Research on distributed temperature sensor (DTS) applied in underground tunnel

NASA Astrophysics Data System (ADS)

Hu, Chuanlong; Wang, Jianfeng; Zhang, Zaixuan; Shen, Changyu; Jin, Yongxing; Jin, Shangzhong

2011-11-01

A distributed temperature sensor (DTS) system with a sensing distance of 4 km was developed for applications in tunnel temperature measurement and fire alarm. Characteristics of DTS and experiment results are introduced. The results show that DTS system can play an important role in tunnel fire alarm.

Feasibility study on low-dosage digital tomosynthesis (DTS) using a multislit collimation technique

NASA Astrophysics Data System (ADS)

Park, S. Y.; Kim, G. A.; Park, C. K.; Cho, H. S.; Seo, C. W.; Lee, D. Y.; Kang, S. Y.; Kim, K. S.; Lim, H. W.; Lee, H. W.; Park, J. E.; Kim, W. S.; Jeon, D. H.; Woo, T. H.

2018-04-01

In this study, we investigated an effective low-dose digital tomosynthesis (DTS) where a multislit collimator placed between the X-ray tube and the patient oscillates during projection data acquisition, partially blocking the X-ray beam to the patient thereby reducing the radiation dosage. We performed a simulation using the proposed DTS with two sets of multislit collimators both having a 50% duty cycle and investigated the image characteristics to demonstrate the feasibility of this proposed approach. In the simulation, all projections were taken at a tomographic angle of θ = ± 50° and an angle step of Δθ =2°. We utilized an iterative algorithm based on a compressed-sensing (CS) scheme for more accurate DTS reconstruction. Using the proposed DTS, we successfully obtained CS-reconstructed DTS images with no bright-band artifacts around the multislit edges of the collimator, thus maintaining the image quality. Therefore, the use of multislit collimation in current real-world DTS systems can reduce the radiation dosage to patients.

WE-AB-303-01: FEATURED PRESENTATION: A Dual-Detector Phase-Matched Digital Tomosynthesis (DTS) Imaging Scheme Using Aggregated KV and MV Projections for Intra-Treatment Lung Tumor Tracking

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Y; Yin, F; Mao, R

2015-06-15

Purpose: To develop a dual-detector phase-matched DTS technique for continuous and fast intra-treatment lung tumor localization. Methods: Tumor localization accuracy of limited-angle DTS imaging is affected by low inter-slice resolution. The dual-detector DTS technique aims to overcome this limitation through combining orthogonally acquired beam’s eye view MV projections and kV projections for intra-treatment DTS reconstruction and localization. To aggregate the kV and MV projections for reconstruction, the MV projections were linearly converted to synthesize corresponding kV projections. To further address the lung motion induced localization errors, this technique uses respiratory phase-matching to match the motion information between on-board DTS andmore » reference DTS to offset the adverse effects of motion blurriness in tumor localization.A study was performed using the CIRS008A lung phantom to simulate different on-board target variation scenarios for localization. The intra-treatment kV and MV acquisition was achieved through the Varian TrueBeam Developer Mode. Four methods were compared for their localization accuracy: 1. the proposed dual-detector phase-matched DTS technique; 2. the single-detector phase-matched DTS technique; 3. the dual-detector 3D-DTS technique without phase-matching; and 4. the single-detector 3D-DTS technique without phase-matching. Results: For scan angles of 2.5°, 5°, 10°, 20° and 30°, the dual-detector phase-matched DTS technique localized the tumor with average(±standard deviations) errors of 0.4±0.3 mm, 0.5±0.3 mm, 0.6±0.2 mm, 0.9±0.4 mm and 1.0±0.3 mm, respectively. The corresponding values of single-detector phase-matched DTS technique were 4.0±2.5 mm, 2.7±1.1 mm, 1.7±1.2 mm, 2.2±0.9 mm and 1.5±0.8 mm, respectively. The values of dual-detector 3D-DTS technique were 6.2±1.7 mm, 6.3±1.2 mm, 5.3±1.3 mm, 2.0±2.2 mm and 1.5±0.5 mm, respectively. And the values of single-detector 3D-DTS technique were 9.7±8.9 mm, 9.8±8.8 mm, 10.0±9.7 mm, 3.9±2.7 mm and 2.2±1.3 mm, respectively. Conclusion: The dual-detector phase-matched DTS technique substantially improves the tumor localization accuracy, which can be applied to real-time intra-treatment lung tumor localization. The research was funded by the National Institutes of Health Grant No. R01-CA184173 and a grant from Varian Medical Systems.« less

A software tool of digital tomosynthesis application for patient positioning in radiotherapy.

PubMed

Yan, Hui; Dai, Jian-Rong

2016-03-08

Digital Tomosynthesis (DTS) is an image modality in reconstructing tomographic images from two-dimensional kV projections covering a narrow scan angles. Comparing with conventional cone-beam CT (CBCT), it requires less time and radiation dose in data acquisition. It is feasible to apply this technique in patient positioning in radiotherapy. To facilitate its clinical application, a software tool was developed and the reconstruction processes were accelerated by graphic process-ing unit (GPU). Two reconstruction and two registration processes are required for DTS application which is different from conventional CBCT application which requires one image reconstruction process and one image registration process. The reconstruction stage consists of productions of two types of DTS. One type of DTS is reconstructed from cone-beam (CB) projections covering a narrow scan angle and is named onboard DTS (ODTS), which represents the real patient position in treatment room. Another type of DTS is reconstructed from digitally reconstructed radiography (DRR) and is named reference DTS (RDTS), which represents the ideal patient position in treatment room. Prior to the reconstruction of RDTS, The DRRs are reconstructed from planning CT using the same acquisition setting of CB projections. The registration stage consists of two matching processes between ODTS and RDTS. The target shift in lateral and longitudinal axes are obtained from the matching between ODTS and RDTS in coronal view, while the target shift in longitudinal and vertical axes are obtained from the matching between ODTS and RDTS in sagittal view. In this software, both DRR and DTS reconstruction algorithms were implemented on GPU environments for acceleration purpose. The comprehensive evaluation of this software tool was performed including geometric accuracy, image quality, registration accuracy, and reconstruction efficiency. The average correlation coefficient between DRR/DTS generated by GPU-based algorithm and CPU-based algorithm is 0.99. Based on the measurements of cube phantom on DTS, the geometric errors are within 0.5 mm in three axes. For both cube phantom and pelvic phantom, the registration errors are within 0.5 mm in three axes. Compared with reconstruction performance of CPU-based algorithms, the performances of DRR and DTS reconstructions are improved by a factor of 15 to 20. A GPU-based software tool was developed for DTS application for patient positioning of radiotherapy. The geometric and registration accuracy met the clinical requirement in patient setup of radiotherapy. The high performance of DRR and DTS reconstruction algorithms was achieved by the GPU-based computation environments. It is a useful software tool for researcher and clinician in evaluating DTS application in patient positioning of radiotherapy.

Comparison of digital tomosynthesis and computed tomography for lung nodule detection in SOS screening program.

PubMed

Grosso, Maurizio; Priotto, Roberto; Ghirardo, Donatella; Talenti, Alberto; Roberto, Emanuele; Bertolaccini, Luca; Terzi, Alberto; Chauvie, Stéphane

2017-08-01

To compare the lung nodules' detection of digital tomosynthesis (DTS) and computed tomography (CT) in the context of the SOS (Studio OSservazionale) prospective screening program for lung cancer detection. One hundred and thirty-two of the 1843 subjects enrolled in the SOS study underwent CT because non-calcified nodules with diameters larger than 5 mm and/or multiple nodules were present in DTS. Two expert radiologists reviewed the exams classifying the nodules based on their radiological appearance and their dimension. LUNG-RADS classification was applied to compare receiver operator characteristics curve between CT and DTS with respect to final diagnosis. CT was used as gold standard. DTS and CT detected 208 and 179 nodules in the 132 subjects, respectively. Of these 208 nodules, 189 (91%) were solid, partially solid, and ground glass opacity. CT confirmed 140/189 (74%) of these nodules but found 4 nodules that were not detected by DTS. DTS and CT were concordant in 62% of the cases applying the 5-point LUNG-RADS scale. The concordance rose to 86% on a suspicious/non-suspicious binary scale. The areas under the curve in receiver operator characteristics were 0.89 (95% CI 0.83-0.94) and 0.80 (95% CI 0.72-0.89) for CT and DTS, respectively. The mean effective dose was 0.09 ± 0.04 mSv for DTS and 4.90 ± 1.20 mSv for CT. The use of a common classification for nodule detection in DTS and CT helps in comparing the two technologies. DTS detected and correctly classified 74% of the nodules seen by CT but lost 4 nodules identified by CT. Concordance between DTS and CT rose to 86% of the nodules when considering LUNG-RADS on a binary scale.

Diagnostic impact of digital tomosynthesis in oncologic patients with suspected pulmonary lesions on chest radiography.

PubMed

Quaia, Emilio; Baratella, Elisa; Poillucci, Gabriele; Gennari, Antonio Giulio; Cova, Maria Assunta

2016-08-01

To assess the actual diagnostic impact of digital tomosynthesis (DTS) in oncologic patients with suspected pulmonary lesions on chest radiography (CXR). A total of 237 patients (135 male, 102 female; age, 70.8 ± 10.4 years) with a known primary malignancy and suspected pulmonary lesion(s) on CXR and who underwent DTS were retrospectively identified. Two radiologists (experience, 10 and 15 years) analysed in consensus CXR and DTS images and proposed a diagnosis according to a confidence score: 1 or 2 = definitely or probably benign pulmonary or extrapulmonary lesion, or pseudolesion; 3 = indeterminate; 4 or 5 = probably or definitely pulmonary lesion. DTS findings were proven by CT (n = 114 patients), CXR during follow-up (n = 105) or histology (n = 18). Final diagnoses included 77 pulmonary opacities, 26 pulmonary scars, 12 pleural lesions and 122 pulmonary pseudolesions. DTS vs CXR presented a higher (P < 0.05) sensitivity (92 vs 15 %), specificity (91 vs 9 %), overall accuracy (92 vs 12 %), and diagnostic confidence (area under ROC, 0.997 vs 0.619). Mean effective dose of CXR vs DTS was 0.06 vs 0.107 mSv (P < 0.05). DTS improved diagnostic accuracy and confidence in comparison to CXR alone in oncologic patients with suspected pulmonary lesions on CXR with only a slight, though significant, increase in radiation dose. • Digital tomosynthesis (DTS) improves accuracy of chest radiography (CXR) in oncologic patients. • DTS improves confidence of CXR in oncologic patients. • DTS allowed avoidance of CT in about 50 % of oncologic patients.

Accurate joint space quantification in knee osteoarthritis: a digital x-ray tomosynthesis phantom study

NASA Astrophysics Data System (ADS)

Sewell, Tanzania S.; Piacsek, Kelly L.; Heckel, Beth A.; Sabol, John M.

2011-03-01

The current imaging standard for diagnosis and monitoring of knee osteoarthritis (OA) is projection radiography. However radiographs may be insensitive to markers of early disease such as osteophytes and joint space narrowing (JSN). Relative to standard radiography, digital X-ray tomosynthesis (DTS) may provide improved visualization of the markers of knee OA without the interference of superimposed anatomy. DTS utilizes a series of low-dose projection images over an arc of +/-20 degrees to reconstruct tomographic images parallel to the detector. We propose that DTS can increase accuracy and precision in JSN quantification. The geometric accuracy of DTS was characterized by quantifying joint space width (JSW) as a function of knee flexion and position using physical and anthropomorphic phantoms. Using a commercially available digital X-ray system, projection and DTS images were acquired for a Lucite rod phantom with known gaps at various source-object-distances, and angles of flexion. Gap width, representative of JSW, was measured using a validated algorithm. Over an object-to-detector-distance range of 5-21cm, a 3.0mm gap width was reproducibly measured in the DTS images, independent of magnification. A simulated 0.50mm (+/-0.13) JSN was quantified accurately (95% CI 0.44-0.56mm) in the DTS images. Angling the rods to represent knee flexion, the minimum gap could be precisely determined from the DTS images and was independent of flexion angle. JSN quantification using DTS was insensitive to distance from patient barrier and flexion angle. Potential exists for the optimization of DTS for accurate radiographic quantification of knee OA independent of patient positioning.

Using Distributed Temperature Sensing for evaporation measurements: background, verification, and future applications.

NASA Astrophysics Data System (ADS)

Schilperoort, Bart; Coenders-Gerrits, Miriam; van Iersel, Tara; Jiménez Rodríguez, Cesar; Luxemburg, Willem; Cisneros Vaca, Cesar; Ucer, Murat

2017-04-01

Distributed temperature sensing (DTS) is a relatively new method for measuring latent and sensible heat fluxes. The method has been successfully tested before on multiple sites (Euser, 2014). It uses a glass fibre optic cable of which the temperature can be measured every 12.5cm. By placing the cable vertically along a structure, the air temperature profile can be measured. If the cable is wrapped with cloth and kept wet (akin to a psychrometer), a vertical wet-bulb temperature gradient over height can be calculated. From these dry and wet-bulb temperatures over the height the Bowen ratio is determined and together with the energy balance the latent and sensible heat can be determined. To verify the measurements of the DTS based Bowen ratio method (BR-DTS) we assessed in detail; the accuracy of the air temperature and wet-bulb temperature measurements, the influence of solar radiation and wind on these temperatures, and a comparison to standard methods of evaporation measurement. We tested the performance of the BR-DTS on a 45m high tower in a tall mixed forest in the centre of the Netherlands in August. The average tree height is 30m, hence we measure temperature gradients above, in, and underneath the canopy. We found that solar radiation has a significant effect on the temperature measurements due to heating of the cable coating and leads to deviations up to 2° C. By using cables with different coating thickness we could theoretically correct for this effect, but this introduces too much uncertainty for calculating the temperature gradient. By installing screens the effect of direct sunlight on the cable is sufficiently reduced, and the correlation of the cable temperature with reference air temperature sensors is very high (R2=0.988 to 0.998). Wind speed seems to have a minimal effect on the measured wet-bulb temperature, both below and above the canopy. The latent heat fluxes of the BR-DTS were compared to an eddy covariance system using data from 10 days, with quality control applied to both methods. When comparing the daytime values, there is a high correlation (R2=0.75), a low bias (mean difference of ±15W/m2) and a good accuracy (standard deviation of the difference of 40W/m2) for both the latent and sensible heat flux. This can lead to a small error. Nonetheless, the results show that when the system is set up with care, and by eliminating sources of errors, the DTS based Bowen ratio is in agreement with an eddy covariance system, even above a tall forest canopy, which is notoriously hard to measure. Further applications of the DTS data in evaporation measurement studies are the flux-variance method (where the standard deviations of the air temperature and absolute humidity are used to estimate the sensible and latent heat fluxes), the surface-renewal method, and correcting the Bowen ratio for the non-unity of the eddy diffusivity ratios. These can all be used to gather additional data on the evaporation to increase the accuracy.

DTS: Building custom, intelligent schedulers

NASA Technical Reports Server (NTRS)

Hansson, Othar; Mayer, Andrew

1994-01-01

DTS is a decision-theoretic scheduler, built on top of a flexible toolkit -- this paper focuses on how the toolkit might be reused in future NASA mission schedulers. The toolkit includes a user-customizable scheduling interface, and a 'Just-For-You' optimization engine. The customizable interface is built on two metaphors: objects and dynamic graphs. Objects help to structure problem specifications and related data, while dynamic graphs simplify the specification of graphical schedule editors (such as Gantt charts). The interface can be used with any 'back-end' scheduler, through dynamically-loaded code, interprocess communication, or a shared database. The 'Just-For-You' optimization engine includes user-specific utility functions, automatically compiled heuristic evaluations, and a postprocessing facility for enforcing scheduling policies. The optimization engine is based on BPS, the Bayesian Problem-Solver (1,2), which introduced a similar approach to solving single-agent and adversarial graph search problems.

Data Transport Subsystem - The SFOC glue

NASA Technical Reports Server (NTRS)

Parr, Stephen J.

1988-01-01

The design and operation of the Data Transport Subsystem (DTS) for the JPL Space Flight Operation Center (SFOC) are described. The SFOC is the ground data system under development to serve interplanetary space probes; in addition to the DTS, it comprises a ground interface facility, a telemetry-input subsystem, data monitor and display facilities, and a digital TV system. DTS links the other subsystems via an ISO OSI presentation layer and an LAN. Here, particular attention is given to the DTS services and service modes (virtual circuit, datagram, and broadcast), the DTS software architecture, the logical-name server, the role of the integrated AI library, and SFOC as a distributed system.

A software tool of digital tomosynthesis application for patient positioning in radiotherapy

PubMed Central

Dai, Jian‐Rong

2016-01-01

Digital Tomosynthesis (DTS) is an image modality in reconstructing tomographic images from two‐dimensional kV projections covering a narrow scan angles. Comparing with conventional cone‐beam CT (CBCT), it requires less time and radiation dose in data acquisition. It is feasible to apply this technique in patient positioning in radiotherapy. To facilitate its clinical application, a software tool was developed and the reconstruction processes were accelerated by graphic processing unit (GPU). Two reconstruction and two registration processes are required for DTS application which is different from conventional CBCT application which requires one image reconstruction process and one image registration process. The reconstruction stage consists of productions of two types of DTS. One type of DTS is reconstructed from cone‐beam (CB) projections covering a narrow scan angle and is named onboard DTS (ODTS), which represents the real patient position in treatment room. Another type of DTS is reconstructed from digitally reconstructed radiography (DRR) and is named reference DTS (RDTS), which represents the ideal patient position in treatment room. Prior to the reconstruction of RDTS, The DRRs are reconstructed from planning CT using the same acquisition setting of CB projections. The registration stage consists of two matching processes between ODTS and RDTS. The target shift in lateral and longitudinal axes are obtained from the matching between ODTS and RDTS in coronal view, while the target shift in longitudinal and vertical axes are obtained from the matching between ODTS and RDTS in sagittal view. In this software, both DRR and DTS reconstruction algorithms were implemented on GPU environments for acceleration purpose. The comprehensive evaluation of this software tool was performed including geometric accuracy, image quality, registration accuracy, and reconstruction efficiency. The average correlation coefficient between DRR/DTS generated by GPU‐based algorithm and CPU‐based algorithm is 0.99. Based on the measurements of cube phantom on DTS, the geometric errors are within 0.5 mm in three axes. For both cube phantom and pelvic phantom, the registration errors are within 0.5 mm in three axes. Compared with reconstruction performance of CPU‐based algorithms, the performances of DRR and DTS reconstructions are improved by a factor of 15 to 20. A GPU‐based software tool was developed for DTS application for patient positioning of radiotherapy. The geometric and registration accuracy met the clinical requirement in patient setup of radiotherapy. The high performance of DRR and DTS reconstruction algorithms was achieved by the GPU‐based computation environments. It is a useful software tool for researcher and clinician in evaluating DTS application in patient positioning of radiotherapy. PACS number(s): 87.57.nf PMID:27074482

Field assessment of dried Plasmodium falciparum samples for malaria rapid diagnostic test quality control and proficiency testing in Ethiopia.

PubMed

Tamiru, Afework; Boulanger, Lucy; Chang, Michelle A; Malone, Joseph L; Aidoo, Michael

2015-01-21

Rapid diagnostic tests (RDTs) are now widely used for laboratory confirmation of suspected malaria cases to comply with the World Health Organization recommendation for universal testing before treatment. However, many malaria programmes lack quality control (QC) processes to assess RDT use under field conditions. Prior research showed the feasibility of using the dried tube specimen (DTS) method for preserving Plasmodium falciparum parasites for use as QC samples for RDTs. This study focused on the use of DTS for RDT QC and proficiency testing under field conditions. DTS were prepared using cultured P. falciparum at densities of 500 and 1,000 parasites/μL; 50 μL aliquots of these along with parasite negative human blood controls (0 parasites/μL) were air-dried in specimen tubes and reactivity verified after rehydration. The DTS were used in a field study in the Oromia Region of Ethiopia. Replicate DTS samples containing 0, 500 and 1,000 parasites/μL were stored at 4°C at a reference laboratory and at ambient temperatures at two nearby health facilities. At weeks 0, 4, 8, 12, 16, 20, and 24, the DTS were rehydrated and tested on RDTs stored under manufacturer-recommended temperatures at the RL and on RDTs stored under site-specific conditions at the two health facilities. Reactivity of DTS stored at 4°C at the reference laboratory on RDTs stored at the reference laboratory was considered the gold standard for assessing DTS stability. A proficiency-testing panel consisting of one negative and three positive samples, monitored with a checklist was administered at weeks 12 and 24. At all the seven time points, DTS stored at both the reference laboratory and health facility were reactive on RDTs stored under the recommended temperature and under field conditions, and the DTS without malaria parasites were negative. At the reference laboratory and one health facility, a 500 parasites/μL DTS from the proficiency panel was falsely reported as negative at week 24 due to errors in interpreting faint test lines. The DTS method can be used under field conditions to supplement other RDT QC methods and health worker proficiency in Ethiopia and possibly other malaria-endemic countries.

[New type distributed optical fiber temperature sensor (DTS) based on Raman scattering and its' application].

PubMed

Wang, Jian-Feng; Liu, Hong-Lin; Zhang, Shu-Qin; Yu, Xiang-Dong; Sun, Zhong-Zhou; Jin, Shang-Zhong; Zhang, Zai-Xuan

2013-04-01

Basic principles, development trends and applications status of distributed optical fiber Raman temperature sensor (DTS) are introduced. Performance parameters of DTS system include the sensing optical fiber length, temperature measurement uncertainty, spatial resolution and measurement time. These parameters have a certain correlation and it is difficult to improve them at the same time by single technology. So a variety of key techniques such as Raman amplification, pulse coding technique, Raman related dual-wavelength self-correction technique and embedding optical switching technique are researched to improve the performance of the DTS system. A 1 467 nm continuous laser is used as pump laser and the light source of DTS system (1 550 nm pulse laser) is amplified. When the length of sensing optical fiber is 50 km the Raman gain is about 17 dB. Raman gain can partially compensate the transmission loss of optical fiber, so that the sensing length can reach 50 km. In DTS system using pulse coding technique, pulse laser is coded by 211 bits loop encoder and correlation calculation is used to demodulate temperature. The encoded laser signal is related, whereas the noise is not relevant. So that signal-to-noise ratio (SNR) of DTS system can be improved significantly. The experiments are carried out in DTS system with single mode optical fiber and multimode optical fiber respectively. Temperature measurement uncertainty can all reach 1 degrees C. In DTS system using Raman related dual-wavelength self-correction technique, the wavelength difference of the two light sources must be one Raman frequency shift in optical fiber. For example, wavelength of the main laser is 1 550 nm and wavelength of the second laser must be 1 450 nm. Spatial resolution of DTS system is improved to 2 m by using dual-wavelength self-correction technique. Optical switch is embedded in DTS system, so that the temperature measurement channel multiply extended and the total length of the sensing optical fiber effectively extended. Optical fiber sensor network is composed.

Benchmarking Ligand-Based Virtual High-Throughput Screening with the PubChem Database

PubMed Central

Butkiewicz, Mariusz; Lowe, Edward W.; Mueller, Ralf; Mendenhall, Jeffrey L.; Teixeira, Pedro L.; Weaver, C. David; Meiler, Jens

2013-01-01

With the rapidly increasing availability of High-Throughput Screening (HTS) data in the public domain, such as the PubChem database, methods for ligand-based computer-aided drug discovery (LB-CADD) have the potential to accelerate and reduce the cost of probe development and drug discovery efforts in academia. We assemble nine data sets from realistic HTS campaigns representing major families of drug target proteins for benchmarking LB-CADD methods. Each data set is public domain through PubChem and carefully collated through confirmation screens validating active compounds. These data sets provide the foundation for benchmarking a new cheminformatics framework BCL::ChemInfo, which is freely available for non-commercial use. Quantitative structure activity relationship (QSAR) models are built using Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Decision Trees (DTs), and Kohonen networks (KNs). Problem-specific descriptor optimization protocols are assessed including Sequential Feature Forward Selection (SFFS) and various information content measures. Measures of predictive power and confidence are evaluated through cross-validation, and a consensus prediction scheme is tested that combines orthogonal machine learning algorithms into a single predictor. Enrichments ranging from 15 to 101 for a TPR cutoff of 25% are observed. PMID:23299552

The value of X-ray digital tomosynthesis in the diagnosis of urinary calculi

PubMed Central

Liu, Shifeng; Wang, Hong; Feng, Weihua; Hu, Xiaokun; Guo, Jian; Shang, Qingjun; Li, Zixiang; Yu, Hongsheng

2018-01-01

Urinary calculus is a common and recurrent condition that affects kidney function. The present study evaluated the use of digital tomosynthesis (DTS) and Kidneys-Ureters-Bladder (KUB) radiography as methods of diagnosing urinary calculi. Unenhanced multidetector computed tomography (UMDCT) was used in the diagnosis of calculi. KUB radiography and DTS procedures were conducted on patients prior to and following bowel preparation to detect kidney, ureteral and bladder calculi. Differences in diagnostic performance of KUB radiography and DTS imaging on prepared and unprepared bowel were evaluated using the χ2 test. The consistency of diagnostic results between two examining physicians was analyzed using the κ test. A total of 138 calculi from 80 patients were detected via UMDCT. The calculi detection rates of KUB prior to and following bowel preparation were 47.8 and 66.7% respectively, and the calculi detection rate of DTS prior to and following bowel preparation were 94.2 and 96.4%, respectively. The detection rates of calculi >5 mm via KUB prior to and following bowel preparation were 56.6 and 73.5% respectively, and in DTS they were 100% prior to and following bowel preparation. Economically, DTS performed on the unprepared bowel was the most cost effective, followed by DTS on the prepared bowel, KUB on the unprepared bowel and KUB on the prepared bowel. Therefore, the current study concluded that DTS may be an appropriate first-line imaging technique in patients with urinary calculi. PMID:29434761

The value of X-ray digital tomosynthesis in the diagnosis of urinary calculi.

PubMed

Liu, Shifeng; Wang, Hong; Feng, Weihua; Hu, Xiaokun; Guo, Jian; Shang, Qingjun; Li, Zixiang; Yu, Hongsheng

2018-02-01

Urinary calculus is a common and recurrent condition that affects kidney function. The present study evaluated the use of digital tomosynthesis (DTS) and Kidneys-Ureters-Bladder (KUB) radiography as methods of diagnosing urinary calculi. Unenhanced multidetector computed tomography (UMDCT) was used in the diagnosis of calculi. KUB radiography and DTS procedures were conducted on patients prior to and following bowel preparation to detect kidney, ureteral and bladder calculi. Differences in diagnostic performance of KUB radiography and DTS imaging on prepared and unprepared bowel were evaluated using the χ 2 test. The consistency of diagnostic results between two examining physicians was analyzed using the κ test. A total of 138 calculi from 80 patients were detected via UMDCT. The calculi detection rates of KUB prior to and following bowel preparation were 47.8 and 66.7% respectively, and the calculi detection rate of DTS prior to and following bowel preparation were 94.2 and 96.4%, respectively. The detection rates of calculi >5 mm via KUB prior to and following bowel preparation were 56.6 and 73.5% respectively, and in DTS they were 100% prior to and following bowel preparation. Economically, DTS performed on the unprepared bowel was the most cost effective, followed by DTS on the prepared bowel, KUB on the unprepared bowel and KUB on the prepared bowel. Therefore, the current study concluded that DTS may be an appropriate first-line imaging technique in patients with urinary calculi.

Practical considerations for coil-wrapped Distributed Temperature Sensing setups

NASA Astrophysics Data System (ADS)

Solcerova, Anna; van Emmerik, Tim; Hilgersom, Koen; van de Giesen, Nick

2015-04-01

Fiber-optic Distributed Temperature Sensing (DTS) has been applied widely in hydrological and meteorological systems. For example, DTS has been used to measure streamflow, groundwater, soil moisture and temperature, air temperature, and lake energy fluxes. Many of these applications require a spatial monitoring resolution smaller than the minimum resolution of the DTS device. Therefore, measuring with these resolutions requires a custom made setup. To obtain both high temporal and high spatial resolution temperature measurements, fiber-optic cable is often wrapped around, and glued to, a coil, for example a PVC conduit. For these setups, it is often assumed that the construction characteristics (e.g., the coil material, shape, diameter) do not influence the DTS temperature measurements significantly. This study compares DTS datasets obtained during four measurement campaigns. The datasets were acquired using different setups, allowing to investigate the influence of the construction characteristics on the monitoring results. This comparative study suggests that the construction material, shape, diameter, and way of attachment can have a significant influence on the results. We present a qualitative and quantitative approximation of errors introduced through the selection of the construction, e.g., choice of coil material, influence of solar radiation, coil diameter, and cable attachment method. Our aim is to provide insight in factors that influence DTS measurements, which designers of future DTS measurements setups can take into account. Moreover, we present a number of solutions to minimize these errors for improved temperature retrieval using DTS.

Human microRNA expression in sporadic and FAP-associated desmoid tumors and correlation with beta-catenin mutations.

PubMed

Cavallini, Aldo; Rotelli, Maria Teresa; Lippolis, Catia; Piscitelli, Domenico; Digennaro, Rosa; Covelli, Claudia; Carella, Nicola; Accetturo, Matteo; Altomare, Donato Francesco

2017-06-27

Desmoid tumors (DT) are rare, benign, fibroblastic neoplasm with challenging histological diagnosis. DTs can occur sporadically or associated with the familial adenomatous polyposis coli (FAP). Most sporadic DTs are associated with β-catenin gene (CTNNB1) mutations, while mutated APC gene causes FAP disease. microRNAs (miRNAs) are involved in many human carcinogenesis.The miRNA profile was analyzed by microarray in formalin-fixed, paraffin-embedded (FFPE) specimens of 12 patients (8 sporadic, 4 FAP-associated) and 4 healthy controls. One hundred and one mRNAs resulted dysregulated, of which 98 in sporadic DTs and 8 in FAP-associated DTs, 5 were shared by both tumors. Twenty-six miRNAs were then validated by RT-qPCR in 23 sporadic and 7 FAP-associated DT samples matched with healthy controls. The qPCR method was also used to evaluate the CTNNB1 mutational status in sporadic DTs. The correlation between sporadic DTs and miRNA expression showed that miR-21-3p increased in mutated versus wild-type DTs, while miR-197-3p was decreased. The mRNA expression of Tetraspanin3 and Serpin family A member 3, as miR-21-3p targets, and L1 Cell Adhesion Molecule, as miR-197-3p target, was also evaluate. CTNNB1 mutations associated to miRNA dysregulation could affect the genesis and the progression of this disease and help histological diagnosis of sporadic DTs.

A dual-view digital tomosynthesis imaging technique for improved chest imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhong, Yuncheng; Lai, Chao-Jen; Wang, Tianpeng

Purpose: Digital tomosynthesis (DTS) has been shown to be useful for reducing the overlapping of abnormalities with anatomical structures at various depth levels along the posterior–anterior (PA) direction in chest radiography. However, DTS provides crude three-dimensional (3D) images that have poor resolution in the lateral view and can only be displayed with reasonable quality in the PA view. Furthermore, the spillover of high-contrast objects from off-fulcrum planes generates artifacts that may impede the diagnostic use of the DTS images. In this paper, the authors describe and demonstrate the use of a dual-view DTS technique to improve the accuracy of themore » reconstructed volume image data for more accurate rendition of the anatomy and slice images with improved resolution and reduced artifacts, thus allowing the 3D image data to be viewed in views other than the PA view. Methods: With the dual-view DTS technique, limited angle scans are performed and projection images are acquired in two orthogonal views: PA and lateral. The dual-view projection data are used together to reconstruct 3D images using the maximum likelihood expectation maximization iterative algorithm. In this study, projection images were simulated or experimentally acquired over 360° using the scanning geometry for cone beam computed tomography (CBCT). While all projections were used to reconstruct CBCT images, selected projections were extracted and used to reconstruct single- and dual-view DTS images for comparison with the CBCT images. For realistic demonstration and comparison, a digital chest phantom derived from clinical CT images was used for the simulation study. An anthropomorphic chest phantom was imaged for the experimental study. The resultant dual-view DTS images were visually compared with the single-view DTS images and CBCT images for the presence of image artifacts and accuracy of CT numbers and anatomy and quantitatively compared with root-mean-square-deviation (RMSD) values computed using the digital chest phantom or the CBCT images as the reference in the simulation and experimental study, respectively. High-contrast wires with vertical, oblique, and horizontal orientations in a PA view plane were also imaged to investigate the spatial resolutions and how the wire signals spread in the PA view and lateral view slice images. Results: Both the digital phantom images (simulated) and the anthropomorphic phantom images (experimentally generated) demonstrated that the dual-view DTS technique resulted in improved spatial resolution in the depth (PA) direction, more accurate representation of the anatomy, and significantly reduced artifacts. The RMSD values corroborate well with visual observations with substantially lower RMSD values measured for the dual-view DTS images as compared to those measured for the single-view DTS images. The imaging experiment with the high-contrast wires shows that while the vertical and oblique wires could be resolved in the lateral view in both single- and dual-view DTS images, the horizontal wire could only be resolved in the dual-view DTS images. This indicates that with single-view DTS, the wire signals spread liberally to off-fulcrum planes and generated wire shadow there. Conclusions: The authors have demonstrated both visually and quantitatively that the dual-view DTS technique can be used to achieve more accurate rendition of the anatomy and to obtain slice images with improved resolution and reduced artifacts as compared to the single-view DTS technique, thus allowing the 3D image data to be viewed in views other than the PA view. These advantages could make the dual-view DTS technique useful in situations where better separation of the objects-of-interest from the off-fulcrum structures or more accurate 3D rendition of the anatomy are required while a regular CT examination is undesirable due to radiation dose considerations.« less

Decision tools in health care: focus on the problem, not the solution.

PubMed

Liu, Joseph; Wyatt, Jeremy C; Altman, Douglas G

2006-01-20

Systematic reviews or randomised-controlled trials usually help to establish the effectiveness of drugs and other health technologies, but are rarely sufficient by themselves to ensure actual clinical use of the technology. The process from innovation to routine clinical use is complex. Numerous computerised decision support systems (DSS) have been developed, but many fail to be taken up into actual use. Some developers construct technologically advanced systems with little relevance to the real world. Others did not determine whether a clinical need exists. With NHS investing 5 billion pounds sterling in computer systems, also occurring in other countries, there is an urgent need to shift from a technology-driven approach to one that identifies and employs the most cost-effective method to manage knowledge, regardless of the technology. The generic term, 'decision tool' (DT), is therefore suggested to demonstrate that these aids, which seem different technically, are conceptually the same from a clinical viewpoint. Many computerised DSSs failed for various reasons, for example, they were not based on best available knowledge; there was insufficient emphasis on their need for high quality clinical data; their development was technology-led; or evaluation methods were misapplied. We argue that DSSs and other computer-based, paper-based and even mechanical decision aids are members of a wider family of decision tools. A DT is an active knowledge resource that uses patient data to generate case specific advice, which supports decision making about individual patients by health professionals, the patients themselves or others concerned about them. The identification of DTs as a consistent and important category of health technology should encourage the sharing of lessons between DT developers and users and reduce the frequency of decision tool projects focusing only on technology. The focus of evaluation should become more clinical, with the impact of computer-based DTs being evaluated against other computer, paper- or mechanical tools, to identify the most cost effective tool for each clinical problem. We suggested the generic term 'decision tool' to demonstrate that decision-making aids, such as computerised DSSs, paper algorithms, and reminders are conceptually the same, so the methods to evaluate them should be the same.

A comparison of digital tomosynthesis and chest radiography in evaluating airway lesions using computed tomography as a reference.

PubMed

Choo, Ji Yung; Lee, Ki Yeol; Yu, Ami; Kim, Je-Hyeong; Lee, Seung Heon; Choi, Jung Won; Kang, Eun-Young; Oh, Yu Whan

2016-09-01

To compare the diagnostic performance of digital tomosynthesis (DTS) and chest radiography for detecting airway abnormalities, using computed tomography (CT) as a reference. We evaluated 161 data sets from 149 patients (91 with and 70 without airway abnormalities) who had undergone radiography, DTS, and CT to detect airway problems. Radiographs and DTS were evaluated to localize and score the severity of the airway abnormalities, and to score the image quality using CT as a reference. Receiver operating characteristics (ROC), McNemar's test, weighted kappa, and the paired t-test were used for statistical analysis. The sensitivity of DTS was higher (reader 1, 93.51 %; reader 2, 94.29 %) than chest radiography (68.83 %; 71.43 %) in detecting airway lesions. The diagnostic accuracy of DTS (90.91 %; 94.70 %) was also significantly better than that of radiography (78.03 %; 82.58 %, all p < 0.05). DTS image quality was significantly better than chest radiography (1.83, 2.74; p < 0.05) in the results of both readers. The inter-observer agreement with respect to DTS findings was moderate and superior when compared to radiography findings. DTS is a more accurate and sensitive modality than radiography for detecting airway lesions that are easily obscured by soft tissue structures in the mediastinum. • Digital tomosynthesis offers new diagnostic options for airway lesions. • Digital tomosynthesis is more sensitive and accurate than radiography for airway lesions. • Digital tomosynthesis shows better image quality than radiography. • Assessment of lesion severity, via tomosynthesis is comparable to computed tomography.

Statistical mapping of zones of focused groundwater/surface-water exchange using fiber-optic distributed temperature sensing

USGS Publications Warehouse

Mwakanyamale, Kisa; Day-Lewis, Frederick D.; Slater, Lee D.

2013-01-01

Fiber-optic distributed temperature sensing (FO-DTS) increasingly is used to map zones of focused groundwater/surface-water exchange (GWSWE). Previous studies of GWSWE using FO-DTS involved identification of zones of focused GWSWE based on arbitrary cutoffs of FO-DTS time-series statistics (e.g., variance, cross-correlation between temperature and stage, or spectral power). New approaches are needed to extract more quantitative information from large, complex FO-DTS data sets while concurrently providing an assessment of uncertainty associated with mapping zones of focused GSWSE. Toward this end, we present a strategy combining discriminant analysis (DA) and spectral analysis (SA). We demonstrate the approach using field experimental data from a reach of the Columbia River adjacent to the Hanford 300 Area site. Results of the combined SA/DA approach are shown to be superior to previous results from qualitative interpretation of FO-DTS spectra alone.

Digital tomosynthesis and high resolution computed tomography as clinical tools for vertebral endplate topography measurements: Comparison with microcomputed tomography.

PubMed

Oravec, Daniel; Quazi, Abrar; Xiao, Angela; Yang, Ellen; Zauel, Roger; Flynn, Michael J; Yeni, Yener N

2015-12-01

Endplate morphology is understood to play an important role in the mechanical behavior of vertebral bone as well as degenerative processes in spinal tissues; however, the utility of clinical imaging modalities in assessment of the vertebral endplate has been limited. The objective of this study was to evaluate the ability of two clinical imaging modalities (digital tomosynthesis, DTS; high resolution computed tomography, HRCT) to assess endplate topography by correlating the measurements to a microcomputed tomography (μCT) standard. DTS, HRCT, and μCT images of 117 cadaveric thoracolumbar vertebrae (T10-L1; 23 male, 19 female; ages 36-100 years) were segmented, and inferior and superior endplate surface topographical distribution parameters were calculated. Both DTS and HRCT showed statistically significant correlations with μCT approaching a moderate level of correlation at the superior endplate for all measured parameters (R(2)Adj=0.19-0.57), including averages, variability, and higher order statistical moments. Correlation of average depths at the inferior endplate was comparable to the superior case for both DTS and HRCT (R(2)Adj=0.14-0.51), while correlations became weak or nonsignificant for higher moments of the topography distribution. DTS was able to capture variations in the endplate topography to a slightly better extent than HRCT, and taken together with the higher speed and lower radiation cost of DTS than HRCT, DTS appears preferable for endplate measurements. Copyright © 2015 Elsevier Inc. All rights reserved.

Decision-theoretic control of EUVE telescope scheduling

NASA Technical Reports Server (NTRS)

Hansson, Othar; Mayer, Andrew

1993-01-01

This paper describes a decision theoretic scheduler (DTS) designed to employ state-of-the-art probabilistic inference technology to speed the search for efficient solutions to constraint-satisfaction problems. Our approach involves assessing the performance of heuristic control strategies that are normally hard-coded into scheduling systems and using probabilistic inference to aggregate this information in light of the features of a given problem. The Bayesian Problem-Solver (BPS) introduced a similar approach to solving single agent and adversarial graph search patterns yielding orders-of-magnitude improvement over traditional techniques. Initial efforts suggest that similar improvements will be realizable when applied to typical constraint-satisfaction scheduling problems.

Experiments with a decision-theoretic scheduler

NASA Technical Reports Server (NTRS)

Hansson, Othar; Holt, Gerhard; Mayer, Andrew

1992-01-01

This paper describes DTS, a decision-theoretic scheduler designed to employ state-of-the-art probabilistic inference technology to speed the search for efficient solutions to constraint-satisfaction problems. Our approach involves assessing the performance of heuristic control strategies that are normally hard-coded into scheduling systems, and using probabilistic inference to aggregate this information in light of features of a given problem. BPS, the Bayesian Problem-Solver, introduced a similar approach to solving single-agent and adversarial graph search problems, yielding orders-of-magnitude improvement over traditional techniques. Initial efforts suggest that similar improvements will be realizable when applied to typical constraint-satisfaction scheduling problems.

The value of digital tomosynthesis of the chest as a problem-solving tool for suspected pulmonary nodules and hilar lesions detected on chest radiography.

PubMed

Galea, Angela; Dubbins, Paul; Riordan, Richard; Adlan, Tarig; Roobottom, Carl; Gay, David

2015-05-01

To assess the capability of digital tomosynthesis (DTS) of the chest compared to a postero-anterior (PA) and lateral chest radiograph (CXR) in the diagnosis of suspected but unconfirmed pulmonary nodules and hilar lesions detected on a CXR. Computed tomography (CT) was used as the reference standard. 78 patients with suspected non-calcified pulmonary nodules or hilar lesions on their CXR were included in the study. Two radiologists, blinded to the history and CT, prospectively analysed the CXR (PA and lateral) and the DTS images using a picture archiving and communication workstation and were asked to designate one of two outcomes: true intrapulmonary lesion or false intrapulmonary lesion. A CT of the chest performed within 4 weeks of the CXR was used as the reference standard. Inter-observer agreement and time to report the modalities were calculated for CXR and DTS. There were 34 true lesions confirmed on CT, 12 were hilar lesions and 22 were peripheral nodules. Of the 44 false lesions, 37 lesions were artefactual or due to composite shadow and 7 lesions were real but extrapulmonary simulating non-calcified intrapulmonary lesions. The PA and lateral CXR correctly classified 39/78 (50%) of the lesions, this improved to 75/78 (96%) with DTS. The sensitivity and specificity was 0.65 and 0.39 for CXR and 0.91 and 1 for DTS. Based on the DTS images, readers correctly classified all the false lesions but missed 3/34 true lesions. Two of the missed lesions were hilar in location and one was a peripheral nodule. All three missed lesions were incorrectly classified on DTS as composite shadow. DTS improves diagnostic confidence when compared to a repeat PA and lateral CXR in the diagnosis of both suspected hilar lesions and pulmonary nodules detected on CXR. DTS is able to exclude most peripheral pulmonary nodules but caution and further studies are needed to assess its ability to exclude hilar lesions. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Multivariate analysis for scanning tunneling spectroscopy data

NASA Astrophysics Data System (ADS)

Yamanishi, Junsuke; Iwase, Shigeru; Ishida, Nobuyuki; Fujita, Daisuke

2018-01-01

We applied principal component analysis (PCA) to two-dimensional tunneling spectroscopy (2DTS) data obtained on a Si(111)-(7 × 7) surface to explore the effectiveness of multivariate analysis for interpreting 2DTS data. We demonstrated that several components that originated mainly from specific atoms at the Si(111)-(7 × 7) surface can be extracted by PCA. Furthermore, we showed that hidden components in the tunneling spectra can be decomposed (peak separation), which is difficult to achieve with normal 2DTS analysis without the support of theoretical calculations. Our analysis showed that multivariate analysis can be an additional powerful way to analyze 2DTS data and extract hidden information from a large amount of spectroscopic data.

Technical Note: Bed conduction impact on fiber optic DTS water temperature measurements

NASA Astrophysics Data System (ADS)

O'Donnell Meininger, T.; Selker, J. S.

2014-07-01

Error in Distributed Temperature Sensor (DTS) water temperature measurements may be introduced by contact of the fiber optic cable sensor with bed materials (e.g., seafloor, lakebed, stream bed). Heat conduction from the bed materials can affect cable temperature and the resulting DTS measurements. In the Middle Fork John Day River, apparent water temperature measurements were influenced by cable sensor contact with aquatic vegetation and fine sediment bed materials. Affected cable segments measured a diurnal temperature range reduced by 10% and lagged by 20-40 min relative to that of ambient stream temperature. The diurnal temperature range deeper within the vegetation-sediment bed material was reduced 70% and lagged 240 min relative to ambient stream temperature. These site-specific results illustrate the potential magnitude of bed-conduction impacts with buried DTS measurements. Researchers who deploy DTS for water temperature monitoring should understand the importance of the environment into which the cable is placed on the range and phase of temperature measurements.

Advances in Using Fiber-Optic Distributed Temperature Sensing to Identify the Mixing of Waters

NASA Astrophysics Data System (ADS)

Briggs, M. A.; Day-Lewis, F. D.; Rosenberry, D. O.; Harvey, J. W.; Lane, J. W., Jr.; Hare, D. K.; Boutt, D. F.; Voytek, E. B.; Buckley, S.

2014-12-01

Fiber-optic distributed temperature sensing (FO-DTS) provides thermal data through space and time along linear cables. When installed along a streambed, FO-DTS can capture the influence of upwelling groundwater (GW) as thermal anomalies. The planning of labor-intensive physical measurements can make use of FO-DTS data to target areas of focused GW discharge that can disproportionately affect surface-water (SW) quality and temperature. Typical longitudinal FO-DTS spatial resolution ranges 0.25 to1.0 m, and cannot resolve small-scale water-column mixing or sub-surface diurnal fluctuations. However, configurations where the cable is wrapped around rods can improve the effective vertical resolution to sub-centimeter scales, and the pipes can be actively heated to induce a thermal tracer. Longitudinal streambed and high-resolution vertical arrays were deployed at the upper Delaware River (PA, USA) and the Quashnet River (MA, USA) for aquatic habitat studies. The resultant datasets exemplify the varied uses of FO-DTS. Cold anomalies found along the Delaware River steambed coincide with zones of known mussel populations, and high-resolution vertical array data showed relatively stable in-channel thermal refugia. Cold anomalies at the Quashnet River identified in 2013 were found to persist in 2014, and seepage measurements and water samples at these locations showed high GW flux with distinctive chemistry. Cable location is paramount to seepage identification, particularly in faster flowing deep streams such as the Quashnet and Delaware Rivers where steambed FO-DTS identified many seepage zones with no surface expression. The temporal characterization of seepage dynamics are unique to FO-DTS. However, data from Tidmarsh Farms, a cranberry bog restoration site in MA, USA indicate that in slower flowing shallow steams GW inflow affects surface temperature; therefore infrared imaging can provide seepage location information similar to FO-DTS with substantially less effort.

Analyzed DTS Data, Guelph, ON Canada

DOE Data Explorer

Coleman, Thomas

2015-07-01

Analyzed DTS datasets from active heat injection experiments in Guelph, ON Canada is included. A .pdf file of images including borehole temperature distributions, temperature difference distributions, temperature profiles, and flow interpretations is included as the primary analyzed dataset. Analyzed data used to create the .pdf images are included as a matlab data file that contains the following 5 types of data: 1) Borehole Temperature (matrix of temperature data collected in the borehole), 2) Borehole Temperature Difference (matrix of temperature difference above ambient for each test), 3) Borehole Time (time in both min and sec since the start of a DTS test), 4) Borehole Depth (channel depth locations for the DTS measurements), 5) Temperature Profiles (ambient, active, active off early time, active off late time, and injection).

Development of the Diabetes Technology Society Blood Glucose Monitor System Surveillance Protocol

PubMed Central

Klonoff, David C.; Lias, Courtney; Beck, Stayce; Parkes, Joan Lee; Kovatchev, Boris; Vigersky, Robert A.; Arreaza-Rubin, Guillermo; Burk, Robert D.; Kowalski, Aaron; Little, Randie; Nichols, James; Petersen, Matt; Rawlings, Kelly; Sacks, David B.; Sampson, Eric; Scott, Steve; Seley, Jane Jeffrie; Slingerland, Robbert; Vesper, Hubert W.

2015-01-01

Background: Inaccurate blood glucsoe monitoring systems (BGMSs) can lead to adverse health effects. The Diabetes Technology Society (DTS) Surveillance Program for cleared BGMSs is intended to protect people with diabetes from inaccurate, unreliable BGMS products that are currently on the market in the United States. The Surveillance Program will provide an independent assessment of the analytical performance of cleared BGMSs. Methods: The DTS BGMS Surveillance Program Steering Committee included experts in glucose monitoring, surveillance testing, and regulatory science. Over one year, the committee engaged in meetings and teleconferences aiming to describe how to conduct BGMS surveillance studies in a scientifically sound manner that is in compliance with good clinical practice and all relevant regulations. Results: A clinical surveillance protocol was created that contains performance targets and analytical accuracy-testing studies with marketed BGMS products conducted by qualified clinical and laboratory sites. This protocol entitled “Protocol for the Diabetes Technology Society Blood Glucose Monitor System Surveillance Program” is attached as supplementary material. Conclusion: This program is needed because currently once a BGMS product has been cleared for use by the FDA, no systematic postmarket Surveillance Program exists that can monitor analytical performance and detect potential problems. This protocol will allow identification of inaccurate and unreliable BGMSs currently available on the US market. The DTS Surveillance Program will provide BGMS manufacturers a benchmark to understand the postmarket analytical performance of their products. Furthermore, patients, health care professionals, payers, and regulatory agencies will be able to use the results of the study to make informed decisions to, respectively, select, prescribe, finance, and regulate BGMSs on the market. PMID:26481642

Development of the Diabetes Technology Society Blood Glucose Monitor System Surveillance Protocol.

PubMed

Klonoff, David C; Lias, Courtney; Beck, Stayce; Parkes, Joan Lee; Kovatchev, Boris; Vigersky, Robert A; Arreaza-Rubin, Guillermo; Burk, Robert D; Kowalski, Aaron; Little, Randie; Nichols, James; Petersen, Matt; Rawlings, Kelly; Sacks, David B; Sampson, Eric; Scott, Steve; Seley, Jane Jeffrie; Slingerland, Robbert; Vesper, Hubert W

2016-05-01

Inaccurate blood glucsoe monitoring systems (BGMSs) can lead to adverse health effects. The Diabetes Technology Society (DTS) Surveillance Program for cleared BGMSs is intended to protect people with diabetes from inaccurate, unreliable BGMS products that are currently on the market in the United States. The Surveillance Program will provide an independent assessment of the analytical performance of cleared BGMSs. The DTS BGMS Surveillance Program Steering Committee included experts in glucose monitoring, surveillance testing, and regulatory science. Over one year, the committee engaged in meetings and teleconferences aiming to describe how to conduct BGMS surveillance studies in a scientifically sound manner that is in compliance with good clinical practice and all relevant regulations. A clinical surveillance protocol was created that contains performance targets and analytical accuracy-testing studies with marketed BGMS products conducted by qualified clinical and laboratory sites. This protocol entitled "Protocol for the Diabetes Technology Society Blood Glucose Monitor System Surveillance Program" is attached as supplementary material. This program is needed because currently once a BGMS product has been cleared for use by the FDA, no systematic postmarket Surveillance Program exists that can monitor analytical performance and detect potential problems. This protocol will allow identification of inaccurate and unreliable BGMSs currently available on the US market. The DTS Surveillance Program will provide BGMS manufacturers a benchmark to understand the postmarket analytical performance of their products. Furthermore, patients, health care professionals, payers, and regulatory agencies will be able to use the results of the study to make informed decisions to, respectively, select, prescribe, finance, and regulate BGMSs on the market. © 2015 Diabetes Technology Society.

Use of a graphics processing unit (GPU) to facilitate real-time 3D graphic presentation of the patient skin-dose distribution during fluoroscopic interventional procedures

PubMed Central

Rana, Vijay; Rudin, Stephen; Bednarek, Daniel R.

2012-01-01

We have developed a dose-tracking system (DTS) that calculates the radiation dose to the patient’s skin in real-time by acquiring exposure parameters and imaging-system-geometry from the digital bus on a Toshiba Infinix C-arm unit. The cumulative dose values are then displayed as a color map on an OpenGL-based 3D graphic of the patient for immediate feedback to the interventionalist. Determination of those elements on the surface of the patient 3D-graphic that intersect the beam and calculation of the dose for these elements in real time demands fast computation. Reducing the size of the elements results in more computation load on the computer processor and therefore a tradeoff occurs between the resolution of the patient graphic and the real-time performance of the DTS. The speed of the DTS for calculating dose to the skin is limited by the central processing unit (CPU) and can be improved by using the parallel processing power of a graphics processing unit (GPU). Here, we compare the performance speed of GPU-based DTS software to that of the current CPU-based software as a function of the resolution of the patient graphics. Results show a tremendous improvement in speed using the GPU. While an increase in the spatial resolution of the patient graphics resulted in slowing down the computational speed of the DTS on the CPU, the speed of the GPU-based DTS was hardly affected. This GPU-based DTS can be a powerful tool for providing accurate, real-time feedback about patient skin-dose to physicians while performing interventional procedures. PMID:24027616

Use of a graphics processing unit (GPU) to facilitate real-time 3D graphic presentation of the patient skin-dose distribution during fluoroscopic interventional procedures.

PubMed

Rana, Vijay; Rudin, Stephen; Bednarek, Daniel R

2012-02-23

We have developed a dose-tracking system (DTS) that calculates the radiation dose to the patient's skin in real-time by acquiring exposure parameters and imaging-system-geometry from the digital bus on a Toshiba Infinix C-arm unit. The cumulative dose values are then displayed as a color map on an OpenGL-based 3D graphic of the patient for immediate feedback to the interventionalist. Determination of those elements on the surface of the patient 3D-graphic that intersect the beam and calculation of the dose for these elements in real time demands fast computation. Reducing the size of the elements results in more computation load on the computer processor and therefore a tradeoff occurs between the resolution of the patient graphic and the real-time performance of the DTS. The speed of the DTS for calculating dose to the skin is limited by the central processing unit (CPU) and can be improved by using the parallel processing power of a graphics processing unit (GPU). Here, we compare the performance speed of GPU-based DTS software to that of the current CPU-based software as a function of the resolution of the patient graphics. Results show a tremendous improvement in speed using the GPU. While an increase in the spatial resolution of the patient graphics resulted in slowing down the computational speed of the DTS on the CPU, the speed of the GPU-based DTS was hardly affected. This GPU-based DTS can be a powerful tool for providing accurate, real-time feedback about patient skin-dose to physicians while performing interventional procedures.

Evaluation of respiration-correlated digital tomosynthesis in lung.

PubMed

Santoro, Joseph; Kriminski, Sergey; Lovelock, D Michael; Rosenzweig, Kenneth; Mostafavi, Hassan; Amols, Howard I; Mageras, Gig S

2010-03-01

Digital tomosynthesis (DTS) with a linear accelerator-mounted imaging system provides a means of reconstructing tomographic images from radiographic projections over a limited gantry arc, thus requiring only a few seconds to acquire. Its application in the thorax, however, often results in blurred images from respiration-induced motion. This work evaluates the feasibility of respiration-correlated (RC) DTS for soft-tissue visualization and patient positioning. Image data acquired with a gantry-mounted kilovoltage imaging system while recording respiration were retrospectively analyzed from patients receiving radiotherapy for non-small-cell lung carcinoma. Projection images spanning an approximately 30 degrees gantry arc were sorted into four respiration phase bins prior to DTS reconstruction, which uses a backprojection, followed by a procedure to suppress structures above and below the reconstruction plane of interest. The DTS images were reconstructed in planes at different depths through the patient and normal to a user-selected angle close to the center of the arc. The localization accuracy of RC-DTS was assessed via a comparison with CBCT. Evaluation of RC-DTS in eight tumors shows visible reduction in image blur caused by the respiratory motion. It also allows the visualization of tumor motion extent. The best image quality is achieved at the end-exhalation phase of the respiratory motion. Comparison of RC-DTS with respiration-correlated cone-beam CT in determining tumor position, motion extent and displacement between treatment sessions shows agreement in most cases within 2-3 mm, comparable in magnitude to the intraobserver repeatability of the measurement. These results suggest the method's applicability for soft-tissue image guidance in lung, but must be confirmed with further studies in larger numbers of patients.

MO-FG-CAMPUS-IeP1-04: Kerma Area Product Calculation for Non-Uniform X-Ray Fields Using a Skin Dose Tracking System

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vijayan, S; Xiong, Z; Rudin, S

Purpose: The functionality of the Dose-Tracking System (DTS) has been expanded to include the calculation of the Kerma-Area Product (KAP) for non-uniform x-ray fields such as result from the use of compensation filters during fluoroscopic procedures Methods: The DTS calculates skin dose during fluoroscopic interventions and provides a color-coded dose map on a patient-graphic model. The KAP is the integral of air kerma over the x-ray field and is usually measured with a transmission-ionization chamber that intercepts the entire x-ray beam. The DTS has been modified to determine KAP when there are beam non-uniformities that can be modeled. For example,more » the DTS includes models of the three compensation filters with tapered edges located in the collimator assembly of the Toshiba Infinix fluoroscopic C-Arm and can track their movement. To determine the air kerma after the filters, DTS includes transmission factors for the compensation filters as a function of kVp and beam filtration. A virtual KAP dosimeter is simulated in the DTS by an array of graphic vertices; the air kerma at each vertex is corrected by the field non-uniformity, which in this case is the attenuation factor for those rays which pass through the filter. The products of individual vertex air-kerma values for all vertices within the beam times the effective-area-per-vertex are summed for each x-ray pulse to yield the KAP per pulse and the cumulative KAP for the procedure is then calculated. Results: The KAP values estimated by DTS with the compensation filter inserted into the x-ray field agree within ± 6% with the values displayed on the fluoroscopy unit monitor, which are measured with a transmission chamber. Conclusion: The DTS can account for field non-uniformities such as result from the use of compensation filters in calculating KAP and can obviate the need for a KAP transmission ionization chamber. Partial support from NIH Grant R01-EB002873 and Toshiba Medical Systems Corp.« less

Using Perturbation Theory to Compute the Morphological Similarity of Diffusion Tensors

PubMed Central

Bansal, Ravi; Staib, Lawrence H.; Xu, Dongrong; Laine, Andrew F.; Royal, Jason; Peterson, Bradley S.

2008-01-01

Computing the morphological similarity of Diffusion Tensors (DTs) at neighboring voxels within a DT image, or at corresponding locations across different DT images, is a fundamental and ubiquitous operation in the post-processing of DT images. The morphological similarity of DTs typically has been computed using either the Principal Directions (PDs) of DTs (i.e., the direction along which water molecules diffuse preferentially) or their tensor elements. Although comparing PDs allows the similarity of one morphological feature of DTs to be visualized directly in eigenspace, this method takes into account only a single eigenvector, and it is therefore sensitive to the presence of noise in the images that can introduce error into the estimation of that vector. Although comparing tensor elements, rather than PDs, is comparatively more robust to the effects of noise, the individual elements of a given tensor do not directly reflect the diffusion properties of water molecules. We propose a measure for computing the morphological similarity of DTs that uses both their eigenvalues and eigenvectors, and that also accounts for the noise levels present in DT images. Our measure presupposes that DTs in a homogeneous region within or across DT images are random perturbations of one another in the presence of noise. The similarity values that are computed using our method are smooth (in the sense that small changes in eigenvalues and eigenvectors cause only small changes in similarity), and they are symmetric when differences in eigenvalues and eigenvectors are also symmetric. In addition, our method does not presuppose that the corresponding eigenvectors across two DTs have been identified accurately, an assumption that is problematic in the presence of noise. Because we compute the similarity between DTs using their eigenspace components, our similarity measure relates directly to both the magnitude and the direction of the diffusion of water molecules. The favorable performance characteristics of our measure offer the prospect of substantially improving additional post-processing operations that are commonly performed on DTI datasets, such as image segmentation, fiber tracking, noise filtering, and spatial normalization. PMID:18450533

Two Trees: Migrating Fault Trees to Decision Trees for Real Time Fault Detection on International Space Station

NASA Technical Reports Server (NTRS)

Lee, Charles; Alena, Richard L.; Robinson, Peter

2004-01-01

We started from ISS fault trees example to migrate to decision trees, presented a method to convert fault trees to decision trees. The method shows that the visualizations of root cause of fault are easier and the tree manipulating becomes more programmatic via available decision tree programs. The visualization of decision trees for the diagnostic shows a format of straight forward and easy understands. For ISS real time fault diagnostic, the status of the systems could be shown by mining the signals through the trees and see where it stops at. The other advantage to use decision trees is that the trees can learn the fault patterns and predict the future fault from the historic data. The learning is not only on the static data sets but also can be online, through accumulating the real time data sets, the decision trees can gain and store faults patterns in the trees and recognize them when they come.

Verifying the distributed temperature sensing Bowen ratio method for measuring evaporation

NASA Astrophysics Data System (ADS)

Schilperoort, Bart; Coenders-Gerrits, Miriam; Luxemburg, Willem; Cisneros Vaca, César; Ucer, Murat

2016-04-01

Evaporation is an important process in the hydrological cycle, therefore measuring evaporation accurately is essential for water resource management, hydrological management and climate change models. Current techniques to measure evaporation, like eddy covariance systems, scintillometers, or lysimeters, have their limitations and therefore cannot always be used to estimate evaporation correctly. Also the conventional Bowen ratio surface energy balance method has as drawback that two sensors are used, which results in large measuring errors. In Euser et al. (2014) a new method was introduced, the DTS-based Bowen ratio (BR-DTS), that overcomes this drawback. It uses a distributed temperature sensing technique (DTS) whereby a fibre optic cable is placed vertically, going up and down along a measurement tower. One stretch of the cable is dry, the other wrapped with cloth and kept wet, akin to a psychrometer. Using this, the wet and dry bulb temperatures are determined every 12.5 cm over the height, from which the Bowen ratio can be determined. As radiation and wind have an effect on the cooling and heating of the cable's sheath as well, the DTS cables do not necessarily always measure dry and wet bulb temperature of the air accurately. In this study the accuracy in representing the dry and wet bulb temperatures of the cable are verified, and evaporation observations of the BR-DTS method are compared to Eddy Covariance (EC) measurements. Two ways to correct for errors due to wind and solar radiation warming up the DTS cables are presented: one for the dry cable and one for the wet cable. The measurements were carried out in a pine forest near Garderen (The Netherlands), along a 46-meter tall scaffold tower (15 meters above the canopy). Both the wet (Twet) and dry (Tdry) temperature of the DTS cable were compared to temperature and humidity (from which Twet is derived) observations from sensors placed along the height of the tower. Underneath the canopy, where there was barely any direct sunlight, the non-corrected temperatures correlated well for both Tdry (R2=0.998) and Twet (R2=0.995). Above the canopy the two temperature corrections worked well Tdry (R2=0.978) and Twet (R2=0.979). The comparison of the latent and sensible heat flux from the BR-DTS and the EC-system was often not possible, due to large energy balance residuals estimated during north-eastern winds (using an averaging interval of 30 minutes). For the limited days with other wind directions the BR-DTS overestimated the latent and sensible heat flux. Additionally, we even found that the applied temperature corrections resulted in a lower performance due to increased uncertainties in the applied corrections. Furthermore, we found that both the corrected and uncorrected DTS-temperatures resulted in rather similar latent and sensible heat fluxes, due to the fact that BR-DTS applies gradients of temperatures over the height, rather than absolute values. Hence, based on our limited data, the correction methods are not recommended if one is interested in the fluxes.

Conceptual design of a divertor Thomson scattering diagnostic for NSTX-U

DOE Office of Scientific and Technical Information (OSTI.GOV)

McLean, A. G., E-mail: mclean@fusion.gat.com; Soukhanovskii, V. A.; Allen, S. L.

2014-11-15

A conceptual design for a divertor Thomson scattering (DTS) diagnostic has been developed for the NSTX-U device to operate in parallel with the existing multipoint Thomson scattering system. Higher projected peak heat flux in NSTX-U will necessitate application of advanced magnetics geometries and divertor detachment. Interpretation and modeling of these divertor scenarios will depend heavily on local measurement of electron temperature, T{sub e}, and density, n{sub e}, which DTS provides in a passive manner. The DTS design for NSTX-U adopts major elements from the successful DIII-D DTS system including 7-channel polychromators measuring T{sub e} to 0.5 eV. If implemented onmore » NSTX-U, the divertor TS system would provide an invaluable diagnostic for the boundary program to characterize the edge plasma.« less

Gene expression promoted by the SV40 DNA targeting sequence and the hypoxia-responsive element under normoxia and hypoxia.

PubMed

Sacramento, C B; Moraes, J Z; Denapolis, P M A; Han, S W

2010-08-01

The main objective of the present study was to find suitable DNA-targeting sequences (DTS) for the construction of plasmid vectors to be used to treat ischemic diseases. The well-known Simian virus 40 nuclear DTS (SV40-DTS) and hypoxia-responsive element (HRE) sequences were used to construct plasmid vectors to express the human vascular endothelial growth factor gene (hVEGF). The rate of plasmid nuclear transport and consequent gene expression under normoxia (20% O2) and hypoxia (less than 5% O2) were determined. Plasmids containing the SV40-DTS or HRE sequences were constructed and used to transfect the A293T cell line (a human embryonic kidney cell line) in vitro and mouse skeletal muscle cells in vivo. Plasmid transport to the nucleus was monitored by real-time PCR, and the expression level of the hVEGF gene was measured by ELISA. The in vitro nuclear transport efficiency of the SV40-DTS plasmid was about 50% lower under hypoxia, while the HRE plasmid was about 50% higher under hypoxia. Quantitation of reporter gene expression in vitro and in vivo, under hypoxia and normoxia, confirmed that the SV40-DTS plasmid functioned better under normoxia, while the HRE plasmid was superior under hypoxia. These results indicate that the efficiency of gene expression by plasmids containing DNA binding sequences is affected by the concentration of oxygen in the medium.

Interplay of Drug-Metabolizing Enzymes and Transporters in Drug Absorption and Disposition.

PubMed

Shi, Shaojun; Li, Yunqiao

2014-01-01

In recent years, the functional interplay between drug-metabolizing enzymes (DMEs) and drug transporters (DTs) in drug absorption and disposition, as well as the complex drug interactions (DIs), has become an intriguing contention, which has also been termed the "transport-metabolism interplay". The current mechanistic understanding for this interplay is first discussed. In the present article, studies investigating the interplay between cytochrome P450 enzymes (CYPs) and efflux transporters have been systematically reviewed in vitro, in situ, in silico, in animals and humans, followed by CYPs-uptake transporters, CYPs-uptake transporters-efflux transporters, and phase II metabolic enzymes-transporters interplay studies. Although several cellular, isolated organ and whole animal studies, in conjunction with simulation and modelling, have addressed the issue that DMEs and DTs can work cooperatively to affect the bioavailability of shared substrate drugs, convincing evidences in human studies are still lacking. Furthermore, the functional interplay between DMEs and DTs will be highly substrate- and dose- dependent. Additionally, we review recent studies to evaluate the influence of genetic variations in the interplay between DMEs and DTs, which might be helpful for the prediction of pharmacokinetics (PK) and possible DIs in human more correctly. There is strong evidence of coordinately regulated DEMs and DTs gene expression and protein activity (e.g. nuclear receptors). Taken together, further investigations and analysis are urgently needed to explore the functional interplay of DMEs and DTs and to delineate the underlying mechanisms.

Dysfunctional tear syndrome: dry eye disease and associated tear film disorders - new strategies for diagnosis and treatment.

PubMed

Milner, Mark S; Beckman, Kenneth A; Luchs, Jodi I; Allen, Quentin B; Awdeh, Richard M; Berdahl, John; Boland, Thomas S; Buznego, Carlos; Gira, Joseph P; Goldberg, Damien F; Goldman, David; Goyal, Raj K; Jackson, Mitchell A; Katz, James; Kim, Terry; Majmudar, Parag A; Malhotra, Ranjan P; McDonald, Marguerite B; Rajpal, Rajesh K; Raviv, Tal; Rowen, Sheri; Shamie, Neda; Solomon, Jonathan D; Stonecipher, Karl; Tauber, Shachar; Trattler, William; Walter, Keith A; Waring, George O; Weinstock, Robert J; Wiley, William F; Yeu, Elizabeth

2017-01-01

Dysfunctional tear syndrome (DTS) is a common and complex condition affecting the ocular surface. The health and normal functioning of the ocular surface is dependent on a stable and sufficient tear film. Clinician awareness of conditions affecting the ocular surface has increased in recent years because of expanded research and the publication of diagnosis and treatment guidelines pertaining to disorders resulting in DTS, including the Delphi panel treatment recommendations for DTS (2006), the International Dry Eye Workshop (DEWS) (2007), the Meibomian Gland Dysfunction (MGD) Workshop (2011), and the updated Preferred Practice Pattern guidelines from the American Academy of Ophthalmology pertaining to dry eye and blepharitis (2013). Since the publication of the existing guidelines, new diagnostic techniques and treatment options that provide an opportunity for better management of patients have become available. Clinicians are now able to access a wealth of information that can help them obtain a differential diagnosis and treatment approach for patients presenting with DTS. This review provides a practical and directed approach to the diagnosis and treatment of patients with DTS, emphasizing treatment that is tailored to the specific disease subtype as well as the severity of the condition.

Dysfunctional tear syndrome: dry eye disease and associated tear film disorders – new strategies for diagnosis and treatment

PubMed Central

Milner, Mark S.; Beckman, Kenneth A.; Luchs, Jodi I.; Allen, Quentin B.; Awdeh, Richard M.; Berdahl, John; Boland, Thomas S.; Buznego, Carlos; Gira, Joseph P.; Goldberg, Damien F.; Goldman, David; Goyal, Raj K.; Jackson, Mitchell A.; Katz, James; Kim, Terry; Majmudar, Parag A.; Malhotra, Ranjan P.; McDonald, Marguerite B.; Rajpal, Rajesh K.; Raviv, Tal; Rowen, Sheri; Shamie, Neda; Solomon, Jonathan D.; Stonecipher, Karl; Tauber, Shachar; Trattler, William; Walter, Keith A.; Waring, George O.; Weinstock, Robert J.; Wiley, William F.; Yeu, Elizabeth

2017-01-01

Dysfunctional tear syndrome (DTS) is a common and complex condition affecting the ocular surface. The health and normal functioning of the ocular surface is dependent on a stable and sufficient tear film. Clinician awareness of conditions affecting the ocular surface has increased in recent years because of expanded research and the publication of diagnosis and treatment guidelines pertaining to disorders resulting in DTS, including the Delphi panel treatment recommendations for DTS (2006), the International Dry Eye Workshop (DEWS) (2007), the Meibomian Gland Dysfunction (MGD) Workshop (2011), and the updated Preferred Practice Pattern guidelines from the American Academy of Ophthalmology pertaining to dry eye and blepharitis (2013). Since the publication of the existing guidelines, new diagnostic techniques and treatment options that provide an opportunity for better management of patients have become available. Clinicians are now able to access a wealth of information that can help them obtain a differential diagnosis and treatment approach for patients presenting with DTS. This review provides a practical and directed approach to the diagnosis and treatment of patients with DTS, emphasizing treatment that is tailored to the specific disease subtype as well as the severity of the condition. PMID:28099212

Validation of the Davidson Trauma Scale in its original and a new shorter version in people exposed to the F-27 earthquake in Chile

PubMed Central

Leiva-Bianchi, Marcelo C.; Araneda, Andrea C.

2013-01-01

Background On February 27, 2010 (F-27), an earthquake and tsunami occurred having a significant impact on the mental health of the Chilean population, leading to an increase in cases of post-traumatic stress disorder (PTSD). Objectives Within this context, validated for the first time in Chile was the Davidson Trauma Scale (DTS) using three samples (each one consisting of 200 participants), two of them random from the Chilean population. Results Reliability analyses (i.e., α=0.933), concurrent validity (63% of the items are significantly correlated with the criteria variable “degree of damage to home”) and construct validity (i.e., CMIN = 3.754, RMSEA = 0.118, NFI = 0.808, CFI = 0.850 and PNFI = 0.689) indicate validity between regular and good for DTS. However, a new short version of the scale (DTS-SF) created using the items with heavier factor weights, presented better fits (CMIN = 2.170, RMSEA = 0.077, NFI = 0.935, CFI = 0.963, PNFI = 0.697). Discussion Finally, the usefulness of DTS and DTS-SF is discussed, the latter being briefer, valid and having better psychometric characteristics. PMID:23983920

A new approach to enhance the performance of decision tree for classifying gene expression data.

PubMed

Hassan, Md; Kotagiri, Ramamohanarao

2013-12-20

Gene expression data classification is a challenging task due to the large dimensionality and very small number of samples. Decision tree is one of the popular machine learning approaches to address such classification problems. However, the existing decision tree algorithms use a single gene feature at each node to split the data into its child nodes and hence might suffer from poor performance specially when classifying gene expression dataset. By using a new decision tree algorithm where, each node of the tree consists of more than one gene, we enhance the classification performance of traditional decision tree classifiers. Our method selects suitable genes that are combined using a linear function to form a derived composite feature. To determine the structure of the tree we use the area under the Receiver Operating Characteristics curve (AUC). Experimental analysis demonstrates higher classification accuracy using the new decision tree compared to the other existing decision trees in literature. We experimentally compare the effect of our scheme against other well known decision tree techniques. Experiments show that our algorithm can substantially boost the classification performance of the decision tree.

Lower levels of serum high-density lipoprotein cholesterol are associated with a worse Duke treadmill score in men but not in women.

PubMed

Erkan, A F; Ekici, B; Demir, G G; Töre, H F

2014-01-01

High-density lipoprotein cholesterol (HDL-C) levels are inversely related to the atherosclerotic burden and are higher in women than in men. We aimed to investigate the sex-specific relationship between serum HDL-C levels and the Duke treadmill score (DTS) in this study. A total of 111 patients (59 men, 42 women) with suspected coronary artery disease (CAD) who underwent exercise treadmill test (EST) were included. Fasting blood samples were obtained for the assessment of serum lipid levels. DTS was calculated for each patient based on EST findings including ST segment deviation and symptoms. Patients were categorized into a moderate to high risk group based on the DTS score (group-I: 38 patients) and a low risk group (group-II: 63 patients). There was a significant positive correlation between serum HDL-C levels and DTS (r = 0.230; P=0.021). The mean HDL-C level was significantly higher in group-II relative to group-I (49.25 ±11.21 vs. 44.43 ± 11.18, respectively, P = 0.04). An HDL-C level less than the cut-off value of 41.39 mg/dL predicted a moderate to severe risk DTS with 65% sensitivity and 69% specificity in men (area under curve = 0.732, P = 0.004), but not in women (area under curve = 0.505, P = 0.958). After adjustment for traditional CAD risk factors (age, sex, and smoking status), the relationship of DTS to HDL-C remained significant. (P = 0.030; adjusted OR = 0.948 [95% CI, 0.904-0.995]). Low HDL-C levels may be associated with a moderate to high risk Duke treadmill score in men, but not in women. Further research is required to clarify the sex-specific relationship between HDL-C and DTS.

The use of distributed temperature sensing technology for monitoring wildland fire intensity and distribution.

NASA Astrophysics Data System (ADS)

Ochoa, C. G.; Cram, D.; Hatch, C. E.; Tyler, S. W.

2014-12-01

Distributed temperature sensing (DTS) technology offers a viable alternative for accurately measuring wildland fire intensity and distribution in real time applications. We conducted an experiment to test the use of DTS as an alternative technology to monitor prescribed fire temperatures in real time and across a broad spatial scale. The custom fiber-optic cable consisted of three fiber optic lines buffered by polyamide, copper, and polyvinyl chloride, respectively, each armored in a stainless steel tube backfilled with Nitrogen gas. The 150 m long cable was deployed in three different 20 by 26 m experimental plots of short-grass rangeland in central New Mexico. Cable was arranged to maximize coverage of the experimental plots and allow cross-comparison between two main parallel straight-line sections approximately 8 m apart. A DTS system recorded fire temperatures every three seconds and integrated every one meter. A series of five thermocouples attached to a datalogger were placed at selected locations along the cable and also recorded temperature data every three seconds on each fiber. Results indicate that in general there is good agreement between thermocouple-measured and DTS-measured temperatures. A close match in temperature between DTS and thermocouples was particularly observed during the rising limb but not so much during the decline. The metal armoring of the fiber-optic cable remained hot longer than the thermocouples after the flames had passed. The relatively short-duration, high-intensity, prescribed burn fire in each plot resulted in temperatures reaching up to 450 degrees Celsius. In addition, DTS data allow for illustration of the irregular nature of flame speed and travel path across the rangeland grasses, a phenomenon that was impossible to quantify without the use of this tool. This study adds to the understanding of using DTS as a new alternative tool for better characterizing wildland fire intensity, distribution and travel patterns, and establishes the baseline for expanding these test plot results to larger spatial scales.

Safety validation of decision trees for hepatocellular carcinoma.

PubMed

Wang, Xian-Qiang; Liu, Zhe; Lv, Wen-Ping; Luo, Ying; Yang, Guang-Yun; Li, Chong-Hui; Meng, Xiang-Fei; Liu, Yang; Xu, Ke-Sen; Dong, Jia-Hong

2015-08-21

To evaluate a different decision tree for safe liver resection and verify its efficiency. A total of 2457 patients underwent hepatic resection between January 2004 and December 2010 at the Chinese PLA General Hospital, and 634 hepatocellular carcinoma (HCC) patients were eligible for the final analyses. Post-hepatectomy liver failure (PHLF) was identified by the association of prothrombin time < 50% and serum bilirubin > 50 μmol/L (the "50-50" criteria), which were assessed at day 5 postoperatively or later. The Swiss-Clavien decision tree, Tokyo University-Makuuchi decision tree, and Chinese consensus decision tree were adopted to divide patients into two groups based on those decision trees in sequence, and the PHLF rates were recorded. The overall mortality and PHLF rate were 0.16% and 3.0%. A total of 19 patients experienced PHLF. The numbers of patients to whom the Swiss-Clavien, Tokyo University-Makuuchi, and Chinese consensus decision trees were applied were 581, 573, and 622, and the PHLF rates were 2.75%, 2.62%, and 2.73%, respectively. Significantly more cases satisfied the Chinese consensus decision tree than the Swiss-Clavien decision tree and Tokyo University-Makuuchi decision tree (P < 0.01，P < 0.01); nevertheless, the latter two shared no difference (P = 0.147). The PHLF rate exhibited no significant difference with respect to the three decision trees. The Chinese consensus decision tree expands the indications for hepatic resection for HCC patients and does not increase the PHLF rate compared to the Swiss-Clavien and Tokyo University-Makuuchi decision trees. It would be a safe and effective algorithm for hepatectomy in patients with hepatocellular carcinoma.

Monitoring the performance of a storm water separating manifold with distributed temperature sensing.

PubMed

Langeveld, J G; de Haan, C; Klootwijk, M; Schilperoort, R P S

2012-01-01

Storm water separating manifolds in house connections have been introduced as a cost effective solution to disconnect impervious areas from combined sewers. Such manifolds have been applied by the municipality of Breda, the Netherlands. In order to investigate the performance of the manifolds, a monitoring technique (distributed temperature sensing or DTS) using fiber optic cables has been applied in the sewer system of Breda. This paper describes the application of DTS as a research tool in sewer systems. DTS proves to be a powerful tool to monitor the performance of (parts of) a sewer system in time and space. The research project showed that DTS is capable of monitoring the performance of house connections and identifying locations of inflow of both sewage and storm runoff. The research results show that the performance of storm water separating manifolds varies over time, thus making them unreliable.

Identifying spatial variability of groundwater discharge in a wetland stream using a distributed temperature sensor

USGS Publications Warehouse

Lowry, Christopher S.; Walker, John F.; Hunt, Randall J.; Anderson, Mary P.

2007-01-01

Discrete zones of groundwater discharge in a stream within a peat‐dominated wetland were identified on the basis of variations in streambed temperature using a distributed temperature sensor (DTS). The DTS gives measurements of the spatial (±1 m) and temporal (15 min) variation of streambed temperature over a much larger reach of stream (>800 m) than previous methods. Isolated temperature anomalies observed along the stream correspond to focused groundwater discharge zones likely caused by soil pipes within the peat. The DTS also recorded variations in the number of temperature anomalies, where higher numbers correlated well with a gaining reach identified by stream gauging. Focused zones of groundwater discharge showed essentially no change in position over successive measurement periods. Results suggest DTS measurements will complement other techniques (e.g., seepage meters and stream gauging) and help further improve our understanding of groundwater–surface water dynamics in wetland streams.

The validity and diagnostic efficiency of the Davidson Trauma Scale in military veterans who have served since September 11th, 2001.

PubMed

McDonald, Scott D; Beckham, Jean C; Morey, Rajendra A; Calhoun, Patrick S

2009-03-01

The present study examined the psychometric properties and diagnostic efficiency of the Davidson Trauma Scale (DTS), a self-report measure of posttraumatic stress disorder (PTSD) symptoms. Participants included 158 U.S. military veterans who have served since September 11, 2001 (post-9/11). Results support the DTS as a valid self-report measure of PTSD symptoms. The DTS demonstrated good internal consistency, concurrent validity, and convergent and divergent validity. Diagnostic efficiency was excellent when discriminating between veterans with PTSD and veterans with no Axis I diagnosis. However, although satisfactory by conventional standards, efficiency was substantially attenuated when discriminating between PTSD and other Axis I diagnoses. Thus, results illustrate that potency of the DTS as a diagnostic aid was highly dependent on the comparison group used for analyses. Results are discussed in terms of applications to clinical practice and research.

Assessment of a vertical high-resolution distributed-temperature-sensing system in a shallow thermohaline environment

NASA Astrophysics Data System (ADS)

Suárez, F.; Aravena, J. E.; Hausner, M. B.; Childress, A. E.; Tyler, S. W.

2011-03-01

In shallow thermohaline-driven lakes it is important to measure temperature on fine spatial and temporal scales to detect stratification or different hydrodynamic regimes. Raman spectra distributed temperature sensing (DTS) is an approach available to provide high spatial and temporal temperature resolution. A vertical high-resolution DTS system was constructed to overcome the problems of typical methods used in the past, i.e., without disturbing the water column, and with resistance to corrosive environments. This paper describes a method to quantitatively assess accuracy, precision and other limitations of DTS systems to fully utilize the capacity of this technology, with a focus on vertical high-resolution to measure temperatures in shallow thermohaline environments. It also presents a new method to manually calibrate temperatures along the optical fiber achieving significant improved resolution. The vertical high-resolution DTS system is used to monitor the thermal behavior of a salt-gradient solar pond, which is an engineered shallow thermohaline system that allows collection and storage of solar energy for a long period of time. The vertical high-resolution DTS system monitors the temperature profile each 1.1 cm vertically and in time averages as small as 10 s. Temperature resolution as low as 0.035 °C is obtained when the data are collected at 5-min intervals.

Charge Separation and Triplet Exciton Formation Pathways in Small-Molecule Solar Cells as Studied by Time-Resolved EPR Spectroscopy

DOE PAGES

Thomson, Stuart A. J.; Niklas, Jens; Mardis, Kristy L.; ...

2017-09-13

Organic solar cells are a promising renewable energy technology, offering the advantages of mechanical flexibility and solution processability. An understanding of the electronic excited states and charge separation pathways in these systems is crucial if efficiencies are to be further improved. Here we use light induced electron paramagnetic resonance (LEPR) spectroscopy and density functional theory calculations (DFT) to study the electronic excited states, charge transfer (CT) dynamics and triplet exciton formation pathways in blends of the small molecule donors (DTS(FBTTh 2) 2, DTS(F2BTTh 2) 2, DTS(PTTh 2) 2, DTG(FBTTh 2) 2 and DTG(F2BTTh 2) 2) with the fullerene derivative PCmore » 61BM. Using high frequency EPR the g-tensor of the positive polaron on the donor molecules was determined. The experimental results are compared with DFT calculations which reveal that the spin density of the polaron is distributed over a dimer or trimer. Time-resolved EPR (TR-EPR) spectra attributed to singlet CT states were identified and the polarization patterns revealed similar charge separation dynamics in the four fluorobenzothiadiazole donors, while charge separation in the DTS(PTTh 2) 2 blend is slower. Using TR-EPR we also investigated the triplet exciton formation pathways in the blend. The polarization patterns reveal that the excitons originate from both intersystem crossing (ISC) and back electron transfer (BET) processes. The DTS(PTTh 2) 2 blend was found to contain substantially more triplet excitons formed by BET than the fluorobenzothiadiazole blends. As a result, the higher BET triplet exciton population in the DTS(PTTh 2) 2 blend is in accordance with the slower charge separation dynamics observed in this blend.« less

Solar radiative heating of fiber-optic cables used to monitor temperatures in water

NASA Astrophysics Data System (ADS)

Neilson, Bethany T.; Hatch, Christine E.; Ban, Heng; Tyler, Scott W.

2010-08-01

In recent years, applications of distributed temperature sensing (DTS) have increased in number and diversity. Because fiber-optic cables used for DTS are typically sheathed in dark UV-resistant materials, the question arises as to how shortwave solar radiation penetrating a water column influences the accuracy of absolute DTS-derived temperatures in aquatic applications. To quantify these effects, we completed a modeling effort that accounts for the effects of radiation and convection on a submersed cable to predict when solar heating may be important. Results indicate that for cables installed at shallow depths in clear, low-velocity water bodies, measurable heating of the cable is likely during peak solar radiation. However, at higher velocities, increased turbidity and/or greater depths, the effects of solar heating are immeasurable. A field study illustrated the effects of solar radiation by installing two types of fiber-optic cable at multiple water depths (from 0.05 to 0.8 m) in the center and along the sidewall of a trapezoidal canal. Thermistors were installed at similar depths and shielded from solar radiation to record absolute water temperatures. During peak radiation, thermistor data showed small temperature differences (˜0.003°C-0.04°C) between depths suggesting minor thermal stratification in the canal center. DTS data from cables at these same depths show differences of 0.01°C-0.17°C. The DTS differences cannot be explained by stratification alone and are likely evidence of additional heating from solar radiation. Sidewall thermistor strings also recorded stratification. However, corresponding DTS data suggested that bed conduction overwhelmed the effects of solar radiation.

High open-circuit voltage small-molecule p-DTS(FBTTh 2 ) 2.ICBA bulk heterojunction solar cells – morphology, excited-state dynamics, and photovoltaic performance

DOE PAGES

Ko Kyaw, Aung Ko; Gehrig, Dominik; Zhang, Jie; ...

2014-11-27

The photovoltaic performance of bulk heterojunction solar cells using the solution-processable small molecule donor 7,7'-(4,4-bis(2-ethylhexyl)-4H-silolo[3,2-b:4,5-b']dithiophene-2,6-diyl)bis(6-fluoro-4-(5'-hexyl-[2,2'-bithiophene]-5-yl)benzo[c][1,2,5]thiadiazole) (p-DTS(FBTTh 2) 2 in combination with indene-C60 bis-adduct (ICBA) as an acceptor is systematically optimized by altering the processing conditions. A high open-circuit voltage of 1 V, more than 0.2 V higher than that of a p-DTS(FBTTh 2) 2:PC 70BM blend, is achieved. However, the power conversion efficiency remains around 5% and thus is lower than ~8% previously reported for p-DTS(FBTTh 2) 2:PC 70BM. Transient absorption (TA) pump–probe spectroscopy over a wide spectral (Vis-NIR) and dynamic (fs to μs) range in combination with multivariate curvemore » resolution analysis of the TA data reveals that generation of free charges is more efficient in the blend with PC 70BM as an acceptor. In contrast, blends with ICBA create more coulombically bound interfacial charge transfer (CT) states, which recombine on the sub-nanosecond timescale by geminate recombination. Furthermore, the ns to μs charge carrier dynamics in p-DTS(FBTTh 2) 2:ICBA blends are only weakly intensity dependent implying a significant contribution of recombination from long-lived CT states and trapped charges, while those in p-DTS(FBTTh 2) 2:PC 70BM decay via an intensity-dependent recombination mechanism indicating that spatially separated (free) charge carriers are observed, which can be extracted as photocurrent from the device.« less

Charge Separation and Triplet Exciton Formation Pathways in Small Molecule Solar Cells as Studied by Time-resolved EPR Spectroscopy.

PubMed

Thomson, Stuart A J; Niklas, Jens; Mardis, Kristy L; Mallares, Christopher; Samuel, Ifor D W; Poluektov, Oleg G

2017-10-19

Organic solar cells are a promising renewable energy technology, offering the advantages of mechanical flexibility and solution processability. An understanding of the electronic excited states and charge separation pathways in these systems is crucial if efficiencies are to be further improved. Here we use light induced electron paramagnetic resonance (LEPR) spectroscopy and density functional theory calculations (DFT) to study the electronic excited states, charge transfer (CT) dynamics and triplet exciton formation pathways in blends of the small molecule donors (DTS(FBTTh 2 ) 2 , DTS(F 2 BTTh 2 ) 2 , DTS(PTTh 2 ) 2 , DTG(FBTTh 2 ) 2 and DTG(F 2 BTTh 2 ) 2 ) with the fullerene derivative PC 61 BM. Using high frequency EPR the g-tensor of the positive polaron on the donor molecules was determined. The experimental results are compared with DFT calculations which reveal that the spin density of the polaron is distributed over a dimer or trimer. Time-resolved EPR (TR-EPR) spectra attributed to singlet CT states were identified and the polarization patterns revealed similar charge separation dynamics in the four fluorobenzothiadiazole donors, while charge separation in the DTS(PTTh 2 ) 2 blend is slower. Using TR-EPR we also investigated the triplet exciton formation pathways in the blend. The polarization patterns reveal that the excitons originate from both intersystem crossing (ISC) and back electron transfer (BET) processes. The DTS(PTTh 2 ) 2 blend was found to contain substantially more triplet excitons formed by BET than the fluorobenzothiadiazole blends. The higher BET triplet exciton population in the DTS(PTTh 2 ) 2 blend is in accordance with the slower charge separation dynamics observed in this blend.

FAP-related desmoid tumors: a series of 44 patients evaluated in a cancer referral center.

PubMed

Colombo, Chiara; Foo, Wai Chin; Whiting, David; Young, Eric D; Lusby, Kristelle; Pollock, Raphael E; Lazar, Alexander J; Lev, Dina

2012-05-01

Desmoid tumors (DTs), the commonest extra-intestinal manifestation of familial adenomatosis polyposis (FAP), are monoclonal neoplasms demonstrating fibroblastic - myofibroblastic differentiation; they are locally invasive without metastatic capacity. FAP-associated DT natural history knowledge is limited; we examined patient and tumor characteristics for a FAP-DT cohort and evaluated anti-DT therapy molecular target expression levels (immunohistochemical analyses, FAP-DT tissue microarray; TMA). Forty-four patients were classified as intra-abdominal (IA; n=26), abdominal wall (AW)/extra-abdominal (EA; n=12) or concomitant IA/AW (n=6) based on DT primary diagnosis location. Positive family histories were found in 62% of FAP versus 10% of DT patients. Surgery was the mainstay therapy for AW/EW patients, whereas IA DTs received surgery, chemotherapy, radiotherapy, tamoxifen, NSAIDs, and/or imatinib. Eight of 20 completely resected DTs in the IA and AW/EA groups recurred; 12 of 38 patients in these groups (33%) developed secondary lesions elsewhere. Two intestinal mesenteric DT patients died of disease, three from other cancers, 27 are alive with disease and 12 are alive without disease. All evaluable FAP-DT exhibited nuclear β-catenin, 65% were positive for cyclin D1, and 66% expressed nuclear p53. No ERα expression was observed, but ERβ was expressed in 72%. COX2 was expressed in all evaluable FAP-DTs. KIT was rarely found in DTs but both PDGFRs and their ligands were expressed. Comparing biomarker expression (IA vs. EA DTs), only nuclear ER-ß staining was significantly higher in EA lesions (p=0.0070); no other markers were site informative. Enhanced knowledge of FAP-DT molecular underpinnings will facilitate development of novel therapeutic strategies.

Charge Separation and Triplet Exciton Formation Pathways in Small-Molecule Solar Cells as Studied by Time-Resolved EPR Spectroscopy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thomson, Stuart A. J.; Niklas, Jens; Mardis, Kristy L.

Organic solar cells are a promising renewable energy technology, offering the advantages of mechanical flexibility and solution processability. An understanding of the electronic excited states and charge separation pathways in these systems is crucial if efficiencies are to be further improved. Here we use light induced electron paramagnetic resonance (LEPR) spectroscopy and density functional theory calculations (DFT) to study the electronic excited states, charge transfer (CT) dynamics and triplet exciton formation pathways in blends of the small molecule donors (DTS(FBTTh 2) 2, DTS(F2BTTh 2) 2, DTS(PTTh 2) 2, DTG(FBTTh 2) 2 and DTG(F2BTTh 2) 2) with the fullerene derivative PCmore » 61BM. Using high frequency EPR the g-tensor of the positive polaron on the donor molecules was determined. The experimental results are compared with DFT calculations which reveal that the spin density of the polaron is distributed over a dimer or trimer. Time-resolved EPR (TR-EPR) spectra attributed to singlet CT states were identified and the polarization patterns revealed similar charge separation dynamics in the four fluorobenzothiadiazole donors, while charge separation in the DTS(PTTh 2) 2 blend is slower. Using TR-EPR we also investigated the triplet exciton formation pathways in the blend. The polarization patterns reveal that the excitons originate from both intersystem crossing (ISC) and back electron transfer (BET) processes. The DTS(PTTh 2) 2 blend was found to contain substantially more triplet excitons formed by BET than the fluorobenzothiadiazole blends. As a result, the higher BET triplet exciton population in the DTS(PTTh 2) 2 blend is in accordance with the slower charge separation dynamics observed in this blend.« less

Bed conduction impact on fiber optic distributed temperature sensing water temperature measurements

NASA Astrophysics Data System (ADS)

O'Donnell Meininger, T.; Selker, J. S.

2015-02-01

Error in distributed temperature sensing (DTS) water temperature measurements may be introduced by contact of the fiber optic cable sensor with bed materials (e.g., seafloor, lakebed, streambed). Heat conduction from the bed materials can affect cable temperature and the resulting DTS measurements. In the Middle Fork John Day River, apparent water temperature measurements were influenced by cable sensor contact with aquatic vegetation and fine sediment bed materials. Affected cable segments measured a diurnal temperature range reduced by 10% and lagged by 20-40 min relative to that of ambient stream temperature. The diurnal temperature range deeper within the vegetation-sediment bed material was reduced 70% and lagged 240 min relative to ambient stream temperature. These site-specific results illustrate the potential magnitude of bed-conduction impacts with buried DTS measurements. Researchers who deploy DTS for water temperature monitoring should understand the importance of the environment into which the cable is placed on the range and phase of temperature measurements.

Effect of microwave disinfection on compressive and tensile strengths of dental stones.

PubMed

Robati Anaraki, Mahmood; Moslehifard, Elnaz; Aminifar, Soran; Ghanati, Hamed

2013-01-01

Although microwave irradiation has been used for disinfection of dental stone casts, there are concerns regarding mechanical damage to casts during the process. The aim of this study was to evaluate the effect of microwave irradiation on the compressive strength (CS) and diametral tensile strength (DTS) of stone casts. In this in vitro study, 80 cylindrical type III and IV stone models (20 × 40 mm) were prepared and divided into 8 groups of 10. The DTS and CS of the specimens were measured by a mechanical testing machine at a crosshead speed of 0.5 cm/min after 7 times of frequent wetting, irradiating at an energy level of 600 W for 3 minutes and cooling. Data were analyzed by Student's t-test. Microwave irradiation significantly increased DTS of type III and IV to 5.23 ± 0.64 and 8.17 ± 0.94, respectively (P < 0.01). According to the results, microwave disinfection increases DTS of type III and IV stone casts without any effects on their CS.

Decision-Tree Formulation With Order-1 Lateral Execution

NASA Technical Reports Server (NTRS)

James, Mark

2007-01-01

A compact symbolic formulation enables mapping of an arbitrarily complex decision tree of a certain type into a highly computationally efficient multidimensional software object. The type of decision trees to which this formulation applies is that known in the art as the Boolean class of balanced decision trees. Parallel lateral slices of an object created by means of this formulation can be executed in constant time considerably less time than would otherwise be required. Decision trees of various forms are incorporated into almost all large software systems. A decision tree is a way of hierarchically solving a problem, proceeding through a set of true/false responses to a conclusion. By definition, a decision tree has a tree-like structure, wherein each internal node denotes a test on an attribute, each branch from an internal node represents an outcome of a test, and leaf nodes represent classes or class distributions that, in turn represent possible conclusions. The drawback of decision trees is that execution of them can be computationally expensive (and, hence, time-consuming) because each non-leaf node must be examined to determine whether to progress deeper into a tree structure or to examine an alternative. The present formulation was conceived as an efficient means of representing a decision tree and executing it in as little time as possible. The formulation involves the use of a set of symbolic algorithms to transform a decision tree into a multi-dimensional object, the rank of which equals the number of lateral non-leaf nodes. The tree can then be executed in constant time by means of an order-one table lookup. The sequence of operations performed by the algorithms is summarized as follows: 1. Determination of whether the tree under consideration can be encoded by means of this formulation. 2. Extraction of decision variables. 3. Symbolic optimization of the decision tree to minimize its form. 4. Expansion and transformation of all nested conjunctive-disjunctive paths to a flattened conjunctive form composed only of equality checks when possible. If each reduced conjunctive form contains only equality checks and all of these forms use the same variables, then the decision tree can be reduced to an order-one operation through a table lookup. The speedup to order one is accomplished by distributing each decision variable over a surface of a multidimensional object by mapping the equality constant to an index

77 FR 27457 - Ocean Transportation Intermediary License; Applicants

Federal Register 2010, 2011, 2012, 2013, 2014

2012-05-10

... FEDERAL MARITIME COMMISSION Ocean Transportation Intermediary License; Applicants Notice is hereby... license as a Non-Vessel-Operating Common Carrier (NVO) and/or Ocean Freight Forwarder (OFF)--Ocean... at (202) 523-5843 or by email at [email protected] . DTS World Cargo Services, Inc. dba DTS World Cargo...

Comparison of digital tomosynthesis and chest radiography for the detection of pulmonary nodules: systematic review and meta-analysis.

PubMed

Kim, Jun H; Lee, Kyung H; Kim, Kyoung-Tae; Kim, Hyun J; Ahn, Hyeong S; Kim, Yeo J; Lee, Ha Y; Jeon, Yong S

2016-12-01

To compare the diagnostic accuracy of digital tomosynthesis (DTS) with that of chest radiography for the detection of pulmonary nodules by meta-analysis. A systematic literature search was performed to identify relevant original studies from 1 January 1 1976 to 31 August 31 2016. The quality of included studies was assessed by quality assessment of diagnostic accuracy studies-2. Per-patient data were used to calculate the sensitivity and specificity and per-lesion data were used to calculate the detection rate. Summary receiver-operating characteristic curves were drawn for pulmonary nodule detection. 16 studies met the inclusion criteria. 1017 patients on a per-patient basis and 2159 lesions on a per-lesion basis from 16 eligible studies were evaluated. The pooled patient-based sensitivity of DTS was 0.85 [95% confidence interval (CI) 0.83-0.88] and the specificity was 0.95 (0.93-0.96). The pooled sensitivity and specificity of chest radiography were 0.47 (0.44-0.51) and 0.37 (0.34-0.40), respectively. The per-lesion detection rate was 2.90 (95% CI 2.63-3.19). DTS has higher diagnostic accuracy than chest radiography for detection of pulmonary nodules. Chest radiography has low sensitivity but similar specificity, comparable with that of DTS. Advances in knowledge: DTS has higher diagnostic accuracy than chest radiography for the detection of pulmonary nodules.

Application of a dual-resolution voxelization scheme to compressed-sensing (CS)-based iterative reconstruction in digital tomosynthesis (DTS)

NASA Astrophysics Data System (ADS)

Park, S. Y.; Kim, G. A.; Cho, H. S.; Park, C. K.; Lee, D. Y.; Lim, H. W.; Lee, H. W.; Kim, K. S.; Kang, S. Y.; Park, J. E.; Kim, W. S.; Jeon, D. H.; Je, U. K.; Woo, T. H.; Oh, J. E.

2018-02-01

In recent digital tomosynthesis (DTS), iterative reconstruction methods are often used owing to the potential to provide multiplanar images of superior image quality to conventional filtered-backprojection (FBP)-based methods. However, they require enormous computational cost in the iterative process, which has still been an obstacle to put them to practical use. In this work, we propose a new DTS reconstruction method incorporated with a dual-resolution voxelization scheme in attempt to overcome these difficulties, in which the voxels outside a small region-of-interest (ROI) containing target diagnosis are binned by 2 × 2 × 2 while the voxels inside the ROI remain unbinned. We considered a compressed-sensing (CS)-based iterative algorithm with a dual-constraint strategy for more accurate DTS reconstruction. We implemented the proposed algorithm and performed a systematic simulation and experiment to demonstrate its viability. Our results indicate that the proposed method seems to be effective for reducing computational cost considerably in iterative DTS reconstruction, keeping the image quality inside the ROI not much degraded. A binning size of 2 × 2 × 2 required only about 31.9% computational memory and about 2.6% reconstruction time, compared to those for no binning case. The reconstruction quality was evaluated in terms of the root-mean-square error (RMSE), the contrast-to-noise ratio (CNR), and the universal-quality index (UQI).

Brady's Geothermal Field DAS and DTS Surface and Borehole Array Metadata

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dante Fratta

This metadata submission includes the coordinates of the DAS and DTS surface and borehole arrays, the list of file names, and the list of recorded files during testing at the PoroTomo Natural Laboratory at Brady Hot Spring in Nevada. Testing was completed during March 2016.

Investigation of controlled flight into terrain : descriptions of flight paths for selected controlled flight into terrain (CFIT) aircraft accidents, 1985-1997

DOT National Transportation Integrated Search

1999-03-01

This report documents an investigation of the flight paths of 13 selected controlled flight into terrain (CFIT) aircraft accidents that occurred between 1985 and 1997. The Operations Assessment Division (DTS-43) and the Aviation Safety Division (DTS-...

Classification and Progression Based on CFS-GA and C5.0 Boost Decision Tree of TCM Zheng in Chronic Hepatitis B.

PubMed

Chen, Xiao Yu; Ma, Li Zhuang; Chu, Na; Zhou, Min; Hu, Yiyang

2013-01-01

Chronic hepatitis B (CHB) is a serious public health problem, and Traditional Chinese Medicine (TCM) plays an important role in the control and treatment for CHB. In the treatment of TCM, zheng discrimination is the most important step. In this paper, an approach based on CFS-GA (Correlation based Feature Selection and Genetic Algorithm) and C5.0 boost decision tree is used for zheng classification and progression in the TCM treatment of CHB. The CFS-GA performs better than the typical method of CFS. By CFS-GA, the acquired attribute subset is classified by C5.0 boost decision tree for TCM zheng classification of CHB, and C5.0 decision tree outperforms two typical decision trees of NBTree and REPTree on CFS-GA, CFS, and nonselection in comparison. Based on the critical indicators from C5.0 decision tree, important lab indicators in zheng progression are obtained by the method of stepwise discriminant analysis for expressing TCM zhengs in CHB, and alterations of the important indicators are also analyzed in zheng progression. In conclusion, all the three decision trees perform better on CFS-GA than on CFS and nonselection, and C5.0 decision tree outperforms the two typical decision trees both on attribute selection and nonselection.

TreePOD: Sensitivity-Aware Selection of Pareto-Optimal Decision Trees.

PubMed

Muhlbacher, Thomas; Linhardt, Lorenz; Moller, Torsten; Piringer, Harald

2018-01-01

Balancing accuracy gains with other objectives such as interpretability is a key challenge when building decision trees. However, this process is difficult to automate because it involves know-how about the domain as well as the purpose of the model. This paper presents TreePOD, a new approach for sensitivity-aware model selection along trade-offs. TreePOD is based on exploring a large set of candidate trees generated by sampling the parameters of tree construction algorithms. Based on this set, visualizations of quantitative and qualitative tree aspects provide a comprehensive overview of possible tree characteristics. Along trade-offs between two objectives, TreePOD provides efficient selection guidance by focusing on Pareto-optimal tree candidates. TreePOD also conveys the sensitivities of tree characteristics on variations of selected parameters by extending the tree generation process with a full-factorial sampling. We demonstrate how TreePOD supports a variety of tasks involved in decision tree selection and describe its integration in a holistic workflow for building and selecting decision trees. For evaluation, we illustrate a case study for predicting critical power grid states, and we report qualitative feedback from domain experts in the energy sector. This feedback suggests that TreePOD enables users with and without statistical background a confident and efficient identification of suitable decision trees.

VC-dimension of univariate decision trees.

PubMed

Yildiz, Olcay Taner

2015-02-01

In this paper, we give and prove the lower bounds of the Vapnik-Chervonenkis (VC)-dimension of the univariate decision tree hypothesis class. The VC-dimension of the univariate decision tree depends on the VC-dimension values of its subtrees and the number of inputs. Via a search algorithm that calculates the VC-dimension of univariate decision trees exhaustively, we show that our VC-dimension bounds are tight for simple trees. To verify that the VC-dimension bounds are useful, we also use them to get VC-generalization bounds for complexity control using structural risk minimization in decision trees, i.e., pruning. Our simulation results show that structural risk minimization pruning using the VC-dimension bounds finds trees that are more accurate as those pruned using cross validation.

Brady's Geothermal Field - DTS Raw Data

DOE Data Explorer

Thomas Coleman

2016-03-26

The submitted data correspond to the complete raw temperature datasets captured by the distributed temperature sensing (DTS) horizontal and vertical arrays during the PoroTomo Experiment. Files in each submitted resource include: .xml (level 0): Data that includes Stokes, Anti-Stokes, and Temperature data .csv (level 1): Data that includes temperature PT100: Reference probe data

VISIBILITY OF STRUCTURES OF RELEVANCE FOR PATIENTS WITH CYSTIC FIBROSIS IN CHEST TOMOSYNTHESIS: INFLUENCE OF ANATOMICAL LOCATION AND OBSERVER EXPERIENCE.

PubMed

Meltzer, Carin; Båth, Magnus; Kheddache, Susanne; Ásgeirsdóttir, Helga; Gilljam, Marita; Johnsson, Åse Allansdotter

2016-06-01

The aims of this study were to assess the visibility of pulmonary structures in patients with cystic fibrosis (CF) in digital tomosynthesis (DTS) using computed tomography (CT) as reference and to investigate the dependency on anatomical location and observer experience. Anatomical structures in predefined regions of CT images from 21 patients were identified. Three observers with different levels of experience rated the visibility of the structures in DTS by performing a head-to-head comparison with visibility in CT. Visibility of the structures in DTS was reported as equal to CT in 34 %, inferior in 52 % and superior in 14 % of the ratings. Central and peripheral lateral structures received higher visibility ratings compared with peripheral structures anteriorly, posteriorly and surrounding the diaphragm (p ≤ 0.001). Reported visibility was significantly higher for the most experienced observer (p ≤ 0.01). The results indicate that minor pathology can be difficult to visualise with DTS depending on location and observer experience. Central and peripheral lateral structures are generally well depicted. © The Author 2016. Published by Oxford University Press.

Geothermal reservoir characterization using distributed temperature sensing at Brady Geothermal Field, Nevada

DOE Office of Scientific and Technical Information (OSTI.GOV)

Patterson, Jeremy R.; Cardiff, Michael; Coleman, Thomas

Distributed temperature sensing (DTS) systems provide near real-time data collection that captures borehole spatiotemporal temperature dynamics. For this study, temperature data was collected in an observation well at an active geothermal site for a period of eight days under geothermal production conditions. Collected temperature data showcase the ability of DTS systems to detect changes to the location of the steam-water interface, visualize borehole temperature recovery — following injection of a coldwater “slug” — and identify anomalously warm and/or cool zones. The high sampling rate and spatial resolution of DTS data also shows borehole temperature dynamics that are not captured bymore » traditional pressure-temperature survey tools. Inversion of thermal recovery data using a finite-difference heat-transfer model produces a thermal-diffusivity profile that is consistent with laboratorymeasured values and correlates with identified lithologic changes within the borehole. Used alone or in conjunction with complementary data sets, DTS systems are useful tools for developing a better understanding of both reservoir rock thermal properties as well as within and near borehole fluid movement.« less

Proposal and Implementation of a Robust Sensing Method for DVB-T Signal

NASA Astrophysics Data System (ADS)

Song, Chunyi; Rahman, Mohammad Azizur; Harada, Hiroshi

This paper proposes a sensing method for TV signals of DVB-T standard to realize effective TV White Space (TVWS) Communication. In the TVWS technology trial organized by the Infocomm Development Authority (iDA) of Singapore, with regard to the sensing level and sensing time, detecting DVB-T signal at the level of -120dBm over an 8MHz channel with a sensing time below 1 second is required. To fulfill such a strict sensing requirement, we propose a smart sensing method which combines feature detection and energy detection (CFED), and is also characterized by using dynamic threshold selection (DTS) based on a threshold table to improve sensing robustness to noise uncertainty. The DTS based CFED (DTS-CFED) is evaluated by computer simulations and is also implemented into a hardware sensing prototype. The results show that the DTS-CFED achieves a detection probability above 0.9 for a target false alarm probability of 0.1 for DVB-T signals at the level of -120dBm over an 8MHz channel with the sensing time equals to 0.1 second.

VISIBILITY OF STRUCTURES OF RELEVANCE FOR PATIENTS WITH CYSTIC FIBROSIS IN CHEST TOMOSYNTHESIS: INFLUENCE OF ANATOMICAL LOCATION AND OBSERVER EXPERIENCE

PubMed Central

Meltzer, Carin; Båth, Magnus; Kheddache, Susanne; Ásgeirsdóttir, Helga; Gilljam, Marita; Johnsson, Åse Allansdotter

2016-01-01

The aims of this study were to assess the visibility of pulmonary structures in patients with cystic fibrosis (CF) in digital tomosynthesis (DTS) using computed tomography (CT) as reference and to investigate the dependency on anatomical location and observer experience. Anatomical structures in predefined regions of CT images from 21 patients were identified. Three observers with different levels of experience rated the visibility of the structures in DTS by performing a head-to-head comparison with visibility in CT. Visibility of the structures in DTS was reported as equal to CT in 34 %, inferior in 52 % and superior in 14 % of the ratings. Central and peripheral lateral structures received higher visibility ratings compared with peripheral structures anteriorly, posteriorly and surrounding the diaphragm (p ≤ 0.001). Reported visibility was significantly higher for the most experienced observer (p ≤ 0.01). The results indicate that minor pathology can be difficult to visualise with DTS depending on location and observer experience. Central and peripheral lateral structures are generally well depicted. PMID:26842827

Geothermal reservoir characterization using distributed temperature sensing at Brady Geothermal Field, Nevada

DOE PAGES

Patterson, Jeremy R.; Cardiff, Michael; Coleman, Thomas; ...

2017-12-01

Distributed temperature sensing (DTS) systems provide near real-time data collection that captures borehole spatiotemporal temperature dynamics. For this study, temperature data was collected in an observation well at an active geothermal site for a period of eight days under geothermal production conditions. Collected temperature data showcase the ability of DTS systems to detect changes to the location of the steam-water interface, visualize borehole temperature recovery — following injection of a coldwater “slug” — and identify anomalously warm and/or cool zones. The high sampling rate and spatial resolution of DTS data also shows borehole temperature dynamics that are not captured bymore » traditional pressure-temperature survey tools. Inversion of thermal recovery data using a finite-difference heat-transfer model produces a thermal-diffusivity profile that is consistent with laboratorymeasured values and correlates with identified lithologic changes within the borehole. Used alone or in conjunction with complementary data sets, DTS systems are useful tools for developing a better understanding of both reservoir rock thermal properties as well as within and near borehole fluid movement.« less

The Decision Tree: A Tool for Achieving Behavioral Change.

ERIC Educational Resources Information Center

Saren, Dru

1999-01-01

Presents a "Decision Tree" process for structuring team decision making and problem solving about specific student behavioral goals. The Decision Tree involves a sequence of questions/decisions that can be answered in "yes/no" terms. Questions address reasonableness of the goal, time factors, importance of the goal, responsibilities, safety,…

Development and acceptability testing of decision trees for self-management of prosthetic socket fit in adults with lower limb amputation.

PubMed

Lee, Daniel Joseph; Veneri, Diana A

2018-05-01

The most common complaint lower limb prosthesis users report is inadequacy of a proper socket fit. Adjustments to the residual limb-socket interface can be made by the prosthesis user without consultation of a clinician in many scenarios through skilled self-management. Decision trees guide prosthesis wearers through the self-management process, empowering them to rectify fit issues, or referring them to a clinician when necessary. This study examines the development and acceptability testing of patient-centered decision trees for lower limb prosthesis users. Decision trees underwent a four-stage process: literature review and expert consultation, designing, two-rounds of expert panel review and revisions, and target audience testing. Fifteen lower limb prosthesis users (average age 61 years) reviewed the decision trees and completed an acceptability questionnaire. Participants reported agreement of 80% or above in five of the eight questions related to acceptability of the decision trees. Disagreement was related to the level of experience of the respondent. Decision trees were found to be easy to use, illustrate correct solutions to common issues, and have terminology consistent with that of a new prosthesis user. Some users with greater than 1.5 years of experience would not use the decision trees based on their own self-management skills. Implications for Rehabilitation Discomfort of the residual limb-prosthetic socket interface is the most common reason for clinician visits. Prosthesis users can use decision trees to guide them through the process of obtaining a proper socket fit independently. Newer users may benefit from using the decision trees more than experienced users.

Minimizing the cost of translocation failure with decision-tree models that predict species' behavioral response in translocation sites.

PubMed

Ebrahimi, Mehregan; Ebrahimie, Esmaeil; Bull, C Michael

2015-08-01

The high number of failures is one reason why translocation is often not recommended. Considering how behavior changes during translocations may improve translocation success. To derive decision-tree models for species' translocation, we used data on the short-term responses of an endangered Australian skink in 5 simulated translocations with different release conditions. We used 4 different decision-tree algorithms (decision tree, decision-tree parallel, decision stump, and random forest) with 4 different criteria (gain ratio, information gain, gini index, and accuracy) to investigate how environmental and behavioral parameters may affect the success of a translocation. We assumed behavioral changes that increased dispersal away from a release site would reduce translocation success. The trees became more complex when we included all behavioral parameters as attributes, but these trees yielded more detailed information about why and how dispersal occurred. According to these complex trees, there were positive associations between some behavioral parameters, such as fight and dispersal, that showed there was a higher chance, for example, of dispersal among lizards that fought than among those that did not fight. Decision trees based on parameters related to release conditions were easier to understand and could be used by managers to make translocation decisions under different circumstances. © 2015 Society for Conservation Biology.

Soft context clustering for F0 modeling in HMM-based speech synthesis

NASA Astrophysics Data System (ADS)

Khorram, Soheil; Sameti, Hossein; King, Simon

2015-12-01

This paper proposes the use of a new binary decision tree, which we call a soft decision tree, to improve generalization performance compared to the conventional `hard' decision tree method that is used to cluster context-dependent model parameters in statistical parametric speech synthesis. We apply the method to improve the modeling of fundamental frequency, which is an important factor in synthesizing natural-sounding high-quality speech. Conventionally, hard decision tree-clustered hidden Markov models (HMMs) are used, in which each model parameter is assigned to a single leaf node. However, this `divide-and-conquer' approach leads to data sparsity, with the consequence that it suffers from poor generalization, meaning that it is unable to accurately predict parameters for models of unseen contexts: the hard decision tree is a weak function approximator. To alleviate this, we propose the soft decision tree, which is a binary decision tree with soft decisions at the internal nodes. In this soft clustering method, internal nodes select both their children with certain membership degrees; therefore, each node can be viewed as a fuzzy set with a context-dependent membership function. The soft decision tree improves model generalization and provides a superior function approximator because it is able to assign each context to several overlapped leaves. In order to use such a soft decision tree to predict the parameters of the HMM output probability distribution, we derive the smoothest (maximum entropy) distribution which captures all partial first-order moments and a global second-order moment of the training samples. Employing such a soft decision tree architecture with maximum entropy distributions, a novel speech synthesis system is trained using maximum likelihood (ML) parameter re-estimation and synthesis is achieved via maximum output probability parameter generation. In addition, a soft decision tree construction algorithm optimizing a log-likelihood measure is developed. Both subjective and objective evaluations were conducted and indicate a considerable improvement over the conventional method.

Decision trees in epidemiological research.

PubMed

Venkatasubramaniam, Ashwini; Wolfson, Julian; Mitchell, Nathan; Barnes, Timothy; JaKa, Meghan; French, Simone

2017-01-01

In many studies, it is of interest to identify population subgroups that are relatively homogeneous with respect to an outcome. The nature of these subgroups can provide insight into effect mechanisms and suggest targets for tailored interventions. However, identifying relevant subgroups can be challenging with standard statistical methods. We review the literature on decision trees, a family of techniques for partitioning the population, on the basis of covariates, into distinct subgroups who share similar values of an outcome variable. We compare two decision tree methods, the popular Classification and Regression tree (CART) technique and the newer Conditional Inference tree (CTree) technique, assessing their performance in a simulation study and using data from the Box Lunch Study, a randomized controlled trial of a portion size intervention. Both CART and CTree identify homogeneous population subgroups and offer improved prediction accuracy relative to regression-based approaches when subgroups are truly present in the data. An important distinction between CART and CTree is that the latter uses a formal statistical hypothesis testing framework in building decision trees, which simplifies the process of identifying and interpreting the final tree model. We also introduce a novel way to visualize the subgroups defined by decision trees. Our novel graphical visualization provides a more scientifically meaningful characterization of the subgroups identified by decision trees. Decision trees are a useful tool for identifying homogeneous subgroups defined by combinations of individual characteristics. While all decision tree techniques generate subgroups, we advocate the use of the newer CTree technique due to its simplicity and ease of interpretation.

All-Printed Differential Temperature Sensor for the Compensation of Bending Effects.

PubMed

Ali, Shawkat; Hassan, Arshad; Bae, Jinho; Lee, Chong Hyun; Kim, Juho

2016-11-08

Because printed resistance temperature detectors (RTDs) are affected by tension and compression of metallic patterns on flexible or curved surfaces, a significant temperature-sensing error occurs in general. Hence, we propose a differential temperature sensor (DTS) to compensate the bending effect of the printed RTDs, which is composed of two serially connected similar meander patterns fabricated back-to-back on a polyimide polyethylene terephthalate substrate through a Dimatix DMP-3000 inkjet printer using silver nanoparticles. Under mechanical deformation, the resistance of the proposed DTS is not varied significantly under the same temperature environment because its patterns vary differentially as one side experiences tension while the opposite side experiences compression. A single meander pattern of the proposed DTS has a total length of 75 mm and device dimensions of 7 × 7 mm 2 . The total resistance variation is observed to be 15.5 Ω against the temperature variation from 0 to 100 °C, and the temperature coefficient of resistance is 1.076 × 10 -3 °C -1 . The proposed DTS exhibits no significant resistance change on bendability testing down to 2 mm diameter because of mechanical deformation. In addition, it is also used to detect the curvature of a body shape down to 2 mm diameter because its resistance changes by ±8.22% using a single meander pattern of DTS. The proposed sensor can be applied on a curved or flexible surface to measure relatively accurate temperature when compared to a single meander pattern.

Comparison of digital tomosynthesis and chest radiography for the detection of pulmonary nodules: systematic review and meta-analysis

PubMed Central

Kim, Jun H; Lee, Kyung H; Kim, Kyoung-Tae; Ahn, Hyeong S; Kim, Yeo J; Lee, Ha Y; Jeon, Yong S

2016-01-01

Objective: To compare the diagnostic accuracy of digital tomosynthesis (DTS) with that of chest radiography for the detection of pulmonary nodules by meta-analysis. Methods: A systematic literature search was performed to identify relevant original studies from 1 January 1 1976 to 31 August 31 2016. The quality of included studies was assessed by quality assessment of diagnostic accuracy studies-2. Per-patient data were used to calculate the sensitivity and specificity and per-lesion data were used to calculate the detection rate. Summary receiver-operating characteristic curves were drawn for pulmonary nodule detection. Results: 16 studies met the inclusion criteria. 1017 patients on a per-patient basis and 2159 lesions on a per-lesion basis from 16 eligible studies were evaluated. The pooled patient-based sensitivity of DTS was 0.85 [95% confidence interval (CI) 0.83–0.88] and the specificity was 0.95 (0.93–0.96). The pooled sensitivity and specificity of chest radiography were 0.47 (0.44–0.51) and 0.37 (0.34–0.40), respectively. The per-lesion detection rate was 2.90 (95% CI 2.63–3.19). Conclusion: DTS has higher diagnostic accuracy than chest radiography for detection of pulmonary nodules. Chest radiography has low sensitivity but similar specificity, comparable with that of DTS. Advances in knowledge: DTS has higher diagnostic accuracy than chest radiography for the detection of pulmonary nodules. PMID:27759428

The effect of instrument lubricant on the diametral tensile strength and water uptake of posterior composite restorative material.

PubMed

Patel, J; Granger, C; Parker, S; Patel, M

2017-01-01

This in-vitro study investigated the effect of 'instrument lubricants' used during placement of composite restorative material, on the diametral tensile strength (DTS) and water uptake of composite specimens. 300 posterior composite cylindrical specimens were manufactured: 60 with each instrument lubricant (ethanol, 3-step, 2-step and 1-step 'bonding agent') and 60 with no lubricant (controls). Each set of 60 specimens was evenly allocated to one of the following test groups (n=100/group): Group 1 - tested for DTS immediately after manufacture; Groups 2 and 3 - tested for DTS after immersion in phosphate-buffered saline (PBS) for 1 and 12-weeks respectively, using a Universal Instron machine. Water uptake was assessed gravimetrically. Data were statistically analysed with two-way ANOVA and Tukey's post hoc test (α=0.05). The mean DTS and percentage weight change of composite specimens ranged between 32.49-53.14MPa and 0.51-1.36% and varied with lubricant used and time incubated in PBS. All control groups exhibited significantly higher DTS (MPa) (groups 1-3: 53.17±1.78; 50.64±1.85; 45.17±1.77) and lower percentage weight change (groups 2-3: 0.51±0.03; 0.61±0.01) than specimens placed with an instrument lubricant, with significant differences between certain lubricant groups. Data from the present study suggest that the use of instrument lubricant may adversely effect the DTS and water uptake of composite restorative material. The use of instrument lubricants to aid composite placement is widespread however based on the data obtained it is suggested that discontinuing or limiting the use of instrument lubricants, and if necessary using the 'bonding agent' from a 3-step adhesive system is recommended as results suggest this has the least deleterious effect upon material properties.​. Copyright © 2016 Elsevier Ltd. All rights reserved.

Time-lapse ERT and DTS for seasonal and short-term monitoring of an alpine river hyporheic zone

NASA Astrophysics Data System (ADS)

Boaga, Jacopo; Laura, Busato; Mariateresa, Perri; Giorgio, Cassiani

2016-04-01

The hyporheic zone (HZ) is the area located beneath and adjacent to rivers and streams, where the interactions between surface water and groundwater take place. This complex physical domain allows the transport of several substances from a stream to the unconfined aquifer below, and vice versa, thus playing a fundamental role in the river ecosystem. The importance of the hyporheic zone makes its characterization a goal shared by several disciplines, which range from applied geophysics to biogeochemistry, from hydraulics to ecology. The frontier field of HZ characterization stays in applied non-invasive methodologies as Electrical Resistivity Tomography - ERT - and Distributed Temperature Sensing - DTS. ERT is commonly applied in cross-well configuration or with a superficial electrodes deployment while DTS is used in hydro-geophysics in the last decade, revealing a wide applicability to the typical issues of this field of study. DTS for hydro-geophysics studies is based on Raman scattering and employs heat as tracer and uses a fiber-optic cable to acquire temperature values. We applied both techniques for an alpine river case studies located in Val di Sole, TN, Italy. The collected measurements allow high-resolution characterization of the hyporheic zone, overcoming the critical problem of invasive measurements under riverbeds. In this work, we present the preliminary results regarding the characterization of the hyporheic zone of the alpine river obtained combining ERT and DTS time-lapse measurements. The data collection benefits from an innovative instrumentation deployment, which consists of both an ERT multicore cable and a DTS fiber-optic located in two separated boreholes drilled 5m under the watercourse and perpendicular to it. In particular we present the first year monitoring results and a short time-lapse monitoring experiment conducted during summer 2015. The site and the results here described are part of the EU FP7 CLIMB (Climate Induced Changes on the Hydrology of Mediterranean Basins) project.

An automated approach to the design of decision tree classifiers

NASA Technical Reports Server (NTRS)

Argentiero, P.; Chin, R.; Beaudet, P.

1982-01-01

An automated technique is presented for designing effective decision tree classifiers predicated only on a priori class statistics. The procedure relies on linear feature extractions and Bayes table look-up decision rules. Associated error matrices are computed and utilized to provide an optimal design of the decision tree at each so-called 'node'. A by-product of this procedure is a simple algorithm for computing the global probability of correct classification assuming the statistical independence of the decision rules. Attention is given to a more precise definition of decision tree classification, the mathematical details on the technique for automated decision tree design, and an example of a simple application of the procedure using class statistics acquired from an actual Landsat scene.

Creating ensembles of decision trees through sampling

DOEpatents

Kamath, Chandrika; Cantu-Paz, Erick

2005-08-30

A system for decision tree ensembles that includes a module to read the data, a module to sort the data, a module to evaluate a potential split of the data according to some criterion using a random sample of the data, a module to split the data, and a module to combine multiple decision trees in ensembles. The decision tree method is based on statistical sampling techniques and includes the steps of reading the data; sorting the data; evaluating a potential split according to some criterion using a random sample of the data, splitting the data, and combining multiple decision trees in ensembles.

Bioinformatics in proteomics: application, terminology, and pitfalls.

PubMed

Wiemer, Jan C; Prokudin, Alexander

2004-01-01

Bioinformatics applies data mining, i.e., modern computer-based statistics, to biomedical data. It leverages on machine learning approaches, such as artificial neural networks, decision trees and clustering algorithms, and is ideally suited for handling huge data amounts. In this article, we review the analysis of mass spectrometry data in proteomics, starting with common pre-processing steps and using single decision trees and decision tree ensembles for classification. Special emphasis is put on the pitfall of overfitting, i.e., of generating too complex single decision trees. Finally, we discuss the pros and cons of the two different decision tree usages.

Feasibility Study For Use Of Commercial Cask Vendor Dry Transfer Systems To Unload Used Fuel Assemblies In L-Area

DOE Office of Scientific and Technical Information (OSTI.GOV)

Krementz, Dan; Rose, David; Dunsmuir, Mike

2014-02-06

The purpose of this study is to determine whether a commercial dry transfer system (DTS) could be used for loading or unloading used nuclear fuel (UNF) in L-Basin and to determine if a DTS pool adapter could be made for L-Basin Transfer Pit #2 that could accommodate a variety of DTS casks and fuel baskets or canisters up to 24” diameter.[1, 2] This study outlines the technical feasibility of accommodating different vendor dry transfer systems in the L-Basin Transfer Bay with a general work scope. It identifies equipment needing development, facility modifications, and describes the needed analyses and calculations. Aftermore » reviewing the L-Basin Transfer Bay area layout and information on the only DTS system currently in use for the Nuclear Assurance Corporation Legal Weight Truck cask (NAC LWT), the authors conclude that use of a dry transfer cask is feasible. AREVA was contacted and acknowledged that they currently do not have a design for a dry transfer cask for their new Transnuclear Long Cask (TN-LC) cask. Nonetheless, this study accounted for a potential future DTS from AREVA to handle fuel baskets up to 18” in diameter. Due to the layout of the Transfer Bay, it was determined that a DTS cask pool adapter designed specifically for spanning Pit #2 and placed just north of the 70 Ton Cask lid lifting superstructure would be needed. The proposed pool adapter could be used to transition a fuel basket up to 24” in diameter and ~11 feet long from a dry transfer cask to the basin. The 18” and 24” applications of the pool adapter are pending vendor development of dry transfer casks that accommodate these diameters. Once a fuel basket has been lowered into Pit #2 through a pool adapter, a basket cart could be used to move the basket out from under the pool adapter for access by the 5 Ton Crane. The cost to install a dry transfer cask handling system in L-Area capable of handling multiple vendor provided transport and dry transfer casks and baskets with different diameters and lengths would likely be on the same order of magnitude as the Basin Modifications project. The cost of a DTS capability is affected by the number of design variations of different vendor transport and dry transfer casks to be considered for design input. Some costs would be incurred for each vendor DTS to be handled. For example, separate analyses would be needed for each dry transfer cask type such as criticality, shielding, dropping a dry transfer cask and basket, handling and auxiliary equipment, procedures, operator training, readiness assessments, and operational readiness reviews. A DTS handling capability in L-Area could serve as a backup to the Shielded Transfer System (STS) for unloading long casks and could support potential future missions such as the Idaho National Laboratory (INL) Exchange or transferring UNF from wet to dry storage.« less

Pharmacy Characteristics Associated with the Provision of Drug Therapy Services in Nonmetropolitan Community Pharmacies

ERIC Educational Resources Information Center

Gadkari, Abhijit S.; Mott, David A.; Kreling, David H.; Bonnarens, Joseph K.

2009-01-01

Context: Higher prevalence of chronic diseases and reduced access to other health professionals in rural areas suggest that rural Medicare enrollees will benefit from pharmacist-provided drug therapy services (DTS). Purpose: The purpose of this study was to describe non-metropolitan community pharmacy sites in Wisconsin, the provision of DTS at…

Mathematics Education ITE Students Examining the Value of Digital Learning Objects

ERIC Educational Resources Information Center

Hawera Ngarewa; Wright, Noeline; Sharma, Sashi

2017-01-01

One issue in mathematics initial teacher education (ITE) is how to best support students to use digital technologies (DTs) to enhance their teaching of mathematics. While most ITE students are probably using DTs on a daily basis for personal use, they are often unfamiliar with using them for educative purposes in New Zealand primary school…

High-resolution distributed temperature sensing with the multiphoton-timing technique

NASA Astrophysics Data System (ADS)

Höbel, M.; Ricka, J.; Wüthrich, M.; Binkert, Th.

1995-06-01

We report on a multiphoton-timing distributed temperature sensor (DTS) based on the concept of distributed anti-Stokes Raman thermometry. The sensor combines the advantage of very high spatial resolution (40 cm) with moderate measurement times. In 5 min it is possible to determine the temperature of as many as 4000 points along an optical fiber with an accuracy Delta T less than 2 deg C. The new feature of the DTS system is the combination of a fast single-photon avalanche diode with specially designed real-time signal-processing electronics. We discuss various parameters that affect the operation of analog and photon-timing DTS systems. Particular emphasis is put on the consequences of the nonideal behavior of sensor components and the corresponding correction procedures.

Learning in data-limited multimodal scenarios: Scandent decision forests and tree-based features.

PubMed

Hor, Soheil; Moradi, Mehdi

2016-12-01

Incomplete and inconsistent datasets often pose difficulties in multimodal studies. We introduce the concept of scandent decision trees to tackle these difficulties. Scandent trees are decision trees that optimally mimic the partitioning of the data determined by another decision tree, and crucially, use only a subset of the feature set. We show how scandent trees can be used to enhance the performance of decision forests trained on a small number of multimodal samples when we have access to larger datasets with vastly incomplete feature sets. Additionally, we introduce the concept of tree-based feature transforms in the decision forest paradigm. When combined with scandent trees, the tree-based feature transforms enable us to train a classifier on a rich multimodal dataset, and use it to classify samples with only a subset of features of the training data. Using this methodology, we build a model trained on MRI and PET images of the ADNI dataset, and then test it on cases with only MRI data. We show that this is significantly more effective in staging of cognitive impairments compared to a similar decision forest model trained and tested on MRI only, or one that uses other kinds of feature transform applied to the MRI data. Copyright © 2016. Published by Elsevier B.V.

Predicting membrane protein types using various decision tree classifiers based on various modes of general PseAAC for imbalanced datasets.

PubMed

Sankari, E Siva; Manimegalai, D

2017-12-21

Predicting membrane protein types is an important and challenging research area in bioinformatics and proteomics. Traditional biophysical methods are used to classify membrane protein types. Due to large exploration of uncharacterized protein sequences in databases, traditional methods are very time consuming, expensive and susceptible to errors. Hence, it is highly desirable to develop a robust, reliable, and efficient method to predict membrane protein types. Imbalanced datasets and large datasets are often handled well by decision tree classifiers. Since imbalanced datasets are taken, the performance of various decision tree classifiers such as Decision Tree (DT), Classification And Regression Tree (CART), C4.5, Random tree, REP (Reduced Error Pruning) tree, ensemble methods such as Adaboost, RUS (Random Under Sampling) boost, Rotation forest and Random forest are analysed. Among the various decision tree classifiers Random forest performs well in less time with good accuracy of 96.35%. Another inference is RUS boost decision tree classifier is able to classify one or two samples in the class with very less samples while the other classifiers such as DT, Adaboost, Rotation forest and Random forest are not sensitive for the classes with fewer samples. Also the performance of decision tree classifiers is compared with SVM (Support Vector Machine) and Naive Bayes classifier. Copyright © 2017 Elsevier Ltd. All rights reserved.

Metric Sex Determination of the Human Coxal Bone on a Virtual Sample using Decision Trees.

PubMed

Savall, Frédéric; Faruch-Bilfeld, Marie; Dedouit, Fabrice; Sans, Nicolas; Rousseau, Hervé; Rougé, Daniel; Telmon, Norbert

2015-11-01

Decision trees provide an alternative to multivariate discriminant analysis, which is still the most commonly used in anthropometric studies. Our study analyzed the metric characterization of a recent virtual sample of 113 coxal bones using decision trees for sex determination. From 17 osteometric type I landmarks, a dataset was built with five classic distances traditionally reported in the literature and six new distances selected using the two-step ratio method. A ten-fold cross-validation was performed, and a decision tree was established on two subsamples (training and test sets). The decision tree established on the training set included three nodes and its application to the test set correctly classified 92% of individuals. This percentage was similar to the data of the literature. The usefulness of decision trees has been demonstrated in numerous fields. They have been already used in sex determination, body mass prediction, and ancestry estimation. This study shows another use of decision trees enabling simple and accurate sex determination. © 2015 American Academy of Forensic Sciences.

Multi-test decision tree and its application to microarray data classification.

PubMed

Czajkowski, Marcin; Grześ, Marek; Kretowski, Marek

2014-05-01

The desirable property of tools used to investigate biological data is easy to understand models and predictive decisions. Decision trees are particularly promising in this regard due to their comprehensible nature that resembles the hierarchical process of human decision making. However, existing algorithms for learning decision trees have tendency to underfit gene expression data. The main aim of this work is to improve the performance and stability of decision trees with only a small increase in their complexity. We propose a multi-test decision tree (MTDT); our main contribution is the application of several univariate tests in each non-terminal node of the decision tree. We also search for alternative, lower-ranked features in order to obtain more stable and reliable predictions. Experimental validation was performed on several real-life gene expression datasets. Comparison results with eight classifiers show that MTDT has a statistically significantly higher accuracy than popular decision tree classifiers, and it was highly competitive with ensemble learning algorithms. The proposed solution managed to outperform its baseline algorithm on 14 datasets by an average 6%. A study performed on one of the datasets showed that the discovered genes used in the MTDT classification model are supported by biological evidence in the literature. This paper introduces a new type of decision tree which is more suitable for solving biological problems. MTDTs are relatively easy to analyze and much more powerful in modeling high dimensional microarray data than their popular counterparts. Copyright © 2014 Elsevier B.V. All rights reserved.

A system to track skin dose for neuro-interventional cone-beam computed tomography (CBCT)

NASA Astrophysics Data System (ADS)

Vijayan, Sarath; Xiong, Zhenyu; Rudin, Stephen; Bednarek, Daniel R.

2016-03-01

The skin-dose tracking system (DTS) provides a color-coded illustration of the cumulative skin-dose distribution on a closely-matching 3D graphic of the patient during fluoroscopic interventions in real-time for immediate feedback to the interventionist. The skin-dose tracking utility of DTS has been extended to include cone-beam computed tomography (CBCT) of neurointerventions. While the DTS was developed to track the entrance skin dose including backscatter, a significant part of the dose in CBCT is contributed by exit primary radiation and scatter due to the many overlapping projections during the rotational scan. The variation of backscatter inside and outside the collimated beam was measured with radiochromic film and a curve was fit to obtain a scatter spread function that could be applied in the DTS. Likewise, the exit dose distribution was measured with radiochromic film for a single projection and a correction factor was determined as a function of path length through the head. Both of these sources of skin dose are added for every projection in the CBCT scan to obtain a total dose mapping over the patient graphic. Results show the backscatter to follow a sigmoidal falloff near the edge of the beam, extending outside the beam as far as 8 cm. The exit dose measured for a cylindrical CTDI phantom was nearly 10 % of the entrance peak skin dose for the central ray. The dose mapping performed by the DTS for a CBCT scan was compared to that measured with radiochromic film and a CTDI-head phantom with good agreement.

Voxel-Wise Comparisons of the Morphology of Diffusion Tensors Across Groups of Experimental Subjects

PubMed Central

Bansal, Ravi; Staib, Lawrence H.; Plessen, Kerstin J.; Xu, Dongrong; Royal, Jason; Peterson, Bradley S.

2007-01-01

Water molecules in the brain diffuse preferentially along the fiber tracts within white matter, which form the anatomical connections across spatially distant brain regions. A diffusion tensor (DT) is a probabilistic ellipsoid composed of 3 orthogonal vectors, each having a direction and an associated scalar magnitude, that represent the probability of water molecules diffusing in each of those directions. The 3D morphologies of DTs can be compared across groups of subjects to reveal disruptions in structural organization and neuroanatomical connectivity of the brains of persons with various neuropsychiatric illnesses. Comparisons of tensor morphology across groups have typically been performed on scalar measures of diffusivity, such as Fractional Anisotropy (FA), rather than directly on the complex 3D morphologies of DTs. Scalar measures, however, are related in nonlinear ways to the eigenvalues and eigenvectors that create the 3D morphologies of DTs. We present a mathematical framework that permits the direct comparison across groups of mean eigenvalues and eigenvectors of individual DTs. We show that group-mean eigenvalues and eigenvectors are multivariate Gaussian distributed, and we use the Delta method to compute their approximate covariance matrices. Our results show that the theoretically computed Mean Tensor (MT) eigenvectors and eigenvalues match well with their respective true values. Furthermore, a comparison of synthetically generated groups of DTs highlights the limitations of using FA to detect group differences. Finally, analyses of in vivo DT data using our method reveal significant between-group differences in diffusivity along fiber tracts within white matter, whereas analyses based on FA values failed to detect some of these differences. PMID:18006284

Comprehensive decision tree models in bioinformatics.

PubMed

Stiglic, Gregor; Kocbek, Simon; Pernek, Igor; Kokol, Peter

2012-01-01

Classification is an important and widely used machine learning technique in bioinformatics. Researchers and other end-users of machine learning software often prefer to work with comprehensible models where knowledge extraction and explanation of reasoning behind the classification model are possible. This paper presents an extension to an existing machine learning environment and a study on visual tuning of decision tree classifiers. The motivation for this research comes from the need to build effective and easily interpretable decision tree models by so called one-button data mining approach where no parameter tuning is needed. To avoid bias in classification, no classification performance measure is used during the tuning of the model that is constrained exclusively by the dimensions of the produced decision tree. The proposed visual tuning of decision trees was evaluated on 40 datasets containing classical machine learning problems and 31 datasets from the field of bioinformatics. Although we did not expected significant differences in classification performance, the results demonstrate a significant increase of accuracy in less complex visually tuned decision trees. In contrast to classical machine learning benchmarking datasets, we observe higher accuracy gains in bioinformatics datasets. Additionally, a user study was carried out to confirm the assumption that the tree tuning times are significantly lower for the proposed method in comparison to manual tuning of the decision tree. The empirical results demonstrate that by building simple models constrained by predefined visual boundaries, one not only achieves good comprehensibility, but also very good classification performance that does not differ from usually more complex models built using default settings of the classical decision tree algorithm. In addition, our study demonstrates the suitability of visually tuned decision trees for datasets with binary class attributes and a high number of possibly redundant attributes that are very common in bioinformatics.

Comprehensive Decision Tree Models in Bioinformatics

PubMed Central

Stiglic, Gregor; Kocbek, Simon; Pernek, Igor; Kokol, Peter

2012-01-01

Purpose Classification is an important and widely used machine learning technique in bioinformatics. Researchers and other end-users of machine learning software often prefer to work with comprehensible models where knowledge extraction and explanation of reasoning behind the classification model are possible. Methods This paper presents an extension to an existing machine learning environment and a study on visual tuning of decision tree classifiers. The motivation for this research comes from the need to build effective and easily interpretable decision tree models by so called one-button data mining approach where no parameter tuning is needed. To avoid bias in classification, no classification performance measure is used during the tuning of the model that is constrained exclusively by the dimensions of the produced decision tree. Results The proposed visual tuning of decision trees was evaluated on 40 datasets containing classical machine learning problems and 31 datasets from the field of bioinformatics. Although we did not expected significant differences in classification performance, the results demonstrate a significant increase of accuracy in less complex visually tuned decision trees. In contrast to classical machine learning benchmarking datasets, we observe higher accuracy gains in bioinformatics datasets. Additionally, a user study was carried out to confirm the assumption that the tree tuning times are significantly lower for the proposed method in comparison to manual tuning of the decision tree. Conclusions The empirical results demonstrate that by building simple models constrained by predefined visual boundaries, one not only achieves good comprehensibility, but also very good classification performance that does not differ from usually more complex models built using default settings of the classical decision tree algorithm. In addition, our study demonstrates the suitability of visually tuned decision trees for datasets with binary class attributes and a high number of possibly redundant attributes that are very common in bioinformatics. PMID:22479449

Investigating Self-Perceptions and Resilience in Looked after Children

ERIC Educational Resources Information Center

Honey, Kyla L.; Rees, Paul; Griffey, Simon

2011-01-01

The perceptions of Looked After Children (LAC; n = 51), their Designated Teachers (DTs), and a sample of non-LAC (n = 99) were elicited. LAC held more positive self-perceptions than the non-LAC, and similarly positive ratings were given for the LAC by their DTs; but LAC held lower career aspirations than the non-LAC. LAC differed in their levels…

Brady's Geothermal Field Distributed Temperature Sensing Data

DOE Data Explorer

Patterson, Jeremy

2016-03-26

This submission is an 8 day time history of vertical temperature measurements in Brady observation well 56-1 collected during the PoroTomo field experiment. The data was collected with a fiber-optic DTS system installed to a depth of 372 m below wellhead. DTS installation uses a double-loop set up. Data includes forward length and backward length temperature measurements.

Assessment of a vertical high-resolution distributed-temperature-sensing system in a shallow thermohaline environment

NASA Astrophysics Data System (ADS)

Suárez, F.; Aravena, J. E.; Hausner, M. B.; Childress, A. E.; Tyler, S. W.

2011-01-01

In shallow thermohaline-driven lakes it is important to measure temperature on fine spatial and temporal scales to detect stratification or different hydrodynamic regimes. Raman spectra distributed temperature sensing (DTS) is an approach available to provide high spatial and temporal temperature resolution. A vertical high-resolution DTS system was constructed to overcome the problems of typical methods used in the past, i.e., without disturbing the water column, and with resistance to corrosive environments. This system monitors the temperature profile each 1.1 cm vertically and in time averages as small as 10 s. Temperature resolution as low as 0.035 °C is obtained when the data are collected at 5-min intervals. The vertical high-resolution DTS system is used to monitor the thermal behavior of a salt-gradient solar pond, which is an engineered shallow thermohaline system that allows collection and storage of solar energy for a long period of time. This paper describes a method to quantitatively assess accuracy, precision and other limitations of DTS systems to fully utilize the capacity of this technology. It also presents, for the first time, a method to manually calibrate temperatures along the optical fiber.

Fibre-optic distributed temperature sensing in combined sewer systems.

PubMed

Schilperoort, R P S; Clemens, F H L R

2009-01-01

This paper introduces the application of fibre-optic distributed temperature sensing (DTS) in combined sewer systems. The DTS-technique uses a fibre-optic cable that is inserted into a combined sewer system in combination with a laser instrument that performs measurements and logs the data. The DTS-technique allows monitoring in-sewer temperatures with dense spatial and temporal resolutions. The installation of a fibre-optic cable in a combined sewer system has proven feasible. The use of a single instrument in an easy accessible and safe location that can simultaneously monitor up to several hundreds of monitoring locations makes the DTS set-up easy in use and nearly free of maintenance. Temperature data from a one-week monitoring campaign in an 1,850 m combined sewer system shows the level of detail with which in-sewer processes that affect wastewater temperatures can be studied. Individual discharges from house-connections can be tracked in time and space. With a dedicated cable configuration the confluence of wastewater flows can be observed with a potential to derive the relative contributions of contributary flows to a total flow. Also, the inflow and in-sewer propagation of stormwater can be monitored.

Using histograms to introduce randomization in the generation of ensembles of decision trees

DOEpatents

Kamath, Chandrika; Cantu-Paz, Erick; Littau, David

2005-02-22

A system for decision tree ensembles that includes a module to read the data, a module to create a histogram, a module to evaluate a potential split according to some criterion using the histogram, a module to select a split point randomly in an interval around the best split, a module to split the data, and a module to combine multiple decision trees in ensembles. The decision tree method includes the steps of reading the data; creating a histogram; evaluating a potential split according to some criterion using the histogram, selecting a split point randomly in an interval around the best split, splitting the data, and combining multiple decision trees in ensembles.

Effect of Solar Radiation on Fiber Optic Cables Used in Distributed Temperature Sensing (DTS) Applications

NASA Astrophysics Data System (ADS)

Neilson, B. T.; Hatch, C. E.; Bingham, Q. G.; Tyler, S. W.

2008-12-01

In recent years, distributed temperature sensing (DTS) has enjoyed steady increases in the number and diversity of applications. Because fiber optic cables used for DTS are typically sheathed in dark materials resistant to UV deterioration, the question arises of how shortwave solar radiation penetrating a water column influences the accuracy of absolute DTS-derived temperatures. Initial calculations of these affects considered: shortwave radiation as a function of time of day, water depth, and water clarity; fiber optic cable dimensions; and fluid velocity. These indicate that for clear waterbodies with low velocities and shallow depths, some heating on the cable is likely during peak daily solar radiation. Given higher water velocities, substantial increases in turbidity, and/or deeper water, there should be negligible solar heating on the cable. To confirm these calculations, a field study was conducted to test the effects of solar radiation by installing two types of fiber optic cable at multiple, uniform depths in a trapezoidal canal with constant flow determined by a controlled release from Porcupine Dam near Paradise, Utah. Cables were installed in water depths from 0.05 to 0.79 m in locations of faster (center of canal) and slower (sidewall) water velocities. Thermister strings were installed at the same depths, but shielded from solar radiation and designed to record absolute water temperatures. Calculations predict that at peak solar radiation, in combination with shallow depths and slow velocities, typical fiber-optic cable is likely to experience heating greater than the ambient water column. In this study, DTS data show differences of 0.1-0.2°C in temperatures as seen by cables separated vertically by 0.31 m on the sidewall and center of the channel. Corresponding thermister data showed smaller vertical differences (~0.03-0.1°C) suggesting thermal stratification was also present in the canal. However, the magnitude of the DTS differences could not be fully explained by stratification alone. Additional information from cables installed in a shallow, near-zero velocity pool showed significantly higher temperature differences with cable depth when compared to the cable in the higher-velocity canal flows. This indicates a higher potential for heating of fiber-optic cable in stagnant, shallow waters. With sufficient water velocities and depths, the effect of shortwave solar radiation on DTS measurement accuracy via heating of the fiber- optic cable is negligible. Particular care in experimental design is recommended in shallow or low-velocity systems, including consideration of solar radiation, and independent quantification of (or calibration for) absolute temperatures.

Using Decision Trees to Detect and Isolate Simulated Leaks in the J-2X Rocket Engine

NASA Technical Reports Server (NTRS)

Schwabacher, Mark A.; Aguilar, Robert; Figueroa, Fernando F.

2009-01-01

The goal of this work was to use data-driven methods to automatically detect and isolate faults in the J-2X rocket engine. It was decided to use decision trees, since they tend to be easier to interpret than other data-driven methods. The decision tree algorithm automatically "learns" a decision tree by performing a search through the space of possible decision trees to find one that fits the training data. The particular decision tree algorithm used is known as C4.5. Simulated J-2X data from a high-fidelity simulator developed at Pratt & Whitney Rocketdyne and known as the Detailed Real-Time Model (DRTM) was used to "train" and test the decision tree. Fifty-six DRTM simulations were performed for this purpose, with different leak sizes, different leak locations, and different times of leak onset. To make the simulations as realistic as possible, they included simulated sensor noise, and included a gradual degradation in both fuel and oxidizer turbine efficiency. A decision tree was trained using 11 of these simulations, and tested using the remaining 45 simulations. In the training phase, the C4.5 algorithm was provided with labeled examples of data from nominal operation and data including leaks in each leak location. From the data, it "learned" a decision tree that can classify unseen data as having no leak or having a leak in one of the five leak locations. In the test phase, the decision tree produced very low false alarm rates and low missed detection rates on the unseen data. It had very good fault isolation rates for three of the five simulated leak locations, but it tended to confuse the remaining two locations, perhaps because a large leak at one of these two locations can look very similar to a small leak at the other location.

Objective consensus from decision trees.

PubMed

Putora, Paul Martin; Panje, Cedric M; Papachristofilou, Alexandros; Dal Pra, Alan; Hundsberger, Thomas; Plasswilm, Ludwig

2014-12-05

Consensus-based approaches provide an alternative to evidence-based decision making, especially in situations where high-level evidence is limited. Our aim was to demonstrate a novel source of information, objective consensus based on recommendations in decision tree format from multiple sources. Based on nine sample recommendations in decision tree format a representative analysis was performed. The most common (mode) recommendations for each eventuality (each permutation of parameters) were determined. The same procedure was applied to real clinical recommendations for primary radiotherapy for prostate cancer. Data was collected from 16 radiation oncology centres, converted into decision tree format and analyzed in order to determine the objective consensus. Based on information from multiple sources in decision tree format, treatment recommendations can be assessed for every parameter combination. An objective consensus can be determined by means of mode recommendations without compromise or confrontation among the parties. In the clinical example involving prostate cancer therapy, three parameters were used with two cut-off values each (Gleason score, PSA, T-stage) resulting in a total of 27 possible combinations per decision tree. Despite significant variations among the recommendations, a mode recommendation could be found for specific combinations of parameters. Recommendations represented as decision trees can serve as a basis for objective consensus among multiple parties.

Effects of neutral sodium hydrogen phosphate on setting reaction and mechanical strength of hydroxyapatite putty.

PubMed

Ishikawa, K; Miyamoto, Y; Takechi, M; Ueyama, Y; Suzuki, K; Nagayama, M; Matsumura, T

1999-03-05

The setting reaction and mechanical strength in terms of diametral tensile strength (DTS) of hydroxyapatite (HAP) putty made of tetracalcium phosphate, dicalcium phosphate anhydrous, and neutral sodium hydrogen phosphate (Na1.8H1.2PO4) solution containing 8 wt % sodium alginate were evaluated as a function of the Na1.8H1.2PO4 concentration. In one condition, HAP putty was placed in an incubator kept at 37 degrees C and 100% relative humidity. In the other condition, immediately after mixing HAP putty was immersed in serum kept at 37 degrees C. Longer setting times and lower DTS values were observed when HAP putty was immersed in serum regardless of the Na1.8H1.2PO4 concentration. The setting times of the HAP putty in both conditions became shorter with an increase in the Na1. 8H1.2PO4 concentration, reaching approximately 7-13 min when the Na1. 8H1.2PO4 concentration was 0.6 mol/L or higher. The DTS value of HAP putty was relatively constant (10 MPa) regardless of the Na1.8H1. 2PO4 concentration (0.2-1.0 mol/L) when HAP putty was kept in an incubator. In contrast, when HAP putty was immersed in serum, the DTS value was dependent on the Na1.8H1.2PO4 concentration. It increased with the Na1.8H1.2PO4 concentration and reached approximately 5 MPa when the Na1.8H1.2PO4 concentration was 0.6 mol/L, after which it showed a relatively constant DTS value. We therefore would recommend a HAP putty that uses 0.6 mol/L Na1.8H1. 2PO4 since at that concentration the putty's setting time (approximately 10 min) is proper for clinical use and it shows good DTS value (approximately 5 MPa) even when it is immersed in serum immediately after mixing. Copyright 1999 John Wiley & Sons, Inc.

SU-E-CAMPUS-I-04: Automatic Skin-Dose Mapping for An Angiographic System with a Region-Of-Interest, High-Resolution Detector

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vijayan, S; Rana, V; Setlur Nagesh, S

2014-06-15

Purpose: Our real-time skin dose tracking system (DTS) has been upgraded to monitor dose for the micro-angiographic fluoroscope (MAF), a high-resolution, small field-of-view x-ray detector. Methods: The MAF has been mounted on a changer on a clinical C-Arm gantry so it can be used interchangeably with the standard flat-panel detector (FPD) during neuro-interventional procedures when high resolution is needed in a region-of-interest. To monitor patient skin dose when using the MAF, our DTS has been modified to automatically account for the change in scatter for the very small MAF FOV and to provide separated dose distributions for each detector. Themore » DTS is able to provide a color-coded mapping of the cumulative skin dose on a 3D graphic model of the patient. To determine the correct entrance skin exposure to be applied by the DTS, a correction factor was determined by measuring the exposure at the entrance surface of a skull phantom with an ionization chamber as a function of entrance beam size for various beam filters and kVps. Entrance exposure measurements included primary radiation, patient backscatter and table forward scatter. To allow separation of the dose from each detector, a parameter log is kept that allows a replay of the procedure exposure events and recalculation of the dose components.The graphic display can then be constructed showing the dose distribution from the MAF and FPD separately or together. Results: The DTS is able to provide separate displays of dose for the MAF and FPD with field-size specific scatter corrections. These measured corrections change from about 49% down to 10% when changing from the FPD to the MAF. Conclusion: The upgraded DTS allows identification of the patient skin dose delivered when using each detector in order to achieve improved dose management as well as to facilitate peak skin-dose reduction through dose spreading. Research supported in part by Toshiba Medical Systems Corporation and NIH Grants R43FD0158401, R44FD0158402 and R01EB002873.« less

The decision tree approach to classification

NASA Technical Reports Server (NTRS)

Wu, C.; Landgrebe, D. A.; Swain, P. H.

1975-01-01

A class of multistage decision tree classifiers is proposed and studied relative to the classification of multispectral remotely sensed data. The decision tree classifiers are shown to have the potential for improving both the classification accuracy and the computation efficiency. Dimensionality in pattern recognition is discussed and two theorems on the lower bound of logic computation for multiclass classification are derived. The automatic or optimization approach is emphasized. Experimental results on real data are reported, which clearly demonstrate the usefulness of decision tree classifiers.

Improving medical diagnosis reliability using Boosted C5.0 decision tree empowered by Particle Swarm Optimization.

PubMed

Pashaei, Elnaz; Ozen, Mustafa; Aydin, Nizamettin

2015-08-01

Improving accuracy of supervised classification algorithms in biomedical applications is one of active area of research. In this study, we improve the performance of Particle Swarm Optimization (PSO) combined with C4.5 decision tree (PSO+C4.5) classifier by applying Boosted C5.0 decision tree as the fitness function. To evaluate the effectiveness of our proposed method, it is implemented on 1 microarray dataset and 5 different medical data sets obtained from UCI machine learning databases. Moreover, the results of PSO + Boosted C5.0 implementation are compared to eight well-known benchmark classification methods (PSO+C4.5, support vector machine under the kernel of Radial Basis Function, Classification And Regression Tree (CART), C4.5 decision tree, C5.0 decision tree, Boosted C5.0 decision tree, Naive Bayes and Weighted K-Nearest neighbor). Repeated five-fold cross-validation method was used to justify the performance of classifiers. Experimental results show that our proposed method not only improve the performance of PSO+C4.5 but also obtains higher classification accuracy compared to the other classification methods.

Decision tree and ensemble learning algorithms with their applications in bioinformatics.

PubMed

Che, Dongsheng; Liu, Qi; Rasheed, Khaled; Tao, Xiuping

2011-01-01

Machine learning approaches have wide applications in bioinformatics, and decision tree is one of the successful approaches applied in this field. In this chapter, we briefly review decision tree and related ensemble algorithms and show the successful applications of such approaches on solving biological problems. We hope that by learning the algorithms of decision trees and ensemble classifiers, biologists can get the basic ideas of how machine learning algorithms work. On the other hand, by being exposed to the applications of decision trees and ensemble algorithms in bioinformatics, computer scientists can get better ideas of which bioinformatics topics they may work on in their future research directions. We aim to provide a platform to bridge the gap between biologists and computer scientists.

A Decision Tree for Psychology Majors: Supplying Questions as Well as Answers.

ERIC Educational Resources Information Center

Poe, Retta E.

1988-01-01

Outlines the development of a psychology careers decision tree to help faculty advise students plan their program. States that students using the decision tree may benefit by learning more about their career options and by acquiring better question-asking skills. (GEA)

Models of practice organisation using dental therapists: English case studies.

PubMed

Sun, N; Harris, R V

2011-08-12

A new dental remuneration system based on bands of activity has changed the reward system operating in dental practices and influenced practitioner behaviour in relation to the delegation of tasks to English dental therapists (DTs). Since dental practitioners operate as independent contractors they are free to innovate. A variety of models incorporating DTs in general practice teams exist, some of which may overcome the apparent delegation constraints embedded within this system of remuneration. To describe the way different practices are organised to take account of DTs in their teams and identify whether any of these models address delegation disincentives arising from the system of remuneration. A purposive sample of six dental practices was identified, comprising two small, two medium and two large dental practices, including a variety of models of practice organisation. Semi-structured interviews were carried out with principal dentists, associate dentists, DTs, practice managers and dental hygienists (35 participants in total). A thematic analysis was applied to interview transcripts. The six dental practices demonstrated six different models of practice organisation which could be grouped into 'practice payment' and 'dentist payment' models according to whether the salary costs of the DT were met by a central practice fund or from the income of individual dentists in the team. In both of the large practices only some of the dentists in the team referred work to the DT because of reimbursement issues. In two practices the system was perceived to be satisfactory to all parties, one of these being a single-handed practice with two DTs. Although the remuneration system contained some potential disincentives to DT delegation, some practices innovated in their organisations to overcome these issues.

Modification of conventional glass-ionomer cements with N-vinylpyrrolidone containing polyacids, nano-hydroxy and fluoroapatite to improve mechanical properties.

PubMed

Moshaverinia, Alireza; Ansari, Sahar; Movasaghi, Zanyar; Billington, Richard W; Darr, Jawwad A; Rehman, Ihtesham U

2008-10-01

The objective of this study was to enhance the mechanical strength of glass-ionomer cements, while preserving their unique clinical properties. Copolymers incorporating several different segments including N-vinylpyrrolidone (NVP) in different molar ratios were synthesized. The synthesized polymers were copolymers of acrylic acid and NVP with side chains containing itaconic acid. In addition, nano-hydroxyapatite and fluoroapatite were synthesized using an ethanol-based sol-gel technique. The synthesized polymers were used in glass-ionomer cement formulations (Fuji II commercial GIC) and the synthesized nanoceramic particles (nano-hydroxy or fluoroapatite) were also incorporated into commercial glass-ionomer powder, respectively. The synthesized materials were characterized using FTIR and Raman spectroscopy and scanning electron microscopy. Compressive, diametral tensile and biaxial flexural strengths of the modified glass-ionomer cements were evaluated. After 24h setting, the NVP modified glass-ionomer cements exhibited higher compressive strength (163-167 MPa), higher diametral tensile strength (DTS) (13-17 MPa) and much higher biaxial flexural strength (23-26 MPa) in comparison to Fuji II GIC (160 MPa in CS, 12MPa in DTS and 15 MPa in biaxial flexural strength). The nano-hydroxyapatite/fluoroapatite added cements also exhibited higher CS (177-179 MPa), higher DTS (19-20 MPa) and much higher biaxial flexural strength (28-30 MPa) as compared to the control group. The highest values for CS, DTS and BFS were found for NVP-nanoceramic powder modified cements (184 MPa for CS, 22 MPa for DTS and 33 MPa for BFS) which were statistically higher than control group. It was concluded that, both NVP modified and nano-HA/FA added glass-ionomer cements are promising restorative dental materials with improved mechanical properties.

A new method to measure Bowen ratios using high-resolution vertical dry and wet bulb temperature profiles

NASA Astrophysics Data System (ADS)

Euser, T.; Luxemburg, W. M. J.; Everson, C. S.; Mengistu, M. G.; Clulow, A. D.; Bastiaanssen, W. G. M.

2014-06-01

The Bowen ratio surface energy balance method is a relatively simple method to determine the latent heat flux and the actual land surface evaporation. The Bowen ratio method is based on the measurement of air temperature and vapour pressure gradients. If these measurements are performed at only two heights, correctness of data becomes critical. In this paper we present the concept of a new measurement method to estimate the Bowen ratio based on vertical dry and wet bulb temperature profiles with high spatial resolution. A short field experiment with distributed temperature sensing (DTS) in a fibre optic cable with 13 measurement points in the vertical was undertaken. A dry and a wetted section of a fibre optic cable were suspended on a 6 m high tower installed over a sugar beet trial plot near Pietermaritzburg (South Africa). Using the DTS cable as a psychrometer, a near continuous observation of vapour pressure and air temperature at 0.20 m intervals was established. These data allowed the computation of the Bowen ratio with a high spatial and temporal precision. The daytime latent and sensible heat fluxes were estimated by combining the Bowen ratio values from the DTS-based system with independent measurements of net radiation and soil heat flux. The sensible heat flux, which is the relevant term to evaluate, derived from the DTS-based Bowen ratio (BR-DTS) was compared with that derived from co-located eddy covariance (R2 = 0.91), surface layer scintillometer (R2 = 0.81) and surface renewal (R2 = 0.86) systems. By using multiple measurement points instead of two, more confidence in the derived Bowen ratio values is obtained.

Relative Impacts of Low Permeability Subsurface Deposits on Recharge Basin Infiltration Rates

NASA Astrophysics Data System (ADS)

Oconnell, P.; Becker, M.; Pham, C.; Rodriguez, G.; Hutchinson, A.; Plumlee, M.

2017-12-01

Artificial recharge of aquifers through spreading basins has become an important component of water management in semi-arid climates. The rate at which water can be recharged in these basins is limited by the natural vertical permeability of the underlying deposits which may be highly variable both laterally and vertically. To help understand hydrostratigraphic controls on recharge, a newly constructed basin was surveyed and instrumented. Prior to flooding the basin, lithology was characterized by shallow hand coring, direct push coring, ground penetrating radar, and electrical resistivity. After flooding, recharge was monitored through piezometers, electrical resistivity, and a network of fiber optic distributed temperature sensing (DTS). The DTS network used temperature as a tracer to measure infiltration rate on 25 cm intervals both laterally and vertically. Several hundred paired DTS time series datasets (from fiber optic cables located at 0 and 0.5 meters below ground surface) were processed with the cross-wavelet transform (XWT) to calculate spatially and temporally continuous infiltration rates, which can be interpolated and animated to visualize heterogeneity. Time series data from 8-meter deep, vertically oriented DTS cables reveal depth intervals where infiltration rates vary. Inverted resistivity sections from repeated dipole-dipole surveys along the sidewall of a spreading basin exhibit a positive correlation with the distribution of relatively high and low infiltration rates, indicating zones of preferential downward (efficient) and lateral (inefficient) flow, respectively. In contrast to other monitored basins, no perching was observed in the vertically oriented DTS cables. The variation in recharge across the basin and the appearance of subsurface lateral flow can be explained in context of the alluvial depositional environment.

Diametral tensile strength and film thickness of an experimental dental luting agent derived from castor oil.

PubMed

Carmello, Juliana Cabrini; Fais, Laiza Maria Grassi; Ribeiro, Lígia Nunes de Moraes; Claro Neto, Salvador; Guaglianoni, Dalton Geraldo; Pinelli, Lígia Antunes Pereira

2012-02-01

The need to develop new dental luting agents in order to improve the success of treatments has greatly motivated research. The aim of this study was to evaluate the diametral tensile strength (DTS) and film thickness (FT) of an experimental dental luting agent derived from castor oil (COP) with or without addition of different quantities of filler (calcium carbonate - CaCO3). Eighty specimens were manufactured (DTS N=40; FT N=40) and divided into 4 groups: Pure COP; COP 10%; COP 50% and zinc phosphate (control). The cements were mixed according to the manufacturers' recommendations and submitted to the tests. The DTS test was performed in the MTS 810 testing machine (10 KN, 0.5 mm/min). For FT test, the cements were sandwiched between two glass plates (2 cm²) and a load of 15 kg was applied vertically on the top of the specimen for 10 min. The data were analyzed by means of one-way ANOVA and Tukey's test (α=0.05). The values of DTS (MPa) were: Pure COP- 10.94 ± 1.30; COP 10%- 30.06 ± 0.64; COP 50%- 29.87 ± 0.27; zinc phosphate- 4.88 ± 0.96. The values of FT (µm) were: Pure COP- 31.09 ± 3.16; COP 10%- 17.05 ± 4.83; COP 50%- 13.03 ± 4.83; Zinc Phosphate- 20.00 ± 0.12. One-way ANOVA showed statistically significant differences among the groups (DTS - p=1.01E-40; FT - p=2.4E-10). The experimental dental luting agent with 50% of filler showed the best diametral tensile strength and film thickness.

Diametral tensile strength and film thickness of an experimental dental luting agent derived from castor oil

PubMed Central

CARMELLO, Juliana Cabrini; FAIS, Laiza Maria Grassi; RIBEIRO, Lígia Nunes de Moraes; CLARO NETO, Salvador; GUAGLIANONI, Dalton Geraldo; PINELLI, Lígia Antunes Pereira

2012-01-01

The need to develop new dental luting agents in order to improve the success of treatments has greatly motivated research. Objective The aim of this study was to evaluate the diametral tensile strength (DTS) and film thickness (FT) of an experimental dental luting agent derived from castor oil (COP) with or without addition of different quantities of filler (calcium carbonate - CaCO3). Material and Methods Eighty specimens were manufactured (DTS N=40; FT N=40) and divided into 4 groups: Pure COP; COP 10%; COP 50% and zinc phosphate (control). The cements were mixed according to the manufacturers' recommendations and submitted to the tests. The DTS test was performed in the MTS 810 testing machine (10 KN, 0.5 mm/min). For FT test, the cements were sandwiched between two glass plates (2 cm2) and a load of 15 kg was applied vertically on the top of the specimen for 10 min. The data were analyzed by means of one-way ANOVA and Tukey's test (α=0.05). Results The values of DTS (MPa) were: Pure COP- 10.94±1.30; COP 10%- 30.06±0.64; COP 50%- 29.87±0.27; zinc phosphate- 4.88±0.96. The values of FT (µm) were: Pure COP- 31.09±3.16; COP 10%- 17.05±4.83; COP 50%- 13.03±4.83; Zinc Phosphate- 20.00±0.12. One-way ANOVA showed statistically significant differences among the groups (DTS - p=1.01E-40; FT - p=2.4E-10). Conclusion The experimental dental luting agent with 50% of filler showed the best diametral tensile strength and film thickness. PMID:22437672

[Prediction of regional soil quality based on mutual information theory integrated with decision tree algorithm].

PubMed

Lin, Fen-Fang; Wang, Ke; Yang, Ning; Yan, Shi-Guang; Zheng, Xin-Yu

2012-02-01

In this paper, some main factors such as soil type, land use pattern, lithology type, topography, road, and industry type that affect soil quality were used to precisely obtain the spatial distribution characteristics of regional soil quality, mutual information theory was adopted to select the main environmental factors, and decision tree algorithm See 5.0 was applied to predict the grade of regional soil quality. The main factors affecting regional soil quality were soil type, land use, lithology type, distance to town, distance to water area, altitude, distance to road, and distance to industrial land. The prediction accuracy of the decision tree model with the variables selected by mutual information was obviously higher than that of the model with all variables, and, for the former model, whether of decision tree or of decision rule, its prediction accuracy was all higher than 80%. Based on the continuous and categorical data, the method of mutual information theory integrated with decision tree could not only reduce the number of input parameters for decision tree algorithm, but also predict and assess regional soil quality effectively.

The value of decision tree analysis in planning anaesthetic care in obstetrics.

PubMed

Bamber, J H; Evans, S A

2016-08-01

The use of decision tree analysis is discussed in the context of the anaesthetic and obstetric management of a young pregnant woman with joint hypermobility syndrome with a history of insensitivity to local anaesthesia and a previous difficult intubation due to a tongue tumour. The multidisciplinary clinical decision process resulted in the woman being delivered without complication by elective caesarean section under general anaesthesia after an awake fibreoptic intubation. The decision process used is reviewed and compared retrospectively to a decision tree analytical approach. The benefits and limitations of using decision tree analysis are reviewed and its application in obstetric anaesthesia is discussed. Copyright © 2016 Elsevier Ltd. All rights reserved.

Building of fuzzy decision trees using ID3 algorithm

NASA Astrophysics Data System (ADS)

Begenova, S. B.; Avdeenko, T. V.

2018-05-01

Decision trees are widely used in the field of machine learning and artificial intelligence. Such popularity is due to the fact that with the help of decision trees graphic models, text rules can be built and they are easily understood by the final user. Because of the inaccuracy of observations, uncertainties, the data, collected in the environment, often take an unclear form. Therefore, fuzzy decision trees becoming popular in the field of machine learning. This article presents a method that includes the features of the two above-mentioned approaches: a graphical representation of the rules system in the form of a tree and a fuzzy representation of the data. The approach uses such advantages as high comprehensibility of decision trees and the ability to cope with inaccurate and uncertain information in fuzzy representation. The received learning method is suitable for classifying problems with both numerical and symbolic features. In the article, solution illustrations and numerical results are given.

Evolutionary Algorithm Based Automated Reverse Engineering and Defect Discovery

DTIC Science & Technology

2007-09-21

a previous application of a GP as a data mining function to evolve fuzzy decision trees symbolically [3-5], the terminal set consisted of fuzzy...of input and output information is required. In the case of fuzzy decision trees, the database represented a collection of scenarios about which the...fuzzy decision tree to be evolved would make decisions . The database also had entries created by experts representing decisions about the scenarios

Dithienogermole as a fused electron donor in bulk heterojunction solar cells.

PubMed

Amb, Chad M; Chen, Song; Graham, Kenneth R; Subbiah, Jegadesan; Small, Cephas E; So, Franky; Reynolds, John R

2011-07-06

We report the synthesis and bulk heterojunction photovoltaic performance of the first dithienogermole (DTG)-containing conjugated polymer. Stille polycondensation of a distannyl-DTG derivative with 1,3-dibromo-N-octyl-thienopyrrolodione (TPD) results in an alternating copolymer which displays light absorption extending to 735 nm, and a higher HOMO level than the analogous copolymer containing the commonly utilized dithienosilole (DTS) heterocycle. When polyDTG-TPD:PC(70)BM blends are utilized in inverted bulk heterojunction solar cells, the cells display average power conversion efficiencies of 7.3%, compared to 6.6% for the DTS-containing cells prepared in parallel under identical conditions. The performance enhancement is a result of a higher short-circuit current and fill factor in the DTG-containing cells, which comes at the cost of a slightly lower open circuit voltage than for the DTS-based cells.

Fiber‐optic distributed temperature sensing: A new tool for assessment and monitoring of hydrologic processes

USGS Publications Warehouse

Lane, John W.; Day-Lewis, Frederick D.; Johnson, Carole D.; Dawson, Cian B.; Nelms, David L.; Miller, Cheryl; Wheeler, Jerrod D.; Harvey, Charles F.; Karam, Hanan N.

2008-01-01

Fiber‐optic distributed temperature sensing (FO DTS) is an emerging technology for characterizing and monitoring a wide range of important earth processes. FO DTS utilizes laser light to measure temperature along the entire length of standard telecommunications optical fibers. The technology can measure temperature every meter over FO cables up to 30 kilometers (km) long. Commercially available systems can measure fiber temperature as often as 4 times per minute, with thermal precision ranging from 0.1 to 0.01 °C depending on measurement integration time. In 2006, the U.S. Geological Survey initiated a project to demonstrate and evaluate DTS as a technology to support hydrologic studies. This paper demonstrates the potential of the technology to assess and monitor hydrologic processes through case‐study examples of FO DTS monitoring of stream‐aquifer interaction on the Shenandoah River near Locke's Mill, Virginia, and on Fish Creek, near Jackson Hole, Wyoming, and estuary‐aquifer interaction on Waquoit Bay, Falmouth, Massachusetts. The ability to continuously observe temperature over large spatial scales with high spatial and temporal resolution provides a new opportunity to observe and monitor a wide range of hydrologic processes with application to other disciplines including hazards, climate‐change, and ecosystem monitoring.

DTS: The NOAO Data Transport System

NASA Astrophysics Data System (ADS)

Fitzpatrick, M.; Semple, T.

2014-05-01

The NOAO Data Transport System (DTS) provides high-throughput, reliable, data transfer between telescopes, pipelines and archive centers located in the Northern and Southern hemispheres. It is a distributed application using XML-RPC for command and control, and either parallel-TCP or UDT protocols for bulk data transport. The system is data-agnostic, allowing arbitrary files or directories to be moved using the same infrastructure. Data paths are configurable in the system by connecting nodes as the source or destination of data in a queue. Each leg of a data path may be configured independently based on the network environment between the sites. A queueing model is currently implemented to manage the automatic movement of data, a streaming model is planned to support arbitrarily large transfers (e.g. as in a disk recovery scenario) or to provide a 'pass-thru' interface to minize overheads. A web-based monitor allows anyone to get a graphical overview of the DTS system as it runs, operators will be able to control individual nodes in the system. Through careful tuning of the network paths DTS is able to achieve in excess of 80-percent of the nominal wire speed using only commodity networks, making it ideal for long-haul transport of large volumes of data.

An online dispatcher training simulator function for real-time analysis and training

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vadari, S.V.; Montstream, M.J.; Ross, H.B. Jr.

1995-11-01

Today`s power systems have become so complex that it is not easy for the system dispatcher to realistically predict the results of outages. The situation is compounded whenever the power grid is not in its normal configuration due to maintenance switching or equipment failure. The authors feel that the DTS is an excellent tool that can be used to teach the dispatcher how to react under these conditions. In this paper, the authors present an on-line implementation of the DTS which allows the user to initialize the DTS to an EMS disturbance using data that was captured at the timemore » of the disturbance; and place the DTS in a playback mode and go back to specific times in the scenario. The former feature allows the analyst to investigate EMS disturbances and then train the various dispatchers to be able to recognize such disturbances and to recover from them when they occur. The latter feature allows the instructor (with the trainee) to review and re-experience desired portions of the scenario. It is the authors` feeling that these two features will help the EMS operational staff understand their power system better and help their dispatchers in dealing with operational problems associated with the proper running of the system.« less

Nonlinear Internal Waves on the Inner Shelf: Observations Using a Distributed Temperature Sensing (DTS) System.

NASA Astrophysics Data System (ADS)

Davis, K. A.; Reid, E. C.; Cohen, A. L.

2016-02-01

Internal waves propagating across the continental slope and shelf are transformed by the competing effects of nonlinear steepening and dispersive spreading, forming nonlinear internal waves (NLIWs) that can penetrate onto the shallow inner shelf, often appearing in the form of bottom-propagating nonlinear internal bores or boluses. NLIWs play a significant role in nearshore dynamics with baroclinic current amplitudes on the order of that of wind- and surface wave-driven flows and rapid temperature changes on the order of annual ranges. In June 2014 we used a Distributed Temperature Sensing (DTS) system to give a continuous cross-shelf view of nonlinear internal wave dynamics on the forereef of Dongsha Atoll, a coral reef in the northern South China Sea. A DTS system measures temperature continuously along the length of an optical fiber, resolving meter-to-kilometer spatial scales. This unique view of cross-shelf temperature structure made it possible to observe internal wave reflection, variable propagation speed across the shelf, bolus formation and dissipation. Additionally, we used the DTS data to track internal waves across the shallow fore reef and onto the reef flat and to quantify spatial patterns in temperature variability. Shoaling internal waves are an important process affecting physical variability and water properties on the reef.

[Analysis of influence on spatial distribution of fishing ground for Antarctic krill fishery in the northern South Shetland Islands based on GWR model].

PubMed

Chen, Lyu Feng; Zhu, Guo Ping

2018-03-01

Based on Antarctic krill fishery and marine environmental data collected by scientific observers, using geographically weighted regression (GWR) model, we analyzed the effects of the factors with spatial attributes, i.e., depth of krill swarm (DKS) and distance from fishing position to shore (DTS), and sea surface temperature (SST), on the spatial distribution of fishing ground in the northern South Shetland Islands. The results showed that there was no significant aggregation in spatial distribution of catch per unit fishing effort (CPUE). Spatial autocorrelations (positive) among three factors were observed in 2010 and 2013, but were not in 2012 and 2016. Results from GWR model showed that the extent for the impacts on spatial distribution of CPUEs varied among those three factors, following the order DKS>SST>DTS. Compared to the DKS and DTS, the impact of SST on the spatial distribution of CPUEs presented adverse trend in the eastern and western parts of the South Shetland Islands. Negative correlations occurred for the spatial effects of DKS and DTS on distribution of CPUEs, though with inter-annual and regional variation. Our results provide metho-dological reference for researches on the underlying mechanism for fishing ground formation for Antarctic krill fishery.

Creating ensembles of oblique decision trees with evolutionary algorithms and sampling

DOEpatents

Cantu-Paz, Erick [Oakland, CA; Kamath, Chandrika [Tracy, CA

2006-06-13

A decision tree system that is part of a parallel object-oriented pattern recognition system, which in turn is part of an object oriented data mining system. A decision tree process includes the step of reading the data. If necessary, the data is sorted. A potential split of the data is evaluated according to some criterion. An initial split of the data is determined. The final split of the data is determined using evolutionary algorithms and statistical sampling techniques. The data is split. Multiple decision trees are combined in ensembles.

The decision tree classifier - Design and potential. [for Landsat-1 data

NASA Technical Reports Server (NTRS)

Hauska, H.; Swain, P. H.

1975-01-01

A new classifier has been developed for the computerized analysis of remote sensor data. The decision tree classifier is essentially a maximum likelihood classifier using multistage decision logic. It is characterized by the fact that an unknown sample can be classified into a class using one or several decision functions in a successive manner. The classifier is applied to the analysis of data sensed by Landsat-1 over Kenosha Pass, Colorado. The classifier is illustrated by a tree diagram which for processing purposes is encoded as a string of symbols such that there is a unique one-to-one relationship between string and decision tree.

Automated rule-base creation via CLIPS-Induce

NASA Technical Reports Server (NTRS)

Murphy, Patrick M.

1994-01-01

Many CLIPS rule-bases contain one or more rule groups that perform classification. In this paper we describe CLIPS-Induce, an automated system for the creation of a CLIPS classification rule-base from a set of test cases. CLIPS-Induce consists of two components, a decision tree induction component and a CLIPS production extraction component. ID3, a popular decision tree induction algorithm, is used to induce a decision tree from the test cases. CLIPS production extraction is accomplished through a top-down traversal of the decision tree. Nodes of the tree are used to construct query rules, and branches of the tree are used to construct classification rules. The learned CLIPS productions may easily be incorporated into a large CLIPS system that perform tasks such as accessing a database or displaying information.

Decision tree methods: applications for classification and prediction.

PubMed

Song, Yan-Yan; Lu, Ying

2015-04-25

Decision tree methodology is a commonly used data mining method for establishing classification systems based on multiple covariates or for developing prediction algorithms for a target variable. This method classifies a population into branch-like segments that construct an inverted tree with a root node, internal nodes, and leaf nodes. The algorithm is non-parametric and can efficiently deal with large, complicated datasets without imposing a complicated parametric structure. When the sample size is large enough, study data can be divided into training and validation datasets. Using the training dataset to build a decision tree model and a validation dataset to decide on the appropriate tree size needed to achieve the optimal final model. This paper introduces frequently used algorithms used to develop decision trees (including CART, C4.5, CHAID, and QUEST) and describes the SPSS and SAS programs that can be used to visualize tree structure.

Learning from examples - Generation and evaluation of decision trees for software resource analysis

NASA Technical Reports Server (NTRS)

Selby, Richard W.; Porter, Adam A.

1988-01-01

A general solution method for the automatic generation of decision (or classification) trees is investigated. The approach is to provide insights through in-depth empirical characterization and evaluation of decision trees for software resource data analysis. The trees identify classes of objects (software modules) that had high development effort. Sixteen software systems ranging from 3,000 to 112,000 source lines were selected for analysis from a NASA production environment. The collection and analysis of 74 attributes (or metrics), for over 4,700 objects, captured information about the development effort, faults, changes, design style, and implementation style. A total of 9,600 decision trees were automatically generated and evaluated. The trees correctly identified 79.3 percent of the software modules that had high development effort or faults, and the trees generated from the best parameter combinations correctly identified 88.4 percent of the modules on the average.

Quantifying the tibiofemoral joint space using x-ray tomosynthesis.

PubMed

Kalinosky, Benjamin; Sabol, John M; Piacsek, Kelly; Heckel, Beth; Gilat Schmidt, Taly

2011-12-01

Digital x-ray tomosynthesis (DTS) has the potential to provide 3D information about the knee joint in a load-bearing posture, which may improve diagnosis and monitoring of knee osteoarthritis compared with projection radiography, the current standard of care. Manually quantifying and visualizing the joint space width (JSW) from 3D tomosynthesis datasets may be challenging. This work developed a semiautomated algorithm for quantifying the 3D tibiofemoral JSW from reconstructed DTS images. The algorithm was validated through anthropomorphic phantom experiments and applied to three clinical datasets. A user-selected volume of interest within the reconstructed DTS volume was enhanced with 1D multiscale gradient kernels. The edge-enhanced volumes were divided by polarity into tibial and femoral edge maps and combined across kernel scales. A 2D connected components algorithm was performed to determine candidate tibial and femoral edges. A 2D joint space width map (JSW) was constructed to represent the 3D tibiofemoral joint space. To quantify the algorithm accuracy, an adjustable knee phantom was constructed, and eleven posterior-anterior (PA) and lateral DTS scans were acquired with the medial minimum JSW of the phantom set to 0-5 mm in 0.5 mm increments (VolumeRad™, GE Healthcare, Chalfont St. Giles, United Kingdom). The accuracy of the algorithm was quantified by comparing the minimum JSW in a region of interest in the medial compartment of the JSW map to the measured phantom setting for each trial. In addition, the algorithm was applied to DTS scans of a static knee phantom and the JSW map compared to values estimated from a manually segmented computed tomography (CT) dataset. The algorithm was also applied to three clinical DTS datasets of osteoarthritic patients. The algorithm segmented the JSW and generated a JSW map for all phantom and clinical datasets. For the adjustable phantom, the estimated minimum JSW values were plotted against the measured values for all trials. A linear fit estimated a slope of 0.887 (R² = 0.962) and a mean error across all trials of 0.34 mm for the PA phantom data. The estimated minimum JSW values for the lateral adjustable phantom acquisitions were found to have low correlation to the measured values (R² = 0.377), with a mean error of 2.13 mm. The error in the lateral adjustable-phantom datasets appeared to be caused by artifacts due to unrealistic features in the phantom bones. JSW maps generated by DTS and CT varied by a mean of 0.6 mm and 0.8 mm across the knee joint, for PA and lateral scans. The tibial and femoral edges were successfully segmented and JSW maps determined for PA and lateral clinical DTS datasets. A semiautomated method is presented for quantifying the 3D joint space in a 2D JSW map using tomosynthesis images. The proposed algorithm quantified the JSW across the knee joint to sub-millimeter accuracy for PA tomosynthesis acquisitions. Overall, the results suggest that x-ray tomosynthesis may be beneficial for diagnosing and monitoring disease progression or treatment of osteoarthritis by providing quantitative images of JSW in the load-bearing knee.

Decision-Tree Models of Categorization Response Times, Choice Proportions, and Typicality Judgments

ERIC Educational Resources Information Center

Lafond, Daniel; Lacouture, Yves; Cohen, Andrew L.

2009-01-01

The authors present 3 decision-tree models of categorization adapted from T. Trabasso, H. Rollins, and E. Shaughnessy (1971) and use them to provide a quantitative account of categorization response times, choice proportions, and typicality judgments at the individual-participant level. In Experiment 1, the decision-tree models were fit to…

Using decision trees to characterize verbal communication during change and stuck episodes in the therapeutic process

PubMed Central

Masías, Víctor H.; Krause, Mariane; Valdés, Nelson; Pérez, J. C.; Laengle, Sigifredo

2015-01-01

Methods are needed for creating models to characterize verbal communication between therapists and their patients that are suitable for teaching purposes without losing analytical potential. A technique meeting these twin requirements is proposed that uses decision trees to identify both change and stuck episodes in therapist-patient communication. Three decision tree algorithms (C4.5, NBTree, and REPTree) are applied to the problem of characterizing verbal responses into change and stuck episodes in the therapeutic process. The data for the problem is derived from a corpus of 8 successful individual therapy sessions with 1760 speaking turns in a psychodynamic context. The decision tree model that performed best was generated by the C4.5 algorithm. It delivered 15 rules characterizing the verbal communication in the two types of episodes. Decision trees are a promising technique for analyzing verbal communication during significant therapy events and have much potential for use in teaching practice on changes in therapeutic communication. The development of pedagogical methods using decision trees can support the transmission of academic knowledge to therapeutic practice. PMID:25914657

Using decision trees to characterize verbal communication during change and stuck episodes in the therapeutic process.

PubMed

Masías, Víctor H; Krause, Mariane; Valdés, Nelson; Pérez, J C; Laengle, Sigifredo

2015-01-01

Methods are needed for creating models to characterize verbal communication between therapists and their patients that are suitable for teaching purposes without losing analytical potential. A technique meeting these twin requirements is proposed that uses decision trees to identify both change and stuck episodes in therapist-patient communication. Three decision tree algorithms (C4.5, NBTree, and REPTree) are applied to the problem of characterizing verbal responses into change and stuck episodes in the therapeutic process. The data for the problem is derived from a corpus of 8 successful individual therapy sessions with 1760 speaking turns in a psychodynamic context. The decision tree model that performed best was generated by the C4.5 algorithm. It delivered 15 rules characterizing the verbal communication in the two types of episodes. Decision trees are a promising technique for analyzing verbal communication during significant therapy events and have much potential for use in teaching practice on changes in therapeutic communication. The development of pedagogical methods using decision trees can support the transmission of academic knowledge to therapeutic practice.

Computerized Adaptive Test vs. decision trees: Development of a support decision system to identify suicidal behavior.

PubMed

Delgado-Gomez, D; Baca-Garcia, E; Aguado, D; Courtet, P; Lopez-Castroman, J

2016-12-01

Several Computerized Adaptive Tests (CATs) have been proposed to facilitate assessments in mental health. These tests are built in a standard way, disregarding useful and usually available information not included in the assessment scales that could increase the precision and utility of CATs, such as the history of suicide attempts. Using the items of a previously developed scale for suicidal risk, we compared the performance of a standard CAT and a decision tree in a support decision system to identify suicidal behavior. We included the history of past suicide attempts as a class for the separation of patients in the decision tree. The decision tree needed an average of four items to achieve a similar accuracy than a standard CAT with nine items. The accuracy of the decision tree, obtained after 25 cross-validations, was 81.4%. A shortened test adapted for the separation of suicidal and non-suicidal patients was developed. CATs can be very useful tools for the assessment of suicidal risk. However, standard CATs do not use all the information that is available. A decision tree can improve the precision of the assessment since they are constructed using a priori information. Copyright © 2016 Elsevier B.V. All rights reserved.

Autonomous docking ground demonstration

NASA Technical Reports Server (NTRS)

Lamkin, Steve L.; Le, Thomas Quan; Othon, L. T.; Prather, Joseph L.; Eick, Richard E.; Baxter, Jim M.; Boyd, M. G.; Clark, Fred D.; Spehar, Peter T.; Teters, Rebecca T.

1991-01-01

The Autonomous Docking Ground Demonstration is an evaluation of the laser sensor system to support the docking phase (12 ft to contact) when operated in conjunction with the guidance, navigation, and control (GN&C) software. The docking mechanism being used was developed for the Apollo/Soyuz Test Program. This demonstration will be conducted using the 6-DOF Dynamic Test System (DTS). The DTS simulates the Space Station Freedom as the stationary or target vehicle and the Orbiter as the active or chase vehicle. For this demonstration, the laser sensor will be mounted on the target vehicle and the retroflectors will be on the chase vehicle. This arrangement was chosen to prevent potential damage to the laser. The laser sensor system, GN&C, and 6-DOF DTS will be operated closed-loop. Initial conditions to simulate vehicle misalignments, translational and rotational, will be introduced within the constraints of the systems involved.

Constructing a superhydrophobic surface on polydimethylsiloxane via spin coating and vapor-liquid sol-gel process.

PubMed

Peng, Yu-Ting; Lo, Kuo-Feng; Juang, Yi-Je

2010-04-06

In this study, a superhydrophobic surface on polydimethylsiloxane (PDMS) substrate was constructed via the proposed vapor-liquid sol-gel process in conjunction with spin coating of dodecyltrichlorosilane (DTS). Unlike the conventional sol-gel process where the reaction takes place in the liquid phase, layers of silica (SiO(2)) particles were formed through the reaction between the reactant spin-coated on the PDMS surface and vapor of the acid solution. This led to the SiO(2) particles inlaid on the PDMS surface. Followed by subsequent spin coating of DTS solution, the wrinkle-like structure was formed, and the static contact angle of the water droplet on the surface could reach 162 degrees with 2 degrees sliding angle and less than 5 degrees contact angle hysteresis. The effect of layers of SiO(2) particles, concentrations of DTS solution and surface topography on superhydrophobicity of the surface is discussed.

Factorial invariance of posttraumatic stress disorder symptoms across three veteran samples.

PubMed

McDonald, Scott D; Beckham, Jean C; Morey, Rajendra; Marx, Christine; Tupler, Larry A; Calhoun, Patrick S

2008-06-01

Research generally supports a 4-factor structure of posttraumatic stress disorder (PTSD) symptoms. However, few studies have established factor invariance by comparing multiple groups. This study examined PTSD symptom structure using the Davidson Trauma Scale (DTS) across three veteran samples: treatment-seeking Vietnam-era veterans, treatment-seeking post-Vietnam-era veterans, and Operation Enduring Freedom/Operation Iraqi Freedom (OEF/OIF) veteran research participants. Confirmatory factor analyses of DTS items demonstrated that a 4-factor structural model of the DTS (reexperiencing, avoidance, numbing, and hyperarousal) was superior to five alternate models, including the conventional 3-factor model proposed by the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV; American Psychiatric Association, 1994). Results supported factor invariance across the three veteran cohorts, suggesting that cross-group comparisons are interpretable. Implications and applications for DSM-IV nosology and the validity of symptom measures are discussed.

Do people trust dentists? Development of the Dentist Trust Scale.

PubMed

Armfield, J M; Ketting, M; Chrisopoulos, S; Baker, S R

2017-09-01

This study aimed to adapt a measure of trust in physicians to trust in dentists and to assess the reliability and validity of the measure. Questionnaire data were collected from a simple random sample of 596 Australian adults. The 11-item General Trust in Physicians Scale was modified to apply to dentists. The Dentist Trust Scale (DTS) had good internal consistency (α = 0.92) and exploratory factor analysis revealed a single-factor solution. Lower DTS scores were associated with less trust in the dentist last visited, having previously changed dentists due to unhappiness with the care received, currently having dental pain, usual visiting frequency, dental avoidance, and with past experiences of discomfort, gagging, fainting, embarrassment and personal problems with the dentist. The majority of people appear to exhibit trust in dentists. The DTS shows promising reliability and validity evidence. © 2017 Australian Dental Association.

Reconciliation of Decision-Making Heuristics Based on Decision Trees Topologies and Incomplete Fuzzy Probabilities Sets

PubMed Central

Doubravsky, Karel; Dohnal, Mirko

2015-01-01

Complex decision making tasks of different natures, e.g. economics, safety engineering, ecology and biology, are based on vague, sparse, partially inconsistent and subjective knowledge. Moreover, decision making economists / engineers are usually not willing to invest too much time into study of complex formal theories. They require such decisions which can be (re)checked by human like common sense reasoning. One important problem related to realistic decision making tasks are incomplete data sets required by the chosen decision making algorithm. This paper presents a relatively simple algorithm how some missing III (input information items) can be generated using mainly decision tree topologies and integrated into incomplete data sets. The algorithm is based on an easy to understand heuristics, e.g. a longer decision tree sub-path is less probable. This heuristic can solve decision problems under total ignorance, i.e. the decision tree topology is the only information available. But in a practice, isolated information items e.g. some vaguely known probabilities (e.g. fuzzy probabilities) are usually available. It means that a realistic problem is analysed under partial ignorance. The proposed algorithm reconciles topology related heuristics and additional fuzzy sets using fuzzy linear programming. The case study, represented by a tree with six lotteries and one fuzzy probability, is presented in details. PMID:26158662

Reconciliation of Decision-Making Heuristics Based on Decision Trees Topologies and Incomplete Fuzzy Probabilities Sets.

PubMed

Doubravsky, Karel; Dohnal, Mirko

2015-01-01

Complex decision making tasks of different natures, e.g. economics, safety engineering, ecology and biology, are based on vague, sparse, partially inconsistent and subjective knowledge. Moreover, decision making economists / engineers are usually not willing to invest too much time into study of complex formal theories. They require such decisions which can be (re)checked by human like common sense reasoning. One important problem related to realistic decision making tasks are incomplete data sets required by the chosen decision making algorithm. This paper presents a relatively simple algorithm how some missing III (input information items) can be generated using mainly decision tree topologies and integrated into incomplete data sets. The algorithm is based on an easy to understand heuristics, e.g. a longer decision tree sub-path is less probable. This heuristic can solve decision problems under total ignorance, i.e. the decision tree topology is the only information available. But in a practice, isolated information items e.g. some vaguely known probabilities (e.g. fuzzy probabilities) are usually available. It means that a realistic problem is analysed under partial ignorance. The proposed algorithm reconciles topology related heuristics and additional fuzzy sets using fuzzy linear programming. The case study, represented by a tree with six lotteries and one fuzzy probability, is presented in details.

Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients.

PubMed

Freitas, Alex A; Limbu, Kriti; Ghafourian, Taravat

2015-01-01

Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug's distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds' molecular descriptors and the compounds' tissue:plasma partition coefficients (Kt:p) - often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds' molecular descriptors but also (a subset of) their predicted Kt:p values. Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied. Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models. Graphical AbstractDecision trees for the prediction of tissue partition coefficient and volume of distribution of drugs.

Hierarchical classification of land use types using multiple vegetation indices to measure the effects of urbanization.

PubMed

Shishir, Sharmin; Tsuyuzaki, Shiro

2018-05-11

Detecting fine-scale spatiotemporal land use changes is a prerequisite for understanding and predicting the effects of urbanization and its related human impacts on the ecosystem. Land use changes are frequently examined using vegetation indices (VIs), although the validation of these indices has not been conducted at a high resolution. Therefore, a hierarchical classification was constructed to obtain accurate land use types at a fine scale. The characteristics of four popular VIs were investigated prior to examining the hierarchical classification by using Purbachal New Town, Bangladesh, which exhibits ongoing urbanization. These four VIs are the normalized difference VI (NDVI), green-red VI (GRVI), enhanced VI (EVI), and two-band EVI (EVI2). The reflectance data were obtained by the IKONOS (0.8-m resolution) and WorldView-2 sensor (0.5-m resolution) in 2001 and 2015, respectively. The hierarchical classification of land use types was constructed using a decision tree (DT) utilizing all four of the examined VIs. The accuracy of the classification was evaluated using ground truth data with multiple comparisons and kappa (κ) coefficients. The DT showed overall accuracies of 96.1 and 97.8% in 2001 and 2015, respectively, while the accuracies of the VIs were less than 91.2%. These results indicate that each VI exhibits unique advantages. In addition, the DT was the best classifier of land use types, particularly for native ecosystems represented by Shorea forests and homestead vegetation, at the fine scale. Since the conservation of these native ecosystems is of prime importance, DTs based on hierarchical classifications should be used more widely.

Cloud Detection from Satellite Imagery: A Comparison of Expert-Generated and Automatically-Generated Decision Trees

NASA Technical Reports Server (NTRS)

Shiffman, Smadar

2004-01-01

Automated cloud detection and tracking is an important step in assessing global climate change via remote sensing. Cloud masks, which indicate whether individual pixels depict clouds, are included in many of the data products that are based on data acquired on- board earth satellites. Many cloud-mask algorithms have the form of decision trees, which employ sequential tests that scientists designed based on empirical astrophysics studies and astrophysics simulations. Limitations of existing cloud masks restrict our ability to accurately track changes in cloud patterns over time. In this study we explored the potential benefits of automatically-learned decision trees for detecting clouds from images acquired using the Advanced Very High Resolution Radiometer (AVHRR) instrument on board the NOAA-14 weather satellite of the National Oceanic and Atmospheric Administration. We constructed three decision trees for a sample of 8km-daily AVHRR data from 2000 using a decision-tree learning procedure provided within MATLAB(R), and compared the accuracy of the decision trees to the accuracy of the cloud mask. We used ground observations collected by the National Aeronautics and Space Administration Clouds and the Earth s Radiant Energy Systems S COOL project as the gold standard. For the sample data, the accuracy of automatically learned decision trees was greater than the accuracy of the cloud masks included in the AVHRR data product.

Modifiable risk factors predicting major depressive disorder at four year follow-up: a decision tree approach.

PubMed

Batterham, Philip J; Christensen, Helen; Mackinnon, Andrew J

2009-11-22

Relative to physical health conditions such as cardiovascular disease, little is known about risk factors that predict the prevalence of depression. The present study investigates the expected effects of a reduction of these risks over time, using the decision tree method favoured in assessing cardiovascular disease risk. The PATH through Life cohort was used for the study, comprising 2,105 20-24 year olds, 2,323 40-44 year olds and 2,177 60-64 year olds sampled from the community in the Canberra region, Australia. A decision tree methodology was used to predict the presence of major depressive disorder after four years of follow-up. The decision tree was compared with a logistic regression analysis using ROC curves. The decision tree was found to distinguish and delineate a wide range of risk profiles. Previous depressive symptoms were most highly predictive of depression after four years, however, modifiable risk factors such as substance use and employment status played significant roles in assessing the risk of depression. The decision tree was found to have better sensitivity and specificity than a logistic regression using identical predictors. The decision tree method was useful in assessing the risk of major depressive disorder over four years. Application of the model to the development of a predictive tool for tailored interventions is discussed.

Implementation of Data Mining to Analyze Drug Cases Using C4.5 Decision Tree

NASA Astrophysics Data System (ADS)

Wahyuni, Sri

2018-03-01

Data mining was the process of finding useful information from a large set of databases. One of the existing techniques in data mining was classification. The method used was decision tree method and algorithm used was C4.5 algorithm. The decision tree method was a method that transformed a very large fact into a decision tree which was presenting the rules. Decision tree method was useful for exploring data, as well as finding a hidden relationship between a number of potential input variables with a target variable. The decision tree of the C4.5 algorithm was constructed with several stages including the selection of attributes as roots, created a branch for each value and divided the case into the branch. These stages would be repeated for each branch until all the cases on the branch had the same class. From the solution of the decision tree there would be some rules of a case. In this case the researcher classified the data of prisoners at Labuhan Deli prison to know the factors of detainees committing criminal acts of drugs. By applying this C4.5 algorithm, then the knowledge was obtained as information to minimize the criminal acts of drugs. From the findings of the research, it was found that the most influential factor of the detainee committed the criminal act of drugs was from the address variable.

An Improved Decision Tree for Predicting a Major Product in Competing Reactions

ERIC Educational Resources Information Center

Graham, Kate J.

2014-01-01

When organic chemistry students encounter competing reactions, they are often overwhelmed by the task of evaluating multiple factors that affect the outcome of a reaction. The use of a decision tree is a useful tool to teach students to evaluate a complex situation and propose a likely outcome. Specifically, a decision tree can help students…

Decision Tree Phytoremediation

DTIC Science & Technology

1999-12-01

aromatic hydrocarbons, and landfill leachates . Phytoremediation has been used for point and nonpoint source hazardous waste control. 1.2 Types of... Phytoremediation Prepared by Interstate Technology and Regulatory Cooperation Work Group Phytoremediation Work Team December 1999 Decision Tree...1999 2. REPORT TYPE N/A 3. DATES COVERED - 4. TITLE AND SUBTITLE Phytoremediation Decision Tree 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c

Mechanical properties and microstructures of glass-ionomer cements.

PubMed

Xie, D; Brantley, W A; Culbertson, B M; Wang, G

2000-03-01

The objective of this study was to determine the flexural strength (FS), compressive strength (CS), diametral tensile strength (DTS), Knoop hardness (KHN) and wear resistance of ten commercial glass-ionomer cements (GICs). The fracture surfaces of these cements were examined using scanning electron microscopic (SEM) techniques to ascertain relationships between the mechanical properties and microstructures of these cements. Specimens were fabricated according to the instructions from each manufacturer. The FS, CS, DTS, KHN and wear rate were measured after conditioning the specimens for 7 d in distilled water at 37 degrees C. One-way analysis of variance with the post hoc Tukey-Kramer multiple range test was used to determine which specimen groups were significantly different for each test. The fracture surface of one representative specimen of each GIC from the FS tests was examined using a scanning electron microscope. The resin-modified GICs (RM GICs) exhibited much higher FS and DTS, not generally higher CS, often lower Knoop hardness and generally lower wear resistance, compared to the conventional GICs (C GICs). Vitremer (3M) had the highest values of FS and DTS; Fuji II LC (GC International) and Ketac-Molar (ESPE) had the highest CS; Ketac-Fil (ESPE) had the highest KHN. Ketac-Bond (ESPE) had the lowest FS; alpha-Silver (DMG-Hamburg) had the lowest CS. Four GICs (alpha-Fil (DMG-Hamburg), alpha-Silver, Ketac-Bond and Fuji II) had the lowest values of DTS, which were not significantly different from each other; alpha-Silver and Ketac-Silver had the lowest values of KHN. The highest wear resistance was exhibited by alpha-Silver and Ketac-Fil; F2LC had the lowest wear resistance. The C GICs exhibited brittle behavior, whereas the RM GICs underwent substantial plastic deformation in compression. The more integrated the microstructure, the higher were the FS and DTS. Higher CS was correlated with smaller glass particles, and higher KHN was found where there was a combination of smaller glass particles and lower porosity. Larger glass particle sizes and a more integrated microstructure contributed to a higher wear resistance. The mechanical properties of GICs were closely related to their microstructures. Factors such as the integrity of the interface between the glass particles and the polymer matrix, the particle size, and the number and size of voids have important roles in determining the mechanical properties.

Lactococcus lactis carrying the pValac eukaryotic expression vector coding for IL-4 reduces chemically-induced intestinal inflammation by increasing the levels of IL-10-producing regulatory cells.

PubMed

Souza, Bianca Mendes; Preisser, Tatiane Melo; Pereira, Vanessa Bastos; Zurita-Turk, Meritxell; de Castro, Camila Prósperi; da Cunha, Vanessa Pecini; de Oliveira, Rafael Pires; Gomes-Santos, Ana Cristina; de Faria, Ana Maria Caetano; Machado, Denise Carmona Cara; Chatel, Jean-Marc; Azevedo, Vasco Ariston de Carvalho; Langella, Philippe; Miyoshi, Anderson

2016-08-30

Inflammatory bowel diseases are characterized by chronic intestinal inflammation that leads to severe destruction of the intestinal mucosa. Therefore, the understanding of their aetiology as well as the development of new medicines is an important step for the treatment of such diseases. Consequently, the development of Lactococcus lactis strains capable of delivering a eukaryotic expression vector encoding the interleukin 4 (IL-4) of Mus musculus would represent a new strategy for the elaboration of a more effective alternative therapy against Crohn's disease. The murine IL-4 ORF was cloned into the eukaryotic expression vector pValac::dts. The resulting plasmid-pValac::dts::IL-4-was transfected into CHO cells so that its functionality could be evaluated in vitro. With fluorescent confocal microscopy, flow cytometry and ELISA, it was observed that pValac::dts::IL-4-transfected cells produced IL-4, while non-transfected cells and cells transfected with the empty vector did not. Then, pValac::dts::IL-4 was inserted into L. lactis MG1363 FnBPA(+) in order to evaluate the therapeutic potential of the recombinant strain against TNBS-induced colitis. Intragastric administration of L. lactis MG1363 FnBPA(+) (pValac::dts::IL-4) was able to decrease the severity of colitis, with animals showing decreased levels of IL-12, IL-6 and MPO activity; and increased levels of IL-4 and IL-10. Finally, LP-isolated cells from mice administered TNBS were immunophenotyped so that the main IL-4 and IL-10 producers were identified. Mice administered the recombinant strain presented significantly higher percentages of F4/80(+)MHCII(+)Ly6C(-)IL-4(+), F4/80(+)MHCII(+)Ly6C(-)IL-10(+), F4/80(+)MHCII(+)Ly6C(-)CD206(+)CD124(+)IL-10(+) and CD4(+)Foxp3(+)IL10(+) cells compared to the other groups. This study shows that L. lactis MG1363 FnBPA(+) (pValac::dts::IL-4) is a good candidate to maintain the anti-inflammatory and proinflammatory balance in the gastrointestinal tract, increasing the levels of IL-10-secreting regulatory cells and, thus, demonstrating the effectiveness of this novel DNA delivery-based strategy.

Automatic design of decision-tree induction algorithms tailored to flexible-receptor docking data.

PubMed

Barros, Rodrigo C; Winck, Ana T; Machado, Karina S; Basgalupp, Márcio P; de Carvalho, André C P L F; Ruiz, Duncan D; de Souza, Osmar Norberto

2012-11-21

This paper addresses the prediction of the free energy of binding of a drug candidate with enzyme InhA associated with Mycobacterium tuberculosis. This problem is found within rational drug design, where interactions between drug candidates and target proteins are verified through molecular docking simulations. In this application, it is important not only to correctly predict the free energy of binding, but also to provide a comprehensible model that could be validated by a domain specialist. Decision-tree induction algorithms have been successfully used in drug-design related applications, specially considering that decision trees are simple to understand, interpret, and validate. There are several decision-tree induction algorithms available for general-use, but each one has a bias that makes it more suitable for a particular data distribution. In this article, we propose and investigate the automatic design of decision-tree induction algorithms tailored to particular drug-enzyme binding data sets. We investigate the performance of our new method for evaluating binding conformations of different drug candidates to InhA, and we analyze our findings with respect to decision tree accuracy, comprehensibility, and biological relevance. The empirical analysis indicates that our method is capable of automatically generating decision-tree induction algorithms that significantly outperform the traditional C4.5 algorithm with respect to both accuracy and comprehensibility. In addition, we provide the biological interpretation of the rules generated by our approach, reinforcing the importance of comprehensible predictive models in this particular bioinformatics application. We conclude that automatically designing a decision-tree algorithm tailored to molecular docking data is a promising alternative for the prediction of the free energy from the binding of a drug candidate with a flexible-receptor.

Automatic design of decision-tree induction algorithms tailored to flexible-receptor docking data

PubMed Central

2012-01-01

Background This paper addresses the prediction of the free energy of binding of a drug candidate with enzyme InhA associated with Mycobacterium tuberculosis. This problem is found within rational drug design, where interactions between drug candidates and target proteins are verified through molecular docking simulations. In this application, it is important not only to correctly predict the free energy of binding, but also to provide a comprehensible model that could be validated by a domain specialist. Decision-tree induction algorithms have been successfully used in drug-design related applications, specially considering that decision trees are simple to understand, interpret, and validate. There are several decision-tree induction algorithms available for general-use, but each one has a bias that makes it more suitable for a particular data distribution. In this article, we propose and investigate the automatic design of decision-tree induction algorithms tailored to particular drug-enzyme binding data sets. We investigate the performance of our new method for evaluating binding conformations of different drug candidates to InhA, and we analyze our findings with respect to decision tree accuracy, comprehensibility, and biological relevance. Results The empirical analysis indicates that our method is capable of automatically generating decision-tree induction algorithms that significantly outperform the traditional C4.5 algorithm with respect to both accuracy and comprehensibility. In addition, we provide the biological interpretation of the rules generated by our approach, reinforcing the importance of comprehensible predictive models in this particular bioinformatics application. Conclusions We conclude that automatically designing a decision-tree algorithm tailored to molecular docking data is a promising alternative for the prediction of the free energy from the binding of a drug candidate with a flexible-receptor. PMID:23171000

A decision tree for differentiating multiple system atrophy from Parkinson's disease using 3-T MR imaging.

PubMed

Nair, Shalini Rajandran; Tan, Li Kuo; Mohd Ramli, Norlisah; Lim, Shen Yang; Rahmat, Kartini; Mohd Nor, Hazman

2013-06-01

To develop a decision tree based on standard magnetic resonance imaging (MRI) and diffusion tensor imaging to differentiate multiple system atrophy (MSA) from Parkinson's disease (PD). 3-T brain MRI and DTI (diffusion tensor imaging) were performed on 26 PD and 13 MSA patients. Regions of interest (ROIs) were the putamen, substantia nigra, pons, middle cerebellar peduncles (MCP) and cerebellum. Linear, volumetry and DTI (fractional anisotropy and mean diffusivity) were measured. A three-node decision tree was formulated, with design goals being 100 % specificity at node 1, 100 % sensitivity at node 2 and highest combined sensitivity and specificity at node 3. Nine parameters (mean width, fractional anisotropy (FA) and mean diffusivity (MD) of MCP; anteroposterior diameter of pons; cerebellar FA and volume; pons and mean putamen volume; mean FA substantia nigra compacta-rostral) showed statistically significant (P < 0.05) differences between MSA and PD with mean MCP width, anteroposterior diameter of pons and mean FA MCP chosen for the decision tree. Threshold values were 14.6 mm, 21.8 mm and 0.55, respectively. Overall performance of the decision tree was 92 % sensitivity, 96 % specificity, 92 % PPV and 96 % NPV. Twelve out of 13 MSA patients were accurately classified. Formation of the decision tree using these parameters was both descriptive and predictive in differentiating between MSA and PD. • Parkinson's disease and multiple system atrophy can be distinguished on MR imaging. • Combined conventional MRI and diffusion tensor imaging improves the accuracy of diagnosis. • A decision tree is descriptive and predictive in differentiating between clinical entities. • A decision tree can reliably differentiate Parkinson's disease from multiple system atrophy.

Application of preprocessing filtering on Decision Tree C4.5 and rough set theory

NASA Astrophysics Data System (ADS)

Chan, Joseph C. C.; Lin, Tsau Y.

2001-03-01

This paper compares two artificial intelligence methods: the Decision Tree C4.5 and Rough Set Theory on the stock market data. The Decision Tree C4.5 is reviewed with the Rough Set Theory. An enhanced window application is developed to facilitate the pre-processing filtering by introducing the feature (attribute) transformations, which allows users to input formulas and create new attributes. Also, the application produces three varieties of data set with delaying, averaging, and summation. The results prove the improvement of pre-processing by applying feature (attribute) transformations on Decision Tree C4.5. Moreover, the comparison between Decision Tree C4.5 and Rough Set Theory is based on the clarity, automation, accuracy, dimensionality, raw data, and speed, which is supported by the rules sets generated by both algorithms on three different sets of data.

Multivariate analysis of flow cytometric data using decision trees.

PubMed

Simon, Svenja; Guthke, Reinhard; Kamradt, Thomas; Frey, Oliver

2012-01-01

Characterization of the response of the host immune system is important in understanding the bidirectional interactions between the host and microbial pathogens. For research on the host site, flow cytometry has become one of the major tools in immunology. Advances in technology and reagents allow now the simultaneous assessment of multiple markers on a single cell level generating multidimensional data sets that require multivariate statistical analysis. We explored the explanatory power of the supervised machine learning method called "induction of decision trees" in flow cytometric data. In order to examine whether the production of a certain cytokine is depended on other cytokines, datasets from intracellular staining for six cytokines with complex patterns of co-expression were analyzed by induction of decision trees. After weighting the data according to their class probabilities, we created a total of 13,392 different decision trees for each given cytokine with different parameter settings. For a more realistic estimation of the decision trees' quality, we used stratified fivefold cross validation and chose the "best" tree according to a combination of different quality criteria. While some of the decision trees reflected previously known co-expression patterns, we found that the expression of some cytokines was not only dependent on the co-expression of others per se, but was also dependent on the intensity of expression. Thus, for the first time we successfully used induction of decision trees for the analysis of high dimensional flow cytometric data and demonstrated the feasibility of this method to reveal structural patterns in such data sets.

15 CFR Supplement 1 to Part 732 - Decision Tree

Code of Federal Regulations, 2010 CFR

2010-01-01

... 15 Commerce and Foreign Trade 2 2010-01-01 2010-01-01 false Decision Tree 1 Supplement 1 to Part 732 Commerce and Foreign Trade Regulations Relating to Commerce and Foreign Trade (Continued) BUREAU... THE EAR Pt. 732, Supp. 1 Supplement 1 to Part 732—Decision Tree ER06FE04.000 [69 FR 5687, Feb. 6, 2004] ...

15 CFR Supplement No 1 to Part 732 - Decision Tree

Code of Federal Regulations, 2013 CFR

2013-01-01

... 15 Commerce and Foreign Trade 2 2013-01-01 2013-01-01 false Decision Tree No Supplement No 1 to Part 732 Commerce and Foreign Trade Regulations Relating to Commerce and Foreign Trade (Continued... THE EAR Pt. 732, Supp. 1 Supplement No 1 to Part 732—Decision Tree ER06FE04.000 [69 FR 5687, Feb. 6...

15 CFR Supplement No 1 to Part 732 - Decision Tree

Code of Federal Regulations, 2014 CFR

2014-01-01

... 15 Commerce and Foreign Trade 2 2014-01-01 2014-01-01 false Decision Tree No Supplement No 1 to Part 732 Commerce and Foreign Trade Regulations Relating to Commerce and Foreign Trade (Continued... THE EAR Pt. 732, Supp. 1 Supplement No 1 to Part 732—Decision Tree ER06FE04.000 [69 FR 5687, Feb. 6...

15 CFR Supplement 1 to Part 732 - Decision Tree

Code of Federal Regulations, 2012 CFR

2012-01-01

... 15 Commerce and Foreign Trade 2 2012-01-01 2012-01-01 false Decision Tree 1 Supplement 1 to Part 732 Commerce and Foreign Trade Regulations Relating to Commerce and Foreign Trade (Continued) BUREAU... THE EAR Pt. 732, Supp. 1 Supplement 1 to Part 732—Decision Tree ER06FE04.000 [69 FR 5687, Feb. 6, 2004] ...

15 CFR Supplement 1 to Part 732 - Decision Tree

Code of Federal Regulations, 2011 CFR

2011-01-01

... 15 Commerce and Foreign Trade 2 2011-01-01 2011-01-01 false Decision Tree 1 Supplement 1 to Part 732 Commerce and Foreign Trade Regulations Relating to Commerce and Foreign Trade (Continued) BUREAU... THE EAR Pt. 732, Supp. 1 Supplement 1 to Part 732—Decision Tree ER06FE04.000 [69 FR 5687, Feb. 6, 2004] ...

Improved Frame Mode Selection for AMR-WB+ Based on Decision Tree

NASA Astrophysics Data System (ADS)

Kim, Jong Kyu; Kim, Nam Soo

In this letter, we propose a coding mode selection method for the AMR-WB+ audio coder based on a decision tree. In order to reduce computation while maintaining good performance, decision tree classifier is adopted with the closed loop mode selection results as the target classification labels. The size of the decision tree is controlled by pruning, so the proposed method does not increase the memory requirement significantly. Through an evaluation test on a database covering both speech and music materials, the proposed method is found to achieve a much better mode selection accuracy compared with the open loop mode selection module in the AMR-WB+.

Activity classification using realistic data from wearable sensors.

PubMed

Pärkkä, Juha; Ermes, Miikka; Korpipää, Panu; Mäntyjärvi, Jani; Peltola, Johannes; Korhonen, Ilkka

2006-01-01

Automatic classification of everyday activities can be used for promotion of health-enhancing physical activities and a healthier lifestyle. In this paper, methods used for classification of everyday activities like walking, running, and cycling are described. The aim of the study was to find out how to recognize activities, which sensors are useful and what kind of signal processing and classification is required. A large and realistic data library of sensor data was collected. Sixteen test persons took part in the data collection, resulting in approximately 31 h of annotated, 35-channel data recorded in an everyday environment. The test persons carried a set of wearable sensors while performing several activities during the 2-h measurement session. Classification results of three classifiers are shown: custom decision tree, automatically generated decision tree, and artificial neural network. The classification accuracies using leave-one-subject-out cross validation range from 58 to 97% for custom decision tree classifier, from 56 to 97% for automatically generated decision tree, and from 22 to 96% for artificial neural network. Total classification accuracy is 82 % for custom decision tree classifier, 86% for automatically generated decision tree, and 82% for artificial neural network.

A universal hybrid decision tree classifier design for human activity classification.

PubMed

Chien, Chieh; Pottie, Gregory J

2012-01-01

A system that reliably classifies daily life activities can contribute to more effective and economical treatments for patients with chronic conditions or undergoing rehabilitative therapy. We propose a universal hybrid decision tree classifier for this purpose. The tree classifier can flexibly implement different decision rules at its internal nodes, and can be adapted from a population-based model when supplemented by training data for individuals. The system was tested using seven subjects each monitored by 14 triaxial accelerometers. Each subject performed fourteen different activities typical of daily life. Using leave-one-out cross validation, our decision tree produced average classification accuracies of 89.9%. In contrast, the MATLAB personalized tree classifiers using Gini's diversity index as the split criterion followed by optimally tuning the thresholds for each subject yielded 69.2%.

Formulation and biopharmaceutical evaluation of bitter taste masking microparticles containing azithromycin loaded in dispersible tablets.

PubMed

Tung, Nguyen-Thach; Tran, Cao-Son; Nguyen, Tran-Linh; Hoang, Tung; Trinh, Thanh-Dat; Nguyen, Thi-Ngan

2018-05-01

The objective of this study was to prepare and evaluate some physiochemical and biopharmaceutical properties of bitter taste masking microparticles containing azithromycin loaded in dispersible tablets. In the first stage of the study, the bitter taste masking microparticles were prepared by solvent evaporation and spray drying method. When compared to the bitter threshold (32.43µg/ml) of azithromycin (AZI), the microparticles using AZI:Eudragit L100=1:4 and having a size distribution of 45-212µm did significantly mask the bitter taste of AZI. Fourier transform infrared spectroscopy (FTIR), and proton nuclear magnetic resonance spectroscopy ( 1 H NMR) proved that the taste masking of microparticles resulted from the intermolecular interaction of the amine group in AZI and the carbonyl group in Eudragit L100. Differential scanning calorimeter (DSC) analysis was used to display the amorphous state of AZI in microparticles. Images obtaining from optical microscopy and scanning electron microscopy (SEM) indicated the existence of microparticles in regular cube shape with many layers. In the second stage, dispersible tablets containing microparticles (DTs-MP) were prepared by direct compression technique. Stability study was conducted to screen pH modulators for DTs-MP, and a combination of alkali agents (CaCO 3 :NaH 2 PO 4 , 2:1) was added into DTs-MP to create microenvironment pH of 5.0-6.0 for the tablets. The disintegration time of optimum DTs-MP was 53±5.29s and strongly depended on the kinds of lubricant and diluent. The pharmacokinetic study in the rabbit model using liquid chromatography tandem mass spectrometry showed that the mean relative bioavailability (AUC) and mean maximum concentration (C max ) of DTs-MP were improved by 2.19 and 2.02 times, respectively, compared to the reference product (Zithromax®, Pfizer). Copyright © 2017 Elsevier B.V. All rights reserved.

High-Resolution Fibre-Optic Temperature Sensing: A New Tool to Study the Two-Dimensional Structure of Atmospheric Surface-Layer Flow

NASA Astrophysics Data System (ADS)

Thomas, Christoph K.; Kennedy, Adam M.; Selker, John S.; Moretti, Ayla; Schroth, Martin H.; Smoot, Alexander R.; Tufillaro, Nicholas B.; Zeeman, Matthias J.

2012-02-01

We present a novel approach based on fibre-optic distributed temperature sensing (DTS) to measure the two-dimensional thermal structure of the surface layer at high resolution (0.25 m, ≈0.5 Hz). Air temperature observations obtained from a vertically-oriented fibre-optics array of approximate dimensions 8 m × 8 m and sonic anemometer data from two levels were collected over a short grass field located in the flat bottom of a wide valley with moderate surface heterogeneity. The objectives of the study were to evaluate the potential of the DTS technique to study small-scale processes in the surface layer over a wide range of atmospheric stability, and to analyze the space-time dynamics of transient cold-air pools in the calm boundary layer. The time response and precision of the fibre-based temperatures were adequate to resolve individual sub-metre sized turbulent and non-turbulent structures, of time scales of seconds, in the convective, neutral, and stable surface layer. Meaningful sensible heat fluxes were computed using the eddy-covariance technique when combined with vertical wind observations. We present a framework that determines the optimal environmental conditions for applying the fibre-optics technique in the surface layer and identifies areas for potentially significant improvements of the DTS performance. The top of the transient cold-air pool was highly non-stationary indicating a superposition of perturbations of different time and length scales. Vertical eddy scales in the strongly stratified transient cold-air pool derived from the DTS data agreed well with the buoyancy length scale computed using the vertical velocity variance and the Brunt-Vaisala frequency, while scales for weak stratification disagreed. The high-resolution DTS technique opens a new window into spatially sampling geophysical fluid flows including turbulent energy exchange.

Development of a New Decision Tree to Rapidly Screen Chemical Estrogenic Activities of Xenopus laevis.

PubMed

Wang, Ting; Li, Weiying; Zheng, Xiaofeng; Lin, Zhifen; Kong, Deyang

2014-02-01

During the last past decades, there is an increasing number of studies about estrogenic activities of the environmental pollutants on amphibians and many determination methods have been proposed. However, these determination methods are time-consuming and expensive, and a rapid and simple method to screen and test the chemicals for estrogenic activities to amphibians is therefore imperative. Herein is proposed a new decision tree formulated not only with physicochemical parameters but also a biological parameter that was successfully used to screen estrogenic activities of the chemicals on amphibians. The biological parameter, CDOCKER interaction energy (Ebinding ) between chemicals and the target proteins was calculated based on the method of molecular docking, and it was used to revise the decision tree formulated by Hong only with physicochemical parameters for screening estrogenic activity of chemicals in rat. According to the correlation between Ebinding of rat and Xenopus laevis, a new decision tree for estrogenic activities in Xenopus laevis is finally proposed. Then it was validated by using the randomly 8 chemicals which can be frequently exposed to Xenopus laevis, and the agreement between the results from the new decision tree and the ones from experiments is generally satisfactory. Consequently, the new decision tree can be used to screen the estrogenic activities of the chemicals, and combinational use of the Ebinding and classical physicochemical parameters can greatly improves Hong's decision tree. Copyright © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Comparing wavefront-optimized, wavefront-guided and topography-guided laser vision correction: clinical outcomes using an objective decision tree.

PubMed

Stonecipher, Karl; Parrish, Joseph; Stonecipher, Megan

2018-05-18

This review is intended to update and educate the reader on the currently available options for laser vision correction, more specifically, laser-assisted in-situ keratomileusis (LASIK). In addition, some related clinical outcomes data from over 1000 cases performed over a 1-year are presented to highlight some differences between the various treatment profiles currently available including the rapidity of visual recovery. The cases in question were performed on the basis of a decision tree to segregate patients on the basis of anatomical, topographic and aberrometry findings; the decision tree was formulated based on the data available in some of the reviewed articles. Numerous recent studies reported in the literature provide data related to the risks and benefits of LASIK; alternatives to a laser refractive procedure are also discussed. The results from these studies have been used to prepare a decision tree to assist the surgeon in choosing the best option for the patient based on the data from several standard preoperative diagnostic tests. The data presented here should aid surgeons in understanding the effects of currently available LASIK treatment profiles. Surgeons should also be able to appreciate how the findings were used to create a decision tree to help choose the most appropriate treatment profile for patients. Finally, the retrospective evaluation of clinical outcomes based on the decision tree should provide surgeons with a realistic expectation for their own outcomes should they adopt such a decision tree in their own practice.

Comparative analysis of tree classification models for detecting fusarium oxysporum f. sp cubense (TR4) based on multi soil sensor parameters

NASA Astrophysics Data System (ADS)

Estuar, Maria Regina Justina; Victorino, John Noel; Coronel, Andrei; Co, Jerelyn; Tiausas, Francis; Señires, Chiara Veronica

2017-09-01

Use of wireless sensor networks and smartphone integration design to monitor environmental parameters surrounding plantations is made possible because of readily available and affordable sensors. Providing low cost monitoring devices would be beneficial, especially to small farm owners, in a developing country like the Philippines, where agriculture covers a significant amount of the labor market. This study discusses the integration of wireless soil sensor devices and smartphones to create an application that will use multidimensional analysis to detect the presence or absence of plant disease. Specifically, soil sensors are designed to collect soil quality parameters in a sink node from which the smartphone collects data from via Bluetooth. Given these, there is a need to develop a classification model on the mobile phone that will report infection status of a soil. Though tree classification is the most appropriate approach for continuous parameter-based datasets, there is a need to determine whether tree models will result to coherent results or not. Soil sensor data that resides on the phone is modeled using several variations of decision tree, namely: decision tree (DT), best-fit (BF) decision tree, functional tree (FT), Naive Bayes (NB) decision tree, J48, J48graft and LAD tree, where decision tree approaches the problem by considering all sensor nodes as one. Results show that there are significant differences among soil sensor parameters indicating that there are variances in scores between the infected and uninfected sites. Furthermore, analysis of variance in accuracy, recall, precision and F1 measure scores from tree classification models homogeneity among NBTree, J48graft and J48 tree classification models.

Experimental study of low-cost fiber optic distributed temperature sensor system performance

NASA Astrophysics Data System (ADS)

Dashkov, Michael V.; Zharkov, Alexander D.

2016-03-01

The distributed control of temperature is an actual task for various application such as oil & gas fields, high-voltage power lines, fire alarm systems etc. The most perspective are optical fiber distributed temperature sensors (DTS). They have advantages on accuracy, resolution and range, but have a high cost. Nevertheless, for some application the accuracy of measurement and localization aren't so important as cost. The results of an experimental study of low-cost Raman based DTS based on standard OTDR are represented.

Defense Travel System (DTS) Airline Ticket Price Analysis: Do DTS Ticket Prices Differ From Other Online Tickets Available for Naval Postgraduate School Travelers

DTIC Science & Technology

2007-12-01

price dispersion at least as large as dispersion for traditional retailers for books, music CDs, and software offered through 52 Internet and...dispersion differences. For instance, for 22 old-hit albums , average price percentage differences are 31% on-line, compared to 11% off-line. But for 21...current-hit albums , differences are smaller at 18% on-line and 19% off-line. This suggests price dispersion levels are related to product

Generation of a Kind of Displaced Thermal States in the Diffusion Process and its Statistical Properties

NASA Astrophysics Data System (ADS)

Xiang-Guo, Meng; Hong-Yi, Fan; Ji-Suo, Wang

2018-04-01

This paper proposes a kind of displaced thermal states (DTS) and explores how this kind of optical field emerges using the entangled state representation. The results show that the DTS can be generated by a coherent state passing through a diffusion channel with the diffusion coefficient ϰ only when there exists κ t = (e^{\\hbar ν /kBT} - 1 )^{-1}. Also, its statistical properties, such as mean photon number, Wigner function and entropy, are investigated.

Aneurysmal subarachnoid hemorrhage prognostic decision-making algorithm using classification and regression tree analysis.

PubMed

Lo, Benjamin W Y; Fukuda, Hitoshi; Angle, Mark; Teitelbaum, Jeanne; Macdonald, R Loch; Farrokhyar, Forough; Thabane, Lehana; Levine, Mitchell A H

2016-01-01

Classification and regression tree analysis involves the creation of a decision tree by recursive partitioning of a dataset into more homogeneous subgroups. Thus far, there is scarce literature on using this technique to create clinical prediction tools for aneurysmal subarachnoid hemorrhage (SAH). The classification and regression tree analysis technique was applied to the multicenter Tirilazad database (3551 patients) in order to create the decision-making algorithm. In order to elucidate prognostic subgroups in aneurysmal SAH, neurologic, systemic, and demographic factors were taken into account. The dependent variable used for analysis was the dichotomized Glasgow Outcome Score at 3 months. Classification and regression tree analysis revealed seven prognostic subgroups. Neurological grade, occurrence of post-admission stroke, occurrence of post-admission fever, and age represented the explanatory nodes of this decision tree. Split sample validation revealed classification accuracy of 79% for the training dataset and 77% for the testing dataset. In addition, the occurrence of fever at 1-week post-aneurysmal SAH is associated with increased odds of post-admission stroke (odds ratio: 1.83, 95% confidence interval: 1.56-2.45, P < 0.01). A clinically useful classification tree was generated, which serves as a prediction tool to guide bedside prognostication and clinical treatment decision making. This prognostic decision-making algorithm also shed light on the complex interactions between a number of risk factors in determining outcome after aneurysmal SAH.

A survey of decision tree classifier methodology

NASA Technical Reports Server (NTRS)

Safavian, S. R.; Landgrebe, David

1991-01-01

Decision tree classifiers (DTCs) are used successfully in many diverse areas such as radar signal classification, character recognition, remote sensing, medical diagnosis, expert systems, and speech recognition. Perhaps the most important feature of DTCs is their capability to break down a complex decision-making process into a collection of simpler decisions, thus providing a solution which is often easier to interpret. A survey of current methods is presented for DTC designs and the various existing issues. After considering potential advantages of DTCs over single-state classifiers, subjects of tree structure design, feature selection at each internal node, and decision and search strategies are discussed.

A survey of decision tree classifier methodology

NASA Technical Reports Server (NTRS)

Safavian, S. Rasoul; Landgrebe, David

1990-01-01

Decision Tree Classifiers (DTC's) are used successfully in many diverse areas such as radar signal classification, character recognition, remote sensing, medical diagnosis, expert systems, and speech recognition. Perhaps, the most important feature of DTC's is their capability to break down a complex decision-making process into a collection of simpler decisions, thus providing a solution which is often easier to interpret. A survey of current methods is presented for DTC designs and the various existing issue. After considering potential advantages of DTC's over single stage classifiers, subjects of tree structure design, feature selection at each internal node, and decision and search strategies are discussed.

Development and Pilot Testing of the Dual Task Screen in Healthy Adolescents.

PubMed

Stephens, Jaclyn; Nicholson, Rachel; Slomine, Beth; Suskauer, Stacy

Athletes with mild traumatic brain injury (mTBI) should refrain from high-risk activities until recovered (symptom free and cognitive and physical exam findings normalize). Studies have suggested that this examination may not be sufficiently sensitive because dual-task paradigms, which typically assess motor performance while a person simultaneously completes a distractor task, can detect residual deficits in athletes who otherwise appear recovered from mTBI. Paradigms used to date are time-intensive procedures conducted in laboratory settings. Here, we report findings from a pilot study of the Dual Task Screen (DTS), which is a brief evaluation with two dual-task paradigms. In 32 healthy female adolescents, the DTS was administered in a mean of 5.63 min in the community, and every participant had poorer dual-condition performance on at least one of the motor tasks. The DTS is a clinically feasible measure and merits additional study regarding utility in adolescents with mTBIs. Copyright © 2018 by the American Occupational Therapy Association, Inc.

Locating illicit connections in storm water sewers using fiber-optic distributed temperature sensing.

PubMed

Hoes, O A C; Schilperoort, R P S; Luxemburg, W M J; Clemens, F H L R; van de Giesen, N C

2009-12-01

A newly developed technique using distributed temperature sensing (DTS) has been developed to find illicit household sewage connections to storm water systems in the Netherlands. DTS allows for the accurate measurement of temperature along a fiber-optic cable, with high spatial (2m) and temporal (30s) resolution. We inserted a fiber-optic cable of 1300m in two storm water drains. At certain locations, significant temperature differences with an intermittent character were measured, indicating inflow of water that was not storm water. In all cases, we found that foul water from households or companies entered the storm water system through an illicit sewage connection. The method of using temperature differences for illicit connection detection in storm water networks is discussed. The technique of using fiber-optic cables for distributed temperature sensing is explained in detail. The DTS method is a reliable, inexpensive and practically feasible method to detect illicit connections to storm water systems, which does not require access to private property.

Development of a diagnostic decision tree for obstructive pulmonary diseases based on real-life data

PubMed Central

in ’t Veen, Johannes C.C.M.; Dekhuijzen, P.N. Richard; van Heijst, Ellen; Kocks, Janwillem W.H.; Muilwijk-Kroes, Jacqueline B.; Chavannes, Niels H.; van der Molen, Thys

2016-01-01

The aim of this study was to develop and explore the diagnostic accuracy of a decision tree derived from a large real-life primary care population. Data from 9297 primary care patients (45% male, mean age 53±17 years) with suspicion of an obstructive pulmonary disease was derived from an asthma/chronic obstructive pulmonary disease (COPD) service where patients were assessed using spirometry, the Asthma Control Questionnaire, the Clinical COPD Questionnaire, history data and medication use. All patients were diagnosed through the Internet by a pulmonologist. The Chi-squared Automatic Interaction Detection method was used to build the decision tree. The tree was externally validated in another real-life primary care population (n=3215). Our tree correctly diagnosed 79% of the asthma patients, 85% of the COPD patients and 32% of the asthma–COPD overlap syndrome (ACOS) patients. External validation showed a comparable pattern (correct: asthma 78%, COPD 83%, ACOS 24%). Our decision tree is considered to be promising because it was based on real-life primary care patients with a specialist's diagnosis. In most patients the diagnosis could be correctly predicted. Predicting ACOS, however, remained a challenge. The total decision tree can be implemented in computer-assisted diagnostic systems for individual patients. A simplified version of this tree can be used in daily clinical practice as a desk tool. PMID:27730177

Evolving optimised decision rules for intrusion detection using particle swarm paradigm

NASA Astrophysics Data System (ADS)

Sivatha Sindhu, Siva S.; Geetha, S.; Kannan, A.

2012-12-01

The aim of this article is to construct a practical intrusion detection system (IDS) that properly analyses the statistics of network traffic pattern and classify them as normal or anomalous class. The objective of this article is to prove that the choice of effective network traffic features and a proficient machine-learning paradigm enhances the detection accuracy of IDS. In this article, a rule-based approach with a family of six decision tree classifiers, namely Decision Stump, C4.5, Naive Baye's Tree, Random Forest, Random Tree and Representative Tree model to perform the detection of anomalous network pattern is introduced. In particular, the proposed swarm optimisation-based approach selects instances that compose training set and optimised decision tree operate over this trained set producing classification rules with improved coverage, classification capability and generalisation ability. Experiment with the Knowledge Discovery and Data mining (KDD) data set which have information on traffic pattern, during normal and intrusive behaviour shows that the proposed algorithm produces optimised decision rules and outperforms other machine-learning algorithm.

A Decision Tree for Nonmetric Sex Assessment from the Skull.

PubMed

Langley, Natalie R; Dudzik, Beatrix; Cloutier, Alesia

2018-01-01

This study uses five well-documented cranial nonmetric traits (glabella, mastoid process, mental eminence, supraorbital margin, and nuchal crest) and one additional trait (zygomatic extension) to develop a validated decision tree for sex assessment. The decision tree was built and cross-validated on a sample of 293 U.S. White individuals from the William M. Bass Donated Skeletal Collection. Ordinal scores from the six traits were analyzed using the partition modeling option in JMP Pro 12. A holdout sample of 50 skulls was used to test the model. The most accurate decision tree includes three variables: glabella, zygomatic extension, and mastoid process. This decision tree yielded 93.5% accuracy on the training sample, 94% on the cross-validated sample, and 96% on a holdout validation sample. Linear weighted kappa statistics indicate acceptable agreement among observers for these variables. Mental eminence should be avoided, and definitions and figures should be referenced carefully to score nonmetric traits. © 2017 American Academy of Forensic Sciences.

A framework for sensitivity analysis of decision trees.

PubMed

Kamiński, Bogumił; Jakubczyk, Michał; Szufel, Przemysław

2018-01-01

In the paper, we consider sequential decision problems with uncertainty, represented as decision trees. Sensitivity analysis is always a crucial element of decision making and in decision trees it often focuses on probabilities. In the stochastic model considered, the user often has only limited information about the true values of probabilities. We develop a framework for performing sensitivity analysis of optimal strategies accounting for this distributional uncertainty. We design this robust optimization approach in an intuitive and not overly technical way, to make it simple to apply in daily managerial practice. The proposed framework allows for (1) analysis of the stability of the expected-value-maximizing strategy and (2) identification of strategies which are robust with respect to pessimistic/optimistic/mode-favoring perturbations of probabilities. We verify the properties of our approach in two cases: (a) probabilities in a tree are the primitives of the model and can be modified independently; (b) probabilities in a tree reflect some underlying, structural probabilities, and are interrelated. We provide a free software tool implementing the methods described.

Learning accurate very fast decision trees from uncertain data streams

NASA Astrophysics Data System (ADS)

Liang, Chunquan; Zhang, Yang; Shi, Peng; Hu, Zhengguo

2015-12-01

Most existing works on data stream classification assume the streaming data is precise and definite. Such assumption, however, does not always hold in practice, since data uncertainty is ubiquitous in data stream applications due to imprecise measurement, missing values, privacy protection, etc. The goal of this paper is to learn accurate decision tree models from uncertain data streams for classification analysis. On the basis of very fast decision tree (VFDT) algorithms, we proposed an algorithm for constructing an uncertain VFDT tree with classifiers at tree leaves (uVFDTc). The uVFDTc algorithm can exploit uncertain information effectively and efficiently in both the learning and the classification phases. In the learning phase, it uses Hoeffding bound theory to learn from uncertain data streams and yield fast and reasonable decision trees. In the classification phase, at tree leaves it uses uncertain naive Bayes (UNB) classifiers to improve the classification performance. Experimental results on both synthetic and real-life datasets demonstrate the strong ability of uVFDTc to classify uncertain data streams. The use of UNB at tree leaves has improved the performance of uVFDTc, especially the any-time property, the benefit of exploiting uncertain information, and the robustness against uncertainty.

Real-Time Speech/Music Classification With a Hierarchical Oblique Decision Tree

DTIC Science & Technology

2008-04-01

REAL-TIME SPEECH/ MUSIC CLASSIFICATION WITH A HIERARCHICAL OBLIQUE DECISION TREE Jun Wang, Qiong Wu, Haojiang Deng, Qin Yan Institute of Acoustics...time speech/ music classification with a hierarchical oblique decision tree. A set of discrimination features in frequency domain are selected...handle signals without discrimination and can not work properly in the existence of multimedia signals. This paper proposes a real-time speech/ music

PCA based feature reduction to improve the accuracy of decision tree c4.5 classification

NASA Astrophysics Data System (ADS)

Nasution, M. Z. F.; Sitompul, O. S.; Ramli, M.

2018-03-01

Splitting attribute is a major process in Decision Tree C4.5 classification. However, this process does not give a significant impact on the establishment of the decision tree in terms of removing irrelevant features. It is a major problem in decision tree classification process called over-fitting resulting from noisy data and irrelevant features. In turns, over-fitting creates misclassification and data imbalance. Many algorithms have been proposed to overcome misclassification and overfitting on classifications Decision Tree C4.5. Feature reduction is one of important issues in classification model which is intended to remove irrelevant data in order to improve accuracy. The feature reduction framework is used to simplify high dimensional data to low dimensional data with non-correlated attributes. In this research, we proposed a framework for selecting relevant and non-correlated feature subsets. We consider principal component analysis (PCA) for feature reduction to perform non-correlated feature selection and Decision Tree C4.5 algorithm for the classification. From the experiments conducted using available data sets from UCI Cervical cancer data set repository with 858 instances and 36 attributes, we evaluated the performance of our framework based on accuracy, specificity and precision. Experimental results show that our proposed framework is robust to enhance classification accuracy with 90.70% accuracy rates.

[Comparison of Discriminant Analysis and Decision Trees for the Detection of Subclinical Keratoconus].

PubMed

Kleinhans, Sonja; Herrmann, Eva; Kohnen, Thomas; Bühren, Jens

2017-08-15

Background Iatrogenic keratectasia is one of the most dreaded complications of refractive surgery. In most cases, keratectasia develops after refractive surgery of eyes suffering from subclinical stages of keratoconus with few or no signs. Unfortunately, there has been no reliable procedure for the early detection of keratoconus. In this study, we used binary decision trees (recursive partitioning) to assess their suitability for discrimination between normal eyes and eyes with subclinical keratoconus. Patients and Methods The method of decision tree analysis was compared with discriminant analysis which has shown good results in previous studies. Input data were 32 eyes of 32 patients with newly diagnosed keratoconus in the contralateral eye and preoperative data of 10 eyes of 5 patients with keratectasia after laser in-situ keratomileusis (LASIK). The control group was made up of 245 normal eyes after LASIK and 12-month follow-up without any signs of iatrogenic keratectasia. Results Decision trees gave better accuracy and specificity than did discriminant analysis. The sensitivity of decision trees was lower than the sensitivity of discriminant analysis. Conclusion On the basis of the patient population of this study, decision trees did not prove to be superior to linear discriminant analysis for the detection of subclinical keratoconus. Georg Thieme Verlag KG Stuttgart · New York.

Pruning a decision tree for selecting computer-related assistive devices for people with disabilities.

PubMed

Chi, Chia-Fen; Tseng, Li-Kai; Jang, Yuh

2012-07-01

Many disabled individuals lack extensive knowledge about assistive technology, which could help them use computers. In 1997, Denis Anson developed a decision tree of 49 evaluative questions designed to evaluate the functional capabilities of the disabled user and choose an appropriate combination of assistive devices, from a selection of 26, that enable the individual to use a computer. In general, occupational therapists guide the disabled users through this process. They often have to go over repetitive questions in order to find an appropriate device. A disabled user may require an alphanumeric entry device, a pointing device, an output device, a performance enhancement device, or some combination of these. Therefore, the current research eliminates redundant questions and divides Anson's decision tree into multiple independent subtrees to meet the actual demand of computer users with disabilities. The modified decision tree was tested by six disabled users to prove it can determine a complete set of assistive devices with a smaller number of evaluative questions. The means to insert new categories of computer-related assistive devices was included to ensure the decision tree can be expanded and updated. The current decision tree can help the disabled users and assistive technology practitioners to find appropriate computer-related assistive devices that meet with clients' individual needs in an efficient manner.

Uncertain decision tree inductive inference

NASA Astrophysics Data System (ADS)

Zarban, L.; Jafari, S.; Fakhrahmad, S. M.

2011-10-01

Induction is the process of reasoning in which general rules are formulated based on limited observations of recurring phenomenal patterns. Decision tree learning is one of the most widely used and practical inductive methods, which represents the results in a tree scheme. Various decision tree algorithms have already been proposed such as CLS, ID3, Assistant C4.5, REPTree and Random Tree. These algorithms suffer from some major shortcomings. In this article, after discussing the main limitations of the existing methods, we introduce a new decision tree induction algorithm, which overcomes all the problems existing in its counterparts. The new method uses bit strings and maintains important information on them. This use of bit strings and logical operation on them causes high speed during the induction process. Therefore, it has several important features: it deals with inconsistencies in data, avoids overfitting and handles uncertainty. We also illustrate more advantages and the new features of the proposed method. The experimental results show the effectiveness of the method in comparison with other methods existing in the literature.

Soil moisture and properties estimation by assimilating soil temperatures using particle batch smoother: A new perspective for DTS

NASA Astrophysics Data System (ADS)

Dong, J.; Steele-Dunne, S. C.; Ochsner, T. E.; Van De Giesen, N.

2015-12-01

Soil moisture, hydraulic and thermal properties are critical for understanding the soil surface energy balance and hydrological processes. Here, we will discuss the potential of using soil temperature observations from Distributed Temperature Sensing (DTS) to investigate the spatial variability of soil moisture and soil properties. With DTS soil temperature can be measured with high resolution (spatial <1m, and temporal < 1min) in cables up to kilometers in length. Soil temperature evolution is primarily controlled by the soil thermal properties, and the energy balance at the soil surface. Hence, soil moisture, which affects both soil thermal properties and the energy that participates the evaporation process, is strongly correlated to the soil temperatures. In addition, the dynamics of the soil moisture is determined by the soil hydraulic properties.Here we will demonstrate that soil moisture, hydraulic and thermal properties can be estimated by assimilating observed soil temperature at shallow depths using the Particle Batch Smoother (PBS). The PBS can be considered as an extension of the particle filter, which allows us to infer soil moisture and soil properties using the dynamics of soil temperature within a batch window. Both synthetic and real field data will be used to demonstrate the robustness of this approach. We will show that the proposed method is shown to be able to handle different sources of uncertainties, which may provide a new view of using DTS observations to estimate sub-meter resolution soil moisture and properties for remote sensing product validation.

Autonomous docking ground demonstration (category 3)

NASA Technical Reports Server (NTRS)

Lamkin, Steve L.; Eick, Richard E.; Baxter, James M.; Boyd, M. G.; Clark, Fred D.; Lee, Thomas Q.; Othon, L. T.; Prather, Joseph L.; Spehar, Peter T.; Teders, Rebecca J.

1991-01-01

The NASA Johnson Space Center (JSC) is involved in the development of an autonomous docking ground demonstration. The demonstration combines the technologies, expertise and facilities of the JSC Tracking and Communications Division (EE), Structures and Mechanics Division (ES), and the Navigation, Guidance and Control Division (EG) and their supporting contractors. The autonomous docking ground demonstration is an evaluation of the capabilities of the laser sensor system to support the docking phase (12ft to contact) when operated in conjunction with the Guidance, Navigation and Control Software. The docking mechanism being used was developed for the Apollo Soyuz Test Program. This demonstration will be conducted using the Six-Degrees of Freedom (6-DOF) Dynamic Test System (DTS). The DTS environment simulates the Space Station Freedom as the stationary or target vehicle and the Orbiter as the active or chase vehicle. For this demonstration the laser sensor will be mounted on the target vehicle and the retroreflectors on the chase vehicle. This arrangement was used to prevent potential damage to the laser. The sensor system. GN&C and 6-DOF DTS will be operated closed-loop. Initial condition to simulate vehicle misalignments, translational and rotational, will be introduced within the constraints of the systems involved. Detailed description of each of the demonstration components (e.g., Sensor System, GN&C, 6-DOF DTS and supporting computer configuration) including their capabilities and limitations will be discussed. A demonstration architecture drawing and photographs of the test configuration will be presented.

Autonomous docking ground demonstration (category 3)

NASA Astrophysics Data System (ADS)

Lamkin, Steve L.; Eick, Richard E.; Baxter, James M.; Boyd, M. G.; Clark, Fred D.; Lee, Thomas Q.; Othon, L. T.; Prather, Joseph L.; Spehar, Peter T.; Teders, Rebecca J.

The NASA Johnson Space Center (JSC) is involved in the development of an autonomous docking ground demonstration. The demonstration combines the technologies, expertise and facilities of the JSC Tracking and Communications Division (EE), Structures and Mechanics Division (ES), and the Navigation, Guidance and Control Division (EG) and their supporting contractors. The autonomous docking ground demonstration is an evaluation of the capabilities of the laser sensor system to support the docking phase (12ft to contact) when operated in conjunction with the Guidance, Navigation and Control Software. The docking mechanism being used was developed for the Apollo Soyuz Test Program. This demonstration will be conducted using the Six-Degrees of Freedom (6-DOF) Dynamic Test System (DTS). The DTS environment simulates the Space Station Freedom as the stationary or target vehicle and the Orbiter as the active or chase vehicle. For this demonstration the laser sensor will be mounted on the target vehicle and the retroreflectors on the chase vehicle. This arrangement was used to prevent potential damage to the laser. The sensor system. GN&C and 6-DOF DTS will be operated closed-loop. Initial condition to simulate vehicle misalignments, translational and rotational, will be introduced within the constraints of the systems involved. Detailed description of each of the demonstration components (e.g., Sensor System, GN&C, 6-DOF DTS and supporting computer configuration) including their capabilities and limitations will be discussed. A demonstration architecture drawing and photographs of the test configuration will be presented.

An Obvious Improvement in the Performance of Ternary Organic Solar Cells with "Guest" Donor Present at the "Host" Donor/Acceptor Interface.

PubMed

Bi, Peng-Qing; Wu, Bo; Zheng, Fei; Xu, Wei-Long; Yang, Xiao-Yu; Feng, Lin; Zhu, Furong; Hao, Xiao-Tao

2016-09-07

A small-molecule material, 7,7-(4,4-bis(2-ethylhexyl)-4H-silolo[3,2-b:4,5-b']dithiophene-2,6-diyl)bis(6-fluoro-4-(5'-hexyl-[2,2'-bithiophen]-5-yl)benzo-[c] [1,2,5]thiadiazole) (p-DTS(FBTTH2)2), was used to modify the morphology and electron-transport properties of the polymer blend of poly(3-hexythiophene) (P3HT) and [6,6]-phenyl-C71-butyric acid methyl ester (PC71BM) bulk heterojunctions. As a result, a 24% increase in the power-conversion efficiency (PCE) of the p-DTS(FBTTH2)2:P3HT:PC71BM ternary organic solar cells (OSCs) is obtained. The improvement in the performance of OSCs is attributed to the constructive energy cascade path in the ternary system that benefits an efficient Förster resonance energy/charge transfer process between P3HT and p-DTS(FBTTH2)2, thereby improving photocurrent generation. It is shown that p-DTS(FBTTH2)2 molecules engage themselves at the P3HT/PC71BM interface. A combination of absorption enhancement, efficient energy transfer process, and ordered nanomorphology in the ternary system favors exciton dissociation and charge transportation in the polymer bulk heterojunction. The finding of this work reveals that distribution of the appropriate "guest" donor at the "host" donor/acceptor interface is an effective approach for attaining high-performance OSCs.

Influence of powder/liquid ratio on the radiodensity and diametral tensile strength of glass ionomer cements

PubMed Central

FONSECA, Rodrigo Borges; BRANCO, Carolina Assaf; QUAGLIATTO, Paulo Sérgio; GONÇALVES, Luciano de Souza; SOARES, Carlos José; CARLO, Hugo Lemes; CORRER-SOBRINHO, Lourenço

2010-01-01

Objective To determine the influence of P/L ratio on the radiodensity and diametral tensile strength (DTS) of glass ionomer cements. Material and Methods There were 2 factors under study: P/L ratio (manufacturer's recommended P/L ratio and a 50% reduced P/L ratio), and materials (Vitro Molar, Vitro Fil, Vitro Cem conventional GICs and Vitro Fil LC, Ortho Glass LC RMGICs). Five 1-mm-thick samples of each material-P/L ratio were produced for radiodensity evaluation. Samples were x-ray exposed onto Digora phosphor plate and radiodensity was obtained using the software Digora for Windows 2.5 Rev 0. For DTS, five (4.0x8.0 mm) cylinder samples of each material were tested (0.5 mm/min). Data were subjected to one- and two-way ANOVA (5x2) followed by Tukey's HSD test, or Kruskal-Wallis and Dunn's method. For paired comparisons, t-test or Mann-Whitney test were used (a=0.05). Results There was a significant interaction (P=0.001) for the studied factors (materials vs. P/L ratio). Reduced P/L ratio resulted in significantly lower DTS for the RMGICs, but radiodensity was affected for all materials (P<0.05). Conclusions Reduced P/L ratio affected properties of the tested glass ionomer cements. RMGICs were more susceptible to lower values of DTS, but radiodensity decreased for all materials following P/L ratio reduction. PMID:21308288

SU-E-J-02: 4D Digital Tomosynthesis Based On Algebraic Image Reconstruction and Total-Variation Minimization for the Improvement of Image Quality

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, D; Kang, S; Kim, T

2014-06-01

Purpose: In this paper, we implemented the four-dimensional (4D) digital tomosynthesis (DTS) imaging based on algebraic image reconstruction technique and total-variation minimization method in order to compensate the undersampled projection data and improve the image quality. Methods: The projection data were acquired as supposed the cone-beam computed tomography system in linear accelerator by the Monte Carlo simulation and the in-house 4D digital phantom generation program. We performed 4D DTS based upon simultaneous algebraic reconstruction technique (SART) among the iterative image reconstruction technique and total-variation minimization method (TVMM). To verify the effectiveness of this reconstruction algorithm, we performed systematic simulation studiesmore » to investigate the imaging performance. Results: The 4D DTS algorithm based upon the SART and TVMM seems to give better results than that based upon the existing method, or filtered-backprojection. Conclusion: The advanced image reconstruction algorithm for the 4D DTS would be useful to validate each intra-fraction motion during radiation therapy. In addition, it will be possible to give advantage to real-time imaging for the adaptive radiation therapy. This research was supported by Leading Foreign Research Institute Recruitment Program (Grant No.2009-00420) and Basic Atomic Energy Research Institute (BAERI); (Grant No. 2009-0078390) through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT and Future Planning (MSIP)« less

Comparative Issues and Methods in Organizational Diagnosis. Report II. The Decision Tree Approach.

DTIC Science & Technology

organizational diagnosis . The advantages and disadvantages of the decision-tree approach generally, and in this study specifically, are examined. A pre-test, using a civilian sample of 174 work groups with Survey of Organizations data, was conducted to assess various decision-tree classification criteria, in terms of their similarity to the distance function used by Bowers and Hausser (1977). The results suggested the use of a large developmental sample, which should result in more distinctly defined boundary lines between classification profiles. Also, the decision matrix

FDT 2.0: Improving scalability of the fuzzy decision tree induction tool - integrating database storage.

PubMed

Durham, Erin-Elizabeth A; Yu, Xiaxia; Harrison, Robert W

2014-12-01

Effective machine-learning handles large datasets efficiently. One key feature of handling large data is the use of databases such as MySQL. The freeware fuzzy decision tree induction tool, FDT, is a scalable supervised-classification software tool implementing fuzzy decision trees. It is based on an optimized fuzzy ID3 (FID3) algorithm. FDT 2.0 improves upon FDT 1.0 by bridging the gap between data science and data engineering: it combines a robust decisioning tool with data retention for future decisions, so that the tool does not need to be recalibrated from scratch every time a new decision is required. In this paper we briefly review the analytical capabilities of the freeware FDT tool and its major features and functionalities; examples of large biological datasets from HIV, microRNAs and sRNAs are included. This work shows how to integrate fuzzy decision algorithms with modern database technology. In addition, we show that integrating the fuzzy decision tree induction tool with database storage allows for optimal user satisfaction in today's Data Analytics world.

Soil Temperature Variability in Complex Terrain measured using Distributed a Fiber-Optic Distributed Temperature Sensing

NASA Astrophysics Data System (ADS)

Seyfried, M. S.; Link, T. E.

2013-12-01

Soil temperature (Ts) exerts critical environmental controls on hydrologic and biogeochemical processes. Rates of carbon cycling, mineral weathering, infiltration and snow melt are all influenced by Ts. Although broadly reflective of the climate, Ts is sensitive to local variations in cover (vegetative, litter, snow), topography (slope, aspect, position), and soil properties (texture, water content), resulting in a spatially and temporally complex distribution of Ts across the landscape. Understanding and quantifying the processes controlled by Ts requires an understanding of that distribution. Relatively few spatially distributed field Ts data exist, partly because traditional Ts data are point measurements. A relatively new technology, fiber optic distributed temperature system (FO-DTS), has the potential to provide such data but has not been rigorously evaluated in the context of remote, long term field research. We installed FO-DTS in a small experimental watershed in the Reynolds Creek Experimental Watershed (RCEW) in the Owyhee Mountains of SW Idaho. The watershed is characterized by complex terrain and a seasonal snow cover. Our objectives are to: (i) evaluate the applicability of fiber optic DTS to remote field environments and (ii) to describe the spatial and temporal variability of soil temperature in complex terrain influenced by a variable snow cover. We installed fiber optic cable at a depth of 10 cm in contrasting snow accumulation and topographic environments and monitored temperature along 750 m with DTS. We found that the DTS can provide accurate Ts data (+/- .4°C) that resolves Ts changes of about 0.03°C at a spatial scale of 1 m with occasional calibration under conditions with an ambient temperature range of 50°C. We note that there are site-specific limitations related cable installation and destruction by local fauna. The FO-DTS provide unique insight into the spatial and temporal variability of Ts in a landscape. We found strong seasonal trends in Ts variability controlled by snow cover and solar radiation as modified by topography. During periods of spatially continuous snow cover Ts was practically homogeneous throughout. In the absence of snow cover, Ts is highly variable, with most of the variability attributable to different topographic units defined by slope and aspect. During transition periods when snow melts out, Ts is highly variable within the watershed and within topographic units. The importance of accounting for these relatively small scale effects is underscored by the fact that the overall range of Ts in study area 600 m long is similar to that of the much large RCEW with 900 m elevation gradient.

Nested heat tracer experiments for identifying heterogeneity of aquifer-river exchange at multiple scales

NASA Astrophysics Data System (ADS)

Krause, Stefan; Hannah, David; Blume, Theresa; Angermann, Lisa; Lewandowski, Joerg; Cassidy, Nigel

2016-04-01

This study presents the nested application of three heat tracing methods for identifying aquifer-river exchange fluxes at multiple scales ranging from centimeter to stream reach-scale. The investigations focus on a UK lowland river where hotspots of redox-reactivity were found to coincide with locations of increased streambed residence times underneath flow confining streambed peat and clay structures. In order to identify the spatial extend and patterns of reactivity hot spots associated with these streambed structures, reach-scale patterns of aquifer-river exchange fluxes have been analysed by Fibre-Optic Distributed Temperature Sensing (FO-DTS) along a cable buried in the streambed of a 250 m reach in combination with 2D thermocouple arrays in a 12 m long pool-riffle-pool sequence and small-scale heat pulse injections for tracing shallow hyporheic flow paths within the uppermost 20cm streambed sediments. FO-DTS observed streambed temperature anomalies caused by the mixing of different temperatures of GW and SW end-members were used to infer information on exchange fluxes at the aquifer-river interface. FO-DTS survey results indicate that patterns of up to 2C colder (Summer) and 3.5C warmer (Winter) temperatures in investigated streambed sediments can be attributed to fast GW up-welling in sandy and gravely sediments. Contrasting conditions were found at locations where streambed temperatures equal SW temperatures and GW-SW exchange was inhibited by the existence of peat or clay lenses within the streambed. FO-DTS observations of regional GW up-welling patterns were complemented by heat pulse injection experiments which provided essential information of the shallow aquifer- river exchange fluxes and confirmed increased SW infiltration and lateral flow in riffle crests and at locations with highly conductive streambed sediments above flow confining low conductivity structures. The propagation of diurnal temperature oscillations from the surface to streambed depths of up to 40cm was observed at thermocouple profiles along a pool-riffle-pool sequence in order to analyse the potential masking of FO-DTS observed temperature patterns by topography induced hyporheic exchange fluxes. The cross-correlation functions based analysis of the depth dampening and offset of diurnal temperature amplitudes revealed that streambed temperature variation due to topography induced hyporheic exchange flow was an order of magnitude lower than the FO-DTS signal strength. The investigations supported the development of a conceptual model of aquifer-river exchange and hyporheic reactivity in lowland rivers including temperature traceable hyporheic exchange fluxes at multiple scales.

Application of decision tree model for the ground subsidence hazard mapping near abandoned underground coal mines.

PubMed

Lee, Saro; Park, Inhye

2013-09-30

Subsidence of ground caused by underground mines poses hazards to human life and property. This study analyzed the hazard to ground subsidence using factors that can affect ground subsidence and a decision tree approach in a geographic information system (GIS). The study area was Taebaek, Gangwon-do, Korea, where many abandoned underground coal mines exist. Spatial data, topography, geology, and various ground-engineering data for the subsidence area were collected and compiled in a database for mapping ground-subsidence hazard (GSH). The subsidence area was randomly split 50/50 for training and validation of the models. A data-mining classification technique was applied to the GSH mapping, and decision trees were constructed using the chi-squared automatic interaction detector (CHAID) and the quick, unbiased, and efficient statistical tree (QUEST) algorithms. The frequency ratio model was also applied to the GSH mapping for comparing with probabilistic model. The resulting GSH maps were validated using area-under-the-curve (AUC) analysis with the subsidence area data that had not been used for training the model. The highest accuracy was achieved by the decision tree model using CHAID algorithm (94.01%) comparing with QUEST algorithms (90.37%) and frequency ratio model (86.70%). These accuracies are higher than previously reported results for decision tree. Decision tree methods can therefore be used efficiently for GSH analysis and might be widely used for prediction of various spatial events. Copyright © 2013. Published by Elsevier Ltd.

MRI-based decision tree model for diagnosis of biliary atresia.

PubMed

Kim, Yong Hee; Kim, Myung-Joon; Shin, Hyun Joo; Yoon, Haesung; Han, Seok Joo; Koh, Hong; Roh, Yun Ho; Lee, Mi-Jung

2018-02-23

To evaluate MRI findings and to generate a decision tree model for diagnosis of biliary atresia (BA) in infants with jaundice. We retrospectively reviewed features of MRI and ultrasonography (US) performed in infants with jaundice between January 2009 and June 2016 under approval of the institutional review board, including the maximum diameter of periportal signal change on MRI (MR triangular cord thickness, MR-TCT) or US (US-TCT), visibility of common bile duct (CBD) and abnormality of gallbladder (GB). Hepatic subcapsular flow was reviewed on Doppler US. We performed conditional inference tree analysis using MRI findings to generate a decision tree model. A total of 208 infants were included, 112 in the BA group and 96 in the non-BA group. Mean age at the time of MRI was 58.7 ± 36.6 days. Visibility of CBD, abnormality of GB and MR-TCT were good discriminators for the diagnosis of BA and the MRI-based decision tree using these findings with MR-TCT cut-off 5.1 mm showed 97.3 % sensitivity, 94.8 % specificity and 96.2 % accuracy. MRI-based decision tree model reliably differentiates BA in infants with jaundice. MRI can be an objective imaging modality for the diagnosis of BA. • MRI-based decision tree model reliably differentiates biliary atresia in neonatal cholestasis. • Common bile duct, gallbladder and periportal signal changes are the discriminators. • MRI has comparable performance to ultrasonography for diagnosis of biliary atresia.

Predictability of the future development of aggressive behavior of cranial dural arteriovenous fistulas based on decision tree analysis.

PubMed

Satomi, Junichiro; Ghaibeh, A Ammar; Moriguchi, Hiroki; Nagahiro, Shinji

2015-07-01

The severity of clinical signs and symptoms of cranial dural arteriovenous fistulas (DAVFs) are well correlated with their pattern of venous drainage. Although the presence of cortical venous drainage can be considered a potential predictor of aggressive DAVF behaviors, such as intracranial hemorrhage or progressive neurological deficits due to venous congestion, accurate statistical analyses are currently not available. Using a decision tree data mining method, the authors aimed at clarifying the predictability of the future development of aggressive behaviors of DAVF and at identifying the main causative factors. Of 266 DAVF patients, 89 were eligible for analysis. Under observational management, 51 patients presented with intracranial hemorrhage/infarction during the follow-up period. The authors created a decision tree able to assess the risk for the development of aggressive DAVF behavior. Evaluated by 10-fold cross-validation, the decision tree's accuracy, sensitivity, and specificity were 85.28%, 88.33%, and 80.83%, respectively. The tree shows that the main factor in symptomatic patients was the presence of cortical venous drainage. In its absence, the lesion location determined the risk of a DAVF developing aggressive behavior. Decision tree analysis accurately predicts the future development of aggressive DAVF behavior.

[Analysis of the characteristics of the older adults with depression using data mining decision tree analysis].

PubMed

Park, Myonghwa; Choi, Sora; Shin, A Mi; Koo, Chul Hoi

2013-02-01

The purpose of this study was to develop a prediction model for the characteristics of older adults with depression using the decision tree method. A large dataset from the 2008 Korean Elderly Survey was used and data of 14,970 elderly people were analyzed. Target variable was depression and 53 input variables were general characteristics, family & social relationship, economic status, health status, health behavior, functional status, leisure & social activity, quality of life, and living environment. Data were analyzed by decision tree analysis, a data mining technique using SPSS Window 19.0 and Clementine 12.0 programs. The decision trees were classified into five different rules to define the characteristics of older adults with depression. Classification & Regression Tree (C&RT) showed the best prediction with an accuracy of 80.81% among data mining models. Factors in the rules were life satisfaction, nutritional status, daily activity difficulty due to pain, functional limitation for basic or instrumental daily activities, number of chronic diseases and daily activity difficulty due to disease. The different rules classified by the decision tree model in this study should contribute as baseline data for discovering informative knowledge and developing interventions tailored to these individual characteristics.

Applied Swarm-based medicine: collecting decision trees for patterns of algorithms analysis.

PubMed

Panje, Cédric M; Glatzer, Markus; von Rappard, Joscha; Rothermundt, Christian; Hundsberger, Thomas; Zumstein, Valentin; Plasswilm, Ludwig; Putora, Paul Martin

2017-08-16

The objective consensus methodology has recently been applied in consensus finding in several studies on medical decision-making among clinical experts or guidelines. The main advantages of this method are an automated analysis and comparison of treatment algorithms of the participating centers which can be performed anonymously. Based on the experience from completed consensus analyses, the main steps for the successful implementation of the objective consensus methodology were identified and discussed among the main investigators. The following steps for the successful collection and conversion of decision trees were identified and defined in detail: problem definition, population selection, draft input collection, tree conversion, criteria adaptation, problem re-evaluation, results distribution and refinement, tree finalisation, and analysis. This manuscript provides information on the main steps for successful collection of decision trees and summarizes important aspects at each point of the analysis.

Comparison of neurofuzzy logic and decision trees in discovering knowledge from experimental data of an immediate release tablet formulation.

PubMed

Shao, Q; Rowe, R C; York, P

2007-06-01

Understanding of the cause-effect relationships between formulation ingredients, process conditions and product properties is essential for developing a quality product. However, the formulation knowledge is often hidden in experimental data and not easily interpretable. This study compares neurofuzzy logic and decision tree approaches in discovering hidden knowledge from an immediate release tablet formulation database relating formulation ingredients (silica aerogel, magnesium stearate, microcrystalline cellulose and sodium carboxymethylcellulose) and process variables (dwell time and compression force) to tablet properties (tensile strength, disintegration time, friability, capping and drug dissolution at various time intervals). Both approaches successfully generated useful knowledge in the form of either "if then" rules or decision trees. Although different strategies are employed by the two approaches in generating rules/trees, similar knowledge was discovered in most cases. However, as decision trees are not able to deal with continuous dependent variables, data discretisation procedures are generally required.

Parallel object-oriented decision tree system

DOEpatents

Kamath,; Chandrika, Cantu-Paz [Dublin, CA; Erick, [Oakland, CA

2006-02-28

A data mining decision tree system that uncovers patterns, associations, anomalies, and other statistically significant structures in data by reading and displaying data files, extracting relevant features for each of the objects, and using a method of recognizing patterns among the objects based upon object features through a decision tree that reads the data, sorts the data if necessary, determines the best manner to split the data into subsets according to some criterion, and splits the data.

Generation and Termination of Binary Decision Trees for Nonparametric Multiclass Classification.

DTIC Science & Technology

1984-10-01

O M coF=F;; UMBER2. GOVT ACCE5SION NO.1 3 . REC,PINS :A7AL:,G NUMBER ( ’eneration and Terminat_,on :)f Binary D-ecision jC j ik; Trees for Nonnararetrc...1-I . v)IAMO 0~I4 EDvt" O F I 00 . 3 15I OR%.OL.ETL - S-S OCTOBER 1984 LIDS-P-1411 GENERATION AND TERMINATION OF BINARY DECISION TREES FOR...minimizes the Bayes risk. Tree generation and termination are based on the training and test samples, respectively. 0 0 0/ 6 0¢ A 3 I. Introduction We state

EEG feature selection method based on decision tree.

PubMed

Duan, Lijuan; Ge, Hui; Ma, Wei; Miao, Jun

2015-01-01

This paper aims to solve automated feature selection problem in brain computer interface (BCI). In order to automate feature selection process, we proposed a novel EEG feature selection method based on decision tree (DT). During the electroencephalogram (EEG) signal processing, a feature extraction method based on principle component analysis (PCA) was used, and the selection process based on decision tree was performed by searching the feature space and automatically selecting optimal features. Considering that EEG signals are a series of non-linear signals, a generalized linear classifier named support vector machine (SVM) was chosen. In order to test the validity of the proposed method, we applied the EEG feature selection method based on decision tree to BCI Competition II datasets Ia, and the experiment showed encouraging results.

The Decision Tree for Teaching Management of Uncertainty

ERIC Educational Resources Information Center

Knaggs, Sara J.; And Others

1974-01-01

A 'decision tree' consists of an outline of the patient's symptoms and a logic for decision and action. It is felt that this approach to the decisionmaking process better facilitates each learner's application of his own level of knowledge and skills. (Author)

Predicting metabolic syndrome using decision tree and support vector machine methods.

PubMed

Karimi-Alavijeh, Farzaneh; Jalili, Saeed; Sadeghi, Masoumeh

2016-05-01

Metabolic syndrome which underlies the increased prevalence of cardiovascular disease and Type 2 diabetes is considered as a group of metabolic abnormalities including central obesity, hypertriglyceridemia, glucose intolerance, hypertension, and dyslipidemia. Recently, artificial intelligence based health-care systems are highly regarded because of its success in diagnosis, prediction, and choice of treatment. This study employs machine learning technics for predict the metabolic syndrome. This study aims to employ decision tree and support vector machine (SVM) to predict the 7-year incidence of metabolic syndrome. This research is a practical one in which data from 2107 participants of Isfahan Cohort Study has been utilized. The subjects without metabolic syndrome according to the ATPIII criteria were selected. The features that have been used in this data set include: gender, age, weight, body mass index, waist circumference, waist-to-hip ratio, hip circumference, physical activity, smoking, hypertension, antihypertensive medication use, systolic blood pressure (BP), diastolic BP, fasting blood sugar, 2-hour blood glucose, triglycerides (TGs), total cholesterol, low-density lipoprotein, high density lipoprotein-cholesterol, mean corpuscular volume, and mean corpuscular hemoglobin. Metabolic syndrome was diagnosed based on ATPIII criteria and two methods of decision tree and SVM were selected to predict the metabolic syndrome. The criteria of sensitivity, specificity and accuracy were used for validation. SVM and decision tree methods were examined according to the criteria of sensitivity, specificity and accuracy. Sensitivity, specificity and accuracy were 0.774 (0.758), 0.74 (0.72) and 0.757 (0.739) in SVM (decision tree) method. The results show that SVM method sensitivity, specificity and accuracy is more efficient than decision tree. The results of decision tree method show that the TG is the most important feature in predicting metabolic syndrome. According to this study, in cases where only the final result of the decision is regarded significant, SVM method can be used with acceptable accuracy in decision making medical issues. This method has not been implemented in the previous research.

Temperature Sensing for Oil, Gas, and Structural Analysis

NASA Technical Reports Server (NTRS)

2006-01-01

In 1996, Systems and Processes Engineering Corporation (SPEC), of Austin, Texas, undertook a NASA Small Business Innovation Research (SBIR) contract with Langley Research Center to develop a compact and lightweight digital thermal sensing (DTS) system for monitoring the cryogenic tanks on the X-33 prototype aircraft. That technology, along with a processor developed by SPEC for Goddard Space Flight Center, was space-qualified and integrated into several NASA missions. SPEC formed an ancillary organization, SensorTran, Inc., to continue work developing the DTS technology for a variety of commercial and industrial applications.

Cost-effectiveness Analysis with Influence Diagrams.

PubMed

Arias, M; Díez, F J

2015-01-01

Cost-effectiveness analysis (CEA) is used increasingly in medicine to determine whether the health benefit of an intervention is worth the economic cost. Decision trees, the standard decision modeling technique for non-temporal domains, can only perform CEA for very small problems. To develop a method for CEA in problems involving several dozen variables. We explain how to build influence diagrams (IDs) that explicitly represent cost and effectiveness. We propose an algorithm for evaluating cost-effectiveness IDs directly, i.e., without expanding an equivalent decision tree. The evaluation of an ID returns a set of intervals for the willingness to pay - separated by cost-effectiveness thresholds - and, for each interval, the cost, the effectiveness, and the optimal intervention. The algorithm that evaluates the ID directly is in general much more efficient than the brute-force method, which is in turn more efficient than the expansion of an equivalent decision tree. Using OpenMarkov, an open-source software tool that implements this algorithm, we have been able to perform CEAs on several IDs whose equivalent decision trees contain millions of branches. IDs can perform CEA on large problems that cannot be analyzed with decision trees.

Discovering Decision Knowledge from Web Log Portfolio for Managing Classroom Processes by Applying Decision Tree and Data Cube Technology.

ERIC Educational Resources Information Center

Chen, Gwo-Dong; Liu, Chen-Chung; Ou, Kuo-Liang; Liu, Baw-Jhiune

2000-01-01

Discusses the use of Web logs to record student behavior that can assist teachers in assessing performance and making curriculum decisions for distance learning students who are using Web-based learning systems. Adopts decision tree and data cube information processing methodologies for developing more effective pedagogical strategies. (LRW)

Assessing School Readiness for a Practice Arrangement Using Decision Tree Methodology.

ERIC Educational Resources Information Center

Barger, Sara E.

1998-01-01

Questions in a decision-tree address mission, faculty interest, administrative support, and practice plan as a way of assessing arrangements for nursing faculty's clinical practice. Decisions should be based on congruence between the human resource allocation and the reward systems. (SK)

Automated Decision Tree Classification of Corneal Shape

PubMed Central

Twa, Michael D.; Parthasarathy, Srinivasan; Roberts, Cynthia; Mahmoud, Ashraf M.; Raasch, Thomas W.; Bullimore, Mark A.

2011-01-01

Purpose The volume and complexity of data produced during videokeratography examinations present a challenge of interpretation. As a consequence, results are often analyzed qualitatively by subjective pattern recognition or reduced to comparisons of summary indices. We describe the application of decision tree induction, an automated machine learning classification method, to discriminate between normal and keratoconic corneal shapes in an objective and quantitative way. We then compared this method with other known classification methods. Methods The corneal surface was modeled with a seventh-order Zernike polynomial for 132 normal eyes of 92 subjects and 112 eyes of 71 subjects diagnosed with keratoconus. A decision tree classifier was induced using the C4.5 algorithm, and its classification performance was compared with the modified Rabinowitz–McDonnell index, Schwiegerling’s Z3 index (Z3), Keratoconus Prediction Index (KPI), KISA%, and Cone Location and Magnitude Index using recommended classification thresholds for each method. We also evaluated the area under the receiver operator characteristic (ROC) curve for each classification method. Results Our decision tree classifier performed equal to or better than the other classifiers tested: accuracy was 92% and the area under the ROC curve was 0.97. Our decision tree classifier reduced the information needed to distinguish between normal and keratoconus eyes using four of 36 Zernike polynomial coefficients. The four surface features selected as classification attributes by the decision tree method were inferior elevation, greater sagittal depth, oblique toricity, and trefoil. Conclusions Automated decision tree classification of corneal shape through Zernike polynomials is an accurate quantitative method of classification that is interpretable and can be generated from any instrument platform capable of raw elevation data output. This method of pattern classification is extendable to other classification problems. PMID:16357645

Decision Trees Predicting Tumor Shrinkage for Head and Neck Cancer: Implications for Adaptive Radiotherapy.

PubMed

Surucu, Murat; Shah, Karan K; Mescioglu, Ibrahim; Roeske, John C; Small, William; Choi, Mehee; Emami, Bahman

2016-02-01

To develop decision trees predicting for tumor volume reduction in patients with head and neck (H&N) cancer using pretreatment clinical and pathological parameters. Forty-eight patients treated with definitive concurrent chemoradiotherapy for squamous cell carcinoma of the nasopharynx, oropharynx, oral cavity, or hypopharynx were retrospectively analyzed. These patients were rescanned at a median dose of 37.8 Gy and replanned to account for anatomical changes. The percentages of gross tumor volume (GTV) change from initial to rescan computed tomography (CT; %GTVΔ) were calculated. Two decision trees were generated to correlate %GTVΔ in primary and nodal volumes with 14 characteristics including age, gender, Karnofsky performance status (KPS), site, human papilloma virus (HPV) status, tumor grade, primary tumor growth pattern (endophytic/exophytic), tumor/nodal/group stages, chemotherapy regimen, and primary, nodal, and total GTV volumes in the initial CT scan. The C4.5 Decision Tree induction algorithm was implemented. The median %GTVΔ for primary, nodal, and total GTVs was 26.8%, 43.0%, and 31.2%, respectively. Type of chemotherapy, age, primary tumor growth pattern, site, KPS, and HPV status were the most predictive parameters for primary %GTVΔ decision tree, whereas for nodal %GTVΔ, KPS, site, age, primary tumor growth pattern, initial primary GTV, and total GTV volumes were predictive. Both decision trees had an accuracy of 88%. There can be significant changes in primary and nodal tumor volumes during the course of H&N chemoradiotherapy. Considering the proposed decision trees, radiation oncologists can select patients predicted to have high %GTVΔ, who would theoretically gain the most benefit from adaptive radiotherapy, in order to better use limited clinical resources. © The Author(s) 2015.

On Parallelism and the Penman Natural Language Generation System.

DTIC Science & Technology

1988-04-01

TagfiniteA Tagsubject L untag ed Figure 2-2: System network with choosers & realization statements 7 decision . We will give a more detailed account of...2: enter the current system. The chooser of the system is in charge of * selection of features. The chooser is itself a decision tree with certain...organization of a chooser is the same as a decision (discrimination) tree, and each branching point in the tree is defined by Ask operation. For example, in

An automated approach to the design of decision tree classifiers

NASA Technical Reports Server (NTRS)

Argentiero, P.; Chin, P.; Beaudet, P.

1980-01-01

The classification of large dimensional data sets arising from the merging of remote sensing data with more traditional forms of ancillary data is considered. Decision tree classification, a popular approach to the problem, is characterized by the property that samples are subjected to a sequence of decision rules before they are assigned to a unique class. An automated technique for effective decision tree design which relies only on apriori statistics is presented. This procedure utilizes a set of two dimensional canonical transforms and Bayes table look-up decision rules. An optimal design at each node is derived based on the associated decision table. A procedure for computing the global probability of correct classfication is also provided. An example is given in which class statistics obtained from an actual LANDSAT scene are used as input to the program. The resulting decision tree design has an associated probability of correct classification of .76 compared to the theoretically optimum .79 probability of correct classification associated with a full dimensional Bayes classifier. Recommendations for future research are included.

Evaluation of Decision Trees for Cloud Detection from AVHRR Data

NASA Technical Reports Server (NTRS)

Shiffman, Smadar; Nemani, Ramakrishna

2005-01-01

Automated cloud detection and tracking is an important step in assessing changes in radiation budgets associated with global climate change via remote sensing. Data products based on satellite imagery are available to the scientific community for studying trends in the Earth's atmosphere. The data products include pixel-based cloud masks that assign cloud-cover classifications to pixels. Many cloud-mask algorithms have the form of decision trees. The decision trees employ sequential tests that scientists designed based on empirical astrophysics studies and simulations. Limitations of existing cloud masks restrict our ability to accurately track changes in cloud patterns over time. In a previous study we compared automatically learned decision trees to cloud masks included in Advanced Very High Resolution Radiometer (AVHRR) data products from the year 2000. In this paper we report the replication of the study for five-year data, and for a gold standard based on surface observations performed by scientists at weather stations in the British Islands. For our sample data, the accuracy of automatically learned decision trees was greater than the accuracy of the cloud masks p < 0.001.

Decision-Tree Analysis for Predicting First-Time Pass/Fail Rates for the NCLEX-RN® in Associate Degree Nursing Students.

PubMed

Chen, Hsiu-Chin; Bennett, Sean

2016-08-01

Little evidence shows the use of decision-tree algorithms in identifying predictors and analyzing their associations with pass rates for the NCLEX-RN(®) in associate degree nursing students. This longitudinal and retrospective cohort study investigated whether a decision-tree algorithm could be used to develop an accurate prediction model for the students' passing or failing the NCLEX-RN. This study used archived data from 453 associate degree nursing students in a selected program. The chi-squared automatic interaction detection analysis of the decision trees module was used to examine the effect of the collected predictors on passing/failing the NCLEX-RN. The actual percentage scores of Assessment Technologies Institute®'s RN Comprehensive Predictor(®) accurately identified students at risk of failing. The classification model correctly classified 92.7% of the students for passing. This study applied the decision-tree model to analyze a sequence database for developing a prediction model for early remediation in preparation for the NCLEXRN. [J Nurs Educ. 2016;55(8):454-457.]. Copyright 2016, SLACK Incorporated.

Distributed Temperature Sensing - a Useful Tool for Investigation of Surface Water - Groundwater Interaction

NASA Astrophysics Data System (ADS)

Vogt, T.; Hahn-Woernle, L.; Sunarjo, B.; Thum, T.; Schneider, P.; Schirmer, M.; Cirpka, O. A.

2009-04-01

In recent years, the transition zone between surface water bodies and groundwater, known as the hyporheic zone, has been identified as crucial for the ecological status of the open-water body and the quality of groundwater. The hyporheic exchange processes vary both in time and space. For the assessment of water quality of both water bodies reliable models and measurements of the exchange rates and their variability are needed. A wide range of methods and technologies exist to estimate water fluxes between surface water and groundwater. Due to recent developments in sensor techniques and data logging work on heat as a tracer in hydrological systems advances, especially with focus on surface water - groundwater interactions. Here, we evaluate the use of Distributed Temperature Sensing (DTS) for the qualitative and quantitative investigation of groundwater discharge into and groundwater recharge from a river. DTS is based on the temperature dependence of Raman scattering. Light from a laser pulse is scattered along an optical fiber of up to several km length, which is the sensor of the DTS system. By sampling the the back-scattered light with high temporal resolution, the temperature along the fiber can be measured with high accuracy (0.1 K) and high spatial resolution (1 m). We used DTS at a test side at River Thur in North-East Switzerland. Here, the river is loosing and the aquifer is drained by two side-channels, enabling us to test DTS for both, groundwater recharge from the river and groundwater discharge into the side-channels. For estimation of seepage rates, we measured highly resolved vertical temperature profiles in the river bed. For this application, we wrapped an optical fiber around a piezometer tube and measured the temperature distribution along the fiber. Due to the wrapping, we obtained a vertical resolution of approximately 5 mm. We analyzed the temperature time series by means of Dynamic Harmonic Regression as presented by Keery et al. (2007). From the travel time and attenuation of the diurnal time signal, we estimated the apparent velocity and diffusivity of temperature propagation, which then can be used to quantify infiltration rates. A particular strength of the new measuring technique lies in the high spatial and temporal resolution, enabling us to detect non-uniformity and temporal changes in vertical water fluxes. In the side-channels, we have laterally laid out optical fibers to detect zones of groundwater discharge. As groundwater temperatures differ from river temperatures, local exfiltration of groundwater leads to a local change of the temperature at the river bottom. A limitation of lateral DTS data is that exchange rates cannot directly be quantified. Therefore, we used DTS for streambed temperature mapping. Then certain exfiltration zones undergo further investigation using time series of streambed temperature profiles obtained in piezometers. J. Keery, A. Binley, N. Crook and J.W.N. Smith (2007) Temporal and spatial variability of groundwater-surface water fluxes: Development and application of an analytical method using temperature time series, Journal of Hydrology, 336, 1-16.

Sequential decision tree using the analytic hierarchy process for decision support in rectal cancer.

PubMed

Suner, Aslı; Çelikoğlu, Can Cengiz; Dicle, Oğuz; Sökmen, Selman

2012-09-01

The aim of the study is to determine the most appropriate method for construction of a sequential decision tree in the management of rectal cancer, using various patient-specific criteria and treatments such as surgery, chemotherapy, and radiotherapy. An analytic hierarchy process (AHP) was used to determine the priorities of variables. Relevant criteria used in two decision steps and their relative priorities were established by a panel of five general surgeons. Data were collected via a web-based application and analyzed using the "Expert Choice" software specifically developed for the AHP. Consistency ratios in the AHP method were calculated for each set of judgments, and the priorities of sub-criteria were determined. A sequential decision tree was constructed for the best treatment decision process, using priorities determined by the AHP method. Consistency ratios in the AHP method were calculated for each decision step, and the judgments were considered consistent. The tumor-related criterion "presence of perforation" (0.331) and the patient-surgeon-related criterion "surgeon's experience" (0.630) had the highest priority in the first decision step. In the second decision step, the tumor-related criterion "the stage of the disease" (0.230) and the patient-surgeon-related criterion "surgeon's experience" (0.281) were the paramount criteria. The results showed some variation in the ranking of criteria between the decision steps. In the second decision step, for instance, the tumor-related criterion "presence of perforation" was just the fifth. The consistency of decision support systems largely depends on the quality of the underlying decision tree. When several choices and variables have to be considered in a decision, it is very important to determine priorities. The AHP method seems to be effective for this purpose. The decision algorithm developed by this method is more realistic and will improve the quality of the decision tree. Copyright © 2012 Elsevier B.V. All rights reserved.

Comparison of Taxi Time Prediction Performance Using Different Taxi Speed Decision Trees

NASA Technical Reports Server (NTRS)

Lee, Hanbong

2017-01-01

In the STBO modeler and tactical surface scheduler for ATD-2 project, taxi speed decision trees are used to calculate the unimpeded taxi times of flights taxiing on the airport surface. The initial taxi speed values in these decision trees did not show good prediction accuracy of taxi times. Using the more recent, reliable surveillance data, new taxi speed values in ramp area and movement area were computed. Before integrating these values into the STBO system, we performed test runs using live data from Charlotte airport, with different taxi speed settings: 1) initial taxi speed values and 2) new ones. Taxi time prediction performance was evaluated by comparing various metrics. The results show that the new taxi speed decision trees can calculate the unimpeded taxi-out times more accurately.

Improved spatial monitoring of air temperature in forested complex terrain: an energy-balance based calibration method

NASA Astrophysics Data System (ADS)

Kennedy, A. M.; Thomas, C. K.; Pypker, T. G.; Bond, B. J.; Selker, J. S.; Unsworth, M. H.

2009-12-01

Fiber-optic distributed temperature sensing (DTS) has great potential for spatial monitoring in hydrology and atmospheric science. DTS systems have an advantage over conventional individual temperature sensors in that thousands of quasi-concurrent temperature measurements may be made along the entire length of a fiber at 1 meter increments by a single instrument, thus increasing measurement precision. However, like any other temperature sensors, the fiber temperature is influenced by energy exchange with its environment, particularly by radiant energy (solar and long-wave) and by wind speed. The objective of this research is to perform an energy-balance based calibration of a DTS fiber system that will reduce the uncertainty of air temperature measurements in open and forested environments. To better understand the physics controlling the fiber temperature reported by the DTS, alternating black and white fiber optic cables were installed on vertical wooden jigs inside a recirculating wind tunnel. A constant irradiance from six 600W halogen lamps was directed on a two meter section of fiber to permit controlled observations of the resulting temperature difference between the black and white fibers as wind speed was varied. The net short and longwave radiation balance of each fiber was measured with an Eppley pyranometer and Kipp and Zonen pyrgeometer. Additionally, accurate air temperature was recorded from a screened platinum resistance thermometer, and sonic anemometers were positioned to record wind speed and turbulence. Relationships between the temperature excess of each fiber, net radiation, and wind speed were developed and will be used to derive correction terms in future field work. Preliminary results indicate that differential heating of fibers (black-white) is driven largely by net radiation with wind having a smaller but consistent effect. Subsequent work will require field verification to confirm that the observed wind tunnel correction algorithms are applicable in both open and forest canopy settings. Our ultimate goal is to use atmospheric DTS measurements of 3D temperature fields in a small steep-walled forested watershed to gain a better understanding and rigorous description of the processes governing air circulation (cold air drainage etc) in the canopy. Such knowledge will assist in the interpretation of observed biological responses.

Improved-resolution real-time skin-dose mapping for interventional fluoroscopic procedures

NASA Astrophysics Data System (ADS)

Rana, Vijay K.; Rudin, Stephen; Bednarek, Daniel R.

2014-03-01

We have developed a dose-tracking system (DTS) that provides a real-time display of the skin-dose distribution on a 3D patient graphic during fluoroscopic procedures. Radiation dose to individual points on the skin is calculated using exposure and geometry parameters from the digital bus on a Toshiba C-arm unit. To accurately define the distribution of dose, it is necessary to use a high-resolution patient graphic consisting of a large number of elements. In the original DTS version, the patient graphics were obtained from a library of population body scans which consisted of larger-sized triangular elements resulting in poor congruence between the graphic points and the x-ray beam boundary. To improve the resolution without impacting real-time performance, the number of calculations must be reduced and so we created software-designed human models and modified the DTS to read the graphic as a list of vertices of the triangular elements such that common vertices of adjacent triangles are listed once. Dose is calculated for each vertex point once instead of the number of times that a given vertex appears in multiple triangles. By reformatting the graphic file, we were able to subdivide the triangular elements by a factor of 64 times with an increase in the file size of only 1.3 times. This allows a much greater number of smaller triangular elements and improves resolution of the patient graphic without compromising the real-time performance of the DTS and also gives a smoother graphic display for better visualization of the dose distribution.

Groundwater temperature estimation and modeling using hydrogeophysics.

NASA Astrophysics Data System (ADS)

Nguyen, F.; Lesparre, N.; Hermans, T.; Dassargues, A.; Klepikova, M.; Kemna, A.; Caers, J.

2017-12-01

Groundwater temperature may be of use as a state variable proxy for aquifer heat storage, highlighting preferential flow paths, or contaminant remediation monitoring. However, its estimation often relies on scarce temperature data collected in boreholes. Hydrogeophysical methods such as electrical resistivity tomography (ERT) and distributed temperature sensing (DTS) may provide more exhaustive spatial information of the bulk properties of interest than samples from boreholes. If a properly calibrated DTS reading provides direct measurements of the groundwater temperature in the well, ERT requires one to determine the fractional change per degree Celsius. One advantage of this petrophysical relationship is its relative simplicity: the fractional change is often found to be around 0.02 per degree Celcius, and represents mainly the variation of electrical resistivity due to the viscosity effect. However, in presence of chemical and kinetics effects, the variation may also depend on the duration of the test and may neglect reactions occurring between the pore water and the solid matrix. Such effects are not expected to be important for low temperature systems (<30 °C), at least for short experiments. In this contribution, we use different field experiments under natural and forced flow conditions to review developments for the joint use of DTS and ERT to map and monitor the temperature distribution within aquifers, to characterize aquifers in terms of heterogeneity and to better understand processes. We show how temperature time-series measurements might be used to constraint the ERT inverse problem in space and time and how combined ERT-derived and DTS estimation of temperature may be used together with hydrogeological modeling to provide predictions of the groundwater temperature field.

Effectiveness of distributed temperature measurements for early detection of piping in river embankments

NASA Astrophysics Data System (ADS)

Bersan, Silvia; Koelewijn, André R.; Simonini, Paolo

2018-02-01

Internal erosion is the cause of a significant percentage of failure and incidents involving both dams and river embankments in many countries. In the past 20 years the use of fibre-optic Distributed Temperature Sensing (DTS) in dams has proved to be an effective tool for the detection of leakages and internal erosion. This work investigates the effectiveness of DTS for dike monitoring, focusing on the early detection of backward erosion piping, a mechanism that affects the foundation layer of structures resting on permeable, sandy soils. The paper presents data from a piping test performed on a large-scale experimental dike equipped with a DTS system together with a large number of accompanying sensors. The effect of seepage and piping on the temperature field is analysed, eventually identifying the processes that cause the onset of thermal anomalies around piping channels and thus enable their early detection. Making use of dimensional analysis, the factors that influence this thermal response of a dike foundation are identified. Finally some tools are provided that can be helpful for the design of monitoring systems and for the interpretation of temperature data.

Progress in distributed fiber optic temperature sensing

NASA Astrophysics Data System (ADS)

Hartog, Arthur H.

2002-02-01

The paper reviews the adoption of distributed temperature sensing (DTS) technology based on Raman backscatter. With one company alone having installed more than 400 units, the DTS is becoming accepted practice in several applications, notably in energy cable monitoring, specialised fire detection and oil production monitoring. The paper will provide case studies in these applications. In each case the benefit (whether economic or safety) will be addressed, together with key application engineering issues. The latter range from the selection and installation of the fibre sensor, the specific performance requirements of the opto-electronic equipment and the issues of data management. The paper will also address advanced applications of distributed sensing, notably the problem of monitoring very long ranges, which apply in subsea DC energy cables or in subsea oil wells linked to platforms through very long (e.g. 30km flowlines). These applications are creating the need for a new generation of DTS systems able to achieve measurements at up to 40km with very high temperature resolution, without sacrificing spatial resolution. This challenge is likely to drive the development of new concepts in the field of distributed sensing.

Detection and localization of building insulation faults using optical-fiber DTS system

NASA Astrophysics Data System (ADS)

Papes, Martin; Liner, Andrej; Koudelka, Petr; Siska, Petr; Cubik, Jakub; Kepak, Stanislav; Jaros, Jakub; Vasinek, Vladimir

2013-05-01

Nowadays the trends in the construction industry are changing at an incredible speed. The new technologies are still emerging on the market. Sphere of building insulation is not an exception as well. One of the major problems in building insulation is usually its failure, whether caused by unwanted mechanical intervention or improper installation. The localization of these faults is quite difficult, often impossible without large intervention into the construction. As a proper solution for this problem might be utilization of Optical-Fiber DTS system based on stimulated Raman scattering. Used DTS system is primary designed for continuous measurement of the temperature along the optical fiber. This system is using standard optical fiber as a sensor, which brings several advantages in its application. First, the optical fiber is relatively inexpensive, which allows to cover a quite large area for a small cost. The other main advantages of the optical fiber are electromagnetic resistance, small size, safety operation in inflammable or explosive area, easy installation, etc. This article is dealing with the detection and localization of building insulation faults using mentioned system.

Distributed temperature sensing inside a 19-rod bundle

DOE PAGES

Lomperski, S.; Bremer, N.; Gerardi, C.

2017-05-23

The temperature field within a model of a sodium-cooled fast reactor fuel rod bundle was measured using Ø155 μm fiber optic distributed temperature sensors (DTS). The bundle consists of 19 electrically-heated rods Ø6.3 mm and 865 mm long. Working fluids were argon and air at atmospheric pressure and Reynolds numbers up to 300. A 20 m-long DTS was threaded through Ø1 mm capillaries wound around rods as wire-wraps. The sensor generated 173 measurements along each rod at 5 mm resolution for a total of 3300 data locations. A second DTS, 58 m long, was suspended between rods to provide 9300more » fluid temperature measurements at 20 mm resolution. Such data density makes it possible to construct 3D maps of the temperature field that are beyond the reach of traditional sensors such as thermocouples. This is illustrated through a series of steady-state and transient tests. As a result, the work demonstrates the feasibility of mapping temperature within the close confines of a rod bundle at resolutions suitable for validation of computational fluid dynamics codes.« less

Monitoring the Impacts of Forest Management on Snowpack Duration

NASA Astrophysics Data System (ADS)

O'Halloran, T.; Tyler, S.; Gaffney, R.; Pai, H.

2017-12-01

Seasonal snowpack constitutes a significant portion of the hydrologic budget in mountain watersheds and influences dynamic (e.g., runoff magnitude and timing, soil moisture availability) and energetic processes (e.g., surface-atmosphere energy fluxes, ground temperature). Altered forest structure can affect snow accumulation and ablation. As part of a long-term monitoring project, this work examines the impact of forest management practices on snow cover in Lassen National Forest, California. We deployed a fiber optic distributed temperature sensing (DTS) cable and multiple meteorological stations in thinned, clear-cut, and untreated areas of forest. The DTS data was collected at 1 meter spatial intervals every 4 hours from February to May 2017. To determine snow cover, daily temperature variations were examined along locations of the DTS cable associated with our areas of interest. Between the various treatments, snow duration was greater in both clear-cut and untreated forest compared to the thinned area. However, snow duration varied by only six days. We also investigated other meteorological forcings, such as average winter temperature and precipitation, which coupled with forest modifications could explain snow duration in our study.

Studies on high-moment soft magnetic FeCo/Co thin films

NASA Astrophysics Data System (ADS)

Fu, Yu; Yang, Zheng; Matsumoto, Mitsunori; Liu, Xiao-Xi; Morisako, Akimitsu

2006-06-01

The dependences of soft magnetic properties and microstructures of the sputtered FeCo (=Fe65Co35) films on Co underlayer thickness tCo, FeCo thickness tFeCo, substrate temperature Ts and target-substrate spacing dT-S are studied. FeCo single layer generally shows a high coercivity with no obvious magnetic anisotropy. Excellent soft magnetic properties with saturation magnetization μ0Ms of 2.35 T and hard axis coercivity Hch of 0.25 kA/m in FeCo films can be achieved by introducing a Co underlayer. It is shown that sandwiching a Co underlayer causes a change in orientation and reduction in grain size from 70 nm to about 10 nm in the FeCo layer. The magnetic softness can be explained by the Hoffmann's ripple theory due to the effect of grain size. The magnetic anisotropy can be controlled by changing dT-S and a maximum of 14.3 kA/m for anisotropic field Hk is obtained with dT-S=18.0 cm.

Sparse Reconstruction for Temperature Distribution Using DTS Fiber Optic Sensors with Applications in Electrical Generator Stator Monitoring.

PubMed

Bazzo, João Paulo; Pipa, Daniel Rodrigues; da Silva, Erlon Vagner; Martelli, Cicero; Cardozo da Silva, Jean Carlos

2016-09-07

This paper presents an image reconstruction method to monitor the temperature distribution of electric generator stators. The main objective is to identify insulation failures that may arise as hotspots in the structure. The method is based on temperature readings of fiber optic distributed sensors (DTS) and a sparse reconstruction algorithm. Thermal images of the structure are formed by appropriately combining atoms of a dictionary of hotspots, which was constructed by finite element simulation with a multi-physical model. Due to difficulties for reproducing insulation faults in real stator structure, experimental tests were performed using a prototype similar to the real structure. The results demonstrate the ability of the proposed method to reconstruct images of hotspots with dimensions down to 15 cm, representing a resolution gain of up to six times when compared to the DTS spatial resolution. In addition, satisfactory results were also obtained to detect hotspots with only 5 cm. The application of the proposed algorithm for thermal imaging of generator stators can contribute to the identification of insulation faults in early stages, thereby avoiding catastrophic damage to the structure.

Bayesian additive decision trees of biomarker by treatment interactions for predictive biomarker detection and subgroup identification.

PubMed

Zhao, Yang; Zheng, Wei; Zhuo, Daisy Y; Lu, Yuefeng; Ma, Xiwen; Liu, Hengchang; Zeng, Zhen; Laird, Glen

2017-10-11

Personalized medicine, or tailored therapy, has been an active and important topic in recent medical research. Many methods have been proposed in the literature for predictive biomarker detection and subgroup identification. In this article, we propose a novel decision tree-based approach applicable in randomized clinical trials. We model the prognostic effects of the biomarkers using additive regression trees and the biomarker-by-treatment effect using a single regression tree. Bayesian approach is utilized to periodically revise the split variables and the split rules of the decision trees, which provides a better overall fitting. Gibbs sampler is implemented in the MCMC procedure, which updates the prognostic trees and the interaction tree separately. We use the posterior distribution of the interaction tree to construct the predictive scores of the biomarkers and to identify the subgroup where the treatment is superior to the control. Numerical simulations show that our proposed method performs well under various settings comparing to existing methods. We also demonstrate an application of our method in a real clinical trial.

RE-Powering’s Electronic Decision Tree

EPA Pesticide Factsheets

Developed by US EPA's RE-Powering America's Land Initiative, the RE-Powering Decision Trees tool guides interested parties through a process to screen sites for their suitability for solar photovoltaics or wind installations

Fast Image Texture Classification Using Decision Trees

NASA Technical Reports Server (NTRS)

Thompson, David R.

2011-01-01

Texture analysis would permit improved autonomous, onboard science data interpretation for adaptive navigation, sampling, and downlink decisions. These analyses would assist with terrain analysis and instrument placement in both macroscopic and microscopic image data products. Unfortunately, most state-of-the-art texture analysis demands computationally expensive convolutions of filters involving many floating-point operations. This makes them infeasible for radiation- hardened computers and spaceflight hardware. A new method approximates traditional texture classification of each image pixel with a fast decision-tree classifier. The classifier uses image features derived from simple filtering operations involving integer arithmetic. The texture analysis method is therefore amenable to implementation on FPGA (field-programmable gate array) hardware. Image features based on the "integral image" transform produce descriptive and efficient texture descriptors. Training the decision tree on a set of training data yields a classification scheme that produces reasonable approximations of optimal "texton" analysis at a fraction of the computational cost. A decision-tree learning algorithm employing the traditional k-means criterion of inter-cluster variance is used to learn tree structure from training data. The result is an efficient and accurate summary of surface morphology in images. This work is an evolutionary advance that unites several previous algorithms (k-means clustering, integral images, decision trees) and applies them to a new problem domain (morphology analysis for autonomous science during remote exploration). Advantages include order-of-magnitude improvements in runtime, feasibility for FPGA hardware, and significant improvements in texture classification accuracy.

Determinants of farmers' tree planting investment decision as a degraded landscape management strategy in the central highlands of Ethiopia

NASA Astrophysics Data System (ADS)

Gessesse, B.; Bewket, W.; Bräuning, A.

2015-11-01

Land degradation due to lack of sustainable land management practices are one of the critical challenges in many developing countries including Ethiopia. This study explores the major determinants of farm level tree planting decision as a land management strategy in a typical framing and degraded landscape of the Modjo watershed, Ethiopia. The main data were generated from household surveys and analysed using descriptive statistics and binary logistic regression model. The model significantly predicted farmers' tree planting decision (Chi-square = 37.29, df = 15, P<0.001). Besides, the computed significant value of the model suggests that all the considered predictor variables jointly influenced the farmers' decision to plant trees as a land management strategy. In this regard, the finding of the study show that local land-users' willingness to adopt tree growing decision is a function of a wide range of biophysical, institutional, socioeconomic and household level factors, however, the likelihood of household size, productive labour force availability, the disparity of schooling age, level of perception of the process of deforestation and the current land tenure system have positively and significantly influence on tree growing investment decisions in the study watershed. Eventually, the processes of land use conversion and land degradation are serious which in turn have had adverse effects on agricultural productivity, local food security and poverty trap nexus. Hence, devising sustainable and integrated land management policy options and implementing them would enhance ecological restoration and livelihood sustainability in the study watershed.

Determinants of farmers' tree-planting investment decisions as a degraded landscape management strategy in the central highlands of Ethiopia

NASA Astrophysics Data System (ADS)

Gessesse, Berhan; Bewket, Woldeamlak; Bräuning, Achim

2016-04-01

Land degradation due to lack of sustainable land management practices is one of the critical challenges in many developing countries including Ethiopia. This study explored the major determinants of farm-level tree-planting decisions as a land management strategy in a typical farming and degraded landscape of the Modjo watershed, Ethiopia. The main data were generated from household surveys and analysed using descriptive statistics and a binary logistic regression model. The model significantly predicted farmers' tree-planting decisions (χ2 = 37.29, df = 15, P < 0.001). Besides, the computed significant value of the model revealed that all the considered predictor variables jointly influenced the farmers' decisions to plant trees as a land management strategy. The findings of the study demonstrated that the adoption of tree-growing decisions by local land users was a function of a wide range of biophysical, institutional, socioeconomic and household-level factors. In this regard, the likelihood of household size, productive labour force availability, the disparity of schooling age, level of perception of the process of deforestation and the current land tenure system had a critical influence on tree-growing investment decisions in the study watershed. Eventually, the processes of land-use conversion and land degradation were serious, which in turn have had adverse effects on agricultural productivity, local food security and poverty trap nexus. Hence, the study recommended that devising and implementing sustainable land management policy options would enhance ecological restoration and livelihood sustainability in the study watershed.

A tracking system to calculate patient skin dose in real-time during neurointerventional procedures using a biplane x-ray imaging system.

PubMed

Rana, V K; Rudin, S; Bednarek, D R

2016-09-01

Neurovascular interventional procedures using biplane fluoroscopic imaging systems can lead to increased risk of radiation-induced skin injuries. The authors developed a biplane dose tracking system (Biplane-DTS) to calculate the cumulative skin dose distribution from the frontal and lateral x-ray tubes and display it in real-time as a color-coded map on a 3D graphic of the patient for immediate feedback to the physician. The agreement of the calculated values with the dose measured on phantoms was evaluated. The Biplane-DTS consists of multiple components including 3D graphic models of the imaging system and patient, an interactive graphical user interface, a data acquisition module to collect geometry and exposure parameters, the computer graphics processing unit, and functions for determining which parts of the patient graphic skin surface are within the beam and for calculating dose. The dose is calculated to individual points on the patient graphic using premeasured calibration files of entrance skin dose per mAs including backscatter; corrections are applied for field area, distance from the focal spot and patient table and pad attenuation when appropriate. The agreement of the calculated patient skin dose and its spatial distribution with measured values was evaluated in 2D and 3D for simulated procedure conditions using a PMMA block phantom and an SK-150 head phantom, respectively. Dose values calculated by the Biplane-DTS were compared to the measurements made on the phantom surface with radiochromic film and a calibrated ionization chamber, which was also used to calibrate the DTS. The agreement with measurements was specifically evaluated with variation in kVp, gantry angle, and field size. The dose tracking system that was developed is able to acquire data from the two x-ray gantries on a biplane imaging system and calculate the skin dose for each exposure pulse to those vertices of a patient graphic that are determined to be in the beam. The calculations are done in real-time with a typical graphic update time of 30 ms and an average vertex separation of 3 mm. With appropriate corrections applied, the Biplane-DTS was able to determine the entrance dose within 6% and the spatial distribution of the dose within 4% compared to the measurements with the ionization chamber and film for the SK150 head phantom. The cumulative dose for overlapping fields from both gantries showed similar agreement. The Biplane-DTS can provide a good estimate of the peak skin dose and cumulative skin dose distribution during biplane neurointerventional procedures. Real-time display of this information should help the physician manage patient dose to reduce the risk of radiation-induced skin injuries.

A tracking system to calculate patient skin dose in real-time during neurointerventional procedures using a biplane x-ray imaging system

PubMed Central

Rana, V. K.; Rudin, S.; Bednarek, D. R.

2016-01-01

Purpose: Neurovascular interventional procedures using biplane fluoroscopic imaging systems can lead to increased risk of radiation-induced skin injuries. The authors developed a biplane dose tracking system (Biplane-DTS) to calculate the cumulative skin dose distribution from the frontal and lateral x-ray tubes and display it in real-time as a color-coded map on a 3D graphic of the patient for immediate feedback to the physician. The agreement of the calculated values with the dose measured on phantoms was evaluated. Methods: The Biplane-DTS consists of multiple components including 3D graphic models of the imaging system and patient, an interactive graphical user interface, a data acquisition module to collect geometry and exposure parameters, the computer graphics processing unit, and functions for determining which parts of the patient graphic skin surface are within the beam and for calculating dose. The dose is calculated to individual points on the patient graphic using premeasured calibration files of entrance skin dose per mAs including backscatter; corrections are applied for field area, distance from the focal spot and patient table and pad attenuation when appropriate. The agreement of the calculated patient skin dose and its spatial distribution with measured values was evaluated in 2D and 3D for simulated procedure conditions using a PMMA block phantom and an SK-150 head phantom, respectively. Dose values calculated by the Biplane-DTS were compared to the measurements made on the phantom surface with radiochromic film and a calibrated ionization chamber, which was also used to calibrate the DTS. The agreement with measurements was specifically evaluated with variation in kVp, gantry angle, and field size. Results: The dose tracking system that was developed is able to acquire data from the two x-ray gantries on a biplane imaging system and calculate the skin dose for each exposure pulse to those vertices of a patient graphic that are determined to be in the beam. The calculations are done in real-time with a typical graphic update time of 30 ms and an average vertex separation of 3 mm. With appropriate corrections applied, the Biplane-DTS was able to determine the entrance dose within 6% and the spatial distribution of the dose within 4% compared to the measurements with the ionization chamber and film for the SK150 head phantom. The cumulative dose for overlapping fields from both gantries showed similar agreement. Conclusions: The Biplane-DTS can provide a good estimate of the peak skin dose and cumulative skin dose distribution during biplane neurointerventional procedures. Real-time display of this information should help the physician manage patient dose to reduce the risk of radiation-induced skin injuries. PMID:27587043

Dependency of image quality on acquisition protocol and image processing in chest tomosynthesis-a visual grading study based on clinical data.

PubMed

Jadidi, Masoud; Båth, Magnus; Nyrén, Sven

2018-04-09

To compare the quality of images obtained with two different protocols with different acquisition time and the influence from image post processing in a chest digital tomosynthesis (DTS) system. 20 patients with suspected lung cancer were imaged with a chest X-ray equipment with tomosynthesis option. Two examination protocols with different acquisition times (6.3 and 12 s) were performed on each patient. Both protocols were presented with two different image post-processing (standard DTS processing and more advanced processing optimised for chest radiography). Thus, 4 series from each patient, altogether 80 series, were presented anonymously and in a random order. Five observers rated the quality of the reconstructed section images according to predefined quality criteria in three different classes. Visual grading characteristics (VGC) was used to analyse the data and the area under the VGC curve (AUC VGC ) was used as figure-of-merit. The 12 s protocol and the standard DTS processing were used as references in the analyses. The protocol with 6.3 s acquisition time had a statistically significant advantage over the vendor-recommended protocol with 12 s acquisition time for the classes of criteria, Demarcation (AUC VGC = 0.56, p = 0.009) and Disturbance (AUC VGC = 0.58, p < 0.001). A similar value of AUC VGC was found also for the class Structure (definition of bone structures in the spine) (0.56) but it could not be statistically separated from 0.5 (p = 0.21). For the image processing, the VGC analysis showed a small but statistically significant advantage for the standard DTS processing over the more advanced processing for the classes of criteria Demarcation (AUC VGC = 0.45, p = 0.017) and Disturbance (AUC VGC = 0.43, p = 0.005). A similar value of AUC VGC was found also for the class Structure (0.46), but it could not be statistically separated from 0.5 (p = 0.31). The study indicates that the protocol with 6.3 s acquisition time yields slightly better image quality than the vender-recommended protocol with acquisition time 12 s for several anatomical structures. Furthermore, the standard gradation processing  (the vendor-recommended post-processing for DTS), yields to some extent advantage over the gradation processing/multiobjective frequency processing/flexible noise control processing in terms of image quality for all classes of criteria. Advances in knowledge: The study proves that the image quality may be strongly affected by the selection of DTS protocol and that the vendor-recommended protocol may not always be the optimal choice.

A tracking system to calculate patient skin dose in real-time during neurointerventional procedures using a biplane x-ray imaging system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rana, V. K., E-mail: vkrana@buffalo.edu

Purpose: Neurovascular interventional procedures using biplane fluoroscopic imaging systems can lead to increased risk of radiation-induced skin injuries. The authors developed a biplane dose tracking system (Biplane-DTS) to calculate the cumulative skin dose distribution from the frontal and lateral x-ray tubes and display it in real-time as a color-coded map on a 3D graphic of the patient for immediate feedback to the physician. The agreement of the calculated values with the dose measured on phantoms was evaluated. Methods: The Biplane-DTS consists of multiple components including 3D graphic models of the imaging system and patient, an interactive graphical user interface, amore » data acquisition module to collect geometry and exposure parameters, the computer graphics processing unit, and functions for determining which parts of the patient graphic skin surface are within the beam and for calculating dose. The dose is calculated to individual points on the patient graphic using premeasured calibration files of entrance skin dose per mAs including backscatter; corrections are applied for field area, distance from the focal spot and patient table and pad attenuation when appropriate. The agreement of the calculated patient skin dose and its spatial distribution with measured values was evaluated in 2D and 3D for simulated procedure conditions using a PMMA block phantom and an SK-150 head phantom, respectively. Dose values calculated by the Biplane-DTS were compared to the measurements made on the phantom surface with radiochromic film and a calibrated ionization chamber, which was also used to calibrate the DTS. The agreement with measurements was specifically evaluated with variation in kVp, gantry angle, and field size. Results: The dose tracking system that was developed is able to acquire data from the two x-ray gantries on a biplane imaging system and calculate the skin dose for each exposure pulse to those vertices of a patient graphic that are determined to be in the beam. The calculations are done in real-time with a typical graphic update time of 30 ms and an average vertex separation of 3 mm. With appropriate corrections applied, the Biplane-DTS was able to determine the entrance dose within 6% and the spatial distribution of the dose within 4% compared to the measurements with the ionization chamber and film for the SK150 head phantom. The cumulative dose for overlapping fields from both gantries showed similar agreement. Conclusions: The Biplane-DTS can provide a good estimate of the peak skin dose and cumulative skin dose distribution during biplane neurointerventional procedures. Real-time display of this information should help the physician manage patient dose to reduce the risk of radiation-induced skin injuries.« less

Ethnographic Decision Tree Modeling: A Research Method for Counseling Psychology.

ERIC Educational Resources Information Center

Beck, Kirk A.

2005-01-01

This article describes ethnographic decision tree modeling (EDTM; C. H. Gladwin, 1989) as a mixed method design appropriate for counseling psychology research. EDTM is introduced and located within a postpositivist research paradigm. Decision theory that informs EDTM is reviewed, and the 2 phases of EDTM are highlighted. The 1st phase, model…

Classification of Parkinsonian syndromes from FDG-PET brain data using decision trees with SSM/PCA features.

PubMed

Mudali, D; Teune, L K; Renken, R J; Leenders, K L; Roerdink, J B T M

2015-01-01

Medical imaging techniques like fluorodeoxyglucose positron emission tomography (FDG-PET) have been used to aid in the differential diagnosis of neurodegenerative brain diseases. In this study, the objective is to classify FDG-PET brain scans of subjects with Parkinsonian syndromes (Parkinson's disease, multiple system atrophy, and progressive supranuclear palsy) compared to healthy controls. The scaled subprofile model/principal component analysis (SSM/PCA) method was applied to FDG-PET brain image data to obtain covariance patterns and corresponding subject scores. The latter were used as features for supervised classification by the C4.5 decision tree method. Leave-one-out cross validation was applied to determine classifier performance. We carried out a comparison with other types of classifiers. The big advantage of decision tree classification is that the results are easy to understand by humans. A visual representation of decision trees strongly supports the interpretation process, which is very important in the context of medical diagnosis. Further improvements are suggested based on enlarging the number of the training data, enhancing the decision tree method by bagging, and adding additional features based on (f)MRI data.

PRIA 3 Fee Determination Decision Tree

EPA Pesticide Factsheets

The PRIA 3 decision tree will help applicants requesting a pesticide registration or certain tolerance action to accurately identify the category of their application and the amount of the required fee before they submit the application.

Solar and Wind Site Screening Decision Trees

EPA Pesticide Factsheets

EPA and NREL created a decision tree to guide state and local governments and other stakeholders through a process for screening sites for their suitability for future redevelopment with solar photovoltaic (PV) energy and wind energy.

Use of electrical imaging and distributed temperature sensing methods to characterize surface water–groundwater exchange regulating uranium transport at the Hanford 300 Area, Washington

USGS Publications Warehouse

Slater, Lee D.; Ntarlagiannis, Dimitrios; Day-Lewis, Frederick D.; Mwakanyamale, Kisa; Versteeg, Roelof J.; Ward, Andy; Strickland, Christopher; Johnson, Carole D.; Lane, John W.

2010-01-01

We explored the use of continuous waterborne electrical imaging (CWEI), in conjunction with fiber‐optic distributed temperature sensor (FO‐DTS) monitoring, to improve the conceptual model for uranium transport within the Columbia River corridor at the Hanford 300 Area, Washington. We first inverted resistivity and induced polarization CWEI data sets for distributions of electrical resistivity and polarizability, from which the spatial complexity of the primary hydrogeologic units was reconstructed. Variations in the depth to the interface between the overlying coarse‐grained, high‐permeability Hanford Formation and the underlying finer‐grained, less permeable Ringold Formation, an important contact that limits vertical migration of contaminants, were resolved along ∼3 km of the river corridor centered on the 300 Area. Polarizability images were translated into lithologic images using established relationships between polarizability and surface area normalized to pore volume (Spor). The FO‐DTS data recorded along 1.5 km of cable with a 1 m spatial resolution and 5 min sampling interval revealed subreaches showing (1) temperature anomalies (relatively warm in winter and cool in summer) and (2) a strong correlation between temperature and river stage (negative in winter and positive in summer), both indicative of reaches of enhanced surface water–groundwater exchange. The FO‐DTS data sets confirm the hydrologic significance of the variability identified in the CWEI and reveal a pattern of highly focused exchange, concentrated at springs where the Hanford Formation is thickest. Our findings illustrate how the combination of CWEI and FO‐DTS technologies can characterize surface water–groundwater exchange in a complex, coupled river‐aquifer system.

Effect of ultrasound application during setting on the mechanical properties of high viscous glass-ionomers used for ART restorations.

PubMed

Daifalla, Lamia E; Mobarak, Enas H

2015-11-01

This study was conducted to evaluate the effect of ultrasound application on the surface microhardness (VHN) and diametral tensile strength (DTS) of three high viscous glass-ionomer restorative materials (HVGIRMs). For each test (VHN and DTS), a total of 180 specimens were prepared from three HVGIRMs (Ketac-Molar Aplicap, Fuji IX GP Fast, and ChemFil Rock). Specimens of each material (n = 60) were further subdivided into three subgroups (n = 20) according to the setting modality whether ultrasound (20 or 40 s) was applied during setting or not (control). Specimens within each subgroup were then equally divided (n = 10) and tested at 24 h or 28 days. For the VHN measurement, five indentations, with a 200 g load and a dwell time for 20 s, were made on the top surface of each specimen. The DTS test was done using Lloyd Testing machine at a cross-head speed of 0.5 mm/min. Ultrasound application had no significant effect on the VHN. Fuji IX GP Fast revealed the highest VHN value, followed by Ketac-Molar Aplicap, and the least was recorded for ChemFil Rock. Fuji IX GP Fast and Ketac-Molar Aplicap VHN values were significantly increased by time. ChemFil Rock recorded the highest DTS value at 24 h and was the only material that showed significant improvement with both US application times. However, this improvement did not sustain till 28 days. The ultrasound did not enhance the surface microhardness, but its positive effect on the diametral tensile strength values was material and time dependent.

Expression of Cyt-c-Mediated Mitochondrial Apoptosis-Related Proteins in Rat Renal Proximal Tubules during Development.

PubMed

Song, Xiao-Feng; Tian, He; Zhang, Ping; Zhang, Zhen-Xing

2017-01-01

Apoptosis regulates embryogenesis, organ metamorphosis and tissue homeostasis. Mitochondrial signaling is an apoptotic pathway, in which Cyt-c and Apaf-1 are transformed into an apoptosome, which activates procaspase-9 and triggers apoptosis. This study evaluated Cyt-c, Apaf-1 and caspase-9 expression during renal development. Kidneys from embryonic (E) 16-, 18-, and 20-day-old fetuses and postnatal (P) 1-, 3-, 5-, 7-, 14-, and 21-day-old pups were obtained. Immunohistochemical analysis, dual-labeled immunofluorescence, terminal deoxynucleotidyl transferase-mediated dUTP nick end-labeling (TUNEL) technique assay and Western blot were performed in addition to histological analysis. Immunohistochemistry showed that Cyt-c was strongly expressed in proximal and distal tubules (DTs) at all time points. Caspase-9 and Apaf-1 were strongly expressed in proximal tubules (PTs) but only weakly expressed in DTs. Dual-labeled immunofluorescence showed that most tubules expressed both Cyt-c and Apaf-1, except for some tubules that only expressed Cyt-c. The TUNEL assay showed a greater percentage of apoptotic cells in PTs compared to DTs. Apaf-1 and cleaved caspase-9 protein expression gradually increased during the embryonic period and peaked during the early postnatal period but apparently declined from P7. Cyt-c protein expression was weak during the embryonic period but obviously increased after P1. This study showed that PTs are more sensitive to apoptosis than DTs during rat renal development, even though both tubule segments contain a large number of mitochondria. Furthermore, Cyt-c-mediated mitochondrial apoptosis-related proteins play an important role in PTs during the early postnatal kidney development. © 2016 S. Karger AG, Basel.

Enhancement of the bulbocavernosus reflex during intraoperative neurophysiological monitoring through the use of double train stimulation: a pilot study.

PubMed

Skinner, Stanley; Chiri, Chala A; Wroblewski, Jill; Transfeldt, Ensor E

2007-02-01

Electrophysiological bulbocavernosus reflex (BCR) testing, during surgeries in which the constituent neural components are at risk, might supplement other low sacral (S2-4) stimulation/recording techniques. However, intraoperative BCR is not always reliably implemented. We proposed to analyze BCR signals in five surgical patients monitored with the novel application of double train stimulation (DTS) to determine if the potential could be enhanced. We prospectively planned a regime of DTS BCR with a series of intertrain delays in five monitored patients at risk for low sacral neural injury. Patients were maintained with propofol, opiate infusion, and low inhalant anesthesia without muscle relaxant. Cutaneous sensory nerves of the penis (or clitoris) were stimulated using two consecutive pulse trains (DTS). Intertrain delays were 75, 100, 125, 150, 175, 200, and 250 ms. For BCR recording, uncoated paired wires were inserted into the external anal sphincter (EAS) bilaterally. For each trial, waveform amplitude, duration, and turn count measures for the first (single train) and second (double train) response were recorded. Percent increase/decrease of the second train response compared to the first train response was calculated. There was at least a 30% increase in measures of amplitude, turn count, and duration of the second train response in 22/28, 22/28, and 14/28 of the total trials respectively. There was an insufficient number of independent observations to determine statistical significance. Intraoperative BCR is currently obtained with some difficulty using pulse train stimulation. Our preliminary evidence has identified BCR waveform enhancement using DTS and suggests that the reliability of intraoperative BCR acquisition may be further improved by the addition of this technique. Our data are insufficient to define the best intertrain interval.

Synthesis and characterization of a novel N-vinylcaprolactam-containing acrylic acid terpolymer for applications in glass-ionomer dental cements.

PubMed

Moshaverinia, Alireza; Roohpour, Nima; Darr, Jawwad A; Rehman, Ihtesham U

2009-07-01

In this study a novel N-vinylcaprolactam (NVC)-containing copolymer of acrylic-itaconic acid was synthesized, characterized and incorporated into Fuji IX conventional glass-ionomer cement (GIC). Subsequently, the effects of incorporation of synthesized terpolymer on the mechanical properties of GIC were studied. The synthesized terpolymer was characterized using (1)H nuclear magnetic resonance, Fourier transform infrared and Raman spectroscopy. The viscosity and molecular weight of the terpolymer were also measured. The compressive strength (CS), diametral tensile strength (DTS) and biaxial flexural strength (BFS) of the modified GICs were evaluated after 24h and 1week of immersion in distilled water at 37 degrees C. The handling properties (working and setting times) of the resulting modified cements were also evaluated. One-way analysis of variance was used to study the statistical significance of the mechanical strengths and handling properties in comparison to the control group. The results showed that NVC-containing GIC samples exhibited significantly higher (P<0.05) DTS (38.3+/-10.9MPa) and BFS (82.2+/-12.8MPa) in comparison to Fuji IX GIC (DTS=19.6+/-11.4MPa; BFS=41.3+/-10.5MPa). The experimental cement also showed higher but not statistically significant values for CS compared to the control material (CS for NVC-containing sample=303+/-32.8MPa; CS for Fuji XI=236+/-41.5MPa). Novel NVC-containing GIC has been developed in this study, with a 28% increase in CS. The presented GIC is capable of doubling the DTS and BFS in comparison to commercial Fuji IX GIC. The working properties of NVC-containing glass-ionomer formulations are comparable and are acceptable for water-based cements.

Effect of ultrasound application during setting on the mechanical properties of high viscous glass-ionomers used for ART restorations

PubMed Central

Daifalla, Lamia E.; Mobarak, Enas H.

2014-01-01

This study was conducted to evaluate the effect of ultrasound application on the surface microhardness (VHN) and diametral tensile strength (DTS) of three high viscous glass-ionomer restorative materials (HVGIRMs). For each test (VHN and DTS), a total of 180 specimens were prepared from three HVGIRMs (Ketac-Molar Aplicap, Fuji IX GP Fast, and ChemFil Rock). Specimens of each material (n = 60) were further subdivided into three subgroups (n = 20) according to the setting modality whether ultrasound (20 or 40 s) was applied during setting or not (control). Specimens within each subgroup were then equally divided (n = 10) and tested at 24 h or 28 days. For the VHN measurement, five indentations, with a 200 g load and a dwell time for 20 s, were made on the top surface of each specimen. The DTS test was done using Lloyd Testing machine at a cross-head speed of 0.5 mm/min. Ultrasound application had no significant effect on the VHN. Fuji IX GP Fast revealed the highest VHN value, followed by Ketac-Molar Aplicap, and the least was recorded for ChemFil Rock. Fuji IX GP Fast and Ketac-Molar Aplicap VHN values were significantly increased by time. ChemFil Rock recorded the highest DTS value at 24 h and was the only material that showed significant improvement with both US application times. However, this improvement did not sustain till 28 days. The ultrasound did not enhance the surface microhardness, but its positive effect on the diametral tensile strength values was material and time dependent. PMID:26644916

Advantages of a Vertical High-Resolution Distributed-Temperature-Sensing System Used to Evaluate the Thermal Behavior of Green Roofs

NASA Astrophysics Data System (ADS)

Hausner, M. B.; Suarez, F. I.; Cousiño, J. A.; Victorero, F.; Bonilla, C. A.; Gironas, J. A.; Vera, S.; Bustamante, W.; Rojas, V.; Leiva, E.; Pasten, P.

2015-12-01

Technological innovations used for sustainable urban development, green roofs offer a range of benefits, including reduced heat island effect, rooftop runoff, roof surface temperatures, energy consumption, and noise levels inside buildings, as well as increased urban biodiversity. Green roofs feature layered construction, with the most important layers being the vegetation and the substrate layers located above the traditional roof. These layers provide both insulation and warm season cooling by latent heat flux, reducing the thermal load to the building. To understand and improve the processes driving this thermal energy reduction, it is important to observe the thermal dynamics of a green roof at the appropriate spatial and temporal scales. Traditionally, to observe the thermal behavior of green roofs, a series of thermocouples have been installed at discrete depths within the layers of the roof. Here, we present a vertical high-resolution distributed-temperature-sensing (DTS) system installed in different green roof modules of the Laboratory of Vegetated Infrastructure for Buildings (LIVE -its acronym in Spanish) of the Pontifical Catholic University of Chile. This DTS system allows near-continuous measurement of the thermal profile at spatial and temporal resolutions of approximately 1 cm and 30 s, respectively. In this investigation, the temperature observations from the DTS system are compared with the measurements of a series of thermocouples installed in the green roofs. This comparison makes it possible to assess the value of thermal observations at better spatial and temporal resolutions. We show that the errors associated with lower resolution observations (i.e., from the thermocouples) are propagated in the calculations of the heat fluxes through the different layers of the green roof. Our results highlight the value of having a vertical high-resolution DTS system to observe the thermal dynamics in green roofs.

Characterizing submarine ground‐water discharge using fiber‐optic distributed temperature sensing and marine electrical resistivity

USGS Publications Warehouse

Henderson, Rory; Day-Lewis, Frederick D.; Lane, John W.; Harvey, Charles F.; Liu, Lanbo

2008-01-01

Submarine ground‐water discharge (SGD) contributes important solute fluxes to coastal waters. Pollutants are transported to coastal ecosystems by SGD at spatially and temporally variable rates. New approaches are needed to characterize the effects of storm‐event, tidal, and seasonal forcing on SGD. Here, we evaluate the utility of two geophysical methods‐fiber‐optic distributed temperature sensing (FO‐DTS) and marine electrical resistivity (MER)—for observing the spatial and temporal variations in SGD and the configuration of the freshwater/saltwater interface within submarine sediments. FO‐DTS and MER cables were permanently installed into the estuary floor on a transect extending 50 meters offshore under Waquoit Bay, Massachusetts, at the Waquoit Bay National Estuarine Research Reserve, and nearly continuous data were collected for 4 weeks in summer 2007. Initial results indicate that the methods are extremely useful for monitoring changes in the complex estuarine environment. The FO‐DTS produced time‐series data at approximately 1‐meter increments along the length of the fiber at approximately 29‐second intervals. The temperature time‐series data show that the temperature at near‐shore locations appears to be dominated by a semi‐diurnal (tidal) signal, whereas the temperature at off‐shore locations is dominated by a diurnal signal (day/night heating and cooling). Dipole‐dipole MER surveys were completed about every 50 minutes, allowing for production of high‐resolution time‐lapse tomograms, which provide insight into the variations of the subsurface freshwater/saltwater interface. Preliminary results from the MER data show a high‐resistivity zone near the shore at low tide, indicative of SGD, and consistent with the FO‐DTS results.

NCAR Earth Observing Laboratory's Data Tracking System

NASA Astrophysics Data System (ADS)

Cully, L. E.; Williams, S. F.

2014-12-01

The NCAR Earth Observing Laboratory (EOL) maintains an extensive collection of complex, multi-disciplinary datasets from national and international, current and historical projects accessible through field project web pages (https://www.eol.ucar.edu/all-field-projects-and-deployments). Data orders are processed through the EOL Metadata Database and Cyberinfrastructure (EMDAC) system. Behind the scenes is the institutionally created EOL Computing, Data, and Software/Data Management Group (CDS/DMG) Data Tracking System (DTS) tool. The DTS is used to track the complete life cycle (from ingest to long term stewardship) of the data, metadata, and provenance for hundreds of projects and thousands of data sets. The DTS is an EOL internal only tool which consists of three subsystems: Data Loading Notes (DLN), Processing Inventory Tool (IVEN), and Project Metrics (STATS). The DLN is used to track and maintain every dataset that comes to the CDS/DMG. The DLN captures general information such as title, physical locations, responsible parties, high level issues, and correspondence. When the CDS/DMG processes a data set, IVEN is used to track the processing status while collecting sufficient information to ensure reproducibility. This includes detailed "How To" documentation, processing software (with direct links to the EOL Subversion software repository), and descriptions of issues and resolutions. The STATS subsystem generates current project metrics such as archive size, data set order counts, "Top 10" most ordered data sets, and general information on who has ordered these data. The DTS was developed over many years to meet the specific needs of the CDS/DMG, and it has been successfully used to coordinate field project data management efforts for the past 15 years. This paper will describe the EOL CDS/DMG Data Tracking System including its basic functionality, the provenance maintained within the system, lessons learned, potential improvements, and future developments.

Monitoring of ground movement in open pit iron mines of Carajás Province (Amazon region) based on A-DInSAR techniques using TerraSAR-X data

NASA Astrophysics Data System (ADS)

Silva, Guilherme Gregório; Mura, José Claudio; Paradella, Waldir Renato; Gama, Fabio Furlan; Temporim, Filipe Altoé

2017-04-01

Persistent scatterer interferometry (PSI) analysis of a large area is always a challenging task regarding the removal of the atmospheric phase component. This work presents an investigation of ground movement measurements based on a combination of differential SAR interferometry time-series (DTS) and PSI techniques, applied on a large area of extent with open pit iron mines located in Carajás (Brazilian Amazon Region), aiming at detecting linear and nonlinear ground movement. These mines have presented a history of instability, and surface monitoring measurements over sectors of the mines (pit walls) have been carried out based on ground-based radar and total station (prisms). Using a priori information regarding the topographic phase error and a phase displacement model derived from DTS, temporal phase unwrapping in the PSI processing and the removal of the atmospheric phases can be performed more efficiently. A set of 33 TerraSAR-X (TSX-1) images, acquired during the period from March 2012 to April 2013, was used to perform this investigation. The DTS analysis was carried out on a stack of multilook unwrapped interferograms using an extension of SVD to obtain the least-square solution. The height errors and deformation rates provided by the DTS approach were subtracted from the stack of interferograms to perform the PSI analysis. This procedure improved the capability of the PSI analysis for detecting high rates of deformation, as well as increased the numbers of point density of the final results. The proposed methodology showed good results for monitoring surface displacement in a large mining area, which is located in a rain forest environment, providing very useful information about the ground movement for planning and risk control.

Monitoring of surface movement in a large area of the open pit iron mines (Carajás, Brazil) based on A-DInSAR techniques using TerraSAR-X data

NASA Astrophysics Data System (ADS)

Mura, José C.; Paradella, Waldir R.; Gama, Fabio F.; Silva, Guilherme G.

2016-10-01

PSI (Persistent Scatterer Interferometry) analysis of large area is always a challenging task regarding the removal of the atmospheric phase component. This work presents an investigation of ground deformation measurements based on a combination of DInSAR Time-Series (DTS) and PSI techniques, applied in a large area of open pit iron mines located in Carajás (Brazilian Amazon Region), aiming at detect high rates of linear and nonlinear ground deformation. These mines have presented a historical of instability and surface monitoring measurements over sectors of the mines (pit walls) have been carried out based on ground based radar and total station (prisms). By using a priori information regarding the topographic phase error and phase displacement model derived from DTS, temporal phase unwrapping in the PSI processing and the removal of the atmospheric phases can be performed more efficiently. A set of 33 TerraSAR-X-1 images, acquired during the period from March 2012 to April 2013, was used to perform this investigation. The DTS analysis was carried out on a stack of multi-look unwrapped interferogram using an extension of SVD to obtain the Least-Square solution. The height errors and deformation rates provided by the DTS approach were subtracted from the stack of interferogram to perform the PSI analysis. This procedure improved the capability of the PSI analysis to detect high rates of deformation as well as increased the numbers of point density of the final results. The proposed methodology showed good results for monitoring surface displacement in a large mining area, which is located in a rain forest environment, providing very useful information about the ground movement for planning and risks control.

Applying of Decision Tree Analysis to Risk Factors Associated with Pressure Ulcers in Long-Term Care Facilities.

PubMed

Moon, Mikyung; Lee, Soo-Kyoung

2017-01-01

The purpose of this study was to use decision tree analysis to explore the factors associated with pressure ulcers (PUs) among elderly people admitted to Korean long-term care facilities. The data were extracted from the 2014 National Inpatient Sample (NIS)-data of Health Insurance Review and Assessment Service (HIRA). A MapReduce-based program was implemented to join and filter 5 tables of the NIS. The outcome predicted by the decision tree model was the prevalence of PUs as defined by the Korean Standard Classification of Disease-7 (KCD-7; code L89 * ). Using R 3.3.1, a decision tree was generated with the finalized 15,856 cases and 830 variables. The decision tree displayed 15 subgroups with 8 variables showing 0.804 accuracy, 0.820 sensitivity, and 0.787 specificity. The most significant primary predictor of PUs was length of stay less than 0.5 day. Other predictors were the presence of an infectious wound dressing, followed by having diagnoses numbering less than 3.5 and the presence of a simple dressing. Among diagnoses, "injuries to the hip and thigh" was the top predictor ranking 5th overall. Total hospital cost exceeding 2,200,000 Korean won (US $2,000) rounded out the top 7. These results support previous studies that showed length of stay, comorbidity, and total hospital cost were associated with PUs. Moreover, wound dressings were commonly used to treat PUs. They also show that machine learning, such as a decision tree, could effectively predict PUs using big data.

Predicting the probability of mortality of gastric cancer patients using decision tree.

PubMed

Mohammadzadeh, F; Noorkojuri, H; Pourhoseingholi, M A; Saadat, S; Baghestani, A R

2015-06-01

Gastric cancer is the fourth most common cancer worldwide. This reason motivated us to investigate and introduce gastric cancer risk factors utilizing statistical methods. The aim of this study was to identify the most important factors influencing the mortality of patients who suffer from gastric cancer disease and to introduce a classification approach according to decision tree model for predicting the probability of mortality from this disease. Data on 216 patients with gastric cancer, who were registered in Taleghani hospital in Tehran,Iran, were analyzed. At first, patients were divided into two groups: the dead and alive. Then, to fit decision tree model to our data, we randomly selected 20% of dataset to the test sample and remaining dataset considered as the training sample. Finally, the validity of the model examined with sensitivity, specificity, diagnosis accuracy and the area under the receiver operating characteristic curve. The CART version 6.0 and SPSS version 19.0 softwares were used for the analysis of the data. Diabetes, ethnicity, tobacco, tumor size, surgery, pathologic stage, age at diagnosis, exposure to chemical weapons and alcohol consumption were determined as effective factors on mortality of gastric cancer. The sensitivity, specificity and accuracy of decision tree were 0.72, 0.75 and 0.74 respectively. The indices of sensitivity, specificity and accuracy represented that the decision tree model has acceptable accuracy to prediction the probability of mortality in gastric cancer patients. So a simple decision tree consisted of factors affecting on mortality of gastric cancer may help clinicians as a reliable and practical tool to predict the probability of mortality in these patients.

Diagnostic classification scheme in Iranian breast cancer patients using a decision tree.

PubMed

Malehi, Amal Saki

2014-01-01

The objective of this study was to determine a diagnostic classification scheme using a decision tree based model. The study was conducted as a retrospective case-control study in Imam Khomeini hospital in Tehran during 2001 to 2009. Data, including demographic and clinical-pathological characteristics, were uniformly collected from 624 females, 312 of them were referred with positive diagnosis of breast cancer (cases) and 312 healthy women (controls). The decision tree was implemented to develop a diagnostic classification scheme using CART 6.0 Software. The AUC (area under curve), was measured as the overall performance of diagnostic classification of the decision tree. Five variables as main risk factors of breast cancer and six subgroups as high risk were identified. The results indicated that increasing age, low age at menarche, single and divorced statues, irregular menarche pattern and family history of breast cancer are the important diagnostic factors in Iranian breast cancer patients. The sensitivity and specificity of the analysis were 66% and 86.9% respectively. The high AUC (0.82) also showed an excellent classification and diagnostic performance of the model. Decision tree based model appears to be suitable for identifying risk factors and high or low risk subgroups. It can also assists clinicians in making a decision, since it can identify underlying prognostic relationships and understanding the model is very explicit.

Ultrasonographic Diagnosis of Biliary Atresia Based on a Decision-Making Tree Model.

PubMed

Lee, So Mi; Cheon, Jung-Eun; Choi, Young Hun; Kim, Woo Sun; Cho, Hyun-Hae; Cho, Hyun-Hye; Kim, In-One; You, Sun Kyoung

2015-01-01

To assess the diagnostic value of various ultrasound (US) findings and to make a decision-tree model for US diagnosis of biliary atresia (BA). From March 2008 to January 2014, the following US findings were retrospectively evaluated in 100 infants with cholestatic jaundice (BA, n = 46; non-BA, n = 54): length and morphology of the gallbladder, triangular cord thickness, hepatic artery and portal vein diameters, and visualization of the common bile duct. Logistic regression analyses were performed to determine the features that would be useful in predicting BA. Conditional inference tree analysis was used to generate a decision-making tree for classifying patients into the BA or non-BA groups. Multivariate logistic regression analysis showed that abnormal gallbladder morphology and greater triangular cord thickness were significant predictors of BA (p = 0.003 and 0.001; adjusted odds ratio: 345.6 and 65.6, respectively). In the decision-making tree using conditional inference tree analysis, gallbladder morphology and triangular cord thickness (optimal cutoff value of triangular cord thickness, 3.4 mm) were also selected as significant discriminators for differential diagnosis of BA, and gallbladder morphology was the first discriminator. The diagnostic performance of the decision-making tree was excellent, with sensitivity of 100% (46/46), specificity of 94.4% (51/54), and overall accuracy of 97% (97/100). Abnormal gallbladder morphology and greater triangular cord thickness (> 3.4 mm) were the most useful predictors of BA on US. We suggest that the gallbladder morphology should be evaluated first and that triangular cord thickness should be evaluated subsequently in cases with normal gallbladder morphology.

Correlation Between the System Capabilities Analytic Process (SCAP) and the Missions and Means Framework (MMF)

DTIC Science & Technology

2013-05-01

specifics of the correlation will be explored followed by discussion of new paradigms— the ordered event list (OEL) and the decision tree — that result from...4.2.1  Brief Overview of the Decision Tree Paradigm ................................................15  4.2.2  OEL Explained...6  Figure 3. A depiction of a notional fault/activation tree . ................................................................7

Personalized Modeling for Prediction with Decision-Path Models

PubMed Central

Visweswaran, Shyam; Ferreira, Antonio; Ribeiro, Guilherme A.; Oliveira, Alexandre C.; Cooper, Gregory F.

2015-01-01

Deriving predictive models in medicine typically relies on a population approach where a single model is developed from a dataset of individuals. In this paper we describe and evaluate a personalized approach in which we construct a new type of decision tree model called decision-path model that takes advantage of the particular features of a given person of interest. We introduce three personalized methods that derive personalized decision-path models. We compared the performance of these methods to that of Classification And Regression Tree (CART) that is a population decision tree to predict seven different outcomes in five medical datasets. Two of the three personalized methods performed statistically significantly better on area under the ROC curve (AUC) and Brier skill score compared to CART. The personalized approach of learning decision path models is a new approach for predictive modeling that can perform better than a population approach. PMID:26098570

Space/age forestry: Implications of planting density and rotation age in SRIC management decisions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Merriam, R.A.; Phillips, V.D.; Liu, W.

1993-12-31

Short-rotation intensive-culture (SRIC) of promising tree crops is being evaluated worldwide for the production of methanol, ethanol, and electricity from renewable biomass resources. Planting density and rotation age are fundamental management decisions associated with SRIC energy plantations. Most studies of these variables have been conducted without the benefit of a unifying theory of the effects of growing space and rotation age on individual tree growth and stand level productivity. A modeling procedure based on field trials of Eucalyptus spp. is presented that evaluates the growth potential of a tree in the absence and presence of competition of neighboring trees inmore » a stand. The results of this analysis are useful in clarifying economic implications of different growing space and rotation age decisions that tree plantation managers must make. The procedure is readily applicable to other species under consideration for SRIC plantations at any location.« less

A conceptual framework for effectively anticipating water-quality changes resulting from changes in agricultural activities

USGS Publications Warehouse

Capel, Paul D.; Wolock, David M.; Coupe, Richard H.; Roth, Jason L.

2018-01-10

Agricultural activities can affect water quality and the health of aquatic ecosystems; many water-quality issues originate with the movement of water, agricultural chemicals, and eroded soil from agricultural areas to streams and groundwater. Most agricultural activities are designed to sustain or increase crop production, while some are designed to protect soil and water resources. Numerous soil- and water-protection practices are designed to reduce the volume and velocity of runoff and increase infiltration. This report presents a conceptual framework that combines generalized concepts on the movement of water, the environmental behavior of chemicals and eroded soil, and the designed functions of various agricultural activities, as they relate to hydrology, to create attainable expectations for the protection of—with the goal of improving—water quality through changes in an agricultural activity.The framework presented uses two types of decision trees to guide decision making toward attainable expectations regarding the effectiveness of changing agricultural activities to protect and improve water quality in streams. One decision tree organizes decision making by considering the hydrologic setting and chemical behaviors, largely at the field scale. This decision tree can help determine which agricultural activities could effectively protect and improve water quality in a stream from the movement of chemicals, or sediment, from a field. The second decision tree is a chemical fate accounting tree. This decision tree helps set attainable expectations for the permanent removal of sediment, elements, and organic chemicals—such as herbicides and insecticides—through trapping or conservation tillage practices. Collectively, this conceptual framework consolidates diverse hydrologic settings, chemicals, and agricultural activities into a single, broad context that can be used to set attainable expectations for agricultural activities. This framework also enables better decision making for future agricultural activities as a means to reduce current, and prevent new, water-quality issues.

DTS Raw Data Guelph, ON Canada

DOE Data Explorer

Thomas Coleman

2013-07-31

Unprocessed active distributed temperature sensing (DTS) data from 3 boreholes in the Guelph, ON Canada region. Data from borehole 1 was collected during a fluid injection while data from boreholes 2 and 3 were collected under natural gradient conditions in a lined borehole. The column labels/headers (in the first row) define the time since start of measurement in seconds and the row labels/headers (in the first column) are the object IDs that are defined in the metadata. Each object ID is a sampling location whose exact location is defined in the metadata file. Data in each cell are temperature in Celsius at time and sampling location as defined above.

F/FB-111 Avionics Test Station and Component Specialist/Technician. Automatic Test Stations Manual and Electronic Warfare Test Stations. Training Requirements Analysis (451X6). Volume 2

DTIC Science & Technology

1991-11-01

F-111D RADAR SST TASK NOTES: SST IS LOCATED ONLY AT CANNON AFB, NM. IT CONSISTS OF AN MRU , EPU, LVPS, MFG, DDPU, ARS RACK, AND TRANSMITTER. THE SST...VOTES: SST IS LOCATED ONLY AT CANNON AFB, NM. IT CONSISTS OF AN MRU , EPU, LVPS, MFG, DDPU, ARS RACK, AND TRANSMITTER. THE SST WILL BE REPLACED BY DTS...NOTES: SST IS LOCATED ONLY AT CANNON AFB, NM. IT CONSISTS OF AN MRU , EPU, LVPS, MFG, DDPU, ARS RACK, AND TRANSMITTER. THE SST WILL BE REPLACED BY DTS

Quantitative evaluation of the disintegration of orally rapid disintegrating tablets by X-ray computed tomography.

PubMed

Otsuka, Makoto; Yamanaka, Azusa; Uchino, Tomohiro; Otsuka, Kuniko; Sadamoto, Kiyomi; Ohshima, Hiroyuki

2012-01-01

To measure the rapid disintegration of Oral Disintegrating Tablets (ODT), a new test (XCT) was developed using X-ray computing tomography (X-ray CT). Placebo ODT, rapid disintegration candy (RDC) and Gaster®-D-Tablets (GAS) were used as model samples. All these ODTs were used to measure oral disintegration time (DT) in distilled water at 37±2°C by XCT. DTs were affected by the width of mesh screens, and degree to which the tablet holder vibrated from air bubbles. An in-vivo tablet disintegration test was performed for RDC using 11 volunteers. DT by the in-vivo method was significantly longer than that using the conventional tester. The experimental conditions for XCT such as the width of the mesh screen and degree of vibration were adjusted to be consistent with human DT values. Since DTs by the XCT method were almost the same as the human data, this method was able to quantitatively evaluate the rapid disintegration of ODT under the same conditions as inside the oral cavity. The DTs of four commercially available ODTs were comparatively evaluated by the XCT method, conventional tablet disintegration test and in-vivo method.

Detoxification and color removal of Congo red by a novel Dietzia sp. (DTS26) - a microcosm approach.

PubMed

Satheesh Babu, S; Mohandass, C; Vijayaraj, A S; Dhale, Mohan A

2015-04-01

The present study deals with the decolorization and detoxification of Congo red (CR) by a novel marine bacterium Dietzia sp. (DTS26) isolated from Divar Island, Goa, India. The maximum decolorization of 94.5% (100 mg L(-1)) was observed under static condition within 30 h at pH 8 and temperature 32±2°C. Bacterially treated samples could enhance the light intensity by 38% and the primary production levels 5 times higher than the untreated. The strain was also able to reduce COD by 86.4% within 30 h at 100 mg L(-1) of CR dye. The degraded metabolites of CR dye were analyzed by FTIR, HPLC, GC-MS and the end product closely matches with 4-amino-3-naphthol-1-sulfonate which is comparatively less toxic than CR. Bioassay experiments conducted in treated samples for Artemia franciscana showed better survival rates (after 72 h) at higher concentration of CR (500 mg L(-1)). This work suggests the potential application of DTS26 in bioremediation of dye wastes and its safe disposal into coastal environment. Copyright © 2015 Elsevier Inc. All rights reserved.

Sparse Reconstruction for Temperature Distribution Using DTS Fiber Optic Sensors with Applications in Electrical Generator Stator Monitoring

PubMed Central

Bazzo, João Paulo; Pipa, Daniel Rodrigues; da Silva, Erlon Vagner; Martelli, Cicero; Cardozo da Silva, Jean Carlos

2016-01-01

This paper presents an image reconstruction method to monitor the temperature distribution of electric generator stators. The main objective is to identify insulation failures that may arise as hotspots in the structure. The method is based on temperature readings of fiber optic distributed sensors (DTS) and a sparse reconstruction algorithm. Thermal images of the structure are formed by appropriately combining atoms of a dictionary of hotspots, which was constructed by finite element simulation with a multi-physical model. Due to difficulties for reproducing insulation faults in real stator structure, experimental tests were performed using a prototype similar to the real structure. The results demonstrate the ability of the proposed method to reconstruct images of hotspots with dimensions down to 15 cm, representing a resolution gain of up to six times when compared to the DTS spatial resolution. In addition, satisfactory results were also obtained to detect hotspots with only 5 cm. The application of the proposed algorithm for thermal imaging of generator stators can contribute to the identification of insulation faults in early stages, thereby avoiding catastrophic damage to the structure. PMID:27618040

Using Distributed Temperature Sensing to Locate and Quantify Thermal Refugia: Insights Into Radiative & Hydrologic Processes

NASA Astrophysics Data System (ADS)

Bond, R. M.; Stubblefield, A. P.

2012-12-01

Stream temperature plays a critical role in determining the overall structure and function of stream ecosystems. Aquatic fauna are particularly vulnerable to projected increases in the magnitude and duration of elevated stream temperatures from global climate change. Northern California cold water salmon and trout fisheries have been declared thermally impacted by the California State Water Resources Control Board. This study employed Distributed Temperature Sensing (DTS) to detect stream heating and cooling at one meter resolution along a one kilometer section of the North Fork of the Salmon River, a tributary of the Klamath River, northern California, USA. The Salmon River has an extensive legacy of hydraulic gold mining tailing which have been reworked into large gravel bars; creating shallow wide runs, possibly filling in pools and disrupting riparian vegetation recruitment. Eight days of temperature data were collected at 15 minute intervals during July 2012. Three remote weather stations were deployed during the study period. The main objectives of this research were: one, quantify thermal inputs that create and maintain thermal refugia for cold water fishes; two, investigate the role of riparian and topographic shading in buffering peak summer temperatures; and three, create and validate a physically based stream heating model to predict effects of riparian management, drought, and climate change on stream temperature. DTS was used to spatially identify cold water seeps and quantify their contribution to the stream's thermal regime. Along the one kilometer reach, hyporheic flow was identified using DTS. The spring was between 16-18°C while the peak mainstem temperature above the spring reached a maximum of 23°C. The study found a diel heating cycle of 5°C with a Maximum Weekly Average Temperature (MWAT) of over 22°C; exceeding salmon and trout protective temperature standards set by USEPA Region 10. Twenty intensive fish counts over five days were conducted to assess the relative abundance of Chinook (Oncorhynchus tshawytscha), coho (O. kisutch), and steelhead (O. mykiss) use of thermal refugia. The North Fork Salmon River is the largest river to be instrumented with DTS technology. The researchers will use the DTS data and thermal model to make suggestions for management actions to improve the Salmon River's thermal regime.

Feasibility of leakage detection in lake pressure pipes using the Distributed Temperature Sensing Technology

NASA Astrophysics Data System (ADS)

Apperl, Benjamin; Pressl, Alexander; Schulz, Karsten

2016-04-01

This contribution describes a feasibility study carried out in the laboratory for the detection of leakages in lake pressure pipes using high-resolution fiber-optic temperature measurements (DTS). The usage of the DTS technology provides spatiotemporal high-resolution temperature measurements along a fibre optic cable. An opto-electrical device serves both as a light emitter as well as a spectrometer for measuring the scattering of light. The fiber optic cable serves as linear sensor. Measurements can be taken at a spatial resolution of up to 25 cm with a temperature accuracy of higher than 0.1 °C. The first warmer days after the winter stagnation provoke a temperature rise of superficial layers of lakes with barely stable temperature stratification. The warmer layer in the epilimnion differs 4 °C to 5 °C compared to the cold layers in the meta- or hypolimnion before water circulation in spring starts. The warmer water from the surface layer can be rinsed on the entire length of the pipe. Water intrudes at leakages by generating a slightly negative pressure in the pipe. This provokes a local temperature change, in case that the penetrating water (seawater) differs in temperature from the water pumped through the pipe. These temperature changes should be detectable and localized with a DTS cable introduced in the pipe. A laboratory experiment was carried out to determine feasibility as well as limits and problems of this methodology. A 6 m long pipe, submerged in a water tank at constant temperature, was rinsed with water 5-10 °C warmer than the water in the tank. Temperature measurements were taken continuously along the pipe. A negative pressure of 0.1 bar provoked the intrusion of colder water from the tank into the pipe through the leakages, resulting in local temperature changes. Experiments where conducted with different temperature gradients, leakage sizes, number of leaks as well as with different positioning of the DTS cable inside the pipe. Results showed that already small leakages (4mm) can be detected. Problems have arisen from the inside positioning of DTS cable, measuring a reduced temperature difference in the transition layer at the inside wall of the pipe.

Evaluating the ability of dental technician students and graduate dentists to match tooth color.

PubMed

Sinmazisik, Gulden; Trakyali, Goksu; Tarcin, Bilge

2014-12-01

The ability of dental technician students to match tooth shade with the Vita 3D-Master shade guide and Toothguide Training Box has not been investigated. The purpose of this study was to evaluate and compare the shade-matching ability of dental technician students and graduate dentists using the Vita 3D-Master shade guide. Twenty-nine dental technician students (DTS group) and 30 graduate dentists (GD group) participated in this study. The Toothguide Training Box (TTB) was used to train the participants and test their shade-matching abilities. Shade-matching ability was evaluated with 3 exercises and a final test, all of which are components of the TTB. The number of mistakes for each participant for value (L), chroma (c), and hue (h) were recorded during the exercises and the final test, and the mistake ratios were calculated. Color difference (ΔE) values for each shade were calculated from the L*, a*, and b* values of the Vita 3D-Master shade guide for each participant in both groups. The Mann-Whitney U test was used to determine statistically significant differences between the L, c, and h mistake ratios of the 2 groups, and the Student t test was used to determine statistically significant differences between the final test scores and the ΔE values of the groups (α=.05). The mistake ratio for L in the GD group was significantly higher than that of the DTS group (P<.05), whereas the mistake ratio for h in the DTS group was higher (P<.001). No significant differences were observed between the groups regarding the mistake ratios for c (P>.05). With regard to the final test scores and the ΔE values, no significant differences were found between the groups (P<.001), and the DTS group received higher scores than the GD group (912 and 851). The mean ΔE values for the DTS and GD groups were 1.72 and 2.92. DTSs made more mistakes in the h parameter than GDs, and GDs made more mistakes in the L parameter than DTSs. With regard to the final test scores and the ΔE values, DTSs were more successful in shade matching than GDs. Copyright © 2014 Editorial Council for the Journal of Prosthetic Dentistry. Published by Elsevier Inc. All rights reserved.

Vlsi implementation of flexible architecture for decision tree classification in data mining

NASA Astrophysics Data System (ADS)

Sharma, K. Venkatesh; Shewandagn, Behailu; Bhukya, Shankar Nayak

2017-07-01

The Data mining algorithms have become vital to researchers in science, engineering, medicine, business, search and security domains. In recent years, there has been a terrific raise in the size of the data being collected and analyzed. Classification is the main difficulty faced in data mining. In a number of the solutions developed for this problem, most accepted one is Decision Tree Classification (DTC) that gives high precision while handling very large amount of data. This paper presents VLSI implementation of flexible architecture for Decision Tree classification in data mining using c4.5 algorithm.

Applying Data Mining Techniques to Extract Hidden Patterns about Breast Cancer Survival in an Iranian Cohort Study.

PubMed

Khalkhali, Hamid Reza; Lotfnezhad Afshar, Hadi; Esnaashari, Omid; Jabbari, Nasrollah

2016-01-01

Breast cancer survival has been analyzed by many standard data mining algorithms. A group of these algorithms belonged to the decision tree category. Ability of the decision tree algorithms in terms of visualizing and formulating of hidden patterns among study variables were main reasons to apply an algorithm from the decision tree category in the current study that has not studied already. The classification and regression trees (CART) was applied to a breast cancer database contained information on 569 patients in 2007-2010. The measurement of Gini impurity used for categorical target variables was utilized. The classification error that is a function of tree size was measured by 10-fold cross-validation experiments. The performance of created model was evaluated by the criteria as accuracy, sensitivity and specificity. The CART model produced a decision tree with 17 nodes, 9 of which were associated with a set of rules. The rules were meaningful clinically. They showed in the if-then format that Stage was the most important variable for predicting breast cancer survival. The scores of accuracy, sensitivity and specificity were: 80.3%, 93.5% and 53%, respectively. The current study model as the first one created by the CART was able to extract useful hidden rules from a relatively small size dataset.

The Utility of Decision Trees in Oncofertility Care in Japan.

PubMed

Ito, Yuki; Shiraishi, Eriko; Kato, Atsuko; Haino, Takayuki; Sugimoto, Kouhei; Okamoto, Aikou; Suzuki, Nao

2017-03-01

To identify the utility and issues associated with the use of decision trees in oncofertility patient care in Japan. A total of 35 women who had been diagnosed with cancer, but had not begun anticancer treatment, were enrolled. We applied the oncofertility decision tree for women published by Gardino et al. to counsel a consecutive series of women on fertility preservation (FP) options following cancer diagnosis. Percentage of women who decided to undergo oocyte retrieval for embryo cryopreservation and the expected live-birth rate for these patients were calculated using the following equation: expected live-birth rate = pregnancy rate at each age per embryo transfer × (1 - miscarriage rate) × No. of cryopreserved embryos. Oocyte retrieval was performed for 17 patients (48.6%; mean ± standard deviation [SD] age, 36.35 ± 3.82 years). The mean ± SD number of cryopreserved embryos was 5.29 ± 4.63. The expected live-birth rate was 0.66. The expected live-birth rate with FP indicated that one in three oncofertility patients would not expect to have a live birth following oocyte retrieval and embryo cryopreservation. While the decision trees were useful as decision-making tools for women contemplating FP, in the context of the current restrictions on oocyte donation and the extremely small number of adoptions in Japan, the remaining options for fertility after cancer are limited. In order for cancer survivors to feel secure in their decisions, the decision tree may need to be adapted simultaneously with improvements to the social environment, such as greater support for adoption.

The diametral tensile strength and hydrostability of polymer-ceramic nano-composite (pcnc) material prototypes

NASA Astrophysics Data System (ADS)

Yepez, Johanna

Statement of the problem: There is a weak connection between the filler and the resin matrix of dental composites caused primarily by hydrolysis of silane coupling agent, therefore, jeopardizing the mechanical properties of the dental restorations. Purpose: The purpose of this study was to compare the diametral tensile strength (DTS) of a nano-mechanically bonded polymer ceramic nano composite (pcnc) versus the chemically bonding prototype polymer ceramic nano composite (pcnc) fabricated by using hydrolytically stable interphase. Materials and Methods: Composites were made with 60wt % filler, 38% triethyleneglycol dimethacrylate (TEDGMA), 1% camphorquinone (CQ) and 1% 2-(dimethylamino) ethyl methacrylate (DMAEMA). Tests for DTS were performed using a universal testing machine. The disk-shaped specimens were loaded in compression between two supporting plates at a crosshead speed of 0.5 mm/min until fracture. The samples, measuring 3 mm in height and 6 mm in diameter, were produced in a round stainless steel (SS) mold. A total of 144 samples were created. Groups of 48 samples were made for each of three different fillers. Specimens were soaked in artificial saliva at 37° for four time periods, dry(t=0), 1 day, 7 days, 28 days). At the end of each soaking time DTS tests were performed. Results: There where statistically significant differences in the DTS between the filler groups and the soaking times (p=<0.001) as well as for the pairwise comparison between the different filler group values and between the different soaking times as an individual treatment. Overall, longer soaking times resulted in lower mean DTS values. The DTS of the PCNC for filler #1 decreased to 82.4% of the original value after 1 day of soaking, 67.2% after 7 days and 27.2 % after 28 days. For filler #2 decreased to 54.8% of the original value after 1 day of soaking, 62.3% after 7 days and 61.2% after 28 days. For filler #3 decreased to 71.2% of the original value, 67.3% after 7 days and 51.4% after 28 days (Fig 8). Conclusions: Within the limitation of this study it can be concluded that the use of coupling agent will significantly influence the degradation of the material under wet environment. Clinical Implication: Changes within matrix composition and bonding interphase of resin base composites promise improvements of mechanical properties, decreasing the incidence of clinical failure of posterior composite restorations, hence resulting in a more ideal restorative material for use in posterior segment. The results of this investigation showed that the deficiency of hydrostability in dental composites is a detrimental factor in the mechanical behavior. The silanation of the filler particles have a positive influence on the mechanical properties of dental composites but the hydrolysis of the silane coupling agent can dramatically reduce the average lifetime of dental composites.

Comparison of Naive Bayes and Decision Tree on Feature Selection Using Genetic Algorithm for Classification Problem

NASA Astrophysics Data System (ADS)

Rahmadani, S.; Dongoran, A.; Zarlis, M.; Zakarias

2018-03-01

This paper discusses the problem of feature selection using genetic algorithms on a dataset for classification problems. The classification model used is the decicion tree (DT), and Naive Bayes. In this paper we will discuss how the Naive Bayes and Decision Tree models to overcome the classification problem in the dataset, where the dataset feature is selectively selected using GA. Then both models compared their performance, whether there is an increase in accuracy or not. From the results obtained shows an increase in accuracy if the feature selection using GA. The proposed model is referred to as GADT (GA-Decision Tree) and GANB (GA-Naive Bayes). The data sets tested in this paper are taken from the UCI Machine Learning repository.

Predictive models for subtypes of autism spectrum disorder based on single-nucleotide polymorphisms and magnetic resonance imaging.

PubMed

Jiao, Y; Chen, R; Ke, X; Cheng, L; Chu, K; Lu, Z; Herskovits, E H

2011-01-01

Autism spectrum disorder (ASD) is a neurodevelopmental disorder, of which Asperger syndrome and high-functioning autism are subtypes. Our goal is: 1) to determine whether a diagnostic model based on single-nucleotide polymorphisms (SNPs), brain regional thickness measurements, or brain regional volume measurements can distinguish Asperger syndrome from high-functioning autism; and 2) to compare the SNP, thickness, and volume-based diagnostic models. Our study included 18 children with ASD: 13 subjects with high-functioning autism and 5 subjects with Asperger syndrome. For each child, we obtained 25 SNPs for 8 ASD-related genes; we also computed regional cortical thicknesses and volumes for 66 brain structures, based on structural magnetic resonance (MR) examination. To generate diagnostic models, we employed five machine-learning techniques: decision stump, alternating decision trees, multi-class alternating decision trees, logistic model trees, and support vector machines. For SNP-based classification, three decision-tree-based models performed better than the other two machine-learning models. The performance metrics for three decision-tree-based models were similar: decision stump was modestly better than the other two methods, with accuracy = 90%, sensitivity = 0.95 and specificity = 0.75. All thickness and volume-based diagnostic models performed poorly. The SNP-based diagnostic models were superior to those based on thickness and volume. For SNP-based classification, rs878960 in GABRB3 (gamma-aminobutyric acid A receptor, beta 3) was selected by all tree-based models. Our analysis demonstrated that SNP-based classification was more accurate than morphometry-based classification in ASD subtype classification. Also, we found that one SNP--rs878960 in GABRB3--distinguishes Asperger syndrome from high-functioning autism.

The application of a decision tree to establish the parameters associated with hypertension.

PubMed

Tayefi, Maryam; Esmaeili, Habibollah; Saberi Karimian, Maryam; Amirabadi Zadeh, Alireza; Ebrahimi, Mahmoud; Safarian, Mohammad; Nematy, Mohsen; Parizadeh, Seyed Mohammad Reza; Ferns, Gordon A; Ghayour-Mobarhan, Majid

2017-02-01

Hypertension is an important risk factor for cardiovascular disease (CVD). The goal of this study was to establish the factors associated with hypertension by using a decision-tree algorithm as a supervised classification method of data mining. Data from a cross-sectional study were used in this study. A total of 9078 subjects who met the inclusion criteria were recruited. 70% of these subjects (6358 cases) were randomly allocated to the training dataset for the constructing of the decision-tree. The remaining 30% (2720 cases) were used as the testing dataset to evaluate the performance of decision-tree. Two models were evaluated in this study. In model I, age, gender, body mass index, marital status, level of education, occupation status, depression and anxiety status, physical activity level, smoking status, LDL, TG, TC, FBG, uric acid and hs-CRP were considered as input variables and in model II, age, gender, WBC, RBC, HGB, HCT MCV, MCH, PLT, RDW and PDW were considered as input variables. The validation of the model was assessed by constructing a receiver operating characteristic (ROC) curve. The prevalence rates of hypertension were 32% in our population. For the decision-tree model I, the accuracy, sensitivity, specificity and area under the ROC curve (AUC) value for identifying the related risk factors of hypertension were 73%, 63%, 77% and 0.72, respectively. The corresponding values for model II were 70%, 61%, 74% and 0.68, respectively. We have developed a decision tree model to identify the risk factors associated with hypertension that maybe used to develop programs for hypertension management. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Identifying the performance characteristics of a winning outcome in elite mixed martial arts competition.

PubMed

James, Lachlan P; Robertson, Sam; Haff, G Gregory; Beckman, Emma M; Kelly, Vincent G

2017-03-01

To determine those performance indicators that have the greatest influence on classifying outcome at the elite level of mixed martial arts (MMA). A secondary objective was to establish the efficacy of decision tree analysis in explaining the characteristics of victory when compared to alternate statistical methods. Cross-sectional observational. Eleven raw performance indicators from male Ultimate Fighting Championship bouts (n=234) from July 2014 to December 2014 were screened for analysis. Each raw performance indicator was also converted to a rate-dependent measure to be scaled to fight duration. Further, three additional performance indicators were calculated from the dataset and included in the analysis. Cohen's d effect sizes were employed to determine the magnitude of the differences between Wins and Losses, while decision tree (chi-square automatic interaction detector (CHAID)) and discriminant function analyses (DFA) were used to classify outcome (Win and Loss). Effect size comparisons revealed differences between Wins and Losses across a number of performance indicators. Decision tree (raw: 71.8%; rate-scaled: 76.3%) and DFA (raw: 71.4%; rate-scaled 71.2%) achieved similar classification accuracies. Grappling and accuracy performance indicators were the most influential in explaining outcome. The decision tree models also revealed multiple combinations of performance indicators leading to victory. The decision tree analyses suggest that grappling activity and technique accuracy are of particular importance in achieving victory in elite-level MMA competition. The DFA results supported the importance of these performance indicators. Decision tree induction represents an intuitive and slightly more accurate approach to explaining bout outcome in this sport when compared to DFA. Copyright © 2016 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Decision tree analysis in subarachnoid hemorrhage: prediction of outcome parameters during the course of aneurysmal subarachnoid hemorrhage using decision tree analysis.

PubMed

Hostettler, Isabel Charlotte; Muroi, Carl; Richter, Johannes Konstantin; Schmid, Josef; Neidert, Marian Christoph; Seule, Martin; Boss, Oliver; Pangalu, Athina; Germans, Menno Robbert; Keller, Emanuela

2018-01-19

OBJECTIVE The aim of this study was to create prediction models for outcome parameters by decision tree analysis based on clinical and laboratory data in patients with aneurysmal subarachnoid hemorrhage (aSAH). METHODS The database consisted of clinical and laboratory parameters of 548 patients with aSAH who were admitted to the Neurocritical Care Unit, University Hospital Zurich. To examine the model performance, the cohort was randomly divided into a derivation cohort (60% [n = 329]; training data set) and a validation cohort (40% [n = 219]; test data set). The classification and regression tree prediction algorithm was applied to predict death, functional outcome, and ventriculoperitoneal (VP) shunt dependency. Chi-square automatic interaction detection was applied to predict delayed cerebral infarction on days 1, 3, and 7. RESULTS The overall mortality was 18.4%. The accuracy of the decision tree models was good for survival on day 1 and favorable functional outcome at all time points, with a difference between the training and test data sets of < 5%. Prediction accuracy for survival on day 1 was 75.2%. The most important differentiating factor was the interleukin-6 (IL-6) level on day 1. Favorable functional outcome, defined as Glasgow Outcome Scale scores of 4 and 5, was observed in 68.6% of patients. Favorable functional outcome at all time points had a prediction accuracy of 71.1% in the training data set, with procalcitonin on day 1 being the most important differentiating factor at all time points. A total of 148 patients (27%) developed VP shunt dependency. The most important differentiating factor was hyperglycemia on admission. CONCLUSIONS The multiple variable analysis capability of decision trees enables exploration of dependent variables in the context of multiple changing influences over the course of an illness. The decision tree currently generated increases awareness of the early systemic stress response, which is seemingly pertinent for prognostication.

Faults Discovery By Using Mined Data

NASA Technical Reports Server (NTRS)

Lee, Charles

2005-01-01

Fault discovery in the complex systems consist of model based reasoning, fault tree analysis, rule based inference methods, and other approaches. Model based reasoning builds models for the systems either by mathematic formulations or by experiment model. Fault Tree Analysis shows the possible causes of a system malfunction by enumerating the suspect components and their respective failure modes that may have induced the problem. The rule based inference build the model based on the expert knowledge. Those models and methods have one thing in common; they have presumed some prior-conditions. Complex systems often use fault trees to analyze the faults. Fault diagnosis, when error occurs, is performed by engineers and analysts performing extensive examination of all data gathered during the mission. International Space Station (ISS) control center operates on the data feedback from the system and decisions are made based on threshold values by using fault trees. Since those decision-making tasks are safety critical and must be done promptly, the engineers who manually analyze the data are facing time challenge. To automate this process, this paper present an approach that uses decision trees to discover fault from data in real-time and capture the contents of fault trees as the initial state of the trees.

Evaluation with Decision Trees of Efficacy and Safety of Semirigid Ureteroscopy in the Treatment of Proximal Ureteral Calculi.

PubMed

Sancak, Eyup Burak; Kılınç, Muhammet Fatih; Yücebaş, Sait Can

2017-01-01

The decision on the choice of proximal ureteral stone therapy depends on many factors, and sometimes urologists have difficulty in choosing the treatment option. This study is aimed at evaluating the factors affecting the success of semirigid ureterorenoscopy (URS) using the "decision tree" method. From January 2005 to November 2015, the data of consecutive patients treated for proximal ureteral stone were retrospectively analyzed. A total of 920 patients with proximal ureteral stone treated with semirigid URS were included in the study. All statistically significant attributes were tested using the decision tree method. The model created using decision tree had a sensitivity of 0.993 and an accuracy of 0.857. While URS treatment was successful in 752 patients (81.7%), it was unsuccessful in 168 patients (18.3%). According to the decision tree method, the most important factor affecting the success of URS is whether the stone is impacted to the ureteral wall. The second most important factor affecting treatment was intramural stricture requiring dilatation if the stone is impacted, and the size of the stone if not impacted. Our study suggests that the impacted stone, intramural stricture requiring dilatation and stone size may have a significant effect on the success rate of semirigid URS for proximal ureteral stone. Further studies with population-based and longitudinal design should be conducted to confirm this finding. © 2017 S. Karger AG, Basel.

C-fuzzy variable-branch decision tree with storage and classification error rate constraints

NASA Astrophysics Data System (ADS)

Yang, Shiueng-Bien

2009-10-01

The C-fuzzy decision tree (CFDT), which is based on the fuzzy C-means algorithm, has recently been proposed. The CFDT is grown by selecting the nodes to be split according to its classification error rate. However, the CFDT design does not consider the classification time taken to classify the input vector. Thus, the CFDT can be improved. We propose a new C-fuzzy variable-branch decision tree (CFVBDT) with storage and classification error rate constraints. The design of the CFVBDT consists of two phases-growing and pruning. The CFVBDT is grown by selecting the nodes to be split according to the classification error rate and the classification time in the decision tree. Additionally, the pruning method selects the nodes to prune based on the storage requirement and the classification time of the CFVBDT. Furthermore, the number of branches of each internal node is variable in the CFVBDT. Experimental results indicate that the proposed CFVBDT outperforms the CFDT and other methods.

A Modified Decision Tree Algorithm Based on Genetic Algorithm for Mobile User Classification Problem

PubMed Central

Liu, Dong-sheng; Fan, Shu-jiang

2014-01-01

In order to offer mobile customers better service, we should classify the mobile user firstly. Aimed at the limitations of previous classification methods, this paper puts forward a modified decision tree algorithm for mobile user classification, which introduced genetic algorithm to optimize the results of the decision tree algorithm. We also take the context information as a classification attributes for the mobile user and we classify the context into public context and private context classes. Then we analyze the processes and operators of the algorithm. At last, we make an experiment on the mobile user with the algorithm, we can classify the mobile user into Basic service user, E-service user, Plus service user, and Total service user classes and we can also get some rules about the mobile user. Compared to C4.5 decision tree algorithm and SVM algorithm, the algorithm we proposed in this paper has higher accuracy and more simplicity. PMID:24688389

Planning effectiveness may grow on fault trees.

PubMed

Chow, C W; Haddad, K; Mannino, B

1991-10-01

The first step of a strategic planning process--identifying and analyzing threats and opportunities--requires subjective judgments. By using an analytical tool known as a fault tree, healthcare administrators can reduce the unreliability of subjective decision making by creating a logical structure for problem solving and decision making. A case study of 11 healthcare administrators showed that an analysis technique called prospective hindsight can add to a fault tree's ability to improve a strategic planning process.

Prescriptive models to support decision making in genetics.

PubMed

Pauker, S G; Pauker, S P

1987-01-01

Formal prescriptive models can help patients and clinicians better understand the risks and uncertainties they face and better formulate well-reasoned decisions. Using Bayes rule, the clinician can interpret pedigrees, historical data, physical findings and laboratory data, providing individualized probabilities of various diagnoses and outcomes of pregnancy. With the advent of screening programs for genetic disease, it becomes increasingly important to consider the prior probabilities of disease when interpreting an abnormal screening test result. Decision trees provide a convenient formalism for structuring diagnostic, therapeutic and reproductive decisions; such trees can also enhance communication between clinicians and patients. Utility theory provides a mechanism for patients to understand the choices they face and to communicate their attitudes about potential reproductive outcomes in a manner which encourages the integration of those attitudes into appropriate decisions. Using a decision tree, the relevant probabilities and the patients' utilities, physicians can estimate the relative worth of various medical and reproductive options by calculating the expected utility of each. By performing relevant sensitivity analyses, clinicians and patients can understand the impact of various soft data, including the patients' attitudes toward various health outcomes, on the decision making process. Formal clinical decision analytic models can provide deeper understanding and improved decision making in clinical genetics.

Applications of urban tree canopy assessment and prioritization tools: supporting collaborative decision making to achieve urban sustainability goals

Treesearch

Dexter H. Locke; J. Morgan Grove; Michael Galvin; Jarlath P.M. ONeil-Dunne; Charles Murphy

2013-01-01

Urban Tree Canopy (UTC) Prioritizations can be both a set of geographic analysis tools and a planning process for collaborative decision-making. In this paper, we describe how UTC Prioritizations can be used as a planning process to provide decision support to multiple government agencies, civic groups and private businesses to aid in reaching a canopy target. Linkages...

Combining binary decision tree and geostatistical methods to estimate snow distribution in a mountain watershed

USGS Publications Warehouse

Balk, Benjamin; Elder, Kelly

2000-01-01

We model the spatial distribution of snow across a mountain basin using an approach that combines binary decision tree and geostatistical techniques. In April 1997 and 1998, intensive snow surveys were conducted in the 6.9‐km2 Loch Vale watershed (LVWS), Rocky Mountain National Park, Colorado. Binary decision trees were used to model the large‐scale variations in snow depth, while the small‐scale variations were modeled through kriging interpolation methods. Binary decision trees related depth to the physically based independent variables of net solar radiation, elevation, slope, and vegetation cover type. These decision tree models explained 54–65% of the observed variance in the depth measurements. The tree‐based modeled depths were then subtracted from the measured depths, and the resulting residuals were spatially distributed across LVWS through kriging techniques. The kriged estimates of the residuals were added to the tree‐based modeled depths to produce a combined depth model. The combined depth estimates explained 60–85% of the variance in the measured depths. Snow densities were mapped across LVWS using regression analysis. Snow‐covered area was determined from high‐resolution aerial photographs. Combining the modeled depths and densities with a snow cover map produced estimates of the spatial distribution of snow water equivalence (SWE). This modeling approach offers improvement over previous methods of estimating SWE distribution in mountain basins.

New Splitting Criteria for Decision Trees in Stationary Data Streams.

PubMed

Jaworski, Maciej; Duda, Piotr; Rutkowski, Leszek; Jaworski, Maciej; Duda, Piotr; Rutkowski, Leszek; Rutkowski, Leszek; Duda, Piotr; Jaworski, Maciej

2018-06-01

The most popular tools for stream data mining are based on decision trees. In previous 15 years, all designed methods, headed by the very fast decision tree algorithm, relayed on Hoeffding's inequality and hundreds of researchers followed this scheme. Recently, we have demonstrated that although the Hoeffding decision trees are an effective tool for dealing with stream data, they are a purely heuristic procedure; for example, classical decision trees such as ID3 or CART cannot be adopted to data stream mining using Hoeffding's inequality. Therefore, there is an urgent need to develop new algorithms, which are both mathematically justified and characterized by good performance. In this paper, we address this problem by developing a family of new splitting criteria for classification in stationary data streams and investigating their probabilistic properties. The new criteria, derived using appropriate statistical tools, are based on the misclassification error and the Gini index impurity measures. The general division of splitting criteria into two types is proposed. Attributes chosen based on type- splitting criteria guarantee, with high probability, the highest expected value of split measure. Type- criteria ensure that the chosen attribute is the same, with high probability, as it would be chosen based on the whole infinite data stream. Moreover, in this paper, two hybrid splitting criteria are proposed, which are the combinations of single criteria based on the misclassification error and Gini index.

MAGIC: Model and Graphic Information Converter

NASA Technical Reports Server (NTRS)

Herbert, W. C.

2009-01-01

MAGIC is a software tool capable of converting highly detailed 3D models from an open, standard format, VRML 2.0/97, into the proprietary DTS file format used by the Torque Game Engine from GarageGames. MAGIC is used to convert 3D simulations from authoritative sources into the data needed to run the simulations in NASA's Distributed Observer Network. The Distributed Observer Network (DON) is a simulation presentation tool built by NASA to facilitate the simulation sharing requirements of the Data Presentation and Visualization effort within the Constellation Program. DON is built on top of the Torque Game Engine (TGE) and has chosen TGE's Dynamix Three Space (DTS) file format to represent 3D objects within simulations.

Diverted Total Synthesis of Promysalin Analogs Demonstrates That an Iron-Binding Motif Is Responsible for Its Narrow-Spectrum Antibacterial Activity.

PubMed

Steele, Andrew D; Keohane, Colleen E; Knouse, Kyle W; Rossiter, Sean E; Williams, Sierra J; Wuest, William M

2016-05-11

Promysalin is a species-specific Pseudomonad metabolite with unique bioactivity. To better understand the mode of action of this natural product, we synthesized 16 analogs utilizing diverted total synthesis (DTS). Our analog studies revealed that the bioactivity of promysalin is sensitive to changes within its hydrogen bond network whereby alteration has drastic biological consequences. The DTS library not only yielded three analogs that retained potency but also provided insights that resulted in the identification of a previously unknown ability of promysalin to bind iron. These findings coupled with previous observations hint at a complex multifaceted role of the natural product within the rhizosphere.

Diverted Total Synthesis of Promysalin Analogs Demonstrates That an Iron-Binding Motif Is Responsible for Its Narrow-Spectrum Antibacterial Activity

PubMed Central

Steele, Andrew D.; Keohane, Colleen E.; Knouse, Kyle W.; Rossiter, Sean E.; Williams, Sierra J.; Wuest, William M.

2016-01-01

Promysalin is a species-specific Pseudomonad metabolite with unique bioactivity. To better understand the mode of action of this natural product, we synthesized 16 analogs utilizing diverted total synthesis (DTS). Our analog studies revealed that the bioactivity of promysalin is sensitive to changes within its hydrogen bond network whereby alteration has drastic biological consequences. The DTS library not only yielded three analogs that retained potency but also provided insights that resulted in the identification of a previously unknown ability of promysalin to bind iron. These findings coupled with previous observations hint at a complex multifaceted role of the natural product within the rhizosphere. PMID:27096543

Decision tree analysis to stratify risk of de novo non-melanoma skin cancer following liver transplantation.

PubMed

Tanaka, Tomohiro; Voigt, Michael D

2018-03-01

Non-melanoma skin cancer (NMSC) is the most common de novo malignancy in liver transplant (LT) recipients; it behaves more aggressively and it increases mortality. We used decision tree analysis to develop a tool to stratify and quantify risk of NMSC in LT recipients. We performed Cox regression analysis to identify which predictive variables to enter into the decision tree analysis. Data were from the Organ Procurement Transplant Network (OPTN) STAR files of September 2016 (n = 102984). NMSC developed in 4556 of the 105984 recipients, a mean of 5.6 years after transplant. The 5/10/20-year rates of NMSC were 2.9/6.3/13.5%, respectively. Cox regression identified male gender, Caucasian race, age, body mass index (BMI) at LT, and sirolimus use as key predictive or protective factors for NMSC. These factors were entered into a decision tree analysis. The final tree stratified non-Caucasians as low risk (0.8%), and Caucasian males > 47 years, BMI < 40 who did not receive sirolimus, as high risk (7.3% cumulative incidence of NMSC). The predictions in the derivation set were almost identical to those in the validation set (r 2  = 0.971, p < 0.0001). Cumulative incidence of NMSC in low, moderate and high risk groups at 5/10/20 year was 0.5/1.2/3.3, 2.1/4.8/11.7 and 5.6/11.6/23.1% (p < 0.0001). The decision tree model accurately stratifies the risk of developing NMSC in the long-term after LT.

Interpretation of diagnostic data: 6. How to do it with more complex maths.

PubMed

1983-11-15

We have now shown you how to use decision analysis in making those rare, tough diagnostic decisions that are not soluble through other, easier routes. In summary, to "use more complex maths" the following steps will be useful: Create a decision tree or map of all the pertinent courses of action and their consequences. Assign probabilities to the branches of each chance node. Assign utilities to each of the potential outcomes shown on the decision tree. Combine the probabilities and utilities for each node on the decision tree. Pick the decision that leads to the highest expected utility. Test your decision for its sensitivity to clinically sensible changes in probabilities and utilities. That concludes this series of clinical epidemiology rounds. You've come a long way from "doing it with pictures" and are now able to extract most of the diagnostic information that can be provided from signs, symptoms and laboratory investigations. We would appreciate learning whether you have found this series useful and how we can do a better job of presenting these and other elements of "the science of the art of medicine".

Policy Route Map for Academic Libraries' Digital Content

ERIC Educational Resources Information Center

Koulouris, Alexandros; Kapidakis, Sarantos

2012-01-01

This paper presents a policy decision tree for digital information management in academic libraries. The decision tree is a policy guide, which offers alternative access and reproduction policy solutions according to the prevailing circumstances (for example acquisition method, copyright ownership). It refers to the digital information life cycle,…

Decision-Tree, Rule-Based, and Random Forest Classification of High-Resolution Multispectral Imagery for Wetland Mapping and Inventory

EPA Science Inventory

Efforts are increasingly being made to classify the world’s wetland resources, an important ecosystem and habitat that is diminishing in abundance. There are multiple remote sensing classification methods, including a suite of nonparametric classifiers such as decision-tree...

Siting a municipal solid waste disposal facility, part II: the effects of external criteria on the final decision.

PubMed

Korucu, M Kemal; Karademir, Aykan

2014-02-01

The procedure of a multi-criteria decision analysis supported by the geographic information systems was applied to the site selection process of a planning municipal solid waste management practice based on twelve different scenarios. The scenarios included two different decision tree modes and two different weighting models for three different area requirements. The suitability rankings of the suitable sites obtained from the application of the decision procedure for the scenarios were assessed by a factorial experimental design concerning the effect of some external criteria on the final decision of the site selection process. The external criteria used in the factorial experimental design were defined as "Risk perception and approval of stakeholders" and "Visibility". The effects of the presence of these criteria in the decision trees were evaluated in detail. For a quantitative expression of the differentiations observed in the suitability rankings, the ranking data were subjected to ANOVA test after a normalization process. Then the results of these tests were evaluated by Tukey test to measure the effects of external criteria on the final decision. The results of Tukey tests indicated that the involvement of the external criteria into the decision trees produced statistically meaningful differentiations in the suitability rankings. Since the external criteria could cause considerable external costs during the operation of the disposal facilities, the presence of these criteria in the decision tree in addition to the other criteria related to environmental and legislative requisites could prevent subsequent external costs in the first place.

Decision support for mitigating the risk of tree induced transmission line failure in utility rights-of-way.

PubMed

Poulos, H M; Camp, A E

2010-02-01

Vegetation management is a critical component of rights-of-way (ROW) maintenance for preventing electrical outages and safety hazards resulting from tree contact with conductors during storms. Northeast Utility's (NU) transmission lines are a critical element of the nation's power grid; NU is therefore under scrutiny from federal agencies charged with protecting the electrical transmission infrastructure of the United States. We developed a decision support system to focus right-of-way maintenance and minimize the potential for a tree fall episode that disables transmission capacity across the state of Connecticut. We used field data on tree characteristics to develop a system for identifying hazard trees (HTs) in the field using limited equipment to manage Connecticut power line ROW. Results from this study indicated that the tree height-to-diameter ratio, total tree height, and live crown ratio were the key characteristics that differentiated potential risk trees (danger trees) from trees with a high probability of tree fall (HTs). Products from this research can be transferred to adaptive right-of-way management, and the methods we used have great potential for future application to other regions of the United States and elsewhere where tree failure can disrupt electrical power.

Digital tomosynthesis (DTS) with a Circular X-ray tube: Its image reconstruction based on total-variation minimization and the image characteristics

NASA Astrophysics Data System (ADS)

Park, Y. O.; Hong, D. K.; Cho, H. S.; Je, U. K.; Oh, J. E.; Lee, M. S.; Kim, H. J.; Lee, S. H.; Jang, W. S.; Cho, H. M.; Choi, S. I.; Koo, Y. S.

2013-09-01

In this paper, we introduce an effective imaging system for digital tomosynthesis (DTS) with a circular X-ray tube, the so-called circular-DTS (CDTS) system, and its image reconstruction algorithm based on the total-variation (TV) minimization method for low-dose, high-accuracy X-ray imaging. Here, the X-ray tube is equipped with a series of cathodes distributed around a rotating anode, and the detector remains stationary throughout the image acquisition. We considered a TV-based reconstruction algorithm that exploited the sparsity of the image with substantially high image accuracy. We implemented the algorithm for the CDTS geometry and successfully reconstructed images of high accuracy. The image characteristics were investigated quantitatively by using some figures of merit, including the universal-quality index (UQI) and the depth resolution. For selected tomographic angles of 20, 40, and 60°, the corresponding UQI values in the tomographic view were estimated to be about 0.94, 0.97, and 0.98, and the depth resolutions were about 4.6, 3.1, and 1.2 voxels in full width at half maximum (FWHM), respectively. We expect the proposed method to be applicable to developing a next-generation dental or breast X-ray imaging system.

Interpreting seasonal convective mixing in Devils Hole, Death Valley National Park, from temperature profiles observed by fiber-optic distributed temperature sensing

NASA Astrophysics Data System (ADS)

Hausner, Mark B.; Wilson, Kevin P.; Gaines, D. Bailey; Tyler, Scott W.

2012-05-01

Devils Hole, a groundwater-filled fracture in the carbonate aquifer of the southern Nevada Mojave Desert, represents a unique ecohydrological setting, as home to the only extant population of Cyprinodon diabolis, the endangered Devils Hole pupfish. Using water column temperatures collected with a fiber-optic distributed temperature sensor (DTS) during four field campaigns in 2009, evidence of deep circulation and nutrient export are, for the first time, documented. The DTS was deployed to measure vertical temperature profiles in the system, and the raw data returned were postprocessed to refine the calibration beyond the precision of the instrument's native calibration routines. Calibrated temperature data serve as a tracer for water movement and reveal a seasonal pattern of convective mixing that is supported by numerical simulations of the system. The periodic presence of divers in the water is considered, and their impacts on the temperature profiles are examined and found to be minimal. The seasonal mixing cycle may deplete the pupfish's food supplies when nutrients are at their scarcest. The spatial and temporal scales of the DTS observations make it possible to observe temperature gradients on the order of 0.001°C m-1, revealing phenomena that would have been lost in instrument noise and uncertainty.

Decision tree modeling using R.

PubMed

Zhang, Zhongheng

2016-08-01

In machine learning field, decision tree learner is powerful and easy to interpret. It employs recursive binary partitioning algorithm that splits the sample in partitioning variable with the strongest association with the response variable. The process continues until some stopping criteria are met. In the example I focus on conditional inference tree, which incorporates tree-structured regression models into conditional inference procedures. While growing a single tree is subject to small changes in the training data, random forests procedure is introduced to address this problem. The sources of diversity for random forests come from the random sampling and restricted set of input variables to be selected. Finally, I introduce R functions to perform model based recursive partitioning. This method incorporates recursive partitioning into conventional parametric model building.

MO-F-CAMPUS-I-01: A System for Automatically Calculating Organ and Effective Dose for Fluoroscopically-Guided Procedures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xiong, Z; Vijayan, S; Rana, V

2015-06-15

Purpose: A system was developed that automatically calculates the organ and effective dose for individual fluoroscopically-guided procedures using a log of the clinical exposure parameters. Methods: We have previously developed a dose tracking system (DTS) to provide a real-time color-coded 3D- mapping of skin dose. This software produces a log file of all geometry and exposure parameters for every x-ray pulse during a procedure. The data in the log files is input into PCXMC, a Monte Carlo program that calculates organ and effective dose for projections and exposure parameters set by the user. We developed a MATLAB program to readmore » data from the log files produced by the DTS and to automatically generate the definition files in the format used by PCXMC. The processing is done at the end of a procedure after all exposures are completed. Since there are thousands of exposure pulses with various parameters for fluoroscopy, DA and DSA and at various projections, the data for exposures with similar parameters is grouped prior to entry into PCXMC to reduce the number of Monte Carlo calculations that need to be performed. Results: The software developed automatically transfers data from the DTS log file to PCXMC and runs the program for each grouping of exposure pulses. When the dose from all exposure events are calculated, the doses for each organ and all effective doses are summed to obtain procedure totals. For a complicated interventional procedure, the calculations can be completed on a PC without manual intervention in less than 30 minutes depending on the level of data grouping. Conclusion: This system allows organ dose to be calculated for individual procedures for every patient without tedious calculations or data entry so that estimates of stochastic risk can be obtained in addition to the deterministic risk estimate provided by the DTS. Partial support from NIH grant R01EB002873 and Toshiba Medical Systems Corp.« less

TU-D-209-02: A Backscatter Point Spread Function for Entrance Skin Dose Determination

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vijayan, S; Xiong, Z; Shankar, A

Purpose: To determine the distribution of backscattered radiation to the skin resulting from a non-uniform distribution of primary radiation through convolution with a backscatter point spread function (PSF). Methods: A backscatter PSF is determined using Monte Carlo simulation of a 1 mm primary beam incident on a 30 × 30 cm × 20 cm thick PMMA phantom using EGSnrc software. A primary profile is similarly obtained without the phantom and the difference from the total provides the backscatter profile. This scatter PSF characterizes the backscatter spread for a “point” primary interaction and can be convolved with the entrance primary dosemore » distribution to obtain the total entrance skin dose. The backscatter PSF was integrated into the skin dose tracking system (DTS), a graphical utility for displaying the color-coded skin dose distribution on a 3D graphic of the patient during interventional fluoroscopic procedures. The backscatter convolution method was validated for the non-uniform beam resulting from the use of an ROI attenuator. The ROI attenuator is a copper sheet with about 20% primary transmission (0.7 mm thick) containing a circular aperture; this attenuator is placed in the beam to reduce dose in the periphery while maintaining full dose in the region of interest. The DTS calculated primary plus backscatter distribution is compared to that measured with GafChromic film and that calculated using EGSnrc Monte-Carlo software. Results: The PSF convolution method used in the DTS software was able to account for the spread of backscatter from the ROI region to the region under the attenuator. The skin dose distribution determined using DTS with the ROI attenuator was in good agreement with the distributions measured with Gafchromic film and determined by Monte Carlo simulation Conclusion: The PSF convolution technique provides an accurate alternative for entrance skin dose determination with non-uniform primary x-ray beams. Partial support from NIH Grant R01-EB002873 and Toshiba Medical Systems Corp.« less

Active Heat Injection to Investigate Seepage Conditions Along the Interface Between a Concrete Diversion Sluiceway and Earthen Embankment Dam

NASA Astrophysics Data System (ADS)

Ringeri, A.; Butler, K. E.; MacQuarrie, K. T. B.

2016-12-01

The interface between embankment dams and adjoining hydraulic structures are regions which can give rise to seepage defects. A field experiment was conducted at the Mactaquac Generating Station in New Brunswick, Canada using active thermometry to investigate seepage conditions along the interface of a diversion sluiceway and earth embankment. The method involved monitoring the time evolution of temperature following the injection of a controlled heat pulse from a subsurface heat cable acting as a line source. Transient anomalies in the induced temperature field can result from the aberration of thermal properties and flow conditions which accompany defects. An industrial heat trace cable and distributed temperature sensing (DTS) fibre optic cable were installed in two parallel, 42 m deep, sub-vertical boreholes separated by 3 m and offset 0.5 m from the core-concrete interface. The heat and DTS cables were installed in the upstream and downstream boreholes respectively. Heat was injected as a box car function at a constant rate of 78.72 W/m for 51 d while the DTS cable, with a 20 cm sampling resolution, was averaged over 10 min at 30 min intervals for 300 d. The DTS cable successfully detected temperature changes induced by the upstream heat pulse. A coherent temperature response occurred along a 13 m section of deep fibre, where mean peak temperatures rose 1.59 ± 0.03 °C above ambient temperatures with an average time lag of 8.2 d following the end of the heating cycle. Two temperature anomalies above this region coincided with the position of the water table and the location of a previously detected fibre break. The method appears to be particularly useful in seepage surveillance of the deeper regions of the interface. Further analysis is required to remove the influence of seasonal temperatures on the heat pulse response at shallow depths.

High-Resolution Monitoring of Soil Water Dynamics in a Vegetated Hillslope by Active Distributed Temperature Sensing

NASA Astrophysics Data System (ADS)

Ciocca, F.; Krause, S.; Blaen, P.; Hannah, D. M.; Chalari, A.; Mondanos, M.; Abesser, C.

2016-12-01

Water and thermal conditions in the shallow vadose zone can be very complex and dynamic across a range of spatiotemporal scales. The efficient analysis of such dynamics requires technologies capable of precise and high-resolution monitoring of soil temperature and moisture across multiple scales. Optical fibre distributed temperature sensors (DTS) allows for precise temperature measurements at high spatio-temporal resolution, over several kilometres of optical fibre cable. In addition to passive temperature monitoring, hybrid optical cables with embedded metal conductors can be electrically heated and allow for distributed heat pulses. Such Active-DTS technique involves the analysis of temperatures during both heating and cooling phases of an optical fibre cable buried in the soil in order to provide distributed soil moisture estimates. In summer 2015, three loops of a 500m hybrid-optical cable have been deployed at 10cm, 25cm and 40cm depths along a hillslope with juvenile forest. Active-DTS surveys have been conducted with the aim to: (i) monitor the post-installation soil settling around the cable; (ii) analyse different heating strategies (intensity, duration) of the cable; (iii) establish a method for inferring soil moisture from Active-DTS results and validate with independent soil moisture readings from point probes; (iv) monitor the soil moisture response to short forcing events such as storms and artificial irrigation. Results from the surveys will be presented, and first assumptions on how the obtained soil water dynamics can be associated to specific triggers such as precipitation, evapotranspiration, soil inclination, will be discussed. This research is part of the British National Environmental Research Council (NERC) funded Distributed intelligent Heat Pulse System (DiHPS) project and is realised in the context of the Free Air Carbon Enrichment (FACE) experiment, in collaboration with the Birmingham Institute of Forest Research (BIFoR).

Understanding Multiscale Surface Water-Groundwater Interactions on Scott River Watershed Temperatures with the use of Distributed Temperature Sensing (DTS) in Support of the Coldwater Salmonid Fishery Beneficial Use

NASA Astrophysics Data System (ADS)

Hines, R. J.; Harter, T.; Tyler, S. W.; McFadin, B.; Yokel, E.

2008-12-01

The Scott River is a major tributary to the Klamath River that provides cold water rearing habitat for wild salmonid populations, including coho salmon (Oncorhynchus kisutch), Chinook salmon (O. tshawytscha), and steelhead trout (O. mykiss). During the summer months (July through September), the main-stem Scott River becomes disconnected from its tributaries throughout much of Scott Valley and relies primarily on baseflow from the groundwater aquifer. Summer stream temperatures in the Scott River are currently at levels that are not considered sustainable for the native salmonid population, resulting in the enactment of a Total Maximum Daily Load (TMDL) for temperature. Two of the conditions affecting stream temperature have been identified as increases in solar radiation due to a reduction in riparian vegetation and decreased accretion of groundwater. In conjunction with a regional scale surface water-groundwater modeling effort to investigate the benefits of various conjunctive use management alternatives on mid- and late summer baseflow in the Scott River, we completed high-resolution field measurements of stream temperature over an approximately 1,050-meter reach. Temperatures were measured using Fiber-Optic Distributed Temperature Sensing (DTS) techniques. The DTS survey in combination with FLIR stream surface temperature data from 2003 indicate that groundwater discharge to the Scott River is highly localized throughout the valley. The results of the DTS survey depict highly localized areas of groundwater accretion, as well as prominent localized temperature effects from riparian vegetation and river geomorphology. While originally modeled as a well-mixed stream during FLIR analysis, the DTS data further suggest that locally strong, vertical thermal gradients are found near the bottom of the active stream channel. The high-resolution temperature measurements were paired with fish surveys in order to determine the correlation between areas of identified lower river temperatures, groundwater accretion and other beneficial salmonid habitat indicators. Our work suggests that understanding of local-scale groundwater-stream interaction and analysis of corresponding local-scale geologic and riparian vegetation controls are critical to understanding the basin-scale groundwater-stream interactions. Preliminary data reviews indicate that groundwater discharge leads to distinct cold temperature pools near the streambed, while the remainder of the stream column is thermally well mixed. This local-scale, three-dimensional understanding is necessary if strategies are to be developed that aim for effective water resource management practices and improved beneficial use habitat. A multi-scale field reconnaissance and modeling approach is suggested to develop water management practices that lead to better habitat protection throughout the watershed.

Prediction of the compression ratio for municipal solid waste using decision tree.

PubMed

Heshmati R, Ali Akbar; Mokhtari, Maryam; Shakiba Rad, Saeed

2014-01-01

The compression ratio of municipal solid waste (MSW) is an essential parameter for evaluation of waste settlement and landfill design. However, no appropriate model has been proposed to estimate the waste compression ratio so far. In this study, a decision tree method was utilized to predict the waste compression ratio (C'c). The tree was constructed using Quinlan's M5 algorithm. A reliable database retrieved from the literature was used to develop a practical model that relates C'c to waste composition and properties, including dry density, dry weight water content, and percentage of biodegradable organic waste using the decision tree method. The performance of the developed model was examined in terms of different statistical criteria, including correlation coefficient, root mean squared error, mean absolute error and mean bias error, recommended by researchers. The obtained results demonstrate that the suggested model is able to evaluate the compression ratio of MSW effectively.

What Satisfies Students?: Mining Student-Opinion Data with Regression and Decision Tree Analysis

ERIC Educational Resources Information Center

Thomas, Emily H.; Galambos, Nora

2004-01-01

To investigate how students' characteristics and experiences affect satisfaction, this study uses regression and decision tree analysis with the CHAID algorithm to analyze student-opinion data. A data mining approach identifies the specific aspects of students' university experience that most influence three measures of general satisfaction. The…

Identification of pests and diseases of Dalbergia hainanensis based on EVI time series and classification of decision tree

NASA Astrophysics Data System (ADS)

Luo, Qiu; Xin, Wu; Qiming, Xiong

2017-06-01

In the process of vegetation remote sensing information extraction, the problem of phenological features and low performance of remote sensing analysis algorithm is not considered. To solve this problem, the method of remote sensing vegetation information based on EVI time-series and the classification of decision-tree of multi-source branch similarity is promoted. Firstly, to improve the time-series stability of recognition accuracy, the seasonal feature of vegetation is extracted based on the fitting span range of time-series. Secondly, the decision-tree similarity is distinguished by adaptive selection path or probability parameter of component prediction. As an index, it is to evaluate the degree of task association, decide whether to perform migration of multi-source decision tree, and ensure the speed of migration. Finally, the accuracy of classification and recognition of pests and diseases can reach 87%--98% of commercial forest in Dalbergia hainanensis, which is significantly better than that of MODIS coverage accuracy of 80%--96% in this area. Therefore, the validity of the proposed method can be verified.

Delirium tremens

MedlinePlus

Alcohol abuse - delirium tremens; DTs; Alcohol withdrawal - delirium tremens; Alcohol withdrawal delirium ... Delirium tremens can occur when you stop drinking alcohol after a period of heavy drinking, especially if ...

A decision tree model for predicting mediastinal lymph node metastasis in non-small cell lung cancer with F-18 FDG PET/CT.

PubMed

Pak, Kyoungjune; Kim, Keunyoung; Kim, Mi-Hyun; Eom, Jung Seop; Lee, Min Ki; Cho, Jeong Su; Kim, Yun Seong; Kim, Bum Soo; Kim, Seong Jang; Kim, In Joo

2018-01-01

We aimed to develop a decision tree model to improve diagnostic performance of positron emission tomography/computed tomography (PET/CT) to detect metastatic lymph nodes (LN) in non-small cell lung cancer (NSCLC). 115 patients with NSCLC were included in this study. The training dataset included 66 patients. A decision tree model was developed with 9 variables, and validated with 49 patients: short and long diameters of LNs, ratio of short and long diameters, maximum standardized uptake value (SUVmax) of LN, mean hounsfield unit, ratio of LN SUVmax and ascending aorta SUVmax (LN/AA), and ratio of LN SUVmax and superior vena cava SUVmax. A total of 301 LNs of 115 patients were evaluated in this study. Nodular calcification was applied as the initial imaging parameter, and LN SUVmax (≥3.95) was assessed as the second. LN/AA (≥2.92) was required to high LN SUVmax. Sensitivity was 50% for training dataset, and 40% for validation dataset. However, specificity was 99.28% for training dataset, and 96.23% for validation dataset. In conclusion, we have developed a new decision tree model for interpreting mediastinal LNs. All LNs with nodular calcification were benign, and LNs with high LN SUVmax and high LN/AA were metastatic Further studies are needed to incorporate subjective parameters and pathologic evaluations into a decision tree model to improve the test performance of PET/CT.

Identifying Risk Factors for Drug Use in an Iranian Treatment Sample: A Prediction Approach Using Decision Trees.

PubMed

Amirabadizadeh, Alireza; Nezami, Hossein; Vaughn, Michael G; Nakhaee, Samaneh; Mehrpour, Omid

2018-05-12

Substance abuse exacts considerable social and health care burdens throughout the world. The aim of this study was to create a prediction model to better identify risk factors for drug use. A prospective cross-sectional study was conducted in South Khorasan Province, Iran. Of the total of 678 eligible subjects, 70% (n: 474) were randomly selected to provide a training set for constructing decision tree and multiple logistic regression (MLR) models. The remaining 30% (n: 204) were employed in a holdout sample to test the performance of the decision tree and MLR models. Predictive performance of different models was analyzed by the receiver operating characteristic (ROC) curve using the testing set. Independent variables were selected from demographic characteristics and history of drug use. For the decision tree model, the sensitivity and specificity for identifying people at risk for drug abuse were 66% and 75%, respectively, while the MLR model was somewhat less effective at 60% and 73%. Key independent variables in the analyses included first substance experience, age at first drug use, age, place of residence, history of cigarette use, and occupational and marital status. While study findings are exploratory and lack generalizability they do suggest that the decision tree model holds promise as an effective classification approach for identifying risk factors for drug use. Convergent with prior research in Western contexts is that age of drug use initiation was a critical factor predicting a substance use disorder.

Exploratory Use of Decision Tree Analysis in Classification of Outcome in Hypoxic-Ischemic Brain Injury.

PubMed

Phan, Thanh G; Chen, Jian; Singhal, Shaloo; Ma, Henry; Clissold, Benjamin B; Ly, John; Beare, Richard

2018-01-01

Prognostication following hypoxic ischemic encephalopathy (brain injury) is important for clinical management. The aim of this exploratory study is to use a decision tree model to find clinical and MRI associates of severe disability and death in this condition. We evaluate clinical model and then the added value of MRI data. The inclusion criteria were as follows: age ≥17 years, cardio-respiratory arrest, and coma on admission (2003-2011). Decision tree analysis was used to find clinical [Glasgow Coma Score (GCS), features about cardiac arrest, therapeutic hypothermia, age, and sex] and MRI (infarct volume) associates of severe disability and death. We used the area under the ROC (auROC) to determine accuracy of model. There were 41 (63.7% males) patients having MRI imaging with the average age 51.5 ± 18.9 years old. The decision trees showed that infarct volume and age were important factors for discrimination between mild to moderate disability and severe disability and death at day 0 and day 2. The auROC for this model was 0.94 (95% CI 0.82-1.00). At day 7, GCS value was the only predictor; the auROC was 0.96 (95% CI 0.86-1.00). Our findings provide proof of concept for further exploration of the role of MR imaging and decision tree analysis in the early prognostication of hypoxic ischemic brain injury.

Fish to meat intake ratio and cooking oils are associated with hepatitis C virus carriers with persistently normal alanine aminotransferase levels.

PubMed

Otsuka, Momoka; Uchida, Yuki; Kawaguchi, Takumi; Taniguchi, Eitaro; Kawaguchi, Atsushi; Kitani, Shingo; Itou, Minoru; Oriishi, Tetsuharu; Kakuma, Tatsuyuki; Tanaka, Suiko; Yagi, Minoru; Sata, Michio

2012-10-01

  Dietary habits are involved in the development of chronic inflammation; however, the impact of dietary profiles of hepatitis C virus carriers with persistently normal alanine transaminase levels (HCV-PNALT) remains unclear. The decision-tree algorithm is a data-mining statistical technique, which uncovers meaningful profiles of factors from a data collection. We aimed to investigate dietary profiles associated with HCV-PNALT using a decision-tree algorithm.   Twenty-seven HCV-PNALT and 41 patients with chronic hepatitis C were enrolled in this study. Dietary habit was assessed using a validated semiquantitative food frequency questionnaire. A decision-tree algorithm was created by dietary variables, and was evaluated by area under the receiver operating characteristic curve analysis (AUROC).   In multivariate analysis, fish to meat ratio, dairy product and cooking oils were identified as independent variables associated with HCV-PNALT. The decision-tree algorithm was created with two variables: a fish to meat ratio and cooking oils/ideal bodyweight. When subjects showed a fish to meat ratio of 1.24 or more, 68.8% of the subjects were HCV-PNALT. On the other hand, 11.5% of the subjects were HCV-PNALT when subjects showed a fish to meat ratio of less than 1.24 and cooking oil/ideal bodyweight of less than 0.23 g/kg. The difference in the proportion of HCV-PNALT between these groups are significant (odds ratio 16.87, 95% CI 3.40-83.67, P = 0.0005). Fivefold cross-validation of the decision-tree algorithm showed an AUROC of 0.6947 (95% CI 0.5656-0.8238, P = 0.0067).   The decision-tree algorithm disclosed that fish to meat ratio and cooking oil/ideal bodyweight were associated with HCV-PNALT. © 2012 The Japan Society of Hepatology.

Data Clustering and Evolving Fuzzy Decision Tree for Data Base Classification Problems

NASA Astrophysics Data System (ADS)

Chang, Pei-Chann; Fan, Chin-Yuan; Wang, Yen-Wen

Data base classification suffers from two well known difficulties, i.e., the high dimensionality and non-stationary variations within the large historic data. This paper presents a hybrid classification model by integrating a case based reasoning technique, a Fuzzy Decision Tree (FDT), and Genetic Algorithms (GA) to construct a decision-making system for data classification in various data base applications. The model is major based on the idea that the historic data base can be transformed into a smaller case-base together with a group of fuzzy decision rules. As a result, the model can be more accurately respond to the current data under classifying from the inductions by these smaller cases based fuzzy decision trees. Hit rate is applied as a performance measure and the effectiveness of our proposed model is demonstrated by experimentally compared with other approaches on different data base classification applications. The average hit rate of our proposed model is the highest among others.

Computerization of guidelines: a knowledge specification method to convert text to detailed decision tree for electronic implementation.

PubMed

Aguirre-Junco, Angel-Ricardo; Colombet, Isabelle; Zunino, Sylvain; Jaulent, Marie-Christine; Leneveut, Laurence; Chatellier, Gilles

2004-01-01

The initial step for the computerization of guidelines is the knowledge specification from the prose text of guidelines. We describe a method of knowledge specification based on a structured and systematic analysis of text allowing detailed specification of a decision tree. We use decision tables to validate the decision algorithm and decision trees to specify and represent this algorithm, along with elementary messages of recommendation. Edition tools are also necessary to facilitate the process of validation and workflow between expert physicians who will validate the specified knowledge and computer scientist who will encode the specified knowledge in a guide-line model. Applied to eleven different guidelines issued by an official agency, the method allows a quick and valid computerization and integration in a larger decision support system called EsPeR (Personalized Estimate of Risks). The quality of the text guidelines is however still to be developed further. The method used for computerization could help to define a framework usable at the initial step of guideline development in order to produce guidelines ready for electronic implementation.

Bacterial Diterpene Synthases: New Opportunities for Mechanistic Enzymology and Engineered Biosynthesis

PubMed Central

Smanski, Michael J.; Peterson, Ryan M.; Huang, Sheng-Xiong; Shen, Ben

2012-01-01

Diterpenoid biosynthesis has been extensively studied in plants and fungi, yet cloning and engineering diterpenoid pathways in these organisms remain challenging. Bacteria are emerging as prolific producers of diterpenoid natural products, and bacterial diterpene synthases are poised to make significant contributions to our understanding of terpenoid biosynthesis. Here we will first survey diterpenoid natural products of bacterial origin and briefly review their biosynthesis with emphasis on diterpene synthases (DTSs) that channel geranylgeranyl diphosphate to various diterpenoid scaffolds. We will then highlight differences of DTSs of bacterial and higher organism origins and discuss the challenges in discovering novel bacterial DTSs. We will conclude by discussing new opportunities for DTS mechanistic enzymology and applications of bacterial DTS in biocatalysis and metabolic pathway engineering. PMID:22445175

Validating a decision tree for serious infection: diagnostic accuracy in acutely ill children in ambulatory care.

PubMed

Verbakel, Jan Y; Lemiengre, Marieke B; De Burghgraeve, Tine; De Sutter, An; Aertgeerts, Bert; Bullens, Dominique M A; Shinkins, Bethany; Van den Bruel, Ann; Buntinx, Frank

2015-08-07

Acute infection is the most common presentation of children in primary care with only few having a serious infection (eg, sepsis, meningitis, pneumonia). To avoid complications or death, early recognition and adequate referral are essential. Clinical prediction rules have the potential to improve diagnostic decision-making for rare but serious conditions. In this study, we aimed to validate a recently developed decision tree in a new but similar population. Diagnostic accuracy study validating a clinical prediction rule. Acutely ill children presenting to ambulatory care in Flanders, Belgium, consisting of general practice and paediatric assessment in outpatient clinics or the emergency department. Physicians were asked to score the decision tree in every child. The outcome of interest was hospital admission for at least 24 h with a serious infection within 5 days after initial presentation. We report the diagnostic accuracy of the decision tree in sensitivity, specificity, likelihood ratios and predictive values. In total, 8962 acute illness episodes were included, of which 283 lead to admission to hospital with a serious infection. Sensitivity of the decision tree was 100% (95% CI 71.5% to 100%) at a specificity of 83.6% (95% CI 82.3% to 84.9%) in the general practitioner setting with 17% of children testing positive. In the paediatric outpatient and emergency department setting, sensitivities were below 92%, with specificities below 44.8%. In an independent validation cohort, this clinical prediction rule has shown to be extremely sensitive to identify children at risk of hospital admission for a serious infection in general practice, making it suitable for ruling out. NCT02024282. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Decay fungi of oaks and associated hardwoods for western arborists

Treesearch

Jessie A. Glaeser; Kevin T. Smith

2010-01-01

Examination of trees for the presence and extent of decay should be part of any hazard tree assessment. Identification of the fungi responsible for the decay improves prediction of tree performance and the quality of management decisions, including tree pruning or removal. Scouting for Sudden Oak Death (SOD) in the West has drawn attention to hardwood tree species,...

Climate change impacts on tree ranges: model intercomparison facilitates understanding and quantification of uncertainty.

PubMed

Cheaib, Alissar; Badeau, Vincent; Boe, Julien; Chuine, Isabelle; Delire, Christine; Dufrêne, Eric; François, Christophe; Gritti, Emmanuel S; Legay, Myriam; Pagé, Christian; Thuiller, Wilfried; Viovy, Nicolas; Leadley, Paul

2012-06-01

Model-based projections of shifts in tree species range due to climate change are becoming an important decision support tool for forest management. However, poorly evaluated sources of uncertainty require more scrutiny before relying heavily on models for decision-making. We evaluated uncertainty arising from differences in model formulations of tree response to climate change based on a rigorous intercomparison of projections of tree distributions in France. We compared eight models ranging from niche-based to process-based models. On average, models project large range contractions of temperate tree species in lowlands due to climate change. There was substantial disagreement between models for temperate broadleaf deciduous tree species, but differences in the capacity of models to account for rising CO(2) impacts explained much of the disagreement. There was good quantitative agreement among models concerning the range contractions for Scots pine. For the dominant Mediterranean tree species, Holm oak, all models foresee substantial range expansion. © 2012 Blackwell Publishing Ltd/CNRS.

A multivariate decision tree analysis of biophysical factors in tropical forest fire occurrence

Treesearch

Rey S. Ofren; Edward Harvey

2000-01-01

A multivariate decision tree model was used to quantify the relative importance of complex hierarchical relationships between biophysical variables and the occurrence of tropical forest fires. The study site is the Huai Kha Kbaeng wildlife sanctuary, a World Heritage Site in northwestern Thailand where annual fires are common and particularly destructive. Thematic...

Which Types of Leadership Styles Do Followers Prefer? A Decision Tree Approach

ERIC Educational Resources Information Center

Salehzadeh, Reza

2017-01-01

Purpose: The purpose of this paper is to propose a new method to find the appropriate leadership styles based on the followers' preferences using the decision tree technique. Design/methodology/approach: Statistical population includes the students of the University of Isfahan. In total, 750 questionnaires were distributed; out of which, 680…

The Americans with Disabilities Act: A Decision Tree for Social Services Administrators

ERIC Educational Resources Information Center

O'Brien, Gerald V.; Ellegood, Christina

2005-01-01

The 1990 Americans with Disabilities Act has had a profound influence on social workers and social services administrators in virtually all work settings. Because of the multiple elements of the act, however, assessing the validity of claims can be a somewhat arduous and complicated task. This article provides a "decision tree" for…

A Decision-Tree-Oriented Guidance Mechanism for Conducting Nature Science Observation Activities in a Context-Aware Ubiquitous Learning

ERIC Educational Resources Information Center

Hwang, Gwo-Jen; Chu, Hui-Chun; Shih, Ju-Ling; Huang, Shu-Hsien; Tsai, Chin-Chung

2010-01-01

A context-aware ubiquitous learning environment is an authentic learning environment with personalized digital supports. While showing the potential of applying such a learning environment, researchers have also indicated the challenges of providing adaptive and dynamic support to individual students. In this paper, a decision-tree-oriented…

A decision tree approach using silvics to guide planning for forest restoration

Treesearch

Sharon M. Hermann; John S. Kush; John C. Gilbert

2013-01-01

We created a decision tree based on silvics of longleaf pine (Pinus palustris) and historical descriptions to develop approaches for restoration management at Horseshoe Bend National Military Park located in central Alabama. A National Park Service goal is to promote structure and composition of a forest that likely surrounded the 1814 battlefield....

What Satisfies Students? Mining Student-Opinion Data with Regression and Decision-Tree Analysis. AIR 2002 Forum Paper.

ERIC Educational Resources Information Center

Thomas, Emily H.; Galambos, Nora

To investigate how students' characteristics and experiences affect satisfaction, this study used regression and decision-tree analysis with the CHAID algorithm to analyze student opinion data from a sample of 1,783 college students. A data-mining approach identifies the specific aspects of students' university experience that most influence three…

Foraging Behaviour in Magellanic Woodpeckers Is Consistent with a Multi-Scale Assessment of Tree Quality

PubMed Central

Vergara, Pablo M.; Soto, Gerardo E.; Rodewald, Amanda D.; Meneses, Luis O.; Pérez-Hernández, Christian G.

2016-01-01

Theoretical models predict that animals should make foraging decisions after assessing the quality of available habitat, but most models fail to consider the spatio-temporal scales at which animals perceive habitat availability. We tested three foraging strategies that explain how Magellanic woodpeckers (Campephilus magellanicus) assess the relative quality of trees: 1) Woodpeckers with local knowledge select trees based on the available trees in the immediate vicinity. 2) Woodpeckers lacking local knowledge select trees based on their availability at previously visited locations. 3) Woodpeckers using information from long-term memory select trees based on knowledge about trees available within the entire landscape. We observed foraging woodpeckers and used a Brownian Bridge Movement Model to identify trees available to woodpeckers along foraging routes. Woodpeckers selected trees with a later decay stage than available trees. Selection models indicated that preferences of Magellanic woodpeckers were based on clusters of trees near the most recently visited trees, thus suggesting that woodpeckers use visual cues from neighboring trees. In a second analysis, Cox’s proportional hazards models showed that woodpeckers used information consolidated across broader spatial scales to adjust tree residence times. Specifically, woodpeckers spent more time at trees with larger diameters and in a more advanced stage of decay than trees available along their routes. These results suggest that Magellanic woodpeckers make foraging decisions based on the relative quality of trees that they perceive and memorize information at different spatio-temporal scales. PMID:27416115

Foraging Behaviour in Magellanic Woodpeckers Is Consistent with a Multi-Scale Assessment of Tree Quality.

PubMed

Vergara, Pablo M; Soto, Gerardo E; Moreira-Arce, Darío; Rodewald, Amanda D; Meneses, Luis O; Pérez-Hernández, Christian G

2016-01-01

Theoretical models predict that animals should make foraging decisions after assessing the quality of available habitat, but most models fail to consider the spatio-temporal scales at which animals perceive habitat availability. We tested three foraging strategies that explain how Magellanic woodpeckers (Campephilus magellanicus) assess the relative quality of trees: 1) Woodpeckers with local knowledge select trees based on the available trees in the immediate vicinity. 2) Woodpeckers lacking local knowledge select trees based on their availability at previously visited locations. 3) Woodpeckers using information from long-term memory select trees based on knowledge about trees available within the entire landscape. We observed foraging woodpeckers and used a Brownian Bridge Movement Model to identify trees available to woodpeckers along foraging routes. Woodpeckers selected trees with a later decay stage than available trees. Selection models indicated that preferences of Magellanic woodpeckers were based on clusters of trees near the most recently visited trees, thus suggesting that woodpeckers use visual cues from neighboring trees. In a second analysis, Cox's proportional hazards models showed that woodpeckers used information consolidated across broader spatial scales to adjust tree residence times. Specifically, woodpeckers spent more time at trees with larger diameters and in a more advanced stage of decay than trees available along their routes. These results suggest that Magellanic woodpeckers make foraging decisions based on the relative quality of trees that they perceive and memorize information at different spatio-temporal scales.

A method of building of decision trees based on data from wearable device during a rehabilitation of patients with tibia fractures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kupriyanov, M. S., E-mail: mikhail.kupriyanov@gmail.com; Shukeilo, E. Y., E-mail: eyshukeylo@gmail.com; Shichkina, J. A., E-mail: strange.y@mail.ru

2015-11-17

Nowadays technologies which are used in traumatology are a combination of mechanical, electronic, calculating and programming tools. Relevance of development of mobile applications for an expeditious data processing which are received from medical devices (in particular, wearable devices), and formulation of management decisions increases. Using of a mathematical method of building of decision trees for an assessment of a patient’s health condition using data from a wearable device considers in this article.

A method of building of decision trees based on data from wearable device during a rehabilitation of patients with tibia fractures

NASA Astrophysics Data System (ADS)

Kupriyanov, M. S.; Shukeilo, E. Y.; Shichkina, J. A.

2015-11-01

Nowadays technologies which are used in traumatology are a combination of mechanical, electronic, calculating and programming tools. Relevance of development of mobile applications for an expeditious data processing which are received from medical devices (in particular, wearable devices), and formulation of management decisions increases. Using of a mathematical method of building of decision trees for an assessment of a patient's health condition using data from a wearable device considers in this article.

Protein attributes contribute to halo-stability, bioinformatics approach

PubMed Central

2011-01-01

Halophile proteins can tolerate high salt concentrations. Understanding halophilicity features is the first step toward engineering halostable crops. To this end, we examined protein features contributing to the halo-toleration of halophilic organisms. We compared more than 850 features for halophilic and non-halophilic proteins with various screening, clustering, decision tree, and generalized rule induction models to search for patterns that code for halo-toleration. Up to 251 protein attributes selected by various attribute weighting algorithms as important features contribute to halo-stability; from them 14 attributes selected by 90% of models and the count of hydrogen gained the highest value (1.0) in 70% of attribute weighting models, showing the importance of this attribute in feature selection modeling. The other attributes mostly were the frequencies of di-peptides. No changes were found in the numbers of groups when K-Means and TwoStep clustering modeling were performed on datasets with or without feature selection filtering. Although the depths of induced trees were not high, the accuracies of trees were higher than 94% and the frequency of hydrophobic residues pointed as the most important feature to build trees. The performance evaluation of decision tree models had the same values and the best correctness percentage recorded with the Exhaustive CHAID and CHAID models. We did not find any significant difference in the percent of correctness, performance evaluation, and mean correctness of various decision tree models with or without feature selection. For the first time, we analyzed the performance of different screening, clustering, and decision tree algorithms for discriminating halophilic and non-halophilic proteins and the results showed that amino acid composition can be used to discriminate between halo-tolerant and halo-sensitive proteins. PMID:21592393

Classification tree for the assessment of sedentary lifestyle among hypertensive.

PubMed

Castelo Guedes Martins, Larissa; Venícios de Oliveira Lopes, Marcos; Gomes Guedes, Nirla; Paixão de Menezes, Angélica; de Oliveira Farias, Odaleia; Alves Dos Santos, Naftale

2016-04-01

To develop a classification tree of clinical indicators for the correct prediction of the nursing diagnosis "Sedentary lifestyle" (SL) in people with high blood pressure (HTN). A cross-sectional study conducted in an outpatient care center specializing in high blood pressure and Mellitus diabetes located in northeastern Brazil. The sample consisted of 285 people between 19 and 59 years old diagnosed with high blood pressure and was applied an interview and physical examination, obtaining socio-demographic information, related factors and signs and symptoms that made the defining characteristics for the diagnosis under study. The tree was generated using the CHAID algorithm (Chi-square Automatic Interaction Detection). The construction of the decision tree allowed establishing the interactions between clinical indicators that facilitate a probabilistic analysis of multiple situations allowing quantify the probability of an individual presenting a sedentary lifestyle. The tree included the clinical indicator Choose daily routine without exercise as the first node. People with this indicator showed a probability of 0.88 of presenting the SL. The second node was composed of the indicator Does not perform physical activity during leisure, with 0.99 probability of presenting the SL with these two indicators. The predictive capacity of the tree was established at 69.5%. Decision trees help nurses who care HTN people in decision-making in assessing the characteristics that increase the probability of SL nursing diagnosis, optimizing the time for diagnostic inference.

An improved classification tree analysis of high cost modules based upon an axiomatic definition of complexity

NASA Technical Reports Server (NTRS)

Tian, Jianhui; Porter, Adam; Zelkowitz, Marvin V.

1992-01-01

Identification of high cost modules has been viewed as one mechanism to improve overall system reliability, since such modules tend to produce more than their share of problems. A decision tree model was used to identify such modules. In this current paper, a previously developed axiomatic model of program complexity is merged with the previously developed decision tree process for an improvement in the ability to identify such modules. This improvement was tested using data from the NASA Software Engineering Laboratory.

A key for the Forest Service hardwood tree grades

Treesearch

Gary W. Miller; Leland F. Hanks; Harry V., Jr. Wiant

1986-01-01

A dichotomous key organizes the USDA Forest Service hardwood tree grade specifications into a stepwise procedure for those learning to grade hardwood sawtimber. The key addresses the major grade factors, tree size, surface characteristics, and allowable cull deductions in a series of paried choices that lead the user to a decision regarding tree grade.

Inferences from growing trees backwards

Treesearch

David W. Green; Kent A. McDonald

1997-01-01

The objective of this paper is to illustrate how longitudinal stress wave techniques can be useful in tracking the future quality of a growing tree. Monitoring the quality of selected trees in a plantation forest could provide early input to decisions on the effectiveness of management practices, or future utilization options, for trees in a plantation. There will...

Acute Mental Discomfort Associated with Suicide Behavior in a Clinical Sample of Patients with Affective Disorders: Ascertaining Critical Variables Using Artificial Intelligence Tools.

PubMed

Morales, Susana; Barros, Jorge; Echávarri, Orietta; García, Fabián; Osses, Alex; Moya, Claudia; Maino, María Paz; Fischman, Ronit; Núñez, Catalina; Szmulewicz, Tita; Tomicic, Alemka

2017-01-01

In efforts to develop reliable methods to detect the likelihood of impending suicidal behaviors, we have proposed the following. To gain a deeper understanding of the state of suicide risk by determining the combination of variables that distinguishes between groups with and without suicide risk. A study involving 707 patients consulting for mental health issues in three health centers in Greater Santiago, Chile. Using 345 variables, an analysis was carried out with artificial intelligence tools, Cross Industry Standard Process for Data Mining processes, and decision tree techniques. The basic algorithm was top-down, and the most suitable division produced by the tree was selected by using the lowest Gini index as a criterion and by looping it until the condition of belonging to the group with suicidal behavior was fulfilled. Four trees distinguishing the groups were obtained, of which the elements of one were analyzed in greater detail, since this tree included both clinical and personality variables. This specific tree consists of six nodes without suicide risk and eight nodes with suicide risk (tree decision 01, accuracy 0.674, precision 0.652, recall 0.678, specificity 0.670, F measure 0.665, receiver operating characteristic (ROC) area under the curve (AUC) 73.35%; tree decision 02, accuracy 0.669, precision 0.642, recall 0.694, specificity 0.647, F measure 0.667, ROC AUC 68.91%; tree decision 03, accuracy 0.681, precision 0.675, recall 0.638, specificity 0.721, F measure, 0.656, ROC AUC 65.86%; tree decision 04, accuracy 0.714, precision 0.734, recall 0.628, specificity 0.792, F measure 0.677, ROC AUC 58.85%). This study defines the interactions among a group of variables associated with suicidal ideation and behavior. By using these variables, it may be possible to create a quick and easy-to-use tool. As such, psychotherapeutic interventions could be designed to mitigate the impact of these variables on the emotional state of individuals, thereby reducing eventual risk of suicide. Such interventions may reinforce psychological well-being, feelings of self-worth, and reasons for living, for each individual in certain groups of patients.

Analytical and CASE study on Limited Search, ID3, CHAID, C4.5, Improved C4.5 and OVA Decision Tree Algorithms to design Decision Support System

NASA Astrophysics Data System (ADS)

Kaur, Parneet; Singh, Sukhwinder; Garg, Sushil; Harmanpreet

2010-11-01

In this paper we study about classification algorithms for farm DSS. By applying classification algorithms i.e. Limited search, ID3, CHAID, C4.5, Improved C4.5 and One VS all Decision Tree on common data set of crop with specified class, results are obtained. The tool used to derive results is SPINA. The graphical results obtained from tool are compared to suggest best technique to develop farm Decision Support System. This analysis would help to researchers to design effective and fast DSS for farmer to take decision for enhancing their yield.

Cases Study of Nonlinear Interaction Between Near-Inertial Waves Induced by Typhoon and Diurnal Tides Near the Xisha Islands

NASA Astrophysics Data System (ADS)

Liu, Junliang; He, Yinghui; Li, Juan; Cai, Shuqun; Wang, Dongxiao; Huang, Yandan

2018-04-01

Nonlinear interaction between near-inertial waves (NIWs) and diurnal tides (DTs) after nine typhoons near the Xisha Islands of the northwestern South China Sea (SCS) were investigated using three-year in situ mooring observation data. It was found that a harmonic wave (f + D1, hereafter referred to as fD1 wave), with a frequency equal to the sum of frequencies of NIWs and DTs (hereafter referred to as f and D1, respectively), was generated via nonlinear interaction between typhoon-induced NIWs and DTs after each typhoon. The fD1 wave mainly concentrates in the subsurface layer, and is mainly induced by the first component of the vertical nonlinear momentum term, the product of the vertical velocity of DT and vertical shear of near-inertial current (hereafter referred to as Component 1), in which the vertical shear of the near-inertial current greatly affects the strength of the fD1 current. The larger the Component 1, the stronger the fD1 currents. The background preexisting mesoscale anticyclonic eddy near the mooring site may also enhance the vertical velocity of DT and thus Component 1, which subsequently facilitates the nonlinear interaction-induced energy transfer to the fD1 wave and enhances the fD1 currents after the passage of a typhoon.

Abdominal wall desmoid tumors: A case report

PubMed Central

MA, JIN-HUI; MA, ZHEN-HAI; DONG, XUE-FENG; YIN, HANG; ZHAO, YONG-FU

2013-01-01

Desmoid tumors (DTs) are rare lesions that do not possess any metastatic potential. However, they have a strong tendency to invade locally and recur. They constitute 3% of all soft tissue tumors and 0.03% of all neoplasms. Abdominal DTs occur sporadically or are associated with certain familial syndromes, such as familial adenomatous polyposis (FAP). The single form of this neoplasm most frequently occurs in females of reproductive age and during pregnancy. A female patient with a DT of the abdominal wall who had no relevant family history was admitted to hospital. The patient, who presented with a painless mass in the left anterolateral abdomen, had no history of trauma, surgery or childbearing. According to the medical history, physical examination and CT report, the patient was diagnosed with DT. Radical resection of the affected abdominal wall musculature was performed, and the defect was replaced with a polypropylene mesh. The histological diagnosis was of DT. The patient remains in good health and complete remission without any other treatment following surgery. DTs exhibit aggressive growth and have a high rate of recurrence. Surgery is the optimal treatment, and subsequent radiotherapy may decrease the local recurrence rate. Further research into their aetiology is required combined with multicentre clinical trials of new treatments in order to improve management of this disease. This case report provides general knowledge of DT, and may be used as a guidance for diagnosis and treatment. PMID:23833679

Experimental study on distributed optical fiber-based approach monitoring saturation line in levee engineering

NASA Astrophysics Data System (ADS)

Su, Huaizhi; Li, Hao; Kang, Yeyuan; Wen, Zhiping

2018-02-01

Seepage is one of key factors which affect the levee engineering safety. The seepage danger without timely detection and rapid response may likely lead to severe accidents such as seepage failure, slope instability, and even levee break. More than 90 percent of levee break events are caused by the seepage. It is very important for seepage behavior identification to determine accurately saturation line in levee engineering. Furthermore, the location of saturation line has a major impact on slope stability in levee engineering. Considering the structure characteristics and service condition of levee engineering, the distributed optical fiber sensing technology is introduced to implement the real-time observation of saturation line in levee engineering. The distributed optical fiber temperature sensor system (DTS)-based monitoring principle of saturation line in levee engineering is investigated. An experimental platform, which consists of DTS, heating system, water-supply system, auxiliary analysis system and levee model, is designed and constructed. The monitoring experiment of saturation line in levee model is implemented on this platform. According to the experimental results, the numerical relationship between moisture content and thermal conductivity in porous medium is identified. A line heat source-based distributed optical fiber method obtaining the thermal conductivity in porous medium is developed. A DTS-based approach is proposed to monitor the saturation line in levee engineering. The embedment pattern of optical fiber for monitoring saturation line is presented.

The role of distress intolerance for panic and nicotine withdrawal symptoms during a biological challenge.

PubMed

Farris, Samantha G; Zvolensky, Michael J; Otto, Michael W; Leyro, Teresa M

2015-07-01

Distress intolerance is linked to the maintenance of panic disorder and cigarette smoking, and may underlie both problems. Smokers (n = 54; 40.7% panic disorder) were recruited for an experimental study; half were randomly assigned to 12-hour nicotine deprivation and half smoked as usual. The current investigation consisted of secondary, exploratory analyses from this larger experimental study. Four distress intolerance indices were examined as predictors of anxious responding to an emotional elicitation task (10% carbon dioxide (CO2)-enriched air challenge); anxious responding was in turn examined as a predictor of post-challenge panic and nicotine withdrawal symptoms. The Distress Tolerance Scale (DTS) was significantly negatively associated with anxious responding to the challenge (β = -0.41, p = 0.017). The DTS was negatively associated with post-challenge increases nicotine withdrawal symptoms indirectly through the effect of anxious responding to the challenge (b = -0.485, CI95% (-1.095, -0.033)). This same indirect effect was found for post-challenge severity of panic symptoms (b = -0.515, CI95% (-0.888, -0.208)). The DTS was directly predictive of post-challenge increases nicotine withdrawal symptoms, in the opposite direction (β = 0.37, p = 0.009), but not panic symptom severity. Anxious responding in response to stressful experiences may explain the impact of perceived distress intolerance on panic and nicotine withdrawal symptom expression. © The Author(s) 2015.

Uninjured trees - a meaningful guide to white-pine weevil control decisions

Treesearch

William E. Waters

1962-01-01

The white-pine weevil, Pissodes strobi, is a particularly insidious forest pest that can render a stand of host trees virtually worthless. It rarely, if ever, kills a tree; but the crooks, forks, and internal defects that develop in attacked trees over a period of years may reduce the merchantable volume and value of the tree at harvest age to zero. Dollar losses are...

Compensatory value of urban trees in the United States

Treesearch

David J. Nowak; Daniel E. Crane; John F. Dwyer

2002-01-01

Understanding the value of an urban forest can give decision makers a better foundation for urban tree namagement. Based on tree-valuation methods of the Council of Tree and Landscape Appraisers and field data from eight cities, total compensatory value of tree populations in U.S. cities ranges from $101 million in Jersey City, New Jersey, to $6.2 billion in New York,...

A P2P Botnet detection scheme based on decision tree and adaptive multilayer neural networks.

PubMed

Alauthaman, Mohammad; Aslam, Nauman; Zhang, Li; Alasem, Rafe; Hossain, M A

2018-01-01

In recent years, Botnets have been adopted as a popular method to carry and spread many malicious codes on the Internet. These malicious codes pave the way to execute many fraudulent activities including spam mail, distributed denial-of-service attacks and click fraud. While many Botnets are set up using centralized communication architecture, the peer-to-peer (P2P) Botnets can adopt a decentralized architecture using an overlay network for exchanging command and control data making their detection even more difficult. This work presents a method of P2P Bot detection based on an adaptive multilayer feed-forward neural network in cooperation with decision trees. A classification and regression tree is applied as a feature selection technique to select relevant features. With these features, a multilayer feed-forward neural network training model is created using a resilient back-propagation learning algorithm. A comparison of feature set selection based on the decision tree, principal component analysis and the ReliefF algorithm indicated that the neural network model with features selection based on decision tree has a better identification accuracy along with lower rates of false positives. The usefulness of the proposed approach is demonstrated by conducting experiments on real network traffic datasets. In these experiments, an average detection rate of 99.08 % with false positive rate of 0.75 % was observed.

Prognostic Factors and Decision Tree for Long-term Survival in Metastatic Uveal Melanoma.

PubMed

Lorenzo, Daniel; Ochoa, María; Piulats, Josep Maria; Gutiérrez, Cristina; Arias, Luis; Català, Jaum; Grau, María; Peñafiel, Judith; Cobos, Estefanía; Garcia-Bru, Pere; Rubio, Marcos Javier; Padrón-Pérez, Noel; Dias, Bruno; Pera, Joan; Caminal, Josep Maria

2017-12-04

The purpose of this study was to demonstrate the existence of a bimodal survival pattern in metastatic uveal melanoma. Secondary aims were to identify the characteristics and prognostic factors associated with long-term survival and to develop a clinical decision tree. The medical records of 99 metastatic uveal melanoma patients were retrospectively reviewed. Patients were classified as either short (≤ 12 months) or long-term survivors (> 12 months) based on a graphical interpretation of the survival curve after diagnosis of the first metastatic lesion. Ophthalmic and oncological characteristics were assessed in both groups. Of the 99 patients, 62 (62.6%) were classified as short-term survivors, and 37 (37.4%) as long-term survivors. The multivariate analysis identified the following predictors of long-term survival: age ≤ 65 years (p=0.012) and unaltered serum lactate dehydrogenase levels (p=0.018); additionally, the size (smaller vs. larger) of the largest liver metastasis showed a trend towards significance (p=0.063). Based on the variables significantly associated with long-term survival, we developed a decision tree to facilitate clinical decision-making. The findings of this study demonstrate the existence of a bimodal survival pattern in patients with metastatic uveal melanoma. The presence of certain clinical characteristics at diagnosis of distant disease is associated with long-term survival. A decision tree was developed to facilitate clinical decision-making and to counsel patients about the expected course of disease.

Test Reviews: Euler, B. L. (2007). "Emotional Disturbance Decision Tree". Lutz, FL: Psychological Assessment Resources

ERIC Educational Resources Information Center

Tansy, Michael

2009-01-01

The Emotional Disturbance Decision Tree (EDDT) is a teacher-completed norm-referenced rating scale published by Psychological Assessment Resources, Inc., in Lutz, Florida. The 156-item EDDT was developed for use as part of a broader assessment process to screen and assist in the identification of 5- to 18-year-old children for the special…

Phytotechnology Technical and Regulatory Guidance Document

DTIC Science & Technology

2001-04-01

contaminated media is rather new. Throughout the development process of this document, we referred to the science as “ phytoremediation .” Recently...the media containing contaminants, we now refer to “phytotechnologies” as the overarching terminology, while using “ phytoremediation ” more...publication of the ITRC document, Phytoremediation Decision Tree. The decision tree was designed to allow potential users to take basic information

Classifying dysmorphic syndromes by using artificial neural network based hierarchical decision tree.

PubMed

Özdemir, Merve Erkınay; Telatar, Ziya; Eroğul, Osman; Tunca, Yusuf

2018-05-01

Dysmorphic syndromes have different facial malformations. These malformations are significant to an early diagnosis of dysmorphic syndromes and contain distinctive information for face recognition. In this study we define the certain features of each syndrome by considering facial malformations and classify Fragile X, Hurler, Prader Willi, Down, Wolf Hirschhorn syndromes and healthy groups automatically. The reference points are marked on the face images and ratios between the points' distances are taken into consideration as features. We suggest a neural network based hierarchical decision tree structure in order to classify the syndrome types. We also implement k-nearest neighbor (k-NN) and artificial neural network (ANN) classifiers to compare classification accuracy with our hierarchical decision tree. The classification accuracy is 50, 73 and 86.7% with k-NN, ANN and hierarchical decision tree methods, respectively. Then, the same images are shown to a clinical expert who achieve a recognition rate of 46.7%. We develop an efficient system to recognize different syndrome types automatically in a simple, non-invasive imaging data, which is independent from the patient's age, sex and race at high accuracy. The promising results indicate that our method can be used for pre-diagnosis of the dysmorphic syndromes by clinical experts.

Applying decision tree for identification of a low risk population for type 2 diabetes. Tehran Lipid and Glucose Study.

PubMed

Ramezankhani, Azra; Pournik, Omid; Shahrabi, Jamal; Khalili, Davood; Azizi, Fereidoun; Hadaegh, Farzad

2014-09-01

The aim of this study was to create a prediction model using data mining approach to identify low risk individuals for incidence of type 2 diabetes, using the Tehran Lipid and Glucose Study (TLGS) database. For a 6647 population without diabetes, aged ≥20 years, followed for 12 years, a prediction model was developed using classification by the decision tree technique. Seven hundred and twenty-nine (11%) diabetes cases occurred during the follow-up. Predictor variables were selected from demographic characteristics, smoking status, medical and drug history and laboratory measures. We developed the predictive models by decision tree using 60 input variables and one output variable. The overall classification accuracy was 90.5%, with 31.1% sensitivity, 97.9% specificity; and for the subjects without diabetes, precision and f-measure were 92% and 0.95, respectively. The identified variables included fasting plasma glucose, body mass index, triglycerides, mean arterial blood pressure, family history of diabetes, educational level and job status. In conclusion, decision tree analysis, using routine demographic, clinical, anthropometric and laboratory measurements, created a simple tool to predict individuals at low risk for type 2 diabetes. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Intelligent Diagnostic Assistant for Complicated Skin Diseases through C5's Algorithm.

PubMed

Jeddi, Fatemeh Rangraz; Arabfard, Masoud; Kermany, Zahra Arab

2017-09-01

Intelligent Diagnostic Assistant can be used for complicated diagnosis of skin diseases, which are among the most common causes of disability. The aim of this study was to design and implement a computerized intelligent diagnostic assistant for complicated skin diseases through C5's Algorithm. An applied-developmental study was done in 2015. Knowledge base was developed based on interviews with dermatologists through questionnaires and checklists. Knowledge representation was obtained from the train data in the database using Excel Microsoft Office. Clementine Software and C5's Algorithms were applied to draw the decision tree. Analysis of test accuracy was performed based on rules extracted using inference chains. The rules extracted from the decision tree were entered into the CLIPS programming environment and the intelligent diagnostic assistant was designed then. The rules were defined using forward chaining inference technique and were entered into Clips programming environment as RULE. The accuracy and error rates obtained in the training phase from the decision tree were 99.56% and 0.44%, respectively. The accuracy of the decision tree was 98% and the error was 2% in the test phase. Intelligent diagnostic assistant can be used as a reliable system with high accuracy, sensitivity, specificity, and agreement.

Data mining for multiagent rules, strategies, and fuzzy decision tree structure

NASA Astrophysics Data System (ADS)

Smith, James F., III; Rhyne, Robert D., II; Fisher, Kristin

2002-03-01

A fuzzy logic based resource manager (RM) has been developed that automatically allocates electronic attack resources in real-time over many dissimilar platforms. Two different data mining algorithms have been developed to determine rules, strategies, and fuzzy decision tree structure. The first data mining algorithm uses a genetic algorithm as a data mining function and is called from an electronic game. The game allows a human expert to play against the resource manager in a simulated battlespace with each of the defending platforms being exclusively directed by the fuzzy resource manager and the attacking platforms being controlled by the human expert or operating autonomously under their own logic. This approach automates the data mining problem. The game automatically creates a database reflecting the domain expert's knowledge. It calls a data mining function, a genetic algorithm, for data mining of the database as required and allows easy evaluation of the information mined in the second step. The criterion for re- optimization is discussed as well as experimental results. Then a second data mining algorithm that uses a genetic program as a data mining function is introduced to automatically discover fuzzy decision tree structures. Finally, a fuzzy decision tree generated through this process is discussed.

Prevalence and Determinants of Preterm Birth in Tehran, Iran: A Comparison between Logistic Regression and Decision Tree Methods.

PubMed

Amini, Payam; Maroufizadeh, Saman; Samani, Reza Omani; Hamidi, Omid; Sepidarkish, Mahdi

2017-06-01

Preterm birth (PTB) is a leading cause of neonatal death and the second biggest cause of death in children under five years of age. The objective of this study was to determine the prevalence of PTB and its associated factors using logistic regression and decision tree classification methods. This cross-sectional study was conducted on 4,415 pregnant women in Tehran, Iran, from July 6-21, 2015. Data were collected by a researcher-developed questionnaire through interviews with mothers and review of their medical records. To evaluate the accuracy of the logistic regression and decision tree methods, several indices such as sensitivity, specificity, and the area under the curve were used. The PTB rate was 5.5% in this study. The logistic regression outperformed the decision tree for the classification of PTB based on risk factors. Logistic regression showed that multiple pregnancies, mothers with preeclampsia, and those who conceived with assisted reproductive technology had an increased risk for PTB ( p < 0.05). Identifying and training mothers at risk as well as improving prenatal care may reduce the PTB rate. We also recommend that statisticians utilize the logistic regression model for the classification of risk groups for PTB.

Challenges in Ecohydrological Monitoring at Soil-Vegetation Interfaces: Exploiting the Potential for Fibre Optic Technologies

NASA Astrophysics Data System (ADS)

Chalari, A.; Ciocca, F.; Krause, S.; Hannah, D. M.; Blaen, P.; Coleman, T. I.; Mondanos, M.

2015-12-01

The Birmingham Institute of Forestry Research (BIFoR) is using Free-Air Carbon Enrichment (FACE) experiments to quantify the long-term impact and resilience of forests into rising atmospheric CO2 concentrations. The FACE campaign critically relies on a successful monitoring and understanding of the large variety of ecohydrological processes occurring across many interfaces, from deep soil to above the tree canopy. At the land-atmosphere interface, soil moisture and temperature are key variables to determine the heat and water exchanges, crucial to the vegetation dynamics as well as to groundwater recharge. Traditional solutions for monitoring soil moisture and temperature such as remote techniques and point sensors show limitations in fast acquisition rates and spatial coverage, respectively. Hence, spatial patterns and temporal dynamics of heat and water fluxes at this interface can only be monitored to a certain degree, limiting deeper knowledge in dynamically evolving systems (e.g. in impact of growing vegetation). Fibre optics Distributed Temperature Sensors (DTS) can measure soil temperatures at high spatiotemporal resolutions and accuracy, along kilometers of optical cable buried in the soil. Heat pulse methods applied to electrical elements embedded in the optical cable can be used to obtain the soil moisture. In July 2015 a monitoring system based on DTS has been installed in a recently forested hillslope at BIFoR in order to quantify high-resolution spatial patterns and high-frequency temporal dynamics of soil heat fluxes and soil moisture conditions. Therefore, 1500m of optical cables have been carefully deployed in three overlapped loops at 0.05m, 0.25m and 0.4m from the soil surface and an electrical system to send heat pulses along the optical cable has been developed. This paper discussed both, installation and design details along with first results of the soil moisture and temperature monitoring carried out since July 2015. Moreover, interpretations of the collected data to investigate the impact on soil moisture dynamics of i) forest evolution (long timescale), (ii) seasonality and, (iii) high-frequency forcing, are discussed.

Decision tree and PCA-based fault diagnosis of rotating machinery

NASA Astrophysics Data System (ADS)

Sun, Weixiang; Chen, Jin; Li, Jiaqing

2007-04-01

After analysing the flaws of conventional fault diagnosis methods, data mining technology is introduced to fault diagnosis field, and a new method based on C4.5 decision tree and principal component analysis (PCA) is proposed. In this method, PCA is used to reduce features after data collection, preprocessing and feature extraction. Then, C4.5 is trained by using the samples to generate a decision tree model with diagnosis knowledge. At last the tree model is used to make diagnosis analysis. To validate the method proposed, six kinds of running states (normal or without any defect, unbalance, rotor radial rub, oil whirl, shaft crack and a simultaneous state of unbalance and radial rub), are simulated on Bently Rotor Kit RK4 to test C4.5 and PCA-based method and back-propagation neural network (BPNN). The result shows that C4.5 and PCA-based diagnosis method has higher accuracy and needs less training time than BPNN.

Decision Tree based Prediction and Rule Induction for Groundwater Trichloroethene (TCE) Pollution Vulnerability

NASA Astrophysics Data System (ADS)

Park, J.; Yoo, K.

2013-12-01

For groundwater resource conservation, it is important to accurately assess groundwater pollution sensitivity or vulnerability. In this work, we attempted to use data mining approach to assess groundwater pollution vulnerability in a TCE (trichloroethylene) contaminated Korean industrial site. The conventional DRASTIC method failed to describe TCE sensitivity data with a poor correlation with hydrogeological properties. Among the different data mining methods such as Artificial Neural Network (ANN), Multiple Logistic Regression (MLR), Case Base Reasoning (CBR), and Decision Tree (DT), the accuracy and consistency of Decision Tree (DT) was the best. According to the following tree analyses with the optimal DT model, the failure of the conventional DRASTIC method in fitting with TCE sensitivity data may be due to the use of inaccurate weight values of hydrogeological parameters for the study site. These findings provide a proof of concept that DT based data mining approach can be used in predicting and rule induction of groundwater TCE sensitivity without pre-existing information on weights of hydrogeological properties.

The application of data mining techniques to oral cancer prognosis.

PubMed

Tseng, Wan-Ting; Chiang, Wei-Fan; Liu, Shyun-Yeu; Roan, Jinsheng; Lin, Chun-Nan

2015-05-01

This study adopted an integrated procedure that combines the clustering and classification features of data mining technology to determine the differences between the symptoms shown in past cases where patients died from or survived oral cancer. Two data mining tools, namely decision tree and artificial neural network, were used to analyze the historical cases of oral cancer, and their performance was compared with that of logistic regression, the popular statistical analysis tool. Both decision tree and artificial neural network models showed superiority to the traditional statistical model. However, as to clinician, the trees created by the decision tree models are relatively easier to interpret compared to that of the artificial neural network models. Cluster analysis also discovers that those stage 4 patients whose also possess the following four characteristics are having an extremely low survival rate: pN is N2b, level of RLNM is level I-III, AJCC-T is T4, and cells mutate situation (G) is moderate.

Cesarean section after abdominal mesh repair for pregnancy-related desmoid tumor: a case report

PubMed Central

Ooi, Sara; Ngo, Harry

2017-01-01

We report the case of a 32-year-old gravida 2 para 1 woman with a background of partially resected desmoid tumor (DT) arising from the previous cesarean section (CS) scar. This case details the management of her DT by surgical resection and mesh repair and second pregnancy following this. Pregnancy-related DTs are a relatively rare entity, and there is a paucity of literature regarding their management during pregnancy. There are only five reported cases of DTs arising from CS scars. To our knowledge, this is the only report to illustrate that subsequent CS is possible after desmoid resection and abdominal mesh repair. It provides evidence that CS can be safely accomplished following abdominal wall reconstructions and further arguments against elective lower segment CS. PMID:28744163

Cesarean section after abdominal mesh repair for pregnancy-related desmoid tumor: a case report.

PubMed

Ooi, Sara; Ngo, Harry

2017-01-01

We report the case of a 32-year-old gravida 2 para 1 woman with a background of partially resected desmoid tumor (DT) arising from the previous cesarean section (CS) scar. This case details the management of her DT by surgical resection and mesh repair and second pregnancy following this. Pregnancy-related DTs are a relatively rare entity, and there is a paucity of literature regarding their management during pregnancy. There are only five reported cases of DTs arising from CS scars. To our knowledge, this is the only report to illustrate that subsequent CS is possible after desmoid resection and abdominal mesh repair. It provides evidence that CS can be safely accomplished following abdominal wall reconstructions and further arguments against elective lower segment CS.

A Geophysical Characterization & Monitoring Strategy for Determining Hydrologic Processes in the Hyporheic Corridor at the Hanford 300-Area

DOE Office of Scientific and Technical Information (OSTI.GOV)

Slater, Lee; Day-Lewis, Frederick; Lane, John

2011-08-31

The primary objective of this research was to advance the prediction of solute transport between the Uranium contaminated Hanford aquifer and the Columbia River at the Hanford 300 Area by improving understanding of how fluctuations in river stage, combined with subsurface heterogeneity, impart spatiotemporal complexity to solute exchange along the Columbia River corridor. Our work explored the use of continuous waterborne electrical imaging (CWEI), in conjunction with fiber-optic distributed temperature sensor (FO-DTS) and time-lapse resistivity monitoring, to improve the conceptual model for how groundwater/surface water exchange regulates uranium transport. We also investigated how resistivity and induced polarization can be usedmore » to generate spatially rich estimates of the variation in depth to the Hanford-Ringold (H-R) contact between the river and the 300 Area Integrated Field Research Challenge (IFRC) site. Inversion of the CWEI datasets (a data rich survey containing {approx}60,000 measurements) provided predictions of the distributions of electrical resistivity and polarizability, from which the spatial complexity of the primary hydrogeologic units along the river corridor was reconstructed. Variation in the depth to the interface between the overlying coarse-grained, high permeability Hanford Formation and the underlying finer-grained, less permeable Ringold Formation, an important contact that limits vertical migration of contaminants, has been resolved along {approx}3 km of the river corridor centered on the IFRC site in the Hanford 300 Area. Spatial variability in the thickness of the Hanford Formation captured in the CWEI datasets indicates that previous studies based on borehole projections and drive-point and multi-level sampling likely overestimate the contributing area for uranium exchange within the Columbia River at the Hanford 300 Area. Resistivity and induced polarization imaging between the river and the 300 Area IFRC further imaged spatial variability in the depth to the Hanford-Ringold inland over a critical region where borehole information is absent, identifying evidence for a continuous depression in the H-R contact between the IFRC and the river corridor. Strong natural contrasts in temperature and specific conductance of river water compared to groundwater at this site, along with periodic river stage fluctuations driven by dam operations, were exploited to yield new insights into the dynamics of groundwater-surface water interaction. Whereas FO-DTS datasets have provided meter-scale measurements of focused groundwater discharge at the riverbed along the corridor, continuous resistivity monitoring has non-invasively imaged spatiotemporal variation in the resistivity inland driven by river stage fluctuations. Time series and time-frequency analysis of FO-DTS and 3D resistivity datasets has provided insights into the role of forcing variables, primarily daily dam operations, in regulating the occurrence of focused exchange at the riverbed and its extension inland. High amplitudes in the DTS and 3D resistivity signals for long periods that dominate the stage time series identify regions along the corridor where stage-driven exchange is preferentially focused. Our work has demonstrated how time-series analysis of both time-lapse resistivity and DTS datasets, in conjunction with resistivity/IP imaging of lithology, can improve understanding of groundwater-surface water exchange along river corridors, offering unique opportunities to connect stage-driven groundwater discharge observed with DTS on the riverbed to stage-driven groundwater and solute fluctuations captured with resistivity inland.« less

FINAL PROJECT REPORT: A Geophysical Characterization & Monitoring Strategy for Determining Hydrologic Processes in the Hyporheic Corridor at the Hanford 300-Area

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee Slater

2011-08-15

The primary objective of this research was to advance the prediction of solute transport between the Uranium contaminated Hanford aquifer and the Columbia River at the Hanford 300 Area by improving understanding of how fluctuations in river stage, combined with subsurface heterogeneity, impart spatiotemporal complexity to solute exchange along the Columbia River corridor. Our work explored the use of continuous waterborne electrical imaging (CWEI), in conjunction with fiber-optic distributed temperature sensor (FO-DTS) and time-lapse resistivity monitoring, to improve the conceptual model for how groundwater/surface water exchange regulates uranium transport. We also investigated how resistivity and induced polarization can be usedmore » to generate spatially rich estimates of the variation in depth to the Hanford-Ringold (H-R) contact between the river and the 300 Area Integrated Field Research Challenge (IFRC) site. Inversion of the CWEI datasets (a data rich survey containing ~60,000 measurements) provided predictions of the distributions of electrical resistivity and polarizability, from which the spatial complexity of the primary hydrogeologic units along the river corridor was reconstructed. Variation in the depth to the interface between the overlying coarse-grained, high permeability Hanford Formation and the underlying finer-grained, less permeable Ringold Formation, an important contact that limits vertical migration of contaminants, has been resolved along ~3 km of the river corridor centered on the IFRC site in the Hanford 300 Area. Spatial variability in the thickness of the Hanford Formation captured in the CWEI datasets indicates that previous studies based on borehole projections and drive-point and multi-level sampling likely overestimate the contributing area for uranium exchange within the Columbia River at the Hanford 300 Area. Resistivity and induced polarization imaging between the river and the 300 Area IFRC further imaged spatial variability in the depth to the Hanford-Ringold inland over a critical region where borehole information is absent, identifying evidence for a continuous depression in the H-R contact between the IFRC and the river corridor. Strong natural contrasts in temperature and specific conductance of river water compared to groundwater at this site, along with periodic river stage fluctuations driven by dam operations, were exploited to yield new insights into the dynamics of groundwater-surface water interaction. Whereas FO-DTS datasets have provided meter-scale measurements of focused groundwater discharge at the riverbed along the corridor, continuous resistivity monitoring has non-invasively imaged spatiotemporal variation in the resistivity inland driven by river stage fluctuations. Time series and time-frequency analysis of FO-DTS and 3D resistivity datasets has provided insights into the role of forcing variables, primarily daily dam operations, in regulating the occurrence of focused exchange at the riverbed and its extension inland. High amplitudes in the DTS and 3D resistivity signals for long periods that dominate the stage time series identify regions along the corridor where stage-driven exchange is preferentially focused. Our work has demonstrated how time-series analysis of both time-lapse resistivity and DTS datasets, in conjunction with resistivity/IP imaging of lithology, can improve understanding of groundwater-surface water exchange along river corridors, offering unique opportunities to connect stage-driven groundwater discharge observed with DTS on the riverbed to stage-driven groundwater and solute fluctuations captured with resistivity inland.« less

The Evolution of DNA-Templated Synthesis as a Tool for Materials Discovery.

PubMed

O'Reilly, Rachel K; Turberfield, Andrew J; Wilks, Thomas R

2017-10-17

Precise control over reactivity and molecular structure is a fundamental goal of the chemical sciences. Billions of years of evolution by natural selection have resulted in chemical systems capable of information storage, self-replication, catalysis, capture and production of light, and even cognition. In all these cases, control over molecular structure is required to achieve a particular function: without structural control, function may be impaired, unpredictable, or impossible. The search for molecules with a desired function is often achieved by synthesizing a combinatorial library, which contains many or all possible combinations of a set of chemical building blocks (BBs), and then screening this library to identify "successful" structures. The largest libraries made by conventional synthesis are currently of the order of 10 8 distinct molecules. To put this in context, there are 10 13 ways of arranging the 21 proteinogenic amino acids in chains up to 10 units long. Given that we know that a number of these compounds have potent biological activity, it would be highly desirable to be able to search them all to identify leads for new drug molecules. Large libraries of oligonucleotides can be synthesized combinatorially and translated into peptides using systems based on biological replication such as mRNA display, with selected molecules identified by DNA sequencing; but these methods are limited to BBs that are compatible with cellular machinery. In order to search the vast tracts of chemical space beyond nucleic acids and natural peptides, an alternative approach is required. DNA-templated synthesis (DTS) could enable us to meet this challenge. DTS controls chemical product formation by using the specificity of DNA hybridization to bring selected reactants into close proximity, and is capable of the programmed synthesis of many distinct products in the same reaction vessel. By making use of dynamic, programmable DNA processes, it is possible to engineer a system that can translate instructions coded as a sequence of DNA bases into a chemical structure-a process analogous to the action of the ribosome in living organisms but with the potential to create a much more chemically diverse set of products. It is also possible to ensure that each product molecule is tagged with its identifying DNA sequence. Compound libraries synthesized in this way can be exposed to selection against suitable targets, enriching successful molecules. The encoding DNA can then be amplified using the polymerase chain reaction and decoded by DNA sequencing. More importantly, the DNA instruction sequences can be mutated and reused during multiple rounds of amplification, translation, and selection. In other words, DTS could be used as the foundation for a system of synthetic molecular evolution, which could allow us to efficiently search a vast chemical space. This has huge potential to revolutionize materials discovery-imagine being able to evolve molecules for light harvesting, or catalysts for CO 2 fixation. The field of DTS has developed to the point where a wide variety of reactions can be performed on a DNA template. Complex architectures and autonomous "DNA robots" have been implemented for the controlled assembly of BBs, and these mechanisms have in turn enabled the one-pot synthesis of large combinatorial libraries. Indeed, DTS libraries are being exploited by pharmaceutical companies and have already found their way into drug lead discovery programs. This Account explores the processes involved in DTS and highlights the challenges that remain in creating a general system for molecular discovery by evolution.

The Evolution of DNA-Templated Synthesis as a Tool for Materials Discovery

PubMed Central

2017-01-01

Conspectus Precise control over reactivity and molecular structure is a fundamental goal of the chemical sciences. Billions of years of evolution by natural selection have resulted in chemical systems capable of information storage, self-replication, catalysis, capture and production of light, and even cognition. In all these cases, control over molecular structure is required to achieve a particular function: without structural control, function may be impaired, unpredictable, or impossible. The search for molecules with a desired function is often achieved by synthesizing a combinatorial library, which contains many or all possible combinations of a set of chemical building blocks (BBs), and then screening this library to identify “successful” structures. The largest libraries made by conventional synthesis are currently of the order of 108 distinct molecules. To put this in context, there are 1013 ways of arranging the 21 proteinogenic amino acids in chains up to 10 units long. Given that we know that a number of these compounds have potent biological activity, it would be highly desirable to be able to search them all to identify leads for new drug molecules. Large libraries of oligonucleotides can be synthesized combinatorially and translated into peptides using systems based on biological replication such as mRNA display, with selected molecules identified by DNA sequencing; but these methods are limited to BBs that are compatible with cellular machinery. In order to search the vast tracts of chemical space beyond nucleic acids and natural peptides, an alternative approach is required. DNA-templated synthesis (DTS) could enable us to meet this challenge. DTS controls chemical product formation by using the specificity of DNA hybridization to bring selected reactants into close proximity, and is capable of the programmed synthesis of many distinct products in the same reaction vessel. By making use of dynamic, programmable DNA processes, it is possible to engineer a system that can translate instructions coded as a sequence of DNA bases into a chemical structure—a process analogous to the action of the ribosome in living organisms but with the potential to create a much more chemically diverse set of products. It is also possible to ensure that each product molecule is tagged with its identifying DNA sequence. Compound libraries synthesized in this way can be exposed to selection against suitable targets, enriching successful molecules. The encoding DNA can then be amplified using the polymerase chain reaction and decoded by DNA sequencing. More importantly, the DNA instruction sequences can be mutated and reused during multiple rounds of amplification, translation, and selection. In other words, DTS could be used as the foundation for a system of synthetic molecular evolution, which could allow us to efficiently search a vast chemical space. This has huge potential to revolutionize materials discovery—imagine being able to evolve molecules for light harvesting, or catalysts for CO2 fixation. The field of DTS has developed to the point where a wide variety of reactions can be performed on a DNA template. Complex architectures and autonomous “DNA robots” have been implemented for the controlled assembly of BBs, and these mechanisms have in turn enabled the one-pot synthesis of large combinatorial libraries. Indeed, DTS libraries are being exploited by pharmaceutical companies and have already found their way into drug lead discovery programs. This Account explores the processes involved in DTS and highlights the challenges that remain in creating a general system for molecular discovery by evolution. PMID:28915003

Machine Learning Through Signature Trees. Applications to Human Speech.

ERIC Educational Resources Information Center

White, George M.

A signature tree is a binary decision tree used to classify unknown patterns. An attempt was made to develop a computer program for manipulating signature trees as a general research tool for exploring machine learning and pattern recognition. The program was applied to the problem of speech recognition to test its effectiveness for a specific…

Modeling individual tree survial

Treesearch

Quang V. Cao

2016-01-01

Information provided by growth and yield models is the basis for forest managers to make decisions on how to manage their forests. Among different types of growth models, whole-stand models offer predictions at stand level, whereas individual-tree models give detailed information at tree level. The well-known logistic regression is commonly used to predict tree...

Predicting Tillage Patterns in the Tiffin River Watershed Using Remote Sensing Methods

NASA Astrophysics Data System (ADS)

Brooks, C.; McCarty, J. L.; Dean, D. B.; Mann, B. F.

2012-12-01

Previous research in tillage mapping has focused primarily on utilizing low to no-cost, moderate (30 m to 15 m) resolution satellite data. Successful data processing techniques published in the scientific literature have focused on extracting and/or classifying tillage patterns through manipulation of spectral bands. For instance, Daughtry et al. (2005) evaluated several spectral indices for crop residue cover using satellite multispectral and hyperspectral data and to categorize soil tillage intensity in agricultural fields. A weak to moderate relationship between Landsat Thematic Mapper (TM) indices and crop residue cover was found; similar results were reported in Minnesota. Building on the findings from the scientific literature and previous work done by MTRI in the heavily agricultural Tiffin watershed of northwest Ohio and southeast Michigan, a decision tree classifier approach (also referred to as a classification tree) was used, linking several satellite data to on-the-ground tillage information in order to boost classification results. This approach included five tillage indices and derived products. A decision tree methodology enabled the development of statistically optimized (i.e., minimizing misclassification rates) classification algorithms at various desired time steps: monthly, seasonally, and annual over the 2006-2010 time period. Due to their flexibility, processing speed, and availability within all major remote sensing and statistical software packages, decision trees can ingest several data inputs from multiple sensors and satellite products, selecting only the bands, band ratios, indices, and products that further reduce misclassification errors. The project team created crop-specific tillage pattern classification trees whereby a training data set (~ 50% of available ground data) was created for production of the actual decision tree and a validation data set was set aside (~ 50% of available ground data) in order to assess the accuracy of the classification. A seasonal time step was used, optimizing a decision tree based on seasonal ground data for tillage patterns and satellite data and products for years 2006 through 2010. Annual crop type maps derived by the project team and the USDA Cropland Data Layer project was used an input to understand locations of corn, soybeans, wheat, etc. on a yearly basis. As previously stated, the robustness of the decision tree approach is the ability to implement various satellite data and products across temporal, spectral, and spatial resolutions, thereby improving the resulting classification and providing a reliable method that is not sensor-dependent. Tillage pattern classification from satellite imagery is not a simple task and has proven a challenge to previous researchers investigating this remote sensing topic. The team's decision tree method produced a practical, usable output within a focused project time period. Daughtry, C.S.T., Hunt Jr., E.R., Doraiswamy, P.C., McMurtrey III, J.E. 2005. Remote sensing the spatial distribution of crop residues. Agron. J. 97, 864-871.

Using decision tree models to depict primary care physicians CRC screening decision heuristics.

PubMed

Wackerbarth, Sarah B; Tarasenko, Yelena N; Curtis, Laurel A; Joyce, Jennifer M; Haist, Steven A

2007-10-01

The purpose of this study was to identify decision heuristics utilized by primary care physicians in formulating colorectal cancer screening recommendations. Qualitative research using in-depth semi-structured interviews. We interviewed 66 primary care internists and family physicians evenly drawn from academic and community practices. A majority of physicians were male, and almost all were white, non-Hispanic. Three researchers independently reviewed each transcript to determine the physician's decision criteria and developed decision trees. Final trees were developed by consensus. The constant comparative methodology was used to define the categories. Physicians were found to use 1 of 4 heuristics ("age 50," "age 50, if family history, then earlier," "age 50, if family history, then screen at age 40," or "age 50, if family history, then adjust relative to reference case") for the timing recommendation and 5 heuristics ["fecal occult blood test" (FOBT), "colonoscopy," "if not colonoscopy, then...," "FOBT and another test," and "a choice between options"] for the type decision. No connection was found between timing and screening type heuristics. We found evidence of heuristic use. Further research is needed to determine the potential impact on quality of care.

Semi-Automated Approach for Mapping Urban Trees from Integrated Aerial LiDAR Point Cloud and Digital Imagery Datasets

NASA Astrophysics Data System (ADS)

Dogon-Yaro, M. A.; Kumar, P.; Rahman, A. Abdul; Buyuksalih, G.

2016-09-01

Mapping of trees plays an important role in modern urban spatial data management, as many benefits and applications inherit from this detailed up-to-date data sources. Timely and accurate acquisition of information on the condition of urban trees serves as a tool for decision makers to better appreciate urban ecosystems and their numerous values which are critical to building up strategies for sustainable development. The conventional techniques used for extracting trees include ground surveying and interpretation of the aerial photography. However, these techniques are associated with some constraints, such as labour intensive field work and a lot of financial requirement which can be overcome by means of integrated LiDAR and digital image datasets. Compared to predominant studies on trees extraction mainly in purely forested areas, this study concentrates on urban areas, which have a high structural complexity with a multitude of different objects. This paper presented a workflow about semi-automated approach for extracting urban trees from integrated processing of airborne based LiDAR point cloud and multispectral digital image datasets over Istanbul city of Turkey. The paper reveals that the integrated datasets is a suitable technology and viable source of information for urban trees management. As a conclusion, therefore, the extracted information provides a snapshot about location, composition and extent of trees in the study area useful to city planners and other decision makers in order to understand how much canopy cover exists, identify new planting, removal, or reforestation opportunities and what locations have the greatest need or potential to maximize benefits of return on investment. It can also help track trends or changes to the urban trees over time and inform future management decisions.

Decision-Tree Program

NASA Technical Reports Server (NTRS)

Buntine, Wray

1994-01-01

IND computer program introduces Bayesian and Markov/maximum-likelihood (MML) methods and more-sophisticated methods of searching in growing trees. Produces more-accurate class-probability estimates important in applications like diagnosis. Provides range of features and styles with convenience for casual user, fine-tuning for advanced user or for those interested in research. Consists of four basic kinds of routines: data-manipulation, tree-generation, tree-testing, and tree-display. Written in C language.

Interpretable Categorization of Heterogeneous Time Series Data

NASA Technical Reports Server (NTRS)

Lee, Ritchie; Kochenderfer, Mykel J.; Mengshoel, Ole J.; Silbermann, Joshua

2017-01-01

We analyze data from simulated aircraft encounters to validate and inform the development of a prototype aircraft collision avoidance system. The high-dimensional and heterogeneous time series dataset is analyzed to discover properties of near mid-air collisions (NMACs) and categorize the NMAC encounters. Domain experts use these properties to better organize and understand NMAC occurrences. Existing solutions either are not capable of handling high-dimensional and heterogeneous time series datasets or do not provide explanations that are interpretable by a domain expert. The latter is critical to the acceptance and deployment of safety-critical systems. To address this gap, we propose grammar-based decision trees along with a learning algorithm. Our approach extends decision trees with a grammar framework for classifying heterogeneous time series data. A context-free grammar is used to derive decision expressions that are interpretable, application-specific, and support heterogeneous data types. In addition to classification, we show how grammar-based decision trees can also be used for categorization, which is a combination of clustering and generating interpretable explanations for each cluster. We apply grammar-based decision trees to a simulated aircraft encounter dataset and evaluate the performance of four variants of our learning algorithm. The best algorithm is used to analyze and categorize near mid-air collisions in the aircraft encounter dataset. We describe each discovered category in detail and discuss its relevance to aircraft collision avoidance.

Application of data mining techniques to explore predictors of upper urinary tract damage in patients with neurogenic bladder.

PubMed

Fang, H; Lu, B; Wang, X; Zheng, L; Sun, K; Cai, W

2017-08-17

This study proposed a decision tree model to screen upper urinary tract damage (UUTD) for patients with neurogenic bladder (NGB). Thirty-four NGB patients with UUTD were recruited in the case group, while 78 without UUTD were included in the control group. A decision tree method, classification and regression tree (CART), was then applied to develop the model in which UUTD was used as a dependent variable and history of urinary tract infections, bladder management, conservative treatment, and urodynamic findings were used as independent variables. The urethra function factor was found to be the primary screening information of patients and treated as the root node of the tree; Pabd max (maximum abdominal pressure, >14 cmH2O), Pves max (maximum intravesical pressure, ≤89 cmH2O), and gender (female) were also variables associated with UUTD. The accuracy of the proposed model was 84.8%, and the area under curve was 0.901 (95%CI=0.844-0.958), suggesting that the decision tree model might provide a new and convenient way to screen UUTD for NGB patients in both undeveloped and developing areas.

Graphic Representations as Tools for Decision Making.

ERIC Educational Resources Information Center

Howard, Judith

2001-01-01

Focuses on the use of graphic representations to enable students to improve their decision making skills in the social studies. Explores three visual aids used in assisting students with decision making: (1) the force field; (2) the decision tree; and (3) the decision making grid. (CMK)

The Effect of Defense R&D Expenditures on Military Capability and Technological Spillover

DTIC Science & Technology

2013-03-01

ix List of Figures Page Figure 1. Decision Tree for Sectoring R&D Units...approach, often called sectoring , categorizes R&D activities by funding source, and the functional approach categorizes R&D activities by their objective...economic objectives (defense, and control and care of environment) (OECD, 2002). Figure 1 shows the decision tree for sectoring R&D units and

Modeling flash floods in ungauged mountain catchments of China: A decision tree learning approach for parameter regionalization

NASA Astrophysics Data System (ADS)

Ragettli, S.; Zhou, J.; Wang, H.; Liu, C.

2017-12-01

Flash floods in small mountain catchments are one of the most frequent causes of loss of life and property from natural hazards in China. Hydrological models can be a useful tool for the anticipation of these events and the issuing of timely warnings. Since sub-daily streamflow information is unavailable for most small basins in China, one of the main challenges is finding appropriate parameter values for simulating flash floods in ungauged catchments. In this study, we use decision tree learning to explore parameter set transferability between different catchments. For this purpose, the physically-based, semi-distributed rainfall-runoff model PRMS-OMS is set up for 35 catchments in ten Chinese provinces. Hourly data from more than 800 storm runoff events are used to calibrate the model and evaluate the performance of parameter set transfers between catchments. For each catchment, 58 catchment attributes are extracted from several data sets available for whole China. We then use a data mining technique (decision tree learning) to identify catchment similarities that can be related to good transfer performance. Finally, we use the splitting rules of decision trees for finding suitable donor catchments for ungauged target catchments. We show that decision tree learning allows to optimally utilize the information content of available catchment descriptors and outperforms regionalization based on a conventional measure of physiographic-climatic similarity by 15%-20%. Similar performance can be achieved with a regionalization method based on spatial proximity, but decision trees offer flexible rules for selecting suitable donor catchments, not relying on the vicinity of gauged catchments. This flexibility makes the method particularly suitable for implementation in sparsely gauged environments. We evaluate the probability to detect flood events exceeding a given return period, considering measured discharge and PRMS-OMS simulated flows with regionalized parameters. Overall, the probability of detection of an event with a return period of 10 years is 62%. 44% of all 10-year flood peaks can be detected with a timing error of 2 hours or less. These results indicate that the modeling system can provide useful information about the timing and magnitude of flood events at ungauged sites.

Blood oxygen level dependent magnetic resonance imaging for detecting pathological patterns in lupus nephritis patients: a preliminary study using a decision tree model.

PubMed

Shi, Huilan; Jia, Junya; Li, Dong; Wei, Li; Shang, Wenya; Zheng, Zhenfeng

2018-02-09

Precise renal histopathological diagnosis will guide therapy strategy in patients with lupus nephritis. Blood oxygen level dependent (BOLD) magnetic resonance imaging (MRI) has been applicable noninvasive technique in renal disease. This current study was performed to explore whether BOLD MRI could contribute to diagnose renal pathological pattern. Adult patients with lupus nephritis renal pathological diagnosis were recruited for this study. Renal biopsy tissues were assessed based on the lupus nephritis ISN/RPS 2003 classification. The Blood oxygen level dependent magnetic resonance imaging (BOLD-MRI) was used to obtain functional magnetic resonance parameter, R2* values. Several functions of R2* values were calculated and used to construct algorithmic models for renal pathological patterns. In addition, the algorithmic models were compared as to their diagnostic capability. Both Histopathology and BOLD MRI were used to examine a total of twelve patients. Renal pathological patterns included five classes III (including 3 as class III + V) and seven classes IV (including 4 as class IV + V). Three algorithmic models, including decision tree, line discriminant, and logistic regression, were constructed to distinguish the renal pathological pattern of class III and class IV. The sensitivity of the decision tree model was better than that of the line discriminant model (71.87% vs 59.48%, P < 0.001) and inferior to that of the Logistic regression model (71.87% vs 78.71%, P < 0.001). The specificity of decision tree model was equivalent to that of the line discriminant model (63.87% vs 63.73%, P = 0.939) and higher than that of the logistic regression model (63.87% vs 38.0%, P < 0.001). The Area under the ROC curve (AUROCC) of the decision tree model was greater than that of the line discriminant model (0.765 vs 0.629, P < 0.001) and logistic regression model (0.765 vs 0.662, P < 0.001). BOLD MRI is a useful non-invasive imaging technique for the evaluation of lupus nephritis. Decision tree models constructed using functions of R2* values may facilitate the prediction of renal pathological patterns.

A Clinical Decision Tree to Predict Whether a Bacteremic Patient Is Infected With an Extended-Spectrum β-Lactamase-Producing Organism.

PubMed

Goodman, Katherine E; Lessler, Justin; Cosgrove, Sara E; Harris, Anthony D; Lautenbach, Ebbing; Han, Jennifer H; Milstone, Aaron M; Massey, Colin J; Tamma, Pranita D

2016-10-01

Timely identification of extended-spectrum β-lactamase (ESBL) bacteremia can improve clinical outcomes while minimizing unnecessary use of broad-spectrum antibiotics, including carbapenems. However, most clinical microbiology laboratories currently require at least 24 additional hours from the time of microbial genus and species identification to confirm ESBL production. Our objective was to develop a user-friendly decision tree to predict which organisms are ESBL producing, to guide appropriate antibiotic therapy. We included patients ≥18 years of age with bacteremia due to Escherichia coli or Klebsiella species from October 2008 to March 2015 at Johns Hopkins Hospital. Isolates with ceftriaxone minimum inhibitory concentrations ≥2 µg/mL underwent ESBL confirmatory testing. Recursive partitioning was used to generate a decision tree to determine the likelihood that a bacteremic patient was infected with an ESBL producer. Discrimination of the original and cross-validated models was evaluated using receiver operating characteristic curves and by calculation of C-statistics. A total of 1288 patients with bacteremia met eligibility criteria. For 194 patients (15%), bacteremia was due to a confirmed ESBL producer. The final classification tree for predicting ESBL-positive bacteremia included 5 predictors: history of ESBL colonization/infection, chronic indwelling vascular hardware, age ≥43 years, recent hospitalization in an ESBL high-burden region, and ≥6 days of antibiotic exposure in the prior 6 months. The decision tree's positive and negative predictive values were 90.8% and 91.9%, respectively. Our findings suggest that a clinical decision tree can be used to estimate a bacteremic patient's likelihood of infection with ESBL-producing bacteria. Recursive partitioning offers a practical, user-friendly approach for addressing important diagnostic questions. © The Author 2016. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail journals.permissions@oup.com.

Effect of Resin-modified Glass Ionomer Cement Dispensing/Mixing Methods on Mechanical Properties.

PubMed

Sulaiman, T A; Abdulmajeed, A A; Altitinchi, A; Ahmed, S N; Donovan, T E

2018-03-23

Resin-modified glass ionomer cements (RMGIs) are often used for luting indirect restorations. Hand-mixing traditional cements demands significant time and may be technique sensitive. Efforts have been made by manufacturers to introduce the same cement using different dispensing/mixing methods. It is not known what effects these changes may have on the mechanical properties of the dental cement. The purpose of this study was to evaluate the mechanical properties (diametral tensile strength [DTS], compressive strength [CS], and fracture toughness [FT]) of RMGIs with different dispensing/mixing systems. The RMGI specimens (n=14)-RelyX Luting (hand mix), RelyX Luting Plus (clicker-hand mix), RelyX Luting Plus (automix) (3M ESPE), GC Fuji PLUS (capsule-automix), and GC FujiCEM 2 (automix) (GC)-were prepared for each mechanical test and examined after thermocycling (n=7/subgroup) for 20,000 cycles to the following: DTS, CS (ISO 9917-1) and FT (ISO standard 6872; Single-edge V-notched beam method). Specimens were mounted and loaded with a universal testing machine until failure occurred. Two-/one-way analysis of variance followed by Tukey honestly significantly different post hoc test was used to analyze data for statistical significance ( p<0.05). The interaction effect of both dispensing/mixing method and thermocycling was significant only for the CS test of the GC group ( p<0.05). The different dispensing/mixing methods had no effect on the DTS of the tested cements. The CS of GC Fuji PLUS was significantly higher than that of the automix version ( p<0.05). The FT decreased significantly when switching from RelyX (hand mix) to RelyX Luting Plus (clicker-hand mix) and to RelyX Luting Plus (automix) ( p<0.05). Except in the case of the DTS of the GC group and the CS of GC Fuji PLUS, thermocycling had a significant effect reducing the mechanical properties of the RMGI cements ( p<0.05). Introducing alternative dispensing/mixing methods for mixing RMGIs to reduce time and technique sensitivity may affect mechanical properties and is brand dependent.

7-Years of Using Distributed Temperature Sensing (DTS) to assess river restoration efforts : synergies of high-resolution observation and modeling on the Middle Fork of the John Day River

NASA Astrophysics Data System (ADS)

Hall, A.; Diabat, M.

2014-12-01

Temperature is a key factor for salmonid health and is an important restoration metric on the Middle Fork of the John Day River, northeast Oregon. The longest undammed tributary to the Columbia, the headwaters of the Middle Fork are crucial to steelhead and spring Chinook and summer Chinook juvenile rearing. In the past century the river has been altered by dredge mining, overgrazing, logging activities, and irrigation resulting in bank erosion, low effective shade, and channelization. These factors decreased fish habitat and led to increased stream temperature maxima. Restoration has focused on restoring fish habitat, creating thermal refugia, and planting native vegetation. The most recent completed restoration project diverted the flow into the historic, meandering stream channel from the dredged, straightened channel. Over the past seven years, Oregon State University researchers (Tara O'Donnell-2012, Julie Huff-2009) have been involved in a planned-to-be 10-year stream temperature monitoring study to assess maximum temperatures during low-flow summer months. The use of fiber optics through distributed temperature sensing (DTS) made it possible to record high resolution temperature data at both temporal and spatial scales; data which is used to assess the efficacy of restoration efforts on the reach. Furthermore, DTS provided temperature data that reveals subtle hydrologic processes such as groundwater or hyporheic inflows and quantifies their effect on the stream. Current research has focused on large scale DTS installations on the Middle Fork of the John Day River on the Oxbow, Forrest, and the upstream Galena ("RPB") conservation properties. In the summers of 2013 and 2014, 16 km of river were monitored. Our study compares temperatures before and after the restoration project and provides essential guidance for future restoration projects. Direct comparisons coupled with a deterministic modeling using HeatSource assist in better understanding the responsiveness of the stream to restoration. Results showed that reconstructing the stream channel influenced stream temperature as a function of modifying channel geometry, hydraulics, and riparian conditions. Special attention in this work is focused on the role of tributary fans in the creation of distributed cold-water emergences.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, D; Kang, S; Kim, T

Purpose: Patient breathing-related sorting method of projections in 4D digital tomosythesis (DTS) can be suffered from severe artifacts due to non-uniform angle distribution of projections and noncoplanar reconstructed images for each phase. In this study, we propose a method for optimally acquiring projection images in 4D DTS. Methods: In this method every pair of projections at x-ray tube’s gantry angles symmetrical with respect to the center of the range of gantry rotation is obtained at the same respiration amplitude. This process is challenging but becomes feasible with visual-biofeedback using a patient specific respiration guide wave which is in sinusoidal shapemore » (i.e., smooth and symmetrical enough). Depending on scan parameters such as the number of acquisition points per cycle, total scan angle and projections per acquisition amplitude, acquisition sequence is pre-determined. A simulation study for feasibility test was performed. To mimic actual situation closely, a group of volunteers were recruited and breathing data were acquired both with/without biofeedback. Then, x-ray projections for a humanoid phantom were virtually performed following (1) the breathing data from volunteers without guide, (2) the breathing data with guide and (3) the planned breathing data (i.e., ideal situation). Images from all of 3 scenarios were compared. Results: Scenario #2 showed significant artifact reduction compared to #1 while did minimal increase from the ideal situation (i.e., scenario #3). We verified the performance of the method with regard to the degree of inaccuracy during respiratory guiding. Also, the scan angle dependence-related differences in the DTS images could reduce between using the proposed method and the established patient breathing-related sorting method. Conclusion: Through the proposed 4D DTS method, it is possible to improve the accuracy of image guidance between intra/inter fractions with relatively low imaging dose. This research was supported by the Mid-career Researcher Program through NRF funded by the Ministry of Science, ICT & Future Planning of Korea (NRF-2014R1A2A1A10050270) and by the Radiation Technology R&D program through the National Research Foundation of Korea funded by the Ministry of Science, ICT & Future Planning (No. 2013M2A2A7038291)« less

Observations of the UTLS: An analysis of the double tropopause and its relationship to Rossby waves and the tropopause inversion layer

NASA Astrophysics Data System (ADS)

Peevey, Tanya

The upper troposphere lower stratosphere (UTLS) is a region of minimum temperatures that contains the tropopause. As a transition region between the troposphere and the stratosphere, the UTLS contains various processes that facilitate stratosphere-troposphere exchange (STE) which can redistribute radiatively important species such as water vapor or ozone. One potential marker for STE is the double tropopause (DT). Therefore this study seeks to further understand how DTs form and how they could enhance the current understanding of some STE processes in the UTLS. Using data from the High Resolution Dynamic Limb Sounder (HIRDLS), a data set with high vertical and horizontal resolution, newly discovered DT structures are found over the Pacific and Atlantic oceans that suggest a relationship between the DT and both storm tracks and Rossby waves. The association between DTs and storm tracks is examined by further analyzing the recently discovered and unexpected relationship between the DT and the tropopause inversion layer (TIL) in a developing baroclinic disturbance. Results show an increase in the number of DTs when the lapse rate of the extratropical TIL is less than -2°C/km, i.e. when the TIL is stronger and the local stability is higher. Composites of ERA-Interim DT profiles for three different TIL strengths shows that the vertical motion and relative vorticity both decrease as the TIL increases, which suggests the warm conveyor belt as a mechanism. This is investigated further with a case study analysis of a developing extratropical cyclone in the Pacific Ocean. Additionally, an analysis of DTs in relation to the large scale flow responsible for storm development shows a strong correlation between monthly Rossby wave activity, ozone laminae and DT variability. Further examination shows that if these waves break a DT will be found with a wave breaking event about 30% of the time in the eastern Pacific and eastern Atlantic oceans, both regions of poleward wave breaking. These results highlight a new and more complicated DT structure that is a product of both large scale dynamics and small scale vertical motions, thus adding new information to the current understanding of the UTLS.

Heat transfer fouling characteristics of microfiltered thin stillage from the dry grind process.

PubMed

Arora, Amit; Dien, Bruce S; Belyea, Ronald L; Singh, Vijay; Tumbleson, M E; Rausch, Kent D

2010-08-01

We investigated effects of microfiltration (MF) on heat transfer fouling tendencies of thin stillage. A stainless steel MF membrane (0.1 micron pore size) was used to remove solids from thin stillage. At filtration conditions of 690kPa, the MF process effectively recovered total solids from thin stillage. Thin stillage was concentrated from 7.0% to 22.4% solids with average permeate flux rates of 180+/-30 L/m(2)/h at 75 degrees C. In retentate streams, protein and fat contents were increased from 23.5 and 16.7% db to 27.6 and 31.1% db, respectively, and ash content was reduced from 10.5% to 3.8% db. Removal of solids, protein and fat generated a microfiltration permeate (MFP) that was used as an input stream to the fouling probe system; MFP fouling tendencies were measured. An annular fouling probe was used to measure fouling tendencies of thin stillage from a commercial dry grind facility. When comparing diluted thin stillage (DTS) stream and MFP, a reduction in solids concentration was not the only reason of fouling decrement. Selective removal of protein and fat played an important role in mitigating the fouling. At t=10h, mean fouling rates of MFP were an order of magnitude lower when compared to thin stillage and diluted streams. When maximum probe temperature (200 degrees C) was reached, mean fouling rates for thin stillage, DTS and MFP were 7.1x10(-4), 4.2x10(-4) and 2.6x10(-4) m(2) degrees C/kW/min, respectively. In DTS and MFP, the induction period was prolonged by factors of 4.3 and 9.5, respectively, compared to the induction period for thin stillage fouling. Mean fouling rates were decreased by factors of 2.3 and 23.4 for DTS and MFP, respectively. Fouling of MFP took twice the time to reach a probe temperature of 200 degrees C than did thin stillage (22 h vs 10 h, respectively). A reduction in heat transfer fouling could be achieved by altering process stream composition using microfiltration. Copyright 2010 Elsevier Ltd. All rights reserved.

Impact of quercetin-induced changes in drug-metabolizing enzyme and transporter expression on the pharmacokinetics of cyclosporine in rats

PubMed Central

Liu, Yani; Luo, Xiaomei; Yang, Chunxiao; Yang, Tingyu; Zhou, Jiali; Shi, Shaojun

2016-01-01

The aim of the present study was to evaluate whether quercetin (Que) modulates the mRNA and protein expression levels of drug-metabolizing enzymes (DMEs) and drug transporters (DTs) in the small intestine and liver, and thus modifies the pharmacokinetic profile of cyclosporine (CsA) in rats. This two-part study evaluated the pharmacokinetic profiles of CsA in the presence or absence of Que (experiment I) and the involvement of DMEs and DTs (experiment II). In experiment I, 24 rats received single-dose CsA (10 mg/kg) on day 1, single-dose Que (25, 50 and 100 mg/kg/day; eight rats in each group) on days 3–8, and concomitant CsA/Que on day 9. In experiment II, the mRNA and protein expression levels of cytochrome P (CYP)3A1, CYP3A2, UDP glucuronosyltransferase family 1 member A complex locus, organic anion-transporting polypeptide (OATP)2B1, OATP1B2, P-glycoprotein, breast cancer resistance protein, and multidrug resistance-associated protein 2 in the small intestine and liver of rats were analyzed following oral administration of Que at 25, 50 and 100 mg/kg in the presence or absence of CsA (10 mg/kg) for seven consecutive days. Co-administration of Que (25,50 and 100 mg/kg) decreased the maximum serum concentration of CsA by 46, 50 and 47% in a dose-independent manner. In addition, the area under the curve to the last measurable concentration and area under the curve to infinite time were decreased, by 21 and 16%, 30 and 33%, and 33 and 34% (P<0.01), respectively. However, the mRNA and protein expression levels of the above-mentioned DMEs and DTs were inhibited by Que in a dose-dependent manner (P<0.01) to a similar extent in the small intestine and liver. It was demonstrated that Que was able to reduce the bioavailability of CsA following multiple concomitant doses in rats. Overlapping modulation of intestinal and hepatic DMEs and DTs, as well as the DME-DT interplay are potential explanations for these observations. PMID:27510982

Ensemble stump classifiers and gene expression signatures in lung cancer.

PubMed

Frey, Lewis; Edgerton, Mary; Fisher, Douglas; Levy, Shawn

2007-01-01

Microarray data sets for cancer tumor tissue generally have very few samples, each sample having thousands of probes (i.e., continuous variables). The sparsity of samples makes it difficult for machine learning techniques to discover probes relevant to the classification of tumor tissue. By combining data from different platforms (i.e., data sources), data sparsity is reduced, but this typically requires normalizing data from the different platforms, which can be non-trivial. This paper proposes a variant on the idea of ensemble learners to circumvent the need for normalization. To facilitate comprehension we build ensembles of very simple classifiers known as decision stumps--decision trees of one test each. The Ensemble Stump Classifier (ESC) identifies an mRNA signature having three probes and high accuracy for distinguishing between adenocarcinoma and squamous cell carcinoma of the lung across four data sets. In terms of accuracy, ESC outperforms a decision tree classifier on all four data sets, outperforms ensemble decision trees on three data sets, and simple stump classifiers on two data sets.

Chi-squared Automatic Interaction Detection Decision Tree Analysis of Risk Factors for Infant Anemia in Beijing, China

PubMed Central

Ye, Fang; Chen, Zhi-Hua; Chen, Jie; Liu, Fang; Zhang, Yong; Fan, Qin-Ying; Wang, Lin

2016-01-01

Background: In the past decades, studies on infant anemia have mainly focused on rural areas of China. With the increasing heterogeneity of population in recent years, available information on infant anemia is inconclusive in large cities of China, especially with comparison between native residents and floating population. This population-based cross-sectional study was implemented to determine the anemic status of infants as well as the risk factors in a representative downtown area of Beijing. Methods: As useful methods to build a predictive model, Chi-squared automatic interaction detection (CHAID) decision tree analysis and logistic regression analysis were introduced to explore risk factors of infant anemia. A total of 1091 infants aged 6–12 months together with their parents/caregivers living at Heping Avenue Subdistrict of Beijing were surveyed from January 1, 2013 to December 31, 2014. Results: The prevalence of anemia was 12.60% with a range of 3.47%–40.00% in different subgroup characteristics. The CHAID decision tree model has demonstrated multilevel interaction among risk factors through stepwise pathways to detect anemia. Besides the three predictors identified by logistic regression model including maternal anemia during pregnancy, exclusive breastfeeding in the first 6 months, and floating population, CHAID decision tree analysis also identified the fourth risk factor, the maternal educational level, with higher overall classification accuracy and larger area below the receiver operating characteristic curve. Conclusions: The infant anemic status in metropolis is complex and should be carefully considered by the basic health care practitioners. CHAID decision tree analysis has demonstrated a better performance in hierarchical analysis of population with great heterogeneity. Risk factors identified by this study might be meaningful in the early detection and prompt treatment of infant anemia in large cities. PMID:27174328

ATLAAS: an automatic decision tree-based learning algorithm for advanced image segmentation in positron emission tomography.

PubMed

Berthon, Beatrice; Marshall, Christopher; Evans, Mererid; Spezi, Emiliano

2016-07-07

Accurate and reliable tumour delineation on positron emission tomography (PET) is crucial for radiotherapy treatment planning. PET automatic segmentation (PET-AS) eliminates intra- and interobserver variability, but there is currently no consensus on the optimal method to use, as different algorithms appear to perform better for different types of tumours. This work aimed to develop a predictive segmentation model, trained to automatically select and apply the best PET-AS method, according to the tumour characteristics. ATLAAS, the automatic decision tree-based learning algorithm for advanced segmentation is based on supervised machine learning using decision trees. The model includes nine PET-AS methods and was trained on a 100 PET scans with known true contour. A decision tree was built for each PET-AS algorithm to predict its accuracy, quantified using the Dice similarity coefficient (DSC), according to the tumour volume, tumour peak to background SUV ratio and a regional texture metric. The performance of ATLAAS was evaluated for 85 PET scans obtained from fillable and printed subresolution sandwich phantoms. ATLAAS showed excellent accuracy across a wide range of phantom data and predicted the best or near-best segmentation algorithm in 93% of cases. ATLAAS outperformed all single PET-AS methods on fillable phantom data with a DSC of 0.881, while the DSC for H&N phantom data was 0.819. DSCs higher than 0.650 were achieved in all cases. ATLAAS is an advanced automatic image segmentation algorithm based on decision tree predictive modelling, which can be trained on images with known true contour, to predict the best PET-AS method when the true contour is unknown. ATLAAS provides robust and accurate image segmentation with potential applications to radiation oncology.

The risk of disabling, surgery and reoperation in Crohn's disease - A decision tree-based approach to prognosis.

PubMed

Dias, Cláudia Camila; Pereira Rodrigues, Pedro; Fernandes, Samuel; Portela, Francisco; Ministro, Paula; Martins, Diana; Sousa, Paula; Lago, Paula; Rosa, Isadora; Correia, Luis; Moura Santos, Paula; Magro, Fernando

2017-01-01

Crohn's disease (CD) is a chronic inflammatory bowel disease known to carry a high risk of disabling and many times requiring surgical interventions. This article describes a decision-tree based approach that defines the CD patients' risk or undergoing disabling events, surgical interventions and reoperations, based on clinical and demographic variables. This multicentric study involved 1547 CD patients retrospectively enrolled and divided into two cohorts: a derivation one (80%) and a validation one (20%). Decision trees were built upon applying the CHAIRT algorithm for the selection of variables. Three-level decision trees were built for the risk of disabling and reoperation, whereas the risk of surgery was described in a two-level one. A receiver operating characteristic (ROC) analysis was performed, and the area under the curves (AUC) Was higher than 70% for all outcomes. The defined risk cut-off values show usefulness for the assessed outcomes: risk levels above 75% for disabling had an odds test positivity of 4.06 [3.50-4.71], whereas risk levels below 34% and 19% excluded surgery and reoperation with an odds test negativity of 0.15 [0.09-0.25] and 0.50 [0.24-1.01], respectively. Overall, patients with B2 or B3 phenotype had a higher proportion of disabling disease and surgery, while patients with later introduction of pharmacological therapeutic (1 months after initial surgery) had a higher proportion of reoperation. The decision-tree based approach used in this study, with demographic and clinical variables, has shown to be a valid and useful approach to depict such risks of disabling, surgery and reoperation.

Chi-squared Automatic Interaction Detection Decision Tree Analysis of Risk Factors for Infant Anemia in Beijing, China.

PubMed

Ye, Fang; Chen, Zhi-Hua; Chen, Jie; Liu, Fang; Zhang, Yong; Fan, Qin-Ying; Wang, Lin

2016-05-20

In the past decades, studies on infant anemia have mainly focused on rural areas of China. With the increasing heterogeneity of population in recent years, available information on infant anemia is inconclusive in large cities of China, especially with comparison between native residents and floating population. This population-based cross-sectional study was implemented to determine the anemic status of infants as well as the risk factors in a representative downtown area of Beijing. As useful methods to build a predictive model, Chi-squared automatic interaction detection (CHAID) decision tree analysis and logistic regression analysis were introduced to explore risk factors of infant anemia. A total of 1091 infants aged 6-12 months together with their parents/caregivers living at Heping Avenue Subdistrict of Beijing were surveyed from January 1, 2013 to December 31, 2014. The prevalence of anemia was 12.60% with a range of 3.47%-40.00% in different subgroup characteristics. The CHAID decision tree model has demonstrated multilevel interaction among risk factors through stepwise pathways to detect anemia. Besides the three predictors identified by logistic regression model including maternal anemia during pregnancy, exclusive breastfeeding in the first 6 months, and floating population, CHAID decision tree analysis also identified the fourth risk factor, the maternal educational level, with higher overall classification accuracy and larger area below the receiver operating characteristic curve. The infant anemic status in metropolis is complex and should be carefully considered by the basic health care practitioners. CHAID decision tree analysis has demonstrated a better performance in hierarchical analysis of population with great heterogeneity. Risk factors identified by this study might be meaningful in the early detection and prompt treatment of infant anemia in large cities.

ATLAAS: an automatic decision tree-based learning algorithm for advanced image segmentation in positron emission tomography

NASA Astrophysics Data System (ADS)

Berthon, Beatrice; Marshall, Christopher; Evans, Mererid; Spezi, Emiliano

2016-07-01

Accurate and reliable tumour delineation on positron emission tomography (PET) is crucial for radiotherapy treatment planning. PET automatic segmentation (PET-AS) eliminates intra- and interobserver variability, but there is currently no consensus on the optimal method to use, as different algorithms appear to perform better for different types of tumours. This work aimed to develop a predictive segmentation model, trained to automatically select and apply the best PET-AS method, according to the tumour characteristics. ATLAAS, the automatic decision tree-based learning algorithm for advanced segmentation is based on supervised machine learning using decision trees. The model includes nine PET-AS methods and was trained on a 100 PET scans with known true contour. A decision tree was built for each PET-AS algorithm to predict its accuracy, quantified using the Dice similarity coefficient (DSC), according to the tumour volume, tumour peak to background SUV ratio and a regional texture metric. The performance of ATLAAS was evaluated for 85 PET scans obtained from fillable and printed subresolution sandwich phantoms. ATLAAS showed excellent accuracy across a wide range of phantom data and predicted the best or near-best segmentation algorithm in 93% of cases. ATLAAS outperformed all single PET-AS methods on fillable phantom data with a DSC of 0.881, while the DSC for H&N phantom data was 0.819. DSCs higher than 0.650 were achieved in all cases. ATLAAS is an advanced automatic image segmentation algorithm based on decision tree predictive modelling, which can be trained on images with known true contour, to predict the best PET-AS method when the true contour is unknown. ATLAAS provides robust and accurate image segmentation with potential applications to radiation oncology.

Trees Are Terrific!

ERIC Educational Resources Information Center

Braus, Judy, Ed.

1992-01-01

Ranger Rick's NatureScope is a creative education series dedicated to inspiring in children an understanding and appreciation of the natural world while developing the skills they will need to make responsible decisions about the environment. Contents are organized into the following sections: (1) "What Makes a Tree a Tree?," including…

A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran.

PubMed

Khosravi, Khabat; Pham, Binh Thai; Chapi, Kamran; Shirzadi, Ataollah; Shahabi, Himan; Revhaug, Inge; Prakash, Indra; Tien Bui, Dieu

2018-06-15

Floods are one of the most damaging natural hazards causing huge loss of property, infrastructure and lives. Prediction of occurrence of flash flood locations is very difficult due to sudden change in climatic condition and manmade factors. However, prior identification of flood susceptible areas can be done with the help of machine learning techniques for proper timely management of flood hazards. In this study, we tested four decision trees based machine learning models namely Logistic Model Trees (LMT), Reduced Error Pruning Trees (REPT), Naïve Bayes Trees (NBT), and Alternating Decision Trees (ADT) for flash flood susceptibility mapping at the Haraz Watershed in the northern part of Iran. For this, a spatial database was constructed with 201 present and past flood locations and eleven flood-influencing factors namely ground slope, altitude, curvature, Stream Power Index (SPI), Topographic Wetness Index (TWI), land use, rainfall, river density, distance from river, lithology, and Normalized Difference Vegetation Index (NDVI). Statistical evaluation measures, the Receiver Operating Characteristic (ROC) curve, and Freidman and Wilcoxon signed-rank tests were used to validate and compare the prediction capability of the models. Results show that the ADT model has the highest prediction capability for flash flood susceptibility assessment, followed by the NBT, the LMT, and the REPT, respectively. These techniques have proven successful in quickly determining flood susceptible areas. Copyright © 2018 Elsevier B.V. All rights reserved.

Finding structure in data using multivariate tree boosting

PubMed Central

Miller, Patrick J.; Lubke, Gitta H.; McArtor, Daniel B.; Bergeman, C. S.

2016-01-01

Technology and collaboration enable dramatic increases in the size of psychological and psychiatric data collections, but finding structure in these large data sets with many collected variables is challenging. Decision tree ensembles such as random forests (Strobl, Malley, & Tutz, 2009) are a useful tool for finding structure, but are difficult to interpret with multiple outcome variables which are often of interest in psychology. To find and interpret structure in data sets with multiple outcomes and many predictors (possibly exceeding the sample size), we introduce a multivariate extension to a decision tree ensemble method called gradient boosted regression trees (Friedman, 2001). Our extension, multivariate tree boosting, is a method for nonparametric regression that is useful for identifying important predictors, detecting predictors with nonlinear effects and interactions without specification of such effects, and for identifying predictors that cause two or more outcome variables to covary. We provide the R package ‘mvtboost’ to estimate, tune, and interpret the resulting model, which extends the implementation of univariate boosting in the R package ‘gbm’ (Ridgeway et al., 2015) to continuous, multivariate outcomes. To illustrate the approach, we analyze predictors of psychological well-being (Ryff & Keyes, 1995). Simulations verify that our approach identifies predictors with nonlinear effects and achieves high prediction accuracy, exceeding or matching the performance of (penalized) multivariate multiple regression and multivariate decision trees over a wide range of conditions. PMID:27918183

Tools of the Future: How Decision Tree Analysis Will Impact Mission Planning

NASA Technical Reports Server (NTRS)

Otterstatter, Matthew R.

2005-01-01

The universe is infinitely complex; however, the human mind has a finite capacity. The multitude of possible variables, metrics, and procedures in mission planning are far too many to address exhaustively. This is unfortunate because, in general, considering more possibilities leads to more accurate and more powerful results. To compensate, we can get more insightful results by employing our greatest tool, the computer. The power of the computer will be utilized through a technology that considers every possibility, decision tree analysis. Although decision trees have been used in many other fields, this is innovative for space mission planning. Because this is a new strategy, no existing software is able to completely accommodate all of the requirements. This was determined through extensive research and testing of current technologies. It was necessary to create original software, for which a short-term model was finished this summer. The model was built into Microsoft Excel to take advantage of the familiar graphical interface for user input, computation, and viewing output. Macros were written to automate the process of tree construction, optimization, and presentation. The results are useful and promising. If this tool is successfully implemented in mission planning, our reliance on old-fashioned heuristics, an error-prone shortcut for handling complexity, will be reduced. The computer algorithms involved in decision trees will revolutionize mission planning. The planning will be faster and smarter, leading to optimized missions with the potential for more valuable data.

The use of decision trees and naïve Bayes algorithms and trace element patterns for controlling the authenticity of free-range-pastured hens' eggs.

PubMed

Barbosa, Rommel Melgaço; Nacano, Letícia Ramos; Freitas, Rodolfo; Batista, Bruno Lemos; Barbosa, Fernando

2014-09-01

This article aims to evaluate 2 machine learning algorithms, decision trees and naïve Bayes (NB), for egg classification (free-range eggs compared with battery eggs). The database used for the study consisted of 15 chemical elements (As, Ba, Cd, Co, Cs, Cu, Fe, Mg, Mn, Mo, Pb, Se, Sr, V, and Zn) determined in 52 eggs samples (20 free-range and 32 battery eggs) by inductively coupled plasma mass spectrometry. Our results demonstrated that decision trees and NB associated with the mineral contents of eggs provide a high level of accuracy (above 80% and 90%, respectively) for classification between free-range and battery eggs and can be used as an alternative method for adulteration evaluation. © 2014 Institute of Food Technologists®

Fiber Optic Distributed Temperature Sensing of Recharge Basin Percolation Dynamics

NASA Astrophysics Data System (ADS)

Becker, M.; Allen, E. M.; Hutchinson, A.

2014-12-01

Infiltration (spreading) basins are a central component of managed aquifer and recovery operations around the world. The concept is simple. Water is percolated into an aquifer where it can be withdrawn at a later date. However, managing infiltration basins can be complicated by entrapped air in sediments, strata of low permeability, clogging of the recharge surface, and biological growth, among other factors. Understanding the dynamics of percolation in light of these complicating factors provides a basis for making management decisions that increase recharge efficiency. As an aid to understanding percolation dynamics, fiber optic distribute temperature sensing (DTS) was used to track heat as a tracer of water movement in an infiltration basin. The diurnal variation of temperature in the basin was sensed at depth. The time lag between the oscillating temperature signal at the surface and at depth indicated the velocity of water percolation. DTS fiber optic cables were installed horizontally along the basin and vertically in boreholes to measure percolation behavior. The horizontal cable was installed in trenches at 0.3 and 1 m depth, and the vertical cable was installed using direct push technology. The vertical cable was tightly wound to produce a factor of 10 increase in spatial resolution of temperature measurements. Temperature was thus measured every meter across the basin and every 10 cm to a depth of 10 m. Data from the trenched cable suggested homogeneous percolation across the basin, but infiltration rates were a function of stage indicating non-ideal percolation. Vertical temperature monitoring showed significant lateral flow in sediments underlying the basin both during saturation and operation of the basin. Deflections in the vertical temperature profile corresponded with fine grained layers identified in core samples indicating a transient perched water table condition. The three-dimensional flow in this relatively homogenous surficial geology calls into question the relevance of simple wetting models for predicting percolation behavior in infiltration basins.

Pollution mitigation and carbon sequestration by an urban forest.

PubMed

Brack, C L

2002-01-01

At the beginning of the 1900s, the Canberra plain was largely treeless. Graziers had carried out extensive clearing of the original trees since the 1820s leaving only scattered remnants and some plantings near homesteads. With the selection of Canberra as the site for the new capital of Australia, extensive tree plantings began in 1911. These trees have delivered a number of benefits, including aesthetic values and the amelioration of climatic extremes. Recently, however, it was considered that the benefits might extend to pollution mitigation and the sequestration of carbon. This paper outlines a case study of the value of the Canberra urban forest with particular reference to pollution mitigation. This study uses a tree inventory, modelling and decision support system developed to collect and use data about trees for tree asset management. The decision support system (DISMUT) was developed to assist in the management of about 400,000 trees planted in Canberra. The size of trees during the 5-year Kyoto Commitment Period was estimated using DISMUT and multiplied by estimates of value per square meter of canopy derived from available literature. The planted trees are estimated to have a combined energy reduction, pollution mitigation and carbon sequestration value of US$20-67 million during the period 2008-2012.

Using real options analysis to support strategic management decisions

NASA Astrophysics Data System (ADS)

Kabaivanov, Stanimir; Markovska, Veneta; Milev, Mariyan

2013-12-01

Decision making is a complex process that requires taking into consideration multiple heterogeneous sources of uncertainty. Standard valuation and financial analysis techniques often fail to properly account for all these sources of risk as well as for all sources of additional flexibility. In this paper we explore applications of a modified binomial tree method for real options analysis (ROA) in an effort to improve decision making process. Usual cases of use of real options are analyzed with elaborate study on the applications and advantages that company management can derive from their application. A numeric results based on extending simple binomial tree approach for multiple sources of uncertainty are provided to demonstrate the improvement effects on management decisions.

Improving ensemble decision tree performance using Adaboost and Bagging

NASA Astrophysics Data System (ADS)

Hasan, Md. Rajib; Siraj, Fadzilah; Sainin, Mohd Shamrie

2015-12-01

Ensemble classifier systems are considered as one of the most promising in medical data classification and the performance of deceision tree classifier can be increased by the ensemble method as it is proven to be better than single classifiers. However, in a ensemble settings the performance depends on the selection of suitable base classifier. This research employed two prominent esemble s namely Adaboost and Bagging with base classifiers such as Random Forest, Random Tree, j48, j48grafts and Logistic Model Regression (LMT) that have been selected independently. The empirical study shows that the performance varries when different base classifiers are selected and even some places overfitting issue also been noted. The evidence shows that ensemble decision tree classfiers using Adaboost and Bagging improves the performance of selected medical data sets.

Knowledge Quality Functions for Rule Discovery

DTIC Science & Technology

1994-09-01

Managers in many organizations finding themselves in the possession of large and rapidly growing databases are beginning to suspect the information in their...missing values (Smyth and Goodman, 1992, p. 303). Decision trees "tend to grow very large for realistic applications and are thus difficult to interpret...by humans" (Holsheimer, 1994, p. 42). Decision trees also grow excessively complicated in the presence of noisy databases (Dhar and Tuzhilin, 1993, p

Structural Equation Model Trees

ERIC Educational Resources Information Center

Brandmaier, Andreas M.; von Oertzen, Timo; McArdle, John J.; Lindenberger, Ulman

2013-01-01

In the behavioral and social sciences, structural equation models (SEMs) have become widely accepted as a modeling tool for the relation between latent and observed variables. SEMs can be seen as a unification of several multivariate analysis techniques. SEM Trees combine the strengths of SEMs and the decision tree paradigm by building tree…

a Rough Set Decision Tree Based Mlp-Cnn for Very High Resolution Remotely Sensed Image Classification

NASA Astrophysics Data System (ADS)

Zhang, C.; Pan, X.; Zhang, S. Q.; Li, H. P.; Atkinson, P. M.

2017-09-01

Recent advances in remote sensing have witnessed a great amount of very high resolution (VHR) images acquired at sub-metre spatial resolution. These VHR remotely sensed data has post enormous challenges in processing, analysing and classifying them effectively due to the high spatial complexity and heterogeneity. Although many computer-aid classification methods that based on machine learning approaches have been developed over the past decades, most of them are developed toward pixel level spectral differentiation, e.g. Multi-Layer Perceptron (MLP), which are unable to exploit abundant spatial details within VHR images. This paper introduced a rough set model as a general framework to objectively characterize the uncertainty in CNN classification results, and further partition them into correctness and incorrectness on the map. The correct classification regions of CNN were trusted and maintained, whereas the misclassification areas were reclassified using a decision tree with both CNN and MLP. The effectiveness of the proposed rough set decision tree based MLP-CNN was tested using an urban area at Bournemouth, United Kingdom. The MLP-CNN, well capturing the complementarity between CNN and MLP through the rough set based decision tree, achieved the best classification performance both visually and numerically. Therefore, this research paves the way to achieve fully automatic and effective VHR image classification.

A retrospective analysis to identify the factors affecting infection in patients undergoing chemotherapy.

PubMed

Park, Ji Hyun; Kim, Hyeon-Young; Lee, Hanna; Yun, Eun Kyoung

2015-12-01

This study compares the performance of the logistic regression and decision tree analysis methods for assessing the risk factors for infection in cancer patients undergoing chemotherapy. The subjects were 732 cancer patients who were receiving chemotherapy at K university hospital in Seoul, Korea. The data were collected between March 2011 and February 2013 and were processed for descriptive analysis, logistic regression and decision tree analysis using the IBM SPSS Statistics 19 and Modeler 15.1 programs. The most common risk factors for infection in cancer patients receiving chemotherapy were identified as alkylating agents, vinca alkaloid and underlying diabetes mellitus. The logistic regression explained 66.7% of the variation in the data in terms of sensitivity and 88.9% in terms of specificity. The decision tree analysis accounted for 55.0% of the variation in the data in terms of sensitivity and 89.0% in terms of specificity. As for the overall classification accuracy, the logistic regression explained 88.0% and the decision tree analysis explained 87.2%. The logistic regression analysis showed a higher degree of sensitivity and classification accuracy. Therefore, logistic regression analysis is concluded to be the more effective and useful method for establishing an infection prediction model for patients undergoing chemotherapy. Copyright © 2015 Elsevier Ltd. All rights reserved.

MODIS Snow Cover Mapping Decision Tree Technique: Snow and Cloud Discrimination

NASA Technical Reports Server (NTRS)

Riggs, George A.; Hall, Dorothy K.

2010-01-01

Accurate mapping of snow cover continues to challenge cryospheric scientists and modelers. The Moderate-Resolution Imaging Spectroradiometer (MODIS) snow data products have been used since 2000 by many investigators to map and monitor snow cover extent for various applications. Users have reported on the utility of the products and also on problems encountered. Three problems or hindrances in the use of the MODIS snow data products that have been reported in the literature are: cloud obscuration, snow/cloud confusion, and snow omission errors in thin or sparse snow cover conditions. Implementation of the MODIS snow algorithm in a decision tree technique using surface reflectance input to mitigate those problems is being investigated. The objective of this work is to use a decision tree structure for the snow algorithm. This should alleviate snow/cloud confusion and omission errors and provide a snow map with classes that convey information on how snow was detected, e.g. snow under clear sky, snow tinder cloud, to enable users' flexibility in interpreting and deriving a snow map. Results of a snow cover decision tree algorithm are compared to the standard MODIS snow map and found to exhibit improved ability to alleviate snow/cloud confusion in some situations allowing up to about 5% increase in mapped snow cover extent, thus accuracy, in some scenes.

hs-CRP is strongly associated with coronary heart disease (CHD): A data mining approach using decision tree algorithm.

PubMed

Tayefi, Maryam; Tajfard, Mohammad; Saffar, Sara; Hanachi, Parichehr; Amirabadizadeh, Ali Reza; Esmaeily, Habibollah; Taghipour, Ali; Ferns, Gordon A; Moohebati, Mohsen; Ghayour-Mobarhan, Majid

2017-04-01

Coronary heart disease (CHD) is an important public health problem globally. Algorithms incorporating the assessment of clinical biomarkers together with several established traditional risk factors can help clinicians to predict CHD and support clinical decision making with respect to interventions. Decision tree (DT) is a data mining model for extracting hidden knowledge from large databases. We aimed to establish a predictive model for coronary heart disease using a decision tree algorithm. Here we used a dataset of 2346 individuals including 1159 healthy participants and 1187 participant who had undergone coronary angiography (405 participants with negative angiography and 782 participants with positive angiography). We entered 10 variables of a total 12 variables into the DT algorithm (including age, sex, FBG, TG, hs-CRP, TC, HDL, LDL, SBP and DBP). Our model could identify the associated risk factors of CHD with sensitivity, specificity, accuracy of 96%, 87%, 94% and respectively. Serum hs-CRP levels was at top of the tree in our model, following by FBG, gender and age. Our model appears to be an accurate, specific and sensitive model for identifying the presence of CHD, but will require validation in prospective studies. Copyright © 2017 Elsevier B.V. All rights reserved.

A Comparison between Decision Tree and Random Forest in Determining the Risk Factors Associated with Type 2 Diabetes.

PubMed

Esmaily, Habibollah; Tayefi, Maryam; Doosti, Hassan; Ghayour-Mobarhan, Majid; Nezami, Hossein; Amirabadizadeh, Alireza

2018-04-24

We aimed to identify the associated risk factors of type 2 diabetes mellitus (T2DM) using data mining approach, decision tree and random forest techniques using the Mashhad Stroke and Heart Atherosclerotic Disorders (MASHAD) Study program. A cross-sectional study. The MASHAD study started in 2010 and will continue until 2020. Two data mining tools, namely decision trees, and random forests, are used for predicting T2DM when some other characteristics are observed on 9528 subjects recruited from MASHAD database. This paper makes a comparison between these two models in terms of accuracy, sensitivity, specificity and the area under ROC curve. The prevalence rate of T2DM was 14% among these subjects. The decision tree model has 64.9% accuracy, 64.5% sensitivity, 66.8% specificity, and area under the ROC curve measuring 68.6%, while the random forest model has 71.1% accuracy, 71.3% sensitivity, 69.9% specificity, and area under the ROC curve measuring 77.3% respectively. The random forest model, when used with demographic, clinical, and anthropometric and biochemical measurements, can provide a simple tool to identify associated risk factors for type 2 diabetes. Such identification can substantially use for managing the health policy to reduce the number of subjects with T2DM .

Recent sheath physics studies on DIII-D

NASA Astrophysics Data System (ADS)

Watkins, J. G.; Labombard, B.; Stangeby, P. C.; Lasnier, C. J.; McLean, A. G.; Nygren, R. E.; Boedo, J. A.; Leonard, A. W.; Rudakov, D. L.

2015-08-01

A study to examine some current issues in the physics of the plasma sheath has been recently carried out in DIII-D low power Ohmic plasmas using both flush and domed Langmuir probes, divertor Thomson scattering (DTS), an infrared camera (IRTV), and a new calorimeter triple probe assembly mounted on the Divertor Materials Evaluation System (DIMES). The sheath power transmission factor was found to be consistent with the theoretically predicted value of 7 (±2) for low power plasmas. Using this factor, the three heat flux profiles derived from the LP, DTS, and calorimeter diagnostic measurements agree. Comparison of flush and domed Langmuir probes and divertor Thomson scattering indicates that proper interpretation of flush probe data to get target plate density and temperature is feasible and could potentially yield accurate measurements of target plate conditions where the probes are located.

The Development of Dispatcher Training Simulator in a Thermal Energy Generation System

NASA Astrophysics Data System (ADS)

Hakim, D. L.; Abdullah, A. G.; Mulyadi, Y.; Hasan, B.

2018-01-01

A dispatcher training simulator (DTS) is a real-time Human Machine Interface (HMI)-based control tool that is able to visualize industrial control system processes. The present study was aimed at developing a simulator tool for boilers in a thermal power station. The DTS prototype was designed using technical data of thermal power station boilers in Indonesia. It was then designed and implemented in Wonderware Intouch 10. The resulting simulator came with component drawing, animation, control display, alarm system, real-time trend, historical trend. This application used 26 tagnames and was equipped with a security system. The test showed that the principles of real-time control worked well. It is expected that this research could significantly contribute to the development of thermal power station, particularly in terms of its application as a training simulator for beginning dispatchers.

Heat Transport upon River-Water Infiltration investigated by Fiber-Optic High-Resolution Temperature Profiling

NASA Astrophysics Data System (ADS)

Vogt, T.; Schirmer, M.; Cirpka, O. A.

2010-12-01

Infiltrating river water is of high relevance for drinking water supply by river bank filtration as well as for riparian groundwater ecology. Quantifying flow patterns and velocities, however, is hampered by temporal and spatial variations of exchange fluxes. In recent years, heat has become a popular natural tracer to estimate exchange rates between rivers and groundwater. Nevertheless, field investigations are often limited by insufficient sensors spacing or simplifying assumptions such as one-dimensional flow. Our interest lies in a detailed local survey of river water infiltration at a restored river section at the losing river Thur in northeast Switzerland. Here, we measured three high-resolution temperature profiles along an assumed flow path by means of distributed temperature sensing (DTS) using fiber optic cables wrapped around poles. Moreover, piezometers were equipped with standard temperature sensors for a comparison to the DTS data. Diurnal temperature oscillations were tracked in the river bed and the riparian groundwater and analyzed by means of dynamic harmonic regression and subsequent modeling of heat transport with sinusoidal boundary conditions to quantify seepage velocities and thermal diffusivities. Compared to the standard temperature sensors, the DTS data give a higher vertical resolution, facilitating the detection of process- and structure-dependent patterns of the spatiotemporal temperature field. This advantage overcompensates the scatter in the data due to instrument noise. In particular, we could demonstrate the impact of heat conduction through the unsaturated zone on the riparian groundwater by the high resolution temperature profiles.

Systems Modeling of Ca2+ Homeostasis and Mobilization in Platelets Mediated by IP3 and Store-Operated Ca2+ Entry

PubMed Central

Dolan, Andrew T.; Diamond, Scott L.

2014-01-01

Resting platelets maintain a stable level of low cytoplasmic calcium ([Ca2+]cyt) and high dense tubular system calcium ([Ca2+]dts). During thrombosis, activators cause a transient rise in inositol trisphosphate (IP3) to trigger calcium mobilization from stores and elevation of [Ca2+]cyt. Another major source of [Ca2+]cyt elevation is store-operated calcium entry (SOCE) through plasmalemmal calcium channels that open in response to store depletion as [Ca2+]dts drops. A 34-species systems model employed kinetics describing IP3-receptor, DTS-plasmalemma puncta formation, SOCE via assembly of STIM1 and Orai1, and the plasmalemma and sarco/endoplasmic reticulum Ca2+-ATPases. Four constraints were imposed: calcium homeostasis before activation; stable in zero extracellular calcium; IP3-activatable; and functional SOCE. Using a Monte Carlo method to sample three unknown parameters and nine initial concentrations in a 12-dimensional space near measured or expected values, we found that model configurations that were responsive to stimuli and demonstrated significant SOCE required high inner membrane electric potential (>−70 mV) and low resting IP3 concentrations. The absence of puncta in resting cells was required to prevent spontaneous store depletion in calcium-free media. Ten-fold increases in IP3 caused saturated calcium mobilization. This systems model represents a critical step in being able to predict platelets’ phenotypes during hemostasis or thrombosis. PMID:24806937

Environmental justice and factors that influence participation in tree planting programs in Portland, Oregon, U.S

Treesearch

Geoffrey H. Donovan; John Mills

2014-01-01

Many cities have policies encouraging homeowners to plant trees. For these policies to be effective, it is important to understand what motivates a homeowner’s tree-planting decision. Researchers address this question by identifying variables that influence participation in a tree-planting program in Portland, Oregon, U.S. According to the study, homeowners with street...

Decision Tree Algorithm-Generated Single-Nucleotide Polymorphism Barcodes of rbcL Genes for 38 Brassicaceae Species Tagging.

PubMed

Yang, Cheng-Hong; Wu, Kuo-Chuan; Chuang, Li-Yeh; Chang, Hsueh-Wei

2018-01-01

DNA barcode sequences are accumulating in large data sets. A barcode is generally a sequence larger than 1000 base pairs and generates a computational burden. Although the DNA barcode was originally envisioned as straightforward species tags, the identification usage of barcode sequences is rarely emphasized currently. Single-nucleotide polymorphism (SNP) association studies provide us an idea that the SNPs may be the ideal target of feature selection to discriminate between different species. We hypothesize that SNP-based barcodes may be more effective than the full length of DNA barcode sequences for species discrimination. To address this issue, we tested a r ibulose diphosphate carboxylase ( rbcL ) S NP b arcoding (RSB) strategy using a decision tree algorithm. After alignment and trimming, 31 SNPs were discovered in the rbcL sequences from 38 Brassicaceae plant species. In the decision tree construction, these SNPs were computed to set up the decision rule to assign the sequences into 2 groups level by level. After algorithm processing, 37 nodes and 31 loci were required for discriminating 38 species. Finally, the sequence tags consisting of 31 rbcL SNP barcodes were identified for discriminating 38 Brassicaceae species based on the decision tree-selected SNP pattern using RSB method. Taken together, this study provides the rational that the SNP aspect of DNA barcode for rbcL gene is a useful and effective sequence for tagging 38 Brassicaceae species.

Kernel and divergence techniques in high energy physics separations

NASA Astrophysics Data System (ADS)

Bouř, Petr; Kůs, Václav; Franc, Jiří

2017-10-01

Binary decision trees under the Bayesian decision technique are used for supervised classification of high-dimensional data. We present a great potential of adaptive kernel density estimation as the nested separation method of the supervised binary divergence decision tree. Also, we provide a proof of alternative computing approach for kernel estimates utilizing Fourier transform. Further, we apply our method to Monte Carlo data set from the particle accelerator Tevatron at DØ experiment in Fermilab and provide final top-antitop signal separation results. We have achieved up to 82 % AUC while using the restricted feature selection entering the signal separation procedure.

A divide and conquer approach to cope with uncertainty, human health risk, and decision making in contaminant hydrology

NASA Astrophysics Data System (ADS)

de Barros, Felipe P. J.; Bolster, Diogo; Sanchez-Vila, Xavier; Nowak, Wolfgang

2011-05-01

Assessing health risk in hydrological systems is an interdisciplinary field. It relies on the expertise in the fields of hydrology and public health and needs powerful translation concepts to provide decision support and policy making. Reliable health risk estimates need to account for the uncertainties and variabilities present in hydrological, physiological, and human behavioral parameters. Despite significant theoretical advancements in stochastic hydrology, there is still a dire need to further propagate these concepts to practical problems and to society in general. Following a recent line of work, we use fault trees to address the task of probabilistic risk analysis and to support related decision and management problems. Fault trees allow us to decompose the assessment of health risk into individual manageable modules, thus tackling a complex system by a structural divide and conquer approach. The complexity within each module can be chosen individually according to data availability, parsimony, relative importance, and stage of analysis. Three differences are highlighted in this paper when compared to previous works: (1) The fault tree proposed here accounts for the uncertainty in both hydrological and health components, (2) system failure within the fault tree is defined in terms of risk being above a threshold value, whereas previous studies that used fault trees used auxiliary events such as exceedance of critical concentration levels, and (3) we introduce a new form of stochastic fault tree that allows us to weaken the assumption of independent subsystems that is required by a classical fault tree approach. We illustrate our concept in a simple groundwater-related setting.

Automated diagnosis of coronary artery disease based on data mining and fuzzy modeling.

PubMed

Tsipouras, Markos G; Exarchos, Themis P; Fotiadis, Dimitrios I; Kotsia, Anna P; Vakalis, Konstantinos V; Naka, Katerina K; Michalis, Lampros K

2008-07-01

A fuzzy rule-based decision support system (DSS) is presented for the diagnosis of coronary artery disease (CAD). The system is automatically generated from an initial annotated dataset, using a four stage methodology: 1) induction of a decision tree from the data; 2) extraction of a set of rules from the decision tree, in disjunctive normal form and formulation of a crisp model; 3) transformation of the crisp set of rules into a fuzzy model; and 4) optimization of the parameters of the fuzzy model. The dataset used for the DSS generation and evaluation consists of 199 subjects, each one characterized by 19 features, including demographic and history data, as well as laboratory examinations. Tenfold cross validation is employed, and the average sensitivity and specificity obtained is 62% and 54%, respectively, using the set of rules extracted from the decision tree (first and second stages), while the average sensitivity and specificity increase to 80% and 65%, respectively, when the fuzzification and optimization stages are used. The system offers several advantages since it is automatically generated, it provides CAD diagnosis based on easily and noninvasively acquired features, and is able to provide interpretation for the decisions made.

48 CFR 47.301-3 - Using the Defense Transportation System (DTS).

Code of Federal Regulations, 2010 CFR

2010-10-01

... implemented on a world-wide basis. (b) Contracting activities are responsible for (1) ensuring that the... directly to a military air or water port terminal without authorization from the designated contract...

Re-Construction of Reference Population and Generating Weights by Decision Tree

DTIC Science & Technology

2017-07-21

2017 Claflin University Orangeburg, SC 29115 DEFENSE EQUAL OPPORTUNITY MANAGEMENT INSTITUTE RESEARCH, DEVELOPMENT, AND STRATEGIC...Original Dataset 32 List of Figures in Appendix B Figure 1: Flow and Components of Project 20 Figure 2: Decision Tree 31 Figure 3: Effects of Weight...can compare the sample data. The dataset of this project has the reference population on unit level for group and gender, which is in red-dotted box

An Approach for Implementing a Microcomputer Based Report Origination System in the Ada Programming Language

DTIC Science & Technology

1983-03-01

Decision Tree -------------------- 62 4-E. PACKAGE unitrep Action/Area Selection flow Chart 82 4-7. PACKAGE unitrep Control Flow Chart...the originetor wculd manually draft simple, readable, formatted iressages using "-i predef.ined forms and decision logic trees . This alternative was...Study Analysis DATA CCNTENT ERRORS PERCENT OF ERRORS Character Type 2.1 Calcvlations/Associations 14.3 Message Identification 4.? Value Pisiratch 22.E

Method and apparatus for detecting a desired behavior in digital image data

DOEpatents

Kegelmeyer, Jr., W. Philip

1997-01-01

A method for detecting stellate lesions in digitized mammographic image data includes the steps of prestoring a plurality of reference images, calculating a plurality of features for each of the pixels of the reference images, and creating a binary decision tree from features of randomly sampled pixels from each of the reference images. Once the binary decision tree has been created, a plurality of features, preferably including an ALOE feature (analysis of local oriented edges), are calculated for each of the pixels of the digitized mammographic data. Each of these plurality of features of each pixel are input into the binary decision tree and a probability is determined, for each of the pixels, corresponding to the likelihood of the presence of a stellate lesion, to create a probability image. Finally, the probability image is spatially filtered to enforce local consensus among neighboring pixels and the spatially filtered image is output.

Method and apparatus for detecting a desired behavior in digital image data

DOEpatents

Kegelmeyer, Jr., W. Philip

1997-01-01

A method for detecting stellate lesions in digitized mammographic image data includes the steps of prestoring a plurality of reference images, calculating a plurality of features for each of the pixels of the reference images, and creating a binary decision tree from features of randomly sampled pixels from each of the reference images. Once the binary decision tree has been created, a plurality of features, preferably including an ALOE feature (analysis of local oriented edges), are calculated for each of the pixels of the digitized mammographic data. Each of these plurality of features of each pixel are input into the binary decision tree and a probability is determined, for each of the pixels, corresponding to the likelihood of the presence of a stellate lesion, to create a probability image. Finally, the probability image is spacially filtered to enforce local consensus among neighboring pixels and the spacially filtered image is output.

Identification of Potential Sources of Mercury (Hg) in Farmland Soil Using a Decision Tree Method in China.

PubMed

Zhong, Taiyang; Chen, Dongmei; Zhang, Xiuying

2016-11-09

Identification of the sources of soil mercury (Hg) on the provincial scale is helpful for enacting effective policies to prevent further contamination and take reclamation measurements. The natural and anthropogenic sources and their contributions of Hg in Chinese farmland soil were identified based on a decision tree method. The results showed that the concentrations of Hg in parent materials were most strongly associated with the general spatial distribution pattern of Hg concentration on a provincial scale. The decision tree analysis gained an 89.70% total accuracy in simulating the influence of human activities on the additions of Hg in farmland soil. Human activities-for example, the production of coke, application of fertilizers, discharge of wastewater, discharge of solid waste, and the production of non-ferrous metals-were the main external sources of a large amount of Hg in the farmland soil.

A Hybrid Approach of Stepwise Regression, Logistic Regression, Support Vector Machine, and Decision Tree for Forecasting Fraudulent Financial Statements

PubMed Central

Goo, Yeong-Jia James; Shen, Zone-De

2014-01-01

As the fraudulent financial statement of an enterprise is increasingly serious with each passing day, establishing a valid forecasting fraudulent financial statement model of an enterprise has become an important question for academic research and financial practice. After screening the important variables using the stepwise regression, the study also matches the logistic regression, support vector machine, and decision tree to construct the classification models to make a comparison. The study adopts financial and nonfinancial variables to assist in establishment of the forecasting fraudulent financial statement model. Research objects are the companies to which the fraudulent and nonfraudulent financial statement happened between years 1998 to 2012. The findings are that financial and nonfinancial information are effectively used to distinguish the fraudulent financial statement, and decision tree C5.0 has the best classification effect 85.71%. PMID:25302338

Identifying Risk and Protective Factors in Recidivist Juvenile Offenders: A Decision Tree Approach

PubMed Central

Ortega-Campos, Elena; García-García, Juan; Gil-Fenoy, Maria José; Zaldívar-Basurto, Flor

2016-01-01

Research on juvenile justice aims to identify profiles of risk and protective factors in juvenile offenders. This paper presents a study of profiles of risk factors that influence young offenders toward committing sanctionable antisocial behavior (S-ASB). Decision tree analysis is used as a multivariate approach to the phenomenon of repeated sanctionable antisocial behavior in juvenile offenders in Spain. The study sample was made up of the set of juveniles who were charged in a court case in the Juvenile Court of Almeria (Spain). The period of study of recidivism was two years from the baseline. The object of study is presented, through the implementation of a decision tree. Two profiles of risk and protective factors are found. Risk factors associated with higher rates of recidivism are antisocial peers, age at baseline S-ASB, problems in school and criminality in family members. PMID:27611313

Circum-Arctic petroleum systems identified using decision-tree chemometrics

USGS Publications Warehouse

Peters, K.E.; Ramos, L.S.; Zumberge, J.E.; Valin, Z.C.; Scotese, C.R.; Gautier, D.L.

2007-01-01

Source- and age-related biomarker and isotopic data were measured for more than 1000 crude oil samples from wells and seeps collected above approximately 55??N latitude. A unique, multitiered chemometric (multivariate statistical) decision tree was created that allowed automated classification of 31 genetically distinct circumArctic oil families based on a training set of 622 oil samples. The method, which we call decision-tree chemometrics, uses principal components analysis and multiple tiers of K-nearest neighbor and SIMCA (soft independent modeling of class analogy) models to classify and assign confidence limits for newly acquired oil samples and source rock extracts. Geochemical data for each oil sample were also used to infer the age, lithology, organic matter input, depositional environment, and identity of its source rock. These results demonstrate the value of large petroleum databases where all samples were analyzed using the same procedures and instrumentation. Copyright ?? 2007. The American Association of Petroleum Geologists. All rights reserved.

Three-dimensional object recognition using similar triangles and decision trees

NASA Technical Reports Server (NTRS)

Spirkovska, Lilly

1993-01-01

A system, TRIDEC, that is capable of distinguishing between a set of objects despite changes in the objects' positions in the input field, their size, or their rotational orientation in 3D space is described. TRIDEC combines very simple yet effective features with the classification capabilities of inductive decision tree methods. The feature vector is a list of all similar triangles defined by connecting all combinations of three pixels in a coarse coded 127 x 127 pixel input field. The classification is accomplished by building a decision tree using the information provided from a limited number of translated, scaled, and rotated samples. Simulation results are presented which show that TRIDEC achieves 94 percent recognition accuracy in the 2D invariant object recognition domain and 98 percent recognition accuracy in the 3D invariant object recognition domain after training on only a small sample of transformed views of the objects.

Identification of Potential Sources of Mercury (Hg) in Farmland Soil Using a Decision Tree Method in China

PubMed Central

Zhong, Taiyang; Chen, Dongmei; Zhang, Xiuying

2016-01-01

Identification of the sources of soil mercury (Hg) on the provincial scale is helpful for enacting effective policies to prevent further contamination and take reclamation measurements. The natural and anthropogenic sources and their contributions of Hg in Chinese farmland soil were identified based on a decision tree method. The results showed that the concentrations of Hg in parent materials were most strongly associated with the general spatial distribution pattern of Hg concentration on a provincial scale. The decision tree analysis gained an 89.70% total accuracy in simulating the influence of human activities on the additions of Hg in farmland soil. Human activities—for example, the production of coke, application of fertilizers, discharge of wastewater, discharge of solid waste, and the production of non-ferrous metals—were the main external sources of a large amount of Hg in the farmland soil. PMID:27834884

A hybrid approach of stepwise regression, logistic regression, support vector machine, and decision tree for forecasting fraudulent financial statements.

PubMed

Chen, Suduan; Goo, Yeong-Jia James; Shen, Zone-De

2014-01-01

As the fraudulent financial statement of an enterprise is increasingly serious with each passing day, establishing a valid forecasting fraudulent financial statement model of an enterprise has become an important question for academic research and financial practice. After screening the important variables using the stepwise regression, the study also matches the logistic regression, support vector machine, and decision tree to construct the classification models to make a comparison. The study adopts financial and nonfinancial variables to assist in establishment of the forecasting fraudulent financial statement model. Research objects are the companies to which the fraudulent and nonfraudulent financial statement happened between years 1998 to 2012. The findings are that financial and nonfinancial information are effectively used to distinguish the fraudulent financial statement, and decision tree C5.0 has the best classification effect 85.71%.

Tree value system: users guide.

Treesearch

J.K. Ayer Sachet; D.G. Briggs; R.D. Fight

1989-01-01

This paper instructs resource analysts on use of the Tree Value System (TREEVAL). TREEVAL is a microcomputer system of programs for calculating tree or stand values and volumes based on predicted product recovery. Designed for analyzing silvicultural decisions, the system can also be used for appraisals and for evaluating log bucking. The system calculates results...

A decision support tool for identifying abuse of controlled substances by ForwardHealth Medicaid members.

PubMed

Mailloux, Allan T; Cummings, Stephen W; Mugdh, Mrinal

2010-01-01

Our objective was to use Wisconsin's Medicaid Evaluation and Decision Support (MEDS) data warehouse to develop and validate a decision support tool (DST) that (1) identifies Wisconsin Medicaid fee-for-service recipients who are abusing controlled substances, (2) effectively replicates clinical pharmacist recommendations for interventions intended to curb abuse of physician and pharmacy services, and (3) automates data extraction, profile generation and tracking of recommendations and interventions. From pharmacist manual reviews of medication profiles, seven measures of overutilization of controlled substances were developed, including (1-2) 6-month and 2-month "shopping" scores, (3-4) 6-month and 2-month forgery scores, (5) duplicate/same day prescriptions, (6) count of controlled substance claims, and the (7) shopping 6-month score for the individual therapeutic class with the highest score. The pattern analysis logic for the measures was encoded into SQL and applied to the medication profiles of 190 recipients who had already undergone manual review. The scores for each measure and numbers of providers were analyzed by exhaustive chi-squared automatic interaction detection (CHAID) to determine significant thresholds and combinations of predictors of pharmacist recommendations, resulting in a decision tree to classify recipients by pharmacist recommendations. The overall correct classification rate of the decision tree was 95.3%, with a 2.4% false positive rate and 4.0% false negative rate for lock-in versus prescriber-alert letter recommendations. Measures used by the decision tree include the 2-month and 6-month shopping scores, and the number of pharmacies and prescribers. The number of pharmacies was the best predictor of abuse of controlled substances. When a Medicaid recipient receives prescriptions for controlled substances at 8 or more pharmacies, the likelihood of a lock-in recommendation is 90%. The availability of the Wisconsin MEDS data warehouse has enabled development and application of a decision tree for detecting recipient fraud and abuse of controlled substance medications. Using standard pharmacy claims data, the decision tree effectively replicates pharmacist manual review recommendations. The DST has automated extraction and evaluation of pharmacy claims data for creating recommendations for guiding pharmacists in the selection of profiles for manual review. The DST is now the primary method used by the Wisconsin Medicaid program to detect fraud and abuse of physician and pharmacy services committed by recipients.

A decision support system using combined-classifier for high-speed data stream in smart grid

NASA Astrophysics Data System (ADS)

Yang, Hang; Li, Peng; He, Zhian; Guo, Xiaobin; Fong, Simon; Chen, Huajun

2016-11-01

Large volume of high-speed streaming data is generated by big power grids continuously. In order to detect and avoid power grid failure, decision support systems (DSSs) are commonly adopted in power grid enterprises. Among all the decision-making algorithms, incremental decision tree is the most widely used one. In this paper, we propose a combined classifier that is a composite of a cache-based classifier (CBC) and a main tree classifier (MTC). We integrate this classifier into a stream processing engine on top of the DSS such that high-speed steaming data can be transformed into operational intelligence efficiently. Experimental results show that our proposed classifier can return more accurate answers than other existing ones.

Challenging the distributed temperature sensing technique for estimating groundwater discharge to streams through controlled artificial point source experiment

NASA Astrophysics Data System (ADS)

Lauer, F.; Frede, H.-G.; Breuer, L.

2012-04-01

Spatially confined groundwater discharge can contribute significantly to stream discharge. Distributed fibre optic temperature sensing (DTS) of stream water has been successfully used to localize- and quantify groundwater discharge from this type "point sources" (PS) in small first-order streams. During periods when stream and groundwater temperatures differ PS appear as abrupt step in longitudinal stream water temperature distribution. Based on stream temperature observation up- and downstream of a point source and estimated or measured groundwater temperature the proportion of groundwater inflow to stream discharge can be quantified using simple mixing models. However so far this method has not been quantitatively verified, nor has a detailed uncertainty analysis of the method been conducted. The relative accuracy of this method is expected to decrease nonlinear with decreasing proportions of lateral inflow. Furthermore it depends on the temperature differences (ΔT) between groundwater and surface water and on the accuracy of temperature measurement itself. The latter could be affected by different sources of errors. For example it has been shown that a direct impact of solar radiation on fibre optic cables can lead to errors in temperature measurements in small streams due to low water depth. Considerable uncertainty might also be related to the determination of groundwater temperature through direct measurements or derived from the DTS signal. In order to directly validate the method and asses it's uncertainty we performed a set of artificial point source experiments with controlled lateral inflow rates to a natural stream. The experiments were carried out at the Vollnkirchener Bach, a small head water stream in Hessen, Germany in November and December 2011 during a low flow period. A DTS system was installed along a 1.2 km sub reach of the stream. Stream discharge was measured using a gauging flume installed directly upstream of the artificial PS. Lateral inflow was simulated using a pumping system connected to a 2 m3 water tank. Pumping rates were controlled using a magnetic inductive flowmeter and kept constant for a time period of 30 minutes to 1.5 hours depending on the simulated inflow rate. Different temperatures of lateral inflow were adjusted by heating the water in the tank (for summer experiments a cooling by ice cubes could be realized). With this setup, different proportions of lateral inflow to stream flow ranging from 2 to 20%, could be simulated for different ΔT's (2-7° C) between stream- and inflowing water. Results indicate that the estimation of groundwater discharge through DTS is working properly, but that the method is very sensitive to the determination of the PS groundwater temperature. The span of adjusted ΔT and inflow rates of the artificial system are currently used to perform a thorough uncertainty analysis of the DTS method and to derive thresholds for detection limits.

Estimation of accuracy of earth-rotation parameters in different frequency bands

NASA Astrophysics Data System (ADS)

Vondrak, J.

1986-11-01

The accuracies of earth-rotation parameters as determined by five different observational techniques now available (i.e., optical astrometry /OA/, Doppler tracking of satellites /DTS/, satellite laser ranging /SLR/, very long-base interferometry /VLBI/ and lunar laser ranging /LLR/) are estimated. The differences between the individual techniques in all possible combinations, separated by appropriate filters into three frequency bands, were used to estimate the accuracies of the techniques for periods from 0 to 200 days, from 200 to 1000 days and longer than 1000 days. It is shown that for polar motion the most accurate results are obtained with VLBI anad SLR, especially in the short-period region; OA and DTS are less accurate, but with longer periods the differences in accuracy are less pronounced. The accuracies of UTI-UTC as determined by OA, VLBI and LLR are practically equivalent, the differences being less than 40 percent.

Psychometric properties of seven self-report measures of posttraumatic stress disorder in college students with mixed civilian trauma exposure.

PubMed

Adkins, Jennifer W; Weathers, Frank W; McDevitt-Murphy, Meghan; Daniels, Jennifer B

2008-12-01

In this study psychometric properties of seven self-report measures of posttraumatic stress disorder (PTSD) were compared. The seven scales evaluated were the Davidson Trauma Scale (DTS), the PTSD Checklist (PCL), the Posttraumatic Stress Diagnostic Scale (PDS), the Civilian Mississippi Scale (CMS), the Impact of Event Scale-Revised (IES-R), the Penn Inventory for Posttraumatic Stress Disorder (Penn), and the PK scale of the MMPI-2 (PK). Participants were 239 (79 male and 160 female) trauma-exposed undergraduates. All seven measures exhibited good test-retest reliability and internal consistency. The PDS, PCL and DTS demonstrated the best convergent validity; the IES-R, PDS, and PCL demonstrated the best discriminant validity; and the PDS, PCL, and IES-R demonstrated the best diagnostic utility. Overall, results most strongly support the use of the PDS and the PCL for the assessment of PTSD in this population.

L-Area STS MTR/NRU/NRX Grapple Assembly Closure Mechanics Review

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huizenga, D. J.

2016-06-08

A review of the closure mechanics associated with the Shielded Transfer System (STS) MTR/NRU/NRX grapple assembly utilized at the Savannah River Site (SRS) was performed. This review was prompted by an operational event which occurred at the Canadian Nuclear Laboratories (CNL) utilizing a DTS-XL grapple assembly which is essentially identical to the STS MTR/NRU/NRX grapple assembly used at the SRS. The CNL operational event occurred when a NRU/NRX fuel basket containing spent nuclear fuel assemblies was inadvertently released by the DTS-XL grapple assembly during a transfer. The SM review of the STS MTR/NRU/NRX grapple assembly will examine the operational aspectsmore » of the STS and the engineered features of the STS which prevent such an event at the SRS. The design requirements for the STS NRU/NRX modifications and the overall layout of the STS are provided in other documents.« less

Advanced Subspace Techniques for Modeling Channel and Session Variability in a Speaker Recognition System

DTIC Science & Technology

2012-03-01

with each SVM discriminating between a pair of the N total speakers in the data set. The (( + 1))/2 classifiers then vote on the final...classification of a test sample. The Random Forest classifier is an ensemble classifier that votes amongst decision trees generated with each node using...Forest vote , and the effects of overtraining will be mitigated by the fact that each decision tree is overtrained differently (due to the random

Using Decision Trees for Estimating Mode Choice of Trips in Buca-Izmir

NASA Astrophysics Data System (ADS)

Oral, L. O.; Tecim, V.

2013-05-01

Decision makers develop transportation plans and models for providing sustainable transport systems in urban areas. Mode Choice is one of the stages in transportation modelling. Data mining techniques can discover factors affecting the mode choice. These techniques can be applied with knowledge process approach. In this study a data mining process model is applied to determine the factors affecting the mode choice with decision trees techniques by considering individual trip behaviours from household survey data collected within Izmir Transportation Master Plan. From this perspective transport mode choice problem is solved on a case in district of Buca-Izmir, Turkey with CRISP-DM knowledge process model.

Interacting with mobile devices by fusion eye and hand gestures recognition systems based on decision tree approach

NASA Astrophysics Data System (ADS)

Elleuch, Hanene; Wali, Ali; Samet, Anis; Alimi, Adel M.

2017-03-01

Two systems of eyes and hand gestures recognition are used to control mobile devices. Based on a real-time video streaming captured from the device's camera, the first system recognizes the motion of user's eyes and the second one detects the static hand gestures. To avoid any confusion between natural and intentional movements we developed a system to fuse the decision coming from eyes and hands gesture recognition systems. The phase of fusion was based on decision tree approach. We conducted a study on 5 volunteers and the results that our system is robust and competitive.

A dynamic fault tree model of a propulsion system

NASA Technical Reports Server (NTRS)

Xu, Hong; Dugan, Joanne Bechta; Meshkat, Leila

2006-01-01

We present a dynamic fault tree model of the benchmark propulsion system, and solve it using Galileo. Dynamic fault trees (DFT) extend traditional static fault trees with special gates to model spares and other sequence dependencies. Galileo solves DFT models using a judicious combination of automatically generated Markov and Binary Decision Diagram models. Galileo easily handles the complexities exhibited by the benchmark problem. In particular, Galileo is designed to model phased mission systems.

Including public-health benefits of trees in urban-forestry decision making

Treesearch

Geoffrey H. Donovan

2017-01-01

Research demonstrating the biophysical benefits of urban trees are often used to justify investments in urban forestry. Far less emphasis, however, is placed on the non-bio-physical benefits such as improvements in public health. Indeed, the public-health benefits of trees may be significantly larger than the biophysical benefits, and, therefore, failure to account for...

Goal Programming: A New Tool for the Christmas Tree Industry

Treesearch

Bruce G. Hansen

1977-01-01

Goal programing (GP) can be useful for decision making in the natural Christmas tree industry. Its usefulness is demonstrated through an analysis of a hypothetical problem in which two potential growers decide how to use 10 acres in growing Christmas trees. Though the physical settings are identical, distinct differences between their goals significantly influence the...

Lessons learned from Applications of a Decision Tree for Confronting Climate Change Uncertainty - the Short Term and the Long Term

NASA Astrophysics Data System (ADS)

Ray, P. A.; Wi, S.; Bonzanigo, L.; Taner, M. U.; Rodriguez, D.; Garcia, L.; Brown, C.

2016-12-01

The Decision Tree for Confronting Climate Change Uncertainty is a hierarchical, staged framework for accomplishing climate change risk management in water resources system investments. Since its development for the World Bank Water Group two years ago, the framework has been applied to pilot demonstration projects in Nepal (hydropower generation), Mexico (water supply), Kenya (multipurpose reservoir operation), and Indonesia (flood risks to dam infrastructure). An important finding of the Decision Tree demonstration projects has been the need to present the risks/opportunities of climate change to stakeholders and investors in proportion to risks/opportunities and hazards of other kinds. This presentation will provide an overview of tools and techniques used to quantify risks/opportunities to each of the project types listed above, with special attention to those found most useful for exploration of the risk space. Careful exploration of the risk/opportunity space shows that some interventions would be better taken now, whereas risks/opportunities of other types would be better instituted incrementally in order to maintain reversibility and flexibility. A number of factors contribute to the robustness/flexibility tradeoff: available capital, magnitude and imminence of potential risk/opportunity, modular (or not) character of investment, and risk aversion of the decision maker, among others. Finally, in each case, nuance was required in the translation of Decision Tree findings into actionable policy recommendations. Though the narrative of stakeholder solicitation, engagement, and ultimate partnership is unique to each case, summary lessons are available from the portfolio that can serve as a guideline to the community of climate change risk managers.

Decision tree analysis of treatment strategies for mild and moderate cases of clinical mastitis occurring in early lactation.

PubMed

Pinzón-Sánchez, C; Cabrera, V E; Ruegg, P L

2011-04-01

The objective of this study was to develop a decision tree to evaluate the economic impact of different durations of intramammary treatment for the first case of mild or moderate clinical mastitis (CM) occurring in early lactation with various scenarios of pathogen distributions and use of on-farm culture. The tree included 2 decision and 3 probability events. The first decision evaluated use of on-farm culture (OFC; 2 programs using OFC and 1 not using OFC) and the second decision evaluated treatment strategies (no intramammary antimicrobials or antimicrobials administered for 2, 5, or 8 d). The tree included probabilities for the distribution of etiologies (gram-positive, gram-negative, or no growth), bacteriological cure, and recurrence. The economic consequences of mastitis included costs of diagnosis and initial treatment, additional treatments, labor, discarded milk, milk production losses due to clinical and subclinical mastitis, culling, and transmission of infection to other cows (only for CM caused by Staphylococcus aureus). Pathogen-specific estimates for bacteriological cure and milk losses were used. The economically optimal path for several scenarios was determined by comparison of expected monetary values. For most scenarios, the optimal economic strategy was to treat CM caused by gram-positive pathogens for 2 d and to avoid antimicrobials for CM cases caused by gram-negative pathogens or when no pathogen was recovered. Use of extended intramammary antimicrobial therapy (5 or 8 d) resulted in the least expected monetary values. Copyright © 2011 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Binary Decision Trees for Preoperative Periapical Cyst Screening Using Cone-beam Computed Tomography.

PubMed

Pitcher, Brandon; Alaqla, Ali; Noujeim, Marcel; Wealleans, James A; Kotsakis, Georgios; Chrepa, Vanessa

2017-03-01

Cone-beam computed tomographic (CBCT) analysis allows for 3-dimensional assessment of periradicular lesions and may facilitate preoperative periapical cyst screening. The purpose of this study was to develop and assess the predictive validity of a cyst screening method based on CBCT volumetric analysis alone or combined with designated radiologic criteria. Three independent examiners evaluated 118 presurgical CBCT scans from cases that underwent apicoectomies and had an accompanying gold standard histopathological diagnosis of either a cyst or granuloma. Lesion volume, density, and specific radiologic characteristics were assessed using specialized software. Logistic regression models with histopathological diagnosis as the dependent variable were constructed for cyst prediction, and receiver operating characteristic curves were used to assess the predictive validity of the models. A conditional inference binary decision tree based on a recursive partitioning algorithm was constructed to facilitate preoperative screening. Interobserver agreement was excellent for volume and density, but it varied from poor to good for the radiologic criteria. Volume and root displacement were strong predictors for cyst screening in all analyses. The binary decision tree classifier determined that if the volume of the lesion was >247 mm 3 , there was 80% probability of a cyst. If volume was <247 mm 3 and root displacement was present, cyst probability was 60% (78% accuracy). The good accuracy and high specificity of the decision tree classifier renders it a useful preoperative cyst screening tool that can aid in clinical decision making but not a substitute for definitive histopathological diagnosis after biopsy. Confirmatory studies are required to validate the present findings. Published by Elsevier Inc.

Rapid decision support tool based on novel ecosystem service variables for retrofitting of permeable pavement systems in the presence of trees.

PubMed

Scholz, Miklas; Uzomah, Vincent C

2013-08-01

The retrofitting of sustainable drainage systems (SuDS) such as permeable pavements is currently undertaken ad hoc using expert experience supported by minimal guidance based predominantly on hard engineering variables. There is a lack of practical decision support tools useful for a rapid assessment of the potential of ecosystem services when retrofitting permeable pavements in urban areas that either feature existing trees or should be planted with trees in the near future. Thus the aim of this paper is to develop an innovative rapid decision support tool based on novel ecosystem service variables for retrofitting of permeable pavement systems close to trees. This unique tool proposes the retrofitting of permeable pavements that obtained the highest ecosystem service score for a specific urban site enhanced by the presence of trees. This approach is based on a novel ecosystem service philosophy adapted to permeable pavements rather than on traditional engineering judgement associated with variables based on quick community and environment assessments. For an example case study area such as Greater Manchester, which was dominated by Sycamore and Common Lime, a comparison with the traditional approach of determining community and environment variables indicates that permeable pavements are generally a preferred SuDS option. Permeable pavements combined with urban trees received relatively high scores, because of their great potential impact in terms of water and air quality improvement, and flood control, respectively. The outcomes of this paper are likely to lead to more combined permeable pavement and tree systems in the urban landscape, which are beneficial for humans and the environment. Copyright © 2013 Elsevier B.V. All rights reserved.

The prediction of breast cancer biopsy outcomes using two CAD approaches that both emphasize an intelligible decision process

DOE Office of Scientific and Technical Information (OSTI.GOV)

Elter, M.; Schulz-Wendtland, R.; Wittenberg, T.

2007-11-15

Mammography is the most effective method for breast cancer screening available today. However, the low positive predictive value of breast biopsy resulting from mammogram interpretation leads to approximately 70% unnecessary biopsies with benign outcomes. To reduce the high number of unnecessary breast biopsies, several computer-aided diagnosis (CAD) systems have been proposed in the last several years. These systems help physicians in their decision to perform a breast biopsy on a suspicious lesion seen in a mammogram or to perform a short term follow-up examination instead. We present two novel CAD approaches that both emphasize an intelligible decision process to predictmore » breast biopsy outcomes from BI-RADS findings. An intelligible reasoning process is an important requirement for the acceptance of CAD systems by physicians. The first approach induces a global model based on decison-tree learning. The second approach is based on case-based reasoning and applies an entropic similarity measure. We have evaluated the performance of both CAD approaches on two large publicly available mammography reference databases using receiver operating characteristic (ROC) analysis, bootstrap sampling, and the ANOVA statistical significance test. Both approaches outperform the diagnosis decisions of the physicians. Hence, both systems have the potential to reduce the number of unnecessary breast biopsies in clinical practice. A comparison of the performance of the proposed decision tree and CBR approaches with a state of the art approach based on artificial neural networks (ANN) shows that the CBR approach performs slightly better than the ANN approach, which in turn results in slightly better performance than the decision-tree approach. The differences are statistically significant (p value <0.001). On 2100 masses extracted from the DDSM database, the CRB approach for example resulted in an area under the ROC curve of A(z)=0.89{+-}0.01, the decision-tree approach in A(z)=0.87{+-}0.01, and the ANN approach in A(z)=0.88{+-}0.01.« less

Temperature Response of a Small Mountain Stream to Thunderstorm Cloud-Cover: Application of DTS Fiber-Optic Temperature Sensing

NASA Astrophysics Data System (ADS)

Thayer, D.; Klatt, A. L.; Miller, S. N.; Ohara, N.

2014-12-01

From a hydrologic point of view, the critical zone in alpine areas contains the first interaction of living systems with water which will flow to streams and rivers that sustain lowland biomes and human civilization. A key to understanding critical zone functions is understanding the flow of energy, and we can measure temperature as a way of looking at energy transfer between related systems. In this study we installed a Distributed Temperature Sensor (DTS) and fiber-optic cable in a zero-order stream at 9,000 ft in the Medicine Bow National Forest in southern Wyoming. We measured the temperature of the stream for 17 days from June 29 to July 16; the first 12 days were mostly sunny with occasional afternoon storms, and the last 5 experienced powerful, long-lasting storms for much of the day. The DTS measurements show a seasonal warming trend of both minimum and maximum stream temperature for the first 12 days, followed by a distinct cooling trend for the five days that experienced heavy storm activity. To gain insights into the timing and mechanisms of energy flow through the critical zone systems, we analyzed the timing of stream temperature change relative to solar short-wave radiation, and compared the stream temperature temporal response to the temporal response of soil temperature adjacent to the stream. Since convective thunderstorms are a dominant summer weather pattern in sub-alpine regions in the Rocky Mountains, this study gives us further insight into interactions of critical zone processes and weather in mountain ecosystems.

Using Distributed Temperature Sensing for measuring vertical temperature profiles and air temperature variance in the roughness sublayer above a forest canopy

NASA Astrophysics Data System (ADS)

Schilperoort, B.; Coenders, M.; Savenije, H. H. G.

2017-12-01

In recent years, the accuracy and resolution of Distributed Temperature Sensing (DTS) machines has increased enough to expand its use in atmospheric sciences. With DTS the temperature of a fiber optic (FO) cable can be measured with a high frequency (1 Hz) and high resolution (0.30 m), for cable lengths up to kilometers. At our measurement site, a patch of 26 to 30 m tall Douglas Fir in mixed forest, we placed FO cables vertically along a 48 m tall flux tower. This gives a high resolution vertical temperature profile above, through, and below the canopy. By using a `bare' FO cable, with a diameter of 0.25 mm, we are able to measure variations in air temperature at a very small timescale, and are able to measure a vertical profile of the air temperature variance. The vertical temperature profiles can be used to study the formation of the stable boundary layer above and in the canopy at a high resolution. It also shows that a stable layer can develop below the canopy, which is not limited to night time conditions but also occurs during daytime. The high frequency measurements can be used to study the gradient of the variance of air temperature over the height. To study how the flux tower itself affects temperature variance measurements, the `bare' FO cable can be placed horizontally under a support structure away from the flux tower. Lastly, by using the hot-wire anemometer principle with DTS, the measurements can be expanded to also include vertical wind profile.

Integration of kerma-area product and cumulative air kerma determination into a skin dose tracking system for fluoroscopic imaging procedures

NASA Astrophysics Data System (ADS)

Vijayan, Sarath; Shankar, Alok; Rudin, Stephen; Bednarek, Daniel R.

2016-03-01

The skin dose tracking system (DTS) that we developed provides a color-coded mapping of the cumulative skin dose distribution on a 3D graphic of the patient during fluoroscopic procedures in real time. The DTS has now been modified to also calculate the kerma area product (KAP) and cumulative air kerma (CAK) for fluoroscopic interventions using data obtained in real-time from the digital bus on a Toshiba Infinix system. KAP is the integral of air kerma over the beam area and is typically measured with a large-area transmission ionization chamber incorporated into the collimator assembly. In this software, KAP is automatically determined for each x-ray pulse as the product of the air kerma/ mAs from a calibration file for the given kVp and beam filtration times the mAs per pulse times the length and width of the beam times a field nonuniformity correction factor. Field nonuniformity is primarily the result of the heel effect and the correction factor was determined from the beam profile measured using radio-chromic film. Dividing the KAP by the beam area at the interventional reference point provides the area averaged CAK. The KAP and CAK per x-ray pulse are summed after each pulse to obtain the total procedure values in real-time. The calculated KAP and CAK were compared to the values displayed by the fluoroscopy machine with excellent agreement. The DTS now is able to automatically calculate both KAP and CAK without the need for measurement by an add-on transmission ionization chamber.

Improved oral absorption of dutasteride via Soluplus®-based supersaturable self-emulsifying drug delivery system (S-SEDDS).

PubMed

Lee, Dong Hoon; Yeom, Dong Woo; Song, Ye Seul; Cho, Ha Ra; Choi, Yong Seok; Kang, Myung Joo; Choi, Young Wook

2015-01-15

A novel supersaturable self-emulsifying drug delivery system (S-SEDDS) was formulated to improve the oral absorption of dutasteride (DTS), a 5α-reductase inhibitor that is poorly water-soluble. A supersaturable system was prepared by employing Soluplus(®) (polyvinyl caprolactam-polyvinyl acetate-polyethylene glycol graft copolymer) as a precipitation inhibitor with a conventional SEDDS vehicle consisted of Capryol™ 90, Cremophor(®) EL and Transcutol(®) HP (DTS:SEDDS vehicle:Soluplus(®)=1.0:67.6:10.0 w/v/w). In an in vitro dissolution test in a non-sink condition, the drug dissolution rate from SEDDS was rapidly increased to 72% for an initial period of 5min, but underwent rapid drug precipitation within 2h, decreasing the amount of drug dissolved to one-seventh of its original amount. On the other hand, S-SEDDS resulted in a slower crystallization of DTS by virtue of a precipitation inhibitor, maintaining a 3 times greater dissolution rate after 2h compared to SEDDS. In an in vivo pharmacokinetic study in rats, the S-SEDDS formulation exhibited 3.9-fold greater area-under-curve value than that of the drug suspension and 1.3-fold greater than that of SEDDS. The maximum plasma concentration of S-SEDDS was 5.6- and 2.0-fold higher compared to drug suspension and SEDDS, respectively. The results of this study suggest that the novel supersaturable system may be a promising tool for improving the physicochemical property and oral absorption of the 5α-reductase inhibitor. Copyright © 2014 Elsevier B.V. All rights reserved.

Basic properties of calcium phosphate cement containing atelocollagen in its liquid or powder phases.

PubMed

Miyamoto, Y; Ishikawa, K; Takechi, M; Toh, T; Yuasa, T; Nagayama, M; Suzuki, K

1998-01-01

The basic properties of calcium phosphate cement (CPC) containing atelocollagen, the main component of the organic substrate in bone, were studied in an initial evaluation for the fabrication of modified CPC. The setting time of conventional CPC (c-CPC) was prolonged to over 100 min when c-CPC contained 1% or more atelocollagen. The diametral tensile strength (DTS) of c-CPC decreased linearly with the collagen content, descending to below the detection limit when the c-CPC contained 3% or more atelocollagen. Therefore, use of c-CPC as the base cement seems inappropriate for the fabrication of atelocollagen-containing CPC. In contrast, the cement set at 9-34 min when fast-setting CPC (FSCPC) was used as the base cement and contained 1-5% atelocollagen, respectively. Although addition of atelocollagen resulted in the decrease of DTS of the set mass, the DTS was approximately the same, 6-8 MPa, at contents of atelocollagen between 1% and 5%. When atelocollagen was added to FSCPC, the handling property was improved significantly. The paste also became more adhesive with increase in atelocollagen content. These properties are desirable for its use in surgical procedures since, for example, bony defects can be filled easily and without a space interposed between the bone and cement paste. Although there are some disadvantages for the addition of atelocollagen to CPC, it can be accepted as long as FSCPC was used as the base cement. We conclude that further evaluations of the effects of atelocollagen, such as biocompatibility, bone synthesis, and bone replacement behaviour should be done, using FSCPC as the base cement.

Application of Decision Tree to Obtain Optimal Operation Rules for Reservoir Flood Control Considering Sediment Desilting-Case Study of Tseng Wen Reservoir

NASA Astrophysics Data System (ADS)

ShiouWei, L.

2014-12-01

Reservoirs are the most important water resources facilities in Taiwan.However,due to the steep slope and fragile geological conditions in the mountain area,storm events usually cause serious debris flow and flood,and the flood then will flush large amount of sediment into reservoirs.The sedimentation caused by flood has great impact on the reservoirs life.Hence,how to operate a reservoir during flood events to increase the efficiency of sediment desilting without risk the reservoir safety and impact the water supply afterward is a crucial issue in Taiwan. 　Therefore,this study developed a novel optimization planning model for reservoir flood operation considering flood control and sediment desilting,and proposed easy to use operating rules represented by decision trees.The decision trees rules have considered flood mitigation,water supply and sediment desilting.The optimal planning model computes the optimal reservoir release for each flood event that minimum water supply impact and maximum sediment desilting without risk the reservoir safety.Beside the optimal flood operation planning model,this study also proposed decision tree based flood operating rules that were trained by the multiple optimal reservoir releases to synthesis flood scenarios.The synthesis flood scenarios consists of various synthesis storm events,reservoir's initial storage and target storages at the end of flood operating. 　Comparing the results operated by the decision tree operation rules(DTOR) with that by historical operation for Krosa Typhoon in 2007,the DTOR removed sediment 15.4% more than that of historical operation with reservoir storage only8.38×106m3 less than that of historical operation.For Jangmi Typhoon in 2008,the DTOR removed sediment 24.4% more than that of historical operation with reservoir storage only 7.58×106m3 less than that of historical operation.The results show that the proposed DTOR model can increase the sediment desilting efficiency and extend the reservoir life.

Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression.

PubMed

Westreich, Daniel; Lessler, Justin; Funk, Michele Jonsson

2010-08-01

Propensity scores for the analysis of observational data are typically estimated using logistic regression. Our objective in this review was to assess machine learning alternatives to logistic regression, which may accomplish the same goals but with fewer assumptions or greater accuracy. We identified alternative methods for propensity score estimation and/or classification from the public health, biostatistics, discrete mathematics, and computer science literature, and evaluated these algorithms for applicability to the problem of propensity score estimation, potential advantages over logistic regression, and ease of use. We identified four techniques as alternatives to logistic regression: neural networks, support vector machines, decision trees (classification and regression trees [CART]), and meta-classifiers (in particular, boosting). Although the assumptions of logistic regression are well understood, those assumptions are frequently ignored. All four alternatives have advantages and disadvantages compared with logistic regression. Boosting (meta-classifiers) and, to a lesser extent, decision trees (particularly CART), appear to be most promising for use in the context of propensity score analysis, but extensive simulation studies are needed to establish their utility in practice. Copyright (c) 2010 Elsevier Inc. All rights reserved.

Type 2 Diabetes Mellitus Screening and Risk Factors Using Decision Tree: Results of Data Mining.

PubMed

Habibi, Shafi; Ahmadi, Maryam; Alizadeh, Somayeh

2015-03-18

The aim of this study was to examine a predictive model using features related to the diabetes type 2 risk factors. The data were obtained from a database in a diabetes control system in Tabriz, Iran. The data included all people referred for diabetes screening between 2009 and 2011. The features considered as "Inputs" were: age, sex, systolic and diastolic blood pressure, family history of diabetes, and body mass index (BMI). Moreover, we used diagnosis as "Class". We applied the "Decision Tree" technique and "J48" algorithm in the WEKA (3.6.10 version) software to develop the model. After data preprocessing and preparation, we used 22,398 records for data mining. The model precision to identify patients was 0.717. The age factor was placed in the root node of the tree as a result of higher information gain. The ROC curve indicates the model function in identification of patients and those individuals who are healthy. The curve indicates high capability of the model, especially in identification of the healthy persons. We developed a model using the decision tree for screening T2DM which did not require laboratory tests for T2DM diagnosis.

Predicting the disease of Alzheimer with SNP biomarkers and clinical data using data mining classification approach: decision tree.

PubMed

Erdoğan, Onur; Aydin Son, Yeşim

2014-01-01

Single Nucleotide Polymorphisms (SNPs) are the most common genomic variations where only a single nucleotide differs between individuals. Individual SNPs and SNP profiles associated with diseases can be utilized as biological markers. But there is a need to determine the SNP subsets and patients' clinical data which is informative for the diagnosis. Data mining approaches have the highest potential for extracting the knowledge from genomic datasets and selecting the representative SNPs as well as most effective and informative clinical features for the clinical diagnosis of the diseases. In this study, we have applied one of the widely used data mining classification methodology: "decision tree" for associating the SNP biomarkers and significant clinical data with the Alzheimer's disease (AD), which is the most common form of "dementia". Different tree construction parameters have been compared for the optimization, and the most accurate tree for predicting the AD is presented.

Comparative Genotypes, Staphylococcal Cassette Chromosome mec (SCCmec) Genes and Antimicrobial Resistance amongst Staphylococcus epidermidis and Staphylococcus haemolyticus Isolates from Infections in Humans and Companion Animals.

PubMed

McManus, Brenda A; Coleman, David C; Deasy, Emily C; Brennan, Gráinne I; O' Connell, Brian; Monecke, Stefan; Ehricht, Ralf; Leggett, Bernadette; Leonard, Nola; Shore, Anna C

2015-01-01

This study compares the characteristics of Staphylococcus epidermidis (SE) and Staphylococcus haemolyticus (SH) isolates from epidemiologically unrelated infections in humans (Hu) (28 SE-Hu; 8 SH-Hu) and companion animals (CpA) (12 SE-CpA; 13 SH-CpA). All isolates underwent antimicrobial susceptibility testing, multilocus sequence typing and DNA microarray profiling to detect antimicrobial resistance and SCCmec-associated genes. All methicillin-resistant (MR) isolates (33/40 SE, 20/21 SH) underwent dru and mecA allele typing. Isolates were predominantly assigned to sequence types (STs) within a single clonal complex (CC2, SE, 84.8%; CC1, SH, 95.2%). SCCmec IV predominated among MRSE with ST2-MRSE-IVc common to both Hu (40.9%) and CpA (54.5%). Identical mecA alleles and nontypeable dru types (dts) were identified in one ST2-MRSE-IVc Hu and CpA isolate, however, all mecA alleles and 2/4 dts detected among 18 ST2-MRSE-IVc isolates were closely related, sharing >96.5% DNA sequence homology. Although only one ST-SCCmec type combination (ST1 with a non-typeable [NT] SCCmec NT9 [class C mec and ccrB4]) was common to four MRSH-Hu and one MRSH-CpA, all MRSH isolates were closely related based on similar STs, SCCmec genes (V/VT or components thereof), mecA alleles and dts. Overall, 39.6% of MR isolates harbored NT SCCmec elements, and ACME was more common amongst MRSE and CpA isolates. Multidrug resistance (MDR) was detected among 96.7% of isolates but they differed in the prevalence of specific macrolide, aminoglycoside and trimethoprim resistance genes amongst SE and SH isolates. Ciprofloxacin, rifampicin, chloramphenicol [fexA, cat-pC221], tetracycline [tet(K)], aminoglycosides [aadD, aphA3] and fusidic acid [fusB] resistance was significantly more common amongst CpA isolates. SE and SH isolates causing infections in Hu and CpA hosts belong predominantly to STs within a single lineage, harboring similar but variable SCCmec genes, mecA alleles and dts. Host and staphylococcal species-specific characteristics were identified in relation to antimicrobial resistance genes and phenotypes, SCCmec and ACME.

Comparative Genotypes, Staphylococcal Cassette Chromosome mec (SCCmec) Genes and Antimicrobial Resistance amongst Staphylococcus epidermidis and Staphylococcus haemolyticus Isolates from Infections in Humans and Companion Animals

PubMed Central

McManus, Brenda A.; Coleman, David C.; Deasy, Emily C.; Brennan, Gráinne I.; O’ Connell, Brian; Monecke, Stefan; Ehricht, Ralf; Leggett, Bernadette; Leonard, Nola; Shore, Anna C.

2015-01-01

This study compares the characteristics of Staphylococcus epidermidis (SE) and Staphylococcus haemolyticus (SH) isolates from epidemiologically unrelated infections in humans (Hu) (28 SE-Hu; 8 SH-Hu) and companion animals (CpA) (12 SE-CpA; 13 SH-CpA). All isolates underwent antimicrobial susceptibility testing, multilocus sequence typing and DNA microarray profiling to detect antimicrobial resistance and SCCmec-associated genes. All methicillin-resistant (MR) isolates (33/40 SE, 20/21 SH) underwent dru and mecA allele typing. Isolates were predominantly assigned to sequence types (STs) within a single clonal complex (CC2, SE, 84.8%; CC1, SH, 95.2%). SCCmec IV predominated among MRSE with ST2-MRSE-IVc common to both Hu (40.9%) and CpA (54.5%). Identical mecA alleles and nontypeable dru types (dts) were identified in one ST2-MRSE-IVc Hu and CpA isolate, however, all mecA alleles and 2/4 dts detected among 18 ST2-MRSE-IVc isolates were closely related, sharing >96.5% DNA sequence homology. Although only one ST-SCCmec type combination (ST1 with a non-typeable [NT] SCCmec NT9 [class C mec and ccrB4]) was common to four MRSH-Hu and one MRSH-CpA, all MRSH isolates were closely related based on similar STs, SCCmec genes (V/VT or components thereof), mecA alleles and dts. Overall, 39.6% of MR isolates harbored NT SCCmec elements, and ACME was more common amongst MRSE and CpA isolates. Multidrug resistance (MDR) was detected among 96.7% of isolates but they differed in the prevalence of specific macrolide, aminoglycoside and trimethoprim resistance genes amongst SE and SH isolates. Ciprofloxacin, rifampicin, chloramphenicol [fexA, cat-pC221], tetracycline [tet(K)], aminoglycosides [aadD, aphA3] and fusidic acid [fusB] resistance was significantly more common amongst CpA isolates. SE and SH isolates causing infections in Hu and CpA hosts belong predominantly to STs within a single lineage, harboring similar but variable SCCmec genes, mecA alleles and dts. Host and staphylococcal species-specific characteristics were identified in relation to antimicrobial resistance genes and phenotypes, SCCmec and ACME. PMID:26379051

Applying Fibre-Optic Distributed Temperature Sensing to Near-surface Temperature Dynamics of Broadacre Cereals During Radiant Frost Events.

NASA Astrophysics Data System (ADS)

Stutsel, B.; Callow, J. N.

2017-12-01

Radiant frost events, particularly those during the reproductive stage of winter cereal growth, cost growers millions of dollars in lost yield. Whilst synoptic drivers of frost and factors influencing temperature variation at the landscape scale are relatively well understood, there is a lack of knowledge surrounding small-scale temperature dynamics within paddocks and plot trials. Other work has also suggested a potential significant temperature gradient (several degrees) vertically from ground to canopy, but this is poorly constrained experimentally. Subtle changes in temperature are important as frost damage generally occurs in a very narrow temperature range (-2 to -5°C). Once a variety's damage threshold is reached, a 1°C difference in minimum temperature can increase damage from 10 to 90%. This study applies Distributed Temperature Sensing (DTS) using fibre optics to understand how minimum temperature evolves during a radiant frost. DTS assesses the difference in attenuation of Raman scattering of a light pulse travelling along a fibre optic cable to measure temperature. A bend insensitive multimode fibre was deployed in a double ended duplex configuration as a "fence" run through four times of sowing at a trial site in the Western Australian Wheatbelt. The fibre optic fence was 160m long and 800mm tall with the fibre optic cable spaced 100mm apart vertically, and calibrated in ambient water ( 10 to 15oC) and a chilled glycol ( -8 to-10 oC) baths. The temperature measurements had a spatial resolution of 0.65m and temporal resolution of 60s, providing 2,215 measurements every minute. The results of this study inform our understanding of the subtle temperature changes from the soil to canopy, providing new insight into how to place traditional temperature loggers to monitor frost damage. It also addresses questions of within-trial temperature variability, and provides an example of how novel techniques such as DTS can be used to improve the way temperature (frost) is incorporated in crop damage models. This data set provided by DTS allows a level of detail that is not possible to record with traditional temperature loggers and shows how this emerging technology can be applied to agricultural applications. This research was supported by the Grains Research and Development Corporation National Frost Initiative.

Pricing and reimbursement frameworks in Central Eastern Europe: a decision tool to support choices.

PubMed

Kolasa, Katarzyna; Kalo, Zoltan; Hornby, Edward

2015-02-01

Given limited financial resources in the Central Eastern European (CEE) region, challenges in obtaining access to innovative medical technologies are formidable. The objective of this research was to develop a decision tree that supports decision makers and drug manufacturers from CEE region in their search for optimal innovative pricing and reimbursement scheme (IPRSs). A systematic literature review was performed to search for published IPRSs, and then ten experts from the CEE region were interviewed to ascertain their opinions on these schemes. In total, 33 articles representing 46 unique IPRSs were analyzed. Based on our literature review and subsequent expert input, key decision nodes and branches of the decision tree were developed. The results indicate that outcome-based schemes are better suited to deal with uncertainties surrounding cost effectiveness, while non-outcome-based schemes are more appropriate for pricing and budget impact challenges.

Development and Validation of a Primary Care-Based Family Health History and Decision Support Program (MeTree)

PubMed Central

Orlando, Lori A.; Buchanan, Adam H.; Hahn, Susan E.; Christianson, Carol A.; Powell, Karen P.; Skinner, Celette Sugg; Chesnut, Blair; Blach, Colette; Due, Barbara; Ginsburg, Geoffrey S.; Henrich, Vincent C.

2016-01-01

INTRODUCTION Family health history is a strong predictor of disease risk. To reduce the morbidity and mortality of many chronic diseases, risk-stratified evidence-based guidelines strongly encourage the collection and synthesis of family health history to guide selection of primary prevention strategies. However, the collection and synthesis of such information is not well integrated into clinical practice. To address barriers to collection and use of family health histories, the Genomedical Connection developed and validated MeTree, a Web-based, patient-facing family health history collection and clinical decision support tool. MeTree is designed for integration into primary care practices as part of the genomic medicine model for primary care. METHODS We describe the guiding principles, operational characteristics, algorithm development, and coding used to develop MeTree. Validation was performed through stakeholder cognitive interviewing, a genetic counseling pilot program, and clinical practice pilot programs in 2 community-based primary care clinics. RESULTS Stakeholder feedback resulted in changes to MeTree’s interface and changes to the phrasing of clinical decision support documents. The pilot studies resulted in the identification and correction of coding errors and the reformatting of clinical decision support documents. MeTree’s strengths in comparison with other tools are its seamless integration into clinical practice and its provision of action-oriented recommendations guided by providers’ needs. LIMITATIONS The tool was validated in a small cohort. CONCLUSION MeTree can be integrated into primary care practices to help providers collect and synthesize family health history information from patients with the goal of improving adherence to risk-stratified evidence-based guidelines. PMID:24044145

Detection of clinical mastitis with sensor data from automatic milking systems is improved by using decision-tree induction.

PubMed

Kamphuis, C; Mollenhorst, H; Heesterbeek, J A P; Hogeveen, H

2010-08-01

The objective was to develop and validate a clinical mastitis (CM) detection model by means of decision-tree induction. For farmers milking with an automatic milking system (AMS), it is desirable that the detection model has a high level of sensitivity (Se), especially for more severe cases of CM, at a very high specificity (Sp). In addition, an alert for CM should be generated preferably at the quarter milking (QM) at which the CM infection is visible for the first time. Data were collected from 9 Dutch dairy herds milking automatically during a 2.5-yr period. Data included sensor data (electrical conductivity, color, and yield) at the QM level and visual observations of quarters with CM recorded by the farmers. Visual observations of quarters with CM were combined with sensor data of the most recent automatic milking recorded for that same quarter, within a 24-h time window before the visual assessment time. Sensor data of 3.5 million QM were collected, of which 348 QM were combined with a CM observation. Data were divided into a training set, including two-thirds of all data, and a test set. Cows in the training set were not included in the test set and vice versa. A decision-tree model was trained using only clear examples of healthy (n=24,717) or diseased (n=243) QM. The model was tested on 105 QM with CM and a random sample of 50,000 QM without CM. While keeping the Se at a level comparable to that of models currently used by AMS, the decision-tree model was able to decrease the number of false-positive alerts by more than 50%. At an Sp of 99%, 40% of the CM cases were detected. Sixty-four percent of the severe CM cases were detected and only 12.5% of the CM that were scored as watery milk. The Se increased considerably from 40% to 66.7% when the time window increased from less than 24h before the CM observation, to a time window from 24h before to 24h after the CM observation. Even at very wide time windows, however, it was impossible to reach an Se of 100%. This indicates the inability to detect all CM cases based on sensor data alone. Sensitivity levels varied largely when the decision tree was validated per herd. This trend was confirmed when decision trees were trained using data from 8 herds and tested on data from the ninth herd. This indicates that when using the decision tree as a generic CM detection model in practice, some herds will continue having difficulties in detecting CM using mastitis alert lists, whereas others will perform well. Copyright (c) 2010 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Predicting post-fire tree mortality for 12 western US conifers using the First-Order Fire Effects Model (FOFEM)

Treesearch

Sharon Hood; Duncan Lutes

2017-01-01

Accurate prediction of fire-caused tree mortality is critical for making sound land management decisions such as developing burning prescriptions and post-fire management guidelines. To improve efforts to predict post-fire tree mortality, we developed 3-year post-fire mortality models for 12 Western conifer species - white fir (Abies concolor [Gord. &...

Context-Sensitive Ethics in School Psychology

ERIC Educational Resources Information Center

Lasser, Jon; Klose, Laurie McGarry; Robillard, Rachel

2013-01-01

Ethical codes and licensing rules provide foundational guidance for practicing school psychologists, but these sources fall short in their capacity to facilitate effective decision-making. When faced with ethical dilemmas, school psychologists can turn to decision-making models, but step-wise decision trees frequently lack the situation…

Branch: an interactive, web-based tool for testing hypotheses and developing predictive models.

PubMed

Gangavarapu, Karthik; Babji, Vyshakh; Meißner, Tobias; Su, Andrew I; Good, Benjamin M

2016-07-01

Branch is a web application that provides users with the ability to interact directly with large biomedical datasets. The interaction is mediated through a collaborative graphical user interface for building and evaluating decision trees. These trees can be used to compose and test sophisticated hypotheses and to develop predictive models. Decision trees are built and evaluated based on a library of imported datasets and can be stored in a collective area for sharing and re-use. Branch is hosted at http://biobranch.org/ and the open source code is available at http://bitbucket.org/sulab/biobranch/ asu@scripps.edu or bgood@scripps.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Block-Based Connected-Component Labeling Algorithm Using Binary Decision Trees

PubMed Central

Chang, Wan-Yu; Chiu, Chung-Cheng; Yang, Jia-Horng

2015-01-01

In this paper, we propose a fast labeling algorithm based on block-based concepts. Because the number of memory access points directly affects the time consumption of the labeling algorithms, the aim of the proposed algorithm is to minimize neighborhood operations. Our algorithm utilizes a block-based view and correlates a raster scan to select the necessary pixels generated by a block-based scan mask. We analyze the advantages of a sequential raster scan for the block-based scan mask, and integrate the block-connected relationships using two different procedures with binary decision trees to reduce unnecessary memory access. This greatly simplifies the pixel locations of the block-based scan mask. Furthermore, our algorithm significantly reduces the number of leaf nodes and depth levels required in the binary decision tree. We analyze the labeling performance of the proposed algorithm alongside that of other labeling algorithms using high-resolution images and foreground images. The experimental results from synthetic and real image datasets demonstrate that the proposed algorithm is faster than other methods. PMID:26393597

Event Classification and Identification Based on the Characteristic Ellipsoid of Phasor Measurement

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ma, Jian; Diao, Ruisheng; Makarov, Yuri V.

2011-09-23

In this paper, a method to classify and identify power system events based on the characteristic ellipsoid of phasor measurement is presented. The decision tree technique is used to perform the event classification and identification. Event types, event locations and clearance times are identified by decision trees based on the indices of the characteristic ellipsoid. A sufficiently large number of transient events were simulated on the New England 10-machine 39-bus system based on different system configurations. Transient simulations taking into account different event types, clearance times and various locations are conducted to simulate phasor measurement. Bus voltage magnitudes and recordedmore » reactive and active power flows are used to build the characteristic ellipsoid. The volume, eccentricity, center and projection of the longest axis in the parameter space coordinates of the characteristic ellipsoids are used to classify and identify events. Results demonstrate that the characteristic ellipsoid and the decision tree are capable to detect the event type, location, and clearance time with very high accuracy.« less

Online adaptive decision trees: pattern classification and function approximation.

PubMed

Basak, Jayanta

2006-09-01

Recently we have shown that decision trees can be trained in the online adaptive (OADT) mode (Basak, 2004), leading to better generalization score. OADTs were bottlenecked by the fact that they are able to handle only two-class classification tasks with a given structure. In this article, we provide an architecture based on OADT, ExOADT, which can handle multiclass classification tasks and is able to perform function approximation. ExOADT is structurally similar to OADT extended with a regression layer. We also show that ExOADT is capable not only of adapting the local decision hyperplanes in the nonterminal nodes but also has the potential of smoothly changing the structure of the tree depending on the data samples. We provide the learning rules based on steepest gradient descent for the new model ExOADT. Experimentally we demonstrate the effectiveness of ExOADT in the pattern classification and function approximation tasks. Finally, we briefly discuss the relationship of ExOADT with other classification models.

A hybrid method for classifying cognitive states from fMRI data.

PubMed

Parida, S; Dehuri, S; Cho, S-B; Cacha, L A; Poznanski, R R

2015-09-01

Functional magnetic resonance imaging (fMRI) makes it possible to detect brain activities in order to elucidate cognitive-states. The complex nature of fMRI data requires under-standing of the analyses applied to produce possible avenues for developing models of cognitive state classification and improving brain activity prediction. While many models of classification task of fMRI data analysis have been developed, in this paper, we present a novel hybrid technique through combining the best attributes of genetic algorithms (GAs) and ensemble decision tree technique that consistently outperforms all other methods which are being used for cognitive-state classification. Specifically, this paper illustrates the combined effort of decision-trees ensemble and GAs for feature selection through an extensive simulation study and discusses the classification performance with respect to fMRI data. We have shown that our proposed method exhibits significant reduction of the number of features with clear edge classification accuracy over ensemble of decision-trees.

Application of the pessimistic pruning to increase the accuracy of C4.5 algorithm in diagnosing chronic kidney disease

NASA Astrophysics Data System (ADS)

Muslim, M. A.; Herowati, A. J.; Sugiharti, E.; Prasetiyo, B.

2018-03-01

A technique to dig valuable information buried or hidden in data collection which is so big to be found an interesting patterns that was previously unknown is called data mining. Data mining has been applied in the healthcare industry. One technique used data mining is classification. The decision tree included in the classification of data mining and algorithm developed by decision tree is C4.5 algorithm. A classifier is designed using applying pessimistic pruning in C4.5 algorithm in diagnosing chronic kidney disease. Pessimistic pruning use to identify and remove branches that are not needed, this is done to avoid overfitting the decision tree generated by the C4.5 algorithm. In this paper, the result obtained using these classifiers are presented and discussed. Using pessimistic pruning shows increase accuracy of C4.5 algorithm of 1.5% from 95% to 96.5% in diagnosing of chronic kidney disease.

The economic impact of pig-associated parasitic zoonosis in Northern Lao PDR.

PubMed

Choudhury, Adnan Ali Khan; Conlan, James V; Racloz, Vanessa Nadine; Reid, Simon Andrew; Blacksell, Stuart D; Fenwick, Stanley G; Thompson, Andrew R C; Khamlome, Boualam; Vongxay, Khamphouth; Whittaker, Maxine

2013-03-01

The parasitic zoonoses human cysticercosis (Taenia solium), taeniasis (other Taenia species) and trichinellosis (Trichinella species) are endemic in the Lao People's Democratic Republic (Lao PDR). This study was designed to quantify the economic burden pig-associated zoonotic disease pose in Lao PDR. In particular, the analysis included estimation of the losses in the pork industry as well as losses due to human illness and lost productivity. A Markov-probability based decision-tree model was chosen to form the basis of the calculations to estimate the economic and public health impacts of taeniasis, trichinellosis and cysticercosis. Two different decision trees were run simultaneously on the model's human cohort. A third decision tree simulated the potential impacts on pig production. The human capital method was used to estimate productivity loss. The results found varied significantly depending on the rate of hospitalisation due to neurocysticerosis. This study is the first systematic estimate of the economic impact of pig-associated zoonotic diseases in Lao PDR that demonstrates the significance of the diseases in that country.

Industrial and occupational ergonomics in the petrochemical process industry: a regression trees approach.

PubMed

Bevilacqua, M; Ciarapica, F E; Giacchetta, G

2008-07-01

This work is an attempt to apply classification tree methods to data regarding accidents in a medium-sized refinery, so as to identify the important relationships between the variables, which can be considered as decision-making rules when adopting any measures for improvement. The results obtained using the CART (Classification And Regression Trees) method proved to be the most precise and, in general, they are encouraging concerning the use of tree diagrams as preliminary explorative techniques for the assessment of the ergonomic, management and operational parameters which influence high accident risk situations. The Occupational Injury analysis carried out in this paper was planned as a dynamic process and can be repeated systematically. The CART technique, which considers a very wide set of objective and predictive variables, shows new cause-effect correlations in occupational safety which had never been previously described, highlighting possible injury risk groups and supporting decision-making in these areas. The use of classification trees must not, however, be seen as an attempt to supplant other techniques, but as a complementary method which can be integrated into traditional types of analysis.

A research of selected textural features for detection of asbestos-cement roofing sheets using orthoimages

NASA Astrophysics Data System (ADS)

Książek, Judyta

2015-10-01

At present, there has been a great interest in the development of texture based image classification methods in many different areas. This study presents the results of research carried out to assess the usefulness of selected textural features for detection of asbestos-cement roofs in orthophotomap classification. Two different orthophotomaps of southern Poland (with ground resolution: 5 cm and 25 cm) were used. On both orthoimages representative samples for two classes: asbestos-cement roofing sheets and other roofing materials were selected. Estimation of texture analysis usefulness was conducted using machine learning methods based on decision trees (C5.0 algorithm). For this purpose, various sets of texture parameters were calculated in MaZda software. During the calculation of decision trees different numbers of texture parameters groups were considered. In order to obtain the best settings for decision trees models cross-validation was performed. Decision trees models with the lowest mean classification error were selected. The accuracy of the classification was held based on validation data sets, which were not used for the classification learning. For 5 cm ground resolution samples, the lowest mean classification error was 15.6%. The lowest mean classification error in the case of 25 cm ground resolution was 20.0%. The obtained results confirm potential usefulness of the texture parameter image processing for detection of asbestos-cement roofing sheets. In order to improve the accuracy another extended study should be considered in which additional textural features as well as spectral characteristics should be analyzed.

Comparison of two data mining techniques in labeling diagnosis to Iranian pharmacy claim dataset: artificial neural network (ANN) versus decision tree model.

PubMed

Rezaei-Darzi, Ehsan; Farzadfar, Farshad; Hashemi-Meshkini, Amir; Navidi, Iman; Mahmoudi, Mahmoud; Varmaghani, Mehdi; Mehdipour, Parinaz; Soudi Alamdari, Mahsa; Tayefi, Batool; Naderimagham, Shohreh; Soleymani, Fatemeh; Mesdaghinia, Alireza; Delavari, Alireza; Mohammad, Kazem

2014-12-01

This study aimed to evaluate and compare the prediction accuracy of two data mining techniques, including decision tree and neural network models in labeling diagnosis to gastrointestinal prescriptions in Iran. This study was conducted in three phases: data preparation, training phase, and testing phase. A sample from a database consisting of 23 million pharmacy insurance claim records, from 2004 to 2011 was used, in which a total of 330 prescriptions were assessed and used to train and test the models simultaneously. In the training phase, the selected prescriptions were assessed by both a physician and a pharmacist separately and assigned a diagnosis. To test the performance of each model, a k-fold stratified cross validation was conducted in addition to measuring their sensitivity and specificity. Generally, two methods had very similar accuracies. Considering the weighted average of true positive rate (sensitivity) and true negative rate (specificity), the decision tree had slightly higher accuracy in its ability for correct classification (83.3% and 96% versus 80.3% and 95.1%, respectively). However, when the weighted average of ROC area (AUC between each class and all other classes) was measured, the ANN displayed higher accuracies in predicting the diagnosis (93.8% compared with 90.6%). According to the result of this study, artificial neural network and decision tree model represent similar accuracy in labeling diagnosis to GI prescription.

Multifunctional imaging signature for V-KI-RAS2 Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations in colorectal cancer.

PubMed

Miles, Kenneth A; Ganeshan, Balaji; Rodriguez-Justo, Manuel; Goh, Vicky J; Ziauddin, Zia; Engledow, Alec; Meagher, Marie; Endozo, Raymondo; Taylor, Stuart A; Halligan, Stephen; Ell, Peter J; Groves, Ashley M

2014-03-01

This study explores the potential for multifunctional imaging to provide a signature for V-KI-RAS2 Kirsten rat sarcoma viral oncogene homolog (KRAS) gene mutations in colorectal cancer. This prospective study approved by the institutional review board comprised 33 patients undergoing PET/CT before surgery for proven primary colorectal cancer. Tumor tissue was examined histologically for presence of the KRAS mutations and for expression of hypoxia-inducible factor-1 (HIF-1) and minichromosome maintenance protein 2 (mcm2). The following imaging parameters were derived for each tumor: (18)F-FDG uptake ((18)F-FDG maximum standardized uptake value [SUVmax]), CT texture (expressed as mean of positive pixels [MPP]), and blood flow measured by dynamic contrast-enhanced CT. A recursive decision tree was developed in which the imaging investigations were applied sequentially to identify tumors with KRAS mutations. Monte Carlo analysis provided mean values and 95% confidence intervals for sensitivity, specificity, and accuracy. The final decision tree comprised 4 decision nodes and 5 terminal nodes, 2 of which identified KRAS mutants. The true-positive rate, false-positive rate, and accuracy (95% confidence intervals) of the decision tree were 82.4% (63.9%-93.9%), 0% (0%-10.4%), and 90.1% (79.2%-96.0%), respectively. KRAS mutants with high (18)F-FDG SUVmax and low MPP showed greater frequency of HIF-1 expression (P = 0.032). KRAS mutants with low (18)F-FDG SUV(max), high MPP, and high blood flow expressed mcm2 (P = 0.036). Multifunctional imaging with PET/CT and recursive decision-tree analysis to combine measurements of tumor (18)F-FDG uptake, CT texture, and perfusion has the potential to identify imaging signatures for colorectal cancers with KRAS mutations exhibiting hypoxic or proliferative phenotypes.

Insurance Contract Analysis for Company Decision Support in Acquisition Management

NASA Astrophysics Data System (ADS)

Chernovita, H. P.; Manongga, D.; Iriani, A.

2017-01-01

One of company activities to retain their business is marketing the products which include in acquisition management to get new customers. Insurance contract analysis using ID3 to produce decision tree and rules to be decision support for the insurance company. The decision tree shows 13 rules that lead to contract termination claim. This could be a guide for the insurance company in acquisition management to prevent contract binding with these contract condition because it has a big chance for the customer to terminate their insurance contract before its expired date. As the result, there are several strong points that could be the determinant of contract termination such as: 1) customer age whether too young or too old, 2) long insurance period (above 10 years), 3) big insurance amount, 4) big amount of premium charges, and 5) payment method.

Distributed Temperature Sensing of hyporheic flux patterns in varied space and time around beaver dams

NASA Astrophysics Data System (ADS)

Briggs, M.; Lautz, L. K.; McKenzie, J. M.

2010-12-01

Small dams enhance hyporheic interaction by creating punctuated head differentials along streams, thereby affecting redox conditions and nutrient cycling in the streambed. As beaver populations return, they create dams that alter hyporheic flowpaths locally, an effect which may integrate at the reach scale to produce a net hydrological and ecological functional change. Streambed heterogeneity around beaver dams combines with varied morphology, head differentials and stream velocities to create patterns of hyporheic seepage flux that vary in both space and time. Heat has been used as a groundwater tracer for many years, but it’s dependence on spatially disperse point measurements has only recently been resolved by the development of Distributed Temperature Sensing (DTS) fiber-optic technology. Modified applications of DTS include wrapping the fiber around a mandrel to increase spatial resolution dramatically. Wrapped configurations can be installed vertically in the streambed to provide data for heat transport modeling of vertical hyporheic flux. The vertically continuous dataset generated with DTS may be more informative regarding subsurface heterogeneity than more commonly used spatially discrete thermocouples. We installed a total of nine wrapped DTS rods with 1.4 cm vertical spatial resolution above two beaver dams in Cherry Creek, a tributary of the Little Popo Agie River in Lander, Wyoming, USA. Data was collected over 20 min periods in dual-ended mode continuously for one month (10-Jul to 10-Aug 2010) during baseflow recession, as discharge dropped from 384 Ls-1 to 211 Ls-1. The temperature rods were installed to at least 0.75 m depth within bed sediments at varied distances upstream of the dams in diverse stream morphological units, which ranged from gravel bars to clay lined pools. Diurnal fluctuations in stream temperature were generally between 4.5 and 5.5 oC in amplitude, imparting a strong potential signal for propagation into the bed due to advective hyporheic flux. In many locations monthly temperature standard deviations at the 10 cm depth were larger than that of the overlying stream water, indicating direct heating of the streambed by solar radiation was an important process, even in that high velocity system. The high-resolution temperature records revealed local heterogeneity in the streambed at each rod and indicated the largest hyporheic flux was within gravel bars close to the dams. The smallest flux was through a gravel bar farther upstream of the dam, and through the deepest portions of pools closer to the dam. High flux regions had monthly temperature standard deviations close to that of the stream (1.5 oC) at shallow depths, while shallow sediments in pools had much more muted temperature oscillations. At 0.5 m depth, all rods had similar, smaller temperature standard deviations, ranging from 0.64-0.80 oC. The extensive and spatially continuous data set generated using DTS allowed us to determine hyporheic flux patterns for virtually any depth and time along the high-resolution temperature rods, a crucial step for understanding transient patterns in biogeochemical processing around beaver dams.

Comparative seed-tree and selection harvesting costs in young-growth mixed-conifer stands

Treesearch

William A. Atkinson; Dale O. Hall

1963-01-01

Little difference was found between yarding and felling costs in seed-tree and selection harvest cuts. The volume per acre logged was 23,800 board feet on the seed-tree compartments and 10,600 board feet on the selection compartments. For a comparable operation with this range of volumes, cutting method decisions should be based on factors other than logging costs....

Merger of three modeling approaches to assess potential effects of climate change on trees in the eastern United States

Treesearch

Louis R. Iverson; Anantha M. Prasad; Stephen N. Matthews; Matthew P. Peters

2010-01-01

Climate change will likely cause impacts that are species specific and significant; modeling is critical to better understand potential changes in suitable habitat. We use empirical, abundance-based habitat models utilizing decision tree-based ensemble methods to explore potential changes of 134 tree species habitats in the eastern United States (http://www.nrs.fs.fed....

Issues Related to Effects of New Maintenance Concept on AF Specialists Working in the Field

DOT National Transportation Integrated Search

1990-03-07

This working paper was prepared by the Transportation Systems Center, Operator : performance and Safety Analysis Division (DTS-45) for the Federal Aviation Ad : ministration. The paper provides initial thinking on groups of human resource issues : re...

Divide and Conquer: A Valid Approach for Risk Assessment and Decision Making under Uncertainty for Groundwater-Related Diseases

NASA Astrophysics Data System (ADS)

Sanchez-Vila, X.; de Barros, F.; Bolster, D.; Nowak, W.

2010-12-01

Assessing the potential risk of hydro(geo)logical supply systems to human population is an interdisciplinary field. It relies on the expertise in fields as distant as hydrogeology, medicine, or anthropology, and needs powerful translation concepts to provide decision support and policy making. Reliable health risk estimates need to account for the uncertainties in hydrological, physiological and human behavioral parameters. We propose the use of fault trees to address the task of probabilistic risk analysis (PRA) and to support related management decisions. Fault trees allow decomposing the assessment of health risk into individual manageable modules, thus tackling a complex system by a structural “Divide and Conquer” approach. The complexity within each module can be chosen individually according to data availability, parsimony, relative importance and stage of analysis. The separation in modules allows for a true inter- and multi-disciplinary approach. This presentation highlights the three novel features of our work: (1) we define failure in terms of risk being above a threshold value, whereas previous studies used auxiliary events such as exceedance of critical concentration levels, (2) we plot an integrated fault tree that handles uncertainty in both hydrological and health components in a unified way, and (3) we introduce a new form of stochastic fault tree that allows to weaken the assumption of independent subsystems that is required by a classical fault tree approach. We illustrate our concept in a simple groundwater-related setting.