Who Will Succeed and Who Will Struggle? Predicting Early College Success with Indiana's Student Information System. REL 2015-078

ERIC Educational Resources Information Center

Stephan, Jennifer L.; Davis, Elisabeth; Lindsay, Jim; Miller, Shazia

2015-01-01

This study examined whether data on Indiana high school students, their high schools, and the Indiana public colleges and universities in which they enroll predict their academic success during the first two years in college. The researchers obtained student-level, school-level, and university-related data from Indiana's state longitudinal data…

The impact of real-time and predictive traffic information on travelers' behavior on the I-4 corridor. Final report.

DOT National Transportation Integrated Search

2003-07-01

Real time and predicted traffic information plays a key role in the successful implementation of advanced traveler information systems (ATIS) and advance traffic management systems (ATMS). Traffic information is essentially valuable to both transport...

Automated System Checkout to Support Predictive Maintenance for the Reusable Launch Vehicle

NASA Technical Reports Server (NTRS)

Patterson-Hine, Ann; Deb, Somnath; Kulkarni, Deepak; Wang, Yao; Lau, Sonie (Technical Monitor)

1998-01-01

The Propulsion Checkout and Control System (PCCS) is a predictive maintenance software system. The real-time checkout procedures and diagnostics are designed to detect components that need maintenance based on their condition, rather than using more conventional approaches such as scheduled or reliability centered maintenance. Predictive maintenance can reduce turn-around time and cost and increase safety as compared to conventional maintenance approaches. Real-time sensor validation, limit checking, statistical anomaly detection, and failure prediction based on simulation models are employed. Multi-signal models, useful for testability analysis during system design, are used during the operational phase to detect and isolate degraded or failed components. The TEAMS-RT real-time diagnostic engine was developed to utilize the multi-signal models by Qualtech Systems, Inc. Capability of predicting the maintenance condition was successfully demonstrated with a variety of data, from simulation to actual operation on the Integrated Propulsion Technology Demonstrator (IPTD) at Marshall Space Flight Center (MSFC). Playback of IPTD valve actuations for feature recognition updates identified an otherwise undetectable Main Propulsion System 12 inch prevalve degradation. The algorithms were loaded into the Propulsion Checkout and Control System for further development and are the first known application of predictive Integrated Vehicle Health Management to an operational cryogenic testbed. The software performed successfully in real-time, meeting the required performance goal of 1 second cycle time.

Psychological factors determining success in a medical career: a 10-year longitudinal study.

PubMed

Tartas, Malgorzata; Walkiewicz, Maciej; Majkowicz, Mikolaj; Budzinski, Waldemar

2011-01-01

Systemic review of predictors of success in medical career is an important tool to recognize the indicators of proper training. To determine psychological factors that predict success in a medical career. The success is defined as professional competence, satisfaction with medicine as a career, occupational stress and burnout and quality of life (QOF). Part I (1999-2005), medical students were examined each subsequent year, beginning with admission. Assessment included academic achievement (high school final examination results, entrance exam results, academic results during medical school) and psychological characteristics (sense of coherence (SOC), depression, anxiety, coping styles, value system and need for social approval). Part II (2008-2009), the same participants completed an Internet survey 4 years after graduation. Results of the postgraduate medical exam were taken under consideration. Academic achievement predicts only professional competence. Coping styles are significant indicators of satisfaction with medicine as a career. SOC, while assessed with anxiety and depression during studies, enabled us to recognize future QOF of medical graduates. Professional stress is not predictable to such an extent as other success indicators. There are significant psychological qualities useful to draw the outline of the future job and life performance of medical graduates.

Success rates of a skeletal anchorage system in orthodontics: A retrospective analysis.

PubMed

Lam, Raymond; Goonewardene, Mithran S; Allan, Brent P; Sugawara, Junji

2018-01-01

To evaluate the premise that skeletal anchorage with SAS miniplates are highly successful and predictable for a range of complex orthodontic movements. This retrospective cross-sectional analysis consisted of 421 bone plates placed by one clinician in 163 patients (95 female, 68 male, mean age 29.4 years ± 12.02). Simple descriptive statistics were performed for a wide range of malocclusions and desired movements to obtain success, complication, and failure rates. The success rate of skeletal anchorage system miniplates was 98.6%, where approximately 40% of cases experienced mild complications. The most common complication was soft tissue inflammation, which was amenable to focused oral hygiene and antiseptic rinses. Infection occurred in approximately 15% of patients where there was a statistically significant correlation with poor oral hygiene. The most common movements were distalization and intrusion of teeth. More than a third of the cases involved complex movements in more than one plane of space. The success rate of skeletal anchorage system miniplates is high and predictable for a wide range of complex orthodontic movements.

Ensemble framework based real-time respiratory motion prediction for adaptive radiotherapy applications.

PubMed

Tatinati, Sivanagaraja; Nazarpour, Kianoush; Tech Ang, Wei; Veluvolu, Kalyana C

2016-08-01

Successful treatment of tumors with motion-adaptive radiotherapy requires accurate prediction of respiratory motion, ideally with a prediction horizon larger than the latency in radiotherapy system. Accurate prediction of respiratory motion is however a non-trivial task due to the presence of irregularities and intra-trace variabilities, such as baseline drift and temporal changes in fundamental frequency pattern. In this paper, to enhance the accuracy of the respiratory motion prediction, we propose a stacked regression ensemble framework that integrates heterogeneous respiratory motion prediction algorithms. We further address two crucial issues for developing a successful ensemble framework: (1) selection of appropriate prediction methods to ensemble (level-0 methods) among the best existing prediction methods; and (2) finding a suitable generalization approach that can successfully exploit the relative advantages of the chosen level-0 methods. The efficacy of the developed ensemble framework is assessed with real respiratory motion traces acquired from 31 patients undergoing treatment. Results show that the developed ensemble framework improves the prediction performance significantly compared to the best existing methods. Copyright © 2016 IPEM. Published by Elsevier Ltd. All rights reserved.

Benchmarking successional progress in a quantitative food web.

PubMed

Boit, Alice; Gaedke, Ursula

2014-01-01

Central to ecology and ecosystem management, succession theory aims to mechanistically explain and predict the assembly and development of ecological communities. Yet processes at lower hierarchical levels, e.g. at the species and functional group level, are rarely mechanistically linked to the under-investigated system-level processes which drive changes in ecosystem properties and functioning and are comparable across ecosystems. As a model system for secondary succession, seasonal plankton succession during the growing season is readily observable and largely driven autogenically. We used a long-term dataset from large, deep Lake Constance comprising biomasses, auto- and heterotrophic production, food quality, functional diversity, and mass-balanced food webs of the energy and nutrient flows between functional guilds of plankton and partly fish. Extracting population- and system-level indices from this dataset, we tested current hypotheses about the directionality of successional progress which are rooted in ecosystem theory, the metabolic theory of ecology, quantitative food web theory, thermodynamics, and information theory. Our results indicate that successional progress in Lake Constance is quantifiable, passing through predictable stages. Mean body mass, functional diversity, predator-prey weight ratios, trophic positions, system residence times of carbon and nutrients, and the complexity of the energy flow patterns increased during succession. In contrast, both the mass-specific metabolic activity and the system export decreased, while the succession rate exhibited a bimodal pattern. The weighted connectance introduced here represents a suitable index for assessing the evenness and interconnectedness of energy flows during succession. Diverging from earlier predictions, ascendency and eco-exergy did not increase during succession. Linking aspects of functional diversity to metabolic theory and food web complexity, we reconcile previously disjoint bodies of ecological theory to form a complete picture of successional progress within a pelagic food web. This comprehensive synthesis may be used as a benchmark for quantifying successional progress in other ecosystems.

Benchmarking Successional Progress in a Quantitative Food Web

PubMed Central

Boit, Alice; Gaedke, Ursula

2014-01-01

Central to ecology and ecosystem management, succession theory aims to mechanistically explain and predict the assembly and development of ecological communities. Yet processes at lower hierarchical levels, e.g. at the species and functional group level, are rarely mechanistically linked to the under-investigated system-level processes which drive changes in ecosystem properties and functioning and are comparable across ecosystems. As a model system for secondary succession, seasonal plankton succession during the growing season is readily observable and largely driven autogenically. We used a long-term dataset from large, deep Lake Constance comprising biomasses, auto- and heterotrophic production, food quality, functional diversity, and mass-balanced food webs of the energy and nutrient flows between functional guilds of plankton and partly fish. Extracting population- and system-level indices from this dataset, we tested current hypotheses about the directionality of successional progress which are rooted in ecosystem theory, the metabolic theory of ecology, quantitative food web theory, thermodynamics, and information theory. Our results indicate that successional progress in Lake Constance is quantifiable, passing through predictable stages. Mean body mass, functional diversity, predator-prey weight ratios, trophic positions, system residence times of carbon and nutrients, and the complexity of the energy flow patterns increased during succession. In contrast, both the mass-specific metabolic activity and the system export decreased, while the succession rate exhibited a bimodal pattern. The weighted connectance introduced here represents a suitable index for assessing the evenness and interconnectedness of energy flows during succession. Diverging from earlier predictions, ascendency and eco-exergy did not increase during succession. Linking aspects of functional diversity to metabolic theory and food web complexity, we reconcile previously disjoint bodies of ecological theory to form a complete picture of successional progress within a pelagic food web. This comprehensive synthesis may be used as a benchmark for quantifying successional progress in other ecosystems. PMID:24587353

Factors predicting labor induction success: a critical analysis.

PubMed

Crane, Joan M G

2006-09-01

Because of the risk of failed induction of labor, a variety of maternal and fetal factors as well as screening tests have been suggested to predict labor induction success. Certain characteristics of the woman (including parity, age, weight, height and body mass index), and of the fetus (including birth weight and gestational age) are associated with the success of labor induction; with parous, young women who are taller and lower weight having a higher rate of induction success. Fetuses with a lower birth weight or increased gestational age are also associated with increased induction success. The condition of the cervix at the start of induction is an important predictor, with the modified Bishop score being a widely used scoring system. The most important element of the Bishop score is dilatation. Other predictors, including transvaginal ultrasound (TVUS) and biochemical markers [including fetal fibronectin (fFN)] have been suggested. Meta-analyses of studies identified from MEDLINE, PubMed, and EMBASE and published from 1990 to October 2005 were performed evaluating the use of TVUS and fFN in predicting labor induction success in women at term with singleton gestations. Both TVUS and Bishop score predicted successful induction [likelihood ratio (LR)=1.82, 95% confidence interval (CI)=1.51-2.20 and LR=2.10, 95%CI=1.67-2.64, respectively]. As well, fFN and Bishop score predicted successful induction (LR=1.49, 95%CI=1.20-1.85, and LR=2.62, 95%CI=1.88-3.64, respectively). Although TVUS and fFN predicted successful labor induction, neither has been shown to be superior to Bishop score. Further research is needed to evaluate these potential predictors and insulin-like growth factor binding protein-1 (IGFBP-1), another potential biochemical marker.

Predicting Academic Success of First-Time College-Bound African American Students at a Predominantly White Four-Year Public Institution: A Preadmission Model

ERIC Educational Resources Information Center

Redmond, M. William, Jr.

2011-01-01

The purpose of this study is to develop a preadmission predictive model of student success for prospective first-time African American college applicants at a predominately White four-year public institution within the Pennsylvania State System of Higher Education. This model will use two types of variables. They are (a) cognitive variables (i.e.,…

Predicting Time Series Outputs and Time-to-Failure for an Aircraft Controller Using Bayesian Modeling

NASA Technical Reports Server (NTRS)

He, Yuning

2015-01-01

Safety of unmanned aerial systems (UAS) is paramount, but the large number of dynamically changing controller parameters makes it hard to determine if the system is currently stable, and the time before loss of control if not. We propose a hierarchical statistical model using Treed Gaussian Processes to predict (i) whether a flight will be stable (success) or become unstable (failure), (ii) the time-to-failure if unstable, and (iii) time series outputs for flight variables. We first classify the current flight input into success or failure types, and then use separate models for each class to predict the time-to-failure and time series outputs. As different inputs may cause failures at different times, we have to model variable length output curves. We use a basis representation for curves and learn the mappings from input to basis coefficients. We demonstrate the effectiveness of our prediction methods on a NASA neuro-adaptive flight control system.

Linking the development and functioning of a carnivorous pitcher plant's microbial digestive community.

PubMed

Armitage, David W

2017-11-01

Ecosystem development theory predicts that successional turnover in community composition can influence ecosystem functioning. However, tests of this theory in natural systems are made difficult by a lack of replicable and tractable model systems. Using the microbial digestive associates of a carnivorous pitcher plant, I tested hypotheses linking host age-driven microbial community development to host functioning. Monitoring the yearlong development of independent microbial digestive communities in two pitcher plant populations revealed a number of trends in community succession matching theoretical predictions. These included mid-successional peaks in bacterial diversity and metabolic substrate use, predictable and parallel successional trajectories among microbial communities, and convergence giving way to divergence in community composition and carbon substrate use. Bacterial composition, biomass, and diversity positively influenced the rate of prey decomposition, which was in turn positively associated with a host leaf's nitrogen uptake efficiency. Overall digestive performance was greatest during late summer. These results highlight links between community succession and ecosystem functioning and extend succession theory to host-associated microbial communities.

Mating system and the evolution of sex-specific mortality rates in two nymphalid butterflies.

PubMed

Wiklund, Christer; Gotthard, Karl; Nylin, Sören

2003-09-07

Life-history theory predicts that organisms should invest resources into intrinsic components of lifespan only to the degree that it pays off in terms of reproductive success. The benefit of a long life may differ between the sexes and different mating systems may therefore select for different sex-specific mortality rates. In insects with polyandrous mating systems, females mate throughout their lives and male reproductive success is likely to increase monotonously with lifespan. In monandrous systems, where the mating season is less protracted because receptive females are available only at the beginning of the flight season, male mating success should be less dependent on a long lifespan. Here, we show, in a laboratory experiment without predation, that the duration of the mating season is longer in the polyandrous comma butterfly, Polygonia c-album, than in the monandrous peacock butterfly, Inachis io, and that, in line with predictions, male lifespan is shorter than female lifespan in I. io, whereas male and female lifespans are similar in P. c-album.

Nonlinear filtering techniques for noisy geophysical data: Using big data to predict the future

NASA Astrophysics Data System (ADS)

Moore, J. M.

2014-12-01

Chaos is ubiquitous in physical systems. Within the Earth sciences it is readily evident in seismology, groundwater flows and drilling data. Models and workflows have been applied successfully to understand and even to predict chaotic systems in other scientific fields, including electrical engineering, neurology and oceanography. Unfortunately, the high levels of noise characteristic of our planet's chaotic processes often render these frameworks ineffective. This contribution presents techniques for the reduction of noise associated with measurements of nonlinear systems. Our ultimate aim is to develop data assimilation techniques for forward models that describe chaotic observations, such as episodic tremor and slip (ETS) events in fault zones. A series of nonlinear filters are presented and evaluated using classical chaotic systems. To investigate whether the filters can successfully mitigate the effect of noise typical of Earth science, they are applied to sunspot data. The filtered data can be used successfully to forecast sunspot evolution for up to eight years (see figure).

Improved therapy-success prediction with GSS estimated from clinical HIV-1 sequences.

PubMed

Pironti, Alejandro; Pfeifer, Nico; Kaiser, Rolf; Walter, Hauke; Lengauer, Thomas

2014-01-01

Rules-based HIV-1 drug-resistance interpretation (DRI) systems disregard many amino-acid positions of the drug's target protein. The aims of this study are (1) the development of a drug-resistance interpretation system that is based on HIV-1 sequences from clinical practice rather than hard-to-get phenotypes, and (2) the assessment of the benefit of taking all available amino-acid positions into account for DRI. A dataset containing 34,934 therapy-naïve and 30,520 drug-exposed HIV-1 pol sequences with treatment history was extracted from the EuResist database and the Los Alamos National Laboratory database. 2,550 therapy-change-episode baseline sequences (TCEB) were assigned to test set A. Test set B contains 1,084 TCEB from the HIVdb TCE repository. Sequences from patients absent in the test sets were used to train three linear support vector machines to produce scores that predict drug exposure pertaining to each of 20 antiretrovirals: the first one uses the full amino-acid sequences (DEfull), the second one only considers IAS drug-resistance positions (DEonlyIAS), and the third one disregards IAS drug-resistance positions (DEnoIAS). For performance comparison, test sets A and B were evaluated with DEfull, DEnoIAS, DEonlyIAS, geno2pheno[resistance], HIVdb, ANRS, HIV-GRADE, and REGA. Clinically-validated cut-offs were used to convert the continuous output of the first four methods into susceptible-intermediate-resistant (SIR) predictions. With each method, a genetic susceptibility score (GSS) was calculated for each therapy episode in each test set by converting the SIR prediction for its compounds to integer: S=2, I=1, and R=0. The GSS were used to predict therapy success as defined by the EuResist standard datum definition. Statistical significance was assessed using a Wilcoxon signed-rank test. A comparison of the therapy-success prediction performances among the different interpretation systems for test set A can be found in Table 1, while those for test set B are found in Figure 1. Therapy-success prediction of first-line therapies with DEnoIAS performed better than DEonlyIAS (p<10-16). Therapy success prediction benefits from the consideration of all available mutations. The increase in performance was largest in first-line therapies with transmitted drug-resistance mutations.

Tuberculosis diagnostic delay and therapy outcomes of non-national migrants in Tel Aviv, 1998-2008.

PubMed

Mor, Z; Kolb, H; Lidji, M; Migliori, Gb; Leventhal, A

2013-03-21

Non-national migrants have limited access to medical therapy. This study compares diagnostic delay and treatment outcomes of non-insured non-national migrants (NINNM) with insured Israeli citizens (IC) in the Tel Aviv tuberculosis (TB) clinic between 1998 and 2008. Patient delay was the time from symptoms onset to doctor's visit, while system delay was measured from doctor visit to anti-TB therapy administration. We randomly sampled 222 NINNM and 265 IC. NINNM were younger than IC, had lower male to female ratio and fewer smoked. They had less drug/alcohol abuse, more cavitations on chest radiography, longer patient and shorter system delay. Mean patient and system delays of all patients were 25 ± 14 and 79 ± 42 days, respectively. In multivariate analysis, being NINNM, asymptomatic or smoking predicted longer patient delay, while being asymptomatic or having additional co-morbidity predicted longer system delay. Treatment success in sputum smear-positive pulmonary TB NINNM was 81% and 95.7% in IC (p=0.01). Treatment success was not associated with patient or system delay. In multivariate analysis, work security and treatment adherence predicted treatment success. NINNM had longer patient delay and worse therapy outcome, while IC had longer system delay. Both delays should be reduced. NINNM should be informed that TB therapy is free and unlinked with deportation.

The role of HATCH score in predicting the success rate of sinus rhythm following electrical cardioversion of atrial fibrillation.

PubMed

Emren, Sadık Volkan; Kocabaş, Uğur; Duygu, Hamza; Levent, Fatih; Şimşek, Ersin Çağrı; Yapan Emren, Zeynep; Tülüce, Selcen

2016-01-01

The HATCH score predicts the development of persistent and permanent atrial fibrillation (AF) one year after spontaneous or pharmacological conversion to sinus rhythm in patients with AF. However, it remains unknown whether HATCH score predicts short-term success of the procedure at early stages for patients who have undergone electrical cardioversion (EC) for AF. The present study evaluated whether HATCH score predicts short-term success of EC in patients with AF. The study included patients aged 18 years and over, who had undergone EC due to AF lasting less than 12 months, between December 2011 and October 2013. HATCH score was calculated for all patients. The acronym HATCH stands for Hypertension, Age (above 75 years), Transient ischaemic attack or stroke, Chronic obstructive pulmonary disease, and Heart failure. This scoring system awards two points for heart failure and transient ischaemic attack or stroke and one point for the remaining items. The study included 227 patients and short-term EC was successful in 163 of the cases. The mean HATCH scores of the patients who had undergone successful or unsuccessful EC were 1.3 ± 1.4 and 2.9 ± 1.4, respectively (p < 0.001). The area of the HATCH score under the curve in receiver operating characteristics analysis was (AUC) 0.792 (95% CI 0.727-0.857, p < 0.001). A HATCH score of two and above yielded 77% sensitivity, 62% specificity, 56% positive predictive value, and 87% negative predictive value in predicting unsuccessful cardioversion. HATCH score is useful in predicting short-term success of EC at early stages for patients with AF, for whom the use of a rhythm-control strategy is planned.

Two signs indicative of successful access in nuclear medicine cerebrospinal fluid diversionary shunt studies.

PubMed

Bermo, Mohammed S; Khalatbari, Hedieh; Parisi, Marguerite T

2018-05-08

Successful shunt access is the first step in a properly performed nuclear medicine cerebrospinal fluid (CSF) shunt study. To determine the significance of the radiotracer configuration at the injection site during initial nuclear medicine CSF shunt imaging and the lack of early systemic radiotracer activity as predictors of successful shunt access. With Institutional Review Board approval, three nuclear medicine physicians performed a retrospective review of all consecutive CSF shunt studies performed in children at our institution in 2015. Antecedent nuclear medicine CSF shunt studies in these patients were also assessed and included in the review. The appearance of the reservoir site immediately after radiotracer injection was classified as either figure-of-eight or round/ovoid configuration. The presence or absence of early systemic distribution of the tracer on the 5-min static images was noted and separately evaluated. A total of 98 nuclear medicine ventriculoperitoneal CSF shunt studies were evaluated. Figure-of-eight configuration was identified in 87% of studies and, when present, had 93% sensitivity, 78% specificity, 92% accuracy, 98% positive predictive value (PPV) and 54% negative predictive value (NPV) as a predictor of successful shunt access. Early systemic activity was absent in 89 of 98 studies. Lack of early systemic distribution of the radiotracer had 98% sensitivity, 78% specificity, 96% accuracy, 98% PPV and 78% NPV as a predictor of successful shunt access. Figure-of-eight configuration in conjunction with the absence of early systemic tracer activity had 99% PPV for successful shunt access. Figure-of-eight configuration at the injection site or lack of early systemic radiotracer activity had moderate specificity for successful shunt access. Specificity and PPV significantly improved when both signs were combined in assessment.

BehavePlus fire modeling system: Past, present, and future

Treesearch

Patricia L. Andrews

2007-01-01

Use of mathematical fire models to predict fire behavior and fire effects plays an important supporting role in wildland fire management. When used in conjunction with personal fire experience and a basic understanding of the fire models, predictions can be successfully applied to a range of fire management activities including wildfire behavior prediction, prescribed...

The Use of an Online Learning and Teaching System for Monitoring Computer Aided Design Student Participation and Predicting Student Success

ERIC Educational Resources Information Center

Akhtar, S.; Warburton, S.; Xu, W.

2017-01-01

In this paper we report on the use of a purpose built Computer Support Collaborative learning environment designed to support lab-based CAD teaching through the monitoring of student participation and identified predictors of success. This was carried out by analysing data from the interactive learning system and correlating student behaviour with…

Condition Assessment and End-of-Life Prediction System for Electric Machines and Their Loads

NASA Technical Reports Server (NTRS)

Parlos, Alexander G.; Toliyat, Hamid A.

2005-01-01

An end-of-life prediction system developed for electric machines and their loads could be used in integrated vehicle health monitoring at NASA and in other government agencies. This system will provide on-line, real-time condition assessment and end-of-life prediction of electric machines (e.g., motors, generators) and/or their loads of mechanically coupled machinery (e.g., pumps, fans, compressors, turbines, conveyor belts, magnetic levitation trains, and others). In long-duration space flight, the ability to predict the lifetime of machinery could spell the difference between mission success or failure. Therefore, the system described here may be of inestimable value to the U.S. space program. The system will provide continuous monitoring for on-line condition assessment and end-of-life prediction as opposed to the current off-line diagnoses.

Fluoroscopic removal of retrievable self-expandable metal stents in patients with malignant oesophageal strictures: Experience with a non-endoscopic removal system.

PubMed

Kim, Pyeong Hwa; Song, Ho-Young; Park, Jung-Hoon; Zhou, Wei-Zhong; Na, Han Kyu; Cho, Young Chul; Jun, Eun Jung; Kim, Jun Ki; Kim, Guk Bae

2017-03-01

To evaluate clinical outcomes of fluoroscopic removal of retrievable self-expandable metal stents (SEMSs) for malignant oesophageal strictures, to compare clinical outcomes of three different removal techniques, and to identify predictive factors of successful removal by the standard technique (primary technical success). A total of 137 stents were removed from 128 patients with malignant oesophageal strictures. Primary overall technical success and removal-related complications were evaluated. Logistic regression models were constructed to identify predictive factors of primary technical success. Primary technical success rate was 78.8 % (108/137). Complications occurred in six (4.4 %) cases. Stent location in the upper oesophagus (P=0.004), stricture length over 8 cm (P=0.030), and proximal granulation tissue (P<0.001) were negative predictive factors of primary technical success. If granulation tissue was present at the proximal end, eversion technique was more frequently required (P=0.002). Fluoroscopic removal of retrievable SEMSs for malignant oesophageal strictures using three different removal techniques appeared to be safe and easy. The standard technique is safe and effective in the majority of patients. The presence of proximal granulation tissue, stent location in the upper oesophagus, and stricture length over 8 cm were negative predictive factors for primary technical success by standard extraction and may require a modified removal technique. • Fluoroscopic retrievable SEMS removal is safe and effective. • Standard removal technique by traction is effective in the majority of patients. • Three negative predictive factors of primary technical success were identified. • Caution should be exercised during the removal in those situations. • Eversion technique is effective in cases of proximal granulation tissue.

Ceramic Matrix Composites (CMC) Life Prediction Development - 2003

NASA Technical Reports Server (NTRS)

Levine, Stanley R.; Calomino, Anthony M.; Verrilli, Michael J.; Thomas, David J.; Halbig, Michael C.; Opila, Elizabeth J.; Ellis, John R.

2003-01-01

Accurate life prediction is critical to successful use of ceramic matrix composites (CMCs). The tools to accomplish this are immature and not oriented toward the behavior of carbon fiber reinforced silicon carbide (C/SiC), the primary system of interest for many reusable and single mission launch vehicle propulsion and airframe applications. This paper describes an approach and progress made to satisfy the need to develop an integrated life prediction system that addresses mechanical durability and environmental degradation of C/SiC.

A study on a robot arm driven by three-dimensional trajectories predicted from non-invasive neural signals.

PubMed

Kim, Yoon Jae; Park, Sung Woo; Yeom, Hong Gi; Bang, Moon Suk; Kim, June Sic; Chung, Chun Kee; Kim, Sungwan

2015-08-20

A brain-machine interface (BMI) should be able to help people with disabilities by replacing their lost motor functions. To replace lost functions, robot arms have been developed that are controlled by invasive neural signals. Although invasive neural signals have a high spatial resolution, non-invasive neural signals are valuable because they provide an interface without surgery. Thus, various researchers have developed robot arms driven by non-invasive neural signals. However, robot arm control based on the imagined trajectory of a human hand can be more intuitive for patients. In this study, therefore, an integrated robot arm-gripper system (IRAGS) that is driven by three-dimensional (3D) hand trajectories predicted from non-invasive neural signals was developed and verified. The IRAGS was developed by integrating a six-degree of freedom robot arm and adaptive robot gripper. The system was used to perform reaching and grasping motions for verification. The non-invasive neural signals, magnetoencephalography (MEG) and electroencephalography (EEG), were obtained to control the system. The 3D trajectories were predicted by multiple linear regressions. A target sphere was placed at the terminal point of the real trajectories, and the system was commanded to grasp the target at the terminal point of the predicted trajectories. The average correlation coefficient between the predicted and real trajectories in the MEG case was [Formula: see text] ([Formula: see text]). In the EEG case, it was [Formula: see text] ([Formula: see text]). The success rates in grasping the target plastic sphere were 18.75 and 7.50 % with MEG and EEG, respectively. The success rates of touching the target were 52.50 and 58.75 % respectively. A robot arm driven by 3D trajectories predicted from non-invasive neural signals was implemented, and reaching and grasping motions were performed. In most cases, the robot closely approached the target, but the success rate was not very high because the non-invasive neural signal is less accurate. However the success rate could be sufficiently improved for practical applications by using additional sensors. Robot arm control based on hand trajectories predicted from EEG would allow for portability, and the performance with EEG was comparable to that with MEG.

A Data mining Technique for Analyzing and Predicting the success of Movie

NASA Astrophysics Data System (ADS)

Meenakshi, K.; Maragatham, G.; Agarwal, Neha; Ghosh, Ishitha

2018-04-01

In real world prediction models and mechanisms can be used to predict the success of a movie. The proposed work aims to develop a system based upon data mining techniques that may help in predicting the success of a movie in advance thereby reducing certain level of uncertainty. An attempt is made to predict the past as well as the future of movie for the purpose of business certainty or simply a theoretical condition in which decision making [the success of the movie] is without risk, because the decision maker [movie makers and stake holders] has all the information about the exact outcome of the decision, before he or she makes the decision [release of the movie]. With over two million spectators a day and films exported to over 100 countries, the impact of Bollywood film industry is formidable We gather a series of interesting facts and relationships using a variety of data mining techniques. In particular, we concentrate on attributes relevant to the success prediction of movies, such as whether any particular actors or actresses are likely to help a movie to succeed. The paper additionally reports on the techniques used, giving their implementation and utility. Additionally, we found some attention-grabbing facts, such as the budget of a movie isn't any indication of how well-rated it'll be, there's a downward trend within the quality of films over time, and also the director and actors/actresses involved in the movie.

Mating system and the evolution of sex-specific mortality rates in two nymphalid butterflies.

PubMed Central

Wiklund, Christer; Gotthard, Karl; Nylin, Sören

2003-01-01

Life-history theory predicts that organisms should invest resources into intrinsic components of lifespan only to the degree that it pays off in terms of reproductive success. The benefit of a long life may differ between the sexes and different mating systems may therefore select for different sex-specific mortality rates. In insects with polyandrous mating systems, females mate throughout their lives and male reproductive success is likely to increase monotonously with lifespan. In monandrous systems, where the mating season is less protracted because receptive females are available only at the beginning of the flight season, male mating success should be less dependent on a long lifespan. Here, we show, in a laboratory experiment without predation, that the duration of the mating season is longer in the polyandrous comma butterfly, Polygonia c-album, than in the monandrous peacock butterfly, Inachis io, and that, in line with predictions, male lifespan is shorter than female lifespan in I. io, whereas male and female lifespans are similar in P. c-album. PMID:12964985

An evaluation of the real-time tropical cyclone forecast skill of the Navy Operational Global Atmospheric Prediction System in the western North Pacific

NASA Technical Reports Server (NTRS)

Fiorino, Michael; Goerss, James S.; Jensen, Jack J.; Harrison, Edward J., Jr.

1993-01-01

The paper evaluates the meteorological quality and operational utility of the Navy Operational Global Atmospheric Prediction System (NOGAPS) in forecasting tropical cyclones. It is shown that the model can provide useful predictions of motion and formation on a real-time basis in the western North Pacific. The meterological characteristics of the NOGAPS tropical cyclone predictions are evaluated by examining the formation of low-level cyclone systems in the tropics and vortex structure in the NOGAPS analysis and verifying 72-h forecasts. The adjusted NOGAPS track forecasts showed equitable skill to the baseline aid and the dynamical model. NOGAPS successfully predicted unusual equatorward turns for several straight-running cyclones.

Personal Factors Predicting College Student Success

ERIC Educational Resources Information Center

Aydin, Gokcen

2017-01-01

Purpose: With the changing perspective in modern education systems, success means more than grades and includes emotional, social, cognitive, and academic development. The aim of this study was to investigate the role of personal factors (academic self-efficacy, organization and attention to study, time utilization, classroom communication, stress…

Nonlinear dynamics and predictability in the atmospheric sciences

NASA Technical Reports Server (NTRS)

Ghil, M.; Kimoto, M.; Neelin, J. D.

1991-01-01

Systematic applications of nonlinear dynamics to studies of the atmosphere and climate are reviewed for the period 1987-1990. Problems discussed include paleoclimatic applications, low-frequency atmospheric variability, and interannual variability of the ocean-atmosphere system. Emphasis is placed on applications of the successive bifurcation approach and the ergodic theory of dynamical systems to understanding and prediction of intraseasonal, interannual, and Quaternary climate changes.

Vehicle dynamic prediction systems with on-line identification of vehicle parameters and road conditions.

PubMed

Hsu, Ling-Yuan; Chen, Tsung-Lin

2012-11-13

This paper presents a vehicle dynamics prediction system, which consists of a sensor fusion system and a vehicle parameter identification system. This sensor fusion system can obtain the six degree-of-freedom vehicle dynamics and two road angles without using a vehicle model. The vehicle parameter identification system uses the vehicle dynamics from the sensor fusion system to identify ten vehicle parameters in real time, including vehicle mass, moment of inertial, and road friction coefficients. With above two systems, the future vehicle dynamics is predicted by using a vehicle dynamics model, obtained from the parameter identification system, to propagate with time the current vehicle state values, obtained from the sensor fusion system. Comparing with most existing literatures in this field, the proposed approach improves the prediction accuracy both by incorporating more vehicle dynamics to the prediction system and by on-line identification to minimize the vehicle modeling errors. Simulation results show that the proposed method successfully predicts the vehicle dynamics in a left-hand turn event and a rollover event. The prediction inaccuracy is 0.51% in a left-hand turn event and 27.3% in a rollover event.

Vehicle Dynamic Prediction Systems with On-Line Identification of Vehicle Parameters and Road Conditions

PubMed Central

Hsu, Ling-Yuan; Chen, Tsung-Lin

2012-01-01

This paper presents a vehicle dynamics prediction system, which consists of a sensor fusion system and a vehicle parameter identification system. This sensor fusion system can obtain the six degree-of-freedom vehicle dynamics and two road angles without using a vehicle model. The vehicle parameter identification system uses the vehicle dynamics from the sensor fusion system to identify ten vehicle parameters in real time, including vehicle mass, moment of inertial, and road friction coefficients. With above two systems, the future vehicle dynamics is predicted by using a vehicle dynamics model, obtained from the parameter identification system, to propagate with time the current vehicle state values, obtained from the sensor fusion system. Comparing with most existing literatures in this field, the proposed approach improves the prediction accuracy both by incorporating more vehicle dynamics to the prediction system and by on-line identification to minimize the vehicle modeling errors. Simulation results show that the proposed method successfully predicts the vehicle dynamics in a left-hand turn event and a rollover event. The prediction inaccuracy is 0.51% in a left-hand turn event and 27.3% in a rollover event. PMID:23202231

Emerging approaches in predictive toxicology.

PubMed

Zhang, Luoping; McHale, Cliona M; Greene, Nigel; Snyder, Ronald D; Rich, Ivan N; Aardema, Marilyn J; Roy, Shambhu; Pfuhler, Stefan; Venkatactahalam, Sundaresan

2014-12-01

Predictive toxicology plays an important role in the assessment of toxicity of chemicals and the drug development process. While there are several well-established in vitro and in vivo assays that are suitable for predictive toxicology, recent advances in high-throughput analytical technologies and model systems are expected to have a major impact on the field of predictive toxicology. This commentary provides an overview of the state of the current science and a brief discussion on future perspectives for the field of predictive toxicology for human toxicity. Computational models for predictive toxicology, needs for further refinement and obstacles to expand computational models to include additional classes of chemical compounds are highlighted. Functional and comparative genomics approaches in predictive toxicology are discussed with an emphasis on successful utilization of recently developed model systems for high-throughput analysis. The advantages of three-dimensional model systems and stem cells and their use in predictive toxicology testing are also described. © 2014 Wiley Periodicals, Inc.

Emerging Approaches in Predictive Toxicology

PubMed Central

Zhang, Luoping; McHale, Cliona M.; Greene, Nigel; Snyder, Ronald D.; Rich, Ivan N.; Aardema, Marilyn J.; Roy, Shambhu; Pfuhler, Stefan; Venkatactahalam, Sundaresan

2016-01-01

Predictive toxicology plays an important role in the assessment of toxicity of chemicals and the drug development process. While there are several well-established in vitro and in vivo assays that are suitable for predictive toxicology, recent advances in high-throughput analytical technologies and model systems are expected to have a major impact on the field of predictive toxicology. This commentary provides an overview of the state of the current science and a brief discussion on future perspectives for the field of predictive toxicology for human toxicity. Computational models for predictive toxicology, needs for further refinement and obstacles to expand computational models to include additional classes of chemical compounds are highlighted. Functional and comparative genomics approaches in predictive toxicology are discussed with an emphasis on successful utilization of recently developed model systems for high-throughput analysis. The advantages of three-dimensional model systems and stem cells and their use in predictive toxicology testing are also described. PMID:25044351

The Mars Exploration Rover (MER) Transverse Impulse Rocket System (TIRS)

NASA Technical Reports Server (NTRS)

SanMartin, Alejandro Miguel; Bailey, Erik

2005-01-01

In a very short period of time the MER project successfully developed and tested a system, TIRS/DIMES, to improve the probability of success in the presence of large Martian winds. The successful development of TIRS/DIMES played a big role in the landing site selection process by enabling the landing of Spirit on Gusev crater, a site of very high scientific interest but with known high wind conditions. The performance of TIRS by Spirit at Gusev Crater was excellent. The velocity prediction error was small and Big TIRS was fired reducing the impact horizontal velocity from approximately 23 meters per second to approximately 11 meters per second, well within the airbag capabilities. The performance of TIRS by Opportunity at Meridiani was good. The velocity prediction error was rather large (approximately 6 meters per second, a less than 2 sigma value, but TIRS did not fire which was the correct action.

Predictive hypotheses are ineffectual in resolving complex biochemical systems.

PubMed

Fry, Michael

2018-03-20

Scientific hypotheses may either predict particular unknown facts or accommodate previously-known data. Although affirmed predictions are intuitively more rewarding than accommodations of established facts, opinions divide whether predictive hypotheses are also epistemically superior to accommodation hypotheses. This paper examines the contribution of predictive hypotheses to discoveries of several bio-molecular systems. Having all the necessary elements of the system known beforehand, an abstract predictive hypothesis of semiconservative mode of DNA replication was successfully affirmed. However, in defining the genetic code whose biochemical basis was unclear, hypotheses were only partially effective and supplementary experimentation was required for its conclusive definition. Markedly, hypotheses were entirely inept in predicting workings of complex systems that included unknown elements. Thus, hypotheses did not predict the existence and function of mRNA, the multiple unidentified components of the protein biosynthesis machinery, or the manifold unknown constituents of the ubiquitin-proteasome system of protein breakdown. Consequently, because of their inability to envision unknown entities, predictive hypotheses did not contribute to the elucidation of cation theories remained the sole instrument to explain complex bio-molecular systems, the philosophical question of alleged advantage of predictive over accommodative hypotheses became inconsequential.

Investigating Academic Success Factors for Undergraduate Business Students

ERIC Educational Resources Information Center

Kaighobadi, Mehdi; Allen, Marcus T.

2008-01-01

Student academic performance is of major interest to all stakeholders of higher education institutions. This study questions whether or not statistical analysis of information that is readily available in most universities' official records system can be used to predict overall academic success. In particular, this study is an attempt to…

Scaling Student Success with Predictive Analytics: Reflections after Four Years in the Data Trenches

ERIC Educational Resources Information Center

Wagner, Ellen; Longanecker, David

2016-01-01

The metrics used in the US to track students do not include adults and part-time students. This has led to the development of a massive data initiative--the Predictive Analytics Reporting (PAR) framework--that uses predictive analytics to trace the progress of all types of students in the system. This development has allowed actionable,…

Towards crystal structure prediction of complex organic compounds – a report on the fifth blind test

PubMed Central

Bardwell, David A.; Adjiman, Claire S.; Arnautova, Yelena A.; Bartashevich, Ekaterina; Boerrigter, Stephan X. M.; Braun, Doris E.; Cruz-Cabeza, Aurora J.; Day, Graeme M.; Della Valle, Raffaele G.; Desiraju, Gautam R.; van Eijck, Bouke P.; Facelli, Julio C.; Ferraro, Marta B.; Grillo, Damian; Habgood, Matthew; Hofmann, Detlef W. M.; Hofmann, Fridolin; Jose, K. V. Jovan; Karamertzanis, Panagiotis G.; Kazantsev, Andrei V.; Kendrick, John; Kuleshova, Liudmila N.; Leusen, Frank J. J.; Maleev, Andrey V.; Misquitta, Alston J.; Mohamed, Sharmarke; Needs, Richard J.; Neumann, Marcus A.; Nikylov, Denis; Orendt, Anita M.; Pal, Rumpa; Pantelides, Constantinos C.; Pickard, Chris J.; Price, Louise S.; Price, Sarah L.; Scheraga, Harold A.; van de Streek, Jacco; Thakur, Tejender S.; Tiwari, Siddharth; Venuti, Elisabetta; Zhitkov, Ilia K.

2011-01-01

Following on from the success of the previous crystal structure prediction blind tests (CSP1999, CSP2001, CSP2004 and CSP2007), a fifth such collaborative project (CSP2010) was organized at the Cambridge Crystallographic Data Centre. A range of methodologies was used by the participating groups in order to evaluate the ability of the current computational methods to predict the crystal structures of the six organic molecules chosen as targets for this blind test. The first four targets, two rigid molecules, one semi-flexible molecule and a 1:1 salt, matched the criteria for the targets from CSP2007, while the last two targets belonged to two new challenging categories – a larger, much more flexible molecule and a hydrate with more than one polymorph. Each group submitted three predictions for each target it attempted. There was at least one successful prediction for each target, and two groups were able to successfully predict the structure of the large flexible molecule as their first place submission. The results show that while not as many groups successfully predicted the structures of the three smallest molecules as in CSP2007, there is now evidence that methodologies such as dispersion-corrected density functional theory (DFT-D) are able to reliably do so. The results also highlight the many challenges posed by more complex systems and show that there are still issues to be overcome. PMID:22101543

Parts and Components Reliability Assessment: A Cost Effective Approach

NASA Technical Reports Server (NTRS)

Lee, Lydia

2009-01-01

System reliability assessment is a methodology which incorporates reliability analyses performed at parts and components level such as Reliability Prediction, Failure Modes and Effects Analysis (FMEA) and Fault Tree Analysis (FTA) to assess risks, perform design tradeoffs, and therefore, to ensure effective productivity and/or mission success. The system reliability is used to optimize the product design to accommodate today?s mandated budget, manpower, and schedule constraints. Stand ard based reliability assessment is an effective approach consisting of reliability predictions together with other reliability analyses for electronic, electrical, and electro-mechanical (EEE) complex parts and components of large systems based on failure rate estimates published by the United States (U.S.) military or commercial standards and handbooks. Many of these standards are globally accepted and recognized. The reliability assessment is especially useful during the initial stages when the system design is still in the development and hard failure data is not yet available or manufacturers are not contractually obliged by their customers to publish the reliability estimates/predictions for their parts and components. This paper presents a methodology to assess system reliability using parts and components reliability estimates to ensure effective productivity and/or mission success in an efficient manner, low cost, and tight schedule.

What is the quickest scoring system to predict percutaneous nephrolithotomy outcomes? A comparative study among S.T.O.N.E score, guy's stone score and croes nomogram.

PubMed

Vicentini, Fabio C; Serzedello, Felipe R; Thomas, Kay; Marchini, Giovanni S; Torricelli, Fabio C M; Srougi, Miguel; Mazzucchi, Eduardo

2017-01-01

To compare the application time and the capacity of the nomograms to predict the success of Guy's Stone Score (GSS), S.T.O.N.E. Nephrolithometry (STONE) and Clinical Research Office of the Endourological Society nephrolithometric nomogram (CROES) of percutaneous nephrolithotomy (PCNL), evaluating the most efficient one for clinical use. We studied 48 patients who underwent PCNL by the same surgeon between 2010 and 2011. We calculated GSS, STONE and CROES based on preoperative non-contrast computed tomography (CT) images and clinical data. A single observer, blinded to the outcomes, reviewed all images and assigned scores. We compared the application time of each nomogram. We used an analysis of variance for repeated measures and multiple comparisons by the Tukey test. We compared the area under the ROC curve (AUC) of the three nomograms two by two to determine the most predictive scoring system. The immediate success rate was 66.7% and complications occurred in 16.7% of cases. The average operative time was 122 minutes. Mean application time was significantly lower for the GSS (27.5 seconds) when compared to 300.6 seconds for STONE and 213.4 seconds for CROES (p<0.001). There was no significant difference among the GSS (AUC=0.653), STONE (AUC=0.563) and CROES (AUC=0.641) in the ability to predict immediate success of PCNL. All three nomograms showed similar ability to predict success of PCNL, however the GSS was the quickest to be applied, what is an important issue for routine clinical use when counseling patients who are candidates to PCNL. Copyright® by the International Brazilian Journal of Urology.

What is the quickest scoring system to predict percutaneous nephrolithotomy outcomes? A comparative study among S.T.O.N.E score, Guy's Stone Ccore and CROES nomogram

PubMed Central

Vicentini, Fabio C.; Serzedello, Felipe R.; Thomas, Kay; Marchini, Giovanni S.; Torricelli, Fabio C. M.; Srougi, Miguel; Mazzucchi, Eduardo

2017-01-01

ABSTRACT Objective: To compare the application time and the capacity of the nomograms to predict the success of Guy's Stone Score (GSS), S.T.O.N.E. Nephrolithometry (STONE) and Clinical Research Office of the Endourological Society nephrolithometric nomogram (CROES) of percutaneous nephrolithotomy (PCNL), evaluating the most efficient one for clinical use. Materials and Methods: We studied 48 patients who underwent PCNL by the same surgeon between 2010 and 2011. We calculated GSS, STONE and CROES based on pre-operative non-contrast computed tomography (CT) images and clinical data. A single observer, blinded to the outcomes, reviewed all images and assigned scores. We compared the application time of each nomogram. We used an analysis of variance for repeated measures and multiple comparisons by the Tukey test. We compared the area under the ROC curve (AUC) of the three nomograms two by two to determine the most predictive scoring system. Results: The immediate success rate was 66.7% and complications occurred in 16.7% of cases. The average operative time was 122 minutes. Mean application time was significantly lower for the GSS (27.5 seconds) when compared to 300.6 seconds for STONE and 213.4 seconds for CROES (p<0.001). There was no significant difference among the GSS (AUC=0.653), STONE (AUC=0.563) and CROES (AUC=0.641) in the ability to predict immediate success of PCNL. Conclusions: All three nomograms showed similar ability to predict success of PCNL, however the GSS was the quickest to be applied, what is an important issue for routine clinical use when counseling patients who are candidates to PCNL. PMID:28338303

The Use of High Performance Computing (HPC) to Strengthen the Development of Army Systems

DTIC Science & Technology

2011-11-01

accurately predicting the supersonic magus effect about spinning cones, ogive- cylinders , and boat-tailed afterbodies. This work led to the successful...successful computer model of the proposed product or system, one can then build prototypes on the computer and study the effects on the performance of...needed. The NRC report discusses the requirements for effective use of such computing power. One needs “models, algorithms, software, hardware

Biomass and fire dynamics in a temperate forest-grassland mosaic: Integrating multi-species herbivory, climate, and fire with the FireBGCv2/GrazeBGC system

Treesearch

Robert A. Riggs; Robert E. Keane; Norm Cimon; Rachel Cook; Lisa Holsinger; John Cook; Timothy DelCurto; L.Scott Baggett; Donald Justice; David Powell; Martin Vavra; Bridgett Naylor

2015-01-01

Landscape fire succession models (LFSMs) predict spatially-explicit interactions between vegetation succession and disturbance, but these models have yet to fully integrate ungulate herbivory as a driver of their processes. We modified a complex LFSM, FireBGCv2, to include a multi-species herbivory module, GrazeBGC. The system is novel in that it explicitly...

Prediction of Ionizing Radiation Resistance in Bacteria Using a Multiple Instance Learning Model.

PubMed

Aridhi, Sabeur; Sghaier, Haïtham; Zoghlami, Manel; Maddouri, Mondher; Nguifo, Engelbert Mephu

2016-01-01

Ionizing-radiation-resistant bacteria (IRRB) are important in biotechnology. In this context, in silico methods of phenotypic prediction and genotype-phenotype relationship discovery are limited. In this work, we analyzed basal DNA repair proteins of most known proteome sequences of IRRB and ionizing-radiation-sensitive bacteria (IRSB) in order to learn a classifier that correctly predicts this bacterial phenotype. We formulated the problem of predicting bacterial ionizing radiation resistance (IRR) as a multiple-instance learning (MIL) problem, and we proposed a novel approach for this purpose. We provide a MIL-based prediction system that classifies a bacterium to either IRRB or IRSB. The experimental results of the proposed system are satisfactory with 91.5% of successful predictions.

Forecasting municipal solid waste generation using artificial intelligence modelling approaches.

PubMed

Abbasi, Maryam; El Hanandeh, Ali

2016-10-01

Municipal solid waste (MSW) management is a major concern to local governments to protect human health, the environment and to preserve natural resources. The design and operation of an effective MSW management system requires accurate estimation of future waste generation quantities. The main objective of this study was to develop a model for accurate forecasting of MSW generation that helps waste related organizations to better design and operate effective MSW management systems. Four intelligent system algorithms including support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and k-nearest neighbours (kNN) were tested for their ability to predict monthly waste generation in the Logan City Council region in Queensland, Australia. Results showed artificial intelligence models have good prediction performance and could be successfully applied to establish municipal solid waste forecasting models. Using machine learning algorithms can reliably predict monthly MSW generation by training with waste generation time series. In addition, results suggest that ANFIS system produced the most accurate forecasts of the peaks while kNN was successful in predicting the monthly averages of waste quantities. Based on the results, the total annual MSW generated in Logan City will reach 9.4×10(7)kg by 2020 while the peak monthly waste will reach 9.37×10(6)kg. Copyright © 2016 Elsevier Ltd. All rights reserved.

Thermomechanical Controls on the Success and Failure of Continental Rift Systems

NASA Astrophysics Data System (ADS)

Brune, S.

2017-12-01

Studies of long-term continental rift evolution are often biased towards rifts that succeed in breaking the continent like the North Atlantic, South China Sea, or South Atlantic rifts. However there are many prominent rift systems on Earth where activity stopped before the formation of a new ocean basin such as the North Sea, the West and Central African Rifts, or the West Antarctic Rift System. The factors controlling the success and failure of rifts can be divided in two groups: (1) Intrinsic processes - for instance frictional weakening, lithospheric thinning, shear heating or the strain-dependent growth of rift strength by replacing weak crust with strong mantle. (2) External processes - such as a change of plate divergence rate, the waning of a far-field driving force, or the arrival of a mantle plume. Here I use numerical and analytical modeling to investigate the role of these processes for the success and failure of rift systems. These models show that a change of plate divergence rate under constant force extension is controlled by the non-linearity of lithospheric materials. For successful rifts, a strong increase in divergence velocity can be expected to take place within few million years, a prediction that agrees with independent plate tectonic reconstructions of major Mesozoic and Cenozoic ocean-forming rift systems. Another model prediction is that oblique rifting is mechanically favored over orthogonal rifting, which means that simultaneous deformation within neighboring rift systems of different obliquity and otherwise identical properties will lead to success and failure of the more and less oblique rift, respectively. This can be exemplified by the Cretaceous activity within the Equatorial Atlantic and the West African Rifts that lead to the formation of a highly oblique oceanic spreading center and the failure of the West African Rift System. While in nature the circumstances of rift success or failure may be manifold, simplified numerical and analytical models allow the isolated analysis of various contributing factors and to define a characteristic time scale for each process.

QSAR models for prediction of chromatographic behavior of homologous Fab variants.

PubMed

Robinson, Julie R; Karkov, Hanne S; Woo, James A; Krogh, Berit O; Cramer, Steven M

2017-06-01

While quantitative structure activity relationship (QSAR) models have been employed successfully for the prediction of small model protein chromatographic behavior, there have been few reports to date on the use of this methodology for larger, more complex proteins. Recently our group generated focused libraries of antibody Fab fragment variants with different combinations of surface hydrophobicities and electrostatic potentials, and demonstrated that the unique selectivities of multimodal resins can be exploited to separate these Fab variants. In this work, results from linear salt gradient experiments with these Fabs were employed to develop QSAR models for six chromatographic systems, including multimodal (Capto MMC, Nuvia cPrime, and two novel ligand prototypes), hydrophobic interaction chromatography (HIC; Capto Phenyl), and cation exchange (CEX; CM Sepharose FF) resins. The models utilized newly developed "local descriptors" to quantify changes around point mutations in the Fab libraries as well as novel cluster descriptors recently introduced by our group. Subsequent rounds of feature selection and linearized machine learning algorithms were used to generate robust, well-validated models with high training set correlations (R 2  > 0.70) that were well suited for predicting elution salt concentrations in the various systems. The developed models then were used to predict the retention of a deamidated Fab and isotype variants, with varying success. The results represent the first successful utilization of QSAR for the prediction of chromatographic behavior of complex proteins such as Fab fragments in multimodal chromatographic systems. The framework presented here can be employed to facilitate process development for the purification of biological products from product-related impurities by in silico screening of resin alternatives. Biotechnol. Bioeng. 2017;114: 1231-1240. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Ceramic Matrix Composites (CMC) Life Prediction Development

NASA Technical Reports Server (NTRS)

Levine, Stanley R.; Verrilli, Michael J.; Thomas, David J.; Halbig, Michael C.; Calomino, Anthony M.; Ellis, John R.; Opila, Elizabeth J.

1990-01-01

Advanced launch systems will very likely incorporate fiber reinforced ceramic matrix composites (CMC) in critical propulsion and airframe components. The use of CMC will save weight, increase operating margin, safety and performance, and improve reuse capability. For reusable and single mission use, accurate life prediction is critical to success. The tools to accomplish this are immature and not oriented toward the behavior of carbon fiber reinforced silicon carbide (C/SiC), the primary system of interest for many applications. This paper describes an approach and progress made to satisfy the need to develop an integrated life prediction system that addresses mechanical durability and environmental degradation.

Predictive sensor method and apparatus

NASA Technical Reports Server (NTRS)

Cambridge, Vivien J.; Koger, Thomas L.

1993-01-01

A microprocessor and electronics package employing predictive methodology was developed to accelerate the response time of slowly responding hydrogen sensors. The system developed improved sensor response time from approximately 90 seconds to 8.5 seconds. The microprocessor works in real-time providing accurate hydrogen concentration corrected for fluctuations in sensor output resulting from changes in atmospheric pressure and temperature. Following the successful development of the hydrogen sensor system, the system and predictive methodology was adapted to a commercial medical thermometer probe. Results of the experiment indicate that, with some customization of hardware and software, response time improvements are possible for medical thermometers as well as other slowly responding sensors.

Heatpipe power system and heatpipe bimodal system design and development options

NASA Technical Reports Server (NTRS)

Houts, M. G.; Poston, D. I.; Emrich, W. J., Jr.

1997-01-01

The Heatpipe Power System (HPS) is a potential, near-term, low-cost space fission power system. The Heatpipe Bimodal System (HBS) is a potential, near-term, low-cost space fission power and/or propulsion system. Both systems will be composed of independent modules, and all components operate within the existing databases. The HPS and HBS have relatively few system integration issues; thus, the successful development of a module is a significant step toward verifying system feasibility and performance estimates. A prototypic HPS module is being fabricated, and testing is scheduled to begin in November 1996. A successful test will provide high confidence that the HPS can achieve its predicted performance.

Progress in Earth System Modeling since the ENIAC Calculation

NASA Astrophysics Data System (ADS)

Fung, I.

2009-05-01

The success of the first numerical weather prediction experiment on the ENIAC computer in 1950 was hinged on the expansion of the meteorological observing network, which led to theoretical advances in atmospheric dynamics and subsequently the implementation of the simplified equations on the computer. This paper briefly reviews the progress in Earth System Modeling and climate observations, and suggests a strategy to sustain and expand the observations needed to advance climate science and prediction.

Shared leadership in multiteam systems: how cockpit and cabin crews lead each other to safety.

PubMed

Bienefeld, Nadine; Grote, Gudela

2014-03-01

In this study, we aimed to examine the effect of shared leadership within and across teams in multiteam systems (MTS) on team goal attainment and MTS success. Due to different and sometimes competing goals in MTS, leadership is required within and across teams. Shared leadership, the effectiveness of which has been proven in single teams, may be an effective strategy to cope with these challenges. We observed leadership in 84 cockpit and cabin crews that collaborated in the form of six-member MTS aircrews (N = 504) during standardized simulations of an in-flight emergency. Leadership was coded by three trained observers using a structured observation system. Team goal attainment was assessed by two subject matter experts using a checklist-based rating tool. MTS goal attainment was measured objectively on the basis of the outcome of the simulated flights. In successful MTS aircrews, formal leaders and team members displayed significantly more leadership behaviors, shared leadership by pursers and flight attendants predicted team goal attainment, and pursers' shared leadership across team boundaries predicted cross-team goal attainment. In cockpit crews, leadership was not shared and captains' vertical leadership predicted team goal attainment regardless of MTS success. The results indicate that in general, shared leadership positively relates to team goal attainment and MTS success,whereby boundary spanners' dual leadership role is key. Leadership training in MTS should address shared rather than merely vertical forms of leadership, and component teams in MTS should be trained together with emphasis on boundary spanners' dual leadership role. Furthermore, team members should be empowered to engage in leadership processes when required.

Control of epileptic seizures in WAG/Rij rats by means of brain-computer interface

NASA Astrophysics Data System (ADS)

Makarov, Vladimir V.; Maksimenko, Vladimir A.; van Luijtelaar, Gilles; Lüttjohann, Annika; Hramov, Alexander E.

2018-02-01

The main issue of epileptology is the elimination of epileptic events. This can be achieved by a system that predicts the emergence of seizures in conjunction with a system that interferes with the process that leads to the onset of seizure. The prediction of seizures remains, for the present, unresolved in the absence epilepsy, due to the sudden onset of seizures. We developed an algorithm for predicting seizures in real time, evaluated it and implemented it into an online closed-loop brain stimulation system designed to prevent typical for the absence of epilepsy of spike waves (SWD) in the genetic rat model. The algorithm correctly predicts more than 85% of the seizures and the rest were successfully detected. Unlike the old beliefs that SWDs are unpredictable, current results show that they can be predicted and that the development of systems for predicting and preventing closed-loop capture is a feasible step on the way to intervention to achieve control and freedom from epileptic seizures.

Evaluating the success of an emergency response medical information system.

PubMed

Petter, Stacie; Fruhling, Ann

2011-07-01

STATPack™ is an information system used to aid in the diagnosis of pathogens in hospitals and state public health laboratories. STATPack™ is used as a communication and telemedicine diagnosis tool during emergencies. This paper explores the success of this emergency response medical information system (ERMIS) using a well-known framework of information systems success developed by DeLone and McLean. Using an online survey, the entire population of STATPack™ users evaluated the success of the information system by considering system quality, information quality, system use, intention to use, user satisfaction, individual impact, and organizational impact. The results indicate that the overall quality of this ERMIS (i.e., system quality, information quality, and service quality) has a positive impact on both user satisfaction and intention to use the system. However, given the nature of ERMIS, overall quality does not necessarily predict use of the system. Moreover, the user's satisfaction with the information system positively affected the intention to use the system. User satisfaction, intention to use, and system use had a positive influence on the system's impact on the individual. Finally, the organizational impacts of the system were positively influenced by use of the system and the system's individual impact on the user. The results of the study demonstrate how to evaluate the success of an ERMIS as well as introduce potential changes in how one applies the DeLone and McLean success model in an emergency response medical information system context. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

TRAC-PF1 code verification with data from the OTIS test facility. [Once-Through Intergral System

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

Childerson, M.T.; Fujita, R.K.

1985-01-01

A computer code (TRAC-PF1/MOD1) developed for predicting transient thermal and hydraulic integral nuclear steam supply system (NSSS) response was benchmarked. Post-small break loss-of-coolant accident (LOCA) data from a scaled, experimental facility, designated the One-Through Integral System (OTIS), were obtained for the Babcock and Wilcox NSSS and compared to TRAC predictions. The OTIS tests provided a challenging small break LOCA data set for TRAC verification. The major phases of a small break LOCA observed in the OTIS tests included pressurizer draining and loop saturation, intermittent reactor coolant system circulation, boiler-condenser mode, and the initial stages of refill. The TRAC code wasmore » successful in predicting OTIS loop conditions (system pressures and temperatures) after modification of the steam generator model. In particular, the code predicted both pool and auxiliary-feedwater initiated boiler-condenser mode heat transfer.« less

Higher levels of salivary alpha-amylase predict failure of cessation efforts in male smokers.

PubMed

Dušková, M; Simůnková, K; Hill, M; Hruškovičová, H; Hoskovcová, P; Králíková, E; Stárka, L

2010-01-01

The ability to predict the success or failure of smoking cessation efforts will be useful for clinical practice. Stress response is regulated by two primary neuroendocrine systems. Salivary cortisol has been used as a marker for the hypothalamus-pituitary-adrenocortical axis and salivary alpha-amylase as a marker for the sympathetic adrenomedullary system. We studied 62 chronic smokers (34 women and 28 men with an average age of 45.2+/-12.9 years). The levels of salivary cortisol and salivary alpha-amylase were measured during the period of active smoking, and 6 weeks and 24 weeks after quitting. We analyzed the men separately from the women. The men who were unsuccessful in cessation showed significantly higher levels of salivary alpha-amylase over the entire course of the cessation attempt. Before stopping smoking, salivary cortisol levels were higher among the men who were unsuccessful in smoking cessation. After quitting, there were no differences between this group and the men who were successful in cessation. In women we found no differences between groups of successful and unsuccessful ex-smokers during cessation. In conclusions, increased levels of salivary alpha-amylase before and during smoking cessation may predict failure to quit in men. On the other hand, no advantage was found in predicting the failure to quit in women. The results of our study support previously described gender differences in smoking cessation.

Computational Gene Expression Modeling Identifies Salivary Biomarker Analysis that Predict Oral Feeding Readiness in the Newborn

PubMed Central

Maron, Jill L.; Hwang, Jooyeon S.; Pathak, Subash; Ruthazer, Robin; Russell, Ruby L.; Alterovitz, Gil

2014-01-01

Objective To combine mathematical modeling of salivary gene expression microarray data and systems biology annotation with RT-qPCR amplification to identify (phase I) and validate (phase II) salivary biomarker analysis for the prediction of oral feeding readiness in preterm infants. Study design Comparative whole transcriptome microarray analysis from 12 preterm newborns pre- and post-oral feeding success was used for computational modeling and systems biology analysis to identify potential salivary transcripts associated with oral feeding success (phase I). Selected gene expression biomarkers (15 from computational modeling; 6 evidence-based; and 3 reference) were evaluated by RT-qPCR amplification on 400 salivary samples from successful (n=200) and unsuccessful (n=200) oral feeders (phase II). Genes, alone and in combination, were evaluated by a multivariate analysis controlling for sex and post-conceptional age (PCA) to determine the probability that newborns achieved successful oral feeding. Results Advancing post-conceptional age (p < 0.001) and female sex (p = 0.05) positively predicted an infant’s ability to feed orally. A combination of five genes, NPY2R (hunger signaling), AMPK (energy homeostasis), PLXNA1 (olfactory neurogenesis), NPHP4 (visual behavior) and WNT3 (facial development), in addition to PCA and sex, demonstrated good accuracy for determining feeding success (AUROC = 0.78). Conclusions We have identified objective and biologically relevant salivary biomarkers that noninvasively assess a newborn’s developing brain, sensory and facial development as they relate to oral feeding success. Understanding the mechanisms that underlie the development of oral feeding readiness through translational and computational methods may improve clinical decision making while decreasing morbidities and health care costs. PMID:25620512

Oxytocin receptor density is associated with male mating tactics and social monogamy

PubMed Central

Ophir, Alexander G.; Gessel, Ana; Zheng, Da-Jiang; Phelps, Steven M.

2012-01-01

Despite its well-described role in female affiliation, the influence of oxytocin on male pairbonding is largely unknown. However, recent human studies indicate that this nonapeptide has a potent influence on male behaviors commonly associated with monogamy. Here we investigated the distribution of oxytocin receptors (OTR) throughout the forebrain of the socially monogamous male prairie vole (Microtus ochrogaster). Because males vary in both sexual and spatial fidelity, we explored the extent to which OTR predicted monogamous or non-monogamous patterns of space use, mating success and sexual fidelity in free-living males. We found that monogamous males expressed higher OTR density in the nucleus accumbens than non-monogamous males, a result that mirrors species differences in voles with different mating systems. OTR density in the posterior portion of the insula predicted mating success. Finally, OTR in the hippocampus and septohippocampal nucleus, which are nuclei associated with spatial memory, predicted patterns of space use and reproductive success within mating tactics. Our data highlight the importance of oxytocin receptor in neural structures associated with pairbonding and socio-spatial memory in male mating tactics. The role of memory in mating systems is often neglected, despite the fact that mating tactics impose an inherently spatial challenge for animals. Identifying mechanisms responsible for relating information about the social world with mechanisms mediating pairbonding and mating tactics is crucial to fully appreciate the suite of factors driving mating systems. PMID:22285648

Bioreactor design for successive culture of anchorage-dependent cells operated in an automated manner.

PubMed

Kino-Oka, Masahiro; Ogawa, Natsuki; Umegaki, Ryota; Taya, Masahito

2005-01-01

A novel bioreactor system was designed to perform a series of batchwise cultures of anchorage-dependent cells by means of automated operations of medium change and passage for cell transfer. The experimental data on contamination frequency ensured the biological cleanliness in the bioreactor system, which facilitated the operations in a closed environment, as compared with that in flask culture system with manual handlings. In addition, the tools for growth prediction (based on growth kinetics) and real-time growth monitoring by measurement of medium components (based on small-volume analyzing machinery) were installed into the bioreactor system to schedule the operations of medium change and passage and to confirm that culture proceeds as scheduled, respectively. The successive culture of anchorage-dependent cells was conducted with the bioreactor running in an automated way. The automated bioreactor gave a successful culture performance with fair accordance to preset scheduling based on the information in the latest subculture, realizing 79- fold cell expansion for 169 h. In addition, the correlation factor between experimental data and scheduled values through the bioreactor performance was 0.998. It was concluded that the proposed bioreactor with the integration of the prediction and monitoring tools could offer a feasible system for the manufacturing process of cultured tissue products.

Motivational Correlates of Academic Success in an Educational Psychology Course

ERIC Educational Resources Information Center

Herman, William E.

2011-01-01

The variables of class attendance and the institution-wide Early Alert Grading System were employed to predict academic success at the end of the semester. Classroom attendance was found to be statistically and significantly related to final average and accounted for 14-16% of the variance in academic performance. Class attendance was found to…

Development of a wideband pulse quaternary modulation system. [for an operational 400 Mbps baseband laser communication system

NASA Technical Reports Server (NTRS)

Federhofer, J. A.

1974-01-01

Laboratory data verifying the pulse quaternary modulation (PQM) theoretical predictions is presented. The first laboratory PQM laser communication system was successfully fabricated, integrated, tested and demonstrated. System bit error rate tests were performed and, in general, indicated approximately a 2 db degradation from the theoretically predicted results. These tests indicated that no gross errors were made in the initial theoretical analysis of PQM. The relative ease with which the entire PQM laboratory system was integrated and tested indicates that PQM is a viable candidate modulation scheme for an operational 400 Mbps baseband laser communication system.

3P: Personalized Pregnancy Prediction in IVF Treatment Process

NASA Astrophysics Data System (ADS)

Uyar, Asli; Ciray, H. Nadir; Bener, Ayse; Bahceci, Mustafa

We present an intelligent learning system for improving pregnancy success rate of IVF treatment. Our proposed model uses an SVM based classification system for training a model from past data and making predictions on implantation outcome of new embryos. This study employs an embryo-centered approach. Each embryo is represented with a data feature vector including 17 features related to patient characteristics, clinical diagnosis, treatment method and embryo morphological parameters. Our experimental results demonstrate a prediction accuracy of 82.7%. We have obtained the IVF dataset from Bahceci Women Health, Care Centre, in Istanbul, Turkey.

[Improvement and prediction of intestinal drug absorption].

PubMed

Miyake, Masateru

2013-01-01

The suppository preparation, which can improve the absorption of poorly absorbable drugs safer than commercially available suppositories, was developed by utilizing sodium laurate and taurine. Additionally, the novel oral absorption-improving system was also established by utilizing polyamines and bile acids. Furthermore, to evaluate the efficacy of these new formulations and estimate the absorbability of new drug candidates in humans, the in vitro prediction system utilizing an isolated human intestinal tissues was developed and successfully predicted the fraction of dose absorbed for several model drugs. These findings would contribute to the development of new dosage forms and new drugs for oral administration.

Filtering sensory information with XCSF: improving learning robustness and robot arm control performance.

PubMed

Kneissler, Jan; Stalph, Patrick O; Drugowitsch, Jan; Butz, Martin V

2014-01-01

It has been shown previously that the control of a robot arm can be efficiently learned using the XCSF learning classifier system, which is a nonlinear regression system based on evolutionary computation. So far, however, the predictive knowledge about how actual motor activity changes the state of the arm system has not been exploited. In this paper, we utilize the forward velocity kinematics knowledge of XCSF to alleviate the negative effect of noisy sensors for successful learning and control. We incorporate Kalman filtering for estimating successive arm positions, iteratively combining sensory readings with XCSF-based predictions of hand position changes over time. The filtered arm position is used to improve both trajectory planning and further learning of the forward velocity kinematics. We test the approach on a simulated kinematic robot arm model. The results show that the combination can improve learning and control performance significantly. However, it also shows that variance estimates of XCSF prediction may be underestimated, in which case self-delusional spiraling effects can hinder effective learning. Thus, we introduce a heuristic parameter, which can be motivated by theory, and which limits the influence of XCSF's predictions on its own further learning input. As a result, we obtain drastic improvements in noise tolerance, allowing the system to cope with more than 10 times higher noise levels.

Species traits and network structure predict the success and impacts of pollinator invasions.

PubMed

Valdovinos, Fernanda S; Berlow, Eric L; Moisset de Espanés, Pablo; Ramos-Jiliberto, Rodrigo; Vázquez, Diego P; Martinez, Neo D

2018-05-31

Species invasions constitute a major and poorly understood threat to plant-pollinator systems. General theory predicting which factors drive species invasion success and subsequent effects on native ecosystems is particularly lacking. We address this problem using a consumer-resource model of adaptive behavior and population dynamics to evaluate the invasion success of alien pollinators into plant-pollinator networks and their impact on native species. We introduce pollinator species with different foraging traits into network models with different levels of species richness, connectance, and nestedness. Among 31 factors tested, including network and alien properties, we find that aliens with high foraging efficiency are the most successful invaders. Networks exhibiting high alien-native diet overlap, fraction of alien-visited plant species, most-generalist plant connectivity, and number of specialist pollinator species are the most impacted by invaders. Our results mimic several disparate observations conducted in the field and potentially elucidate the mechanisms responsible for their variability.

STONE score versus Guy's Stone Score - prospective comparative evaluation for success rate and complications in percutaneous nephrolithotomy

PubMed Central

Kumar, Ujwal; Tomar, Vinay; Yadav, Sher Singh; Priyadarshi, Shivam; Vyas, Nachiket; Agarwal, Neeraj; Dayal, Ram

2018-01-01

Purpose: The aim of the current study was to compare Guy's score and STONE score in predicting the success and complication rate of percutaneous nephrolithotomy (PCNL). Materials and Methods: A total of 445 patients were included in the study between July 2015 and December 2016. The patients were given STONE score and Guy's Stone Score (GSS) grades based on CT scan done preoperatively and intra- and post-operative complications were graded using the modified Clavien grading system. The PCNL were done by a standard technique in prone positions. Results: The success rate in our study was 86.29% and both the GSS and STONE score were significantly associated with a success rate of the procedure. Both the scoring systems correlated with operative time and postoperative hospital stay. Of the total cases, 102 patients (22.92%) experienced complications. A correlation between STONE score stratified into low, moderate, and high nephrolithometry score risk groups (low scores 4–5, moderate scores 6–8, high scores 9–13), and complication was also found (P = 0.04) but not between the GSS and complication rate (P = 0.054). Conclusion: Both GSS and STONE scores are equally effective in predicting success rate of the procedure. PMID:29416280

A system identification approach for developing model predictive controllers of antibody quality attributes in cell culture processes

PubMed Central

Schmitt, John; Beller, Justin; Russell, Brian; Quach, Anthony; Hermann, Elizabeth; Lyon, David; Breit, Jeffrey

2017-01-01

As the biopharmaceutical industry evolves to include more diverse protein formats and processes, more robust control of Critical Quality Attributes (CQAs) is needed to maintain processing flexibility without compromising quality. Active control of CQAs has been demonstrated using model predictive control techniques, which allow development of processes which are robust against disturbances associated with raw material variability and other potentially flexible operating conditions. Wide adoption of model predictive control in biopharmaceutical cell culture processes has been hampered, however, in part due to the large amount of data and expertise required to make a predictive model of controlled CQAs, a requirement for model predictive control. Here we developed a highly automated, perfusion apparatus to systematically and efficiently generate predictive models using application of system identification approaches. We successfully created a predictive model of %galactosylation using data obtained by manipulating galactose concentration in the perfusion apparatus in serialized step change experiments. We then demonstrated the use of the model in a model predictive controller in a simulated control scenario to successfully achieve a %galactosylation set point in a simulated fed‐batch culture. The automated model identification approach demonstrated here can potentially be generalized to many CQAs, and could be a more efficient, faster, and highly automated alternative to batch experiments for developing predictive models in cell culture processes, and allow the wider adoption of model predictive control in biopharmaceutical processes. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 33:1647–1661, 2017 PMID:28786215

Effects of using visualization and animation in presentations to communities about forest succession and fire behavior potential

Treesearch

Jane Kapler Smith; Donald E. Zimmerman; Carol Akerelrea; Garrett O' Keefe

2008-01-01

Natural resource managers use a variety of computer-mediated presentation methods to communicate management practices to the public. We explored the effects of using the Stand Visualization System to visualize and animate predictions from the Forest Vegetation Simulator-Fire and Fuels Extension in presentations explaining forest succession (forest growth and change...

Tailoring biocontrol to maximize top-down effects: on the importance of underlying site fertility.

PubMed

Hovick, Stephen M; Carson, Walter P

2015-01-01

The degree to which biocontrol agents impact invasive plants varies widely across landscapes, often for unknown reasons. Understanding this variability can help optimize invasive species management while also informing our understanding of trophic linkages. To address these issues, we tested three hypotheses with contrasting predictions regarding the likelihood of biocontrol success. (1) The biocontrol effort hypothesis: invasive populations are regulated primarily by top-down effects, predicting that increased biocontrol efforts alone (e.g., more individuals of a given biocontrol agent or more time since agent release) will enhance biocontrol success. (2) The relative fertility hypothesis: invasive populations are regulated primarily by bottom-up effects, predicting that nutrient enrichment will increase dominance by invasives and thus reduce biocontrol success, regardless of biocontrol efforts. (3) The fertility-dependent biocontrol effort hypothesis: top-down effects will only regulate invasive populations if bottom-up effects are weak. It predicts that greater biocontrol efforts will increase biocontrol success, but only in low-nutrient sites. To test these hypotheses, we surveyed 46 sites across three states with prior releases of Galerucella beetles, the most common biocontrol agents used against invasive purple loosestrife (Lythrum salicaria). We found strong support for the fertility-dependent biocontrol effort hypothesis, as biocontrol success occurred most often with greater biocontrol efforts, but only in low-fertility sites. This result held for early stage metrics of biocontrol success (higher Galerucella abundance) and ultimate biocontrol outcomes (decreased loosestrife plant size and abundance). Presence of the invasive grass Phalaris arundinacea was also inversely related to loosestrife abundance, suggesting that biocontrol-based reductions in loosestrife made secondary invasion by P. arundinacea more likely. Our data suggest that low-nutrient sites be prioritized for loosestrife biocontrol and that future monitoring account for variation in site fertility or work to mitigate it. We introduce a new framework that integrates our findings with conflicting patterns previously reported from other biocontrol systems, proposing a unimodal relationship whereby nutrient availability enhances biocontrol success in low-nutrient sites but hampers it in high-nutrient sites. Our results represent one of the first examples of biocontrol success depending on site fertility, which has the potential to inform biocontrol-based management decisions across entire regions and among contrasting systems.

Japan Meteorological Agency/Meteorological Research Institute-Coupled Prediction System version 1 (JMA/MRI-CPS1) for operational seasonal forecasting

NASA Astrophysics Data System (ADS)

Takaya, Yuhei; Yasuda, Tamaki; Fujii, Yosuke; Matsumoto, Satoshi; Soga, Taizo; Mori, Hirotoshi; Hirai, Masayuki; Ishikawa, Ichiro; Sato, Hitoshi; Shimpo, Akihiko; Kamachi, Masafumi; Ose, Tomoaki

2017-01-01

This paper describes the operational seasonal prediction system of the Japan Meteorological Agency (JMA), the Japan Meteorological Agency/Meteorological Research Institute-Coupled Prediction System version 1 (JMA/MRI-CPS1), which was in operation at JMA during the period between February 2010 and May 2015. The predictive skill of the system was assessed with a set of retrospective seasonal predictions (reforecasts) covering 30 years (1981-2010). JMA/MRI-CPS1 showed reasonable predictive skill for the El Niño-Southern Oscillation, comparable to the skills of other state-of-the-art systems. The one-tiered approach adopted in JMA/MRI-CPS1 improved its overall predictive skills for atmospheric predictions over those of the two-tiered approach of the previous uncoupled system. For 3-month predictions with a 1-month lead, JMA/MRI-CPS1 showed statistically significant skills in predicting 500-hPa geopotential height and 2-m temperature in East Asia in most seasons; thus, it is capable of providing skillful seasonal predictions for that region. Furthermore, JMA/MRI-CPS1 was superior overall to the previous system for atmospheric predictions with longer (4-month) lead times. In particular, JMA/MRI-CPS1 was much better able to predict the Asian Summer Monsoon than the previous two-tiered system. This enhanced performance was attributed to the system's ability to represent atmosphere-ocean coupled variability over the Indian Ocean and the western North Pacific from boreal winter to summer following winter El Niño events, which in turn influences the East Asian summer climate through the Pacific-Japan teleconnection pattern. These substantial improvements obtained by using an atmosphere-ocean coupled general circulation model underpin its success in providing more skillful seasonal forecasts on an operational basis.

Prognostics

NASA Technical Reports Server (NTRS)

Goebel, Kai; Vachtsevanos, George; Orchard, Marcos E.

2013-01-01

Knowledge discovery, statistical learning, and more specifically an understanding of the system evolution in time when it undergoes undesirable fault conditions, are critical for an adequate implementation of successful prognostic systems. Prognosis may be understood as the generation of long-term predictions describing the evolution in time of a particular signal of interest or fault indicator, with the purpose of estimating the remaining useful life (RUL) of a failing component/subsystem. Predictions are made using a thorough understanding of the underlying processes and factor in the anticipated future usage.

Initialization and Predictability of a Coupled ENSO Forecast Model

NASA Technical Reports Server (NTRS)

Chen, Dake; Zebiak, Stephen E.; Cane, Mark A.; Busalacchi, Antonio J.

1997-01-01

The skill of a coupled ocean-atmosphere model in predicting ENSO has recently been improved using a new initialization procedure in which initial conditions are obtained from the coupled model, nudged toward observations of wind stress. The previous procedure involved direct insertion of wind stress observations, ignoring model feedback from ocean to atmosphere. The success of the new scheme is attributed to its explicit consideration of ocean-atmosphere coupling and the associated reduction of "initialization shock" and random noise. The so-called spring predictability barrier is eliminated, suggesting that such a barrier is not intrinsic to the real climate system. Initial attempts to generalize the nudging procedure to include SST were not successful; possible explanations are offered. In all experiments forecast skill is found to be much higher for the 1980s than for the 1970s and 1990s, suggesting decadal variations in predictability.

Gender Interacts with Opioid Receptor Polymorphism A118G and Serotonin Receptor Polymorphism -1438 A/G on Speed-Dating Success.

PubMed

Wu, Karen; Chen, Chuansheng; Moyzis, Robert K; Greenberger, Ellen; Yu, Zhaoxia

2016-09-01

We examined an understudied but potentially important source of romantic attraction-genetics-using a speed-dating paradigm. The mu opioid receptor (OPRM1) polymorphism A118G (rs1799971) and the serotonin receptor (HTR2A) polymorphism -1438 A/G (rs6311) were studied because they have been implicated in social affiliation. Guided by the social role theory of mate selection and prior genetic evidence, we examined these polymorphisms' gender-specific associations with speed-dating success (i.e., date offers, mate desirability). A total of 262 single Asian Americans went on speed-dates with members of the opposite gender and completed interaction questionnaires about their partners. Consistent with our prediction, significant gender-by-genotype interactions were found for speed-dating success. Specifically, the minor variant of A118G (G-allele), which has been linked to submissiveness/social sensitivity, predicted greater speed-dating success for women, whereas the minor variant of -1438 A/G (G-allele), which has been linked to leadership/social dominance, predicted greater speed-dating success for men. For both polymorphisms, reverse "dampening" effects of minor variants were found for opposite-gender counterparts. These results support previous research on the importance of the opioid and serotonergic systems in social affiliation, indicating that their influence extends to dating success, with opposite, yet gender-norm consistent, effects for men and women.

HESI admission assessment (A(2)) examination scores, program progression, and NCLEX-RN success in baccalaureate nursing: an exploratory study of dependable academic indicators of success.

PubMed

Hinderer, Katherine A; DiBartolo, Mary C; Walsh, Catherine M

2014-01-01

In an effort to meet the demand for well-educated, high-quality nurses, schools of nursing seek to admit those candidates most likely to have both timely progression and first-time success on the National Council Licensure Examination for Registered Nurses (NCLEX-RN). Finding the right combination of academic indicators, which are most predictive of success, continues to be an ongoing challenge for entry-level baccalaureate nursing programs across the United States. This pilot study explored the relationship of a standardized admission examination, the Health Education Systems, Inc. (HESI) Admission Assessment (A(2)) Examination to preadmission grade point average (GPA), science GPA, and nursing GPA using a retrospective descriptive design. In addition, the predictive ability of the A(2) Examination, preadmission GPA, and science GPA related to timely progression and NCLEX-RN success were explored. In a sample of 89 students, no relationship was found between the A(2) Examination and preadmission GPA or science GPA. The A(2) Examination was correlated with nursing GPA and NCLEX-RN success but not with timely progression. Further studies are needed to explore the utility and predictive ability of standardized examinations such as the A(2) Examination and the contribution of such examinations to evidence-based admission decision making. Copyright © 2014 Elsevier Inc. All rights reserved.

Laying the Groundwork for NCLEX Success: An Exploration of Adaptive Quizzing as an Examination Preparation Method.

PubMed

Cox-Davenport, Rebecca A; Phelan, Julia C

2015-05-01

First-time NCLEX-RN pass rates are an important indicator of nursing school success and quality. Nursing schools use different methods to anticipate NCLEX outcomes and help prevent student failure and possible threat to accreditation. This study evaluated the impact of a shift in NCLEX preparation policy at a BSN program in the southeast United States. The policy shifted from the use of predictor score thresholds to determine graduation eligibility to a more proactive remediation strategy involving adaptive quizzing. A descriptive correlational design evaluated the impact of an adaptive quizzing system designed to give students ongoing active practice and feedback and explored the relationship between predictor examinations and NCLEX success. Data from student usage of the system as well as scores on predictor tests were collected for three student cohorts. Results revealed a positive correlation between adaptive quizzing system usage and content mastery. Two of the 69 students in the sample did not pass the NCLEX. With so few students failing the NCLEX, predictability of any course variables could not be determined. The power of predictor examinations to predict NCLEX failure could also not be supported. The most consistent factor among students, however, was their content mastery level within the adaptive quizzing system. Implications of these findings are discussed.

Predicting length of stay from an electronic patient record system: a primary total knee replacement example.

PubMed

Carter, Evelene M; Potts, Henry W W

2014-04-04

To investigate whether factors can be identified that significantly affect hospital length of stay from those available in an electronic patient record system, using primary total knee replacements as an example. To investigate whether a model can be produced to predict the length of stay based on these factors to help resource planning and patient expectations on their length of stay. Data were extracted from the electronic patient record system for discharges from primary total knee operations from January 2007 to December 2011 (n=2,130) at one UK hospital and analysed for their effect on length of stay using Mann-Whitney and Kruskal-Wallis tests for discrete data and Spearman's correlation coefficient for continuous data. Models for predicting length of stay for primary total knee replacements were tested using the Poisson regression and the negative binomial modelling techniques. Factors found to have a significant effect on length of stay were age, gender, consultant, discharge destination, deprivation and ethnicity. Applying a negative binomial model to these variables was successful. The model predicted the length of stay of those patients who stayed 4-6 days (~50% of admissions) with 75% accuracy within 2 days (model data). Overall, the model predicted the total days stayed over 5 years to be only 88 days more than actual, a 6.9% uplift (test data). Valuable information can be found about length of stay from the analysis of variables easily extracted from an electronic patient record system. Models can be successfully created to help improve resource planning and from which a simple decision support system can be produced to help patient expectation on their length of stay.

Adaptive neuro-fuzzy and expert systems for power quality analysis and prediction of abnormal operation

NASA Astrophysics Data System (ADS)

Ibrahim, Wael Refaat Anis

The present research involves the development of several fuzzy expert systems for power quality analysis and diagnosis. Intelligent systems for the prediction of abnormal system operation were also developed. The performance of all intelligent modules developed was either enhanced or completely produced through adaptive fuzzy learning techniques. Neuro-fuzzy learning is the main adaptive technique utilized. The work presents a novel approach to the interpretation of power quality from the perspective of the continuous operation of a single system. The research includes an extensive literature review pertaining to the applications of intelligent systems to power quality analysis. Basic definitions and signature events related to power quality are introduced. In addition, detailed discussions of various artificial intelligence paradigms as well as wavelet theory are included. A fuzzy-based intelligent system capable of identifying normal from abnormal operation for a given system was developed. Adaptive neuro-fuzzy learning was applied to enhance its performance. A group of fuzzy expert systems that could perform full operational diagnosis were also developed successfully. The developed systems were applied to the operational diagnosis of 3-phase induction motors and rectifier bridges. A novel approach for learning power quality waveforms and trends was developed. The technique, which is adaptive neuro fuzzy-based, learned, compressed, and stored the waveform data. The new technique was successfully tested using a wide variety of power quality signature waveforms, and using real site data. The trend-learning technique was incorporated into a fuzzy expert system that was designed to predict abnormal operation of a monitored system. The intelligent system learns and stores, in compressed format, trends leading to abnormal operation. The system then compares incoming data to the retained trends continuously. If the incoming data matches any of the learned trends, an alarm is instigated predicting the advent of system abnormal operation. The incoming data could be compared to previous trends as well as matched to trends developed through computer simulations and stored using fuzzy learning.

Long term impact of emotional, social and cognitive intelligence competencies and GMAT on career and life satisfaction and career success

PubMed Central

Amdurer, Emily; Boyatzis, Richard E.; Saatcioglu, Argun; Smith, Melvin L.; Taylor, Scott N.

2014-01-01

Career scholars have called for a broader definition of career success by inviting greater exploration of its antecedents. While success in various jobs has been predicted by intelligence and in other studies by competencies, especially in management, long term impact of having intelligence and using competencies has not been examined. Even in collegiate outcome studies, few have examined the longer term impact on graduates' careers or lives. This study assesses the impact of demonstrated emotional, social, and cognitive intelligence competencies assessed at graduation and g measured through GMAT at entry from an MBA program on career and life satisfaction, and career success assessed 5 to 19 years after graduation. Using behavioral measures of competencies (i.e., as assessed by others), we found that emotional intelligence competencies predict career satisfaction and success. Adaptability had a positive impact, but influence had the opposite effect on these career measures and life satisfaction. Life satisfaction was negatively affected by achievement orientation and positively affected by teamwork. Current salary, length of marriage, and being younger at time of graduation positively affect all three measures of life and career satisfaction and career success. GMAT (as a measure of g) predicted life satisfaction and career success to a slight but significant degree in the final model analyzed. Meanwhile, being female and number of children positively affected life satisfaction but cognitive intelligence competencies negatively affected it, and in particular demonstrated systems thinking was negative. PMID:25566128

Long term impact of emotional, social and cognitive intelligence competencies and GMAT on career and life satisfaction and career success.

PubMed

Amdurer, Emily; Boyatzis, Richard E; Saatcioglu, Argun; Smith, Melvin L; Taylor, Scott N

2014-01-01

Career scholars have called for a broader definition of career success by inviting greater exploration of its antecedents. While success in various jobs has been predicted by intelligence and in other studies by competencies, especially in management, long term impact of having intelligence and using competencies has not been examined. Even in collegiate outcome studies, few have examined the longer term impact on graduates' careers or lives. This study assesses the impact of demonstrated emotional, social, and cognitive intelligence competencies assessed at graduation and g measured through GMAT at entry from an MBA program on career and life satisfaction, and career success assessed 5 to 19 years after graduation. Using behavioral measures of competencies (i.e., as assessed by others), we found that emotional intelligence competencies predict career satisfaction and success. Adaptability had a positive impact, but influence had the opposite effect on these career measures and life satisfaction. Life satisfaction was negatively affected by achievement orientation and positively affected by teamwork. Current salary, length of marriage, and being younger at time of graduation positively affect all three measures of life and career satisfaction and career success. GMAT (as a measure of g) predicted life satisfaction and career success to a slight but significant degree in the final model analyzed. Meanwhile, being female and number of children positively affected life satisfaction but cognitive intelligence competencies negatively affected it, and in particular demonstrated systems thinking was negative.

Direct modeling parameter signature analysis and failure mode prediction of physical systems using hybrid computer optimization

NASA Technical Reports Server (NTRS)

Drake, R. L.; Duvoisin, P. F.; Asthana, A.; Mather, T. W.

1971-01-01

High speed automated identification and design of dynamic systems, both linear and nonlinear, are discussed. Special emphasis is placed on developing hardware and techniques which are applicable to practical problems. The basic modeling experiment and new results are described. Using the improvements developed successful identification of several systems, including a physical example as well as simulated systems, was obtained. The advantages of parameter signature analysis over signal signature analysis in go-no go testing of operational systems were demonstrated. The feasibility of using these ideas in failure mode prediction in operating systems was also investigated. An improved digital controlled nonlinear function generator was developed, de-bugged, and completely documented.

Development and initial evaluation of the Clinical Information Systems Success Model (CISSM).

PubMed

Garcia-Smith, Dianna; Effken, Judith A

2013-06-01

Most clinical information systems (CIS) today are technically sound, but the number of successful implementations of these systems is low. The purpose of this study was to develop and test a theoretically based integrated CIS Success Model (CISSM) from the nurse perspective. Model predictors of CIS success were taken from existing research on information systems acceptance, user satisfaction, use intention, user behavior and perceptions, as well as clinical research. Data collected online from 234 registered nurses in four hospitals were used to test the model. Each nurse had used the Cerner Power Chart Admission Health Profile for at least 3 months. Psychometric testing and factor analysis of the 23-item CISSM instrument established its construct validity and reliability. Initial analysis showed nurses' satisfaction with and dependency on CIS use predicted their perceived CIS use Net Benefit. Further analysis identified Social Influence and Facilitating Conditions as other predictors of CIS user Net Benefit. The level of hospital CIS integration may account for the role of CIS Use Dependency in the success of CIS. Based on our experience, CISSM provides a formative as well as summative tool for evaluating CIS success from the nurse's perspective. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Developments in Science and Technology.

DTIC Science & Technology

1980-01-01

control. Sucessful completion of the testing and cer- a single unduplicated track file, thereby reducing tification of readiness represents a...Navy shipboard surveillance radar systems Service Corp., is called the single radar performance has been successfully designed, developed, and tested at...for Navy deteciion/disclosure ranges. The single radar per- shipboard surveillance radar systems are reduced by formance prediction system can be

The ‘prediction imperative’ as the basis for self-awareness

PubMed Central

Llinás, Rodolfo R.; Roy, Sisir

2009-01-01

Here, we propose that global brain function is geared towards the implementation of intelligent motricity. Motricity is the only possible external manifestation of nervous system function (other than endocrine and exocrine secretion and the control of vascular tone). The intelligence component of motricity requires, for its successful wheeling, a prediction imperative to approximate the consequences of the impending motion. We address how such predictive function may originate from the dynamic properties of neuronal networks. PMID:19528011

Oxytocin receptor density is associated with male mating tactics and social monogamy.

PubMed

Ophir, Alexander G; Gessel, Ana; Zheng, Da-Jiang; Phelps, Steven M

2012-03-01

Despite its well-described role in female affiliation, the influence of oxytocin on male pairbonding is largely unknown. However, recent human studies indicate that this nonapeptide has a potent influence on male behaviors commonly associated with monogamy. Here we investigated the distribution of oxytocin receptors (OTR) throughout the forebrain of the socially monogamous male prairie vole (Microtus ochrogaster). Because males vary in both sexual and spatial fidelity, we explored the extent to which OTR predicted monogamous or non-monogamous patterns of space use, mating success and sexual fidelity in free-living males. We found that monogamous males expressed higher OTR density in the nucleus accumbens than non-monogamous males, a result that mirrors species differences in voles with different mating systems. OTR density in the posterior portion of the insula predicted mating success. Finally, OTR in the hippocampus and septohippocampal nucleus, which are nuclei associated with spatial memory, predicted patterns of space use and reproductive success within mating tactics. Our data highlight the importance of oxytocin receptor in neural structures associated with pairbonding and socio-spatial memory in male mating tactics. The role of memory in mating systems is often neglected, despite the fact that mating tactics impose an inherently spatial challenge for animals. Identifying mechanisms responsible for relating information about the social world with mechanisms mediating pairbonding and mating tactics is crucial to fully appreciate the suite of factors driving mating systems. This article is part of a Special Issue entitled Oxytocin, Vasopressin, and Social Behavior. Published by Elsevier Inc.

Comparing Weighted and Unweighted Grade Point Averages in Predicting College Success of Diverse and Low-Income College Students

ERIC Educational Resources Information Center

Warne, Russell T.; Nagaishi, Chanel; Slade, Michael K.; Hermesmeyer, Paul; Peck, Elizabeth Kimberli

2014-01-01

While research has shown the statistical significance of high school grade point averages (HSGPAs) in predicting future academic outcomes, the systems with which HSGPAs are calculated vary drastically across schools. Some schools employ unweighted grades that carry the same point value regardless of the course in which they are earned; other…

Dynamic contraction behaviour of pneumatic artificial muscle

NASA Astrophysics Data System (ADS)

Doumit, Marc D.; Pardoel, Scott

2017-07-01

The development of a dynamic model for the Pneumatic Artificial Muscle (PAM) is an imperative undertaking for understanding and analyzing the behaviour of the PAM as a function of time. This paper proposes a Newtonian based dynamic PAM model that includes the modeling of the muscle geometry, force, inertia, fluid dynamic, static and dynamic friction, heat transfer and valve flow while ignoring the effect of bladder elasticity. This modeling contribution allows the designer to predict, analyze and optimize PAM performance prior to its development. Thus advancing successful implementations of PAM based powered exoskeletons and medical systems. To date, most muscle dynamic properties are determined experimentally, furthermore, no analytical models that can accurately predict the muscle's dynamic behaviour are found in the literature. Most developed analytical models adequately predict the muscle force in static cases but neglect the behaviour of the system in the transient response. This could be attributed to the highly challenging task of deriving such a dynamic model given the number of system elements that need to be identified and the system's highly non-linear properties. The proposed dynamic model in this paper is successfully simulated through MATLAB programing and validated the pressure, contraction distance and muscle temperature with experimental testing that is conducted with in-house built prototype PAM's.

A Genetically Optimized Predictive System for Success in General Chemistry Using a Diagnostic Algebra Test

NASA Astrophysics Data System (ADS)

Cooper, Cameron I.; Pearson, Paul T.

2012-02-01

In higher education, many high-enrollment introductory courses have evolved into "gatekeeper" courses due to their high failure rates. These courses prevent many students from attaining their educational goals and often become graduation roadblocks. At the authors' home institution, general chemistry has become a gatekeeper course in which approximately 25% of students do not pass. This failure rate in chemistry is common, and often higher, at many other institutions of higher education, and mathematical deficiencies are perceived to be a large contributing factor. This paper details the development of a highly accurate predictive system that identifies students at the beginning of the semester who are "at-risk" for earning a grade of C- or below in chemistry. The predictive accuracy of this system is maximized by using a genetically optimized neural network to analyze the results of a diagnostic algebra test designed for a specific population. Once at-risk students have been identified, they can be helped to improve their chances of success using techniques such as concurrent support courses, online tutorials, "just-in-time" instructional aides, study skills, motivational interviewing, and/or peer mentoring.

Validation of the GILLS score for tongue-lip adhesion in Robin sequence patients.

PubMed

Abramowicz, Shelly; Bacic, Janine D; Mulliken, John B; Rogers, Gary F

2012-03-01

The GILLS score consists of gastroesophageal reflux disease, preoperative intubation, late surgical intervention, low birth weight, and syndromic diagnosis. The purpose of this study was to test the validity of the GILLS score in predicting success of tongue-lip adhesion (TLA) in managing Robin sequence. Infants with Robin sequence were included in the study if they had a TLA for airway compromise subsequent to formulation of the GILLS scoring system, that is, they were not included in the original GILLS analysis. The patients were prospectively considered based on the presence of the 5 factors that constitute the GILLS score. A score of ≤ 2 predicts success of TLA. Twenty patients met the inclusion criteria. Tongue-lip adhesion managed the compromised airway in 18 (90%) of 20 patients. Overall, the GILLS score had a sensitivity of 83%, specificity of 50%, positive predictive value of 94%, and negative predictive value of 25%. The GILLS score accurately predicts a successful outcome for TLA in infants with Robin sequence. For infants with a score of 2 or less, TLA is the procedure of choice. Infants with a GILLS score of 3 or greater were 5 times more likely to fail TLA than those with a score of 2 or less. In these patients, other methods of managing the airway should be considered.

Reliability Driven Space Logistics Demand Analysis

NASA Technical Reports Server (NTRS)

Knezevic, J.

1995-01-01

Accurate selection of the quantity of logistic support resources has a strong influence on mission success, system availability and the cost of ownership. At the same time the accurate prediction of these resources depends on the accurate prediction of the reliability measures of the items involved. This paper presents a method for the advanced and accurate calculation of the reliability measures of complex space systems which are the basis for the determination of the demands for logistics resources needed during the operational life or mission of space systems. The applicability of the method presented is demonstrated through several examples.

A theoretical approach for estimation of ultimate size of bimetallic nanocomposites synthesized in microemulsion systems

NASA Astrophysics Data System (ADS)

Salabat, Alireza; Saydi, Hassan

2012-12-01

In this research a new idea for prediction of ultimate sizes of bimetallic nanocomposites synthesized in water-in-oil microemulsion system is proposed. In this method, by modifying Tabor Winterton approximation equation, an effective Hamaker constant was introduced. This effective Hamaker constant was applied in the van der Waals attractive interaction energy. The obtained effective van der Waals interaction energy was used as attractive contribution in the total interaction energy. The modified interaction energy was applied successfully to predict some bimetallic nanoparticles, at different mass fraction, synthesized in microemulsion system of dioctyl sodium sulfosuccinate (AOT)/isooctane.

From Data-Sharing to Model-Sharing: SCEC and the Development of Earthquake System Science (Invited)

NASA Astrophysics Data System (ADS)

Jordan, T. H.

2009-12-01

Earthquake system science seeks to construct system-level models of earthquake phenomena and use them to predict emergent seismic behavior—an ambitious enterprise that requires high degree of interdisciplinary, multi-institutional collaboration. This presentation will explore model-sharing structures that have been successful in promoting earthquake system science within the Southern California Earthquake Center (SCEC). These include disciplinary working groups to aggregate data into community models; numerical-simulation working groups to investigate system-specific phenomena (process modeling) and further improve the data models (inverse modeling); and interdisciplinary working groups to synthesize predictive system-level models. SCEC has developed a cyberinfrastructure, called the Community Modeling Environment, that can distribute the community models; manage large suites of numerical simulations; vertically integrate the hardware, software, and wetware needed for system-level modeling; and promote the interactions among working groups needed for model validation and refinement. Various socio-scientific structures contribute to successful model-sharing. Two of the most important are “communities of trust” and collaborations between government and academic scientists on mission-oriented objectives. The latter include improvements of earthquake forecasts and seismic hazard models and the use of earthquake scenarios in promoting public awareness and disaster management.

fMRI Reactivity to High-Calorie Food Pictures Predicts Short- and Long-Term Outcome in a Weight-Loss Program

PubMed Central

Murdaugh, Donna L.; Cox, James E.; Cook, Edwin W.; Weller, Rosalyn E.

2011-01-01

Behavioral studies have suggested that food cues have stronger motivating effects in obese than in normal-weight individuals, which may be a risk factor underlying obesity. Previous cross-sectional neuroimaging studies have suggested that this difference is mediated by increased reactivity to food cues in parts of the reward system in obese individuals. To date, however, only a few prospective neuroimaging studies have been conducted to examine whether individual differences in brain activation elicited by food cues can predict differences in weight change. We used functional magnetic resonance imaging (fMRI) to investigate activation in reward-system as well as other brain regions in response to viewing high-calorie food vs. control pictures in 25 obese individuals before and after a 12-week psychosocial weight-loss treatment and at 9-mo follow-up. In those obese individuals who were least successful in losing weight during the treatment, we found greater pre-treatment activation to high-calorie food vs. control pictures in brain regions implicated in reward-system processes, such as the nucleus accumbens, anterior cingulate, and insula. We found similar correlations with weight loss in brain regions implicated by other studies in vision and attention, such as superior occipital cortex, inferior and superior parietal lobule, and prefrontal cortex. Furthermore, less successful weight maintenance at 9-mo follow-up was predicted by greater post-treatment activation in such brain regions as insula, ventral tegmental area, putamen, and fusiform gyrus. In summary, we found that greater activation in brain regions mediating motivational and attentional salience of food cues in obese individuals at the start of a weight-loss program was predictive of less success in the program and that such activation following the program predicted poorer weight control over a 9-mo follow-up period. PMID:22332246

Designing and benchmarking the MULTICOM protein structure prediction system

PubMed Central

2013-01-01

Background Predicting protein structure from sequence is one of the most significant and challenging problems in bioinformatics. Numerous bioinformatics techniques and tools have been developed to tackle almost every aspect of protein structure prediction ranging from structural feature prediction, template identification and query-template alignment to structure sampling, model quality assessment, and model refinement. How to synergistically select, integrate and improve the strengths of the complementary techniques at each prediction stage and build a high-performance system is becoming a critical issue for constructing a successful, competitive protein structure predictor. Results Over the past several years, we have constructed a standalone protein structure prediction system MULTICOM that combines multiple sources of information and complementary methods at all five stages of the protein structure prediction process including template identification, template combination, model generation, model assessment, and model refinement. The system was blindly tested during the ninth Critical Assessment of Techniques for Protein Structure Prediction (CASP9) in 2010 and yielded very good performance. In addition to studying the overall performance on the CASP9 benchmark, we thoroughly investigated the performance and contributions of each component at each stage of prediction. Conclusions Our comprehensive and comparative study not only provides useful and practical insights about how to select, improve, and integrate complementary methods to build a cutting-edge protein structure prediction system but also identifies a few new sources of information that may help improve the design of a protein structure prediction system. Several components used in the MULTICOM system are available at: http://sysbio.rnet.missouri.edu/multicom_toolbox/. PMID:23442819

Development and Validation of a Novel Scoring System for Predicting Technical Success of Chronic Total Occlusion Percutaneous Coronary Interventions: The PROGRESS CTO (Prospective Global Registry for the Study of Chronic Total Occlusion Intervention) Score.

PubMed

Christopoulos, Georgios; Kandzari, David E; Yeh, Robert W; Jaffer, Farouc A; Karmpaliotis, Dimitri; Wyman, Michael R; Alaswad, Khaldoon; Lombardi, William; Grantham, J Aaron; Moses, Jeffrey; Christakopoulos, Georgios; Tarar, Muhammad Nauman J; Rangan, Bavana V; Lembo, Nicholas; Garcia, Santiago; Cipher, Daisha; Thompson, Craig A; Banerjee, Subhash; Brilakis, Emmanouil S

2016-01-11

This study sought to develop a novel parsimonious score for predicting technical success of chronic total occlusion (CTO) percutaneous coronary intervention (PCI) performed using the hybrid approach. Predicting technical success of CTO PCI can facilitate clinical decision making and procedural planning. We analyzed clinical and angiographic parameters from 781 CTO PCIs included in PROGRESS CTO (Prospective Global Registry for the Study of Chronic Total Occlusion Intervention) using a derivation and validation cohort (2:1 sampling ratio). Variables with strong association with technical success in multivariable analysis were assigned 1 point, and a 4-point score was developed from summing all points. The PROGRESS CTO score was subsequently compared with the J-CTO (Multicenter Chronic Total Occlusion Registry in Japan) score in the validation cohort. Technical success was 92.9%. On multivariable analysis, factors associated with technical success included proximal cap ambiguity (beta coefficient [b] = 0.88), moderate/severe tortuosity (b = 1.18), circumflex artery CTO (b = 0.99), and absence of "interventional" collaterals (b = 0.88). The resulting score demonstrated good calibration and discriminatory capacity in the derivation (Hosmer-Lemeshow chi-square = 2.633; p = 0.268, and receiver-operator characteristic [ROC] area = 0.778) and validation (Hosmer-Lemeshow chi-square = 5.333; p = 0.070, and ROC area = 0.720) subset. In the validation cohort, the PROGRESS CTO and J-CTO scores performed similarly in predicting technical success (ROC area 0.720 vs. 0.746, area under the curve difference = 0.026, 95% confidence interval = -0.093 to 0.144). The PROGRESS CTO score is a novel useful tool for estimating technical success in CTO PCI performed using the hybrid approach. Copyright © 2016 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Bona fide colour: DNA prediction of human eye and hair colour from ancient and contemporary skeletal remains

PubMed Central

2013-01-01

Background DNA analysis of ancient skeletal remains is invaluable in evolutionary biology for exploring the history of species, including humans. Contemporary human bones and teeth, however, are relevant in forensic DNA analyses that deal with the identification of perpetrators, missing persons, disaster victims or family relationships. They may also provide useful information towards unravelling controversies that surround famous historical individuals. Retrieving information about a deceased person’s externally visible characteristics can be informative in both types of DNA analyses. Recently, we demonstrated that human eye and hair colour can be reliably predicted from DNA using the HIrisPlex system. Here we test the feasibility of the novel HIrisPlex system at establishing eye and hair colour of deceased individuals from skeletal remains of various post-mortem time ranges and storage conditions. Methods Twenty-one teeth between 1 and approximately 800 years of age and 5 contemporary bones were subjected to DNA extraction using standard organic protocol followed by analysis using the HIrisPlex system. Results Twenty-three out of 26 bone DNA extracts yielded the full 24 SNP HIrisPlex profile, therefore successfully allowing model-based eye and hair colour prediction. HIrisPlex analysis of a tooth from the Polish general Władysław Sikorski (1881 to 1943) revealed blue eye colour and blond hair colour, which was positively verified from reliable documentation. The partial profiles collected in the remaining three cases (two contemporary samples and a 14th century sample) were sufficient for eye colour prediction. Conclusions Overall, we demonstrate that the HIrisPlex system is suitable, sufficiently sensitive and robust to successfully predict eye and hair colour from ancient and contemporary skeletal remains. Our findings, therefore, highlight the HIrisPlex system as a promising tool in future routine forensic casework involving skeletal remains, including ancient DNA studies, for the prediction of eye and hair colour of deceased individuals. PMID:23317428

Bona fide colour: DNA prediction of human eye and hair colour from ancient and contemporary skeletal remains.

PubMed

Draus-Barini, Jolanta; Walsh, Susan; Pośpiech, Ewelina; Kupiec, Tomasz; Głąb, Henryk; Branicki, Wojciech; Kayser, Manfred

2013-01-14

DNA analysis of ancient skeletal remains is invaluable in evolutionary biology for exploring the history of species, including humans. Contemporary human bones and teeth, however, are relevant in forensic DNA analyses that deal with the identification of perpetrators, missing persons, disaster victims or family relationships. They may also provide useful information towards unravelling controversies that surround famous historical individuals. Retrieving information about a deceased person's externally visible characteristics can be informative in both types of DNA analyses. Recently, we demonstrated that human eye and hair colour can be reliably predicted from DNA using the HIrisPlex system. Here we test the feasibility of the novel HIrisPlex system at establishing eye and hair colour of deceased individuals from skeletal remains of various post-mortem time ranges and storage conditions. Twenty-one teeth between 1 and approximately 800 years of age and 5 contemporary bones were subjected to DNA extraction using standard organic protocol followed by analysis using the HIrisPlex system. Twenty-three out of 26 bone DNA extracts yielded the full 24 SNP HIrisPlex profile, therefore successfully allowing model-based eye and hair colour prediction. HIrisPlex analysis of a tooth from the Polish general Władysław Sikorski (1881 to 1943) revealed blue eye colour and blond hair colour, which was positively verified from reliable documentation. The partial profiles collected in the remaining three cases (two contemporary samples and a 14th century sample) were sufficient for eye colour prediction. Overall, we demonstrate that the HIrisPlex system is suitable, sufficiently sensitive and robust to successfully predict eye and hair colour from ancient and contemporary skeletal remains. Our findings, therefore, highlight the HIrisPlex system as a promising tool in future routine forensic casework involving skeletal remains, including ancient DNA studies, for the prediction of eye and hair colour of deceased individuals.

Individualized Prediction of Heat Stress in Firefighters: A Data-Driven Approach Using Classification and Regression Trees.

PubMed

Mani, Ashutosh; Rao, Marepalli; James, Kelley; Bhattacharya, Amit

2015-01-01

The purpose of this study was to explore data-driven models, based on decision trees, to develop practical and easy to use predictive models for early identification of firefighters who are likely to cross the threshold of hyperthermia during live-fire training. Predictive models were created for three consecutive live-fire training scenarios. The final predicted outcome was a categorical variable: will a firefighter cross the upper threshold of hyperthermia - Yes/No. Two tiers of models were built, one with and one without taking into account the outcome (whether a firefighter crossed hyperthermia or not) from the previous training scenario. First tier of models included age, baseline heart rate and core body temperature, body mass index, and duration of training scenario as predictors. The second tier of models included the outcome of the previous scenario in the prediction space, in addition to all the predictors from the first tier of models. Classification and regression trees were used independently for prediction. The response variable for the regression tree was the quantitative variable: core body temperature at the end of each scenario. The predicted quantitative variable from regression trees was compared to the upper threshold of hyperthermia (38°C) to predict whether a firefighter would enter hyperthermia. The performance of classification and regression tree models was satisfactory for the second (success rate = 79%) and third (success rate = 89%) training scenarios but not for the first (success rate = 43%). Data-driven models based on decision trees can be a useful tool for predicting physiological response without modeling the underlying physiological systems. Early prediction of heat stress coupled with proactive interventions, such as pre-cooling, can help reduce heat stress in firefighters.

A system identification approach for developing model predictive controllers of antibody quality attributes in cell culture processes.

PubMed

Downey, Brandon; Schmitt, John; Beller, Justin; Russell, Brian; Quach, Anthony; Hermann, Elizabeth; Lyon, David; Breit, Jeffrey

2017-11-01

As the biopharmaceutical industry evolves to include more diverse protein formats and processes, more robust control of Critical Quality Attributes (CQAs) is needed to maintain processing flexibility without compromising quality. Active control of CQAs has been demonstrated using model predictive control techniques, which allow development of processes which are robust against disturbances associated with raw material variability and other potentially flexible operating conditions. Wide adoption of model predictive control in biopharmaceutical cell culture processes has been hampered, however, in part due to the large amount of data and expertise required to make a predictive model of controlled CQAs, a requirement for model predictive control. Here we developed a highly automated, perfusion apparatus to systematically and efficiently generate predictive models using application of system identification approaches. We successfully created a predictive model of %galactosylation using data obtained by manipulating galactose concentration in the perfusion apparatus in serialized step change experiments. We then demonstrated the use of the model in a model predictive controller in a simulated control scenario to successfully achieve a %galactosylation set point in a simulated fed-batch culture. The automated model identification approach demonstrated here can potentially be generalized to many CQAs, and could be a more efficient, faster, and highly automated alternative to batch experiments for developing predictive models in cell culture processes, and allow the wider adoption of model predictive control in biopharmaceutical processes. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 33:1647-1661, 2017. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers.

Prediction and validation of diffusion coefficients in a model drug delivery system using microsecond atomistic molecular dynamics simulation and vapour sorption analysis.

PubMed

Forrey, Christopher; Saylor, David M; Silverstein, Joshua S; Douglas, Jack F; Davis, Eric M; Elabd, Yossef A

2014-10-14

Diffusion of small to medium sized molecules in polymeric medical device materials underlies a broad range of public health concerns related to unintended leaching from or uptake into implantable medical devices. However, obtaining accurate diffusion coefficients for such systems at physiological temperature represents a formidable challenge, both experimentally and computationally. While molecular dynamics simulation has been used to accurately predict the diffusion coefficients, D, of a handful of gases in various polymers, this success has not been extended to molecules larger than gases, e.g., condensable vapours, liquids, and drugs. We present atomistic molecular dynamics simulation predictions of diffusion in a model drug eluting system that represent a dramatic improvement in accuracy compared to previous simulation predictions for comparable systems. We find that, for simulations of insufficient duration, sub-diffusive dynamics can lead to dramatic over-prediction of D. We present useful metrics for monitoring the extent of sub-diffusive dynamics and explore how these metrics correlate to error in D. We also identify a relationship between diffusion and fast dynamics in our system, which may serve as a means to more rapidly predict diffusion in slowly diffusing systems. Our work provides important precedent and essential insights for utilizing atomistic molecular dynamics simulations to predict diffusion coefficients of small to medium sized molecules in condensed soft matter systems.

Characterizing Ship Navigation Patterns Using Automatic Identification System (AIS) Data in the Baltic Sea

DTIC Science & Technology

in the Saint Petersburg area. We use three random forest models, that differ in their use of past information , to predict a vessels next port of visit...network where past information is used to more accurately predict the future state. The transitional probabilities change when predictor variables are...added that reach deeper into the past. Our findings suggest that successful prediction of the movement of a vessel depends on having accurate information on its recent history.

Prediction on carbon dioxide emissions based on fuzzy rules

NASA Astrophysics Data System (ADS)

Pauzi, Herrini; Abdullah, Lazim

2014-06-01

There are several ways to predict air quality, varying from simple regression to models based on artificial intelligence. Most of the conventional methods are not sufficiently able to provide good forecasting performances due to the problems with non-linearity uncertainty and complexity of the data. Artificial intelligence techniques are successfully used in modeling air quality in order to cope with the problems. This paper describes fuzzy inference system (FIS) to predict CO2 emissions in Malaysia. Furthermore, adaptive neuro-fuzzy inference system (ANFIS) is used to compare the prediction performance. Data of five variables: energy use, gross domestic product per capita, population density, combustible renewable and waste and CO2 intensity are employed in this comparative study. The results from the two model proposed are compared and it is clearly shown that the ANFIS outperforms FIS in CO2 prediction.

Web-based decision support system to predict risk level of long term rice production

NASA Astrophysics Data System (ADS)

Mukhlash, Imam; Maulidiyah, Ratna; Sutikno; Setiyono, Budi

2017-09-01

Appropriate decision making in risk management of rice production is very important in agricultural planning, especially for Indonesia which is an agricultural country. Good decision would be obtained if the supporting data required are satisfied and using appropriate methods. This study aims to develop a Decision Support System that can be used to predict the risk level of rice production in some districts which are central of rice production in East Java. Web-based decision support system is constructed so that the information can be easily accessed and understood. Components of the system are data management, model management, and user interface. This research uses regression models of OLS and Copula. OLS model used to predict rainfall while Copula model used to predict harvested area. Experimental results show that the models used are successfully predict the harvested area of rice production in some districts which are central of rice production in East Java at any given time based on the conditions and climate of a region. Furthermore, it can predict the amount of rice production with the level of risk. System generates prediction of production risk level in the long term for some districts that can be used as a decision support for the authorities.

Modeling natural regeneration establishment in the northern Rocky Mountains of the U.S.A

Treesearch

D. E. Ferguson

1996-01-01

Retrospective examination of cutover forests enables the development of models that predict regeneration success as a function of plot conditions and time since disturbance. The modeling process uses a two-state system. In the first state, all plots are analyzed to predict the probability of stocking (at least one established seedling on the plot). In the second state...

Sexual selection gradients change over time in a simultaneous hermaphrodite

PubMed Central

Hoffer, Jeroen NA; Mariën, Janine; Ellers, Jacintha; Koene, Joris M

2017-01-01

Sexual selection is generally predicted to act more strongly on males than on females. The Darwin-Bateman paradigm predicts that this should also hold for hermaphrodites. However, measuring this strength of selection is less straightforward when both sexual functions are performed throughout the organism’s lifetime. Besides, quantifications of sexual selection are usually done during a short time window, while many animals store sperm and are long-lived. To explore whether the chosen time frame affects estimated measures of sexual selection, we recorded mating success and reproductive success over time, using a simultaneous hermaphrodite. Our results show that male sexual selection gradients are consistently positive. However, an individual’s female mating success seems to negatively affect its own male reproductive success, an effect that only becomes visible several weeks into the experiment, highlighting that the time frame is crucial for the quantification and interpretation of sexual selection measures, an insight that applies to any iteroparous mating system. DOI: http://dx.doi.org/10.7554/eLife.25139.001 PMID:28613158

Cryogenic Boil-Off Reduction System Testing

NASA Technical Reports Server (NTRS)

Plachta, David W.; Johnson, Wesley L.; Feller, Jeffery

2014-01-01

The Cryogenic Boil-Off Reduction System was tested with LH2 and LOX in a vacuum chamber to simulate space vacuum and the temperatures of low Earth orbit. Testing was successful and results validated the scaling study model that predicts active cooling reduces upper stage cryogenic propulsion mass for loiter periods greater than 2 weeks.

Validation of the close-to-delivery prediction model for vaginal birth after cesarean delivery in a Middle Eastern cohort.

PubMed

Abdel Aziz, Ahmed; Abd Rabbo, Amal; Sayed Ahmed, Waleed A; Khamees, Rasha E; Atwa, Khaled A

2016-07-01

To validate a prediction model for vaginal birth after cesarean (VBAC) that incorporates variables available at admission for delivery among Middle Eastern women. The present prospective cohort study enrolled women at 37weeks of pregnancy or more with cephalic presentation who were willing to attempt a trial of labor (TOL) after a single prior low transverse cesarean delivery at Al-Jahra Hospital, Kuwait, between June 2013 and June 2014. The predicted success rate of VBAC determined via the close-to-delivery prediction model of Grobman et al. was compared between participants whose TOL was and was not successful. Among 203 enrolled women, 140 (69.0%) had successful VBAC. The predicted VBAC success rate was higher among women with successful TOL (82.4%±13.1%) than among those with failed TOL (67.7%±18.3%; P<0.001). There was a high positive correlation between actual and predicted success rates. For deciles of predicted success rate increasing from >30%-40% to >90%-100%, the actual success rate was 20%, 30.7%, 38.5%, 59.1%, 71.4%, 76%, and 84.5%, respectively (r=0.98, P=0.013). The close-to-delivery prediction model was found to be applicable to Middle Eastern women and might predict VBAC success rates, thereby decreasing morbidities associated with failed TOL. Copyright © 2016 International Federation of Gynecology and Obstetrics. Published by Elsevier Ireland Ltd. All rights reserved.

New Space Weather Systems Under Development and Their Contribution to Space Weather Management

NASA Astrophysics Data System (ADS)

Tobiska, W.; Bouwer, D.; Schunk, R.; Garrett, H.; Mertens, C.; Bowman, B.

2008-12-01

There have been notable successes during the past decade in the development of operational space environment systems. Examples include the Magnetospheric Specification Model (MSM) of the Earth's magnetosphere, 2000; SOLAR2000 (S2K) solar spectral irradiances, 2001; High Accuracy Satellite Drag Model (HASDM) neutral atmosphere densities, 2004; Global Assimilation of Ionospheric Measurements (GAIM) ionosphere specification, 2006; Hakamada-Akasofu-Fry (HAF) solar wind parameters, 2007; Communication Alert and Prediction System (CAPS) ionosphere, high frequency radio, and scintillation S4 index prediction, 2008; and GEO Alert and Prediction System (GAPS) geosynchronous environment satellite charging specification and forecast, 2008. Operational systems that are in active operational implementation include the Jacchia-Bowman 2006/2008 (JB2006/2008) neutral atmosphere, 2009, and the Nowcast of Atmospheric Ionizing Radiation for Aviation Safety (NAIRAS) aviation radiation model using the Radiation Alert and Prediction System (RAPS), 2010. U.S. national agency and commercial assets will soon reach a state where specification and prediction will become ubiquitous and where coordinated management of the space environment and space weather will become a necessity. We describe the status of the CAPS, GAPS, RAPS, and JB2008 operational development. We additionally discuss the conditions that are laying the groundwork for space weather management and estimate the unfilled needs as we move beyond specification and prediction efforts.

A Comparative Analysis of Factors Related to a Candidate's Success or Failure in the 1975 and 1977 Community School Board Elections in New York City.

ERIC Educational Resources Information Center

Levine, Jonathan; Clawar, Harry J.

In 1960, the New York City Public School System was decentralized into 32 school districts with limited authority over elementary and junior high schools. Locally elected district community school boards were provided for by State legislation. In this study factors relevant to predicting a candidate's success or failure in the 1975 and 1977 school…

The Relationship of the Officer Evaluation Report to Captain Attrition

DTIC Science & Technology

2001-05-31

especially, Microsoft licensing. The OER plays a significant role in each of these stages, determining or predicting an officer’s potential for career success . The...company command greatly raises expectations for continued career success . Those that have a positive command experience with at least one above center of...system, especially pertaining to branch qualification was reviewed. Nearly 85% had been advised by their branch assignment officer, that future career

Successful Nd:Yag Laser Photocoagulation Of Arrhythmogenic Myocardium: Potential Limitations Of Current Optical Delivery Systems.

NASA Astrophysics Data System (ADS)

Svenson, Robert H.; Marroum, Marie-Claire; Frank, Frank; Selle, Jay G.; Gallagher, John J.; Bou-Saba, George; Seifert, Kathleen T.; Linder, Kathy; Tatsis, George P.

1987-04-01

Canine myocardial lesions of predictable dimensions can be achieved with Nd:YAG laser photocoagulation. These lesions are well demarcated from surrounding normal tissue and heal with homogeneous scar formation. Intraoperative Nd:YAG laser photocoagulation successfully ablated 52 of 55 ventricular tachycardias in 17 patients. Histologic examination of tissues from these arrhythmogenic areas showed differences from lesions produced on canine epicardium. Lesions from the human cases were less predictable and not well circumscribed. These differences are felt to be due to optical inhomogeneities present in diseased, scarred human myocardium, geometric irregularities of the endocardial surface, anatomical constraints on tissue-fiber distance, and the angle of incidence of the beam with the tissue. Modifications of current delivery systems may overcome some of these limitations. Ablation of ventricular tachycardia arising deeper than 4.0 to 6.0 mm. from the irradiated surface may require interstitial probes coupled to the fiberoptic.

Weather Research and Forecasting Model Sensitivity Comparisons for Warm Season Convective Initiation

NASA Technical Reports Server (NTRS)

Watson, Leela R.

2007-01-01

This report describes the work done by the Applied Meteorology Unit (AMU) in assessing the success of different model configurations in predicting warm season convection over East-Central Florida. The Weather Research and Forecasting Environmental Modeling System (WRF EMS) software allows users to choose among two dynamical cores - the Advanced Research WRF (ARW) and the Non-hydrostatic Mesoscale Model (NMM). There are also data assimilation analysis packages available for the initialization of the WRF model - the Local Analysis and Prediction System (LAPS) and the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS). Besides model core and initialization options, the WRF model can be run with one- or two-way nesting. Having a series of initialization options and WRF cores, as well as many options within each core, creates challenges for local forecasters, such as determining which configuration options are best to address specific forecast concerns. This project assessed three different model intializations available to determine which configuration best predicts warm season convective initiation in East-Central Florida. The project also examined the use of one- and two-way nesting in predicting warm season convection.

Laboratory evaluation of the pointing stability of the ASPS Vernier System

NASA Technical Reports Server (NTRS)

1980-01-01

The annular suspension and pointing system (ASPS) is an end-mount experiment pointing system designed for use in the space shuttle. The results of the ASPS Vernier System (AVS) pointing stability tests conducted in a laboratory environment are documented. A simulated zero-G suspension was used to support the test payload in the laboratory. The AVS and the suspension were modelled and incorporated into a simulation of the laboratory test. Error sources were identified and pointing stability sensitivities were determined via simulation. Statistical predictions of laboratory test performance were derived and compared to actual laboratory test results. The predicted mean pointing stability during simulated shuttle disturbances was 1.22 arc seconds; the actual mean laboratory test pointing stability was 1.36 arc seconds. The successful prediction of laboratory test results provides increased confidence in the analytical understanding of the AVS magnetic bearing technology and allows confident prediction of in-flight performance. Computer simulations of ASPS, operating in the shuttle disturbance environment, predict in-flight pointing stability errors less than 0.01 arc seconds.

Adaptive vibration control of structures under earthquakes

NASA Astrophysics Data System (ADS)

Lew, Jiann-Shiun; Juang, Jer-Nan; Loh, Chin-Hsiung

2017-04-01

techniques, for structural vibration suppression under earthquakes. Various control strategies have been developed to protect structures from natural hazards and improve the comfort of occupants in buildings. However, there has been little development of adaptive building control with the integration of real-time system identification and control design. Generalized predictive control, which combines the process of real-time system identification and the process of predictive control design, has received widespread acceptance and has been successfully applied to various test-beds. This paper presents a formulation of the predictive control scheme for adaptive vibration control of structures under earthquakes. Comprehensive simulations are performed to demonstrate and validate the proposed adaptive control technique for earthquake-induced vibration of a building.

A unifying concept: pancreatic ductal anatomy both predicts and determines the major complications resulting from pancreatitis.

PubMed

Nealon, William H; Bhutani, Manoop; Riall, Taylor S; Raju, Gottumukkala; Ozkan, Orhan; Neilan, Ryan

2009-05-01

Precepts about acute pancreatitis, necrotizing pancreatitis, and pancreatic fluid collections or pseudocyst rarely include the impact of pancreatic ductal injuries on their natural course and outcomes. We previously examined and established a system to categorize ductal changes. We sought a unifying concept that may predict course and direct therapies in these complex patients. We use our system categorizing ductal changes in pseudocyst of the pancreas and severe necrotizing pancreatitis (type I, normal duct; type II, duct stricture; type III, duct occlusion or "disconnected duct"; and type IV, chronic pancreatitis). From 1985 to 2006, a policy was implemented of routine imaging (cross-sectional, endoscopic retrograde cholangiopancreatography, or magnetic resonance cholangiopancreatography). Clinical outcomes were measured. Among 563 patients with pseudocyst, 142 resolved spontaneously (87% of type I, 5% of type II, and no type III, and 3% of type IV). Percutaneous drainage was successful in 83% of type I, 49% of type II, and no type III or type IV. Among 174 patients with severe acute pancreatitis percutaneous drainage was successful in 64% of type I, 38% of type II, and no type III. Operative debridement was required in 39% of type I and 83% and 85% of types II and III, respectively. Persistent fistula after debridement occurred in 27%, 54%, and 85% of types I, II, and III ducts, respectively. Late complications correlated with duct injury. Pancreatic ductal changes predict spontaneous resolution, success of nonoperative measures, and direct therapies in pseudocyst. Ductal changes also predict patients with necrotizing pancreatitis who are most likely to have immediate and delayed complications.

Developing a theoretical model and questionnaire survey instrument to measure the success of electronic health records in residential aged care

PubMed Central

Yu, Ping; Qian, Siyu

2018-01-01

Electronic health records (EHR) are introduced into healthcare organizations worldwide to improve patient safety, healthcare quality and efficiency. A rigorous evaluation of this technology is important to reduce potential negative effects on patient and staff, to provide decision makers with accurate information for system improvement and to ensure return on investment. Therefore, this study develops a theoretical model and questionnaire survey instrument to assess the success of organizational EHR in routine use from the viewpoint of nursing staff in residential aged care homes. The proposed research model incorporates six variables in the reformulated DeLone and McLean information systems success model: system quality, information quality, service quality, use, user satisfaction and net benefits. Two variables training and self-efficacy were also incorporated into the model. A questionnaire survey instrument was designed to measure the eight variables in the model. After a pilot test, the measurement scale was used to collect data from 243 nursing staff members in 10 residential aged care homes belonging to three management groups in Australia. Partial least squares path modeling was conducted to validate the model. The validated EHR systems success model predicts the impact of the four antecedent variables—training, self-efficacy, system quality and information quality—on the net benefits, the indicator of EHR systems success, through the intermittent variables use and user satisfaction. A 24-item measurement scale was developed to quantitatively evaluate the performance of an EHR system. The parsimonious EHR systems success model and the measurement scale can be used to benchmark EHR systems success across organizations and units and over time. PMID:29315323

Developing a theoretical model and questionnaire survey instrument to measure the success of electronic health records in residential aged care.

PubMed

Yu, Ping; Qian, Siyu

2018-01-01

Electronic health records (EHR) are introduced into healthcare organizations worldwide to improve patient safety, healthcare quality and efficiency. A rigorous evaluation of this technology is important to reduce potential negative effects on patient and staff, to provide decision makers with accurate information for system improvement and to ensure return on investment. Therefore, this study develops a theoretical model and questionnaire survey instrument to assess the success of organizational EHR in routine use from the viewpoint of nursing staff in residential aged care homes. The proposed research model incorporates six variables in the reformulated DeLone and McLean information systems success model: system quality, information quality, service quality, use, user satisfaction and net benefits. Two variables training and self-efficacy were also incorporated into the model. A questionnaire survey instrument was designed to measure the eight variables in the model. After a pilot test, the measurement scale was used to collect data from 243 nursing staff members in 10 residential aged care homes belonging to three management groups in Australia. Partial least squares path modeling was conducted to validate the model. The validated EHR systems success model predicts the impact of the four antecedent variables-training, self-efficacy, system quality and information quality-on the net benefits, the indicator of EHR systems success, through the intermittent variables use and user satisfaction. A 24-item measurement scale was developed to quantitatively evaluate the performance of an EHR system. The parsimonious EHR systems success model and the measurement scale can be used to benchmark EHR systems success across organizations and units and over time.

Identifying Model-Based Reconfiguration Goals through Functional Deficiencies

NASA Technical Reports Server (NTRS)

Benazera, Emmanuel; Trave-Massuyes, Louise

2004-01-01

Model-based diagnosis is now advanced to the point autonomous systems face some uncertain and faulty situations with success. The next step toward more autonomy is to have the system recovering itself after faults occur, a process known as model-based reconfiguration. After faults occur, given a prediction of the nominal behavior of the system and the result of the diagnosis operation, this paper details how to automatically determine the functional deficiencies of the system. These deficiencies are characterized in the case of uncertain state estimates. A methodology is then presented to determine the reconfiguration goals based on the deficiencies. Finally, a recovery process interleaves planning and model predictive control to restore the functionalities in prioritized order.

Flight Tests of the Turbulence Prediction and Warning System (TPAWS)

NASA Technical Reports Server (NTRS)

Hamilton, David W.; Proctor, Fred H.; Ahmad, Nashat N.

2012-01-01

Flight tests of the National Aeronautics and Space Administration's Turbulence Prediction And Warning System (TPAWS) were conducted in the Fall of 2000 and Spring of 2002. TPAWS is a radar-based airborne turbulence detection system. During twelve flights, NASA's B-757 tallied 53 encounters with convectively induced turbulence. Analysis of data collected during 49 encounters in the Spring of 2002 showed that the TPAWS Airborne Turbulence Detection System (ATDS) successfully detected 80% of the events at least 30 seconds prior to the encounter, achieving FAA recommended performance criteria. Details of the flights, the prevailing weather conditions, and each of the turbulence events are presented in this report. Sensor and environmental characterizations are also provided.

A test to evaluate the earthquake prediction algorithm, M8

USGS Publications Warehouse

Healy, John H.; Kossobokov, Vladimir G.; Dewey, James W.

1992-01-01

A test of the algorithm M8 is described. The test is constructed to meet four rules, which we propose to be applicable to the test of any method for earthquake prediction:  1. An earthquake prediction technique should be presented as a well documented, logical algorithm that can be used by  investigators without restrictions. 2. The algorithm should be coded in a common programming language and implementable on widely available computer systems. 3. A test of the earthquake prediction technique should involve future predictions with a black box version of the algorithm in which potentially adjustable parameters are fixed in advance. The source of the input data must be defined and ambiguities in these data must be resolved automatically by the algorithm. 4. At least one reasonable null hypothesis should be stated in advance of testing the earthquake prediction method, and it should be stated how this null hypothesis will be used to estimate the statistical significance of the earthquake predictions. The M8 algorithm has successfully predicted several destructive earthquakes, in the sense that the earthquakes occurred inside regions with linear dimensions from 384 to 854 km that the algorithm had identified as being in times of increased probability for strong earthquakes. In addition, M8 has successfully "post predicted" high percentages of strong earthquakes in regions to which it has been applied in retroactive studies. The statistical significance of previous predictions has not been established, however, and post-prediction studies in general are notoriously subject to success-enhancement through hindsight. Nor has it been determined how much more precise an M8 prediction might be than forecasts and probability-of-occurrence estimates made by other techniques. We view our test of M8 both as a means to better determine the effectiveness of M8 and as an experimental structure within which to make observations that might lead to improvements in the algorithm or conceivably lead to a radically different approach to earthquake prediction.

Using Empirical Models for Communication Prediction of Spacecraft

NASA Technical Reports Server (NTRS)

Quasny, Todd

2015-01-01

A viable communication path to a spacecraft is vital for its successful operation. For human spaceflight, a reliable and predictable communication link between the spacecraft and the ground is essential not only for the safety of the vehicle and the success of the mission, but for the safety of the humans on board as well. However, analytical models of these communication links are challenged by unique characteristics of space and the vehicle itself. For example, effects of radio frequency during high energy solar events while traveling through a solar array of a spacecraft can be difficult to model, and thus to predict. This presentation covers the use of empirical methods of communication link predictions, using the International Space Station (ISS) and its associated historical data as the verification platform and test bed. These empirical methods can then be incorporated into communication prediction and automation tools for the ISS in order to better understand the quality of the communication path given a myriad of variables, including solar array positions, line of site to satellites, position of the sun, and other dynamic structures on the outside of the ISS. The image on the left below show the current analytical model of one of the communication systems on the ISS. The image on the right shows a rudimentary empirical model of the same system based on historical archived data from the ISS.

A computer system to analyze showers in nuclear emulsions: Center Director's discretionary fund report

NASA Technical Reports Server (NTRS)

Meegan, C. A.; Fountain, W. F.; Berry, F. A., Jr.

1987-01-01

A system to rapidly digitize data from showers in nuclear emulsions is described. A TV camera views the emulsions though a microscope. The TV output is superimposed on the monitor of a minicomputer. The operator uses the computer's graphics capability to mark the positions of particle tracks. The coordinates of each track are stored on a disk. The computer then predicts the coordinates of each track through successive layers of emulsion. The operator, guided by the predictions, thus tracks and stores the development of the shower. The system provides a significant improvement over purely manual methods of recording shower development in nuclear emulsion stacks.

Modeling the prediction of business intelligence system effectiveness.

PubMed

Weng, Sung-Shun; Yang, Ming-Hsien; Koo, Tian-Lih; Hsiao, Pei-I

2016-01-01

Although business intelligence (BI) technologies are continually evolving, the capability to apply BI technologies has become an indispensable resource for enterprises running in today's complex, uncertain and dynamic business environment. This study performed pioneering work by constructing models and rules for the prediction of business intelligence system effectiveness (BISE) in relation to the implementation of BI solutions. For enterprises, effectively managing critical attributes that determine BISE to develop prediction models with a set of rules for self-evaluation of the effectiveness of BI solutions is necessary to improve BI implementation and ensure its success. The main study findings identified the critical prediction indicators of BISE that are important to forecasting BI performance and highlighted five classification and prediction rules of BISE derived from decision tree structures, as well as a refined regression prediction model with four critical prediction indicators constructed by logistic regression analysis that can enable enterprises to improve BISE while effectively managing BI solution implementation and catering to academics to whom theory is important.

Applying predictive analytics to develop an intelligent risk detection application for healthcare contexts.

PubMed

Moghimi, Fatemeh Hoda; Cheung, Michael; Wickramasinghe, Nilmini

2013-01-01

Healthcare is an information rich industry where successful outcomes require the processing of multi-spectral data and sound decision making. The exponential growth of data and big data issues coupled with a rapid increase of service demands in healthcare contexts today, requires a robust framework enabled by IT (information technology) solutions as well as real-time service handling in order to ensure superior decision making and successful healthcare outcomes. Such a context is appropriate for the application of real time intelligent risk detection decision support systems using predictive analytic techniques such as data mining. To illustrate the power and potential of data science technologies in healthcare decision making scenarios, the use of an intelligent risk detection (IRD) model is proffered for the context of Congenital Heart Disease (CHD) in children, an area which requires complex high risk decisions that need to be made expeditiously and accurately in order to ensure successful healthcare outcomes.

Cloud prediction of protein structure and function with PredictProtein for Debian.

PubMed

Kaján, László; Yachdav, Guy; Vicedo, Esmeralda; Steinegger, Martin; Mirdita, Milot; Angermüller, Christof; Böhm, Ariane; Domke, Simon; Ertl, Julia; Mertes, Christian; Reisinger, Eva; Staniewski, Cedric; Rost, Burkhard

2013-01-01

We report the release of PredictProtein for the Debian operating system and derivatives, such as Ubuntu, Bio-Linux, and Cloud BioLinux. The PredictProtein suite is available as a standard set of open source Debian packages. The release covers the most popular prediction methods from the Rost Lab, including methods for the prediction of secondary structure and solvent accessibility (profphd), nuclear localization signals (predictnls), and intrinsically disordered regions (norsnet). We also present two case studies that successfully utilize PredictProtein packages for high performance computing in the cloud: the first analyzes protein disorder for whole organisms, and the second analyzes the effect of all possible single sequence variants in protein coding regions of the human genome.

Cloud Prediction of Protein Structure and Function with PredictProtein for Debian

PubMed Central

Kaján, László; Yachdav, Guy; Vicedo, Esmeralda; Steinegger, Martin; Mirdita, Milot; Angermüller, Christof; Böhm, Ariane; Domke, Simon; Ertl, Julia; Mertes, Christian; Reisinger, Eva; Rost, Burkhard

2013-01-01

We report the release of PredictProtein for the Debian operating system and derivatives, such as Ubuntu, Bio-Linux, and Cloud BioLinux. The PredictProtein suite is available as a standard set of open source Debian packages. The release covers the most popular prediction methods from the Rost Lab, including methods for the prediction of secondary structure and solvent accessibility (profphd), nuclear localization signals (predictnls), and intrinsically disordered regions (norsnet). We also present two case studies that successfully utilize PredictProtein packages for high performance computing in the cloud: the first analyzes protein disorder for whole organisms, and the second analyzes the effect of all possible single sequence variants in protein coding regions of the human genome. PMID:23971032

Damage prognosis: the future of structural health monitoring.

PubMed

Farrar, Charles R; Lieven, Nick A J

2007-02-15

This paper concludes the theme issue on structural health monitoring (SHM) by discussing the concept of damage prognosis (DP). DP attempts to forecast system performance by assessing the current damage state of the system (i.e. SHM), estimating the future loading environments for that system, and predicting through simulation and past experience the remaining useful life of the system. The successful development of a DP capability will require the further development and integration of many technology areas including both measurement/processing/telemetry hardware and a variety of deterministic and probabilistic predictive modelling capabilities, as well as the ability to quantify the uncertainty in these predictions. The multidisciplinary and challenging nature of the DP problem, its current embryonic state of development, and its tremendous potential for life-safety and economic benefits qualify DP as a 'grand challenge' problem for engineers in the twenty-first century.

Estimating the Reliability of Electronic Parts in High Radiation Fields

NASA Technical Reports Server (NTRS)

Everline, Chester; Clark, Karla; Man, Guy; Rasmussen, Robert; Johnston, Allan; Kohlhase, Charles; Paulos, Todd

2008-01-01

Radiation effects on materials and electronic parts constrain the lifetime of flight systems visiting Europa. Understanding mission lifetime limits is critical to the design and planning of such a mission. Therefore, the operational aspects of radiation dose are a mission success issue. To predict and manage mission lifetime in a high radiation environment, system engineers need capable tools to trade radiation design choices against system design and reliability, and science achievements. Conventional tools and approaches provided past missions with conservative designs without the ability to predict their lifetime beyond the baseline mission.This paper describes a more systematic approach to understanding spacecraft design margin, allowing better prediction of spacecraft lifetime. This is possible because of newly available electronic parts radiation effects statistics and an enhanced spacecraft system reliability methodology. This new approach can be used in conjunction with traditional approaches for mission design. This paper describes the fundamentals of the new methodology.

Design of teleoperation system with a force-reflecting real-time simulator

NASA Technical Reports Server (NTRS)

Hirata, Mitsunori; Sato, Yuichi; Nagashima, Fumio; Maruyama, Tsugito

1994-01-01

We developed a force-reflecting teleoperation system that uses a real-time graphic simulator. This system eliminates the effects of communication time delays in remote robot manipulation. The simulator provides the operator with predictive display and feedback of computed contact forces through a six-degree of freedom (6-DOF) master arm on a real-time basis. With this system, peg-in-hole tasks involving round-trip communication time delays of up to a few seconds were performed at three support levels: a real image alone, a predictive display with a real image, and a real-time graphic simulator with computed-contact-force reflection and a predictive display. The experimental results indicate the best teleoperation efficiency was achieved by using the force-reflecting simulator with two images. The shortest work time, lowest sensor maximum, and a 100 percent success rate were obtained. These results demonstrate the effectiveness of simulated-force-reflecting teleoperation efficiency.

The Development and Field Test of an Employment Interview System to Identify Successful School Principals

ERIC Educational Resources Information Center

Kobler, Angelique L.

2010-01-01

The purpose of the study was to examine the reliability and validity of the employment interview system for Principals (ICIS-Principal). This instrument attempts to predict the effectiveness of principal applicants as building leaders, determined through a set of employment interview questions aligned with the primary themes found within the…

Critical Success Factors for Adoption of Web-Based Learning Management Systems in Tanzania

ERIC Educational Resources Information Center

Lwoga, Edda Tandi

2014-01-01

This paper examines factors that predict students' continual usage intention of web-based learning content management systems in Tanzania, with a specific focus at Muhimbili University of Health and Allied Science (MUHAS). This study sent a questionnaire surveys to 408 first year undergraduate students, with a rate of return of 66.7. This study…

Bowen Family Systems Theory and Its Relationship to Teachers: Does Differentiation of Self Predict Teacher Job Satisfaction?

ERIC Educational Resources Information Center

Cochran, Noal Baxter

2011-01-01

Educational leaders are charged with maintaining the academic success of students, the faith of stakeholders in the educational process, and the growth of the educational profession. These objectives have become difficult during a time of noticeable discontent among the stakeholders of educational systems. The discontent is noted strongly among…

The efficacy of IntraFlow intraosseous injection as a primary anesthesia technique.

PubMed

Remmers, Todd; Glickman, Gerald; Spears, Robert; He, Jianing

2008-03-01

The purpose of this study was to compare the efficacy of intraosseous injection and inferior alveolar (IA) nerve block in anesthetizing mandibular posterior teeth with irreversible pulpitis. Thirty human subjects were randomly assigned to receive either intraosseous injection using the IntraFlow system (Pro-Dex Inc, Santa Ana, CA) or IA block as the primary anesthesia method. Pulpal anesthesia was evaluated via electric pulp testing at 4-minute intervals for 20 minutes. Two consecutive 80/80 readings were considered successful pulpal anesthesia. Anesthesia success or failure was recorded and groups compared. Intraosseous injection provided successful anesthesia in 13 of 15 subjects (87%). The IA block provided successful anesthesia in 9 of 15 subjects (60%). Although this difference was not statistically significant (p = 0.2148), the results of this preliminary study indicate that the IntraFlow system can be used as the primary anesthesia method in teeth with irreversible pulpitis to achieve predictable pulpal anesthesia.

Endodontic implications of the variability of the root canal systems of posterior teeth.

PubMed

Biggs, J T; Benenati, F W

1995-01-01

Variations in the morphology of roots and root canal systems create challenges which the dental practitioner must be able to recognize. Endodontic therapy is predictable and successful only to the extent that the root canal system can be debrided, disinfected and sealed against future contamination. In order to accomplish these goals it is necessary to become familiar with the variability of the system we seek to treat.

Successful emotion regulation is predicted by amygdala activity and aspects of personality: A latent variable approach.

PubMed

Morawetz, Carmen; Alexandrowicz, Rainer W; Heekeren, Hauke R

2017-04-01

The experience of emotions and their cognitive control are based upon neural responses in prefrontal and subcortical regions and could be affected by personality and temperamental traits. Previous studies established an association between activity in reappraisal-related brain regions (e.g., inferior frontal gyrus and amygdala) and emotion regulation success. Given these relationships, we aimed to further elucidate how individual differences in emotion regulation skills relate to brain activity within the emotion regulation network on the one hand, and personality/temperamental traits on the other. We directly examined the relationship between personality and temperamental traits, emotion regulation success and its underlying neuronal network in a large sample (N = 82) using an explicit emotion regulation task and functional MRI (fMRI). We applied a multimethodological analysis approach, combing standard activation-based analyses with structural equation modeling. First, we found that successful downregulation is predicted by activity in key regions related to emotion processing. Second, the individual ability to successfully upregulate emotions is strongly associated with the ability to identify feelings, conscientiousness, and neuroticism. Third, the successful downregulation of emotion is modulated by openness to experience and habitual use of reappraisal. Fourth, the ability to regulate emotions is best predicted by a combination of brain activity and personality as well temperamental traits. Using a multimethodological analysis approach, we provide a first step toward a causal model of individual differences in emotion regulation ability by linking biological systems underlying emotion regulation with descriptive constructs. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Space Shuttle Program Primary Avionics Software System (PASS) Success Legacy - Quality and Reliability Date

NASA Technical Reports Server (NTRS)

Orr, James K.; Peltier, Daryl

2010-01-01

Thsi slide presentation reviews the avionics software system on board the space shuttle, with particular emphasis on the quality and reliability. The Primary Avionics Software System (PASS) provides automatic and fly-by-wire control of critical shuttle systems which executes in redundant computers. Charts given show the number of space shuttle flights vs time, PASS's development history, and other charts that point to the reliability of the system's development. The reliability of the system is also compared to predicted reliability.

Implementation Intention and Reminder Effects on Behavior Change in a Mobile Health System: A Predictive Cognitive Model

PubMed Central

Mohan, Shiwali; Venkatakrishnan, Anusha; Nelson, Les; Silva, Michael; Springer, Aaron

2017-01-01

Background Implementation intentions are mental representations of simple plans to translate goal intentions into behavior under specific conditions. Studies show implementation intentions can produce moderate to large improvements in behavioral goal achievement. Human associative memory mechanisms have been implicated in the processes by which implementation intentions produce effects. On the basis of the adaptive control of thought-rational (ACT-R) theory of cognition, we hypothesized that the strength of implementation intention effect could be manipulated in predictable ways using reminders delivered by a mobile health (mHealth) app. Objective The aim of this experiment was to manipulate the effects of implementation intentions on daily behavioral goal success in ways predicted by the ACT-R theory concerning mHealth reminder scheduling. Methods An incomplete factorial design was used in this mHealth study. All participants were asked to choose a healthy behavior goal associated with eat slowly, walking, or eating more vegetables and were asked to set implementation intentions. N=64 adult participants were in the study for 28 days. Participants were stratified by self-efficacy and assigned to one of two reminder conditions: reminders-presented versus reminders-absent. Self-efficacy and reminder conditions were crossed. Nested within the reminders-presented condition was a crossing of frequency of reminders sent (high, low) by distribution of reminders sent (distributed, massed). Participants in the low frequency condition got 7 reminders over 28 days; those in the high frequency condition were sent 14. Participants in the distributed conditions were sent reminders at uniform intervals. Participants in the massed distribution conditions were sent reminders in clusters. Results There was a significant overall effect of reminders on achieving a daily behavioral goal (coefficient=2.018, standard error [SE]=0.572, odds ratio [OR]=7.52, 95% CI 0.9037-3.2594, P<.001). As predicted by ACT-R, using default theoretical parameters, there was an interaction of reminder frequency by distribution on daily goal success (coefficient=0.7994, SE=0.2215, OR=2.2242, 95% CI 0.3656-1.2341, P<.001). The total number of times a reminder was acknowledged as received by a participant had a marginal effect on daily goal success (coefficient=0.0694, SE=0.0410, OR=1.0717, 95% CI −0.01116 to 0.1505, P=.09), and the time since acknowledging receipt of a reminder was highly significant (coefficient=−0.0490, SE=0.0104, OR=0.9522, 95% CI −0.0700 to −0.2852], P<.001). A dual system ACT-R mathematical model was fit to individuals’ daily goal successes and reminder acknowledgments: a goal-striving system dependent on declarative memory plus a habit-forming system that acquires automatic procedures for performance of behavioral goals. Conclusions Computational cognitive theory such as ACT-R can be used to make precise quantitative predictions concerning daily health behavior goal success in response to implementation intentions and the dosing schedules of reminders. PMID:29191800

Potential for western US seasonal snowpack prediction

USGS Publications Warehouse

Kapnick, Sarah B.; Yang, Xiaosong; Vecchi, Gabriel A.; Delworth, Thomas L.; Gudgel, Rich; Malyshev, Sergey; Milly, Paul C. D.; Shevliakova, Elena; Underwood, Seth; Margulis, Steven A.

2018-01-01

Western US snowpack—snow that accumulates on the ground in the mountains—plays a critical role in regional hydroclimate and water supply, with 80% of snowmelt runoff being used for agriculture. While climate projections provide estimates of snowpack loss by the end of th ecentury and weather forecasts provide predictions of weather conditions out to 2 weeks, less progress has been made for snow predictions at seasonal timescales (months to 2 years), crucial for regional agricultural decisions (e.g., plant choice and quantity). Seasonal predictions with climate models first took the form of El Niño predictions 3 decades ago, with hydroclimate predictions emerging more recently. While the field has been focused on single-season predictions (3 months or less), we are now poised to advance our predictions beyond this timeframe. Utilizing observations, climate indices, and a suite of global climate models, we demonstrate the feasibility of seasonal snowpack predictions and quantify the limits of predictive skill 8 month sin advance. This physically based dynamic system outperforms observation-based statistical predictions made on July 1 for March snowpack everywhere except the southern Sierra Nevada, a region where prediction skill is nonexistent for every predictor presently tested. Additionally, in the absence of externally forced negative trends in snowpack, narrow maritime mountain ranges with high hydroclimate variability pose a challenge for seasonal prediction in our present system; natural snowpack variability may inherently be unpredictable at this timescale. This work highlights present prediction system successes and gives cause for optimism for developing seasonal predictions for societal needs.

On-line determination of transient stability status using multilayer perceptron neural network

NASA Astrophysics Data System (ADS)

Frimpong, Emmanuel Asuming; Okyere, Philip Yaw; Asumadu, Johnson

2018-01-01

A scheme to predict transient stability status following a disturbance is presented. The scheme is activated upon the tripping of a line or bus and operates as follows: Two samples of frequency deviation values at all generator buses are obtained. At each generator bus, the maximum frequency deviation within the two samples is extracted. A vector is then constructed from the extracted maximum frequency deviations. The Euclidean norm of the constructed vector is calculated and then fed as input to a trained multilayer perceptron neural network which predicts the stability status of the system. The scheme was tested using data generated from the New England test system. The scheme successfully predicted the stability status of all two hundred and five disturbance test cases.

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

Mishra, Manoj K., E-mail: manoj.qit@gmail.com; Space Applications Centre, Indian Space Research Organization; Prakash, Hari

Long distance atomic teleportation (LDAT) is of prime importance in long distance quantum communication. Scheme proposed by Bose et al. (1999) in principle enables us to have LDAT using cavity decay. However it gives message state dependent fidelity and success rate. Here, using interaction of entangled coherent states with atom–cavity systems and a two-step measurement, we show how, LDAT can be achieved with unit fidelity and as good success as desired under ideal conditions. The scheme is unique in that, the first measurement predicts success or failure. If success is predicted then second measurement gives perfect teleportation. If failure is predictedmore » the message-qubit remains conserved therefore a second attempt may be started. We found that even in presence of decoherence due to dissipation of energy our scheme gives message state independent success rate and almost perfect teleportation in single attempt with mean fidelity of teleportation equal to 0.9 at long distances. However if first attempt fails, unlike ideal case where message-qubit remains conserved with unit fidelity, in presence of decoherence the message-qubit remains conserved to some degree, therefore mean fidelity of teleportation can be increased beyond 0.9 by repeating the process.« less

Predicting risky choices from brain activity patterns

PubMed Central

Helfinstein, Sarah M.; Schonberg, Tom; Congdon, Eliza; Karlsgodt, Katherine H.; Mumford, Jeanette A.; Sabb, Fred W.; Cannon, Tyrone D.; London, Edythe D.; Bilder, Robert M.; Poldrack, Russell A.

2014-01-01

Previous research has implicated a large network of brain regions in the processing of risk during decision making. However, it has not yet been determined if activity in these regions is predictive of choices on future risky decisions. Here, we examined functional MRI data from a large sample of healthy subjects performing a naturalistic risk-taking task and used a classification analysis approach to predict whether individuals would choose risky or safe options on upcoming trials. We were able to predict choice category successfully in 71.8% of cases. Searchlight analysis revealed a network of brain regions where activity patterns were reliably predictive of subsequent risk-taking behavior, including a number of regions known to play a role in control processes. Searchlights with significant predictive accuracy were primarily located in regions more active when preparing to avoid a risk than when preparing to engage in one, suggesting that risk taking may be due, in part, to a failure of the control systems necessary to initiate a safe choice. Additional analyses revealed that subject choice can be successfully predicted with minimal decrements in accuracy using highly condensed data, suggesting that information relevant for risky choice behavior is encoded in coarse global patterns of activation as well as within highly local activation within searchlights. PMID:24550270

Effect of vibration on retention characteristics of screen acquisition systems. [for surface tension propellant acquisition

NASA Technical Reports Server (NTRS)

Tegart, J. R.; Aydelott, J. C.

1978-01-01

The design of surface tension propellant acquisition systems using fine-mesh screen must take into account all factors that influence the liquid pressure differentials within the system. One of those factors is spacecraft vibration. Analytical models to predict the effects of vibration have been developed. A test program to verify the analytical models and to allow a comparative evaluation of the parameters influencing the response to vibration was performed. Screen specimens were tested under conditions simulating the operation of an acquisition system, considering the effects of such parameters as screen orientation and configuration, screen support method, screen mesh, liquid flow and liquid properties. An analytical model, based on empirical coefficients, was most successful in predicting the effects of vibration.

Reward contingencies and the recalibration of task monitoring and reward systems: a high-density electrical mapping study.

PubMed

Morie, K P; De Sanctis, P; Foxe, J J

2014-07-25

Task execution almost always occurs in the context of reward-seeking or punishment-avoiding behavior. As such, ongoing task-monitoring systems are influenced by reward anticipation systems. In turn, when a task has been executed either successfully or unsuccessfully, future iterations of that task will be re-titrated on the basis of the task outcome. Here, we examined the neural underpinnings of the task-monitoring and reward-evaluation systems to better understand how they govern reward-seeking behavior. Twenty-three healthy adult participants performed a task where they accrued points that equated to real world value (gift cards) by responding as rapidly as possible within an allotted timeframe, while success rate was titrated online by changing the duration of the timeframe dependent on participant performance. Informative cues initiated each trial, indicating the probability of potential reward or loss (four levels from very low to very high). We manipulated feedback by first informing participants of task success/failure, after which a second feedback signal indicated actual magnitude of reward/loss. High-density electroencephalography (EEG) recordings allowed for examination of event-related potentials (ERPs) to the informative cues and in turn, to both feedback signals. Distinct ERP components associated with reward cues, task-preparatory and task-monitoring processes, and reward feedback processes were identified. Unsurprisingly, participants displayed increased ERP amplitudes associated with task-preparatory processes following cues that predicted higher chances of reward. They also rapidly updated reward and loss prediction information dependent on task performance after the first feedback signal. Finally, upon reward receipt, initial reward probability was no longer taken into account. Rather, ERP measures suggested that only the magnitude of actual reward or loss was now processed. Reward and task-monitoring processes are clearly dissociable, but interact across very fast timescales to update reward predictions as information about task success or failure is accrued. Careful delineation of these processes will be useful in future investigations in clinical groups where such processes are suspected of having gone awry. Copyright © 2014 IBRO. Published by Elsevier Ltd. All rights reserved.

Reward Contingencies and the Recalibration of Task Monitoring and Reward Systems: A high-density electrical mapping study

PubMed Central

Morie, Kristen P.; De Sanctis, Pierfilippo; Foxe, John J.

2014-01-01

Task execution almost always occurs in the context of reward-seeking or punishment-avoiding behavior. As such, ongoing task monitoring systems are influenced by reward anticipation systems. In turn, when a task has been executed either successfully or unsuccessfully, future iterations of that task will be re-titrated on the basis of the task outcome. Here, we examined the neural underpinnings of the task-monitoring and reward-evaluation systems to better understand how they govern reward seeking behavior. Twenty-three healthy adult participants performed a task where they accrued points that equated to real world value (gift cards) by responding as rapidly as possible within an allotted timeframe, while success rate was titrated online by changing the duration of the timeframe dependent on participant performance. Informative cues initiated each trial, indicating the probability of potential reward or loss (four levels from very low to very high). We manipulated feedback by first informing participants of task success/failure, after which a second feedback signal indicated actual magnitude of reward/loss. High-density EEG recordings allowed for examination of event-related potentials (ERPs) to the informative cues and in turn, to both feedback signals. Distinct ERP components associated with reward cues, task preparatory and task monitoring processes, and reward feedback processes were identified. Unsurprisingly, participants displayed increased ERP amplitudes associated with task preparatory processes following cues that predicted higher chances of reward. They also rapidly updated reward and loss prediction information dependent on task performance after the first feedback signal. Finally, upon reward receipt, initial reward probability was no longer taken into account. Rather, ERP measures suggested that only the magnitude of actual reward or loss was now processed. Reward and task monitoring processes are clearly dissociable, but interact across very fast timescales to update reward predictions as information about task success or failure is accrued. Careful delineation of these processes will be useful in future investigations in clinical groups where such processes are suspected of having gone awry. PMID:24836852

IASI Radiance Data Assimilation in Local Ensemble Transform Kalman Filter

NASA Astrophysics Data System (ADS)

Cho, K.; Hyoung-Wook, C.; Jo, Y.

2016-12-01

Korea institute of Atmospheric Prediction Systems (KIAPS) is developing NWP model with data assimilation systems. Local Ensemble Transform Kalman Filter (LETKF) system, one of the data assimilation systems, has been developed for KIAPS Integrated Model (KIM) based on cubed-sphere grid and has successfully assimilated real data. LETKF data assimilation system has been extended to 4D- LETKF which considers time-evolving error covariance within assimilation window and IASI radiance data assimilation using KPOP (KIAPS package for observation processing) with RTTOV (Radiative Transfer for TOVS). The LETKF system is implementing semi operational prediction including conventional (sonde, aircraft) observation and AMSU-A (Advanced Microwave Sounding Unit-A) radiance data from April. Recently, the semi operational prediction system updated radiance observations including GPS-RO, AMV, IASI (Infrared Atmospheric Sounding Interferometer) data at July. A set of simulation of KIM with ne30np4 and 50 vertical levels (of top 0.3hPa) were carried out for short range forecast (10days) within semi operation prediction LETKF system with ensemble forecast 50 members. In order to only IASI impact, our experiments used only conventional and IAIS radiance data to same semi operational prediction set. We carried out sensitivity test for IAIS thinning method (3D and 4D). IASI observation number was increased by temporal (4D) thinning and the improvement of IASI radiance data impact on the forecast skill of model will expect.

Performance Measures for Adaptive Decisioning Systems

DTIC Science & Technology

1991-09-11

set to hypothesis space mapping best approximates the known map. Two assumptions, a sufficiently representative training set and the ability of the...successful prediction of LINEXT performance. The LINEXT algorithm above performs the decision space mapping on the training-set ele- ments exactly. For a

Performance of Earthquake Early Warning Systems during the Major Events of the 2016-2017 Central Italy Seismic Sequence.

NASA Astrophysics Data System (ADS)

Festa, G.; Picozzi, M.; Alessandro, C.; Colombelli, S.; Cattaneo, M.; Chiaraluce, L.; Elia, L.; Martino, C.; Marzorati, S.; Supino, M.; Zollo, A.

2017-12-01

Earthquake early warning systems (EEWS) are systems nowadays contributing to the seismic risk mitigation actions, both in terms of losses and societal resilience, by issuing an alert promptly after the earthquake origin and before the ground shaking impacts the targets to be protected. EEWS systems can be grouped in two main classes: network based and stand-alone systems. Network based EEWS make use of dense seismic networks surrounding the fault (e.g. Near Fault Observatory; NFO) generating the event. The rapid processing of the P-wave early portion allows for the location and magnitude estimation of the event then used to predict the shaking through ground motion prediction equations. Stand-alone systems instead analyze the early P-wave signal to predict the ground shaking carried by the late S or surface waves, through empirically calibrated scaling relationships, at the recording site itself. We compared the network-based (PRESTo, PRobabilistic and Evolutionary early warning SysTem, www.prestoews.org, Satriano et al., 2011) and the stand-alone (SAVE, on-Site-Alert-leVEl, Caruso et al., 2017) systems, by analyzing their performance during the 2016-2017 Central Italy sequence. We analyzed 9 earthquakes having magnitude 5.0 < M < 6.5 at about 200 stations located within 200 km from the epicentral area, including stations of The Altotiberina NFO (TABOO). Performances are evaluated in terms of rate of success of ground shaking intensity prediction and available lead-time, i.e. the time available for security actions. PRESTo also evaluated the accuracy of location and magnitude. Both systems well predict the ground shaking nearby the event source, with a success rate around 90% within the potential damage zone. The lead-time is significantly larger for the network based system, increasing to more than 10s at 40 km from the event epicentre. The stand-alone system better performs in the near-source region showing a positive albeit small lead-time (<3s). Far away from the source, the performances slightly degrade, mostly owing to uncertain calibration of attenuation relationships. This study opens to the possibility of making EEWS operational in Italy, based on the available acceleration networks, by improving the capability of reducing the lead-time related to data telemetry.

Vital Signs: How Early Can Resident Evaluation Predict Acquisition of Competency in Surgical Pathology?

PubMed Central

Ducatman, Barbara S.; Williams, H. James; Hobbs, Gerald; Gyure, Kymberly A.

2009-01-01

Objectives To determine whether a longitudinal, case-based evaluation system can predict acquisition of competency in surgical pathology and how trainees at risk can be identified early. Design Data were collected for trainee performance on surgical pathology cases (how well their diagnosis agreed with the faculty diagnosis) and compared with training outcomes. Negative training outcomes included failure to complete the residency, failure to pass the anatomic pathology component of the American Board of Pathology examination, and/or failure to obtain or hold a position immediately following training. Findings Thirty-three trainees recorded diagnoses for 54 326 surgical pathology cases, with outcome data available for 15 residents. Mean case-based performance was significantly higher for those with positive outcomes, and outcome status could be predicted as early as postgraduate year-1 (P  =  .0001). Performance on the first postgraduate year-1 rotation was significantly associated with the outcome (P  =  .02). Although trainees with unsuccessful outcomes improved their performance more rapidly, they started below residents with successful outcomes and did not make up the difference during training. There was no significant difference in Step 1 or 2 United States Medical Licensing Examination (USMLE) scores when compared with performance or final outcomes (P  =  .43 and P  =  .68, respectively) and the resident in-service examination (RISE) had limited predictive ability. Discussion Differences between successful- and unsuccessful-outcome residents were most evident in early residency, ideal for designing interventions or counseling residents to consider another specialty. Conclusion Our longitudinal case-based system successfully identified trainees at risk for failure to acquire critical competencies for surgical pathology early in the program. PMID:21975705

Pre-clinical methods for detecting the hypersensitivity potential of pharmaceuticals: regulatory considerations.

PubMed

Hastings, K L

2001-02-02

Immune-based systemic hypersensitivities account for a significant number of adverse drug reactions. There appear to be no adequate nonclinical models to predict systemic hypersensitivity to small molecular weight drugs. Although there are very good methods for detecting drugs that can induce contact sensitization, these have not been successfully adapted for prediction of systemic hypersensitivity. Several factors have made the development of adequate models difficult. The term systemic hypersensitivity encompases many discrete immunopathologies. Each type of immunopathology presumably is the result of a specific cluster of immunologic and biochemical phenomena. Certainly other factors, such as genetic predisposition, metabolic idiosyncrasies, and concomitant diseases, further complicate the problem. Therefore, it may be difficult to find common mechanisms upon which to construct adequate models to predict specific types of systemic hypersensitivity reactions. There is some reason to hope, however, that adequate methods could be developed for at least identifying drugs that have the potential to produce signs indicative of a general hazard for immune-based reactions.

An Examination of Faculty and Student Online Activity: Predictive Relationships of Student Academic Success in a Learning Management System (LMS)

ERIC Educational Resources Information Center

Stamm, Randy Lee

2013-01-01

The purpose of this mixed method research study was to examine relationships in student and instructor activity logs and student performance benchmarks specific to enabling early intervention by the instructor in a Learning Management System (LMS). Instructor feedback was collected through a survey instrument to demonstrate perceived importance of…

Weather Research and Forecasting Model Wind Sensitivity Study at Edwards Air Force Base, CA

NASA Technical Reports Server (NTRS)

Watson, Leela R.; Bauman, William H., III; Hoeth, Brian

2009-01-01

This abstract describes work that will be done by the Applied Meteorology Unit (AMU) in assessing the success of different model configurations in predicting "wind cycling" cases at Edwards Air Force Base, CA (EAFB), in which the wind speeds and directions oscillate among towers near the EAFB runway. The Weather Research and Forecasting (WRF) model allows users to choose among two dynamical cores - the Advanced Research WRF (ARW) and the Non-hydrostatic Mesoscale Model (NMM). There are also data assimilation analysis packages available for the initialization of the WRF model - the Local Analysis and Prediction System (LAPS) and the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS). Having a series of initialization options and WRF cores, as well as many options within each core, creates challenges for local forecasters, such as determining which configuration options are best to address specific forecast concerns. The goal of this project is to assess the different configurations available and determine which configuration will best predict surface wind speed and direction at EAFB.

Predictive Surface Complexation Modeling

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

Sverjensky, Dimitri A.

Surface complexation plays an important role in the equilibria and kinetics of processes controlling the compositions of soilwaters and groundwaters, the fate of contaminants in groundwaters, and the subsurface storage of CO 2 and nuclear waste. Over the last several decades, many dozens of individual experimental studies have addressed aspects of surface complexation that have contributed to an increased understanding of its role in natural systems. However, there has been no previous attempt to develop a model of surface complexation that can be used to link all the experimental studies in order to place them on a predictive basis. Overall,more » my research has successfully integrated the results of the work of many experimentalists published over several decades. For the first time in studies of the geochemistry of the mineral-water interface, a practical predictive capability for modeling has become available. The predictive correlations developed in my research now enable extrapolations of experimental studies to provide estimates of surface chemistry for systems not yet studied experimentally and for natural and anthropogenically perturbed systems.« less

Ceramic Matrix Composites (CMC) Life Prediction Method Development

NASA Technical Reports Server (NTRS)

Levine, Stanley R.; Calomino, Anthony M.; Ellis, John R.; Halbig, Michael C.; Mital, Subodh K.; Murthy, Pappu L.; Opila, Elizabeth J.; Thomas, David J.; Thomas-Ogbuji, Linus U.; Verrilli, Michael J.

2000-01-01

Advanced launch systems (e.g., Reusable Launch Vehicle and other Shuttle Class concepts, Rocket-Based Combine Cycle, etc.), and interplanetary vehicles will very likely incorporate fiber reinforced ceramic matrix composites (CMC) in critical propulsion components. The use of CMC is highly desirable to save weight, to improve reuse capability, and to increase performance. CMC candidate applications are mission and cycle dependent and may include turbopump rotors, housings, combustors, nozzle injectors, exit cones or ramps, and throats. For reusable and single mission uses, accurate prediction of life is critical to mission success. The tools to accomplish life prediction are very immature and not oriented toward the behavior of carbon fiber reinforced silicon carbide (C/SiC), the primary system of interest for a variety of space propulsion applications. This paper describes an approach to satisfy the need to develop an integrated life prediction system for CMC that addresses mechanical durability due to cyclic and steady thermomechanical loads, and takes into account the impact of environmental degradation.

Predicting Dishonorable Discharge Among Military Recruits

DTIC Science & Technology

2013-03-01

train its members to give them the highest chance possible at a successful career. Jacob Rodriquez’s study, “Predicting the Military Career Success of...society as a whole. To improve the enlistment process and attract recruits with the highest probability of future career success , based on our...00036840801964450 Rodriguez, John J. (2008, January 1). Predicting the career success of air force academy cadets (Paper AAI3309209). ETD collection for

Predictive Modeling of Implantation Outcome in an In Vitro Fertilization Setting: An Application of Machine Learning Methods.

PubMed

Uyar, Asli; Bener, Ayse; Ciray, H Nadir

2015-08-01

Multiple embryo transfers in in vitro fertilization (IVF) treatment increase the number of successful pregnancies while elevating the risk of multiple gestations. IVF-associated multiple pregnancies exhibit significant financial, social, and medical implications. Clinicians need to decide the number of embryos to be transferred considering the tradeoff between successful outcomes and multiple pregnancies. To predict implantation outcome of individual embryos in an IVF cycle with the aim of providing decision support on the number of embryos transferred. Retrospective cohort study. Electronic health records of one of the largest IVF clinics in Turkey. The study data set included 2453 embryos transferred at day 2 or day 3 after intracytoplasmic sperm injection (ICSI). Each embryo was represented with 18 clinical features and a class label, +1 or -1, indicating positive and negative implantation outcomes, respectively. For each classifier tested, a model was developed using two-thirds of the data set, and prediction performance was evaluated on the remaining one-third of the samples using receiver operating characteristic (ROC) analysis. The training-testing procedure was repeated 10 times on randomly split (two-thirds to one-third) data. The relative predictive values of clinical input characteristics were assessed using information gain feature weighting and forward feature selection methods. The naïve Bayes model provided 80.4% accuracy, 63.7% sensitivity, and 17.6% false alarm rate in embryo-based implantation prediction. Multiple embryo implantations were predicted at a 63.8% sensitivity level. Predictions using the proposed model resulted in higher accuracy compared with expert judgment alone (on average, 75.7% and 60.1%, respectively). A machine learning-based decision support system would be useful in improving the success rates of IVF treatment. © The Author(s) 2014.

Development of a Wake Vortex Spacing System for Airport Capacity Enhancement and Delay Reduction

NASA Technical Reports Server (NTRS)

Hinton, David A.; OConnor, Cornelius J.

2000-01-01

The Terminal Area Productivity project has developed the technologies required (weather measurement, wake prediction, and wake measurement) to determine the aircraft spacing needed to prevent wake vortex encounters in various weather conditions. The system performs weather measurements, predicts bounds on wake vortex behavior in those conditions, derives safe wake spacing criteria, and validates the wake predictions with wake vortex measurements. System performance to date indicates that the potential runway arrival rate increase with Aircraft VOrtex Spacing System (AVOSS), considering common path effects and ATC delivery variance, is 5% to 12% depending on the ratio of large and heavy aircraft. The concept demonstration system, using early generation algorithms and minimal optimization, is performing the wake predictions with adequate robustness such that only 4 hard exceedances have been observed in 1235 wake validation cases. This performance demonstrates the feasibility of predicting wake behavior bounds with multiple uncertainties present, including the unknown aircraft weight and speed, weather persistence between the wake prediction and the observations, and the location of the weather sensors several kilometers from the approach location. A concept for the use of the AVOSS system for parallel runway operations has been suggested, and an initial study at the JFK International Airport suggests that a simplified AVOSS system can be successfully operated using only a single lidar as both the weather sensor and the wake validation instrument. Such a selfcontained AVOSS would be suitable for wake separation close to the airport, as is required for parallel approach concepts such as SOIA.

An evaluation of selected (Q)SARs/expert systems for predicting skin sensitisation potential.

PubMed

Fitzpatrick, J M; Roberts, D W; Patlewicz, G

2018-06-01

Predictive testing to characterise substances for their skin sensitisation potential has historically been based on animal models such as the Local Lymph Node Assay (LLNA) and the Guinea Pig Maximisation Test (GPMT). In recent years, EU regulations, have provided a strong incentive to develop non-animal alternatives, such as expert systems software. Here we selected three different types of expert systems: VEGA (statistical), Derek Nexus (knowledge-based) and TIMES-SS (hybrid), and evaluated their performance using two large sets of animal data: one set of 1249 substances from eChemportal and a second set of 515 substances from NICEATM. A model was considered successful at predicting skin sensitisation potential if it had at least the same balanced accuracy as the LLNA and the GPMT had in predicting the other outcomes, which ranged from 79% to 86%. We found that the highest balanced accuracy of any of the expert systems evaluated was 65% when making global predictions. For substances within the domain of TIMES-SS, however, balanced accuracies for the two datasets were found to be 79% and 82%. In those cases where a chemical was within the TIMES-SS domain, the TIMES-SS skin sensitisation hazard prediction had the same confidence as the result from LLNA or GPMT.

Predictors of the patency of self-expandable metallic stents in malignant gastroduodenal obstruction.

PubMed

Kim, Seung Han; Chun, Hoon Jai; Yoo, In Kyung; Lee, Jae Min; Nam, Seung Joo; Choi, Hyuk Soon; Kim, Eun Sun; Keum, Bora; Seo, Yeon Seok; Jeen, Yoon Tae; Lee, Hong Sik; Um, Soon Ho; Kim, Chang Duck

2015-08-14

To investigate the predictive factors of self-expandable metallic stent patency after stent placement in patients with inoperable malignant gastroduodenal obstruction. A total of 116 patients underwent stent placements for inoperable malignant gastroduodenal obstruction at a tertiary academic center. Clinical success was defined as acceptable decompression of the obstructive lesion within the malignant gastroduodenal neoplasm. We evaluated patient comorbidities and clinical statuses using the World Health Organization's scoring system and categorized patient responses to chemotherapy using the Response Evaluation Criteria in Solid Tumors criteria. We analyzed the relationships between possible predictive factors and stent patency. Self-expandable metallic stent placement was technically successful in all patients (100%), and the clinical success rate was 84.2%. In a multivariate Cox proportional hazards model, carcinoembryonic antigen (CEA) levels were correlated with a reduction in stent patency [P = 0.006; adjusted hazard ratio (aHR) = 2.92, 95%CI: 1.36-6.25]. Palliative chemotherapy was statistically associated with an increase in stent patency (P = 0.009; aHR = 0.27, 95%CI: 0.10-0.72). CEA levels can easily be measured at the time of stent placement and may help clinicians to predict stent patency and determine the appropriate stent procedure.

Anticipatory Understanding of Adversary Intent: A Signature-Based Knowledge System

DTIC Science & Technology

2009-06-01

concept of logical positivism has been applied more recently to all human knowledge and reflected in current data fusion research, information mining...this work has been successfully translated into useful analytical tools that can provide a rigorous and quantitative basis for predictive analysis

Expansion of the neuropeptidome of the globally invasive marine crab Carcinus maenas.

PubMed

Christie, Andrew E

2016-09-01

Carcinus maenas is widely recognized as one of the world's most successful marine invasive species; its success as an invader is due largely to its ability to thrive under varied environmental conditions. The physiological/behavioral control systems that allow C. maenas to adapt to new environments are undoubtedly under hormonal control, the largest single class of hormones being peptides. While numerous studies have focused on identifying native C. maenas peptides, none has taken advantage of mining transcriptome shotgun assembly (TSA) sequence data, a strategy proven highly successful for peptide discovery in other crustaceans. Here, a C. maenas peptidome was predicted via in silico transcriptome mining. Thirty-seven peptide families were searched for in the extant TSA database, with transcripts encoding precursors for 29 groups identified. The pre/preprohormones deduced from the identified sequences allowed for the prediction of 263 distinct mature peptides, 193 of which are new discoveries for C. maenas. The predicted peptides include isoforms of adipokinetic hormone-corazonin-like peptide, allatostatin A, allatostatin B, allatostatin C, bursicon, CCHamide, corazonin, crustacean cardioactive peptide, crustacean hyperglycemic hormone, diuretic hormone 31, diuretic hormone 44, eclosion hormone, FMRFamide-like peptide, HIGSLYRamide, intocin, leucokinin, myosuppressin, neuroparsin, neuropeptide F, orcokinin, pigment dispersing hormone, proctolin, pyrokinin, red pigment concentrating hormone, RYamide, short neuropeptide F, SIFamide, and tachykinin-related peptide. This peptidome is the largest predicted from any single crustacean using the in silico approach, and provides a platform for investigating peptidergic signaling in C. maenas, including control of the processes that allow for its success as a global marine invader. Copyright © 2016 Elsevier Inc. All rights reserved.

Using predictive analytics and big data to optimize pharmaceutical outcomes.

PubMed

Hernandez, Inmaculada; Zhang, Yuting

2017-09-15

The steps involved, the resources needed, and the challenges associated with applying predictive analytics in healthcare are described, with a review of successful applications of predictive analytics in implementing population health management interventions that target medication-related patient outcomes. In healthcare, the term big data typically refers to large quantities of electronic health record, administrative claims, and clinical trial data as well as data collected from smartphone applications, wearable devices, social media, and personal genomics services; predictive analytics refers to innovative methods of analysis developed to overcome challenges associated with big data, including a variety of statistical techniques ranging from predictive modeling to machine learning to data mining. Predictive analytics using big data have been applied successfully in several areas of medication management, such as in the identification of complex patients or those at highest risk for medication noncompliance or adverse effects. Because predictive analytics can be used in predicting different outcomes, they can provide pharmacists with a better understanding of the risks for specific medication-related problems that each patient faces. This information will enable pharmacists to deliver interventions tailored to patients' needs. In order to take full advantage of these benefits, however, clinicians will have to understand the basics of big data and predictive analytics. Predictive analytics that leverage big data will become an indispensable tool for clinicians in mapping interventions and improving patient outcomes. Copyright © 2017 by the American Society of Health-System Pharmacists, Inc. All rights reserved.

An advance forecasting system for ship originated oil spills in the Mediterranean

NASA Astrophysics Data System (ADS)

Zodiatis, G.; Lardner, R.; De Dominicis, M.; Coppini, G.; Pinardi, N.

2012-04-01

One of the permanent risks from an oil spill incident in the Mediterranean is associated with the heavy traffic in maritime transport, as well nowadays with the coastal and offshore installations related to the oil and gas industry. Such dense activity imposes on the coastal countries the need for preparing an operational response to major oil spill incidents. In the recent past, several policies related to oil spill response have been adopted internationally. At the regional level the Barcelona convention, recognizing pollution from oil spills as one of the major threats to the marine environment of the Mediterranean, initiated the preparedness for responding to major oil spill incidents, through various national and sub-regional contingency plans. At the European level the Member States was obliged to implement the EU Directive 2005/35, aimed at identifying the polluter and bringing them to prosecution. The response to an oil spill incident employs various measures and equipment. However, the success of such response depends greatly on the prediction of the movement and weathering of the oil spills. Such predictions may obtained through the operational application of advanced numerical oil spill models integrated with met-ocean forecasting data. A well established operational system for oil spill predictions in the Mediterranean is the MEDSLIK three dimensional model that predicts the transport, diffusion and spreading of oil spill and incorporates the fate processes of evaporation, emulsification, viscosity changes, dispersion into the water column and coastal impact and adhesion. MEDSLIK is integrated with the MyOCEAN regional and several downscaled ocean forecasting systems in the Mediterranean, contributing to the development of the GMES marine services. Moreover, MEDSLIK has been coupled with EMSA-CSN and ESA ASAR imageries, for short forward and backward predictions, to assist the response agencies in the implementation of the EU Directive 2005/35. From 2007 to 2011 more than a thousand possible ship originated oil slicks were detected by ASAR imageries in the Levantine Basin and then used for operational predictions by MEDSLIK. The successful operation of the MEDSLIK oil spill prediction system in the Levantine Basin has initiated efforts to implement a multi model approach to oil spill predictions in the Mediterranean through the realization of the recently approved project known as MedDESS4MS-Mediterranean Decision Support System for Maritime Safety, funded under the MED program. MEDESS4MS project is dedicated to the prevention of maritime risks and strengthening of maritime safety related to oil spill pollution in the Mediterranean. MEDESS4MS will deliver an integrated operational multi model oil spill prediction service in the Mediterranean, connected to existing monitoring platforms (EMSA-CSN, REMPEC, AIS), using the well established oil spill modeling systems, the data from the GMES Marine Core Services and the national ocean forecasting systems.

Can hospital-based doctors change their working hours? Evidence from Australia.

PubMed

Norman, R; Hall, J

2014-07-01

To explore factors predicting hospital-based doctors' desire to work less, and then their success in making that change. Consecutive waves of an Australian longitudinal survey of doctors (Medicine in Australia-Balancing Employment and Life). There were 6285 and 6337 hospital-based completers in the two waves, consisting of specialists, hospital-based non-specialists and specialist registrars. Forty-eight per cent stated a preference to reduce hours. Predictive characteristics were being female and working more than 40 h/week (both P < 0.01). An inverted U-shape relationship was observed for age, with younger and older doctors less likely to state the preference. Factors associated with not wanting to reduce working hours were being in excellent health and being satisfied with work (both P < 0.01). Of those who wanted to reduce working hours, only 32% successfully managed to do so in the subsequent year (defined by a reduction of at least 5 h/week). Predictors of successfully reducing hours were being older, female and working more than 40 h/week (all P < 0.01). Several factors predict the desire of hospital-based doctors to reduce hours and then their subsequent success in doing so. Designing policies that seek to reduce attrition may alleviate some of the ongoing pressures in the Australian hospital system. © 2014 The Authors; Internal Medicine Journal © 2014 Royal Australasian College of Physicians.

Developing a New Computer-Aided Clinical Decision Support System for Prediction of Successful Postcardioversion Patients with Persistent Atrial Fibrillation.

PubMed

Sterling, Mark; Huang, David T; Ghoraani, Behnaz

2015-01-01

We propose a new algorithm to predict the outcome of direct-current electric (DCE) cardioversion for atrial fibrillation (AF) patients. AF is the most common cardiac arrhythmia and DCE cardioversion is a noninvasive treatment to end AF and return the patient to sinus rhythm (SR). Unfortunately, there is a high risk of AF recurrence in persistent AF patients; hence clinically it is important to predict the DCE outcome in order to avoid the procedure's side effects. This study develops a feature extraction and classification framework to predict AF recurrence patients from the underlying structure of atrial activity (AA). A multiresolution signal decomposition technique, based on matching pursuit (MP), was used to project the AA over a dictionary of wavelets. Seven novel features were derived from the decompositions and were employed in a quadratic discrimination analysis classification to predict the success of post-DCE cardioversion in 40 patients with persistent AF. The proposed algorithm achieved 100% sensitivity and 95% specificity, indicating that the proposed computational approach captures detailed structural information about the underlying AA and could provide reliable information for effective management of AF.

Analytical and Experimental Vibration Analysis of a Faulty Gear System.

DTIC Science & Technology

1994-10-01

Wigner - Ville Distribution ( WVD ) was used to give a comprehensive comparison of the predicted and...experimental results. The WVD method applied to the experimental results were also compared to other fault detection techniques to verify the WVD’s ability to...of the damaged test gear and the predicted vibration from the model with simulated gear tooth pitting damage. Results also verified that the WVD method can successfully detect and locate gear tooth wear and pitting damage.

Predicting patchy particle crystals: variable box shape simulations and evolutionary algorithms.

PubMed

Bianchi, Emanuela; Doppelbauer, Günther; Filion, Laura; Dijkstra, Marjolein; Kahl, Gerhard

2012-06-07

We consider several patchy particle models that have been proposed in literature and we investigate their candidate crystal structures in a systematic way. We compare two different algorithms for predicting crystal structures: (i) an approach based on Monte Carlo simulations in the isobaric-isothermal ensemble and (ii) an optimization technique based on ideas of evolutionary algorithms. We show that the two methods are equally successful and provide consistent results on crystalline phases of patchy particle systems.

Evaluation of the ASVAB (Armed Services Vocational Aptitude Battery) 8/ 9/10 Clerical Composite for Predicting Training School Performance

DTIC Science & Technology

1983-10-01

specific predictor such as clerical speed or psychomotor skill , since the AR test would probably predict success equally well in many different areas...to specific occupational skills . Ř? When the aptitude area system was reconstituted in 1958, each composite contained only two tests, one measuring... literacy into each composite was that the composites were highly intercorrelated. The same aptitude composites developed for ACB-73 were also used

Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models

PubMed Central

2017-01-01

We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem. The end-to-end trained model has an encoder–decoder architecture that consists of two recurrent neural networks, which has previously shown great success in solving other sequence-to-sequence prediction tasks such as machine translation. The model is trained on 50,000 experimental reaction examples from the United States patent literature, which span 10 broad reaction types that are commonly used by medicinal chemists. We find that our model performs comparably with a rule-based expert system baseline model, and also overcomes certain limitations associated with rule-based expert systems and with any machine learning approach that contains a rule-based expert system component. Our model provides an important first step toward solving the challenging problem of computational retrosynthetic analysis. PMID:29104927

Development and Implementation of a Hardware In-the-Loop Test Bed for Unmanned Aerial Vehicle Control Algorithms

NASA Technical Reports Server (NTRS)

Nyangweso, Emmanuel; Bole, Brian

2014-01-01

Successful prediction and management of battery life using prognostic algorithms through ground and flight tests is important for performance evaluation of electrical systems. This paper details the design of test beds suitable for replicating loading profiles that would be encountered in deployed electrical systems. The test bed data will be used to develop and validate prognostic algorithms for predicting battery discharge time and battery failure time. Online battery prognostic algorithms will enable health management strategies. The platform used for algorithm demonstration is the EDGE 540T electric unmanned aerial vehicle (UAV). The fully designed test beds developed and detailed in this paper can be used to conduct battery life tests by controlling current and recording voltage and temperature to develop a model that makes a prediction of end-of-charge and end-of-life of the system based on rapid state of health (SOH) assessment.

"Eyeball test" of thermographic patterns for predicting a successful lateral infraclavicular block.

PubMed

Andreasen, Asger M; Linnet, Karen E; Asghar, Semera; Rothe, Christian; Rosenstock, Charlotte V; Lange, Kai H W; Lundstrøm, Lars H

2017-11-01

Increased distal skin temperature can be used to predict the success of lateral infraclavicular (LIC) block. We hypothesized that an "eyeball test" of specific infrared thermographic patterns after LIC block could be used to determine block success. In this observational study, five observers trained in four distinct thermographic patterns independently evaluated thermographic images of the hands of 40 patients at baseline and at one-minute intervals for 30 min after a LIC block. Sensitivity, specificity, and predictive values of a positive and a negative test were estimated to evaluate the validity of specific thermographic patterns for predicting a successful block. Sensory and motor block of the musculocutaneous, radial, ulnar, and median nerves defined block success. Fleiss' kappa statistics of multiple interobserver agreements were used to evaluate reliability. As a diagnostic test, the defined specific thermographic patterns of the hand predicted a successful block with increasing accuracy over the 30-min observation period. Block success was predicted with a sensitivity of 92.4% (95% confidence interval [CI], 86.8 to 96.2) and with a specificity of 84.0% (95% CI, 70.3 to 92.4) at min 30. The Fleiss' kappa for the five observers was 0.87 (95% CI, 0.77 to 0.96). We conclude that visual evaluation by an eyeball test of specific thermographic patterns of the blocked hands may be useful as a valid and reliable diagnostic test for predicting a successful LIC block.

Online EEG-Based Workload Adaptation of an Arithmetic Learning Environment.

PubMed

Walter, Carina; Rosenstiel, Wolfgang; Bogdan, Martin; Gerjets, Peter; Spüler, Martin

2017-01-01

In this paper, we demonstrate a closed-loop EEG-based learning environment, that adapts instructional learning material online, to improve learning success in students during arithmetic learning. The amount of cognitive workload during learning is crucial for successful learning and should be held in the optimal range for each learner. Based on EEG data from 10 subjects, we created a prediction model that estimates the learner's workload to obtain an unobtrusive workload measure. Furthermore, we developed an interactive learning environment that uses the prediction model to estimate the learner's workload online based on the EEG data and adapt the difficulty of the learning material to keep the learner's workload in an optimal range. The EEG-based learning environment was used by 13 subjects to learn arithmetic addition in the octal number system, leading to a significant learning effect. The results suggest that it is feasible to use EEG as an unobtrusive measure of cognitive workload to adapt the learning content. Further it demonstrates that a promptly workload prediction is possible using a generalized prediction model without the need for a user-specific calibration.

A Step Made Toward Designing Microelectromechanical System (MEMS) Structures With High Reliability

NASA Technical Reports Server (NTRS)

Nemeth, Noel N.

2003-01-01

The mechanical design of microelectromechanical systems-particularly for micropower generation applications-requires the ability to predict the strength capacity of load-carrying components over the service life of the device. These microdevices, which typically are made of brittle materials such as polysilicon, show wide scatter (stochastic behavior) in strength as well as a different average strength for different sized structures (size effect). These behaviors necessitate either costly and time-consuming trial-and-error designs or, more efficiently, the development of a probabilistic design methodology for MEMS. Over the years, the NASA Glenn Research Center s Life Prediction Branch has developed the CARES/Life probabilistic design methodology to predict the reliability of advanced ceramic components. In this study, done in collaboration with Johns Hopkins University, the ability of the CARES/Life code to predict the reliability of polysilicon microsized structures with stress concentrations is successfully demonstrated.

Predictive modeling of multicellular structure formation by using Cellular Particle Dynamics simulations

NASA Astrophysics Data System (ADS)

McCune, Matthew; Shafiee, Ashkan; Forgacs, Gabor; Kosztin, Ioan

2014-03-01

Cellular Particle Dynamics (CPD) is an effective computational method for describing and predicting the time evolution of biomechanical relaxation processes of multicellular systems. A typical example is the fusion of spheroidal bioink particles during post bioprinting structure formation. In CPD cells are modeled as an ensemble of cellular particles (CPs) that interact via short-range contact interactions, characterized by an attractive (adhesive interaction) and a repulsive (excluded volume interaction) component. The time evolution of the spatial conformation of the multicellular system is determined by following the trajectories of all CPs through integration of their equations of motion. CPD was successfully applied to describe and predict the fusion of 3D tissue construct involving identical spherical aggregates. Here, we demonstrate that CPD can also predict tissue formation involving uneven spherical aggregates whose volumes decrease during the fusion process. Work supported by NSF [PHY-0957914]. Computer time provided by the University of Missouri Bioinformatics Consortium.

Male mate choice influences female promiscuity in Soay sheep

PubMed Central

Preston, B.T.; Stevenson, I.R.; Pemberton, J.M.; Coltman, D.W.; Wilson, K.

2005-01-01

In most animal species, males are predicted to compete for reproductive opportunities, while females are expected to choose between potential mates. However, when males’ rate of reproduction is constrained, or females vary widely in ‘quality’, male mate choice is also predicted to occur. Such conditions exist in the promiscuous mating system of feral Soay sheep on St Kilda, Scotland, where a highly synchronized mating season, intense sperm competition and limitations on sperm production constrain males’ potential reproductive rate, and females vary substantially in their ability to produce successful offspring. We show that, consistent with predictions, competitive rams focus their mating activity and siring success towards heavier females with higher inclusive fitness. To our knowledge, this is the first time that male mate choice has been identified and shown to lead to assortative patterns of parentage in a natural mammalian system, and occurs despite fierce male–male competition for mates. An additional consequence of assortative mating in this population is that lighter females experience a series of unstable consorts with less adept rams, and hence are mated by a greater number of males during their oestrus. We have thus also identified a novel male-driven mechanism that generates variation in female promiscuity, which suggests that the high levels of female promiscuity in this system are not part of an adaptive female tactic to intensify post-copulatory competition between males. PMID:15734690

Male mate choice influences female promiscuity in Soay sheep.

PubMed

Preston, B T; Stevenson, I R; Pemberton, J M; Coltman, D W; Wilson, K

2005-02-22

In most animal species, males are predicted to compete for reproductive opportunities, while females are expected to choose between potential mates. However, when males' rate of reproduction is constrained, or females vary widely in 'quality', male mate choice is also predicted to occur. Such conditions exist in the promiscuous mating system of feral Soay sheep on St Kilda, Scotland, where a highly synchronized mating season, intense sperm competition and limitations on sperm production constrain males' potential reproductive rate, and females vary substantially in their ability to produce successful offspring. We show that, consistent with predictions, competitive rams focus their mating activity and siring success towards heavier females with higher inclusive fitness. To our knowledge, this is the first time that male mate choice has been identified and shown to lead to assortative patterns of parentage in a natural mammalian system, and occurs despite fierce male-male competition for mates. An additional consequence of assortative mating in this population is that lighter females experience a series of unstable consorts with less adept rams, and hence are mated by a greater number of males during their oestrus. We have thus also identified a novel male-driven mechanism that generates variation in female promiscuity, which suggests that the high levels of female promiscuity in this system are not part of an adaptive female tactic to intensify post-copulatory competition between males.

Reproductive success of a keystone herbivore is more variable and responsive to climate in habitats with lower resource diversity.

PubMed

Iles, David T; Rockwell, Robert F; Koons, David N

2018-07-01

The effects of climate on wild populations are often channelled through species interactions. Population responses to climate variation can therefore differ across habitats, owing to variation in the biotic community. Theory predicts that consumer demography should be less variable and less responsive to climate in habitats with greater resource diversity. We tested these predictions using a long-term study of breeding lesser snow geese along the western coast of Hudson Bay, Manitoba, Canada. Reproductive success was measured in 22 years from 114 locations, in either coastal or inland habitat types. We used Bayesian analysis to estimate the response of reproductive success to climate in each habitat type, along with residual variation not explained by climate. We then quantified gosling diet composition in each habitat type to test the prediction that reproductive success would be less variable and more responsive to climate in habitats with lower resource diversity. Reproductive success responded positively to seasonal warmness, but this response was much stronger in inland habitats than in coastal habitats. Site- and year-level random effects were also three to five times more variable in inland habitats. Simultaneously, land cover diversity and gosling diet diversity were lower in inland habitats. Our study illustrates that spatial variation in resource diversity (and thus, species interactions) can have important effects on consumer responses to climate. In this system, climate change is expected to disproportionately increase the reproductive success of snow geese in vast inland habitats, potentially counteracting management efforts to reduce the abundance of this keystone herbivore. © 2018 The Authors. Journal of Animal Ecology © 2018 British Ecological Society.

Functional brain imaging predicts public health campaign success

PubMed Central

O’Donnell, Matthew Brook; Tompson, Steven; Gonzalez, Richard; Dal Cin, Sonya; Strecher, Victor; Cummings, Kenneth Michael; An, Lawrence

2016-01-01

Mass media can powerfully affect health decision-making. Pre-testing through focus groups or surveys is a standard, though inconsistent, predictor of effectiveness. Converging evidence demonstrates that activity within brain systems associated with self-related processing can predict individual behavior in response to health messages. Preliminary evidence also suggests that neural activity in small groups can forecast population-level campaign outcomes. Less is known about the psychological processes that link neural activity and population-level outcomes, or how these predictions are affected by message content. We exposed 50 smokers to antismoking messages and used their aggregated neural activity within a ‘self-localizer’ defined region of medial prefrontal cortex to predict the success of the same campaign messages at the population level (n = 400 000 emails). Results demonstrate that: (i) independently localized neural activity during health message exposure complements existing self-report data in predicting population-level campaign responses (model combined R2 up to 0.65) and (ii) this relationship depends on message content—self-related neural processing predicts outcomes in response to strong negative arguments against smoking and not in response to compositionally similar neutral images. These data advance understanding of the psychological link between brain and large-scale behavior and may aid the construction of more effective media health campaigns. PMID:26400858

The Paradox of Equipoise: The Principle That Drives and Limits Therapeutic Discoveries in Clinical Research

PubMed Central

Djulbegovic, Benjamin

2009-01-01

Background Progress in clinical medicine relies on the willingness of patients to take part in experimental clinical trials, particularly randomized controlled trials (RCTs). Before agreeing to enroll in clinical trials, patients require guarantees that they will not knowingly be harmed and will have the best possible chances of receiving the most favorable treatments. This guarantee is provided by the acknowledgment of uncertainty (equipoise), which removes ethical dilemmas and makes it easier for patients to enroll in clinical trials. Methods Since the design of clinical trials is mostly affected by clinical equipoise, the “clinical equipoise hypothesis” has been postulated. If the uncertainty requirement holds, this means that investigators cannot predict what they are going to discover in any individual trial that they undertake. In some instances, new treatments will be superior to standard treatments, while in others, standard treatments will be superior to experimental treatments, and in still others, no difference will be detected between new and standard treatments. It is hypothesized that there must be a relationship between the overall pattern of treatment successes and the uncertainties that RCTs are designed to address. Results An analysis of published trials shows that the results cannot be predicted at the level of individual trials. However, the results also indicate that the overall pattern of discovery of treatment success across a series of trials is predictable and is consistent with clinical equipoise hypothesis. The analysis shows that we can discover no more than 25% to 50% of successful treatments when they are tested in RCTs. The analysis also indicates that this discovery rate is optimal in helping to preserve the clinical trial system; a high discovery rate (eg, a 90% to 100% probability of success) is neither feasible nor desirable since under these circumstances, neither the patient nor the researcher has an interest in randomization. This in turn would halt the RCT system as we know it. Conclusions The “principle or law of clinical discovery” described herein predicts the efficiency of the current system of RCTs at generating discoveries of new treatments. The principle is derived from the requirement for uncertainty or equipoise as a precondition for RCTs, the precept that paradoxically drives discoveries of new treatments while limiting the proportion and rate of new therapeutic discoveries. PMID:19910921

Should Non-Cognitive Skills Be Included in School Accountability Systems? Preliminary Evidence from California's CORE Districts. Evidence Speaks Reports, Vol 1, #13

ERIC Educational Resources Information Center

West, Martin R.

2016-01-01

Evidence confirms that student skills other than academic achievement and ability predict a broad range of academic and life outcomes. This evidence, along with a new federal requirement that state accountability systems include an indicator of school quality or student success not based on test scores, has sparked interest in incorporating such…

Space Environmental Effects (SEE) Testing Capability: NASA/Marshall Space Flight Center

NASA Technical Reports Server (NTRS)

DeWittBurns, H.; Crave, Paul; Finckenor, Miria; Finchum, Charles; Nehls, Mary; Schneider, Todd; Vaughn, Jason

2012-01-01

Understanding the effects of the space environment on materials and systems is fundamental and essential for mission success. If not properly understood and designed for, the space environment can lead to materials degradation, reduction of functional lifetime, and system failure. Ground based testing is critical in predicting performance NASA/MSFC's expertise and capabilities make up the most complete SEE testing capability available.

The computational challenges of Earth-system science.

PubMed

O'Neill, Alan; Steenman-Clark, Lois

2002-06-15

The Earth system--comprising atmosphere, ocean, land, cryosphere and biosphere--is an immensely complex system, involving processes and interactions on a wide range of space- and time-scales. To understand and predict the evolution of the Earth system is one of the greatest challenges of modern science, with success likely to bring enormous societal benefits. High-performance computing, along with the wealth of new observational data, is revolutionizing our ability to simulate the Earth system with computer models that link the different components of the system together. There are, however, considerable scientific and technical challenges to be overcome. This paper will consider four of them: complexity, spatial resolution, inherent uncertainty and time-scales. Meeting these challenges requires a significant increase in the power of high-performance computers. The benefits of being able to make reliable predictions about the evolution of the Earth system should, on their own, amply repay this investment.

Pattern recognition of satellite cloud imagery for improved weather prediction

NASA Technical Reports Server (NTRS)

Gautier, Catherine; Somerville, Richard C. J.; Volfson, Leonid B.

1986-01-01

The major accomplishment was the successful development of a method for extracting time derivative information from geostationary meteorological satellite imagery. This research is a proof-of-concept study which demonstrates the feasibility of using pattern recognition techniques and a statistical cloud classification method to estimate time rate of change of large-scale meteorological fields from remote sensing data. The cloud classification methodology is based on typical shape function analysis of parameter sets characterizing the cloud fields. The three specific technical objectives, all of which were successfully achieved, are as follows: develop and test a cloud classification technique based on pattern recognition methods, suitable for the analysis of visible and infrared geostationary satellite VISSR imagery; develop and test a methodology for intercomparing successive images using the cloud classification technique, so as to obtain estimates of the time rate of change of meteorological fields; and implement this technique in a testbed system incorporating an interactive graphics terminal to determine the feasibility of extracting time derivative information suitable for comparison with numerical weather prediction products.

Soil Systems for Upscaling Saturated Hydraulic Conductivity (Ksat) for Hydrological Modeling in the Critical Zone

USDA-ARS?s Scientific Manuscript database

Successful hydrological model predictions depend on appropriate framing of scale and the spatial-temporal accuracy of input parameters describing soil hydraulic properties. Saturated soil hydraulic conductivity (Ksat) is one of the most important properties influencing water movement through soil un...

Orbiter windward surface entry Heating: Post-orbital flight test program update

NASA Technical Reports Server (NTRS)

Harthun, M. H.; Blumer, C. B.; Miller, B. A.

1983-01-01

Correlations of orbiter windward surface entry heating data from the first five flights are presented with emphasis on boundary layer transition and the effects of catalytic recombination. Results show that a single roughness boundary layer transition correlation developed for spherical element trips works well for the orbiter tile system. Also, an engineering approach for predicting heating in nonequilibrium flow conditions shows good agreement with the flight test data in the time period of significant heating. The results of these correlations, when used to predict orbiter heating for a high cross mission, indicate that the thermal protection system on the windward surface will perform successfully in such a mission.

Systems Training for Emotional Predictability and Problem Solving (STEPPS) group treatment for offenders with borderline personality disorder.

PubMed

Black, Donald W; Blum, Nancee; McCormick, Brett; Allen, Jeff

2013-02-01

Systems Training for Emotional Predictability and Problem Solving (STEPPS) is a manual-based group treatment of persons with borderline personality disorder (BPD). We report results from a study of offenders supervised by the Iowa Department of Corrections. Seventy-seven offenders participated in STEPPS groups. The offenders experienced clinically significant improvement in BPD-related symptoms (d = 1.30), mood, and negative affectivity. Suicidal behaviors and disciplinary infractions were reduced. Baseline severity was inversely associated with improvement. The offenders indicated satisfaction with STEPPS. We conclude that STEPPS can be successfully integrated into the care of offenders with BPD in prison and community corrections settings.

Prediction of Excitation Energies for Conjugated Oligomers and Polymers from Time-Dependent Density Functional Theory

PubMed Central

Tao, Jianmin; Tretiak, Sergei; Zhu, Jian-Xin

2010-01-01

With technological advances, light-emitting conjugated oligomers and polymers have become competitive candidates in the commercial market of light-emitting diodes for display and other technologies, due to the ultralow cost, light weight, and flexibility. Prediction of excitation energies of these systems plays a crucial role in the understanding of their optical properties and device design. In this review article, we discuss the calculation of excitation energies with time-dependent density functional theory, which is one of the most successful methods in the investigation of the dynamical response of molecular systems to external perturbation, owing to its high computational efficiency.

How robust are distributed systems

NASA Technical Reports Server (NTRS)

Birman, Kenneth P.

1989-01-01

A distributed system is made up of large numbers of components operating asynchronously from one another and hence with imcomplete and inaccurate views of one another's state. Load fluctuations are common as new tasks arrive and active tasks terminate. Jointly, these aspects make it nearly impossible to arrive at detailed predictions for a system's behavior. It is important to the successful use of distributed systems in situations in which humans cannot provide the sorts of predictable realtime responsiveness of a computer, that the system be robust. The technology of today can too easily be affected by worn programs or by seemingly trivial mechanisms that, for example, can trigger stock market disasters. Inventors of a technology have an obligation to overcome flaws that can exact a human cost. A set of principles for guiding solutions to distributed computing problems is presented.

DoD-GEIS Rift Valley Fever Monitoring and Prediction System as a Tool for Defense and US Diplomacy

NASA Technical Reports Server (NTRS)

Anyamba, Assaf; Tucker, Compton J.; Linthicum, Kenneth J.; Witt, Clara J.; Gaydos, Joel C.; Russell, Kevin L.

2011-01-01

Over the last 10 years the Armed Forces Health Surveillance Center's Global Emerging Infections Surveillance and Response System (GEIS) partnering with NASA'S Goddard Space Flight Center and USDA's USDA-Center for Medical, Agricultural & Veterinary Entomology established and have operated the Rift Valley fever Monitoring and Prediction System to monitor, predict and assess the risk of Rift Valley fever outbreaks and other vector-borne diseases over Africa and the Middle East. This system is built on legacy DoD basic research conducted by Walter Reed Army Institute of Research overseas laboratory (US Army Medical Research Unit-Kenya) and the operational satellite environmental monitoring by NASA GSFC. Over the last 10 years of operation the system has predicted outbreaks of Rift Valley fever in the Horn of Africa, Sudan, South Africa and Mauritania. The ability to predict an outbreak several months before it occurs provides early warning to protect deployed forces, enhance public health in concerned countries and is a valuable tool use.d by the State Department in US Diplomacy. At the international level the system has been used by the Food and Agricultural Organization (FAD) and the World Health Organization (WHO) to support their monitoring, surveillance and response programs in the livestock sector and human health. This project is a successful testament of leveraging resources of different federal agencies to achieve objectives of force health protection, health and diplomacy.

The Relationship Between the California Critical Thinking Disposition Inventory and Student Learning Outcomes in Baccalaureate Nursing Students.

PubMed

Searing, Lisabeth Meade; Kooken, Wendy Carter

2016-04-01

Critical thinking is the foundation for nurses' decision making. One school of nursing used the California Critical Thinking Disposition Inventory (CCTDI) to document improvement in critical thinking dispositions. A retrospective study of 96 nursing students' records examined the relationships between the CCTDI and learning outcomes. Correlational statistics assessed relationships between CCTDI scores and cumulative grade point averages (GPA) and scores on two Health Education Systems Incorporated (HESI) examinations. Ordinal regression assessed predictive relationships between CCTDI scores and science course grades and NCLEX-RN success. First-year CCTDI scores did not predict first-year science grades. Senior-year CCTDI scores did not correlate with cumulative GPA or HESI RN Exit Exam scores, but were weakly correlated with HESI Pharmacology Exam scores. CCTDI scores did not predict NCLEX-RN success. This study did not identify meaningful relationships between critical thinking dispositions, as measured by the CCTDI, and important learning outcomes. The results do not support the efficacy of using the CCTDI in nursing education. Copyright 2016, SLACK Incorporated.

Biomedical systems analysis program

NASA Technical Reports Server (NTRS)

1979-01-01

Biomedical monitoring programs which were developed to provide a system analysis context for a unified hypothesis for adaptation to space flight are presented and discussed. A real-time system of data analysis and decision making to assure the greatest possible crew safety and mission success is described. Information about man's abilities, limitations, and characteristic reactions to weightless space flight was analyzed and simulation models were developed. The predictive capabilities of simulation models for fluid-electrolyte regulation, erythropoiesis regulation, and calcium regulation are discussed.

Implementation Intention and Reminder Effects on Behavior Change in a Mobile Health System: A Predictive Cognitive Model.

PubMed

Pirolli, Peter; Mohan, Shiwali; Venkatakrishnan, Anusha; Nelson, Les; Silva, Michael; Springer, Aaron

2017-11-30

Implementation intentions are mental representations of simple plans to translate goal intentions into behavior under specific conditions. Studies show implementation intentions can produce moderate to large improvements in behavioral goal achievement. Human associative memory mechanisms have been implicated in the processes by which implementation intentions produce effects. On the basis of the adaptive control of thought-rational (ACT-R) theory of cognition, we hypothesized that the strength of implementation intention effect could be manipulated in predictable ways using reminders delivered by a mobile health (mHealth) app. The aim of this experiment was to manipulate the effects of implementation intentions on daily behavioral goal success in ways predicted by the ACT-R theory concerning mHealth reminder scheduling. An incomplete factorial design was used in this mHealth study. All participants were asked to choose a healthy behavior goal associated with eat slowly, walking, or eating more vegetables and were asked to set implementation intentions. N=64 adult participants were in the study for 28 days. Participants were stratified by self-efficacy and assigned to one of two reminder conditions: reminders-presented versus reminders-absent. Self-efficacy and reminder conditions were crossed. Nested within the reminders-presented condition was a crossing of frequency of reminders sent (high, low) by distribution of reminders sent (distributed, massed). Participants in the low frequency condition got 7 reminders over 28 days; those in the high frequency condition were sent 14. Participants in the distributed conditions were sent reminders at uniform intervals. Participants in the massed distribution conditions were sent reminders in clusters. There was a significant overall effect of reminders on achieving a daily behavioral goal (coefficient=2.018, standard error [SE]=0.572, odds ratio [OR]=7.52, 95% CI 0.9037-3.2594, P<.001). As predicted by ACT-R, using default theoretical parameters, there was an interaction of reminder frequency by distribution on daily goal success (coefficient=0.7994, SE=0.2215, OR=2.2242, 95% CI 0.3656-1.2341, P<.001). The total number of times a reminder was acknowledged as received by a participant had a marginal effect on daily goal success (coefficient=0.0694, SE=0.0410, OR=1.0717, 95% CI -0.01116 to 0.1505, P=.09), and the time since acknowledging receipt of a reminder was highly significant (coefficient=-0.0490, SE=0.0104, OR=0.9522, 95% CI -0.0700 to -0.2852], P<.001). A dual system ACT-R mathematical model was fit to individuals' daily goal successes and reminder acknowledgments: a goal-striving system dependent on declarative memory plus a habit-forming system that acquires automatic procedures for performance of behavioral goals. Computational cognitive theory such as ACT-R can be used to make precise quantitative predictions concerning daily health behavior goal success in response to implementation intentions and the dosing schedules of reminders. ©Peter Pirolli, Shiwali Mohan, Anusha Venkatakrishnan, Les Nelson, Michael Silva, Aaron Springer. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 30.11.2017.

Real-time predictive seasonal influenza model in Catalonia, Spain

PubMed Central

Basile, Luca; Oviedo de la Fuente, Manuel; Torner, Nuria; Martínez, Ana; Jané, Mireia

2018-01-01

Influenza surveillance is critical to monitoring the situation during epidemic seasons and predictive mathematic models may aid the early detection of epidemic patterns. The objective of this study was to design a real-time spatial predictive model of ILI (Influenza Like Illness) incidence rate in Catalonia using one- and two-week forecasts. The available data sources used to select explanatory variables to include in the model were the statutory reporting disease system and the sentinel surveillance system in Catalonia for influenza incidence rates, the official climate service in Catalonia for meteorological data, laboratory data and Google Flu Trend. Time series for every explanatory variable with data from the last 4 seasons (from 2010–2011 to 2013–2014) was created. A pilot test was conducted during the 2014–2015 season to select the explanatory variables to be included in the model and the type of model to be applied. During the 2015–2016 season a real-time model was applied weekly, obtaining the intensity level and predicted incidence rates with 95% confidence levels one and two weeks away for each health region. At the end of the season, the confidence interval success rate (CISR) and intensity level success rate (ILSR) were analysed. For the 2015–2016 season a CISR of 85.3% at one week and 87.1% at two weeks and an ILSR of 82.9% and 82% were observed, respectively. The model described is a useful tool although it is hard to evaluate due to uncertainty. The accuracy of prediction at one and two weeks was above 80% globally, but was lower during the peak epidemic period. In order to improve the predictive power, new explanatory variables should be included. PMID:29513710

Updating The Navy’s Recruit Quality Matrix: An Analysis of Educational Credentials and the Success of First-Term Sailors

DTIC Science & Technology

2004-03-01

Allison , Logistic Regression: Using the SAS System (Cary, NC: SAS Institute, Inc, 2001), 57. 23 using the likelihood ratio that SAS generates...21, respectively. 33 Jesse M. Rothstein, College Performance Predictions and the SAT ( Berkely , CA: UC

Transition of Incarcerated Youth With Disabilities Across Systems and Into Adulthood

ERIC Educational Resources Information Center

Baltodano, Heather M.; Mathur, Sarup R.; Rutherford, Robert B.

2005-01-01

Identifying factors that contribute to delinquency and recidivism is critical in predicting the success of incarcerated youth transitioning back to the community. The task of identifying these salient factors becomes increasingly complex when compounded by the disproportionate representation of youth with disabilities in juvenile corrections.…

Modeling the toxicokinetics of 24-hour toluene exposure in rats, impact of activity patterns and enzyme induction

EPA Science Inventory

Toluene, a solvent used in numerous consumer and industrial applications, exerts its critical effects on the brain and nervous system following inhalation exposure. Our previously published PBPK model successfully predicted toluene concentrations in blood and brain over a range o...

Preliminary assessment of the Mars Science Laboratory entry, descent, and landing simulation

NASA Astrophysics Data System (ADS)

Way, David W.

On August 5, 2012, the Mars Science Laboratory rover, Curiosity, successfully landed inside Gale Crater. This landing was the seventh successful landing and fourth rover to be delivered to Mars. Weighing nearly one metric ton, Curiosity is the largest and most complex rover ever sent to investigate another planet. Safely landing such a large payload required an innovative Entry, Descent, and Landing system, which included the first guided entry at Mars, the largest supersonic parachute ever flown at Mars, and the novel Sky Crane landing system. A complete, end-to-end, six degree-of-freedom, multi-body computer simulation of the Mars Science Laboratory Entry, Descent, and Landing sequence was developed at the NASA Langley Research Center. In-flight data gathered during the successful landing is compared to pre-flight statistical distributions, predicted by the simulation. These comparisons provide insight into both the accuracy of the simulation and the overall performance of the Entry, Descent, and Landing system.

Impact of Cloud Analysis on Numerical Weather Prediction in the Galician Region of Spain.

NASA Astrophysics Data System (ADS)

Souto, M. J.; Balseiro, C. F.; Pérez-Muñuzuri, V.; Xue, M.; Brewster, K.

2003-01-01

The Advanced Regional Prediction System (ARPS) is applied to operational numerical weather prediction in Galicia, northwest Spain. The model is run daily for 72-h forecasts at a 10-km horizontal spacing. Located on the northwest coast of Spain and influenced by the Atlantic weather systems, Galicia has a high percentage (nearly 50%) of rainy days per year. For these reasons, the precipitation processes and the initialization of moisture and cloud fields are very important. Even though the ARPS model has a sophisticated data analysis system (`ADAS') that includes a 3D cloud analysis package, because of operational constraints, the current forecast starts from the 12-h forecast of the National Centers for Environmental Prediction Aviation Model (AVN). Still, procedures from the ADAS cloud analysis are being used to construct the cloud fields based on AVN data and then are applied to initialize the microphysical variables in ARPS. Comparisons of the ARPS predictions with local observations show that ARPS can predict very well both the daily total precipitation and its spatial distribution. ARPS also shows skill in predicting heavy rains and high winds, as observed during November 2000, and especially in the prediction of the 5 November 2000 storm that caused widespread wind and rain damage in Galicia. It is demonstrated that the cloud analysis contributes to the success of the precipitation forecasts.

Early identification systems for emerging foodborne hazards.

PubMed

Marvin, H J P; Kleter, G A; Prandini, A; Dekkers, S; Bolton, D J

2009-05-01

This paper provides a non-exhausting overview of early warning systems for emerging foodborne hazards that are operating in the various places in the world. Special attention is given to endpoint-focussed early warning systems (i.e. ECDC, ISIS and GPHIN) and hazard-focussed early warning systems (i.e. FVO, RASFF and OIE) and their merit to successfully identify a food safety problem in an early stage is discussed. Besides these early warning systems which are based on monitoring of either disease symptoms or hazards, also early warning systems and/or activities that intend to predict the occurrence of a food safety hazard in its very beginning of development or before that are described. Examples are trend analysis, horizon scanning, early warning systems for mycotoxins in maize and/or wheat and information exchange networks (e.g. OIE and GIEWS). Furthermore, recent initiatives that aim to develop predictive early warning systems based on the holistic principle are discussed. The assumption of the researchers applying this principle is that developments outside the food production chain that are either directly or indirectly related to the development of a particular food safety hazard may also provide valuable information to predict the development of this hazard.

Statistical Teleodynamics: Toward a Theory of Emergence.

PubMed

Venkatasubramanian, Venkat

2017-10-24

The central scientific challenge of the 21st century is developing a mathematical theory of emergence that can explain and predict phenomena such as consciousness and self-awareness. The most successful research program of the 20th century, reductionism, which goes from the whole to parts, seems unable to address this challenge. This is because addressing this challenge inherently requires an opposite approach, going from parts to the whole. In addition, reductionism, by the very nature of its inquiry, typically does not concern itself with teleology or purposeful behavior. Modeling emergence, in contrast, requires the addressing of teleology. Together, these two requirements present a formidable challenge in developing a successful mathematical theory of emergence. In this article, I describe a new theory of emergence, called statistical teleodynamics, that addresses certain aspects of the general problem. Statistical teleodynamics is a mathematical framework that unifies three seemingly disparate domains-purpose-free entities in statistical mechanics, human engineered teleological systems in systems engineering, and nature-evolved teleological systems in biology and sociology-within the same conceptual formalism. This theory rests on several key conceptual insights, the most important one being the recognition that entropy mathematically models the concept of fairness in economics and philosophy and, equivalently, the concept of robustness in systems engineering. These insights help prove that the fairest inequality of income is a log-normal distribution, which will emerge naturally at equilibrium in an ideal free market society. Similarly, the theory predicts the emergence of the three classes of network organization-exponential, scale-free, and Poisson-seen widely in a variety of domains. Statistical teleodynamics is the natural generalization of statistical thermodynamics, the most successful parts-to-whole systems theory to date, but this generalization is only a modest step toward a more comprehensive mathematical theory of emergence.

Continuation Power Flow with Variable-Step Variable-Order Nonlinear Predictor

NASA Astrophysics Data System (ADS)

Kojima, Takayuki; Mori, Hiroyuki

This paper proposes a new continuation power flow calculation method for drawing a P-V curve in power systems. The continuation power flow calculation successively evaluates power flow solutions through changing a specified value of the power flow calculation. In recent years, power system operators are quite concerned with voltage instability due to the appearance of deregulated and competitive power markets. The continuation power flow calculation plays an important role to understand the load characteristics in a sense of static voltage instability. In this paper, a new continuation power flow with a variable-step variable-order (VSVO) nonlinear predictor is proposed. The proposed method evaluates optimal predicted points confirming with the feature of P-V curves. The proposed method is successfully applied to IEEE 118-bus and IEEE 300-bus systems.

Real-Time Analysis of a Sensor's Data for Automated Decision Making in an IoT-Based Smart Home.

PubMed

Khan, Nida Saddaf; Ghani, Sayeed; Haider, Sajjad

2018-05-25

IoT devices frequently generate large volumes of streaming data and in order to take advantage of this data, their temporal patterns must be learned and identified. Streaming data analysis has become popular after being successfully used in many applications including forecasting electricity load, stock market prices, weather conditions, etc. Artificial Neural Networks (ANNs) have been successfully utilized in understanding the embedded interesting patterns/behaviors in the data and forecasting the future values based on it. One such pattern is modelled and learned in the present study to identify the occurrence of a specific pattern in a Water Management System (WMS). This prediction aids in making an automatic decision support system, to switch OFF a hydraulic suction pump at the appropriate time. Three types of ANN, namely Multi-Input Multi-Output (MIMO), Multi-Input Single-Output (MISO), and Recurrent Neural Network (RNN) have been compared, for multi-step-ahead forecasting, on a sensor's streaming data. Experiments have shown that RNN has the best performance among three models and based on its prediction, a system can be implemented to make the best decision with 86% accuracy.

Optimality approaches to describe characteristic fluvial patterns on landscapes

PubMed Central

Paik, Kyungrock; Kumar, Praveen

2010-01-01

Mother Nature has left amazingly regular geomorphic patterns on the Earth's surface. These patterns are often explained as having arisen as a result of some optimal behaviour of natural processes. However, there is little agreement on what is being optimized. As a result, a number of alternatives have been proposed, often with little a priori justification with the argument that successful predictions will lend a posteriori support to the hypothesized optimality principle. Given that maximum entropy production is an optimality principle attempting to predict the microscopic behaviour from a macroscopic characterization, this paper provides a review of similar approaches with the goal of providing a comparison and contrast between them to enable synthesis. While assumptions of optimal behaviour approach a system from a macroscopic viewpoint, process-based formulations attempt to resolve the mechanistic details whose interactions lead to the system level functions. Using observed optimality trends may help simplify problem formulation at appropriate levels of scale of interest. However, for such an approach to be successful, we suggest that optimality approaches should be formulated at a broader level of environmental systems' viewpoint, i.e. incorporating the dynamic nature of environmental variables and complex feedback mechanisms between fluvial and non-fluvial processes. PMID:20368257

Mechatronics technology in predictive maintenance method

NASA Astrophysics Data System (ADS)

Majid, Nurul Afiqah A.; Muthalif, Asan G. A.

2017-11-01

This paper presents recent mechatronics technology that can help to implement predictive maintenance by combining intelligent and predictive maintenance instrument. Vibration Fault Simulation System (VFSS) is an example of mechatronics system. The focus of this study is the prediction on the use of critical machines to detect vibration. Vibration measurement is often used as the key indicator of the state of the machine. This paper shows the choice of the appropriate strategy in the vibration of diagnostic process of the mechanical system, especially rotating machines, in recognition of the failure during the working process. In this paper, the vibration signature analysis is implemented to detect faults in rotary machining that includes imbalance, mechanical looseness, bent shaft, misalignment, missing blade bearing fault, balancing mass and critical speed. In order to perform vibration signature analysis for rotating machinery faults, studies have been made on how mechatronics technology is used as predictive maintenance methods. Vibration Faults Simulation Rig (VFSR) is designed to simulate and understand faults signatures. These techniques are based on the processing of vibrational data in frequency-domain. The LabVIEW-based spectrum analyzer software is developed to acquire and extract frequency contents of faults signals. This system is successfully tested based on the unique vibration fault signatures that always occur in a rotating machinery.

Surfactant enhanced recovery of tetrachloroethylene from a porous medium containing low permeability lenses. 2. Numerical simulation.

PubMed

Rathfelder, K M; Abriola, L M; Taylor, T P; Pennell, K D

2001-04-01

A numerical model of surfactant enhanced solubilization was developed and applied to the simulation of nonaqueous phase liquid recovery in two-dimensional heterogeneous laboratory sand tank systems. Model parameters were derived from independent, small-scale, batch and column experiments. These parameters included viscosity, density, solubilization capacity, surfactant sorption, interfacial tension, permeability, capillary retention functions, and interphase mass transfer correlations. Model predictive capability was assessed for the evaluation of the micellar solubilization of tetrachloroethylene (PCE) in the two-dimensional systems. Predicted effluent concentrations and mass recovery agreed reasonably well with measured values. Accurate prediction of enhanced solubilization behavior in the sand tanks was found to require the incorporation of pore-scale, system-dependent, interphase mass transfer limitations, including an explicit representation of specific interfacial contact area. Predicted effluent concentrations and mass recovery were also found to depend strongly upon the initial NAPL entrapment configuration. Numerical results collectively indicate that enhanced solubilization processes in heterogeneous, laboratory sand tank systems can be successfully simulated using independently measured soil parameters and column-measured mass transfer coefficients, provided that permeability and NAPL distributions are accurately known. This implies that the accuracy of model predictions at the field scale will be constrained by our ability to quantify soil heterogeneity and NAPL distribution.

The Prediction-Focused Approach: An opportunity for hydrogeophysical data integration and interpretation

NASA Astrophysics Data System (ADS)

Hermans, Thomas; Nguyen, Frédéric; Klepikova, Maria; Dassargues, Alain; Caers, Jef

2017-04-01

Hydrogeophysics is an interdisciplinary field of sciences aiming at a better understanding of subsurface hydrological processes. If geophysical surveys have been successfully used to qualitatively characterize the subsurface, two important challenges remain for a better quantification of hydrological processes: (1) the inversion of geophysical data and (2) their integration in hydrological subsurface models. The classical inversion approach using regularization suffers from spatially and temporally varying resolution and yields geologically unrealistic solutions without uncertainty quantification, making their utilization for hydrogeological calibration less consistent. More advanced techniques such as coupled inversion allow for a direct use of geophysical data for conditioning groundwater and solute transport model calibration. However, the technique is difficult to apply in complex cases and remains computationally demanding to estimate uncertainty. In a recent study, we investigate a prediction-focused approach (PFA) to directly estimate subsurface physical properties from geophysical data, circumventing the need for classic inversions. In PFA, we seek a direct relationship between the data and the subsurface variables we want to predict (the forecast). This relationship is obtained through a prior set of subsurface models for which both data and forecast are computed. A direct relationship can often be derived through dimension reduction techniques. PFA offers a framework for both hydrogeophysical "inversion" and hydrogeophysical data integration. For hydrogeophysical "inversion", the considered forecast variable is the subsurface variable, such as the salinity. An ensemble of possible solutions is generated, allowing uncertainty quantification. For hydrogeophysical data integration, the forecast variable becomes the prediction we want to make with our subsurface models, such as the concentration of contaminant in a drinking water production well. Geophysical and hydrological data are combined to derive a direct relationship between data and forecast. We illustrate the process for the design of an aquifer thermal energy storage (ATES) system. An ATES system can theoretically recover in winter the heat stored in the aquifer during summer. In practice, the energy efficiency is often lower than expected due to spatial heterogeneity of hydraulic properties combined to a non-favorable hydrogeological gradient. A proper design of ATES systems should consider the uncertainty of the prediction related to those parameters. With a global sensitivity analysis, we identify sensitive parameters for heat storage prediction and validate the use of a short term heat tracing experiment monitored with geophysics to generate informative data. First, we illustrate how PFA can be used to successfully derive the distribution of temperature in the aquifer from ERT during the heat tracing experiment. Then, we successfully integrate the geophysical data to predict medium-term heat storage in the aquifer using PFA. The result is a full quantification of the posterior distribution of the prediction conditioned to observed data in a relatively limited time budget.

A Data Assimilation System For Operational Weather Forecast In Galicia Region (nw Spain)

NASA Astrophysics Data System (ADS)

Balseiro, C. F.; Souto, M. J.; Pérez-Muñuzuri, V.; Brewster, K.; Xue, M.

Regional weather forecast models, such as the Advanced Regional Prediction System (ARPS), over complex environments with varying local influences require an accurate meteorological analysis that should include all local meteorological measurements available. In this work, the ARPS Data Analysis System (ADAS) (Xue et al. 2001) is applied as a three-dimensional weather analysis tool to include surface station and rawinsonde data with the NCEP AVN forecasts as the analysis background. Currently in ADAS, a set of five meteorological variables are considered during the analysis: horizontal grid-relative wind components, pressure, potential temperature and spe- cific humidity. The analysis is used for high resolution numerical weather prediction for the Galicia region. The analysis method used in ADAS is based on the successive corrective scheme of Bratseth (1986), which asymptotically approaches the result of a statistical (optimal) interpolation, but at lower computational cost. As in the optimal interpolation scheme, the Bratseth interpolation method can take into account the rel- ative error between background and observational data, therefore they are relatively insensitive to large variations in data density and can integrate data of mixed accuracy. This method can be applied economically in an operational setting, providing signifi- cant improvement over the background model forecast as well as any analysis without high-resolution local observations. A one-way nesting is applied for weather forecast in Galicia region, and the use of this assimilation system in both domains shows better results not only in initial conditions but also in all forecast periods. Bratseth, A.M. (1986): "Statistical interpolation by means of successive corrections." Tellus, 38A, 439-447. Souto, M. J., Balseiro, C. F., Pérez-Muñuzuri, V., Xue, M. Brewster, K., (2001): "Im- pact of cloud analysis on numerical weather prediction in the galician region of Spain". Submitted to Journal of Applied Meteorology. Xue, M., Wang. D., Gao, J., Brewster, K, Droegemeier, K. K., (2001): "The Advanced Regional Prediction System (ARPS), storm-scale numerical weather prediction and data assimilation". Meteor. Atmos Physics. Accepted

Can root biomass of white oak advance regeneration be reliably predicted from diameter and height?

Treesearch

Benjamin O. Knapp; G. Geoff Wang; Joan L. Walker; David H. van Lear

2006-01-01

The successful regeneration of oak-dominated stands is an issue of concern for foresters today. The size of the root system is directly related to the rate of growth following release and therefore to the chances of establishment of regrowth. The size of the root system is difficult to measure without destructive sampling, but it may be assessed through modeling. The...

Observation planning tools for the ESO VLT interferometer

NASA Astrophysics Data System (ADS)

McKay, Derek J.; Ballester, Pascal; Vinther, Jakob

2004-09-01

Now that the Very Large Telescope Interferometer (VLTI) is producing regular scientific observations, the field of optical interferometry has moved from being a specialist niche area into mainstream astronomy. Making such instruments available to the general community involves difficult challenges in modelling, presentation and automation. The planning of each interferometric observation requires calibrator source selection, visibility prediction, signal-to-noise estimation and exposure time calculation. These planning tools require detailed physical models simulating the complete telescope system - including the observed source, atmosphere, array configuration, optics, detector and data processing. Only then can these software utilities provide accurate predictions about instrument performance, robust noise estimation and reliable metrics indicating the anticipated success of an observation. The information must be presented in a clear, intelligible manner, sufficiently abstract to hide the details of telescope technicalities, but still giving the user a degree of control over the system. The Data Flow System group has addressed the needs of the VLTI and, in doing so, has gained some new insights into the planning of observations, and the modelling and simulation of interferometer performance. This paper reports these new techniques, as well as the successes of the Data Flow System group in this area and a summary of what is now offered as standard to VLTI observers.

Bike and run pacing on downhill segments predict Ironman triathlon relative success.

PubMed

Johnson, Evan C; Pryor, J Luke; Casa, Douglas J; Belval, Luke N; Vance, James S; DeMartini, Julie K; Maresh, Carl M; Armstrong, Lawrence E

2015-01-01

Determine if performance and physiological based pacing characteristics over the varied terrain of a triathlon predicted relative bike, run, and/or overall success. Poor self-regulation of intensity during long distance (Full Iron) triathlon can manifest in adverse discontinuities in performance. Observational study of a random sample of Ironman World Championship athletes. High performing and low performing groups were established upon race completion. Participants wore global positioning system and heart rate enabled watches during the race. Percentage difference from pre-race disclosed goal pace (%off) and mean HR were calculated for nine segments of the bike and 11 segments of the run. Normalized graded running pace (accounting for changes in elevation) was computed via analysis software. Step-wise regression analyses identified segments predictive of relative success and HP and LP were compared at these segments to confirm importance. %Off of goal velocity during two downhill segments of the bike (HP: -6.8±3.2%, -14.2±2.6% versus LP: -1.2±4.2%, -5.1±11.5%; p<0.020) and %off from NGP during one downhill segment of the run (HP: 4.8±5.2% versus LP: 33.3±38.7%; p=0.033) significantly predicted relative performance. Also, HP displayed more consistency in mean HR (141±12 to 138±11 bpm) compared to LP (139±17 to 131±16 bpm; p=0.019) over the climb and descent from the turn-around point during the bike component. Athletes who maintained faster relative speeds on downhill segments, and who had smaller changes in HR between consecutive up and downhill segments were more successful relative to their goal times. Copyright © 2013 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Detecting, anticipating, and predicting critical transitions in spatially extended systems.

PubMed

Kwasniok, Frank

2018-03-01

A data-driven linear framework for detecting, anticipating, and predicting incipient bifurcations in spatially extended systems based on principal oscillation pattern (POP) analysis is discussed. The dynamics are assumed to be governed by a system of linear stochastic differential equations which is estimated from the data. The principal modes of the system together with corresponding decay or growth rates and oscillation frequencies are extracted as the eigenvectors and eigenvalues of the system matrix. The method can be applied to stationary datasets to identify the least stable modes and assess the proximity to instability; it can also be applied to nonstationary datasets using a sliding window approach to track the changing eigenvalues and eigenvectors of the system. As a further step, a genuinely nonstationary POP analysis is introduced. Here, the system matrix of the linear stochastic model is time-dependent, allowing for extrapolation and prediction of instabilities beyond the learning data window. The methods are demonstrated and explored using the one-dimensional Swift-Hohenberg equation as an example, focusing on the dynamics of stochastic fluctuations around the homogeneous stable state prior to the first bifurcation. The POP-based techniques are able to extract and track the least stable eigenvalues and eigenvectors of the system; the nonstationary POP analysis successfully predicts the timing of the first instability and the unstable mode well beyond the learning data window.

Detecting, anticipating, and predicting critical transitions in spatially extended systems

NASA Astrophysics Data System (ADS)

Kwasniok, Frank

2018-03-01

A data-driven linear framework for detecting, anticipating, and predicting incipient bifurcations in spatially extended systems based on principal oscillation pattern (POP) analysis is discussed. The dynamics are assumed to be governed by a system of linear stochastic differential equations which is estimated from the data. The principal modes of the system together with corresponding decay or growth rates and oscillation frequencies are extracted as the eigenvectors and eigenvalues of the system matrix. The method can be applied to stationary datasets to identify the least stable modes and assess the proximity to instability; it can also be applied to nonstationary datasets using a sliding window approach to track the changing eigenvalues and eigenvectors of the system. As a further step, a genuinely nonstationary POP analysis is introduced. Here, the system matrix of the linear stochastic model is time-dependent, allowing for extrapolation and prediction of instabilities beyond the learning data window. The methods are demonstrated and explored using the one-dimensional Swift-Hohenberg equation as an example, focusing on the dynamics of stochastic fluctuations around the homogeneous stable state prior to the first bifurcation. The POP-based techniques are able to extract and track the least stable eigenvalues and eigenvectors of the system; the nonstationary POP analysis successfully predicts the timing of the first instability and the unstable mode well beyond the learning data window.

Setting the vision: applied patient-reported outcomes and smart, connected digital healthcare systems to improve patient-centered outcomes prediction in critical illness.

PubMed

Wysham, Nicholas G; Abernethy, Amy P; Cox, Christopher E

2014-10-01

Prediction models in critical illness are generally limited to short-term mortality and uncommonly include patient-centered outcomes. Current outcome prediction tools are also insensitive to individual context or evolution in healthcare practice, potentially limiting their value over time. Improved prognostication of patient-centered outcomes in critical illness could enhance decision-making quality in the ICU. Patient-reported outcomes have emerged as precise methodological measures of patient-centered variables and have been successfully employed using diverse platforms and technologies, enhancing the value of research in critical illness survivorship and in direct patient care. The learning health system is an emerging ideal characterized by integration of multiple data sources into a smart and interconnected health information technology infrastructure with the goal of rapidly optimizing patient care. We propose a vision of a smart, interconnected learning health system with integrated electronic patient-reported outcomes to optimize patient-centered care, including critical care outcome prediction. A learning health system infrastructure integrating electronic patient-reported outcomes may aid in the management of critical illness-associated conditions and yield tools to improve prognostication of patient-centered outcomes in critical illness.

Predictors of Success in Dental Hygiene Education: A Six-Year Review.

ERIC Educational Resources Information Center

Downey, Mary C.; Collins, Marie A.; Browning, William D.

2002-01-01

Examined the predictive reliability of incoming grade point average (GPA), incoming math/science GPA, and Scholastic Aptitude Test (SAT) scores in predicting success in dental hygiene education. Found that GPA was the most significant predictor of success. (EV)

Prediction of successful weight reduction after bariatric surgery by data mining technologies.

PubMed

Lee, Yi-Chih; Lee, Wei-Jei; Lee, Tian-Shyug; Lin, Yang-Chu; Wang, Weu; Liew, Phui-Ly; Huang, Ming-Te; Chien, Ching-Wen

2007-09-01

Surgery is the only long-lasting effective treatment for morbid obesity. Prediction on successful weight loss after surgery by data mining technologies is lacking. We analyze the available information during the initial evaluation of patients referred to bariatric surgery by data mining methods for predictors of successful weight loss. 249 patients undergoing laparoscopic mini-gastric bypass (LMGB) or adjustable gastric banding (LAGB) were enrolled. Logistic Regression and Artificial Neural Network (ANN) technologies were used to predict weight loss. Overall classification capability of the designed diagnostic models was evaluated by the misclassification costs. We studied 249 patients consisting of 72 men and 177 women over 2 years. Mean age was 33 +/- 9 years. 208 (83.5%) patients had successful weight reduction while 41 (16.5%) did not. Logistic Regression revealed that the type of operation had a significant prediction effect (P = 0.000). Patients receiving LMGB had a better weight loss than those receiving LAGB (78.54% +/- 26.87 vs 43.65% +/- 26.08). ANN provided the same predicted factor on the type of operation but it further proposed that HbAlc and triglyceride were associated with success. HbAlc is lower in the successful than failed group (5.81 +/- 1.06 vs 6.05 +/- 1.49; P = NS), and triglyceride in the successful group is higher than in the failed group (171.29 +/- 112.62 vs 144.07 +/- 89.90; P = NS). Artificial neural network is a better modeling technique and the overall predictive accuracy is higher on the basis of multiple variables related to laboratory tests. LMGB, high preoperative triglyceride level, and low HbAlc level can predict successful weight reduction at 2 years.

Initialization shock in decadal hindcasts due to errors in wind stress over the tropical Pacific

NASA Astrophysics Data System (ADS)

Pohlmann, Holger; Kröger, Jürgen; Greatbatch, Richard J.; Müller, Wolfgang A.

2017-10-01

Low prediction skill in the tropical Pacific is a common problem in decadal prediction systems, especially for lead years 2-5 which, in many systems, is lower than in uninitialized experiments. On the other hand, the tropical Pacific is of almost worldwide climate relevance through its teleconnections with other tropical and extratropical regions and also of importance for global mean temperature. Understanding the causes of the reduced prediction skill is thus of major interest for decadal climate predictions. We look into the problem of reduced prediction skill by analyzing the Max Planck Institute Earth System Model (MPI-ESM) decadal hindcasts for the fifth phase of the Climate Model Intercomparison Project and performing a sensitivity experiment in which hindcasts are initialized from a model run forced only by surface wind stress. In both systems, sea surface temperature variability in the tropical Pacific is successfully initialized, but most skill is lost at lead years 2-5. Utilizing the sensitivity experiment enables us to pin down the reason for the reduced prediction skill in MPI-ESM to errors in wind stress used for the initialization. A spurious trend in the wind stress forcing displaces the equatorial thermocline in MPI-ESM unrealistically. When the climate model is then switched into its forecast mode, the recovery process triggers artificial El Niño and La Niña events at the surface. Our results demonstrate the importance of realistic wind stress products for the initialization of decadal predictions.

Precipitation Modeling in Nitriding in Fe-M Binary System

NASA Astrophysics Data System (ADS)

Tomio, Yusaku; Miyamoto, Goro; Furuhara, Tadashi

2016-10-01

Precipitation of fine alloy nitrides near the specimen surface results in significant surface hardening in nitriding of alloyed steels. In this study, a simulation model of alloy nitride precipitation during nitriding is developed for Fe-M binary system based upon the Kampmann-Wagner numerical model in order to predict variations in the distribution of precipitates with depth. The model can predict the number density, average radius, and volume fraction of alloy nitrides as a function of depth from the surface and nitriding time. By a comparison with the experimental observation in a nitrided Fe-Cr alloy, it was found that the model can predict successfully the observed particle distribution from the surface into depth when appropriate solubility of CrN, interfacial energy between CrN and α, and nitrogen flux at the surface are selected.

Predictors of the patency of self-expandable metallic stents in malignant gastroduodenal obstruction

PubMed Central

Kim, Seung Han; Chun, Hoon Jai; Yoo, In Kyung; Lee, Jae Min; Nam, Seung Joo; Choi, Hyuk Soon; Kim, Eun Sun; Keum, Bora; Seo, Yeon Seok; Jeen, Yoon Tae; Lee, Hong Sik; Um, Soon Ho; Kim, Chang Duck

2015-01-01

AIM: To investigate the predictive factors of self-expandable metallic stent patency after stent placement in patients with inoperable malignant gastroduodenal obstruction. METHODS: A total of 116 patients underwent stent placements for inoperable malignant gastroduodenal obstruction at a tertiary academic center. Clinical success was defined as acceptable decompression of the obstructive lesion within the malignant gastroduodenal neoplasm. We evaluated patient comorbidities and clinical statuses using the World Health Organization’s scoring system and categorized patient responses to chemotherapy using the Response Evaluation Criteria in Solid Tumors criteria. We analyzed the relationships between possible predictive factors and stent patency. RESULTS: Self-expandable metallic stent placement was technically successful in all patients (100%), and the clinical success rate was 84.2%. In a multivariate Cox proportional hazards model, carcinoembryonic antigen (CEA) levels were correlated with a reduction in stent patency [P = 0.006; adjusted hazard ratio (aHR) = 2.92, 95%CI: 1.36-6.25]. Palliative chemotherapy was statistically associated with an increase in stent patency (P = 0.009; aHR = 0.27, 95%CI: 0.10-0.72). CONCLUSION: CEA levels can easily be measured at the time of stent placement and may help clinicians to predict stent patency and determine the appropriate stent procedure. PMID:26290640

A Theory-Based Methodology for Analyzing Domain Suitability for Expert Systems Technology Applications

DTIC Science & Technology

1989-06-01

amount of data now available as a result of these experiments, we are still unable to explain the "why" or " how " of successful systems or to predict for...order to better understand the potential contribution of this research, it is important to first discuss how expert systems are developed and the...task performance through internal and external feedback. 9. Knowing how to act upon the feedback received. 10. Implementing the action based on the

Moving beyond GPA: Alternative Measures of Success and Predictive Factors in Honors Programs

ERIC Educational Resources Information Center

Mould, Tom; DeLoach, Stephen B.

2017-01-01

While studies of predictive factors for success in honors have been increasingly creative and expansive on what these factors might include, they have rarely challenged the dominant, virtually monolithic definitions of success. The majority of studies measure success either by collegiate grade point averages (GPAs) or retention rates in honors,…

Predicting Success in an Online Course Using Expectancies, Values, and Typical Mode of Instruction

ERIC Educational Resources Information Center

Zimmerman, Whitney Alicia

2017-01-01

Expectancies of success and values were used to predict success in an online undergraduate-level introductory statistics course. Students who identified as primarily face-to-face learners were compared to students who identified as primarily online learners. Expectancy value theory served as a model. Expectancies of success were operationalized as…

Investigation of chaos and its control in a Duffing-type nano beam model

NASA Astrophysics Data System (ADS)

Jha, Abhishek Kumar; Dasgupta, Sovan Sundar

2018-04-01

The prediction of chaos of a nano beam with harmonic excitation is investigated. Using the Galerkin method the nonlinear lumped model of a clamped-clamped nano beam with nonlinear cubic stiffness is obtained. This is a Duffing system with hardening type of nonlinearity. Based on the energy function and the phase portrait of the system, the resonator dynamics is categorized into four situations in which Using Malnikov function, an analytical criterion for homoclinic intersection in the form of inequality is written in terms of the system parameters. A numerical study including largest lyapunov exponent, Poincare diagram and phase portrait confirm the analytical prediction of chaos and effect of forcing amplitude. Subsequently, a linear velocity feedback controller is introduced into the system to successfully control the chaotic motion of the system at a faster rate at larger value of gain parameter.

Predicting Mission Success in Small Satellite Missions

NASA Technical Reports Server (NTRS)

Saunders, Mark; Richie, Wayne; Rogers, John; Moore, Arlene

1992-01-01

In our global society with its increasing international competition and tighter financial resources, governments, commercial entities and other organizations are becoming critically aware of the need to ensure that space missions can be achieved on time and within budget. This has become particularly true for the National Aeronautics and Space Administration's (NASA) Office of Space Science (OSS) which has developed their Discovery and Explorer programs to meet this need. As technologies advance, space missions are becoming smaller and more capable than their predecessors. The ability to predict the mission success of these small satellite missions is critical to the continued achievement of NASA science mission objectives. The NASA Office of Space Science, in cooperation with the NASA Langley Research Center, has implemented a process to predict the likely success of missions proposed to its Discovery and Explorer Programs. This process is becoming the basis for predicting mission success in many other NASA programs as well. This paper describes the process, methodology, tools and synthesis techniques used to predict mission success for this class of mission.

Predicting Mission Success in Small Satellite Missions

NASA Technical Reports Server (NTRS)

Saunders, Mark; Richie, R. Wayne; Moore, Arlene; Rogers, John

1999-01-01

In our global society with its increasing international competition and tighter financial resources, governments, commercial entities and other organizations are becoming critically aware of the need to ensure that space missions can be achieved on time and within budget. This has become particularly true for the National Aeronautics and Space Administration's (NASA's) Office of Space Science (OSS) which has developed their Discovery and Explorer programs to meet this need. As technologies advance, space missions are becoming smaller and more capable than their predecessors. The ability to predict the mission success of these small satellite missions is critical to the continued achievement of NASA science mission objectives. The NASA Office of Space Science, in cooperation with the NASA Langley Research Center, has implemented a process to predict the likely success of missions proposed to its Discovery and Explorer Programs. This process is becoming the basis for predicting mission success in many other NASA programs as well. This paper describes the process, methodology, tools and synthesis techniques used to predict mission success for this class of mission.

Multivariable Time Series Prediction for the Icing Process on Overhead Power Transmission Line

PubMed Central

Li, Peng; Zhao, Na; Zhou, Donghua; Cao, Min; Li, Jingjie; Shi, Xinling

2014-01-01

The design of monitoring and predictive alarm systems is necessary for successful overhead power transmission line icing. Given the characteristics of complexity, nonlinearity, and fitfulness in the line icing process, a model based on a multivariable time series is presented here to predict the icing load of a transmission line. In this model, the time effects of micrometeorology parameters for the icing process have been analyzed. The phase-space reconstruction theory and machine learning method were then applied to establish the prediction model, which fully utilized the history of multivariable time series data in local monitoring systems to represent the mapping relationship between icing load and micrometeorology factors. Relevant to the characteristic of fitfulness in line icing, the simulations were carried out during the same icing process or different process to test the model's prediction precision and robustness. According to the simulation results for the Tao-Luo-Xiong Transmission Line, this model demonstrates a good accuracy of prediction in different process, if the prediction length is less than two hours, and would be helpful for power grid departments when deciding to take action in advance to address potential icing disasters. PMID:25136653

Single-Rooted Extraction Sockets: Classification and Treatment Protocol.

PubMed

El Chaar, Edgar; Oshman, Sarah; Fallah Abed, Pooria

2016-09-01

Clinicians have many treatment techniques from which to choose when extracting a failing tooth and replacing it with an implant-supported restoration and when successful management of an extraction socket during the course of tooth replacement is necessary to achieve predictable and esthetic outcomes. This article presents a straightforward, yet thorough, classification for extraction sockets of single-rooted teeth and provides guidance to clinicians in the selection of appropriate and predictable treatment. The presented classification of extraction sockets for single-rooted teeth focuses on the topography of the extraction socket, while the protocol for treatment of each socket type factors in the shape of the remaining bone, the biotype, and the location of the socket whether it be in the mandible or maxilla. This system is based on the biologic foundations of wound healing and can help guide clinicians to successful treatment outcomes.

2 kWe Solar Dynamic Ground Test Demonstration Project. Volume 1; Executive Summary

NASA Technical Reports Server (NTRS)

Alexander, Dennis

1997-01-01

The Solar Dynamic Ground Test Demonstration (SDGTD) successfully demonstrated a solar-powered closed Brayton cycle system in a relevant space thermal environment. In addition to meeting technical requirements the project was completed 4 months ahead of schedule and under budget. The following conclusions can be supported: 1. The component technology for solar dynamic closed Brayton cycle technology has clearly been demonstrated. 2. The thermal, optical, control, and electrical integration aspects of systems integration have also been successfully demonstrated. Physical integration aspects were not attempted as these tend to be driven primarily by mission-specific requirements. 3. System efficiency of greater than 15 percent (all losses fully accounted for) was demonstrated using equipment and designs which were not optimized. Some preexisting hardware was used to minimize cost and schedule. 4. Power generation of 2 kWe. 5. A NASA/industry team was developed that successfully worked together to accomplish project goals. The material presented in this report will show that the technology necessary to design and fabricate solar dynamic electrical power systems for space has been successfully developed and demonstrated. The data will further show that achieved results compare well with pretest predictions. The next step in the development of solar dynamic space power will be a flight test.

Basic state lower-tropospheric humidity distribution: key to successful simulation and prediction of the Madden-Julian oscillation

NASA Astrophysics Data System (ADS)

Kim, D.; Ahn, M. S.; DeMott, C. A.; Jiang, X.; Klingaman, N. P.; Kim, H. M.; Lee, J. H.; Lim, Y.; Xavier, P. K.

2017-12-01

The Madden-Julian Oscillation (MJO) influences the global weather-climate system, thereby providing the source of predictability on the intraseasonal timescales worldwide. An accurate representation of the MJO, however, is still one of the most challenging tasks for many contemporary global climate models (GCMs). Identifying aspects of the GCMs that are tightly linked to GCMs' MJO simulation capability is a step toward improving the GCM representation of the MJO. This study surveys recent modeling work that collectively evidence that the horizontal distribution of the basic state low-tropospheric humidity is crucial to a successful simulation and prediction of the MJO. Specifically, the simulated horizontal and meridional gradients of the mean low-tropospheric humidity determine the magnitude of the moistening (drying) to the east (west) of the enhance MJO, thereby enabling or disabling the eastward propagation of the MJO. Supporting this argument, many MJO-incompetent GCMs also exhibit biases in the mean humidity that weaken the horizontal moisture gradient. Also, MJO prediction skill of the S2S models is tightly related to the biases in the mean moisture gradient. Implications of the robust relationship between the MJO and the mean state on MJO modeling and prediction will be discussed.

RobOKoD: microbial strain design for (over)production of target compounds.

PubMed

Stanford, Natalie J; Millard, Pierre; Swainston, Neil

2015-01-01

Sustainable production of target compounds such as biofuels and high-value chemicals for pharmaceutical, agrochemical, and chemical industries is becoming an increasing priority given their current dependency upon diminishing petrochemical resources. Designing these strains is difficult, with current methods focusing primarily on knocking-out genes, dismissing other vital steps of strain design including the overexpression and dampening of genes. The design predictions from current methods also do not translate well-into successful strains in the laboratory. Here, we introduce RobOKoD (Robust, Overexpression, Knockout and Dampening), a method for predicting strain designs for overproduction of targets. The method uses flux variability analysis to profile each reaction within the system under differing production percentages of target-compound and biomass. Using these profiles, reactions are identified as potential knockout, overexpression, or dampening targets. The identified reactions are ranked according to their suitability, providing flexibility in strain design for users. The software was tested by designing a butanol-producing Escherichia coli strain, and was compared against the popular OptKnock and RobustKnock methods. RobOKoD shows favorable design predictions, when predictions from these methods are compared to a successful butanol-producing experimentally-validated strain. Overall RobOKoD provides users with rankings of predicted beneficial genetic interventions with which to support optimized strain design.

RobOKoD: microbial strain design for (over)production of target compounds

PubMed Central

Stanford, Natalie J.; Millard, Pierre; Swainston, Neil

2015-01-01

Sustainable production of target compounds such as biofuels and high-value chemicals for pharmaceutical, agrochemical, and chemical industries is becoming an increasing priority given their current dependency upon diminishing petrochemical resources. Designing these strains is difficult, with current methods focusing primarily on knocking-out genes, dismissing other vital steps of strain design including the overexpression and dampening of genes. The design predictions from current methods also do not translate well-into successful strains in the laboratory. Here, we introduce RobOKoD (Robust, Overexpression, Knockout and Dampening), a method for predicting strain designs for overproduction of targets. The method uses flux variability analysis to profile each reaction within the system under differing production percentages of target-compound and biomass. Using these profiles, reactions are identified as potential knockout, overexpression, or dampening targets. The identified reactions are ranked according to their suitability, providing flexibility in strain design for users. The software was tested by designing a butanol-producing Escherichia coli strain, and was compared against the popular OptKnock and RobustKnock methods. RobOKoD shows favorable design predictions, when predictions from these methods are compared to a successful butanol-producing experimentally-validated strain. Overall RobOKoD provides users with rankings of predicted beneficial genetic interventions with which to support optimized strain design. PMID:25853130

Dissertation Defense Computational Fluid Dynamics Uncertainty Analysis for Payload Fairing Spacecraft Environmental Control Systems

NASA Technical Reports Server (NTRS)

Groves, Curtis Edward

2014-01-01

Spacecraft thermal protection systems are at risk of being damaged due to airflow produced from Environmental Control Systems. There are inherent uncertainties and errors associated with using Computational Fluid Dynamics to predict the airflow field around a spacecraft from the Environmental Control System. This paper describes an approach to quantify the uncertainty in using Computational Fluid Dynamics to predict airflow speeds around an encapsulated spacecraft without the use of test data. Quantifying the uncertainty in analytical predictions is imperative to the success of any simulation-based product. The method could provide an alternative to traditional "validation by test only" mentality. This method could be extended to other disciplines and has potential to provide uncertainty for any numerical simulation, thus lowering the cost of performing these verifications while increasing the confidence in those predictions. Spacecraft requirements can include a maximum airflow speed to protect delicate instruments during ground processing. Computational Fluid Dynamics can be used to verify these requirements; however, the model must be validated by test data. This research includes the following three objectives and methods. Objective one is develop, model, and perform a Computational Fluid Dynamics analysis of three (3) generic, non-proprietary, environmental control systems and spacecraft configurations. Several commercially available and open source solvers have the capability to model the turbulent, highly three-dimensional, incompressible flow regime. The proposed method uses FLUENT, STARCCM+, and OPENFOAM. Objective two is to perform an uncertainty analysis of the Computational Fluid Dynamics model using the methodology found in "Comprehensive Approach to Verification and Validation of Computational Fluid Dynamics Simulations". This method requires three separate grids and solutions, which quantify the error bars around Computational Fluid Dynamics predictions. The method accounts for all uncertainty terms from both numerical and input variables. Objective three is to compile a table of uncertainty parameters that could be used to estimate the error in a Computational Fluid Dynamics model of the Environmental Control System /spacecraft system. Previous studies have looked at the uncertainty in a Computational Fluid Dynamics model for a single output variable at a single point, for example the re-attachment length of a backward facing step. For the flow regime being analyzed (turbulent, three-dimensional, incompressible), the error at a single point can propagate into the solution both via flow physics and numerical methods. Calculating the uncertainty in using Computational Fluid Dynamics to accurately predict airflow speeds around encapsulated spacecraft in is imperative to the success of future missions.

Dissertation Defense: Computational Fluid Dynamics Uncertainty Analysis for Payload Fairing Spacecraft Environmental Control Systems

NASA Technical Reports Server (NTRS)

Groves, Curtis Edward

2014-01-01

Spacecraft thermal protection systems are at risk of being damaged due to airflow produced from Environmental Control Systems. There are inherent uncertainties and errors associated with using Computational Fluid Dynamics to predict the airflow field around a spacecraft from the Environmental Control System. This paper describes an approach to quantify the uncertainty in using Computational Fluid Dynamics to predict airflow speeds around an encapsulated spacecraft without the use of test data. Quantifying the uncertainty in analytical predictions is imperative to the success of any simulation-based product. The method could provide an alternative to traditional validation by test only mentality. This method could be extended to other disciplines and has potential to provide uncertainty for any numerical simulation, thus lowering the cost of performing these verifications while increasing the confidence in those predictions.Spacecraft requirements can include a maximum airflow speed to protect delicate instruments during ground processing. Computational Fluid Dynamics can be used to verify these requirements; however, the model must be validated by test data. This research includes the following three objectives and methods. Objective one is develop, model, and perform a Computational Fluid Dynamics analysis of three (3) generic, non-proprietary, environmental control systems and spacecraft configurations. Several commercially available and open source solvers have the capability to model the turbulent, highly three-dimensional, incompressible flow regime. The proposed method uses FLUENT, STARCCM+, and OPENFOAM. Objective two is to perform an uncertainty analysis of the Computational Fluid Dynamics model using the methodology found in Comprehensive Approach to Verification and Validation of Computational Fluid Dynamics Simulations. This method requires three separate grids and solutions, which quantify the error bars around Computational Fluid Dynamics predictions. The method accounts for all uncertainty terms from both numerical and input variables. Objective three is to compile a table of uncertainty parameters that could be used to estimate the error in a Computational Fluid Dynamics model of the Environmental Control System spacecraft system.Previous studies have looked at the uncertainty in a Computational Fluid Dynamics model for a single output variable at a single point, for example the re-attachment length of a backward facing step. For the flow regime being analyzed (turbulent, three-dimensional, incompressible), the error at a single point can propagate into the solution both via flow physics and numerical methods. Calculating the uncertainty in using Computational Fluid Dynamics to accurately predict airflow speeds around encapsulated spacecraft in is imperative to the success of future missions.

Computational Fluid Dynamics Uncertainty Analysis for Payload Fairing Spacecraft Environmental Control Systems

NASA Technical Reports Server (NTRS)

Groves, Curtis E.

2013-01-01

Spacecraft thermal protection systems are at risk of being damaged due to airflow produced from Environmental Control Systems. There are inherent uncertainties and errors associated with using Computational Fluid Dynamics to predict the airflow field around a spacecraft from the Environmental Control System. This proposal describes an approach to validate the uncertainty in using Computational Fluid Dynamics to predict airflow speeds around an encapsulated spacecraft. The research described here is absolutely cutting edge. Quantifying the uncertainty in analytical predictions is imperative to the success of any simulation-based product. The method could provide an alternative to traditional"validation by test only'' mentality. This method could be extended to other disciplines and has potential to provide uncertainty for any numerical simulation, thus lowering the cost of performing these verifications while increasing the confidence in those predictions. Spacecraft requirements can include a maximum airflow speed to protect delicate instruments during ground processing. Computationaf Fluid Dynamics can be used to veritY these requirements; however, the model must be validated by test data. The proposed research project includes the following three objectives and methods. Objective one is develop, model, and perform a Computational Fluid Dynamics analysis of three (3) generic, non-proprietary, environmental control systems and spacecraft configurations. Several commercially available solvers have the capability to model the turbulent, highly three-dimensional, incompressible flow regime. The proposed method uses FLUENT and OPEN FOAM. Objective two is to perform an uncertainty analysis of the Computational Fluid . . . Dynamics model using the methodology found in "Comprehensive Approach to Verification and Validation of Computational Fluid Dynamics Simulations". This method requires three separate grids and solutions, which quantify the error bars around Computational Fluid Dynamics predictions. The method accounts for all uncertainty terms from both numerical and input variables. Objective three is to compile a table of uncertainty parameters that could be used to estimate the error in a Computational Fluid Dynamics model of the Environmental Control System /spacecraft system. Previous studies have looked at the uncertainty in a Computational Fluid Dynamics model for a single output variable at a single point, for example the re-attachment length of a backward facing step. To date, the author is the only person to look at the uncertainty in the entire computational domain. For the flow regime being analyzed (turbulent, threedimensional, incompressible), the error at a single point can propagate into the solution both via flow physics and numerical methods. Calculating the uncertainty in using Computational Fluid Dynamics to accurately predict airflow speeds around encapsulated spacecraft in is imperative to the success of future missions.

Prepharmacy predictors of success in pharmacy school: grade point averages, pharmacy college admissions test, communication abilities, and critical thinking skills.

PubMed

Allen, D D; Bond, C A

2001-07-01

Good admissions decisions are essential for identifying successful students and good practitioners. Various parameters have been shown to have predictive power for academic success. Previous academic performance, the Pharmacy College Admissions Test (PCAT), and specific prepharmacy courses have been suggested as academic performance indicators. However, critical thinking abilities have not been evaluated. We evaluated the connection between academic success and each of the following predictive parameters: the California Critical Thinking Skills Test (CCTST) score, PCAT score, interview score, overall academic performance prior to admission at a pharmacy school, and performance in specific prepharmacy courses. We confirmed previous reports but demonstrated intriguing results in predicting practice-based skills. Critical thinking skills predict practice-based course success. Also, the CCTST and PCAT scores (Pearson correlation [pc] = 0.448, p < 0.001) were closely related in our students. The strongest predictors of practice-related courses and clerkship success were PCAT (pc=0.237, p<0.001) and CCTST (pc = 0.201, p < 0.001). These findings and other analyses suggest that PCAT may predict critical thinking skills in pharmacy practice courses and clerkships. Further study is needed to confirm this finding and determine which PCAT components predict critical thinking abilities.

Applying the Newman-Peacock Prognostic System to a Portuguese Obstetrical Population - A Useful Tool?

PubMed

Mendes Silva, Rita; Clode, Nuno

2018-01-01

 External cephalic version (ECV) is a maneuver that enables the rotation of the non-cephalic fetus to a cephalic presentation. The Newman-Peacock (NP) index, which was proposed by Newman et al. in a study published in 1993, was described as a prediction tool of the success of this procedure; it was validated in a North-American population, and three prognostic groups were identified.  To evaluate the value of the NP score for the prediction of a successful ECV in a Portuguese obstetrical population, and to evaluate maternal and fetal safety.  We present an observational study conducted from 1997-2016 with pregnant women at 36-38 weeks of pregnancy who were candidates for external cephalic version in our department. Demographic and obstetrical data were collected, including the parameters included in the NP index (parity, cervical dilatation, estimated fetal weight, placental location and fetal station). The calculation of the NP score was performed, and the percentages of success were compared among the three prognostic groups and with the original study by Newman et al. The performance of the score was determined using the Student t -test, the Chi-squared test, and a receiver operating characteristic (ROC) curve.  In total, 337 women were included. The overall success rate was of 43.6%. The univariate analysis revealed that multiparity, posterior placentation and a less engaged fetus were factors that favored a successful maneuver ( p  < 0.05). Moreover, a higher amniotic fluid index was also a relevant predictive factor ( p  < 0.05). The Newman-Peacock score had a poorer performance in our population compared with that of the sample of the original study, but we still found a positive relationship between higher scores and higher prediction of success ( p  < 0.001). No fetal or maternal morbidities were registered.  The Newman-Peacock score had a poorer performance among our population compared to its performance in the original study, but the results suggest that this score is still a useful tool to guide our clinical practice and counsel the candidate regarding ECV. Thieme Revinter Publicações Ltda Rio de Janeiro, Brazil.

Impaction and Prediction: Does Ureteral Wall Thickness Affect the Success of Medical Expulsive Therapy in Pediatric Ureteral Stones?

PubMed

Tuerxun, Aierken; Batuer, Abudukahaer; Erturhan, Sakip; Eryildirim, Bilal; Camur, Emre; Sarica, Kemal

2017-01-01

The study aimed to evaluate the predictive value of ureteral wall thickness (UWT) and stone-related parameters for medical expulsive therapy (MET) success with an alpha blocker in pediatric upper ureteral stones. A total of 35 children receiving MET ureteral stones (<10 mm) were evaluated. Patients were divided into 2 subgroups where MET was successful in 18 children (51.4%) and unsuccessful in 17 children (48.6%). Prior to management, stone size, stone density (in Hounsfield unit), degree of hydronephrosis, and UWT were evaluated with patient demographics and recorded. The possible predictive value of these parameters in success rates and time to stone expulsion were evaluated in a comparative manner between the 2 groups. The overall mean patient age and stone size values were 5.40 ± 0.51 years and 6.24 ± 0.28 mm, respectively. Regarding the predictive values of these parameters for the success of MET, while stone size and UWT were found to be highly predictive for MET success, patients age, body mass index, stone density, and degree of hydronephrosis had no predictive value on this aspect. Our findings indicated that some stone and anatomical factors may be used to predict the success of MET in pediatric ureteral stones in an effective manner. With this approach, unnecessary use of these drugs that may cause a delay in removing the stone will be avoided, and the possible adverse effects of obstruction as well as stone-related clinical symptoms could be minimized. © 2017 S. Karger AG, Basel.

Resolved Observations of the Patroclus-Menoetius Binary

NASA Astrophysics Data System (ADS)

Noll, Keith S.; Grundy, William M.; Buie, Marc W.; Levison, Harold F.

2017-10-01

The Trojan binary (617) Patroclus-Menoetius is one of the targets of the Lucy Discovery mission. Lucy is scheduled to launch in October 2021. We observed this system with the Hubble Space Telescope in May and June 2017 in order to resolve the individual components and use the relative positions to update the binary orbit. The updated orbit is required to predict the upcoming mutual event season. A precise determination of the orbit phase, period, orbit plane and pole position that will result from observations of mutual events is essential for planning the Lucy mission’s encounter with this system. We present results of the successful HST observations including preliminary predictions for mutual events observable in semester 2018A.

Functional brain imaging predicts public health campaign success.

PubMed

Falk, Emily B; O'Donnell, Matthew Brook; Tompson, Steven; Gonzalez, Richard; Dal Cin, Sonya; Strecher, Victor; Cummings, Kenneth Michael; An, Lawrence

2016-02-01

Mass media can powerfully affect health decision-making. Pre-testing through focus groups or surveys is a standard, though inconsistent, predictor of effectiveness. Converging evidence demonstrates that activity within brain systems associated with self-related processing can predict individual behavior in response to health messages. Preliminary evidence also suggests that neural activity in small groups can forecast population-level campaign outcomes. Less is known about the psychological processes that link neural activity and population-level outcomes, or how these predictions are affected by message content. We exposed 50 smokers to antismoking messages and used their aggregated neural activity within a 'self-localizer' defined region of medial prefrontal cortex to predict the success of the same campaign messages at the population level (n = 400,000 emails). Results demonstrate that: (i) independently localized neural activity during health message exposure complements existing self-report data in predicting population-level campaign responses (model combined R(2) up to 0.65) and (ii) this relationship depends on message content-self-related neural processing predicts outcomes in response to strong negative arguments against smoking and not in response to compositionally similar neutral images. These data advance understanding of the psychological link between brain and large-scale behavior and may aid the construction of more effective media health campaigns. © The Author (2015). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Using waveform information in nonlinear data assimilation

NASA Astrophysics Data System (ADS)

Rey, Daniel; Eldridge, Michael; Morone, Uriel; Abarbanel, Henry D. I.; Parlitz, Ulrich; Schumann-Bischoff, Jan

2014-12-01

Information in measurements of a nonlinear dynamical system can be transferred to a quantitative model of the observed system to establish its fixed parameters and unobserved state variables. After this learning period is complete, one may predict the model response to new forces and, when successful, these predictions will match additional observations. This adjustment process encounters problems when the model is nonlinear and chaotic because dynamical instability impedes the transfer of information from the data to the model when the number of measurements at each observation time is insufficient. We discuss the use of information in the waveform of the data, realized through a time delayed collection of measurements, to provide additional stability and accuracy to this search procedure. Several examples are explored, including a few familiar nonlinear dynamical systems and small networks of Colpitts oscillators.

Analytical and experimental vibration analysis of a faulty gear system

NASA Astrophysics Data System (ADS)

Choy, F. K.; Braun, M. J.; Polyshchuk, V.; Zakrajsek, J. J.; Townsend, D. P.; Handschuh, R. F.

1994-10-01

A comprehensive analytical procedure was developed for predicting faults in gear transmission systems under normal operating conditions. A gear tooth fault model is developed to simulate the effects of pitting and wear on the vibration signal under normal operating conditions. The model uses changes in the gear mesh stiffness to simulate the effects of gear tooth faults. The overall dynamics of the gear transmission system is evaluated by coupling the dynamics of each individual gear-rotor system through gear mesh forces generated between each gear-rotor system and the bearing forces generated between the rotor and the gearbox structures. The predicted results were compared with experimental results obtained from a spiral bevel gear fatigue test rig at NASA Lewis Research Center. The Wigner-Ville Distribution (WVD) was used to give a comprehensive comparison of the predicted and experimental results. The WVD method applied to the experimental results were also compared to other fault detection techniques to verify the WVD's ability to detect the pitting damage, and to determine its relative performance. Overall results show good correlation between the experimental vibration data of the damaged test gear and the predicted vibration from the model with simulated gear tooth pitting damage. Results also verified that the WVD method can successfully detect and locate gear tooth wear and pitting damage.

Analytical and experimental vibration analysis of a faulty gear system

NASA Astrophysics Data System (ADS)

Choy, F. K.; Braun, M. J.; Polyshchuk, V.; Zakrajsek, J. J.; Townsend, D. P.; Handschuh, R. F.

1994-10-01

A comprehensive analytical procedure was developed for predicting faults in gear transmission systems under normal operating conditions. A gear tooth fault model is developed to simulate the effects of pitting and wear on the vibration signal under normal operating conditions. The model uses changes in the gear mesh stiffness to simulate the effects of gear tooth faults. The overall dynamics of the gear transmission system is evaluated by coupling the dynamics of each individual gear-rotor system through gear mesh forces generated between each gear-rotor system and the bearing forces generated between the rotor and the gearbox structure. The predicted results were compared with experimental results obtained from a spiral bevel gear fatigue test rig at NASA Lewis Research Center. The Wigner-Ville distribution (WVD) was used to give a comprehensive comparison of the predicted and experimental results. The WVD method applied to the experimental results were also compared to other fault detection techniques to verify the WVD's ability to detect the pitting damage, and to determine its relative performance. Overall results show good correlation between the experimental vibration data of the damaged test gear and the predicted vibration from the model with simulated gear tooth pitting damage. Results also verified that the WVD method can successfully detect and locate gear tooth wear and pitting damage.

Analytical and Experimental Vibration Analysis of a Faulty Gear System

NASA Technical Reports Server (NTRS)

Choy, F. K.; Braun, M. J.; Polyshchuk, V.; Zakrajsek, J. J.; Townsend, D. P.; Handschuh, R. F.

1994-01-01

A comprehensive analytical procedure was developed for predicting faults in gear transmission systems under normal operating conditions. A gear tooth fault model is developed to simulate the effects of pitting and wear on the vibration signal under normal operating conditions. The model uses changes in the gear mesh stiffness to simulate the effects of gear tooth faults. The overall dynamics of the gear transmission system is evaluated by coupling the dynamics of each individual gear-rotor system through gear mesh forces generated between each gear-rotor system and the bearing forces generated between the rotor and the gearbox structure. The predicted results were compared with experimental results obtained from a spiral bevel gear fatigue test rig at NASA Lewis Research Center. The Wigner-Ville distribution (WVD) was used to give a comprehensive comparison of the predicted and experimental results. The WVD method applied to the experimental results were also compared to other fault detection techniques to verify the WVD's ability to detect the pitting damage, and to determine its relative performance. Overall results show good correlation between the experimental vibration data of the damaged test gear and the predicted vibration from the model with simulated gear tooth pitting damage. Results also verified that the WVD method can successfully detect and locate gear tooth wear and pitting damage.

Toward an Omnibus Assessment of Noncognitive Skills: A Longitudinal Multitrait-Multimethod Application in Middle Schools

ERIC Educational Resources Information Center

Rikoon, Samuel H.

2013-01-01

Beyond the traditional notions of intelligence and academic achievement, the successful development of noncognitive or "soft" skills (e.g., personality factors, motivation, creativity) among school-aged youth represents an important objective for any educational system. Such skills have been shown to be significantly predictive of…

A Job Analysis for K-8 Principals in a Nationwide Charter School System

ERIC Educational Resources Information Center

Cumings, Laura; Coryn, Chris L. S.

2009-01-01

Background: Although no single technique on its own can predict job performance, a job analysis is a customary approach for identifying the relevant knowledge, skills, abilities, and other characteristics (KSAO) necessary to successfully complete the job tasks of a position. Once the position requirements are identified, the hiring process is…

Do the Critical Success Factors from Learning Analytics Predict Student Outcomes?

ERIC Educational Resources Information Center

Strang, Kenneth David

2016-01-01

This article starts with a detailed literature review of recent studies that focused on using learning analytics software or learning management system data to determine the nature of any relationships between online student activity and their academic outcomes within university-level business courses. The article then describes how data was…

Optimizing Technical Education Pathways: Does Dual-Credit Course Completion Predict Students' College and Labor Market Success?

ERIC Educational Resources Information Center

Phelps, L. Allen; Chan, Hsun-Yu

2016-01-01

Post-recession Federal policy initiatives, such as secondary/postsecondary career pathways and gainful employment higher education accountability standards, prioritize the alignment of education practices with market-driven outcomes. Using longitudinal student record data merged from college and state K-12 data systems with the Unemployment…

Benchmarking performance measurement and lean manufacturing in the rough mill

Treesearch

Dan Cumbo; D. Earl Kline; Matthew S. Bumgardner

2006-01-01

Lean manufacturing represents a set of tools and a stepwise strategy for achieving smooth, predictable product flow, maximum product flexibility, and minimum system waste. While lean manufacturing principles have been successfully applied to some components of the secondary wood products value stream (e.g., moulding, turning, assembly, and finishing), the rough mill is...

Is ATAR Useful for Predicting the Success of Australian Students in Initial Teacher Education?

ERIC Educational Resources Information Center

Wright, Vince J.

2015-01-01

Quality teaching is the most significant systemic factor contributing to student achievement. Attracting, developing and retaining effective teachers are important goals for Australia as they are for all nations. Debate rages currently about criteria for selection of students into Initial Teacher Education (ITE). The Australian Tertiary Admission…

The role of creep in the time-dependent resistance of Ohmic gold contacts in radio frequency microelectromechanical system devices

NASA Astrophysics Data System (ADS)

Rezvanian, O.; Brown, C.; Zikry, M. A.; Kingon, A. I.; Krim, J.; Irving, D. L.; Brenner, D. W.

2008-07-01

It is shown that measured and calculated time-dependent electrical resistances of closed gold Ohmic switches in radio frequency microelectromechanical system (rf-MEMS) devices are well described by a power law that can be derived from a single asperity creep model. The analysis reveals that the exponent and prefactor in the power law arise, respectively, from the coefficient relating creep rate to applied stress and the initial surface roughness. The analysis also shows that resistance plateaus are not, in fact, limiting resistances but rather result from the small coefficient in the power law. The model predicts that it will take a longer time for the contact resistance to attain a power law relation with each successive closing of the switch due to asperity blunting. Analysis of the first few seconds of the measured resistance for three successive openings and closings of one of the MEMS devices supports this prediction. This work thus provides guidance toward the rational design of Ohmic contacts with enhanced reliabilities by better defining variables that can be controlled through material selection, interface processing, and switch operation.

Successful child psychotherapy of attention deficit/hyperactive disorder: an agitated depression explanation.

PubMed

Seitler, Burton Norman

2008-09-01

Science tries to explain phenomena in ways that are demonstrable and replicable to develop logical, coherent, parsimonious, and predictive theoretical systems. Yet hyperactive children are given stimulants to "calm" them down, despite the fact that science would predict stimulants would increase hyperactivity. Bradley (1937, 1950) observed that half of the behavior-problem children to whom he administered a stimulant for one week became subdued. He called this finding paradoxical, speculating that inhibitory centers of the central nervous system were stimulated. While Bradley's assertion of a paradoxical reverse effect in children may be an empirical observation, it is not an explanation. The Attention Deficit/Hyperactive Disorder (ADHD) is inferred to exist from hyperactive behavior, which in turn, is inferred to be neurological in origin, a circular argument. An inevitable consequence of the belief in the hypothetical neurological etiology of ADHD is that children are typically given stimulants. Using the case of a seven-year old child, described as experiencing ADHD, who was treated successfully without medication as an illustration, the author provides an alternative, more parsimonious explanation of the etiology, suggesting that ADHD is related to agitated depression.

Youth Sport Readiness: A Predictive Model for Success.

ERIC Educational Resources Information Center

Aicinena, Steven

1992-01-01

A model for predicting organized youth sport participation readiness has four predictive components: sport-related fundamental motor skill development; sport-specific knowledge; motivation; and socialization. Physical maturation is also important. The model emphasizes the importance of preparing children for successful participation through…

Predicting athletic success motivation using mental skin and emotional intelligence and its components in male athletes.

PubMed

Kajbafnezhad, H; Ahadi, H; Heidarie, A; Askari, P; Enayati, M

2012-10-01

The aim of this study was to predict athletic success motivation by mental skills, emotional intelligence and its components. The research sample consisted of 153 male athletes who were selected through random multistage sampling. The subjects completed the Mental Skills Questionnaire, Bar-On Emotional Intelligence questionnaire and the perception of sport success questionnaire. Data were analyzed using Pearson correlation coefficient and multiple regressions. Regression analysis shows that between the two variables of mental skill and emotional intelligence, mental skill is the best predictor for athletic success motivation and has a better ability to predict the success rate of the participants. Regression analysis results showed that among all the components of emotional intelligence, self-respect had a significantly higher ability to predict athletic success motivation. The use of psychological skills and emotional intelligence as an mediating and regulating factor and organizer cause leads to improved performance and can not only can to help athletes in making suitable and effective decisions for reaching a desired goal.

Disentangling the Predictive Validity of High School Grades for Academic Success in University

ERIC Educational Resources Information Center

Vulperhorst, Jonne; Lutz, Christel; de Kleijn, Renske; van Tartwijk, Jan

2018-01-01

To refine selective admission models, we investigate which measure of prior achievement has the best predictive validity for academic success in university. We compare the predictive validity of three core high school subjects to the predictive validity of high school grade point average (GPA) for academic achievement in a liberal arts university…

The Complex Route to Success: Complex Problem-Solving Skills in the Prediction of University Success

ERIC Educational Resources Information Center

Stadler, Matthias J.; Becker, Nicolas; Greiff, Samuel; Spinath, Frank M.

2016-01-01

Successful completion of a university degree is a complex matter. Based on considerations regarding the demands of acquiring a university degree, the aim of this paper was to investigate the utility of complex problem-solving (CPS) skills in the prediction of objective and subjective university success (SUS). The key finding of this study was that…

A Gaussian Processes Technique for Short-term Load Forecasting with Considerations of Uncertainty

NASA Astrophysics Data System (ADS)

Ohmi, Masataro; Mori, Hiroyuki

In this paper, an efficient method is proposed to deal with short-term load forecasting with the Gaussian Processes. Short-term load forecasting plays a key role to smooth power system operation such as economic load dispatching, unit commitment, etc. Recently, the deregulated and competitive power market increases the degree of uncertainty. As a result, it is more important to obtain better prediction results to save the cost. One of the most important aspects is that power system operator needs the upper and lower bounds of the predicted load to deal with the uncertainty while they require more accurate predicted values. The proposed method is based on the Bayes model in which output is expressed in a distribution rather than a point. To realize the model efficiently, this paper proposes the Gaussian Processes that consists of the Bayes linear model and kernel machine to obtain the distribution of the predicted value. The proposed method is successively applied to real data of daily maximum load forecasting.

Smallholder farms as stepping stone corridors for crop-raiding elephant in northern Tanzania: integration of Bayesian expert system and network simulator.

PubMed

Pittiglio, Claudia; Skidmore, Andrew K; van Gils, Hein A M J; McCall, Michael K; Prins, Herbert H T

2014-03-01

Crop-raiding elephants affect local livelihoods, undermining conservation efforts. Yet, crop-raiding patterns are poorly understood, making prediction and protection difficult. We hypothesized that raiding elephants use corridors between daytime refuges and farmland. Elephant counts, crop-raiding records, household surveys, Bayesian expert system, and least-cost path simulation were used to predict four alternative categories of daily corridors: (1) footpaths, (2) dry river beds, (3) stepping stones along scattered small farms, and (4) trajectories of shortest distance to refuges. The corridor alignments were compared in terms of their minimum cumulative resistance to elephant movement and related to crop-raiding zones quantified by a kernel density function. The "stepping stone" corridors predicted the crop-raiding patterns. Elephant presence was confirmed along these corridors, demonstrating that small farms located between refuges and contiguous farmland increase habitat connectivity for elephant. Our analysis successfully predicted elephant occurrence in farmland where daytime counts failed to detect nocturnal presence. These results have conservation management implications.

Assessment of a remote sensing-based model for predicting malaria transmission risk in villages of Chiapas, Mexico

NASA Technical Reports Server (NTRS)

Beck, L. R.; Rodriguez, M. H.; Dister, S. W.; Rodriguez, A. D.; Washino, R. K.; Roberts, D. R.; Spanner, M. A.

1997-01-01

A blind test of two remote sensing-based models for predicting adult populations of Anopheles albimanus in villages, an indicator of malaria transmission risk, was conducted in southern Chiapas, Mexico. One model was developed using a discriminant analysis approach, while the other was based on regression analysis. The models were developed in 1992 for an area around Tapachula, Chiapas, using Landsat Thematic Mapper (TM) satellite data and geographic information system functions. Using two remotely sensed landscape elements, the discriminant model was able to successfully distinguish between villages with high and low An. albimanus abundance with an overall accuracy of 90%. To test the predictive capability of the models, multitemporal TM data were used to generate a landscape map of the Huixtla area, northwest of Tapachula, where the models were used to predict risk for 40 villages. The resulting predictions were not disclosed until the end of the test. Independently, An. albimanus abundance data were collected in the 40 randomly selected villages for which the predictions had been made. These data were subsequently used to assess the models' accuracies. The discriminant model accurately predicted 79% of the high-abundance villages and 50% of the low-abundance villages, for an overall accuracy of 70%. The regression model correctly identified seven of the 10 villages with the highest mosquito abundance. This test demonstrated that remote sensing-based models generated for one area can be used successfully in another, comparable area.

Gene network inherent in genomic big data improves the accuracy of prognostic prediction for cancer patients.

PubMed

Kim, Yun Hak; Jeong, Dae Cheon; Pak, Kyoungjune; Goh, Tae Sik; Lee, Chi-Seung; Han, Myoung-Eun; Kim, Ji-Young; Liangwen, Liu; Kim, Chi Dae; Jang, Jeon Yeob; Cha, Wonjae; Oh, Sae-Ock

2017-09-29

Accurate prediction of prognosis is critical for therapeutic decisions regarding cancer patients. Many previously developed prognostic scoring systems have limitations in reflecting recent progress in the field of cancer biology such as microarray, next-generation sequencing, and signaling pathways. To develop a new prognostic scoring system for cancer patients, we used mRNA expression and clinical data in various independent breast cancer cohorts (n=1214) from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and Gene Expression Omnibus (GEO). A new prognostic score that reflects gene network inherent in genomic big data was calculated using Network-Regularized high-dimensional Cox-regression (Net-score). We compared its discriminatory power with those of two previously used statistical methods: stepwise variable selection via univariate Cox regression (Uni-score) and Cox regression via Elastic net (Enet-score). The Net scoring system showed better discriminatory power in prediction of disease-specific survival (DSS) than other statistical methods (p=0 in METABRIC training cohort, p=0.000331, 4.58e-06 in two METABRIC validation cohorts) when accuracy was examined by log-rank test. Notably, comparison of C-index and AUC values in receiver operating characteristic analysis at 5 years showed fewer differences between training and validation cohorts with the Net scoring system than other statistical methods, suggesting minimal overfitting. The Net-based scoring system also successfully predicted prognosis in various independent GEO cohorts with high discriminatory power. In conclusion, the Net-based scoring system showed better discriminative power than previous statistical methods in prognostic prediction for breast cancer patients. This new system will mark a new era in prognosis prediction for cancer patients.

Gene network inherent in genomic big data improves the accuracy of prognostic prediction for cancer patients

PubMed Central

Kim, Yun Hak; Jeong, Dae Cheon; Pak, Kyoungjune; Goh, Tae Sik; Lee, Chi-Seung; Han, Myoung-Eun; Kim, Ji-Young; Liangwen, Liu; Kim, Chi Dae; Jang, Jeon Yeob; Cha, Wonjae; Oh, Sae-Ock

2017-01-01

Accurate prediction of prognosis is critical for therapeutic decisions regarding cancer patients. Many previously developed prognostic scoring systems have limitations in reflecting recent progress in the field of cancer biology such as microarray, next-generation sequencing, and signaling pathways. To develop a new prognostic scoring system for cancer patients, we used mRNA expression and clinical data in various independent breast cancer cohorts (n=1214) from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and Gene Expression Omnibus (GEO). A new prognostic score that reflects gene network inherent in genomic big data was calculated using Network-Regularized high-dimensional Cox-regression (Net-score). We compared its discriminatory power with those of two previously used statistical methods: stepwise variable selection via univariate Cox regression (Uni-score) and Cox regression via Elastic net (Enet-score). The Net scoring system showed better discriminatory power in prediction of disease-specific survival (DSS) than other statistical methods (p=0 in METABRIC training cohort, p=0.000331, 4.58e-06 in two METABRIC validation cohorts) when accuracy was examined by log-rank test. Notably, comparison of C-index and AUC values in receiver operating characteristic analysis at 5 years showed fewer differences between training and validation cohorts with the Net scoring system than other statistical methods, suggesting minimal overfitting. The Net-based scoring system also successfully predicted prognosis in various independent GEO cohorts with high discriminatory power. In conclusion, the Net-based scoring system showed better discriminative power than previous statistical methods in prognostic prediction for breast cancer patients. This new system will mark a new era in prognosis prediction for cancer patients. PMID:29100405

Psycholegal abilities and restoration of competence to stand trial.

PubMed

Morris, Douglas R; Deyoung, Nathaniel J

2012-01-01

Criminal defendants adjudicated incompetent to stand trial are typically hospitalized for competence restoration in state institutions. Prolonged restoration hospitalizations involve civil rights concerns and increasing financial costs, and there remains interest in determining which individuals are likely to be successfully restored. We retrospectively reviewed hospital records of 455 male defendants admitted to a forensic treatment center for competence restoration in an effort to determine whether psychiatric diagnoses, demographic factors, or psycholegal abilities were predictive of successful or failed restoration. At varying stages of restoration efforts, psychotic disorder, mental retardation, and previous state hospitalization predicted unsuccessful restoration, while substance use and personality disorders were predictive of successful restoration. Psycholegal abilities were predictive of successful restoration and appeared to form a continuum, with basic behavior and outlook, factual legal understanding, and rational attorney assistance factors demonstrating progressively increased importance in successful restoration. Copyright © 2012 John Wiley & Sons, Ltd.

Generating Adaptive Behaviour within a Memory-Prediction Framework

PubMed Central

Rawlinson, David; Kowadlo, Gideon

2012-01-01

The Memory-Prediction Framework (MPF) and its Hierarchical-Temporal Memory implementation (HTM) have been widely applied to unsupervised learning problems, for both classification and prediction. To date, there has been no attempt to incorporate MPF/HTM in reinforcement learning or other adaptive systems; that is, to use knowledge embodied within the hierarchy to control a system, or to generate behaviour for an agent. This problem is interesting because the human neocortex is believed to play a vital role in the generation of behaviour, and the MPF is a model of the human neocortex. We propose some simple and biologically-plausible enhancements to the Memory-Prediction Framework. These cause it to explore and interact with an external world, while trying to maximize a continuous, time-varying reward function. All behaviour is generated and controlled within the MPF hierarchy. The hierarchy develops from a random initial configuration by interaction with the world and reinforcement learning only. Among other demonstrations, we show that a 2-node hierarchy can learn to successfully play “rocks, paper, scissors” against a predictable opponent. PMID:22272231

Predicting Success in ISCS Level II.

ERIC Educational Resources Information Center

McDuffie, Thomas E., Jr.

1979-01-01

Investigates a method to predict best and least suited students for the ISCS instructional approach. Aptitude-treatment interactions associated with ISCS instruction and a set of aptitude, attitude, and skill factors were utilized to make and verify predictions on two dependent variables--achievement and success. (Author/GA)

Predicting HIV RNA virologic outcome at 52-weeks follow-up in antiretroviral clinical trials. The INCAS and AVANTI Study Groups.

PubMed

Raboud, J M; Rae, S; Montaner, J S

2000-08-15

To determine the ability of intermediate plasma viral load (pVL) measurements to predict virologic outcome at 52 weeks of follow-up in clinical trials of antiretroviral therapy. Individual patient data from three clinical trials (INCAS, AVANTI-2 and AVANTI-3) were combined into a single database. Virologic success was defined to be plasma viral load (pVL) <500 copies/ml at week 52. The sensitivity and specificity of intermediate pVL measurements below the limit of detection, 100, 500, 1000, and 5000 copies/ml to predict virologic success were calculated. The sensitivity, specificity, and positive and negative predictive values of a pVL measurement <1000 copies/ml at week 16 to predict virologic outcome at week 52 were 74%, 74%, 48%, and 90%, respectively, for patients on double therapy. For patients on triple therapy, the sensitivity, specificity, and positive and negative predictive values of a pVL measurement <50 copies/ml at week 16 to predict virologic outcome were 68%, 68%, 80%, and 47%, respectively. For patients receiving double therapy, a poor virologic result at an intermediate week of follow-up is a strong indicator of virologic failure at 52 weeks whereas intermediate virologic success is no guarantee of success at 1 year. For patients on triple therapy, disappointing intermediate results do not preclude virologic success at 1 year and intermediate successes are more likely to be sustained.

Adaptive Data-based Predictive Control for Short Take-off and Landing (STOL) Aircraft

NASA Technical Reports Server (NTRS)

Barlow, Jonathan Spencer; Acosta, Diana Michelle; Phan, Minh Q.

2010-01-01

Data-based Predictive Control is an emerging control method that stems from Model Predictive Control (MPC). MPC computes current control action based on a prediction of the system output a number of time steps into the future and is generally derived from a known model of the system. Data-based predictive control has the advantage of deriving predictive models and controller gains from input-output data. Thus, a controller can be designed from the outputs of complex simulation code or a physical system where no explicit model exists. If the output data happens to be corrupted by periodic disturbances, the designed controller will also have the built-in ability to reject these disturbances without the need to know them. When data-based predictive control is implemented online, it becomes a version of adaptive control. The characteristics of adaptive data-based predictive control are particularly appropriate for the control of nonlinear and time-varying systems, such as Short Take-off and Landing (STOL) aircraft. STOL is a capability of interest to NASA because conceptual Cruise Efficient Short Take-off and Landing (CESTOL) transport aircraft offer the ability to reduce congestion in the terminal area by utilizing existing shorter runways at airports, as well as to lower community noise by flying steep approach and climb-out patterns that reduce the noise footprint of the aircraft. In this study, adaptive data-based predictive control is implemented as an integrated flight-propulsion controller for the outer-loop control of a CESTOL-type aircraft. Results show that the controller successfully tracks velocity while attempting to maintain a constant flight path angle, using longitudinal command, thrust and flap setting as the control inputs.

Emotional intelligence predicts success in medical school.

PubMed

Libbrecht, Nele; Lievens, Filip; Carette, Bernd; Côté, Stéphane

2014-02-01

Accumulating evidence suggests that effective communication and interpersonal sensitivity during interactions between doctors and patients impact therapeutic outcomes. There is an important need to identify predictors of these behaviors, because traditional tests used in medical admissions offer limited predictions of "bedside manners" in medical practice. This study examined whether emotional intelligence would predict the performance of 367 medical students in medical school courses on communication and interpersonal sensitivity. One of the dimensions of emotional intelligence, the ability to regulate emotions, predicted performance in courses on communication and interpersonal sensitivity over the next 3 years of medical school, over and above cognitive ability and conscientiousness. Emotional intelligence did not predict performance on courses on medical subject domains. The results suggest that medical schools may better predict who will communicate effectively and show interpersonal sensitivity if they include measures of emotional intelligence in their admission systems. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Graph distance for complex networks

NASA Astrophysics Data System (ADS)

Shimada, Yutaka; Hirata, Yoshito; Ikeguchi, Tohru; Aihara, Kazuyuki

2016-10-01

Networks are widely used as a tool for describing diverse real complex systems and have been successfully applied to many fields. The distance between networks is one of the most fundamental concepts for properly classifying real networks, detecting temporal changes in network structures, and effectively predicting their temporal evolution. However, this distance has rarely been discussed in the theory of complex networks. Here, we propose a graph distance between networks based on a Laplacian matrix that reflects the structural and dynamical properties of networked dynamical systems. Our results indicate that the Laplacian-based graph distance effectively quantifies the structural difference between complex networks. We further show that our approach successfully elucidates the temporal properties underlying temporal networks observed in the context of face-to-face human interactions.

An increased estimate of the merger rate of double neutron stars from observations of a highly relativistic system.

PubMed

Burgay, M; D'Amico, N; Possenti, A; Manchester, R N; Lyne, A G; Joshi, B C; McLaughlin, M A; Kramer, M; Sarkissian, J M; Camilo, F; Kalogera, V; Kim, C; Lorimer, D R

2003-12-04

The merger of close binary systems containing two neutron stars should produce a burst of gravitational waves, as predicted by the theory of general relativity. A reliable estimate of the double-neutron-star merger rate in the Galaxy is crucial in order to predict whether current gravity wave detectors will be successful in detecting such bursts. Present estimates of this rate are rather low, because we know of only a few double-neutron-star binaries with merger times less than the age of the Universe. Here we report the discovery of a 22-ms pulsar, PSR J0737-3039, which is a member of a highly relativistic double-neutron-star binary with an orbital period of 2.4 hours. This system will merge in about 85 Myr, a time much shorter than for any other known neutron-star binary. Together with the relatively low radio luminosity of PSR J0737-3039, this timescale implies an order-of-magnitude increase in the predicted merger rate for double-neutron-star systems in our Galaxy (and in the rest of the Universe).

Towards Engineering Biological Systems in a Broader Context.

PubMed

Venturelli, Ophelia S; Egbert, Robert G; Arkin, Adam P

2016-02-27

Significant advances have been made in synthetic biology to program information processing capabilities in cells. While these designs can function predictably in controlled laboratory environments, the reliability of these devices in complex, temporally changing environments has not yet been characterized. As human society faces global challenges in agriculture, human health and energy, synthetic biology should develop predictive design principles for biological systems operating in complex environments. Natural biological systems have evolved mechanisms to overcome innumerable and diverse environmental challenges. Evolutionary design rules should be extracted and adapted to engineer stable and predictable ecological function. We highlight examples of natural biological responses spanning the cellular, population and microbial community levels that show promise in synthetic biology contexts. We argue that synthetic circuits embedded in host organisms or designed ecologies informed by suitable measurement of biotic and abiotic environmental parameters could be used as engineering substrates to achieve target functions in complex environments. Successful implementation of these methods will broaden the context in which synthetic biological systems can be applied to solve important problems. Copyright © 2015 Elsevier Ltd. All rights reserved.

Arctic foxes, lemmings, and canada goose nest survival at cape Churchill, Manitoba

USGS Publications Warehouse

Reiter, M.E.; Andersen, D.E.

2011-01-01

We examined factors influencing Canada Goose (Branta canadensis interior) annual nest success, including the relative abundance of collared lemmings (Dicrostonyx richardsoni), arctic fox (Alopex lagopus) den occupancy, nest density, and spring phenology using data collected during annual Canada Goose breeding area surveys at Cape Churchill, Manitoba. Nest density and arctic fox den occupancy strongly influenced Canada Goose nest success. High nest density resulted in higher nest success and high den occupancy reduced nest success. Nest success was not influenced by lemming abundance in the current or previous year as predicted by the "bird-lemming" hypothesis. Reducing arctic fox abundance through targeted management increased nest survival of Canada Geese; a result that further emphasizes the importance of arctic fox as nest predators in this system. The spatial distribution of nest predators, at least for dispersed-nesting geese, may be most important for nest survival, regardless of the abundance of small mammals in the local ecosystem. Further understanding of the factors influencing the magnitude and variance in arctic fox abundance in this region, and the spatial scale at which these factors are realized, is necessary to fully explain predator-prey-alternative prey dynamics in this system. ?? 2011 by the Wilson Ornithological Society.

Cognitive ability is heritable and predicts the success of an alternative mating tactic

PubMed Central

Smith, Carl; Philips, André; Reichard, Martin

2015-01-01

The ability to attract mates, acquire resources for reproduction, and successfully outcompete rivals for fertilizations may make demands on cognitive traits—the mechanisms by which an animal acquires, processes, stores and acts upon information from its environment. Consequently, cognitive traits potentially undergo sexual selection in some mating systems. We investigated the role of cognitive traits on the reproductive performance of male rose bitterling (Rhodeus ocellatus), a freshwater fish with a complex mating system and alternative mating tactics. We quantified the learning accuracy of males and females in a spatial learning task and scored them for learning accuracy. Males were subsequently allowed to play the roles of a guarder and a sneaker in competitive mating trials, with reproductive success measured using paternity analysis. We detected a significant interaction between male mating role and learning accuracy on reproductive success, with the best-performing males in maze trials showing greater reproductive success in a sneaker role than as a guarder. Using a cross-classified breeding design, learning accuracy was demonstrated to be heritable, with significant additive maternal and paternal effects. Our results imply that male cognitive traits may undergo intra-sexual selection. PMID:26041347

Cognitive ability is heritable and predicts the success of an alternative mating tactic.

PubMed

Smith, Carl; Philips, André; Reichard, Martin

2015-06-22

The ability to attract mates, acquire resources for reproduction, and successfully outcompete rivals for fertilizations may make demands on cognitive traits--the mechanisms by which an animal acquires, processes, stores and acts upon information from its environment. Consequently, cognitive traits potentially undergo sexual selection in some mating systems. We investigated the role of cognitive traits on the reproductive performance of male rose bitterling (Rhodeus ocellatus), a freshwater fish with a complex mating system and alternative mating tactics. We quantified the learning accuracy of males and females in a spatial learning task and scored them for learning accuracy. Males were subsequently allowed to play the roles of a guarder and a sneaker in competitive mating trials, with reproductive success measured using paternity analysis. We detected a significant interaction between male mating role and learning accuracy on reproductive success, with the best-performing males in maze trials showing greater reproductive success in a sneaker role than as a guarder. Using a cross-classified breeding design, learning accuracy was demonstrated to be heritable, with significant additive maternal and paternal effects. Our results imply that male cognitive traits may undergo intra-sexual selection. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Systems face challenges. Interview by Donald E.L. Johnson.

PubMed

Brown, F L

1991-04-01

The regional hospital system has become a mainstay in the structuring of health care during the past decade. How will it fare in the next 10 years? In the following interview with Health Care Strategic Management's editor and publisher Donald E.L. Johnson, Fred L. Brown, president and chief executive officer of Christian Health System, the St. Louis, Mo.-based umbrella organization for nine hospitals, six nursing facilities, and one retirement community, predicts a bright future for the health care system. He also discusses the strategies that are unique to the success of such a system.

GA-based fuzzy reinforcement learning for control of a magnetic bearing system.

PubMed

Lin, C T; Jou, C P

2000-01-01

This paper proposes a TD (temporal difference) and GA (genetic algorithm)-based reinforcement (TDGAR) learning method and applies it to the control of a real magnetic bearing system. The TDGAR learning scheme is a new hybrid GA, which integrates the TD prediction method and the GA to perform the reinforcement learning task. The TDGAR learning system is composed of two integrated feedforward networks. One neural network acts as a critic network to guide the learning of the other network (the action network) which determines the outputs (actions) of the TDGAR learning system. The action network can be a normal neural network or a neural fuzzy network. Using the TD prediction method, the critic network can predict the external reinforcement signal and provide a more informative internal reinforcement signal to the action network. The action network uses the GA to adapt itself according to the internal reinforcement signal. The key concept of the TDGAR learning scheme is to formulate the internal reinforcement signal as the fitness function for the GA such that the GA can evaluate the candidate solutions (chromosomes) regularly, even during periods without external feedback from the environment. This enables the GA to proceed to new generations regularly without waiting for the arrival of the external reinforcement signal. This can usually accelerate the GA learning since a reinforcement signal may only be available at a time long after a sequence of actions has occurred in the reinforcement learning problem. The proposed TDGAR learning system has been used to control an active magnetic bearing (AMB) system in practice. A systematic design procedure is developed to achieve successful integration of all the subsystems including magnetic suspension, mechanical structure, and controller training. The results show that the TDGAR learning scheme can successfully find a neural controller or a neural fuzzy controller for a self-designed magnetic bearing system.

Usefulness of hounsfield unit and density in the assessment and treatment of urinary stones

PubMed Central

Gücük, Adnan; Üyetürk, Uğur

2014-01-01

Computed tomography (CT) is widely used to examine stones in the urinary system. In addition to the size and location of the stone and the overall health of the kidney, CT can also assess the density of the stone in Hounsfield units (HU). The HU, or Hounsfield density, measured by CT, is related to the density of the tissue or stone. A number of studies have assessed the use of HU in urology. HUs have been used to predict the type and opacity of stones during diagnosis, and the efficacy has been assessed using methods including extracorporeal shock wave lithotripsy (ESWL), percutaneous nephrolithotomy (PCNL), ureterorenoscopic ureterolithotripsy (URSL), and medical expulsive treatment (MET). Previous studies have focused on the success rate of HU for predicting the type of stone and of ESWL treatment. Understanding the composition of the stone plays a key role in determining the most appropriate treatment modality. The most recent reports have suggested that the HU value and its variants facilitate prediction of stone composition. However, the inclusion of data regarding urine, such as pH and presence of crystals, increases the predictive accuracy. HUs, which now form part of the clinical guidelines, allow us to predict the success of ESWL; therefore, they should be taken into account when ESWL is considered as a treatment option. However, there are currently insufficient data available regarding the value of HU for assessing the efficacy of PCNL, URSL, and MET. Studies performed to date suggest that these values would make a significant contribution to the diagnosis and treatment of urinary system stones. However, more data are required to assess this further. PMID:25374823

Applications of a simulation model to decisions in mallard management

USGS Publications Warehouse

Cowardin, L.M.; Johnson, D.H.; Shaffer, T.L.; Sparling, D.W.

1988-01-01

A system comprising simulation models and data bases for habitat availability and nest success rates was used to predict results from a mallard (Anas platyrhynchos) management plan and to compare six management methods with a control. Individual treatments in the applications included land purchase for waterfowl production, wetland easement purchase, lease of uplands for waterfowl management, cropland retirement, use of no-till winter wheat, delayed cutting of alfalfa, installation of nest baskets, nesting island construction, and use of predator-resistant fencing.The simulations predicted that implementation of the management plan would increase recruits by 24%. Nest baskets were the most effective treatment, accounting for 20.4% of the recruits. No-till winter wheat was the second most effective, accounting for 5.9% of the recruits. Wetland loss due to drainage would cause an 11% loss of breeding population in 10 years.The models were modified to account for migrational homing. The modification indicated that migrational homing would enhance the effects of management. Nest success rates were critical contributions to individual management methods. The most effective treatments, such as nest baskets, had high success rates and affected a large portion of the breeding population.Economic analyses indicated that nest baskets would be the most economical of the three techniques tested. The applications indicated that the system is a useful tool to aid management decisions, but data are scarce for several important variables. Basic research will be required to adequately model the effect of migrational homing and density dependence on production. The comprehensive nature of predictions desired by managers will also require that production models like the one described here be extended to encompass the entire annual cycle of waterfowl.

Can We Predict Foraging Success in a Marine Predator from Dive Patterns Only? Validation with Prey Capture Attempt Data

PubMed Central

Viviant, Morgane; Monestiez, Pascal; Guinet, Christophe

2014-01-01

Predicting how climatic variations will affect marine predator populations relies on our ability to assess foraging success, but evaluating foraging success in a marine predator at sea is particularly difficult. Dive metrics are commonly available for marine mammals, diving birds and some species of fish. Bottom duration or dive duration are usually used as proxies for foraging success. However, few studies have tried to validate these assumptions and identify the set of behavioral variables that best predict foraging success at a given time scale. The objective of this study was to assess if foraging success in Antarctic fur seals could be accurately predicted from dive parameters only, at different temporal scales. For this study, 11 individuals were equipped with either Hall sensors or accelerometers to record dive profiles and detect mouth-opening events, which were considered prey capture attempts. The number of prey capture attempts was best predicted by descent and ascent rates at the dive scale; bottom duration and descent rates at 30-min, 1-h, and 2-h scales; and ascent rates and maximum dive depths at the all-night scale. Model performances increased with temporal scales, but rank and sign of the factors varied according to the time scale considered, suggesting that behavioral adjustment in response to prey distribution could occur at certain scales only. The models predicted the foraging intensity of new individuals with good accuracy despite high inter-individual differences. Dive metrics that predict foraging success depend on the species and the scale considered, as verified by the literature and this study. The methodology used in our study is easy to implement, enables an assessment of model performance, and could be applied to any other marine predator. PMID:24603534

Authentic Leadership and Emotional Intelligence: Predicting Student Success

ERIC Educational Resources Information Center

Jasso, Sonia Lizette

2016-01-01

Student success has been predicted conservatively, using academic, demographic, and economic variables. Since many colleges are feeling the pressure to produce more graduates, student success is at the forefront of all universities. This study looks to find a relationship between traditional and non-traditional variables. The objective of the…

Psychosocial Factors Predicting First-Year College Student Success

ERIC Educational Resources Information Center

Krumrei-Mancuso, Elizabeth J.; Newton, Fred B.; Kim, Eunhee; Wilcox, Dan

2013-01-01

This study made use of a model of college success that involves students achieving academic goals and life satisfaction. Hierarchical regressions examined the role of six psychosocial factors for college success among 579 first-year college students. Academic self-efficacy and organization and attention to study were predictive of first semester…

Does High School Performance Predict College Math Placement?

ERIC Educational Resources Information Center

Kowski, Lynne E.

2013-01-01

Predicting student success has long been a question of interest for postsecondary admission counselors throughout the United States. Past research has examined the validity of several methods designed for predicting undergraduate success. High school record, standardized test scores, extracurricular activities, and combinations of all three have…

Potential Predictability of the Monsoon Subclimate Systems

NASA Technical Reports Server (NTRS)

Yang, Song; Lau, K.-M.; Chang, Y.; Schubert, S.

1999-01-01

While El Nino/Southern Oscillation (ENSO) phenomenon can be predicted with some success using coupled oceanic-atmospheric models, the skill of predicting the tropical monsoons is low regardless of the methods applied. The low skill of monsoon prediction may be either because the monsoons are not defined appropriately or because they are not influenced significantly by boundary forcing. The latter characterizes the importance of internal dynamics in monsoon variability and leads to many eminent chaotic features of the monsoons. In this study, we analyze results from nine AMIP-type ensemble experiments with the NASA/GEOS-2 general circulation model to assess the potential predictability of the tropical climate system. We will focus on the variability and predictability of tropical monsoon rainfall on seasonal-to-interannual time scales. It is known that the tropical climate is more predictable than its extratropical counterpart. However, predictability is different from one climate subsystem to another within the tropics. It is important to understand the differences among these subsystems in order to increase our skill of seasonal-to-interannual prediction. We assess potential predictability by comparing the magnitude of internal and forced variances as defined by Harzallah and Sadourny (1995). The internal variance measures the spread among the various ensemble members. The forced part of rainfall variance is determined by the magnitude of the ensemble mean rainfall anomaly and by the degree of consistency of the results from the various experiments.

Narrowing the scope of failure prediction using targeted fault load injection

NASA Astrophysics Data System (ADS)

Jordan, Paul L.; Peterson, Gilbert L.; Lin, Alan C.; Mendenhall, Michael J.; Sellers, Andrew J.

2018-05-01

As society becomes more dependent upon computer systems to perform increasingly critical tasks, ensuring that those systems do not fail becomes increasingly important. Many organizations depend heavily on desktop computers for day-to-day operations. Unfortunately, the software that runs on these computers is written by humans and, as such, is still subject to human error and consequent failure. A natural solution is to use statistical machine learning to predict failure. However, since failure is still a relatively rare event, obtaining labelled training data to train these models is not a trivial task. This work presents new simulated fault-inducing loads that extend the focus of traditional fault injection techniques to predict failure in the Microsoft enterprise authentication service and Apache web server. These new fault loads were successful in creating failure conditions that were identifiable using statistical learning methods, with fewer irrelevant faults being created.

New efficient optimizing techniques for Kalman filters and numerical weather prediction models

NASA Astrophysics Data System (ADS)

Famelis, Ioannis; Galanis, George; Liakatas, Aristotelis

2016-06-01

The need for accurate local environmental predictions and simulations beyond the classical meteorological forecasts are increasing the last years due to the great number of applications that are directly or not affected: renewable energy resource assessment, natural hazards early warning systems, global warming and questions on the climate change can be listed among them. Within this framework the utilization of numerical weather and wave prediction systems in conjunction with advanced statistical techniques that support the elimination of the model bias and the reduction of the error variability may successfully address the above issues. In the present work, new optimization methods are studied and tested in selected areas of Greece where the use of renewable energy sources is of critical. The added value of the proposed work is due to the solid mathematical background adopted making use of Information Geometry and Statistical techniques, new versions of Kalman filters and state of the art numerical analysis tools.

Predictability of Extreme Climate Events via a Complex Network Approach

NASA Astrophysics Data System (ADS)

Muhkin, D.; Kurths, J.

2017-12-01

We analyse climate dynamics from a complex network approach. This leads to an inverse problem: Is there a backbone-like structure underlying the climate system? For this we propose a method to reconstruct and analyze a complex network from data generated by a spatio-temporal dynamical system. This approach enables us to uncover relations to global circulation patterns in oceans and atmosphere. This concept is then applied to Monsoon data; in particular, we develop a general framework to predict extreme events by combining a non-linear synchronization technique with complex networks. Applying this method, we uncover a new mechanism of extreme floods in the eastern Central Andes which could be used for operational forecasts. Moreover, we analyze the Indian Summer Monsoon (ISM) and identify two regions of high importance. By estimating an underlying critical point, this leads to an improved prediction of the onset of the ISM; this scheme was successful in 2016 and 2017.

Nonlinear wave chaos: statistics of second harmonic fields.

PubMed

Zhou, Min; Ott, Edward; Antonsen, Thomas M; Anlage, Steven M

2017-10-01

Concepts from the field of wave chaos have been shown to successfully predict the statistical properties of linear electromagnetic fields in electrically large enclosures. The Random Coupling Model (RCM) describes these properties by incorporating both universal features described by Random Matrix Theory and the system-specific features of particular system realizations. In an effort to extend this approach to the nonlinear domain, we add an active nonlinear frequency-doubling circuit to an otherwise linear wave chaotic system, and we measure the statistical properties of the resulting second harmonic fields. We develop an RCM-based model of this system as two linear chaotic cavities coupled by means of a nonlinear transfer function. The harmonic field strengths are predicted to be the product of two statistical quantities and the nonlinearity characteristics. Statistical results from measurement-based calculation, RCM-based simulation, and direct experimental measurements are compared and show good agreement over many decades of power.

A support vector machine based control application to the experimental three-tank system.

PubMed

Iplikci, Serdar

2010-07-01

This paper presents a support vector machine (SVM) approach to generalized predictive control (GPC) of multiple-input multiple-output (MIMO) nonlinear systems. The possession of higher generalization potential and at the same time avoidance of getting stuck into the local minima have motivated us to employ SVM algorithms for modeling MIMO systems. Based on the SVM model, detailed and compact formulations for calculating predictions and gradient information, which are used in the computation of the optimal control action, are given in the paper. The proposed MIMO SVM-based GPC method has been verified on an experimental three-tank liquid level control system. Experimental results have shown that the proposed method can handle the control task successfully for different reference trajectories. Moreover, a detailed discussion on data gathering, model selection and effects of the control parameters have been given in this paper. 2010 ISA. Published by Elsevier Ltd. All rights reserved.

Individual differences in children's innovative problem-solving are not predicted by divergent thinking or executive functions

PubMed Central

2016-01-01

Recent studies of children's tool innovation have revealed that there is variation in children's success in middle-childhood. In two individual differences studies, we sought to identify personal characteristics that might predict success on an innovation task. In Study 1, we found that although measures of divergent thinking were related to each other they did not predict innovation success. In Study 2, we measured executive functioning including: inhibition, working memory, attentional flexibility and ill-structured problem-solving. None of these measures predicted innovation, but, innovation was predicted by children's performance on a receptive vocabulary scale that may function as a proxy for general intelligence. We did not find evidence that children's innovation was predicted by specific personal characteristics. PMID:26926280

A multidimensional model of the effect of gravity on the spatial orientation of the monkey

NASA Technical Reports Server (NTRS)

Merfeld, D. M.; Young, L. R.; Oman, C. M.; Shelhamer, M. J.

1993-01-01

A "sensory conflict" model of spatial orientation was developed. This mathematical model was based on concepts derived from observer theory, optimal observer theory, and the mathematical properties of coordinate rotations. The primary hypothesis is that the central nervous system of the squirrel monkey incorporates information about body dynamics and sensory dynamics to develop an internal model. The output of this central model (expected sensory afference) is compared to the actual sensory afference, with the difference defined as "sensory conflict." The sensory conflict information is, in turn, used to drive central estimates of angular velocity ("velocity storage"), gravity ("gravity storage"), and linear acceleration ("acceleration storage") toward more accurate values. The model successfully predicts "velocity storage" during rotation about an earth-vertical axis. The model also successfully predicts that the time constant of the horizontal vestibulo-ocular reflex is reduced and that the axis of eye rotation shifts toward alignment with gravity following postrotatory tilt. Finally, the model predicts the bias, modulation, and decay components that have been observed during off-vertical axis rotations (OVAR).

Hybrid neural network for density limit disruption prediction and avoidance on J-TEXT tokamak

NASA Astrophysics Data System (ADS)

Zheng, W.; Hu, F. R.; Zhang, M.; Chen, Z. Y.; Zhao, X. Q.; Wang, X. L.; Shi, P.; Zhang, X. L.; Zhang, X. Q.; Zhou, Y. N.; Wei, Y. N.; Pan, Y.; J-TEXT team

2018-05-01

Increasing the plasma density is one of the key methods in achieving an efficient fusion reaction. High-density operation is one of the hot topics in tokamak plasmas. Density limit disruptions remain an important issue for safe operation. An effective density limit disruption prediction and avoidance system is the key to avoid density limit disruptions for long pulse steady state operations. An artificial neural network has been developed for the prediction of density limit disruptions on the J-TEXT tokamak. The neural network has been improved from a simple multi-layer design to a hybrid two-stage structure. The first stage is a custom network which uses time series diagnostics as inputs to predict plasma density, and the second stage is a three-layer feedforward neural network to predict the probability of density limit disruptions. It is found that hybrid neural network structure, combined with radiation profile information as an input can significantly improve the prediction performance, especially the average warning time ({{T}warn} ). In particular, the {{T}warn} is eight times better than that in previous work (Wang et al 2016 Plasma Phys. Control. Fusion 58 055014) (from 5 ms to 40 ms). The success rate for density limit disruptive shots is above 90%, while, the false alarm rate for other shots is below 10%. Based on the density limit disruption prediction system and the real-time density feedback control system, the on-line density limit disruption avoidance system has been implemented on the J-TEXT tokamak.

A Predictive Study of Pre-Service Teachers and Success in Final Student Internship

ERIC Educational Resources Information Center

Ingle, Karen M.

2017-01-01

Student teaching provides the final pre-service clinical teaching experience of an initial teacher preparation program. Research that specifically studies the pre-service student teacher and predictive factors of student teaching is limited. Identifying predictive factors that contribute to the success of student interns' student teaching…

Using Neural Networks to Predict MBA Student Success

ERIC Educational Resources Information Center

Naik, Bijayananda; Ragothaman, Srinivasan

2004-01-01

Predicting MBA student performance for admission decisions is crucial for educational institutions. This paper evaluates the ability of three different models--neural networks, logit, and probit to predict MBA student performance in graduate programs. The neural network technique was used to classify applicants into successful and marginal student…

The Prediction of Success in Intensive Foreign Language Training.

ERIC Educational Resources Information Center

Carroll, John B.

After a review of the problem of predicting foreign language success, this booklet describes the development, refinement, and validation of a battery of psychological tests, some involving tape-recorded auditory stimuli, for predicting rate of progress in learning a foreign language. Although the battery was developed for more general application…

A Framework for Modeling Emerging Diseases to Inform Management

PubMed Central

Katz, Rachel A.; Richgels, Katherine L.D.; Walsh, Daniel P.; Grant, Evan H.C.

2017-01-01

The rapid emergence and reemergence of zoonotic diseases requires the ability to rapidly evaluate and implement optimal management decisions. Actions to control or mitigate the effects of emerging pathogens are commonly delayed because of uncertainty in the estimates and the predicted outcomes of the control tactics. The development of models that describe the best-known information regarding the disease system at the early stages of disease emergence is an essential step for optimal decision-making. Models can predict the potential effects of the pathogen, provide guidance for assessing the likelihood of success of different proposed management actions, quantify the uncertainty surrounding the choice of the optimal decision, and highlight critical areas for immediate research. We demonstrate how to develop models that can be used as a part of a decision-making framework to determine the likelihood of success of different management actions given current knowledge. PMID:27983501

A novel adjuvant to the resident selection process: the hartman value profile.

PubMed

Cone, Jeffrey D; Byrum, C Stephen; Payne, Wyatt G; Smith, David J

2012-01-01

The goal of resident selection is twofold: (1) select candidates who will be successful residents and eventually successful practitioners and (2) avoid selecting candidates who will be unsuccessful residents and/or eventually unsuccessful practitioners. Traditional tools used to select residents have well-known limitations. The Hartman Value Profile (HVP) is a proven adjuvant tool to predicting future performance in candidates for advanced positions in the corporate setting. No literature exists to indicate use of the HVP for resident selection. The HVP evaluates the structure and the dynamics of an individual value system. Given the potential impact, we implemented its use beginning in 2007 as an adjuvant tool to the traditional selection process. Experience gained from incorporating the HVP into the residency selection process suggests that it may add objectivity and refinement in predicting resident performance. Further evaluation is warranted with longer follow-up times.

A Novel Adjuvant to the Resident Selection Process: the Hartman Value Profile

PubMed Central

Cone, Jeffrey D.; Byrum, C. Stephen; Payne, Wyatt G.; Smith, David J.

2012-01-01

Objectives: The goal of resident selection is twofold: (1) select candidates who will be successful residents and eventually successful practitioners and (2) avoid selecting candidates who will be unsuccessful residents and/or eventually unsuccessful practitioners. Traditional tools used to select residents have well-known limitations. The Hartman Value Profile (HVP) is a proven adjuvant tool to predicting future performance in candidates for advanced positions in the corporate setting. Methods: No literature exists to indicate use of the HVP for resident selection. Results: The HVP evaluates the structure and the dynamics of an individual value system. Given the potential impact, we implemented its use beginning in 2007 as an adjuvant tool to the traditional selection process. Conclusions: Experience gained from incorporating the HVP into the residency selection process suggests that it may add objectivity and refinement in predicting resident performance. Further evaluation is warranted with longer follow-up times. PMID:22720114

A Framework for Modeling Emerging Diseases to Inform Management.

PubMed

Russell, Robin E; Katz, Rachel A; Richgels, Katherine L D; Walsh, Daniel P; Grant, Evan H C

2017-01-01

The rapid emergence and reemergence of zoonotic diseases requires the ability to rapidly evaluate and implement optimal management decisions. Actions to control or mitigate the effects of emerging pathogens are commonly delayed because of uncertainty in the estimates and the predicted outcomes of the control tactics. The development of models that describe the best-known information regarding the disease system at the early stages of disease emergence is an essential step for optimal decision-making. Models can predict the potential effects of the pathogen, provide guidance for assessing the likelihood of success of different proposed management actions, quantify the uncertainty surrounding the choice of the optimal decision, and highlight critical areas for immediate research. We demonstrate how to develop models that can be used as a part of a decision-making framework to determine the likelihood of success of different management actions given current knowledge.

A framework for modeling emerging diseases to inform management

USGS Publications Warehouse

Russell, Robin E.; Katz, Rachel A.; Richgels, Katherine L. D.; Walsh, Daniel P.; Grant, Evan H. Campbell

2017-01-01

The rapid emergence and reemergence of zoonotic diseases requires the ability to rapidly evaluate and implement optimal management decisions. Actions to control or mitigate the effects of emerging pathogens are commonly delayed because of uncertainty in the estimates and the predicted outcomes of the control tactics. The development of models that describe the best-known information regarding the disease system at the early stages of disease emergence is an essential step for optimal decision-making. Models can predict the potential effects of the pathogen, provide guidance for assessing the likelihood of success of different proposed management actions, quantify the uncertainty surrounding the choice of the optimal decision, and highlight critical areas for immediate research. We demonstrate how to develop models that can be used as a part of a decision-making framework to determine the likelihood of success of different management actions given current knowledge.

What predicts successful literacy acquisition in a second language?

PubMed Central

Frost, Ram; Siegelman, Noam; Narkiss, Alona; Afek, Liron

2013-01-01

We examined whether success (or failure) in assimilating the structure of a second language could be predicted by general statistical learning abilities that are non-linguistic in nature. We employed a visual statistical learning (VSL) task, monitoring our participants’ implicit learning of the transitional probabilities of visual shapes. A pretest revealed that performance in the VSL task is not correlated with abilities related to a general G factor or working memory. We found that native speakers of English who picked up the implicit statistical structure embedded in the continuous stream of shapes, on average, better assimilated the Semitic structure of Hebrew words. Our findings thus suggest that languages and their writing systems are characterized by idiosyncratic correlations of form and meaning, and these are picked up in the process of literacy acquisition, as they are picked up in any other type of learning, for the purpose of making sense of the environment. PMID:23698615

Outcome prediction of third ventriculostomy: a proposed hydrocephalus grading system.

PubMed

Kehler, U; Regelsberger, J; Gliemroth, J; Westphal, M

2006-08-01

An important factor in making a recommendation for different treatment modalities in hydrocephalus patients (VP shunt versus endoscopic third ventriculostomy) is the definition of the underlying pathology which determines the prognosis/outcome of the surgical procedure. Third ventriculostomies (3rd VS) are successful mainly in obstructive hydrocephalus but also in some subtypes of communicating hydrocephalus. A simple, easily applicable grading system that is designed to predict the outcome of 3rd VS is proposed. The hydrocephalus is graded on the basis of the extent of downward bulging of the floor of the third ventricle, which reflects the pressure gradient between the 3rd ventricle and the basal cisterns, presence of directly visualised CSF pathway obstruction in MRI, and the progression of the clinical symptoms resulting in five different grades. In this proposed grading system, grade 1 hydrocephalus subtype shows no downward bulged floor of the 3rd ventricle, no obstruction of the CSF pathway, and no progressive symptoms of hydrocephalus. There is no indication for 3rd VS. Grades 2 to 4 show different combinations of the described parameters. Grade 5 subtype shows a markedly downward bulged floor of the 3rd ventricle and direct detection of the CSF pathway obstruction (i.e., aqueductal stenosis) with progressive clinical deterioration. Retrospective application of this grading scheme to a series of 72 3rd VS has demonstrated a high correlation with the outcome: The success rate in grade 3 reached 40%, in grade 4: 58%, and in grade 5: 95%. This standardised grading system predicts the outcome of 3rd VS and helps in decision making for 3rd VS versus VP shunting.

Architecture and biological applications of artificial neural networks: a tuberculosis perspective.

PubMed

Darsey, Jerry A; Griffin, William O; Joginipelli, Sravanthi; Melapu, Venkata Kiran

2015-01-01

Advancement of science and technology has prompted researchers to develop new intelligent systems that can solve a variety of problems such as pattern recognition, prediction, and optimization. The ability of the human brain to learn in a fashion that tolerates noise and error has attracted many researchers and provided the starting point for the development of artificial neural networks: the intelligent systems. Intelligent systems can acclimatize to the environment or data and can maximize the chances of success or improve the efficiency of a search. Due to massive parallelism with large numbers of interconnected processers and their ability to learn from the data, neural networks can solve a variety of challenging computational problems. Neural networks have the ability to derive meaning from complicated and imprecise data; they are used in detecting patterns, and trends that are too complex for humans, or other computer systems. Solutions to the toughest problems will not be found through one narrow specialization; therefore we need to combine interdisciplinary approaches to discover the solutions to a variety of problems. Many researchers in different disciplines such as medicine, bioinformatics, molecular biology, and pharmacology have successfully applied artificial neural networks. This chapter helps the reader in understanding the basics of artificial neural networks, their applications, and methodology; it also outlines the network learning process and architecture. We present a brief outline of the application of neural networks to medical diagnosis, drug discovery, gene identification, and protein structure prediction. We conclude with a summary of the results from our study on tuberculosis data using neural networks, in diagnosing active tuberculosis, and predicting chronic vs. infiltrative forms of tuberculosis.

Updating Algebra for All?: Evidence of a Middle-Grades Math Acceleration Policy

ERIC Educational Resources Information Center

Dougherty, Shaun M.; Goodman, Joshua; Hill, Darryl; Litke, Erica; Page, Lindsay

2014-01-01

The Wake County Public School System (WCPSS) in North Carolina recently addressed the issue of advancement in and equitable access to advanced mathematics. Under a recent policy, WCPSS uses a SAS-generated predicted probabilities of students' success in obtaining a passing score on the NC Algebra I End-of-Course (EOC) exam, to determine…

Comparing Language and Literacy Environments in Two Types of Infant-Toddler Child Care Centers

ERIC Educational Resources Information Center

Norris, Deborah J.

2017-01-01

Language development is a significant milestone in the infant/toddler years; vocabulary by 2 years of age is predictive of later school success. It has been recognized within the bioecological systems theoretical framework that language develops as a result of an interplay between characteristics of the child, features of the setting, and…

Elements of Success: How Type of Secondary Education Credential Helps Predict Enlistee Attrition

ERIC Educational Resources Information Center

Burkhauser, Susan; Hanser, Lawrence M.; Hardison, Chaitra M.

2014-01-01

The U.S. military services have traditionally used a tiering system, including education credentials such as high school diplomas, in combination with Armed Forces Qualification Test (AFQT) scores to help gauge the likelihood of a recruit persevering through his or her first term of service. But what about less traditional credentials, such as…

Teaching as a Career Choice: Attractors and Deterrents Identified by Grade 11 Learners

ERIC Educational Resources Information Center

Park, Thomas

2006-01-01

Any strategic plan to address the predicted shortage of teachers will have to include the promotion of the teaching profession as an attractive career. This will, however, depend largely on how successfully the campaign takes into account the favourable and less favourable opinions and perceptions of learners about the education system as a whole…

Advancing Development of Intercultural Competence through Supporting Predictions in Narrative Video

ERIC Educational Resources Information Center

Ogan, Amy; Aleven, Vincent; Jones, Christopher

2009-01-01

Most successes in intelligent tutoring systems have come in well-defined domains like algebra or physics. We investigate how to support students in acquiring ill-defined skills of intercultural competence using an online environment that employs clips of feature films from a target culture. To test the effectiveness of a set of attention-focusing…

Theater Nuclear Force Survivability, Security and Safety Instrumentation. Volume I. Engineering Development Phase - Fiscal Year 1980.

DTIC Science & Technology

1980-12-31

development and acquisition program. It is generally agreed that the measures of merit in system acquisition programs are costs, schedule, and achievement...very few system acquisitions have successfully achieved their predicted measures of merit. The reasons for the poor record have been attributed to a...and Logistics -- The instrumentation must be easily maintained and easily transported to remote test sites in CONUS and Europe. 13 4. Useful Lifetime

Developing a case-mix model for PPS.

PubMed

Goldberg, H B; Delargy, D

2000-01-01

Agencies are pinning hopes for success under PPS on an accurate case-mix adjustor. The Health Care Financing Administration (HCFA) tasked Abt Associates Inc. to develop a system to accurately predict the volume and type of home health services each patient requires, based on his or her characteristics (not the service actually received). HCFA wanted this system to be feasible, clinically logical, and valid and accurate. Authors Goldberg and Delargy explain how Abt approached this daunting task.

Predicting Firm Success From the Facial Appearance of Chief Executive Officers of Non-Profit Organizations.

PubMed

Re, Daniel E; Rule, Nicholas O

2016-10-01

Recent research has demonstrated that judgments of Chief Executive Officers' (CEOs') faces predict their firms' financial performance, finding that characteristics associated with higher power (e.g., dominance) predict greater profits. Most of these studies have focused on CEOs of profit-based businesses, where the main criterion for success is financial gain. Here, we examined whether facial appearance might predict measures of success in a sample of CEOs of non-profit organizations (NPOs). Indeed, contrary to findings for the CEOs of profit-based businesses, judgments of leadership and power from the faces of CEOs of NPOs negatively correlated with multiple measures of charitable success (Study 1). Moreover, CEOs of NPOs looked less powerful than the CEOs of profit-based businesses (Study 2) and leadership ratings positively associated with warmth-based traits and NPO success when participants knew the faces belonged to CEOs of NPOs (Study 3). CEOs who look less dominant may therefore achieve greater success in leading NPOs, opposite the relationship found for the CEOs of profit-based companies. Thus, the relationship between facial appearance and leadership success varies by organizational context. © The Author(s) 2016.

Combining chemoinformatics with bioinformatics: in silico prediction of bacterial flavor-forming pathways by a chemical systems biology approach "reverse pathway engineering".

PubMed

Liu, Mengjin; Bienfait, Bruno; Sacher, Oliver; Gasteiger, Johann; Siezen, Roland J; Nauta, Arjen; Geurts, Jan M W

2014-01-01

The incompleteness of genome-scale metabolic models is a major bottleneck for systems biology approaches, which are based on large numbers of metabolites as identified and quantified by metabolomics. Many of the revealed secondary metabolites and/or their derivatives, such as flavor compounds, are non-essential in metabolism, and many of their synthesis pathways are unknown. In this study, we describe a novel approach, Reverse Pathway Engineering (RPE), which combines chemoinformatics and bioinformatics analyses, to predict the "missing links" between compounds of interest and their possible metabolic precursors by providing plausible chemical and/or enzymatic reactions. We demonstrate the added-value of the approach by using flavor-forming pathways in lactic acid bacteria (LAB) as an example. Established metabolic routes leading to the formation of flavor compounds from leucine were successfully replicated. Novel reactions involved in flavor formation, i.e. the conversion of alpha-hydroxy-isocaproate to 3-methylbutanoic acid and the synthesis of dimethyl sulfide, as well as the involved enzymes were successfully predicted. These new insights into the flavor-formation mechanisms in LAB can have a significant impact on improving the control of aroma formation in fermented food products. Since the input reaction databases and compounds are highly flexible, the RPE approach can be easily extended to a broad spectrum of applications, amongst others health/disease biomarker discovery as well as synthetic biology.

Prediction of high-speed rotor noise with a Kirchhoff formula

NASA Technical Reports Server (NTRS)

Purcell, Timothy W.; Strawn, Roger C.; Yu, Yung H.

1987-01-01

A new methodology has been developed to predict the impulsive noise generated by a transonic rotor blade. The formulation uses a full-potential finite-difference method to obtain the pressure field close to the blade. A Kirchhoff integral formulation is then used to extend these finite-difference results into the far-field. This Kirchhoff formula is written in a blade-fixed coordinate system. It requires initial data across a plane at the sonic radius. This data is provided by the finite-difference solution. Acoustic pressure predictions show excellent agreement with hover experimental data for two hover cases of 0.88 and 0.90 tip Mach number, the latter of which has delocalized transonic flow. These results represent the first successful prediction technique for peak pressure amplitudes using a computational code.

Application of Micro-segmentation Algorithms to the Healthcare Market:A Case Study

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

Sukumar, Sreenivas R; Aline, Frank

We draw inspiration from the recent success of loyalty programs and targeted personalized market campaigns of retail companies such as Kroger, Netflix, etc. to understand beneficiary behaviors in the healthcare system. Our posit is that we can emulate the financial success the companies have achieved by better understanding and predicting customer behaviors and translating such success to healthcare operations. Towards that goal, we survey current practices in market micro-segmentation research and analyze health insurance claims data using those algorithms. We present results and insights from micro-segmentation of the beneficiaries using different techniques and discuss how the interpretation can assist withmore » matching the cost-effective insurance payment models to the beneficiary micro-segments.« less

The GMAT as a Predictor of MBA Performance: Less Success than Meets the Eye

ERIC Educational Resources Information Center

Kass, Darrin; Grandzol, Christian; Bommer, William

2012-01-01

Consistent with previous research, the authors found that the combined use of undergraduate grade point average and the Graduate Management Admission Test (GMAT) verbal and quantitative sections successfully predicted performance in a master of business administration (MBA) program. However, these measures did not successfully predict the…

Social Factors That Predict Fear of Academic Success

ERIC Educational Resources Information Center

Gore, Jonathan S.; Thomas, Jessica; Jones, Stevy; Mahoney, Lauren; Dukes, Kristina; Treadway, Jodi

2016-01-01

Fear of academic success is ultimately a fear of social exclusion. Therefore, various forms of social inclusion may alleviate this fear. Three studies tested the hypothesis that social inclusion variables negatively predict fear of success. In Study 1, middle and high school students (n = 129) completed surveys of parental involvement, parental…

Emotional Intelligence as a Predictor for Success in Online Learning

ERIC Educational Resources Information Center

Berenson, Robin; Boyles, Gary; Weaver, Ann

2008-01-01

As students increasingly opt for online classes, it becomes more important for administrators to predict levels of potential academic success. This study examined the intrinsic factors of emotional intelligence (EI) and personality to determine the extent to which they predict grade point average (GPA), a measure of academic success, among…

Predicting solubilisation features of ternary phase diagrams of fully dilutable lecithin linker microemulsions.

PubMed

Nouraei, Mehdi; Acosta, Edgar J

2017-06-01

Fully dilutable microemulsions (μEs), used to design self-microemulsifying delivery system (SMEDS), are formulated as concentrate solutions containing oil and surfactants, without water. As water is added to dilute these systems, various μEs are produced (water-swollen reverse micelles, bicontinuous systems, and oil-swollen micelles), without the onset of phase separation. Currently, the formulation dilutable μEs follows a trial and error approach that has had a limited success. The objective of this work is to introduce the use of the hydrophilic-lipophilic-difference (HLD) and net-average-curvature (NAC) frameworks to predict the solubilisation features of ternary phase diagrams of lecithin-linker μEs and the use of these predictions to guide the formulation of dilutable μEs. To this end, the characteristic curvatures (Cc) of soybean lecithin (surfactant), glycerol monooleate (lipophilic linker) and polyglycerol caprylate (hydrophilic linker) and the equivalent alkane carbon number (EACN) of ethyl caprate (oil) were obtained via phase scans with reference surfactant-oil systems. These parameters were then used to calculate the HLD of lecithin-linkers-ethyl caprate microemulsions. The calculated HLDs were able to predict the phase transitions observed in the phase scans. The NAC was then used to fit and predict phase volumes obtained from salinity phase scans, and to predict the solubilisation features of ternary phase diagrams of the lecithin-linker formulations. The HLD-NAC predictions were reasonably accurate, and indicated that the largest region for dilutable μEs was obtained with slightly negative HLD values. The NAC framework also predicted, and explained, the changes in microemulsion properties along dilution lines. Copyright © 2017 Elsevier Inc. All rights reserved.

Recent tests of the equilibrium-point hypothesis (lambda model).

PubMed

Feldman, A G; Ostry, D J; Levin, M F; Gribble, P L; Mitnitski, A B

1998-07-01

The lambda model of the equilibrium-point hypothesis (Feldman & Levin, 1995) is an approach to motor control which, like physics, is based on a logical system coordinating empirical data. The model has gone through an interesting period. On one hand, several nontrivial predictions of the model have been successfully verified in recent studies. In addition, the explanatory and predictive capacity of the model has been enhanced by its extension to multimuscle and multijoint systems. On the other hand, claims have recently appeared suggesting that the model should be abandoned. The present paper focuses on these claims and concludes that they are unfounded. Much of the experimental data that have been used to reject the model are actually consistent with it.

Mass and stiffness estimation using mobile devices for structural health monitoring

NASA Astrophysics Data System (ADS)

Le, Viet; Yu, Tzuyang

2015-04-01

In the structural health monitoring (SHM) of civil infrastructure, dynamic methods using mass, damping, and stiffness for characterizing structural health have been a traditional and widely used approach. Changes in these system parameters over time indicate the progress of structural degradation or deterioration. In these methods, capability of predicting system parameters is essential to their success. In this paper, research work on the development of a dynamic SHM method based on perturbation analysis is reported. The concept is to use externally applied mass to perturb an unknown system and measure the natural frequency of the system. Derived theoretical expressions for mass and stiffness prediction are experimentally verified by a building model. Dynamic responses of the building model perturbed by various masses in free vibration were experimentally measured by a mobile device (cell phone) to extract the natural frequency of the building model. Single-degreeof- freedom (SDOF) modeling approach was adopted for the sake of using a cell phone. From the experimental result, it is shown that the percentage error of predicted mass increases when the mass ratio increases, while the percentage error of predicted stiffness decreases when the mass ratio increases. This work also demonstrated the potential use of mobile devices in the health monitoring of civil infrastructure.

Development of a Physiologically-Based Pharmacokinetic Model of the Rat Central Nervous System

PubMed Central

Badhan, Raj K. Singh; Chenel, Marylore; Penny, Jeffrey I.

2014-01-01

Central nervous system (CNS) drug disposition is dictated by a drug’s physicochemical properties and its ability to permeate physiological barriers. The blood–brain barrier (BBB), blood-cerebrospinal fluid barrier and centrally located drug transporter proteins influence drug disposition within the central nervous system. Attainment of adequate brain-to-plasma and cerebrospinal fluid-to-plasma partitioning is important in determining the efficacy of centrally acting therapeutics. We have developed a physiologically-based pharmacokinetic model of the rat CNS which incorporates brain interstitial fluid (ISF), choroidal epithelial and total cerebrospinal fluid (CSF) compartments and accurately predicts CNS pharmacokinetics. The model yielded reasonable predictions of unbound brain-to-plasma partition ratio (Kpuu,brain) and CSF:plasma ratio (CSF:Plasmau) using a series of in vitro permeability and unbound fraction parameters. When using in vitro permeability data obtained from L-mdr1a cells to estimate rat in vivo permeability, the model successfully predicted, to within 4-fold, Kpuu,brain and CSF:Plasmau for 81.5% of compounds simulated. The model presented allows for simultaneous simulation and analysis of both brain biophase and CSF to accurately predict CNS pharmacokinetics from preclinical drug parameters routinely available during discovery and development pathways. PMID:24647103

Developing and validating a model to predict the success of an IHCS implementation: the Readiness for Implementation Model.

PubMed

Wen, Kuang-Yi; Gustafson, David H; Hawkins, Robert P; Brennan, Patricia F; Dinauer, Susan; Johnson, Pauley R; Siegler, Tracy

2010-01-01

To develop and validate the Readiness for Implementation Model (RIM). This model predicts a healthcare organization's potential for success in implementing an interactive health communication system (IHCS). The model consists of seven weighted factors, with each factor containing five to seven elements. Two decision-analytic approaches, self-explicated and conjoint analysis, were used to measure the weights of the RIM with a sample of 410 experts. The RIM model with weights was then validated in a prospective study of 25 IHCS implementation cases. Orthogonal main effects design was used to develop 700 conjoint-analysis profiles, which varied on seven factors. Each of the 410 experts rated the importance and desirability of the factors and their levels, as well as a set of 10 different profiles. For the prospective 25-case validation, three time-repeated measures of the RIM scores were collected for comparison with the implementation outcomes. Two of the seven factors, 'organizational motivation' and 'meeting user needs,' were found to be most important in predicting implementation readiness. No statistically significant difference was found in the predictive validity of the two approaches (self-explicated and conjoint analysis). The RIM was a better predictor for the 1-year implementation outcome than the half-year outcome. The expert sample, the order of the survey tasks, the additive model, and basing the RIM cut-off score on experience are possible limitations of the study. The RIM needs to be empirically evaluated in institutions adopting IHCS and sustaining the system in the long term.

Ordinary Differential Equation Models for Adoptive Immunotherapy.

PubMed

Talkington, Anne; Dantoin, Claudia; Durrett, Rick

2018-05-01

Modified T cells that have been engineered to recognize the CD19 surface marker have recently been shown to be very successful at treating acute lymphocytic leukemias. Here, we explore four previous approaches that have used ordinary differential equations to model this type of therapy, compare their properties, and modify the models to address their deficiencies. Although the four models treat the workings of the immune system in slightly different ways, they all predict that adoptive immunotherapy can be successful to move a patient from the large tumor fixed point to an equilibrium with little or no tumor.

A theoretical physicist's journey into biology: from quarks and strings to cells and whales.

PubMed

West, Geoffrey B

2014-10-08

Biology will almost certainly be the predominant science of the twenty-first century but, for it to become successfully so, it will need to embrace some of the quantitative, analytic, predictive culture that has made physics so successful. This includes the search for underlying principles, systemic thinking at all scales, the development of coarse-grained models, and closer ongoing collaboration between theorists and experimentalists. This article presents a personal, slightly provocative, perspective of a theoretical physicist working in close collaboration with biologists at the interface between the physical and biological sciences.

Studying the Power of the Integrative Weaning Index in Predicting the Success Rate of the Spontaneous Breathing Trial in Patients under Mechanical Ventilation.

PubMed

Ebrahimabadi, Sahar; Moghadam, Ahmad Bagheri; Vakili, Mohammadali; Modanloo, Mahnaz; Khoddam, Homeira

2017-08-01

The use of weaning predictive indicators can avoid early extubation and wrongful prolonged mechanical ventilation. This study aimed to determine the power of the integrative weaning index (IWI) in predicting the success rate of the spontaneous breathing trial (SBT) in patients under mechanical ventilation. In this prospective study, 105 patients undergoing mechanical ventilation for over 48 h were enrolled. Before weaning initiation, the IWI was calculated and based on the defined cutoff point (≥25), the success rate of the SBT was predicted. In case of weaning from the device, 2-h SBT was performed and the physiologic and respiratory indices were continuously studied while being intubated. If they were in the normal range besides the patient's tolerance, the test was considered as a success. The result was then compared with the IWI and further analyzed. The SBT was successful in 90 (85.7%) and unsuccessful in 15 (14.3%) cases. The difference between the true patient outcome after SBT, and the IWI prediction was 0.143 according to the Kappa agreement coefficient ( P < 0.001). Moreover, regarding the predictive power, IWI had high sensitivity (95.6%), specificity (40%), positive and negative predictive values (90.5% and 60), positive and negative likelihood ratios (1.59 and 0.11), and accuracy (86.7%). The IWI as a more objective indicator has acceptable accuracy and power for predicting the 2-h SBT result. Therefore, in addition to the reliable prediction of the final weaning outcome, it has favorable power to predict if the patient is ready to breathe spontaneously as the first step to weaning.

Studying the Power of the Integrative Weaning Index in Predicting the Success Rate of the Spontaneous Breathing Trial in Patients under Mechanical Ventilation

PubMed Central

Ebrahimabadi, Sahar; Moghadam, Ahmad Bagheri; Vakili, Mohammadali; Modanloo, Mahnaz; Khoddam, Homeira

2017-01-01

Background and Aims: The use of weaning predictive indicators can avoid early extubation and wrongful prolonged mechanical ventilation. This study aimed to determine the power of the integrative weaning index (IWI) in predicting the success rate of the spontaneous breathing trial (SBT) in patients under mechanical ventilation. Materials and Methods: In this prospective study, 105 patients undergoing mechanical ventilation for over 48 h were enrolled. Before weaning initiation, the IWI was calculated and based on the defined cutoff point (≥25), the success rate of the SBT was predicted. In case of weaning from the device, 2-h SBT was performed and the physiologic and respiratory indices were continuously studied while being intubated. If they were in the normal range besides the patient's tolerance, the test was considered as a success. The result was then compared with the IWI and further analyzed. Results: The SBT was successful in 90 (85.7%) and unsuccessful in 15 (14.3%) cases. The difference between the true patient outcome after SBT, and the IWI prediction was 0.143 according to the Kappa agreement coefficient (P < 0.001). Moreover, regarding the predictive power, IWI had high sensitivity (95.6%), specificity (40%), positive and negative predictive values (90.5% and 60), positive and negative likelihood ratios (1.59 and 0.11), and accuracy (86.7%). Conclusion: The IWI as a more objective indicator has acceptable accuracy and power for predicting the 2-h SBT result. Therefore, in addition to the reliable prediction of the final weaning outcome, it has favorable power to predict if the patient is ready to breathe spontaneously as the first step to weaning. PMID:28904477

Satellite Data Assimilation within KIAPS-LETKF system

NASA Astrophysics Data System (ADS)

Jo, Y.; Lee, S., Sr.; Cho, K.

2016-12-01

Korea Institute of Atmospheric Prediction Systems (KIAPS) has been developing an ensemble data assimilation system using four-dimensional local ensemble transform kalman filter (LETKF; Hunt et al., 2007) within KIAPS Integrated Model (KIM), referred to as "KIAPS-LETKF". KIAPS-LETKF system was successfully evaluated with various Observing System Simulation Experiments (OSSEs) with NCAR Community Atmospheric Model - Spectral Element (Kang et al., 2013), which has fully unstructured quadrilateral meshes based on the cubed-sphere grid as the same grid system of KIM. Recently, assimilation of real observations has been conducted within the KIAPS-LETKF system with four-dimensional covariance functions over the 6-hr assimilation window. Then, conventional (e.g., sonde, aircraft, and surface) and satellite (e.g., AMSU-A, IASI, GPS-RO, and AMV) observations have been provided by the KIAPS Package for Observation Processing (KPOP). Wind speed prediction was found most beneficial due to ingestion of AMV and for the temperature prediction the improvement in assimilation is mostly due to ingestion of AMSU-A and IASI. However, some degradation in the simulation of the GPS-RO is presented in the upper stratosphere, even though GPS-RO leads positive impacts on the analysis and forecasts. We plan to test the bias correction method and several vertical localization strategies for radiance observations to improve analysis and forecast impacts.

A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS

NASA Astrophysics Data System (ADS)

Pradhan, Biswajeet

2013-02-01

The purpose of the present study is to compare the prediction performances of three different approaches such as decision tree (DT), support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) for landslide susceptibility mapping at Penang Hill area, Malaysia. The necessary input parameters for the landslide susceptibility assessments were obtained from various sources. At first, landslide locations were identified by aerial photographs and field surveys and a total of 113 landslide locations were constructed. The study area contains 340,608 pixels while total 8403 pixels include landslides. The landslide inventory was randomly partitioned into two subsets: (1) part 1 that contains 50% (4000 landslide grid cells) was used in the training phase of the models; (2) part 2 is a validation dataset 50% (4000 landslide grid cells) for validation of three models and to confirm its accuracy. The digitally processed images of input parameters were combined in GIS. Finally, landslide susceptibility maps were produced, and the performances were assessed and discussed. Total fifteen landslide susceptibility maps were produced using DT, SVM and ANFIS based models, and the resultant maps were validated using the landslide locations. Prediction performances of these maps were checked by receiver operating characteristics (ROC) by using both success rate curve and prediction rate curve. The validation results showed that, area under the ROC curve for the fifteen models produced using DT, SVM and ANFIS varied from 0.8204 to 0.9421 for success rate curve and 0.7580 to 0.8307 for prediction rate curves, respectively. Moreover, the prediction curves revealed that model 5 of DT has slightly higher prediction performance (83.07), whereas the success rate showed that model 5 of ANFIS has better prediction (94.21) capability among all models. The results of this study showed that landslide susceptibility mapping in the Penang Hill area using the three approaches (e.g., DT, SVM and ANFIS) is viable. As far as the performance of the models are concerned, the results appeared to be quite satisfactory, i.e., the zones determined on the map being zones of relative susceptibility.

Multi-target QSPR modeling for simultaneous prediction of multiple gas-phase kinetic rate constants of diverse chemicals

NASA Astrophysics Data System (ADS)

Basant, Nikita; Gupta, Shikha

2018-03-01

The reactions of molecular ozone (O3), hydroxyl (•OH) and nitrate (NO3) radicals are among the major pathways of removal of volatile organic compounds (VOCs) in the atmospheric environment. The gas-phase kinetic rate constants (kO3, kOH, kNO3) are thus, important in assessing the ultimate fate and exposure risk of atmospheric VOCs. Experimental data for rate constants are not available for many emerging VOCs and the computational methods reported so far address a single target modeling only. In this study, we have developed a multi-target (mt) QSPR model for simultaneous prediction of multiple kinetic rate constants (kO3, kOH, kNO3) of diverse organic chemicals considering an experimental data set of VOCs for which values of all the three rate constants are available. The mt-QSPR model identified and used five descriptors related to the molecular size, degree of saturation and electron density in a molecule, which were mechanistically interpretable. These descriptors successfully predicted three rate constants simultaneously. The model yielded high correlations (R2 = 0.874-0.924) between the experimental and simultaneously predicted endpoint rate constant (kO3, kOH, kNO3) values in test arrays for all the three systems. The model also passed all the stringent statistical validation tests for external predictivity. The proposed multi-target QSPR model can be successfully used for predicting reactivity of new VOCs simultaneously for their exposure risk assessment.

Humor Ability Reveals Intelligence, Predicts Mating Success, and Is Higher in Males

ERIC Educational Resources Information Center

Greengross, Gil; Miller, Geoffrey

2011-01-01

A good sense of humor is sexually attractive, perhaps because it reveals intelligence, creativity, and other "good genes" or "good parent" traits. If so, intelligence should predict humor production ability, which in turn should predict mating success. In this study, 400 university students (200 men and 200 women) completed…

The Odds of Success: Predicting Registered Health Information Administrator Exam Success

PubMed Central

Dolezel, Diane; McLeod, Alexander

2017-01-01

The purpose of this study was to craft a predictive model to examine the relationship between grades in specific academic courses, overall grade point average (GPA), on-campus versus online course delivery, and success in passing the Registered Health Information Administrator (RHIA) exam on the first attempt. Because student success in passing the exam on the first attempt is assessed as part of the accreditation process, this study is important to health information management (HIM) programs. Furthermore, passing the exam greatly expands the graduate's job possibilities because the demand for credentialed graduates far exceeds the supply of credentialed graduates. Binary logistic regression was utilized to explore the relationships between the predictor variables and success in passing the RHIA exam on the first attempt. Results indicate that the student's cumulative GPA, specific HIM course grades, and course delivery method were predictive of success. PMID:28566994

Evaluation of a nurse leadership development programme.

PubMed

West, Margaret; Smithgall, Lisa; Rosler, Greta; Winn, Erin

2016-03-01

The challenge for nursing leaders responsible for workforce planning is to predict the knowledge, skills and abilities required to lead future healthcare delivery systems effectively. Succession planning requires a constant, competitive pool of qualified nursing leader candidates, and retention of those interested in career growth. Formal nursing leadership education in the United States is available through graduate education and professional nursing organisation programmes, such as the Emerging Nurse Leader Institute of the American Organization of Nurse Executives. However, there is also a need for local development programmes tailored to the needs of individual organisations. Leaders at Geisinger Health System, one of the largest rural health systems in the US, identified the need for an internal professional development scheme for nurses. In 2013 the Nurses Emerging as Leaders programme was developed to prepare nurse leaders for effective leadership and successful role transition. This article describes the programme and an evaluation of its effectiveness.

Omics AnalySIs System for PRecision Oncology (OASISPRO): A Web-based Omics Analysis Tool for Clinical Phenotype Prediction.

PubMed

Yu, Kun-Hsing; Fitzpatrick, Michael R; Pappas, Luke; Chan, Warren; Kung, Jessica; Snyder, Michael

2017-09-12

Precision oncology is an approach that accounts for individual differences to guide cancer management. Omics signatures have been shown to predict clinical traits for cancer patients. However, the vast amount of omics information poses an informatics challenge in systematically identifying patterns associated with health outcomes, and no general-purpose data-mining tool exists for physicians, medical researchers, and citizen scientists without significant training in programming and bioinformatics. To bridge this gap, we built the Omics AnalySIs System for PRecision Oncology (OASISPRO), a web-based system to mine the quantitative omics information from The Cancer Genome Atlas (TCGA). This system effectively visualizes patients' clinical profiles, executes machine-learning algorithms of choice on the omics data, and evaluates the prediction performance using held-out test sets. With this tool, we successfully identified genes strongly associated with tumor stage, and accurately predicted patients' survival outcomes in many cancer types, including mesothelioma and adrenocortical carcinoma. By identifying the links between omics and clinical phenotypes, this system will facilitate omics studies on precision cancer medicine and contribute to establishing personalized cancer treatment plans. This web-based tool is available at http://tinyurl.com/oasispro ;source codes are available at http://tinyurl.com/oasisproSourceCode . © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Human-Robot Interaction in High Vulnerability Domains

NASA Technical Reports Server (NTRS)

Gore, Brian F.

2016-01-01

Future NASA missions will require successful integration of the human with highly complex systems. Highly complex systems are likely to involve humans, automation, and some level of robotic assistance. The complex environments will require successful integration of the human with automation, with robots, and with human-automation-robot teams to accomplish mission critical goals. Many challenges exist for the human performing in these types of operational environments with these kinds of systems. Systems must be designed to optimally integrate various levels of inputs and outputs based on the roles and responsibilities of the human, the automation, and the robots; from direct manual control, shared human-robotic control, or no active human control (i.e. human supervisory control). It is assumed that the human will remain involved at some level. Technologies that vary based on contextual demands and on operator characteristics (workload, situation awareness) will be needed when the human integrates into these systems. Predictive models that estimate the impact of the technologies on the system performance and the on the human operator are also needed to meet the challenges associated with such future complex human-automation-robot systems in extreme environments.

Applying data mining techniques for increasing implantation rate by selecting best sperms for intra-cytoplasmic sperm injection treatment.

PubMed

Mirroshandel, Seyed Abolghasem; Ghasemian, Fatemeh; Monji-Azad, Sara

2016-12-01

Aspiration of a good-quality sperm during intracytoplasmic sperm injection (ICSI) is one of the main concerns. Understanding the influence of individual sperm morphology on fertilization, embryo quality, and pregnancy probability is one of the most important subjects in male factor infertility. Embryologists need to decide the best sperm for injection in real time during ICSI cycle. Our objective is to predict the quality of zygote, embryo, and implantation outcome before injection of each sperm in an ICSI cycle for male factor infertility with the aim of providing a decision support system on the sperm selection. The information was collected from 219 patients with male factor infertility at the infertility therapy center of Alzahra hospital in Rasht from 2012 through 2014. The prepared dataset included the quality of zygote, embryo, and implantation outcome of 1544 injected sperms into the related oocytes. In our study, embryo transfer was performed at day 3. Each sperm was represented with thirteen clinical features. Data preprocessing was the first step in the proposed data mining algorithm. After applying more than 30 classifiers, 9 successful classifiers were selected and evaluated by 10-fold cross validation technique using precision, recall, F1, and AUC measures. Another important experiment was measuring the effect of each feature in prediction process. In zygote and embryo quality prediction, IBK and RandomCommittee models provided 79.2% and 83.8% F1, respectively. In implantation outcome prediction, KStar model achieved 95.9% F1, which is even better than prediction of human experts. All these predictions can be done in real time. A machine learning-based decision support system would be helpful in sperm selection phase of ICSI cycle to improve the success rate of ICSI treatment. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Validation of a prediction model for predicting the probability of morbidity related to a trial of labour in Quebec.

PubMed

Chaillet, Nils; Bujold, Emmanuel; Dubé, Eric; Grobman, William A

2012-09-01

Pregnant women with a history of previous Caesarean section face the decision either to undergo an elective repeat Caesarean section (ERCS) or to attempt a trial of labour with the goal of achieving a vaginal birth after Caesarean (VBAC). Both choices are associated with their own risks of maternal and neonatal morbidity. We aimed to determine the external validity of a prediction model for the success of trial of labour after Caesarean section (TOLAC) that could help these women in their decision-making. We used a perinatal database including 185,437 deliveries from 32 obstetrical centres in Quebec between 2007 and 2011 and selected women with one previous Caesarean section who were eligible for a TOLAC. We compared the frequency of maternal and neonatal morbidity between women who underwent TOLAC and those who underwent an ERCS according to the probability of success of TOLAC calculated from a published model of prediction. Of 8508 eligible women, including 3113 who underwent TOLAC, both maternal and neonatal morbidities became less frequent as the predicted chance of VBAC increased (P < 0.05). Women undergoing a TOLAC were more likely to have maternal morbidity than those who underwent an ERCS when the predicted probability of VBAC was less than 60% (relative risk [RR] 2.3; 95% CI 1.4 to 4.0); conversely, maternal morbidity was not different between the two groups when the predicted probability of VBAC was at least 60% (RR 0.8; 95% CI 0.6 to 1.1). Neonatal morbidity was similar between groups when the probability of VBAC success was 70% or greater (RR 1.2; 95% CI 0.9 to 1.5). The use of a prediction model for TOLAC success could be useful in the prediction of TOLAC success and perinatal morbidity in a Canadian population. Neither maternal nor neonatal morbidity are increased with a TOLAC when the probability of VBAC success is at least 70%.

Prediction of biochar yield from cattle manure pyrolysis via least squares support vector machine intelligent approach.

PubMed

Cao, Hongliang; Xin, Ya; Yuan, Qiaoxia

2016-02-01

To predict conveniently the biochar yield from cattle manure pyrolysis, intelligent modeling approach was introduced in this research. A traditional artificial neural networks (ANN) model and a novel least squares support vector machine (LS-SVM) model were developed. For the identification and prediction evaluation of the models, a data set with 33 experimental data was used, which were obtained using a laboratory-scale fixed bed reaction system. The results demonstrated that the intelligent modeling approach is greatly convenient and effective for the prediction of the biochar yield. In particular, the novel LS-SVM model has a more satisfying predicting performance and its robustness is better than the traditional ANN model. The introduction and application of the LS-SVM modeling method gives a successful example, which is a good reference for the modeling study of cattle manure pyrolysis process, even other similar processes. Copyright © 2015 Elsevier Ltd. All rights reserved.

Predicting the magnetospheric plasma of weather

NASA Technical Reports Server (NTRS)

Dawson, John M.

1986-01-01

The prediction of the plasma environment in time, the plasma weather, is discussed. It is important to be able to predict when large magnetic storms will produce auroras, which will affect the space station operating in low orbit, and what precautions to take both for personnel and sensitive control (computer) equipment onboard. It is also important to start to establish a set of plasma weather records and a record of the ability to predict this weather. A successful forecasting system requires a set of satellite weather stations to provide data from which predictions can be made and a set of plasma weather codes capable of accurately forecasting the status of the Earth's magnetosphere. A numerical magnetohydrodynamic fluid model which is used to model the flow in the magnetosphere, the currents flowing into and out of the auroral regions, the magnetopause, the bow shock location and the magnetotail of the Earth is discussed.

Predicting success of prescribed fires in pinyon-juniper woodlands in Nevada

Treesearch

Allen D. Bruner; Donald A. Klebenow

1979-01-01

Thirty prescribed burns were attempted in pinyon-juniper woodlands from fall 1974 to fall 1976. These attempts were made out of fire season, during varied atmospheric conditions, and in several pinyon-juniper communities. An analysis of the successful burns provided us with a method for predicting burning success from windspeed, air temperature, and vegetation cover....

Most Likely to Achieve: Predicting Early Success of the Practical Nurse Student

ERIC Educational Resources Information Center

Cline, April P.

2013-01-01

It is important that practical nurse (PN) educators be able to identify which students are likely to be successful in their programs. However, the majority of literature related to predicting success of nursing students has been done on baccalaureate nursing students in the university setting. This study sought to determine whether the same…

Value of College Education Mediating the Predictive Effects of Causal Attributions on Academic Success

ERIC Educational Resources Information Center

Dong, Ying; Stupnisky, Robert H.; Obade, Masela; Gerszewski, Tammy; Ruthig, Joelle C.

2015-01-01

Causal attributions (explanations for outcomes) have been found to predict college students' academic success; however, not all students attributing success or failure to adaptive (i.e., controllable) causes perform well in university. Eccles et al.'s ("Achievement and achievement motives." W.H. Freeman, San Francisco, pp 75-145, 1983)…

The Use of Psychological Tests in Predicting Vocational Success of Disadvantaged Adults.

ERIC Educational Resources Information Center

Stanley, Charlton S.

A study of the relationship between certain test scores and probable training and vocational success was made. Examined were three major training areas: power sewing machine, nurse aide, and clerical office work. Six tests were tested for their ability to predict success: the WAIS Revised Beta; Purdue Pegboard; English, California Surveys of…

Social support, stress, health, and academic success in Ghanaian adolescents: a path analysis.

PubMed

Glozah, Franklin N; Pevalin, David J

2014-06-01

The aim of this study is to gain a better understanding of the role psychosocial factors play in promoting the health and academic success of adolescents. A total of 770 adolescent boys and girls in Senior High Schools were randomly selected to complete a self-report questionnaire. School reported latest terminal examination grades were used as the measure of academic success. Structural equation modelling indicated a relatively good fit to the posteriori model with four of the hypothesised paths fully supported and two partially supported. Perceived social support was negatively related to stress and predictive of health and wellbeing but not academic success. Stress was predictive of health but not academic success. Finally, health and wellbeing was able to predict academic success. These findings have policy implications regarding efforts aimed at promoting the health and wellbeing as well as the academic success of adolescents in Ghana. Copyright © 2014 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

Assessment of the Mars Science Laboratory Entry, Descent, and Landing Simulation

NASA Technical Reports Server (NTRS)

Way, David W.; Davis, J. L.; Shidner, Jeremy D.

2013-01-01

On August 5, 2012, the Mars Science Laboratory rover, Curiosity, successfully landed inside Gale Crater. This landing was only the seventh successful landing and fourth rover to be delivered to Mars. Weighing nearly one metric ton, Curiosity is the largest and most complex rover ever sent to investigate another planet. Safely landing such a large payload required an innovative Entry, Descent, and Landing system, which included the first guided entry at Mars, the largest supersonic parachute ever flown at Mars, and a novel and untested Sky Crane landing system. A complete, end-to-end, six degree-of-freedom, multi-body computer simulation of the Mars Science Laboratory Entry, Descent, and Landing sequence was developed at the NASA Langley Research Center. In-flight data gathered during the successful landing is compared to pre-flight statistical distributions, predicted by the simulation. These comparisons provide insight into both the accuracy of the simulation and the overall performance of the vehicle.

Preliminary Assessment of the Mars Science Laboratory Entry, Descent, and Landing Simulation

NASA Technical Reports Server (NTRS)

Way, David W.

2013-01-01

On August 5, 2012, the Mars Science Laboratory rover, Curiosity, successfully landed inside Gale Crater. This landing was only the seventh successful landing and fourth rover to be delivered to Mars. Weighing nearly one metric ton, Curiosity is the largest and most complex rover ever sent to investigate another planet. Safely landing such a large payload required an innovative Entry, Descent, and Landing system, which included the first guided entry at Mars, the largest supersonic parachute ever flown at Mars, and a novel and untested Sky Crane landing system. A complete, end-to-end, six degree-of-freedom, multibody computer simulation of the Mars Science Laboratory Entry, Descent, and Landing sequence was developed at the NASA Langley Research Center. In-flight data gathered during the successful landing is compared to pre-flight statistical distributions, predicted by the simulation. These comparisons provide insight into both the accuracy of the simulation and the overall performance of the vehicle.

Low GWP Refrigerants Modelling Study for a Room Air Conditioner Having Microchannel Heat Exchangers

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

Shen, Bo; Bhandari, Mahabir S

Microchannel heat exchangers (MHX) have found great successes in residential and commercial air conditioning applications, being compact heat exchangers, to reduce refrigerant charge and material cost. This investigation aims to extend the application of MHXs in split, room air conditioners (RAC), per fundamental heat exchanger and system modelling. For this paper, microchannel condenser and evaporator models were developed, using a segment-to-segment modelling approach. The microchannel heat exchanger models were integrated to a system design model. The system model is able to predict the performance indices, such as cooling capacity, efficiency, sensible heat ratio, etc. Using the calibrated system and heatmore » exchanger models, we evaluated numerous low GWP (global warming potential) refrigerants. The predicted system performance indices, e.g. cooling efficiency, compressor discharge temperature, and required compressor displacement volume etc., are compared. Suitable replacements for R22 and R-410A for the room air conditioner application are recommended.« less

System characteristics of healthcare organizations conducting successful improvements.

PubMed

Olsson, Jesper; Elg, Mattias; Lindblad, Staffan

2007-01-01

In a previous study, based on a survey to all clinical department and primary care center managers in Sweden, it was concluded that the prevailing general improvement strategy is characterized by: drivers for improvement are staff needs; patients and data are not as important; improvements mainly focus on administrative routines and stress management; improvements are mainly reached, by writing guidelines, and conducting meetings; the majority of managers perceive outcomes from this strategy as successful. The purpose of current research in this paper is to investigate whether there is any other improvement strategy at play in Swedish health care. Data from the study of all Swedish managers were stratified into two populations based on an instrument predicting successful improvement. One population represented organizations with exceptionally high probability of successful imrpovement and remaining organizations represented the general improvement strategy. The paper finds that organizations with high probability for successful change differed from the comparison population at the p = 0.05 level in many of the surveyed characteristics. They put emphasis on patient focus, measuring outcomes, feedback of data, interorganizational collaboration, learning and knowledge, communication/information, culture, and development of administration and management. Thus these organizations center their attention towards behavioral changes supported by data. Organizations predicted to conduct successful improvement apply comprehensive improvement strategies as suggested in the literature. Such actions are part of the Patient Centered Task Alignment strategy and it is suggested that this concept has managerial implications as well, as it might be useful in further studies on improvement work in health care. This paper provides empirically based findings on a successful improvement strategy that can aid research-informed policy decisions on organizational improvement strategies.

Bringing colour back after 70 years: Predicting eye and hair colour from skeletal remains of World War II victims using the HIrisPlex system.

PubMed

Chaitanya, Lakshmi; Pajnič, Irena Zupanič; Walsh, Susan; Balažic, Jože; Zupanc, Tomaž; Kayser, Manfred

2017-01-01

Retrieving information about externally visible characteristics from DNA can provide investigative leads to find unknown perpetrators, and can also help in disaster victim and other missing person identification cases. Aiming for the application to both types of forensic casework, we previously developed and forensically validated the HIrisPlex test system enabling parallel DNA prediction of eye and hair colour. Although a recent proof-of-principle study demonstrated the general suitability of the HIrisPlex system for successfully analysing DNA from bones and teeth of various storage times and conditions, practical case applications to human remains are scarce. In this study, we applied the HIrisPlex system to 49 DNA samples obtained from bones or teeth of World War II victims excavated at six sites, mostly mass graves, in Slovenia. PCR-based DNA quantification ranged from 4pg/μl to 313pg/μl and on an average was 41pg/μl across all samples. All 49 samples generated complete HIrisPlex profiles with the exception of one MC1R DNA marker (N29insA) missing in 83.7% of the samples. In 44 of the 49 samples (89.8%) complete 15-loci autosomal STR (plus amelogenin) profiles were obtained. Of 5 pairs of skeletal remains for which STR profiling suggested an origin in the same individuals, respectively, 4 showed the same HIrisPlex profiles and predicted eye and hair colours, respectively, while discrepancies in one pair (sample 26 and 43) are likely to be explained by DNA quantity and quality issues observed in sample 43. Sample 43 had the lowest DNA concentration of only 4pg/μl, producing least reliable STR results and could be misleading in concluding that samples 43 and 26 originate from the same individual. The HIrisPlex-predicted eye and hair colours from two skeletal samples, suggested to derive from two brothers via STR profiling together with a living sister, were confirmed by the living sister's report. Overall, we demonstrate that after more than 70 years, HIrisPlex-based eye and hair colour prediction from skeletal remains is feasible with high success rate. Our results further encourage the use of the HIrisPlex system in missing person/disaster victim identification to aid the identification process in cases where ante-mortem samples or putative relatives are not directly available, and DNA predicted eye and hair colour information provides leads for locating them, allowing STRbased individual identification. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Big Data Analytic, Big Step for Patient Management and Care in Puerto Rico.

PubMed

Borrero, Ernesto E

2018-01-01

This letter provides an overview of the application of big data in health care system to improve quality of care, including predictive modelling for risk and resource use, precision medicine and clinical decision support, quality of care and performance measurement, public health and research applications, among others. The author delineates the tremendous potential for big data analytics and discuss how it can be successfully implemented in clinical practice, as an important component of a learning health-care system.

Early Benchmarks of Product Generation Capabilities of the GOES-R Ground System for Operational Weather Prediction

NASA Astrophysics Data System (ADS)

Kalluri, S. N.; Haman, B.; Vititoe, D.

2014-12-01

The ground system under development for Geostationary Operational Environmental Satellite-R (GOES-R) series of weather satellite has completed a key milestone in implementing the science algorithms that process raw sensor data to higher level products in preparation for launch. Real time observations from GOES-R are expected to make significant contributions to Earth and space weather prediction, and there are stringent requirements to product weather products at very low latency to meet NOAA's operational needs. Simulated test data from all the six GOES-R sensors are being processed by the system to test and verify performance of the fielded system. Early results show that the system development is on track to meet functional and performance requirements to process science data. Comparison of science products generated by the ground system from simulated data with those generated by the algorithm developers show close agreement among data sets which demonstrates that the algorithms are implemented correctly. Successful delivery of products to AWIPS and the Product Distribution and Access (PDA) system from the core system demonstrate that the external interfaces are working.

Low technology systems for wastewater treatment: perspectives.

PubMed

Brissaud, F

2007-01-01

Low technology systems for the treatment of wastewater are sometimes presented as remnants of the past, nowadays supposedly only meant to serve developing countries and remote rural areas. However, considering their advantages and disadvantages together with enhanced treatment requirements and recent research and technological developments, the future of these systems still appears promising. Successful applications of low technology systems require that more care is taken of their design and operation than often observed. Correlatively, more efforts should be made to decipher the treatment mechanisms and determine the related reaction parameters, so as to provide more deterministic approaches of the natural wastewater treatment systems and better predict their performance.

Prediction models for successful external cephalic version: a systematic review.

PubMed

Velzel, Joost; de Hundt, Marcella; Mulder, Frederique M; Molkenboer, Jan F M; Van der Post, Joris A M; Mol, Ben W; Kok, Marjolein

2015-12-01

To provide an overview of existing prediction models for successful ECV, and to assess their quality, development and performance. We searched MEDLINE, EMBASE and the Cochrane Library to identify all articles reporting on prediction models for successful ECV published from inception to January 2015. We extracted information on study design, sample size, model-building strategies and validation. We evaluated the phases of model development and summarized their performance in terms of discrimination, calibration and clinical usefulness. We collected different predictor variables together with their defined significance, in order to identify important predictor variables for successful ECV. We identified eight articles reporting on seven prediction models. All models were subjected to internal validation. Only one model was also validated in an external cohort. Two prediction models had a low overall risk of bias, of which only one showed promising predictive performance at internal validation. This model also completed the phase of external validation. For none of the models their impact on clinical practice was evaluated. The most important predictor variables for successful ECV described in the selected articles were parity, placental location, breech engagement and the fetal head being palpable. One model was assessed using discrimination and calibration using internal (AUC 0.71) and external validation (AUC 0.64), while two other models were assessed with discrimination and calibration, respectively. We found one prediction model for breech presentation that was validated in an external cohort and had acceptable predictive performance. This model should be used to council women considering ECV. Copyright © 2015. Published by Elsevier Ireland Ltd.

Modeling student success in engineering education

NASA Astrophysics Data System (ADS)

Jin, Qu

In order for the United States to maintain its global competitiveness, the long-term success of our engineering students in specific courses, programs, and colleges is now, more than ever, an extremely high priority. Numerous studies have focused on factors that impact student success, namely academic performance, retention, and/or graduation. However, there are only a limited number of works that have systematically developed models to investigate important factors and to predict student success in engineering. Therefore, this research presents three separate but highly connected investigations to address this gap. The first investigation involves explaining and predicting engineering students' success in Calculus I courses using statistical models. The participants were more than 4000 first-year engineering students (cohort years 2004 - 2008) who enrolled in Calculus I courses during the first semester in a large Midwestern university. Predictions from statistical models were proposed to be used to place engineering students into calculus courses. The success rates were improved by 12% in Calculus IA using predictions from models developed over traditional placement method. The results showed that these statistical models provided a more accurate calculus placement method than traditional placement methods and help improve success rates in those courses. In the second investigation, multi-outcome and single-outcome neural network models were designed to understand and to predict first-year retention and first-year GPA of engineering students. The participants were more than 3000 first year engineering students (cohort years 2004 - 2005) enrolled in a large Midwestern university. The independent variables include both high school academic performance factors and affective factors measured prior to entry. The prediction performances of the multi-outcome and single-outcome models were comparable. The ability to predict cumulative GPA at the end of an engineering student's first year of college was about a half of a grade point for both models. The predictors of retention and cumulative GPA while being similar differ in that high school academic metrics play a more important role in predicting cumulative GPA with the affective measures playing a more important role in predicting retention. In the last investigation, multi-outcome neural network models were used to understand and to predict engineering students' retention, GPA, and graduation from entry to departure. The participants were more than 4000 engineering students (cohort years 2004 - 2006) enrolled in a large Midwestern university. Different patterns of important predictors were identified for GPA, retention, and graduation. Overall, this research explores the feasibility of using modeling to enhance a student's educational experience in engineering. Student success modeling was used to identify the most important cognitive and affective predictors for a student's first calculus course retention, GPA, and graduation. The results suggest that the statistical modeling methods have great potential to assist decision making and help ensure student success in engineering education.

Predicting Academic Success from Academic Motivation and Learning Approaches in Classroom Teaching Students

ERIC Educational Resources Information Center

Çetin, Baris

2015-01-01

Our aim was to determine whether learning approaches and academic motivation together predict academic success of classroom teaching students. The sample of the study included 536 students (386 female, 150 male) studying at the Classroom Teaching Division of Canakkale 18 Mart University. Our research was designed as a prediction study. Data was…

California Community College Administrators' Use of Predictive Modeling to Improve Student Course Completions

ERIC Educational Resources Information Center

Grogan, Rita D.

2017-01-01

Purpose: The purpose of this case study was to determine the impact of utilizing predictive modeling to improve successful course completion rates for at-risk students at California community colleges. A secondary purpose of the study was to identify factors of predictive modeling that have the most importance for improving successful course…

Predicting Success in College: SAT® Studies of Classes Graduating since 1980. Research Report No. 2001-2

ERIC Educational Resources Information Center

Burton, Nancy W.; Ramist, Leonard

2001-01-01

Studies predicting success in college for students graduating since 1980 are reviewed. SAT scores and high school records are the most common predictors, but a few studies of other predictors are included. The review establishes that SAT scores and high school records predict academic performance, nonacademic accomplishments, leadership in…

Implementation of the Disruption Predictor APODIS in JET's Real-Time Network Using the MARTe Framework

NASA Astrophysics Data System (ADS)

López, Juan Manuel; Vega, J.; Alves, D.; Dormido-Canto, S.; Murari, A.; Ramírez, J. M.; Felton, R.; Ruiz, M.; de Arcas, G.

2014-04-01

This paper describes the implementation of a real-time disruption predictor that is based on support vector machine (SVM) classifiers. The implementation was performed under the MARTe framework on a six-core x86 architecture. The system is connected via JET's Real-time Data Network (RTDN). The online results show a high degree of successful predictions and a low rate of false alarms, thus confirming the usefulness of this approach in a disruption mitigation scheme. The implementation shows a low computational load, which will be exploited in the immediate future to increase the prediction's temporal resolution.

Applications of LANCE Data at SPoRT

NASA Technical Reports Server (NTRS)

Molthan, Andrew

2014-01-01

Short term Prediction Research and Transition (SPoRT) Center: Mission: Apply NASA and NOAA measurement systems and unique Earth science research to improve the accuracy of short term weather prediction at the regional/local scale. Goals: Evaluate and assess the utility of NASA and NOAA Earth science data and products and unique research capabilities to address operational weather forecast problems; Provide an environment which enables the development and testing of new capabilities to improve short term weather forecasts on a regional scale; Help ensure successful transition of new capabilities to operational weather entities for the benefit of society

Limitations on scientific prediction and how they could affect repository licensing

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

Van Konynenburg, R.A.

The best possibility for gaining an understanding of the likely future behavior of a high level nuclear waste disposal system is to use the scientific method. However, the scientific approach has inherent limitations when it comes to making long-term predictions with confidence. This paper examines some of these limiting factors as well as the criteria for admissibility of scientific evidence in the legal arena, and concludes that the prospects are doubtful for successful licensing of a potential repository under the regulations that are now being reconsidered. Suggestions am made for remedying this situation.

A Predictable Alternative to a Smile in Six Months?

PubMed

Kalantzis, Elizabeth; Waring, David T; Malik, Ovais H

2017-03-01

The aim of this article is to explore the use of Incognito™ Lite as an alternative to competitors advertising a ‘Smile in Six Months’ or rather short-term orthodontics for improving dental appearance through tooth movement. The focus is on the varied clinical use of this appliance system and its comparative advantages, disadvantages and placement techniques. Some alternatives are discussed and appraised. Two cases successfully treated with Incognito™ Lite are then presented. Clinical relevance: A reliable and predictable tool for aesthetic alignment of teeth, creating a broader range of treatment options for both the clinician and the patient.

Improving Student Success Using Predictive Models and Data Visualisations

ERIC Educational Resources Information Center

Essa, Alfred; Ayad, Hanan

2012-01-01

The need to educate a competitive workforce is a global problem. In the US, for example, despite billions of dollars spent to improve the educational system, approximately 35% of students never finish high school. The drop rate among some demographic groups is as high as 50-60%. At the college level in the US only 30% of students graduate from…

Models of vegetation change for landscape planning: a comparison of FETM, LANDSUM, SIMPPLLE, and VDDT

Treesearch

T. M. Barrett

2001-01-01

Landscape assessment and planning often depend on the ability to predict change of vegetation. This report compares four modeling systems (FETM, LANDSUM, SIMPPLLE, and VDDT) that can be used to understand changes resulting from succession, natural disturbance, and management activities. The four models may be useful for regional or local assessments in National Forest...

Predicting the Weather and Building the Boats: Full Service Schools as One Avenue to School Success for All of America's Children

ERIC Educational Resources Information Center

Kronick, Robert F.

2003-01-01

This article describes the evolution of full service schools. Full service schools stress prevention, collaboration and systems change. Prevention is geared toward corrections, mental health and welfare, all topics of keen interest to people working in and studying criminal justice. By providing mental health services at the school for both…

Supersonics Project - Airport Noise Tech Challenge

NASA Technical Reports Server (NTRS)

Bridges, James

2010-01-01

The Airport Noise Tech Challenge research effort under the Supersonics Project is reviewed. While the goal of "Improved supersonic jet noise models validated on innovative nozzle concepts" remains the same, the success of the research effort has caused the thrust of the research to be modified going forward in time. The main activities from FY06-10 focused on development and validation of jet noise prediction codes. This required innovative diagnostic techniques to be developed and deployed, extensive jet noise and flow databases to be created, and computational tools to be developed and validated. Furthermore, in FY09-10 systems studies commissioned by the Supersonics Project showed that viable supersonic aircraft were within reach using variable cycle engine architectures if exhaust nozzle technology could provide 3-5dB of suppression. The Project then began to focus on integrating the technologies being developed in its Tech Challenge areas to bring about successful system designs. Consequently, the Airport Noise Tech Challenge area has shifted efforts from developing jet noise prediction codes to using them to develop low-noise nozzle concepts for integration into supersonic aircraft. The new plan of research is briefly presented by technology and timelines.

Predicting therapy success for treatment as usual and blended treatment in the domain of depression.

PubMed

van Breda, Ward; Bremer, Vincent; Becker, Dennis; Hoogendoorn, Mark; Funk, Burkhardt; Ruwaard, Jeroen; Riper, Heleen

2018-06-01

In this paper, we explore the potential of predicting therapy success for patients in mental health care. Such predictions can eventually improve the process of matching effective therapy types to individuals. In the EU project E-COMPARED, a variety of information is gathered about patients suffering from depression. We use this data, where 276 patients received treatment as usual and 227 received blended treatment, to investigate to what extent we are able to predict therapy success. We utilize different encoding strategies for preprocessing, varying feature selection techniques, and different statistical procedures for this purpose. Significant predictive power is found with average AUC values up to 0.7628 for treatment as usual and 0.7765 for blended treatment. Adding daily assessment data for blended treatment does currently not add predictive accuracy. Cost effectiveness analysis is needed to determine the added potential for real-world applications.

Multiple mechanisms enable invasive species to suppress native species.

PubMed

Bennett, Alison E; Thomsen, Meredith; Strauss, Sharon Y

2011-07-01

Invasive plants represent a significant threat to ecosystem biodiversity. To decrease the impacts of invasive species, a major scientific undertaking of the last few decades has been aimed at understanding the mechanisms that drive invasive plant success. Most studies and theories have focused on a single mechanism for predicting the success of invasive plants and therefore cannot provide insight as to the relative importance of multiple interactions in predicting invasive species' success. We examine four mechanisms that potentially contribute to the success of invasive velvetgrass Holcus lanatus: direct competition, indirect competition mediated by mammalian herbivores, interference competition via allelopathy, and indirect competition mediated by changes in the soil community. Using a combination of field and greenhouse approaches, we focus on the effects of H. lanatus on a common species in California coastal prairies, Erigeron glaucus, where the invasion is most intense. We found that H. lanatus had the strongest effects on E. glaucus via direct competition, but it also influenced the soil community in ways that feed back to negatively influence E. glaucus and other native species after H. lanatus removal. This approach provided evidence for multiple mechanisms contributing to negative effects of invasive species, and it identified when particular strategies were most likely to be important. These mechanisms can be applied to eradication of H. lanatus and conservation of California coastal prairie systems, and they illustrate the utility of an integrated set of experiments for determining the potential mechanisms of invasive species' success.

Basic PK/PD principles of drug effects in circular/proliferative systems for disease modelling.

PubMed

Jacqmin, Philippe; McFadyen, Lynn; Wade, Janet R

2010-04-01

Disease progression modelling can provide information about the time course and outcome of pharmacological intervention on the disease. The basic PK/PD principles of proliferative and circular systems within the context of modelling disease progression and the effect of treatment thereupon are illustrated with the goal to better understand/predict eventual clinical outcome. Circular/proliferative systems can be very complex. To facilitate the understanding of how a dosing regimen can be defined in such systems we have shown the derivation of a system parameter named the Reproduction Minimum Inhibitory Concentration (RMIC) which represents the critical concentration at which the system switches from growth to extinction. The RMIC depends on two parameters (RMIC = (R(0) - 1) x IC(50)): the basic reproductive ratio (R(0)) a fundamental parameter of the circular/proliferative system that represents the number of offspring produced by one replicating species during its lifespan, and the IC(50), the potency of the drug to inhibit the proliferation of the system. The RMIC is constant for a given system and a given drug and represents the lowest concentration that needs to be achieved for eradication of the system. When exposure is higher than the RMIC, success can be expected in the long term. Time varying inhibition of replicating species proliferation is a natural consequence of the time varying inhibitor drug concentrations and when combined with the dynamics of the circular/proliferative system makes it difficult to predict the eventual outcome. Time varying inhibition of proliferative/circular systems can be handled by calculating the equivalent effective constant concentration (ECC), the constant plasma concentration that would give rise to the average inhibition at steady state. When ECC is higher than the RMIC, eradication of the system can be expected. In addition, it is shown that scenarios that have the same steady state ECC whatever the dose, dosage schedule or PK parameters have also the same average R (0) in the presence of the inhibitor (i.e. R (0-INH)) and therefore lead to the same outcome. This allows predicting equivalent active doses and dosing schedules in circular and proliferative systems when the IC(50) and pharmacokinetic characteristics of the drugs are known. The results from the simulations performed demonstrate that, for a given system (defined by its RMIC), treatment success depends mainly on the pharmacokinetic characteristics of the drug and the dosing schedule.

Effects of fire on golden eagle territory occupancy and reproductive success

USGS Publications Warehouse

Kochert, Michael N.; Steenhof, Karen; Marzluff, J.M.; Carpenter, L.B.

1999-01-01

We examined effects of fire on golden eagle (Aquila chrysaetos) territory occupancy and reproductive success in southwestern Idaho because wildfires since 1980 have resulted in large-scale losses of shrub habitat in the Snake River Plain. Success (percentage of pairs that raised young) at burned territories declined after major fires (P = 0.004). Pairs in burned areas that could expand into adjacent vacant territories were as successful as pairs in unburned territories and more successful than pairs in burned territories that could not expand. Success at extensively burned territories was lowest 4-6 years after burning but increased 4-5 years later. The incidence and extent of fires did not help predict territories that would have low occupancy and success rates in postburn years. The presence of a vacant neighboring territory and the amount of agriculture and proportion of shrubs within 3 km of the nesting centroid best predicted probability of territory occupancy. Nesting success during preburn years best predicted the probability of a territory being successful in postburn years. Burned territories with high success rates during preburn years continued to have high success rates during postburn years, and those with low success in preburn years continued to be less successful after burning. In areas where much shrub habitat has been lost to fire, management for golden eagles should include active fire suppression and rehabilitation of burned areas.

Integrated Liquid Bismuth Propellant Feed System

NASA Technical Reports Server (NTRS)

Polzin, Kurt A.; Markusic, Thomas E.; Stanojev, Boris J.

2006-01-01

A prototype bismuth propellant feed and control system was constructed and tested. An electromagnetic pump was used in this system to provide fine control of the hydrostatic pressure, and a new type of in-line flow sensor was developed to provide an accurate, real-time measurement of the mass flow rate. High-temperature material compatibility was a driving design requirement for the pump and flow sensor, leading to the selection of macor for the main body of both components. Post-test inspections of both components revealed no cracks or leaking in either. In separate proof-of-concept experiments, the pump produced a linear pressure rise as a function of current that compared favorably with theoretical pump pressure predictions, with a pressure of 10 kPa at 30 A. Flow sensing was successfully demonstrated in a bench-top test using gallium as a substitute liquid metal. A real-time controller was successfully used to control the entire system, simultaneously monitoring all power supplies and performing data acquisition duties.

A proposal for a comprehensive risk scoring system for predicting postoperative complications in octogenarian patients with medically operable lung cancer: JACS1303.

PubMed

Saji, Hisashi; Ueno, Takahiko; Nakamura, Hiroshige; Okumura, Norihito; Tsuchida, Masanori; Sonobe, Makoto; Miyazaki, Takuro; Aokage, Keiju; Nakao, Masayuki; Haruki, Tomohiro; Ito, Hiroyuki; Kataoka, Kazuhiko; Okabe, Kazunori; Tomizawa, Kenji; Yoshimoto, Kentaro; Horio, Hirotoshi; Sugio, Kenji; Ode, Yasuhisa; Takao, Motoshi; Okada, Morihito; Chida, Masayuki

2018-04-01

Although some retrospective studies have reported clinicopathological scoring systems for predicting postoperative complications and survival outcomes for elderly lung cancer patients, optimized scoring systems remain controversial. The Japanese Association for Chest Surgery (JACS) conducted a nationwide multicentre prospective cohort and enrolled a total of 1019 octogenarians with medically operable lung cancer. Details of the clinical factors, comorbidities and comprehensive geriatric assessment were recorded for 895 patients to develop a comprehensive risk scoring (RS) system capable of predicting severe complications. Operative (30 days) and hospital mortality rates were 1.0% and 1.6%, respectively. Complications were observed in 308 (34%) patients, of whom 81 (8.4%) had Grade 3-4 severe complications. Pneumonia was the most common severe complication, observed in 27 (3.0%) patients. Five predictive factors, gender, comprehensive geriatric assessment75: memory and Simplified Comorbidity Score (SCS): diabetes mellitus, albumin and percentage vital capacity, were identified as independent predictive factors for severe postoperative complications (odds ratio = 2.73, 1.86, 1.54, 1.66 and 1.61, respectively) through univariate and multivariate analyses. A 5-fold cross-validation was performed as an internal validation to reconfirm these 5 predictive factors (average area under the curve 0.70). We developed a simplified RS system as follows: RS = 3 (gender: male) + 2 (comprehensive geriatric assessment 75: memory: yes) + 2 (albumin: <3.8 ng/ml) + 1 (percentage vital capacity: ≤90) + 1 (SCS: diabetes mellitus: yes). The current series shows that octogenarians can be successfully treated for lung cancer with surgical resection with an acceptable rate of severe complications and mortality. We propose a simplified RS system to predict severe complications in octogenarian patients with medically operative lung cancer. JACS1303 (UMIN000016756).

Predictability of the geospace variations and measuring the capability to model the state of the system

NASA Astrophysics Data System (ADS)

Pulkkinen, A.

2012-12-01

Empirical modeling has been the workhorse of the past decades in predicting the state of the geospace. For example, numerous empirical studies have shown that global geoeffectiveness indices such as Kp and Dst are generally well predictable from the solar wind input. These successes have been facilitated partly by the strongly externally driven nature of the system. Although characterizing the general state of the system is valuable and empirical modeling will continue playing an important role, refined physics-based quantification of the state of the system has been the obvious next step in moving toward more mature science. Importantly, more refined and localized products are needed also for space weather purposes. Predictions of local physical quantities are necessary to make physics-based links to the impacts on specific systems. As we have introduced more localized predictions of the geospace state one central question is how predictable these local quantities are? This complex question can be addressed by rigorously measuring the model performance against the observed data. Space sciences community has made great advanced on this topic over the past few years and there are ongoing efforts in SHINE, CEDAR and GEM to carry out community-wide evaluations of the state-of-the-art solar and heliospheric, ionosphere-thermosphere and geospace models, respectively. These efforts will help establish benchmarks and thus provide means to measure the progress in the field analogous to monitoring of the improvement in lower atmospheric weather predictions carried out rigorously since 1980s. In this paper we will discuss some of the latest advancements in predicting the local geospace parameters and give an overview of some of the community efforts to rigorously measure the model performances. We will also briefly discuss some of the future opportunities for advancing the geospace modeling capability. These will include further development in data assimilation and ensemble modeling (e.g. taking into account uncertainty in the inflow boundary conditions).

Identifying and Tracking Pedestrians Based on Sensor Fusion and Motion Stability Predictions

PubMed Central

Musleh, Basam; García, Fernando; Otamendi, Javier; Armingol, José Mª; de la Escalera, Arturo

2010-01-01

The lack of trustworthy sensors makes development of Advanced Driver Assistance System (ADAS) applications a tough task. It is necessary to develop intelligent systems by combining reliable sensors and real-time algorithms to send the proper, accurate messages to the drivers. In this article, an application to detect and predict the movement of pedestrians in order to prevent an imminent collision has been developed and tested under real conditions. The proposed application, first, accurately measures the position of obstacles using a two-sensor hybrid fusion approach: a stereo camera vision system and a laser scanner. Second, it correctly identifies pedestrians using intelligent algorithms based on polylines and pattern recognition related to leg positions (laser subsystem) and dense disparity maps and u-v disparity (vision subsystem). Third, it uses statistical validation gates and confidence regions to track the pedestrian within the detection zones of the sensors and predict their position in the upcoming frames. The intelligent sensor application has been experimentally tested with success while tracking pedestrians that cross and move in zigzag fashion in front of a vehicle. PMID:22163639

Identifying and tracking pedestrians based on sensor fusion and motion stability predictions.

PubMed

Musleh, Basam; García, Fernando; Otamendi, Javier; Armingol, José Maria; de la Escalera, Arturo

2010-01-01

The lack of trustworthy sensors makes development of Advanced Driver Assistance System (ADAS) applications a tough task. It is necessary to develop intelligent systems by combining reliable sensors and real-time algorithms to send the proper, accurate messages to the drivers. In this article, an application to detect and predict the movement of pedestrians in order to prevent an imminent collision has been developed and tested under real conditions. The proposed application, first, accurately measures the position of obstacles using a two-sensor hybrid fusion approach: a stereo camera vision system and a laser scanner. Second, it correctly identifies pedestrians using intelligent algorithms based on polylines and pattern recognition related to leg positions (laser subsystem) and dense disparity maps and u-v disparity (vision subsystem). Third, it uses statistical validation gates and confidence regions to track the pedestrian within the detection zones of the sensors and predict their position in the upcoming frames. The intelligent sensor application has been experimentally tested with success while tracking pedestrians that cross and move in zigzag fashion in front of a vehicle.

Mentoring Support and Power: A Three Year Predictive Field Study on Protege Networking and Career Success

ERIC Educational Resources Information Center

Blickle, Gerhard; Witzki, Alexander H.; Schneider, Paula B.

2009-01-01

Career success of early employees was analyzed from a power perspective and a developmental network perspective. In a predictive field study with 112 employees mentoring support and mentors' power were assessed in the first wave, employees' networking was assessed after two years, and career success (i.e. income and hierarchical position) and…

Predicting Success for College Students Enrolled in an Online, Lab-Based, Biology Course for Non-Majors

ERIC Educational Resources Information Center

Foster, Regina

2012-01-01

Online education has exploded in popularity. While there is ample research on predictors of traditional college student success, little research has been done on effective methods of predicting student success in online education. In this study, a number of demographic variables including GPA, ACT, gender, age and others were examined to determine…

Predicting Educational Success and Attrition in Problem-Based Learning: Do First Impressions Count?

ERIC Educational Resources Information Center

Wijnia, Lisette; Loyens, Sofie M. M.; Derous, Eva; Koendjie, Nitaasha S.; Schmidt, Henk G.

2014-01-01

This study examines whether tutors (N?=?15) in a problem-based learning curriculum were able to predict students' success in their first year and their entire bachelor programme. Tutors were asked to rate each student in their tutorial group in terms of the chance that this student would successfully finish their first year and the entire…

Using Standardized Tests to Identify Prior Knowledge Necessary for Success in Algebra: A Predictive Analysis

ERIC Educational Resources Information Center

Jensen, Jennifer

2014-01-01

This study sought to determine if there is a relationship between students' scores on the eighth-grade Indiana State Test of Education Progress Plus (ISTEP+) exam and success on Indiana's Algebra End-of-Course Assessment (ECA). Additionally, it sought to determine if algebra success could be significantly predicted by the achievement in one or…

Changes in the glucosinolate-myrosinase defense system in Brassica juncea cotyledons during seedling development.

PubMed

Wallace, S K; Eigenbrode, Sanford D

2002-02-01

Optimal defense theory (ODT) predicts that plant defenses will be allocated to plant organs and tissues in proportion to their relative fitness values and susceptibilities to attack. This study was designed to test ODT predictions on the myrosinase-glucosinolate defense system in Brassica juncea by examining the relationships between the fitness value of B. juncea cotyledons and the levels and effectiveness of cotyledon defenses. Specifically, we estimated fitness value of cotyledons during plant development by measuring plant growth and seed production after cotyledon damage or removal at successive seedling ages. Cotyledon removal within five days of emergence had a significant impact on growth and seed production, but cotyledon removal at later stages did not. Consistent with ODT, glucosinolate and myrosinase levels in cotyledons also declined with seedling age, as did relative defenses against a generalist herbivore, Spodoptera eridania, as estimated by bioassay. Declines in glucosinolates were as predicted by a passive, allometric dilution model based on cotyledon expansion. Declines in myrosinase activity were significantly more gradual than predicted by allometric dilution, suggesting active retention of myrosinase activity as young cotyledons expand.

Autoregressive-moving-average hidden Markov model for vision-based fall prediction-An application for walker robot.

PubMed

Taghvaei, Sajjad; Jahanandish, Mohammad Hasan; Kosuge, Kazuhiro

2017-01-01

Population aging of the societies requires providing the elderly with safe and dependable assistive technologies in daily life activities. Improving the fall detection algorithms can play a major role in achieving this goal. This article proposes a real-time fall prediction algorithm based on the acquired visual data of a user with walking assistive system from a depth sensor. In the lack of a coupled dynamic model of the human and the assistive walker a hybrid "system identification-machine learning" approach is used. An autoregressive-moving-average (ARMA) model is fitted on the time-series walking data to forecast the upcoming states, and a hidden Markov model (HMM) based classifier is built on the top of the ARMA model to predict falling in the upcoming time frames. The performance of the algorithm is evaluated through experiments with four subjects including an experienced physiotherapist while using a walker robot in five different falling scenarios; namely, fall forward, fall down, fall back, fall left, and fall right. The algorithm successfully predicts the fall with a rate of 84.72%.

Modification of Hazen's equation in coarse grained soils by soft computing techniques

NASA Astrophysics Data System (ADS)

Kaynar, Oguz; Yilmaz, Isik; Marschalko, Marian; Bednarik, Martin; Fojtova, Lucie

2013-04-01

Hazen first proposed a Relationship between coefficient of permeability (k) and effective grain size (d10) was first proposed by Hazen, and it was then extended by some other researchers. However many attempts were done for estimation of k, correlation coefficients (R2) of the models were generally lower than ~0.80 and whole grain size distribution curves were not included in the assessments. Soft computing techniques such as; artificial neural networks, fuzzy inference systems, genetic algorithms, etc. and their hybrids are now being successfully used as an alternative tool. In this study, use of some soft computing techniques such as Artificial Neural Networks (ANNs) (MLP, RBF, etc.) and Adaptive Neuro-Fuzzy Inference System (ANFIS) for prediction of permeability of coarse grained soils was described, and Hazen's equation was then modificated. It was found that the soft computing models exhibited high performance in prediction of permeability coefficient. However four different kinds of ANN algorithms showed similar prediction performance, results of MLP was found to be relatively more accurate than RBF models. The most reliable prediction was obtained from ANFIS model.

Shuttle TPS thermal performance and analysis methodology

NASA Technical Reports Server (NTRS)

Neuenschwander, W. E.; Mcbride, D. U.; Armour, G. A.

1983-01-01

Thermal performance of the thermal protection system was approximately as predicted. The only extensive anomalies were filler bar scorching and over-predictions in the high Delta p gap heating regions of the orbiter. A technique to predict filler bar scorching has been developed that can aid in defining a solution. Improvement in high Delta p gap heating methodology is still under study. Minor anomalies were also examined for improvements in modeling techniques and prediction capabilities. These include improved definition of low Delta p gap heating, an analytical model for inner mode line convection heat transfer, better modeling of structure, and inclusion of sneak heating. The limited number of problems related to penetration items that presented themselves during orbital flight tests were resolved expeditiously, and designs were changed and proved successful within the time frame of that program.

Implementation of model predictive control for resistive wall mode stabilization on EXTRAP T2R

NASA Astrophysics Data System (ADS)

Setiadi, A. C.; Brunsell, P. R.; Frassinetti, L.

2015-10-01

A model predictive control (MPC) method for stabilization of the resistive wall mode (RWM) in the EXTRAP T2R reversed-field pinch is presented. The system identification technique is used to obtain a linearized empirical model of EXTRAP T2R. MPC employs the model for prediction and computes optimal control inputs that satisfy performance criterion. The use of a linearized form of the model allows for compact formulation of MPC, implemented on a millisecond timescale, that can be used for real-time control. The design allows the user to arbitrarily suppress any selected Fourier mode. The experimental results from EXTRAP T2R show that the designed and implemented MPC successfully stabilizes the RWM.

Numerical Modeling of STARx for Ex Situ Soil Remediation

NASA Astrophysics Data System (ADS)

Gerhard, J.; Solinger, R. L.; Grant, G.; Scholes, G.

2016-12-01

Growing stockpiles of contaminated soils contaminated with petroleum hydrocarbons are an outstanding problem worldwide. Self-sustaining Treatment for Active Remediation (STAR) is an emerging technology based on smouldering combustion that has been successfully deployed for in situ remediation. STAR has also been developed for ex situ applications (STARx). This work used a two-dimensional numerical model to systematically explore the sensitivity of ex situ remedial performance to key design and operational parameters. First the model was calibrated and validated against pilot scale experiments, providing confidence that the rate and extent of treatment were correctly predicted. Simulations then investigated sensitivity of remedial performance to injected air flux, contaminant saturation, system configuration, heterogeneity of intrinsic permeability, heterogeneity of contaminant saturation, and system scale. Remedial performance was predicted to be most sensitive to the injected air flux, with higher air fluxes achieving higher treatment rates and remediating larger fractions of the initial contaminant mass. The uniformity of the advancing smouldering front was predicted to be highly dependent on effective permeability contrasts between treated and untreated sections of the contaminant pack. As a result, increased heterogeneity (of intrinsic permeability in particular) is predicted to lower remedial performance. Full-scale systems were predicted to achieve treatment rates an order of magnitude higher than the pilot scale for similar contaminant saturation and injected air flux. This work contributed to the large scale STARx treatment system that is being tested at a field site in Fall 2016.

Blood and small intestine cell kinetics under radiation exposures: Mathematical modeling

NASA Astrophysics Data System (ADS)

Smirnova, Olga

Biophysical models, which describe the dynamics of vital body systems (namely, hematopoiesis and small intestinal epithelium) in mammals exposed to acute and chronic radiation, are developed. These models, based on conventional biological theories, are realized as the systems of nonlinear differential equations. Their variables and constant parameters have real biological meaning, that provides successful identification and verification of the models in hand. The explanation of a number of radiobiological effects, including those of the low-level long-term exposures, is proposed proceeding from the modeling results. It is proved that the predictions the models agree with the respective experimental data at both qualitative and quantitative levels. All this testifies to the efficiency of employment of the developed models in investigation and prediction of radiation effects on the hematopoietic and small intestinal epithelium systems, that can be used for the radiation risk assessment in the long-term space missions such as lunar colony and Mars voyage.

What happens to the motor theory of perception when the motor system is damaged?

PubMed

Stasenko, Alena; Garcea, Frank E; Mahon, Bradford Z

2013-09-01

Motor theories of perception posit that motor information is necessary for successful recognition of actions. Perhaps the most well known of this class of proposals is the motor theory of speech perception, which argues that speech recognition is fundamentally a process of identifying the articulatory gestures (i.e. motor representations) that were used to produce the speech signal. Here we review neuropsychological evidence from patients with damage to the motor system, in the context of motor theories of perception applied to both manual actions and speech. Motor theories of perception predict that patients with motor impairments will have impairments for action recognition. Contrary to that prediction, the available neuropsychological evidence indicates that recognition can be spared despite profound impairments to production. These data falsify strong forms of the motor theory of perception, and frame new questions about the dynamical interactions that govern how information is exchanged between input and output systems.

What happens to the motor theory of perception when the motor system is damaged?

PubMed Central

Stasenko, Alena; Garcea, Frank E.; Mahon, Bradford Z.

2016-01-01

Motor theories of perception posit that motor information is necessary for successful recognition of actions. Perhaps the most well known of this class of proposals is the motor theory of speech perception, which argues that speech recognition is fundamentally a process of identifying the articulatory gestures (i.e. motor representations) that were used to produce the speech signal. Here we review neuropsychological evidence from patients with damage to the motor system, in the context of motor theories of perception applied to both manual actions and speech. Motor theories of perception predict that patients with motor impairments will have impairments for action recognition. Contrary to that prediction, the available neuropsychological evidence indicates that recognition can be spared despite profound impairments to production. These data falsify strong forms of the motor theory of perception, and frame new questions about the dynamical interactions that govern how information is exchanged between input and output systems. PMID:26823687

Parsimonious description for predicting high-dimensional dynamics

PubMed Central

Hirata, Yoshito; Takeuchi, Tomoya; Horai, Shunsuke; Suzuki, Hideyuki; Aihara, Kazuyuki

2015-01-01

When we observe a system, we often cannot observe all its variables and may have some of its limited measurements. Under such a circumstance, delay coordinates, vectors made of successive measurements, are useful to reconstruct the states of the whole system. Although the method of delay coordinates is theoretically supported for high-dimensional dynamical systems, practically there is a limitation because the calculation for higher-dimensional delay coordinates becomes more expensive. Here, we propose a parsimonious description of virtually infinite-dimensional delay coordinates by evaluating their distances with exponentially decaying weights. This description enables us to predict the future values of the measurements faster because we can reuse the calculated distances, and more accurately because the description naturally reduces the bias of the classical delay coordinates toward the stable directions. We demonstrate the proposed method with toy models of the atmosphere and real datasets related to renewable energy. PMID:26510518

Learnability and generalisation of Arabic broken plural nouns

PubMed Central

Dawdy-Hesterberg, Lisa Garnand; Pierrehumbert, Janet Breckenridge

2014-01-01

The noun plural system in Modern Standard Arabic lies at a nexus of critical issues in morphological learnability. The suffixing “sound” plural competes with as many as 31 non-concatenative “broken” plural patterns. Our computational analysis of singular–plural pairs in the Corpus of Contemporary Arabic explores what types of linguistic information are statistically relevant to morphological generalisation for this highly complex system. We show that an analogical approach with the generalised context model is highly successful in predicting the plural form for any given singular form. This model proves to be robust to variation, as evidenced by its stability across 10 rounds of cross-validation. The predictive power is carried almost entirely by the CV template, a representation which specifies a segment's status as a consonant or vowel only, providing further support for the abstraction of prosodic templates in the Arabic morphological system as proposed by McCarthy and Prince. PMID:25346932

New way to produce dense double-antikaonic dibaryon system, 𝐾̄𝐾̄NN, through Λ(1405)-doorway sticking in p + p collisions

PubMed Central

YAMAZAKI, Toshimitsu; AKAISHI, Yoshinori; HASSANVAND, Maryam

2011-01-01

A recent successful observation of a dense and deeply bound 𝐾̄ nuclear system, K−pp, in the p + p → K+ + K−pp reaction in a DISTO experiment indicates that the double-𝐾̄ dibaryon, K−K−pp, which was predicted to be a dense nuclear system, can also be formed in p + p collisions. We find theoretically that the K−-K− repulsion plays no significant role in reducing the density and binding energy of K−K−pp and that, when two Λ(1405) resonances are produced simultaneously in a short-range p + p collision, they act as doorways to copious formation of K−K−pp, if and only if K−K−pp is a dense object, as predicted. PMID:21670568

Topological defect formation and spontaneous symmetry breaking in ion Coulomb crystals.

PubMed

Pyka, K; Keller, J; Partner, H L; Nigmatullin, R; Burgermeister, T; Meier, D M; Kuhlmann, K; Retzker, A; Plenio, M B; Zurek, W H; del Campo, A; Mehlstäubler, T E

2013-01-01

Symmetry breaking phase transitions play an important role in nature. When a system traverses such a transition at a finite rate, its causally disconnected regions choose the new broken symmetry state independently. Where such local choices are incompatible, topological defects can form. The Kibble-Zurek mechanism predicts the defect densities to follow a power law that scales with the rate of the transition. Owing to its ubiquitous nature, this theory finds application in a wide field of systems ranging from cosmology to condensed matter. Here we present the successful creation of defects in ion Coulomb crystals by a controlled quench of the confining potential, and observe an enhanced power law scaling in accordance with numerical simulations and recent predictions. This simple system with well-defined critical exponents opens up ways to investigate the physics of non-equilibrium dynamics from the classical to the quantum regime.

Numerical Modeling of Flow Distribution in Micro-Fluidics Systems

NASA Technical Reports Server (NTRS)

Majumdar, Alok; Cole, Helen; Chen, C. P.

2005-01-01

This paper describes an application of a general purpose computer program, GFSSP (Generalized Fluid System Simulation Program) for calculating flow distribution in a network of micro-channels. GFSSP employs a finite volume formulation of mass and momentum conservation equations in a network consisting of nodes and branches. Mass conservation equation is solved for pressures at the nodes while the momentum conservation equation is solved at the branches to calculate flowrate. The system of equations describing the fluid network is solved by a numerical method that is a combination of the Newton-Raphson and successive substitution methods. The numerical results have been compared with test data and detailed CFD (computational Fluid Dynamics) calculations. The agreement between test data and predictions is satisfactory. The discrepancies between the predictions and test data can be attributed to the frictional correlation which does not include the effect of surface tension or electro-kinetic effect.

Adaptive DIT-Based Fringe Tracking and Prediction at IOTA

NASA Technical Reports Server (NTRS)

Wilson, Edward; Pedretti, Ettore; Bregman, Jesse; Mah, Robert W.; Traub, Wesley A.

2004-01-01

An automatic fringe tracking system has been developed and implemented at the Infrared Optical Telescope Array (IOTA). In testing during May 2002, the system successfully minimized the optical path differences (OPDs) for all three baselines at IOTA. Based on sliding window discrete Fourier transform (DFT) calculations that were optimized for computational efficiency and robustness to atmospheric disturbances, the algorithm has also been tested extensively on off-line data. Implemented in ANSI C on the 266 MHZ PowerPC processor running the VxWorks real-time operating system, the algorithm runs in approximately 2.0 milliseconds per scan (including all three interferograms), using the science camera and piezo scanners to measure and correct the OPDs. Preliminary analysis on an extension of this algorithm indicates a potential for predictive tracking, although at present, real-time implementation of this extension would require significantly more computational capacity.

[Effect of stock abundance and environmental factors on the recruitment success of small yellow croaker in the East China Sea].

PubMed

Liu, Zun-lei; Yuan, Xing-wei; Yang, Lin-lin; Yan, Li-ping; Zhang, Hui; Cheng, Jia-hua

2015-02-01

Multiple hypotheses are available to explain recruitment rate. Model selection methods can be used to identify the best model that supports a particular hypothesis. However, using a single model for estimating recruitment success is often inadequate for overexploited population because of high model uncertainty. In this study, stock-recruitment data of small yellow croaker in the East China Sea collected from fishery dependent and independent surveys between 1992 and 2012 were used to examine density-dependent effects on recruitment success. Model selection methods based on frequentist (AIC, maximum adjusted R2 and P-values) and Bayesian (Bayesian model averaging, BMA) methods were applied to identify the relationship between recruitment and environment conditions. Interannual variability of the East China Sea environment was indicated by sea surface temperature ( SST) , meridional wind stress (MWS), zonal wind stress (ZWS), sea surface pressure (SPP) and runoff of Changjiang River ( RCR). Mean absolute error, mean squared predictive error and continuous ranked probability score were calculated to evaluate the predictive performance of recruitment success. The results showed that models structures were not consistent based on three kinds of model selection methods, predictive variables of models were spawning abundance and MWS by AIC, spawning abundance by P-values, spawning abundance, MWS and RCR by maximum adjusted R2. The recruitment success decreased linearly with stock abundance (P < 0.01), suggesting overcompensation effect in the recruitment success might be due to cannibalism or food competition. Meridional wind intensity showed marginally significant and positive effects on the recruitment success (P = 0.06), while runoff of Changjiang River showed a marginally negative effect (P = 0.07). Based on mean absolute error and continuous ranked probability score, predictive error associated with models obtained from BMA was the smallest amongst different approaches, while that from models selected based on the P-value of the independent variables was the highest. However, mean squared predictive error from models selected based on the maximum adjusted R2 was highest. We found that BMA method could improve the prediction of recruitment success, derive more accurate prediction interval and quantitatively evaluate model uncertainty.

Estimating the state of a geophysical system with sparse observations: time delay methods to achieve accurate initial states for prediction

NASA Astrophysics Data System (ADS)

An, Zhe; Rey, Daniel; Ye, Jingxin; Abarbanel, Henry D. I.

2017-01-01

The problem of forecasting the behavior of a complex dynamical system through analysis of observational time-series data becomes difficult when the system expresses chaotic behavior and the measurements are sparse, in both space and/or time. Despite the fact that this situation is quite typical across many fields, including numerical weather prediction, the issue of whether the available observations are "sufficient" for generating successful forecasts is still not well understood. An analysis by Whartenby et al. (2013) found that in the context of the nonlinear shallow water equations on a β plane, standard nudging techniques require observing approximately 70 % of the full set of state variables. Here we examine the same system using a method introduced by Rey et al. (2014a), which generalizes standard nudging methods to utilize time delayed measurements. We show that in certain circumstances, it provides a sizable reduction in the number of observations required to construct accurate estimates and high-quality predictions. In particular, we find that this estimate of 70 % can be reduced to about 33 % using time delays, and even further if Lagrangian drifter locations are also used as measurements.

A Machine Learning Recommender System to Tailor Preference Assessments to Enhance Person-Centered Care Among Nursing Home Residents.

PubMed

Gannod, Gerald C; Abbott, Katherine M; Van Haitsma, Kimberly; Martindale, Nathan; Heppner, Alexandra

2018-05-21

Nursing homes (NHs) using the Preferences for Everyday Living Inventory (PELI-NH) to assess important preferences and provide person-centered care find the number of items (72) to be a barrier to using the assessment. Using a sample of n = 255 NH resident responses to the PELI-NH, we used the 16 preference items from the MDS 3.0 Section F to develop a machine learning recommender system to identify additional PELI-NH items that may be important to specific residents. Much like the Netflix recommender system, our system is based on the concept of collaborative filtering whereby insights and predictions (e.g., filters) are created using the interests and preferences of many users. The algorithm identifies multiple sets of "you might also like" patterns called association rules, based upon responses to the 16 MDS preferences that recommends an additional set of preferences with a high likelihood of being important to a specific resident. In the evaluation of the combined apriori and logistic regression approach, we obtained a high recall performance (i.e., the ratio of correctly predicted preferences compared with all predicted preferences and nonpreferences) and high precision (i.e., the ratio of correctly predicted rules with respect to the rules predicted to be true) of 80.2% and 79.2%, respectively. The recommender system successfully provides guidance on how to best tailor the preference items asked of residents and can support preference capture in busy clinical environments, contributing to the feasibility of delivering person-centered care.

EEG-based learning system for online motion sickness level estimation in a dynamic vehicle environment.

PubMed

Lin, Chin-Teng; Tsai, Shu-Fang; Ko, Li-Wei

2013-10-01

Motion sickness is a common experience for many people. Several previous researches indicated that motion sickness has a negative effect on driving performance and sometimes leads to serious traffic accidents because of a decline in a person's ability to maintain self-control. This safety issue has motivated us to find a way to prevent vehicle accidents. Our target was to determine a set of valid motion sickness indicators that would predict the occurrence of a person's motion sickness as soon as possible. A successful method for the early detection of motion sickness will help us to construct a cognitive monitoring system. Such a monitoring system can alert people before they become sick and prevent them from being distracted by various motion sickness symptoms while driving or riding in a car. In our past researches, we investigated the physiological changes that occur during the transition of a passenger's cognitive state using electroencephalography (EEG) power spectrum analysis, and we found that the EEG power responses in the left and right motors, parietal, lateral occipital, and occipital midline brain areas were more highly correlated to subjective sickness levels than other brain areas. In this paper, we propose the use of a self-organizing neural fuzzy inference network (SONFIN) to estimate a driver's/passenger's sickness level based on EEG features that have been extracted online from five motion sickness-related brain areas, while either in real or virtual vehicle environments. The results show that our proposed learning system is capable of extracting a set of valid motion sickness indicators that originated from EEG dynamics, and through SONFIN, a neuro-fuzzy prediction model, we successfully translated the set of motion sickness indicators into motion sickness levels. The overall performance of this proposed EEG-based learning system can achieve an average prediction accuracy of ~82%.

CLASS: Coherent Lidar Airborne Shear Sensor. Windshear avoidance

NASA Technical Reports Server (NTRS)

Targ, Russell

1991-01-01

The coherent lidar airborne shear sensor (CLASS) is an airborne CO2 lidar system being designed and developed by Lockheed Missiles and Space Company, Inc. (LMSC) under contract to NASA Langley Research Center. The goal of this program is to develop a system with a 2- to 4-kilometer range that will provide a warning time of 20 to 40 seconds, so that the pilot can avoid the hazards of low-altitude wind shear under all weather conditions. It is a predictive system which will warn the pilot about a hazard that the aircraft will experience at some later time. The ability of the system to provide predictive warnings of clear air turbulence will also be evaluated. A one-year flight evaluation program will measure the line-of-sight wind velocity from a wide variety of wind fields obtained by an airborne radar, an accelerometer-based reactive wind-sensing system, and a ground-based Doppler radar. The success of the airborne lidar system will be determined by its correlation with the windfield as indicated by the onboard reactive system, which indicates the winds actually experienced by the NASA Boeing 737 aircraft.

Temporal effects in trend prediction: identifying the most popular nodes in the future.

PubMed

Zhou, Yanbo; Zeng, An; Wang, Wei-Hong

2015-01-01

Prediction is an important problem in different science domains. In this paper, we focus on trend prediction in complex networks, i.e. to identify the most popular nodes in the future. Due to the preferential attachment mechanism in real systems, nodes' recent degree and cumulative degree have been successfully applied to design trend prediction methods. Here we took into account more detailed information about the network evolution and proposed a temporal-based predictor (TBP). The TBP predicts the future trend by the node strength in the weighted network with the link weight equal to its exponential aging. Three data sets with time information are used to test the performance of the new method. We find that TBP have high general accuracy in predicting the future most popular nodes. More importantly, it can identify many potential objects with low popularity in the past but high popularity in the future. The effect of the decay speed in the exponential aging on the results is discussed in detail.

Temporal Effects in Trend Prediction: Identifying the Most Popular Nodes in the Future

PubMed Central

Zhou, Yanbo; Zeng, An; Wang, Wei-Hong

2015-01-01

Prediction is an important problem in different science domains. In this paper, we focus on trend prediction in complex networks, i.e. to identify the most popular nodes in the future. Due to the preferential attachment mechanism in real systems, nodes’ recent degree and cumulative degree have been successfully applied to design trend prediction methods. Here we took into account more detailed information about the network evolution and proposed a temporal-based predictor (TBP). The TBP predicts the future trend by the node strength in the weighted network with the link weight equal to its exponential aging. Three data sets with time information are used to test the performance of the new method. We find that TBP have high general accuracy in predicting the future most popular nodes. More importantly, it can identify many potential objects with low popularity in the past but high popularity in the future. The effect of the decay speed in the exponential aging on the results is discussed in detail. PMID:25806810

Predicting Student Success using Analytics in Course Learning Management Systems

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

Olama, Mohammed M; Thakur, Gautam; McNair, Wade

Educational data analytics is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from the educational context. For example, predicting college student performance is crucial for both the student and educational institutions. It can support timely intervention to prevent students from failing a course, increasing efficacy of advising functions, and improving course completion rate. In this paper, we present the efforts carried out at Oak Ridge National Laboratory (ORNL) toward conducting predictive analytics to academic data collected from 2009 through 2013 and available in one of the most commonly used learning management systems,more » called Moodle. First, we have identified the data features useful for predicting student outcomes such as students scores in homework assignments, quizzes, exams, in addition to their activities in discussion forums and their total GPA at the same term they enrolled in the course. Then, Logistic Regression and Neural Network predictive models are used to identify students as early as possible that are in danger of failing the course they are currently enrolled in. These models compute the likelihood of any given student failing (or passing) the current course. Numerical results are presented to evaluate and compare the performance of the developed models and their predictive accuracy.« less

Predicting student success using analytics in course learning management systems

NASA Astrophysics Data System (ADS)

Olama, Mohammed M.; Thakur, Gautam; McNair, Allen W.; Sukumar, Sreenivas R.

2014-05-01

Educational data analytics is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from the educational context. For example, predicting college student performance is crucial for both the student and educational institutions. It can support timely intervention to prevent students from failing a course, increasing efficacy of advising functions, and improving course completion rate. In this paper, we present the efforts carried out at Oak Ridge National Laboratory (ORNL) toward conducting predictive analytics to academic data collected from 2009 through 2013 and available in one of the most commonly used learning management systems, called Moodle. First, we have identified the data features useful for predicting student outcomes such as students' scores in homework assignments, quizzes, exams, in addition to their activities in discussion forums and their total GPA at the same term they enrolled in the course. Then, Logistic Regression and Neural Network predictive models are used to identify students as early as possible that are in danger of failing the course they are currently enrolled in. These models compute the likelihood of any given student failing (or passing) the current course. Numerical results are presented to evaluate and compare the performance of the developed models and their predictive accuracy.

Extended-range ensemble forecasting of tropical cyclogenesis in the northern Indian Ocean: Modulation of Madden-Julian Oscillation

NASA Astrophysics Data System (ADS)

Fu, Xiouhua; Hsu, Pang-chi

2011-08-01

A conventional atmosphere-ocean coupled system initialized with NCEP FNL analysis has successfully predicted a tropical cyclogenesis event in the northern Indian Ocean with a lead time of two weeks. The coupled forecasting system reproduces the westerly wind bursts in the equatorial Indian Ocean associated with an eastward-propagating Madden-Julian Oscillation (MJO) event as well as the accompanying northward-propagating westerly and convective disturbances. After reaching the Bay of Bengal, this northward-propagating Intra-Seasonal Variability (ISV) fosters the tropical cyclogenesis. The present finding demonstrates that a realistic MJO/ISV prediction will make the extended-range forecasting of tropical cyclogenesis possible and also calls for improved representation of the MJO/ISV in contemporary weather and climate forecast models.

Long-wave instabilities of two interlaced helical vortices

NASA Astrophysics Data System (ADS)

Quaranta, H. U.; Brynjell-Rahkola, M.; Leweke, T.; Henningson, D. S.

2016-09-01

We present a comparison between experimental observations and theoretical predictions concerning long-wave displacement instabilities of the helical vortices in the wake of a two-bladed rotor. Experiments are performed with a small-scale rotor in a water channel, using a set-up that allows the individual triggering of various instability modes at different azimuthal wave numbers, leading to local or global pairing of successive vortex loops. The initial development of the instability and the measured growth rates are in good agreement with the predictions from linear stability theory, based on an approach where the helical vortex system is represented by filaments. At later times, local pairing develops into large-scale distortions of the vortices, whereas for global pairing the non-linear evolution returns the system almost to its initial geometry.

Aptitude Tests and Successful College Students: The Predictive Validity of the General Aptitude Test (GAT) in Saudi Arabia

ERIC Educational Resources Information Center

Alnahdi, Ghaleb Hamad

2015-01-01

Aptitude tests should predict student success at the university level. This study examined the predictive validity of the General Aptitude Test (GAT) in Saudi Arabia. Data for 27420 students enrolled at Prince Sattam bin Abdulaziz University were analyzed. Of these students, 17565 were male students, and 9855 were female students. Multiple…

Testing predictions of forest succession using long-term measurements: 100 yrs of observations in the Oregon Cascades

Treesearch

Mark E. Harmon; Robert J. Pabst

2015-01-01

Question: Many predictions about forest succession have been based on chronosequences. Are these predictions – at the population, community and ecosystemlevel – consistent with long-termmeasurements in permanent plots? Location: Pseudotsuga menziesii (Mirb.) Franco dominated forest in western Oregon, US.Methods: Over a 100-yr period,...

Probabilistic Forecasting of Coastal Morphodynamic Storm Response at Fire Island, New York

NASA Astrophysics Data System (ADS)

Wilson, K.; Adams, P. N.; Hapke, C. J.; Lentz, E. E.; Brenner, O.

2013-12-01

Site-specific probabilistic models of shoreline change are useful because they are derived from direct observations so that local factors, which greatly influence coastal response, are inherently considered by the model. Fire Island, a 50-km barrier island off Long Island, New York, is periodically subject to large storms, whose waves and storm surge dramatically alter beach morphology. Nor'Ida, which impacted the Fire Island coast in 2009, was one of the larger storms to occur in the early 2000s. In this study, we improve upon a Bayesian Network (BN) model informed with historical data to predict shoreline change from Nor'Ida. We present two BN models, referred to as 'original' model (BNo) and 'revised' model (BNr), designed to predict the most probable magnitude of net shoreline movement (NSM), as measured at 934 cross-shore transects, spanning 46 km. Both are informed with observational data (wave impact hours, shoreline and dune toe change rates, pre-storm beach width, and measured NSM) organized within five nodes, but the revised model contains a sixth node to represent the distribution of material added during an April 2009 nourishment project. We evaluate model success by examining the percentage of transects on which the model chooses the correct (observed) bin value of NSM. Comparisons of observed to model-predicted NSM show BNr has slightly higher predictive success over the total study area and significantly higher success at nourished locations. The BNo, which neglects anthropogenic modification history, correctly predicted the most probable NSM in 66.6% of transects, with ambiguous prediction at 12.7% of the locations. BNr, which incorporates anthropogenic modification history, resulted in 69.4% predictive accuracy and 13.9% ambiguity. However, across nourished transects, BNr reported 72.9% predictive success, while BNo reported 61.5% success. Further, at nourished transects, BNr reported higher ambiguity of 23.5% compared to 9.9% in BNo. These results demonstrate that BNr recognizes that nourished transects may behave differently from the expectation derived from historical data and therefore is more 'cautious' in its predictions at these locations. In contrast, BNo is more confident, but less accurate, demonstrating the risk of ignoring the influences of anthropogenic modification in a probabilistic model. Over the entire study region, both models produced greatest predictive accuracy for low retreat observations (BNo: 77.6%; BNr: 76.0%) and least success at predicting low advance observations, although BNr shows considerable improvement over BNo (39.4% vs. 28.6%, respectively). BNr also was significantly more accurate at predicting observations of no shoreline change (BNo: 56.2%; BNr: 68.93%). Both models were accurate for 60% of high advance observations, and reported high predictive success for high retreat observations (BNo: 69.1%; BNr: 67.6%), the scenario of greatest concern to coastal managers.

Neural correlates of encoding processes predicting subsequent cued recall and source memory.

PubMed

Angel, Lucie; Isingrini, Michel; Bouazzaoui, Badiâa; Fay, Séverine

2013-03-06

In this experiment, event-related potentials were used to examine whether the neural correlates of encoding processes predicting subsequent successful recall differed from those predicting successful source memory retrieval. During encoding, participants studied lists of words and were instructed to memorize each word and the list in which it occurred. At test, they had to complete stems (the first four letters) with a studied word and then make a judgment of the initial temporal context (i.e. list). Event-related potentials recorded during encoding were segregated according to subsequent memory performance to examine subsequent memory effects (SMEs) reflecting successful cued recall (cued recall SME) and successful source retrieval (source memory SME). Data showed a cued recall SME on parietal electrode sites from 400 to 1200 ms and a late inversed cued recall SME on frontal sites in the 1200-1400 ms period. Moreover, a source memory SME was reported from 400 to 1400 ms on frontal areas. These findings indicate that patterns of encoding-related activity predicting successful recall and source memory are clearly dissociated.

Phytoplankton succession in recurrently fluctuating environments.

PubMed

Roelke, Daniel L; Spatharis, Sofie

2015-01-01

Coastal marine systems are affected by seasonal variations in biogeochemical and physical processes, sometimes leading to alternating periods of reproductive growth limitation within an annual cycle. Transitions between these periods can be sudden or gradual. Human activities, such as reservoir construction and interbasin water transfers, influence these processes and can affect the type of transition between resource loading conditions. How such human activities might influence phytoplankton succession is largely unknown. Here, we employ a multispecies, multi-nutrient model to explore how nutrient loading switching mode might affect phytoplankton succession. The model is based on the Monod-relationship, predicting an instantaneous reproductive growth rate from ambient inorganic nutrient concentrations whereas the limiting nutrient at any given time was determined by Liebig's Law of the Minimum. When these relationships are combined with population loss factors, such as hydraulic displacement of cells associated with inflows, a characterization of a species' niche can be achieved through application of the R* conceptual model, thus enabling an ecological interpretation of modeling results. We found that the mode of reversal in resource supply concentrations had a profound effect. When resource supply reversals were sudden, as expected in systems influenced by pulsed inflows or wind-driven mixing events, phytoplankton were characterized by alternating succession dynamics, a phenomenon documented in inland water bodies of temperate latitudes. When resource supply reversals were gradual, as expected in systems influenced by seasonally developing wet and dry seasons, or annually occurring periods of upwelling, phytoplankton dynamics were characterized by mirror-image succession patterns. This phenomenon has not been reported previously in plankton systems but has been observed in some terrestrial plant systems. These findings suggest that a transition from alternating to "mirror-image" succession patterns might arise with continued coastal zone development, with crucial implications for ecosystems dependent on time-sensitive processes, e.g., spawning events and migration patterns.

Is the First-Year Predictive for Study Success in Subsequent Years? Findings from an Academy of Music

ERIC Educational Resources Information Center

Mennen, Josien; van der Klink, Marcel

2017-01-01

In higher education, departments are under increasing pressure to improve study success. Research in this field focusing on higher music education is scarce. The aim of this study was to gain insight into the predictive capability of the first year for study success of students at an academy of music in subsequent years. Data on study progression…

Testing a Model of Environmental Risk and Protective Factors to Predict Middle and High School Students' Academic Success

ERIC Educational Resources Information Center

Peters, S. Colby; Woolley, Michael E.

2015-01-01

Data from the School Success Profile generated by 19,228 middle and high school students were organized into three broad categories of risk and protective factors--control, support, and challenge--to examine the relative and combined power of aggregate scale scores in each category so as to predict academic success. It was hypothesized that higher…

Differential Validities for Shop Courses: Proposal B: Follow-Up of Subjects' Work Experiences. Final Report. Vol I: Procedures and Results.

ERIC Educational Resources Information Center

Isabelle, L. A.; Lokan, J. J.

Follow-up information was collected on 1500 students who attended a two-year occupational high school, in order to relate predictor measures to success during training and subsequent job success. Although not predictive of dropouts, variables in the pre-test battery did predict performance in academic and shop courses; ratings of job success were…

Localization of magnetic pills

PubMed Central

Laulicht, Bryan; Gidmark, Nicholas J.; Tripathi, Anubhav; Mathiowitz, Edith

2011-01-01

Numerous therapeutics demonstrate optimal absorption or activity at specific sites in the gastrointestinal (GI) tract. Yet, safe, effective pill retention within a desired region of the GI remains an elusive goal. We report a safe, effective method for localizing magnetic pills. To ensure safety and efficacy, we monitor and regulate attractive forces between a magnetic pill and an external magnet, while visualizing internal dose motion in real time using biplanar videofluoroscopy. Real-time monitoring yields direct visual confirmation of localization completely noninvasively, providing a platform for investigating the therapeutic benefits imparted by localized oral delivery of new and existing drugs. Additionally, we report the in vitro measurements and calculations that enabled prediction of successful magnetic localization in the rat small intestines for 12 h. The designed system for predicting and achieving successful magnetic localization can readily be applied to any area of the GI tract within any species, including humans. The described system represents a significant step forward in the ability to localize magnetic pills safely and effectively anywhere within the GI tract. What our magnetic pill localization strategy adds to the state of the art, if used as an oral drug delivery system, is the ability to monitor the force exerted by the pill on the tissue and to locate the magnetic pill within the test subject all in real time. This advance ensures both safety and efficacy of magnetic localization during the potential oral administration of any magnetic pill-based delivery system. PMID:21257903

Quickprop method to speed up learning process of Artificial Neural Network in money's nominal value recognition case

NASA Astrophysics Data System (ADS)

Swastika, Windra

2017-03-01

A money's nominal value recognition system has been developed using Artificial Neural Network (ANN). ANN with Back Propagation has one disadvantage. The learning process is very slow (or never reach the target) in the case of large number of iteration, weight and samples. One way to speed up the learning process is using Quickprop method. Quickprop method is based on Newton's method and able to speed up the learning process by assuming that the weight adjustment (E) is a parabolic function. The goal is to minimize the error gradient (E'). In our system, we use 5 types of money's nominal value, i.e. 1,000 IDR, 2,000 IDR, 5,000 IDR, 10,000 IDR and 50,000 IDR. One of the surface of each nominal were scanned and digitally processed. There are 40 patterns to be used as training set in ANN system. The effectiveness of Quickprop method in the ANN system was validated by 2 factors, (1) number of iterations required to reach error below 0.1; and (2) the accuracy to predict nominal values based on the input. Our results shows that the use of Quickprop method is successfully reduce the learning process compared to Back Propagation method. For 40 input patterns, Quickprop method successfully reached error below 0.1 for only 20 iterations, while Back Propagation method required 2000 iterations. The prediction accuracy for both method is higher than 90%.

Forecasting success via early adoptions analysis: A data-driven study

PubMed Central

Milli, Letizia; Giannotti, Fosca; Pedreschi, Dino

2017-01-01

Innovations are continuously launched over markets, such as new products over the retail market or new artists over the music scene. Some innovations become a success; others don’t. Forecasting which innovations will succeed at the beginning of their lifecycle is hard. In this paper, we provide a data-driven, large-scale account of the existence of a special niche among early adopters, individuals that consistently tend to adopt successful innovations before they reach success: we will call them Hit-Savvy. Hit-Savvy can be discovered in very different markets and retain over time their ability to anticipate the success of innovations. As our second contribution, we devise a predictive analytical process, exploiting Hit-Savvy as signals, which achieves high accuracy in the early-stage prediction of successful innovations, far beyond the reach of state-of-the-art time series forecasting models. Indeed, our findings and predictive model can be fruitfully used to support marketing strategies and product placement. PMID:29216255

Forecasting success via early adoptions analysis: A data-driven study.

PubMed

Rossetti, Giulio; Milli, Letizia; Giannotti, Fosca; Pedreschi, Dino

2017-01-01

Innovations are continuously launched over markets, such as new products over the retail market or new artists over the music scene. Some innovations become a success; others don't. Forecasting which innovations will succeed at the beginning of their lifecycle is hard. In this paper, we provide a data-driven, large-scale account of the existence of a special niche among early adopters, individuals that consistently tend to adopt successful innovations before they reach success: we will call them Hit-Savvy. Hit-Savvy can be discovered in very different markets and retain over time their ability to anticipate the success of innovations. As our second contribution, we devise a predictive analytical process, exploiting Hit-Savvy as signals, which achieves high accuracy in the early-stage prediction of successful innovations, far beyond the reach of state-of-the-art time series forecasting models. Indeed, our findings and predictive model can be fruitfully used to support marketing strategies and product placement.

Parameterizing the Spatial Markov Model From Breakthrough Curve Data Alone

NASA Astrophysics Data System (ADS)

Sherman, Thomas; Fakhari, Abbas; Miller, Savannah; Singha, Kamini; Bolster, Diogo

2017-12-01

The spatial Markov model (SMM) is an upscaled Lagrangian model that effectively captures anomalous transport across a diverse range of hydrologic systems. The distinct feature of the SMM relative to other random walk models is that successive steps are correlated. To date, with some notable exceptions, the model has primarily been applied to data from high-resolution numerical simulations and correlation effects have been measured from simulated particle trajectories. In real systems such knowledge is practically unattainable and the best one might hope for is breakthrough curves (BTCs) at successive downstream locations. We introduce a novel methodology to quantify velocity correlation from BTC data alone. By discretizing two measured BTCs into a set of arrival times and developing an inverse model, we estimate velocity correlation, thereby enabling parameterization of the SMM in studies where detailed Lagrangian velocity statistics are unavailable. The proposed methodology is applied to two synthetic numerical problems, where we measure all details and thus test the veracity of the approach by comparison of estimated parameters with known simulated values. Our results suggest that our estimated transition probabilities agree with simulated values and using the SMM with this estimated parameterization accurately predicts BTCs downstream. Our methodology naturally allows for estimates of uncertainty by calculating lower and upper bounds of velocity correlation, enabling prediction of a range of BTCs. The measured BTCs fall within the range of predicted BTCs. This novel method to parameterize the SMM from BTC data alone is quite parsimonious, thereby widening the SMM's practical applicability.

Investigation of metabolic objectives in cultured hepatocytes.

PubMed

Uygun, Korkut; Matthew, Howard W T; Huang, Yinlun

2007-06-15

Using optimization based methods to predict fluxes in metabolic flux balance models has been a successful approach for some microorganisms, enabling construction of in silico models and even inference of some regulatory motifs. However, this success has not been translated to mammalian cells. The lack of knowledge about metabolic objectives in mammalian cells is a major obstacle that prevents utilization of various metabolic engineering tools and methods for tissue engineering and biomedical purposes. In this work, we investigate and identify possible metabolic objectives for hepatocytes cultured in vitro. To achieve this goal, we present a special data-mining procedure for identifying metabolic objective functions in mammalian cells. This multi-level optimization based algorithm enables identifying the major fluxes in the metabolic objective from MFA data in the absence of information about critical active constraints of the system. Further, once the objective is determined, active flux constraints can also be identified and analyzed. This information can be potentially used in a predictive manner to improve cell culture results or clinical metabolic outcomes. As a result of the application of this method, it was found that in vitro cultured hepatocytes maximize oxygen uptake, coupling of urea and TCA cycles, and synthesis of serine and urea. Selection of these fluxes as the metabolic objective enables accurate prediction of the flux distribution in the system given a limited amount of flux data; thus presenting a workable in silico model for cultured hepatocytes. It is observed that an overall homeostasis picture is also emergent in the findings.

Variable terrestrial GPS telemetry detection rates: Addressing the probability of successful acquisitions

USGS Publications Warehouse

Ironside, Kirsten E.; Mattson, David J.; Choate, David; Stoner, David; Arundel, Terry; Hansen, Jered R.; Theimer, Tad; Holton, Brandon; Jansen, Brian; Sexton, Joseph O.; Longshore, Kathleen M.; Edwards, Thomas C.; Peters, Michael

2017-01-01

Studies using global positioning system (GPS) telemetry rarely result in 100% fix success rates (FSR), which may bias datasets because data loss is systematic rather than a random process. Previous spatially explicit models developed to correct for sampling bias have been limited to small study areas, a small range of data loss, or were study-area specific. We modeled environmental effects on FSR from desert to alpine biomes, investigated the full range of potential data loss (0–100% FSR), and evaluated whether animal body position can contribute to lower FSR because of changes in antenna orientation based on GPS detection rates for 4 focal species: cougars (Puma concolor), desert bighorn sheep (Ovis canadensis nelsoni), Rocky Mountain elk (Cervus elaphus nelsoni), and mule deer (Odocoileus hemionus). Terrain exposure and height of over story vegetation were the most influential factors affecting FSR. Model evaluation showed a strong correlation (0.88) between observed and predicted FSR and no significant differences between predicted and observed FSRs using 2 independent validation datasets. We found that cougars and canyon-dwelling bighorn sheep may select for environmental features that influence their detectability by GPS technology, mule deer may select against these features, and elk appear to be nonselective. We observed temporal patterns in missed fixes only for cougars. We provide a model for cougars, predicting fix success by time of day that is likely due to circadian changes in collar orientation and selection of daybed sites. We also provide a model predicting the probability of GPS fix acquisitions given environmental conditions, which had a strong relationship (r 2 = 0.82) with deployed collar FSRs across species.

A new method for analysis of limit cycle behavior of the NASA/JPL 70-meter antenna axis servos

NASA Technical Reports Server (NTRS)

Hill, R. E.

1989-01-01

A piecewise linear method of analyzing the effects of discontinuous nonlinearities on control system performance is described. The limit cycle oscillatory behavior of the system resulting from the nonlinearities is described in terms of a sequence of linear system transient responses. The equations are derived which relate the initial and the terminal conditions of successive transients and the boundary conditions imposed by the non-linearities. The method leads to a convenient computation algorithm for prediction of limit cycle characteristics resulting from discontinuous nonlinearities such as friction, deadzones, and hysteresis.

Use of computed tomography to predict the outcome of a noninvasive intranasal infusion in dogs with nasal aspergillosis

PubMed Central

Saunders, Jimmy H.; Duchateau, Luc; Störk, Christophe; van Bree, Henri

2003-01-01

Computed tomography (CT) was performed on 36 dogs with nasal aspergillosis to assess whether this imaging technique can be used to predict the success of a noninvasive intranasal infusion of enilconazole. A CT score based on the severity of the disease was given to each dog, prior to treatment, by dividing the nasal cavities and frontal sinuses into 8 anatomical regions. After therapy, the dogs were classified into 2 response groups (success group: dogs cured after 1 treatment; failure group: dogs needing more than 1 treatment or with treatment failure). No significant relationship on the logistic scale was found between the CT score and the response to treatment. High sensitivity (treatment failures correctly predicted) and specificity (treatment successes correctly predicted) could not be obtained at the same time, whatever the cut-off value chosen. The results of this study suggest that CT cannot predict the therapeutic success of nasal aspergillosis in dogs treated with a 1-hour infusion of enilconazole. However, dogs with a low score seem to be good candidates to respond after 1 treatment. PMID:12715982

Piecewise multivariate modelling of sequential metabolic profiling data.

PubMed

Rantalainen, Mattias; Cloarec, Olivier; Ebbels, Timothy M D; Lundstedt, Torbjörn; Nicholson, Jeremy K; Holmes, Elaine; Trygg, Johan

2008-02-19

Modelling the time-related behaviour of biological systems is essential for understanding their dynamic responses to perturbations. In metabolic profiling studies, the sampling rate and number of sampling points are often restricted due to experimental and biological constraints. A supervised multivariate modelling approach with the objective to model the time-related variation in the data for short and sparsely sampled time-series is described. A set of piecewise Orthogonal Projections to Latent Structures (OPLS) models are estimated, describing changes between successive time points. The individual OPLS models are linear, but the piecewise combination of several models accommodates modelling and prediction of changes which are non-linear with respect to the time course. We demonstrate the method on both simulated and metabolic profiling data, illustrating how time related changes are successfully modelled and predicted. The proposed method is effective for modelling and prediction of short and multivariate time series data. A key advantage of the method is model transparency, allowing easy interpretation of time-related variation in the data. The method provides a competitive complement to commonly applied multivariate methods such as OPLS and Principal Component Analysis (PCA) for modelling and analysis of short time-series data.

White light emitting diode as potential replacement of tungsten-halogen lamp for visible spectroscopy system: a case study in the measurement of mango qualities

NASA Astrophysics Data System (ADS)

Chiong, W. L.; Omar, A. F.

2017-07-01

Non-destructive technique based on visible (VIS) spectroscopy using light emitting diode (LED) as lighting was used for evaluation of the internal quality of mango fruit. The objective of this study was to investigate feasibility of white LED as lighting in spectroscopic instrumentation to predict the acidity and soluble solids content of intact Sala Mango. The reflectance spectra of the mango samples were obtained and measured in the visible range (400-700 nm) using VIS spectroscopy illuminated under different white LEDs and tungsten-halogen lamp (pro lamp). Regression models were developed by multiple linear regression to establish the relationship between spectra and internal quality. Direct calibration transfer procedure was then applied between master and slave lighting to check on the acidity prediction results after transfer. Determination of mango acidity under white LED lighting was successfully performed through VIS spectroscopy using multiple linear regression but otherwise for soluble solids content. Satisfactory results were obtained for calibration transfer between LEDs with different correlated colour temperature indicated this technique was successfully used in spectroscopy measurement between two similar light sources in prediction of internal quality of mango.

A multichannel model-based methodology for extubation readiness decision of patients on weaning trials.

PubMed

Casaseca-de-la-Higuera, Pablo; Simmross-Wattenberg, Federico; Martín-Fernández, Marcos; Alberola-López, Carlos

2009-07-01

Discontinuation of mechanical ventilation is a challenging task that involves a number of subtle clinical issues. The gradual removal of the respiratory support (referred to as weaning) should be performed as soon as autonomous respiration can be sustained. However, the prediction rate of successful extubation is still below 25% based on previous studies. Construction of an automatic system that provides information on extubation readiness is thus desirable. Recent works have demonstrated that the breathing pattern variability is a useful extubation readiness indicator, with improving performance when multiple respiratory signals are jointly processed. However, the existing methods for predictor extraction present several drawbacks when length-limited time series are to be processed in heterogeneous groups of patients. In this paper, we propose a model-based methodology for automatic readiness prediction. It is intended to deal with multichannel, nonstationary, short records of the breathing pattern. Results on experimental data yield an 87.27% of successful readiness prediction, which is in line with the best figures reported in the literature. A comparative analysis shows that our methodology overcomes the shortcomings of so far proposed methods when applied to length-limited records on heterogeneous groups of patients.

Flexible and fast: linguistic shortcut affects both shallow and deep conceptual processing.

PubMed

Connell, Louise; Lynott, Dermot

2013-06-01

Previous research has shown that people use linguistic distributional information during conceptual processing, and that it is especially useful for shallow tasks and rapid responding. Using two conceptual combination tasks, we showed that this linguistic shortcut extends to the processing of novel stimuli, is used in both successful and unsuccessful conceptual processing, and is evident in both shallow and deep conceptual tasks. Specifically, as predicted by the ECCo theory of conceptual combination, people use the linguistic shortcut as a "quick-and-dirty" guide to whether the concepts are likely to combine into a coherent conceptual representation, in both shallow sensibility judgment and deep interpretation generation tasks. Linguistic distributional frequency predicts both the likelihood and the time course of rejecting a novel word compound as nonsensical or uninterpretable. However, it predicts the time course of successful processing only in shallow sensibility judgment, because the deeper conceptual process of interpretation generation does not allow the linguistic shortcut to suffice. Furthermore, the effects of linguistic distributional frequency are independent of any effects of conventional word frequency. We discuss the utility of the linguistic shortcut as a cognitive triage mechanism that can optimize processing in a limited-resource conceptual system.

Predicting success in the treatment of psychopaths.

PubMed

Copas, J B; Whiteley, J S

1976-10-01

Factors from the social history of male psychopaths were examined in relation to their success or failure as measured by re-convictions of psychiatric hospital re-admissions 2-3 years after treatment at Henderson Hospital. From these data a weighted prediction formula was calculated and was tested out on a further cohort of patients. As a prediction instrument it was found to be reliable to a degree of significance just above the 1 per cent level. Other aspects of the subsequent career of treated patients are noted, in particular a tendency to early but often short-lived relapse and then longer standing success.

Multicomponent ionic liquid CMC prediction.

PubMed

Kłosowska-Chomiczewska, I E; Artichowicz, W; Preiss, U; Jungnickel, C

2017-09-27

We created a model to predict CMC of ILs based on 704 experimental values published in 43 publications since 2000. Our model was able to predict CMC of variety of ILs in binary or ternary system in a presence of salt or alcohol. The molecular volume of IL (V m ), solvent-accessible surface (Ŝ), solvation enthalpy (Δ solv G ∞ ), concentration of salt (C s ) or alcohol (C a ) and their molecular volumes (V ms and V ma , respectively) were chosen as descriptors, and Kernel Support Vector Machine (KSVM) and Evolutionary Algorithm (EA) as regression methodologies to create the models. Data was split into training and validation set (80/20) and subjected to bootstrap aggregation. KSVM provided better fit with average R 2 of 0.843, and MSE of 0.608, whereas EA resulted in R 2 of 0.794 and MSE of 0.973. From the sensitivity analysis it was shown that V m and Ŝ have the highest impact on ILs micellization in both binary and ternary systems, however surprisingly in the presence of alcohol the V m becomes insignificant/irrelevant. Micelle stabilizing or destabilizing influence of the descriptors depends upon the additives. Previous attempts at modelling the CMC of ILs was generally limited to small number of ILs in simplified (binary) systems. We however showed successful prediction of the CMC over a range of different systems (binary and ternary).

On the limitations of standard statistical modeling in biological systems: a full Bayesian approach for biology.

PubMed

Gomez-Ramirez, Jaime; Sanz, Ricardo

2013-09-01

One of the most important scientific challenges today is the quantitative and predictive understanding of biological function. Classical mathematical and computational approaches have been enormously successful in modeling inert matter, but they may be inadequate to address inherent features of biological systems. We address the conceptual and methodological obstacles that lie in the inverse problem in biological systems modeling. We introduce a full Bayesian approach (FBA), a theoretical framework to study biological function, in which probability distributions are conditional on biophysical information that physically resides in the biological system that is studied by the scientist. Copyright © 2013 Elsevier Ltd. All rights reserved.

Improved cyberinfrastructure for integrated hydrometeorological predictions within the fully-coupled WRF-Hydro modeling system

NASA Astrophysics Data System (ADS)

gochis, David; hooper, Rick; parodi, Antonio; Jha, Shantenu; Yu, Wei; Zaslavsky, Ilya; Ganapati, Dinesh

2014-05-01

The community WRF-Hydro system is currently being used in a variety of flood prediction and regional hydroclimate impacts assessment applications around the world. Despite its increasingly wide use certain cyberinfrastructure bottlenecks exist in the setup, execution and post-processing of WRF-Hydro model runs. These bottlenecks result in wasted time, labor, data transfer bandwidth and computational resource use. Appropriate development and use of cyberinfrastructure to setup and manage WRF-Hydro modeling applications will streamline the entire workflow of hydrologic model predictions. This talk will present recent advances in the development and use of new open-source cyberinfrastructure tools for the WRF-Hydro architecture. These tools include new web-accessible pre-processing applications, supercomputer job management applications and automated verification and visualization applications. The tools will be described successively and then demonstrated in a set of flash flood use cases for recent destructive flood events in the U.S. and in Europe. Throughout, an emphasis on the implementation and use of community data standards for data exchange is made.

Directed Nanopatterning with Nonlinear Laser Lithography

NASA Astrophysics Data System (ADS)

Tokel, Onur; Yavuz, Ozgun; Ergecen, Emre; Pavlov, Ihor; Makey, Ghaith; Ilday, Fatih Omer

In spite of the successes of maskless optical nanopatterning methods, it remains extremely challenging to create any isotropic, periodic nanopattern. Further, available optical techniques lack the long-range coverage and high periodicity demanded by photonics and photovoltaics applications. Here, we provide a novel solution with Nonlinear Laser Lithography (NLL) approach. Notably, we demonstrate that self-organized nanopatterns can be produced in all possible Bravais lattice types. Further, we show that carefully chosen defects or structued noise can direct NLL symmetries. Exploitation of directed self-organizatio to select or guide to predetermined symmetries is a new capability. Predictive capabilities for such far-from-equilibrium, dissipative systems is very limited due to a lack of experimental systems with predictive models. Here we also present a completely predictive model, and experimentally confirm that the emergence of motifs can be regulated by engineering defects, while the polarization of the ultrafast laser prescribes lattice symmetry, which in turn reinforces translational invariance. Thus, NLL enables a novel, maskless nanofabrication approach, where laser-induced nanopatterns can be rapidly created in any lattice symmetry

Metabolomics for organic food authentication: Results from a long-term field study in carrots.

PubMed

Cubero-Leon, Elena; De Rudder, Olivier; Maquet, Alain

2018-01-15

Increasing demand for organic products and their premium prices make them an attractive target for fraudulent malpractices. In this study, a large-scale comparative metabolomics approach was applied to investigate the effect of the agronomic production system on the metabolite composition of carrots and to build statistical models for prediction purposes. Orthogonal projections to latent structures-discriminant analysis (OPLS-DA) was applied successfully to predict the origin of the agricultural system of the harvested carrots on the basis of features determined by liquid chromatography-mass spectrometry. When the training set used to build the OPLS-DA models contained samples representative of each harvest year, the models were able to classify unknown samples correctly (100% correct classification). If a harvest year was left out of the training sets and used for predictions, the correct classification rates achieved ranged from 76% to 100%. The results therefore highlight the potential of metabolomic fingerprinting for organic food authentication purposes. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

An Analytical Explanation for the X-43A Flush Air Data Sensing System Pressure Mismatch Between Flight and Theory

NASA Technical Reports Server (NTRS)

Ellsworth, Joel C.

2010-01-01

Following the successful Mach 7 flight test of the X-43A, unexpectedly low pressures were measured by the aft set of the onboard Flush Air Data Sensing System s pressure ports. These in-flight aft port readings were significantly lower below Mach 3.5 than was predicted by theory. The same lower readings were also seen in the Mach 10 flight of the X-43A and in wind-tunnel data. The pre-flight predictions were developed based on 2-dimensional wedge flow, which fails to predict some of the significant 3-dimensional flow features in this geometry at lower Mach numbers. Using Volterra s solution to the wave equation as a starting point, a three-dimensional finite wedge approximation to flow over the X-43A forebody is presented. The surface pressures from this approximation compare favorably with the measured wind tunnel and flight data at speeds of Mach 2.5 and 3.

Knowing what to sell, when, and to whom.

PubMed

Kumar, V; Venkatesan, Rajkumar; Reinartz, Werner

2006-03-01

Despite an abundance of data, most companies do a poor job of predicting the behavior of their customers. In fact, the authors' research suggests that even companies that take the greatest trouble over their predictions about whether a particular customer will buy a particular product are correct only around 55% of the time--a result that hardly justifies the costs of having a CRM system in the first place. Businesses usually conclude from studies like this that it's impossible to use the past to predict the future, so they revert to the timeworn marketing practice of inundating their customers with offers. But as the authors explain, the reason for the poor predictions is not any basic limitation of CRM systems or the predictive power of past behavior, but rather of the mathematical methods that companies use to interpret the data. The authors have developed a new way of predicting customer behavior, based on the work of the Nobel Prize-winning economist Daniel McFadden, that delivers vastly improved results. Indeed, the methodology increases the odds of successfully predicting a specific purchase by a specific customer at a specific time to about 85%, a number that will have a major impact on any company's marketing ROI. What's more, using this methodology, companies can increase revenues while reducing their frequency of customer contact-evidence that overcommunication with customers may actually damage a company's sales.

Loss-of-flow-without-scram tests in Experimental Breeder Reactor-II and comparison with pretest predictions

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

Chang, L.K.; Mohr, D.; Planchon, H.P.

This article discusses a series of successful loss-of-flow-without-scram tests conducted in Experimental Breeder Reactor-II (EBR-II), a metal-fueled, sodium-cooled fast reactor. These May 1985 tests demonstrated the capability of the EBR to reduce reactor power passively during a loss of flow and to maintain reactor temperatures within bounds without any reliance on an active safety system. The tests were run from reduced power to ensure that temperatures could be maintained well below the fuel-clad eutectic temperature. Good agreement was found between selected test data and pretest predictions made with the EBR-II system analysis code NATDEMO and the hot channel analysis codemore » HOTCHAN. The article also discusses safety assessments of the tests as well as modifications required on the EBR-II reactor safety system for conducting required on the EBR-II reactor safety system for the conducting the tests.« less

Predicting Treatment Success in Child and Parent Therapy Among Families in Poverty.

PubMed

Mattek, Ryan J; Harris, Sara E; Fox, Robert A

2016-01-01

Behavior problems are prevalent in young children and those living in poverty are at increased risk for stable, high-intensity behavioral problems. Research has demonstrated that participation in child and parent therapy (CPT) programs significantly reduces problematic child behaviors while increasing positive behaviors. However, CPT programs, particularly those implemented with low-income populations, frequently report high rates of attrition (over 50%). Parental attributional style has shown some promise as a contributing factor to treatment attendance and termination in previous research. The authors examined if parental attributional style could predict treatment success in a CPT program, specifically targeting low-income urban children with behavior problems. A hierarchical logistic regression was used with a sample of 425 families to assess if parent- and child-referent attributions variables predicted treatment success over and above demographic variables and symptom severity. Parent-referent attributions, child-referent attributions, and child symptom severity were found to be significant predictors of treatment success. Results indicated that caregivers who viewed themselves as a contributing factor for their child's behavior problems were significantly more likely to demonstrate treatment success. Alternatively, caregivers who viewed their child as more responsible for their own behavior problems were less likely to demonstrate treatment success. Additionally, more severe behavior problems were also predictive of treatment success. Clinical and research implications of these results are discussed.

The temporalis muscle flap and temporoparietal fascial flap.

PubMed

Lam, Din; Carlson, Eric R

2014-08-01

The temporal arterial system provides reliable vascular anatomy for the temporalis muscle flap and temporoparietal fascial flap that can support multiple reconstructive needs of the oral and maxillofacial region. The minimal donor site morbidity and ease of development of these flaps result in their predictable and successful transfer for reconstructive surgery of the oral and maxillofacial region. Copyright © 2014 Elsevier Inc. All rights reserved.

Predicting Urban Elementary Student Success and Passage on Ohio's High-Stakes Achievement Measures Using DIBELS Oral Reading Fluency and Informal Math Concepts and Applications: An Exploratory Study Employing Hierarchical Linear Modeling

ERIC Educational Resources Information Center

Merkle, Erich Robert

2011-01-01

Contemporary education is experiencing substantial reform across legislative, pedagogical, and assessment dimensions. The increase in school-based accountability systems has brought forth a culture where states, school districts, teachers, and individual students are required to demonstrate their efficacy towards improvement of the educational…

Cyberdeterrence and Cyberwar

DTIC Science & Technology

2009-01-01

introduce subtle but vehicle-damaging changes to the chemical mix in the gasoline. Battle damage prediction is critical in establishing deterrence at all...the emotional resonance of the sort engen - dered by bloody terrorist attacks. If the target system is private (which it would practically have to be...secondary deterrence are the successful dissuasion of Nazi Germany and, half a century later, Saddam’s Iraq from using chemical weapons. Strategic

Volcano deformation--Geodetic monitoring techniques

USGS Publications Warehouse

Dzurisin, Daniel; Lu, Zhong

2007-01-01

This book describes the techniques used by volcanologists to successfully predict several recent volcanic eruptions by combining information from various scientific disciplines, including geodetic techniques. Many recent developments in the use of state-of-the-art and emerging techniques, including Global Positioning System and Synthetic Aperture Radar Interferometry, mean that most books on volcanology are out of date, and this book includes chapters devoted entirely to these two techniques.

Prediction of Significant Wave Heights in the Tropics at Sub-seasonal Time Scales

NASA Astrophysics Data System (ADS)

Kinter, J. L.; Shukla, R. P.; Shin, C. S.

2017-12-01

Skillfully predicting the 14-day mean significant wave height (SWH) forecasts at 3 weeks lead-time over the Western Pacific and Indian Oceans has been demonstrated using the WAVEWATCH-3 (WW3) model coupled to a modified version of the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2). In this paper, we present results on the effect of the Madden Julian Oscillation (MJO) events and El Niño and the Southern Oscillation (ENSO) on such predictions. Forecasts initialized with multiple ocean analyses in both January and May for 1979-2008 are evaluated. A significant anomaly correlation of predicted and observed SWH anomalies (SWHA) at 3 weeks lead-time is found over portions of the domain in both January and May cases. The model successfully predicts almost all the important features of the observed SWHA during El Niño events in January, including negative SWHA in the central Indian Ocean and northern western tropical Pacific, and positive SWHA over the southern Ocean and western Pacific. The model also reproduces the spatial pattern of the inverse relationship between SWHA and sea level pressure anomalies during both composite El Niño and La Niña events at 3 weeks lead-time. The model successfully predicts the sign and magnitude of SWHA in May over the Bay of Bengal and South China Sea in composites of phases 2 and 6 of MJO. The observed leading mode of SWHA in May and the third mode of SWHA in January are influenced by the combined effects of MJO and ENSO. Analysis of the mechanisms for these relationships is described.

Exploration of government policy structure which support and block energy transition process in indonesia using system dynamics model

NASA Astrophysics Data System (ADS)

Destyanto, A. R.; Silalahi, T. D.; Hidayatno, A.

2017-11-01

System dynamic modeling is widely used to predict and simulate the energy system in several countries. One of the applications of system dynamics is to evaluate national energy policy alternatives, and energy efficiency analysis. Using system dynamic modeling, this research aims to evaluate the energy transition policy that has been implemented in Indonesia on the past conversion program of kerosene to LPG for household cook fuel consumption, which considered as successful energy transition program implemented since 2007. This research is important since Indonesia considered not yet succeeded to execute another energy transition program on conversion program of oil fuel to gas fuel for transportation that has started since 1989. The aim of this research is to explore which policy intervention that has significant contribution to support or even block the conversion program. Findings in this simulation show that policy intervention to withdraw the kerosene supply and government push to increase production capacity of the support equipment industries (gas stove, regulator, and LPG Cylinder) is the main influence on the success of the program conversion program.

Managing living marine resources in a dynamic environment: The role of seasonal to decadal climate forecasts

NASA Astrophysics Data System (ADS)

Tommasi, Desiree; Stock, Charles A.; Hobday, Alistair J.; Methot, Rick; Kaplan, Isaac C.; Eveson, J. Paige; Holsman, Kirstin; Miller, Timothy J.; Gaichas, Sarah; Gehlen, Marion; Pershing, Andrew; Vecchi, Gabriel A.; Msadek, Rym; Delworth, Tom; Eakin, C. Mark; Haltuch, Melissa A.; Séférian, Roland; Spillman, Claire M.; Hartog, Jason R.; Siedlecki, Samantha; Samhouri, Jameal F.; Muhling, Barbara; Asch, Rebecca G.; Pinsky, Malin L.; Saba, Vincent S.; Kapnick, Sarah B.; Gaitan, Carlos F.; Rykaczewski, Ryan R.; Alexander, Michael A.; Xue, Yan; Pegion, Kathleen V.; Lynch, Patrick; Payne, Mark R.; Kristiansen, Trond; Lehodey, Patrick; Werner, Francisco E.

2017-03-01

Recent developments in global dynamical climate prediction systems have allowed for skillful predictions of climate variables relevant to living marine resources (LMRs) at a scale useful to understanding and managing LMRs. Such predictions present opportunities for improved LMR management and industry operations, as well as new research avenues in fisheries science. LMRs respond to climate variability via changes in physiology and behavior. For species and systems where climate-fisheries links are well established, forecasted LMR responses can lead to anticipatory and more effective decisions, benefitting both managers and stakeholders. Here, we provide an overview of climate prediction systems and advances in seasonal to decadal prediction of marine-resource relevant environmental variables. We then describe a range of climate-sensitive LMR decisions that can be taken at lead-times of months to decades, before highlighting a range of pioneering case studies using climate predictions to inform LMR decisions. The success of these case studies suggests that many additional applications are possible. Progress, however, is limited by observational and modeling challenges. Priority developments include strengthening of the mechanistic linkages between climate and marine resource responses, development of LMR models able to explicitly represent such responses, integration of climate driven LMR dynamics in the multi-driver context within which marine resources exist, and improved prediction of ecosystem-relevant variables at the fine regional scales at which most marine resource decisions are made. While there are fundamental limits to predictability, continued advances in these areas have considerable potential to make LMR managers and industry decision more resilient to climate variability and help sustain valuable resources. Concerted dialog between scientists, LMR managers and industry is essential to realizing this potential.

Does the Number of College Credits Earned in a Tech Prep and Postsecondary Enrollment Options Program Predict College Success?

ERIC Educational Resources Information Center

Meyer, Bruce A.

2011-01-01

The purpose of this study was to examine a Tech Prep Program located in Northwest Ohio and determine the degree to which college credits earned in high school through the Tech Prep and PSEO Programs predict college success and if there were any significant gender/race differences in credits earned and college success as well as high school…

Multivariate Brain Prediction of Heart Rate and Skin Conductance Responses to Social Threat.

PubMed

Eisenbarth, Hedwig; Chang, Luke J; Wager, Tor D

2016-11-23

Psychosocial stressors induce autonomic nervous system (ANS) responses in multiple body systems that are linked to health risks. Much work has focused on the common effects of stress, but ANS responses in different body systems are dissociable and may result from distinct patterns of cortical-subcortical interactions. Here, we used machine learning to develop multivariate patterns of fMRI activity predictive of heart rate (HR) and skin conductance level (SCL) responses during social threat in humans (N = 18). Overall, brain patterns predicted both HR and SCL in cross-validated analyses successfully (r HR = 0.54, r SCL = 0.58, both p < 0.0001). These patterns partly reflected central stress mechanisms common to both responses because each pattern predicted the other signal to some degree (r HR→SCL = 0.21 and r SCL→HR = 0.22, both p < 0.01), but they were largely physiological response specific. Both patterns included positive predictive weights in dorsal anterior cingulate and cerebellum and negative weights in ventromedial PFC and local pattern similarity analyses within these regions suggested that they encode common central stress mechanisms. However, the predictive maps and searchlight analysis suggested that the patterns predictive of HR and SCL were substantially different across most of the brain, including significant differences in ventromedial PFC, insula, lateral PFC, pre-SMA, and dmPFC. Overall, the results indicate that specific patterns of cerebral activity track threat-induced autonomic responses in specific body systems. Physiological measures of threat are not interchangeable, but rather reflect specific interactions among brain systems. We show that threat-induced increases in heart rate and skin conductance share some common representations in the brain, located mainly in the vmPFC, temporal and parahippocampal cortices, thalamus, and brainstem. However, despite these similarities, the brain patterns that predict these two autonomic responses are largely distinct. This evidence for largely output-measure-specific regulation of autonomic responses argues against a common system hypothesis and provides evidence that different autonomic measures reflect distinct, measurable patterns of cortical-subcortical interactions. Copyright © 2016 the authors 0270-6474/16/3611987-12$15.00/0.

Using data mining to predict success in a weight loss trial.

PubMed

Batterham, M; Tapsell, L; Charlton, K; O'Shea, J; Thorne, R

2017-08-01

Traditional methods for predicting weight loss success use regression approaches, which make the assumption that the relationships between the independent and dependent (or logit of the dependent) variable are linear. The aim of the present study was to investigate the relationship between common demographic and early weight loss variables to predict weight loss success at 12 months without making this assumption. Data mining methods (decision trees, generalised additive models and multivariate adaptive regression splines), in addition to logistic regression, were employed to predict: (i) weight loss success (defined as ≥5%) at the end of a 12-month dietary intervention using demographic variables [body mass index (BMI), sex and age]; percentage weight loss at 1 month; and (iii) the difference between actual and predicted weight loss using an energy balance model. The methods were compared by assessing model parsimony and the area under the curve (AUC). The decision tree provided the most clinically useful model and had a good accuracy (AUC 0.720 95% confidence interval = 0.600-0.840). Percentage weight loss at 1 month (≥0.75%) was the strongest predictor for successful weight loss. Within those individuals losing ≥0.75%, individuals with a BMI (≥27 kg m -2 ) were more likely to be successful than those with a BMI between 25 and 27 kg m -2 . Data mining methods can provide a more accurate way of assessing relationships when conventional assumptions are not met. In the present study, a decision tree provided the most parsimonious model. Given that early weight loss cannot be predicted before randomisation, incorporating this information into a post randomisation trial design may give better weight loss results. © 2017 The British Dietetic Association Ltd.

Identification and Management of Pump Thrombus in the HeartWare Left Ventricular Assist Device System: A Novel Approach Using Log File Analysis.

PubMed

Jorde, Ulrich P; Aaronson, Keith D; Najjar, Samer S; Pagani, Francis D; Hayward, Christopher; Zimpfer, Daniel; Schlöglhofer, Thomas; Pham, Duc T; Goldstein, Daniel J; Leadley, Katrin; Chow, Ming-Jay; Brown, Michael C; Uriel, Nir

2015-11-01

The study sought to characterize patterns in the HeartWare (HeartWare Inc., Framingham, Massachusetts) ventricular assist device (HVAD) log files associated with successful medical treatment of device thrombosis. Device thrombosis is a serious adverse event for mechanical circulatory support devices and is often preceded by increased power consumption. Log files of the pump power are easily accessible on the bedside monitor of HVAD patients and may allow early diagnosis of device thrombosis. Furthermore, analysis of the log files may be able to predict the success rate of thrombolysis or the need for pump exchange. The log files of 15 ADVANCE trial patients (algorithm derivation cohort) with 16 pump thrombus events treated with tissue plasminogen activator (tPA) were assessed for changes in the absolute and rate of increase in power consumption. Successful thrombolysis was defined as a clinical resolution of pump thrombus including normalization of power consumption and improvement in biochemical markers of hemolysis. Significant differences in log file patterns between successful and unsuccessful thrombolysis treatments were verified in 43 patients with 53 pump thrombus events implanted outside of clinical trials (validation cohort). The overall success rate of tPA therapy was 57%. Successful treatments had significantly lower measures of percent of expected power (130.9% vs. 196.1%, p = 0.016) and rate of increase in power (0.61 vs. 2.87, p < 0.0001). Medical therapy was successful in 77.7% of the algorithm development cohort and 81.3% of the validation cohort when the rate of power increase and percent of expected power values were <1.25% and 200%, respectively. Log file parameters can potentially predict the likelihood of successful tPA treatments and if validated prospectively, could substantially alter the approach to thrombus management. Copyright © 2015 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Musical trends and predictability of success in contemporary songs in and out of the top charts

PubMed Central

Interiano, Myra; Kazemi, Kamyar; Wang, Lijia; Yang, Jienian; Yu, Zhaoxia

2018-01-01

We analyse more than 500 000 songs released in the UK between 1985 and 2015 to understand the dynamics of success (defined as ‘making it’ into the top charts), correlate success with acoustic features and explore the predictability of success. Several multi-decadal trends have been uncovered. For example, there is a clear downward trend in ‘happiness’ and ‘brightness’, as well as a slight upward trend in ‘sadness’. Furthermore, songs are becoming less ‘male’. Interestingly, successful songs exhibit their own distinct dynamics. In particular, they tend to be ‘happier’, more ‘party-like’, less ‘relaxed’ and more ‘female’ than most. The difference between successful and average songs is not straightforward. In the context of some features, successful songs pre-empt the dynamics of all songs, and in others they tend to reflect the past. We used random forests to predict the success of songs, first based on their acoustic features, and then adding the ‘superstar’ variable (informing us whether the song’s artist had appeared in the top charts in the near past). This allowed quantification of the contribution of purely musical characteristics in the songs’ success, and suggested the time scale of fashion dynamics in popular music. PMID:29892348

Predictive Success Factors in Selective Upper Airway Stimulation.

PubMed

Heiser, Clemens; Hofauer, Benedikt

2017-01-01

Obstructive sleep apnea is one of the most common diseases in Western industrialized countries. A variety of conservative and surgical treatment options are available for its treatment. In recent years, selective upper airway stimulation (sUAS) has been shown to be effective and safe. Different biomarkers have been investigated as predictive clinical success factors in a number of clinical trials. Age does not matter in sUAS, as compared to its predictive role in other therapies. Weight seems to play a limited role, depending on drug-induced sleep endoscopy to rule out a complete concentric collapse with an increased body mass index. For surgical success and the related postoperative tongue motions, a nerve integrity monitoring methodology has been developed for predicting correct cuff placement. Postoperative sonography remains a promising method for the future assessment of predictive markers in sUAS. © 2017 S. Karger AG, Basel.

Comprehensive predictions of target proteins based on protein-chemical interaction using virtual screening and experimental verifications.

PubMed

Kobayashi, Hiroki; Harada, Hiroko; Nakamura, Masaomi; Futamura, Yushi; Ito, Akihiro; Yoshida, Minoru; Iemura, Shun-Ichiro; Shin-Ya, Kazuo; Doi, Takayuki; Takahashi, Takashi; Natsume, Tohru; Imoto, Masaya; Sakakibara, Yasubumi

2012-04-05

Identification of the target proteins of bioactive compounds is critical for elucidating the mode of action; however, target identification has been difficult in general, mostly due to the low sensitivity of detection using affinity chromatography followed by CBB staining and MS/MS analysis. We applied our protocol of predicting target proteins combining in silico screening and experimental verification for incednine, which inhibits the anti-apoptotic function of Bcl-xL by an unknown mechanism. One hundred eighty-two target protein candidates were computationally predicted to bind to incednine by the statistical prediction method, and the predictions were verified by in vitro binding of incednine to seven proteins, whose expression can be confirmed in our cell system.As a result, 40% accuracy of the computational predictions was achieved successfully, and we newly found 3 incednine-binding proteins. This study revealed that our proposed protocol of predicting target protein combining in silico screening and experimental verification is useful, and provides new insight into a strategy for identifying target proteins of small molecules.

Competitive assessment of aerospace systems using system dynamics

NASA Astrophysics Data System (ADS)

Pfaender, Jens Holger

Aircraft design has recently experienced a trend away from performance centric design towards a more balanced approach with increased emphasis on engineering an economically successful system. This approach focuses on bringing forward a comprehensive economic and life-cycle cost analysis. Since the success of any system also depends on many external factors outside of the control of the designer, this traditionally has been modeled as noise affecting the uncertainty of the design. However, this approach is currently lacking a strategic treatment of necessary early decisions affecting the probability of success of a given concept in a dynamic environment. This suggests that the introduction of a dynamic method into a life-cycle cost analysis should allow the analysis of the future attractiveness of such a concept in the presence of uncertainty. One way of addressing this is through the use of a competitive market model. However, existing market models do not focus on the dynamics of the market. Instead, they focus on modeling and predicting market share through logit regression models. The resulting models exhibit relatively poor predictive capabilities. The method proposed here focuses on a top-down approach that integrates a competitive model based on work in the field of system dynamics into the aircraft design process. Demonstrating such integration is one of the primary contributions of this work, which previously has not been demonstrated. This integration is achieved through the use of surrogate models, in this case neural networks. This enabled not only the practical integration of analysis techniques, but also reduced the computational requirements so that interactive exploration as envisioned was actually possible. The example demonstration of this integration is built on the competition in the 250 seat large commercial aircraft market exemplified by the Boeing 767-400ER and the Airbus A330-200. Both aircraft models were calibrated to existing performance and certification data and then integrated into the system dynamics market model. The market model was then calibrated with historical market data. This calibration showed a much improved predictive capability as compared to the conventional logit regression models. An additional advantage of this dynamic model is that to realize this improved capability, no additional explanatory variables were required. Furthermore, the resulting market model was then integrated into a prediction profiler environment with a time variant Monte-Carlo analysis resulting in a unique trade-off environment. This environment was shown to allow interactive trade-off between aircraft design decisions and economic considerations while allowing the exploration potential market success in the light of varying external market conditions and scenarios. The resulting method is capable of reduced decision support uncertainty and identification of robust design decisions in future scenarios with a high likelihood of occurrence with special focus on the path dependent nature of future implications of decisions. Furthermore, it was possible to demonstrate the increased importance of design and technology choices on the competitiveness in scenarios with drastic increases in commodity prices during the time period modeled. Another use of the existing outputs of the Monte-Carlo analysis was then realized by showing them on a multivariate scatter plot. This plot was then shown to enable by appropriate grouping of variables to enable the top down definition of an aircraft design, also known as inverse design. In other words this enables the designer to define strategic market and return on investment goals for a number of scenarios, for example the development of fuel prices, and then directly see which specific aircraft designs meet these goals.

Real-time implementation of an interactive jazz accompaniment system

NASA Astrophysics Data System (ADS)

Deshpande, Nikhil

Modern computational algorithms and digital signal processing (DSP) are able to combine with human performers without forced or predetermined structure in order to create dynamic and real-time accompaniment systems. With modern computing power and intelligent algorithm layout and design, it is possible to achieve more detailed auditory analysis of live music. Using this information, computer code can follow and predict how a human's musical performance evolves, and use this to react in a musical manner. This project builds a real-time accompaniment system to perform together with live musicians, with a focus on live jazz performance and improvisation. The system utilizes a new polyphonic pitch detector and embeds it in an Ableton Live system - combined with Max for Live - to perform elements of audio analysis, generation, and triggering. The system also relies on tension curves and information rate calculations from the Creative Artificially Intuitive and Reasoning Agent (CAIRA) system to help understand and predict human improvisation. These metrics are vital to the core system and allow for extrapolated audio analysis. The system is able to react dynamically to a human performer, and can successfully accompany the human as an entire rhythm section.

Predicting the spread of aquatic invaders: insight from 200 years of invasion by zebra mussels.

PubMed

Karatayev, Alexander Y; Burlakova, Lyubov E; Mastitsky, Sergey E; Padilla, Dianna K

2015-03-01

Understanding factors controlling the introduction and spread of species is crucial to improving the management of both natural populations and introduced species. The zebra mussel, Dreissena polymorpha, is considered the most aggressive freshwater invader in the Northern Hemisphere, and is a convenient model system for invasion biology, offering one of the best aquatic examples for examining the invasion process. We used data on 553 of the 1040 glacial lakes in the Republic of Belarus that were examined for the presence of zebra mussels. We used these data to build, test, and construct modified models to predict the spread of this invader, including selection of important parameters that could limit the spread of this invader. In spite of 200 years of continuous invasion, by 1996, zebra mussels were found in only 16.8% of all lakes studied. Of those lakes without zebra mussels in 1996, 66% were predicted to be susceptible to invasion by zebra mussels in the future, and 33% were predicted to be immune to successful invasion due to their water chemistry. Eighty lakes free of zebra mussels in 1996 were reexamined from 1997 to 2008. Of these, zebra mussels successfully invaded an additional 31 lakes, all of which were classified initially as suitable for zebra mussels; none of the lakes previously classified as unsuitable were invaded. We used the Random Forests classification algorithm with 16 environmental variables to determine the most important factors that differed between invaded lakes and those lakes suitable for invasion that have not yet been invaded. Distance to the nearest infested lakes was found to be the most important variable, followed by the lake area, color, average depth, and concentration of chloride, magnesium, and bicarbonate. This study provides a useful approach for predicting the spread of an invader across a landscape with variable habitat suitability that can be applied to a variety of species and systems.

Belief state representation in the dopamine system.

PubMed

Babayan, Benedicte M; Uchida, Naoshige; Gershman, Samuel J

2018-05-14

Learning to predict future outcomes is critical for driving appropriate behaviors. Reinforcement learning (RL) models have successfully accounted for such learning, relying on reward prediction errors (RPEs) signaled by midbrain dopamine neurons. It has been proposed that when sensory data provide only ambiguous information about which state an animal is in, it can predict reward based on a set of probabilities assigned to hypothetical states (called the belief state). Here we examine how dopamine RPEs and subsequent learning are regulated under state uncertainty. Mice are first trained in a task with two potential states defined by different reward amounts. During testing, intermediate-sized rewards are given in rare trials. Dopamine activity is a non-monotonic function of reward size, consistent with RL models operating on belief states. Furthermore, the magnitude of dopamine responses quantitatively predicts changes in behavior. These results establish the critical role of state inference in RL.

Executive function predicts artificial language learning

PubMed Central

Kapa, Leah L.; Colombo, John

2017-01-01

Previous research suggests executive function (EF) advantages among bilinguals compared to monolingual peers, and these advantages are generally attributed to experience controlling two linguistic systems. However, the possibility that the relationship between bilingualism and EF might be bidirectional has not been widely considered; while experience with two languages might improve EF, better EF skills might also facilitate language learning. In the current studies, we tested whether adults’ and preschool children’s EF abilities predicted success in learning a novel artificial language. After controlling for working memory and English receptive vocabulary, adults’ artificial language performance was predicted by their inhibitory control ability (Study 1) and children’s performance was predicted by their attentional monitoring and shifting ability (Study 2). These findings provide preliminary evidence suggesting that EF processes may be employed during initial stages of language learning, particularly vocabulary acquisition, and support the possibility of a bidirectional relationship between EF and language acquisition. PMID:29129958

Predicting Defects Using Information Intelligence Process Models in the Software Technology Project

PubMed Central

Selvaraj, Manjula Gandhi; Jayabal, Devi Shree; Srinivasan, Thenmozhi; Balasubramanie, Palanisamy

2015-01-01

A key differentiator in a competitive market place is customer satisfaction. As per Gartner 2012 report, only 75%–80% of IT projects are successful. Customer satisfaction should be considered as a part of business strategy. The associated project parameters should be proactively managed and the project outcome needs to be predicted by a technical manager. There is lot of focus on the end state and on minimizing defect leakage as much as possible. Focus should be on proactively managing and shifting left in the software life cycle engineering model. Identify the problem upfront in the project cycle and do not wait for lessons to be learnt and take reactive steps. This paper gives the practical applicability of using predictive models and illustrates use of these models in a project to predict system testing defects thus helping to reduce residual defects. PMID:26495427

The spatial structure of a nonlinear receptive field.

PubMed

Schwartz, Gregory W; Okawa, Haruhisa; Dunn, Felice A; Morgan, Josh L; Kerschensteiner, Daniel; Wong, Rachel O; Rieke, Fred

2012-11-01

Understanding a sensory system implies the ability to predict responses to a variety of inputs from a common model. In the retina, this includes predicting how the integration of signals across visual space shapes the outputs of retinal ganglion cells. Existing models of this process generalize poorly to predict responses to new stimuli. This failure arises in part from properties of the ganglion cell response that are not well captured by standard receptive-field mapping techniques: nonlinear spatial integration and fine-scale heterogeneities in spatial sampling. Here we characterize a ganglion cell's spatial receptive field using a mechanistic model based on measurements of the physiological properties and connectivity of only the primary excitatory circuitry of the retina. The resulting simplified circuit model successfully predicts ganglion-cell responses to a variety of spatial patterns and thus provides a direct correspondence between circuit connectivity and retinal output.

Nondestructive Characterization Techniques Used for Ceramic Matrix Composite Life Determination

NASA Technical Reports Server (NTRS)

Effinger, Michael; Koenig, John; Ellingson, Bill; Spohnholtz, Todd

2000-01-01

Recent results indicate that the specific damping capacity and resonant frequency measurements taken periodically during a component's lifetime is able to quantify the mechanical fatigue of CMCS. This gives hope for the potential of determining the actual and residual life of CMC materials using a combination of nondestructive techniques. If successful, then this new paradigm for life prediction of CMCs could revolutionize the approach for designing and servicing CMC components, thereby significantly reducing costs for design, development, health monitoring, and maintenance of CMC components and systems. The Nondestructive Characterization (NDC) life prediction approach would complement life prediction using micromechanics and continuum finite element models. This paper reports on the initial concept of NDC life prediction, a review of the C/SiC blisk damping data, and how changes in the specific damping capacity & ultrasonic elastic modulus data have established the concept as a possibility.

Concept for Determining the Life of Ceramic Matrix Composites Using Nondestructive Characterization Techniques

NASA Technical Reports Server (NTRS)

Effinger, Michael; Ellingson, Bill; Spohnholtz, Todd; Koenig, John

2000-01-01

Damping measurements have been taken on ceramic matrix composite (CMC) turbopump blisks in the as fabricated, post proof testing, and post turbopump testing conditions. These results indicate that damping is able to quantify fatigue of the CMC blisk. This gives hope for the potential of determining the actual and residual life of CMC materials using a combination of nondestructive techniques. If successful, then this new paradigm for life prediction of CMCs could revolutionize the approach for designing and servicing CMC components, thereby significantly reducing costs for design, development, health monitoring, and maintenance of CMC components and systems. The Nondestructive Characterization (NDC) life prediction approach would complement life prediction using micromechanics and continuum finite element models. This paper reports on the initial concept of NDC life prediction and how changes in damping and ultrasonic elastic modulus data have established the concept as a possibility.

Predicting Defects Using Information Intelligence Process Models in the Software Technology Project.

PubMed

Selvaraj, Manjula Gandhi; Jayabal, Devi Shree; Srinivasan, Thenmozhi; Balasubramanie, Palanisamy

2015-01-01

A key differentiator in a competitive market place is customer satisfaction. As per Gartner 2012 report, only 75%-80% of IT projects are successful. Customer satisfaction should be considered as a part of business strategy. The associated project parameters should be proactively managed and the project outcome needs to be predicted by a technical manager. There is lot of focus on the end state and on minimizing defect leakage as much as possible. Focus should be on proactively managing and shifting left in the software life cycle engineering model. Identify the problem upfront in the project cycle and do not wait for lessons to be learnt and take reactive steps. This paper gives the practical applicability of using predictive models and illustrates use of these models in a project to predict system testing defects thus helping to reduce residual defects.

The nature and use of prediction skills in a biological computer simulation

NASA Astrophysics Data System (ADS)

Lavoie, Derrick R.; Good, Ron

The primary goal of this study was to examine the science process skill of prediction using qualitative research methodology. The think-aloud interview, modeled after Ericsson and Simon (1984), let to the identification of 63 program exploration and prediction behaviors.The performance of seven formal and seven concrete operational high-school biology students were videotaped during a three-phase learning sequence on water pollution. Subjects explored the effects of five independent variables on two dependent variables over time using a computer-simulation program. Predictions were made concerning the effect of the independent variables upon dependent variables through time. Subjects were identified according to initial knowledge of the subject matter and success at solving three selected prediction problems.Successful predictors generally had high initial knowledge of the subject matter and were formal operational. Unsuccessful predictors generally had low initial knowledge and were concrete operational. High initial knowledge seemed to be more important to predictive success than stage of Piagetian cognitive development.Successful prediction behaviors involved systematic manipulation of the independent variables, note taking, identification and use of appropriate independent-dependent variable relationships, high interest and motivation, and in general, higher-level thinking skills. Behaviors characteristic of unsuccessful predictors were nonsystematic manipulation of independent variables, lack of motivation and persistence, misconceptions, and the identification and use of inappropriate independent-dependent variable relationships.

Evaluation of single nucleotide polymorphisms in chromosomal regions impacting pregnancy status in cattle

USDA-ARS?s Scientific Manuscript database

Reproductive success is an important component of commercial beef cattle production, and identification of DNA markers with predictive merit for reproductive success would facilitate accurate prediction of mean daughter pregnancy rate, enabling effective selection of bulls to improve female fertilit...

Prediction of dissolved oxygen concentration in hypoxic river systems using support vector machine: a case study of Wen-Rui Tang River, China.

PubMed

Ji, Xiaoliang; Shang, Xu; Dahlgren, Randy A; Zhang, Minghua

2017-07-01

Accurate quantification of dissolved oxygen (DO) is critically important for managing water resources and controlling pollution. Artificial intelligence (AI) models have been successfully applied for modeling DO content in aquatic ecosystems with limited data. However, the efficacy of these AI models in predicting DO levels in the hypoxic river systems having multiple pollution sources and complicated pollutants behaviors is unclear. Given this dilemma, we developed a promising AI model, known as support vector machine (SVM), to predict the DO concentration in a hypoxic river in southeastern China. Four different calibration models, specifically, multiple linear regression, back propagation neural network, general regression neural network, and SVM, were established, and their prediction accuracy was systemically investigated and compared. A total of 11 hydro-chemical variables were used as model inputs. These variables were measured bimonthly at eight sampling sites along the rural-suburban-urban portion of Wen-Rui Tang River from 2004 to 2008. The performances of the established models were assessed through the mean square error (MSE), determination coefficient (R 2 ), and Nash-Sutcliffe (NS) model efficiency. The results indicated that the SVM model was superior to other models in predicting DO concentration in Wen-Rui Tang River. For SVM, the MSE, R 2 , and NS values for the testing subset were 0.9416 mg/L, 0.8646, and 0.8763, respectively. Sensitivity analysis showed that ammonium-nitrogen was the most significant input variable of the proposal SVM model. Overall, these results demonstrated that the proposed SVM model can efficiently predict water quality, especially for highly impaired and hypoxic river systems.

Nomogram to predict successful placement in surgical subspecialty fellowships using applicant characteristics.

PubMed

Muffly, Tyler M; Barber, Matthew D; Karafa, Matthew T; Kattan, Michael W; Shniter, Abigail; Jelovsek, J Eric

2012-01-01

The purpose of the study was to develop a model that predicts an individual applicant's probability of successful placement into a surgical subspecialty fellowship program. Candidates who applied to surgical fellowships during a 3-year period were identified in a set of databases that included the electronic application materials. Of the 1281 applicants who were available for analysis, 951 applicants (74%) successfully placed into a colon and rectal surgery, thoracic surgery, vascular surgery, or pediatric surgery fellowship. The optimal final prediction model, which was based on a logistic regression, included 14 variables. This model, with a c statistic of 0.74, allowed for the determination of a useful estimate of the probability of placement for an individual candidate. Of the factors that are available at the time of fellowship application, 14 were used to predict accurately the proportion of applicants who will successfully gain a fellowship position. Copyright © 2012 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

Temperature-dependence of biomass accumulation rates during secondary succession.

PubMed

Anderson, Kristina J; Allen, Andrew P; Gillooly, James F; Brown, James H

2006-06-01

Rates of ecosystem recovery following disturbance affect many ecological processes, including carbon cycling in the biosphere. Here, we present a model that predicts the temperature dependence of the biomass accumulation rate following disturbances in forests. Model predictions are derived based on allometric and biochemical principles that govern plant energetics and are tested using a global database of 91 studies of secondary succession compiled from the literature. The rate of biomass accumulation during secondary succession increases with average growing season temperature as predicted based on the biochemical kinetics of photosynthesis in chloroplasts. In addition, the rate of biomass accumulation is greater in angiosperm-dominated communities than in gymnosperm-dominated ones and greater in plantations than in naturally regenerating stands. By linking the temperature-dependence of photosynthesis to the rate of whole-ecosystem biomass accumulation during secondary succession, our model and results provide one example of how emergent, ecosystem-level rate processes can be predicted based on the kinetics of individual metabolic rate.

Predicting the integration of overlapping memories by decoding mnemonic processing states during learning

PubMed Central

Richter, Franziska R.; Chanales, Avi J. H.; Kuhl, Brice A.

2015-01-01

The hippocampal memory system is thought to alternate between two opposing processing states: encoding and retrieval. When present experience overlaps with past experience, this creates a potential tradeoff between encoding the present and retrieving the past. This tradeoff may be resolved by memory integration—that is, by forming a mnemonic representation that links present experience with overlapping past experience. Here, we used fMRI decoding analyses to predict when—and establish how—past and present experiences become integrated in memory. In an initial experiment, we alternately instructed subjects to adopt encoding, retrieval or integration states during overlapping learning. We then trained across-subject pattern classifiers to ‘read out’ the instructed processing states from fMRI activity patterns. We show that an integration state was clearly dissociable from encoding or retrieval states. Moreover, trial-by-trial fluctuations in decoded evidence for an integration state during learning reliably predicted behavioral expressions of successful memory integration. Strikingly, the decoding algorithm also successfully predicted specific instances of spontaneous memory integration in an entirely independent sample of subjects for whom processing state instructions were not administered. Finally, we show that medial prefrontal cortex and hippocampus differentially contribute to encoding, retrieval, and integration states: whereas hippocampus signals the tradeoff between encoding vs. retrieval states, medial prefrontal cortex actively represents past experience in relation to new learning. PMID:26327243

Laparoscopic sleeve gastrectomy for type 2 diabetes mellitus: predicting the success by ABCD score.

PubMed

Lee, Wei-Jei; Almulaifi, Abdullah; Tsou, Ju Juin; Ser, Kong-Han; Lee, Yi-Chih; Chen, Shu-Chun

2015-01-01

Laparoscopic sleeve gastrectomy (LSG) is becoming a primary bariatric surgery for obesity and related diseases. This study presents the outcome of LSG with regard to the remission of type 2 diabetes mellitus (T2 DM) and the usefulness of a grading system to categorize and predict outcome of T2 DM remission. A total of 157 patients with T2 DM (82 women and 75 men) with morbid obesity (mean body mass index 39.0±7.4 kg/m(2)) who underwent LSG from 2006 to 2013 were selected for the present study. The ABCD score is composed of the patient's age, body mass index, C-peptide level, and duration of T2 DM (yr). The remission of T2 DM after LSG was evaluated using the ABCD score. At 12 months after surgery, 85 of the patients had complete follow-up data. The weight loss was 26.5% and the mean HbA1c decreased from 8.1% to 6.1%. A significant number of patients had improvement in their glycemic control, including 45 (52.9%) patients who had complete remission (HbA1c<6.0%), another 18 (21.2%) who had partial remission (HbA1c<6.5%), and 9 (10.6%) who improved (HbA1c<7%). Patients who had T2 DM remission after surgery had a higher ABCD score than those who did not (7.3±1.7 versus 5.2±2.1, P<.05). Patients with a higher ABCD score were also at a higher rate of success in T2 DM remission (from 0% in score 0 to 100% in score 10). LSG is an effective and well-tolerated procedure for achieving weight loss and T2 DM remission. The ABCD score, a simple multidimensional grading system, can predict the success of T2 DM treatment by LSG. Copyright © 2015 American Society for Bariatric Surgery. Published by Elsevier Inc. All rights reserved.

Prediction of Beck Depression Inventory (BDI-II) Score Using Acoustic Measurements in a Sample of Iium Engineering Students

NASA Astrophysics Data System (ADS)

Fikri Zanil, Muhamad; Nur Wahidah Nik Hashim, Nik; Azam, Huda

2017-11-01

Psychiatrist currently relies on questionnaires and interviews for psychological assessment. These conservative methods often miss true positives and might lead to death, especially in cases where a patient might be experiencing suicidal predisposition but was only diagnosed as major depressive disorder (MDD). With modern technology, an assessment tool might aid psychiatrist with a more accurate diagnosis and thus hope to reduce casualty. This project will explore on the relationship between speech features of spoken audio signal (reading) in Bahasa Malaysia with the Beck Depression Inventory scores. The speech features used in this project were Power Spectral Density (PSD), Mel-frequency Ceptral Coefficients (MFCC), Transition Parameter, formant and pitch. According to analysis, the optimum combination of speech features to predict BDI-II scores include PSD, MFCC and Transition Parameters. The linear regression approach with sequential forward/backward method was used to predict the BDI-II scores using reading speech. The result showed 0.4096 mean absolute error (MAE) for female reading speech. For male, the BDI-II scores successfully predicted 100% less than 1 scores difference with MAE of 0.098437. A prediction system called Depression Severity Evaluator (DSE) was developed. The DSE managed to predict one out of five subjects. Although the prediction rate was low, the system precisely predict the score within the maximum difference of 4.93 for each person. This demonstrates that the scores are not random numbers.

Regioisomer-Specific Mechanochromism of Naphthopyran in Polymeric Materials.

PubMed

Robb, Maxwell J; Kim, Tae Ann; Halmes, Abigail J; White, Scott R; Sottos, Nancy R; Moore, Jeffrey S

2016-09-28

Transformation of naphthopyran into a colored merocyanine species in polymeric materials is achieved using mechanical force. We demonstrate that the mechanochemical reactivity of naphthopyran is critically dependent on the regiochemistry, with only one particular substitution pattern leading to successful mechanochemical activation. Two alternative regioisomers with different polymer attachment points are demonstrated to be mechanochemically inactive. This trend in reactivity is accurately predicted by DFT calculations, reinforcing predictive capabilities in mechanochemical systems. We rationalize the reactivity differences between naphthopyran regioisomers in terms of the alignment of the target C-O pyran bond with the direction of the applied mechanical force and its effect on mechanochemical transduction along the reaction coordinate.

Development of a prototype automatic controller for liquid cooling garment inlet temperature

NASA Technical Reports Server (NTRS)

Weaver, C. S.; Webbon, B. W.; Montgomery, L. D.

1982-01-01

The development of a computer control of a liquid cooled garment (LCG) inlet temperature is descirbed. An adaptive model of the LCG is used to predict the heat-removal rates for various inlet temperatures. An experimental system that contains a microcomputer was constructed. The LCG inlet and outlet temperatures and the heat exchanger outlet temperature form the inputs to the computer. The adaptive model prediction method of control is successful during tests where the inlet temperature is automatically chosen by the computer. It is concluded that the program can be implemented in a microprocessor of a size that is practical for a life support back-pack.

Space motion sickness

NASA Technical Reports Server (NTRS)

Vanderploeg, J. M.; Stewart, D. F.; Davis, J. R.

1986-01-01

Space motion sickness clinical characteristics, time course, prediction of susceptibility, and effectiveness of countermeasures were evaluated. Although there is wide individual variability, there appear to be typical patterns of symptom development. The duration of symptoms ranges from several hours to four days with the majority of individuals being symptom free by the end of third day. The etiology of this malady remains uncertain but evidence points to reinterpretation of otolith inputs as being a key factor in the response of the neurovestibular system. Prediction of susceptibility and severity remains unsatisfactory. Countermeasures tried include medications, preflight adaptation, and autogenic feedback training. No countermeasure is entirely successful in eliminating or alleviating symptoms.

Prediction of Phase Formation in Nanoscale Sn-Ag-Cu Solder Alloy

NASA Astrophysics Data System (ADS)

Wu, Min; Lv, Bailin

2016-01-01

In a dynamic nonequilibrium process, the effective heat of formation allows the heat of formation to be calculated as a function of concentrations of the reacting atoms. In this work, we used the effective heat of formation rule to predict the formation and size of compound phases in a nanoscale Sn-Ag-Cu lead-free solder. We calculated the formation enthalpy and effective formation enthalpy of compounds in the Sn-Ag, Sn-Cu, and Ag-Cu systems by using the Miedema model and effective heat of formation. Our results show that, considering the surface effect of the nanoparticle, the effective heat of formation rule successfully predicts the phase formation and sizes of Ag3Sn and Cu6Sn5 compounds, which agrees well with experimental data.

Validation of the CME Geomagnetic Forecast Alerts Under the COMESEP Alert System

NASA Astrophysics Data System (ADS)

Dumbović, Mateja; Srivastava, Nandita; Rao, Yamini K.; Vršnak, Bojan; Devos, Andy; Rodriguez, Luciano

2017-08-01

Under the European Union 7th Framework Programme (EU FP7) project Coronal Mass Ejections and Solar Energetic Particles (COMESEP, http://comesep.aeronomy.be), an automated space weather alert system has been developed to forecast solar energetic particles (SEP) and coronal mass ejection (CME) risk levels at Earth. The COMESEP alert system uses the automated detection tool called Computer Aided CME Tracking (CACTus) to detect potentially threatening CMEs, a drag-based model (DBM) to predict their arrival, and a CME geoeffectiveness tool (CGFT) to predict their geomagnetic impact. Whenever CACTus detects a halo or partial halo CME and issues an alert, the DBM calculates its arrival time at Earth and the CGFT calculates its geomagnetic risk level. The geomagnetic risk level is calculated based on an estimation of the CME arrival probability and its likely geoeffectiveness, as well as an estimate of the geomagnetic storm duration. We present the evaluation of the CME risk level forecast with the COMESEP alert system based on a study of geoeffective CMEs observed during 2014. The validation of the forecast tool is made by comparing the forecasts with observations. In addition, we test the success rate of the automatic forecasts (without human intervention) against the forecasts with human intervention using advanced versions of the DBM and CGFT (independent tools available at the Hvar Observatory website, http://oh.geof.unizg.hr). The results indicate that the success rate of the forecast in its current form is unacceptably low for a realistic operation system. Human intervention improves the forecast, but the false-alarm rate remains unacceptably high. We discuss these results and their implications for possible improvement of the COMESEP alert system.

Dynamics Simulation Model for Space Tethers

NASA Technical Reports Server (NTRS)

Levin, E. M.; Pearson, J.; Oldson, J. C.

2006-01-01

This document describes the development of an accurate model for the dynamics of the Momentum Exchange Electrodynamic Reboost (MXER) system. The MXER is a rotating tether about 100-km long in elliptical Earth orbit designed to catch payloads in low Earth orbit and throw them to geosynchronous orbit or to Earth escape. To ensure successful rendezvous between the MXER tip catcher and a payload, a high-fidelity model of the system dynamics is required. The model developed here quantifies the major environmental perturbations, and can predict the MXER tip position to within meters over one orbit.

Intelligent tutoring systems as tools for investigating individual differences in learning

NASA Technical Reports Server (NTRS)

Shute, Valerie J.

1987-01-01

The ultimate goal of this research is to build an improved model-based selection and classification system for the United States Air Force. Researchers are developing innovative approaches to ability testing. The Learning Abilities Measurement Program (LAMP) examines individual differences in learning abilities, seeking answers to the questions of why some people learn more and better than others and whether there are basic cognitive processes applicable across tasks and domains that are predictive of successful performance (or whether there are more complex problem solving behaviors involved).

Predicting Individuals' Learning Success from Patterns of Pre-Learning MRI Activity

PubMed Central

Vo, Loan T. K.; Walther, Dirk B.; Kramer, Arthur F.; Erickson, Kirk I.; Boot, Walter R.; Voss, Michelle W.; Prakash, Ruchika S.; Lee, Hyunkyu; Fabiani, Monica; Gratton, Gabriele; Simons, Daniel J.; Sutton, Bradley P.; Wang, Michelle Y.

2011-01-01

Performance in most complex cognitive and psychomotor tasks improves with training, yet the extent of improvement varies among individuals. Is it possible to forecast the benefit that a person might reap from training? Several behavioral measures have been used to predict individual differences in task improvement, but their predictive power is limited. Here we show that individual differences in patterns of time-averaged T2*-weighted MRI images in the dorsal striatum recorded at the initial stage of training predict subsequent learning success in a complex video game with high accuracy. These predictions explained more than half of the variance in learning success among individuals, suggesting that individual differences in neuroanatomy or persistent physiology predict whether and to what extent people will benefit from training in a complex task. Surprisingly, predictions from white matter were highly accurate, while voxels in the gray matter of the dorsal striatum did not contain any information about future training success. Prediction accuracy was higher in the anterior than the posterior half of the dorsal striatum. The link between trainability and the time-averaged T2*-weighted signal in the dorsal striatum reaffirms the role of this part of the basal ganglia in learning and executive functions, such as task-switching and task coordination processes. The ability to predict who will benefit from training by using neuroimaging data collected in the early training phase may have far-reaching implications for the assessment of candidates for specific training programs as well as the study of populations that show deficiencies in learning new skills. PMID:21264257

Projecting technology change to improve space technology planning and systems management

NASA Astrophysics Data System (ADS)

Walk, Steven Robert

2011-04-01

Projecting technology performance evolution has been improving over the years. Reliable quantitative forecasting methods have been developed that project the growth, diffusion, and performance of technology in time, including projecting technology substitutions, saturation levels, and performance improvements. These forecasts can be applied at the early stages of space technology planning to better predict available future technology performance, assure the successful selection of technology, and improve technology systems management strategy. Often what is published as a technology forecast is simply scenario planning, usually made by extrapolating current trends into the future, with perhaps some subjective insight added. Typically, the accuracy of such predictions falls rapidly with distance in time. Quantitative technology forecasting (QTF), on the other hand, includes the study of historic data to identify one of or a combination of several recognized universal technology diffusion or substitution patterns. In the same manner that quantitative models of physical phenomena provide excellent predictions of system behavior, so do QTF models provide reliable technological performance trajectories. In practice, a quantitative technology forecast is completed to ascertain with confidence when the projected performance of a technology or system of technologies will occur. Such projections provide reliable time-referenced information when considering cost and performance trade-offs in maintaining, replacing, or migrating a technology, component, or system. This paper introduces various quantitative technology forecasting techniques and illustrates their practical application in space technology and technology systems management.

Soil-Bacterium Compatibility Model as a Decision-Making Tool for Soil Bioremediation.

PubMed

Horemans, Benjamin; Breugelmans, Philip; Saeys, Wouter; Springael, Dirk

2017-02-07

Bioremediation of organic pollutant contaminated soil involving bioaugmentation with dedicated bacteria specialized in degrading the pollutant is suggested as a green and economically sound alternative to physico-chemical treatment. However, intrinsic soil characteristics impact the success of bioaugmentation. The feasibility of using partial least-squares regression (PLSR) to predict the success of bioaugmentation in contaminated soil based on the intrinsic physico-chemical soil characteristics and, hence, to improve the success of bioaugmentation, was examined. As a proof of principle, PLSR was used to build soil-bacterium compatibility models to predict the bioaugmentation success of the phenanthrene-degrading Novosphingobium sp. LH128. The survival and biodegradation activity of strain LH128 were measured in 20 soils and correlated with the soil characteristics. PLSR was able to predict the strain's survival using 12 variables or less while the PAH-degrading activity of strain LH128 in soils that show survival was predicted using 9 variables. A three-step approach using the developed soil-bacterium compatibility models is proposed as a decision making tool and first estimation to select compatible soils and organisms and increase the chance of success of bioaugmentation.

Time-programmable drug dosing allows the manipulation, suppression and reversal of antibiotic drug resistance in vitro

NASA Astrophysics Data System (ADS)

Yoshida, Mari; Reyes, Sabrina Galiñanes; Tsuda, Soichiro; Horinouchi, Takaaki; Furusawa, Chikara; Cronin, Leroy

2017-06-01

Multi-drug strategies have been attempted to prolong the efficacy of existing antibiotics, but with limited success. Here we show that the evolution of multi-drug-resistant Escherichia coli can be manipulated in vitro by administering pairs of antibiotics and switching between them in ON/OFF manner. Using a multiplexed cell culture system, we find that switching between certain combinations of antibiotics completely suppresses the development of resistance to one of the antibiotics. Using this data, we develop a simple deterministic model, which allows us to predict the fate of multi-drug evolution in this system. Furthermore, we are able to reverse established drug resistance based on the model prediction by modulating antibiotic selection stresses. Our results support the idea that the development of antibiotic resistance may be potentially controlled via continuous switching of drugs.

Predictive factors for successful clinical outcome 1 year after an intensive combined physical and psychological programme for chronic low back pain.

PubMed

van Hooff, Miranda L; Spruit, Maarten; O'Dowd, John K; van Lankveld, Wim; Fairbank, Jeremy C T; van Limbeek, Jacques

2014-01-01

The aim of this longitudinal study is to determine the factors which predict a successful 1-year outcome from an intensive combined physical and psychological (CPP) programme in chronic low back pain (CLBP) patients. A prospective cohort of 524 selected consecutive CLBP patients was followed. Potential predictive factors included demographic characteristics, disability, pain and cognitive behavioural factors as measured at pre-treatment assessment. The primary outcome measure was the oswestry disability index (ODI). A successful 1-year follow-up outcome was defined as a functional status equivalent to 'normal' and healthy populations (ODI ≤22). The 2-week residential programme fulfills the recommendations in international guidelines. For statistical analysis we divided the database into two equal samples. A random sample was used to develop a prediction model with multivariate logistic regression. The remaining cases were used to validate this model. The final predictive model suggested being 'in employment' at pre-treatment [OR 3.61 (95 % CI 1.80-7.26)] and an initial 'disability score' [OR 0.94 (95 % CI 0.92-0.97)] as significant predictive factors for a successful 1-year outcome (R (2) = 22 %; 67 % correctly classified). There was no predictive value from measures of psychological distress. CLBP patients who are in work and mild to moderately disabled at the start of a CPP programme are most likely to benefit from it and to have a successful treatment outcome. In these patients, the disability score falls to values seen in healthy populations. This small set of factors is easily identified, allowing selection for programme entry and triage to alternative treatment regimes.

The spike trains of inhibited pacemaker neurons seen through the magnifying glass of nonlinear analyses.

PubMed

Segundo, J P; Sugihara, G; Dixon, P; Stiber, M; Bersier, L F

1998-12-01

This communication describes the new information that may be obtained by applying nonlinear analytical techniques to neurobiological time-series. Specifically, we consider the sequence of interspike intervals Ti (the "timing") of trains recorded from synaptically inhibited crayfish pacemaker neurons. As reported earlier, different postsynaptic spike train forms (sets of timings with shared properties) are generated by varying the average rate and/or pattern (implying interval dispersions and sequences) of presynaptic spike trains. When the presynaptic train is Poisson (independent exponentially distributed intervals), the form is "Poisson-driven" (unperturbed and lengthened intervals succeed each other irregularly). When presynaptic trains are pacemaker (intervals practically equal), forms are either "p:q locked" (intervals repeat periodically), "intermittent" (mostly almost locked but disrupted irregularly), "phase walk throughs" (intermittencies with briefer regular portions), or "messy" (difficult to predict or describe succinctly). Messy trains are either "erratic" (some intervals natural and others lengthened irregularly) or "stammerings" (intervals are integral multiples of presynaptic intervals). The individual spike train forms were analysed using attractor reconstruction methods based on the lagged coordinates provided by successive intervals from the time-series Ti. Numerous models were evaluated in terms of their predictive performance by a trial-and-error procedure: the most successful model was taken as best reflecting the true nature of the system's attractor. Each form was characterized in terms of its dimensionality, nonlinearity and predictability. (1) The dimensionality of the underlying dynamical attractor was estimated by the minimum number of variables (coordinates Ti) required to model acceptably the system's dynamics, i.e. by the system's degrees of freedom. Each model tested was based on a different number of Ti; the smallest number whose predictions were judged successful provided the best integer approximation of the attractor's true dimension (not necessarily an integer). Dimensionalities from three to five provided acceptable fits. (2) The degree of nonlinearity was estimated by: (i) comparing the correlations between experimental results and data from linear and nonlinear models, and (ii) tuning model nonlinearity via a distance-weighting function and identifying the either local or global neighborhood size. Lockings were compatible with linear models and stammerings were marginal; nonlinear models were best for Poisson-driven, intermittent and erratic forms. (3) Finally, prediction accuracy was plotted against increasingly long sequences of intervals forecast: the accuracies for Poisson-driven, locked and stammering forms were invariant, revealing irregularities due to uncorrelated noise, but those of intermittent and messy erratic forms decayed rapidly, indicating an underlying deterministic process. The excellent reconstructions possible for messy erratic and for some intermittent forms are especially significant because of their relatively low dimensionality (around 4), high degree of nonlinearity and prediction decay with time. This is characteristic of chaotic systems, and provides evidence that nonlinear couplings between relatively few variables are the major source of the apparent complexity seen in these cases. This demonstration of different dimensions, degrees of nonlinearity and predictabilities provides rigorous support for the categorization of different synaptically driven discharge forms proposed earlier on the basis of more heuristic criteria. This has significant implications. (1) It demonstrates that heterogeneous postsynaptic forms can indeed be induced by manipulating a few presynaptic variables. (2) Each presynaptic timing induces a form with characteristic dimensionality, thus breaking up the preparation into subsystems such that the physical variables in each operate as one

Tell me who you are and I tell you how you feel: expected emotional reactions to success and failure are influenced by knowledge about a person's personality.

PubMed

Hareli, Shlomo; Sharabi, Moshe; Hess, Ursula

2011-08-01

The present research investigated the influence of knowledge about a person's modesty or arrogance on people's expectations regarding that person's emotional reactions to success and failure. Arrogance and modesty reflect the extent to which someone is likely to publicize their ability. Accordingly, we predicted that observers' expectations regarding a person's tendency to publicize their ability should inform expectations about the person's emotional reactions to success and failure. In two vignette studies, observers predicted the emotional state of a protagonist, as well as the probability that s/he will actually express that emotion and share the experience with others. For success, participants predicted a protagonist's pride, happiness, schadenfreude, and embarrassment if praised for a positive outcome. For failure, participants predicted anger, shame, guilt, sadness, and fear reactions. Across studies, personality information explained more variance than did gender or status. Results showed that the expectations for an arrogant person matched modal expectations for success, whereas for failure the expectations for the modest individual were closest to the modal expectations. Specifically, both modest and arrogant individuals were expected to suppress emotions that do not fit their self-presentational styles rather than to exaggerate expressions that do. This paper adds to our understanding of the information that people use to predict others' emotional reactions.

Mathematical Model of a Telomerase Transcriptional Regulatory Network Developed by Cell-Based Screening: Analysis of Inhibitor Effects and Telomerase Expression Mechanisms

PubMed Central

Bilsland, Alan E.; Stevenson, Katrina; Liu, Yu; Hoare, Stacey; Cairney, Claire J.; Roffey, Jon; Keith, W. Nicol

2014-01-01

Cancer cells depend on transcription of telomerase reverse transcriptase (TERT). Many transcription factors affect TERT, though regulation occurs in context of a broader network. Network effects on telomerase regulation have not been investigated, though deeper understanding of TERT transcription requires a systems view. However, control over individual interactions in complex networks is not easily achievable. Mathematical modelling provides an attractive approach for analysis of complex systems and some models may prove useful in systems pharmacology approaches to drug discovery. In this report, we used transfection screening to test interactions among 14 TERT regulatory transcription factors and their respective promoters in ovarian cancer cells. The results were used to generate a network model of TERT transcription and to implement a dynamic Boolean model whose steady states were analysed. Modelled effects of signal transduction inhibitors successfully predicted TERT repression by Src-family inhibitor SU6656 and lack of repression by ERK inhibitor FR180204, results confirmed by RT-QPCR analysis of endogenous TERT expression in treated cells. Modelled effects of GSK3 inhibitor 6-bromoindirubin-3′-oxime (BIO) predicted unstable TERT repression dependent on noise and expression of JUN, corresponding with observations from a previous study. MYC expression is critical in TERT activation in the model, consistent with its well known function in endogenous TERT regulation. Loss of MYC caused complete TERT suppression in our model, substantially rescued only by co-suppression of AR. Interestingly expression was easily rescued under modelled Ets-factor gain of function, as occurs in TERT promoter mutation. RNAi targeting AR, JUN, MXD1, SP3, or TP53, showed that AR suppression does rescue endogenous TERT expression following MYC knockdown in these cells and SP3 or TP53 siRNA also cause partial recovery. The model therefore successfully predicted several aspects of TERT regulation including previously unknown mechanisms. An extrapolation suggests that a dominant stimulatory system may programme TERT for transcriptional stability. PMID:24550717

Prophage-mediated defense against viral attack and viral counter-defense

PubMed Central

Dedrick, Rebekah M.; Jacobs-Sera, Deborah; Guerrero Bustamante, Carlos A.; Garlena, Rebecca A.; Mavrich, Travis N.; Pope, Welkin H.; Reyes, Juan C Cervantes; Russell, Daniel A.; Adair, Tamarah; Alvey, Richard; Bonilla, J. Alfred; Bricker, Jerald S.; Brown, Bryony R.; Byrnes, Deanna; Cresawn, Steven G.; Davis, William B.; Dickson, Leon A.; Edgington, Nicholas P.; Findley, Ann M.; Golebiewska, Urszula; Grose, Julianne H.; Hayes, Cory F.; Hughes, Lee E.; Hutchison, Keith W.; Isern, Sharon; Johnson, Allison A.; Kenna, Margaret A.; Klyczek, Karen K.; Mageeney, Catherine M.; Michael, Scott F.; Molloy, Sally D.; Montgomery, Matthew T.; Neitzel, James; Page, Shallee T.; Pizzorno, Marie C.; Poxleitner, Marianne K.; Rinehart, Claire A.; Robinson, Courtney J.; Rubin, Michael R.; Teyim, Joseph N.; Vazquez, Edwin; Ware, Vassie C.; Washington, Jacqueline; Hatfull, Graham F.

2017-01-01

Temperate phages are common and prophages are abundant residents of sequenced bacterial genomes. Mycobacteriophages are viruses infecting mycobacterial hosts including Mycobacterium tuberculosis and Mycobacterium smegmatis, encompass substantial genetic diversity, and are commonly temperate. Characterization of ten Cluster N temperate mycobacteriophages reveals at least five distinct prophage-expressed viral defense systems that interfere with infection of lytic and temperate phages that are either closely-related (homotypic defense) or unrelated (heterotypic defense). Target specificity is unpredictable, ranging from a single target phage to one-third of those tested. The defense systems include a single-subunit restriction system, a heterotypic exclusion system, and a predicted (p)ppGpp synthetase, which blocks lytic phage growth, promotes bacterial survival, and enables efficient lysogeny. The predicted (p)ppGpp synthetase coded by the Phrann prophage defends against phage Tweety infection, but Tweety codes for a tetrapeptide repeat protein, gp54, that acts as a highly effective counter-defense system. Prophage-mediated viral defense offers an efficient mechanism for bacterial success in host-virus dynamics, and counter-defense promotes phage co-evolution. PMID:28067906

Joint nonlinearity effects in the design of a flexible truss structure control system

NASA Technical Reports Server (NTRS)

Mercadal, Mathieu

1986-01-01

Nonlinear effects are introduced in the dynamics of large space truss structures by the connecting joints which are designed with rather important tolerances to facilitate the assembly of the structures in space. The purpose was to develop means to investigate the nonlinear dynamics of the structures, particularly the limit cycles that might occur when active control is applied to the structures. An analytical method was sought and derived to predict the occurrence of limit cycles and to determine their stability. This method is mainly based on the quasi-linearization of every joint using describing functions. This approach was proven successful when simple dynamical systems were tested. Its applicability to larger systems depends on the amount of computations it requires, and estimates of the computational task tend to indicate that the number of individual sources of nonlinearity should be limited. Alternate analytical approaches, which do not account for every single nonlinearity, or the simulation of a simplified model of the dynamical system should, therefore, be investigated to determine a more effective way to predict limit cycles in large dynamical systems with an important number of distributed nonlinearities.

Building a generalized distributed system model

NASA Technical Reports Server (NTRS)

Mukkamala, R.

1992-01-01

The key elements in the second year (1991-92) of our project are: (1) implementation of the distributed system prototype; (2) successful passing of the candidacy examination and a PhD proposal acceptance by the funded student; (3) design of storage efficient schemes for replicated distributed systems; and (4) modeling of gracefully degrading reliable computing systems. In the third year of the project (1992-93), we propose to: (1) complete the testing of the prototype; (2) enhance the functionality of the modules by enabling the experimentation with more complex protocols; (3) use the prototype to verify the theoretically predicted performance of locking protocols, etc.; and (4) work on issues related to real-time distributed systems. This should result in efficient protocols for these systems.

Predicting Student Success in a Major's Introductory Biology Course via Logistic Regression Analysis of Scientific Reasoning Ability and Mathematics Scores

NASA Astrophysics Data System (ADS)

Thompson, E. David; Bowling, Bethany V.; Markle, Ross E.

2018-02-01

Studies over the last 30 years have considered various factors related to student success in introductory biology courses. While much of the available literature suggests that the best predictors of success in a college course are prior college grade point average (GPA) and class attendance, faculty often require a valuable predictor of success in those courses wherein the majority of students are in the first semester and have no previous record of college GPA or attendance. In this study, we evaluated the efficacy of the ACT Mathematics subject exam and Lawson's Classroom Test of Scientific Reasoning in predicting success in a major's introductory biology course. A logistic regression was utilized to determine the effectiveness of a combination of scientific reasoning (SR) scores and ACT math (ACT-M) scores to predict student success. In summary, we found that the model—with both SR and ACT-M as significant predictors—could be an effective predictor of student success and thus could potentially be useful in practical decision making for the course, such as directing students to support services at an early point in the semester.

Preoperative Ultrasound Prediction of Essential Landmarks for Successful Fetoscopic Laser Treatment of Twin-Twin Transfusion Syndrome.

PubMed

Miller, Jena L; Block-Abraham, Dana M; Blakemore, Karin J; Baschat, Ahmet A

2018-06-06

The insertion site of the fetoscope for laser occlusion (FLOC) treatment of twin-twin transfusion syndrome (TTTS) determines the likelihood of treatment success. We assessed a standardized preoperative ultrasound approach for its ability to identify critical landmarks for successful FLOC. Three surgeons independently performed preoperative ultrasound and deduced the likely orientation of the intertwin membrane (ITM) and vascular equator (VE) based on the sites of the cord insertion, the lie of the donor, and the size discordance between twins. At FLOC, these landmarks were visually verified and compared to preoperative assessments. Fifty consecutive FLOC surgeries had 127 preoperative assessments. Basic ITM and VE orientation were accurately predicted in 115 (90.6%), 109 (85.8%), and 105 (82.7%) assessments. Predictions were anatomically correct in 96 (75.6%), 70 (55.1%), and 58 (45.7%) assessments with no differences in accuracy between operators of different training level. The ITM/VE relationship was most poorly predicted in stage-3 TTTS (χ2, p = 0.016). In TTTS, preoperative ultrasound identification of placental cord insertion sites, lie of the donor twin, and size discordance enables preoperative prediction of key landmarks for successful FLOC. © 2018 S. Karger AG, Basel.