C*-algebras associated with reversible extensions of logistic maps

NASA Astrophysics Data System (ADS)

Kwaśniewski, Bartosz K.

2012-10-01

The construction of reversible extensions of dynamical systems presented in a previous paper by the author and A.V. Lebedev is enhanced, so that it applies to arbitrary mappings (not necessarily with open range). It is based on calculating the maximal ideal space of C*-algebras that extends endomorphisms to partial automorphisms via partial isometric representations, and involves a new set of 'parameters' (the role of parameters is played by chosen sets or ideals). As model examples, we give a thorough description of reversible extensions of logistic maps and a classification of systems associated with compression of unitaries generating homeomorphisms of the circle. Bibliography: 34 titles.

Centralized versus decentralized decision-making for recycled material flows.

PubMed

Hong, I-Hsuan; Ammons, Jane C; Realff, Matthew J

2008-02-15

A reverse logistics system is a network of transportation logistics and processing functions that collect, consolidate, refurbish, and demanufacture end-of-life products. This paper examines centralized and decentralized models of decision-making for material flows and associated transaction prices in reverse logistics networks. We compare the application of a centralized model for planning reverse production systems, where a single planner is acquainted with all of the system information and has the authority to determine decision variables for the entire system, to a decentralized approach. In the decentralized approach, the entities coordinate between tiers of the system using a parametrized flow function and compete within tiers based on reaching a price equilibrium. We numerically demonstrate the increase in the total net profit of the centralized system relative to the decentralized one. This implies that one may overestimate the system material flows and profit if the system planner utilizes a centralized viewto predict behaviors of independent entities in the system and that decentralized contract mechanisms will require careful design to avoid losses in the efficiency and scope of these systems.

Reverse logistics in the construction industry.

PubMed

Hosseini, M Reza; Rameezdeen, Raufdeen; Chileshe, Nicholas; Lehmann, Steffen

2015-06-01

Reverse logistics in construction refers to the movement of products and materials from salvaged buildings to a new construction site. While there is a plethora of studies looking at various aspects of the reverse logistics chain, there is no systematic review of literature on this important subject as applied to the construction industry. Therefore, the objective of this study is to integrate the fragmented body of knowledge on reverse logistics in construction, with the aim of promoting the concept among industry stakeholders and the wider construction community. Through a qualitative meta-analysis, the study synthesises the findings of previous studies and presents some actions needed by industry stakeholders to promote this concept within the real-life context. First, the trend of research and terminology related with reverse logistics is introduced. Second, it unearths the main advantages and barriers of reverse logistics in construction while providing some suggestions to harness the advantages and mitigate these barriers. Finally, it provides a future research direction based on the review. © The Author(s) 2015.

Analysis of efficiency of waste reverse logistics for recycling.

PubMed

Veiga, Marcelo M

2013-10-01

Brazil is an agricultural country with the highest pesticide consumption in the world. Historically, pesticide packaging has not been disposed of properly. A federal law requires the chemical industry to provide proper waste management for pesticide-related products. A reverse logistics program was implemented, which has been hailed a great success. This program was designed to target large rural communities, where economy of scale can take place. Over the last 10 years, the recovery rate has been very poor in most small rural communities. The objective of this study was to analyze the case of this compulsory reverse logistics program for pesticide packaging under the recent Brazilian Waste Management Policy, which enforces recycling as the main waste management solution. This results of this exploratory research indicate that despite its aggregate success, the reverse logistics program is not efficient for small rural communities. It is not possible to use the same logistic strategy for small and large communities. The results also indicate that recycling might not be the optimal solution, especially in developing countries with unsatisfactory recycling infrastructure and large transportation costs. Postponement and speculation strategies could be applied for improving reverse logistics performance. In most compulsory reverse logistics programs, there is no economical solution. Companies should comply with the law by ranking cost-effective alternatives.

Cost-benefit study of consumer product take-back programs using IBM's WIT reverse logistics optimization tool

NASA Astrophysics Data System (ADS)

Veerakamolmal, Pitipong; Lee, Yung-Joon; Fasano, J. P.; Hale, Rhea; Jacques, Mary

2002-02-01

In recent years, there has been increased focus by regulators, manufacturers, and consumers on the issue of product end of life management for electronics. This paper presents an overview of a conceptual study designed to examine the costs and benefits of several different Product Take Back (PTB) scenarios for used electronics equipment. The study utilized a reverse logistics supply chain model to examine the effects of several different factors in PTB programs. The model was done using the IBM supply chain optimization tool known as WIT (Watson Implosion Technology). Using the WIT tool, we were able to determine a theoretical optimal cost scenario for PTB programs. The study was designed to assist IBM internally in determining theoretical optimal Product Take Back program models and determining potential incentives for increasing participation rates.

Reverse logistics in the Brazilian construction industry.

PubMed

Nunes, K R A; Mahler, C F; Valle, R A

2009-09-01

In Brazil most Construction and Demolition Waste (C&D waste) is not recycled. This situation is expected to change significantly, since new federal regulations oblige municipalities to create and implement sustainable C&D waste management plans which assign an important role to recycling activities. The recycling organizational network and its flows and components are fundamental to C&D waste recycling feasibility. Organizational networks, flows and components involve reverse logistics. The aim of this work is to introduce the concepts of reverse logistics and reverse distribution channel networks and to study the Brazilian C&D waste case.

Reverse Logistics at the Commander, Naval Surface Forces Real-Time and Reutilization Asset Management (R-RAM) San Diego Warehouse

DTIC Science & Technology

2008-11-20

in December 2000 when the system was converted from UADPS to a Commercial-off-the-shelf (COTS) product from a company called Lawson Insight (2008...In 1998, Carter and Ellram stated that Reverse Logistics is a process whereby companies can become more environmentally efficient through recycling...by companies practicing reverse logistics:  In 1996, Baxter’s environmental initiatives saved the company $11 million; cost avoidance efforts (e.g

Optimising reverse logistics network to support policy-making in the case of Electrical and Electronic Equipment.

PubMed

Achillas, Ch; Vlachokostas, Ch; Aidonis, D; Moussiopoulos, N; Iakovou, E; Banias, G

2010-12-01

Due to the rapid growth of Waste Electrical and Electronic Equipment (WEEE) volumes, as well as the hazardousness of obsolete electr(on)ic goods, this type of waste is now recognised as a priority stream in the developed countries. Policy-making related to the development of the necessary infrastructure and the coordination of all relevant stakeholders is crucial for the efficient management and viability of individually collected waste. This paper presents a decision support tool for policy-makers and regulators to optimise electr(on)ic products' reverse logistics network. To that effect, a Mixed Integer Linear Programming mathematical model is formulated taking into account existing infrastructure of collection points and recycling facilities. The applicability of the developed model is demonstrated employing a real-world case study for the Region of Central Macedonia, Greece. The paper concludes with presenting relevant obtained managerial insights. Copyright © 2010 Elsevier Ltd. All rights reserved.

Analysis on the cost structure of product recall for reverse supply chain

NASA Astrophysics Data System (ADS)

Yanhua, Feng; Xuhui, Xia; Zheng, Yang

2017-12-01

The research on the reverse supply chain of product recall mainly focused on the recall network structure, logistics mode and so on. In this paper, when product recall and supply channel are fixed, the specific structure and function expression of cost are analyzed according to the peak season and off-season of recall activities, and whether the assembly manufacturer, supplier and recyclers are cooperated situation, respectively, to build the total cost structure of the function model. Finally, the model is validated correctly through the automotive industry and the electromechanical industry.

Reverse logistics system and recycling potential at a landfill: A case study from Kampala City

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

Kinobe, J.R., E-mail: joel.kinobe@slu.se; Department of Civil and Environmental Engineering, Makerere University College of Engineering, Design, Art and Technology; Gebresenbet, G.

Highlights: • Quantifies the different waste streams delivered at the landfill. • Evaluates the amount of potential waste products that enters into the reverse cycle. • Drawing out the reverse logistics activities from Kampala City to Kiteezi landfill. • Identify the storage, collection and transportation mechanisms of products to the various destinations; and finally. • The study suggests efficient measures to improve reverse logistics system. - Abstract: The rapid growing population and high urbanisation rates in Sub-Saharan Africa has caused enormous pressure on collection services of the generated waste in the urban areas. This has put a burden on landfilling,more » which is the major waste disposal method. Waste reduction, re-use and recycling opportunities exist but are not fully utilized. The common items that are re-used and re-cycled are plastics, paper, aluminum, glass, steel, cardboard, and yard waste. This paper develops an overview of reverse logistics at Kiteezi landfill, the only officially recognised waste disposal facility for Kampala City. The paper analyses, in details the collection, re-processing, re-distribution and final markets of these products into a reversed supply chain network. Only 14% of the products at Kiteezi landfill are channeled into the reverse chain while 63% could be included in the distribution chain but are left out and disposed of while the remaining 23% is buried. This is because of the low processing power available, lack of market value, lack of knowledge and limited value addition activities to the products. This paper proposes possible strategies of efficient and effective reverse logistics development, applicable to Kampala City and other similar cities.« less

The management challenge for household waste in emerging economies like Brazil: realistic source separation and activation of reverse logistics.

PubMed

Fehr, M

2014-09-01

Business opportunities in the household waste sector in emerging economies still evolve around the activities of bulk collection and tipping with an open material balance. This research, conducted in Brazil, pursued the objective of shifting opportunities from tipping to reverse logistics in order to close the balance. To do this, it illustrated how specific knowledge of sorted waste composition and reverse logistics operations can be used to determine realistic temporal and quantitative landfill diversion targets in an emerging economy context. Experimentation constructed and confirmed the recycling trilogy that consists of source separation, collection infrastructure and reverse logistics. The study on source separation demonstrated the vital difference between raw and sorted waste compositions. Raw waste contained 70% biodegradable and 30% inert matter. Source separation produced 47% biodegradable, 20% inert and 33% mixed material. The study on collection infrastructure developed the necessary receiving facilities. The study on reverse logistics identified private operators capable of collecting and processing all separated inert items. Recycling activities for biodegradable material were scarce and erratic. Only farmers would take the material as animal feed. No composting initiatives existed. The management challenge was identified as stimulating these activities in order to complete the trilogy and divert the 47% source-separated biodegradable discards from the landfills. © The Author(s) 2014.

Reverse Logistics

DTIC Science & Technology

2001-05-01

reverse logistics was to pick up the damage or obsolete items from the vendor and discard them into a land fill. Estee Lauder Companies, Inc. dumped as...Quality Center, Benchmarking and Leveraging “Best Practices” Strategies , Houston, TX, AQPC, 1995. 2. Brauner, Marygail, “Evaluating Five Proposed Price

77 FR 39662 - Hazardous Materials; Reverse Logistics (RRR)

Federal Register 2010, 2011, 2012, 2013, 2014

2012-07-05

... used batteries from multiple shippers for the purposes of recycling. The petition also notes that, when... recycling falls within the realm of ``reverse logistics.'' Currently Sec. 173.159(e)(4) prevents a battery... comment on how the retail industry should handle the recycling or disposal of these batteries for use in...

Chaotic and stable perturbed maps: 2-cycles and spatial models

NASA Astrophysics Data System (ADS)

Braverman, E.; Haroutunian, J.

2010-06-01

As the growth rate parameter increases in the Ricker, logistic and some other maps, the models exhibit an irreversible period doubling route to chaos. If a constant positive perturbation is introduced, then the Ricker model (but not the classical logistic map) experiences period doubling reversals; the break of chaos finally gives birth to a stable two-cycle. We outline the maps which demonstrate a similar behavior and also study relevant discrete spatial models where the value in each cell at the next step is defined only by the values at the cell and its nearest neighbors. The stable 2-cycle in a scalar map does not necessarily imply 2-cyclic-type behavior in each cell for the spatial generalization of the map.

Impact of RFID Information-Sharing Coordination over a Supply Chain with Reverse Logistics

ERIC Educational Resources Information Center

Nativi Nicolau, Juan Jose

2016-01-01

Companies have adopted environmental practices such as reverse logistics over the past few decades. However, studies show that aligning partners inside the green supply chain can be a substantial problem. This lack of coordination can increase overall supply chain cost. Information technology such as Radio Frequency Identification (RFID) has the…

A hybrid solution approach for a multi-objective closed-loop logistics network under uncertainty

NASA Astrophysics Data System (ADS)

Mehrbod, Mehrdad; Tu, Nan; Miao, Lixin

2015-06-01

The design of closed-loop logistics (forward and reverse logistics) has attracted growing attention with the stringent pressures of customer expectations, environmental concerns and economic factors. This paper considers a multi-product, multi-period and multi-objective closed-loop logistics network model with regard to facility expansion as a facility location-allocation problem, which more closely approximates real-world conditions. A multi-objective mixed integer nonlinear programming formulation is linearized by defining new variables and adding new constraints to the model. By considering the aforementioned model under uncertainty, this paper develops a hybrid solution approach by combining an interactive fuzzy goal programming approach and robust counterpart optimization based on three well-known robust counterpart optimization formulations. Finally, this paper compares the results of the three formulations using different test scenarios and parameter-sensitive analysis in terms of the quality of the final solution, CPU time, the level of conservatism, the degree of closeness to the ideal solution, the degree of balance involved in developing a compromise solution, and satisfaction degree.

Reverse Logistics at the Commander, Naval Surface Forces Real-time & Reutilization Asset Management (R-RAM) San Diego Warehouse

DTIC Science & Technology

2008-12-01

Asset Management) in December 2000 when the system was converted from UADPS to a Commercial-of-the-Shelf (COTS) product from a company called Lawson...materials and disposal (Stock, 1992, p. 25). In 1998, Carter and Ellram stated that Reverse Logistics is a process whereby companies can become...35 billion (p. 275). In the white paper authored by Dr. James Stock in 1998, he highlighted the benefits achieved by companies practicing reverse

A Food Chain Algorithm for Capacitated Vehicle Routing Problem with Recycling in Reverse Logistics

NASA Astrophysics Data System (ADS)

Song, Qiang; Gao, Xuexia; Santos, Emmanuel T.

2015-12-01

This paper introduces the capacitated vehicle routing problem with recycling in reverse logistics, and designs a food chain algorithm for it. Some illustrative examples are selected to conduct simulation and comparison. Numerical results show that the performance of the food chain algorithm is better than the genetic algorithm, particle swarm optimization as well as quantum evolutionary algorithm.

Empirical research on coordination evaluation and sustainable development mechanism of regional logistics and new-type urbanization: a panel data analysis from 2000 to 2015 for Liaoning Province in China.

PubMed

Sun, Qiang

2017-06-01

As the largest developing country in the world, China has witnessed fast-paced urbanization over the past three decades with rapid economic growth. In fact, urbanization has been not only shown to promote economic growth and improve the livelihood of people but also can increase demands of regional logistics. Therefore, a better understanding of the relationship between urbanization and regional logistics is important for China's future sustainable development. The development of urban residential area and heterogeneous, modern society as well regional logistics are running two abreast. The regional logistics can promote the development of new-type urbanization jointly by promoting industrial concentration and logistics demand, enhancing the residents' quality of life and improving the infrastructure and logistics technology. In this paper, the index system and evaluation model for evaluating the development of regional logistics and the new-type urbanization are constructed. Further, the econometric analysis is utilized such as correlation analysis, co-integration test, and error correction model to explore relationships of the new-type urbanization development and regional logistics development in Liaoning Province. The results showed that there was a long-term stable equilibrium relationship between the new-type urbanization and regional logistics. The findings have important implications for Chinese policymakers that on the path towards a sustainable urbanization and regional reverse, this must be taken into consideration. The paper concludes providing some strategies that might be helpful to the policymakers in formulating development policies for sustainable urbanization.

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

Kwasniewski, Bartosz K

The construction of reversible extensions of dynamical systems presented in a previous paper by the author and A.V. Lebedev is enhanced, so that it applies to arbitrary mappings (not necessarily with open range). It is based on calculating the maximal ideal space of C*-algebras that extends endomorphisms to partial automorphisms via partial isometric representations, and involves a new set of 'parameters' (the role of parameters is played by chosen sets or ideals). As model examples, we give a thorough description of reversible extensions of logistic maps and a classification of systems associated with compression of unitaries generating homeomorphisms of themore » circle. Bibliography: 34 titles.« less

Reverse logistics system and recycling potential at a landfill: A case study from Kampala City.

PubMed

Kinobe, J R; Gebresenbet, G; Niwagaba, C B; Vinnerås, B

2015-08-01

The rapid growing population and high urbanisation rates in Sub-Saharan Africa has caused enormous pressure on collection services of the generated waste in the urban areas. This has put a burden on landfilling, which is the major waste disposal method. Waste reduction, re-use and recycling opportunities exist but are not fully utilized. The common items that are re-used and re-cycled are plastics, paper, aluminum, glass, steel, cardboard, and yard waste. This paper develops an overview of reverse logistics at Kiteezi landfill, the only officially recognised waste disposal facility for Kampala City. The paper analyses, in details the collection, re-processing, re-distribution and final markets of these products into a reversed supply chain network. Only 14% of the products at Kiteezi landfill are channeled into the reverse chain while 63% could be included in the distribution chain but are left out and disposed of while the remaining 23% is buried. This is because of the low processing power available, lack of market value, lack of knowledge and limited value addition activities to the products. This paper proposes possible strategies of efficient and effective reverse logistics development, applicable to Kampala City and other similar cities. Copyright © 2015 Elsevier Ltd. All rights reserved.

The optimal manufacturing batch size with rework under time-varying demand process for a finite time horizon

NASA Astrophysics Data System (ADS)

Musa, Sarah; Supadi, Siti Suzlin; Omar, Mohd

2014-07-01

Rework is one of the solutions to some of the main issues in reverse logistic and green supply chain as it reduces production cost and environmental problem. Many researchers focus on developing rework model, but to the knowledge of the author, none of them has developed a model for time-varying demand rate. In this paper, we extend previous works and develop multiple batch production system for time-varying demand rate with rework. In this model, the rework is done within the same production cycle.

Reverse bifurcation and fractal of the compound logistic map

NASA Astrophysics Data System (ADS)

Wang, Xingyuan; Liang, Qingyong

2008-07-01

The nature of the fixed points of the compound logistic map is researched and the boundary equation of the first bifurcation of the map in the parameter space is given out. Using the quantitative criterion and rule of chaotic system, the paper reveal the general features of the compound logistic map transforming from regularity to chaos, the following conclusions are shown: (1) chaotic patterns of the map may emerge out of double-periodic bifurcation and (2) the chaotic crisis phenomena and the reverse bifurcation are found. At the same time, we analyze the orbit of critical point of the compound logistic map and put forward the definition of Mandelbrot-Julia set of compound logistic map. We generalize the Welstead and Cromer's periodic scanning technology and using this technology construct a series of Mandelbrot-Julia sets of compound logistic map. We investigate the symmetry of Mandelbrot-Julia set and study the topological inflexibility of distributing of period region in the Mandelbrot set, and finds that Mandelbrot set contain abundant information of structure of Julia sets by founding the whole portray of Julia sets based on Mandelbrot set qualitatively.

A novel image encryption algorithm using chaos and reversible cellular automata

NASA Astrophysics Data System (ADS)

Wang, Xingyuan; Luan, Dapeng

2013-11-01

In this paper, a novel image encryption scheme is proposed based on reversible cellular automata (RCA) combining chaos. In this algorithm, an intertwining logistic map with complex behavior and periodic boundary reversible cellular automata are used. We split each pixel of image into units of 4 bits, then adopt pseudorandom key stream generated by the intertwining logistic map to permute these units in confusion stage. And in diffusion stage, two-dimensional reversible cellular automata which are discrete dynamical systems are applied to iterate many rounds to achieve diffusion on bit-level, in which we only consider the higher 4 bits in a pixel because the higher 4 bits carry almost the information of an image. Theoretical analysis and experimental results demonstrate the proposed algorithm achieves a high security level and processes good performance against common attacks like differential attack and statistical attack. This algorithm belongs to the class of symmetric systems.

Real-world evaluation of the effectiveness of reversing camera and parking sensor technologies in preventing backover pedestrian injuries.

PubMed

Keall, M D; Fildes, B; Newstead, S

2017-02-01

Backover injuries to pedestrians are a significant road safety issue, but their prevalence is underestimated as the majority of such injuries are often outside the scope of official road injury recording systems, which just focus on public roads. Based on experimental evidence, reversing cameras have been found to be effective in reducing the rate of collisions when reversing; the evidence for the effectiveness of reverse parking sensors has been mixed. The wide availability of these technologies in recent model vehicles provides impetus for real-world evaluations using crash data. A logistic model was fitted to data from crashes that occurred on public roads constituting 3172 pedestrian injuries in New Zealand and four Australian States to estimate the odds of backover injury (compared to other sorts of pedestrian injury crashes) for the different technology combinations fitted as standard equipment (both reversing cameras and sensors; just reversing cameras; just sensors; neither cameras nor sensors) controlling for vehicle type, jurisdiction, speed limit area and year of manufacture restricted to the range 2007-2013. Compared to vehicles without any of these technologies, reduced odds of backover injury were estimated for all three of these technology configurations: 0.59 (95% CI 0.39-0.88) for reversing cameras by themselves; 0.70 (95% CI 0.49-1.01) for both reversing cameras and sensors; 0.69 (95% CI 0.47-1.03) for reverse parking sensors by themselves. These findings are important as they are the first to our knowledge to present an assessment of real-world safety effectiveness of these technologies. Copyright © 2016 Elsevier Ltd. All rights reserved.

Experiments of reconstructing discrete atmospheric dynamic models from data (I)

NASA Astrophysics Data System (ADS)

Lin, Zhenshan; Zhu, Yanyu; Deng, Ziwang

1995-03-01

In this paper, we give some experimental results of our study in reconstructing discrete atmospheric dynamic models from data. After a great deal of numerical experiments, we found that the logistic map, x n + 1 = 1- μx {2/n}, could be used in monthly mean temperature prediction when it was approaching the chaotic region, and its predictive results were in reverse states to the practical data. This means that the nonlinear developing behavior of the monthly mean temperature system is bifurcating back into the critical chaotic states from the chaotic ones.

ARES: A System for Real-Time Operational and Tactical Decision Support

DTIC Science & Technology

1986-12-01

In B]LE LCLGf. 9 NAVAL POSTGRADUATE SCHOOL Monterey, California Vi,-. %*.. THESIS - ’ A RE S A SYSTEM -OR REAL- 1I I .-.. --- OPERATIONAL AND...able) aval Postgraduate School 54 Naval Postgraduate School NN DRESS (City,. State,. and ZIP Code) 7b ADDRESS (City,. State,. and ZIP Code...SUBJECT TERMS (Continue on reverse if necessaty and identify by block number) LD GROUP SUB-GROUP Decision Support System, Logistics Model, Operational

Predictors of participant engagement and naloxone utilization in a community-based naloxone distribution program.

PubMed

Rowe, Christopher; Santos, Glenn-Milo; Vittinghoff, Eric; Wheeler, Eliza; Davidson, Peter; Coffin, Philip O

2015-08-01

To describe characteristics of participants and overdose reversals associated with a community-based naloxone distribution program and identify predictors of obtaining naloxone refills and using naloxone for overdose reversal. Bivariate statistical tests were used to compare characteristics of participants who obtained refills and reported overdose reversals versus those who did not. We fitted multiple logistic regression models to identify predictors of refills and reversals; zero-inflated multiple Poisson regression models were used to identify predictors of number of refills and reversals. San Francisco, California, USA. Naloxone program participants registered and reversals reported from 2010 to 2013. Baseline characteristics of participants and reported characteristics of reversals. A total of 2500 participants were registered and 702 reversals were reported from 2010 to 2013. Participants who had witnessed an overdose [adjusted odds ratio (AOR)=2.02, 95% confidence interval (CI)= 1.53-2.66; AOR = 2.73, 95% CI = 1.73-4.30] or used heroin (AOR = 1.85, 95% CI =  1.44-2.37; AOR = 2.19, 95% CI = 1.54-3.13) or methamphetamine (AOR=1.71, 95% CI=1.37-2.15; AOR=1.61, 95% CI=1.18-2.19) had higher odds of obtaining a refill and reporting a reversal, respectively. African American (AOR = 0.63, 95% CI = 0.45-0.88) and Latino (AOR = 0.65, 95% CI =  0.43-1.00) participants had lower odds of obtaining a naloxone refill, whereas Latino participants who obtained at least one refill reported a higher number of refills [incidence rate ratio (IRR) = 1.33 (1.05-1.69)]. Community naloxone distribution programs are capable of reaching sizeable populations of high-risk individuals and facilitating large numbers of overdose reversals. Community members most likely to engage with a naloxone program and use naloxone to reverse an overdose are active drug users. © 2015 Society for the Study of Addiction.

Reversal of Glaucoma Hemifield Test Results and Visual Field Features in Glaucoma.

PubMed

Wang, Mengyu; Pasquale, Louis R; Shen, Lucy Q; Boland, Michael V; Wellik, Sarah R; De Moraes, Carlos Gustavo; Myers, Jonathan S; Wang, Hui; Baniasadi, Neda; Li, Dian; Silva, Rafaella Nascimento E; Bex, Peter J; Elze, Tobias

2018-03-01

To develop a visual field (VF) feature model to predict the reversal of glaucoma hemifield test (GHT) results to within normal limits (WNL) after 2 consecutive outside normal limits (ONL) results. Retrospective cohort study. Visual fields of 44 503 eyes from 26 130 participants. Eyes with 3 or more consecutive reliable VFs measured with the Humphrey Field Analyzer (Swedish interactive threshold algorithm standard 24-2) were included. Eyes with ONL GHT results for the 2 baseline VFs were selected. We extracted 3 categories of VF features from the baseline tests: (1) VF global indices (mean deviation [MD] and pattern standard deviation), (2) mismatch between baseline VFs, and (3) VF loss patterns (archetypes). Logistic regression was applied to predict the GHT results reversal. Cross-validation was applied to evaluate the model on testing data by the area under the receiver operating characteristic curve (AUC). We ascertained clinical glaucoma status on a patient subset (n = 97) to determine the usefulness of our model. Predictive models for GHT results reversal using VF features. For the 16 604 eyes with 2 initial ONL results, the prevalence of a subsequent WNL result increased from 0.1% for MD < -12 dB to 13.8% for MD ≥-3 dB. Compared with models with VF global indices, the AUC of predictive models increased from 0.669 (MD ≥-3 dB) and 0.697 (-6 dB ≤ MD < -3 dB) to 0.770 and 0.820, respectively, by adding VF mismatch features and computationally derived VF archetypes (P < 0.001 for both). The GHT results reversal was associated with a large mismatch between baseline VFs. Moreover, the GHT results reversal was associated more with VF archetypes of nonglaucomatous loss, severe widespread loss, and lens rim artifacts. For a subset of 97 eyes, using our model to predict absence of glaucoma based on clinical evidence after 2 ONL results yielded significantly better prediction accuracy (87.7%; P < 0.001) than predicting GHT results reversal (68.8%) with a prescribed specificity 67.7%. Using VF features may predict the GHT results reversal to WNL after 2 consecutive ONL results. Copyright © 2017 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

Epidemic models for phase transitions: application to a physical gel

NASA Astrophysics Data System (ADS)

Bilge, A. H.; Pekcan, O.; Kara, S.; Ogrenci, A. S.

2017-09-01

Carrageenan gels are characterized by reversible sol-gel and gel-sol transitions under cooling and heating processes and these transitions are approximated by generalized logistic growth curves. We express the transitions of carrageenan-water system, as a representative of reversible physical gels, in terms of a modified Susceptible-Infected-Susceptible epidemic model, as opposed to the Susceptible-Infected-Removed model used to represent the (irreversible) chemical gel formation in the previous work. We locate the gel point Tc of sol-gel and gel-sol transitions and we find that, for the sol-gel transition (cooling), Tc > Tsg (transition temperature), i.e. Tc is earlier in time for all carrageenan contents and moves forward in time and gets closer to Tsg as the carrageenan content increases. For the gel-sol transition (heating), Tc is relatively closer to Tgs; it is greater than Tgs, i.e. later in time for low carrageenan contents and moves backward as carrageenan content increases.

The Life Cycle Evaluation Model of External Diseconomy of Open-loop Supply Chain

NASA Astrophysics Data System (ADS)

Liu, Qian; Hu, Tianjun

2017-08-01

In recent years, with the continuous deterioration of pollution, resource space is gradually narrowed, the number of waste items increased, people began to use the method of recycling on waste products to ease the pressure on the environment. This paper adopted the external diseconomy of open-loop supply chain as the research object and constructed the model by the life cycle evaluation method, comparative analysis through the case. This paper also concludes that the key to solving the problem is to realize the closed-loop supply chain and building reverse logistics system is of great significance.

E-waste management and sustainability: a case study in Brazil.

PubMed

Azevedo, Luís Peres; da Silva Araújo, Fernando Gabriel; Lagarinhos, Carlos Alberto Ferreira; Tenório, Jorge Alberto Soares; Espinosa, Denise Crocce Romano

2017-11-01

The advancement of technology and development of new electronic and electrical equipment with a reduced life cycle has increased the need for the disposal of them (called Waste of Electric and Electronic Equipment or simply e-waste) due to defects presented during use, replacement of obsolete equipment, and ease of acquisition of new equipment. There is a lack of consumer awareness regarding the use, handling storage, and disposal of this equipment. In Brazil, the disposal of post-consumer waste is regulated by the National Solid Waste Policy, established by Law No. 12305 and regulated on the 23rd December 2010. Under this legislation, manufacturers and importers are required to perform a project for the Reverse Logistics of e-waste, though its implementation is not well defined. This work focuses on the verification of the sustainability of reverse logistics suggested by the legislation and the mandatory points, evaluating its costs and the possible financial gain with recycling of the waste. The management of reverse logistics and recycling of waste electrical and electronic equipment, or simply recycling of e-waste, as suggested by the government, will be the responsibility of the managing organization to be formed by the manufacturers/importers in Brazil.

Investigation of possibility of surface rupture derived from PFDHA and calculation of surface displacement based on dislocation

NASA Astrophysics Data System (ADS)

Inoue, N.; Kitada, N.; Irikura, K.

2013-12-01

A probability of surface rupture is important to configure the seismic source, such as area sources or fault models, for a seismic hazard evaluation. In Japan, Takemura (1998) estimated the probability based on the historical earthquake data. Kagawa et al. (2004) evaluated the probability based on a numerical simulation of surface displacements. The estimated probability indicates a sigmoid curve and increases between Mj (the local magnitude defined and calculated by Japan Meteorological Agency) =6.5 and Mj=7.0. The probability of surface rupture is also used in a probabilistic fault displacement analysis (PFDHA). The probability is determined from the collected earthquake catalog, which were classified into two categories: with surface rupture or without surface rupture. The logistic regression is performed for the classified earthquake data. Youngs et al. (2003), Ross and Moss (2011) and Petersen et al. (2011) indicate the logistic curves of the probability of surface rupture by normal, reverse and strike-slip faults, respectively. Takao et al. (2013) shows the logistic curve derived from only Japanese earthquake data. The Japanese probability curve shows the sharply increasing in narrow magnitude range by comparison with other curves. In this study, we estimated the probability of surface rupture applying the logistic analysis to the surface displacement derived from a surface displacement calculation. A source fault was defined in according to the procedure of Kagawa et al. (2004), which determined a seismic moment from a magnitude and estimated the area size of the asperity and the amount of slip. Strike slip and reverse faults were considered as source faults. We applied Wang et al. (2003) for calculations. The surface displacements with defined source faults were calculated by varying the depth of the fault. A threshold value as 5cm of surface displacement was used to evaluate whether a surface rupture reach or do not reach to the surface. We carried out the logistic regression analysis to the calculated displacements, which were classified by the above threshold. The estimated probability curve indicated the similar trend to the result of Takao et al. (2013). The probability of revere faults is larger than that of strike slip faults. On the other hand, PFDHA results show different trends. The probability of reverse faults at higher magnitude is lower than that of strike slip and normal faults. Ross and Moss (2011) suggested that the sediment and/or rock over the fault compress and not reach the displacement to the surface enough. The numerical theory applied in this study cannot deal with a complex initial situation such as topography.

Late pregnancy reversal from excessive gestational weight gain lowers risk of childhood overweight--a cohort study.

PubMed

von Kries, Rüdiger; Chmitorz, Andrea; Rasmussen, Kathleen M; Bayer, Otmar; Ensenauer, Regina

2013-06-01

Whether reversal to adequate gestational weight gain (GWG) in the third trimester reverses the risk for childhood overweight associated with excessive GWG is assessed. In a retrospective cohort study in 6,665 mother-child pairs, pre-pregnancy weight and the temporal course of GWG were collected from medical records. Overweight as defined by International Obesity Task Force was assessed at a mean age of 5.8 years. Main exposures were exceeding week-specific cut-off values for GWG in the third trimester or any previous trimester. Logistic regression models, adjusted for possible confounding factors, were used to predict the risk of childhood overweight from excessive GWG in the third trimester with stratification by excessive GWG in previous trimesters. In the final model, women who avoided excessive GWG in the third trimester had children with a 31% (odds ratio [OR]: 0.69, 95% confidence interval [CI]: 0.59, 0.82) lower probability being overweight. A similar association was observed for reversing from excessive GWG in the first or second trimester to normal GWG in the third trimester: 27% (OR: 0.73, 95% CI: 0.53, 0.99). Avoidance of excessive GWG in the third trimester is associated with lower risk of childhood overweight even in case of excessive GWG in the first or second trimester. Copyright © 2013 The Obesity Society.

Highly dissipative Hénon map behavior in the four-level model of the CO 2 laser with modulated losses

NASA Astrophysics Data System (ADS)

Pando L., C. L.; Acosta, G. A. Luna; Meucci, R.; Ciofini, M.

1995-02-01

We show that the four-level model for the CO 2 laser with modulated losses behaves in a qualitatively similar way as the highly dissipative Hénon map. The ubiquity of elements of the universal sequence, their related symbolic dynamics, and the presence of reverse bifurcations of chaotic bands in the model are reminiscent of the logistic map which is the limit of the Hénon map when the Jacobian equals zero. The coexistence of attractors, its dynamics related to contraction of volumes in phase space and the associated return maps can be correlated with those of the highly dissipative Hénon map.

Problem parental care and teenage deliberate self-harm in young community adults.

PubMed

Bifulco, Antonia; Schimmenti, Adriano; Moran, Patricia; Jacobs, Catherine; Bunn, Amanda; Rusu, Adina Carmen

2014-01-01

Deliberate self-harm (DSH) in young people is a clinical and social problem related to early maltreatment but with little specificity in type of care or abuse determined. A community sample of 160 high-risk young people (aged 16-30) were the offspring of mothers' previously interviewed as vulnerable to major depression. The youth were interviewed to determine DSH (both suicidal and nonsuicidal), childhood maltreatment (using the Childhood Experience of Care and Abuse interview) and major depression (using SCID for DSMIV) before age 17. Around one fifth reported DSH; equal proportions were suicidal and nonsuicidal with a fourth of these with both. DSH was highly related to family context (single mother upbringing and family discord) and poor parental care (including antipathy, neglect, inadequate supervision, and role reversal). Highest odds ratios were for role reversal (OR = 17) and neglect (OR = 11). DSH was unrelated to any type of abuse. Logistic regression showed that role reversal, inadequate supervision, and teenage depression all modeled DSH. There was some specificity, with single mother upbringing, role reversal, and inadequate supervision predicting nonsuicidal DSH, and neglect and role reversal alone predicting suicidal DSH. Role reversal remained a key predictor for both types of DSH when controls were applied. Poor childhood care, which has implications for problematic emotion regulation and empoverished social development, needs to be understood to improve interventions and treatment for DSH in young people.

Control of nonlinear systems using periodic parametric perturbations with application to a reversed field pinch

NASA Astrophysics Data System (ADS)

Mirus, Kevin Andrew

In this thesis, the possibility of controlling low- and high-dimensional chaotic systems by periodically driving an accessible system parameter is examined. This method has been carried out on several numerical systems and the MST Reversed Field Pinch. The numerical systems investigated include the logistic equation, the Lorenz equations, the Rossler equations, a coupled lattice of logistic equations, a coupled lattice of Lorenz equations, the Yoshida equations, which model tearing mode fluctuations in a plasma, and a neural net model for magnetic fluctuations on MST. This method was tested on the MST by sinusoidally driving a magnetic flux through the toroidal gap of the device. Numerically, periodic drives were found to be most effective at producing limit cycle behavior or significantly reducing the dimension of the system when the perturbation frequency was near natural frequencies of unstable periodic orbits embedded in the attractor of the unperturbed system. Several different unstable periodic orbits have been stabilized in this way for the low-dimensional numerical systems, sometimes with perturbation amplitudes that were less than 5% of the nominal value of the parameter being perturbed. In high- dimensional systems, limit cycle behavior and significant decreases in the system dimension were also achieved using perturbations with frequencies near the natural unstable periodic orbit frequencies. Results for the MST were not this encouraging, most likely because of an insufficient drive amplitude, the extremely high dimension of the plasma behavior, large amounts of noise, and a lack of stationarity in the transient plasma pulses.

Comprehension of texts by deaf elementary school students: The role of grammatical understanding.

PubMed

Barajas, Carmen; González-Cuenca, Antonia M; Carrero, Francisco

2016-12-01

The aim of this study was to analyze how the reading process of deaf Spanish elementary school students is affected both by those components that explain reading comprehension according to the Simple View of Reading model: decoding and linguistic comprehension (both lexical and grammatical) and by other variables that are external to the reading process: the type of assistive technology used, the age at which it is implanted or fitted, the participant's socioeconomic status and school stage. Forty-seven students aged between 6 and 13 years participated in the study; all presented with profound or severe prelingual bilateral deafness, and all used digital hearing aids or cochlear implants. Students' text comprehension skills, decoding skills and oral comprehension skills (both lexical and grammatical) were evaluated. Logistic regression analysis indicated that neither the type of assistive technology, age at time of fitting or activation, socioeconomic status, nor school stage could predict the presence or absence of difficulties in text comprehension. Furthermore, logistic regression analysis indicated that neither decoding skills, nor lexical age could predict competency in text comprehension; however, grammatical age could explain 41% of the variance. Probing deeper into the effect of grammatical understanding, logistic regression analysis indicated that a participant's understanding of reversible passive object-verb-subject sentences and reversible predicative subject-verb-object sentences accounted for 38% of the variance in text comprehension. Based on these results, we suggest that it might be beneficial to devise and evaluate interventions that focus specifically on grammatical comprehension. Copyright © 2016 Elsevier Ltd. All rights reserved.

The use of reverse logistics for waste management in a Brazilian grocery retailer.

PubMed

Dias, Karina T S; Braga Junior, Sergio S

2016-01-01

Retail growth is a result of the diversification of departments with the intention to look to consumer's needs and level of demand. Pressed by consumers and by the law, the adoption of environmental preservation practices is becoming stronger among grocery retailers. The objective of this research was to analyse the practices of reverse logistics performed by a retailer and measure the amount of waste generated by each department. To reach the proposed goal, a field research study was conducted to directly observe a grocery retailer in the state of Sao Paulo, Brazil, for a period of 6 months and monitor the amounts of cardboard and plastic discarded by each department. Using the Wuppertal method, the first result observed was that the retailer stopped its monthly production of approximately 20 tonne of biotic and abiotic material, which influence global warming and degradation of the ozone layer. Another result observed with the implementation of reverse logistics, was that the general grocery department mostly used cardboard and plastic. This sector includes products such as food cupboard, drinks, household, health and beauty, and pet articles. The fresh fruit and vegetable department and the meat, chicken and frozen department were increasingly using less plastic and cardboard packaging, increasing the use of returnable and durable packaging and thus promoting sustainability. © The Author(s) 2015.

Contraceptive Sterilization: Introducing A Couple Perspective to Examine Sociodemographic Differences in Use.

PubMed

Eeckhaut, Mieke C W

2017-09-01

Most studies of contraceptive use have relied solely on the woman's perspective, but because men's attitudes and preferences are also important, analytic approaches based on couples should also be explored. Data from the 2006-2010 and 2011-2013 rounds of the National Survey of Family Growth yielded a sample of 4,591 men and women who were married or cohabiting with an opposite-sex partner and who had completed their intended childbearing. Respondents' reports of both their own and their partners' characteristics and behaviors were employed in two sets of analyses examining educational and racial and ethnic differences in contraceptive use: an individualistic approach (using multinomial logistic regression) and a couple approach (using multinomial logistic diagonal reference models). In the full model using the individualistic approach, respondents with less than a high school education were less likely than those with at least a college degree to rely on male sterilization (odds ratios, 0.1-0.2) or a reversible method (0.4-0.5), as opposed to female sterilization. Parallel analyses limited to couples in which partners had the same educational levels (i.e., educationally homogamous couples) showed an even greater difference between those with the least and those with the most schooling (0.03 for male sterilization and 0.2 for a reversible method). When race and ethnicity, which had a much higher level of homogamy, were examined, the approaches yielded more similar results. Research on contraceptive use can benefit from a couple approach, particularly when focusing on partners' characteristics for which homogamy is relatively low. Copyright © 2017 by the Guttmacher Institute.

Reverse preferential spread in complex networks

NASA Astrophysics Data System (ADS)

Toyoizumi, Hiroshi; Tani, Seiichi; Miyoshi, Naoto; Okamoto, Yoshio

2012-08-01

Large-degree nodes may have a larger influence on the network, but they can be bottlenecks for spreading information since spreading attempts tend to concentrate on these nodes and become redundant. We discuss that the reverse preferential spread (distributing information inversely proportional to the degree of the receiving node) has an advantage over other spread mechanisms. In large uncorrelated networks, we show that the mean number of nodes that receive information under the reverse preferential spread is an upper bound among any other weight-based spread mechanisms, and this upper bound is indeed a logistic growth independent of the degree distribution.

Sleep Duration and Midday Napping with 5-Year Incidence and Reversion of Metabolic Syndrome in Middle-Aged and Older Chinese.

PubMed

Yang, Liangle; Xu, Zengguang; He, Meian; Yang, Handong; Li, Xiulou; Min, Xinwen; Zhang, Ce; Xu, Chengwei; Angileri, Francesca; Légaré, Sébastien; Yuan, Jing; Miao, Xiaoping; Guo, Huan; Yao, Ping; Wu, Tangchun; Zhang, Xiaomin

2016-11-01

Prospective evidence on the association of sleep duration and midday napping with metabolic syndrome (MetS) is limited. We aimed to examine the associations of sleep duration and midday napping with risk of incidence and reversion of MetS and its components among a middle-aged and older Chinese population. We included 14,399 subjects from the Dongfeng-Tongji (DFTJ) Cohort Study (2008-2013) who were free of coronary heart disease, stroke, and cancer at baseline. Baseline data were obtained by questionnaires and health examinations. Odds ratios (ORs) and 95% confidence interval (CI) were derived from multivariate logistic regression models. After controlling for potential covariates, longer sleep duration (≥ 9 h) was associated with a higher risk of MetS incidence (OR, 1.29; 95% CI, 1.08-1.55) and lower reversion of MetS (OR, 0.80; 95% CI, 0.66-0.96) compared with sleep duration of 7 to < 8 h; whereas shorter sleep duration (< 6 h) was not related to incidence or reversion of MetS. For midday napping, subjects with longer napping (≥ 90 min) was also associated with a higher risk of MetS incidence and a lower risk of MetS reversion compared with those with napping of 1 to < 30 min (OR, 1.48; 95% CI, 1.05-2.10 and OR, 0.70; 95% CI, 0.52-0.94, respectively). Significance for incidence or reversion of certain MetS components remained in shorter and longer sleepers but disappeared across napping categories. Both longer sleep duration and longer midday napping were potential risk factors for MetS incidence, and concurrently exert adverse effects on MetS reversion. © 2016 Associated Professional Sleep Societies, LLC.

Unified heuristics to solve routing problem of reverse logistics in sustainable supply chain

NASA Astrophysics Data System (ADS)

Anbuudayasankar, S. P.; Ganesh, K.; Lenny Koh, S. C.; Mohandas, K.

2010-03-01

A reverse logistics problem, motivated by many real-life applications, is examined where bottles/cans in which products are delivered from a processing depot to customers in one period are available for return to the depot in the following period. The picked-up bottles/cans need to be adjusted in the place of delivery load. This problem is termed as simultaneous delivery and pick-up problem with constrained capacity (SDPC). We develop three unified heuristics based on extended branch and bound heuristic, genetic algorithm and simulated annealing to solve SDPC. These heuristics are also designed to solve standard travelling salesman problem (TSP) and TSP with simultaneous delivery and pick-up (TSDP). We tested the heuristics on standard, derived and randomly generated datasets of TSP, TSDP and SDPC and obtained satisfying results with high convergence in reasonable time.

Global reverse supply chain design for solid waste recycling under uncertainties and carbon emission constraint.

PubMed

Xu, Zhitao; Elomri, Adel; Pokharel, Shaligram; Zhang, Qin; Ming, X G; Liu, Wenjie

2017-06-01

The emergence of concerns over environmental protection, resource conservation as well as the development of logistics operations and manufacturing technology has led several countries to implement formal collection and recycling systems of solid waste. Such recycling system has the benefits of reducing environmental pollution, boosting the economy by creating new jobs, and generating income from trading the recyclable materials. This leads to the formation of a global reverse supply chain (GRSC) of solid waste. In this paper, we investigate the design of such a GRSC with a special emphasis on three aspects; (1) uncertainty of waste collection levels, (2) associated carbon emissions, and (3) challenges posed by the supply chain's global aspect, particularly the maritime transportation costs and currency exchange rates. To the best of our knowledge, this paper is the first attempt to integrate the three above-mentioned important aspects in the design of a GRSC. We have used mixed integer-linear programming method along with robust optimization to develop the model which is validated using a sample case study of e-waste management. Our results show that using a robust model by taking the complex interactions characterizing global reverse supply chain networks into account, we can create a better GRSC. The effect of uncertainties and carbon constraints on decisions to reduce costs and emissions are also shown. Copyright © 2017 Elsevier Ltd. All rights reserved.

Reliable design of a closed loop supply chain network under uncertainty: An interval fuzzy possibilistic chance-constrained model

NASA Astrophysics Data System (ADS)

Vahdani, Behnam; Tavakkoli-Moghaddam, Reza; Jolai, Fariborz; Baboli, Arman

2013-06-01

This article seeks to offer a systematic approach to establishing a reliable network of facilities in closed loop supply chains (CLSCs) under uncertainties. Facilities that are located in this article concurrently satisfy both traditional objective functions and reliability considerations in CLSC network designs. To attack this problem, a novel mathematical model is developed that integrates the network design decisions in both forward and reverse supply chain networks. The model also utilizes an effective reliability approach to find a robust network design. In order to make the results of this article more realistic, a CLSC for a case study in the iron and steel industry has been explored. The considered CLSC is multi-echelon, multi-facility, multi-product and multi-supplier. Furthermore, multiple facilities exist in the reverse logistics network leading to high complexities. Since the collection centres play an important role in this network, the reliability concept of these facilities is taken into consideration. To solve the proposed model, a novel interactive hybrid solution methodology is developed by combining a number of efficient solution approaches from the recent literature. The proposed solution methodology is a bi-objective interval fuzzy possibilistic chance-constraint mixed integer linear programming (BOIFPCCMILP). Finally, computational experiments are provided to demonstrate the applicability and suitability of the proposed model in a supply chain environment and to help decision makers facilitate their analyses.

Comment on ``Correlated noise in a logistic growth model''

NASA Astrophysics Data System (ADS)

Behera, Anita; O'Rourke, S. Francesca C.

2008-01-01

We argue that the results published by Ai [Phys. Rev. E 67, 022903 (2003)] on “correlated noise in logistic growth” are not correct. Their conclusion that, for larger values of the correlation parameter λ , the cell population is peaked at x=0 , which denotes a high extinction rate, is also incorrect. We find the reverse behavior to their results, that increasing λ promotes the stable growth of tumor cells. In particular, their results for the steady-state probability, as a function of cell number, at different correlation strengths, presented in Figs. 1 and 2 of their paper show different behavior than one would expect from the simple mathematical expression for the steady-state probability. Additionally, their interpretation that at small values of cell number the steady-state probability increases as the correlation parameter is increased is also questionable. Another striking feature in their Figs. 1 and 3 is that, for the same values of the parameters λ and α , their simulation produces two different curves, both qualitatively and quantitatively.

An Optimal Hierarchical Decision Model for a Regional Logistics Network with Environmental Impact Consideration

PubMed Central

Zhang, Dezhi; Li, Shuangyan

2014-01-01

This paper proposes a new model of simultaneous optimization of three-level logistics decisions, for logistics authorities, logistics operators, and logistics users, for regional logistics network with environmental impact consideration. The proposed model addresses the interaction among the three logistics players in a complete competitive logistics service market with CO2 emission charges. We also explicitly incorporate the impacts of the scale economics of the logistics park and the logistics users' demand elasticity into the model. The logistics authorities aim to maximize the total social welfare of the system, considering the demand of green logistics development by two different methods: optimal location of logistics nodes and charging a CO2 emission tax. Logistics operators are assumed to compete with logistics service fare and frequency, while logistics users minimize their own perceived logistics disutility given logistics operators' service fare and frequency. A heuristic algorithm based on the multinomial logit model is presented for the three-level decision model, and a numerical example is given to illustrate the above optimal model and its algorithm. The proposed model provides a useful tool for modeling competitive logistics services and evaluating logistics policies at the strategic level. PMID:24977209

An optimal hierarchical decision model for a regional logistics network with environmental impact consideration.

PubMed

Zhang, Dezhi; Li, Shuangyan; Qin, Jin

2014-01-01

This paper proposes a new model of simultaneous optimization of three-level logistics decisions, for logistics authorities, logistics operators, and logistics users, for regional logistics network with environmental impact consideration. The proposed model addresses the interaction among the three logistics players in a complete competitive logistics service market with CO2 emission charges. We also explicitly incorporate the impacts of the scale economics of the logistics park and the logistics users' demand elasticity into the model. The logistics authorities aim to maximize the total social welfare of the system, considering the demand of green logistics development by two different methods: optimal location of logistics nodes and charging a CO2 emission tax. Logistics operators are assumed to compete with logistics service fare and frequency, while logistics users minimize their own perceived logistics disutility given logistics operators' service fare and frequency. A heuristic algorithm based on the multinomial logit model is presented for the three-level decision model, and a numerical example is given to illustrate the above optimal model and its algorithm. The proposed model provides a useful tool for modeling competitive logistics services and evaluating logistics policies at the strategic level.

Structured Analysis of the Logistics Support Analysis (LSA) Task, and Integrated Logistic Support (ILS) Element, ’Standardization and Interoperability (S and I)’.

DTIC Science & Technology

1988-11-01

system, using graphic techniques which enable users, analysts, and designers to get a clear and common picture of the system and how its parts fit...boxes into hierarchies suitable for computer implementation. ŗ. Structured Design uses tools, especially graphic ones, to render systems readily...LSA, PROCESSES, DATA FLOWS, DATA STORES, EX"RNAL ENTITIES, OVERALL SYSTEMS DESIGN PROCESS, over 19, ABSTRACT (Continue on reverse if necessary and

System dynamics applied to closed loop supply chains of desktops and laptops in Brazil: A perspective for social inclusion of waste pickers.

PubMed

Ghisolfi, Verônica; Diniz Chaves, Gisele de Lorena; Ribeiro Siman, Renato; Xavier, Lúcia Helena

2017-02-01

The structure of reverse logistics for waste electrical and electronic equipment (WEEE) is essential to minimize the impacts of their improper disposal. In this context, the Brazilian Solid Waste Policy (BSWP) was a regulatory milestone in Brazil, submitting WEEE to the mandatory implementation of reverse logistics systems, involving the integration of waste pickers on the shared responsibility for the life cycle of products. This article aims to measure the impact of such legal incentives and the bargaining power obtained by the volume of collected waste on the effective formalization of waste pickers. The proposed model evaluates the sustainability of supply chains in terms of the use of raw materials due to disposal fees, collection, recycling and return of some materials from desktops and laptops using system dynamics methodology. The results show that even in the absence of bargaining power, the formalization of waste pickers occurs due to legal incentives. It is important to ensure the waste pickers cooperatives access to a minimum amount, which requires a level of protection against unfair competition with companies. Regarding the optimal level of environmental policies, even though the formalization time is long, it is still not enough to guarantee the formalization of waste picker cooperatives, which is dependent on their bargaining power. Steel is the material with the largest decrease in acquisition rate of raw material. Copyright © 2016 Elsevier Ltd. All rights reserved.

Environmental factors and flow paths related to Escherichia coli concentrations at two beaches on Lake St. Clair, Michigan, 2002–2005

USGS Publications Warehouse

Holtschlag, David J.; Shively, Dawn; Whitman, Richard L.; Haack, Sheridan K.; Fogarty, Lisa R.

2008-01-01

Regression analyses and hydrodynamic modeling were used to identify environmental factors and flow paths associated with Escherichia coli (E. coli) concentrations at Memorial and Metropolitan Beaches on Lake St. Clair in Macomb County, Mich. Lake St. Clair is part of the binational waterway between the United States and Canada that connects Lake Huron with Lake Erie in the Great Lakes Basin. Linear regression, regression-tree, and logistic regression models were developed from E. coli concentration and ancillary environmental data. Linear regression models on log10 E. coli concentrations indicated that rainfall prior to sampling, water temperature, and turbidity were positively associated with bacteria concentrations at both beaches. Flow from Clinton River, changes in water levels, wind conditions, and log10 E. coli concentrations 2 days before or after the target bacteria concentrations were statistically significant at one or both beaches. In addition, various interaction terms were significant at Memorial Beach. Linear regression models for both beaches explained only about 30 percent of the variability in log10 E. coli concentrations. Regression-tree models were developed from data from both Memorial and Metropolitan Beaches but were found to have limited predictive capability in this study. The results indicate that too few observations were available to develop reliable regression-tree models. Linear logistic models were developed to estimate the probability of E. coli concentrations exceeding 300 most probable number (MPN) per 100 milliliters (mL). Rainfall amounts before bacteria sampling were positively associated with exceedance probabilities at both beaches. Flow of Clinton River, turbidity, and log10 E. coli concentrations measured before or after the target E. coli measurements were related to exceedances at one or both beaches. The linear logistic models were effective in estimating bacteria exceedances at both beaches. A receiver operating characteristic (ROC) analysis was used to determine cut points for maximizing the true positive rate prediction while minimizing the false positive rate. A two-dimensional hydrodynamic model was developed to simulate horizontal current patterns on Lake St. Clair in response to wind, flow, and water-level conditions at model boundaries. Simulated velocity fields were used to track hypothetical massless particles backward in time from the beaches along flow paths toward source areas. Reverse particle tracking for idealized steady-state conditions shows changes in expected flow paths and traveltimes with wind speeds and directions from 24 sectors. The results indicate that three to four sets of contiguous wind sectors have similar effects on flow paths in the vicinity of the beaches. In addition, reverse particle tracking was used for transient conditions to identify expected flow paths for 10 E. coli sampling events in 2004. These results demonstrate the ability to track hypothetical particles from the beaches, backward in time, to likely source areas. This ability, coupled with a greater frequency of bacteria sampling, may provide insight into changes in bacteria concentrations between source and sink areas.

Pharmacopollution and Household Waste Medicine (HWM): how reverse logistics is environmentally important to Brazil.

PubMed

Pereira, André Luiz; de Vasconcelos Barros, Raphael Tobias; Pereira, Sandra Rosa

2017-11-01

Pharmacopollution is a public health and environmental outcome of some active pharmaceutical ingredients (API) and endocrine-disrupting compounds (EDC) dispersed through water and/or soil. Its most important sources are the pharmaceutical industry, healthcare facilities (e.g., hospitals), livestock, aquaculture, and households (patients' excretion and littering). The last source is the focus of this article. Research questions are "What is the Household Waste Medicine (HWM) phenomenon?", "How HWM and pharmacopollution are related?", and "Why is a reverse logistic system necessary for HWM in Brazil?" This article followed the seven steps proposed by Rother (2007) for a systematic review based on the Cochrane Handbook and the National Health Service (NHS) Center for Reviews Dissemination (CDR) Report. The HWM phenomenon brings many environmental, public health, and, social challenges. The insufficient data is a real challenge to assessing potential human health risks and API concentrations. Therefore, the hazard of long-term exposure to low concentrations of pharmacopollutants and the combined effects of API mixtures is still uncertain. HWM are strongly related to pharmacopollution, as this review shows. The Brazilian HWM case is remarkable because it is the fourth pharmaceutical market (US$ 65,971 billion), with a wide number of private pharmacies and drugstores (3.3: 10,000 pharmacy/inhabitants), self-medication habits, and no national take-back program. The HWM generation is estimated in 56.6 g/per capita, or 10,800 t/year. The absence of a reverse logistics for HWM can lead to serious environmental and public health challenges. The sector agreement for HWM is currently under public consultation.

77 FR 40026 - 36(b)(1) Arms Sales Notification

Federal Register 2010, 2011, 2012, 2013, 2014

2012-07-06

... and contractor logistics, Quality Assurance Team support services, engineering and technical support..., engineering and technical support, and other related elements of program support. The estimated cost is $49..., maintenance, or training is Confidential. Reverse engineering could reveal Confidential information...

Unacceptable treatment outcomes and associated factors among India's initial cohorts of multidrug-resistant tuberculosis (MDR-TB) patients under the revised national TB control programme (2007-2011): Evidence leading to policy enhancement.

PubMed

Parmar, Malik M; Sachdeva, Kuldeep Singh; Dewan, Puneet K; Rade, Kiran; Nair, Sreenivas A; Pant, Rashmi; Khaparde, Sunil D

2018-01-01

Globally, India has the world's highest burden of multidrug-resistant tuberculosis (MDR-TB). Programmatic Management of Drug Resistant TB (PMDT) in India began in 2007 and nationwide coverage was achieved in early 2013. Poor initial microbiological outcomes under the Revised National Tuberculosis Control Programme (RNTCP) prompted detailed analysis. This is the first study on factors significantly associated with poor outcomes in MDR-TB patients treated under the RNTCP. To evaluate initial sputum culture conversion, culture reversion and final treatment outcomes among MDR-TB patients registered in India from 2007 to early 2011 who were treated with a standard 24-month regimen under daily-observed treatment. This is a retrospective cohort study. Clinical and microbiological data were abstracted from PMDT records. Initial sputum culture conversion, culture reversion and treatment outcomes were defined by country adaptation of the standard WHO definitions (2008). Cox proportional hazards modeling with logistic regression, multinomial logistic regression and adjusted odds ratio was used to evaluate factors associated with interim and final outcomes respectively, controlling for demographic and clinical characteristics. In the cohort of 3712 MDR-TB patients, 2735 (73.6%) had initial sputum culture conversion at 100 median days (IQR 92-125), of which 506 (18.5%) had culture reversion at 279 median days (IQR 202-381). Treatment outcomes were available for 2264 (60.9%) patients while 1448 (39.0%) patients were still on treatment or yet to have a definite outcome at the time of analysis. Of 2264 patients, 781 (34.5%) had treatment success, 644 (28.4%) died, 670 (29.6%) were lost to follow up, 169 (7.5%) experienced treatment failure or were changed to XDR-TB treatment. Factors significantly associated with either culture non-conversion, culture reversion and/or unfavorable treatment outcomes were baseline BMI < 18; ≥ seven missed doses in intensive phase (IP) and continuation phase (CP); cavitary disease; prior treatment episodes characterized by re-treatment regimen taken twice, longer duration and more episodes of treatment; any weight loss during treatment; males and additional resistance to first line drugs (Ethambutol, Streptomycin). In a subgroup of 104 MDR-TB patients, 62 (59.6%) had Ofloxacin resistance among whom only 25.8% had treatment success, half of the success (54.8%) seen in Ofloxacin sensitive patients. Baseline susceptibility to Ofloxacin (HR 2.04) and Kanamycin (HR 4.55) significantly doubled and quadrupled the chances for culture conversion respectively while baseline susceptibility to Ofloxacin (AOR 0.37) also significantly reduced the odds of unfavorable treatment outcomes (p value ≤0.05) in multinomial logistic regression model. India's initial MDR-TB patients' cohort treated under the RNTCP experienced poor treatment outcomes. To address the factors associated with poor treatment outcomes revealed in our study, a systematic multi-pronged approach would be needed. A series of policies and interventions have been developed to address these factors to improve DR-TB treatment outcomes and are being scaled-up in India.

Unacceptable treatment outcomes and associated factors among India's initial cohorts of multidrug-resistant tuberculosis (MDR-TB) patients under the revised national TB control programme (2007–2011): Evidence leading to policy enhancement

PubMed Central

Sachdeva, Kuldeep Singh; Dewan, Puneet K.; Rade, Kiran; Nair, Sreenivas A.; Pant, Rashmi; Khaparde, Sunil D.

2018-01-01

Background Globally, India has the world’s highest burden of multidrug-resistant tuberculosis (MDR-TB). Programmatic Management of Drug Resistant TB (PMDT) in India began in 2007 and nationwide coverage was achieved in early 2013. Poor initial microbiological outcomes under the Revised National Tuberculosis Control Programme (RNTCP) prompted detailed analysis. This is the first study on factors significantly associated with poor outcomes in MDR-TB patients treated under the RNTCP. Objective To evaluate initial sputum culture conversion, culture reversion and final treatment outcomes among MDR-TB patients registered in India from 2007 to early 2011 who were treated with a standard 24-month regimen under daily-observed treatment. Methods This is a retrospective cohort study. Clinical and microbiological data were abstracted from PMDT records. Initial sputum culture conversion, culture reversion and treatment outcomes were defined by country adaptation of the standard WHO definitions (2008). Cox proportional hazards modeling with logistic regression, multinomial logistic regression and adjusted odds ratio was used to evaluate factors associated with interim and final outcomes respectively, controlling for demographic and clinical characteristics. Results In the cohort of 3712 MDR-TB patients, 2735 (73.6%) had initial sputum culture conversion at 100 median days (IQR 92–125), of which 506 (18.5%) had culture reversion at 279 median days (IQR 202–381). Treatment outcomes were available for 2264 (60.9%) patients while 1448 (39.0%) patients were still on treatment or yet to have a definite outcome at the time of analysis. Of 2264 patients, 781 (34.5%) had treatment success, 644 (28.4%) died, 670 (29.6%) were lost to follow up, 169 (7.5%) experienced treatment failure or were changed to XDR-TB treatment. Factors significantly associated with either culture non-conversion, culture reversion and/or unfavorable treatment outcomes were baseline BMI < 18; ≥ seven missed doses in intensive phase (IP) and continuation phase (CP); cavitary disease; prior treatment episodes characterized by re-treatment regimen taken twice, longer duration and more episodes of treatment; any weight loss during treatment; males and additional resistance to first line drugs (Ethambutol, Streptomycin). In a subgroup of 104 MDR-TB patients, 62 (59.6%) had Ofloxacin resistance among whom only 25.8% had treatment success, half of the success (54.8%) seen in Ofloxacin sensitive patients. Baseline susceptibility to Ofloxacin (HR 2.04) and Kanamycin (HR 4.55) significantly doubled and quadrupled the chances for culture conversion respectively while baseline susceptibility to Ofloxacin (AOR 0.37) also significantly reduced the odds of unfavorable treatment outcomes (p value ≤0.05) in multinomial logistic regression model. Conclusion India’s initial MDR-TB patients’ cohort treated under the RNTCP experienced poor treatment outcomes. To address the factors associated with poor treatment outcomes revealed in our study, a systematic multi-pronged approach would be needed. A series of policies and interventions have been developed to address these factors to improve DR-TB treatment outcomes and are being scaled-up in India. PMID:29641576

Chaotic Behaviour of a Driven P-N Junction

NASA Astrophysics Data System (ADS)

Perez, Jose Maria

The chaotic behavior of a driven p-n junction is experimentally examined. Bifurcation diagrams for the system are measured, showing period doubling bifurcations up to f/32, onset of chaos, reverse bifurcations of chaotic bands, and periodic windows. Some of the measured bifurcation diagrams are similar to the bifurcation diagram of the logistic map x(,n+1) = (lamda)x(,n)(1 - x(,n)). A return map is also measured showing approximately a one-dimensional map with a single extremum at low driving voltages. The intermittency route to chaos is experimentally observed to occur near a tangent bifurcation as the system approaches a period 5 window at (lamda) = (lamda)(,5). Data are presented for the dependence of the average laminar length on (epsilon) = (lamda)(,5) - (lamda), and for the probability distribution P(l) vs. l. The effects of additive stochastic noise on period doubling, chaos, windows, and intermittency are examined and are found to agree with the logistic model and universal predictions. Three examples of crisis of the attractor are observed. The crises occur when an unstable orbit intersects the chaotic attractor. A period adding sequence is reported in which wide periodic windows of period 2, 3, 4, ... are observed for increasing driving voltage. The initial period doubling cascade and the period adding sequence are compared to two theoretical models, with reasonable success.

Application of a time-dependent coalescence process for inferring the history of population size changes from DNA sequence data.

PubMed

Polanski, A; Kimmel, M; Chakraborty, R

1998-05-12

Distribution of pairwise differences of nucleotides from data on a sample of DNA sequences from a given segment of the genome has been used in the past to draw inferences about the past history of population size changes. However, all earlier methods assume a given model of population size changes (such as sudden expansion), parameters of which (e.g., time and amplitude of expansion) are fitted to the observed distributions of nucleotide differences among pairwise comparisons of all DNA sequences in the sample. Our theory indicates that for any time-dependent population size, N(tau) (in which time tau is counted backward from present), a time-dependent coalescence process yields the distribution, p(tau), of the time of coalescence between two DNA sequences randomly drawn from the population. Prediction of p(tau) and N(tau) requires the use of a reverse Laplace transform known to be unstable. Nevertheless, simulated data obtained from three models of monotone population change (stepwise, exponential, and logistic) indicate that the pattern of a past population size change leaves its signature on the pattern of DNA polymorphism. Application of the theory to the published mtDNA sequences indicates that the current mtDNA sequence variation is not inconsistent with a logistic growth of the human population.

Effects of adolescent exposure to methylmercury and d-amphetamine on reversal learning and an extradimensional shift in male mice

PubMed Central

Boomhower, Steven R.; Newland, M. Christopher

2016-01-01

Adolescence is associated with the continued maturation of dopamine neurotransmission and is implicated in the etiology of many psychiatric illnesses. Adolescent exposure to neurotoxicants that distort dopamine neurotransmission, such as methylmercury (MeHg), may modify the effects of chronic d-amphetamine (d-AMP) administration on reversal learning and attentional-set shifting. Male C57Bl/6n mice were randomly assigned to two MeHg-exposure groups (0 ppm and 3 ppm) and two d-AMP-exposure groups (saline and 1 mg/kg/day), producing four treatment groups (n = 10–12/group): Control, MeHg, d-AMP, and MeHg + d-AMP. MeHg exposure (via drinking water) spanned postnatal day 21–59 (the murine adolescent period), and once daily i.p. injections of d-AMP or saline spanned postnatal day 28–42. As adults, mice were trained on a spatial-discrimination-reversal (SDR) task in which the spatial location of a lever press predicted reinforcement. Following two SDRs, a visual-discrimination task (extradimensional shift) was instated in which the presence of a stimulus light above a lever predicted reinforcement. Responding was modeled using a logistic function, which estimated the rate (slope) of a behavioral transition and trials required to complete half a transition (half-max). MeHg, d-AMP, and MeHg + d-AMP exposure increased estimates of half-max on the second reversal. MeHg exposure increased half-max and decreased the slope term following the extradimensional shift, but these effects did not occur following MeHg + d-AMP exposure. MeHg + d-AMP exposure produced more perseverative errors and omissions following a reversal. Adolescent exposure to MeHg can modify the behavioral effects of chronic d-AMP administration. PMID:28287789

Effects of adolescent exposure to methylmercury and d-amphetamine on reversal learning and an extradimensional shift in male mice.

PubMed

Boomhower, Steven R; Newland, M Christopher

2017-04-01

Adolescence is associated with the continued maturation of dopamine neurotransmission and is implicated in the etiology of many psychiatric illnesses. Adolescent exposure to neurotoxicants that distort dopamine neurotransmission, such as methylmercury (MeHg), may modify the effects of chronic d -amphetamine ( d -AMP) administration on reversal learning and attentional-set shifting. Male C57Bl/6n mice were randomly assigned to two MeHg-exposure groups (0 ppm and 3 ppm) and two d -AMP-exposure groups (saline and 1 mg/kg/day), producing four treatment groups (n = 10-12/group): control, MeHg , d -AMP, and MeHg + d -AMP. MeHg exposure (via drinking water) spanned postnatal days 21-59 (the murine adolescent period), and once daily intraperitoneal injections of d -AMP or saline spanned postnatal days 28-42. As adults, mice were trained on a spatial-discrimination-reversal (SDR) task in which the spatial location of a lever press predicted reinforcement. Following 2 SDRs, a visual-discrimination task (extradimensional shift) was instated in which the presence of a stimulus light above a lever predicted reinforcement. Responding was modeled using a logistic function, which estimated the rate (slope) of a behavioral transition and trials required to complete half a transition (half-max). MeHg, d -AMP, and MeHg + d -AMP exposure increased estimates of half-max on the second reversal. MeHg exposure increased half-max and decreased the slope term following the extradimensional shift, but these effects did not occur following MeHg + d -AMP exposure. MeHg + d -AMP exposure produced more perseverative errors and omissions following a reversal. Adolescent exposure to MeHg can modify the behavioral effects of chronic d -AMP administration. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

An economic production model for deteriorating items and time dependent demand with rework and multiple production setups

NASA Astrophysics Data System (ADS)

Uthayakumar, R.; Tharani, S.

2017-12-01

Recently, much emphasis has given to study the control and maintenance of production inventories of the deteriorating items. Rework is one of the main issues in reverse logistic and green supply chain, since it can reduce production cost and the environmental problem. Many researchers have focused on developing rework model, but few of them have developed model for deteriorating items. Due to this fact, we take up productivity and rework with deterioration as the major concern in this paper. In this paper, a production-inventory model with deteriorative items in which one cycle has n production setups and one rework setup (n, 1) policy is considered for deteriorating items with stock-dependent demand in case 1 and exponential demand in case 2. An effective iterative solution procedure is developed to achieve optimal time, so that the total cost of the system is minimized. Numerical and sensitivity analyses are discussed to examine the outcome of the proposed solution procedure presented in this research.

An assessment of the relationship between the World Health Organization HIV drug resistance early warning indicators and HIV drug resistance acquisition.

PubMed

St-Jean, M; Harrigan, P R; Sereda, P; Montaner, Jsg; Lima, V D

2017-05-01

The World Health Organization (WHO)'s HIV drug resistance (HIVDR) early warning indicators (EWIs) measure antiretroviral therapy (ART)-site factors associated with HIVDR prevention, without HIVDR laboratory testing. We assessed the relationship between EWIs and HIVDR acquisition using data from British Columbia, Canada. Eligible patients were ART-naïve, were ≥ 19 years old, had initiated ART between 1 January 2000 and 31 December 2012, had ≥ 15 months of follow-up, and were without transmitted HIVDR. Patients were followed for acquired HIVDR until 31 March 2014, the last contact date, or death. We built logistic regression models to assess the associations and predictive ability of individual indicators and of the EWI Score (the number of indicators for which a patient did not meet the criteria) on HIVDR acquisition (to any class of HIVDR, lamivudine (3TC)/emtricitabine (FTC), nonnucleoside reverse transcriptase inhibitors (NNRTIs), nucleoside reverse transcriptase inhibitors (NRTIs) or protease inhibitors (PIs)]). All explored EWIs were associated with at least one class of HIVDR, with the exception of 'ART prescribing practices'. We observed a dose-response relationship between acquiring HIVDR to any antiretroviral class and an increasing EWI score in our predictive logistic regression model. The area under the curve was 0.848 (excellent discrimination). The adjusted odds ratios for acquiring any class of HIVDR for an EWI score of 1, 2 and ≥ 3 versus 0 were 2.30 [95% confidence Interval (CI) 1.21-4.38], 3.35 (95% CI: 1.86-6.03) and 7.26 (95% CI: 4.18-12.61), respectively. Several EWIs were associated with and predictive of HIVDR, supporting the WHO EWIs as a component of the HIVDR prevention method in settings where HIVDR testing is not routinely or widely available. © 2016 British HIV Association.

Assessment of utilization of long acting reversible contraceptive and associated factors among women of reproductive age in Harar City, Ethiopia.

PubMed

Shiferaw, Kasiye; Musa, Abdulbasit

2017-01-01

World health organization report indicated that, in 2013 alone, over 289,000 maternal death that resulted from pregnancy and delivery related complication were reported worldwide indicating a decline of 45% from 1990. The sub-Saharan Africa region alone accounted for 62% of maternal death followed by southern Asian country (24%). Provision of family planning is one of the effective intervention that prevent unwanted and ill spaced pregnancy there by reducing maternal mortality and morbidity. Given that its effectiveness and, associated fewer visits to health facilities, LARC are very important in tackling maternal mortality and morbidity. However, little is known regarding its prevalence in eastern Ethiopia. Thus, this study aimed to assess utilization of long acting reversible contraceptives and associated factors among women of reproductive age groups. A facility based cross-sectional study conducted in Harar city among 402 study participants. The study participants selected by using systematic random sampling method. The quantitative data collected using structured interviewer administered questionnaires. All variables with p-value of ≤ 0.25 in bivariate logistic regression were taken into multivariable model. Variables having p value ≤ 0.05 in the multivariate analysis were taken as significant predictors. Crude and adjusted odds ratios with their 95% confidence intervals were calculated. The study identified that the utilization of long acting reversible contraceptive among mother of reproductive age was 38%. Study participants whose occupation was daily laborer were less likely to utilize long acting reversible contraceptive compared to those whose occupation was house wife (adjusted OR = 0.3; 95% CI 0.01 to 0.8). Moreover, those mothers who were unable to read and write utilize long acting reversible contraceptive 5 times more likely compared to those who were above grade 12 (adjusted OR = 4.9; 95% CI 1.2 to 19.6). The prevalence of long acting reversible contraceptive was found to be low. Maternal education and occupation were factors found to have a significant association with utilization of long acting reversible contraceptive. Community and facility level awareness creation should be reinforced to improve utilization of long acting reversible contraceptives.

Sleep Duration and Midday Napping with 5-Year Incidence and Reversion of Metabolic Syndrome in Middle-Aged and Older Chinese

PubMed Central

Yang, Liangle; Xu, Zengguang; He, Meian; Yang, Handong; Li, Xiulou; Min, Xinwen; Zhang, Ce; Xu, Chengwei; Angileri, Francesca; Légaré, Sébastien; Yuan, Jing; Miao, Xiaoping; Guo, Huan; Yao, Ping; Wu, Tangchun; Zhang, Xiaomin

2016-01-01

Study Objectives: Prospective evidence on the association of sleep duration and midday napping with metabolic syndrome (MetS) is limited. We aimed to examine the associations of sleep duration and midday napping with risk of incidence and reversion of MetS and its components among a middle-aged and older Chinese population. Methods: We included 14,399 subjects from the Dongfeng-Tongji (DFTJ) Cohort Study (2008–2013) who were free of coronary heart disease, stroke, and cancer at baseline. Baseline data were obtained by questionnaires and health examinations. Odds ratios (ORs) and 95% confidence interval (CI) were derived from multivariate logistic regression models. Results: After controlling for potential covariates, longer sleep duration (≥ 9 h) was associated with a higher risk of MetS incidence (OR, 1.29; 95% CI, 1.08–1.55) and lower reversion of MetS (OR, 0.80; 95% CI, 0.66–0.96) compared with sleep duration of 7 to < 8 h; whereas shorter sleep duration (< 6 h) was not related to incidence or reversion of MetS. For midday napping, subjects with longer napping (≥ 90 min) was also associated with a higher risk of MetS incidence and a lower risk of MetS reversion compared with those with napping of 1 to < 30 min (OR, 1.48; 95% CI, 1.05–2.10 and OR, 0.70; 95% CI, 0.52–0.94, respectively). Significance for incidence or reversion of certain MetS components remained in shorter and longer sleepers but disappeared across napping categories. Conclusions: Both longer sleep duration and longer midday napping were potential risk factors for MetS incidence, and concurrently exert adverse effects on MetS reversion. Citation: Yang L, Xu Z, He M, Yang H, Li X, Min X, Zhang C, Xu C, Angileri F, Légaré S, Yuan J, Miao X, Guo H, Yao P, Wu T, Zhang X. Sleep duration and midday napping with 5-year incidence and reversion of metabolic syndrome in middle-aged and older Chinese. SLEEP 2016;39(11):1911–1918. PMID:27450688

Design and optimization of photovoltaics recycling infrastructure.

PubMed

Choi, Jun-Ki; Fthenakis, Vasilis

2010-11-15

With the growing production and installation of photovoltaics (PV) around the world constrained by the limited availability of resources, end-of-life management of PV is becoming very important. A few major PV manufacturers currently are operating several PV recycling technologies at the process level. The management of the total recycling infrastructure, including reverse-logistics planning, is being started in Europe. In this paper, we overview the current status of photovoltaics recycling planning and discuss our mathematic modeling of the economic feasibility and the environmental viability of several PV recycling infrastructure scenarios in Germany; our findings suggest the optimum locations of the anticipated PV take-back centers. Short-term 5-10 year planning for PV manufacturing scraps is the focus of this article. Although we discuss the German situation, we expect the generic model will be applicable to any region, such as the whole of Europe and the United States.

P.C. disposal decisions: a banking industry case study

NASA Astrophysics Data System (ADS)

Shah, Sejal P.; Sarkis, Joseph

2002-02-01

The service industry and the manufacturing industry are interlinked in a supply chain situation. Part of the effectiveness of some manufacturing industry environmental performance based on remanufacturing and recycling is dependent on service industry decisions. In the information technology arena, personal computers (PCs) are the hard equipment of the service industry. The end-of-life decisions made by the service industry, and in this case the banking industry will have implications for the amount of systems within the waste or reverse logistics stream for manufacturers. Looking at some of the issues (and presenting a model for evaluation) related to decision making concerning end-of-life disposition for PCs is something this paper investigates. The analytical hierarchy process (AHP) is applied in this circumstance. The development of the model, its application, and results, provide the basis for much of the discussion in this paper.

Incomplete reversibility of estimated glomerular filtration rate decline following tenofovir disoproxil fumarate exposure.

PubMed

Jose, Sophie; Hamzah, Lisa; Campbell, Lucy J; Hill, Teresa; Fisher, Martin; Leen, Clifford; Gilson, Richard; Walsh, John; Nelson, Mark; Hay, Phillip; Johnson, Margaret; Chadwick, David; Nitsch, Dorothea; Jones, Rachael; Sabin, Caroline A; Post, Frank A

2014-08-01

Tenofovir disoproxil fumarate (TDF) has been linked to renal impairment, but the extent to which this impairment is reversible is unclear. We aimed to investigate the reversibility of renal decline during TDF therapy. Cox proportional hazards models assessed factors associated with discontinuing TDF in those with an exposure duration of >6 months. In those who discontinued TDF therapy, linear piecewise regression models estimated glomerular filtration rate (eGFR) slopes before initiation of, during, and after discontinuation of TDF therapy. Factors associated with not achieving eGFR recovery 6 months after discontinuing TDF were assessed using multivariable logistic regression. We observed declines in the eGFR during TDF exposure (mean slopes, -15.7 mL/minute/1.73 m(2)/year [95% confidence interval {CI}, -20.5 to -10.9] during the first 3 months and -3.1 mL/minute/1.73 m(2)/year [95% CI, -4.6 to -1.7] thereafter) and evidence of eGFR increases following discontinuation of TDF therapy (mean slopes, 12.5 mL/minute/1.73 m(2)/year [95% CI, 8.9-16.1] during the first 3 months and 0.8 mL/minute/1.73 m(2)/year [95% CI, .1-1.5] thereafter). Following TDF discontinuation, 38.6% of patients with a decline in the eGFR did not experience recovery. A higher eGFR at baseline, a lower eGFR after discontinuation of TDF therapy, and more-prolonged exposure to TDF were associated with an increased risk of incomplete recovery 6 months after discontinuation of TDF therapy. This study shows that a decline in the eGFR during TDF therapy was not fully reversible in one third of patients and suggests that prolonged TDF exposure at a low eGFR should be avoided. © The Author 2014. Published by Oxford University Press on behalf of the Infectious Diseases Society of America.

Incomplete Reversibility of Estimated Glomerular Filtration Rate Decline Following Tenofovir Disoproxil Fumarate Exposure

PubMed Central

Jose, Sophie; Hamzah, Lisa; Campbell, Lucy J.; Hill, Teresa; Fisher, Martin; Leen, Clifford; Gilson, Richard; Walsh, John; Nelson, Mark; Hay, Phillip; Johnson, Margaret; Chadwick, David; Nitsch, Dorothea; Jones, Rachael; Sabin, Caroline A.; Post, Frank A.; Ainsworth, Jonathan; Anderson, Jane; Babiker, Abdel; Chadwick, David; Delpech, Valerie; Dunn, David; Fisher, Martin; Gazzard, Brian; Gilson, Richard; Gompels, Mark; Hay, Phillip; Hill, Teresa; Johnson, Margaret; Kegg, Stephen; Leen, Clifford; Nelson, Mark; Orkin, Chloe; Palfreeman, Adrian; Phillips, Andrew; Pillay, Deenan; Post, Frank; Sabin, Caroline; Sachikonye, Memory; Schwenk, Achim; Walsh, John; Hill, Teresa; Huntington, Susie; Josie, Sophie; Phillips, Andrew; Sabin, Caroline; Thornton, Alicia; Dunn, David; Glabay, Adam; Orkin, C.; Garrett, N.; Lynch, J.; Hand, J.; de Souza, C.; Fisher, M.; Perry, N.; Tilbury, S.; Churchill, D.; Gazzard, B.; Nelson, M.; Waxman, M.; Asboe, D.; Mandalia, S.; Delpech, V.; Anderson, J.; Munshi, S.; Korat, H.; Poulton, M.; Taylor, C.; Gleisner, Z.; Campbell, L.; Babiker, Abdel; Dunn, David; Glabay, Adam; Gilson, R.; Brima, N.; Williams, I.; Schwenk, A.; Ainsworth, J.; Wood, C.; Miller, S.; Johnson, M.; Youle, M.; Lampe, F.; Smith, C.; Grabowska, H.; Chaloner, C.; Puradiredja, D.; Walsh, J.; Weber, J.; Ramzan, F.; Mackie, N.; Winston, A.; Leen, C.; Wilson, A.; Gompels, M.; Allan, S.; Palfreeman, A.; Moore, A.; Chadwick, D.; Wakeman, K.; Kegg, Stephen; Main, Paul; Mitchell; Hunter; Sachikonye, Memory; Hay, Phillip; Dhillon, Mandip

2014-01-01

Background. Tenofovir disoproxil fumarate (TDF) has been linked to renal impairment, but the extent to which this impairment is reversible is unclear. We aimed to investigate the reversibility of renal decline during TDF therapy. Methods. Cox proportional hazards models assessed factors associated with discontinuing TDF in those with an exposure duration of >6 months. In those who discontinued TDF therapy, linear piecewise regression models estimated glomerular filtration rate (eGFR) slopes before initiation of, during, and after discontinuation of TDF therapy. Factors associated with not achieving eGFR recovery 6 months after discontinuing TDF were assessed using multivariable logistic regression. Results. We observed declines in the eGFR during TDF exposure (mean slopes, −15.7 mL/minute/1.73 m2/year [95% confidence interval {CI}, −20.5 to −10.9] during the first 3 months and −3.1 mL/minute/1.73 m2/year [95% CI, −4.6 to −1.7] thereafter) and evidence of eGFR increases following discontinuation of TDF therapy (mean slopes, 12.5 mL/minute/1.73 m2/year [95% CI, 8.9–16.1] during the first 3 months and 0.8 mL/minute/1.73 m2/year [95% CI, .1–1.5] thereafter). Following TDF discontinuation, 38.6% of patients with a decline in the eGFR did not experience recovery. A higher eGFR at baseline, a lower eGFR after discontinuation of TDF therapy, and more-prolonged exposure to TDF were associated with an increased risk of incomplete recovery 6 months after discontinuation of TDF therapy. Conclusions. This study shows that a decline in the eGFR during TDF therapy was not fully reversible in one third of patients and suggests that prolonged TDF exposure at a low eGFR should be avoided. PMID:24585896

Higher outcomes of vasectomy reversal in men with the same female partner as before vasectomy.

PubMed

Ostrowski, Kevin A; Polackwich, A Scott; Kent, Joe; Conlin, Michael J; Hedges, Jason C; Fuchs, Eugene F

2015-01-01

We reviewed fertility outcomes of vasectomy reversal at a high surgical volume center in men with the same female partner as before vasectomy. We retrospectively studied a prospective database. All vasectomy reversals were performed by a single surgeon (EFF). Patients who underwent microsurgical vasectomy reversal and had the same female partner as before vasectomy were identified from 1978 to 2011. Pregnancy and live birth rates, procedure type (bilateral vasovasostomy, bilateral vasoepididymostomy, unilateral vasovasostomy or unilateral vasoepididymostomy), patency rate, time from reversal and spouse age were evaluated. We reviewed the records of 3,135 consecutive microsurgical vasectomy reversals. Of these patients 524 (17%) who underwent vasectomy reversal had the same female partner as before vasectomy. Complete information was available on 258 patients (49%), who had a 94% vas patency rate. The clinical pregnancy rate was 83% by natural means compared to 60% in our general vasectomy reversal population (p <0.0001). On logistic regression analysis controlling for female partner and patient ages, years from vasectomy and vasectomy reversal with the same female partner the OR was 2 (p <0.007). Average time from vasectomy was 5.7 years. Average patient and female partner age at reversal was 38.9 and 33.2 years, respectively. Outcomes of clinical pregnancy and live birth rates are higher in men who undergo microsurgical vasectomy reversal with the same female partner. These outcomes may be related to a shorter interval from vasectomy, previous fertility and couple motivation. Copyright © 2015 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

The association between rainfall rate and occurrence of an enterovirus epidemic due to a contaminated well.

PubMed

Jean, J-S; Guo, H-R; Chen, S-H; Liu, C-C; Chang, W-T; Yang, Y-J; Huang, M-C

2006-12-01

To determine the association between rainfall rate and occurrence of enterovirus infection related to contamination of drinking water. One fatality case and three cases of severe illness were observed during the enterovirus epidemic in a village in southern Taiwan from 16 September to 3 October 1998. Groundwater samples were collected from the public well in the village after heavy rainfall to test for enterovirus using the reverse transcription-polymerase chain reaction (RT-PCR) assay. The RT-PCR assay detected the enterovirus in the groundwater sample collected on 26 September 1998. The logistic regression model also revealed a statistically significant association between the rainfall rate and the observation of cases of enterovirus infection. According to the fitted logistic regression model, the probability of detecting cases of enterovirus infection was greater than 50% at rainfall rates >31 mm h(-1). The higher the rainfall rate, the higher the probability of enterovirus epidemic. Contamination of drinking water by the enterovirus may lead to epidemics that cause deaths and severe illness, and such contamination may be caused by heavy rainfall. The major finding in this study is that the enterovirus could be flushed to groundwater in an unconfined aquifer after a heavy rainfall. This work allows for a warning level so that an action can be taken to minimize future outbreaks and so protect public health.

Applying Kaplan-Meier to Item Response Data

ERIC Educational Resources Information Center

McNeish, Daniel

2018-01-01

Some IRT models can be equivalently modeled in alternative frameworks such as logistic regression. Logistic regression can also model time-to-event data, which concerns the probability of an event occurring over time. Using the relation between time-to-event models and logistic regression and the relation between logistic regression and IRT, this…

On the Usefulness of a Multilevel Logistic Regression Approach to Person-Fit Analysis

ERIC Educational Resources Information Center

Conijn, Judith M.; Emons, Wilco H. M.; van Assen, Marcel A. L. M.; Sijtsma, Klaas

2011-01-01

The logistic person response function (PRF) models the probability of a correct response as a function of the item locations. Reise (2000) proposed to use the slope parameter of the logistic PRF as a person-fit measure. He reformulated the logistic PRF model as a multilevel logistic regression model and estimated the PRF parameters from this…

Design logistics performance measurement model of automotive component industry for srengthening competitiveness of dealing AEC 2015

NASA Astrophysics Data System (ADS)

Amran, T. G.; Janitra Yose, Mindy

2018-03-01

As the free trade Asean Economic Community (AEC) causes the tougher competition, it is important that Indonesia’s automotive industry have high competitiveness as well. A model of logistics performance measurement was designed as an evaluation tool for automotive component companies to improve their logistics performance in order to compete in AEC. The design of logistics performance measurement model was based on the Logistics Scorecard perspectives, divided into two stages: identifying the logistics business strategy to get the KPI and arranging the model. 23 KPI was obtained. The measurement result can be taken into consideration of determining policies to improve the performance logistics competitiveness.

Research on support effectiveness modeling and simulating of aviation materiel autonomic logistics system

NASA Astrophysics Data System (ADS)

Zhou, Yan; Zhou, Yang; Yuan, Kai; Jia, Zhiyu; Li, Shuo

2018-05-01

Aiming at the demonstration of autonomic logistics system to be used at the new generation of aviation materiel in our country, the modeling and simulating method of aviation materiel support effectiveness considering autonomic logistics are studied. Firstly, this paper introduced the idea of JSF autonomic logistics and analyzed the influence of autonomic logistics on support effectiveness from aspects of reliability, false alarm rate, troubleshooting time, and support delay time and maintenance level. On this basis, the paper studies the modeling and simulating methods of support effectiveness considering autonomic logistics, and puts forward the maintenance support simulation process considering autonomic logistics. Finally, taking the typical aviation materiel as an example, this paper analyzes and verifies the above-mentioned support effectiveness modeling and simulating method of aviation materiel considering autonomic logistics.

Reverse Inclusion: Providing Peer Social Interaction Opportunities to Students Placed in Self-Contained Special Education Classrooms

ERIC Educational Resources Information Center

Schoger, Kimberly D.

2006-01-01

The social and academic benefits of inclusion for students with disabilities have been well researched and well documented. Unfortunately, inclusion opportunities are limited by lack of qualified staff, logistics, scheduling and other difficulties encountered when attempting to meet students' unique needs in the general education setting. As a…

Multi scale habitat relationships of Martes americana in northern Idaho, U.S.A.

Treesearch

Tzeidle N. Wasserman; Samuel A. Cushman; David O. Wallin; Jim Hayden

2012-01-01

We used bivariate scaling and logistic regression to investigate multiple-scale habitat selection by American marten (Martes americana). Bivariate scaling reveals dramatic differences in the apparent nature and strength of relationships between marten occupancy and a number of habitat variables across a range of spatial scales. These differences include reversals in...

Multi-objective reverse logistics model for integrated computer waste management.

PubMed

Ahluwalia, Poonam Khanijo; Nema, Arvind K

2006-12-01

This study aimed to address the issues involved in the planning and design of a computer waste management system in an integrated manner. A decision-support tool is presented for selecting an optimum configuration of computer waste management facilities (segregation, storage, treatment/processing, reuse/recycle and disposal) and allocation of waste to these facilities. The model is based on an integer linear programming method with the objectives of minimizing environmental risk as well as cost. The issue of uncertainty in the estimated waste quantities from multiple sources is addressed using the Monte Carlo simulation technique. An illustrated example of computer waste management in Delhi, India is presented to demonstrate the usefulness of the proposed model and to study tradeoffs between cost and risk. The results of the example problem show that it is possible to reduce the environmental risk significantly by a marginal increase in the available cost. The proposed model can serve as a powerful tool to address the environmental problems associated with exponentially growing quantities of computer waste which are presently being managed using rudimentary methods of reuse, recovery and disposal by various small-scale vendors.

Identification of Reversible Disruption of the Human Blood-Brain Barrier Following Acute Ischemia.

PubMed

Simpkins, Alexis N; Dias, Christian; Leigh, Richard

2016-09-01

Animal models of acute cerebral ischemia have demonstrated that diffuse blood-brain barrier (BBB) disruption can be reversible after early reperfusion. However, irreversible, focal BBB disruption in humans is associated with hemorrhagic transformation in patients receiving intravenous thrombolytic therapy. The goal of this study was to use a magnetic resonance imaging biomarker of BBB permeability to differentiate these 2 forms of BBB disruption. Acute stroke patients imaged with magnetic resonance imaging before, 2 hours after, and 24 hours after treatment with intravenous tissue-type plasminogen activator were included. The average BBB permeability of the acute ischemic region before and 2 hours after treatment was calculated using a T2* perfusion-weighted source images. Change in average permeability was compared with percent reperfusion using linear regression. Focal regions of maximal BBB permeability from the pretreatment magnetic resonance imaging were compared with the occurrence of parenchymal hematoma (PH) formation on the 24-hour magnetic resonance imaging scan using logistic regression. Signals indicating reversible BBB permeability were detected in 18/36 patients. Change in average BBB permeability correlated inversely with percent reperfusion (P=0.006), indicating that early reperfusion is associated with decreased BBB permeability, whereas sustained ischemia is associated with increased BBB disruption. Focal regions of maximal BBB permeability were significantly associated with subsequent formation of PH (P=0.013). This study demonstrates that diffuse, mild BBB disruption in the acutely ischemic human brain is reversible with reperfusion. This study also confirms prior findings that focal severe BBB disruption confers an increased risk of hemorrhagic transformation in patients treated with intravenous tissue-type plasminogen activator. © 2016 American Heart Association, Inc.

Identification of the population density of a species model with nonlocal diffusion and nonlinear reaction

NASA Astrophysics Data System (ADS)

Tuan, Nguyen Huy; Van Au, Vo; Khoa, Vo Anh; Lesnic, Daniel

2017-05-01

The identification of the population density of a logistic equation backwards in time associated with nonlocal diffusion and nonlinear reaction, motivated by biology and ecology fields, is investigated. The diffusion depends on an integral average of the population density whilst the reaction term is a global or local Lipschitz function of the population density. After discussing the ill-posedness of the problem, we apply the quasi-reversibility method to construct stable approximation problems. It is shown that the regularized solutions stemming from such method not only depend continuously on the final data, but also strongly converge to the exact solution in L 2-norm. New error estimates together with stability results are obtained. Furthermore, numerical examples are provided to illustrate the theoretical results.

SPD-based Logistics Management Model of Medical Consumables in Hospitals.

PubMed

Liu, Tongzhu; Shen, Aizong; Hu, Xiaojian; Tong, Guixian; Gu, Wei; Yang, Shanlin

2016-10-01

With the rapid development of health services, the progress of medical science and technology, and the improvement of materials research, the consumption of medical consumables (MCs) in medical activities has increased in recent years. However, owing to the lack of effective management methods and the complexity of MCs, there are several management problems including MC waste, low management efficiency, high management difficulty, and frequent medical accidents. Therefore, there is urgent need for an effective logistics management model to handle these problems and challenges in hospitals. We reviewed books and scientific literature (by searching the articles published from 2010 to 2015 in Engineering Village database) to understand supply chain related theories and methods and performed field investigations in hospitals across many cities to determine the actual state of MC logistics management of hospitals in China. We describe the definition, physical model, construction, and logistics operation processes of the supply, processing, and distribution (SPD) of MC logistics because of the traditional SPD model. With the establishment of a supply-procurement platform and a logistics lean management system, we applied the model to the MC logistics management of Anhui Provincial Hospital with good effects. The SPD model plays a critical role in optimizing the logistics procedures of MCs, improving the management efficiency of logistics, and reducing the costs of logistics of hospitals in China.

Does a History of Unintended Pregnancy Lessen the Likelihood of Desire for Sterilization Reversal?

PubMed Central

Grady, Cynthia D.; Schwarz, Eleanor Bimla; Emeremni, Chetachi A.; Yabes, Jonathan; Akers, Aletha; Zite, Nikki

2013-01-01

Abstract Background Unintended pregnancy has been significantly associated with subsequent female sterilization. Whether women who are sterilized after experiencing an unintended pregnancy are less likely to express desire for sterilization reversal is unknown. Methods This study used national, cross-sectional data collected by the 2006–2010 National Survey of Family Growth. The study sample included women ages 15–44 who were surgically sterile from a tubal sterilization at the time of interview. Multivariable logistic regression was used to examine the relationship between a history of unintended pregnancy and desire for sterilization reversal while controlling for potential confounders. Results In this nationally representative sample of 1,418 women who were sterile from a tubal sterilization, 78% had a history of at least one unintended pregnancy and 28% expressed a desire to have their sterilization reversed. In unadjusted analysis, having a prior unintended pregnancy was associated with higher odds of expressing desire for sterilization reversal (odds ratio [OR]: 1.80; 95% confidence interval [CI]: 1.15–2.79). In adjusted analysis controlling for sociodemographic factors, unintended pregnancy was no longer significantly associated with desire for reversal (OR: 1.46; 95% CI: 0.91–2.34). Conclusion Among women who had undergone tubal sterilization, a prior history of unintended pregnancy did not decrease desire for sterilization reversal. PMID:23621776

Evolution dynamics modeling and simulation of logistics enterprise's core competence based on service innovation

NASA Astrophysics Data System (ADS)

Yang, Bo; Tong, Yuting

2017-04-01

With the rapid development of economy, the development of logistics enterprises in China is also facing a huge challenge, especially the logistics enterprises generally lack of core competitiveness, and service innovation awareness is not strong. Scholars in the process of studying the core competitiveness of logistics enterprises are mainly from the perspective of static stability, not from the perspective of dynamic evolution to explore. So the author analyzes the influencing factors and the evolution process of the core competence of logistics enterprises, using the method of system dynamics to study the cause and effect of the evolution of the core competence of logistics enterprises, construct a system dynamics model of evolution of core competence logistics enterprises, which can be simulated by vensim PLE. The analysis for the effectiveness and sensitivity of simulation model indicates the model can be used as the fitting of the evolution process of the core competence of logistics enterprises and reveal the process and mechanism of the evolution of the core competence of logistics enterprises, and provide management strategies for improving the core competence of logistics enterprises. The construction and operation of computer simulation model offers a kind of effective method for studying the evolution of logistics enterprise core competence.

Additive Manufacturing in Offsite Repair of Consumer Electronics

NASA Astrophysics Data System (ADS)

Chekurov, Sergei; Salmi, Mika

Spare parts for products that are at the end of their life cycles, but still under warranty, are logistically difficult because they are commonly not stored in the central warehouse. These uncommon spare parts occupy valuable space in smaller inventories and take a long time to be transported to the point of need, thus delaying the repair process. This paper proposes that storing the spare parts on a server and producing them with additive manufacturing (AM) on demand can shorten the repair cycle by simplifying the logistics. Introducing AM in the repair supply chain lowers the number of products that need to be reimbursed to the customer due to lengthy repairs, improves the repair statistics of the repair shops, and reduces the number of items that are held in stock. For this paper, the functionality of the concept was verified by reverse engineering a memory cover of a portable computer and laser sintering it from polyamide 12. The additively manufactured component fit well and the computer operated normally after the replacement. The current spare part supply chain model and models with AM machinery located at the repair shop, the centralized spare part provider, and the original equipment manufacturer were provided. The durations of the repair process in the models were compared by simulating two scenarios with the Monte Carlo method. As the biggest improvement, the model with the AM machine in the repair shop reduced the duration of the repair process from 14 days to three days. The result points to the conclusion that placing the machine as close to the need as possible is the best option, if there is enough demand. The spare parts currently compatible with AM are plastic components without strict surface roughness requirements, but more spare parts will become compatible with the development of AM.

Research on JD e-commerce's delivery model

NASA Astrophysics Data System (ADS)

Fan, Zhiguo; Ma, Mengkun; Feng, Chaoying

2017-03-01

E-commerce enterprises represented by JD have made a great contribution to the economic growth and economic development of our country. Delivery, as an important part of logistics, has self-evident importance. By establishing efficient and perfect self-built logistics systems and building good cooperation models with third-party logistics enterprises, e-commerce enterprises have created their own logistics advantages. Characterized by multi-batch and small-batch, e-commerce is much more complicated than traditional transaction. It's not easy to decide which delivery model e-commerce enterprises should adopt. Having e-commerce's logistics delivery as the main research object, this essay aims to find a more suitable logistics delivery model for JD's development.

SPD-based Logistics Management Model of Medical Consumables in Hospitals

PubMed Central

LIU, Tongzhu; SHEN, Aizong; HU, Xiaojian; TONG, Guixian; GU, Wei; YANG, Shanlin

2016-01-01

Background: With the rapid development of health services, the progress of medical science and technology, and the improvement of materials research, the consumption of medical consumables (MCs) in medical activities has increased in recent years. However, owing to the lack of effective management methods and the complexity of MCs, there are several management problems including MC waste, low management efficiency, high management difficulty, and frequent medical accidents. Therefore, there is urgent need for an effective logistics management model to handle these problems and challenges in hospitals. Methods: We reviewed books and scientific literature (by searching the articles published from 2010 to 2015 in Engineering Village database) to understand supply chain related theories and methods and performed field investigations in hospitals across many cities to determine the actual state of MC logistics management of hospitals in China. Results: We describe the definition, physical model, construction, and logistics operation processes of the supply, processing, and distribution (SPD) of MC logistics because of the traditional SPD model. With the establishment of a supply-procurement platform and a logistics lean management system, we applied the model to the MC logistics management of Anhui Provincial Hospital with good effects. Conclusion: The SPD model plays a critical role in optimizing the logistics procedures of MCs, improving the management efficiency of logistics, and reducing the costs of logistics of hospitals in China. PMID:27957435

Selected Logistics Models and Techniques.

DTIC Science & Technology

1984-09-01

TI - 59 Programmable Calculator LCC...Program 27 TI - 59 Programmable Calculator LCC Model 30 Unmanned Spacecraft Cost Model 31 iv I: TABLE OF CONTENTS (CONT’D) (Subject Index) LOGISTICS...34"" - % - "° > - " ° .° - " .’ > -% > ]*° - LOGISTICS ANALYSIS MODEL/TECHNIQUE DATA MODEL/TECHNIQUE NAME: TI - 59 Programmable Calculator LCC Model TYPE MODEL: Cost Estimating DEVELOPED BY:

Parameters Estimation of Geographically Weighted Ordinal Logistic Regression (GWOLR) Model

NASA Astrophysics Data System (ADS)

Zuhdi, Shaifudin; Retno Sari Saputro, Dewi; Widyaningsih, Purnami

2017-06-01

A regression model is the representation of relationship between independent variable and dependent variable. The dependent variable has categories used in the logistic regression model to calculate odds on. The logistic regression model for dependent variable has levels in the logistics regression model is ordinal. GWOLR model is an ordinal logistic regression model influenced the geographical location of the observation site. Parameters estimation in the model needed to determine the value of a population based on sample. The purpose of this research is to parameters estimation of GWOLR model using R software. Parameter estimation uses the data amount of dengue fever patients in Semarang City. Observation units used are 144 villages in Semarang City. The results of research get GWOLR model locally for each village and to know probability of number dengue fever patient categories.

What is the Right RFID for Your Process?

DTIC Science & Technology

2006-04-30

chain efficiency at the US Department of Defense (DoD) and at major retailers such as Wal-Mart, Tesco and others has prompted these organizations...areas of expertise include global operations, supply- chain management, sustainable technologies, product stewardship, reverse logistics and...time MBA programs. Areas of Apte’s research interests include managing service operations, supply- chain management, technology management, and

Yogurt consumption and abdominal obesity reversion in the PREDIMED study.

PubMed

Santiago, S; Sayón-Orea, C; Babio, N; Ruiz-Canela, M; Martí, A; Corella, D; Estruch, R; Fitó, M; Aros, F; Ros, E; Gómez-García, E; Fiol, M; Lapetra, J; Serra-Majem, Ll; Becerra-Tomás, N; Salas-Salvadó, J; Pinto, X; Schröder, H; Martínez, J A

2016-06-01

Evidence on the association yogurt consumption and obesity is not conclusive. The aim of this study was to prospectively evaluate the association between yogurt consumption, reversion of abdominal obesity status and waist circumference change in elderly. 4545 individuals at high cardiovascular risk were prospectively followed. Total, whole-fat and low-fat yogurt consumption were assessed using food frequency questionnaires. Generalized estimating equations were used to analyze the association between yogurt consumption and waist circumference change (measured at baseline and yearly during the follow-up). Logistic regression models were used to evaluate the odds ratios (ORs) and 95% CIs of the reversion rate of abdominal obesity for each quintile of yogurt consumption compared with the lowest quintile. After multivariable adjustment, the average yearly waist circumference change in the quintiles of whole-fat yogurt consumption was: Q1: 0.00, Q2: 0.00 (-0.23 to 0.23), Q3: -0.15 (-0.42 to 0.13), Q4: 0.10 (-0.21 to 0.42), and Q5: -0.23 (-0.46 to -0.00) cm; p for trend = 0.05. The ORs for the reversion of abdominal obesity for whole-fat yogurt consumption were Q1: 1.00, Q2: 1.40 (1.04-1.90), Q3: 1.33 (0.94-1.89), Q4: 1.21 (0.83-1.77), and Q5: 1.43 (1.06-1.93); p for trend = 0.26. Total yogurt consumption was not significantly associated with reversion of abdominal obesity status and a lower waist circumference. However, consumption of whole-fat yogurt was associated with changes in waist circumference and higher probability for reversion of abdominal obesity. Therefore, it seems that whole-fat yogurt has more beneficial effects in management of abdominal obesity in elderly population at high cardiovascular risk. Copyright © 2015 The Italian Society of Diabetology, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition, and the Department of Clinical Medicine and Surgery, Federico II University. Published by Elsevier B.V. All rights reserved.

GIS-based spatial decision support system for grain logistics management

NASA Astrophysics Data System (ADS)

Zhen, Tong; Ge, Hongyi; Jiang, Yuying; Che, Yi

2010-07-01

Grain logistics is the important component of the social logistics, which can be attributed to frequent circulation and the great quantity. At present time, there is no modern grain logistics distribution management system, and the logistics cost is the high. Geographic Information Systems (GIS) have been widely used for spatial data manipulation and model operations and provide effective decision support through its spatial database management capabilities and cartographic visualization. In the present paper, a spatial decision support system (SDSS) is proposed to support policy makers and to reduce the cost of grain logistics. The system is composed of two major components: grain logistics goods tracking model and vehicle routing problem optimization model and also allows incorporation of data coming from external sources. The proposed system is an effective tool to manage grain logistics in order to increase the speed of grain logistics and reduce the grain circulation cost.

Bayesian Estimation of the Logistic Positive Exponent IRT Model

ERIC Educational Resources Information Center

Bolfarine, Heleno; Bazan, Jorge Luis

2010-01-01

A Bayesian inference approach using Markov Chain Monte Carlo (MCMC) is developed for the logistic positive exponent (LPE) model proposed by Samejima and for a new skewed Logistic Item Response Theory (IRT) model, named Reflection LPE model. Both models lead to asymmetric item characteristic curves (ICC) and can be appropriate because a symmetric…

The relationship between red blood cell distribution width and blood pressure abnormal dipping in patients with essential hypertension: a cross-sectional study.

PubMed

Su, Dan; Guo, Qi; Gao, Ya; Han, Jin; Yan, Bin; Peng, Liyuan; Song, Anqi; Zhou, Fuling; Wang, Gang

2016-02-23

To investigate whether red blood cell distribution width (RDW) is associated with the blood pressure (BP) reverse-dipper pattern in patients with hypertension. Cross-sectional study. Single centre. Patients with essential hypertension were included in our study (n=708). The exclusion criteria included age <18 or >90 years, incomplete clinical data, night workers, diagnosis of secondary hypertension, under antihypertensive treatment, intolerance for the 24 h ambulatory BP monitoring (ABPM) and BP reading success rate <70%. Physical examination and ABPM were performed for all patients in our study. The value of RDW was measured using an automated haematology analyser. The distribution of RDW in patients with hypertension among different circadian BP pattern groups was analyzed using analysis of variance (ANOVA). Multinomial logistic regression was applied to explore the associations of RDW and other relevant variables with ABPM results. There was significantly increased RDW in reverse dippers (13.52 ± 1.05) than dippers (13.25 ± 0.85) of hypertension (p=0.012). Moreover, multinomial logistic regression analysis showed that RDW (OR 1.325, 95% CI 1.037 to 1.692, p=0.024) and diabetes mellitus (OR 2.286, 95% CI 1.380 to 3.788, p=0.001) were significantly different when comparing the reverse-dipper BP pattern with the dipper pattern. However, there was no difference of RDW between the non-dipper pattern and the reverse-dipper pattern (OR 1.036, 95% CI 0.867 to 1.238, p=0.693). In addition to this, RDW was negatively correlated with the decline rate of nocturnal systolic BP (r=-0.113; p=0.003) and diastolic BP (r=-0.101; p=0.007). Our results suggested that RDW might associate with the abnormal dipper BP patterns of either reverse dipping or non-dipping homogeneously examined with 24 h ABPM. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Are Health Plan Design and Prior Use of Long-Acting Reversible Contraception Associated with Pregnancy Intention?

PubMed

Armstrong, Mary Anne; Postlethwaite, Debbie A; Darbinian, Jeanne A; McCoy, Mark; Law, Amy

2017-05-01

In 2007, high-deductible plans were added to the primarily nondeductible Kaiser Permanente Northern California (KPNC) integrated health plan, which had covered 100% of device and procedure costs of long-acting reversible contraception (LARC) for members regardless of prescription/visit copay amount. We hypothesized that nondeductible plans and prior LARC use decreased unintended pregnancy. The purpose of this study was to determine if health plan design (nondeductible vs. deductible) and LARC use before pregnancy were associated with pregnancy intention. In this retrospective cohort study, women aged 15-44 as of the index date of June 30, 2010 were followed from January 1, 2010 to December 31, 2012 for evidence of pregnancy (n = 65,989). Health plan design, copays, contraceptive method used most recently before the pregnancy, and self-reported pregnancy intention status (intended, mistimed, unwanted) were obtained from electronic medical records. Logistic regression models were developed to determine if various health plan designs, copays, or prior LARC use were associated with pregnancy intention, controlling for potential confounders such as age, race/ethnicity, marital status, education/income, parity, and comorbidities. In all models, LARC use before pregnancy versus non-LARC use was significantly related to intended pregnancies (all models: odds ratio [OR] = 2.26, 95% confidence interval [CI] 2.06-2.48). Women with deductible plans with healthcare spending accounts (HSA) were more likely to report intended pregnancies versus women with nondeductible plans (all models: OR = 1.2, 95% CI 1.04-1.30). In stratified analyses, high income/high education was a significant predictor of intended pregnancy regardless of race/ethnicity. LARC use before pregnancy and having an HSA were associated with intended pregnancy.

An Entropy-Based Measure for Assessing Fuzziness in Logistic Regression

PubMed Central

Weiss, Brandi A.; Dardick, William

2015-01-01

This article introduces an entropy-based measure of data–model fit that can be used to assess the quality of logistic regression models. Entropy has previously been used in mixture-modeling to quantify how well individuals are classified into latent classes. The current study proposes the use of entropy for logistic regression models to quantify the quality of classification and separation of group membership. Entropy complements preexisting measures of data–model fit and provides unique information not contained in other measures. Hypothetical data scenarios, an applied example, and Monte Carlo simulation results are used to demonstrate the application of entropy in logistic regression. Entropy should be used in conjunction with other measures of data–model fit to assess how well logistic regression models classify cases into observed categories. PMID:29795897

An Entropy-Based Measure for Assessing Fuzziness in Logistic Regression.

PubMed

Weiss, Brandi A; Dardick, William

2016-12-01

This article introduces an entropy-based measure of data-model fit that can be used to assess the quality of logistic regression models. Entropy has previously been used in mixture-modeling to quantify how well individuals are classified into latent classes. The current study proposes the use of entropy for logistic regression models to quantify the quality of classification and separation of group membership. Entropy complements preexisting measures of data-model fit and provides unique information not contained in other measures. Hypothetical data scenarios, an applied example, and Monte Carlo simulation results are used to demonstrate the application of entropy in logistic regression. Entropy should be used in conjunction with other measures of data-model fit to assess how well logistic regression models classify cases into observed categories.

Comparing the Discrete and Continuous Logistic Models

ERIC Educational Resources Information Center

Gordon, Sheldon P.

2008-01-01

The solutions of the discrete logistic growth model based on a difference equation and the continuous logistic growth model based on a differential equation are compared and contrasted. The investigation is conducted using a dynamic interactive spreadsheet. (Contains 5 figures.)

Joint optimization of logistics infrastructure investments and subsidies in a regional logistics network with CO 2 emission reduction targets

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

Zhang, Dezhi; Zhan, Qingwen; Chen, Yuche

This study proposes an optimization model that simultaneously incorporates the selection of logistics infrastructure investments and subsidies for green transport modes to achieve specific CO 2 emission targets in a regional logistics network. The proposed model is formulated as a bi-level formulation, in which the upper level determines the optimal selection of logistics infrastructure investments and subsidies for green transport modes such that the benefit-cost ratio of the entire logistics system is maximized. The lower level describes the selected service routes of logistics users. A genetic and Frank-Wolfe hybrid algorithm is introduced to solve the proposed model. The proposed modelmore » is applied to the regional logistics network of Changsha City, China. Findings show that using the joint scheme of the selection of logistics infrastructure investments and green subsidies is more effective than using them solely. In conclusion, carbon emission reduction targets can significantly affect logistics infrastructure investments and subsidy levels.« less

Joint optimization of logistics infrastructure investments and subsidies in a regional logistics network with CO 2 emission reduction targets

DOE PAGES

Zhang, Dezhi; Zhan, Qingwen; Chen, Yuche; ...

2016-03-14

This study proposes an optimization model that simultaneously incorporates the selection of logistics infrastructure investments and subsidies for green transport modes to achieve specific CO 2 emission targets in a regional logistics network. The proposed model is formulated as a bi-level formulation, in which the upper level determines the optimal selection of logistics infrastructure investments and subsidies for green transport modes such that the benefit-cost ratio of the entire logistics system is maximized. The lower level describes the selected service routes of logistics users. A genetic and Frank-Wolfe hybrid algorithm is introduced to solve the proposed model. The proposed modelmore » is applied to the regional logistics network of Changsha City, China. Findings show that using the joint scheme of the selection of logistics infrastructure investments and green subsidies is more effective than using them solely. In conclusion, carbon emission reduction targets can significantly affect logistics infrastructure investments and subsidy levels.« less

Logistics Modeling for Lunar Exploration Systems

NASA Technical Reports Server (NTRS)

Andraschko, Mark R.; Merrill, R. Gabe; Earle, Kevin D.

2008-01-01

The extensive logistics required to support extended crewed operations in space make effective modeling of logistics requirements and deployment critical to predicting the behavior of human lunar exploration systems. This paper discusses the software that has been developed as part of the Campaign Manifest Analysis Tool in support of strategic analysis activities under the Constellation Architecture Team - Lunar. The described logistics module enables definition of logistics requirements across multiple surface locations and allows for the transfer of logistics between those locations. A key feature of the module is the loading algorithm that is used to efficiently load logistics by type into carriers and then onto landers. Attention is given to the capabilities and limitations of this loading algorithm, particularly with regard to surface transfers. These capabilities are described within the context of the object-oriented software implementation, with details provided on the applicability of using this approach to model other human exploration scenarios. Some challenges of incorporating probabilistics into this type of logistics analysis model are discussed at a high level.

A multi-objective model for closed-loop supply chain optimization and efficient supplier selection in a competitive environment considering quantity discount policy

NASA Astrophysics Data System (ADS)

Jahangoshai Rezaee, Mustafa; Yousefi, Samuel; Hayati, Jamileh

2017-06-01

Supplier selection and allocation of optimal order quantity are two of the most important processes in closed-loop supply chain (CLSC) and reverse logistic (RL). So that providing high quality raw material is considered as a basic requirement for a manufacturer to produce popular products, as well as achieve more market shares. On the other hand, considering the existence of competitive environment, suppliers have to offer customers incentives like discounts and enhance the quality of their products in a competition with other manufacturers. Therefore, in this study, a model is presented for CLSC optimization, efficient supplier selection, as well as orders allocation considering quantity discount policy. It is modeled using multi-objective programming based on the integrated simultaneous data envelopment analysis-Nash bargaining game. In this study, maximizing profit and efficiency and minimizing defective and functions of delivery delay rate are taken into accounts. Beside supplier selection, the suggested model selects refurbishing sites, as well as determining the number of products and parts in each network's sector. The suggested model's solution is carried out using global criteria method. Furthermore, based on related studies, a numerical example is examined to validate it.

Equal Area Logistic Estimation for Item Response Theory

NASA Astrophysics Data System (ADS)

Lo, Shih-Ching; Wang, Kuo-Chang; Chang, Hsin-Li

2009-08-01

Item response theory (IRT) models use logistic functions exclusively as item response functions (IRFs). Applications of IRT models require obtaining the set of values for logistic function parameters that best fit an empirical data set. However, success in obtaining such set of values does not guarantee that the constructs they represent actually exist, for the adequacy of a model is not sustained by the possibility of estimating parameters. In this study, an equal area based two-parameter logistic model estimation algorithm is proposed. Two theorems are given to prove that the results of the algorithm are equivalent to the results of fitting data by logistic model. Numerical results are presented to show the stability and accuracy of the algorithm.

Evolution Model and Simulation of Profit Model of Agricultural Products Logistics Financing

NASA Astrophysics Data System (ADS)

Yang, Bo; Wu, Yan

2018-03-01

Agricultural products logistics financial warehousing business mainly involves agricultural production and processing enterprises, third-party logistics enterprises and financial institutions tripartite, to enable the three parties to achieve win-win situation, the article first gives the replication dynamics and evolutionary stability strategy between the three parties in business participation, and then use NetLogo simulation platform, using the overall modeling and simulation method of Multi-Agent, established the evolutionary game simulation model, and run the model under different revenue parameters, finally, analyzed the simulation results. To achieve the agricultural products logistics financial financing warehouse business to participate in tripartite mutually beneficial win-win situation, thus promoting the smooth flow of agricultural products logistics business.

Supply Chain Engineering and the Use of a Supporting Knowledge Management Application

NASA Astrophysics Data System (ADS)

Laakmann, Frank

The future competition in markets will happen between logistics networks and no longer between enterprises. A new approach for supporting the engineering of logistics networks is developed by this research as a part of the Collaborative Research Centre (SFB) 559: "Modeling of Large Networks in Logistics" at the University of Dortmund together with the Fraunhofer-Institute of Material Flow and Logistics founded by Deutsche Forschungsgemeinschaft (DFG). Based on a reference model for logistics processes, the process chain model, a guideline for logistics engineers is developed to manage the different types of design tasks of logistics networks. The technical background of this solution is a collaborative knowledge management application. This paper will introduce how new Internet-based technologies support supply chain design projects.

Determining factors influencing survival of breast cancer by fuzzy logistic regression model.

PubMed

Nikbakht, Roya; Bahrampour, Abbas

2017-01-01

Fuzzy logistic regression model can be used for determining influential factors of disease. This study explores the important factors of actual predictive survival factors of breast cancer's patients. We used breast cancer data which collected by cancer registry of Kerman University of Medical Sciences during the period of 2000-2007. The variables such as morphology, grade, age, and treatments (surgery, radiotherapy, and chemotherapy) were applied in the fuzzy logistic regression model. Performance of model was determined in terms of mean degree of membership (MDM). The study results showed that almost 41% of patients were in neoplasm and malignant group and more than two-third of them were still alive after 5-year follow-up. Based on the fuzzy logistic model, the most important factors influencing survival were chemotherapy, morphology, and radiotherapy, respectively. Furthermore, the MDM criteria show that the fuzzy logistic regression have a good fit on the data (MDM = 0.86). Fuzzy logistic regression model showed that chemotherapy is more important than radiotherapy in survival of patients with breast cancer. In addition, another ability of this model is calculating possibilistic odds of survival in cancer patients. The results of this study can be applied in clinical research. Furthermore, there are few studies which applied the fuzzy logistic models. Furthermore, we recommend using this model in various research areas.

The use of the logistic model in space motion sickness prediction

NASA Technical Reports Server (NTRS)

Lin, Karl K.; Reschke, Millard F.

1987-01-01

The one-equation and the two-equation logistic models were used to predict subjects' susceptibility to motion sickness in KC-135 parabolic flights using data from other ground-based motion sickness tests. The results show that the logistic models correctly predicted substantially more cases (an average of 13 percent) in the data subset used for model building. Overall, the logistic models ranged from 53 to 65 percent predictions of the three endpoint parameters, whereas the Bayes linear discriminant procedure ranged from 48 to 65 percent correct for the cross validation sample.

Risk adjustment in the American College of Surgeons National Surgical Quality Improvement Program: a comparison of logistic versus hierarchical modeling.

PubMed

Cohen, Mark E; Dimick, Justin B; Bilimoria, Karl Y; Ko, Clifford Y; Richards, Karen; Hall, Bruce Lee

2009-12-01

Although logistic regression has commonly been used to adjust for risk differences in patient and case mix to permit quality comparisons across hospitals, hierarchical modeling has been advocated as the preferred methodology, because it accounts for clustering of patients within hospitals. It is unclear whether hierarchical models would yield important differences in quality assessments compared with logistic models when applied to American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) data. Our objective was to evaluate differences in logistic versus hierarchical modeling for identifying hospitals with outlying outcomes in the ACS-NSQIP. Data from ACS-NSQIP patients who underwent colorectal operations in 2008 at hospitals that reported at least 100 operations were used to generate logistic and hierarchical prediction models for 30-day morbidity and mortality. Differences in risk-adjusted performance (ratio of observed-to-expected events) and outlier detections from the two models were compared. Logistic and hierarchical models identified the same 25 hospitals as morbidity outliers (14 low and 11 high outliers), but the hierarchical model identified 2 additional high outliers. Both models identified the same eight hospitals as mortality outliers (five low and three high outliers). The values of observed-to-expected events ratios and p values from the two models were highly correlated. Results were similar when data were permitted from hospitals providing < 100 patients. When applied to ACS-NSQIP data, logistic and hierarchical models provided nearly identical results with respect to identification of hospitals' observed-to-expected events ratio outliers. As hierarchical models are prone to implementation problems, logistic regression will remain an accurate and efficient method for performing risk adjustment of hospital quality comparisons.

The Applicability of Confidence Intervals of Quantiles for the Generalized Logistic Distribution

NASA Astrophysics Data System (ADS)

Shin, H.; Heo, J.; Kim, T.; Jung, Y.

2007-12-01

The generalized logistic (GL) distribution has been widely used for frequency analysis. However, there is a little study related to the confidence intervals that indicate the prediction accuracy of distribution for the GL distribution. In this paper, the estimation of the confidence intervals of quantiles for the GL distribution is presented based on the method of moments (MOM), maximum likelihood (ML), and probability weighted moments (PWM) and the asymptotic variances of each quantile estimator are derived as functions of the sample sizes, return periods, and parameters. Monte Carlo simulation experiments are also performed to verify the applicability of the derived confidence intervals of quantile. As the results, the relative bias (RBIAS) and relative root mean square error (RRMSE) of the confidence intervals generally increase as return period increases and reverse as sample size increases. And PWM for estimating the confidence intervals performs better than the other methods in terms of RRMSE when the data is almost symmetric while ML shows the smallest RBIAS and RRMSE when the data is more skewed and sample size is moderately large. The GL model was applied to fit the distribution of annual maximum rainfall data. The results show that there are little differences in the estimated quantiles between ML and PWM while distinct differences in MOM.

Robust mislabel logistic regression without modeling mislabel probabilities.

PubMed

Hung, Hung; Jou, Zhi-Yu; Huang, Su-Yun

2018-03-01

Logistic regression is among the most widely used statistical methods for linear discriminant analysis. In many applications, we only observe possibly mislabeled responses. Fitting a conventional logistic regression can then lead to biased estimation. One common resolution is to fit a mislabel logistic regression model, which takes into consideration of mislabeled responses. Another common method is to adopt a robust M-estimation by down-weighting suspected instances. In this work, we propose a new robust mislabel logistic regression based on γ-divergence. Our proposal possesses two advantageous features: (1) It does not need to model the mislabel probabilities. (2) The minimum γ-divergence estimation leads to a weighted estimating equation without the need to include any bias correction term, that is, it is automatically bias-corrected. These features make the proposed γ-logistic regression more robust in model fitting and more intuitive for model interpretation through a simple weighting scheme. Our method is also easy to implement, and two types of algorithms are included. Simulation studies and the Pima data application are presented to demonstrate the performance of γ-logistic regression. © 2017, The International Biometric Society.

Comparison of the binary logistic and skewed logistic (Scobit) models of injury severity in motor vehicle collisions.

PubMed

Tay, Richard

2016-03-01

The binary logistic model has been extensively used to analyze traffic collision and injury data where the outcome of interest has two categories. However, the assumption of a symmetric distribution may not be a desirable property in some cases, especially when there is a significant imbalance in the two categories of outcome. This study compares the standard binary logistic model with the skewed logistic model in two cases in which the symmetry assumption is violated in one but not the other case. The differences in the estimates, and thus the marginal effects obtained, are significant when the assumption of symmetry is violated. Copyright © 2015 Elsevier Ltd. All rights reserved.

An Entropy-Based Measure for Assessing Fuzziness in Logistic Regression

ERIC Educational Resources Information Center

Weiss, Brandi A.; Dardick, William

2016-01-01

This article introduces an entropy-based measure of data-model fit that can be used to assess the quality of logistic regression models. Entropy has previously been used in mixture-modeling to quantify how well individuals are classified into latent classes. The current study proposes the use of entropy for logistic regression models to quantify…

Immigrant children's reliance on public health insurance in the wake of immigration reform.

PubMed

Pati, Susmita; Danagoulian, Shooshan

2008-11-01

We sought to determine whether the reversal of the public charge rule of the Illegal Immigration Reform and Immigrant Responsibility Act, which may have required families to pay for benefits previously received at no cost, led to immigrant children becoming increasingly reliant on public health insurance programs. We conducted a secondary data analysis focusing on low-income children sampled in the 1997 through 2004 versions of the National Health Interview Survey. Between 1997 and 2004, public health insurance enrollments and the numbers of uninsured foreign-born children in the United States increased by 3.1% and 2.7%, respectively. Using multinomial logistic regression models to account for the substantial differences in socioeconomic status between foreign-born and US-born children, we found that low-income US-born children were just as likely as foreign-born children to have public health insurance coverage (odds ratio [OR] = 1.16; 95% confidence interval [CI] = 0.89, 1.52) and that, after 2000, foreign-born children were 1.59 times (95% CI = 1.24, 2.05) more likely than were US-born children to be uninsured (vs publicly insured). In the wake of the reversal of the public charge rule, immigrant children are increasingly likely to be uninsured as opposed to relying on public health insurance.

Immigrant Children's Reliance on Public Health Insurance in the Wake of Immigration Reform

PubMed Central

Danagoulian, Shooshan

2008-01-01

Objectives. We sought to determine whether the reversal of the public charge rule of the Illegal Immigration Reform and Immigrant Responsibility Act, which may have required families to pay for benefits previously received at no cost, led to immigrant children becoming increasingly reliant on public health insurance programs. Methods. We conducted a secondary data analysis focusing on low-income children sampled in the 1997 through 2004 versions of the National Health Interview Survey. Results. Between 1997 and 2004, public health insurance enrollments and the numbers of uninsured foreign-born children in the United States increased by 3.1% and 2.7%, respectively. Using multinomial logistic regression models to account for the substantial differences in socioeconomic status between foreign-born and US-born children, we found that low-income US-born children were just as likely as foreign-born children to have public health insurance coverage (odds ratio [OR] = 1.16; 95% confidence interval [CI] = 0.89, 1.52) and that, after 2000, foreign-born children were 1.59 times (95% CI = 1.24, 2.05) more likely than were US-born children to be uninsured (vs publicly insured). Conclusions. In the wake of the reversal of the public charge rule, immigrant children are increasingly likely to be uninsured as opposed to relying on public health insurance. PMID:18799772

Logistics in a low carbon concept: Connotation and realization way

NASA Astrophysics Data System (ADS)

Zheng, Chaocheng; Qiu, Xiaoying; Mao, Jiarong

2017-01-01

Low-carbon logistics has become a trend for the logistics industry-as a high-energy consumption industry, continuation of its previous operating mode has been significantly behind the times. So logistics industry must release lower carbon emissions. This paper sort out the literature home and abroad from three aspects, that is, the definition of low-carbon logistics, low-carbon logistics implementation mechanisms or measures, and low carbon design quantitative models. The research shows: low-carbon logistics needed to implemented both in enterprise' macro and micro level, which means the government should provide relevant policy support and micro enterprises should be actively sought from all sectors of the logistics in energy saving. In practice, low-carbon logistics optimization models are effective tools for enterprises to implement emission reduction.

Analyzing Student Learning Outcomes: Usefulness of Logistic and Cox Regression Models. IR Applications, Volume 5

ERIC Educational Resources Information Center

Chen, Chau-Kuang

2005-01-01

Logistic and Cox regression methods are practical tools used to model the relationships between certain student learning outcomes and their relevant explanatory variables. The logistic regression model fits an S-shaped curve into a binary outcome with data points of zero and one. The Cox regression model allows investigators to study the duration…

Satellite rainfall retrieval by logistic regression

NASA Technical Reports Server (NTRS)

Chiu, Long S.

1986-01-01

The potential use of logistic regression in rainfall estimation from satellite measurements is investigated. Satellite measurements provide covariate information in terms of radiances from different remote sensors.The logistic regression technique can effectively accommodate many covariates and test their significance in the estimation. The outcome from the logistical model is the probability that the rainrate of a satellite pixel is above a certain threshold. By varying the thresholds, a rainrate histogram can be obtained, from which the mean and the variant can be estimated. A logistical model is developed and applied to rainfall data collected during GATE, using as covariates the fractional rain area and a radiance measurement which is deduced from a microwave temperature-rainrate relation. It is demonstrated that the fractional rain area is an important covariate in the model, consistent with the use of the so-called Area Time Integral in estimating total rain volume in other studies. To calibrate the logistical model, simulated rain fields generated by rainfield models with prescribed parameters are needed. A stringent test of the logistical model is its ability to recover the prescribed parameters of simulated rain fields. A rain field simulation model which preserves the fractional rain area and lognormality of rainrates as found in GATE is developed. A stochastic regression model of branching and immigration whose solutions are lognormally distributed in some asymptotic limits has also been developed.

Reverse engineering of aircraft wing data using a partial differential equation surface model

NASA Astrophysics Data System (ADS)

Huband, Jacalyn Mann

Reverse engineering is a multi-step process used in industry to determine a production representation of an existing physical object. This representation is in the form of mathematical equations that are compatible with computer-aided design and computer-aided manufacturing (CAD/CAM) equipment. The four basic steps to the reverse engineering process are data acquisition, data separation, surface or curve fitting, and CAD/CAM production. The surface fitting step determines the design representation of the object, and thus is critical to the success or failure of the reverse engineering process. Although surface fitting methods described in the literature are used to model a variety of surfaces, they are not suitable for reversing aircraft wings. In this dissertation, we develop and demonstrate a new strategy for reversing a mathematical representation of an aircraft wing. The basis of our strategy is to take an aircraft design model and determine if an inverse model can be derived. A candidate design model for this research is the partial differential equation (PDE) surface model, proposed by Bloor and Wilson and used in the Rapid Airplane Parameter Input Design (RAPID) tool at the NASA-LaRC Geolab. There are several basic mathematical problems involved in reversing the PDE surface model: (i) deriving a computational approximation of the surface function; (ii) determining a radial parametrization of the wing; (iii) choosing mathematical models or classes of functions for representation of the boundary functions; (iv) fitting the boundary data points by the chosen boundary functions; and (v) simultaneously solving for the axial parameterization and the derivative boundary functions. The study of the techniques to solve the above mathematical problems has culminated in a reverse PDE surface model and two reverse PDE surface algorithms. One reverse PDE surface algorithm recovers engineering design parameters for the RAPID tool from aircraft wing data and the other generates a PDE surface model with spline boundary functions from an arbitrary set of grid points. Our numerical tests show that the reverse PDE surface model and the reverse PDE surface algorithms can be used for the reverse engineering of aircraft wing data.

Should metacognition be measured by logistic regression?

PubMed

Rausch, Manuel; Zehetleitner, Michael

2017-03-01

Are logistic regression slopes suitable to quantify metacognitive sensitivity, i.e. the efficiency with which subjective reports differentiate between correct and incorrect task responses? We analytically show that logistic regression slopes are independent from rating criteria in one specific model of metacognition, which assumes (i) that rating decisions are based on sensory evidence generated independently of the sensory evidence used for primary task responses and (ii) that the distributions of evidence are logistic. Given a hierarchical model of metacognition, logistic regression slopes depend on rating criteria. According to all considered models, regression slopes depend on the primary task criterion. A reanalysis of previous data revealed that massive numbers of trials are required to distinguish between hierarchical and independent models with tolerable accuracy. It is argued that researchers who wish to use logistic regression as measure of metacognitive sensitivity need to control the primary task criterion and rating criteria. Copyright © 2017 Elsevier Inc. All rights reserved.

Naval Supply Systems Command Fleet Logistics Center

DTIC Science & Technology

2012-08-08

Partial small business set - aside is a potential consideration   12-month Base plus two options   Synopsis N00604-11-R-3006 on NECO and FedBizOpps...2012 Navy Gold Coast Small Business Procurement Event 8 August 2012 #1 PRIORITY = Operating Forces Support …while ensuring Joint...while ensuring Joint Base Success FedBid.com Reverse Auction Website 8 Small Business Assistance #1 PRIORITY = Operating Forces

Prioritizing critical success factors for reverse logistics implementation using fuzzy-TOPSIS methodology

NASA Astrophysics Data System (ADS)

Agrawal, Saurabh; Singh, Rajesh K.; Murtaza, Qasim

2016-03-01

Electronics industry is one of the fastest growing industries in the world. In India also, there are high turnovers and growing demand of electronics product especially after post liberalization in early nineties. These products generate e-waste which has become big environmental issue. Industries can handle these e-waste and product returns efficiently by developing reverse logistics (RL) system. A thorough study of critical success factors (CSFs) and their ordered implementation is essential for successful RL implementation. The aim of the study is to review the CSFs, and to prioritize them for RL implementation in Indian electronics industry. Twelve CSFs were identified through literature review, and discussion with the experts from the Indian electronics industry. Fuzzy-Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach is proposed for prioritizing these CSFs. Perusal of literature indicates that fuzzy-TOPSIS has not been applied earlier for prioritization of CSFs in Indian electronics industry. Five Indian electronics companies were selected for evaluation of this methodology. Results indicate that most of the identified factors are crucial for the RL implementation. Top management awareness, resource management, economic factors, and contracts terms and conditions are top four prioritized factor, and process capabilities and skilled workers is the least prioritized factor. The findings will be useful for successful RL implementation in Indian electronics industry.

Generation of and control measures for, e-waste in Hong Kong

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

Chung Shanshan, E-mail: sschung@hkbu.edu.hk; Lau Kayan; Zhang Chan

2011-03-15

While accurately estimating electrical and electronic waste (e-waste) generation is important for building appropriate infrastructure for its collection and recycling, making reliable estimates of this kind is difficult in Hong Kong owing to the fact that neither accurate trade statistics nor sales data of relevant products are available. In view of this, data of e-products consumption at household level was collected by a tailor-made questionnaire survey from the public for obtaining a reasonable e-waste generation estimate. It was estimated that on average no more than 80,443 tonnes (11.5 kg/capita) of waste is generated from non-plasma and non-liquid crystal display televisions,more » refrigerators, washing machines, air-conditioners and personal computers each year by Hong Kong households. However, not more than 17% of this is disposed as waste despite a producer responsibility scheme (PRS) not being in place because of the existence of a vibrant e-waste trading sector. The form of PRS control that can possibly win most public support is one that would involve the current e-waste traders as a major party in providing the reverse logistics with a visible recycling charge levied at the point of importation. This reverse logistic service should be convenient, reliable and highly accessible to the consumers.« less

Generation of and control measures for, e-waste in Hong Kong.

PubMed

Chung, Shan-shan; Lau, Ka-yan; Zhang, Chan

2011-03-01

While accurately estimating electrical and electronic waste (e-waste) generation is important for building appropriate infrastructure for its collection and recycling, making reliable estimates of this kind is difficult in Hong Kong owing to the fact that neither accurate trade statistics nor sales data of relevant products are available. In view of this, data of e-products consumption at household level was collected by a tailor-made questionnaire survey from the public for obtaining a reasonable e-waste generation estimate. It was estimated that on average no more than 80,443 tones (11.5 kg/capita) of waste is generated from non-plasma and non-liquid crystal display televisions, refrigerators, washing machines, air-conditioners and personal computers each year by Hong Kong households. However, not more than 17% of this is disposed as waste despite a producer responsibility scheme (PRS) not being in place because of the existence of a vibrant e-waste trading sector. The form of PRS control that can possibly win most public support is one that would involve the current e-waste traders as a major party in providing the reverse logistics with a visible recycling charge levied at the point of importation. This reverse logistic service should be convenient, reliable and highly accessible to the consumers. Copyright © 2010 Elsevier Ltd. All rights reserved.

Measuring decision weights in recognition experiments with multiple response alternatives: comparing the correlation and multinomial-logistic-regression methods.

PubMed

Dai, Huanping; Micheyl, Christophe

2012-11-01

Psychophysical "reverse-correlation" methods allow researchers to gain insight into the perceptual representations and decision weighting strategies of individual subjects in perceptual tasks. Although these methods have gained momentum, until recently their development was limited to experiments involving only two response categories. Recently, two approaches for estimating decision weights in m-alternative experiments have been put forward. One approach extends the two-category correlation method to m > 2 alternatives; the second uses multinomial logistic regression (MLR). In this article, the relative merits of the two methods are discussed, and the issues of convergence and statistical efficiency of the methods are evaluated quantitatively using Monte Carlo simulations. The results indicate that, for a range of values of the number of trials, the estimated weighting patterns are closer to their asymptotic values for the correlation method than for the MLR method. Moreover, for the MLR method, weight estimates for different stimulus components can exhibit strong correlations, making the analysis and interpretation of measured weighting patterns less straightforward than for the correlation method. These and other advantages of the correlation method, which include computational simplicity and a close relationship to other well-established psychophysical reverse-correlation methods, make it an attractive tool to uncover decision strategies in m-alternative experiments.

Ultrasound predictors of neonatal outcome in intrauterine growth restriction.

PubMed

Craigo, S D; Beach, M L; Harvey-Wilkes, K B; D'Alton, M E

1996-11-01

Our purpose was to assess the value of commonly performed ultrasound parameters in predicting neonatal outcome of fetuses with intrauterine growth restriction (IUGR). One hundred twenty-seven patients were identified on ultrasound examination to have IUGR. Estimated weight percentile, amniotic fluid volume, umbilical artery Doppler velocimetry, and head circumference/abdominal circumference ratio were compared with neonatal outcome. Thirty infants had severely adverse courses. The degree of growth restriction was strongly associated with adverse outcome and neonatal death. Umbilical artery Doppler waveforms with absent or reverse end-diastolic flow were predicted of neonatal death, bronchopulmonary dysplasia (BPD), necrotizing enterocolitis (NEC), and adverse outcome in general. Oligohydramnios was predictive of adverse outcome and neonatal death. Logistic regression also showed that absent or reverse end-diastolic flow and oligohydramnios were independent predictors of adverse outcome. Ultrasound findings of low estimated weight percentile, absent or reverse end-diastolic umbilical blood flow, and oligohydramnios are independent predictors of adverse neonatal outcome of growth restricted fetuses.

Bias in logistic regression due to imperfect diagnostic test results and practical correction approaches.

PubMed

Valle, Denis; Lima, Joanna M Tucker; Millar, Justin; Amratia, Punam; Haque, Ubydul

2015-11-04

Logistic regression is a statistical model widely used in cross-sectional and cohort studies to identify and quantify the effects of potential disease risk factors. However, the impact of imperfect tests on adjusted odds ratios (and thus on the identification of risk factors) is under-appreciated. The purpose of this article is to draw attention to the problem associated with modelling imperfect diagnostic tests, and propose simple Bayesian models to adequately address this issue. A systematic literature review was conducted to determine the proportion of malaria studies that appropriately accounted for false-negatives/false-positives in a logistic regression setting. Inference from the standard logistic regression was also compared with that from three proposed Bayesian models using simulations and malaria data from the western Brazilian Amazon. A systematic literature review suggests that malaria epidemiologists are largely unaware of the problem of using logistic regression to model imperfect diagnostic test results. Simulation results reveal that statistical inference can be substantially improved when using the proposed Bayesian models versus the standard logistic regression. Finally, analysis of original malaria data with one of the proposed Bayesian models reveals that microscopy sensitivity is strongly influenced by how long people have lived in the study region, and an important risk factor (i.e., participation in forest extractivism) is identified that would have been missed by standard logistic regression. Given the numerous diagnostic methods employed by malaria researchers and the ubiquitous use of logistic regression to model the results of these diagnostic tests, this paper provides critical guidelines to improve data analysis practice in the presence of misclassification error. Easy-to-use code that can be readily adapted to WinBUGS is provided, enabling straightforward implementation of the proposed Bayesian models.

A Note on the Item Information Function of the Four-Parameter Logistic Model

ERIC Educational Resources Information Center

Magis, David

2013-01-01

This article focuses on four-parameter logistic (4PL) model as an extension of the usual three-parameter logistic (3PL) model with an upper asymptote possibly different from 1. For a given item with fixed item parameters, Lord derived the value of the latent ability level that maximizes the item information function under the 3PL model. The…

Right ventricular relative wall thickness as a predictor of outcomes and of right ventricular reverse remodeling for patients with pulmonary hypertension.

PubMed

Sano, Hiroyuki; Tanaka, Hidekazu; Motoji, Yoshiki; Fukuda, Yuko; Mochizuki, Yasuhide; Hatani, Yutaka; Matsuzoe, Hiroki; Hatazawa, Keiko; Shimoura, Hiroyuki; Ooka, Junichi; Ryo-Koriyama, Keiko; Nakayama, Kazuhiko; Matsumoto, Kensuke; Emoto, Noriaki; Hirata, Ken-Ichi

2017-03-01

Mid-term right ventricular (RV) reverse remodeling after treatment in patients with pulmonary hypertension (PH) is associated with long-term outcome as well as baseline RV remodeling. However, baseline factors influencing mid-term RV reverse remodeling after treatment and its prognostic capability remain unclear. We studied 54 PH patients. Mid-term RV remodeling was assessed in terms of the RV area, which was traced planimetrically at the end-systole (RVESA). RV reverse remodeling was defined as a relative decrease in the RVESA of at least 15% at 10.2 ± 9.4 months after treatment. Long-term follow-up was 5 years. Adverse events occurred in ten patients (19%) and mid-term RV reverse remodeling after treatment was observed in 37 (69%). Patients with mid-term RV reverse remodeling had more favorable long-term outcomes than those without (log-rank: p = 0.01). Multivariate logistic regression analysis showed that RV relative wall thickness (RV-RWT), as calculated as RV free-wall thickness/RV basal linear dimension at end-diastole, was an independent predictor of mid-term RV reverse remodeling (OR 1.334; 95% CI, 1.039-1.713; p = 0.03). Moreover, patients with RV-RWT ≥0.21 showed better long-term outcomes than did those without (log-rank p = 0.03), while those with RV-RWT ≥0.21 and mid-term RV reverse remodeling had the best long-term outcomes. Patients with RV-RWT <0.21 and without mid-term RV reverse remodeling, on the other hand, had worse long-term outcomes than other sub-groups. In conclusions, RV-RWT could predict mid-term RV reverse remodeling after treatment in PH patients, and was associated with long-term outcomes. Our finding may have clinical implications for better management of PH patients.

Reverse time migration by Krylov subspace reduced order modeling

NASA Astrophysics Data System (ADS)

Basir, Hadi Mahdavi; Javaherian, Abdolrahim; Shomali, Zaher Hossein; Firouz-Abadi, Roohollah Dehghani; Gholamy, Shaban Ali

2018-04-01

Imaging is a key step in seismic data processing. To date, a myriad of advanced pre-stack depth migration approaches have been developed; however, reverse time migration (RTM) is still considered as the high-end imaging algorithm. The main limitations associated with the performance cost of reverse time migration are the intensive computation of the forward and backward simulations, time consumption, and memory allocation related to imaging condition. Based on the reduced order modeling, we proposed an algorithm, which can be adapted to all the aforementioned factors. Our proposed method benefit from Krylov subspaces method to compute certain mode shapes of the velocity model computed by as an orthogonal base of reduced order modeling. Reverse time migration by reduced order modeling is helpful concerning the highly parallel computation and strongly reduces the memory requirement of reverse time migration. The synthetic model results showed that suggested method can decrease the computational costs of reverse time migration by several orders of magnitudes, compared with reverse time migration by finite element method.

Logistic regression models of factors influencing the location of bioenergy and biofuels plants

Treesearch

T.M. Young; R.L. Zaretzki; J.H. Perdue; F.M. Guess; X. Liu

2011-01-01

Logistic regression models were developed to identify significant factors that influence the location of existing wood-using bioenergy/biofuels plants and traditional wood-using facilities. Logistic models provided quantitative insight for variables influencing the location of woody biomass-using facilities. Availability of "thinnings to a basal area of 31.7m2/ha...

Using phenomenological models for forecasting the 2015 Ebola challenge.

PubMed

Pell, Bruce; Kuang, Yang; Viboud, Cecile; Chowell, Gerardo

2018-03-01

The rising number of novel pathogens threatening the human population has motivated the application of mathematical modeling for forecasting the trajectory and size of epidemics. We summarize the real-time forecasting results of the logistic equation during the 2015 Ebola challenge focused on predicting synthetic data derived from a detailed individual-based model of Ebola transmission dynamics and control. We also carry out a post-challenge comparison of two simple phenomenological models. In particular, we systematically compare the logistic growth model and a recently introduced generalized Richards model (GRM) that captures a range of early epidemic growth profiles ranging from sub-exponential to exponential growth. Specifically, we assess the performance of each model for estimating the reproduction number, generate short-term forecasts of the epidemic trajectory, and predict the final epidemic size. During the challenge the logistic equation consistently underestimated the final epidemic size, peak timing and the number of cases at peak timing with an average mean absolute percentage error (MAPE) of 0.49, 0.36 and 0.40, respectively. Post-challenge, the GRM which has the flexibility to reproduce a range of epidemic growth profiles ranging from early sub-exponential to exponential growth dynamics outperformed the logistic growth model in ascertaining the final epidemic size as more incidence data was made available, while the logistic model underestimated the final epidemic even with an increasing amount of data of the evolving epidemic. Incidence forecasts provided by the generalized Richards model performed better across all scenarios and time points than the logistic growth model with mean RMS decreasing from 78.00 (logistic) to 60.80 (GRM). Both models provided reasonable predictions of the effective reproduction number, but the GRM slightly outperformed the logistic growth model with a MAPE of 0.08 compared to 0.10, averaged across all scenarios and time points. Our findings further support the consideration of transmission models that incorporate flexible early epidemic growth profiles in the forecasting toolkit. Such models are particularly useful for quickly evaluating a developing infectious disease outbreak using only case incidence time series of the early phase of an infectious disease outbreak. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Predicting 30-day Hospital Readmission with Publicly Available Administrative Database. A Conditional Logistic Regression Modeling Approach.

PubMed

Zhu, K; Lou, Z; Zhou, J; Ballester, N; Kong, N; Parikh, P

2015-01-01

This article is part of the Focus Theme of Methods of Information in Medicine on "Big Data and Analytics in Healthcare". Hospital readmissions raise healthcare costs and cause significant distress to providers and patients. It is, therefore, of great interest to healthcare organizations to predict what patients are at risk to be readmitted to their hospitals. However, current logistic regression based risk prediction models have limited prediction power when applied to hospital administrative data. Meanwhile, although decision trees and random forests have been applied, they tend to be too complex to understand among the hospital practitioners. Explore the use of conditional logistic regression to increase the prediction accuracy. We analyzed an HCUP statewide inpatient discharge record dataset, which includes patient demographics, clinical and care utilization data from California. We extracted records of heart failure Medicare beneficiaries who had inpatient experience during an 11-month period. We corrected the data imbalance issue with under-sampling. In our study, we first applied standard logistic regression and decision tree to obtain influential variables and derive practically meaning decision rules. We then stratified the original data set accordingly and applied logistic regression on each data stratum. We further explored the effect of interacting variables in the logistic regression modeling. We conducted cross validation to assess the overall prediction performance of conditional logistic regression (CLR) and compared it with standard classification models. The developed CLR models outperformed several standard classification models (e.g., straightforward logistic regression, stepwise logistic regression, random forest, support vector machine). For example, the best CLR model improved the classification accuracy by nearly 20% over the straightforward logistic regression model. Furthermore, the developed CLR models tend to achieve better sensitivity of more than 10% over the standard classification models, which can be translated to correct labeling of additional 400 - 500 readmissions for heart failure patients in the state of California over a year. Lastly, several key predictor identified from the HCUP data include the disposition location from discharge, the number of chronic conditions, and the number of acute procedures. It would be beneficial to apply simple decision rules obtained from the decision tree in an ad-hoc manner to guide the cohort stratification. It could be potentially beneficial to explore the effect of pairwise interactions between influential predictors when building the logistic regression models for different data strata. Judicious use of the ad-hoc CLR models developed offers insights into future development of prediction models for hospital readmissions, which can lead to better intuition in identifying high-risk patients and developing effective post-discharge care strategies. Lastly, this paper is expected to raise the awareness of collecting data on additional markers and developing necessary database infrastructure for larger-scale exploratory studies on readmission risk prediction.

Two-echelon logistics service supply chain decision game considering quality supervision

NASA Astrophysics Data System (ADS)

Shi, Jiaying

2017-10-01

Due to the increasing importance of supply chain logistics service, we established the Stackelberg game model between single integrator and single subcontractors under decentralized and centralized circumstances, and found that logistics services integrators as a leader prefer centralized decision-making but logistics service subcontractors tend to the decentralized decision-making. Then, we further analyzed why subcontractor chose to deceive and rebuilt a principal-agent game model to monitor the logistics services quality of them. Mixed Strategy Nash equilibrium and related parameters were discussed. The results show that strengthening the supervision and coordination can improve the quality level of logistics service supply chain.

Reserve Component Logistics Responsibilities in the Total Force,

DTIC Science & Technology

1982-10-01

It diferent from Report) 14. SUPPLEMENTARY NOTES Four Service-specific Working Notes are included as Appendices. 19. KEY WORDS (Continue on reverse...During the balance of the task, we will augment the data presented in this working note with: - time phasing of RC units after mobilization for a NATO or...aerial refueling During the balance of the task, we will augment the data presented in this working paper with: - time phasings of RC units after

Logistic regression for dichotomized counts.

PubMed

Preisser, John S; Das, Kalyan; Benecha, Habtamu; Stamm, John W

2016-12-01

Sometimes there is interest in a dichotomized outcome indicating whether a count variable is positive or zero. Under this scenario, the application of ordinary logistic regression may result in efficiency loss, which is quantifiable under an assumed model for the counts. In such situations, a shared-parameter hurdle model is investigated for more efficient estimation of regression parameters relating to overall effects of covariates on the dichotomous outcome, while handling count data with many zeroes. One model part provides a logistic regression containing marginal log odds ratio effects of primary interest, while an ancillary model part describes the mean count of a Poisson or negative binomial process in terms of nuisance regression parameters. Asymptotic efficiency of the logistic model parameter estimators of the two-part models is evaluated with respect to ordinary logistic regression. Simulations are used to assess the properties of the models with respect to power and Type I error, the latter investigated under both misspecified and correctly specified models. The methods are applied to data from a randomized clinical trial of three toothpaste formulations to prevent incident dental caries in a large population of Scottish schoolchildren. © The Author(s) 2014.

Predicting U.S. Army Reserve Unit Manning Using Market Demographics

DTIC Science & Technology

2015-06-01

develops linear regression , classification tree, and logistic regression models to determine the ability of the location to support manning requirements... logistic regression model delivers predictive results that allow decision-makers to identify locations with a high probability of meeting unit...manning requirements. The recommendation of this thesis is that the USAR implement the logistic regression model. 14. SUBJECT TERMS U.S

A review of logistic regression models used to predict post-fire tree mortality of western North American conifers

Treesearch

Travis Woolley; David C. Shaw; Lisa M. Ganio; Stephen Fitzgerald

2012-01-01

Logistic regression models used to predict tree mortality are critical to post-fire management, planning prescribed bums and understanding disturbance ecology. We review literature concerning post-fire mortality prediction using logistic regression models for coniferous tree species in the western USA. We include synthesis and review of: methods to develop, evaluate...

Differentially private distributed logistic regression using private and public data.

PubMed

Ji, Zhanglong; Jiang, Xiaoqian; Wang, Shuang; Xiong, Li; Ohno-Machado, Lucila

2014-01-01

Privacy protecting is an important issue in medical informatics and differential privacy is a state-of-the-art framework for data privacy research. Differential privacy offers provable privacy against attackers who have auxiliary information, and can be applied to data mining models (for example, logistic regression). However, differentially private methods sometimes introduce too much noise and make outputs less useful. Given available public data in medical research (e.g. from patients who sign open-consent agreements), we can design algorithms that use both public and private data sets to decrease the amount of noise that is introduced. In this paper, we modify the update step in Newton-Raphson method to propose a differentially private distributed logistic regression model based on both public and private data. We try our algorithm on three different data sets, and show its advantage over: (1) a logistic regression model based solely on public data, and (2) a differentially private distributed logistic regression model based on private data under various scenarios. Logistic regression models built with our new algorithm based on both private and public datasets demonstrate better utility than models that trained on private or public datasets alone without sacrificing the rigorous privacy guarantee.

Carbon emissions, logistics volume and GDP in China: empirical analysis based on panel data model.

PubMed

Guo, Xiaopeng; Ren, Dongfang; Shi, Jiaxing

2016-12-01

This paper studies the relationship among carbon emissions, GDP, and logistics by using a panel data model and a combination of statistics and econometrics theory. The model is based on the historical data of 10 typical provinces and cities in China during 2005-2014. The model in this paper adds the variability of logistics on the basis of previous studies, and this variable is replaced by the freight turnover of the provinces. Carbon emissions are calculated by using the annual consumption of coal, oil, and natural gas. GDP is the gross domestic product. The results showed that the amount of logistics and GDP have a contribution to carbon emissions and the long-term relationships are different between different cities in China, mainly influenced by the difference among development mode, economic structure, and level of logistic development. After the testing of panel model setting, this paper established a variable coefficient model of the panel. The influence of GDP and logistics on carbon emissions is obtained according to the influence factors among the variables. The paper concludes with main findings and provides recommendations toward rational planning of urban sustainable development and environmental protection for China.

Associations between physical activity parenting practices and adolescent girls' self-perceptions and physical activity intentions.

PubMed

Sebire, Simon J; Haase, Anne M; Montgomery, Alan A; McNeill, Jade; Jago, Russ

2014-05-01

The current study investigated cross-sectional associations between maternal and paternal logistic and modeling physical activity support and the self-efficacy, self-esteem, and physical activity intentions of 11- to 12-year-old girls. 210 girls reported perceptions of maternal and paternal logistic and modeling support and their self-efficacy, self-esteem and intention to be physically active. Data were analyzed using multivariable regression models. Maternal logistic support was positively associated with participants' self-esteem, physical activity self-efficacy, and intention to be active. Maternal modeling was positively associated with self-efficacy. Paternal modeling was positively associated with self-esteem and self-efficacy but there was no evidence that paternal logistic support was associated with the psychosocial variables. Activity-related parenting practices were associated with psychosocial correlates of physical activity among adolescent girls. Logistic support from mothers, rather than modeling support or paternal support may be a particularly important target when designing interventions aimed at preventing the age-related decline in physical activity among girls.

Regional Logistics Information Resources Integration Patterns and Countermeasures

NASA Astrophysics Data System (ADS)

Wu, Hui; Shangguan, Xu-ming

Effective integration of regional logistics information resources can provide collaborative services in information flow, business flow and logistics for regional logistics enterprises, which also can reduce operating costs and improve market responsiveness. First, this paper analyzes the realistic significance on the integration of regional logistics information. Second, this paper brings forward three feasible patterns on the integration of regional logistics information resources, These three models have their own strengths and the scope of application and implementation, which model is selected will depend on the specific business and the regional distribution of enterprises. Last, this paper discusses the related countermeasures on the integration of regional logistics information resources, because the integration of regional logistics information is a systems engineering, when the integration is advancing, the countermeasures should pay close attention to the current needs and long-term development of regional enterprises.

Do Community and Caregiver Factors Influence Hospice Use at the End of Life Among Older Adults With Alzheimer Disease?

PubMed

Karikari-Martin, Pauline; McCann, Judith J; Hebert, Liesi E; Haffer, Samuel C; Phillips, Marcia

2012-05-01

Hospice is an underused service among people with Alzheimer disease. This study used the Hospice Use Model to examine community, care recipient, and caregiver characteristics associated with hospice use before death among 145 community-dwelling care recipients with Alzheimer disease and their caregivers. Secondary analysis using logistic regression modeling indicated that older age, male gender, black race, and better functional health of care recipients with Alzheimer disease were associated with a decreased likelihood of using hospice (model χ 2 5 = 23.5, P = .0003). Moreover, care recipients recruited from an Alzheimer clinic were more likely to use hospice than those recruited from adult day-care centers. Caregiver factors were not independent predictors of hospice use. However, there was a significant interaction between hours of care provided each week and recruitment site. Among care recipients from the Alzheimer clinic, the probability of hospice use increased as caregiving intensity increased. This relationship was reversed in care recipients from day-care centers. Results suggest that adult day-care centers need to partner with hospice programs in the community. In conclusion, care recipient and community service factors influence hospice use in individuals with Alzheimer disease.

Transitional paleointensities from Kauai, Hawaii, and geomagnetic reversal models

USGS Publications Warehouse

Bogue, Scott W.; Coe, Robert S.

1984-01-01

Previously presented paleointensity results from an R-N transition zone in Kauai, Hawaii, show that field intensity dropped from 0. 431 Oe to 0. 101 Oe while the field remained within 30 degree of the reversed axial dipole direction. A recovery in intensity and the main directional change followed this presumably short period of low field strength. As the reversal neared completion, the field has an intensity of 0. 217 Oe while still 40 degree from the final direction. The relationship of paleointensity to field direction during the early part of the reversal thus differs from that toward the end, a feature that only some reversal models are consistent with. For example, a model in which a standing nondipole component persists through the dipole reversal predicts only symmetric intensity patterns. In contrast, zonal flooding models generate suitably complex field behavior if multiple flooding schemes operate during a single reversal or if the flooding process is itself asymmetric.

Added sugar intake and metabolic syndrome in US adolescents: cross-sectional analysis of the National Health and Nutrition Examination Survey 2005-2012.

PubMed

Rodríguez, Luis A; Madsen, Kristine A; Cotterman, Carolyn; Lustig, Robert H

2016-09-01

To examine the association between added sugar intake and metabolic syndrome among adolescents. Dietary, serum biomarker, anthropometric and physical activity data from the US National Health and Nutrition Examination Survey cycles between 2005 and 2012 were analysed using multivariate logistic regression models. Added sugar intake in grams per day was estimated from two 24 h standardized dietary recalls and then separated into quintiles from lowest to highest consumption. Multivariate logistic regression analyses were adjusted for physical activity, age, BMI Z-score and energy intake, and their interactions with race were included. Nationally representative sample, USA. US adolescents aged 12-19 years (n 1623). Added sugar was significantly associated with metabolic syndrome. The adjusted prevalence odds ratios for having metabolic syndrome comparing adolescents in the third, fourth and fifth quintiles v. those in the lowest quintile of added sugar were 5·3 (95 % CI 1·4, 20·6), 9·9 (95 % CI 1·9, 50·9) and 8·7 (95 % CI 1·4, 54·9), respectively. Our findings suggest that higher added sugar intake, independent of total energy intake, physical activity or BMI Z-score, is associated with increased prevalence of metabolic syndrome in US adolescents. Further studies are needed to determine if reducing intake of added sugar may help US adolescents prevent or reverse metabolic syndrome.

The Application of Collaborative Business Intelligence Technology in the Hospital SPD Logistics Management Model.

PubMed

Liu, Tongzhu; Shen, Aizong; Hu, Xiaojian; Tong, Guixian; Gu, Wei

2017-06-01

We aimed to apply collaborative business intelligence (BI) system to hospital supply, processing and distribution (SPD) logistics management model. We searched Engineering Village database, China National Knowledge Infrastructure (CNKI) and Google for articles (Published from 2011 to 2016), books, Web pages, etc., to understand SPD and BI related theories and recent research status. For the application of collaborative BI technology in the hospital SPD logistics management model, we realized this by leveraging data mining techniques to discover knowledge from complex data and collaborative techniques to improve the theories of business process. For the application of BI system, we: (i) proposed a layered structure of collaborative BI system for intelligent management in hospital logistics; (ii) built data warehouse for the collaborative BI system; (iii) improved data mining techniques such as supporting vector machines (SVM) and swarm intelligence firefly algorithm to solve key problems in hospital logistics collaborative BI system; (iv) researched the collaborative techniques oriented to data and business process optimization to improve the business processes of hospital logistics management. Proper combination of SPD model and BI system will improve the management of logistics in the hospitals. The successful implementation of the study requires: (i) to innovate and improve the traditional SPD model and make appropriate implement plans and schedules for the application of BI system according to the actual situations of hospitals; (ii) the collaborative participation of internal departments in hospital including the department of information, logistics, nursing, medical and financial; (iii) timely response of external suppliers.

An appraisal of convergence failures in the application of logistic regression model in published manuscripts.

PubMed

Yusuf, O B; Bamgboye, E A; Afolabi, R F; Shodimu, M A

2014-09-01

Logistic regression model is widely used in health research for description and predictive purposes. Unfortunately, most researchers are sometimes not aware that the underlying principles of the techniques have failed when the algorithm for maximum likelihood does not converge. Young researchers particularly postgraduate students may not know why separation problem whether quasi or complete occurs, how to identify it and how to fix it. This study was designed to critically evaluate convergence issues in articles that employed logistic regression analysis published in an African Journal of Medicine and medical sciences between 2004 and 2013. Problems of quasi or complete separation were described and were illustrated with the National Demographic and Health Survey dataset. A critical evaluation of articles that employed logistic regression was conducted. A total of 581 articles was reviewed, of which 40 (6.9%) used binary logistic regression. Twenty-four (60.0%) stated the use of logistic regression model in the methodology while none of the articles assessed model fit. Only 3 (12.5%) properly described the procedures. Of the 40 that used the logistic regression model, the problem of convergence occurred in 6 (15.0%) of the articles. Logistic regression tends to be poorly reported in studies published between 2004 and 2013. Our findings showed that the procedure may not be well understood by researchers since very few described the process in their reports and may be totally unaware of the problem of convergence or how to deal with it.

High stoma prevalence and stoma reversal complications following anterior resection for rectal cancer: a population-based multicentre study.

PubMed

Holmgren, K; Kverneng Hultberg, D; Haapamäki, M M; Matthiessen, P; Rutegård, J; Rutegård, M

2017-12-01

Fashioning a defunctioning stoma is common when performing an anterior resection for rectal cancer in order to avoid and mitigate the consequences of an anastomotic leakage. We investigated the permanent stoma prevalence, factors influencing stoma outcome and complication rates following stoma reversal surgery. Patients who had undergone an anterior resection for rectal cancer between 2007 and 2013 in the northern healthcare region were identified using the Swedish Colorectal Cancer Registry and were followed until the end of 2014 regarding stoma outcome. Data were retrieved by a review of medical records. Multiple logistic regression was used to evaluate predefined risk factors for stoma permanence. Risk factors for non-reversal of a defunctioning stoma were also analysed, using Cox proportional-hazards regression. A total of 316 patients who underwent anterior resection were included, of whom 274 (87%) were defunctioned primarily. At the end of the follow-up period 24% had a permanent stoma, and 9% of patients who underwent reversal of a stoma experienced major complications requiring a return to theatre, need for intensive care or mortality. Anastomotic leakage and tumour Stage IV were significant risk factors for stoma permanence. In this series, partial mesorectal excision correlated with a stoma-free outcome. Non-reversal was considerably more prevalent among patients with leakage and Stage IV; Stage III patients at first had a decreased reversal rate, which increased after the initial year of surgery. Stoma permanence is common after anterior resection, while anastomotic leakage and advanced tumour stage decrease the chances of a stoma-free outcome. Stoma reversal surgery entails a significant risk of major complications. Colorectal Disease © 2017 The Association of Coloproctology of Great Britain and Ireland.

A development of logistics management models for the Space Transportation System

NASA Technical Reports Server (NTRS)

Carrillo, M. J.; Jacobsen, S. E.; Abell, J. B.; Lippiatt, T. F.

1983-01-01

A new analytic queueing approach was described which relates stockage levels, repair level decisions, and the project network schedule of prelaunch operations directly to the probability distribution of the space transportation system launch delay. Finite source population and limited repair capability were additional factors included in this logistics management model developed specifically for STS maintenance requirements. Data presently available to support logistics decisions were based on a comparability study of heavy aircraft components. A two-phase program is recommended by which NASA would implement an integrated data collection system, assemble logistics data from previous STS flights, revise extant logistics planning and resource requirement parameters using Bayes-Lin techniques, and adjust for uncertainty surrounding logistics systems performance parameters. The implementation of these recommendations can be expected to deliver more cost-effective logistics support.

Sperm harvesting and cryopreservation during vasectomy reversal is not cost effective.

PubMed

Boyle, Karen E; Thomas, Anthony J; Marmar, Joel L; Hirshberg, Steven; Belker, Arnold M; Jarow, Jonathan P

2006-04-01

To determine whether sperm harvesting and cryopreservation at the time of vasectomy reversal is cost-effective. Model of actual costs and results at five institutions. Multicenter study comprising five centers, including university hospitals and private practices. Men undergoing vasectomy reversal. We established two models for vasectomy reversal. The first model was sperm harvesting and cryopreservation at the time of vasectomy reversal. The second model was sperm harvesting at the time of IVF only if the patient remained azoospermic after vasectomy reversal. Vasectomy reversal procedures modeled included bilateral vasovasostomy and bilateral epididymovasostomy. The costs for each procedure at the five institutions were collated and median costs determined. Median cost of procedure and calculated financial comparisons. The median cost of testicular sperm extraction/cryopreservation performed at the time of bilateral vasovasostomy was $1,765 (range, $1,025-$2,800). The median cost of microsurgical epididymal sperm aspiration or testicular sperm extraction with cryopreservation performed at the time of epididymovasostomy was $1,209 (range, $905-$2,488). The average of the median costs for percutaneous sperm aspiration or testicular sperm aspiration for those patients with a failed vasectomy reversal was $725 (range, $400-$1,455). Sperm retrieval with cryopreservation at the time of vasectomy reversal is not a cost-effective management strategy.

4D-Fingerprint Categorical QSAR Models for Skin Sensitization Based on Classification Local Lymph Node Assay Measures

PubMed Central

Li, Yi; Tseng, Yufeng J.; Pan, Dahua; Liu, Jianzhong; Kern, Petra S.; Gerberick, G. Frank; Hopfinger, Anton J.

2008-01-01

Currently, the only validated methods to identify skin sensitization effects are in vivo models, such as the Local Lymph Node Assay (LLNA) and guinea pig studies. There is a tremendous need, in particular due to novel legislation, to develop animal alternatives, eg. Quantitative Structure-Activity Relationship (QSAR) models. Here, QSAR models for skin sensitization using LLNA data have been constructed. The descriptors used to generate these models are derived from the 4D-molecular similarity paradigm and are referred to as universal 4D-fingerprints. A training set of 132 structurally diverse compounds and a test set of 15 structurally diverse compounds were used in this study. The statistical methodologies used to build the models are logistic regression (LR), and partial least square coupled logistic regression (PLS-LR), which prove to be effective tools for studying skin sensitization measures expressed in the two categorical terms of sensitizer and non-sensitizer. QSAR models with low values of the Hosmer-Lemeshow goodness-of-fit statistic, χHL2, are significant and predictive. For the training set, the cross-validated prediction accuracy of the logistic regression models ranges from 77.3% to 78.0%, while that of PLS-logistic regression models ranges from 87.1% to 89.4%. For the test set, the prediction accuracy of logistic regression models ranges from 80.0%-86.7%, while that of PLS-logistic regression models ranges from 73.3%-80.0%. The QSAR models are made up of 4D-fingerprints related to aromatic atoms, hydrogen bond acceptors and negatively partially charged atoms. PMID:17226934

A multimodal logistics service network design with time windows and environmental concerns

PubMed Central

Zhang, Dezhi; He, Runzhong; Wang, Zhongwei

2017-01-01

The design of a multimodal logistics service network with customer service time windows and environmental costs is an important and challenging issue. Accordingly, this work established a model to minimize the total cost of multimodal logistics service network design with time windows and environmental concerns. The proposed model incorporates CO2 emission costs to determine the optimal transportation mode combinations and investment selections for transfer nodes, which consider transport cost, transport time, carbon emission, and logistics service time window constraints. Furthermore, genetic and heuristic algorithms are proposed to set up the abovementioned optimal model. A numerical example is provided to validate the model and the abovementioned two algorithms. Then, comparisons of the performance of the two algorithms are provided. Finally, this work investigates the effects of the logistics service time windows and CO2 emission taxes on the optimal solution. Several important management insights are obtained. PMID:28934272

A multimodal logistics service network design with time windows and environmental concerns.

PubMed

Zhang, Dezhi; He, Runzhong; Li, Shuangyan; Wang, Zhongwei

2017-01-01

The design of a multimodal logistics service network with customer service time windows and environmental costs is an important and challenging issue. Accordingly, this work established a model to minimize the total cost of multimodal logistics service network design with time windows and environmental concerns. The proposed model incorporates CO2 emission costs to determine the optimal transportation mode combinations and investment selections for transfer nodes, which consider transport cost, transport time, carbon emission, and logistics service time window constraints. Furthermore, genetic and heuristic algorithms are proposed to set up the abovementioned optimal model. A numerical example is provided to validate the model and the abovementioned two algorithms. Then, comparisons of the performance of the two algorithms are provided. Finally, this work investigates the effects of the logistics service time windows and CO2 emission taxes on the optimal solution. Several important management insights are obtained.

Differentially private distributed logistic regression using private and public data

PubMed Central

2014-01-01

Background Privacy protecting is an important issue in medical informatics and differential privacy is a state-of-the-art framework for data privacy research. Differential privacy offers provable privacy against attackers who have auxiliary information, and can be applied to data mining models (for example, logistic regression). However, differentially private methods sometimes introduce too much noise and make outputs less useful. Given available public data in medical research (e.g. from patients who sign open-consent agreements), we can design algorithms that use both public and private data sets to decrease the amount of noise that is introduced. Methodology In this paper, we modify the update step in Newton-Raphson method to propose a differentially private distributed logistic regression model based on both public and private data. Experiments and results We try our algorithm on three different data sets, and show its advantage over: (1) a logistic regression model based solely on public data, and (2) a differentially private distributed logistic regression model based on private data under various scenarios. Conclusion Logistic regression models built with our new algorithm based on both private and public datasets demonstrate better utility than models that trained on private or public datasets alone without sacrificing the rigorous privacy guarantee. PMID:25079786

Estimating Contraceptive Prevalence Using Logistics Data for Short-Acting Methods: Analysis Across 30 Countries.

PubMed

Cunningham, Marc; Bock, Ariella; Brown, Niquelle; Sacher, Suzy; Hatch, Benjamin; Inglis, Andrew; Aronovich, Dana

2015-09-01

Contraceptive prevalence rate (CPR) is a vital indicator used by country governments, international donors, and other stakeholders for measuring progress in family planning programs against country targets and global initiatives as well as for estimating health outcomes. Because of the need for more frequent CPR estimates than population-based surveys currently provide, alternative approaches for estimating CPRs are being explored, including using contraceptive logistics data. Using data from the Demographic and Health Surveys (DHS) in 30 countries, population data from the United States Census Bureau International Database, and logistics data from the Procurement Planning and Monitoring Report (PPMR) and the Pipeline Monitoring and Procurement Planning System (PipeLine), we developed and evaluated 3 models to generate country-level, public-sector contraceptive prevalence estimates for injectable contraceptives, oral contraceptives, and male condoms. Models included: direct estimation through existing couple-years of protection (CYP) conversion factors, bivariate linear regression, and multivariate linear regression. Model evaluation consisted of comparing the referent DHS prevalence rates for each short-acting method with the model-generated prevalence rate using multiple metrics, including mean absolute error and proportion of countries where the modeled prevalence rate for each method was within 1, 2, or 5 percentage points of the DHS referent value. For the methods studied, family planning use estimates from public-sector logistics data were correlated with those from the DHS, validating the quality and accuracy of current public-sector logistics data. Logistics data for oral and injectable contraceptives were significantly associated (P<.05) with the referent DHS values for both bivariate and multivariate models. For condoms, however, that association was only significant for the bivariate model. With the exception of the CYP-based model for condoms, models were able to estimate public-sector prevalence rates for each short-acting method to within 2 percentage points in at least 85% of countries. Public-sector contraceptive logistics data are strongly correlated with public-sector prevalence rates for short-acting methods, demonstrating the quality of current logistics data and their ability to provide relatively accurate prevalence estimates. The models provide a starting point for generating interim estimates of contraceptive use when timely survey data are unavailable. All models except the condoms CYP model performed well; the regression models were most accurate but the CYP model offers the simplest calculation method. Future work extending the research to other modern methods, relating subnational logistics data with prevalence rates, and tracking that relationship over time is needed. © Cunningham et al.

Estimating Contraceptive Prevalence Using Logistics Data for Short-Acting Methods: Analysis Across 30 Countries

PubMed Central

Cunningham, Marc; Brown, Niquelle; Sacher, Suzy; Hatch, Benjamin; Inglis, Andrew; Aronovich, Dana

2015-01-01

Background: Contraceptive prevalence rate (CPR) is a vital indicator used by country governments, international donors, and other stakeholders for measuring progress in family planning programs against country targets and global initiatives as well as for estimating health outcomes. Because of the need for more frequent CPR estimates than population-based surveys currently provide, alternative approaches for estimating CPRs are being explored, including using contraceptive logistics data. Methods: Using data from the Demographic and Health Surveys (DHS) in 30 countries, population data from the United States Census Bureau International Database, and logistics data from the Procurement Planning and Monitoring Report (PPMR) and the Pipeline Monitoring and Procurement Planning System (PipeLine), we developed and evaluated 3 models to generate country-level, public-sector contraceptive prevalence estimates for injectable contraceptives, oral contraceptives, and male condoms. Models included: direct estimation through existing couple-years of protection (CYP) conversion factors, bivariate linear regression, and multivariate linear regression. Model evaluation consisted of comparing the referent DHS prevalence rates for each short-acting method with the model-generated prevalence rate using multiple metrics, including mean absolute error and proportion of countries where the modeled prevalence rate for each method was within 1, 2, or 5 percentage points of the DHS referent value. Results: For the methods studied, family planning use estimates from public-sector logistics data were correlated with those from the DHS, validating the quality and accuracy of current public-sector logistics data. Logistics data for oral and injectable contraceptives were significantly associated (P<.05) with the referent DHS values for both bivariate and multivariate models. For condoms, however, that association was only significant for the bivariate model. With the exception of the CYP-based model for condoms, models were able to estimate public-sector prevalence rates for each short-acting method to within 2 percentage points in at least 85% of countries. Conclusions: Public-sector contraceptive logistics data are strongly correlated with public-sector prevalence rates for short-acting methods, demonstrating the quality of current logistics data and their ability to provide relatively accurate prevalence estimates. The models provide a starting point for generating interim estimates of contraceptive use when timely survey data are unavailable. All models except the condoms CYP model performed well; the regression models were most accurate but the CYP model offers the simplest calculation method. Future work extending the research to other modern methods, relating subnational logistics data with prevalence rates, and tracking that relationship over time is needed. PMID:26374805

Experience of low-dose aminophylline use to relieve minor adverse effects of dipyridamole in patients undergoing stress myocardial perfusion imaging.

PubMed

Lin, Li-Fan; Cheng, Cheng-Yi; Hou, Cheng-Han; Ku, Chih-Hung; Tseng, Neng-Chuan; Shen, Daniel H Y

2014-06-01

Intravenous administration of aminophylline is widely adopted to reverse dipyridamole-related adverse effects (AEs) during stress myocardial perfusion imaging (MPI). The study aimed to investigate the efficacy of lower-dose aminophylline to relieve minor AEs. 2,250 consecutive patients undergoing dipyridamole-stressed MPI were enrolled. Information concerning AE occurrence and dosages of aminophylline was collected to evaluate the efficacy of lower-dose aminophylline. A logistic regression was used to determine independent predictors of dipyridamole-related AE occurrence. No severe AE was noted. Overall mild AE incidence was 37.0% (833/2,250 patients). Initial low-dose (25 mg) aminophylline relieved symptoms in 98.8% of patients with mild AEs (823/833 patients). An extra 25 mg aminophylline sufficed to reverse all such AEs. Mean body mass index (BMI) differed significantly between patients with and without any AE [25.6 vs 25.1 (P = .009)]. There was no significant difference between two subgroups in mean age, male gender prevalence, body height and weight, dipyridamole dose/BMI, or prevalence of significant perfusion defect(s) on MPI. Multivariable logistic regression demonstrated BMI remained the independent predictor of dipyridamole-related AE occurrence (odds ratio 1.028, 95% confidence interval 1.007-1.049, P = .01). Low-dose (≦50 mg, and usually 25 mg) aminophylline seems sufficient to relieve mild dipyridamole-related AEs during stress MPI.

On the applicability of low-dimensional models for convective flow reversals at extreme Prandtl numbers

NASA Astrophysics Data System (ADS)

Mannattil, Manu; Pandey, Ambrish; Verma, Mahendra K.; Chakraborty, Sagar

2017-12-01

Constructing simpler models, either stochastic or deterministic, for exploring the phenomenon of flow reversals in fluid systems is in vogue across disciplines. Using direct numerical simulations and nonlinear time series analysis, we illustrate that the basic nature of flow reversals in convecting fluids can depend on the dimensionless parameters describing the system. Specifically, we find evidence of low-dimensional behavior in flow reversals occurring at zero Prandtl number, whereas we fail to find such signatures for reversals at infinite Prandtl number. Thus, even in a single system, as one varies the system parameters, one can encounter reversals that are fundamentally different in nature. Consequently, we conclude that a single general low-dimensional deterministic model cannot faithfully characterize flow reversals for every set of parameter values.

Use and interpretation of logistic regression in habitat-selection studies

USGS Publications Warehouse

Keating, Kim A.; Cherry, Steve

2004-01-01

 Logistic regression is an important tool for wildlife habitat-selection studies, but the method frequently has been misapplied due to an inadequate understanding of the logistic model, its interpretation, and the influence of sampling design. To promote better use of this method, we review its application and interpretation under 3 sampling designs: random, case-control, and use-availability. Logistic regression is appropriate for habitat use-nonuse studies employing random sampling and can be used to directly model the conditional probability of use in such cases. Logistic regression also is appropriate for studies employing case-control sampling designs, but careful attention is required to interpret results correctly. Unless bias can be estimated or probability of use is small for all habitats, results of case-control studies should be interpreted as odds ratios, rather than probability of use or relative probability of use. When data are gathered under a use-availability design, logistic regression can be used to estimate approximate odds ratios if probability of use is small, at least on average. More generally, however, logistic regression is inappropriate for modeling habitat selection in use-availability studies. In particular, using logistic regression to fit the exponential model of Manly et al. (2002:100) does not guarantee maximum-likelihood estimates, valid probabilities, or valid likelihoods. We show that the resource selection function (RSF) commonly used for the exponential model is proportional to a logistic discriminant function. Thus, it may be used to rank habitats with respect to probability of use and to identify important habitat characteristics or their surrogates, but it is not guaranteed to be proportional to probability of use. Other problems associated with the exponential model also are discussed. We describe an alternative model based on Lancaster and Imbens (1996) that offers a method for estimating conditional probability of use in use-availability studies. Although promising, this model fails to converge to a unique solution in some important situations. Further work is needed to obtain a robust method that is broadly applicable to use-availability studies.

Logistic models--an odd(s) kind of regression.

PubMed

Jupiter, Daniel C

2013-01-01

The logistic regression model bears some similarity to the multivariable linear regression with which we are familiar. However, the differences are great enough to warrant a discussion of the need for and interpretation of logistic regression. Copyright © 2013 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

Modelling of binary logistic regression for obesity among secondary students in a rural area of Kedah

NASA Astrophysics Data System (ADS)

Kamaruddin, Ainur Amira; Ali, Zalila; Noor, Norlida Mohd.; Baharum, Adam; Ahmad, Wan Muhamad Amir W.

2014-07-01

Logistic regression analysis examines the influence of various factors on a dichotomous outcome by estimating the probability of the event's occurrence. Logistic regression, also called a logit model, is a statistical procedure used to model dichotomous outcomes. In the logit model the log odds of the dichotomous outcome is modeled as a linear combination of the predictor variables. The log odds ratio in logistic regression provides a description of the probabilistic relationship of the variables and the outcome. In conducting logistic regression, selection procedures are used in selecting important predictor variables, diagnostics are used to check that assumptions are valid which include independence of errors, linearity in the logit for continuous variables, absence of multicollinearity, and lack of strongly influential outliers and a test statistic is calculated to determine the aptness of the model. This study used the binary logistic regression model to investigate overweight and obesity among rural secondary school students on the basis of their demographics profile, medical history, diet and lifestyle. The results indicate that overweight and obesity of students are influenced by obesity in family and the interaction between a student's ethnicity and routine meals intake. The odds of a student being overweight and obese are higher for a student having a family history of obesity and for a non-Malay student who frequently takes routine meals as compared to a Malay student.

Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia

NASA Astrophysics Data System (ADS)

Pradhan, Biswajeet

2010-05-01

This paper presents the results of the cross-validation of a multivariate logistic regression model using remote sensing data and GIS for landslide hazard analysis on the Penang, Cameron, and Selangor areas in Malaysia. Landslide locations in the study areas were identified by interpreting aerial photographs and satellite images, supported by field surveys. SPOT 5 and Landsat TM satellite imagery were used to map landcover and vegetation index, respectively. Maps of topography, soil type, lineaments and land cover were constructed from the spatial datasets. Ten factors which influence landslide occurrence, i.e., slope, aspect, curvature, distance from drainage, lithology, distance from lineaments, soil type, landcover, rainfall precipitation, and normalized difference vegetation index (ndvi), were extracted from the spatial database and the logistic regression coefficient of each factor was computed. Then the landslide hazard was analysed using the multivariate logistic regression coefficients derived not only from the data for the respective area but also using the logistic regression coefficients calculated from each of the other two areas (nine hazard maps in all) as a cross-validation of the model. For verification of the model, the results of the analyses were then compared with the field-verified landslide locations. Among the three cases of the application of logistic regression coefficient in the same study area, the case of Selangor based on the Selangor logistic regression coefficients showed the highest accuracy (94%), where as Penang based on the Penang coefficients showed the lowest accuracy (86%). Similarly, among the six cases from the cross application of logistic regression coefficient in other two areas, the case of Selangor based on logistic coefficient of Cameron showed highest (90%) prediction accuracy where as the case of Penang based on the Selangor logistic regression coefficients showed the lowest accuracy (79%). Qualitatively, the cross application model yields reasonable results which can be used for preliminary landslide hazard mapping.

Wind tunnel test of model target thrust reversers for the Pratt and Whitney aircraft JT8D-100 series engines installed on a 727-200 airplane

NASA Technical Reports Server (NTRS)

Hambly, D.

1974-01-01

The results of a low speed wind tunnel test of 0.046 scale model target thrust reversers installed on a 727-200 model airplane are presented. The full airplane model was mounted on a force balance, except for the nacelles and thrust reversers, which were independently mounted and isolated from it. The installation had the capability of simulating the inlet airflows and of supplying the correct proportions of primary and secondary air to the nozzles. The objectives of the test were to assess the compatibility of the thrust reversers target door design with the engine and airplane. The following measurements were made: hot gas ingestion at the nacelle inlets; model lift, drag, and pitching moment; hot gas impingement on the airplane structure; and qualitative assessment of the rudder effectiveness. The major parameters controlling hot gas ingestion were found to be thrust reverser orientation, engine power setting, and the lip height of the bottom thrust reverser doors on the side nacelles. The thrust reversers tended to increase the model lift, decrease the drag, and decrease the pitching moment.

Applying Simulation and Logistics Modeling to Transportation Issues

DOT National Transportation Integrated Search

1995-08-15

This paper describes an application where transportation logistics and simulation tools are integrated to create a modeling environment for transportation planning. The Transportation Planning Model (TPM) is a tool developed for the Department of Ene...

The Application of Collaborative Business Intelligence Technology in the Hospital SPD Logistics Management Model

PubMed Central

LIU, Tongzhu; SHEN, Aizong; HU, Xiaojian; TONG, Guixian; GU, Wei

2017-01-01

Background: We aimed to apply collaborative business intelligence (BI) system to hospital supply, processing and distribution (SPD) logistics management model. Methods: We searched Engineering Village database, China National Knowledge Infrastructure (CNKI) and Google for articles (Published from 2011 to 2016), books, Web pages, etc., to understand SPD and BI related theories and recent research status. For the application of collaborative BI technology in the hospital SPD logistics management model, we realized this by leveraging data mining techniques to discover knowledge from complex data and collaborative techniques to improve the theories of business process. Results: For the application of BI system, we: (i) proposed a layered structure of collaborative BI system for intelligent management in hospital logistics; (ii) built data warehouse for the collaborative BI system; (iii) improved data mining techniques such as supporting vector machines (SVM) and swarm intelligence firefly algorithm to solve key problems in hospital logistics collaborative BI system; (iv) researched the collaborative techniques oriented to data and business process optimization to improve the business processes of hospital logistics management. Conclusion: Proper combination of SPD model and BI system will improve the management of logistics in the hospitals. The successful implementation of the study requires: (i) to innovate and improve the traditional SPD model and make appropriate implement plans and schedules for the application of BI system according to the actual situations of hospitals; (ii) the collaborative participation of internal departments in hospital including the department of information, logistics, nursing, medical and financial; (iii) timely response of external suppliers. PMID:28828316

Modeling logistic performance in quantitative microbial risk assessment.

PubMed

Rijgersberg, Hajo; Tromp, Seth; Jacxsens, Liesbeth; Uyttendaele, Mieke

2010-01-01

In quantitative microbial risk assessment (QMRA), food safety in the food chain is modeled and simulated. In general, prevalences, concentrations, and numbers of microorganisms in media are investigated in the different steps from farm to fork. The underlying rates and conditions (such as storage times, temperatures, gas conditions, and their distributions) are determined. However, the logistic chain with its queues (storages, shelves) and mechanisms for ordering products is usually not taken into account. As a consequence, storage times-mutually dependent in successive steps in the chain-cannot be described adequately. This may have a great impact on the tails of risk distributions. Because food safety risks are generally very small, it is crucial to model the tails of (underlying) distributions as accurately as possible. Logistic performance can be modeled by describing the underlying planning and scheduling mechanisms in discrete-event modeling. This is common practice in operations research, specifically in supply chain management. In this article, we present the application of discrete-event modeling in the context of a QMRA for Listeria monocytogenes in fresh-cut iceberg lettuce. We show the potential value of discrete-event modeling in QMRA by calculating logistic interventions (modifications in the logistic chain) and determining their significance with respect to food safety.

Conditional Poisson models: a flexible alternative to conditional logistic case cross-over analysis.

PubMed

Armstrong, Ben G; Gasparrini, Antonio; Tobias, Aurelio

2014-11-24

The time stratified case cross-over approach is a popular alternative to conventional time series regression for analysing associations between time series of environmental exposures (air pollution, weather) and counts of health outcomes. These are almost always analyzed using conditional logistic regression on data expanded to case-control (case crossover) format, but this has some limitations. In particular adjusting for overdispersion and auto-correlation in the counts is not possible. It has been established that a Poisson model for counts with stratum indicators gives identical estimates to those from conditional logistic regression and does not have these limitations, but it is little used, probably because of the overheads in estimating many stratum parameters. The conditional Poisson model avoids estimating stratum parameters by conditioning on the total event count in each stratum, thus simplifying the computing and increasing the number of strata for which fitting is feasible compared with the standard unconditional Poisson model. Unlike the conditional logistic model, the conditional Poisson model does not require expanding the data, and can adjust for overdispersion and auto-correlation. It is available in Stata, R, and other packages. By applying to some real data and using simulations, we demonstrate that conditional Poisson models were simpler to code and shorter to run than are conditional logistic analyses and can be fitted to larger data sets than possible with standard Poisson models. Allowing for overdispersion or autocorrelation was possible with the conditional Poisson model but when not required this model gave identical estimates to those from conditional logistic regression. Conditional Poisson regression models provide an alternative to case crossover analysis of stratified time series data with some advantages. The conditional Poisson model can also be used in other contexts in which primary control for confounding is by fine stratification.

Modeling Population Growth and Extinction

ERIC Educational Resources Information Center

Gordon, Sheldon P.

2009-01-01

The exponential growth model and the logistic model typically introduced in the mathematics curriculum presume that a population grows exclusively. In reality, species can also die out and more sophisticated models that take the possibility of extinction into account are needed. In this article, two extensions of the logistic model are considered,…

The association between maternal antioxidant levels in midpregnancy and preeclampsia.

PubMed

Cohen, Jacqueline M; Kramer, Michael S; Platt, Robert W; Basso, Olga; Evans, Rhobert W; Kahn, Susan R

2015-11-01

We sought to determine whether midpregnancy antioxidant levels are associated with preeclampsia, overall and by timing of onset. We carried out a case-control study, nested within a cohort of 5337 pregnant women in Montreal, Quebec, Canada. Blood samples obtained at 24-26 weeks were assayed for nonenzymatic antioxidant levels among cases of preeclampsia (n = 111) and unaffected controls (n = 441). We excluded women diagnosed with gestational hypertension only. We used logistic regression with the z-score of each antioxidant level as the main predictor variable for preeclampsia risk. We further stratified early-onset (<34 weeks) and late-onset preeclampsia and carried out multinomial logistic regression. Finally, we assessed associations between antioxidant biomarkers and timing of onset (in weeks) by Cox regression, with appropriate selection weights. We summed levels of correlated biomarkers (r(2) > 0.3) and log-transformed positively skewed distributions. We adjusted for body mass index, nulliparity, preexisting diabetes, hypertension, smoking, and proxies for ethnicity and socioeconomic status. The odds ratios for α-tocopherol, α-tocopherol:cholesterol, lycopene, lutein, and carotenoids (sum of α-carotene, β-carotene, anhydrolutein, α-cryptoxanthin, and β-cryptoxanthin) suggested an inverse association between antioxidant levels and overall preeclampsia risk; however, only lutein was significantly associated with overall preeclampsia in adjusted models (odds ratio, 0.60; 95% confidence interval, 0.46-0.77) per SD. In multinomial logistic models, the relative risk ratio (RRR) estimates for the early-onset subgroup were farther from the null than those for the late-onset subgroup. The ratio of α-tocopherol to cholesterol and retinol were significantly associated with early- but not late-onset preeclampsia: RRRs (95% confidence intervals) for early-onset preeclampsia 0.67 (0.46-0.99) and 1.61 (1.12-2.33), respectively. Lutein was significantly associated with both early- and late-onset subtypes in adjusted models; RRRs 0.53 (0.35-0.80) and 0.62 (0.47-0.82), respectively. Survival analyses confirmed these trends. Most antioxidants were more strongly associated with early-onset preeclampsia, suggesting that oxidative stress may play a greater role in the pathophysiology of early-onset preeclampsia. Alternatively, reverse causality may explain this pattern. Lutein was associated with both early- and late-onset preeclampsia and may be a promising nutrient to consider in preeclampsia prevention trials, if this finding is corroborated. Copyright © 2015 Elsevier Inc. All rights reserved.

Optimization of Location-Routing Problem for Cold Chain Logistics Considering Carbon Footprint.

PubMed

Wang, Songyi; Tao, Fengming; Shi, Yuhe

2018-01-06

In order to solve the optimization problem of logistics distribution system for fresh food, this paper provides a low-carbon and environmental protection point of view, based on the characteristics of perishable products, and combines with the overall optimization idea of cold chain logistics distribution network, where the green and low-carbon location-routing problem (LRP) model in cold chain logistics is developed with the minimum total costs as the objective function, which includes carbon emission costs. A hybrid genetic algorithm with heuristic rules is designed to solve the model, and an example is used to verify the effectiveness of the algorithm. Furthermore, the simulation results obtained by a practical numerical example show the applicability of the model while provide green and environmentally friendly location-distribution schemes for the cold chain logistics enterprise. Finally, carbon tax policies are introduced to analyze the impact of carbon tax on the total costs and carbon emissions, which proves that carbon tax policy can effectively reduce carbon dioxide emissions in cold chain logistics network.

Serum microRNAs as biomarkers for recurrence in melanoma

PubMed Central

2012-01-01

Background Identification of melanoma patients at high risk for recurrence and monitoring for recurrence are critical for informed management decisions. We hypothesized that serum microRNAs (miRNAs) could provide prognostic information at the time of diagnosis unaccounted for by the current staging system and could be useful in detecting recurrence after resection. Methods We screened 355 miRNAs in sera from 80 melanoma patients at primary diagnosis (discovery cohort) using a unique quantitative reverse transcription-PCR (qRT-PCR) panel. Cox proportional hazard models and Kaplan-Meier recurrence-free survival (RFS) curves were used to identify a miRNA signature with prognostic potential adjusting for stage. We then tested the miRNA signature in an independent cohort of 50 primary melanoma patients (validation cohort). Logistic regression analysis was performed to determine if the miRNA signature can determine risk of recurrence in both cohorts. Selected miRNAs were measured longitudinally in subsets of patients pre-/post-operatively and pre-/post-recurrence. Results A signature of 5 miRNAs successfully classified melanoma patients into high and low recurrence risk groups with significant separation of RFS in both discovery and validation cohorts (p = 0.0036, p = 0.0093, respectively). Significant separation of RFS was maintained when a logistic model containing the same signature set was used to predict recurrence risk in both discovery and validation cohorts (p < 0.0001, p = 0.033, respectively). Longitudinal expression of 4 miRNAs in a subset of patients was dynamic, suggesting miRNAs can be associated with tumor burden. Conclusion Our data demonstrate that serum miRNAs can improve accuracy in identifying primary melanoma patients with high recurrence risk and in monitoring melanoma tumor burden over time. PMID:22857597

LGALS4, CEACAM6, TSPAN8, and COL1A2: Blood Markers for Colorectal Cancer-Validation in a Cohort of Subjects With Positive Fecal Immunochemical Test Result.

PubMed

Rodia, Maria Teresa; Solmi, Rossella; Pasini, Francesco; Nardi, Elena; Mattei, Gabriella; Ugolini, Giampaolo; Ricciardiello, Luigi; Strippoli, Pierluigi; Miglio, Rossella; Lauriola, Mattia

2018-06-01

A noninvasive blood test for the early detection of colorectal cancer (CRC) is highly required. We evaluated a panel of 4 mRNAs as putative markers of CRC. We tested LGALS4, CEACAM6, TSPAN8, and COL1A2, referred to as the CELTiC panel, using quantitative reverse transcription polymerase chain reaction, on subjects with positive fecal immunochemical test (FIT) results and undergoing colonoscopy. Using a nonparametric test and multinomial logistic model, FIT-positive subjects were compared with CRC patients and healthy individuals. All the genes of the CELTiC panel displayed statistically significant differences between the healthy subjects (n = 67), both low-risk (n = 36) and high-risk/CRC (n = 92) subjects, and those in the negative-colonoscopy, FIT-positive group (n = 36). The multinomial logistic model revealed LGALS4 was the most powerful marker discriminating the 4 groups. When assessing the diagnostic values by analysis of the areas under the receiver operating characteristic curves (AUCs), the CELTiC panel reached an AUC of 0.91 (sensitivity, 79%; specificity, 94%) comparing normal subjects to low-risk subjects, and 0.88 (sensitivity, 75%; specificity, 87%) comparing normal and high-risk/CRC subjects. The comparison between the normal subjects and the negative-colonoscopy, FIT-positive group revealed an AUC of 0.93 (sensitivity, 82%; specificity, 97%). The CELTiC panel could represent a useful tool for discriminating subjects with positive FIT findings and for the early detection of precancerous adenomatous lesions and CRC. Copyright © 2017 Elsevier Inc. All rights reserved.

Models of health behaviour predict intention to use long-acting reversible contraception

PubMed Central

Roderique-Davies, Gareth; McKnight, Christine; John, Bev; Faulkner, Susan; Lancastle, Deborah

2016-01-01

The aim of this study was to investigate women’s intention to use long-acting reversible contraception using two established models of health behaviour: the theory of planned behaviour and the health belief model. A questionnaire was completed by a convenience sample of 128 women attending a community sexual health clinic. The independent variables were constructs of theory of planned behaviour (attitude, subjective norm and perceived behavioural control) and health belief model (perceived susceptibility, perceived severity, perceived benefits, perceived barriers, health motivation and cues to action). The dependent variable was intention to use long-acting reversible contraception. The theory of planned behaviour and the health belief model accounted for 75% of the variance in intention to use. Perceived behavioural control, perceived barriers and health motivation predict the use of long-acting reversible contraception. Public health information for women considering using long-acting reversible contraception should be based around addressing the perceived barriers and promoting long-acting reversible contraception as a reliable contraceptive method. PMID:27864572

Transport spatial model for the definition of green routes for city logistics centers

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

Pamučar, Dragan, E-mail: dpamucar@gmail.com; Gigović, Ljubomir, E-mail: gigoviclj@gmail.com; Ćirović, Goran, E-mail: cirovic@sezampro.rs

This paper presents a transport spatial decision support model (TSDSM) for carrying out the optimization of green routes for city logistics centers. The TSDSM model is based on the integration of the multi-criteria method of Weighted Linear Combination (WLC) and the modified Dijkstra algorithm within a geographic information system (GIS). The GIS is used for processing spatial data. The proposed model makes it possible to plan routes for green vehicles and maximize the positive effects on the environment, which can be seen in the reduction of harmful gas emissions and an increase in the air quality in highly populated areas.more » The scheduling of delivery vehicles is given as a problem of optimization in terms of the parameters of: the environment, health, use of space and logistics operating costs. Each of these input parameters was thoroughly examined and broken down in the GIS into criteria which further describe them. The model presented here takes into account the fact that logistics operators have a limited number of environmentally friendly (green) vehicles available. The TSDSM was tested on a network of roads with 127 links for the delivery of goods from the city logistics center to the user. The model supports any number of available environmentally friendly or environmentally unfriendly vehicles consistent with the size of the network and the transportation requirements. - Highlights: • Model for routing light delivery vehicles in urban areas. • Optimization of green routes for city logistics centers. • The proposed model maximizes the positive effects on the environment. • The model was tested on a real network.« less

[Logistic regression model of noninvasive prediction for portal hypertensive gastropathy in patients with hepatitis B associated cirrhosis].

PubMed

Wang, Qingliang; Li, Xiaojie; Hu, Kunpeng; Zhao, Kun; Yang, Peisheng; Liu, Bo

2015-05-12

To explore the risk factors of portal hypertensive gastropathy (PHG) in patients with hepatitis B associated cirrhosis and establish a Logistic regression model of noninvasive prediction. The clinical data of 234 hospitalized patients with hepatitis B associated cirrhosis from March 2012 to March 2014 were analyzed retrospectively. The dependent variable was the occurrence of PHG while the independent variables were screened by binary Logistic analysis. Multivariate Logistic regression was used for further analysis of significant noninvasive independent variables. Logistic regression model was established and odds ratio was calculated for each factor. The accuracy, sensitivity and specificity of model were evaluated by the curve of receiver operating characteristic (ROC). According to univariate Logistic regression, the risk factors included hepatic dysfunction, albumin (ALB), bilirubin (TB), prothrombin time (PT), platelet (PLT), white blood cell (WBC), portal vein diameter, spleen index, splenic vein diameter, diameter ratio, PLT to spleen volume ratio, esophageal varices (EV) and gastric varices (GV). Multivariate analysis showed that hepatic dysfunction (X1), TB (X2), PLT (X3) and splenic vein diameter (X4) were the major occurring factors for PHG. The established regression model was Logit P=-2.667+2.186X1-2.167X2+0.725X3+0.976X4. The accuracy of model for PHG was 79.1% with a sensitivity of 77.2% and a specificity of 80.8%. Hepatic dysfunction, TB, PLT and splenic vein diameter are risk factors for PHG and the noninvasive predicted Logistic regression model was Logit P=-2.667+2.186X1-2.167X2+0.725X3+0.976X4.

Humanitarian response: improving logistics to save lives.

PubMed

McCoy, Jessica

2008-01-01

Each year, millions of people worldwide are affected by disasters, underscoring the importance of effective relief efforts. Many highly visible disaster responses have been inefficient and ineffective. Humanitarian agencies typically play a key role in disaster response (eg, procuring and distributing relief items to an affected population, assisting with evacuation, providing healthcare, assisting in the development of long-term shelter), and thus their efficiency is critical for a successful disaster response. The field of disaster and emergency response modeling is well established, but the application of such techniques to humanitarian logistics is relatively recent. This article surveys models of humanitarian response logistics and identifies promising opportunities for future work. Existing models analyze a variety of preparation and response decisions (eg, warehouse location and the distribution of relief supplies), consider both natural and manmade disasters, and typically seek to minimize cost or unmet demand. Opportunities to enhance the logistics of humanitarian response include the adaptation of models developed for general disaster response; the use of existing models, techniques, and insights from the literature on commercial supply chain management; the development of working partnerships between humanitarian aid organizations and private companies with expertise in logistics; and the consideration of behavioral factors relevant to a response. Implementable, realistic models that support the logistics of humanitarian relief can improve the preparation for and the response to disasters, which in turn can save lives.

Multinomial Logistic Regression Predicted Probability Map To Visualize The Influence Of Socio-Economic Factors On Breast Cancer Occurrence in Southern Karnataka

NASA Astrophysics Data System (ADS)

Madhu, B.; Ashok, N. C.; Balasubramanian, S.

2014-11-01

Multinomial logistic regression analysis was used to develop statistical model that can predict the probability of breast cancer in Southern Karnataka using the breast cancer occurrence data during 2007-2011. Independent socio-economic variables describing the breast cancer occurrence like age, education, occupation, parity, type of family, health insurance coverage, residential locality and socioeconomic status of each case was obtained. The models were developed as follows: i) Spatial visualization of the Urban- rural distribution of breast cancer cases that were obtained from the Bharat Hospital and Institute of Oncology. ii) Socio-economic risk factors describing the breast cancer occurrences were complied for each case. These data were then analysed using multinomial logistic regression analysis in a SPSS statistical software and relations between the occurrence of breast cancer across the socio-economic status and the influence of other socio-economic variables were evaluated and multinomial logistic regression models were constructed. iii) the model that best predicted the occurrence of breast cancer were identified. This multivariate logistic regression model has been entered into a geographic information system and maps showing the predicted probability of breast cancer occurrence in Southern Karnataka was created. This study demonstrates that Multinomial logistic regression is a valuable tool for developing models that predict the probability of breast cancer Occurrence in Southern Karnataka.

Multinomial logistic regression modelling of obesity and overweight among primary school students in a rural area of Negeri Sembilan

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

Ghazali, Amirul Syafiq Mohd; Ali, Zalila; Noor, Norlida Mohd

Multinomial logistic regression is widely used to model the outcomes of a polytomous response variable, a categorical dependent variable with more than two categories. The model assumes that the conditional mean of the dependent categorical variables is the logistic function of an affine combination of predictor variables. Its procedure gives a number of logistic regression models that make specific comparisons of the response categories. When there are q categories of the response variable, the model consists of q-1 logit equations which are fitted simultaneously. The model is validated by variable selection procedures, tests of regression coefficients, a significant test ofmore » the overall model, goodness-of-fit measures, and validation of predicted probabilities using odds ratio. This study used the multinomial logistic regression model to investigate obesity and overweight among primary school students in a rural area on the basis of their demographic profiles, lifestyles and on the diet and food intake. The results indicated that obesity and overweight of students are related to gender, religion, sleep duration, time spent on electronic games, breakfast intake in a week, with whom meals are taken, protein intake, and also, the interaction between breakfast intake in a week with sleep duration, and the interaction between gender and protein intake.« less

Multinomial logistic regression modelling of obesity and overweight among primary school students in a rural area of Negeri Sembilan

NASA Astrophysics Data System (ADS)

Ghazali, Amirul Syafiq Mohd; Ali, Zalila; Noor, Norlida Mohd; Baharum, Adam

2015-10-01

Multinomial logistic regression is widely used to model the outcomes of a polytomous response variable, a categorical dependent variable with more than two categories. The model assumes that the conditional mean of the dependent categorical variables is the logistic function of an affine combination of predictor variables. Its procedure gives a number of logistic regression models that make specific comparisons of the response categories. When there are q categories of the response variable, the model consists of q-1 logit equations which are fitted simultaneously. The model is validated by variable selection procedures, tests of regression coefficients, a significant test of the overall model, goodness-of-fit measures, and validation of predicted probabilities using odds ratio. This study used the multinomial logistic regression model to investigate obesity and overweight among primary school students in a rural area on the basis of their demographic profiles, lifestyles and on the diet and food intake. The results indicated that obesity and overweight of students are related to gender, religion, sleep duration, time spent on electronic games, breakfast intake in a week, with whom meals are taken, protein intake, and also, the interaction between breakfast intake in a week with sleep duration, and the interaction between gender and protein intake.

A decision support model for investment on P2P lending platform.

PubMed

Zeng, Xiangxiang; Liu, Li; Leung, Stephen; Du, Jiangze; Wang, Xun; Li, Tao

2017-01-01

Peer-to-peer (P2P) lending, as a novel economic lending model, has triggered new challenges on making effective investment decisions. In a P2P lending platform, one lender can invest N loans and a loan may be accepted by M investors, thus forming a bipartite graph. Basing on the bipartite graph model, we built an iteration computation model to evaluate the unknown loans. To validate the proposed model, we perform extensive experiments on real-world data from the largest American P2P lending marketplace-Prosper. By comparing our experimental results with those obtained by Bayes and Logistic Regression, we show that our computation model can help borrowers select good loans and help lenders make good investment decisions. Experimental results also show that the Logistic classification model is a good complement to our iterative computation model, which motivates us to integrate the two classification models. The experimental results of the hybrid classification model demonstrate that the logistic classification model and our iteration computation model are complementary to each other. We conclude that the hybrid model (i.e., the integration of iterative computation model and Logistic classification model) is more efficient and stable than the individual model alone.

A decision support model for investment on P2P lending platform

PubMed Central

Liu, Li; Leung, Stephen; Du, Jiangze; Wang, Xun; Li, Tao

2017-01-01

Peer-to-peer (P2P) lending, as a novel economic lending model, has triggered new challenges on making effective investment decisions. In a P2P lending platform, one lender can invest N loans and a loan may be accepted by M investors, thus forming a bipartite graph. Basing on the bipartite graph model, we built an iteration computation model to evaluate the unknown loans. To validate the proposed model, we perform extensive experiments on real-world data from the largest American P2P lending marketplace—Prosper. By comparing our experimental results with those obtained by Bayes and Logistic Regression, we show that our computation model can help borrowers select good loans and help lenders make good investment decisions. Experimental results also show that the Logistic classification model is a good complement to our iterative computation model, which motivates us to integrate the two classification models. The experimental results of the hybrid classification model demonstrate that the logistic classification model and our iteration computation model are complementary to each other. We conclude that the hybrid model (i.e., the integration of iterative computation model and Logistic classification model) is more efficient and stable than the individual model alone. PMID:28877234

Conduction at the onset of chaos

NASA Astrophysics Data System (ADS)

Baldovin, Fulvio

2017-02-01

After a general discussion of the thermodynamics of conductive processes, we introduce specific observables enabling the connection of the diffusive transport properties with the microscopic dynamics. We solve the case of Brownian particles, both analytically and numerically, and address then whether aspects of the classic Onsager's picture generalize to the non-local non-reversible dynamics described by logistic map iterates. While in the chaotic case numerical evidence of a monotonic relaxation is found, at the onset of chaos complex relaxation patterns emerge.

Logistic Mixed Models to Investigate Implicit and Explicit Belief Tracking.

PubMed

Lages, Martin; Scheel, Anne

2016-01-01

We investigated the proposition of a two-systems Theory of Mind in adults' belief tracking. A sample of N = 45 participants predicted the choice of one of two opponent players after observing several rounds in an animated card game. Three matches of this card game were played and initial gaze direction on target and subsequent choice predictions were recorded for each belief task and participant. We conducted logistic regressions with mixed effects on the binary data and developed Bayesian logistic mixed models to infer implicit and explicit mentalizing in true belief and false belief tasks. Although logistic regressions with mixed effects predicted the data well a Bayesian logistic mixed model with latent task- and subject-specific parameters gave a better account of the data. As expected explicit choice predictions suggested a clear understanding of true and false beliefs (TB/FB). Surprisingly, however, model parameters for initial gaze direction also indicated belief tracking. We discuss why task-specific parameters for initial gaze directions are different from choice predictions yet reflect second-order perspective taking.

Linear Logistic Test Modeling with R

ERIC Educational Resources Information Center

Baghaei, Purya; Kubinger, Klaus D.

2015-01-01

The present paper gives a general introduction to the linear logistic test model (Fischer, 1973), an extension of the Rasch model with linear constraints on item parameters, along with eRm (an R package to estimate different types of Rasch models; Mair, Hatzinger, & Mair, 2014) functions to estimate the model and interpret its parameters. The…

Time Reversal Method for Pipe Inspection with Guided Wave

NASA Astrophysics Data System (ADS)

Deng, Fei; He, Cunfu; Wu, Bin

2008-02-01

The temporal-spatial focusing effect of the time reversal method on the guided wave inspection in pipes is investigated. A steel pipe model with outer diameter of 70 mm and wall thickness of 3.5 mm is numerically built to analyse the reflection coefficient of L(0,2) mode when the time reversal method is applied in the model. According to the calculated results, it is shown that a synthetic time reversal array method is effective to improve the signal-to-noise ratio of a guided wave inspection system. As an intercepting window is widened, more energy can be included in a re-emitted signal, which leads to a large reflection coefficient of L(0,2) mode. It is also shown that when a time reversed signal is reapplied in the pipe model, by analysing the motion of the time reversed wave propagating along the pipe model, a defect can be identified. Therefore, it is demonstrated that the time reversal method can be used to locate the circumferential position of a defect in a pipe. Finally, through an experiment corresponding with the pipe model, the experimental result shows that the above-mentioned method can be valid in the inspection of a pipe.

Mixed conditional logistic regression for habitat selection studies.

PubMed

Duchesne, Thierry; Fortin, Daniel; Courbin, Nicolas

2010-05-01

1. Resource selection functions (RSFs) are becoming a dominant tool in habitat selection studies. RSF coefficients can be estimated with unconditional (standard) and conditional logistic regressions. While the advantage of mixed-effects models is recognized for standard logistic regression, mixed conditional logistic regression remains largely overlooked in ecological studies. 2. We demonstrate the significance of mixed conditional logistic regression for habitat selection studies. First, we use spatially explicit models to illustrate how mixed-effects RSFs can be useful in the presence of inter-individual heterogeneity in selection and when the assumption of independence from irrelevant alternatives (IIA) is violated. The IIA hypothesis states that the strength of preference for habitat type A over habitat type B does not depend on the other habitat types also available. Secondly, we demonstrate the significance of mixed-effects models to evaluate habitat selection of free-ranging bison Bison bison. 3. When movement rules were homogeneous among individuals and the IIA assumption was respected, fixed-effects RSFs adequately described habitat selection by simulated animals. In situations violating the inter-individual homogeneity and IIA assumptions, however, RSFs were best estimated with mixed-effects regressions, and fixed-effects models could even provide faulty conclusions. 4. Mixed-effects models indicate that bison did not select farmlands, but exhibited strong inter-individual variations in their response to farmlands. Less than half of the bison preferred farmlands over forests. Conversely, the fixed-effect model simply suggested an overall selection for farmlands. 5. Conditional logistic regression is recognized as a powerful approach to evaluate habitat selection when resource availability changes. This regression is increasingly used in ecological studies, but almost exclusively in the context of fixed-effects models. Fitness maximization can imply differences in trade-offs among individuals, which can yield inter-individual differences in selection and lead to departure from IIA. These situations are best modelled with mixed-effects models. Mixed-effects conditional logistic regression should become a valuable tool for ecological research.

Understanding and Predicting Geomagnetic Dipole Reversals Via Low Dimensional Models and Data Assimilation

NASA Astrophysics Data System (ADS)

Morzfeld, M.; Fournier, A.; Hulot, G.

2014-12-01

We investigate the geophysical relevance of low-dimensional models of the geomagnetic dipole fieldby comparing these models to the signed relative paleomagnetic intensity over the past 2 Myr.The comparison is done via Bayesian statistics, implemented numerically by Monte Carlo (MC) sampling.We consider several MC schemes, as well as two data sets to show the robustness of our approach with respect to its numerical implementation and to the details of how the data are collected.The data we consider are the Sint-2000 [1] and PADM2M [2] data sets.We consider three stochastic differential equation (SDE) models and one deterministic model. Experiments with synthetic data show that it is feasible that a low dimensional modelcan learn the geophysical state from data of only the dipole field,and reveal the limitations of the low-dimensional models.For example, the G12 model [3] (a deterministic model that generates dipole reversals by crisis induced intermittency)can only match either one of the two important time scales we find in the data. The MC sampling approach also allows usto use the models to make predictions of the dipole field.We assess how reliably dipole reversals can be predictedwith our approach by hind-casting five reversals documented over the past 2 Myr. We find that, besides its limitations, G12 can be used to predict reversals reliably,however only with short lead times and over short horizons. The scalar SDE models on the other hand are not useful for prediction of dipole reversals.References Valet, J.P., Maynadier,L and Guyodo, Y., 2005, Geomagnetic field strength and reversal rate over the past 2 Million years, Nature, 435, 802-805. Ziegler, L.B., Constable, C.G., Johnson, C.L. and Tauxe, L., 2011, PADM2M: a penalized maximum likelihood model of the 0-2 Ma paleomagnetic axial dipole moment, Geophysical Journal International, 184, 1069-1089. Gissinger, C., 2012, A new deterministic model for chaotic reversals, European Physical Journal B, 85:137.

Evaluation of trade-offs in costs and environmental impacts for returnable packaging implementation

NASA Astrophysics Data System (ADS)

Jarupan, Lerpong; Kamarthi, Sagar V.; Gupta, Surendra M.

2004-02-01

The main thrust of returnable packaging these days is to provide logistical services through transportation and distribution of products and be environmentally friendly. Returnable packaging and reverse logistics concepts have converged to mitigate the adverse effect of packaging materials entering the solid waste stream. Returnable packaging must be designed by considering the trade-offs between costs and environmental impact to satisfy manufacturers and environmentalists alike. The cost of returnable packaging entails such items as materials, manufacturing, collection, storage and disposal. Environmental impacts are explicitly linked with solid waste, air pollution, and water pollution. This paper presents a multi-criteria evaluation technique to assist decision-makers for evaluating the trade-offs in costs and environmental impact during the returnable packaging design process. The proposed evaluation technique involves a combination of multiple objective integer linear programming and analytic hierarchy process. A numerical example is used to illustrate the methodology.

Using Dominance Analysis to Determine Predictor Importance in Logistic Regression

ERIC Educational Resources Information Center

Azen, Razia; Traxel, Nicole

2009-01-01

This article proposes an extension of dominance analysis that allows researchers to determine the relative importance of predictors in logistic regression models. Criteria for choosing logistic regression R[superscript 2] analogues were determined and measures were selected that can be used to perform dominance analysis in logistic regression. A…

Logistics of a Lunar Based Solar Power Satellite Scenario

NASA Technical Reports Server (NTRS)

Melissopoulos, Stefanos

1995-01-01

A logistics system comprised of two orbital stations for the support of a 500 GW space power satellite scenario in a geostationary orbit was investigated in this study. A subsystem mass model, a mass flow model and a life cycle cost model were developed. The results regarding logistics cost and burden rates show that the transportation cost contributed the most (96%) to the overall cost of the scenario. The orbital stations at a geostationary and at a lunar orbit contributed 4 % to that cost.

Evaluation of MIh Scoring System in Diagnosis of Obstructive Sleep Apnea Syndrome.

PubMed

Xu, Qinxing; Du, Junwei; Ling, Xiaobo; Lu, Yangfei

2017-10-02

BACKGROUND The objective of the present study was to investigate whether the analysis of magnesium (Mg), high-sensitivity C-reactive protein (hsCRP), and ischemia-modified albumin (IMA) concentrations can be used as a non-invasive and convenient method for diagnosing obstructive sleep apnea syndrome (OSAS). MATERIAL AND METHODS After polysomnography, venous blood was collected from 33 patients with OSAS and 30 control individuals. Serum levels of Mg, hsCRP, and IMA were investigated. The relationship between these factors and apnea-hypopnea index (AHI) was analyzed using the Pearson correlation coefficient. The role of the factors was determined using a receiver operating characteristic (ROC) curve and multivariate logistic regression analysis. RESULTS The levels of hsCRP and IMA were significantly higher in patients with OSAS than in control subjects, while the levels of Mg were lower (P<0.05 for all). A significant correlation was noted between serum IMA (r=0.614; P<0.001) and hsCRP (r=0.453; P<0.001) levels and the AHI. The ROC showed that serum Mg (AUC=0.74(0.62-0.85)), hsCRP (AUC=0.77(0.65-0.87)), and IMA (AUC=0.78(0.66-0.87)) levels could be used as markers to diagnose OSAS. Moreover, our new model, MIh, which is obtained by multivariate analysis, yielded an AUC value of 0.93 (0.83-0.98). Continuous positive airway pressure (CPAP) treatment reversed the changes in the serum levels of Mg, hsCRP, and IMA. CONCLUSIONS Patients with OSAS show reduced serum Mg levels and elevated serum hsCRP and IMA levels. These observed alterations can be reversed by CPAP treatment. A novel model, named MIh, may be a promising tool for OSAS diagnosis.

Magnetic field reversals in the Milky Way- "cherchez le champ magnetique".

NASA Astrophysics Data System (ADS)

Vallee, J. P.

1996-04-01

Radio observations of nearby spiral galaxies have tremendously enhanced our knowledge of their global magnetic field distributions. Recent theoretical developments in the area of dynamos have also helped in the interpretation of magnetic field data in spiral galaxies. When it comes to the magnetic field in the Milky Way galaxy, our position in the Milky Way's galactic disk hinders our attempts at interpreting the observational data. This makes the proposition of "cherchez le champ magnetique" a difficult one to follow. Some recent papers have attempted to fit magnetic field models to spiral galaxies, and in particular to the Milky Way galaxy. Magnetic field reversals in the Milky Way are crucial to all interpretations, be they axisymmetric spiral (ASS) or bisymmetric spiral (BSS) global magnetic field models. Magnetic field reversals can be found in both ASS and BSS magnetic field models, not just BSS ones. The axisymmetric spiral (ASS) magnetic field models produced by the dynamo theory already predict magnetic field reversals, and they are of the type observed in the Milky Way. The small number of magnetic field reversals observed in the Milky Way is compatible with the ASS magnetic field models. The bisymmetric spiral (BSS) magnetic field models as applied to the pulsar RM data and to the QSO and galaxies data have many problems, due to the many pitfalls in model fitting the magnetic field reversals observed in the Milky Way. Many pitfalls are discussed here, including the incomplete comparisons of BSS versus ASS models, the number of spiral arms to be used in modelling, and the proper distance to pulsars via the more accurate distribution of thermal electrons within spiral arms. The two magnetic field reversals in our Milky Way are clearly located in the interarm regions. Predicted magnetic field reversals are periodic, while observed ones are not periodic. Magnetic field reversals cannot be masked effectively by local interstellar magnetised shells. The strength and direction of the magnetic field with galactic radius show that the BSS magnetic field models are less suitable to explain the RM data in the Milky Way. The prediction by the BSS magnetic field models of a large number of magnetic field reversals differs from the available observations.

An integrative fuzzy Kansei engineering and Kano model for logistics services

NASA Astrophysics Data System (ADS)

Hartono, M.; Chuan, T. K.; Prayogo, D. N.; Santoso, A.

2017-11-01

Nowadays, customer emotional needs (known as Kansei) in product and especially in services become a major concern. One of the emerging services is the logistics services. In obtaining a global competitive advantage, logistics services should understand and satisfy their customer affective impressions (Kansei). How to capture, model and analyze the customer emotions has been well structured by Kansei Engineering, equipped with Kano model to strengthen its methodology. However, its methodology lacks of the dynamics of customer perception. More specifically, there is a criticism of perceived scores on user preferences, in both perceived service quality and Kansei response, whether they represent an exact numerical value. Thus, this paper is proposed to discuss an approach of fuzzy Kansei in logistics service experiences. A case study in IT-based logistics services involving 100 subjects has been conducted. Its findings including the service gaps accompanied with prioritized improvement initiatives are discussed.

Toward understanding of the role of reversibility of phenotypic switching in the evolution of resistance to therapy

NASA Astrophysics Data System (ADS)

Horvath, D.; Brutovsky, B.

2018-06-01

Reversibility of state transitions is intensively studied topic in many scientific disciplines over many years. In cell biology, it plays an important role in epigenetic variation of phenotypes, known as phenotypic plasticity. More interestingly, the cell state reversibility is probably crucial in the adaptation of population phenotypic heterogeneity to environmental fluctuations by evolving bet-hedging strategy, which might confer to cancer cells resistance to therapy. In this article, we propose a formalization of the evolution of highly reversible states in the environments of periodic variability. Two interrelated models of heterogeneous cell populations are proposed and their behavior is studied. The first model captures selection dynamics of the cell clones for the respective levels of phenotypic reversibility. The second model focuses on the interplay between reversibility and drug resistance in the particular case of cancer. Overall, our results show that the threshold dependencies are emergent features of the investigated model with eventual therapeutic relevance. Presented examples demonstrate importance of taking into account cell to cell heterogeneity within a system of clones with different reversibility quantified by appropriately chosen genetic and epigenetic entropy measures.

Over-the-wing model thrust reverser noise tests

NASA Technical Reports Server (NTRS)

Goodykoontz, J.; Gutierrez, O.

1977-01-01

Static acoustic tests were conducted on a 1/12 scale model over-the-wing target type thrust reverser. The model configuration simulates a design that is applicable to the over-the-wing short-haul advanced technology engine. Aerodynamic screening tests of a variety of reverser designs identified configurations that satisfied a reverse thrust requirement of 35 percent of forward thrust at a nozzle pressure ratio of 1.29. The variations in the reverser configuration included, blocker door angle, blocker door lip angle and shape, and side skirt shape. Acoustic data are presented and compared for the various configurations. The model data scaled to a single full size engine show that peak free field perceived noise (PN) levels at a 152.4 meter sideline distance range from 98 to 104 PNdb.

Binary logistic regression-Instrument for assessing museum indoor air impact on exhibits.

PubMed

Bucur, Elena; Danet, Andrei Florin; Lehr, Carol Blaziu; Lehr, Elena; Nita-Lazar, Mihai

2017-04-01

This paper presents a new way to assess the environmental impact on historical artifacts using binary logistic regression. The prediction of the impact on the exhibits during certain pollution scenarios (environmental impact) was calculated by a mathematical model based on the binary logistic regression; it allows the identification of those environmental parameters from a multitude of possible parameters with a significant impact on exhibitions and ranks them according to their severity effect. Air quality (NO 2 , SO 2 , O 3 and PM 2.5 ) and microclimate parameters (temperature, humidity) monitoring data from a case study conducted within exhibition and storage spaces of the Romanian National Aviation Museum Bucharest have been used for developing and validating the binary logistic regression method and the mathematical model. The logistic regression analysis was used on 794 data combinations (715 to develop of the model and 79 to validate it) by a Statistical Package for Social Sciences (SPSS 20.0). The results from the binary logistic regression analysis demonstrated that from six parameters taken into consideration, four of them present a significant effect upon exhibits in the following order: O 3 >PM 2.5 >NO 2 >humidity followed at a significant distance by the effects of SO 2 and temperature. The mathematical model, developed in this study, correctly predicted 95.1 % of the cumulated effect of the environmental parameters upon the exhibits. Moreover, this model could also be used in the decisional process regarding the preventive preservation measures that should be implemented within the exhibition space. The paper presents a new way to assess the environmental impact on historical artifacts using binary logistic regression. The mathematical model developed on the environmental parameters analyzed by the binary logistic regression method could be useful in a decision-making process establishing the best measures for pollution reduction and preventive preservation of exhibits.

One Size Does Not Fit All: The Impact of Primary Vaccine Container Size on Vaccine Distribution and Delivery

PubMed Central

Haidari, Leila A.; Wahl, Brian; Brown, Shawn T.; Privor-Dumm, Lois; Wallman-Stokes, Cecily; Gorham, Katie; Connor, Diana L.; Wateska, Angela R.; Schreiber, Benjamin; Dicko, Hamadou; Jaillard, Philippe; Avella, Melanie; Lee, Bruce Y.

2015-01-01

BACKGROUND While the size and type of a vaccine container (i.e., primary container) can have many implications on the safety and convenience of a vaccination session, another important but potentially overlooked consideration is how the design of the primary container may affect the distribution of the vaccine, its resulting cost, and whether the vial is ultimately opened. METHODS Using our HERMES software platform, we developed a simulation model of the World Health Organization Expanded Program on Immunization supply chain for the Republic of Benin and used the model to explore the effects of different primary containers for various vaccine antigens. RESULTS Replacing vaccines with presentations containing fewer doses per vial reduced vaccine availability (proportion of people arriving for vaccines who are successfully immunized) by as much as 13% (from 73% at baseline) and raised logistics costs by up to $0.06 per dose administered (from $0.25 at baseline) due to increased bottlenecks, while reducing total costs by as much as $0.15 per dose administered (from $2.52 at baseline) due to lower open vial wastage. Primary containers with a greater number of doses per vial each improved vaccine availability by 19% and reduced logistics costs by $0.05 per dose administered, while raising the total costs by up to $0.25 per dose administered due to greater vaccine procurement needs. Changes in supply chain performance were more extreme in departments with greater constraints. Implementing a vial opening threshold reversed the direction of many of these effects. CONCLUSIONS Our results show that one size may not fit all when choosing a primary vaccine container. Rather, the choice depends on characteristics of the vaccine, the vaccine supply chain, immunization session size, and goals of decision-makers. In fact, the optimal vial size may vary among locations within a country. Simulation modeling can help identify tailored approaches to improve availability and efficiency. PMID:25889160

One size does not fit all: The impact of primary vaccine container size on vaccine distribution and delivery.

PubMed

Haidari, Leila A; Wahl, Brian; Brown, Shawn T; Privor-Dumm, Lois; Wallman-Stokes, Cecily; Gorham, Katie; Connor, Diana L; Wateska, Angela R; Schreiber, Benjamin; Dicko, Hamadou; Jaillard, Philippe; Avella, Melanie; Lee, Bruce Y

2015-06-22

While the size and type of a vaccine container (i.e., primary container) can have many implications on the safety and convenience of a vaccination session, another important but potentially overlooked consideration is how the design of the primary container may affect the distribution of the vaccine, its resulting cost, and whether the vial is ultimately opened. Using our HERMES software platform, we developed a simulation model of the World Health Organization Expanded Program on Immunization supply chain for the Republic of Benin and used the model to explore the effects of different primary containers for various vaccine antigens. Replacing vaccines with presentations containing fewer doses per vial reduced vaccine availability (proportion of people arriving for vaccines who are successfully immunized) by as much as 13% (from 73% at baseline) and raised logistics costs by up to $0.06 per dose administered (from $0.25 at baseline) due to increased bottlenecks, while reducing total costs by as much as $0.15 per dose administered (from $2.52 at baseline) due to lower open vial wastage. Primary containers with a greater number of doses per vial each improved vaccine availability by 19% and reduced logistics costs by $0.05 per dose administered, while reducing the total costs by up to $0.25 per dose administered. Changes in supply chain performance were more extreme in departments with greater constraints. Implementing a vial opening threshold reversed the direction of many of these effects. Our results show that one size may not fit all when choosing a primary vaccine container. Rather, the choice depends on characteristics of the vaccine, the vaccine supply chain, immunization session size, and goals of decision makers. In fact, the optimal vial size may vary among locations within a country. Simulation modeling can help identify tailored approaches to improve availability and efficiency. Copyright © 2015 Elsevier Ltd. All rights reserved.

Molecular dynamics simulations of fluoropolymers in the solid state

NASA Astrophysics Data System (ADS)

Holt, David Bryan

1998-10-01

Molecular mechanics and dynamics simulations have been utilized to address the behavior of helix reversal defects in fluoropolymers. The results of the simulations confirm that helix reversals do form and migrate in PTFE crystals. The most important defect structure is a helix reversal band: two helix reversals which bracker a small chain segment (typically 6-7 backbone atoms) having the opposite helical sense from the parent molecule. Small reversal bands had velocities ranging between 100 m/s (low temperature)-250 m/s (high temperature). The size of this reversal band defect is dependent upon the helical conformation and is equal to approximately half of the helical repeat unit in the low and intermediate temperature phases. In the high temperature phase where intermolecular effects are diminished, a wider distribution of reversal band sizes was observed during the simulations. A mechanism is identified by which significant reorientation of a chain segment about the molecular axis can occur when it is bracketed by two helix reversal bands. Simulations with a model containing a perfluoromethyl (PFM) group at low temperature showed that the presence of the PFM group significantly restricts chain mobility locally. However, a significant reduction in the helix reversal defect density was observed on neighboring chains as well. During simulations in which a shear deformation was applied to the models with and without a PFM group, an increase in reversal defect density was observed. However, the helix reversal density in the sheared model containing the PFM branch was less than that in the model without a PFM branch under no shear. These data implicate helix reversal defects and associated chain segment motions in the mechanical behavior of fluoropolymer materials.

Accounting for Slipping and Other False Negatives in Logistic Models of Student Learning

ERIC Educational Resources Information Center

MacLellan, Christopher J.; Liu, Ran; Koedinger, Kenneth R.

2015-01-01

Additive Factors Model (AFM) and Performance Factors Analysis (PFA) are two popular models of student learning that employ logistic regression to estimate parameters and predict performance. This is in contrast to Bayesian Knowledge Tracing (BKT) which uses a Hidden Markov Model formalism. While all three models tend to make similar predictions,…

Some Observations on the Identification and Interpretation of the 3PL IRT Model

ERIC Educational Resources Information Center

Azevedo, Caio Lucidius Naberezny

2009-01-01

The paper by Maris, G., & Bechger, T. (2009) entitled, "On the Interpreting the Model Parameters for the Three Parameter Logistic Model," addressed two important questions concerning the three parameter logistic (3PL) item response theory (IRT) model (and in a broader sense, concerning all IRT models). The first one is related to the model…

A High Resolution Ammunition Resupply Model.

DTIC Science & Technology

1982-03-01

LOU ............... 104 3. Requests for Resupply . . ........ 108 a. Weapon Systems . . . . . . . . . . . . 108 b. Platoon . ... 109 c. Company...essence, the fundamental question, "Can it be done?", is never adequately answered. B. LOGISTICS MODELS Current logistics models then, although...19 .._ " Development of a detailed model that responds to requests for ammunition resupply, maintains a simplified stockage system , and models the

Easy and low-cost identification of metabolic syndrome in patients treated with second-generation antipsychotics: artificial neural network and logistic regression models.

PubMed

Lin, Chao-Cheng; Bai, Ya-Mei; Chen, Jen-Yeu; Hwang, Tzung-Jeng; Chen, Tzu-Ting; Chiu, Hung-Wen; Li, Yu-Chuan

2010-03-01

Metabolic syndrome (MetS) is an important side effect of second-generation antipsychotics (SGAs). However, many SGA-treated patients with MetS remain undetected. In this study, we trained and validated artificial neural network (ANN) and multiple logistic regression models without biochemical parameters to rapidly identify MetS in patients with SGA treatment. A total of 383 patients with a diagnosis of schizophrenia or schizoaffective disorder (DSM-IV criteria) with SGA treatment for more than 6 months were investigated to determine whether they met the MetS criteria according to the International Diabetes Federation. The data for these patients were collected between March 2005 and September 2005. The input variables of ANN and logistic regression were limited to demographic and anthropometric data only. All models were trained by randomly selecting two-thirds of the patient data and were internally validated with the remaining one-third of the data. The models were then externally validated with data from 69 patients from another hospital, collected between March 2008 and June 2008. The area under the receiver operating characteristic curve (AUC) was used to measure the performance of all models. Both the final ANN and logistic regression models had high accuracy (88.3% vs 83.6%), sensitivity (93.1% vs 86.2%), and specificity (86.9% vs 83.8%) to identify MetS in the internal validation set. The mean +/- SD AUC was high for both the ANN and logistic regression models (0.934 +/- 0.033 vs 0.922 +/- 0.035, P = .63). During external validation, high AUC was still obtained for both models. Waist circumference and diastolic blood pressure were the common variables that were left in the final ANN and logistic regression models. Our study developed accurate ANN and logistic regression models to detect MetS in patients with SGA treatment. The models are likely to provide a noninvasive tool for large-scale screening of MetS in this group of patients. (c) 2010 Physicians Postgraduate Press, Inc.

Inappropriate use of the quasi-reversible electrode kinetic model in simulation-experiment comparisons of voltammetric processes that approach the reversible limit.

PubMed

Simonov, Alexandr N; Morris, Graham P; Mashkina, Elena A; Bethwaite, Blair; Gillow, Kathryn; Baker, Ruth E; Gavaghan, David J; Bond, Alan M

2014-08-19

Many electrode processes that approach the "reversible" (infinitely fast) limit under voltammetric conditions have been inappropriately analyzed by comparison of experimental data and theory derived from the "quasi-reversible" model. Simulations based on "reversible" and "quasi-reversible" models have been fitted to an extensive series of a.c. voltammetric experiments undertaken at macrodisk glassy carbon (GC) electrodes for oxidation of ferrocene (Fc(0/+)) in CH3CN (0.10 M (n-Bu)4NPF6) and reduction of [Ru(NH3)6](3+) and [Fe(CN)6](3-) in 1 M KCl aqueous electrolyte. The confidence with which parameters such as standard formal potential (E(0)), heterogeneous electron transfer rate constant at E(0) (k(0)), charge transfer coefficient (α), uncompensated resistance (Ru), and double layer capacitance (CDL) can be reported using the "quasi-reversible" model has been assessed using bootstrapping and parameter sweep (contour plot) techniques. Underparameterization, such as that which occurs when modeling CDL with a potential independent value, results in a less than optimal level of experiment-theory agreement. Overparameterization may improve the agreement but easily results in generation of physically meaningful but incorrect values of the recovered parameters, as is the case with the very fast Fc(0/+) and [Ru(NH3)6](3+/2+) processes. In summary, for fast electrode kinetics approaching the "reversible" limit, it is recommended that the "reversible" model be used for theory-experiment comparisons with only E(0), Ru, and CDL being quantified and a lower limit of k(0) being reported; e.g., k(0) ≥ 9 cm s(-1) for the Fc(0/+) process.

Recovering time-varying networks of dependencies in social and biological studies.

PubMed

Ahmed, Amr; Xing, Eric P

2009-07-21

A plausible representation of the relational information among entities in dynamic systems such as a living cell or a social community is a stochastic network that is topologically rewiring and semantically evolving over time. Although there is a rich literature in modeling static or temporally invariant networks, little has been done toward recovering the network structure when the networks are not observable in a dynamic context. In this article, we present a machine learning method called TESLA, which builds on a temporally smoothed l(1)-regularized logistic regression formalism that can be cast as a standard convex-optimization problem and solved efficiently by using generic solvers scalable to large networks. We report promising results on recovering simulated time-varying networks and on reverse engineering the latent sequence of temporally rewiring political and academic social networks from longitudinal data, and the evolving gene networks over >4,000 genes during the life cycle of Drosophila melanogaster from a microarray time course at a resolution limited only by sample frequency.

Monitoring tetracycline through a solid-state nanopore sensor

NASA Astrophysics Data System (ADS)

Zhang, Yuechuan; Chen, Yanling; Fu, Yongqi; Ying, Cuifeng; Feng, Yanxiao; Huang, Qimeng; Wang, Chao; Pei, De-Sheng; Wang, Deqiang

2016-06-01

Antibiotics as emerging environmental contaminants, are widely used in both human and veterinary medicines. A solid-state nanopore sensing method is reported in this article to detect Tetracycline, which is based on Tet-off and Tet-on systems. rtTA (reverse tetracycline-controlled trans-activator) and TRE (Tetracycline Responsive Element) could bind each other under the action of Tetracycline to form one complex. When the complex passes through nanopores with 8 ~ 9 nanometers in diameter, we could detect the concentrations of Tet from 2 ng/mL to 2000 ng/mL. According to the Logistic model, we could define three growth zones of Tetracycline for rtTA and TRE. The slow growth zone is 0-39.5 ng/mL. The rapid growth zone is 39.5-529.7 ng/mL. The saturated zone is > 529.7 ng/mL. Compared to the previous methods, the nanopore sensor could detect and quantify these different kinds of molecule at the single-molecule level.

Magnetic field evolution and reversals in spiral galaxies

NASA Astrophysics Data System (ADS)

Dobbs, C. L.; Price, D. J.; Pettitt, A. R.; Bate, M. R.; Tricco, T. S.

2016-10-01

We study the evolution of galactic magnetic fields using 3D smoothed particle magnetohydrodynamics (SPMHD) simulations of galaxies with an imposed spiral potential. We consider the appearance of reversals of the field, and amplification of the field. We find that magnetic field reversals occur when the velocity jump across the spiral shock is above ≈20 km s-1, occurring where the velocity change is highest, typically at the inner Lindblad resonance in our models. Reversals also occur at corotation, where the direction of the velocity field reverses in the corotating frame of a spiral arm. They occur earlier with a stronger amplitude spiral potential, and later or not at all with weaker or no spiral arms. The presence of a reversal at radii of around 4-6 kpc in our fiducial model is consistent with a reversal identified in the Milky Way, though we caution that alternative Galaxy models could give a similar reversal. We find that relatively high resolution, a few million particles in SPMHD, is required to produce consistent behaviour of the magnetic field. Amplification of the magnetic field occurs in the models, and while some may be genuinely attributable to differential rotation or spiral arms, some may be a numerical artefact. We check our results using ATHENA, finding reversals but less amplification of the field, suggesting that some of the amplification of the field with SPMHD is numerical.

Optimization of Location–Routing Problem for Cold Chain Logistics Considering Carbon Footprint

PubMed Central

Wang, Songyi; Tao, Fengming; Shi, Yuhe

2018-01-01

In order to solve the optimization problem of logistics distribution system for fresh food, this paper provides a low-carbon and environmental protection point of view, based on the characteristics of perishable products, and combines with the overall optimization idea of cold chain logistics distribution network, where the green and low-carbon location–routing problem (LRP) model in cold chain logistics is developed with the minimum total costs as the objective function, which includes carbon emission costs. A hybrid genetic algorithm with heuristic rules is designed to solve the model, and an example is used to verify the effectiveness of the algorithm. Furthermore, the simulation results obtained by a practical numerical example show the applicability of the model while provide green and environmentally friendly location-distribution schemes for the cold chain logistics enterprise. Finally, carbon tax policies are introduced to analyze the impact of carbon tax on the total costs and carbon emissions, which proves that carbon tax policy can effectively reduce carbon dioxide emissions in cold chain logistics network. PMID:29316639

A 4 K tactical cryocooler using reverse-Brayton machines

NASA Astrophysics Data System (ADS)

Zagarola, M.; Cragin, K.; McCormick, J.; Hill, R.

2017-12-01

Superconducting electronics and spectral-spatial holography have the potential to revolutionize digital communications, but must operate at cryogenic temperatures, near 4 K. Liquid helium is undesirable for military missions due to logistics and scarcity, and commercial low temperature cryocoolers are unable to meet size, weight, power, and environmental requirements for many missions. To address this need, Creare is developing a reverse turbo-Brayton cryocooler that provides refrigeration at 4.2 K and rejects heat at 77 K to an upper-stage cryocooler or through boil-off of liquid nitrogen. The cooling system is predicted to reduce size, weight, and input power by at least an order of magnitude as compared to the current state-of-the-art 4.2 K cryocooler. For systems utilizing nitrogen boil-off, the boil-off rate is reasonable. This paper reviews the design of the cryocooler, the key components, and component test results.

Modeling Polytomous Item Responses Using Simultaneously Estimated Multinomial Logistic Regression Models

ERIC Educational Resources Information Center

Anderson, Carolyn J.; Verkuilen, Jay; Peyton, Buddy L.

2010-01-01

Survey items with multiple response categories and multiple-choice test questions are ubiquitous in psychological and educational research. We illustrate the use of log-multiplicative association (LMA) models that are extensions of the well-known multinomial logistic regression model for multiple dependent outcome variables to reanalyze a set of…

Model building strategy for logistic regression: purposeful selection.

PubMed

Zhang, Zhongheng

2016-03-01

Logistic regression is one of the most commonly used models to account for confounders in medical literature. The article introduces how to perform purposeful selection model building strategy with R. I stress on the use of likelihood ratio test to see whether deleting a variable will have significant impact on model fit. A deleted variable should also be checked for whether it is an important adjustment of remaining covariates. Interaction should be checked to disentangle complex relationship between covariates and their synergistic effect on response variable. Model should be checked for the goodness-of-fit (GOF). In other words, how the fitted model reflects the real data. Hosmer-Lemeshow GOF test is the most widely used for logistic regression model.

MESSOC capabilities and results. [Model for Estimating Space Station Opertions Costs

NASA Technical Reports Server (NTRS)

Shishko, Robert

1990-01-01

MESSOC (Model for Estimating Space Station Operations Costs) is the result of a multi-year effort by NASA to understand and model the mature operations cost of Space Station Freedom. This paper focuses on MESSOC's ability to contribute to life-cycle cost analyses through its logistics equations and databases. Together, these afford MESSOC the capability to project not only annual logistics costs for a variety of Space Station scenarios, but critical non-cost logistics results such as annual Station maintenance crewhours, upweight/downweight, and on-orbit sparing availability as well. MESSOC results using current logistics databases and baseline scenario have already shown important implications for on-orbit maintenance approaches, space transportation systems, and international operations cost sharing.

Requirement analysis for the one-stop logistics management of fresh agricultural products

NASA Astrophysics Data System (ADS)

Li, Jun; Gao, Hongmei; Liu, Yuchuan

2017-08-01

Issues and concerns for food safety, agro-processing, and the environmental and ecological impact of food production have been attracted many research interests. Traceability and logistics management of fresh agricultural products is faced with the technological challenges including food product label and identification, activity/process characterization, information systems for the supply chain, i.e., from farm to table. Application of one-stop logistics service focuses on the whole supply chain process integration for fresh agricultural products is studied. A collaborative research project for the supply and logistics of fresh agricultural products in Tianjin was performed. Requirement analysis for the one-stop logistics management information system is studied. The model-driven business transformation, an approach uses formal models to explicitly define the structure and behavior of a business, is applied for the review and analysis process. Specific requirements for the logistic management solutions are proposed. Development of this research is crucial for the solution of one-stop logistics management information system integration platform for fresh agricultural products.

Two models for evaluating landslide hazards

USGS Publications Warehouse

Davis, J.C.; Chung, C.-J.; Ohlmacher, G.C.

2006-01-01

Two alternative procedures for estimating landslide hazards were evaluated using data on topographic digital elevation models (DEMs) and bedrock lithologies in an area adjacent to the Missouri River in Atchison County, Kansas, USA. The two procedures are based on the likelihood ratio model but utilize different assumptions. The empirical likelihood ratio model is based on non-parametric empirical univariate frequency distribution functions under an assumption of conditional independence while the multivariate logistic discriminant model assumes that likelihood ratios can be expressed in terms of logistic functions. The relative hazards of occurrence of landslides were estimated by an empirical likelihood ratio model and by multivariate logistic discriminant analysis. Predictor variables consisted of grids containing topographic elevations, slope angles, and slope aspects calculated from a 30-m DEM. An integer grid of coded bedrock lithologies taken from digitized geologic maps was also used as a predictor variable. Both statistical models yield relative estimates in the form of the proportion of total map area predicted to already contain or to be the site of future landslides. The stabilities of estimates were checked by cross-validation of results from random subsamples, using each of the two procedures. Cell-by-cell comparisons of hazard maps made by the two models show that the two sets of estimates are virtually identical. This suggests that the empirical likelihood ratio and the logistic discriminant analysis models are robust with respect to the conditional independent assumption and the logistic function assumption, respectively, and that either model can be used successfully to evaluate landslide hazards. ?? 2006.

The Reversal of Direct Oral Anticoagulants in Animal Models

PubMed Central

Honickel, Markus; Akman, Necib; Grottke, Oliver

2017-01-01

ABSTRACT Several direct oral anticoagulants (DOACs), including direct thrombin and factor Xa inhibitors, have been approved as alternatives to vitamin K antagonist anticoagulants. As with any anticoagulant, DOAC use carries a risk of bleeding. In patients with major bleeding or needing urgent surgery, reversal of DOAC anticoagulation may be required, presenting a clinical challenge. The optimal strategy for DOAC reversal is being refined, and may include use of hemostatic agents such as prothrombin complex concentrates (PCCs; a source of concentrated clotting factors), or DOAC-specific antidotes (which bind their target DOAC to abrogate its activity). Though promising, most specific antidotes are still in development. Preclinical animal research is the key to establishing the efficacy and safety of potential reversal agents. Here, we summarize published preclinical animal studies on reversal of DOAC anticoagulation. These studies (n = 26) were identified via a PubMed search, and used rodent, rabbit, pig, and non-human primate models. The larger of these animals have the advantages of similar blood volume/hemodynamics to humans, and can be used to model polytrauma. We find that in addition to varied species being used, there is variability in the models and assays used between studies; we suggest that blood loss (bleeding volume) is the most clinically relevant measure of DOAC anticoagulation-related bleeding and its reversal. The studies covered indicate that both PCCs and specific reversal agents have the potential to be used as part of a clinical strategy for DOAC reversal. For the future, we advocate the development and use of standardized, clinically, and pharmacologically relevant animal models to study novel DOAC reversal strategies. PMID:28471371

An Extension of the Concept of Specific Objectivity.

ERIC Educational Resources Information Center

Irtel, Hans

1995-01-01

Comparisons of subjects are specifically objective if they do not depend on the items involved. Such comparisons are not restricted to the one-parameter logistic latent trait model but may also be defined within ordinal independence models and even within the two-parameter logistic model. (Author)

Artificial neural networks predict the incidence of portosplenomesenteric venous thrombosis in patients with acute pancreatitis.

PubMed

Fei, Y; Hu, J; Li, W-Q; Wang, W; Zong, G-Q

2017-03-01

Essentials Predicting the occurrence of portosplenomesenteric vein thrombosis (PSMVT) is difficult. We studied 72 patients with acute pancreatitis. Artificial neural networks modeling was more accurate than logistic regression in predicting PSMVT. Additional predictive factors may be incorporated into artificial neural networks. Objective To construct and validate artificial neural networks (ANNs) for predicting the occurrence of portosplenomesenteric venous thrombosis (PSMVT) and compare the predictive ability of the ANNs with that of logistic regression. Methods The ANNs and logistic regression modeling were constructed using simple clinical and laboratory data of 72 acute pancreatitis (AP) patients. The ANNs and logistic modeling were first trained on 48 randomly chosen patients and validated on the remaining 24 patients. The accuracy and the performance characteristics were compared between these two approaches by SPSS17.0 software. Results The training set and validation set did not differ on any of the 11 variables. After training, the back propagation network training error converged to 1 × 10 -20 , and it retained excellent pattern recognition ability. When the ANNs model was applied to the validation set, it revealed a sensitivity of 80%, specificity of 85.7%, a positive predictive value of 77.6% and negative predictive value of 90.7%. The accuracy was 83.3%. Differences could be found between ANNs modeling and logistic regression modeling in these parameters (10.0% [95% CI, -14.3 to 34.3%], 14.3% [95% CI, -8.6 to 37.2%], 15.7% [95% CI, -9.9 to 41.3%], 11.8% [95% CI, -8.2 to 31.8%], 22.6% [95% CI, -1.9 to 47.1%], respectively). When ANNs modeling was used to identify PSMVT, the area under receiver operating characteristic curve was 0.849 (95% CI, 0.807-0.901), which demonstrated better overall properties than logistic regression modeling (AUC = 0.716) (95% CI, 0.679-0.761). Conclusions ANNs modeling was a more accurate tool than logistic regression in predicting the occurrence of PSMVT following AP. More clinical factors or biomarkers may be incorporated into ANNs modeling to improve its predictive ability. © 2016 International Society on Thrombosis and Haemostasis.

A Comparison of the One-and Three-Parameter Logistic Models on Measures of Test Efficiency.

ERIC Educational Resources Information Center

Benson, Jeri

Two methods of item selection were used to select sets of 40 items from a 50-item verbal analogies test, and the resulting item sets were compared for relative efficiency. The BICAL program was used to select the 40 items having the best mean square fit to the one parameter logistic (Rasch) model. The LOGIST program was used to select the 40 items…

Adjusting for Confounding in Early Postlaunch Settings: Going Beyond Logistic Regression Models.

PubMed

Schmidt, Amand F; Klungel, Olaf H; Groenwold, Rolf H H

2016-01-01

Postlaunch data on medical treatments can be analyzed to explore adverse events or relative effectiveness in real-life settings. These analyses are often complicated by the number of potential confounders and the possibility of model misspecification. We conducted a simulation study to compare the performance of logistic regression, propensity score, disease risk score, and stabilized inverse probability weighting methods to adjust for confounding. Model misspecification was induced in the independent derivation dataset. We evaluated performance using relative bias confidence interval coverage of the true effect, among other metrics. At low events per coefficient (1.0 and 0.5), the logistic regression estimates had a large relative bias (greater than -100%). Bias of the disease risk score estimates was at most 13.48% and 18.83%. For the propensity score model, this was 8.74% and >100%, respectively. At events per coefficient of 1.0 and 0.5, inverse probability weighting frequently failed or reduced to a crude regression, resulting in biases of -8.49% and 24.55%. Coverage of logistic regression estimates became less than the nominal level at events per coefficient ≤5. For the disease risk score, inverse probability weighting, and propensity score, coverage became less than nominal at events per coefficient ≤2.5, ≤1.0, and ≤1.0, respectively. Bias of misspecified disease risk score models was 16.55%. In settings with low events/exposed subjects per coefficient, disease risk score methods can be useful alternatives to logistic regression models, especially when propensity score models cannot be used. Despite better performance of disease risk score methods than logistic regression and propensity score models in small events per coefficient settings, bias, and coverage still deviated from nominal.

Logistic Mixed Models to Investigate Implicit and Explicit Belief Tracking

PubMed Central

Lages, Martin; Scheel, Anne

2016-01-01

We investigated the proposition of a two-systems Theory of Mind in adults’ belief tracking. A sample of N = 45 participants predicted the choice of one of two opponent players after observing several rounds in an animated card game. Three matches of this card game were played and initial gaze direction on target and subsequent choice predictions were recorded for each belief task and participant. We conducted logistic regressions with mixed effects on the binary data and developed Bayesian logistic mixed models to infer implicit and explicit mentalizing in true belief and false belief tasks. Although logistic regressions with mixed effects predicted the data well a Bayesian logistic mixed model with latent task- and subject-specific parameters gave a better account of the data. As expected explicit choice predictions suggested a clear understanding of true and false beliefs (TB/FB). Surprisingly, however, model parameters for initial gaze direction also indicated belief tracking. We discuss why task-specific parameters for initial gaze directions are different from choice predictions yet reflect second-order perspective taking. PMID:27853440

Using Logistic Regression To Predict the Probability of Debris Flows Occurring in Areas Recently Burned By Wildland Fires

USGS Publications Warehouse

Rupert, Michael G.; Cannon, Susan H.; Gartner, Joseph E.

2003-01-01

Logistic regression was used to predict the probability of debris flows occurring in areas recently burned by wildland fires. Multiple logistic regression is conceptually similar to multiple linear regression because statistical relations between one dependent variable and several independent variables are evaluated. In logistic regression, however, the dependent variable is transformed to a binary variable (debris flow did or did not occur), and the actual probability of the debris flow occurring is statistically modeled. Data from 399 basins located within 15 wildland fires that burned during 2000-2002 in Colorado, Idaho, Montana, and New Mexico were evaluated. More than 35 independent variables describing the burn severity, geology, land surface gradient, rainfall, and soil properties were evaluated. The models were developed as follows: (1) Basins that did and did not produce debris flows were delineated from National Elevation Data using a Geographic Information System (GIS). (2) Data describing the burn severity, geology, land surface gradient, rainfall, and soil properties were determined for each basin. These data were then downloaded to a statistics software package for analysis using logistic regression. (3) Relations between the occurrence/non-occurrence of debris flows and burn severity, geology, land surface gradient, rainfall, and soil properties were evaluated and several preliminary multivariate logistic regression models were constructed. All possible combinations of independent variables were evaluated to determine which combination produced the most effective model. The multivariate model that best predicted the occurrence of debris flows was selected. (4) The multivariate logistic regression model was entered into a GIS, and a map showing the probability of debris flows was constructed. The most effective model incorporates the percentage of each basin with slope greater than 30 percent, percentage of land burned at medium and high burn severity in each basin, particle size sorting, average storm intensity (millimeters per hour), soil organic matter content, soil permeability, and soil drainage. The results of this study demonstrate that logistic regression is a valuable tool for predicting the probability of debris flows occurring in recently-burned landscapes.

Ability Estimation and Item Calibration Using the One and Three Parameter Logistic Models: A Comparative Study. Research Report 77-1.

ERIC Educational Resources Information Center

Reckase, Mark D.

Latent trait model calibration procedures were used on data obtained from a group testing program. The one-parameter model of Wright and Panchapakesan and the three-parameter logistic model of Wingersky, Wood, and Lord were selected for comparison. These models and their corresponding estimation procedures were compared, using actual and simulated…

Predicting risk for portal vein thrombosis in acute pancreatitis patients: A comparison of radical basis function artificial neural network and logistic regression models.

PubMed

Fei, Yang; Hu, Jian; Gao, Kun; Tu, Jianfeng; Li, Wei-Qin; Wang, Wei

2017-06-01

To construct a radical basis function (RBF) artificial neural networks (ANNs) model to predict the incidence of acute pancreatitis (AP)-induced portal vein thrombosis. The analysis included 353 patients with AP who had admitted between January 2011 and December 2015. RBF ANNs model and logistic regression model were constructed based on eleven factors relevant to AP respectively. Statistical indexes were used to evaluate the value of the prediction in two models. The predict sensitivity, specificity, positive predictive value, negative predictive value and accuracy by RBF ANNs model for PVT were 73.3%, 91.4%, 68.8%, 93.0% and 87.7%, respectively. There were significant differences between the RBF ANNs and logistic regression models in these parameters (P<0.05). In addition, a comparison of the area under receiver operating characteristic curves of the two models showed a statistically significant difference (P<0.05). The RBF ANNs model is more likely to predict the occurrence of PVT induced by AP than logistic regression model. D-dimer, AMY, Hct and PT were important prediction factors of approval for AP-induced PVT. Copyright © 2017 Elsevier Inc. All rights reserved.

Kinetic compensation effect in logistic distributed activation energy model for lignocellulosic biomass pyrolysis.

PubMed

Xu, Di; Chai, Meiyun; Dong, Zhujun; Rahman, Md Maksudur; Yu, Xi; Cai, Junmeng

2018-06-04

The kinetic compensation effect in the logistic distributed activation energy model (DAEM) for lignocellulosic biomass pyrolysis was investigated. The sum of square error (SSE) surface tool was used to analyze two theoretically simulated logistic DAEM processes for cellulose and xylan pyrolysis. The logistic DAEM coupled with the pattern search method for parameter estimation was used to analyze the experimental data of cellulose pyrolysis. The results showed that many parameter sets of the logistic DAEM could fit the data at different heating rates very well for both simulated and experimental processes, and a perfect linear relationship between the logarithm of the frequency factor and the mean value of the activation energy distribution was found. The parameters of the logistic DAEM can be estimated by coupling the optimization method and isoconversional kinetic methods. The results would be helpful for chemical kinetic analysis using DAEM. Copyright © 2018 Elsevier Ltd. All rights reserved.

Scenario analysis and disaster preparedness for port and maritime logistics risk management.

PubMed

Kwesi-Buor, John; Menachof, David A; Talas, Risto

2016-08-01

System Dynamics (SD) modelling is used to investigate the impacts of policy interventions on industry actors' preparedness to mitigate risks and to recover from disruptions along the maritime logistics and supply chain network. The model suggests a bi-directional relation between regulation and industry actors' behaviour towards Disaster Preparedness (DP) in maritime logistics networks. The model also showed that the level of DP is highly contingent on forecast accuracy, technology change, attitude to risk prevention, port activities, and port environment. Copyright © 2016 Elsevier Ltd. All rights reserved.

Mortality risk prediction in burn injury: Comparison of logistic regression with machine learning approaches.

PubMed

Stylianou, Neophytos; Akbarov, Artur; Kontopantelis, Evangelos; Buchan, Iain; Dunn, Ken W

2015-08-01

Predicting mortality from burn injury has traditionally employed logistic regression models. Alternative machine learning methods have been introduced in some areas of clinical prediction as the necessary software and computational facilities have become accessible. Here we compare logistic regression and machine learning predictions of mortality from burn. An established logistic mortality model was compared to machine learning methods (artificial neural network, support vector machine, random forests and naïve Bayes) using a population-based (England & Wales) case-cohort registry. Predictive evaluation used: area under the receiver operating characteristic curve; sensitivity; specificity; positive predictive value and Youden's index. All methods had comparable discriminatory abilities, similar sensitivities, specificities and positive predictive values. Although some machine learning methods performed marginally better than logistic regression the differences were seldom statistically significant and clinically insubstantial. Random forests were marginally better for high positive predictive value and reasonable sensitivity. Neural networks yielded slightly better prediction overall. Logistic regression gives an optimal mix of performance and interpretability. The established logistic regression model of burn mortality performs well against more complex alternatives. Clinical prediction with a small set of strong, stable, independent predictors is unlikely to gain much from machine learning outside specialist research contexts. Copyright © 2015 Elsevier Ltd and ISBI. All rights reserved.

Survey of how different groups of veterinarians manage the use of neuromuscular blocking agents in anesthetized dogs.

PubMed

Martin-Flores, Manuel; Sakai, Daniel M; Campoy, Luis; Gleed, Robin D

2018-03-23

To analyze practice habits associated with the use, reversal and monitoring of nondepolarizing neuromuscular blocking agents (NMBAs) in dogs by different groups of veterinarians. Online anonymous survey to veterinarians. Data from 390 answered surveys. A questionnaire was sent to e-mail list servers of the American College of Veterinary Anesthesia and Analgesia (ACVAA-list), Sociedad Española de Anestesia y Analgesia Veterinaria (SEEAV-list), Colégio Brasileiro de Anestesiologia Veterinária (Brazilian College of Veterinary Anesthesiology; CBAV-list) and American College of Veterinary Ophthalmologists (ACVO-list) to elicit information regarding use of NMBAs and reversal agents, monitoring techniques, criteria for redosing, reversing and assessing adequacy of recovery of neuromuscular function. Binomial logistic regression was used to test for association between responses and group of veterinarians in selected questions. Veterinarians of the ACVO-list use NMBAs on a higher fraction of their caseload than other groups (all p < 0.0001). Subjective assessment (observation) of spontaneous movement, including spontaneous breathing, is the most common method for assessing neuromuscular function (43% of pooled responses); 18% of participants always reverse NMBAs, whereas 16% never reverse them. Restoration of neuromuscular function is assessed subjectively by 35% of respondents. Residual neuromuscular block is the most common concern regarding the use of NMBAs for all groups of veterinarians. Side effects of reversal agents (anticholinesterases) were of least concern for all groups. While most veterinarians are concerned about residual neuromuscular block, relatively few steps are implemented to reduce the risks of this complication, such as routine use of quantitative neuromuscular monitoring or routine reversal of NMBAs. These results suggest a limitation in transferring information among groups of veterinarians, or in implementing techniques suggested by scientific research. Copyright © 2018 Association of Veterinary Anaesthetists and American College of Veterinary Anesthesia and Analgesia. Published by Elsevier Ltd. All rights reserved.

Study on conditional probability of surface rupture: effect of fault dip and width of seismogenic layer

NASA Astrophysics Data System (ADS)

Inoue, N.

2017-12-01

The conditional probability of surface ruptures is affected by various factors, such as shallow material properties, process of earthquakes, ground motions and so on. Toda (2013) pointed out difference of the conditional probability of strike and reverse fault by considering the fault dip and width of seismogenic layer. This study evaluated conditional probability of surface rupture based on following procedures. Fault geometry was determined from the randomly generated magnitude based on The Headquarters for Earthquake Research Promotion (2017) method. If the defined fault plane was not saturated in the assumed width of the seismogenic layer, the fault plane depth was randomly provided within the seismogenic layer. The logistic analysis was performed to two data sets: surface displacement calculated by dislocation methods (Wang et al., 2003) from the defined source fault, the depth of top of the defined source fault. The estimated conditional probability from surface displacement indicated higher probability of reverse faults than that of strike faults, and this result coincides to previous similar studies (i.e. Kagawa et al., 2004; Kataoka and Kusakabe, 2005). On the contrary, the probability estimated from the depth of the source fault indicated higher probability of thrust faults than that of strike and reverse faults, and this trend is similar to the conditional probability of PFDHA results (Youngs et al., 2003; Moss and Ross, 2011). The probability of combined simulated results of thrust and reverse also shows low probability. The worldwide compiled reverse fault data include low fault dip angle earthquake. On the other hand, in the case of Japanese reverse fault, there is possibility that the conditional probability of reverse faults with less low dip angle earthquake shows low probability and indicates similar probability of strike fault (i.e. Takao et al., 2013). In the future, numerical simulation by considering failure condition of surface by the source fault would be performed in order to examine the amount of the displacement and conditional probability quantitatively.

Reverse Osmosis Processing of Organic Model Compounds and Fermentation Broths

DTIC Science & Technology

2006-04-01

AFRL-ML-TY-TP-2007-4545 POSTPRINT REVERSE OSMOSIS PROCESSING OF ORGANIC MODEL COMPOUNDS AND FERMENTATION BROTHS Robert Diltz...TELEPHONE NUMBER (Include area code) Bioresource Technology 98 (2007) 686–695Reverse osmosis processing of organic model compounds and fermentation broths...December 2005; accepted 31 January 2006 Available online 4 April 2006Abstract Post-treatment of an anaerobic fermentation broth was evaluated using a 150

Reverse total shoulder arthroplasty: research models

PubMed Central

PETRILLO, STEFANO; LONGO, UMILE GIUSEPPE; GULOTTA, LAWRENCE V.; BERTON, ALESSANDRA; KONTAXIS, ANDREAS; WRIGHT, TIMOTHY; DENARO, VINCENZO

2016-01-01

Purpose the past decade has seen a considerable increase in the use of research models to study reverse total shoulder arthroplasty (RTSA). Nevertheless, none of these models has been shown to completely reflect real in vivo conditions. Methods we performed a systematic review of the literature matching the following key words: “reverse total shoulder arthroplasty” or “reverse total shoulder replacement” or “reverse total shoulder prosthesis” and “research models” or “biomechanical models” or “physical simulators” or “virtual simulators”. The following databases were screened: Medline, Google Scholar, EMBASE, CINAHIL and Ovid. We identified and included all articles reporting research models of any kind, such as physical or virtual simulators, in which RTSA and the glenohumeral joint were reproduced. Results computer models and cadaveric models are the most commonly used, and they were shown to be reliable in simulating in vivo conditions. Bone substitute models have been used in a few studies. Mechanical testing machines provided useful information on stability factors in RTSA. Conclusion because of the limitations of each individual model, additional research is required to develop a research model of RTSA that may reduce the limitations of those presently available, and increase the reproducibility of this technique in the clinical setting. PMID:28217660

Strategies on the Implementation of China's Logistics Information Network

NASA Astrophysics Data System (ADS)

Dong, Yahui; Li, Wei; Guo, Xuwen

The economic globalization and trend of e-commerce network have determined that the logistics industry will be rapidly developed in the 21st century. In order to achieve the optimal allocation of resources, a worldwide rapid and sound customer service system should be established. The establishment of a corresponding modern logistics system is the inevitable choice of this requirement. It is also the inevitable choice for the development of modern logistics industry in China. The perfect combination of modern logistics and information network can better promote the development of the logistics industry. Through the analysis of Status of Logistics Industry in China, this paper summed up the domestic logistics enterprise logistics information system in the building of some common problems. According to logistics information systems planning methods and principles set out logistics information system to optimize the management model.

The 727 airplane target thrust reverser static performance model test for refanned JT8D engines

NASA Technical Reports Server (NTRS)

Chow, C. T. P.; Atkey, E. N.

1974-01-01

The results of a scale model static performance test of target thrust reverser configurations for the Pratt and Whitney Aircraft JT8D-100 series engine are presented. The objective of the test was to select a series of suitable candidate reverser configurations for the subsequent airplane model wind tunnel ingestion and flight controls tests. Test results indicate that adequate reverse thrust performance with compatible engine airflow match is achievable for the selected configurations. Tapering of the lips results in loss of performance and only minimal flow directivity. Door pressure surveys were conducted on a selected number of lip and fence configurations to obtain data to support the design of the thrust reverser system.

Options as information: rational reversals of evaluation and preference.

PubMed

Sher, Shlomi; McKenzie, Craig R M

2014-06-01

This article develops a rational analysis of an important class of apparent preference reversals-joint-separate reversals traditionally explained by the evaluability hypothesis. The "options-as-information" model considers a hypothetical rational actor with limited knowledge about the market distribution of a stimulus attribute. The actor's evaluations are formed via a 2-stage process-an inferential stage in which beliefs are updated on the basis of the sample of options received, followed by an assessment stage in which options are evaluated in light of these updated beliefs. This process generates joint-separate reversals in standard experimental designs. The normative model explains why the evaluability hypothesis works when it does, identifies boundary conditions for the hypothesis, and clarifies some common misconceptions about these effects. In particular, it implies that joint-separate reversals are not irrational; in fact, they are not preference reversals. However, in expanded designs where more than 2 options are jointly evaluated, the model predicts that genuine (and rational) preference reversals will sometimes emerge. Results of 3 experiments suggest an excellent fit between the rational actor model and the judgments of human actors in joint-separate experiments. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Ramsay-Curve Item Response Theory for the Three-Parameter Logistic Item Response Model

ERIC Educational Resources Information Center

Woods, Carol M.

2008-01-01

In Ramsay-curve item response theory (RC-IRT), the latent variable distribution is estimated simultaneously with the item parameters of a unidimensional item response model using marginal maximum likelihood estimation. This study evaluates RC-IRT for the three-parameter logistic (3PL) model with comparisons to the normal model and to the empirical…

On Interpreting the Model Parameters for the Three Parameter Logistic Model

ERIC Educational Resources Information Center

Maris, Gunter; Bechger, Timo

2009-01-01

This paper addresses two problems relating to the interpretability of the model parameters in the three parameter logistic model. First, it is shown that if the values of the discrimination parameters are all the same, the remaining parameters are nonidentifiable in a nontrivial way that involves not only ability and item difficulty, but also the…

Population pharmacokinetic–pharmacodynamic analysis for sugammadex-mediated reversal of rocuronium-induced neuromuscular blockade

PubMed Central

Kleijn, Huub J; Zollinger, Daniel P; van den Heuvel, Michiel W; Kerbusch, Thomas

2011-01-01

AIMS An integrated population pharmacokinetic–pharmacodynamic model was developed with the following aims: to simultaneously describe pharmacokinetic behaviour of sugammadex and rocuronium; to establish the pharmacokinetic–pharmacodynamic model for rocuronium-induced neuromuscular blockade and reversal by sugammadex; to evaluate covariate effects; and to explore, by simulation, typical covariate effects on reversal time. METHODS Data (n = 446) from eight sugammadex clinical studies covering men, women, non-Asians, Asians, paediatrics, adults and the elderly, with various degrees of renal impairment, were used. Modelling and simulation techniques based on physiological principles were applied to capture rocuronium and sugammadex pharmacokinetics and pharmacodynamics and to identify and quantify covariate effects. RESULTS Sugammadex pharmacokinetics were affected by renal function, bodyweight and race, and rocuronium pharmacokinetics were affected by age, renal function and race. Sevoflurane potentiated rocuronium-induced neuromuscular blockade. Posterior predictive checks and bootstrapping illustrated the accuracy and robustness of the model. External validation showed concordance between observed and predicted reversal times, but interindividual variability in reversal time was pronounced. Simulated reversal times in typical adults were 0.8, 1.5 and 1.4 min upon reversal with sugammadex 16 mg kg−1 3 min after rocuronium, sugammadex 4 mg kg−1 during deep neuromuscular blockade and sugammadex 2 mg kg−1 during moderate blockade, respectively. Simulations indicated that reversal times were faster in paediatric patients and slightly slower in elderly patients compared with adults. Renal function did not affect reversal time. CONCLUSIONS Simulations of the therapeutic dosing regimens demonstrated limited impact of age, renal function and sevoflurane use, as predicted reversal time in typical subjects was always <2 min. PMID:21535448

Ring test evaluation of the detection of influenza A virus in swine oral fluids by real-time reverse-transcription polymerase chain reaction and virus isolation

PubMed Central

Goodell, Christa K.; Zhang, Jianqiang; Strait, Erin; Harmon, Karen; Patnayak, Devi; Otterson, Tracy; Culhane, Marie; Christopher-Hennings, Jane; Clement, Travis; Leslie-Steen, Pamela; Hesse, Richard; Anderson, Joe; Skarbek, Kevin; Vincent, Amy; Kitikoon, Pravina; Swenson, Sabrina; Jenkins-Moore, Melinda; McGill, Jodi; Rauh, Rolf; Nelson, William; O’Connell, Catherine; Shah, Rohan; Wang, Chong; Main, Rodger; Zimmerman, Jeffrey J.

2016-01-01

The probability of detecting influenza A virus (IAV) in oral fluid (OF) specimens was calculated for each of 13 assays based on real-time reverse-transcription polymerase chain reaction (rRT-PCR) and 7 assays based on virus isolation (VI). The OF specimens were inoculated with H1N1 or H3N2 IAV and serially diluted 10-fold (10−1 to 10−8). Eight participating laboratories received 180 randomized OF samples (10 replicates × 8 dilutions × 2 IAV subtypes plus 20 IAV-negative samples) and performed the rRT-PCR and VI procedure(s) of their choice. Analysis of the results with a mixed-effect logistic-regression model identified dilution and assay as variables significant (P < 0.0001) for IAV detection in OF by rRT-PCR or VI. Virus subtype was not significant for IAV detection by either rRT-PCR (P = 0.457) or VI (P = 0.101). For rRT-PCR the cycle threshold (Ct) values increased consistently with dilution but varied widely. Therefore, it was not possible to predict VI success on the basis of Ct values. The success of VI was inversely related to the dilution of the sample; the assay was generally unsuccessful at lower virus concentrations. Successful swine health monitoring and disease surveillance require assays with consistent performance, but significant differences in reproducibility were observed among the assays evaluated. PMID:26733728

Trust and health: testing the reverse causality hypothesis

PubMed Central

Giordano, Giuseppe Nicola; Lindström, Martin

2016-01-01

Background Social capital research has consistently shown positive associations between generalised trust and health outcomes over 2 decades. Longitudinal studies attempting to test causal relationships further support the theory that trust is an independent predictor of health. However, as the reverse causality hypothesis has yet to be empirically tested, a knowledge gap remains. The aim of this study, therefore, was to investigate if health status predicts trust. Methods Data employed in this study came from 4 waves of the British Household Panel Survey between years 2000 and 2007 (N=8114). The sample was stratified by baseline trust to investigate temporal relationships between prior self-rated health (SRH) and changes in trust. We used logistic regression models with random effects, as trust was expected to be more similar within the same individuals over time. Results From the ‘Can trust at baseline’ cohort, poor SRH at time (t−1) predicted low trust at time (t) (OR=1.38). Likewise, good health predicted high trust within the ‘Cannot’ trust cohort (OR=1.30). These patterns of positive association remained after robustness checks, which adjusted for misclassification of outcome (trust) status and the existence of other temporal pathways. Conclusions This study offers empirical evidence to support the circular nature of trust/health relationship. The stability of association between prior health status and changes in trust over time differed between cohorts, hinting at the existence of complex pathways rather than a simple positive feedback loop. PMID:26546287

Orthographic similarity: the case of "reversed anagrams".

PubMed

Morris, Alison L; Still, Mary L

2012-07-01

How orthographically similar are words such as paws and swap, flow and wolf, or live and evil? According to the letter position coding schemes used in models of visual word recognition, these reversed anagrams are considered to be less similar than words that share letters in the same absolute or relative positions (such as home and hose or plan and lane). Therefore, reversed anagrams should not produce the standard orthographic similarity effects found using substitution neighbors (e.g., home, hose). Simulations using the spatial coding model (Davis, Psychological Review 117, 713-758, 2010), for example, predict an inhibitory masked-priming effect for substitution neighbor word pairs but a null effect for reversed anagrams. Nevertheless, we obtained significant inhibitory priming using both stimulus types (Experiment 1). We also demonstrated that robust repetition blindness can be obtained for reversed anagrams (Experiment 2). Reversed anagrams therefore provide a new test for models of visual word recognition and orthographic similarity.

[Application of SAS macro to evaluated multiplicative and additive interaction in logistic and Cox regression in clinical practices].

PubMed

Nie, Z Q; Ou, Y Q; Zhuang, J; Qu, Y J; Mai, J Z; Chen, J M; Liu, X Q

2016-05-01

Conditional logistic regression analysis and unconditional logistic regression analysis are commonly used in case control study, but Cox proportional hazard model is often used in survival data analysis. Most literature only refer to main effect model, however, generalized linear model differs from general linear model, and the interaction was composed of multiplicative interaction and additive interaction. The former is only statistical significant, but the latter has biological significance. In this paper, macros was written by using SAS 9.4 and the contrast ratio, attributable proportion due to interaction and synergy index were calculated while calculating the items of logistic and Cox regression interactions, and the confidence intervals of Wald, delta and profile likelihood were used to evaluate additive interaction for the reference in big data analysis in clinical epidemiology and in analysis of genetic multiplicative and additive interactions.

Prediction of unwanted pregnancies using logistic regression, probit regression and discriminant analysis

PubMed Central

Ebrahimzadeh, Farzad; Hajizadeh, Ebrahim; Vahabi, Nasim; Almasian, Mohammad; Bakhteyar, Katayoon

2015-01-01

Background: Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. Methods: In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling; relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used. Results: The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively. Conclusion: Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended. PMID:26793655

Prediction of unwanted pregnancies using logistic regression, probit regression and discriminant analysis.

PubMed

Ebrahimzadeh, Farzad; Hajizadeh, Ebrahim; Vahabi, Nasim; Almasian, Mohammad; Bakhteyar, Katayoon

2015-01-01

Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling; relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used. The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively. Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended.

Modeling of pathogen survival during simulated gastric digestion.

PubMed

Koseki, Shige; Mizuno, Yasuko; Sotome, Itaru

2011-02-01

The objective of the present study was to develop a mathematical model of pathogenic bacterial inactivation kinetics in a gastric environment in order to further understand a part of the infectious dose-response mechanism. The major bacterial pathogens Listeria monocytogenes, Escherichia coli O157:H7, and Salmonella spp. were examined by using simulated gastric fluid adjusted to various pH values. To correspond to the various pHs in a stomach during digestion, a modified logistic differential equation model and the Weibull differential equation model were examined. The specific inactivation rate for each pathogen was successfully described by a square-root model as a function of pH. The square-root models were combined with the modified logistic differential equation to obtain a complete inactivation curve. Both the modified logistic and Weibull models provided a highly accurate fitting of the static pH conditions for every pathogen. However, while the residuals plots of the modified logistic model indicated no systematic bias and/or regional prediction problems, the residuals plots of the Weibull model showed a systematic bias. The modified logistic model appropriately predicted the pathogen behavior in the simulated gastric digestion process with actual food, including cut lettuce, minced tuna, hamburger, and scrambled egg. Although the developed model enabled us to predict pathogen inactivation during gastric digestion, its results also suggested that the ingested bacteria in the stomach would barely be inactivated in the real digestion process. The results of this study will provide important information on a part of the dose-response mechanism of bacterial pathogens.

Modeling of Pathogen Survival during Simulated Gastric Digestion ▿

PubMed Central

Koseki, Shige; Mizuno, Yasuko; Sotome, Itaru

2011-01-01

The objective of the present study was to develop a mathematical model of pathogenic bacterial inactivation kinetics in a gastric environment in order to further understand a part of the infectious dose-response mechanism. The major bacterial pathogens Listeria monocytogenes, Escherichia coli O157:H7, and Salmonella spp. were examined by using simulated gastric fluid adjusted to various pH values. To correspond to the various pHs in a stomach during digestion, a modified logistic differential equation model and the Weibull differential equation model were examined. The specific inactivation rate for each pathogen was successfully described by a square-root model as a function of pH. The square-root models were combined with the modified logistic differential equation to obtain a complete inactivation curve. Both the modified logistic and Weibull models provided a highly accurate fitting of the static pH conditions for every pathogen. However, while the residuals plots of the modified logistic model indicated no systematic bias and/or regional prediction problems, the residuals plots of the Weibull model showed a systematic bias. The modified logistic model appropriately predicted the pathogen behavior in the simulated gastric digestion process with actual food, including cut lettuce, minced tuna, hamburger, and scrambled egg. Although the developed model enabled us to predict pathogen inactivation during gastric digestion, its results also suggested that the ingested bacteria in the stomach would barely be inactivated in the real digestion process. The results of this study will provide important information on a part of the dose-response mechanism of bacterial pathogens. PMID:21131530

Model describing the effect of employment of the United States military in a complex emergency.

PubMed

MacMillan, Donald S

2005-01-01

The end of the Cold War vastly altered the worldwide political landscape. With the loss of a main competitor, the United States (US) military has had to adapt its strategic, operational, and tactical doctrines to an ever-increasing variety of non-traditional missions, including humanitarian operations. Complex emergencies (CEs) are defined in this paper from a political and military perspective, various factors that contribute to their development are described, and issues resulting from the employment of US military forces are discussed. A model was developed to illustrate the course of a humanitarian emergency and the potential impact of a military response. The US intervention in Haiti, Northern Iraq, Kosovo, Somalia, Bosnia, and Rwanda serve as examples. A CE develops when there is civil conflict, loss of national governmental authority, a mass population movement, and massive economic failure, each leading to a general decline in food security. The military can alleviate a CE in four ways: (1) provide security for relief efforts; (2) enforce negotiated settlements; (3) provide security for non-combatants; and/or (4) employ logistical capabilities. The model incorporates Norton and Miskel's taxonomy of identifying failing states and helps illustrate the factors that lead to a CE. The model can be used to determine if and when military intervention will have the greatest impact. The model demonstrates that early military intervention and mission assignment within the core competencies of the forces can reverse the course of a CE. Further study will be needed to verify the model.

Simulating Sustainment for an Unmanned Logistics System Concept of Operation in Support of Distributed Operations

DTIC Science & Technology

2017-06-01

designed experiment to model and explore a ship-to-shore logistics process supporting dispersed units via three types of ULSs, which vary primarily in...systems, simulation, discrete event simulation, design of experiments, data analysis, simplekit, nearly orthogonal and balanced designs 15. NUMBER OF... designed experiment to model and explore a ship-to-shore logistics process supporting dispersed units via three types of ULSs, which vary primarily

Predictors of course in obsessive-compulsive disorder: logistic regression versus Cox regression for recurrent events.

PubMed

Kempe, P T; van Oppen, P; de Haan, E; Twisk, J W R; Sluis, A; Smit, J H; van Dyck, R; van Balkom, A J L M

2007-09-01

Two methods for predicting remissions in obsessive-compulsive disorder (OCD) treatment are evaluated. Y-BOCS measurements of 88 patients with a primary OCD (DSM-III-R) diagnosis were performed over a 16-week treatment period, and during three follow-ups. Remission at any measurement was defined as a Y-BOCS score lower than thirteen combined with a reduction of seven points when compared with baseline. Logistic regression models were compared with a Cox regression for recurrent events model. Logistic regression yielded different models at different evaluation times. The recurrent events model remained stable when fewer measurements were used. Higher baseline levels of neuroticism and more severe OCD symptoms were associated with a lower chance of remission, early age of onset and more depressive symptoms with a higher chance. Choice of outcome time affects logistic regression prediction models. Recurrent events analysis uses all information on remissions and relapses. Short- and long-term predictors for OCD remission show overlap.

Derivation of the linear-logistic model and Cox's proportional hazard model from a canonical system description.

PubMed

Voit, E O; Knapp, R G

1997-08-15

The linear-logistic regression model and Cox's proportional hazard model are widely used in epidemiology. Their successful application leaves no doubt that they are accurate reflections of observed disease processes and their associated risks or incidence rates. In spite of their prominence, it is not a priori evident why these models work. This article presents a derivation of the two models from the framework of canonical modeling. It begins with a general description of the dynamics between risk sources and disease development, formulates this description in the canonical representation of an S-system, and shows how the linear-logistic model and Cox's proportional hazard model follow naturally from this representation. The article interprets the model parameters in terms of epidemiological concepts as well as in terms of general systems theory and explains the assumptions and limitations generally accepted in the application of these epidemiological models.

The Benefits of Including Clinical Factors in Rectal Normal Tissue Complication Probability Modeling After Radiotherapy for Prostate Cancer

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

Defraene, Gilles, E-mail: gilles.defraene@uzleuven.be; Van den Bergh, Laura; Al-Mamgani, Abrahim

2012-03-01

Purpose: To study the impact of clinical predisposing factors on rectal normal tissue complication probability modeling using the updated results of the Dutch prostate dose-escalation trial. Methods and Materials: Toxicity data of 512 patients (conformally treated to 68 Gy [n = 284] and 78 Gy [n = 228]) with complete follow-up at 3 years after radiotherapy were studied. Scored end points were rectal bleeding, high stool frequency, and fecal incontinence. Two traditional dose-based models (Lyman-Kutcher-Burman (LKB) and Relative Seriality (RS) and a logistic model were fitted using a maximum likelihood approach. Furthermore, these model fits were improved by including themore » most significant clinical factors. The area under the receiver operating characteristic curve (AUC) was used to compare the discriminating ability of all fits. Results: Including clinical factors significantly increased the predictive power of the models for all end points. In the optimal LKB, RS, and logistic models for rectal bleeding and fecal incontinence, the first significant (p = 0.011-0.013) clinical factor was 'previous abdominal surgery.' As second significant (p = 0.012-0.016) factor, 'cardiac history' was included in all three rectal bleeding fits, whereas including 'diabetes' was significant (p = 0.039-0.048) in fecal incontinence modeling but only in the LKB and logistic models. High stool frequency fits only benefitted significantly (p = 0.003-0.006) from the inclusion of the baseline toxicity score. For all models rectal bleeding fits had the highest AUC (0.77) where it was 0.63 and 0.68 for high stool frequency and fecal incontinence, respectively. LKB and logistic model fits resulted in similar values for the volume parameter. The steepness parameter was somewhat higher in the logistic model, also resulting in a slightly lower D{sub 50}. Anal wall DVHs were used for fecal incontinence, whereas anorectal wall dose best described the other two endpoints. Conclusions: Comparable prediction models were obtained with LKB, RS, and logistic NTCP models. Including clinical factors improved the predictive power of all models significantly.« less

Enhanced model of photovoltaic cell/panel/array considering the direct and reverse modes

NASA Astrophysics Data System (ADS)

Zegaoui, Abdallah; Boutoubat, Mohamed; Sawicki, Jean-Paul; Kessaissia, Fatma Zohra; Djahbar, Abdelkader; Aillerie, Michel

2018-05-01

This paper presents an improved generalized physical model for photovoltaic, PV cells, panels and arrays taking into account the behavior of these devices when considering their biasing existing in direct and reverse modes. Existing PV physical models generally are very efficient for simulating influence of irradiation changes on the short circuit current but they could not visualize the influences of temperature changes. The Enhanced Direct and Reverse Mode model, named EDRM model, enlightens the influence on the short-circuit current of both temperature and irradiation in the reverse mode of the considered PV devices. Due to its easy implementation, the proposed model can be a useful power tool for the development of new photovoltaic systems taking into account and in a more exhaustive manner, environmental conditions. The developed model was tested on a marketed PV panel and it gives a satisfactory results compared with parameters given in the manufacturer datasheet.

Large unbalanced credit scoring using Lasso-logistic regression ensemble.

PubMed

Wang, Hong; Xu, Qingsong; Zhou, Lifeng

2015-01-01

Recently, various ensemble learning methods with different base classifiers have been proposed for credit scoring problems. However, for various reasons, there has been little research using logistic regression as the base classifier. In this paper, given large unbalanced data, we consider the plausibility of ensemble learning using regularized logistic regression as the base classifier to deal with credit scoring problems. In this research, the data is first balanced and diversified by clustering and bagging algorithms. Then we apply a Lasso-logistic regression learning ensemble to evaluate the credit risks. We show that the proposed algorithm outperforms popular credit scoring models such as decision tree, Lasso-logistic regression and random forests in terms of AUC and F-measure. We also provide two importance measures for the proposed model to identify important variables in the data.

Habitat features and predictive habitat modeling for the Colorado chipmunk in southern New Mexico

USGS Publications Warehouse

Rivieccio, M.; Thompson, B.C.; Gould, W.R.; Boykin, K.G.

2003-01-01

Two subspecies of Colorado chipmunk (state threatened and federal species of concern) occur in southern New Mexico: Tamias quadrivittatus australis in the Organ Mountains and T. q. oscuraensis in the Oscura Mountains. We developed a GIS model of potentially suitable habitat based on vegetation and elevation features, evaluated site classifications of the GIS model, and determined vegetation and terrain features associated with chipmunk occurrence. We compared GIS model classifications with actual vegetation and elevation features measured at 37 sites. At 60 sites we measured 18 habitat variables regarding slope, aspect, tree species, shrub species, and ground cover. We used logistic regression to analyze habitat variables associated with chipmunk presence/absence. All (100%) 37 sample sites (28 predicted suitable, 9 predicted unsuitable) were classified correctly by the GIS model regarding elevation and vegetation. For 28 sites predicted suitable by the GIS model, 18 sites (64%) appeared visually suitable based on habitat variables selected from logistic regression analyses, of which 10 sites (36%) were specifically predicted as suitable habitat via logistic regression. We detected chipmunks at 70% of sites deemed suitable via the logistic regression models. Shrub cover, tree density, plant proximity, presence of logs, and presence of rock outcrop were retained in the logistic model for the Oscura Mountains; litter, shrub cover, and grass cover were retained in the logistic model for the Organ Mountains. Evaluation of predictive models illustrates the need for multi-stage analyses to best judge performance. Microhabitat analyses indicate prospective needs for different management strategies between the subspecies. Sensitivities of each population of the Colorado chipmunk to natural and prescribed fire suggest that partial burnings of areas inhabited by Colorado chipmunks in southern New Mexico may be beneficial. These partial burnings may later help avoid a fire that could substantially reduce habitat of chipmunks over a mountain range.

Application of wireless sensor network technology in logistics information system

NASA Astrophysics Data System (ADS)

Xu, Tao; Gong, Lina; Zhang, Wei; Li, Xuhong; Wang, Xia; Pan, Wenwen

2017-04-01

This paper introduces the basic concepts of active RFID (WSN-ARFID) based on wireless sensor networks and analyzes the shortcomings of the existing RFID-based logistics monitoring system. Integrated wireless sensor network technology and the scrambling point of RFID technology. A new real-time logistics detection system based on WSN and RFID, a model of logistics system based on WSN-ARFID is proposed, and the feasibility of this technology applied to logistics field is analyzed.

The scopolamine-reversal paradigm in rats and monkeys: the importance of computer-assisted operant-conditioning memory tasks for screening drug candidates.

PubMed

Buccafusco, Jerry J; Terry, Alvin V; Webster, Scott J; Martin, Daniel; Hohnadel, Elizabeth J; Bouchard, Kristy A; Warner, Samantha E

2008-08-01

The scopolamine-reversal model is enjoying a resurgence of interest in clinical studies as a reversible pharmacological model for Alzheimer's disease (AD). The cognitive impairment associated with scopolamine is similar to that in AD. The scopolamine model is not simply a cholinergic model, as it can be reversed by drugs that are noncholinergic cognition-enhancing agents. The objective of the study was to determine relevance of computer-assisted operant-conditioning tasks in the scopolamine-reversal model in rats and monkeys. Rats were evaluated for their acquisition of a spatial reference memory task in the Morris water maze. A separate cohort was proficient in performance of an automated delayed stimulus discrimination task (DSDT). Rhesus monkeys were proficient in the performance of an automated delayed matching-to-sample task (DMTS). The AD drug donepezil was evaluated for its ability to reverse the decrements in accuracy induced by scopolamine administration in all three tasks. In the DSDT and DMTS tasks, the effects of donepezil were delay (retention interval)-dependent, affecting primarily short delay trials. Donepezil produced significant but partial reversals of the scopolamine-induced impairment in task accuracies after 2 mg/kg in the water maze, after 1 mg/kg in the DSDT, and after 50 microg/kg in the DMTS task. The two operant-conditioning tasks (DSDT and DMTS) provided data most in keeping with those reported in clinical studies with these drugs. The model applied to nonhuman primates provides an excellent transitional model for new cognition-enhancing drugs before clinical trials.

A model for 'reverse innovation' in health care.

PubMed

Depasse, Jacqueline W; Lee, Patrick T

2013-08-30

'Reverse innovation,' a principle well established in the business world, describes the flow of ideas from emerging to more developed economies. There is strong and growing interest in applying this concept to health care, yet there is currently no framework for describing the stages of reverse innovation or identifying opportunities to accelerate the development process. This paper combines the business concept of reverse innovation with diffusion of innovation theory to propose a model for reverse innovation as a way to innovate in health care. Our model includes the following steps: (1) identifying a problem common to lower- and higher-income countries; (2) innovation and spread in the low-income country (LIC); (3) crossover to the higher-income country (HIC); and (4) innovation and spread in the HIC. The crucial populations in this pathway, drawing from diffusion of innovation theory, are LIC innovators, LIC early adopters, and HIC innovators. We illustrate the model with three examples of current reverse innovations. We then propose four sets of specific actions that forward-looking policymakers, entrepreneurs, health system leaders, and researchers may take to accelerate the movement of promising solutions through the reverse innovation pipeline: (1) identify high-priority problems shared by HICs and LICs; (2) create slack for change, especially for LIC innovators, LIC early adopters, and HIC innovators; (3) create spannable social distances between LIC early adopters and HIC innovators; and (4) measure reverse innovation activity globally.

Preserving Institutional Privacy in Distributed binary Logistic Regression.

PubMed

Wu, Yuan; Jiang, Xiaoqian; Ohno-Machado, Lucila

2012-01-01

Privacy is becoming a major concern when sharing biomedical data across institutions. Although methods for protecting privacy of individual patients have been proposed, it is not clear how to protect the institutional privacy, which is many times a critical concern of data custodians. Built upon our previous work, Grid Binary LOgistic REgression (GLORE)1, we developed an Institutional Privacy-preserving Distributed binary Logistic Regression model (IPDLR) that considers both individual and institutional privacy for building a logistic regression model in a distributed manner. We tested our method using both simulated and clinical data, showing how it is possible to protect the privacy of individuals and of institutions using a distributed strategy.

The application of virtual reality systems as a support of digital manufacturing and logistics

NASA Astrophysics Data System (ADS)

Golda, G.; Kampa, A.; Paprocka, I.

2016-08-01

Modern trends in development of computer aided techniques are heading toward the integration of design competitive products and so-called "digital manufacturing and logistics", supported by computer simulation software. All phases of product lifecycle: starting from design of a new product, through planning and control of manufacturing, assembly, internal logistics and repairs, quality control, distribution to customers and after-sale service, up to its recycling or utilization should be aided and managed by advanced packages of product lifecycle management software. Important problems for providing the efficient flow of materials in supply chain management of whole product lifecycle, using computer simulation will be described on that paper. Authors will pay attention to the processes of acquiring relevant information and correct data, necessary for virtual modeling and computer simulation of integrated manufacturing and logistics systems. The article describes possibilities of use an applications of virtual reality software for modeling and simulation the production and logistics processes in enterprise in different aspects of product lifecycle management. The authors demonstrate effective method of creating computer simulations for digital manufacturing and logistics and show modeled and programmed examples and solutions. They pay attention to development trends and show options of the applications that go beyond enterprise.

Logistic regression analysis of conventional ultrasonography, strain elastosonography, and contrast-enhanced ultrasound characteristics for the differentiation of benign and malignant thyroid nodules

PubMed Central

Deng, Yingyuan; Wang, Tianfu; Chen, Siping; Liu, Weixiang

2017-01-01

The aim of the study is to screen the significant sonographic features by logistic regression analysis and fit a model to diagnose thyroid nodules. A total of 525 pathological thyroid nodules were retrospectively analyzed. All the nodules underwent conventional ultrasonography (US), strain elastosonography (SE), and contrast -enhanced ultrasound (CEUS). Those nodules’ 12 suspicious sonographic features were used to assess thyroid nodules. The significant features of diagnosing thyroid nodules were picked out by logistic regression analysis. All variables that were statistically related to diagnosis of thyroid nodules, at a level of p < 0.05 were embodied in a logistic regression analysis model. The significant features in the logistic regression model of diagnosing thyroid nodules were calcification, suspected cervical lymph node metastasis, hypoenhancement pattern, margin, shape, vascularity, posterior acoustic, echogenicity, and elastography score. According to the results of logistic regression analysis, the formula that could predict whether or not thyroid nodules are malignant was established. The area under the receiver operating curve (ROC) was 0.930 and the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 83.77%, 89.56%, 87.05%, 86.04%, and 87.79% respectively. PMID:29228030

Logistic regression analysis of conventional ultrasonography, strain elastosonography, and contrast-enhanced ultrasound characteristics for the differentiation of benign and malignant thyroid nodules.

PubMed

Pang, Tiantian; Huang, Leidan; Deng, Yingyuan; Wang, Tianfu; Chen, Siping; Gong, Xuehao; Liu, Weixiang

2017-01-01

The aim of the study is to screen the significant sonographic features by logistic regression analysis and fit a model to diagnose thyroid nodules. A total of 525 pathological thyroid nodules were retrospectively analyzed. All the nodules underwent conventional ultrasonography (US), strain elastosonography (SE), and contrast -enhanced ultrasound (CEUS). Those nodules' 12 suspicious sonographic features were used to assess thyroid nodules. The significant features of diagnosing thyroid nodules were picked out by logistic regression analysis. All variables that were statistically related to diagnosis of thyroid nodules, at a level of p < 0.05 were embodied in a logistic regression analysis model. The significant features in the logistic regression model of diagnosing thyroid nodules were calcification, suspected cervical lymph node metastasis, hypoenhancement pattern, margin, shape, vascularity, posterior acoustic, echogenicity, and elastography score. According to the results of logistic regression analysis, the formula that could predict whether or not thyroid nodules are malignant was established. The area under the receiver operating curve (ROC) was 0.930 and the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 83.77%, 89.56%, 87.05%, 86.04%, and 87.79% respectively.

Second-hand market as an alternative in reverse logistics

NASA Astrophysics Data System (ADS)

Pochampally, Kishore K.; Gupta, Surendra M.

2004-02-01

Collectors of discarded products seldom know when those products were bought and why they are discarded. Also, the products do not indicate their remaining life periods. So, it is difficult to decide if it is "sensible" to repair (if necessary) a particular product for subsequent sale on the second-hand market or to disassemble it partially or completely for subsequent remanufacture and/or recycle. To this end, we build an expert system using Bayesian updating process and fuzzy set theory, to aid such decision-making. A numerical example demonstrates the building approach.

Inventory Optimization of USMC Uniforms Through Reverse Logistics

DTIC Science & Technology

2016-05-01

Blouse, Desert, MCCUU wiPermethrin 3 $ 38.86 $ 116.58 8415-01-527-1430 03092 Blouse, Woodland, MCCUU wiPermethrin 2 $ 38.90 $ 77.80 8430-01-591· 1103 ...1 $ 11.66 $ 11.66 8455-01-503- 1103 10054 Clasp, Necktie, Gold Plated 1 $ 1.46 $ 1.46 8405-01-279-5579 02049 Coat, Man’s PolyiWool Gabardne, Green. w...Woodland, MCCUU wiPermethrin 2 $ 38.90 $ 77.80 8430-01-591· 1103 04094 Boot, Rugged All Terrain (RAT), Hot Weather $ 141.70 $ 141.70 8430-01-563-6897 03544

Using Logistic Regression to Predict the Probability of Debris Flows in Areas Burned by Wildfires, Southern California, 2003-2006

USGS Publications Warehouse

Rupert, Michael G.; Cannon, Susan H.; Gartner, Joseph E.; Michael, John A.; Helsel, Dennis R.

2008-01-01

Logistic regression was used to develop statistical models that can be used to predict the probability of debris flows in areas recently burned by wildfires by using data from 14 wildfires that burned in southern California during 2003-2006. Twenty-eight independent variables describing the basin morphology, burn severity, rainfall, and soil properties of 306 drainage basins located within those burned areas were evaluated. The models were developed as follows: (1) Basins that did and did not produce debris flows soon after the 2003 to 2006 fires were delineated from data in the National Elevation Dataset using a geographic information system; (2) Data describing the basin morphology, burn severity, rainfall, and soil properties were compiled for each basin. These data were then input to a statistics software package for analysis using logistic regression; and (3) Relations between the occurrence or absence of debris flows and the basin morphology, burn severity, rainfall, and soil properties were evaluated, and five multivariate logistic regression models were constructed. All possible combinations of independent variables were evaluated to determine which combinations produced the most effective models, and the multivariate models that best predicted the occurrence of debris flows were identified. Percentage of high burn severity and 3-hour peak rainfall intensity were significant variables in all models. Soil organic matter content and soil clay content were significant variables in all models except Model 5. Soil slope was a significant variable in all models except Model 4. The most suitable model can be selected from these five models on the basis of the availability of independent variables in the particular area of interest and field checking of probability maps. The multivariate logistic regression models can be entered into a geographic information system, and maps showing the probability of debris flows can be constructed in recently burned areas of southern California. This study demonstrates that logistic regression is a valuable tool for developing models that predict the probability of debris flows occurring in recently burned landscapes.

An Application of a Multidimensional Extension of the Two-Parameter Logistic Latent Trait Model.

ERIC Educational Resources Information Center

McKinley, Robert L.; Reckase, Mark D.

A latent trait model is described that is appropriate for use with tests that measure more than one dimension, and its application to both real and simulated test data is demonstrated. Procedures for estimating the parameters of the model are presented. The research objectives are to determine whether the two-parameter logistic model more…

Graded Response Model Based on the Logistic Positive Exponent Family of Models for Dichotomous Responses

ERIC Educational Resources Information Center

Samejima, Fumiko

2008-01-01

Samejima ("Psychometrika "65:319--335, 2000) proposed the logistic positive exponent family of models (LPEF) for dichotomous responses in the unidimensional latent space. The objective of the present paper is to propose and discuss a graded response model that is expanded from the LPEF, in the context of item response theory (IRT). This…

Locally Dependent Linear Logistic Test Model with Person Covariates

ERIC Educational Resources Information Center

Ip, Edward H.; Smits, Dirk J. M.; De Boeck, Paul

2009-01-01

The article proposes a family of item-response models that allow the separate and independent specification of three orthogonal components: item attribute, person covariate, and local item dependence. Special interest lies in extending the linear logistic test model, which is commonly used to measure item attributes, to tests with embedded item…

A Bayesian Semiparametric Item Response Model with Dirichlet Process Priors

ERIC Educational Resources Information Center

Miyazaki, Kei; Hoshino, Takahiro

2009-01-01

In Item Response Theory (IRT), item characteristic curves (ICCs) are illustrated through logistic models or normal ogive models, and the probability that examinees give the correct answer is usually a monotonically increasing function of their ability parameters. However, since only limited patterns of shapes can be obtained from logistic models…

Unitary Response Regression Models

ERIC Educational Resources Information Center

Lipovetsky, S.

2007-01-01

The dependent variable in a regular linear regression is a numerical variable, and in a logistic regression it is a binary or categorical variable. In these models the dependent variable has varying values. However, there are problems yielding an identity output of a constant value which can also be modelled in a linear or logistic regression with…

The alarming problems of confounding equivalence using logistic regression models in the perspective of causal diagrams.

PubMed

Yu, Yuanyuan; Li, Hongkai; Sun, Xiaoru; Su, Ping; Wang, Tingting; Liu, Yi; Yuan, Zhongshang; Liu, Yanxun; Xue, Fuzhong

2017-12-28

Confounders can produce spurious associations between exposure and outcome in observational studies. For majority of epidemiologists, adjusting for confounders using logistic regression model is their habitual method, though it has some problems in accuracy and precision. It is, therefore, important to highlight the problems of logistic regression and search the alternative method. Four causal diagram models were defined to summarize confounding equivalence. Both theoretical proofs and simulation studies were performed to verify whether conditioning on different confounding equivalence sets had the same bias-reducing potential and then to select the optimum adjusting strategy, in which logistic regression model and inverse probability weighting based marginal structural model (IPW-based-MSM) were compared. The "do-calculus" was used to calculate the true causal effect of exposure on outcome, then the bias and standard error were used to evaluate the performances of different strategies. Adjusting for different sets of confounding equivalence, as judged by identical Markov boundaries, produced different bias-reducing potential in the logistic regression model. For the sets satisfied G-admissibility, adjusting for the set including all the confounders reduced the equivalent bias to the one containing the parent nodes of the outcome, while the bias after adjusting for the parent nodes of exposure was not equivalent to them. In addition, all causal effect estimations through logistic regression were biased, although the estimation after adjusting for the parent nodes of exposure was nearest to the true causal effect. However, conditioning on different confounding equivalence sets had the same bias-reducing potential under IPW-based-MSM. Compared with logistic regression, the IPW-based-MSM could obtain unbiased causal effect estimation when the adjusted confounders satisfied G-admissibility and the optimal strategy was to adjust for the parent nodes of outcome, which obtained the highest precision. All adjustment strategies through logistic regression were biased for causal effect estimation, while IPW-based-MSM could always obtain unbiased estimation when the adjusted set satisfied G-admissibility. Thus, IPW-based-MSM was recommended to adjust for confounders set.

London Measure of Unplanned Pregnancy: guidance for its use as an outcome measure

PubMed Central

Hall, Jennifer A; Barrett, Geraldine; Copas, Andrew; Stephenson, Judith

2017-01-01

Background The London Measure of Unplanned Pregnancy (LMUP) is a psychometrically validated measure of the degree of intention of a current or recent pregnancy. The LMUP is increasingly being used worldwide, and can be used to evaluate family planning or preconception care programs. However, beyond recommending the use of the full LMUP scale, there is no published guidance on how to use the LMUP as an outcome measure. Ordinal logistic regression has been recommended informally, but studies published to date have all used binary logistic regression and dichotomized the scale at different cut points. There is thus a need for evidence-based guidance to provide a standardized methodology for multivariate analysis and to enable comparison of results. This paper makes recommendations for the regression method for analysis of the LMUP as an outcome measure. Materials and methods Data collected from 4,244 pregnant women in Malawi were used to compare five regression methods: linear, logistic with two cut points, and ordinal logistic with either the full or grouped LMUP score. The recommendations were then tested on the original UK LMUP data. Results There were small but no important differences in the findings across the regression models. Logistic regression resulted in the largest loss of information, and assumptions were violated for the linear and ordinal logistic regression. Consequently, robust standard errors were used for linear regression and a partial proportional odds ordinal logistic regression model attempted. The latter could only be fitted for grouped LMUP score. Conclusion We recommend the linear regression model with robust standard errors to make full use of the LMUP score when analyzed as an outcome measure. Ordinal logistic regression could be considered, but a partial proportional odds model with grouped LMUP score may be required. Logistic regression is the least-favored option, due to the loss of information. For logistic regression, the cut point for un/planned pregnancy should be between nine and ten. These recommendations will standardize the analysis of LMUP data and enhance comparability of results across studies. PMID:28435343

Hierarchical Bayesian Logistic Regression to forecast metabolic control in type 2 DM patients.

PubMed

Dagliati, Arianna; Malovini, Alberto; Decata, Pasquale; Cogni, Giulia; Teliti, Marsida; Sacchi, Lucia; Cerra, Carlo; Chiovato, Luca; Bellazzi, Riccardo

2016-01-01

In this work we present our efforts in building a model able to forecast patients' changes in clinical conditions when repeated measurements are available. In this case the available risk calculators are typically not applicable. We propose a Hierarchical Bayesian Logistic Regression model, which allows taking into account individual and population variability in model parameters estimate. The model is used to predict metabolic control and its variation in type 2 diabetes mellitus. In particular we have analyzed a population of more than 1000 Italian type 2 diabetic patients, collected within the European project Mosaic. The results obtained in terms of Matthews Correlation Coefficient are significantly better than the ones gathered with standard logistic regression model, based on data pooling.

Reverse Flood Routing with the Lag-and-Route Storage Model

NASA Astrophysics Data System (ADS)

Mazi, K.; Koussis, A. D.

2010-09-01

This work presents a method for reverse routing of flood waves in open channels, which is an inverse problem of the signal identification type. Inflow determination from outflow measurements is useful in hydrologic forensics and in optimal reservoir control, but has been seldom studied. Such problems are ill posed and their solution is sensitive to small perturbations present in the data, or to any related uncertainty. Therefore the major difficulty in solving this inverse problem consists in controlling the amplification of errors that inevitably befall flow measurements, from which the inflow signal is to be determined. The lag-and-route model offers a convenient framework for reverse routing, because not only is formal deconvolution not required, but also reverse routing is through a single linear reservoir. In addition, this inversion degenerates to calculating the intermediate inflow (prior to the lag step) simply as the sum of the outflow and of its time derivative multiplied by the reservoir’s time constant. The remaining time shifting (lag) of the intermediate, reversed flow presents no complications, as pure translation causes no error amplification. Note that reverse routing with the inverted Muskingum scheme (Koussis et al., submitted to the 12th Plinius Conference) fails when that scheme is specialised to the Kalinin-Miljukov model (linear reservoirs in series). The principal functioning of the reverse routing procedure was verified first with perfect field data (outflow hydrograph generated by forward routing of a known inflow hydrograph). The field data were then seeded with random error. To smooth the oscillations caused by the imperfect (measured) outflow data, we applied a multipoint Savitzky-Golay low-pass filter. The combination of reverse routing and filtering achieved an effective recovery of the inflow signal extremely efficiently. Specifically, we compared the reverse routing results of the inverted lag-and-route model and of the inverted Kalinin-Miljukov model. The latter applies the lag-and-route model’s single-reservoir inversion scheme sequentially to its cascade of linear reservoirs, the number of which is related to the stream's hydromorphology. For this purpose, we used the example of Bruen & Dooge (2007), who back-routed flow hydrographs in a 100-km long prismatic channel using a scheme for the reverse solution of the St. Venant equations of flood wave motion. The lag-and-route reverse routing model recovered the inflow hydrograph with comparable accuracy to that of the multi-reservoir, inverted Kalinin-Miljukov model, both performing as well as the box-scheme for reverse routing with the St. Venant equations. In conclusion, the success in the regaining of the inflow signal by the devised single-reservoir reverse routing procedure, with multipoint low-pass filtering, can be attributed to its simple computational structure that endows it with remarkable robustness and exceptional efficiency.

A simple approximation of moments of the quasi-equilibrium distribution of an extended stochastic theta-logistic model with non-integer powers.

PubMed

Bhowmick, Amiya Ranjan; Bandyopadhyay, Subhadip; Rana, Sourav; Bhattacharya, Sabyasachi

2016-01-01

The stochastic versions of the logistic and extended logistic growth models are applied successfully to explain many real-life population dynamics and share a central body of literature in stochastic modeling of ecological systems. To understand the randomness in the population dynamics of the underlying processes completely, it is important to have a clear idea about the quasi-equilibrium distribution and its moments. Bartlett et al. (1960) took a pioneering attempt for estimating the moments of the quasi-equilibrium distribution of the stochastic logistic model. Matis and Kiffe (1996) obtain a set of more accurate and elegant approximations for the mean, variance and skewness of the quasi-equilibrium distribution of the same model using cumulant truncation method. The method is extended for stochastic power law logistic family by the same and several other authors (Nasell, 2003; Singh and Hespanha, 2007). Cumulant truncation and some alternative methods e.g. saddle point approximation, derivative matching approach can be applied if the powers involved in the extended logistic set up are integers, although plenty of evidence is available for non-integer powers in many practical situations (Sibly et al., 2005). In this paper, we develop a set of new approximations for mean, variance and skewness of the quasi-equilibrium distribution under more general family of growth curves, which is applicable for both integer and non-integer powers. The deterministic counterpart of this family of models captures both monotonic and non-monotonic behavior of the per capita growth rate, of which theta-logistic is a special case. The approximations accurately estimate the first three order moments of the quasi-equilibrium distribution. The proposed method is illustrated with simulated data and real data from global population dynamics database. Copyright © 2015 Elsevier Inc. All rights reserved.

Research and application of genetic algorithm in path planning of logistics distribution vehicle

NASA Astrophysics Data System (ADS)

Wang, Yong; Zhou, Heng; Wang, Ying

2017-08-01

The core of the logistics distribution system is the vehicle routing planning, research path planning problem, provide a better solution has become an important issue. In order to provide the decision support for logistics and distribution operations, this paper studies the problem of vehicle routing with capacity constraints (CVRP). By establishing a mathematical model, the genetic algorithm is used to plan the path of the logistics vehicle to meet the minimum logistics and transportation costs.

Analysis of Jingdong Mall Logistics Distribution Model

NASA Astrophysics Data System (ADS)

Shao, Kang; Cheng, Feng

In recent years, the development of electronic commerce in our country to speed up the pace. The role of logistics has been highlighted, more and more electronic commerce enterprise are beginning to realize the importance of logistics in the success or failure of the enterprise. In this paper, the author take Jingdong Mall for example, performing a SWOT analysis of their current situation of self-built logistics system, find out the problems existing in the current Jingdong Mall logistics distribution and give appropriate recommendations.

Assessing a cross-border logistics policy using a performance measurement system framework: the case of Hong Kong and the Pearl River Delta region

NASA Astrophysics Data System (ADS)

Wong, David W. C.; Choy, K. L.; Chow, Harry K. H.; Lin, Canhong

2014-06-01

For the most rapidly growing economic entity in the world, China, a new logistics operation called the indirect cross-border supply chain model has recently emerged. The primary idea of this model is to reduce logistics costs by storing goods at a bonded warehouse with low storage cost in certain Chinese regions, such as the Pearl River Delta (PRD). This research proposes a performance measurement system (PMS) framework to assess the direct and indirect cross-border supply chain models. The PMS covers four categories including cost, time, quality and flexibility in the assessment of the performance of direct and indirect models. Furthermore, a survey was conducted to investigate the logistics performance of third party logistics (3PLs) at the PRD regions, including Guangzhou, Shenzhen and Hong Kong. The significance of the proposed PMS framework allows 3PLs accurately pinpoint the weakness and strengths of it current operations policy at four major performance measurement categories. Hence, this helps 3PLs further enhance the competitiveness and operations efficiency through better resources allocation at the area of warehousing and transportation.

Complementary nonparametric analysis of covariance for logistic regression in a randomized clinical trial setting.

PubMed

Tangen, C M; Koch, G G

1999-03-01

In the randomized clinical trial setting, controlling for covariates is expected to produce variance reduction for the treatment parameter estimate and to adjust for random imbalances of covariates between the treatment groups. However, for the logistic regression model, variance reduction is not obviously obtained. This can lead to concerns about the assumptions of the logistic model. We introduce a complementary nonparametric method for covariate adjustment. It provides results that are usually compatible with expectations for analysis of covariance. The only assumptions required are based on randomization and sampling arguments. The resulting treatment parameter is a (unconditional) population average log-odds ratio that has been adjusted for random imbalance of covariates. Data from a randomized clinical trial are used to compare results from the traditional maximum likelihood logistic method with those from the nonparametric logistic method. We examine treatment parameter estimates, corresponding standard errors, and significance levels in models with and without covariate adjustment. In addition, we discuss differences between unconditional population average treatment parameters and conditional subpopulation average treatment parameters. Additional features of the nonparametric method, including stratified (multicenter) and multivariate (multivisit) analyses, are illustrated. Extensions of this methodology to the proportional odds model are also made.

An order insertion scheduling model of logistics service supply chain considering capacity and time factors.

PubMed

Liu, Weihua; Yang, Yi; Wang, Shuqing; Liu, Yang

2014-01-01

Order insertion often occurs in the scheduling process of logistics service supply chain (LSSC), which disturbs normal time scheduling especially in the environment of mass customization logistics service. This study analyses order similarity coefficient and order insertion operation process and then establishes an order insertion scheduling model of LSSC with service capacity and time factors considered. This model aims to minimize the average unit volume operation cost of logistics service integrator and maximize the average satisfaction degree of functional logistics service providers. In order to verify the viability and effectiveness of our model, a specific example is numerically analyzed. Some interesting conclusions are obtained. First, along with the increase of completion time delay coefficient permitted by customers, the possible inserting order volume first increases and then trends to be stable. Second, supply chain performance reaches the best when the volume of inserting order is equal to the surplus volume of the normal operation capacity in mass service process. Third, the larger the normal operation capacity in mass service process is, the bigger the possible inserting order's volume will be. Moreover, compared to increasing the completion time delay coefficient, improving the normal operation capacity of mass service process is more useful.

Mechanism of cell alignment in groups of Myxococcus xanthus bacteria

NASA Astrophysics Data System (ADS)

Balgam, Rajesh; Igoshin, Oleg

2015-03-01

Myxococcus xanthus is a model for studying self-organization in bacteria. These flexible cylindrical bacteria move along. In groups, M. xanthus cells align themselves into dynamic cell clusters but the mechanism underlying their formation is unknown. It has been shown that steric interactions can cause alignment in self-propelled hard rods but it is not clear how flexibility and reversals affect the alignment and cluster formation. We have investigated cell alignment process using our biophysical model of M. xanthus cell in an agent-based simulation framework under realistic cell flexibility values. We observed that flexible model cells can form aligned cell clusters when reversals are suppressed but these clusters disappeared when reversals frequency becomes similar to the observed value. However, M. xanthus cells follow slime (polysaccharide gel like material) trails left by other cells and we show that implementing this into our model rescues cell clustering for reversing cells. Our results show that slime following along with periodic cell reversals act as positive feedback to reinforce existing slime trails and recruit more cells. Furthermore, we have observed that mechanical cell alignment combined with slime following is sufficient to explain the distinct clustering patterns of reversing and non-reversing cells as observed in recent experiments. This work is supported by NSF MCB 0845919 and 1411780.

Logistics modelling: improving resource management and public information strategies in Florida.

PubMed

Walsh, Daniel M; Van Groningen, Chuck; Craig, Brian

2011-10-01

One of the most time-sensitive and logistically-challenging emergency response operations today is to provide mass prophylaxis to every man, woman and child in a community within 48 hours of a bioterrorism attack. To meet this challenge, federal, state and local public health departments in the USA have joined forces to develop, test and execute large-scale bioterrorism response plans. This preparedness and response effort is funded through the US Centers for Disease Control and Prevention's Cities Readiness Initiative, a programme dedicated to providing oral antibiotics to an entire population within 48 hours of a weaponised inhalation anthrax attack. This paper will demonstrate how the State of Florida used a logistics modelling tool to improve its CRI mass prophylaxis plans. Special focus will be on how logistics modelling strengthened Florida's resource management policies and validated its public information strategies.

Discrete post-processing of total cloud cover ensemble forecasts

NASA Astrophysics Data System (ADS)

Hemri, Stephan; Haiden, Thomas; Pappenberger, Florian

2017-04-01

This contribution presents an approach to post-process ensemble forecasts for the discrete and bounded weather variable of total cloud cover. Two methods for discrete statistical post-processing of ensemble predictions are tested. The first approach is based on multinomial logistic regression, the second involves a proportional odds logistic regression model. Applying them to total cloud cover raw ensemble forecasts from the European Centre for Medium-Range Weather Forecasts improves forecast skill significantly. Based on station-wise post-processing of raw ensemble total cloud cover forecasts for a global set of 3330 stations over the period from 2007 to early 2014, the more parsimonious proportional odds logistic regression model proved to slightly outperform the multinomial logistic regression model. Reference Hemri, S., Haiden, T., & Pappenberger, F. (2016). Discrete post-processing of total cloud cover ensemble forecasts. Monthly Weather Review 144, 2565-2577.

Analysis of oil-pipeline distribution of multiple products subject to delivery time-windows

NASA Astrophysics Data System (ADS)

Jittamai, Phongchai

This dissertation defines the operational problems of, and develops solution methodologies for, a distribution of multiple products into oil pipeline subject to delivery time-windows constraints. A multiple-product oil pipeline is a pipeline system composing of pipes, pumps, valves and storage facilities used to transport different types of liquids. Typically, products delivered by pipelines are petroleum of different grades moving either from production facilities to refineries or from refineries to distributors. Time-windows, which are generally used in logistics and scheduling areas, are incorporated in this study. The distribution of multiple products into oil pipeline subject to delivery time-windows is modeled as multicommodity network flow structure and mathematically formulated. The main focus of this dissertation is the investigation of operating issues and problem complexity of single-source pipeline problems and also providing solution methodology to compute input schedule that yields minimum total time violation from due delivery time-windows. The problem is proved to be NP-complete. The heuristic approach, a reversed-flow algorithm, is developed based on pipeline flow reversibility to compute input schedule for the pipeline problem. This algorithm is implemented in no longer than O(T·E) time. This dissertation also extends the study to examine some operating attributes and problem complexity of multiple-source pipelines. The multiple-source pipeline problem is also NP-complete. A heuristic algorithm modified from the one used in single-source pipeline problems is introduced. This algorithm can also be implemented in no longer than O(T·E) time. Computational results are presented for both methodologies on randomly generated problem sets. The computational experience indicates that reversed-flow algorithms provide good solutions in comparison with the optimal solutions. Only 25% of the problems tested were more than 30% greater than optimal values and approximately 40% of the tested problems were solved optimally by the algorithms.

Large Unbalanced Credit Scoring Using Lasso-Logistic Regression Ensemble

PubMed Central

Wang, Hong; Xu, Qingsong; Zhou, Lifeng

2015-01-01

Recently, various ensemble learning methods with different base classifiers have been proposed for credit scoring problems. However, for various reasons, there has been little research using logistic regression as the base classifier. In this paper, given large unbalanced data, we consider the plausibility of ensemble learning using regularized logistic regression as the base classifier to deal with credit scoring problems. In this research, the data is first balanced and diversified by clustering and bagging algorithms. Then we apply a Lasso-logistic regression learning ensemble to evaluate the credit risks. We show that the proposed algorithm outperforms popular credit scoring models such as decision tree, Lasso-logistic regression and random forests in terms of AUC and F-measure. We also provide two importance measures for the proposed model to identify important variables in the data. PMID:25706988

Numerical modeling of reverse recovery characteristic in silicon pin diodes

NASA Astrophysics Data System (ADS)

Yamashita, Yusuke; Tadano, Hiroshi

2018-07-01

A new numerical reverse recovery model of silicon pin diode is proposed by the approximation of the reverse recovery waveform as a simple shape. This is the first model to calculate the reverse recovery characteristics using numerical equations without adjusted by fitting equations and fitting parameters. In order to verify the validity and the accuracy of the numerical model, the calculation result from the model is verified through the device simulation result. In 1980, he joined Toyota Central R&D Labs, Inc., where he was involved in the research and development of power devices such as SIT, IGBT, diodes and power MOSFETs. Since 2013 he has been a professor at the Graduate School of Pure and Applied Science, University of Tsukuba, Tsukuba, Japan. His current research interest is high-efficiency power conversion circuits for electric vehicles using advanced power devices.

Modelling the growth of plants with a uniform growth logistics.

PubMed

Kilian, H G; Bartkowiak, D; Kazda, M; Kaufmann, D

2014-05-21

The increment model has previously been used to describe the growth of plants in general. Here, we examine how the same logistics enables the development of different superstructures. Data from the literature are analyzed with the increment model. Increments are growth-invariant molecular clusters, treated as heuristic particles. This approach formulates the law of mass action for multi-component systems, describing the general properties of superstructures which are optimized via relaxation processes. The daily growth patterns of hypocotyls can be reproduced implying predetermined growth invariant model parameters. In various species, the coordinated formation and death of fine roots are modeled successfully. Their biphasic annual growth follows distinct morphological programs but both use the same logistics. In tropical forests, distributions of the diameter in breast height of trees of different species adhere to the same pattern. Beyond structural fluctuations, competition and cooperation within and between the species may drive optimization. All superstructures of plants examined so far could be reproduced with our approach. With genetically encoded growth-invariant model parameters (interaction with the environment included) perfect morphological development runs embedded in the uniform logistics of the increment model. Copyright © 2014 Elsevier Ltd. All rights reserved.

To Use or Not to Use--(The One- or Three-Parameter Logistic Model) That Is the Question.

ERIC Educational Resources Information Center

Reckase, Mark D.

Definition of the issues to the use of latent trait models, specifically one- and three-parameter logistic models, in conjunction with multi-level achievement batteries, forms the basis of this paper. Research results related to these issues are also documented in an attempt to provide a rational basis for model selection. The application of the…

A Regular Production-Remanufacturing Inventory Model for a Two-Echelon System with Price-dependent Return Rate and Environmental Effects Investigation

NASA Astrophysics Data System (ADS)

Dwicahyani, A. R.; Jauhari, W. A.; Jonrinaldi

2017-06-01

Product take-back recovery has currently became a promising effort for companies in order to create a sustainable supply chain. In addition, some restrictions including government regulations, social-ethical responsibilities, and up to economic factors have contributed to the reasons for the importance of product take-back recovery. This study aims to develop an inventory model in a system of reverse logistic management consisting of a manufacturer and a collector. Recycle dealer collects used products from the market and ships it to manufacturer. Manufacturer then recovers the used products and sell it eventually to the market. Some recovered products that can not be recovered as good as new one will be sold to the secondary market. In this study, we investigate the effects of environmental factors including GHG emissions and energy usage from transportation, regular production, and remanufacturing operations conducted by manufacturer and solve the model to get the maximum annual joint total profit for both parties. The model also considers price-dependent return rate and determine it as a decision variable as well as number of shipments from collector to manufacturer and optimal cycle period. An iterative procedure is proposed to determine the optimal solutions. We present a numerical example to illustrate the application of the model and perform a sensitivity analysis to study the effects of the changes in environmental related costs on the model’s decision.

Examination of environmentally friendly "green" logistics behavior of managers in the pharmaceutical sector using the Theory of Planned Behavior.

PubMed

Arslan, Miray; Şar, Sevgi

2017-12-11

Logistics activities play a prominent role in enabling manufacturers, distribution channels, and pharmacies to work in harmony. Nowadays these activities have become increasingly striking in the pharmaceutical industry and seen as a development area for this sector. Additionally, green practices are beginning to be more attracting particularly in decreasing costs and increasing image of pharmaceutical companies. The main objective of this study was modeling green logistics (GL) behavior of the managers in the pharmaceutical sector in the theory of planned behavior (TPB) frame via structural equation modeling (SEM). A measurement tool was developed according to TPB. Exploratory factor analysis was conducted to determine subfactors of GL behavior. In the second step, confirmatory factor analysis (CFA) was conducted for confirming whether there is a relationship between the observed variables and their underlying latent constructs. Finally, structural equation model was conducted to specify the relationships between latent variables. In the proposed green logistics behavior (GLB) model, the positive effect of environmental attitude towards GL, perceived behavioral control related GL, and subjective norm about GL on intention towards GL were found statistically significant. Nevertheless, the effect of attitude towards costs of GL on intention towards GL was not found statistically significant. Intention towards GL has been found to have a positive statistically significant effect on the GL behavior. Based on the results of this study, it is possible to say that TPB is an appropriate theory for modeling green logistics behavior of managers. This model can be seen as a guide to the companies in the pharmaceutical sector to participate in green logistics. Copyright © 2017 Elsevier Inc. All rights reserved.

CRISPR: a Versatile Tool for Both Forward and Reverse Genetics Research

PubMed Central

Gurumurthy, Channabasavaiah B.; Grati, M'hamed; Ohtsuka, Masato; Schilit, Samantha L.P.; Quadros, Rolen M.; Liu, Xue Zhong

2016-01-01

Human genetics research employs the two opposing approaches of forward and reverse genetics. While forward genetics identifies and links a mutation to an observed disease etiology, reverse genetics induces mutations in model organisms to study their role in disease. In most cases, causality for mutations identified by forward genetics is confirmed by reverse genetics through the development of genetically engineered animal models and an assessment of whether the model can recapitulate the disease. While many technological advances have helped improve these approaches, some gaps still remain. CRISPR/Cas (clustered regularly interspaced short palindromic repeats/CRISPR-associated) system, which has emerged as a revolutionary genetic engineering tool, holds great promise for closing such gaps. By combining the benefits of forward and reverse genetics, it has dramatically expedited human genetics research. We provide a perspective on the power of CRISPR-based forward and reverse genetics tools in human genetics and discuss its applications using some disease examples. PMID:27384229

Sustained diffusion reversal with in-bore reperfusion in monkey stroke models: Confirmed by prospective magnetic resonance imaging

PubMed Central

Yi, Kyung Sik; Choi, Chi-Hoon; Lee, Sang-Rae; Lee, Hong Jun; Lee, Youngjeon; Jeong, Kang-Jin; Hwang, Jinwoo; Chang, Kyu-Tae

2016-01-01

Although early diffusion lesion reversal after recanalization treatment of acute ischaemic stroke has been observed in clinical settings, the reversibility of lesions observed by diffusion-weighted imaging remains controversial. Here, we present consistent observations of sustained diffusion lesion reversal after transient middle cerebral artery occlusion in a monkey stroke model. Seven rhesus macaques were subjected to endovascular transient middle cerebral artery occlusion with in-bore reperfusion confirmed by repeated prospective diffusion-weighted imaging. Early diffusion lesion reversal was defined as lesion reversal at 3 h after reperfusion. Sustained diffusion lesion reversal was defined as the difference between the ADC-derived pre-reperfusion maximal ischemic lesion volume (ADCD-P Match) and the lesion on 4-week follow-up FLAIR magnetic resonance imaging. Diffusion lesions were spatiotemporally assessed using a 3-D voxel-based quantitative technique. The ADCD-P Match was 9.7 ± 6.0% (mean ± SD) and the final infarct was 1.2–6.0% of the volume of the ipsilateral hemisphere. Early diffusion lesion reversal and sustained diffusion lesion reversal were observed in all seven animals, and the calculated percentages compared with their ADCD-P Match ranged from 8.3 to 51.9% (mean ± SD, 26.9 ± 15.3%) and 41.7–77.8% (mean ± SD, 65.4 ± 12.2%), respectively. Substantial sustained diffusion lesion reversal and early reversal were observed in all animals in this monkey model of transient focal cerebral ischaemia. PMID:27401804

Towards random matrix model of breaking the time-reversal invariance of elastic waves in chaotic cavities by feedback

NASA Astrophysics Data System (ADS)

Antoniuk, Oleg; Sprik, Rudolf

2010-03-01

We developed a random matrix model to describe the statistics of resonances in an acoustic cavity with broken time-reversal invariance. Time-reversal invariance braking is achieved by connecting an amplified feedback loop between two transducers on the surface of the cavity. The model is based on approach [1] that describes time- reversal properties of the cavity without a feedback loop. Statistics of eigenvalues (nearest neighbor resonance spacing distributions and spectral rigidity) has been calculated and compared to the statistics obtained from our experimental data. Experiments have been performed on aluminum block of chaotic shape confining ultrasound waves. [1] Carsten Draeger and Mathias Fink, One-channel time- reversal in chaotic cavities: Theoretical limits, Journal of Acoustical Society of America, vol. 105, Nr. 2, pp. 611-617 (1999)

Pulse cleaning flow models and numerical computation of candle ceramic filters.

PubMed

Tian, Gui-shan; Ma, Zhen-ji; Zhang, Xin-yi; Xu, Ting-xiang

2002-04-01

Analytical and numerical computed models are developed for reverse pulse cleaning system of candle ceramic filters. A standard turbulent model is demonstrated suitably to the designing computation of reverse pulse cleaning system from the experimental and one-dimensional computational result. The computed results can be used to guide the designing of reverse pulse cleaning system, which is optimum Venturi geometry. From the computed results, the general conclusions and the designing methods are obtained.

Model selection for logistic regression models

NASA Astrophysics Data System (ADS)

Duller, Christine

2012-09-01

Model selection for logistic regression models decides which of some given potential regressors have an effect and hence should be included in the final model. The second interesting question is whether a certain factor is heterogeneous among some subsets, i.e. whether the model should include a random intercept or not. In this paper these questions will be answered with classical as well as with Bayesian methods. The application show some results of recent research projects in medicine and business administration.

Computational reverse shoulder prosthesis model: Experimental data and verification.

PubMed

Martins, A; Quental, C; Folgado, J; Ambrósio, J; Monteiro, J; Sarmento, M

2015-09-18

The reverse shoulder prosthesis aims to restore the stability and function of pathological shoulders, but the biomechanical aspects of the geometrical changes induced by the implant are yet to be fully understood. Considering a large-scale musculoskeletal model of the upper limb, the aim of this study is to evaluate how the Delta reverse shoulder prosthesis influences the biomechanical behavior of the shoulder joint. In this study, the kinematic data of an unloaded abduction in the frontal plane and an unloaded forward flexion in the sagittal plane were experimentally acquired through video-imaging for a control group, composed of 10 healthy shoulders, and a reverse shoulder group, composed of 3 reverse shoulders. Synchronously, the EMG data of 7 superficial muscles were also collected. The muscle force sharing problem was solved through the minimization of the metabolic energy consumption. The evaluation of the shoulder kinematics shows an increase in the lateral rotation of the scapula in the reverse shoulder group, and an increase in the contribution of the scapulothoracic joint to the shoulder joint. Regarding the muscle force sharing problem, the musculoskeletal model estimates an increased activity of the deltoid, teres minor, clavicular fibers of the pectoralis major, and coracobrachialis muscles in the reverse shoulder group. The comparison between the muscle forces predicted and the EMG data acquired revealed a good correlation, which provides further confidence in the model. Overall, the shoulder joint reaction force was lower in the reverse shoulder group than in the control group. Copyright © 2015 Elsevier Ltd. All rights reserved.

A model for ‘reverse innovation’ in health care

PubMed Central

2013-01-01

‘Reverse innovation,’ a principle well established in the business world, describes the flow of ideas from emerging to more developed economies. There is strong and growing interest in applying this concept to health care, yet there is currently no framework for describing the stages of reverse innovation or identifying opportunities to accelerate the development process. This paper combines the business concept of reverse innovation with diffusion of innovation theory to propose a model for reverse innovation as a way to innovate in health care. Our model includes the following steps: (1) identifying a problem common to lower- and higher-income countries; (2) innovation and spread in the low-income country (LIC); (3) crossover to the higher-income country (HIC); and (4) innovation and spread in the HIC. The crucial populations in this pathway, drawing from diffusion of innovation theory, are LIC innovators, LIC early adopters, and HIC innovators. We illustrate the model with three examples of current reverse innovations. We then propose four sets of specific actions that forward-looking policymakers, entrepreneurs, health system leaders, and researchers may take to accelerate the movement of promising solutions through the reverse innovation pipeline: (1) identify high-priority problems shared by HICs and LICs; (2) create slack for change, especially for LIC innovators, LIC early adopters, and HIC innovators; (3) create spannable social distances between LIC early adopters and HIC innovators; and (4) measure reverse innovation activity globally. PMID:24001367

Filtering data from the collaborative initial glaucoma treatment study for improved identification of glaucoma progression.

PubMed

Schell, Greggory J; Lavieri, Mariel S; Stein, Joshua D; Musch, David C

2013-12-21

Open-angle glaucoma (OAG) is a prevalent, degenerate ocular disease which can lead to blindness without proper clinical management. The tests used to assess disease progression are susceptible to process and measurement noise. The aim of this study was to develop a methodology which accounts for the inherent noise in the data and improve significant disease progression identification. Longitudinal observations from the Collaborative Initial Glaucoma Treatment Study (CIGTS) were used to parameterize and validate a Kalman filter model and logistic regression function. The Kalman filter estimates the true value of biomarkers associated with OAG and forecasts future values of these variables. We develop two logistic regression models via generalized estimating equations (GEE) for calculating the probability of experiencing significant OAG progression: one model based on the raw measurements from CIGTS and another model based on the Kalman filter estimates of the CIGTS data. Receiver operating characteristic (ROC) curves and associated area under the ROC curve (AUC) estimates are calculated using cross-fold validation. The logistic regression model developed using Kalman filter estimates as data input achieves higher sensitivity and specificity than the model developed using raw measurements. The mean AUC for the Kalman filter-based model is 0.961 while the mean AUC for the raw measurements model is 0.889. Hence, using the probability function generated via Kalman filter estimates and GEE for logistic regression, we are able to more accurately classify patients and instances as experiencing significant OAG progression. A Kalman filter approach for estimating the true value of OAG biomarkers resulted in data input which improved the accuracy of a logistic regression classification model compared to a model using raw measurements as input. This methodology accounts for process and measurement noise to enable improved discrimination between progression and nonprogression in chronic diseases.

A Bayesian goodness of fit test and semiparametric generalization of logistic regression with measurement data.

PubMed

Schörgendorfer, Angela; Branscum, Adam J; Hanson, Timothy E

2013-06-01

Logistic regression is a popular tool for risk analysis in medical and population health science. With continuous response data, it is common to create a dichotomous outcome for logistic regression analysis by specifying a threshold for positivity. Fitting a linear regression to the nondichotomized response variable assuming a logistic sampling model for the data has been empirically shown to yield more efficient estimates of odds ratios than ordinary logistic regression of the dichotomized endpoint. We illustrate that risk inference is not robust to departures from the parametric logistic distribution. Moreover, the model assumption of proportional odds is generally not satisfied when the condition of a logistic distribution for the data is violated, leading to biased inference from a parametric logistic analysis. We develop novel Bayesian semiparametric methodology for testing goodness of fit of parametric logistic regression with continuous measurement data. The testing procedures hold for any cutoff threshold and our approach simultaneously provides the ability to perform semiparametric risk estimation. Bayes factors are calculated using the Savage-Dickey ratio for testing the null hypothesis of logistic regression versus a semiparametric generalization. We propose a fully Bayesian and a computationally efficient empirical Bayesian approach to testing, and we present methods for semiparametric estimation of risks, relative risks, and odds ratios when parametric logistic regression fails. Theoretical results establish the consistency of the empirical Bayes test. Results from simulated data show that the proposed approach provides accurate inference irrespective of whether parametric assumptions hold or not. Evaluation of risk factors for obesity shows that different inferences are derived from an analysis of a real data set when deviations from a logistic distribution are permissible in a flexible semiparametric framework. © 2013, The International Biometric Society.

Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat—Turkey)

NASA Astrophysics Data System (ADS)

Yilmaz, Işık

2009-06-01

The purpose of this study is to compare the landslide susceptibility mapping methods of frequency ratio (FR), logistic regression and artificial neural networks (ANN) applied in the Kat County (Tokat—Turkey). Digital elevation model (DEM) was first constructed using GIS software. Landslide-related factors such as geology, faults, drainage system, topographical elevation, slope angle, slope aspect, topographic wetness index (TWI) and stream power index (SPI) were used in the landslide susceptibility analyses. Landslide susceptibility maps were produced from the frequency ratio, logistic regression and neural networks models, and they were then compared by means of their validations. The higher accuracies of the susceptibility maps for all three models were obtained from the comparison of the landslide susceptibility maps with the known landslide locations. However, respective area under curve (AUC) values of 0.826, 0.842 and 0.852 for frequency ratio, logistic regression and artificial neural networks showed that the map obtained from ANN model is more accurate than the other models, accuracies of all models can be evaluated relatively similar. The results obtained in this study also showed that the frequency ratio model can be used as a simple tool in assessment of landslide susceptibility when a sufficient number of data were obtained. Input process, calculations and output process are very simple and can be readily understood in the frequency ratio model, however logistic regression and neural networks require the conversion of data to ASCII or other formats. Moreover, it is also very hard to process the large amount of data in the statistical package.

PREDICTION OF MALIGNANT BREAST LESIONS FROM MRI FEATURES: A COMPARISON OF ARTIFICIAL NEURAL NETWORK AND LOGISTIC REGRESSION TECHNIQUES

PubMed Central

McLaren, Christine E.; Chen, Wen-Pin; Nie, Ke; Su, Min-Ying

2009-01-01

Rationale and Objectives Dynamic contrast enhanced MRI (DCE-MRI) is a clinical imaging modality for detection and diagnosis of breast lesions. Analytical methods were compared for diagnostic feature selection and performance of lesion classification to differentiate between malignant and benign lesions in patients. Materials and Methods The study included 43 malignant and 28 benign histologically-proven lesions. Eight morphological parameters, ten gray level co-occurrence matrices (GLCM) texture features, and fourteen Laws’ texture features were obtained using automated lesion segmentation and quantitative feature extraction. Artificial neural network (ANN) and logistic regression analysis were compared for selection of the best predictors of malignant lesions among the normalized features. Results Using ANN, the final four selected features were compactness, energy, homogeneity, and Law_LS, with area under the receiver operating characteristic curve (AUC) = 0.82, and accuracy = 0.76. The diagnostic performance of these 4-features computed on the basis of logistic regression yielded AUC = 0.80 (95% CI, 0.688 to 0.905), similar to that of ANN. The analysis also shows that the odds of a malignant lesion decreased by 48% (95% CI, 25% to 92%) for every increase of 1 SD in the Law_LS feature, adjusted for differences in compactness, energy, and homogeneity. Using logistic regression with z-score transformation, a model comprised of compactness, NRL entropy, and gray level sum average was selected, and it had the highest overall accuracy of 0.75 among all models, with AUC = 0.77 (95% CI, 0.660 to 0.880). When logistic modeling of transformations using the Box-Cox method was performed, the most parsimonious model with predictors, compactness and Law_LS, had an AUC of 0.79 (95% CI, 0.672 to 0.898). Conclusion The diagnostic performance of models selected by ANN and logistic regression was similar. The analytic methods were found to be roughly equivalent in terms of predictive ability when a small number of variables were chosen. The robust ANN methodology utilizes a sophisticated non-linear model, while logistic regression analysis provides insightful information to enhance interpretation of the model features. PMID:19409817

Is the bronchodilator test an useful tool to measure asthma control?

PubMed

Ferrer Galván, Marta; Javier Alvarez Gutiérrez, Francisco; Romero Falcón, Auxiliadora; Romero Romero, Beatriz; Sáez, Antonia; Medina Gallardo, Juan Francisco

2017-05-01

Asthma control includes the control of symptoms and future risk. We sought to evaluate the usefulness of the degree of spirometric reversibility of the forced expiratory volume in one second (FEV 1 ) as the target parameter of control. Patients with bronchial asthma were followed up for one year. The clinical, functional, inflammatory and control parameters of the asthma were collected. The area under the curve (AUC) was estimated to establish the cutoff point of the post-bronchodilator FEV 1 reversibility in relation to non-control asthma. In the univariate analysis, the differences between groups were studied based on the degree of estimated reversibility. Factors with a significance <0.1 were included in the multivariate analysis by binary logistic regression. A total of 407 patients with a mean age of 38.1 ± 16.7 years were included. When the patients were grouped into controlled and non-controlled groups, compared with post-bronchodilator FEV 1 reversibility, the cutoff point obtained for the non-controlled group was ≥10% (sensitivity: 65.8%, specificity: 48.4%, positive predictive value: 69.5%, and AUC: 0.619 [0.533-0.700], p < 0.01). In the year-long follow-up of this group (post-bronchodilator FEV 1 ≥10), an increased use of relief medication was observed, along with a significantly progressive drop in post-bronchodilator FEV 1 and post-bronchodilator FEV 1 /FVC (forced expiratory volume in one second/forced vital capacity). Spirometric reversibility can be useful in assessing control in asthmatic patients and can predict future risk parameters. The cutoff point related to the non-control of asthma found in our work was ≥10%. Copyright © 2017 Elsevier Ltd. All rights reserved.

Time irreversibility in reversible shell models of turbulence.

PubMed

De Pietro, Massimo; Biferale, Luca; Boffetta, Guido; Cencini, Massimo

2018-04-06

Turbulent flows governed by the Navier-Stokes equations (NSE) generate an out-of-equilibrium time irreversible energy cascade from large to small scales. In the NSE, the energy transfer is due to the nonlinear terms that are formally symmetric under time reversal. As for the dissipative term: first, it explicitly breaks time reversibility; second, it produces a small-scale sink for the energy transfer that remains effective even in the limit of vanishing viscosity. As a result, it is not clear how to disentangle the time irreversibility originating from the non-equilibrium energy cascade from the explicit time-reversal symmetry breaking due to the viscous term. To this aim, in this paper we investigate the properties of the energy transfer in turbulent shell models by using a reversible viscous mechanism, avoiding any explicit breaking of the [Formula: see text] symmetry. We probe time irreversibility by studying the statistics of Lagrangian power, which is found to be asymmetric under time reversal also in the time-reversible model. This suggests that the turbulent dynamics converges to a strange attractor where time reversibility is spontaneously broken and whose properties are robust for what concerns purely inertial degrees of freedoms, as verified by the anomalous scaling behavior of the velocity structure functions.

[Calculating Pearson residual in logistic regressions: a comparison between SPSS and SAS].

PubMed

Xu, Hao; Zhang, Tao; Li, Xiao-song; Liu, Yuan-yuan

2015-01-01

To compare the results of Pearson residual calculations in logistic regression models using SPSS and SAS. We reviewed Pearson residual calculation methods, and used two sets of data to test logistic models constructed by SPSS and STATA. One model contained a small number of covariates compared to the number of observed. The other contained a similar number of covariates as the number of observed. The two software packages produced similar Pearson residual estimates when the models contained a similar number of covariates as the number of observed, but the results differed when the number of observed was much greater than the number of covariates. The two software packages produce different results of Pearson residuals, especially when the models contain a small number of covariates. Further studies are warranted.

Vehicle Scheduling Schemes for Commercial and Emergency Logistics Integration

PubMed Central

Li, Xiaohui; Tan, Qingmei

2013-01-01

In modern logistics operations, large-scale logistics companies, besides active participation in profit-seeking commercial business, also play an essential role during an emergency relief process by dispatching urgently-required materials to disaster-affected areas. Therefore, an issue has been widely addressed by logistics practitioners and caught researchers' more attention as to how the logistics companies achieve maximum commercial profit on condition that emergency tasks are effectively and performed satisfactorily. In this paper, two vehicle scheduling models are proposed to solve the problem. One is a prediction-related scheme, which predicts the amounts of disaster-relief materials and commercial business and then accepts the business that will generate maximum profits; the other is a priority-directed scheme, which, firstly groups commercial and emergency business according to priority grades and then schedules both types of business jointly and simultaneously by arriving at the maximum priority in total. Moreover, computer-based simulations are carried out to evaluate the performance of these two models by comparing them with two traditional disaster-relief tactics in China. The results testify the feasibility and effectiveness of the proposed models. PMID:24391724

Vehicle scheduling schemes for commercial and emergency logistics integration.

PubMed

Li, Xiaohui; Tan, Qingmei

2013-01-01

In modern logistics operations, large-scale logistics companies, besides active participation in profit-seeking commercial business, also play an essential role during an emergency relief process by dispatching urgently-required materials to disaster-affected areas. Therefore, an issue has been widely addressed by logistics practitioners and caught researchers' more attention as to how the logistics companies achieve maximum commercial profit on condition that emergency tasks are effectively and performed satisfactorily. In this paper, two vehicle scheduling models are proposed to solve the problem. One is a prediction-related scheme, which predicts the amounts of disaster-relief materials and commercial business and then accepts the business that will generate maximum profits; the other is a priority-directed scheme, which, firstly groups commercial and emergency business according to priority grades and then schedules both types of business jointly and simultaneously by arriving at the maximum priority in total. Moreover, computer-based simulations are carried out to evaluate the performance of these two models by comparing them with two traditional disaster-relief tactics in China. The results testify the feasibility and effectiveness of the proposed models.

Static performance and noise tests on a thrust reverser for an augmentor wing aircraft

NASA Technical Reports Server (NTRS)

Harkonen, D. L.; Marrs, C. C.; Okeefe, J. V.

1974-01-01

A 1/3 scale model static test program was conducted to measure the noise levels and reverse thrust performance characteristics of wing-mounted thrust reverser that could be used on an advanced augmentor wing airplane. The configuration tested represents only the most fundamental designs where installation and packaging restraints are not considered. The thrust reverser performance is presented in terms of horizontal, vertical, and resultant effectiveness ratios and the reverser noise is compared on the basis of peak perceived noise level (PNL) and one-third octave band data (OASPL). From an analysis of the model force and acoustic data, an assessment is made on the stopping distance versus noise for a 90,900 kg (200,000 lb) airplane using this type of thrust reverser.

Product unit neural network models for predicting the growth limits of Listeria monocytogenes.

PubMed

Valero, A; Hervás, C; García-Gimeno, R M; Zurera, G

2007-08-01

A new approach to predict the growth/no growth interface of Listeria monocytogenes as a function of storage temperature, pH, citric acid (CA) and ascorbic acid (AA) is presented. A linear logistic regression procedure was performed and a non-linear model was obtained by adding new variables by means of a Neural Network model based on Product Units (PUNN). The classification efficiency of the training data set and the generalization data of the new Logistic Regression PUNN model (LRPU) were compared with Linear Logistic Regression (LLR) and Polynomial Logistic Regression (PLR) models. 92% of the total cases from the LRPU model were correctly classified, an improvement on the percentage obtained using the PLR model (90%) and significantly higher than the results obtained with the LLR model, 80%. On the other hand predictions of LRPU were closer to data observed which permits to design proper formulations in minimally processed foods. This novel methodology can be applied to predictive microbiology for describing growth/no growth interface of food-borne microorganisms such as L. monocytogenes. The optimal balance is trying to find models with an acceptable interpretation capacity and with good ability to fit the data on the boundaries of variable range. The results obtained conclude that these kinds of models might well be very a valuable tool for mathematical modeling.

Evaluation of the Logistic Model for GAC Performance in Water Treatment

EPA Science Inventory

Full-scale field measurement and rapid small-scale column test data from the Greater Cincinnati (Ohio) Water Works (GCWW) were used to calibrate and investigate the application of the logistic model for simulating breakthrough of total organic carbon (TOC) in granular activated c...

A multicriteria decision making approach based on fuzzy theory and credibility mechanism for logistics center location selection.

PubMed

Wang, Bowen; Xiong, Haitao; Jiang, Chengrui

2014-01-01

As a hot topic in supply chain management, fuzzy method has been widely used in logistics center location selection to improve the reliability and suitability of the logistics center location selection with respect to the impacts of both qualitative and quantitative factors. However, it does not consider the consistency and the historical assessments accuracy of experts in predecisions. So this paper proposes a multicriteria decision making model based on credibility of decision makers by introducing priority of consistency and historical assessments accuracy mechanism into fuzzy multicriteria decision making approach. In this way, only decision makers who pass the credibility check are qualified to perform the further assessment. Finally, a practical example is analyzed to illustrate how to use the model. The result shows that the fuzzy multicriteria decision making model based on credibility mechanism can improve the reliability and suitability of site selection for the logistics center.

A Multicriteria Decision Making Approach Based on Fuzzy Theory and Credibility Mechanism for Logistics Center Location Selection

PubMed Central

Wang, Bowen; Jiang, Chengrui

2014-01-01

As a hot topic in supply chain management, fuzzy method has been widely used in logistics center location selection to improve the reliability and suitability of the logistics center location selection with respect to the impacts of both qualitative and quantitative factors. However, it does not consider the consistency and the historical assessments accuracy of experts in predecisions. So this paper proposes a multicriteria decision making model based on credibility of decision makers by introducing priority of consistency and historical assessments accuracy mechanism into fuzzy multicriteria decision making approach. In this way, only decision makers who pass the credibility check are qualified to perform the further assessment. Finally, a practical example is analyzed to illustrate how to use the model. The result shows that the fuzzy multicriteria decision making model based on credibility mechanism can improve the reliability and suitability of site selection for the logistics center. PMID:25215319

A Collection of Technical Papers

NASA Technical Reports Server (NTRS)

1995-01-01

Papers presented at the 6th Space Logistics Symposium covered such areas as: The International Space Station; The Hubble Space Telescope; Launch site computer simulation; Integrated logistics support; The Baikonur Cosmodrome; Probabalistic tools for high confidence repair; A simple space station rescue vehicle; Integrated Traffic Model for the International Space Station; Packaging the maintenance shop; Leading edge software support; Storage information management system; Consolidated maintenance inventory logistics planning; Operation concepts for a single stage to orbit vehicle; Mission architecture for human lunar exploration; Logistics of a lunar based solar power satellite scenario; Just in time in space; NASA acquisitions/logistics; Effective transition management; Shuttle logistics; and Revitalized space operations through total quality control management.

Overestimation of physical activity level is associated with lower BMI: a cross-sectional analysis.

PubMed

Watkinson, Clare; van Sluijs, Esther Mf; Sutton, Stephen; Hardeman, Wendy; Corder, Kirsten; Griffin, Simon J

2010-09-20

Poor recognition of physical inactivity may be an important barrier to healthy behaviour change, but little is known about this phenomenon. We aimed to characterize a high-risk population according to the discrepancies between objective and self-rated physical activity (PA), defined as awareness. An exploratory cross-sectional analysis of PA awareness using baseline data collected from 365 ProActive participants between 2001 and 2003 in East Anglia, England. Self-rated PA was defined as 'active' or 'inactive' (assessed via questionnaire). Objective PA was defined according to achievement of guideline activity levels (≥30 minutes or <30 minutes spent at least moderate intensity PA, assessed by heart rate monitoring). Four awareness groups were created: 'Realistic Actives', 'Realistic Inactives', 'Overestimators' and 'Underestimators'. Logistic regression was used to assess associations between awareness group and 17 personal, social and biological correlates. 63.3% of participants (N = 231) were inactive according to objective measurement. Of these, 45.9% rated themselves as active ('Overestimators'). In a multiple logistic regression model adjusted for age and smoking, males (OR = 2.11, 95% CI = 1.12, 3.98), those with lower BMI (OR = 0.89, 95% CI = 0.84, 0.95), younger age at completion of full-time education (OR = 0.83, 95% CI = 0.74, 0.93) and higher general health perception (OR = 1.02 CI = 1.00, 1.04) were more likely to overestimate their PA. Overestimation of PA is associated with favourable indicators of relative slimness and general health. Feedback about PA levels could help reverse misperceptions.

The role of time and risk preferences in smoking inequalities: a population-based study.

PubMed

Jusot, Florence; Khlat, Myriam

2013-05-01

Heterogeneity in time and risk preferences has been proposed as one of the mechanisms involved in the educational gradient in smoking, but this mechanism has scarcely been explored empirically. Subjective scales were introduced in the 2008 French National Health, Health Care and Insurance Survey in order to elicit measures of time and risk preferences for a representative sample of 5188 men and 5684 women. Men and women were treated separately. First, logistic regressions were used to test the associations between preferences and education and between preferences and smoking. Second, nested logistic models were built to investigate the mediating role of preferences in the educational gradient in smoking, with an econometric treatment of the rescaling problem. Preference for the present and risk loving were found to be: inversely related to educational level; strongly related to each other, and; strongly associated to current smoking, even after adjustment for educational level. There was a weakening of the educational gradient after the control for preferences, which supports the role of these two preferences as partial mediators in the educational gradient in smoking. Among men, time preference was more strongly associated with smoking than risk aversion, while the reverse was found for women. We provide convincing evidence in favour of the mediating role of time preference and risk aversion in educational inequalities in smoking and highlight the connection between those two dimensions. Gender patterns are discussed and potential implications in terms of designing targeted anti-tobacco programmes are delineated. Copyright © 2012 Elsevier Ltd. All rights reserved.

Selecting risk factors: a comparison of discriminant analysis, logistic regression and Cox's regression model using data from the Tromsø Heart Study.

PubMed

Brenn, T; Arnesen, E

1985-01-01

For comparative evaluation, discriminant analysis, logistic regression and Cox's model were used to select risk factors for total and coronary deaths among 6595 men aged 20-49 followed for 9 years. Groups with mortality between 5 and 93 per 1000 were considered. Discriminant analysis selected variable sets only marginally different from the logistic and Cox methods which always selected the same sets. A time-saving option, offered for both the logistic and Cox selection, showed no advantage compared with discriminant analysis. Analysing more than 3800 subjects, the logistic and Cox methods consumed, respectively, 80 and 10 times more computer time than discriminant analysis. When including the same set of variables in non-stepwise analyses, all methods estimated coefficients that in most cases were almost identical. In conclusion, discriminant analysis is advocated for preliminary or stepwise analysis, otherwise Cox's method should be used.

Forest biomass supply logistics for a power plant using the discrete-event simulation approach

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

Mobini, Mahdi; Sowlati, T.; Sokhansanj, Shahabaddine

This study investigates the logistics of supplying forest biomass to a potential power plant. Due to the complexities in such a supply logistics system, a simulation model based on the framework of Integrated Biomass Supply Analysis and Logistics (IBSAL) is developed in this study to evaluate the cost of delivered forest biomass, the equilibrium moisture content, and carbon emissions from the logistics operations. The model is applied to a proposed case of 300 MW power plant in Quesnel, BC, Canada. The results show that the biomass demand of the power plant would not be met every year. The weighted averagemore » cost of delivered biomass to the gate of the power plant is about C$ 90 per dry tonne. Estimates of equilibrium moisture content of delivered biomass and CO2 emissions resulted from the processes are also provided.« less

A New TS Algorithm for Solving Low-Carbon Logistics Vehicle Routing Problem with Split Deliveries by Backpack-From a Green Operation Perspective.

PubMed

Xia, Yangkun; Fu, Zhuo; Tsai, Sang-Bing; Wang, Jiangtao

2018-05-10

In order to promote the development of low-carbon logistics and economize logistics distribution costs, the vehicle routing problem with split deliveries by backpack is studied. With the help of the model of classical capacitated vehicle routing problem, in this study, a form of discrete split deliveries was designed in which the customer demand can be split only by backpack. A double-objective mathematical model and the corresponding adaptive tabu search (TS) algorithm were constructed for solving this problem. By embedding the adaptive penalty mechanism, and adopting the random neighborhood selection strategy and reinitialization principle, the global optimization ability of the new algorithm was enhanced. Comparisons with the results in the literature show the effectiveness of the proposed algorithm. The proposed method can save the costs of low-carbon logistics and reduce carbon emissions, which is conducive to the sustainable development of low-carbon logistics.

Modeling Governance KB with CATPCA to Overcome Multicollinearity in the Logistic Regression

NASA Astrophysics Data System (ADS)

Khikmah, L.; Wijayanto, H.; Syafitri, U. D.

2017-04-01

The problem often encounters in logistic regression modeling are multicollinearity problems. Data that have multicollinearity between explanatory variables with the result in the estimation of parameters to be bias. Besides, the multicollinearity will result in error in the classification. In general, to overcome multicollinearity in regression used stepwise regression. They are also another method to overcome multicollinearity which involves all variable for prediction. That is Principal Component Analysis (PCA). However, classical PCA in only for numeric data. Its data are categorical, one method to solve the problems is Categorical Principal Component Analysis (CATPCA). Data were used in this research were a part of data Demographic and Population Survey Indonesia (IDHS) 2012. This research focuses on the characteristic of women of using the contraceptive methods. Classification results evaluated using Area Under Curve (AUC) values. The higher the AUC value, the better. Based on AUC values, the classification of the contraceptive method using stepwise method (58.66%) is better than the logistic regression model (57.39%) and CATPCA (57.39%). Evaluation of the results of logistic regression using sensitivity, shows the opposite where CATPCA method (99.79%) is better than logistic regression method (92.43%) and stepwise (92.05%). Therefore in this study focuses on major class classification (using a contraceptive method), then the selected model is CATPCA because it can raise the level of the major class model accuracy.

Tennis Elbow Diagnosis Using Equivalent Uniform Voltage to Fit the Logistic and the Probit Diseased Probability Models

PubMed Central

Lin, Wei-Chun; Lin, Shu-Yuan; Wu, Li-Fu; Guo, Shih-Sian; Huang, Hsiang-Jui; Chao, Pei-Ju

2015-01-01

To develop the logistic and the probit models to analyse electromyographic (EMG) equivalent uniform voltage- (EUV-) response for the tenderness of tennis elbow. In total, 78 hands from 39 subjects were enrolled. In this study, surface EMG (sEMG) signal is obtained by an innovative device with electrodes over forearm region. The analytical endpoint was defined as Visual Analog Score (VAS) 3+ tenderness of tennis elbow. The logistic and the probit diseased probability (DP) models were established for the VAS score and EMG absolute voltage-time histograms (AVTH). TV50 is the threshold equivalent uniform voltage predicting a 50% risk of disease. Twenty-one out of 78 samples (27%) developed VAS 3+ tenderness of tennis elbow reported by the subject and confirmed by the physician. The fitted DP parameters were TV50 = 153.0 mV (CI: 136.3–169.7 mV), γ 50 = 0.84 (CI: 0.78–0.90) and TV50 = 155.6 mV (CI: 138.9–172.4 mV), m = 0.54 (CI: 0.49–0.59) for logistic and probit models, respectively. When the EUV ≥ 153 mV, the DP of the patient is greater than 50% and vice versa. The logistic and the probit models are valuable tools to predict the DP of VAS 3+ tenderness of tennis elbow. PMID:26380281

Quantitative microbial risk assessment for Escherichia coli O157:H7, Salmonella enterica, and Listeria monocytogenes in leafy green vegetables consumed at salad bars, based on modeling supply chain logistics.

PubMed

Tromp, S O; Rijgersberg, H; Franz, E

2010-10-01

Quantitative microbial risk assessments do not usually account for the planning and ordering mechanisms (logistics) of a food supply chain. These mechanisms and consumer demand determine the storage and delay times of products. The aim of this study was to quantitatively assess the difference between simulating supply chain logistics (MOD) and assuming fixed storage times (FIX) in microbial risk estimation for the supply chain of fresh-cut leafy green vegetables destined for working-canteen salad bars. The results of the FIX model were previously published (E. Franz, S. O. Tromp, H. Rijgersberg, and H. J. van der Fels-Klerx, J. Food Prot. 73:274-285, 2010). Pathogen growth was modeled using stochastic discrete-event simulation of the applied logistics concept. The public health effects were assessed by conducting an exposure assessment and risk characterization. The relative growths of Escherichia coli O157 (17%) and Salmonella enterica (15%) were identical in the MOD and FIX models. In contrast, the relative growth of Listeria monocytogenes was considerably higher in the MOD model (1,156%) than in the FIX model (194%). The probability of L. monocytogenes infection in The Netherlands was higher in the MOD model (5.18×10(-8)) than in the FIX model (1.23×10(-8)). The risk of listeriosis-induced fetal mortality in the perinatal population increased from 1.24×10(-4) (FIX) to 1.66×10(-4) (MOD). Modeling the probabilistic nature of supply chain logistics is of additional value for microbial risk assessments regarding psychrotrophic pathogens in food products for which time and temperature are the postharvest preventive measures in guaranteeing food safety.

Organizational Learning, Strategic Flexibility and Business Model Innovation: An Empirical Research Based on Logistics Enterprises

NASA Astrophysics Data System (ADS)

Bao, Yaodong; Cheng, Lin; Zhang, Jian

Using the data of 237 Jiangsu logistics firms, this paper empirically studies the relationship among organizational learning capability, business model innovation, strategic flexibility. The results show as follows; organizational learning capability has positive impacts on business model innovation performance; strategic flexibility plays mediating roles on the relationship between organizational learning capability and business model innovation; interaction among strategic flexibility, explorative learning and exploitative learning play significant roles in radical business model innovation and incremental business model innovation.

Asymptotic Properties of Induced Maximum Likelihood Estimates of Nonlinear Models for Item Response Variables: The Finite-Generic-Item-Pool Case.

ERIC Educational Resources Information Center

Jones, Douglas H.

The progress of modern mental test theory depends very much on the techniques of maximum likelihood estimation, and many popular applications make use of likelihoods induced by logistic item response models. While, in reality, item responses are nonreplicate within a single examinee and the logistic models are only ideal, practitioners make…

A Method for Calculating the Probability of Successfully Completing a Rocket Propulsion Ground Test

NASA Technical Reports Server (NTRS)

Messer, Bradley

2007-01-01

Propulsion ground test facilities face the daily challenge of scheduling multiple customers into limited facility space and successfully completing their propulsion test projects. Over the last decade NASA s propulsion test facilities have performed hundreds of tests, collected thousands of seconds of test data, and exceeded the capabilities of numerous test facility and test article components. A logistic regression mathematical modeling technique has been developed to predict the probability of successfully completing a rocket propulsion test. A logistic regression model is a mathematical modeling approach that can be used to describe the relationship of several independent predictor variables X(sub 1), X(sub 2),.., X(sub k) to a binary or dichotomous dependent variable Y, where Y can only be one of two possible outcomes, in this case Success or Failure of accomplishing a full duration test. The use of logistic regression modeling is not new; however, modeling propulsion ground test facilities using logistic regression is both a new and unique application of the statistical technique. Results from this type of model provide project managers with insight and confidence into the effectiveness of rocket propulsion ground testing.

Effect of reverse shoulder design philosophy on muscle moment arms.

PubMed

Hamilton, Matthew A; Diep, Phong; Roche, Chris; Flurin, Pierre Henri; Wright, Thomas W; Zuckerman, Joseph D; Routman, Howard

2015-04-01

This study analyzes the muscle moment arms of three different reverse shoulder design philosophies using a previously published method. Digital bone models of the shoulder were imported into a 3D modeling software and markers placed for the origin and insertion of relevant muscles. The anatomic model was used as a baseline for moment arm calculations. Subsequently, three different reverse shoulder designs were virtually implanted and moment arms were analyzed in abduction and external rotation. The results indicate that the lateral offset between the joint center and the axis of the humerus specific to one reverse shoulder design increased the external rotation moment arms of the posterior deltoid relative to the other reverse shoulder designs. The other muscles analyzed demonstrated differences in the moment arms, but none of the differences reached statistical significance. This study demonstrated how the combination of variables making up different reverse shoulder designs can affect the moment arms of the muscles in different and statistically significant ways. The role of humeral offset in reverse shoulder design has not been previously reported and could have an impact on external rotation and stability achieved post-operatively. © 2015 Orthopaedic Research Society. Published by Wiley Periodicals, Inc.

A computational approach to compare regression modelling strategies in prediction research.

PubMed

Pajouheshnia, Romin; Pestman, Wiebe R; Teerenstra, Steven; Groenwold, Rolf H H

2016-08-25

It is often unclear which approach to fit, assess and adjust a model will yield the most accurate prediction model. We present an extension of an approach for comparing modelling strategies in linear regression to the setting of logistic regression and demonstrate its application in clinical prediction research. A framework for comparing logistic regression modelling strategies by their likelihoods was formulated using a wrapper approach. Five different strategies for modelling, including simple shrinkage methods, were compared in four empirical data sets to illustrate the concept of a priori strategy comparison. Simulations were performed in both randomly generated data and empirical data to investigate the influence of data characteristics on strategy performance. We applied the comparison framework in a case study setting. Optimal strategies were selected based on the results of a priori comparisons in a clinical data set and the performance of models built according to each strategy was assessed using the Brier score and calibration plots. The performance of modelling strategies was highly dependent on the characteristics of the development data in both linear and logistic regression settings. A priori comparisons in four empirical data sets found that no strategy consistently outperformed the others. The percentage of times that a model adjustment strategy outperformed a logistic model ranged from 3.9 to 94.9 %, depending on the strategy and data set. However, in our case study setting the a priori selection of optimal methods did not result in detectable improvement in model performance when assessed in an external data set. The performance of prediction modelling strategies is a data-dependent process and can be highly variable between data sets within the same clinical domain. A priori strategy comparison can be used to determine an optimal logistic regression modelling strategy for a given data set before selecting a final modelling approach.

Differential item functioning analysis with ordinal logistic regression techniques. DIFdetect and difwithpar.

PubMed

Crane, Paul K; Gibbons, Laura E; Jolley, Lance; van Belle, Gerald

2006-11-01

We present an ordinal logistic regression model for identification of items with differential item functioning (DIF) and apply this model to a Mini-Mental State Examination (MMSE) dataset. We employ item response theory ability estimation in our models. Three nested ordinal logistic regression models are applied to each item. Model testing begins with examination of the statistical significance of the interaction term between ability and the group indicator, consistent with nonuniform DIF. Then we turn our attention to the coefficient of the ability term in models with and without the group term. If including the group term has a marked effect on that coefficient, we declare that it has uniform DIF. We examined DIF related to language of test administration in addition to self-reported race, Hispanic ethnicity, age, years of education, and sex. We used PARSCALE for IRT analyses and STATA for ordinal logistic regression approaches. We used an iterative technique for adjusting IRT ability estimates on the basis of DIF findings. Five items were found to have DIF related to language. These same items also had DIF related to other covariates. The ordinal logistic regression approach to DIF detection, when combined with IRT ability estimates, provides a reasonable alternative for DIF detection. There appear to be several items with significant DIF related to language of test administration in the MMSE. More attention needs to be paid to the specific criteria used to determine whether an item has DIF, not just the technique used to identify DIF.

Basic Diagnosis and Prediction of Persistent Contrail Occurrence using High-resolution Numerical Weather Analyses/Forecasts and Logistic Regression. Part I: Effects of Random Error

NASA Technical Reports Server (NTRS)

Duda, David P.; Minnis, Patrick

2009-01-01

Straightforward application of the Schmidt-Appleman contrail formation criteria to diagnose persistent contrail occurrence from numerical weather prediction data is hindered by significant bias errors in the upper tropospheric humidity. Logistic models of contrail occurrence have been proposed to overcome this problem, but basic questions remain about how random measurement error may affect their accuracy. A set of 5000 synthetic contrail observations is created to study the effects of random error in these probabilistic models. The simulated observations are based on distributions of temperature, humidity, and vertical velocity derived from Advanced Regional Prediction System (ARPS) weather analyses. The logistic models created from the simulated observations were evaluated using two common statistical measures of model accuracy, the percent correct (PC) and the Hanssen-Kuipers discriminant (HKD). To convert the probabilistic results of the logistic models into a dichotomous yes/no choice suitable for the statistical measures, two critical probability thresholds are considered. The HKD scores are higher when the climatological frequency of contrail occurrence is used as the critical threshold, while the PC scores are higher when the critical probability threshold is 0.5. For both thresholds, typical random errors in temperature, relative humidity, and vertical velocity are found to be small enough to allow for accurate logistic models of contrail occurrence. The accuracy of the models developed from synthetic data is over 85 percent for both the prediction of contrail occurrence and non-occurrence, although in practice, larger errors would be anticipated.

An Order Insertion Scheduling Model of Logistics Service Supply Chain Considering Capacity and Time Factors

PubMed Central

Yang, Yi; Wang, Shuqing; Liu, Yang

2014-01-01

Order insertion often occurs in the scheduling process of logistics service supply chain (LSSC), which disturbs normal time scheduling especially in the environment of mass customization logistics service. This study analyses order similarity coefficient and order insertion operation process and then establishes an order insertion scheduling model of LSSC with service capacity and time factors considered. This model aims to minimize the average unit volume operation cost of logistics service integrator and maximize the average satisfaction degree of functional logistics service providers. In order to verify the viability and effectiveness of our model, a specific example is numerically analyzed. Some interesting conclusions are obtained. First, along with the increase of completion time delay coefficient permitted by customers, the possible inserting order volume first increases and then trends to be stable. Second, supply chain performance reaches the best when the volume of inserting order is equal to the surplus volume of the normal operation capacity in mass service process. Third, the larger the normal operation capacity in mass service process is, the bigger the possible inserting order's volume will be. Moreover, compared to increasing the completion time delay coefficient, improving the normal operation capacity of mass service process is more useful. PMID:25276851

An Evaluation of Hierarchical Bayes Estimation for the Two- Parameter Logistic Model.

ERIC Educational Resources Information Center

Kim, Seock-Ho

Hierarchical Bayes procedures for the two-parameter logistic item response model were compared for estimating item parameters. Simulated data sets were analyzed using two different Bayes estimation procedures, the two-stage hierarchical Bayes estimation (HB2) and the marginal Bayesian with known hyperparameters (MB), and marginal maximum…

Applying the Principles of Specific Objectivity and of Generalizability to the Measurement of Change.

ERIC Educational Resources Information Center

Fischer, Gerhard H.

1987-01-01

A natural parameterization and formalization of the problem of measuring change in dichotomous data is developed. Mathematically-exact definitions of specific objectivity are presented, and the basic structures of the linear logistic test model and the linear logistic model with relaxed assumptions are clarified. (SLD)

Multivariate logistic regression analysis of postoperative complications and risk model establishment of gastrectomy for gastric cancer: A single-center cohort report.

PubMed

Zhou, Jinzhe; Zhou, Yanbing; Cao, Shougen; Li, Shikuan; Wang, Hao; Niu, Zhaojian; Chen, Dong; Wang, Dongsheng; Lv, Liang; Zhang, Jian; Li, Yu; Jiao, Xuelong; Tan, Xiaojie; Zhang, Jianli; Wang, Haibo; Zhang, Bingyuan; Lu, Yun; Sun, Zhenqing

2016-01-01

Reporting of surgical complications is common, but few provide information about the severity and estimate risk factors of complications. If have, but lack of specificity. We retrospectively analyzed data on 2795 gastric cancer patients underwent surgical procedure at the Affiliated Hospital of Qingdao University between June 2007 and June 2012, established multivariate logistic regression model to predictive risk factors related to the postoperative complications according to the Clavien-Dindo classification system. Twenty-four out of 86 variables were identified statistically significant in univariate logistic regression analysis, 11 significant variables entered multivariate analysis were employed to produce the risk model. Liver cirrhosis, diabetes mellitus, Child classification, invasion of neighboring organs, combined resection, introperative transfusion, Billroth II anastomosis of reconstruction, malnutrition, surgical volume of surgeons, operating time and age were independent risk factors for postoperative complications after gastrectomy. Based on logistic regression equation, p=Exp∑BiXi / (1+Exp∑BiXi), multivariate logistic regression predictive model that calculated the risk of postoperative morbidity was developed, p = 1/(1 + e((4.810-1.287X1-0.504X2-0.500X3-0.474X4-0.405X5-0.318X6-0.316X7-0.305X8-0.278X9-0.255X10-0.138X11))). The accuracy, sensitivity and specificity of the model to predict the postoperative complications were 86.7%, 76.2% and 88.6%, respectively. This risk model based on Clavien-Dindo grading severity of complications system and logistic regression analysis can predict severe morbidity specific to an individual patient's risk factors, estimate patients' risks and benefits of gastric surgery as an accurate decision-making tool and may serve as a template for the development of risk models for other surgical groups.

STOL landing thrust: Reverser jet flowfields

NASA Technical Reports Server (NTRS)

Kotansky, D. R.; Glaze, L. W.

1987-01-01

Analysis tools and modeling concepts for jet flow fields encountered upon use of thrust reversers for high performance military aircraft are described. A semi-empirical model of the reverser ground wall jet interaction with the uniform cross flow due to aircraft forward velocity is described. This ground interaction model is used to demonstrate exhaust gas ingestion conditions. The effects of control of exhaust jet vector angle, lateral splay, and moving versus fixed ground simulation are discussed. The Adler/Baron jet-in-cross flow model is used in conjunction with three dimensional panel methods to investigate the upper surface jet induced flow field.

Cluster analysis: a new approach for identification of underlying risk factors for coronary artery disease in essential hypertensive patients.

PubMed

Guo, Qi; Lu, Xiaoni; Gao, Ya; Zhang, Jingjing; Yan, Bin; Su, Dan; Song, Anqi; Zhao, Xi; Wang, Gang

2017-03-07

Grading of essential hypertension according to blood pressure (BP) level may not adequately reflect clinical heterogeneity of hypertensive patients. This study was carried out to explore clinical phenotypes in essential hypertensive patients using cluster analysis. This study recruited 513 hypertensive patients and evaluated BP variations with ambulatory blood pressure monitoring. Four distinct hypertension groups were identified using cluster analysis: (1) younger male smokers with relatively high BP had the most severe carotid plaque thickness but no coronary artery disease (CAD); (2) older women with relatively low diastolic BP had more diabetes; (3) non-smokers with a low systolic BP level had neither diabetes nor CAD; (4) hypertensive patients with BP reverse dipping were most likely to have CAD but had least severe carotid plaque thickness. In binary logistic analysis, reverse dipping was significantly associated with prevalence of CAD. Cluster analysis was shown to be a feasible approach for investigating the heterogeneity of essential hypertension in clinical studies. BP reverse dipping might be valuable for prediction of CAD in hypertensive patients when compared with carotid plaque thickness. However, large-scale prospective trials with more information of plaque morphology are necessary to further compare the predicative power between BP dipping pattern and carotid plaque.

Cluster analysis: a new approach for identification of underlying risk factors for coronary artery disease in essential hypertensive patients

PubMed Central

Guo, Qi; Lu, Xiaoni; Gao, Ya; Zhang, Jingjing; Yan, Bin; Su, Dan; Song, Anqi; Zhao, Xi; Wang, Gang

2017-01-01

Grading of essential hypertension according to blood pressure (BP) level may not adequately reflect clinical heterogeneity of hypertensive patients. This study was carried out to explore clinical phenotypes in essential hypertensive patients using cluster analysis. This study recruited 513 hypertensive patients and evaluated BP variations with ambulatory blood pressure monitoring. Four distinct hypertension groups were identified using cluster analysis: (1) younger male smokers with relatively high BP had the most severe carotid plaque thickness but no coronary artery disease (CAD); (2) older women with relatively low diastolic BP had more diabetes; (3) non-smokers with a low systolic BP level had neither diabetes nor CAD; (4) hypertensive patients with BP reverse dipping were most likely to have CAD but had least severe carotid plaque thickness. In binary logistic analysis, reverse dipping was significantly associated with prevalence of CAD. Cluster analysis was shown to be a feasible approach for investigating the heterogeneity of essential hypertension in clinical studies. BP reverse dipping might be valuable for prediction of CAD in hypertensive patients when compared with carotid plaque thickness. However, large-scale prospective trials with more information of plaque morphology are necessary to further compare the predicative power between BP dipping pattern and carotid plaque. PMID:28266630

Quiet Clean Short-Haul Experimental Engine (QCSEE) acoustic and aerodynamic tests on a scale model over-the-wing thrust reverser and forward thrust nozzle

NASA Technical Reports Server (NTRS)

Stimpert, D. L.

1978-01-01

An acoustic and aerodynamic test program was conducted on a 1/6.25 scale model of the Quiet, Clean, Short-Haul Experimental Engine (QCSEE) forward thrust over-the-wing (OTW) nozzle and OTW thrust reverser. In reverse thrust, the effect of reverser geometry was studied by parametric variations in blocker spacing, blocker height, lip angle, and lip length. Forward thrust nozzle tests determined the jet noise levels of the cruise and takeoff nozzles, the effect of opening side doors to achieve takeoff thrust, and scrubbing noise of the cruise and takeoff jet on a simulated wing surface. Velocity profiles are presented for both forward and reverse thrust nozzles. An estimate of the reverse thrust was made utilizing the measured centerline turning angle.

NRMC - A GPU code for N-Reverse Monte Carlo modeling of fluids in confined media

NASA Astrophysics Data System (ADS)

Sánchez-Gil, Vicente; Noya, Eva G.; Lomba, Enrique

2017-08-01

NRMC is a parallel code for performing N-Reverse Monte Carlo modeling of fluids in confined media [V. Sánchez-Gil, E.G. Noya, E. Lomba, J. Chem. Phys. 140 (2014) 024504]. This method is an extension of the usual Reverse Monte Carlo method to obtain structural models of confined fluids compatible with experimental diffraction patterns, specifically designed to overcome the problem of slow diffusion that can appear under conditions of tight confinement. Most of the computational time in N-Reverse Monte Carlo modeling is spent in the evaluation of the structure factor for each trial configuration, a calculation that can be easily parallelized. Implementation of the structure factor evaluation in NVIDIA® CUDA so that the code can be run on GPUs leads to a speed up of up to two orders of magnitude.

Interpretation of commonly used statistical regression models.

PubMed

Kasza, Jessica; Wolfe, Rory

2014-01-01

A review of some regression models commonly used in respiratory health applications is provided in this article. Simple linear regression, multiple linear regression, logistic regression and ordinal logistic regression are considered. The focus of this article is on the interpretation of the regression coefficients of each model, which are illustrated through the application of these models to a respiratory health research study. © 2013 The Authors. Respirology © 2013 Asian Pacific Society of Respirology.

Estimating the Probability of Rare Events Occurring Using a Local Model Averaging.

PubMed

Chen, Jin-Hua; Chen, Chun-Shu; Huang, Meng-Fan; Lin, Hung-Chih

2016-10-01

In statistical applications, logistic regression is a popular method for analyzing binary data accompanied by explanatory variables. But when one of the two outcomes is rare, the estimation of model parameters has been shown to be severely biased and hence estimating the probability of rare events occurring based on a logistic regression model would be inaccurate. In this article, we focus on estimating the probability of rare events occurring based on logistic regression models. Instead of selecting a best model, we propose a local model averaging procedure based on a data perturbation technique applied to different information criteria to obtain different probability estimates of rare events occurring. Then an approximately unbiased estimator of Kullback-Leibler loss is used to choose the best one among them. We design complete simulations to show the effectiveness of our approach. For illustration, a necrotizing enterocolitis (NEC) data set is analyzed. © 2016 Society for Risk Analysis.

Multibody system of the upper limb including a reverse shoulder prosthesis.

PubMed

Quental, C; Folgado, J; Ambrósio, J; Monteiro, J

2013-11-01

The reverse shoulder replacement, recommended for the treatment of several shoulder pathologies such as cuff tear arthropathy and fractures in elderly people, changes the biomechanics of the shoulder when compared to the normal anatomy. Although several musculoskeletal models of the upper limb have been presented to study the shoulder joint, only a few of them focus on the biomechanics of the reverse shoulder. This work presents a biomechanical model of the upper limb, including a reverse shoulder prosthesis, to evaluate the impact of the variation of the joint geometry and position on the biomechanical function of the shoulder. The biomechanical model of the reverse shoulder is based on a musculoskeletal model of the upper limb, which is modified to account for the properties of the DELTA® reverse prosthesis. Considering two biomechanical models, which simulate the anatomical and reverse shoulder joints, the changes in muscle lengths, muscle moment arms, and muscle and joint reaction forces are evaluated. The muscle force sharing problem is solved for motions of unloaded abduction in the coronal plane and unloaded anterior flexion in the sagittal plane, acquired using video-imaging, through the minimization of an objective function related to muscle metabolic energy consumption. After the replacement of the shoulder joint, significant changes in the length of the pectoralis major, latissimus dorsi, deltoid, teres major, teres minor, coracobrachialis, and biceps brachii muscles are observed for a reference position considered for the upper limb. The shortening of the teres major and teres minor is the most critical since they become unable to produce active force in this position. Substantial changes of muscle moment arms are also observed, which are consistent with the literature. As expected, there is a significant increase of the deltoid moment arms and more fibers are able to elevate the arm. The solutions to the muscle force sharing problem support the biomechanical advantages attributed to the reverse shoulder design and show an increase in activity from the deltoid, teres minor, and coracobrachialis muscles. The glenohumeral joint reaction forces estimated for the reverse shoulder are up to 15% lower than those in the normal shoulder anatomy. The data presented here complements previous publications, which, all together, allow researchers to build a biomechanical model of the upper limb including a reverse shoulder prosthesis.

Access disparities to Magnet hospitals for patients undergoing neurosurgical operations

PubMed Central

Missios, Symeon; Bekelis, Kimon

2017-01-01

Background Centers of excellence focusing on quality improvement have demonstrated superior outcomes for a variety of surgical interventions. We investigated the presence of access disparities to hospitals recognized by the Magnet Recognition Program of the American Nurses Credentialing Center (ANCC) for patients undergoing neurosurgical operations. Methods We performed a cohort study of all neurosurgery patients who were registered in the New York Statewide Planning and Research Cooperative System (SPARCS) database from 2009–2013. We examined the association of African-American race and lack of insurance with Magnet status hospitalization for neurosurgical procedures. A mixed effects propensity adjusted multivariable regression analysis was used to control for confounding. Results During the study period, 190,535 neurosurgical patients met the inclusion criteria. Using a multivariable logistic regression, we demonstrate that African-Americans had lower admission rates to Magnet institutions (OR 0.62; 95% CI, 0.58–0.67). This persisted in a mixed effects logistic regression model (OR 0.77; 95% CI, 0.70–0.83) to adjust for clustering at the patient county level, and a propensity score adjusted logistic regression model (OR 0.75; 95% CI, 0.69–0.82). Additionally, lack of insurance was associated with lower admission rates to Magnet institutions (OR 0.71; 95% CI, 0.68–0.73), in a multivariable logistic regression model. This persisted in a mixed effects logistic regression model (OR 0.72; 95% CI, 0.69–0.74), and a propensity score adjusted logistic regression model (OR 0.72; 95% CI, 0.69–0.75). Conclusions Using a comprehensive all-payer cohort of neurosurgery patients in New York State we identified an association of African-American race and lack of insurance with lower rates of admission to Magnet hospitals. PMID:28684152

Three methods to construct predictive models using logistic regression and likelihood ratios to facilitate adjustment for pretest probability give similar results.

PubMed

Chan, Siew Foong; Deeks, Jonathan J; Macaskill, Petra; Irwig, Les

2008-01-01

To compare three predictive models based on logistic regression to estimate adjusted likelihood ratios allowing for interdependency between diagnostic variables (tests). This study was a review of the theoretical basis, assumptions, and limitations of published models; and a statistical extension of methods and application to a case study of the diagnosis of obstructive airways disease based on history and clinical examination. Albert's method includes an offset term to estimate an adjusted likelihood ratio for combinations of tests. Spiegelhalter and Knill-Jones method uses the unadjusted likelihood ratio for each test as a predictor and computes shrinkage factors to allow for interdependence. Knottnerus' method differs from the other methods because it requires sequencing of tests, which limits its application to situations where there are few tests and substantial data. Although parameter estimates differed between the models, predicted "posttest" probabilities were generally similar. Construction of predictive models using logistic regression is preferred to the independence Bayes' approach when it is important to adjust for dependency of tests errors. Methods to estimate adjusted likelihood ratios from predictive models should be considered in preference to a standard logistic regression model to facilitate ease of interpretation and application. Albert's method provides the most straightforward approach.

NASA Researcher Examines an Aircraft Model with a Four-Fan Thrust Reverser

NASA Image and Video Library

1972-03-21

National Aeronautics and Space Administration (NASA) researcher John Carpenter inspects an aircraft model with a four-fan thrust reverser which would be studied in the 9- by 15-Foot Low Speed Wind Tunnel at the Lewis Research Center. Thrust reversers were introduced in the 1950s as a means for slowing high-speed jet aircraft during landing. Engineers sought to apply the technology to Vertical and Short Takeoff and Landing (VSTOL) aircraft in the 1970s. The new designs would have to take into account shorter landing areas, noise levels, and decreased thrust levels. A balance was needed between the thrust reverser’s efficiency, its noise generation, and the engine’s power setting. This model underwent a series of four tests in the 9- by 15-foot tunnel during April and May 1974. The model, with a high-wing configuration and no tail, was equipped with four thrust-reverser engines. The investigations included static internal aerodynamic tests on a single fan/reverser, wind tunnel isolated fan/reverser thrust tests, installation effects on a four-fan airplane model in a wind tunnel, and single reverser acoustic tests. The 9-by 15 was built inside the return leg of the 8- by 6-Foot Supersonic Wind Tunnel in 1968. The facility generates airspeeds from 0 to 175 miles per hour to evaluate the aerodynamic performance and acoustic characteristics of nozzles, inlets, and propellers, and investigate hot gas re-ingestion of advanced VSTOL concepts. John Carpenter was a technician in the Wind Tunnels Service Section of the Test Installations Division.

Modeling of the attenuation of stress waves in concrete based on the Rayleigh damping model using time-reversal and PZT transducers

NASA Astrophysics Data System (ADS)

Tian, Zhen; Huo, Linsheng; Gao, Weihang; Li, Hongnan; Song, Gangbing

2017-10-01

Wave-based concrete structural health monitoring has attracted much attention. A stress wave experiences significant attenuation in concrete, however there is a lack of a unified method for predicting the attenuation coefficient of the stress wave. In this paper, a simple and effective absorption attenuation model of stress waves in concrete is developed based on the Rayleigh damping model, which indicates that the absorption attenuation coefficient of stress waves in concrete is directly proportional to the square of the stress wave frequency when the damping ratio is small. In order to verify the theoretical model, related experiments were carried out. During the experiments, a concrete beam was designed in which the d33-model piezoelectric smart aggregates were embedded to detect the propagation of stress waves. It is difficult to distinguish direct stress waves due to the complex propagation paths and the reflection and scattering of stress waves in concrete. Hence, as another innovation of this paper, a new method for computing the absorption attenuation coefficient based on the time-reversal method is developed. Due to the self-adaptive focusing properties of the time-reversal method, the time-reversed stress wave focuses and generates a peak value. The time-reversal method eliminates the adverse effects of multipaths, reflection, and scattering. The absorption attenuation coefficient is computed by analyzing the peak value changes of the time-reversal focused signal. Finally, the experimental results are found to be in good agreement with the theoretical model.

Building a Decision Support System for Inpatient Admission Prediction With the Manchester Triage System and Administrative Check-in Variables.

PubMed

Zlotnik, Alexander; Alfaro, Miguel Cuchí; Pérez, María Carmen Pérez; Gallardo-Antolín, Ascensión; Martínez, Juan Manuel Montero

2016-05-01

The usage of decision support tools in emergency departments, based on predictive models, capable of estimating the probability of admission for patients in the emergency department may give nursing staff the possibility of allocating resources in advance. We present a methodology for developing and building one such system for a large specialized care hospital using a logistic regression and an artificial neural network model using nine routinely collected variables available right at the end of the triage process.A database of 255.668 triaged nonobstetric emergency department presentations from the Ramon y Cajal University Hospital of Madrid, from January 2011 to December 2012, was used to develop and test the models, with 66% of the data used for derivation and 34% for validation, with an ordered nonrandom partition. On the validation dataset areas under the receiver operating characteristic curve were 0.8568 (95% confidence interval, 0.8508-0.8583) for the logistic regression model and 0.8575 (95% confidence interval, 0.8540-0. 8610) for the artificial neural network model. χ Values for Hosmer-Lemeshow fixed "deciles of risk" were 65.32 for the logistic regression model and 17.28 for the artificial neural network model. A nomogram was generated upon the logistic regression model and an automated software decision support system with a Web interface was built based on the artificial neural network model.

Comparison of multinomial logistic regression and logistic regression: which is more efficient in allocating land use?

NASA Astrophysics Data System (ADS)

Lin, Yingzhi; Deng, Xiangzheng; Li, Xing; Ma, Enjun

2014-12-01

Spatially explicit simulation of land use change is the basis for estimating the effects of land use and cover change on energy fluxes, ecology and the environment. At the pixel level, logistic regression is one of the most common approaches used in spatially explicit land use allocation models to determine the relationship between land use and its causal factors in driving land use change, and thereby to evaluate land use suitability. However, these models have a drawback in that they do not determine/allocate land use based on the direct relationship between land use change and its driving factors. Consequently, a multinomial logistic regression method was introduced to address this flaw, and thereby, judge the suitability of a type of land use in any given pixel in a case study area of the Jiangxi Province, China. A comparison of the two regression methods indicated that the proportion of correctly allocated pixels using multinomial logistic regression was 92.98%, which was 8.47% higher than that obtained using logistic regression. Paired t-test results also showed that pixels were more clearly distinguished by multinomial logistic regression than by logistic regression. In conclusion, multinomial logistic regression is a more efficient and accurate method for the spatial allocation of land use changes. The application of this method in future land use change studies may improve the accuracy of predicting the effects of land use and cover change on energy fluxes, ecology, and environment.

Power and Sample Size Calculations for Logistic Regression Tests for Differential Item Functioning

ERIC Educational Resources Information Center

Li, Zhushan

2014-01-01

Logistic regression is a popular method for detecting uniform and nonuniform differential item functioning (DIF) effects. Theoretical formulas for the power and sample size calculations are derived for likelihood ratio tests and Wald tests based on the asymptotic distribution of the maximum likelihood estimators for the logistic regression model.…

Logistic Achievement Test Scaling and Equating with Fixed versus Estimated Lower Asymptotes.

ERIC Educational Resources Information Center

Phillips, S. E.

This study compared the lower asymptotes estimated by the maximum likelihood procedures of the LOGIST computer program with those obtained via application of the Norton methodology. The study also compared the equating results from the three-parameter logistic model with those obtained from the equipercentile, Rasch, and conditional…

A Model for Logistics Systems Engineering Management Education in Europe.

ERIC Educational Resources Information Center

Naim, M.; Lalwani, C.; Fortuin, L.; Schmidt, T.; Taylor, J.; Aronsson, H.

2000-01-01

Presents the need for a systems and process perspective of logistics, and develops a template for a logistics education course. The template addresses functional, process, and supply chain needs and was developed by a number of university partners with core skills in different traditional disciplines. (Contains 31 references.) (Author/WRM)

A Methodology for Generating Placement Rules that Utilizes Logistic Regression

ERIC Educational Resources Information Center

Wurtz, Keith

2008-01-01

The purpose of this article is to provide the necessary tools for institutional researchers to conduct a logistic regression analysis and interpret the results. Aspects of the logistic regression procedure that are necessary to evaluate models are presented and discussed with an emphasis on cutoff values and choosing the appropriate number of…

A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery - part II: an illustrative example.

PubMed

Cevenini, Gabriele; Barbini, Emanuela; Scolletta, Sabino; Biagioli, Bonizella; Giomarelli, Pierpaolo; Barbini, Paolo

2007-11-22

Popular predictive models for estimating morbidity probability after heart surgery are compared critically in a unitary framework. The study is divided into two parts. In the first part modelling techniques and intrinsic strengths and weaknesses of different approaches were discussed from a theoretical point of view. In this second part the performances of the same models are evaluated in an illustrative example. Eight models were developed: Bayes linear and quadratic models, k-nearest neighbour model, logistic regression model, Higgins and direct scoring systems and two feed-forward artificial neural networks with one and two layers. Cardiovascular, respiratory, neurological, renal, infectious and hemorrhagic complications were defined as morbidity. Training and testing sets each of 545 cases were used. The optimal set of predictors was chosen among a collection of 78 preoperative, intraoperative and postoperative variables by a stepwise procedure. Discrimination and calibration were evaluated by the area under the receiver operating characteristic curve and Hosmer-Lemeshow goodness-of-fit test, respectively. Scoring systems and the logistic regression model required the largest set of predictors, while Bayesian and k-nearest neighbour models were much more parsimonious. In testing data, all models showed acceptable discrimination capacities, however the Bayes quadratic model, using only three predictors, provided the best performance. All models showed satisfactory generalization ability: again the Bayes quadratic model exhibited the best generalization, while artificial neural networks and scoring systems gave the worst results. Finally, poor calibration was obtained when using scoring systems, k-nearest neighbour model and artificial neural networks, while Bayes (after recalibration) and logistic regression models gave adequate results. Although all the predictive models showed acceptable discrimination performance in the example considered, the Bayes and logistic regression models seemed better than the others, because they also had good generalization and calibration. The Bayes quadratic model seemed to be a convincing alternative to the much more usual Bayes linear and logistic regression models. It showed its capacity to identify a minimum core of predictors generally recognized as essential to pragmatically evaluate the risk of developing morbidity after heart surgery.

An extension of trust and TAM model with IDT in the adoption of the electronic logistics information system in HIS in the medical industry.

PubMed

Tung, Feng-Cheng; Chang, Su-Chao; Chou, Chi-Min

2008-05-01

Ever since National Health Insurance was introduced in 1995, the number of insurants increased to over 96% from 50 to 60%, with a continuous satisfaction rating of about 70%. However, the premium accounted for 5.77% of GDP in 2001 and the Bureau of National Health Insurance had pressing financial difficulties, so it reformed its expenditure systems, such as fee for service, capitation, case payment and the global budget system in order to control the rising medical costs. Since the change in health insurance policy, most hospitals attempted to reduce their operating expenses and improve efficiency. Introducing the electronic logistics information system is one way of reducing the cost of the department of central warehouse and the nursing stations. Hence, the study proposes a technology acceptance research model and examines how nurses' acceptance of the e-logistics information system has been affected in the medical industry. This research combines innovation diffusion theory, technology acceptance model and added two research parameters, trust and perceived financial cost to propose a new hybrid technology acceptance model. Taking Taiwan's medical industry as an experimental example, this paper studies nurses' acceptance of the electronic logistics information system. The structural equation modeling technique was used to evaluate the causal model and confirmatory factor analysis was performed to examine the reliability and validity of the measurement model. The results of the survey strongly support the new hybrid technology acceptance model in predicting nurses' intention to use the electronic logistics information system. The study shows that 'compatibility', 'perceived usefulness', 'perceived ease of use', and 'trust' all have great positive influence on 'behavioral intention to use'. On the other hand 'perceived financial cost' has great negative influence on behavioral intention to use.

Alternative approach to modeling bacterial lag time, using logistic regression as a function of time, temperature, pH, and sodium chloride concentration.

PubMed

Koseki, Shige; Nonaka, Junko

2012-09-01

The objective of this study was to develop a probabilistic model to predict the end of lag time (λ) during the growth of Bacillus cereus vegetative cells as a function of temperature, pH, and salt concentration using logistic regression. The developed λ model was subsequently combined with a logistic differential equation to simulate bacterial numbers over time. To develop a novel model for λ, we determined whether bacterial growth had begun, i.e., whether λ had ended, at each time point during the growth kinetics. The growth of B. cereus was evaluated by optical density (OD) measurements in culture media for various pHs (5.5 ∼ 7.0) and salt concentrations (0.5 ∼ 2.0%) at static temperatures (10 ∼ 20°C). The probability of the end of λ was modeled using dichotomous judgments obtained at each OD measurement point concerning whether a significant increase had been observed. The probability of the end of λ was described as a function of time, temperature, pH, and salt concentration and showed a high goodness of fit. The λ model was validated with independent data sets of B. cereus growth in culture media and foods, indicating acceptable performance. Furthermore, the λ model, in combination with a logistic differential equation, enabled a simulation of the population of B. cereus in various foods over time at static and/or fluctuating temperatures with high accuracy. Thus, this newly developed modeling procedure enables the description of λ using observable environmental parameters without any conceptual assumptions and the simulation of bacterial numbers over time with the use of a logistic differential equation.

Greenhouse gas network design using backward Lagrangian particle dispersion modelling - Part 1: Methodology and Australian test case

NASA Astrophysics Data System (ADS)

Ziehn, T.; Nickless, A.; Rayner, P. J.; Law, R. M.; Roff, G.; Fraser, P.

2014-09-01

This paper describes the generation of optimal atmospheric measurement networks for determining carbon dioxide fluxes over Australia using inverse methods. A Lagrangian particle dispersion model is used in reverse mode together with a Bayesian inverse modelling framework to calculate the relationship between weekly surface fluxes, comprising contributions from the biosphere and fossil fuel combustion, and hourly concentration observations for the Australian continent. Meteorological driving fields are provided by the regional version of the Australian Community Climate and Earth System Simulator (ACCESS) at 12 km resolution at an hourly timescale. Prior uncertainties are derived on a weekly timescale for biosphere fluxes and fossil fuel emissions from high-resolution model runs using the Community Atmosphere Biosphere Land Exchange (CABLE) model and the Fossil Fuel Data Assimilation System (FFDAS) respectively. The influence from outside the modelled domain is investigated, but proves to be negligible for the network design. Existing ground-based measurement stations in Australia are assessed in terms of their ability to constrain local flux estimates from the land. We find that the six stations that are currently operational are already able to reduce the uncertainties on surface flux estimates by about 30%. A candidate list of 59 stations is generated based on logistic constraints and an incremental optimisation scheme is used to extend the network of existing stations. In order to achieve an uncertainty reduction of about 50%, we need to double the number of measurement stations in Australia. Assuming equal data uncertainties for all sites, new stations would be mainly located in the northern and eastern part of the continent.

Declining rates of sterilisation reversal procedures in western Australian women from 1990 to 2008: the relationship with age, hospital type and government policy changes.

PubMed

Jama-Alol, Khadra A; Bremner, Alexandra P; Pereira, Gavin; Stewart, Louise M; Malacova, Eva; Moorin, Rachael; Preen, David B

2017-11-25

Female sterilisation is usually performed on an elective basis at perceived family completion, however, around 1-3% of women who have undergone sterilisation elect to undergo sterilisation reversal (SR) at a later stage. The trends in SR rates in Western Australia (WA), proportions of SR procedures between hospital types (public and private), and the effects of Federal Government policies on these trends are unknown. Using records from statutory state-wide data collections of hospital separations and births, we conducted a retrospective descriptive study of all women aged 15-49 years who underwent a SR procedure during the period 1st January 1990 to 31st December 2008 (n = 1868 procedures). From 1991 to 2007 the annual incidence rate of SR procedures per 10,000 women declined from 47.0 to 3.6. Logistic regression modelling showed that from 1997 to 2001 the odds of women undergoing SR in a private hospital as opposed to all other hospitals were 1.39 times higher (95% CI 1.07-1.81) and 7.51 times higher (95% CI 5.46-10.31) from 2002 to 2008. There were significant decreases in SR rates overall and among different age groups after the Federal Government interventions. Rates of SR procedures in WA have declined from 1990 to 2008, particularly following policy changes such as the introduction of private health insurance (PHI) policies. This suggests decisions to undergo SR may be influenced by Federal Government interventions.

Proteomic analysis of cerebrospinal fluid from children with central nervous system tumors identifies candidate proteins relating to tumor metastatic spread.

PubMed

Spreafico, Filippo; Bongarzone, Italia; Pizzamiglio, Sara; Magni, Ruben; Taverna, Elena; De Bortoli, Maida; Ciniselli, Chiara M; Barzanò, Elena; Biassoni, Veronica; Luchini, Alessandra; Liotta, Lance A; Zhou, Weidong; Signore, Michele; Verderio, Paolo; Massimino, Maura

2017-07-11

Central nervous system (CNS) tumors are the most common solid tumors in childhood. Since the sensitivity of combined cerebrospinal fluid (CSF) cytology and radiological neuroimaging in detecting meningeal metastases remains relatively low, we sought to characterize the CSF proteome of patients with CSF tumors to identify biomarkers predictive of metastatic spread. CSF samples from 27 children with brain tumors and 13 controls (extra-CNS non-Hodgkin lymphoma) were processed using core-shell hydrogel nanoparticles, and analyzed with reverse-phase liquid chromatography/electrospray tandem mass spectrometry (LC-MS/MS). Candidate proteins were identified with Fisher's exact test and/or a univariate logistic regression model. Reverse phase protein array (RPPA), Western blot (WB), and ELISA were used in the training set and in an independent set of CFS samples (60 cases, 14 controls) to validate our discovery findings. Among the 558 non-redundant proteins identified by LC-MS/MS, 147 were missing from the CSF database at http://www.biosino.org. Fourteen of the 26 final top-candidate proteins were chosen for validation with WB, RPPA and ELISA methods. Six proteins (type 1 collagen, insulin-like growth factor binding protein 4, procollagen C-endopeptidase enhancer 1, glial cell-line derived neurotrophic factor receptor α2, inter-alpha-trypsin inhibitor heavy chain 4, neural proliferation and differentiation control protein-1) revealed the ability to discriminate metastatic cases from controls. Combining a unique dataset of CSFs from pediatric CNS tumors with a novel enabling nanotechnology led us to identify CSF proteins potentially related to metastatic status.

Neural network modeling for surgical decisions on traumatic brain injury patients.

PubMed

Li, Y C; Liu, L; Chiu, W T; Jian, W S

2000-01-01

Computerized medical decision support systems have been a major research topic in recent years. Intelligent computer programs were implemented to aid physicians and other medical professionals in making difficult medical decisions. This report compares three different mathematical models for building a traumatic brain injury (TBI) medical decision support system (MDSS). These models were developed based on a large TBI patient database. This MDSS accepts a set of patient data such as the types of skull fracture, Glasgow Coma Scale (GCS), episode of convulsion and return the chance that a neurosurgeon would recommend an open-skull surgery for this patient. The three mathematical models described in this report including a logistic regression model, a multi-layer perceptron (MLP) neural network and a radial-basis-function (RBF) neural network. From the 12,640 patients selected from the database. A randomly drawn 9480 cases were used as the training group to develop/train our models. The other 3160 cases were in the validation group which we used to evaluate the performance of these models. We used sensitivity, specificity, areas under receiver-operating characteristics (ROC) curve and calibration curves as the indicator of how accurate these models are in predicting a neurosurgeon's decision on open-skull surgery. The results showed that, assuming equal importance of sensitivity and specificity, the logistic regression model had a (sensitivity, specificity) of (73%, 68%), compared to (80%, 80%) from the RBF model and (88%, 80%) from the MLP model. The resultant areas under ROC curve for logistic regression, RBF and MLP neural networks are 0.761, 0.880 and 0.897, respectively (P < 0.05). Among these models, the logistic regression has noticeably poorer calibration. This study demonstrated the feasibility of applying neural networks as the mechanism for TBI decision support systems based on clinical databases. The results also suggest that neural networks may be a better solution for complex, non-linear medical decision support systems than conventional statistical techniques such as logistic regression.

Dynamics of a stochastic HIV-1 infection model with logistic growth

NASA Astrophysics Data System (ADS)

Jiang, Daqing; Liu, Qun; Shi, Ningzhong; Hayat, Tasawar; Alsaedi, Ahmed; Xia, Peiyan

2017-03-01

This paper is concerned with a stochastic HIV-1 infection model with logistic growth. Firstly, by constructing suitable stochastic Lyapunov functions, we establish sufficient conditions for the existence of ergodic stationary distribution of the solution to the HIV-1 infection model. Then we obtain sufficient conditions for extinction of the infection. The stationary distribution shows that the infection can become persistent in vivo.

Enhancement of the Logistics Battle Command Model: Architecture Upgrades and Attrition Module Development

DTIC Science & Technology

2017-01-05

module. 15. SUBJECT TERMS Logistics, attrition, discrete event simulation, Simkit, LBC 16. SECURITY CLASSIFICATION OF: Unclassified 17. LIMITATION...stochastics, and discrete event model programmed in Java building largely on the Simkit library. The primary purpose of the LBC model is to support...equations makes them incompatible with the discrete event construct of LBC. Bullard further advances this methodology by developing a stochastic

Use of Robust z in Detecting Unstable Items in Item Response Theory Models

ERIC Educational Resources Information Center

Huynh, Huynh; Meyer, Patrick

2010-01-01

The first part of this paper describes the use of the robust z[subscript R] statistic to link test forms using the Rasch (or one-parameter logistic) model. The procedure is then extended to the two-parameter and three-parameter logistic and two-parameter partial credit (2PPC) models. A real set of data was used to illustrate the extension. The…

Simulating land-use changes by incorporating spatial autocorrelation and self-organization in CLUE-S modeling: a case study in Zengcheng District, Guangzhou, China

NASA Astrophysics Data System (ADS)

Mei, Zhixiong; Wu, Hao; Li, Shiyun

2018-06-01

The Conversion of Land Use and its Effects at Small regional extent (CLUE-S), which is a widely used model for land-use simulation, utilizes logistic regression to estimate the relationships between land use and its drivers, and thus, predict land-use change probabilities. However, logistic regression disregards possible spatial autocorrelation and self-organization in land-use data. Autologistic regression can depict spatial autocorrelation but cannot address self-organization, while logistic regression by considering only self-organization (NElogistic regression) fails to capture spatial autocorrelation. Therefore, this study developed a regression (NE-autologistic regression) method, which incorporated both spatial autocorrelation and self-organization, to improve CLUE-S. The Zengcheng District of Guangzhou, China was selected as the study area. The land-use data of 2001, 2005, and 2009, as well as 10 typical driving factors, were used to validate the proposed regression method and the improved CLUE-S model. Then, three future land-use scenarios in 2020: the natural growth scenario, ecological protection scenario, and economic development scenario, were simulated using the improved model. Validation results showed that NE-autologistic regression performed better than logistic regression, autologistic regression, and NE-logistic regression in predicting land-use change probabilities. The spatial allocation accuracy and kappa values of NE-autologistic-CLUE-S were higher than those of logistic-CLUE-S, autologistic-CLUE-S, and NE-logistic-CLUE-S for the simulations of two periods, 2001-2009 and 2005-2009, which proved that the improved CLUE-S model achieved the best simulation and was thereby effective to a certain extent. The scenario simulation results indicated that under all three scenarios, traffic land and residential/industrial land would increase, whereas arable land and unused land would decrease during 2009-2020. Apparent differences also existed in the simulated change sizes and locations of each land-use type under different scenarios. The results not only demonstrate the validity of the improved model but also provide a valuable reference for relevant policy-makers.

SpaceNet: Modeling and Simulating Space Logistics

NASA Technical Reports Server (NTRS)

Lee, Gene; Jordan, Elizabeth; Shishko, Robert; de Weck, Olivier; Armar, Nii; Siddiqi, Afreen

2008-01-01

This paper summarizes the current state of the art in interplanetary supply chain modeling and discusses SpaceNet as one particular method and tool to address space logistics modeling and simulation challenges. Fundamental upgrades to the interplanetary supply chain framework such as process groups, nested elements, and cargo sharing, enabled SpaceNet to model an integrated set of missions as a campaign. The capabilities and uses of SpaceNet are demonstrated by a step-by-step modeling and simulation of a lunar campaign.

Pump dependence of the dynamics of quantum dot based waveguide absorbers

NASA Astrophysics Data System (ADS)

Viktorov, Evgeny A.; Erneux, Thomas; Piwonski, Tomasz; Pulka, Jaroslaw; Huyet, Guillaume; Houlihan, John

2012-06-01

The nonlinear two stage recovery of quantum dot based reverse-biased waveguide absorbers is investigated experimentally and analytically as a function of the initial ground state occupation probability of the dot. The latter is controlled experimentally by the pump pulse power. The slow stage of the recovery is exponential and its basic timescale is independent of pump power. The fast stage of the recovery is a logistic function which we analyze in detail. The relative strength of slow to fast components is highlighted and the importance of higher order absorption processes at the highest pump level is demonstrated.

A universal approach to the study of nonlinear systems

NASA Astrophysics Data System (ADS)

Hwa, Rudolph C.

2004-07-01

A large variety of nonlinear systems have been treated by a common approach that emphasizes the fluctuation of spatial patterns. By using the same method of analysis it is possible to discuss the chaotic behaviors of quark jets and logistic map in the same language. Critical behaviors of quark-hadron phase transition in heavy-ion collisions and of photon production at the threshold of lasing can also be described by a common scaling behavior. The universal approach also makes possible an insight into the recently discovered phenomenon of wind reversal in cryogenic turbulence as a manifestation of self-organized criticality.

Growth dependent magnetization reversal in Co2MnAl full Heusler alloy thin films

NASA Astrophysics Data System (ADS)

Barwal, Vineet; Husain, Sajid; Behera, Nilamani; Goyat, Ekta; Chaudhary, Sujeet

2018-02-01

Angular dependent magnetization reversal has been investigated in Co2MnAl (CMA) full Heusler alloy thin films grown on Si(100) at different growth temperatures (Ts) by DC-magnetron sputtering. An M -shaped curve is observed in the in-plane angular (0°-360°) dependent coercivity (ADC) by magneto-optical Kerr effect measurements. The dependence of the magnetization reversal on Ts is investigated in detail to bring out the structure-property correlation with regards to ADC in these polycrystalline CMA thin films. This magnetization reversal ( M -shaped ADC behavior) is well described by the two-phase model, which is a combination of Kondorsky (domain wall motion) and Stoner Wohlfarth (coherent rotation) models. In this model, magnetization reversal starts with depinning of domain walls, with their gradual displacement explained by the Kondorsky model, and at a higher field (when the domain walls merge), the system follows coherent rotation before reaching its saturation following the Stoner Wohlfarth model. Further, the analysis of angular dependent squareness ratio (Mr/Ms) indicates that our films clearly exhibited twofold uniaxial anisotropy, which is related to self-steering effect arising due to the obliquely incident flux during the film-growth.

On the effects of nonlinear boundary conditions in diffusive logistic equations on bounded domains

NASA Astrophysics Data System (ADS)

Cantrell, Robert Stephen; Cosner, Chris

We study a diffusive logistic equation with nonlinear boundary conditions. The equation arises as a model for a population that grows logistically inside a patch and crosses the patch boundary at a rate that depends on the population density. Specifically, the rate at which the population crosses the boundary is assumed to decrease as the density of the population increases. The model is motivated by empirical work on the Glanville fritillary butterfly. We derive local and global bifurcation results which show that the model can have multiple equilibria and in some parameter ranges can support Allee effects. The analysis leads to eigenvalue problems with nonstandard boundary conditions.

Basic Diagnosis and Prediction of Persistent Contrail Occurrence using High-resolution Numerical Weather Analyses/Forecasts and Logistic Regression. Part II: Evaluation of Sample Models

NASA Technical Reports Server (NTRS)

Duda, David P.; Minnis, Patrick

2009-01-01

Previous studies have shown that probabilistic forecasting may be a useful method for predicting persistent contrail formation. A probabilistic forecast to accurately predict contrail formation over the contiguous United States (CONUS) is created by using meteorological data based on hourly meteorological analyses from the Advanced Regional Prediction System (ARPS) and from the Rapid Update Cycle (RUC) as well as GOES water vapor channel measurements, combined with surface and satellite observations of contrails. Two groups of logistic models were created. The first group of models (SURFACE models) is based on surface-based contrail observations supplemented with satellite observations of contrail occurrence. The second group of models (OUTBREAK models) is derived from a selected subgroup of satellite-based observations of widespread persistent contrails. The mean accuracies for both the SURFACE and OUTBREAK models typically exceeded 75 percent when based on the RUC or ARPS analysis data, but decreased when the logistic models were derived from ARPS forecast data.

Does Farming Have an Effect on Health Status? A Comparison Study in West Greece

PubMed Central

Demos, Konstantinos; Sazakli, Eleni; Jelastopulu, Eleni; Charokopos, Nikolaos; Ellul, John; Leotsinidis, Michalis

2013-01-01

Investigating the health status of agricultural workers is a challenging goal. Contradictory outcomes concerning farmers’ health are reported in the literature. In this cross-sectional study, certain clinical and neurobehavioral health outcomes were compared between farmers and non-farmers living in the same rural area. Farmers (328) and non-farmers (347), matched per age and sex, were selected randomly in an agricultural area in West Greece. Both groups underwent haematological and biochemical examinations and were administered two neurobehavioral tests, namely the Mini-Mental State Examination (MMSE) and the Montgomery-Åsberg Depression Rating Scale (MADRS). Sociodemographic, personal medical, nutritional and lifestyle data were recorded. According to personal statements, farmers suffered from hypertension, cardiovascular, orthopaedic and ENT problems in higher frequency. Haematocrit, haemoglobin and serum cholinesterase’s activity were found to be lower among farmers. Lower prevalence of hypertension and better performances on MMSE and MADRS tests were recorded in young farmers in relation to young non-farmers, while these findings were reversed in older ages. Odds Ratios were calculated through multivariate logistic regression models. Factors affecting these impairments remain to be clarified. PMID:23442558

Examining Sexual Orientation Disparities in Unmet Medical Needs among Men and Women

PubMed Central

Everett, Bethany G.; Mollborn, Stefanie

2013-01-01

Using the National Longitudinal Study of Adolescent Health (N = 13,810), this study examines disparities in unmet medical needs by sexual orientation identity during young adulthood. We use binary logistic regression and expand Andersen’s health care utilization framework to identify factors that shape disparities in unmet medical needs by sexual orientation. We also investigate whether the well-established gender disparity in health-seeking behaviors among heterosexual persons holds for sexual minorities. The results show that sexual minority women are more likely to report unmet medical needs than heterosexual women, but no differences are found between sexual minority and heterosexual men. Moreover, we find a reversal in the gender disparity between heterosexual and sexual minority populations: heterosexual women are less likely to report unmet medical needs than heterosexual men, whereas sexual minority women are more likely to report unmet medical needs compared to sexual minority men. Finally, this work advances Andersen’s model by articulating the importance of including social psychological factors for reducing disparities in unmet medical needs by sexual orientation for women. PMID:25382887

Examining Sexual Orientation Disparities in Unmet Medical Needs among Men and Women.

PubMed

Everett, Bethany G; Mollborn, Stefanie

2014-08-01

Using the National Longitudinal Study of Adolescent Health (N = 13,810), this study examines disparities in unmet medical needs by sexual orientation identity during young adulthood. We use binary logistic regression and expand Andersen's health care utilization framework to identify factors that shape disparities in unmet medical needs by sexual orientation. We also investigate whether the well-established gender disparity in health-seeking behaviors among heterosexual persons holds for sexual minorities. The results show that sexual minority women are more likely to report unmet medical needs than heterosexual women, but no differences are found between sexual minority and heterosexual men. Moreover, we find a reversal in the gender disparity between heterosexual and sexual minority populations: heterosexual women are less likely to report unmet medical needs than heterosexual men, whereas sexual minority women are more likely to report unmet medical needs compared to sexual minority men. Finally, this work advances Andersen's model by articulating the importance of including social psychological factors for reducing disparities in unmet medical needs by sexual orientation for women.

Does farming have an effect on health status? A comparison study in west Greece.

PubMed

Demos, Konstantinos; Sazakli, Eleni; Jelastopulu, Eleni; Charokopos, Nikolaos; Ellul, John; Leotsinidis, Michalis

2013-02-26

Investigating the health status of agricultural workers is a challenging goal. Contradictory outcomes concerning farmers' health are reported in the literature. In this cross-sectional study, certain clinical and neurobehavioral health outcomes were compared between farmers and non-farmers living in the same rural area. Farmers (328) and non-farmers (347), matched per age and sex, were selected randomly in an agricultural area in West Greece. Both groups underwent haematological and biochemical examinations and were administered two neurobehavioral tests, namely the Mini-Mental State Examination (MMSE) and the Montgomery-Åsberg Depression Rating Scale (MADRS). Sociodemographic, personal medical, nutritional and lifestyle data were recorded. According to personal statements, farmers suffered from hypertension, cardiovascular, orthopaedic and ENT problems in higher frequency. Haematocrit, haemoglobin and serum cholinesterase's activity were found to be lower among farmers. Lower prevalence of hypertension and better performances on MMSE and MADRS tests were recorded in young farmers in relation to young non-farmers, while these findings were reversed in older ages. Odds Ratios were calculated through multivariate logistic regression models. Factors affecting these impairments remain to be clarified.

Model comparison for Escherichia coli growth in pouched food.

PubMed

Fujikawa, Hiroshi; Yano, Kazuyoshi; Morozumi, Satoshi

2006-06-01

We recently studied the growth characteristics of Escherichia coli cells in pouched mashed potatoes (Fujikawa et al., J. Food Hyg. Soc. Japan, 47, 95-98 (2006)). Using those experimental data, in the present study, we compared a logistic model newly developed by us with the modified Gompertz and the Baranyi models, which are used as growth models worldwide. Bacterial growth curves at constant temperatures in the range of 12 to 34 degrees C were successfully described with the new logistic model, as well as with the other models. The Baranyi gave the least error in cell number and our model gave the least error in the rate constant and the lag period. For dynamic temperature, our model successfully predicted the bacterial growth, whereas the Baranyi model considerably overestimated it. Also, there was a discrepancy between the growth curves described with the differential equations of the Baranyi model and those obtained with DMfit, a software program for Baranyi model fitting. These results indicate that the new logistic model can be used to predict bacterial growth in pouched food.

CFD simulation of reverse water-hammer induced by collapse of draft-tube cavity in a model pump-turbine during runaway process

NASA Astrophysics Data System (ADS)

Zhang, Xiaoxi; Cheng, Yongguang; Xia, Linsheng; Yang, Jiandong

2016-11-01

This paper reports the preliminary progress in the CFD simulation of the reverse water-hammer induced by the collapse of a draft-tube cavity in a model pump-turbine during the runaway process. Firstly, the Fluent customized 1D-3D coupling model for hydraulic transients and the Schnerr & Sauer cavitation model for cavity development are introduced. Then, the methods are validated by simulating the benchmark reverse water-hammer in a long pipe caused by a valve instant closure. The simulated head history at the valve agrees well with the measured data in literature. After that, the more complicated reverse water-hammer in the draft-tube of a runaway model pump-turbine, which is installed in a model pumped-storage power plant, is simulated. The dynamic processes of a vapor cavity, from generation, expansion, shrink to collapse, are shown. After the cavity collapsed, a sudden increase of pressure can be evidently observed. The process is featured by a locally expending and collapsing vapor cavity that is around the runner cone, which is different from the conventional recognition of violent water- column separation. This work reveals the possibility for simulating the reverse water-hammer phenomenon in turbines by 3D CFD.

Predicting geomagnetic reversals via data assimilation: a feasibility study

NASA Astrophysics Data System (ADS)

Morzfeld, Matthias; Fournier, Alexandre; Hulot, Gauthier

2014-05-01

The system of three ordinary differential equations (ODE) presented by Gissinger in [1] was shown to exhibit chaotic reversals whose statistics compared well with those from the paleomagnetic record. We explore the geophysical relevance of this low-dimensional model via data assimilation, i.e. we update the solution of the ODE with information from data of the dipole variable. The data set we use is 'SINT' (Valet et al. [2]), and it provides the signed virtual axial dipole moment over the past 2 millions years. We can obtain an accurate reconstruction of these dipole data using implicit sampling (a fully nonlinear Monte Carlo sampling strategy) and assimilating 5 kyr of data per sweep. We confirm our calibration of the model using the PADM2M dipole data set of Ziegler et al. [3]. The Monte Carlo sampling strategy provides us with quantitative information about the uncertainty of our estimates, and -in principal- we can use this information for making (robust) predictions under uncertainty. We perform synthetic data experiments to explore the predictive capability of the ODE model updated by data assimilation. For each experiment, we produce 2 Myr of synthetic data (with error levels similar to the ones found in the SINT data), calibrate the model to this record, and then check if this calibrated model can reliably predict a reversal within the next 5 kyr. By performing a large number of such experiments, we can estimate the statistics that describe how reliably our calibrated model can predict a reversal of the geomagnetic field. It is found that the 1 kyr-ahead predictions of reversals produced by the model appear to be accurate and reliable. These encouraging results prompted us to also test predictions of the five reversals of the SINT (and PADM2M) data set, using a similarly calibrated model. Results will be presented and discussed. References Gissinger, C., 2012, A new deterministic model for chaotic reversals, European Physical Journal B, 85:137 Valet, J.P., Maynadier,L and Guyodo, Y., 2005, Geomagnetic field strength and reversal rate over the past 2 Million years, Nature, 435, 802-805. Ziegler, L.B., Constable, C.G., Johnson, C.L. and Tauxe, L., 2011, PADM2M: a penalized maximum likelihood moidel of the 0-2 Ma paleomagnetic axial dipole moment, Geophysical Journal International, 184, 1069-1089.

EXpectation Propagation LOgistic REgRession (EXPLORER): Distributed Privacy-Preserving Online Model Learning

PubMed Central

Wang, Shuang; Jiang, Xiaoqian; Wu, Yuan; Cui, Lijuan; Cheng, Samuel; Ohno-Machado, Lucila

2013-01-01

We developed an EXpectation Propagation LOgistic REgRession (EXPLORER) model for distributed privacy-preserving online learning. The proposed framework provides a high level guarantee for protecting sensitive information, since the information exchanged between the server and the client is the encrypted posterior distribution of coefficients. Through experimental results, EXPLORER shows the same performance (e.g., discrimination, calibration, feature selection etc.) as the traditional frequentist Logistic Regression model, but provides more flexibility in model updating. That is, EXPLORER can be updated one point at a time rather than having to retrain the entire data set when new observations are recorded. The proposed EXPLORER supports asynchronized communication, which relieves the participants from coordinating with one another, and prevents service breakdown from the absence of participants or interrupted communications. PMID:23562651

Large-Scale Wind-Tunnel Tests of Exhaust Ingestion Due to Thrust Reversal on a Four-Engine Jet Transport during Ground Roll

NASA Technical Reports Server (NTRS)

Tolhurst, William H., Jr.; Hickey, David H.; Aoyagi, Kiyoshi

1961-01-01

Wind-tunnel tests have been conducted on a large-scale model of a swept-wing jet transport type airplane to study the factors affecting exhaust gas ingestion into the engine inlets when thrust reversal is used during ground roll. The model was equipped with four small jet engines mounted in nacelles beneath the wing. The tests included studies of both cascade and target type reversers. The data obtained included the free-stream velocity at the occurrence of exhaust gas ingestion in the outboard engine and the increment of drag due to thrust reversal for various modifications of thrust reverser configuration. Motion picture films of smoke flow studies were also obtained to supplement the data. The results show that the free-stream velocity at which ingestion occurred in the outboard engines could be reduced considerably, by simple modifications to the reversers, without reducing the effective drag due to reversed thrust.

Adiabatic model of field reversal by fast ions in an axisymmetric open trap

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

Tsidulko, Yu. A., E-mail: tsidulko@mail.ru

2016-06-15

A model of field reversal by fast ions has been developed under the assumption of preservation of fast-ion adiabatic invariants. Analytical solutions obtained in the approximation of a narrow fast-ion layer and numerical solutions to the evolutionary problem are presented. The solutions demonstrate the process of formation of a field reversed configuration with parameters close to those of the planned experiment.

Plasma wall sheath contributions to flux retention during the formation of field-reversed configurations

NASA Astrophysics Data System (ADS)

Milroy, R. D.; Slough, J. T.; Hoffman, A. L.

1984-06-01

Flux loss during field reversal on the TRX-1 field-reversed θ pinch is found to be much less than predicted by the inertial model of Green and Newton. This can be explained by a pressure bearing, conducting sheath which naturally forms at the wall and limits the flux loss. A one-dimensional (r-t) magnetohydrodynamic (MHD) numerical model has been used to study the formation and effectiveness of the sheath. The calculations are in excellent agreement with experimental measurements over a wide range of operating parameters. The results indicate that good flux trapping can be achieved through the field reversal phase of FRC formation with much slower external field reversal rates than in current experiments.

The possibility of a reversal of material flammability ranking from normal gravity to microgravity

NASA Technical Reports Server (NTRS)

T'Ien, James S.

1990-01-01

The purpose of the discussion is to show, by a theoretical model, that one of the material flammability indices, the flammability limit, can be reversed in proper circumstances. A stagnation-point diffusion flame adjacent to a spherical solid-fuel surface is considered. It is shown that a reversal of the limiting oxygen indices from normal gravity and microgravity is possible. Although the example is based on a particular theoretical model with a particular flame configuration and specifically for an oxygen limit, the flammability-limit reversal phenomenon is believed to be more general.

Product lifetime, energy efficiency and climate change: A case study of air conditioners in Japan.

PubMed

Nishijima, Daisuke

2016-10-01

This study proposed a modelling technique for estimating life-cycle CO2 emissions of durable goods by considering changes in product lifetime and energy efficiency. The stock and flow of durable goods was modelled by Weibull lifetime distributions and the trend in annual energy efficiency (i.e., annual electricity consumption) of an "average" durable good was formulated as a reverse logistic curve including a technologically critical value (i.e., limit energy efficiency) with respect to time. I found that when the average product lifetime is reduced, there is a trade-off between the reduction in emissions during product use (use phase), due to the additional purchases of new, more energy-efficient air conditioners, and the increase in emissions arising from the additional production of new air conditioners stimulated by the reduction of the average product lifetime. A scenario analysis focused on residential air conditioners in Japan during 1972-2013 showed that for a reduction of average lifetime of 1 year, if the air conditioner energy efficiency limit can be improved by 1.4% from the estimated current efficiency level, then CO2 emissions can be reduced by approximately the same amount as for an extension of average product lifetime of 1 year. Copyright © 2016 Elsevier Ltd. All rights reserved.

Application of reverse engineering in the medical industry.

NASA Astrophysics Data System (ADS)

Kaleev, A. A.; Kashapov, L. N.; Kashapov, N. F.; Kashapov, R. N.

2017-09-01

The purpose of this research is to develop on the basis of existing analogs new design of ophthalmologic microsurgical tweezers by using reverse engineering techniques. Virtual model was obtained by using a three-dimensional scanning system Solutionix Rexcan 450 MP. Geomagic Studio program was used to remove defects and inaccuracies of the obtained parametric model. A prototype of the finished model was made on the installation of laser stereolithography Projet 6000. Total time of the creation was 16 hours from the reverse engineering procedure to 3D-printing of the prototype.

Case Study on Optimal Routing in Logistics Network by Priority-based Genetic Algorithm

NASA Astrophysics Data System (ADS)

Wang, Xiaoguang; Lin, Lin; Gen, Mitsuo; Shiota, Mitsushige

Recently, research on logistics caught more and more attention. One of the important issues on logistics system is to find optimal delivery routes with the least cost for products delivery. Numerous models have been developed for that reason. However, due to the diversity and complexity of practical problem, the existing models are usually not very satisfying to find the solution efficiently and convinently. In this paper, we treat a real-world logistics case with a company named ABC Co. ltd., in Kitakyusyu Japan. Firstly, based on the natures of this conveyance routing problem, as an extension of transportation problem (TP) and fixed charge transportation problem (fcTP) we formulate the problem as a minimum cost flow (MCF) model. Due to the complexity of fcTP, we proposed a priority-based genetic algorithm (pGA) approach to find the most acceptable solution to this problem. In this pGA approach, a two-stage path decoding method is adopted to develop delivery paths from a chromosome. We also apply the pGA approach to this problem, and compare our results with the current logistics network situation, and calculate the improvement of logistics cost to help the management to make decisions. Finally, in order to check the effectiveness of the proposed method, the results acquired are compared with those come from the two methods/ software, such as LINDO and CPLEX.

Comparison of Logistic Regression and Artificial Neural Network in Low Back Pain Prediction: Second National Health Survey

PubMed Central

Parsaeian, M; Mohammad, K; Mahmoudi, M; Zeraati, H

2012-01-01

Background: The purpose of this investigation was to compare empirically predictive ability of an artificial neural network with a logistic regression in prediction of low back pain. Methods: Data from the second national health survey were considered in this investigation. This data includes the information of low back pain and its associated risk factors among Iranian people aged 15 years and older. Artificial neural network and logistic regression models were developed using a set of 17294 data and they were validated in a test set of 17295 data. Hosmer and Lemeshow recommendation for model selection was used in fitting the logistic regression. A three-layer perceptron with 9 inputs, 3 hidden and 1 output neurons was employed. The efficiency of two models was compared by receiver operating characteristic analysis, root mean square and -2 Loglikelihood criteria. Results: The area under the ROC curve (SE), root mean square and -2Loglikelihood of the logistic regression was 0.752 (0.004), 0.3832 and 14769.2, respectively. The area under the ROC curve (SE), root mean square and -2Loglikelihood of the artificial neural network was 0.754 (0.004), 0.3770 and 14757.6, respectively. Conclusions: Based on these three criteria, artificial neural network would give better performance than logistic regression. Although, the difference is statistically significant, it does not seem to be clinically significant. PMID:23113198

Comparison of logistic regression and artificial neural network in low back pain prediction: second national health survey.

PubMed

Parsaeian, M; Mohammad, K; Mahmoudi, M; Zeraati, H

2012-01-01

The purpose of this investigation was to compare empirically predictive ability of an artificial neural network with a logistic regression in prediction of low back pain. Data from the second national health survey were considered in this investigation. This data includes the information of low back pain and its associated risk factors among Iranian people aged 15 years and older. Artificial neural network and logistic regression models were developed using a set of 17294 data and they were validated in a test set of 17295 data. Hosmer and Lemeshow recommendation for model selection was used in fitting the logistic regression. A three-layer perceptron with 9 inputs, 3 hidden and 1 output neurons was employed. The efficiency of two models was compared by receiver operating characteristic analysis, root mean square and -2 Loglikelihood criteria. The area under the ROC curve (SE), root mean square and -2Loglikelihood of the logistic regression was 0.752 (0.004), 0.3832 and 14769.2, respectively. The area under the ROC curve (SE), root mean square and -2Loglikelihood of the artificial neural network was 0.754 (0.004), 0.3770 and 14757.6, respectively. Based on these three criteria, artificial neural network would give better performance than logistic regression. Although, the difference is statistically significant, it does not seem to be clinically significant.

Effect of folic acid on appetite in children: ordinal logistic and fuzzy logistic regressions.

PubMed

Namdari, Mahshid; Abadi, Alireza; Taheri, S Mahmoud; Rezaei, Mansour; Kalantari, Naser; Omidvar, Nasrin

2014-03-01

Reduced appetite and low food intake are often a concern in preschool children, since it can lead to malnutrition, a leading cause of impaired growth and mortality in childhood. It is occasionally considered that folic acid has a positive effect on appetite enhancement and consequently growth in children. The aim of this study was to assess the effect of folic acid on the appetite of preschool children 3 to 6 y old. The study sample included 127 children ages 3 to 6 who were randomly selected from 20 preschools in the city of Tehran in 2011. Since appetite was measured by linguistic terms, a fuzzy logistic regression was applied for modeling. The obtained results were compared with a statistical ordinal logistic model. After controlling for the potential confounders, in a statistical ordinal logistic model, serum folate showed a significantly positive effect on appetite. A small but positive effect of folate was detected by fuzzy logistic regression. Based on fuzzy regression, the risk for poor appetite in preschool children was related to the employment status of their mothers. In this study, a positive association was detected between the levels of serum folate and improved appetite. For further investigation, a randomized controlled, double-blind clinical trial could be helpful to address causality. Copyright © 2014 Elsevier Inc. All rights reserved.

Interplanetary Supply Chain Risk Management

NASA Technical Reports Server (NTRS)

Galluzzi, Michael C.

2018-01-01

Emphasis on KSC ground processing operations, reduced spares up-mass lift requirements and campaign-level flexible path perspective for space systems support as Regolith-based ISM is achieved by; Network modeling for sequencing space logistics and in-space logistics nodal positioning to include feedstock. Economic modeling to assess ISM 3D printing adaption and supply chain risk.

Strategies for Testing Statistical and Practical Significance in Detecting DIF with Logistic Regression Models

ERIC Educational Resources Information Center

Fidalgo, Angel M.; Alavi, Seyed Mohammad; Amirian, Seyed Mohammad Reza

2014-01-01

This study examines three controversial aspects in differential item functioning (DIF) detection by logistic regression (LR) models: first, the relative effectiveness of different analytical strategies for detecting DIF; second, the suitability of the Wald statistic for determining the statistical significance of the parameters of interest; and…

Flower Power: Sunflowers as a Model for Logistic Growth

ERIC Educational Resources Information Center

Fernandez, Eileen; Geist, Kristi A.

2011-01-01

Logistic growth displays an interesting pattern: It starts fast, exhibiting the rapid growth characteristic of exponential models. As time passes, it slows in response to constraints such as limited resources or reallocation of energy. The growth continues to slow until it reaches a limit, called capacity. When the growth describes a population,…

Scheduling Algorithm for Mission Planning and Logistics Evaluation (SAMPLE). Volume 2: Mission payloads subsystem description

NASA Technical Reports Server (NTRS)

Dupnick, E.; Wiggins, D.

1980-01-01

The scheduling algorithm for mission planning and logistics evaluation (SAMPLE) is presented. Two major subsystems are included: The mission payloads program; and the set covering program. Formats and parameter definitions for the payload data set (payload model), feasible combination file, and traffic model are documented.

Automatic Generation of Customized, Model Based Information Systems for Operations Management.

DTIC Science & Technology

The paper discusses the need for developing a customized, model based system to support management decision making in the field of operations ... management . It provides a critique of the current approaches available, formulates a framework to classify logistics decisions, and suggests an approach for the automatic development of logistics systems. (Author)

Item Vector Plots for the Multidimensional Three-Parameter Logistic Model

ERIC Educational Resources Information Center

Bryant, Damon; Davis, Larry

2011-01-01

This brief technical note describes how to construct item vector plots for dichotomously scored items fitting the multidimensional three-parameter logistic model (M3PLM). As multidimensional item response theory (MIRT) shows promise of being a very useful framework in the test development life cycle, graphical tools that facilitate understanding…

Semiparametric Item Response Functions in the Context of Guessing

ERIC Educational Resources Information Center

Falk, Carl F.; Cai, Li

2016-01-01

We present a logistic function of a monotonic polynomial with a lower asymptote, allowing additional flexibility beyond the three-parameter logistic model. We develop a maximum marginal likelihood-based approach to estimate the item parameters. The new item response model is demonstrated on math assessment data from a state, and a computationally…

The Performance of the Linear Logistic Test Model When the Q-Matrix Is Misspecified: A Simulation Study

ERIC Educational Resources Information Center

MacDonald, George T.

2014-01-01

A simulation study was conducted to explore the performance of the linear logistic test model (LLTM) when the relationships between items and cognitive components were misspecified. Factors manipulated included percent of misspecification (0%, 1%, 5%, 10%, and 15%), form of misspecification (under-specification, balanced misspecification, and…

Resolution of an uncertain closed-loop logistics model: an application to fuzzy linear programs with risk analysis.

PubMed

Wang, Hsiao-Fan; Hsu, Hsin-Wei

2010-11-01

With the urgency of global warming, green supply chain management, logistics in particular, has drawn the attention of researchers. Although there are closed-loop green logistics models in the literature, most of them do not consider the uncertain environment in general terms. In this study, a generalized model is proposed where the uncertainty is expressed by fuzzy numbers. An interval programming model is proposed by the defined means and mean square imprecision index obtained from the integrated information of all the level cuts of fuzzy numbers. The resolution for interval programming is based on the decision maker (DM)'s preference. The resulting solution provides useful information on the expected solutions under a confidence level containing a degree of risk. The results suggest that the more optimistic the DM is, the better is the resulting solution. However, a higher risk of violation of the resource constraints is also present. By defining this probable risk, a solution procedure was developed with numerical illustrations. This provides a DM trade-off mechanism between logistic cost and the risk. Copyright 2010 Elsevier Ltd. All rights reserved.

The structure of molten CuCl: Reverse Monte Carlo modeling with high-energy X-ray diffraction data and molecular dynamics of a polarizable ion model

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

Alcaraz, Olga; Trullàs, Joaquim, E-mail: quim.trullas@upc.edu; Tahara, Shuta

2016-09-07

The results of the structural properties of molten copper chloride are reported from high-energy X-ray diffraction measurements, reverse Monte Carlo modeling method, and molecular dynamics simulations using a polarizable ion model. The simulated X-ray structure factor reproduces all trends observed experimentally, in particular the shoulder at around 1 Å{sup −1} related to intermediate range ordering, as well as the partial copper-copper correlations from the reverse Monte Carlo modeling, which cannot be reproduced by using a simple rigid ion model. It is shown that the shoulder comes from intermediate range copper-copper correlations caused by the polarized chlorides.

Advanced colorectal neoplasia risk stratification by penalized logistic regression.

PubMed

Lin, Yunzhi; Yu, Menggang; Wang, Sijian; Chappell, Richard; Imperiale, Thomas F

2016-08-01

Colorectal cancer is the second leading cause of death from cancer in the United States. To facilitate the efficiency of colorectal cancer screening, there is a need to stratify risk for colorectal cancer among the 90% of US residents who are considered "average risk." In this article, we investigate such risk stratification rules for advanced colorectal neoplasia (colorectal cancer and advanced, precancerous polyps). We use a recently completed large cohort study of subjects who underwent a first screening colonoscopy. Logistic regression models have been used in the literature to estimate the risk of advanced colorectal neoplasia based on quantifiable risk factors. However, logistic regression may be prone to overfitting and instability in variable selection. Since most of the risk factors in our study have several categories, it was tempting to collapse these categories into fewer risk groups. We propose a penalized logistic regression method that automatically and simultaneously selects variables, groups categories, and estimates their coefficients by penalizing the [Formula: see text]-norm of both the coefficients and their differences. Hence, it encourages sparsity in the categories, i.e. grouping of the categories, and sparsity in the variables, i.e. variable selection. We apply the penalized logistic regression method to our data. The important variables are selected, with close categories simultaneously grouped, by penalized regression models with and without the interactions terms. The models are validated with 10-fold cross-validation. The receiver operating characteristic curves of the penalized regression models dominate the receiver operating characteristic curve of naive logistic regressions, indicating a superior discriminative performance. © The Author(s) 2013.

An Optimization Model for Expired Drug Recycling Logistics Networks and Government Subsidy Policy Design Based on Tri-level Programming

PubMed Central

Huang, Hui; Li, Yuyu; Huang, Bo; Pi, Xing

2015-01-01

In order to recycle and dispose of all people’s expired drugs, the government should design a subsidy policy to stimulate users to return their expired drugs, and drug-stores should take the responsibility of recycling expired drugs, in other words, to be recycling stations. For this purpose it is necessary for the government to select the right recycling stations and treatment stations to optimize the expired drug recycling logistics network and minimize the total costs of recycling and disposal. This paper establishes a tri-level programming model to study how the government can optimize an expired drug recycling logistics network and the appropriate subsidy policies. Furthermore, a Hybrid Genetic Simulated Annealing Algorithm (HGSAA) is proposed to search for the optimal solution of the model. An experiment is discussed to illustrate the good quality of the recycling logistics network and government subsides obtained by the HGSAA. The HGSAA is proven to have the ability to converge on the global optimal solution, and to act as an effective algorithm for solving the optimization problem of expired drug recycling logistics network and government subsidies. PMID:26184252

An Optimization Model for Expired Drug Recycling Logistics Networks and Government Subsidy Policy Design Based on Tri-level Programming.

PubMed

Huang, Hui; Li, Yuyu; Huang, Bo; Pi, Xing

2015-07-09

In order to recycle and dispose of all people's expired drugs, the government should design a subsidy policy to stimulate users to return their expired drugs, and drug-stores should take the responsibility of recycling expired drugs, in other words, to be recycling stations. For this purpose it is necessary for the government to select the right recycling stations and treatment stations to optimize the expired drug recycling logistics network and minimize the total costs of recycling and disposal. This paper establishes a tri-level programming model to study how the government can optimize an expired drug recycling logistics network and the appropriate subsidy policies. Furthermore, a Hybrid Genetic Simulated Annealing Algorithm (HGSAA) is proposed to search for the optimal solution of the model. An experiment is discussed to illustrate the good quality of the recycling logistics network and government subsides obtained by the HGSAA. The HGSAA is proven to have the ability to converge on the global optimal solution, and to act as an effective algorithm for solving the optimization problem of expired drug recycling logistics network and government subsidies.

Country logistics performance and disaster impact.

PubMed

Vaillancourt, Alain; Haavisto, Ira

2016-04-01

The aim of this paper is to deepen the understanding of the relationship between country logistics performance and disaster impact. The relationship is analysed through correlation analysis and regression models for 117 countries for the years 2007 to 2012 with disaster impact variables from the International Disaster Database (EM-DAT) and logistics performance indicators from the World Bank. The results show a significant relationship between country logistics performance and disaster impact overall and for five out of six specific logistic performance indicators. These specific indicators were further used to explore the relationship between country logistic performance and disaster impact for three specific disaster types (epidemic, flood and storm). The findings enhance the understanding of the role of logistics in a humanitarian context with empirical evidence of the importance of country logistics performance in disaster response operations. © 2016 The Author(s). Disasters © Overseas Development Institute, 2016.

Analysis of Logistics in Support of a Human Lunar Outpost

NASA Technical Reports Server (NTRS)

Cirillo, William; Earle, Kevin; Goodliff, Kandyce; Reeves, j. D.; Andrashko, Mark; Merrill, R. Gabe; Stromgren, Chel

2008-01-01

Strategic level analysis of the integrated behavior of lunar transportation system and lunar surface system architecture options is performed to inform NASA Constellation Program senior management on the benefit, viability, affordability, and robustness of system design choices. This paper presents an overview of the approach used to perform the campaign (strategic) analysis, with an emphasis on the logistics modeling and the impacts of logistics resupply on campaign behavior. An overview of deterministic and probabilistic analysis approaches is provided, with a discussion of the importance of each approach to understanding the integrated system behavior. The logistics required to support lunar surface habitation are analyzed from both 'macro-logistics' and 'micro-logistics' perspectives, where macro-logistics focuses on the delivery of goods to a destination and micro-logistics focuses on local handling of re-supply goods at a destination. An example campaign is provided to tie the theories of campaign analysis to results generation capabilities.

Comparison of Centralized-Manual, Centralized-Computerized, and Decentralized-Computerized Order and Management Information Models for the Turkish Air Force Logistics System.

DTIC Science & Technology

1986-09-01

differentiation between the systems. This study will investigate an appropriate Order Processing and Management Information System (OP&MIS) to link base-level...methodology: 1. Reviewed the current order processing and information model of the TUAF Logistics System. (centralized-manual model) 2. Described the...RDS program’s order processing and information system. (centralized-computerized model) 3. Described the order irocessing and information system of

Prediction of geomagnetic reversals using low-dimensional dynamical models and advanced data assimilation: a feasibility study

NASA Astrophysics Data System (ADS)

Fournier, A.; Morzfeld, M.; Hulot, G.

2013-12-01

For a suitable choice of parameters, the system of three ordinary differential equations (ODE) presented by Gissinger [1] was shown to exhibit chaotic reversals whose statistics compared well with those from the paleomagnetic record. In order to further assess the geophysical relevance of this low-dimensional model, we resort to data assimilation methods to calibrate it using reconstructions of the fluctuation of the virtual axial dipole moment spanning the past 2 millions years. Moreover, we test to which extent a properly calibrated model could possibly be used to predict a reversal of the geomagnetic field. We calibrate the ODE model to the geomagnetic field over the past 2 Ma using the SINT data set of Valet et al. [2]. To this end, we consider four data assimilation algorithms: the ensemble Kalman filter (EnKF), a variational method and two Monte Carlo (MC) schemes, prior importance sampling and implicit sampling. We observe that EnKF performs poorly and that prior importance sampling is inefficient. We obtain the most accurate reconstructions of the geomagnetic data using implicit sampling with five data points per assimilation sweep (of duration 5 kyr). The variational scheme performs equally well, but it does not provide us with quantitative information about the uncertainty of the estimates, which makes this method difficult to use for robust prediction under uncertainty. A calibration of the model using the PADM2M data set of Ziegler et al. [3] confirms these findings. We study the predictive capability of the ODE model using statistics computed from synthetic data experiments. For each experiment, we produce 2 Myr of synthetic data (with error levels similar to the ones found in real data), then calibrate the model to this record and then check if this calibrated model can correctly and reliably predict a reversal within the next 10 kyr (say). By performing 100 such experiments, we can assess how reliably our calibrated model can predict a (non-) reversal. It is found that the 5 kyr ahead predictions of reversals produced by the model appear to be accurate and reliable.These encouraging results prompted us to also test predictions of the five reversals of the SINT (and PADM2M) data set, using a similarly calibrated model. Results will be presented and discussed. [1] Gissinger, C., 2012, A new deterministic model for chaotic reversals, European Physical Journal B, 85:137 [2] Valet, J.-P., Meynadier, L. and Guyodo, Y., 2005, Geomagnetic field strength and reversal rate over the past 2 Million years, Nature, 435, 802-805. [3] Ziegler, L. B., Constable, C. G., Johnson, C. L. and Tauxe, L., 2011, PADM2M: a penalized maximum likelihood model of the 0-2 Ma paleomagnetic axial dipole moment, Geophysical Journal International, 184, 1069-1089.

Gene set differential analysis of time course expression profiles via sparse estimation in functional logistic model with application to time-dependent biomarker detection.

PubMed

Kayano, Mitsunori; Matsui, Hidetoshi; Yamaguchi, Rui; Imoto, Seiya; Miyano, Satoru

2016-04-01

High-throughput time course expression profiles have been available in the last decade due to developments in measurement techniques and devices. Functional data analysis, which treats smoothed curves instead of originally observed discrete data, is effective for the time course expression profiles in terms of dimension reduction, robustness, and applicability to data measured at small and irregularly spaced time points. However, the statistical method of differential analysis for time course expression profiles has not been well established. We propose a functional logistic model based on elastic net regularization (F-Logistic) in order to identify the genes with dynamic alterations in case/control study. We employ a mixed model as a smoothing method to obtain functional data; then F-Logistic is applied to time course profiles measured at small and irregularly spaced time points. We evaluate the performance of F-Logistic in comparison with another functional data approach, i.e. functional ANOVA test (F-ANOVA), by applying the methods to real and synthetic time course data sets. The real data sets consist of the time course gene expression profiles for long-term effects of recombinant interferon β on disease progression in multiple sclerosis. F-Logistic distinguishes dynamic alterations, which cannot be found by competitive approaches such as F-ANOVA, in case/control study based on time course expression profiles. F-Logistic is effective for time-dependent biomarker detection, diagnosis, and therapy. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Temporal association between the influenza virus and respiratory syncytial virus (RSV): RSV as a predictor of seasonal influenza.

PubMed

Míguez, A; Iftimi, A; Montes, F

2016-09-01

Epidemiologists agree that there is a prevailing seasonality in the presentation of epidemic waves of respiratory syncytial virus (RSV) infections and influenza. The aim of this study is to quantify the potential relationship between the activity of RSV, with respect to the influenza virus, in order to use the RSV seasonal curve as a predictor of the evolution of an influenza virus epidemic wave. Two statistical tools, logistic regression and time series, are used for predicting the evolution of influenza. Both logistic models and time series of influenza consider RSV information from previous weeks. Data consist of influenza and confirmed RSV cases reported in Comunitat Valenciana (Spain) during the period from week 40 (2010) to week 8 (2014). Binomial logistic regression models used to predict the two states of influenza wave, basal or peak, result in a rate of correct classification higher than 92% with the validation set. When a finer three-states categorization is established, basal, increasing peak and decreasing peak, the multinomial logistic model performs well in 88% of cases of the validation set. The ARMAX model fits well for influenza waves and shows good performance for short-term forecasts up to 3 weeks. The seasonal evolution of influenza virus can be predicted a minimum of 4 weeks in advance using logistic models based on RSV. It would be necessary to study more inter-pandemic seasons to establish a stronger relationship between the epidemic waves of both viruses.

Modeling of agent-based complex network under cyber-violence

NASA Astrophysics Data System (ADS)

Huang, Chuanchao; Hu, Bin; Jiang, Guoyin; Yang, Ruixian

2016-09-01

Public opinion reversal arises frequently in modern society, due to the continual interactions between individuals and their surroundings. To explore the underlying mechanism of the interesting social phenomenon, we introduce here a new model which takes the relationship between the individual cognitive bias and their corresponding choice behavior into account. Experimental results show that the proposed model can provide an accurate description of the entire process of public opinion reversal under the internet environment and the distribution of cognitive bias plays the role of a measure for the reversal probability. In particular, the application to cyber violence, a typical example of public opinion reversal, suggests that public opinion is prone to be seriously affected by the spread of misleading and harmful information. Furthermore, our model is very robust and thus can be employed to other empirical studies that concern the sudden change of public and personal opinion on internet.

Reverse engineering systems models of regulation: discovery, prediction and mechanisms.

PubMed

Ashworth, Justin; Wurtmann, Elisabeth J; Baliga, Nitin S

2012-08-01

Biological systems can now be understood in comprehensive and quantitative detail using systems biology approaches. Putative genome-scale models can be built rapidly based upon biological inventories and strategic system-wide molecular measurements. Current models combine statistical associations, causative abstractions, and known molecular mechanisms to explain and predict quantitative and complex phenotypes. This top-down 'reverse engineering' approach generates useful organism-scale models despite noise and incompleteness in data and knowledge. Here we review and discuss the reverse engineering of biological systems using top-down data-driven approaches, in order to improve discovery, hypothesis generation, and the inference of biological properties. Copyright © 2011 Elsevier Ltd. All rights reserved.

What Are the Odds of that? A Primer on Understanding Logistic Regression

ERIC Educational Resources Information Center

Huang, Francis L.; Moon, Tonya R.

2013-01-01

The purpose of this Methodological Brief is to present a brief primer on logistic regression, a commonly used technique when modeling dichotomous outcomes. Using data from the National Education Longitudinal Study of 1988 (NELS:88), logistic regression techniques were used to investigate student-level variables in eighth grade (i.e., enrolled in a…

Dietary Fiber Intake Is Inversely Associated with Periodontal Disease among US Adults.

PubMed

Nielsen, Samara Joy; Trak-Fellermeier, Maria Angelica; Joshipura, Kaumudi; Dye, Bruce A

2016-12-01

Approximately 47% of adults in the United States have periodontal disease. Dietary guidelines recommend a diet providing adequate fiber. Healthier dietary habits, particularly an increased fiber intake, may contribute to periodontal disease prevention. Our objective was to evaluate the relation of dietary fiber intake and its sources with periodontal disease in the US adult population (≥30 y of age). Data from 6052 adults participating in NHANES 2009-2012 were used. Periodontal disease was defined (according to the CDC/American Academy of Periodontology) as severe, moderate, mild, and none. Intake was assessed by 24-h dietary recalls. The relation between periodontal disease and dietary fiber, whole-grain, and fruit and vegetable intakes were evaluated by using multivariate models, adjusting for sociodemographic characteristics and dentition status. In the multivariate logistic model, the lowest quartile of dietary fiber was associated with moderate-severe periodontitis (compared with mild-none) compared with the highest dietary fiber intake quartile (OR: 1.30; 95% CI: 1.00, 1.69). In the multivariate multinomial logistic model, intake in the lowest quartile of dietary fiber was associated with higher severity of periodontitis than dietary fiber intake in the highest quartile (OR: 1.27; 95% CI: 1.00, 1.62). In the adjusted logistic model, whole-grain intake was not associated with moderate-severe periodontitis. However, in the adjusted multinomial logistic model, adults consuming whole grains in the lowest quartile were more likely to have more severe periodontal disease than were adults consuming whole grains in the highest quartile (OR: 1.32; 95% CI: 1.08, 1.62). In fully adjusted logistic and multinomial logistic models, fruit and vegetable intake was not significantly associated with periodontitis. We found an inverse relation between dietary fiber intake and periodontal disease among US adults ≥30 y old. Periodontal disease was associated with low whole-grain intake but not with low fruit and vegetable intake. © 2016 American Society for Nutrition.

Large scale landslide susceptibility assessment using the statistical methods of logistic regression and BSA - study case: the sub-basin of the small Niraj (Transylvania Depression, Romania)

NASA Astrophysics Data System (ADS)

Roşca, S.; Bilaşco, Ş.; Petrea, D.; Fodorean, I.; Vescan, I.; Filip, S.; Măguţ, F.-L.

2015-11-01

The existence of a large number of GIS models for the identification of landslide occurrence probability makes difficult the selection of a specific one. The present study focuses on the application of two quantitative models: the logistic and the BSA models. The comparative analysis of the results aims at identifying the most suitable model. The territory corresponding to the Niraj Mic Basin (87 km2) is an area characterised by a wide variety of the landforms with their morphometric, morphographical and geological characteristics as well as by a high complexity of the land use types where active landslides exist. This is the reason why it represents the test area for applying the two models and for the comparison of the results. The large complexity of input variables is illustrated by 16 factors which were represented as 72 dummy variables, analysed on the basis of their importance within the model structures. The testing of the statistical significance corresponding to each variable reduced the number of dummy variables to 12 which were considered significant for the test area within the logistic model, whereas for the BSA model all the variables were employed. The predictability degree of the models was tested through the identification of the area under the ROC curve which indicated a good accuracy (AUROC = 0.86 for the testing area) and predictability of the logistic model (AUROC = 0.63 for the validation area).

Relevance of multiple spatial scales in habitat models: A case study with amphibians and grasshoppers

NASA Astrophysics Data System (ADS)

Altmoos, Michael; Henle, Klaus

2010-11-01

Habitat models for animal species are important tools in conservation planning. We assessed the need to consider several scales in a case study for three amphibian and two grasshopper species in the post-mining landscapes near Leipzig (Germany). The two species groups were selected because habitat analyses for grasshoppers are usually conducted on one scale only whereas amphibians are thought to depend on more than one spatial scale. First, we analysed how the preference to single habitat variables changed across nested scales. Most environmental variables were only significant for a habitat model on one or two scales, with the smallest scale being particularly important. On larger scales, other variables became significant, which cannot be recognized on lower scales. Similar preferences across scales occurred in only 13 out of 79 cases and in 3 out of 79 cases the preference and avoidance for the same variable were even reversed among scales. Second, we developed habitat models by using a logistic regression on every scale and for all combinations of scales and analysed how the quality of habitat models changed with the scales considered. To achieve a sufficient accuracy of the habitat models with a minimum number of variables, at least two scales were required for all species except for Bufo viridis, for which a single scale, the microscale, was sufficient. Only for the European tree frog ( Hyla arborea), at least three scales were required. The results indicate that the quality of habitat models increases with the number of surveyed variables and with the number of scales, but costs increase too. Searching for simplifications in multi-scaled habitat models, we suggest that 2 or 3 scales should be a suitable trade-off, when attempting to define a suitable microscale.

Mixture models for undiagnosed prevalent disease and interval-censored incident disease: applications to a cohort assembled from electronic health records.

PubMed

Cheung, Li C; Pan, Qing; Hyun, Noorie; Schiffman, Mark; Fetterman, Barbara; Castle, Philip E; Lorey, Thomas; Katki, Hormuzd A

2017-09-30

For cost-effectiveness and efficiency, many large-scale general-purpose cohort studies are being assembled within large health-care providers who use electronic health records. Two key features of such data are that incident disease is interval-censored between irregular visits and there can be pre-existing (prevalent) disease. Because prevalent disease is not always immediately diagnosed, some disease diagnosed at later visits are actually undiagnosed prevalent disease. We consider prevalent disease as a point mass at time zero for clinical applications where there is no interest in time of prevalent disease onset. We demonstrate that the naive Kaplan-Meier cumulative risk estimator underestimates risks at early time points and overestimates later risks. We propose a general family of mixture models for undiagnosed prevalent disease and interval-censored incident disease that we call prevalence-incidence models. Parameters for parametric prevalence-incidence models, such as the logistic regression and Weibull survival (logistic-Weibull) model, are estimated by direct likelihood maximization or by EM algorithm. Non-parametric methods are proposed to calculate cumulative risks for cases without covariates. We compare naive Kaplan-Meier, logistic-Weibull, and non-parametric estimates of cumulative risk in the cervical cancer screening program at Kaiser Permanente Northern California. Kaplan-Meier provided poor estimates while the logistic-Weibull model was a close fit to the non-parametric. Our findings support our use of logistic-Weibull models to develop the risk estimates that underlie current US risk-based cervical cancer screening guidelines. Published 2017. This article has been contributed to by US Government employees and their work is in the public domain in the USA. Published 2017. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Dynamic modeling of reversible methanolysis of Jatropha curcas oil to biodiesel.

PubMed

Syam, Azhari M; Hamid, Hamidah A; Yunus, Robiah; Rashid, Umer

2013-01-01

Many kinetics studies on methanolysis assumed the reactions to be irreversible. The aim of the present work was to study the dynamic modeling of reversible methanolysis of Jatropha curcas oil (JCO) to biodiesel. The experimental data were collected under the optimal reaction conditions: molar ratio of methanol to JCO at 6 : 1, reaction temperature of 60°C, 60 min of reaction time, and 1% w/w of catalyst concentration. The dynamic modeling involved the derivation of differential equations for rates of three stepwise reactions. The simulation study was then performed on the resulting equations using MATLAB. The newly developed reversible models were fitted with various rate constants and compared with the experimental data for fitting purposes. In addition, analysis of variance was done statistically to evaluate the adequacy and quality of model parameters. The kinetics study revealed that the reverse reactions were significantly slower than forward reactions. The activation energies ranged from 6.5 to 44.4 KJ mol⁻¹.

Dynamic Modeling of Reversible Methanolysis of Jatropha curcas Oil to Biodiesel

PubMed Central

Syam, Azhari M.; Hamid, Hamidah A.; Yunus, Robiah; Rashid, Umer

2013-01-01

Many kinetics studies on methanolysis assumed the reactions to be irreversible. The aim of the present work was to study the dynamic modeling of reversible methanolysis of Jatropha curcas oil (JCO) to biodiesel. The experimental data were collected under the optimal reaction conditions: molar ratio of methanol to JCO at 6 : 1, reaction temperature of 60°C, 60 min of reaction time, and 1% w/w of catalyst concentration. The dynamic modeling involved the derivation of differential equations for rates of three stepwise reactions. The simulation study was then performed on the resulting equations using MATLAB. The newly developed reversible models were fitted with various rate constants and compared with the experimental data for fitting purposes. In addition, analysis of variance was done statistically to evaluate the adequacy and quality of model parameters. The kinetics study revealed that the reverse reactions were significantly slower than forward reactions. The activation energies ranged from 6.5 to 44.4 KJ mol−1. PMID:24363616

Structure-preserving model reduction of large-scale logistics networks. Applications for supply chains

NASA Astrophysics Data System (ADS)

Scholz-Reiter, B.; Wirth, F.; Dashkovskiy, S.; Makuschewitz, T.; Schönlein, M.; Kosmykov, M.

2011-12-01

We investigate the problem of model reduction with a view to large-scale logistics networks, specifically supply chains. Such networks are modeled by means of graphs, which describe the structure of material flow. An aim of the proposed model reduction procedure is to preserve important features within the network. As a new methodology we introduce the LogRank as a measure for the importance of locations, which is based on the structure of the flows within the network. We argue that these properties reflect relative importance of locations. Based on the LogRank we identify subgraphs of the network that can be neglected or aggregated. The effect of this is discussed for a few motifs. Using this approach we present a meta algorithm for structure-preserving model reduction that can be adapted to different mathematical modeling frameworks. The capabilities of the approach are demonstrated with a test case, where a logistics network is modeled as a Jackson network, i.e., a particular type of queueing network.

Development of the Integrated Biomass Supply Analysis and Logistics Model (IBSAL)

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

Sokhansanj, Shahabaddine; Webb, Erin; Turhollow Jr, Anthony F

2008-06-01

The Integrated Biomass Supply & Logistics (IBSAL) model is a dynamic (time dependent) model of operations that involve collection, harvest, storage, preprocessing, and transportation of feedstock for use at a biorefinery. The model uses mathematical equations to represent individual unit operations. These unit operations can be assembled by the user to represent the working rate of equipment and queues to represent storage at facilities. The model calculates itemized costs, energy input, and carbon emissions. It estimates resource requirements and operational characteristics of the entire supply infrastructure. Weather plays an important role in biomass management and thus in IBSAL, dictating themore » moisture content of biomass and whether or not it can be harvested on a given day. The model calculates net biomass yield based on a soil conservation allowance (for crop residue) and dry matter losses during harvest and storage. This publication outlines the development of the model and provides examples of corn stover harvest and logistics.« less

A Survey of Statistical Models for Reverse Engineering Gene Regulatory Networks

PubMed Central

Huang, Yufei; Tienda-Luna, Isabel M.; Wang, Yufeng

2009-01-01

Statistical models for reverse engineering gene regulatory networks are surveyed in this article. To provide readers with a system-level view of the modeling issues in this research, a graphical modeling framework is proposed. This framework serves as the scaffolding on which the review of different models can be systematically assembled. Based on the framework, we review many existing models for many aspects of gene regulation; the pros and cons of each model are discussed. In addition, network inference algorithms are also surveyed under the graphical modeling framework by the categories of point solutions and probabilistic solutions and the connections and differences among the algorithms are provided. This survey has the potential to elucidate the development and future of reverse engineering GRNs and bring statistical signal processing closer to the core of this research. PMID:20046885

Applications of Evolutionary Technology to Manufacturing and Logistics Systems : State-of-the Art Survey

NASA Astrophysics Data System (ADS)

Gen, Mitsuo; Lin, Lin

Many combinatorial optimization problems from industrial engineering and operations research in real-world are very complex in nature and quite hard to solve them by conventional techniques. Since the 1960s, there has been an increasing interest in imitating living beings to solve such kinds of hard combinatorial optimization problems. Simulating the natural evolutionary process of human beings results in stochastic optimization techniques called evolutionary algorithms (EAs), which can often outperform conventional optimization methods when applied to difficult real-world problems. In this survey paper, we provide a comprehensive survey of the current state-of-the-art in the use of EA in manufacturing and logistics systems. In order to demonstrate the EAs which are powerful and broadly applicable stochastic search and optimization techniques, we deal with the following engineering design problems: transportation planning models, layout design models and two-stage logistics models in logistics systems; job-shop scheduling, resource constrained project scheduling in manufacturing system.

Grey-Theory-Based Optimization Model of Emergency Logistics Considering Time Uncertainty.

PubMed

Qiu, Bao-Jian; Zhang, Jiang-Hua; Qi, Yuan-Tao; Liu, Yang

2015-01-01

Natural disasters occur frequently in recent years, causing huge casualties and property losses. Nowadays, people pay more and more attention to the emergency logistics problems. This paper studies the emergency logistics problem with multi-center, multi-commodity, and single-affected-point. Considering that the path near the disaster point may be damaged, the information of the state of the paths is not complete, and the travel time is uncertainty, we establish the nonlinear programming model that objective function is the maximization of time-satisfaction degree. To overcome these drawbacks: the incomplete information and uncertain time, this paper firstly evaluates the multiple roads of transportation network based on grey theory and selects the reliable and optimal path. Then simplify the original model under the scenario that the vehicle only follows the optimal path from the emergency logistics center to the affected point, and use Lingo software to solve it. The numerical experiments are presented to show the feasibility and effectiveness of the proposed method.

Grey-Theory-Based Optimization Model of Emergency Logistics Considering Time Uncertainty

PubMed Central

Qiu, Bao-Jian; Zhang, Jiang-Hua; Qi, Yuan-Tao; Liu, Yang

2015-01-01

Natural disasters occur frequently in recent years, causing huge casualties and property losses. Nowadays, people pay more and more attention to the emergency logistics problems. This paper studies the emergency logistics problem with multi-center, multi-commodity, and single-affected-point. Considering that the path near the disaster point may be damaged, the information of the state of the paths is not complete, and the travel time is uncertainty, we establish the nonlinear programming model that objective function is the maximization of time-satisfaction degree. To overcome these drawbacks: the incomplete information and uncertain time, this paper firstly evaluates the multiple roads of transportation network based on grey theory and selects the reliable and optimal path. Then simplify the original model under the scenario that the vehicle only follows the optimal path from the emergency logistics center to the affected point, and use Lingo software to solve it. The numerical experiments are presented to show the feasibility and effectiveness of the proposed method. PMID:26417946

A Numerical Study of New Logistic Map

NASA Astrophysics Data System (ADS)

Khmou, Youssef

In this paper, we propose a new logistic map based on the relation of the information entropy, we study the bifurcation diagram comparatively to the standard logistic map. In the first part, we compare the obtained diagram, by numerical simulations, with that of the standard logistic map. It is found that the structures of both diagrams are similar where the range of the growth parameter is restricted to the interval [0,e]. In the second part, we present an application of the proposed map in traffic flow using macroscopic model. It is found that the bifurcation diagram is an exact model of the Greenberg’s model of traffic flow where the growth parameter corresponds to the optimal velocity and the random sequence corresponds to the density. In the last part, we present a second possible application of the proposed map which consists of random number generation. The results of the analysis show that the excluded initial values of the sequences are (0,1).

Research on robust optimization of emergency logistics network considering the time dependence characteristic

NASA Astrophysics Data System (ADS)

WANG, Qingrong; ZHU, Changfeng; LI, Ying; ZHANG, Zhengkun

2017-06-01

Considering the time dependence of emergency logistic network and complexity of the environment that the network exists in, in this paper the time dependent network optimization theory and robust discrete optimization theory are combined, and the emergency logistics dynamic network optimization model with characteristics of robustness is built to maximize the timeliness of emergency logistics. On this basis, considering the complexity of dynamic network and the time dependence of edge weight, an improved ant colony algorithm is proposed to realize the coupling of the optimization algorithm and the network time dependence and robustness. Finally, a case study has been carried out in order to testify validity of this robustness optimization model and its algorithm, and the value of different regulation factors was analyzed considering the importance of the value of the control factor in solving the optimal path. Analysis results show that this model and its algorithm above-mentioned have good timeliness and strong robustness.

High pressure rotating reverse osmosis for long term space missions

NASA Astrophysics Data System (ADS)

Christensen Pederson, Cynthia Lynn

Rotating reverse osmosis, which uses reverse osmosis to purify water and rotating filtration to improve the efficacy of filtration, has great potential for wastewater recycling on a long term space mission. Previous investigations of a proof-of-concept device indicated that the most efficient method to improve rotating reverse osmosis performance is to increase the operational pressure. Thus, a second generation device and fluid circuit were designed, fabricated, and tested to permit high pressure operation for long time periods. The design overcame several obstacles including membrane attachment, rotating seal design, and fluid and pressure management. A theoretical model of rotating reverse osmosis was modified to properly account for the flow conditions in the new design. Tests lasting a week were conducted with a variety of model wastewaters. Significant fouling and a decrease in flux were observed after three days of testing regardless of the operational parameters. A semi-empirical model, the fouling potential, was added to the theoretical model to account for the fouling. This allowed the simulation of 48 hour cleaning cycles that significantly increased the flux of the device. Experimental investigation of the rotational speed and concentrate flow rate indicated that an increase in either parameter decreased the fouling slightly. A week long test of a wastewater ersatz with a biocide did not exhibit a decrease in flux around day three that otherwise occurred. Therefore, biofouling was identified as the primary mechanism of fouling. Rotating reverse osmosis was compared with conventional spiral wound reverse osmosis and displayed increased rejection under dead end filtration conditions. The rotating device exhibited similar rejection and increased flux compared to a tubular reverse osmosis device previously used in a NASA wastewater recovery system. The integration of the rotating device into a NASA water recovery management system was evaluated. Lastly, a theoretical model of rotating hemofiltration was developed that demonstrated that the device is not clinically feasible given the permeability of available hemofiltration membranes.

A New TS Algorithm for Solving Low-Carbon Logistics Vehicle Routing Problem with Split Deliveries by Backpack—From a Green Operation Perspective

PubMed Central

Fu, Zhuo; Wang, Jiangtao

2018-01-01

In order to promote the development of low-carbon logistics and economize logistics distribution costs, the vehicle routing problem with split deliveries by backpack is studied. With the help of the model of classical capacitated vehicle routing problem, in this study, a form of discrete split deliveries was designed in which the customer demand can be split only by backpack. A double-objective mathematical model and the corresponding adaptive tabu search (TS) algorithm were constructed for solving this problem. By embedding the adaptive penalty mechanism, and adopting the random neighborhood selection strategy and reinitialization principle, the global optimization ability of the new algorithm was enhanced. Comparisons with the results in the literature show the effectiveness of the proposed algorithm. The proposed method can save the costs of low-carbon logistics and reduce carbon emissions, which is conducive to the sustainable development of low-carbon logistics. PMID:29747469

Striatal dysfunction during reversal learning in unmedicated schizophrenia patients☆

PubMed Central

Schlagenhauf, Florian; Huys, Quentin J.M.; Deserno, Lorenz; Rapp, Michael A.; Beck, Anne; Heinze, Hans-Joachim; Dolan, Ray; Heinz, Andreas

2014-01-01

Subjects with schizophrenia are impaired at reinforcement-driven reversal learning from as early as their first episode. The neurobiological basis of this deficit is unknown. We obtained behavioral and fMRI data in 24 unmedicated, primarily first episode, schizophrenia patients and 24 age-, IQ- and gender-matched healthy controls during a reversal learning task. We supplemented our fMRI analysis, focusing on learning from prediction errors, with detailed computational modeling to probe task solving strategy including an ability to deploy an internal goal directed model of the task. Patients displayed reduced functional activation in the ventral striatum (VS) elicited by prediction errors. However, modeling task performance revealed that a subgroup did not adjust their behavior according to an accurate internal model of the task structure, and these were also the more severely psychotic patients. In patients who could adapt their behavior, as well as in controls, task solving was best described by cognitive strategies according to a Hidden Markov Model. When we compared patients and controls who acted according to this strategy, patients still displayed a significant reduction in VS activation elicited by informative errors that precede salient changes of behavior (reversals). Thus, our study shows that VS dysfunction in schizophrenia patients during reward-related reversal learning remains a core deficit even when controlling for task solving strategies. This result highlights VS dysfunction is tightly linked to a reward-related reversal learning deficit in early, unmedicated schizophrenia patients. PMID:24291614

Logistics Enterprise Evaluation Model Based On Fuzzy Clustering Analysis

NASA Astrophysics Data System (ADS)

Fu, Pei-hua; Yin, Hong-bo

In this thesis, we introduced an evaluation model based on fuzzy cluster algorithm of logistics enterprises. First of all,we present the evaluation index system which contains basic information, management level, technical strength, transport capacity,informatization level, market competition and customer service. We decided the index weight according to the grades, and evaluated integrate ability of the logistics enterprises using fuzzy cluster analysis method. In this thesis, we introduced the system evaluation module and cluster analysis module in detail and described how we achieved these two modules. At last, we gave the result of the system.

Research on logistics scheduling based on PSO

NASA Astrophysics Data System (ADS)

Bao, Huifang; Zhou, Linli; Liu, Lei

2017-08-01

With the rapid development of e-commerce based on the network, the logistics distribution support of e-commerce is becoming more and more obvious. The optimization of vehicle distribution routing can improve the economic benefit and realize the scientific of logistics [1]. Therefore, the study of logistics distribution vehicle routing optimization problem is not only of great theoretical significance, but also of considerable value of value. Particle swarm optimization algorithm is a kind of evolutionary algorithm, which is based on the random solution and the optimal solution by iteration, and the quality of the solution is evaluated through fitness. In order to obtain a more ideal logistics scheduling scheme, this paper proposes a logistics model based on particle swarm optimization algorithm.

Accurate diode behavioral model with reverse recovery

NASA Astrophysics Data System (ADS)

Banáš, Stanislav; Divín, Jan; Dobeš, Josef; Paňko, Václav

2018-01-01

This paper deals with the comprehensive behavioral model of p-n junction diode containing reverse recovery effect, applicable to all standard SPICE simulators supporting Verilog-A language. The model has been successfully used in several production designs, which require its full complexity, robustness and set of tuning parameters comparable with standard compact SPICE diode model. The model is like standard compact model scalable with area and temperature and can be used as a stand-alone diode or as a part of more complex device macro-model, e.g. LDMOS, JFET, bipolar transistor. The paper briefly presents the state of the art followed by the chapter describing the model development and achieved solutions. During precise model verification some of them were found non-robust or poorly converging and replaced by more robust solutions, demonstrated in the paper. The measurement results of different technologies and different devices compared with a simulation using the new behavioral model are presented as the model validation. The comparison of model validation in time and frequency domains demonstrates that the implemented reverse recovery effect with correctly extracted parameters improves the model simulation results not only in switching from ON to OFF state, which is often published, but also its impedance/admittance frequency dependency in GHz range. Finally the model parameter extraction and the comparison with SPICE compact models containing reverse recovery effect is presented.

Detecting DIF in Polytomous Items Using MACS, IRT and Ordinal Logistic Regression

ERIC Educational Resources Information Center

Elosua, Paula; Wells, Craig

2013-01-01

The purpose of the present study was to compare the Type I error rate and power of two model-based procedures, the mean and covariance structure model (MACS) and the item response theory (IRT), and an observed-score based procedure, ordinal logistic regression, for detecting differential item functioning (DIF) in polytomous items. A simulation…

Semi-Parametric Item Response Functions in the Context of Guessing. CRESST Report 844

ERIC Educational Resources Information Center

Falk, Carl F.; Cai, Li

2015-01-01

We present a logistic function of a monotonic polynomial with a lower asymptote, allowing additional flexibility beyond the three-parameter logistic model. We develop a maximum marginal likelihood based approach to estimate the item parameters. The new item response model is demonstrated on math assessment data from a state, and a computationally…

The Impact of Three Factors on the Recovery of Item Parameters for the Three-Parameter Logistic Model

ERIC Educational Resources Information Center

Kim, Kyung Yong; Lee, Won-Chan

2017-01-01

This article provides a detailed description of three factors (specification of the ability distribution, numerical integration, and frame of reference for the item parameter estimates) that might affect the item parameter estimation of the three-parameter logistic model, and compares five item calibration methods, which are combinations of the…

Comparison of particular logistic models' adoption in the Czech Republic

NASA Astrophysics Data System (ADS)

Vrbová, Petra; Cempírek, Václav

2016-12-01

Managing inventory is considered as one of the most challenging tasks facing supply chain managers and specialists. Decisions related to inventory locations along with level of inventory kept throughout the supply chain have a fundamental impact on the response time, service level, delivery lead-time and the total cost of the supply chain. The main objective of this paper is to identify and analyse the share of a particular logistic model adopted in the Czech Republic (Consignment stock, Buffer stock, Safety stock) and also compare their usage and adoption according to different industries. This paper also aims to specify possible reasons of particular logistic model preferences in comparison to the others. The analysis is based on quantitative survey held in the Czech Republic.

An empirical study of statistical properties of variance partition coefficients for multi-level logistic regression models

USGS Publications Warehouse

Li, Ji; Gray, B.R.; Bates, D.M.

2008-01-01

Partitioning the variance of a response by design levels is challenging for binomial and other discrete outcomes. Goldstein (2003) proposed four definitions for variance partitioning coefficients (VPC) under a two-level logistic regression model. In this study, we explicitly derived formulae for multi-level logistic regression model and subsequently studied the distributional properties of the calculated VPCs. Using simulations and a vegetation dataset, we demonstrated associations between different VPC definitions, the importance of methods for estimating VPCs (by comparing VPC obtained using Laplace and penalized quasilikehood methods), and bivariate dependence between VPCs calculated at different levels. Such an empirical study lends an immediate support to wider applications of VPC in scientific data analysis.

Risk estimation using probability machines

PubMed Central

2014-01-01

Background Logistic regression has been the de facto, and often the only, model used in the description and analysis of relationships between a binary outcome and observed features. It is widely used to obtain the conditional probabilities of the outcome given predictors, as well as predictor effect size estimates using conditional odds ratios. Results We show how statistical learning machines for binary outcomes, provably consistent for the nonparametric regression problem, can be used to provide both consistent conditional probability estimation and conditional effect size estimates. Effect size estimates from learning machines leverage our understanding of counterfactual arguments central to the interpretation of such estimates. We show that, if the data generating model is logistic, we can recover accurate probability predictions and effect size estimates with nearly the same efficiency as a correct logistic model, both for main effects and interactions. We also propose a method using learning machines to scan for possible interaction effects quickly and efficiently. Simulations using random forest probability machines are presented. Conclusions The models we propose make no assumptions about the data structure, and capture the patterns in the data by just specifying the predictors involved and not any particular model structure. So they do not run the same risks of model mis-specification and the resultant estimation biases as a logistic model. This methodology, which we call a “risk machine”, will share properties from the statistical machine that it is derived from. PMID:24581306

Analysis of a database to predict the result of allergy testing in vivo in patients with chronic nasal symptoms.

PubMed

Lacagnina, Valerio; Leto-Barone, Maria S; La Piana, Simona; Seidita, Aurelio; Pingitore, Giuseppe; Di Lorenzo, Gabriele

2014-01-01

This article uses the logistic regression model for diagnostic decision making in patients with chronic nasal symptoms. We studied the ability of the logistic regression model, obtained by the evaluation of a database, to detect patients with positive allergy skin-prick test (SPT) and patients with negative SPT. The model developed was validated using the data set obtained from another medical institution. The analysis was performed using a database obtained from a questionnaire administered to the patients with nasal symptoms containing personal data, clinical data, and results of allergy testing (SPT). All variables found to be significantly different between patients with positive and negative SPT (p < 0.05) were selected for the logistic regression models and were analyzed with backward stepwise logistic regression, evaluated with area under the curve of the receiver operating characteristic curve. A second set of patients from another institution was used to prove the model. The accuracy of the model in identifying, over the second set, both patients whose SPT will be positive and negative was high. The model detected 96% of patients with nasal symptoms and positive SPT and classified 94% of those with negative SPT. This study is preliminary to the creation of a software that could help the primary care doctors in a diagnostic decision making process (need of allergy testing) in patients complaining of chronic nasal symptoms.

Risk estimation using probability machines.

PubMed

Dasgupta, Abhijit; Szymczak, Silke; Moore, Jason H; Bailey-Wilson, Joan E; Malley, James D

2014-03-01

Logistic regression has been the de facto, and often the only, model used in the description and analysis of relationships between a binary outcome and observed features. It is widely used to obtain the conditional probabilities of the outcome given predictors, as well as predictor effect size estimates using conditional odds ratios. We show how statistical learning machines for binary outcomes, provably consistent for the nonparametric regression problem, can be used to provide both consistent conditional probability estimation and conditional effect size estimates. Effect size estimates from learning machines leverage our understanding of counterfactual arguments central to the interpretation of such estimates. We show that, if the data generating model is logistic, we can recover accurate probability predictions and effect size estimates with nearly the same efficiency as a correct logistic model, both for main effects and interactions. We also propose a method using learning machines to scan for possible interaction effects quickly and efficiently. Simulations using random forest probability machines are presented. The models we propose make no assumptions about the data structure, and capture the patterns in the data by just specifying the predictors involved and not any particular model structure. So they do not run the same risks of model mis-specification and the resultant estimation biases as a logistic model. This methodology, which we call a "risk machine", will share properties from the statistical machine that it is derived from.

Nowcasting of Low-Visibility Procedure States with Ordered Logistic Regression at Vienna International Airport

NASA Astrophysics Data System (ADS)

Kneringer, Philipp; Dietz, Sebastian; Mayr, Georg J.; Zeileis, Achim

2017-04-01

Low-visibility conditions have a large impact on aviation safety and economic efficiency of airports and airlines. To support decision makers, we develop a statistical probabilistic nowcasting tool for the occurrence of capacity-reducing operations related to low visibility. The probabilities of four different low visibility classes are predicted with an ordered logistic regression model based on time series of meteorological point measurements. Potential predictor variables for the statistical models are visibility, humidity, temperature and wind measurements at several measurement sites. A stepwise variable selection method indicates that visibility and humidity measurements are the most important model inputs. The forecasts are tested with a 30 minute forecast interval up to two hours, which is a sufficient time span for tactical planning at Vienna Airport. The ordered logistic regression models outperform persistence and are competitive with human forecasters.

EXpectation Propagation LOgistic REgRession (EXPLORER): distributed privacy-preserving online model learning.

PubMed

Wang, Shuang; Jiang, Xiaoqian; Wu, Yuan; Cui, Lijuan; Cheng, Samuel; Ohno-Machado, Lucila

2013-06-01

We developed an EXpectation Propagation LOgistic REgRession (EXPLORER) model for distributed privacy-preserving online learning. The proposed framework provides a high level guarantee for protecting sensitive information, since the information exchanged between the server and the client is the encrypted posterior distribution of coefficients. Through experimental results, EXPLORER shows the same performance (e.g., discrimination, calibration, feature selection, etc.) as the traditional frequentist logistic regression model, but provides more flexibility in model updating. That is, EXPLORER can be updated one point at a time rather than having to retrain the entire data set when new observations are recorded. The proposed EXPLORER supports asynchronized communication, which relieves the participants from coordinating with one another, and prevents service breakdown from the absence of participants or interrupted communications. Copyright © 2013 Elsevier Inc. All rights reserved.

Greenhouse gas network design using backward Lagrangian particle dispersion modelling - Part 1: Methodology and Australian test case

NASA Astrophysics Data System (ADS)

Ziehn, T.; Nickless, A.; Rayner, P. J.; Law, R. M.; Roff, G.; Fraser, P.

2014-03-01

This paper describes the generation of optimal atmospheric measurement networks for determining carbon dioxide fluxes over Australia using inverse methods. A Lagrangian particle dispersion model is used in reverse mode together with a Bayesian inverse modelling framework to calculate the relationship between weekly surface fluxes and hourly concentration observations for the Australian continent. Meteorological driving fields are provided by the regional version of the Australian Community Climate and Earth System Simulator (ACCESS) at 12 km resolution at an hourly time scale. Prior uncertainties are derived on a weekly time scale for biosphere fluxes and fossil fuel emissions from high resolution BIOS2 model runs and from the Fossil Fuel Data Assimilation System (FFDAS), respectively. The influence from outside the modelled domain is investigated, but proves to be negligible for the network design. Existing ground based measurement stations in Australia are assessed in terms of their ability to constrain local flux estimates from the land. We find that the six stations that are currently operational are already able to reduce the uncertainties on surface flux estimates by about 30%. A candidate list of 59 stations is generated based on logistic constraints and an incremental optimization scheme is used to extend the network of existing stations. In order to achieve an uncertainty reduction of about 50% we need to double the number of measurement stations in Australia. Assuming equal data uncertainties for all sites, new stations would be mainly located in the northern and eastern part of the continent.

Building interpretable predictive models for pediatric hospital readmission using Tree-Lasso logistic regression.

PubMed

Jovanovic, Milos; Radovanovic, Sandro; Vukicevic, Milan; Van Poucke, Sven; Delibasic, Boris

2016-09-01

Quantification and early identification of unplanned readmission risk have the potential to improve the quality of care during hospitalization and after discharge. However, high dimensionality, sparsity, and class imbalance of electronic health data and the complexity of risk quantification, challenge the development of accurate predictive models. Predictive models require a certain level of interpretability in order to be applicable in real settings and create actionable insights. This paper aims to develop accurate and interpretable predictive models for readmission in a general pediatric patient population, by integrating a data-driven model (sparse logistic regression) and domain knowledge based on the international classification of diseases 9th-revision clinical modification (ICD-9-CM) hierarchy of diseases. Additionally, we propose a way to quantify the interpretability of a model and inspect the stability of alternative solutions. The analysis was conducted on >66,000 pediatric hospital discharge records from California, State Inpatient Databases, Healthcare Cost and Utilization Project between 2009 and 2011. We incorporated domain knowledge based on the ICD-9-CM hierarchy in a data driven, Tree-Lasso regularized logistic regression model, providing the framework for model interpretation. This approach was compared with traditional Lasso logistic regression resulting in models that are easier to interpret by fewer high-level diagnoses, with comparable prediction accuracy. The results revealed that the use of a Tree-Lasso model was as competitive in terms of accuracy (measured by area under the receiver operating characteristic curve-AUC) as the traditional Lasso logistic regression, but integration with the ICD-9-CM hierarchy of diseases provided more interpretable models in terms of high-level diagnoses. Additionally, interpretations of models are in accordance with existing medical understanding of pediatric readmission. Best performing models have similar performances reaching AUC values 0.783 and 0.779 for traditional Lasso and Tree-Lasso, respectfully. However, information loss of Lasso models is 0.35 bits higher compared to Tree-Lasso model. We propose a method for building predictive models applicable for the detection of readmission risk based on Electronic Health records. Integration of domain knowledge (in the form of ICD-9-CM taxonomy) and a data-driven, sparse predictive algorithm (Tree-Lasso Logistic Regression) resulted in an increase of interpretability of the resulting model. The models are interpreted for the readmission prediction problem in general pediatric population in California, as well as several important subpopulations, and the interpretations of models comply with existing medical understanding of pediatric readmission. Finally, quantitative assessment of the interpretability of the models is given, that is beyond simple counts of selected low-level features. Copyright © 2016 Elsevier B.V. All rights reserved.

Analysis of reversed torsion of FCC metals using polycrystal plasticity models

DOE PAGES

Guo, Xiao Qian; Wang, Huamiao; Wu, Pei Dong; ...

2015-06-19

Large strain behavior of FCC polycrystals during reversed torsion are investigated through the special purpose finite element based on the classical Taylor model and the elastic-viscoplastic self-consistent (EVPSC) model with various Self-Consistent Schemes (SCSs). It is found that the response of both the fixed-end and free-end torsion is very sensitive to the constitutive models. The models are assessed through comparing their predictions to the corresponding experiments in terms of the stress and strain curves, the Swift effect and texture evolution. It is demonstrated that none of the models examined can precisely predict all the experimental results. However, more careful observationmore » reveals that, among the models considered, the tangent model gives the worst overall performance. As a result, it is also demonstrated that the intensity of residual texture during reverse twisting is dependent on the amounts of pre-shear strain during forward twisting and the model used.« less

Geophysics: A reversal of geomagnetic polarity

USGS Publications Warehouse

Mankinen, Edward A.

1986-01-01

The detailed behaviour of the geomagnetic field during reversals is documented by palaeomagnetists to constrain models of the geomagnetic dynamo. Reversals are studied by measuring the magnetic remanence preserved in rocks to obtain both the direction and intensity of the ancient magnetic field.

And the first one now will later be last: Time-reversal in cormack-jolly-seber models

USGS Publications Warehouse

Nichols, James D.

2016-01-01

The models of Cormack, Jolly and Seber (CJS) are remarkable in providing a rich set of inferences about population survival, recruitment, abundance and even sampling probabilities from a seemingly limited data source: a matrix of 1's and 0's reflecting animal captures and recaptures at multiple sampling occasions. Survival and sampling probabilities are estimated directly in CJS models, whereas estimators for recruitment and abundance were initially obtained as derived quantities. Various investigators have noted that just as standard modeling provides direct inferences about survival, reversing the time order of capture history data permits direct modeling and inference about recruitment. Here we review the development of reverse-time modeling efforts, emphasizing the kinds of inferences and questions to which they seem well suited.

Study on Spatial Spillover Effects of Logistics Industry Development for Economic Growth in the Yangtze River Delta City Cluster Based on Spatial Durbin Model

PubMed Central

Xu, Xinxing

2017-01-01

The overall entropy method is used to evaluate the development level of the logistics industry in the city based on a mechanism analysis of the spillover effect of the development of the logistics industry on economic growth, according to the panel data of 26 cities in the Yangtze River delta. On this basis, the paper uses the spatial durbin model to study the direct impact of the development of the logistics industry on economic growth and the spatial spillover effect. The results show that the direct impact coefficient of the development of the logistics industry in the Yangtze River Delta urban agglomeration on local economic growth is 0.092, and the significant spatial spillover effect on the economic growth in the surrounding area is 0.197. Compared with the labor force input, capital investment and the degree of opening to the world, and government functions, the logistics industry’s direct impact coefficient is the largest, other than capital investment; the coefficient of the spillover effect is higher than other control variables, making it a “strong engine” of the Yangtze River Delta urban agglomeration economic growth. PMID:29207555

Study on Spatial Spillover Effects of Logistics Industry Development for Economic Growth in the Yangtze River Delta City Cluster Based on Spatial Durbin Model.

PubMed

Xu, Xinxing; Wang, Yuhong

2017-12-04

The overall entropy method is used to evaluate the development level of the logistics industry in the city based on a mechanism analysis of the spillover effect of the development of the logistics industry on economic growth, according to the panel data of 26 cities in the Yangtze River delta. On this basis, the paper uses the spatial durbin model to study the direct impact of the development of the logistics industry on economic growth and the spatial spillover effect. The results show that the direct impact coefficient of the development of the logistics industry in the Yangtze River Delta urban agglomeration on local economic growth is 0.092, and the significant spatial spillover effect on the economic growth in the surrounding area is 0.197. Compared with the labor force input, capital investment and the degree of opening to the world, and government functions, the logistics industry's direct impact coefficient is the largest, other than capital investment; the coefficient of the spillover effect is higher than other control variables, making it a "strong engine" of the Yangtze River Delta urban agglomeration economic growth.

Reverse engineering biomolecular systems using -omic data: challenges, progress and opportunities.

PubMed

Quo, Chang F; Kaddi, Chanchala; Phan, John H; Zollanvari, Amin; Xu, Mingqing; Wang, May D; Alterovitz, Gil

2012-07-01

Recent advances in high-throughput biotechnologies have led to the rapid growing research interest in reverse engineering of biomolecular systems (REBMS). 'Data-driven' approaches, i.e. data mining, can be used to extract patterns from large volumes of biochemical data at molecular-level resolution while 'design-driven' approaches, i.e. systems modeling, can be used to simulate emergent system properties. Consequently, both data- and design-driven approaches applied to -omic data may lead to novel insights in reverse engineering biological systems that could not be expected before using low-throughput platforms. However, there exist several challenges in this fast growing field of reverse engineering biomolecular systems: (i) to integrate heterogeneous biochemical data for data mining, (ii) to combine top-down and bottom-up approaches for systems modeling and (iii) to validate system models experimentally. In addition to reviewing progress made by the community and opportunities encountered in addressing these challenges, we explore the emerging field of synthetic biology, which is an exciting approach to validate and analyze theoretical system models directly through experimental synthesis, i.e. analysis-by-synthesis. The ultimate goal is to address the present and future challenges in reverse engineering biomolecular systems (REBMS) using integrated workflow of data mining, systems modeling and synthetic biology.

Mixture Rasch model for guessing group identification

NASA Astrophysics Data System (ADS)

Siow, Hoo Leong; Mahdi, Rasidah; Siew, Eng Ling

2013-04-01

Several alternative dichotomous Item Response Theory (IRT) models have been introduced to account for guessing effect in multiple-choice assessment. The guessing effect in these models has been considered to be itemrelated. In the most classic case, pseudo-guessing in the three-parameter logistic IRT model is modeled to be the same for all the subjects but may vary across items. This is not realistic because subjects can guess worse or better than the pseudo-guessing. Derivation from the three-parameter logistic IRT model improves the situation by incorporating ability in guessing. However, it does not model non-monotone function. This paper proposes to study guessing from a subject-related aspect which is guessing test-taking behavior. Mixture Rasch model is employed to detect latent groups. A hybrid of mixture Rasch and 3-parameter logistic IRT model is proposed to model the behavior based guessing from the subjects' ways of responding the items. The subjects are assumed to simply choose a response at random. An information criterion is proposed to identify the behavior based guessing group. Results show that the proposed model selection criterion provides a promising method to identify the guessing group modeled by the hybrid model.

Exactly soluble local bosonic cocycle models, statistical transmutation, and simplest time-reversal symmetric topological orders in 3+1 dimensions

NASA Astrophysics Data System (ADS)

Wen, Xiao-Gang

2017-05-01

We propose a generic construction of exactly soluble local bosonic models that realize various topological orders with gappable boundaries. In particular, we construct an exactly soluble bosonic model that realizes a (3+1)-dimensional [(3+1)D] Z2-gauge theory with emergent fermionic Kramers doublet. We show that the emergence of such a fermion will cause the nucleation of certain topological excitations in space-time without pin+ structure. The exactly soluble model also leads to a statistical transmutation in (3+1)D. In addition, we construct exactly soluble bosonic models that realize 2 types of time-reversal symmetry-enriched Z2 topological orders in 2+1 dimensions, and 20 types of simplest time-reversal symmetry-enriched topological (SET) orders which have only one nontrivial pointlike and stringlike topological excitation. Many physical properties of those topological states are calculated using the exactly soluble models. We find that some time-reversal SET orders have pointlike excitations that carry Kramers doublet, a fractionalized time-reversal symmetry. We also find that some Z2 SET orders have stringlike excitations that carry anomalous (nononsite) Z2 symmetry, which can be viewed as a fractionalization of Z2 symmetry on strings. Our construction is based on cochains and cocycles in algebraic topology, which is very versatile. In principle, it can also realize emergent topological field theory beyond the twisted gauge theory.

Reversible surgical model of biliary inflammation and obstructive jaundice in mice.

PubMed

Kirkland, Jacob G; Godfrey, Cody B; Garrett, Ryan; Kakar, Sanjay; Yeh, Benjamin M; Corvera, Carlos U

2010-12-01

Common bile duct (CBD) ligation is used in animal models to induce biliary inflammation, fibrosis, and cholestatic liver injury, but results in a high early postoperative mortality rate, probably from traumatic pancreatitis. We modified the CBD ligation model in mice by placing a small metal clip across the lower end of the CBD. To reverse biliary obstruction, a suture was incorporated within the clip during its placement. The suture and clip were removed on postoperative d 5 or 10 for biliary decompression. After 5 d of biliary obstruction, the gallbladder showed an 8-fold increase in wall thickness and a 17-fold increase in tissue myeloperoxidase activity. Markedly elevated serum levels of alkaline phosphatase and bilirubin indicated injury to the biliary epithelium and hepatocytes. Early postoperative (d 0-2) survival was 100% and later (d 3-5) survival was 85% (n=54 mice). We successfully reversed biliary obstruction in 20 mice (37%). Overall survival after reversal was 70%. In surviving mice, biliary decompression was complete, inflammation was reduced, and jaundice resolved. Histologic features confirmed reduced epithelial damage, edema, and neutrophil infiltration. Our technique minimized postoperative death, maintained an effective inflammatory response, and was easily reversible without requiring repeat laparotomy. This reversible model can be used to further define molecular mechanisms of biliary inflammation, fibrosis, and liver injury in genetically altered mice. Copyright © 2010. Published by Elsevier Inc.

Identification of total reversible cysteine oxidation in an atherosclerosis model using a modified biotin switch assay.

PubMed

Li, Ru; Huang, Jiqing; Kast, Juergen

2015-05-01

Oxidative stress due to the imbalance of reactive oxygen species (ROS) and the resulting reversible cysteine oxidation (CysOX) are involved in the early proatherogenic aspect of atherosclerosis. Given that the corresponding redox signaling pathways are still unclear, a modified biotin switch assay was developed to quantify the reversible CysOX in an atherosclerosis model established by using a monocytic cell line treated with platelet releasate. The accumulation of ROS was observed in the model system and validated in human primary monocytes. Through the application of the modified biotin switch assay, we obtained the first reversible CysOX proteome for this model. A total of 75 peptides, corresponding to 53 proteins, were quantified with oxidative modification. The bioinformatics analysis of these CysOX-containing proteins highlighted biological processes including glycolysis, cytoskeleton arrangement, and redox regulation. Moreover, the reversible oxidation of three glycolysis enzymes was observed using this method, and the regulation influence was verified by an enzyme activity assay. NADPH oxidase (NOX) inhibition treatment, in conjunction with the modified biotin switch method, was used to evaluate the global CysOX status. In conclusion, this versatile modified biotin switch assay provides an approach for the quantification of all reversible CysOX and for the study of redox signaling in atherosclerosis as well as in diseases in other biological systems.

Intermediate and advanced topics in multilevel logistic regression analysis

PubMed Central

Merlo, Juan

2017-01-01

Multilevel data occur frequently in health services, population and public health, and epidemiologic research. In such research, binary outcomes are common. Multilevel logistic regression models allow one to account for the clustering of subjects within clusters of higher‐level units when estimating the effect of subject and cluster characteristics on subject outcomes. A search of the PubMed database demonstrated that the use of multilevel or hierarchical regression models is increasing rapidly. However, our impression is that many analysts simply use multilevel regression models to account for the nuisance of within‐cluster homogeneity that is induced by clustering. In this article, we describe a suite of analyses that can complement the fitting of multilevel logistic regression models. These ancillary analyses permit analysts to estimate the marginal or population‐average effect of covariates measured at the subject and cluster level, in contrast to the within‐cluster or cluster‐specific effects arising from the original multilevel logistic regression model. We describe the interval odds ratio and the proportion of opposed odds ratios, which are summary measures of effect for cluster‐level covariates. We describe the variance partition coefficient and the median odds ratio which are measures of components of variance and heterogeneity in outcomes. These measures allow one to quantify the magnitude of the general contextual effect. We describe an R 2 measure that allows analysts to quantify the proportion of variation explained by different multilevel logistic regression models. We illustrate the application and interpretation of these measures by analyzing mortality in patients hospitalized with a diagnosis of acute myocardial infarction. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. PMID:28543517

Intermediate and advanced topics in multilevel logistic regression analysis.

PubMed

Austin, Peter C; Merlo, Juan

2017-09-10

Multilevel data occur frequently in health services, population and public health, and epidemiologic research. In such research, binary outcomes are common. Multilevel logistic regression models allow one to account for the clustering of subjects within clusters of higher-level units when estimating the effect of subject and cluster characteristics on subject outcomes. A search of the PubMed database demonstrated that the use of multilevel or hierarchical regression models is increasing rapidly. However, our impression is that many analysts simply use multilevel regression models to account for the nuisance of within-cluster homogeneity that is induced by clustering. In this article, we describe a suite of analyses that can complement the fitting of multilevel logistic regression models. These ancillary analyses permit analysts to estimate the marginal or population-average effect of covariates measured at the subject and cluster level, in contrast to the within-cluster or cluster-specific effects arising from the original multilevel logistic regression model. We describe the interval odds ratio and the proportion of opposed odds ratios, which are summary measures of effect for cluster-level covariates. We describe the variance partition coefficient and the median odds ratio which are measures of components of variance and heterogeneity in outcomes. These measures allow one to quantify the magnitude of the general contextual effect. We describe an R 2 measure that allows analysts to quantify the proportion of variation explained by different multilevel logistic regression models. We illustrate the application and interpretation of these measures by analyzing mortality in patients hospitalized with a diagnosis of acute myocardial infarction. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

First-order reversal curves of single domain particles: diluted random assemblages and chains

NASA Astrophysics Data System (ADS)

Egli, R.

2009-04-01

Exact magnetic models can be used to calculate first-order reversal curves (FORC) of single domain (SD) particle assemblages, as shown by Newell [2005] for the case of isolated Stoner-Wohlfarth particles. After overcoming experimental difficulties, a FORC diagram sharing many similarities to Newell's model has been measured on a lake sediment sample (see A.P. Chen et al., "Quantification of magnetofossils using first-order reversal curves", EGU General Assembly 2009, Abstracts Vol. 11, EGU2009-10719). This sample contains abundant magnetofossils, as shown by coercivity analysis and electron microscopy, therefore suggesting that well dispersed, intact magnetosome chains are the main SD carriers. Subtle differences between the reversible and the irreversible contributions of the measured FORC distribution suggest that magnetosome chains might not be correctly described by the Stoner-Wohlfarth model. To better understand the hysteresis properties of such chains, a simple magnetic model has been implemented, taking dipole-dipole interactions between particles within the same chain into account. The model results depend on the magnetosome elongation, the number of magnetosomes in a chain, and the gap between them. If the chain axis is subparallel to the applied field, the magnetic moment reverses by a pseudo-fanning mode, which is replaced by a pseudo-coherent rotation mode at greater angles. These reversal modes are intrinsically different from coherent rotation assumed Stoner-Wohlfarth model, resulting in FORC diagrams with a smaller reversible component. On the other hand, isolated authigenic SD particles can precipitate in the sediment matrix, as it might occur for pedogenic magnetite. In this case, an assembly of randomly located particles provides a possible model for the resulting FORC diagram. If the concentration of the particles is small, each particle is affected by a random interaction field whose statistical distribution can be calculated from first principles. In this case, the irreversible component of the FORC diagram, which is described by a Dirac delta function in the non-interacting case, converts into a continuous function that directly reflects the distribution of interaction fields. Such models provide a way to identify and characterize authigenic SD particles in sediments, and in some case allow one to isolate their magnetic contribution from that of other magnetic components. Newell, A.J. (2005), A high-precision model of first-order reversal curve (FORC) functions for single-domain ferromagnets with uniaxial anisotropy, Gechem. Geophys. Geosyst., 6, Q05010, doi:10.1029/2004GC00877.

Comparison of four methods for deriving hospital standardised mortality ratios from a single hierarchical logistic regression model.

PubMed

Mohammed, Mohammed A; Manktelow, Bradley N; Hofer, Timothy P

2016-04-01

There is interest in deriving case-mix adjusted standardised mortality ratios so that comparisons between healthcare providers, such as hospitals, can be undertaken in the controversial belief that variability in standardised mortality ratios reflects quality of care. Typically standardised mortality ratios are derived using a fixed effects logistic regression model, without a hospital term in the model. This fails to account for the hierarchical structure of the data - patients nested within hospitals - and so a hierarchical logistic regression model is more appropriate. However, four methods have been advocated for deriving standardised mortality ratios from a hierarchical logistic regression model, but their agreement is not known and neither do we know which is to be preferred. We found significant differences between the four types of standardised mortality ratios because they reflect a range of underlying conceptual issues. The most subtle issue is the distinction between asking how an average patient fares in different hospitals versus how patients at a given hospital fare at an average hospital. Since the answers to these questions are not the same and since the choice between these two approaches is not obvious, the extent to which profiling hospitals on mortality can be undertaken safely and reliably, without resolving these methodological issues, remains questionable. © The Author(s) 2012.

Latin hypercube approach to estimate uncertainty in ground water vulnerability

USGS Publications Warehouse

Gurdak, J.J.; McCray, J.E.; Thyne, G.; Qi, S.L.

2007-01-01

A methodology is proposed to quantify prediction uncertainty associated with ground water vulnerability models that were developed through an approach that coupled multivariate logistic regression with a geographic information system (GIS). This method uses Latin hypercube sampling (LHS) to illustrate the propagation of input error and estimate uncertainty associated with the logistic regression predictions of ground water vulnerability. Central to the proposed method is the assumption that prediction uncertainty in ground water vulnerability models is a function of input error propagation from uncertainty in the estimated logistic regression model coefficients (model error) and the values of explanatory variables represented in the GIS (data error). Input probability distributions that represent both model and data error sources of uncertainty were simultaneously sampled using a Latin hypercube approach with logistic regression calculations of probability of elevated nonpoint source contaminants in ground water. The resulting probability distribution represents the prediction intervals and associated uncertainty of the ground water vulnerability predictions. The method is illustrated through a ground water vulnerability assessment of the High Plains regional aquifer. Results of the LHS simulations reveal significant prediction uncertainties that vary spatially across the regional aquifer. Additionally, the proposed method enables a spatial deconstruction of the prediction uncertainty that can lead to improved prediction of ground water vulnerability. ?? 2007 National Ground Water Association.

Prediction of siRNA potency using sparse logistic regression.

PubMed

Hu, Wei; Hu, John

2014-06-01

RNA interference (RNAi) can modulate gene expression at post-transcriptional as well as transcriptional levels. Short interfering RNA (siRNA) serves as a trigger for the RNAi gene inhibition mechanism, and therefore is a crucial intermediate step in RNAi. There have been extensive studies to identify the sequence characteristics of potent siRNAs. One such study built a linear model using LASSO (Least Absolute Shrinkage and Selection Operator) to measure the contribution of each siRNA sequence feature. This model is simple and interpretable, but it requires a large number of nonzero weights. We have introduced a novel technique, sparse logistic regression, to build a linear model using single-position specific nucleotide compositions which has the same prediction accuracy of the linear model based on LASSO. The weights in our new model share the same general trend as those in the previous model, but have only 25 nonzero weights out of a total 84 weights, a 54% reduction compared to the previous model. Contrary to the linear model based on LASSO, our model suggests that only a few positions are influential on the efficacy of the siRNA, which are the 5' and 3' ends and the seed region of siRNA sequences. We also employed sparse logistic regression to build a linear model using dual-position specific nucleotide compositions, a task LASSO is not able to accomplish well due to its high dimensional nature. Our results demonstrate the superiority of sparse logistic regression as a technique for both feature selection and regression over LASSO in the context of siRNA design.

The cross-validated AUC for MCP-logistic regression with high-dimensional data.

PubMed

Jiang, Dingfeng; Huang, Jian; Zhang, Ying

2013-10-01

We propose a cross-validated area under the receiving operator characteristic (ROC) curve (CV-AUC) criterion for tuning parameter selection for penalized methods in sparse, high-dimensional logistic regression models. We use this criterion in combination with the minimax concave penalty (MCP) method for variable selection. The CV-AUC criterion is specifically designed for optimizing the classification performance for binary outcome data. To implement the proposed approach, we derive an efficient coordinate descent algorithm to compute the MCP-logistic regression solution surface. Simulation studies are conducted to evaluate the finite sample performance of the proposed method and its comparison with the existing methods including the Akaike information criterion (AIC), Bayesian information criterion (BIC) or Extended BIC (EBIC). The model selected based on the CV-AUC criterion tends to have a larger predictive AUC and smaller classification error than those with tuning parameters selected using the AIC, BIC or EBIC. We illustrate the application of the MCP-logistic regression with the CV-AUC criterion on three microarray datasets from the studies that attempt to identify genes related to cancers. Our simulation studies and data examples demonstrate that the CV-AUC is an attractive method for tuning parameter selection for penalized methods in high-dimensional logistic regression models.

Expression of p53 Breast Cancer in Kurdish Women in the West of Iran: a Reverse Correlation with Lymph Node Metastasis.

PubMed

Payandeh, Mehrdad; Sadeghi, Masoud; Sadeghi, Edris; Madani, Seyed-Hamid

2016-01-01

In breast cancer (BC), it has been suggested that nuclear overexpression of p53 protein might be an indicator of poor prognosis. The aim of the current study was to evaluate the expression of p53 BC in Kurdish women from the West of Iran and its correlation with other clinicopathology figures. In the present retrospective study, 231 patients were investigated for estrogen receptor (ER) and progesterone receptor (PR) positivity, defined as ≥10% positive tumor cells with nuclear staining. A binary logistic regression model was selected using Akaike Information Criteria (AIC) in stepwise selection for determination of important factors. ER, PR, the human epidermal growth factor receptor 2 (HER2) and p53 were positive in 58.4%, 55.4%, 59.7% and 45% of cases, respectively. Ki67 index was divided into two groups: 54.5% had Ki67<20% and 45.5% had Ki67 ≥20%. Of 214 patients, 137(64%) had lymph node metastasis and of 186 patients, 122(65.6%) had vascular invasion. Binary logistic regression analysis showed that there was inverse significant correlation between lymph node metastasis (P=0.008, OR 0.120 and 95%CI 0.025-0.574), ER status (P=0.006, OR 0.080, 95%CI 0.014-0.477) and a direct correlation between HER2 (P=005, OR 3.047, 95%CI 1.407-6.599) with the expression of p53. As in a number of studies, expression of p53 had a inverse correlation with lymph node metastasis and ER status and also a direct correlation with HER2 status. Also, p53-positivity is more likely in triple negative BC compared to other subtypes.

Careful with Those Priors: A Note on Bayesian Estimation in Two-Parameter Logistic Item Response Theory Models

ERIC Educational Resources Information Center

Marcoulides, Katerina M.

2018-01-01

This study examined the use of Bayesian analysis methods for the estimation of item parameters in a two-parameter logistic item response theory model. Using simulated data under various design conditions with both informative and non-informative priors, the parameter recovery of Bayesian analysis methods were examined. Overall results showed that…

An Evaluation of a Markov Chain Monte Carlo Method for the Two-Parameter Logistic Model.

ERIC Educational Resources Information Center

Kim, Seock-Ho; Cohen, Allan S.

The accuracy of the Markov Chain Monte Carlo (MCMC) procedure Gibbs sampling was considered for estimation of item parameters of the two-parameter logistic model. Data for the Law School Admission Test (LSAT) Section 6 were analyzed to illustrate the MCMC procedure. In addition, simulated data sets were analyzed using the MCMC, marginal Bayesian…

On the Relationships between Jeffreys Modal and Weighted Likelihood Estimation of Ability under Logistic IRT Models

ERIC Educational Resources Information Center

Magis, David; Raiche, Gilles

2012-01-01

This paper focuses on two estimators of ability with logistic item response theory models: the Bayesian modal (BM) estimator and the weighted likelihood (WL) estimator. For the BM estimator, Jeffreys' prior distribution is considered, and the corresponding estimator is referred to as the Jeffreys modal (JM) estimator. It is established that under…

Computerized Classification Testing under the One-Parameter Logistic Response Model with Ability-Based Guessing

ERIC Educational Resources Information Center

Wang, Wen-Chung; Huang, Sheng-Yun

2011-01-01

The one-parameter logistic model with ability-based guessing (1PL-AG) has been recently developed to account for effect of ability on guessing behavior in multiple-choice items. In this study, the authors developed algorithms for computerized classification testing under the 1PL-AG and conducted a series of simulations to evaluate their…

Standard Errors and Confidence Intervals from Bootstrapping for Ramsay-Curve Item Response Theory Model Item Parameters

ERIC Educational Resources Information Center

Gu, Fei; Skorupski, William P.; Hoyle, Larry; Kingston, Neal M.

2011-01-01

Ramsay-curve item response theory (RC-IRT) is a nonparametric procedure that estimates the latent trait using splines, and no distributional assumption about the latent trait is required. For item parameters of the two-parameter logistic (2-PL), three-parameter logistic (3-PL), and polytomous IRT models, RC-IRT can provide more accurate estimates…

Estimation of Logistic Regression Models in Small Samples. A Simulation Study Using a Weakly Informative Default Prior Distribution

ERIC Educational Resources Information Center

Gordovil-Merino, Amalia; Guardia-Olmos, Joan; Pero-Cebollero, Maribel

2012-01-01

In this paper, we used simulations to compare the performance of classical and Bayesian estimations in logistic regression models using small samples. In the performed simulations, conditions were varied, including the type of relationship between independent and dependent variable values (i.e., unrelated and related values), the type of variable…

Challenges and models in supporting logistics system design for dedicated-biomass-based bioenergy industry.

PubMed

Zhu, Xiaoyan; Li, Xueping; Yao, Qingzhu; Chen, Yuerong

2011-01-01

This paper analyzed the uniqueness and challenges in designing the logistics system for dedicated biomass-to-bioenergy industry, which differs from the other industries, due to the unique features of dedicated biomass (e.g., switchgrass) including its low bulk density, restrictions on harvesting season and frequency, content variation with time and circumambient conditions, weather effects, scattered distribution over a wide geographical area, and so on. To design it, this paper proposed a mixed integer linear programming model. It covered from planting and harvesting switchgrass to delivering to a biorefinery and included the residue handling, concentrating on integrating strategic decisions on the supply chain design and tactical decisions on the annual operation schedules. The present numerical examples verified the model and demonstrated its use in practice. This paper showed that the operations of the logistics system were significantly different for harvesting and non-harvesting seasons, and that under the well-designed biomass logistics system, the mass production with a steady and sufficient supply of biomass can increase the unit profit of bioenergy. The analytical model and practical methodology proposed in this paper will help realize the commercial production in biomass-to-bioenergy industry. Copyright © 2010 Elsevier Ltd. All rights reserved.

Generalized logistic map and its application in chaos based cryptography

NASA Astrophysics Data System (ADS)

Lawnik, M.

2017-12-01

The logistic map is commonly used in, for example, chaos based cryptography. However, its properties do not render a safe construction of encryption algorithms. Thus, the scope of the paper is a proposal of generalization of the logistic map by means of a wellrecognized family of chaotic maps. In the next step, an analysis of Lyapunov exponent and the distribution of the iterative variable are studied. The obtained results confirm that the analyzed model can safely and effectively replace a classic logistic map for applications involving chaotic cryptography.

Considering the reversibility of passive and reactive transport problems: Are forward-in-time and backward-in-time models ever equivalent?

NASA Astrophysics Data System (ADS)

Engdahl, N.

2017-12-01

Backward in time (BIT) simulations of passive tracers are often used for capture zone analysis, source area identification, and generation of travel time and age distributions. The BIT approach has the potential to become an immensely powerful tool for direct inverse modeling but the necessary relationships between the processes modeled in the forward and backward models have yet to be formally established. This study explores the time reversibility of passive and reactive transport models in a variety of 2D heterogeneous domains using particle-based random walk methods for the transport and nonlinear reaction steps. Distributed forward models are used to generate synthetic observations that form the initial conditions for the backward in time models and we consider both linear-flood and point injections. The results for passive travel time distributions show that forward and backward models are not exactly equivalent but that the linear-flood BIT models are reasonable approximations. Point based BIT models fall within the travel time range of the forward models, though their distributions can be distinctive in some cases. The BIT approximation is not as robust when nonlinear reactive transport is considered and we find that this reaction system is only exactly reversible under uniform flow conditions. We use a series of simplified, longitudinally symmetric, but heterogeneous, domains to illustrate the causes of these discrepancies between the two model types. Many of the discrepancies arise because diffusion is a "self-adjoint" operator, which causes mass to spread in the forward and backward models. This allows particles to enter low velocity regions in the both models, which has opposite effects in the forward and reverse models. It may be possible to circumvent some of these limitations using an anti-diffusion model to undo mixing when time is reversed, but this is beyond the capabilities of the existing Lagrangian methods.

Logistics Supply of the Distributed Air Wing

DTIC Science & Technology

2014-09-01

distribution is unlimited LOGISTICS SUPPLY OF THE DISTRIBUTED AIR WING Chee Siong Ong Civilian, Defence Science and Technology Agency B.Eng., Nanyang... Technological University, 2004 Submitted in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE IN MODELING, VIRTUAL...Department, for his advice on the Marine Aviation Logistics Supply Program. Finally, I am very grateful to my company, Defence Science and Technology

International Space Station Logistics Approach: Partnership and Dialog for a Successful Future

NASA Technical Reports Server (NTRS)

Banasik, Natalie

2000-01-01

This article seeks to investigate trends and challenges for establishing a successful partnership in a multi-cultural Logistics environment. The U.S. - Russian relationship in the field of space studies is used as the model for this inquiry. Case studies of culture specific responses to a variety of Logistics situations developed during the initial phase of this cooperation are discussed.

An Evaluation of One- and Three-Parameter Logistic Tailored Testing Procedures for Use with Small Item Pools.

ERIC Educational Resources Information Center

McKinley, Robert L.; Reckase, Mark D.

A two-stage study was conducted to compare the ability estimates yielded by tailored testing procedures based on the one-parameter logistic (1PL) and three-parameter logistic (3PL) models. The first stage of the study employed real data, while the second stage employed simulated data. In the first stage, response data for 3,000 examinees were…

Evaluating a common semi-mechanistic mathematical model of gene-regulatory networks

PubMed Central

2015-01-01

Modeling and simulation of gene-regulatory networks (GRNs) has become an important aspect of modern systems biology investigations into mechanisms underlying gene regulation. A key challenge in this area is the automated inference (reverse-engineering) of dynamic, mechanistic GRN models from gene expression time-course data. Common mathematical formalisms for representing such models capture two aspects simultaneously within a single parameter: (1) Whether or not a gene is regulated, and if so, the type of regulator (activator or repressor), and (2) the strength of influence of the regulator (if any) on the target or effector gene. To accommodate both roles, "generous" boundaries or limits for possible values of this parameter are commonly allowed in the reverse-engineering process. This approach has several important drawbacks. First, in the absence of good guidelines, there is no consensus on what limits are reasonable. Second, because the limits may vary greatly among different reverse-engineering experiments, the concrete values obtained for the models may differ considerably, and thus it is difficult to compare models. Third, if high values are chosen as limits, the search space of the model inference process becomes very large, adding unnecessary computational load to the already complex reverse-engineering process. In this study, we demonstrate that restricting the limits to the [−1, +1] interval is sufficient to represent the essential features of GRN systems and offers a reduction of the search space without loss of quality in the resulting models. To show this, we have carried out reverse-engineering studies on data generated from artificial and experimentally determined from real GRN systems. PMID:26356485

Estimating multilevel logistic regression models when the number of clusters is low: a comparison of different statistical software procedures.

PubMed

Austin, Peter C

2010-04-22

Multilevel logistic regression models are increasingly being used to analyze clustered data in medical, public health, epidemiological, and educational research. Procedures for estimating the parameters of such models are available in many statistical software packages. There is currently little evidence on the minimum number of clusters necessary to reliably fit multilevel regression models. We conducted a Monte Carlo study to compare the performance of different statistical software procedures for estimating multilevel logistic regression models when the number of clusters was low. We examined procedures available in BUGS, HLM, R, SAS, and Stata. We found that there were qualitative differences in the performance of different software procedures for estimating multilevel logistic models when the number of clusters was low. Among the likelihood-based procedures, estimation methods based on adaptive Gauss-Hermite approximations to the likelihood (glmer in R and xtlogit in Stata) or adaptive Gaussian quadrature (Proc NLMIXED in SAS) tended to have superior performance for estimating variance components when the number of clusters was small, compared to software procedures based on penalized quasi-likelihood. However, only Bayesian estimation with BUGS allowed for accurate estimation of variance components when there were fewer than 10 clusters. For all statistical software procedures, estimation of variance components tended to be poor when there were only five subjects per cluster, regardless of the number of clusters.

A 3D QSAR pharmacophore model and quantum chemical structure--activity analysis of chloroquine(CQ)-resistance reversal.

PubMed

Bhattacharjee, Apurba K; Kyle, Dennis E; Vennerstrom, Jonathan L; Milhous, Wilbur K

2002-01-01

Using CATALYST, a three-dimensional QSAR pharmacophore model for chloroquine(CQ)-resistance reversal was developed from a training set of 17 compounds. These included imipramine (1), desipramine (2), and 15 of their analogues (3-17), some of which fully reversed CQ-resistance, while others were without effect. The generated pharmacophore model indicates that two aromatic hydrophobic interaction sites on the tricyclic ring and a hydrogen bond acceptor (lipid) site at the side chain, preferably on a nitrogen atom, are necessary for potent activity. Stereoelectronic properties calculated by using AM1 semiempirical calculations were consistent with the model, particularly the electrostatic potential profiles characterized by a localized negative potential region by the side chain nitrogen atom and a large region covering the aromatic ring. The calculated data further revealed that aminoalkyl substitution at the N5-position of the heterocycle and a secondary or tertiary aliphatic aminoalkyl nitrogen atom with a two or three carbon bridge to the heteroaromatic nitrogen (N5) are required for potent "resistance reversal activity". Lowest energy conformers for 1-17 were determined and optimized to afford stereoelectronic properties such as molecular orbital energies, electrostatic potentials, atomic charges, proton affinities, octanol-water partition coefficients (log P), and structural parameters. For 1-17, fairly good correlation exists between resistance reversal activity and intrinsic basicity of the nitrogen atom at the tricyclic ring system, frontier orbital energies, and lipophilicity. Significantly, nine out of 11 of a group of structurally diverse CQ-resistance reversal agents mapped very well on the 3D QSAR pharmacophore model.

Development of S-ARIMA Model for Forecasting Demand in a Beverage Supply Chain

NASA Astrophysics Data System (ADS)

Mircetic, Dejan; Nikolicic, Svetlana; Maslaric, Marinko; Ralevic, Nebojsa; Debelic, Borna

2016-11-01

Demand forecasting is one of the key activities in planning the freight flows in supply chains, and accordingly it is essential for planning and scheduling of logistic activities within observed supply chain. Accurate demand forecasting models directly influence the decrease of logistics costs, since they provide an assessment of customer demand. Customer demand is a key component for planning all logistic processes in supply chain, and therefore determining levels of customer demand is of great interest for supply chain managers. In this paper we deal with exactly this kind of problem, and we develop the seasonal Autoregressive IntegratedMoving Average (SARIMA) model for forecasting demand patterns of a major product of an observed beverage company. The model is easy to understand, flexible to use and appropriate for assisting the expert in decision making process about consumer demand in particular periods.

Application of fuzzy neural network technologies in management of transport and logistics processes in Arctic

NASA Astrophysics Data System (ADS)

Levchenko, N. G.; Glushkov, S. V.; Sobolevskaya, E. Yu; Orlov, A. P.

2018-05-01

The method of modeling the transport and logistics process using fuzzy neural network technologies has been considered. The analysis of the implemented fuzzy neural network model of the information management system of transnational multimodal transportation of the process showed the expediency of applying this method to the management of transport and logistics processes in the Arctic and Subarctic conditions. The modular architecture of this model can be expanded by incorporating additional modules, since the working conditions in the Arctic and the subarctic themselves will present more and more realistic tasks. The architecture allows increasing the information management system, without affecting the system or the method itself. The model has a wide range of application possibilities, including: analysis of the situation and behavior of interacting elements; dynamic monitoring and diagnostics of management processes; simulation of real events and processes; prediction and prevention of critical situations.

Fractional Order Spatiotemporal Chaos with Delay in Spatial Nonlinear Coupling

NASA Astrophysics Data System (ADS)

Zhang, Yingqian; Wang, Xingyuan; Liu, Liyan; Liu, Jia

We investigate the spatiotemporal dynamics with fractional order differential logistic map with delay under nonlinear chaotic maps for spatial coupling connections. Here, the coupling methods between lattices are the nonlinear chaotic map coupling of lattices. The fractional order differential logistic map with delay breaks the limits of the range of parameter μ ∈ [3.75, 4] in the classical logistic map for chaotic states. The Kolmogorov-Sinai entropy density and universality, and bifurcation diagrams are employed to investigate the chaotic behaviors of the proposed model in this paper. The proposed model can also be applied for cryptography, which is verified in a color image encryption scheme in this paper.

Coevolution can reverse predator–prey cycles

PubMed Central

Cortez, Michael H.; Weitz, Joshua S.

2014-01-01

A hallmark of Lotka–Volterra models, and other ecological models of predator–prey interactions, is that in predator–prey cycles, peaks in prey abundance precede peaks in predator abundance. Such models typically assume that species life history traits are fixed over ecologically relevant time scales. However, the coevolution of predator and prey traits has been shown to alter the community dynamics of natural systems, leading to novel dynamics including antiphase and cryptic cycles. Here, using an eco-coevolutionary model, we show that predator–prey coevolution can also drive population cycles where the opposite of canonical Lotka–Volterra oscillations occurs: predator peaks precede prey peaks. These reversed cycles arise when selection favors extreme phenotypes, predator offense is costly, and prey defense is effective against low-offense predators. We present multiple datasets from phage–cholera, mink–muskrat, and gyrfalcon–rock ptarmigan systems that exhibit reversed-peak ordering. Our results suggest that such cycles are a potential signature of predator–prey coevolution and reveal unique ways in which predator–prey coevolution can shape, and possibly reverse, community dynamics. PMID:24799689

Coevolution can reverse predator-prey cycles.

PubMed

Cortez, Michael H; Weitz, Joshua S

2014-05-20

A hallmark of Lotka-Volterra models, and other ecological models of predator-prey interactions, is that in predator-prey cycles, peaks in prey abundance precede peaks in predator abundance. Such models typically assume that species life history traits are fixed over ecologically relevant time scales. However, the coevolution of predator and prey traits has been shown to alter the community dynamics of natural systems, leading to novel dynamics including antiphase and cryptic cycles. Here, using an eco-coevolutionary model, we show that predator-prey coevolution can also drive population cycles where the opposite of canonical Lotka-Volterra oscillations occurs: predator peaks precede prey peaks. These reversed cycles arise when selection favors extreme phenotypes, predator offense is costly, and prey defense is effective against low-offense predators. We present multiple datasets from phage-cholera, mink-muskrat, and gyrfalcon-rock ptarmigan systems that exhibit reversed-peak ordering. Our results suggest that such cycles are a potential signature of predator-prey coevolution and reveal unique ways in which predator-prey coevolution can shape, and possibly reverse, community dynamics.

Monte Carlo study of magnetization reversal in the model of a hard/soft magnetic bilayer

NASA Astrophysics Data System (ADS)

Taaev, T. A.; Khizriev, K. Sh.; Murtazaev, A. K.

2017-06-01

Magnetization reversal in the model of a hard/soft magnetic bilayer under the action of an external magnetic field has been investigated by the Monte Carlo method. Calculations have been performed for three systems: (i) the model without a soft-magnetic layer (hard-magnetic layer), (ii) the model with a soft-magnetic layer of thickness 25 atomic layers (predominantly exchange-coupled system), and (iii) with 50 (weak exchange coupling) atomic layers. The effect of a soft-magnetic phase on the magnetization reversal of the magnetic bilayer and on the formation of a 1D spin spring in the magnetic bilayer has been demonstrated. An inf lection that has been detected on the arch of the hysteresis loop only for the system with weak exchange coupling is completely determined by the behavior of the soft layer in the external magnetic field. The critical fields of magnetization reversal decrease with increasing thickness of the soft phase.

California Family Planning Health Care Providers' Challenges to Same-Day Long-Acting Reversible Contraception Provision.

PubMed

Biggs, M Antonia; Harper, Cynthia C; Brindis, Claire D

2015-08-01

To assess the extent to which practices offering family planning services are able to offer intrauterine devices (IUDs) and implants in one visit and to identify the reasons why multiple visits may be required. In the fall of 2011, 1,000 California family planning providers were asked about their long-acting reversible contraception delivery practices in a probability survey. We used multivariable logistic regression to examine practice characteristics associated with same-day provision of IUDs and implants. Among the 636 responding practices, 67% offered an IUD and 40% offered a contraceptive implant onsite. Among those with onsite provision, the majority required two or more visits to place an IUD (58%); almost half required two visits to place an implant (47%). Nearly all Planned Parenthood practices could place an IUD (95%) or implant (95%) at the initial visit, whereas the majority of all other practice types could not. The main reasons for delaying IUD and contraceptive implant provision included the need to screen and wait for test results (68% and 24%, respectively) and clinic flow and scheduling issues (50% and 64%, respectively). Multivariable analyses indicated that Planned Parenthood practices were significantly more likely than private practices to have same-day insertion protocols. Most of the family planning providers surveyed have not adopted same-day long-acting reversible contraception insertion protocols and face barriers to same-day provision. III.

Magnetization Reversal of Nanoscale Islands: How Size and Shape Affect the Arrhenius Prefactor

NASA Astrophysics Data System (ADS)

Krause, S.; Herzog, G.; Stapelfeldt, T.; Berbil-Bautista, L.; Bode, M.; Vedmedenko, E. Y.; Wiesendanger, R.

2009-09-01

The thermal switching behavior of individual in-plane magnetized Fe/W(110) nanoislands is investigated by a combined study of variable-temperature spin-polarized scanning tunneling microscopy and Monte Carlo simulations. Even for islands consisting of less than 100 atoms the magnetization reversal takes place via nucleation and propagation. The Arrhenius prefactor is found to strongly depend on the individual island size and shape, and based on the experimental results a simple model is developed to describe the magnetization reversal in terms of metastable states. Complementary Monte Carlo simulations confirm the model and provide new insight into the microscopic processes involved in magnetization reversal of smallest nanomagnets.

Dynamic Models and Coordination Analysis of Reverse Supply Chain with Remanufacturing

NASA Astrophysics Data System (ADS)

Yan, Nina

In this paper, we establish a reverse chain system with one manufacturer and one retailer under demand uncertainties. Distinguishing between the recycling process of the retailer and the remanufacturing process of the manufacturer, we formulate a two-stage dynamic model for reverse supply chain based on remanufacturing. Using buyback contract as coordination mechanism and applying dynamic programming the optimal decision problems for each stage are analyzed. It concluded that the reverse supply chain system could be coordinated under the given condition. Finally, we carry out numerical calculations to analyze the expected profits for the manufacturer and the retailer under different recovery rates and recovery prices and the outcomes validate the theoretical analyses.

A feed-forward spiking model of shape-coding by IT cells

PubMed Central

Romeo, August; Supèr, Hans

2014-01-01

The ability to recognize a shape is linked to figure-ground (FG) organization. Cell preferences appear to be correlated across contrast-polarity reversals and mirror reversals of polygon displays, but not so much across FG reversals. Here we present a network structure which explains both shape-coding by simulated IT cells and suppression of responses to FG reversed stimuli. In our model FG segregation is achieved before shape discrimination, which is itself evidenced by the difference in spiking onsets of a pair of output cells. The studied example also includes feature extraction and illustrates a classification of binary images depending on the dominance of vertical or horizontal borders. PMID:24904494

Modeling of membrane processes for air revitalization and water recovery

NASA Technical Reports Server (NTRS)

Lange, Kevin E.; Foerg, Sandra L.; Dall-Bauman, Liese A.

1992-01-01

Gas-separation and reverse-osmosis membrane models are being developed in conjunction with membrane testing at NASA JSC. The completed gas-separation membrane model extracts effective component permeabilities from multicomponent test data, and predicts the effects of flow configuration, operating conditions, and membrane dimensions on module performance. Variable feed- and permeate-side pressures are considered. The model has been applied to test data for hollow-fiber membrane modules with simulated cabin-air feeds. Results are presented for a membrane designed for air drying applications. Extracted permeabilities are used to predict the effect of operating conditions on water enrichment in the permeate. A first-order reverse-osmosis model has been applied to test data for spiral wound membrane modules with a simulated hygiene water feed. The model estimates an effective local component rejection coefficient under pseudosteady-state conditions. Results are used to define requirements for a detailed reverse-osmosis model.

Scheduling algorithm for mission planning and logistics evaluation users' guide

NASA Technical Reports Server (NTRS)

Chang, H.; Williams, J. M.

1976-01-01

The scheduling algorithm for mission planning and logistics evaluation (SAMPLE) program is a mission planning tool composed of three subsystems; the mission payloads subsystem (MPLS), which generates a list of feasible combinations from a payload model for a given calendar year; GREEDY, which is a heuristic model used to find the best traffic model; and the operations simulation and resources scheduling subsystem (OSARS), which determines traffic model feasibility for available resources. The SAMPLE provides the user with options to allow the execution of MPLS, GREEDY, GREEDY-OSARS, or MPLS-GREEDY-OSARS.

Improving virtual screening predictive accuracy of Human kallikrein 5 inhibitors using machine learning models.

PubMed

Fang, Xingang; Bagui, Sikha; Bagui, Subhash

2017-08-01

The readily available high throughput screening (HTS) data from the PubChem database provides an opportunity for mining of small molecules in a variety of biological systems using machine learning techniques. From the thousands of available molecular descriptors developed to encode useful chemical information representing the characteristics of molecules, descriptor selection is an essential step in building an optimal quantitative structural-activity relationship (QSAR) model. For the development of a systematic descriptor selection strategy, we need the understanding of the relationship between: (i) the descriptor selection; (ii) the choice of the machine learning model; and (iii) the characteristics of the target bio-molecule. In this work, we employed the Signature descriptor to generate a dataset on the Human kallikrein 5 (hK 5) inhibition confirmatory assay data and compared multiple classification models including logistic regression, support vector machine, random forest and k-nearest neighbor. Under optimal conditions, the logistic regression model provided extremely high overall accuracy (98%) and precision (90%), with good sensitivity (65%) in the cross validation test. In testing the primary HTS screening data with more than 200K molecular structures, the logistic regression model exhibited the capability of eliminating more than 99.9% of the inactive structures. As part of our exploration of the descriptor-model-target relationship, the excellent predictive performance of the combination of the Signature descriptor and the logistic regression model on the assay data of the Human kallikrein 5 (hK 5) target suggested a feasible descriptor/model selection strategy on similar targets. Copyright © 2017 Elsevier Ltd. All rights reserved.

A simple statistical model for geomagnetic reversals

NASA Technical Reports Server (NTRS)

Constable, Catherine

1990-01-01

The diversity of paleomagnetic records of geomagnetic reversals now available indicate that the field configuration during transitions cannot be adequately described by simple zonal or standing field models. A new model described here is based on statistical properties inferred from the present field and is capable of simulating field transitions like those observed. Some insight is obtained into what one can hope to learn from paleomagnetic records. In particular, it is crucial that the effects of smoothing in the remanence acquisition process be separated from true geomagnetic field behavior. This might enable us to determine the time constants associated with the dominant field configuration during a reversal.

Magnetic reversals from planetary dynamo waves.

PubMed

Sheyko, Andrey; Finlay, Christopher C; Jackson, Andrew

2016-11-24

A striking feature of many natural dynamos is their ability to undergo polarity reversals. The best documented example is Earth's magnetic field, which has reversed hundreds of times during its history. The origin of geomagnetic polarity reversals lies in a magnetohydrodynamic process that takes place in Earth's core, but the precise mechanism is debated. The majority of numerical geodynamo simulations that exhibit reversals operate in a regime in which the viscosity of the fluid remains important, and in which the dynamo mechanism primarily involves stretching and twisting of field lines by columnar convection. Here we present an example of another class of reversing-geodynamo model, which operates in a regime of comparatively low viscosity and high magnetic diffusivity. This class does not fit into the paradigm of reversal regimes that are dictated by the value of the local Rossby number (the ratio of advection to Coriolis force). Instead, stretching of the magnetic field by a strong shear in the east-west flow near the imaginary cylinder just touching the inner core and parallel to the axis of rotation is crucial to the reversal mechanism in our models, which involves a process akin to kinematic dynamo waves. Because our results are relevant in a regime of low viscosity and high magnetic diffusivity, and with geophysically appropriate boundary conditions, this form of dynamo wave may also be involved in geomagnetic reversals.

Static internal performance of a single-engine onaxisymmetric-nozzle vaned-thrust-reverser design with thrust modulation capabilities

NASA Technical Reports Server (NTRS)

Leavitt, L. D.; Burley, J. R., II

1985-01-01

An investigation has been conducted at wind-off conditions in the stati-test facility of the Langley 16-Foot Transonic Tunnel. The tests were conducted on a single-engine reverser configuration with partial and full reverse-thrust modulation capabilities. The reverser design had four ports with equal areas. These ports were angled outboard 30 deg from the vertical impart of a splay angle to the reverse exhaust flow. This splaying of reverser flow was intended to prevent impingement of exhaust flow on empennage surfaces and to help avoid inlet reingestion of exhaust gas when the reverser is integrated into an actual airplane configuration. External vane boxes were located directly over each of the four ports to provide variation of reverser efflux angle from 140 deg to 26 deg (measured forward from the horizontal reference axis). The reverser model was tested with both a butterfly-type inner door and an internal slider door to provide area control for each individual port. In addition, main nozzle throat area and vector angle were varied to examine various methods of modulating thrust levels. Other model variables included vane box configuration (four or six vanes per box), orientation of external vane boxes with respect to internal port walls (splay angle shims), and vane box sideplates. Nozzle pressure ratio was varied from 2.0 approximately 7.0.