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.

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.

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.

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.

Logistics, electronic commerce, and the environment

NASA Astrophysics Data System (ADS)

Sarkis, Joseph; Meade, Laura; Talluri, Srinivas

2002-02-01

Organizations realize that a strong supporting logistics or electronic logistics (e-logistics) function is important from both commercial and consumer perspectives. The implications of e-logistics models and practices cover the forward and reverse logistics functions of organizations. They also have direct and profound impact on the natural environment. This paper will focus on a discussion of forward and reverse e-logistics and their relationship to the natural environment. After discussion of the many pertinent issues in these areas, directions of practice and implications for study and research are then described.

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

Logistics Management: New trends in the Reverse Logistics

NASA Astrophysics Data System (ADS)

Antonyová, A.; Antony, P.; Soewito, B.

2016-04-01

Present level and quality of the environment are directly dependent on our access to natural resources, as well as their sustainability. In particular production activities and phenomena associated with it have a direct impact on the future of our planet. Recycling process, which in large enterprises often becomes an important and integral part of the production program, is usually in small and medium-sized enterprises problematic. We can specify a few factors, which have direct impact on the development and successful application of the effective reverse logistics system. Find the ways to economically acceptable model of reverse logistics, focusing on converting waste materials for renewable energy, is the task in progress.

On the Simulation-Based Reliability of Complex Emergency Logistics Networks in Post-Accident Rescues.

PubMed

Wang, Wei; Huang, Li; Liang, Xuedong

2018-01-06

This paper investigates the reliability of complex emergency logistics networks, as reliability is crucial to reducing environmental and public health losses in post-accident emergency rescues. Such networks' statistical characteristics are analyzed first. After the connected reliability and evaluation indices for complex emergency logistics networks are effectively defined, simulation analyses of network reliability are conducted under two different attack modes using a particular emergency logistics network as an example. The simulation analyses obtain the varying trends in emergency supply times and the ratio of effective nodes and validates the effects of network characteristics and different types of attacks on network reliability. The results demonstrate that this emergency logistics network is both a small-world and a scale-free network. When facing random attacks, the emergency logistics network steadily changes, whereas it is very fragile when facing selective attacks. Therefore, special attention should be paid to the protection of supply nodes and nodes with high connectivity. The simulation method provides a new tool for studying emergency logistics networks and a reference for similar studies.

On the Simulation-Based Reliability of Complex Emergency Logistics Networks in Post-Accident Rescues

PubMed Central

Wang, Wei; Huang, Li; Liang, Xuedong

2018-01-01

This paper investigates the reliability of complex emergency logistics networks, as reliability is crucial to reducing environmental and public health losses in post-accident emergency rescues. Such networks’ statistical characteristics are analyzed first. After the connected reliability and evaluation indices for complex emergency logistics networks are effectively defined, simulation analyses of network reliability are conducted under two different attack modes using a particular emergency logistics network as an example. The simulation analyses obtain the varying trends in emergency supply times and the ratio of effective nodes and validates the effects of network characteristics and different types of attacks on network reliability. The results demonstrate that this emergency logistics network is both a small-world and a scale-free network. When facing random attacks, the emergency logistics network steadily changes, whereas it is very fragile when facing selective attacks. Therefore, special attention should be paid to the protection of supply nodes and nodes with high connectivity. The simulation method provides a new tool for studying emergency logistics networks and a reference for similar studies. PMID:29316614

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...

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.

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…

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

Risk assessment of logistics outsourcing based on BP neural network

NASA Astrophysics Data System (ADS)

Liu, Xiaofeng; Tian, Zi-you

The purpose of this article is to evaluate the risk of the enterprises logistics outsourcing. To get this goal, the paper first analysed he main risks existing in the logistics outsourcing, and then set up a risk evaluation index system of the logistics outsourcing; second applied BP neural network into the logistics outsourcing risk evaluation and used MATLAB to the simulation. It proved that the network error is small and has strong practicability. And this method can be used by enterprises to evaluate the risks of logistics outsourcing.

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.

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.

Advancing reversible shape memory by tuning the polymer network architecture

DOE PAGES

Li, Qiaoxi; Zhou, Jing; Vatankhah-Varnoosfaderani, Mohammad; ...

2016-02-02

Because of counteraction of a chemical network and a crystalline scaffold, semicrystalline polymer networks exhibit a peculiar behavior—reversible shape memory (RSM), which occurs naturally without applying any external force and particular structural design. There are three RSM properties: (i) range of reversible strain, (ii) rate of strain recovery, and (iii) decay of reversibility with time, which can be improved by tuning the architecture of the polymer network. Different types of poly(octylene adipate) networks were synthesized, allowing for control of cross-link density and network topology, including randomly cross-linked network by free-radical polymerization, thiol–ene clicked network with enhanced mesh uniformity, and loosemore » network with deliberately incorporated dangling chains. It is shown that the RSM properties are controlled by average cross-link density and crystal size, whereas topology of a network greatly affects its extensibility. In conclusion, we have achieved 80% maximum reversible range, 15% minimal decrease in reversibility, and fast strain recovery rate up to 0.05 K –1, i.e., ca. 5% per 10 s at a cooling rate of 5 K/min.« less

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.

Thermodynamic Constraints Improve Metabolic Networks.

PubMed

Krumholz, Elias W; Libourel, Igor G L

2017-08-08

In pursuit of establishing a realistic metabolic phenotypic space, the reversibility of reactions is thermodynamically constrained in modern metabolic networks. The reversibility constraints follow from heuristic thermodynamic poise approximations that take anticipated cellular metabolite concentration ranges into account. Because constraints reduce the feasible space, draft metabolic network reconstructions may need more extensive reconciliation, and a larger number of genes may become essential. Notwithstanding ubiquitous application, the effect of reversibility constraints on the predictive capabilities of metabolic networks has not been investigated in detail. Instead, work has focused on the implementation and validation of the thermodynamic poise calculation itself. With the advance of fast linear programming-based network reconciliation, the effects of reversibility constraints on network reconciliation and gene essentiality predictions have become feasible and are the subject of this study. Networks with thermodynamically informed reversibility constraints outperformed gene essentiality predictions compared to networks that were constrained with randomly shuffled constraints. Unconstrained networks predicted gene essentiality as accurately as thermodynamically constrained networks, but predicted substantially fewer essential genes. Networks that were reconciled with sequence similarity data and strongly enforced reversibility constraints outperformed all other networks. We conclude that metabolic network analysis confirmed the validity of the thermodynamic constraints, and that thermodynamic poise information is actionable during network reconciliation. Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Building of Reusable Reverse Logistics Model and its Optimization Considering the Decision of Backorder or Next Arrival of Goods

NASA Astrophysics Data System (ADS)

Lee, Jeong-Eun; Gen, Mitsuo; Rhee, Kyong-Gu; Lee, Hee-Hyol

This paper deals with the building of the reusable reverse logistics model considering the decision of the backorder or the next arrival of goods. The optimization method to minimize the transportation cost and to minimize the volume of the backorder or the next arrival of goods occurred by the Just in Time delivery of the final delivery stage between the manufacturer and the processing center is proposed. Through the optimization algorithms using the priority-based genetic algorithm and the hybrid genetic algorithm, the sub-optimal delivery routes are determined. Based on the case study of a distilling and sale company in Busan in Korea, the new model of the reusable reverse logistics of empty bottles is built and the effectiveness of the proposed method is verified.

Designing optimal greenhouse gas monitoring networks for Australia

NASA Astrophysics Data System (ADS)

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

2016-01-01

Atmospheric transport inversion is commonly used to infer greenhouse gas (GHG) flux estimates from concentration measurements. The optimal location of ground-based observing stations that supply these measurements can be determined by network design. Here, we use a Lagrangian particle dispersion model (LPDM) in reverse mode together with a Bayesian inverse modelling framework to derive optimal GHG observing networks for Australia. This extends the network design for carbon dioxide (CO2) performed by Ziehn et al. (2014) to also minimise the uncertainty on the flux estimates for methane (CH4) and nitrous oxide (N2O), both individually and in a combined network using multiple objectives. Optimal networks are generated by adding up to five new stations to the base network, which is defined as two existing stations, Cape Grim and Gunn Point, in southern and northern Australia respectively. The individual networks for CO2, CH4 and N2O and the combined observing network show large similarities because the flux uncertainties for each GHG are dominated by regions of biologically productive land. There is little penalty, in terms of flux uncertainty reduction, for the combined network compared to individually designed networks. The location of the stations in the combined network is sensitive to variations in the assumed data uncertainty across locations. A simple assessment of economic costs has been included in our network design approach, considering both establishment and maintenance costs. Our results suggest that, while site logistics change the optimal network, there is only a small impact on the flux uncertainty reductions achieved with increasing network size.

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

Experimental Study of Quantum Graphs With and Without Time-Reversal Invariance

NASA Astrophysics Data System (ADS)

Anlage, Steven Mark; Fu, Ziyuan; Koch, Trystan; Antonsen, Thomas; Ott, Edward

An experimental setup consisting of a microwave network is used to simulate quantum graphs. The random coupling model (RCM) is applied to describe the universal statistical properties of the system with and without time-reversal invariance. The networks which are large compared to the wavelength, are constructed from coaxial cables connected by T junctions, and by making nodes with circulators time-reversal invariance for microwave propagation in the networks can be broken. The results of experimental study of microwave networks with and without time-reversal invariance are presented both in frequency domain and time domain. With the measured S-parameter data of two-port networks, the impedance statistics and the nearest-neighbor spacing statistics are examined. Moreover, the experiments of time reversal mirrors for networks demonstrate that the reconstruction quality can be used to quantify the degree of the time-reversal invariance for wave propagation. Numerical models of networks are also presented to verify the time domain experiments. We acknowledge support under contract AFOSR COE Grant FA9550-15-1-0171 and the ONR Grant N000141512134.

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.

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.

A reverse engineering algorithm for neural networks, applied to the subthalamopallidal network of basal ganglia.

PubMed

Floares, Alexandru George

2008-01-01

Modeling neural networks with ordinary differential equations systems is a sensible approach, but also very difficult. This paper describes a new algorithm based on linear genetic programming which can be used to reverse engineer neural networks. The RODES algorithm automatically discovers the structure of the network, including neural connections, their signs and strengths, estimates its parameters, and can even be used to identify the biophysical mechanisms involved. The algorithm is tested on simulated time series data, generated using a realistic model of the subthalamopallidal network of basal ganglia. The resulting ODE system is highly accurate, and results are obtained in a matter of minutes. This is because the problem of reverse engineering a system of coupled differential equations is reduced to one of reverse engineering individual algebraic equations. The algorithm allows the incorporation of common domain knowledge to restrict the solution space. To our knowledge, this is the first time a realistic reverse engineering algorithm based on linear genetic programming has been applied to neural networks.

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.

Research challenges in municipal solid waste logistics management.

PubMed

Bing, Xiaoyun; Bloemhof, Jacqueline M; Ramos, Tania Rodrigues Pereira; Barbosa-Povoa, Ana Paula; Wong, Chee Yew; van der Vorst, Jack G A J

2016-02-01

During the last two decades, EU legislation has put increasing pressure on member countries to achieve specified recycling targets for municipal household waste. These targets can be obtained in various ways choosing collection methods, separation methods, decentral or central logistic systems, etc. This paper compares municipal solid waste (MSW) management practices in various EU countries to identify the characteristics and key issues from a waste management and reverse logistics point of view. Further, we investigate literature on modelling municipal solid waste logistics in general. Comparing issues addressed in literature with the identified issues in practice result in a research agenda for modelling municipal solid waste logistics in Europe. We conclude that waste recycling is a multi-disciplinary problem that needs to be considered at different decision levels simultaneously. A holistic view and taking into account the characteristics of different waste types are necessary when modelling a reverse supply chain for MSW recycling. Copyright © 2015 Elsevier Ltd. All rights reserved.

Determine the optimal carrier selection for a logistics network based on multi-commodity reliability criterion

NASA Astrophysics Data System (ADS)

Lin, Yi-Kuei; Yeh, Cheng-Ta

2013-05-01

From the perspective of supply chain management, the selected carrier plays an important role in freight delivery. This article proposes a new criterion of multi-commodity reliability and optimises the carrier selection based on such a criterion for logistics networks with routes and nodes, over which multiple commodities are delivered. Carrier selection concerns the selection of exactly one carrier to deliver freight on each route. The capacity of each carrier has several available values associated with a probability distribution, since some of a carrier's capacity may be reserved for various orders. Therefore, the logistics network, given any carrier selection, is a multi-commodity multi-state logistics network. Multi-commodity reliability is defined as a probability that the logistics network can satisfy a customer's demand for various commodities, and is a performance indicator for freight delivery. To solve this problem, this study proposes an optimisation algorithm that integrates genetic algorithm, minimal paths and Recursive Sum of Disjoint Products. A practical example in which multi-sized LCD monitors are delivered from China to Germany is considered to illustrate the solution procedure.

Designing a multistage supply chain in cross-stage reverse logistics environments: application of particle swarm optimization algorithms.

PubMed

Chiang, Tzu-An; Che, Z H; Cui, Zhihua

2014-01-01

This study designed a cross-stage reverse logistics course for defective products so that damaged products generated in downstream partners can be directly returned to upstream partners throughout the stages of a supply chain for rework and maintenance. To solve this reverse supply chain design problem, an optimal cross-stage reverse logistics mathematical model was developed. In addition, we developed a genetic algorithm (GA) and three particle swarm optimization (PSO) algorithms: the inertia weight method (PSOA_IWM), V(Max) method (PSOA_VMM), and constriction factor method (PSOA_CFM), which we employed to find solutions to support this mathematical model. Finally, a real case and five simulative cases with different scopes were used to compare the execution times, convergence times, and objective function values of the four algorithms used to validate the model proposed in this study. Regarding system execution time, the GA consumed more time than the other three PSOs did. Regarding objective function value, the GA, PSOA_IWM, and PSOA_CFM could obtain a lower convergence value than PSOA_VMM could. Finally, PSOA_IWM demonstrated a faster convergence speed than PSOA_VMM, PSOA_CFM, and the GA did.

Designing a Multistage Supply Chain in Cross-Stage Reverse Logistics Environments: Application of Particle Swarm Optimization Algorithms

PubMed Central

Chiang, Tzu-An; Che, Z. H.

2014-01-01

This study designed a cross-stage reverse logistics course for defective products so that damaged products generated in downstream partners can be directly returned to upstream partners throughout the stages of a supply chain for rework and maintenance. To solve this reverse supply chain design problem, an optimal cross-stage reverse logistics mathematical model was developed. In addition, we developed a genetic algorithm (GA) and three particle swarm optimization (PSO) algorithms: the inertia weight method (PSOA_IWM), V Max method (PSOA_VMM), and constriction factor method (PSOA_CFM), which we employed to find solutions to support this mathematical model. Finally, a real case and five simulative cases with different scopes were used to compare the execution times, convergence times, and objective function values of the four algorithms used to validate the model proposed in this study. Regarding system execution time, the GA consumed more time than the other three PSOs did. Regarding objective function value, the GA, PSOA_IWM, and PSOA_CFM could obtain a lower convergence value than PSOA_VMM could. Finally, PSOA_IWM demonstrated a faster convergence speed than PSOA_VMM, PSOA_CFM, and the GA did. PMID:24772026

Planning the location of facilities to implement a reverse logistic system of post-consumer packaging using a location mathematical model.

PubMed

Couto, Maria Claudia Lima; Lange, Liséte Celina; Rosa, Rodrigo de Alvarenga; Couto, Paula Rogeria Lima

2017-12-01

The implementation of reverse logistics systems (RLS) for post-consumer products provides environmental and economic benefits, since it increases recycling potential. However, RLS implantation and consolidation still face problems. The main shortcomings are the high costs and the low expectation of broad implementation worldwide. This paper presents two mathematical models to decide the number and the location of screening centers (SCs) and valorization centers (VCs) to implement reverse logistics of post-consumer packages, defining the optimum territorial arrangements (OTAs), allowing the inclusion of small and medium size municipalities. The paper aims to fill a gap in the literature on RLS location facilities that not only aim at revenue optimization, but also the participation of the population, the involvement of pickers and the service universalization. The results showed that implementation of VCs can lead to revenue/cost ratio higher than 100%. The results of this study can supply companies and government agencies with a global view on the parameters that influence RLS sustainability and help them make decisions about the location of these facilities and the best reverse flows with the social inclusion of pickers and serving the population of small and medium-sized municipalities.

Bifurcation behaviors of synchronized regions in logistic map networks with coupling delay

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

Tang, Longkun, E-mail: tomlk@hqu.edu.cn, E-mail: xqwu@whu.edu.cn; Wu, Xiaoqun, E-mail: tomlk@hqu.edu.cn, E-mail: xqwu@whu.edu.cn; Lu, Jun-an, E-mail: jalu@whu.edu.cn

2015-03-15

Network synchronized regions play an extremely important role in network synchronization according to the master stability function framework. This paper focuses on network synchronous state stability via studying the effects of nodal dynamics, coupling delay, and coupling way on synchronized regions in Logistic map networks. Theoretical and numerical investigations show that (1) network synchronization is closely associated with its nodal dynamics. Particularly, the synchronized region bifurcation points through which the synchronized region switches from one type to another are in good agreement with those of the uncoupled node system, and chaotic nodal dynamics can greatly impede network synchronization. (2) Themore » coupling delay generally impairs the synchronizability of Logistic map networks, which is also dominated by the parity of delay for some nodal parameters. (3) A simple nonlinear coupling facilitates network synchronization more than the linear one does. The results found in this paper will help to intensify our understanding for the synchronous state stability in discrete-time networks with coupling delay.« less

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.

Reverse Engineering Validation using a Benchmark Synthetic Gene Circuit in Human Cells

PubMed Central

Kang, Taek; White, Jacob T.; Xie, Zhen; Benenson, Yaakov; Sontag, Eduardo; Bleris, Leonidas

2013-01-01

Multi-component biological networks are often understood incompletely, in large part due to the lack of reliable and robust methodologies for network reverse engineering and characterization. As a consequence, developing automated and rigorously validated methodologies for unraveling the complexity of biomolecular networks in human cells remains a central challenge to life scientists and engineers. Today, when it comes to experimental and analytical requirements, there exists a great deal of diversity in reverse engineering methods, which renders the independent validation and comparison of their predictive capabilities difficult. In this work we introduce an experimental platform customized for the development and verification of reverse engineering and pathway characterization algorithms in mammalian cells. Specifically, we stably integrate a synthetic gene network in human kidney cells and use it as a benchmark for validating reverse engineering methodologies. The network, which is orthogonal to endogenous cellular signaling, contains a small set of regulatory interactions that can be used to quantify the reconstruction performance. By performing successive perturbations to each modular component of the network and comparing protein and RNA measurements, we study the conditions under which we can reliably reconstruct the causal relationships of the integrated synthetic network. PMID:23654266

Reverse engineering validation using a benchmark synthetic gene circuit in human cells.

PubMed

Kang, Taek; White, Jacob T; Xie, Zhen; Benenson, Yaakov; Sontag, Eduardo; Bleris, Leonidas

2013-05-17

Multicomponent biological networks are often understood incompletely, in large part due to the lack of reliable and robust methodologies for network reverse engineering and characterization. As a consequence, developing automated and rigorously validated methodologies for unraveling the complexity of biomolecular networks in human cells remains a central challenge to life scientists and engineers. Today, when it comes to experimental and analytical requirements, there exists a great deal of diversity in reverse engineering methods, which renders the independent validation and comparison of their predictive capabilities difficult. In this work we introduce an experimental platform customized for the development and verification of reverse engineering and pathway characterization algorithms in mammalian cells. Specifically, we stably integrate a synthetic gene network in human kidney cells and use it as a benchmark for validating reverse engineering methodologies. The network, which is orthogonal to endogenous cellular signaling, contains a small set of regulatory interactions that can be used to quantify the reconstruction performance. By performing successive perturbations to each modular component of the network and comparing protein and RNA measurements, we study the conditions under which we can reliably reconstruct the causal relationships of the integrated synthetic network.

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.

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.

Time reversibility of quantum diffusion in small-world networks

NASA Astrophysics Data System (ADS)

Han, Sung-Guk; Kim, Beom Jun

2012-02-01

We study the time-reversal dynamics of a tight-binding electron in the Watts-Strogatz (WS) small-world networks. The localized initial wave packet at time t = 0 diffuses as time proceeds until the time-reversal operation, together with the momentum perturbation of the strength η, is made at the reversal time T. The time irreversibility is measured by I = |Π( t = 2 T) - Π( t = 0)|, where Π is the participation ratio gauging the extendedness of the wavefunction and for convenience, t is measured forward even after the time reversal. When η = 0, the time evolution after T makes the wavefunction at t = 2 T identical to the one at t = 0, and we find I = 0, implying a null irreversibility or a complete reversibility. On the other hand, as η is increased from zero, the reversibility becomes weaker, and we observe enhancement of the irreversibility. We find that I linearly increases with increasing η in the weakly-perturbed region, and that the irreversibility is much stronger in the WS network than in the local regular network.

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.

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.

A parallel implementation of the network identification by multiple regression (NIR) algorithm to reverse-engineer regulatory gene networks.

PubMed

Gregoretti, Francesco; Belcastro, Vincenzo; di Bernardo, Diego; Oliva, Gennaro

2010-04-21

The reverse engineering of gene regulatory networks using gene expression profile data has become crucial to gain novel biological knowledge. Large amounts of data that need to be analyzed are currently being produced due to advances in microarray technologies. Using current reverse engineering algorithms to analyze large data sets can be very computational-intensive. These emerging computational requirements can be met using parallel computing techniques. It has been shown that the Network Identification by multiple Regression (NIR) algorithm performs better than the other ready-to-use reverse engineering software. However it cannot be used with large networks with thousands of nodes--as is the case in biological networks--due to the high time and space complexity. In this work we overcome this limitation by designing and developing a parallel version of the NIR algorithm. The new implementation of the algorithm reaches a very good accuracy even for large gene networks, improving our understanding of the gene regulatory networks that is crucial for a wide range of biomedical applications.

Protocol vulnerability detection based on network traffic analysis and binary reverse engineering.

PubMed

Wen, Shameng; Meng, Qingkun; Feng, Chao; Tang, Chaojing

2017-01-01

Network protocol vulnerability detection plays an important role in many domains, including protocol security analysis, application security, and network intrusion detection. In this study, by analyzing the general fuzzing method of network protocols, we propose a novel approach that combines network traffic analysis with the binary reverse engineering method. For network traffic analysis, the block-based protocol description language is introduced to construct test scripts, while the binary reverse engineering method employs the genetic algorithm with a fitness function designed to focus on code coverage. This combination leads to a substantial improvement in fuzz testing for network protocols. We build a prototype system and use it to test several real-world network protocol implementations. The experimental results show that the proposed approach detects vulnerabilities more efficiently and effectively than general fuzzing methods such as SPIKE.

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.

A gene network simulator to assess reverse engineering algorithms.

PubMed

Di Camillo, Barbara; Toffolo, Gianna; Cobelli, Claudio

2009-03-01

In the context of reverse engineering of biological networks, simulators are helpful to test and compare the accuracy of different reverse-engineering approaches in a variety of experimental conditions. A novel gene-network simulator is presented that resembles some of the main features of transcriptional regulatory networks related to topology, interaction among regulators of transcription, and expression dynamics. The simulator generates network topology according to the current knowledge of biological network organization, including scale-free distribution of the connectivity and clustering coefficient independent of the number of nodes in the network. It uses fuzzy logic to represent interactions among the regulators of each gene, integrated with differential equations to generate continuous data, comparable to real data for variety and dynamic complexity. Finally, the simulator accounts for saturation in the response to regulation and transcription activation thresholds and shows robustness to perturbations. It therefore provides a reliable and versatile test bed for reverse engineering algorithms applied to microarray data. Since the simulator describes regulatory interactions and expression dynamics as two distinct, although interconnected aspects of regulation, it can also be used to test reverse engineering approaches that use both microarray and protein-protein interaction data in the process of learning. A first software release is available at http://www.dei.unipd.it/~dicamill/software/netsim as an R programming language package.

Limitations of a metabolic network-based reverse ecology method for inferring host-pathogen interactions.

PubMed

Takemoto, Kazuhiro; Aie, Kazuki

2017-05-25

Host-pathogen interactions are important in a wide range of research fields. Given the importance of metabolic crosstalk between hosts and pathogens, a metabolic network-based reverse ecology method was proposed to infer these interactions. However, the validity of this method remains unclear because of the various explanations presented and the influence of potentially confounding factors that have thus far been neglected. We re-evaluated the importance of the reverse ecology method for evaluating host-pathogen interactions while statistically controlling for confounding effects using oxygen requirement, genome, metabolic network, and phylogeny data. Our data analyses showed that host-pathogen interactions were more strongly influenced by genome size, primary network parameters (e.g., number of edges), oxygen requirement, and phylogeny than the reserve ecology-based measures. These results indicate the limitations of the reverse ecology method; however, they do not discount the importance of adopting reverse ecology approaches altogether. Rather, we highlight the need for developing more suitable methods for inferring host-pathogen interactions and conducting more careful examinations of the relationships between metabolic networks and host-pathogen interactions.

Reverse engineering biological networks :applications in immune responses to bio-toxins.

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

Martino, Anthony A.; Sinclair, Michael B.; Davidson, George S.

Our aim is to determine the network of events, or the regulatory network, that defines an immune response to a bio-toxin. As a model system, we are studying T cell regulatory network triggered through tyrosine kinase receptor activation using a combination of pathway stimulation and time-series microarray experiments. Our approach is composed of five steps (1) microarray experiments and data error analysis, (2) data clustering, (3) data smoothing and discretization, (4) network reverse engineering, and (5) network dynamics analysis and fingerprint identification. The technological outcome of this study is a suite of experimental protocols and computational tools that reverse engineermore » regulatory networks provided gene expression data. The practical biological outcome of this work is an immune response fingerprint in terms of gene expression levels. Inferring regulatory networks from microarray data is a new field of investigation that is no more than five years old. To the best of our knowledge, this work is the first attempt that integrates experiments, error analyses, data clustering, inference, and network analysis to solve a practical problem. Our systematic approach of counting, enumeration, and sampling networks matching experimental data is new to the field of network reverse engineering. The resulting mathematical analyses and computational tools lead to new results on their own and should be useful to others who analyze and infer networks.« less

Dynamic modeling and optimization for space logistics using time-expanded networks

NASA Astrophysics Data System (ADS)

Ho, Koki; de Weck, Olivier L.; Hoffman, Jeffrey A.; Shishko, Robert

2014-12-01

This research develops a dynamic logistics network formulation for lifecycle optimization of mission sequences as a system-level integrated method to find an optimal combination of technologies to be used at each stage of the campaign. This formulation can find the optimal transportation architecture considering its technology trades over time. The proposed methodologies are inspired by the ground logistics analysis techniques based on linear programming network optimization. Particularly, the time-expanded network and its extension are developed for dynamic space logistics network optimization trading the quality of the solution with the computational load. In this paper, the methodologies are applied to a human Mars exploration architecture design problem. The results reveal multiple dynamic system-level trades over time and give recommendation of the optimal strategy for the human Mars exploration architecture. The considered trades include those between In-Situ Resource Utilization (ISRU) and propulsion technologies as well as the orbit and depot location selections over time. This research serves as a precursor for eventual permanent settlement and colonization of other planets by humans and us becoming a multi-planet species.

Organizational Analysis of Energy Manpower Requirements in the United States Navy

DTIC Science & Technology

2013-06-01

ix LIST OF FIGURES Figure 1.  A 1.5 Megawatt wind turbine set up at the Marine Corps Logistics Base in Barstow, CA. (From Flores, 2010...Figure 1. A 1.5 Megawatt wind turbine set up at the Marine Corps Logistics Base in Barstow, CA. (From Flores, 2010) 9 In an effort to capture...electronic and information warfare systems ) (h ) Network Engineering (including wireless networks, sensor networks, high speed data networking, and

Tough Self-Healing Elastomers by Molecular Enforced Integration of Covalent and Reversible Networks.

PubMed

Wu, Jinrong; Cai, Li-Heng; Weitz, David A

2017-10-01

Self-healing polymers crosslinked by solely reversible bonds are intrinsically weaker than common covalently crosslinked networks. Introducing covalent crosslinks into a reversible network would improve mechanical strength. It is challenging, however, to apply this concept to "dry" elastomers, largely because reversible crosslinks such as hydrogen bonds are often polar motifs, whereas covalent crosslinks are nonpolar motifs. These two types of bonds are intrinsically immiscible without cosolvents. Here, we design and fabricate a hybrid polymer network by crosslinking randomly branched polymers carrying motifs that can form both reversible hydrogen bonds and permanent covalent crosslinks. The randomly branched polymer links such two types of bonds and forces them to mix on the molecular level without cosolvents. This enables a hybrid "dry" elastomer that is very tough with fracture energy 13500 Jm -2 comparable to that of natural rubber. Moreover, the elastomer can self-heal at room temperature with a recovered tensile strength 4 MPa, which is 30% of its original value, yet comparable to the pristine strength of existing self-healing polymers. The concept of forcing covalent and reversible bonds to mix at molecular scale to create a homogenous network is quite general and should enable development of tough, self-healing polymers of practical usage. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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.

Efficient Reverse-Engineering of a Developmental Gene Regulatory Network

PubMed Central

Cicin-Sain, Damjan; Ashyraliyev, Maksat; Jaeger, Johannes

2012-01-01

Understanding the complex regulatory networks underlying development and evolution of multi-cellular organisms is a major problem in biology. Computational models can be used as tools to extract the regulatory structure and dynamics of such networks from gene expression data. This approach is called reverse engineering. It has been successfully applied to many gene networks in various biological systems. However, to reconstitute the structure and non-linear dynamics of a developmental gene network in its spatial context remains a considerable challenge. Here, we address this challenge using a case study: the gap gene network involved in segment determination during early development of Drosophila melanogaster. A major problem for reverse-engineering pattern-forming networks is the significant amount of time and effort required to acquire and quantify spatial gene expression data. We have developed a simplified data processing pipeline that considerably increases the throughput of the method, but results in data of reduced accuracy compared to those previously used for gap gene network inference. We demonstrate that we can infer the correct network structure using our reduced data set, and investigate minimal data requirements for successful reverse engineering. Our results show that timing and position of expression domain boundaries are the crucial features for determining regulatory network structure from data, while it is less important to precisely measure expression levels. Based on this, we define minimal data requirements for gap gene network inference. Our results demonstrate the feasibility of reverse-engineering with much reduced experimental effort. This enables more widespread use of the method in different developmental contexts and organisms. Such systematic application of data-driven models to real-world networks has enormous potential. Only the quantitative investigation of a large number of developmental gene regulatory networks will allow us to discover whether there are rules or regularities governing development and evolution of complex multi-cellular organisms. PMID:22807664

Phase-synchronisation in continuous flow models of production networks

NASA Astrophysics Data System (ADS)

Scholz-Reiter, Bernd; Tervo, Jan Topi; Freitag, Michael

2006-04-01

To improve their position at the market, many companies concentrate on their core competences and hence cooperate with suppliers and distributors. Thus, between many independent companies strong linkages develop and production and logistics networks emerge. These networks are characterised by permanently increasing complexity, and are nowadays forced to adapt to dynamically changing markets. This factor complicates an enterprise-spreading production planning and control enormously. Therefore, a continuous flow model for production networks will be derived regarding these special logistic problems. Furthermore, phase-synchronisation effects will be presented and their dependencies to the set of network parameters will be investigated.

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.

Stakeholder engagement in quattro helix model for mobile phone reverse logistics in Indonesia: a conceptual framework

NASA Astrophysics Data System (ADS)

Maheswari, H.; Yudoko, G.; Adhiutama, A.

2017-12-01

The number of e-waste from mobile phone industry is still dominating until now. This is happened because there is no mutual commitment from all of parties i.e. businesses, government, and societies to reduce the use of mobile phone that has the shortest product life cycle. There are many researches study about firms’ motivation and government’s role, other discuss about actions of communities in supporting reverse logistics implementation. Unfortunately, research about engagement mechanism that involving all parties is still rare. Therefore, it is important to find the engagement model through this conceptual paper and it is expected useful to build the novel model. Through literature review, the results of this research are establishing the Quattro helix model as the appropriate structure to build the robust team by exploring stakeholder theories; mapping the engagement model either in form of collaboration or participation that consider stakeholders’ role and motivation and finding six types of engagement that consider their interest; and determining the novel model of engagement through Quattro helix model for implementing reverse logistics in handling e-waste by describing the linkage and the gaps among existing model.

Chimera states in networks of logistic maps with hierarchical connectivities

NASA Astrophysics Data System (ADS)

zur Bonsen, Alexander; Omelchenko, Iryna; Zakharova, Anna; Schöll, Eckehard

2018-04-01

Chimera states are complex spatiotemporal patterns consisting of coexisting domains of coherence and incoherence. We study networks of nonlocally coupled logistic maps and analyze systematically how the dilution of the network links influences the appearance of chimera patterns. The network connectivities are constructed using an iterative Cantor algorithm to generate fractal (hierarchical) connectivities. Increasing the hierarchical level of iteration, we compare the resulting spatiotemporal patterns. We demonstrate that a high clustering coefficient and symmetry of the base pattern promotes chimera states, and asymmetric connectivities result in complex nested chimera patterns.

A novel hybrid method of beta-turn identification in protein using binary logistic regression and neural network

PubMed Central

Asghari, Mehdi Poursheikhali; Hayatshahi, Sayyed Hamed Sadat; Abdolmaleki, Parviz

2012-01-01

From both the structural and functional points of view, β-turns play important biological roles in proteins. In the present study, a novel two-stage hybrid procedure has been developed to identify β-turns in proteins. Binary logistic regression was initially used for the first time to select significant sequence parameters in identification of β-turns due to a re-substitution test procedure. Sequence parameters were consisted of 80 amino acid positional occurrences and 20 amino acid percentages in sequence. Among these parameters, the most significant ones which were selected by binary logistic regression model, were percentages of Gly, Ser and the occurrence of Asn in position i+2, respectively, in sequence. These significant parameters have the highest effect on the constitution of a β-turn sequence. A neural network model was then constructed and fed by the parameters selected by binary logistic regression to build a hybrid predictor. The networks have been trained and tested on a non-homologous dataset of 565 protein chains. With applying a nine fold cross-validation test on the dataset, the network reached an overall accuracy (Qtotal) of 74, which is comparable with results of the other β-turn prediction methods. In conclusion, this study proves that the parameter selection ability of binary logistic regression together with the prediction capability of neural networks lead to the development of more precise models for identifying β-turns in proteins. PMID:27418910

A novel hybrid method of beta-turn identification in protein using binary logistic regression and neural network.

PubMed

Asghari, Mehdi Poursheikhali; Hayatshahi, Sayyed Hamed Sadat; Abdolmaleki, Parviz

2012-01-01

From both the structural and functional points of view, β-turns play important biological roles in proteins. In the present study, a novel two-stage hybrid procedure has been developed to identify β-turns in proteins. Binary logistic regression was initially used for the first time to select significant sequence parameters in identification of β-turns due to a re-substitution test procedure. Sequence parameters were consisted of 80 amino acid positional occurrences and 20 amino acid percentages in sequence. Among these parameters, the most significant ones which were selected by binary logistic regression model, were percentages of Gly, Ser and the occurrence of Asn in position i+2, respectively, in sequence. These significant parameters have the highest effect on the constitution of a β-turn sequence. A neural network model was then constructed and fed by the parameters selected by binary logistic regression to build a hybrid predictor. The networks have been trained and tested on a non-homologous dataset of 565 protein chains. With applying a nine fold cross-validation test on the dataset, the network reached an overall accuracy (Qtotal) of 74, which is comparable with results of the other β-turn prediction methods. In conclusion, this study proves that the parameter selection ability of binary logistic regression together with the prediction capability of neural networks lead to the development of more precise models for identifying β-turns in proteins.

Improving link prediction in complex networks by adaptively exploiting multiple structural features of networks

NASA Astrophysics Data System (ADS)

Ma, Chuang; Bao, Zhong-Kui; Zhang, Hai-Feng

2017-10-01

So far, many network-structure-based link prediction methods have been proposed. However, these methods only highlight one or two structural features of networks, and then use the methods to predict missing links in different networks. The performances of these existing methods are not always satisfied in all cases since each network has its unique underlying structural features. In this paper, by analyzing different real networks, we find that the structural features of different networks are remarkably different. In particular, even in the same network, their inner structural features are utterly different. Therefore, more structural features should be considered. However, owing to the remarkably different structural features, the contributions of different features are hard to be given in advance. Inspired by these facts, an adaptive fusion model regarding link prediction is proposed to incorporate multiple structural features. In the model, a logistic function combing multiple structural features is defined, then the weight of each feature in the logistic function is adaptively determined by exploiting the known structure information. Last, we use the "learnt" logistic function to predict the connection probabilities of missing links. According to our experimental results, we find that the performance of our adaptive fusion model is better than many similarity indices.

Variable neighborhood search for reverse engineering of gene regulatory networks.

PubMed

Nicholson, Charles; Goodwin, Leslie; Clark, Corey

2017-01-01

A new search heuristic, Divided Neighborhood Exploration Search, designed to be used with inference algorithms such as Bayesian networks to improve on the reverse engineering of gene regulatory networks is presented. The approach systematically moves through the search space to find topologies representative of gene regulatory networks that are more likely to explain microarray data. In empirical testing it is demonstrated that the novel method is superior to the widely employed greedy search techniques in both the quality of the inferred networks and computational time. Copyright © 2016 Elsevier Inc. All rights reserved.

PageRank and rank-reversal dependence on the damping factor

NASA Astrophysics Data System (ADS)

Son, S.-W.; Christensen, C.; Grassberger, P.; Paczuski, M.

2012-12-01

PageRank (PR) is an algorithm originally developed by Google to evaluate the importance of web pages. Considering how deeply rooted Google's PR algorithm is to gathering relevant information or to the success of modern businesses, the question of rank stability and choice of the damping factor (a parameter in the algorithm) is clearly important. We investigate PR as a function of the damping factor d on a network obtained from a domain of the World Wide Web, finding that rank reversal happens frequently over a broad range of PR (and of d). We use three different correlation measures, Pearson, Spearman, and Kendall, to study rank reversal as d changes, and we show that the correlation of PR vectors drops rapidly as d changes from its frequently cited value, d0=0.85. Rank reversal is also observed by measuring the Spearman and Kendall rank correlation, which evaluate relative ranks rather than absolute PR. Rank reversal happens not only in directed networks containing rank sinks but also in a single strongly connected component, which by definition does not contain any sinks. We relate rank reversals to rank pockets and bottlenecks in the directed network structure. For the network studied, the relative rank is more stable by our measures around d=0.65 than at d=d0.

DEFINING THE PLAYERS IN HIGHER-ORDER NETWORKS: PREDICTIVE MODELING FOR REVERSE ENGINEERING FUNCTIONAL INFLUENCE NETWORKS

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

McDermott, Jason E.; Costa, Michelle N.; Stevens, S.L.

A difficult problem that is currently growing rapidly due to the sharp increase in the amount of high-throughput data available for many systems is that of determining useful and informative causative influence networks. These networks can be used to predict behavior given observation of a small number of components, predict behavior at a future time point, or identify components that are critical to the functioning of the system under particular conditions. In these endeavors incorporating observations of systems from a wide variety of viewpoints can be particularly beneficial, but has often been undertaken with the objective of inferring networks thatmore » are generally applicable. The focus of the current work is to integrate both general observations and measurements taken for a particular pathology, that of ischemic stroke, to provide improved ability to produce useful predictions of systems behavior. A number of hybrid approaches have recently been proposed for network generation in which the Gene Ontology is used to filter or enrich network links inferred from gene expression data through reverse engineering methods. These approaches have been shown to improve the biological plausibility of the inferred relationships determined, but still treat knowledge-based and machine-learning inferences as incommensurable inputs. In this paper, we explore how further improvements may be achieved through a full integration of network inference insights achieved through application of the Gene Ontology and reverse engineering methods with specific reference to the construction of dynamic models of transcriptional regulatory networks. We show that integrating two approaches to network construction, one based on reverse-engineering from conditional transcriptional data, one based on reverse-engineering from in situ hybridization data, and another based on functional associations derived from Gene Ontology, using probabilities can improve results of clustering as evaluated by a predictive model of transcriptional expression levels.« less

Optimal design of reverse osmosis module networks

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

Maskan, F.; Wiley, D.E.; Johnston, L.P.M.

2000-05-01

The structure of individual reverse osmosis modules, the configuration of the module network, and the operating conditions were optimized for seawater and brackish water desalination. The system model included simple mathematical equations to predict the performance of the reverse osmosis modules. The optimization problem was formulated as a constrained multivariable nonlinear optimization. The objective function was the annual profit for the system, consisting of the profit obtained from the permeate, capital cost for the process units, and operating costs associated with energy consumption and maintenance. Optimization of several dual-stage reverse osmosis systems were investigated and compared. It was found thatmore » optimal network designs are the ones that produce the most permeate. It may be possible to achieve economic improvements by refining current membrane module designs and their operating pressures.« less

Blackmail propagation on small-world networks

NASA Astrophysics Data System (ADS)

Shao, Zhi-Gang; Jian-Ping Sang; Zou, Xian-Wu; Tan, Zhi-Jie; Jin, Zhun-Zhi

2005-06-01

The dynamics of the blackmail propagation model based on small-world networks is investigated. It is found that for a given transmitting probability λ the dynamical behavior of blackmail propagation transits from linear growth type to logistical growth one with the network randomness p increases. The transition takes place at the critical network randomness pc=1/N, where N is the total number of nodes in the network. For a given network randomness p the dynamical behavior of blackmail propagation transits from exponential decrease type to logistical growth one with the transmitting probability λ increases. The transition occurs at the critical transmitting probability λc=1/, where is the average number of the nearest neighbors. The present work will be useful for understanding computer virus epidemics and other spreading phenomena on communication and social networks.

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.

The Logistics Knowledge Portal: Gateway to More Individualized Learning in Logistics.

ERIC Educational Resources Information Center

Neumann, Gaby; Krzyzaniak, Stanislaw; Lassen, Carl Christian

This paper describes a research and development project initiated by a network of European logistics educators to promote all types, forms, and levels of logistics education by benefiting from the educational potential of multimedia/hypermedia as well as information technology and telecommunications. The main outcome of this project will be a…

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.

Reverse Engineering of Modified Genes by Bayesian Network Analysis Defines Molecular Determinants Critical to the Development of Glioblastoma

PubMed Central

Kunkle, Brian W.; Yoo, Changwon; Roy, Deodutta

2013-01-01

In this study we have identified key genes that are critical in development of astrocytic tumors. Meta-analysis of microarray studies which compared normal tissue to astrocytoma revealed a set of 646 differentially expressed genes in the majority of astrocytoma. Reverse engineering of these 646 genes using Bayesian network analysis produced a gene network for each grade of astrocytoma (Grade I–IV), and ‘key genes’ within each grade were identified. Genes found to be most influential to development of the highest grade of astrocytoma, Glioblastoma multiforme were: COL4A1, EGFR, BTF3, MPP2, RAB31, CDK4, CD99, ANXA2, TOP2A, and SERBP1. All of these genes were up-regulated, except MPP2 (down regulated). These 10 genes were able to predict tumor status with 96–100% confidence when using logistic regression, cross validation, and the support vector machine analysis. Markov genes interact with NFkβ, ERK, MAPK, VEGF, growth hormone and collagen to produce a network whose top biological functions are cancer, neurological disease, and cellular movement. Three of the 10 genes - EGFR, COL4A1, and CDK4, in particular, seemed to be potential ‘hubs of activity’. Modified expression of these 10 Markov Blanket genes increases lifetime risk of developing glioblastoma compared to the normal population. The glioblastoma risk estimates were dramatically increased with joint effects of 4 or more than 4 Markov Blanket genes. Joint interaction effects of 4, 5, 6, 7, 8, 9 or 10 Markov Blanket genes produced 9, 13, 20.9, 26.7, 52.8, 53.2, 78.1 or 85.9%, respectively, increase in lifetime risk of developing glioblastoma compared to normal population. In summary, it appears that modified expression of several ‘key genes’ may be required for the development of glioblastoma. Further studies are needed to validate these ‘key genes’ as useful tools for early detection and novel therapeutic options for these tumors. PMID:23737970

75 FR 76037 - HAVI Logistics, North America a Subsidiary of HAVI Group, LP Including On-Site Leased Workers of...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-12-07

... Logistics, North America a Subsidiary of HAVI Group, LP Including On-Site Leased Workers of Express Personnel Services and the La Salle Network, Bloomingdale, IL; Havi Logistics, North America, Lisle, IL..., applicable to workers of HAVI Logistics, North America, a subsidiary of HAVI Group, LP, including on-site...

MORE: mixed optimization for reverse engineering--an application to modeling biological networks response via sparse systems of nonlinear differential equations.

PubMed

Sambo, Francesco; de Oca, Marco A Montes; Di Camillo, Barbara; Toffolo, Gianna; Stützle, Thomas

2012-01-01

Reverse engineering is the problem of inferring the structure of a network of interactions between biological variables from a set of observations. In this paper, we propose an optimization algorithm, called MORE, for the reverse engineering of biological networks from time series data. The model inferred by MORE is a sparse system of nonlinear differential equations, complex enough to realistically describe the dynamics of a biological system. MORE tackles separately the discrete component of the problem, the determination of the biological network topology, and the continuous component of the problem, the strength of the interactions. This approach allows us both to enforce system sparsity, by globally constraining the number of edges, and to integrate a priori information about the structure of the underlying interaction network. Experimental results on simulated and real-world networks show that the mixed discrete/continuous optimization approach of MORE significantly outperforms standard continuous optimization and that MORE is competitive with the state of the art in terms of accuracy of the inferred networks.

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.

Dentist practice patterns and therapeutic confidence in the treatment of pain related to temporomandibular disorders in a dental practice-based research network

PubMed Central

Kakudate, Naoki; Yokoyama, Yoko; Sumida, Futoshi; Matsumoto, Yuki; Gordan, Valeria V; Gilbert, Gregg H; Velly, Ana M; Schiffman, Eric L

2018-01-01

Aims This study quantified the practice pattern of Japanese dentists in the management of pain related to temporomandibular disorders (TMDs), and identified associations between dentist characteristics and the decision to perform occlusal adjustment for TMD-related pain. Methods A cross-sectional study was conducted consisting of a questionnaire survey of dentists affiliated with the Dental Practice-based Research Network Japan (JDPBRN) (n=148). Participants were asked how they diagnosed and treated TMD-related pain. Associations between dentist characteristics and their decision to perform occlusal adjustment were analyzed via multiple logistic regression. Results 113 clinicians responded the questionnaire for a 76% response rate. 81% of the participants (n=89) treated TMDs during the previous year. Dentists treated an average of 1.9±1.8 (SD) patients with TMD-related pain monthly. Most JDPBRN dentists used similar diagnostic protocols, including questions and examinations. The most frequent treatments were splints or mouthguards (97%), medications (85%), and self-care (69%). Fifty eight percent of the participants performed occlusal adjustment for TMD-related pain. Multiple logistic regression analysis identified two factors significantly associated with the decision to perform occlusal adjustment. Odds ratios (95%CI) were “dentist lack of confidence in curing TMD-related acute pain”, 5.60 (1.260–24.861) and “proportion of patients with severe TMD-related pain”, 0.95 (0.909–0.999). Conclusions The most common treatments for TMD-related pain were reversible treatments. However, over half of dentists performed occlusal adjustment for TMD-related pain. There was a significant association between the decision to perform occlusal adjustment and lack of therapeutic confidence. The results of this study suggest that an evidence-practice gap exists regarding occlusal adjustment for TMD-related pain. PMID:28437512

Dynamic Network Logistic Regression: A Logistic Choice Analysis of Inter- and Intra-Group Blog Citation Dynamics in the 2004 US Presidential Election

PubMed Central

2013-01-01

Methods for analysis of network dynamics have seen great progress in the past decade. This article shows how Dynamic Network Logistic Regression techniques (a special case of the Temporal Exponential Random Graph Models) can be used to implement decision theoretic models for network dynamics in a panel data context. We also provide practical heuristics for model building and assessment. We illustrate the power of these techniques by applying them to a dynamic blog network sampled during the 2004 US presidential election cycle. This is a particularly interesting case because it marks the debut of Internet-based media such as blogs and social networking web sites as institutionally recognized features of the American political landscape. Using a longitudinal sample of all Democratic National Convention/Republican National Convention–designated blog citation networks, we are able to test the influence of various strategic, institutional, and balance-theoretic mechanisms as well as exogenous factors such as seasonality and political events on the propensity of blogs to cite one another over time. Using a combination of deviance-based model selection criteria and simulation-based model adequacy tests, we identify the combination of processes that best characterizes the choice behavior of the contending blogs. PMID:24143060

Using ROC curves to compare neural networks and logistic regression for modeling individual noncatastrophic tree mortality

Treesearch

Susan L. King

2003-01-01

The performance of two classifiers, logistic regression and neural networks, are compared for modeling noncatastrophic individual tree mortality for 21 species of trees in West Virginia. The output of the classifier is usually a continuous number between 0 and 1. A threshold is selected between 0 and 1 and all of the trees below the threshold are classified as...

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 Predictive Approach to Network Reverse-Engineering

NASA Astrophysics Data System (ADS)

Wiggins, Chris

2005-03-01

A central challenge of systems biology is the ``reverse engineering" of transcriptional networks: inferring which genes exert regulatory control over which other genes. Attempting such inference at the genomic scale has only recently become feasible, via data-intensive biological innovations such as DNA microrrays (``DNA chips") and the sequencing of whole genomes. In this talk we present a predictive approach to network reverse-engineering, in which we integrate DNA chip data and sequence data to build a model of the transcriptional network of the yeast S. cerevisiae capable of predicting the response of genes in unseen experiments. The technique can also be used to extract ``motifs,'' sequence elements which act as binding sites for regulatory proteins. We validate by a number of approaches and present comparison of theoretical prediction vs. experimental data, along with biological interpretations of the resulting model. En route, we will illustrate some basic notions in statistical learning theory (fitting vs. over-fitting; cross- validation; assessing statistical significance), highlighting ways in which physicists can make a unique contribution in data- driven approaches to reverse engineering.

Artificial Neural Network for the Prediction of Chromosomal Abnormalities in Azoospermic Males.

PubMed

Akinsal, Emre Can; Haznedar, Bulent; Baydilli, Numan; Kalinli, Adem; Ozturk, Ahmet; Ekmekçioğlu, Oğuz

2018-02-04

To evaluate whether an artifical neural network helps to diagnose any chromosomal abnormalities in azoospermic males. The data of azoospermic males attending to a tertiary academic referral center were evaluated retrospectively. Height, total testicular volume, follicle stimulating hormone, luteinising hormone, total testosterone and ejaculate volume of the patients were used for the analyses. In artificial neural network, the data of 310 azoospermics were used as the education and 115 as the test set. Logistic regression analyses and discriminant analyses were performed for statistical analyses. The tests were re-analysed with a neural network. Both logistic regression analyses and artificial neural network predicted the presence or absence of chromosomal abnormalities with more than 95% accuracy. The use of artificial neural network model has yielded satisfactory results in terms of distinguishing patients whether they have any chromosomal abnormality or not.

Effects of Edge Directions on the Structural Controllability of Complex Networks

PubMed Central

Xiao, Yandong; Lao, Songyang; Hou, Lvlin; Small, Michael; Bai, Liang

2015-01-01

Recent advances indicate that assigning or reversing edge direction can significantly improve the structural controllability of complex networks. For directed networks, approaching the optimal structural controllability can be achieved by detecting and reversing certain “inappropriate” edge directions. However, the existence of multiple sets of “inappropriate” edge directions suggests that different edges have different effects on optimal controllability—that is, different combinations of edges can be reversed to achieve the same structural controllability. Therefore, we classify edges into three categories based on their direction: critical, redundant and intermittent. We then investigate the effects of changing these edge directions on network controllability, and demonstrate that the existence of more critical edge directions implies not only a lower cost of modifying inappropriate edges but also better controllability. Motivated by this finding, we present a simple edge orientation method aimed at producing more critical edge directions—utilizing only local information—which achieves near optimal controllability. Furthermore, we explore the effects of edge direction on the controllability of several real networks. PMID:26281042

Effects of Edge Directions on the Structural Controllability of Complex Networks.

PubMed

Xiao, Yandong; Lao, Songyang; Hou, Lvlin; Small, Michael; Bai, Liang

2015-01-01

Recent advances indicate that assigning or reversing edge direction can significantly improve the structural controllability of complex networks. For directed networks, approaching the optimal structural controllability can be achieved by detecting and reversing certain "inappropriate" edge directions. However, the existence of multiple sets of "inappropriate" edge directions suggests that different edges have different effects on optimal controllability-that is, different combinations of edges can be reversed to achieve the same structural controllability. Therefore, we classify edges into three categories based on their direction: critical, redundant and intermittent. We then investigate the effects of changing these edge directions on network controllability, and demonstrate that the existence of more critical edge directions implies not only a lower cost of modifying inappropriate edges but also better controllability. Motivated by this finding, we present a simple edge orientation method aimed at producing more critical edge directions-utilizing only local information-which achieves near optimal controllability. Furthermore, we explore the effects of edge direction on the controllability of several real networks.

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.

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.

Science of Test Research Consortium: Year Two Final Report

DTIC Science & Technology

2012-10-02

July 2012. Analysis of an Intervention for Small Unmanned Aerial System ( SUAS ) Accidents, submitted to Quality Engineering, LQEN-2012-0056. Stone... Systems Engineering. Wolf, S. E., R. R. Hill, and J. J. Pignatiello. June 2012. Using Neural Networks and Logistic Regression to Model Small Unmanned ...Human Retina. 6. Wolf, S. E. March 2012. Modeling Small Unmanned Aerial System Mishaps using Logistic Regression and Artificial Neural Networks. 7

Parameter estimation in spiking neural networks: a reverse-engineering approach.

PubMed

Rostro-Gonzalez, H; Cessac, B; Vieville, T

2012-04-01

This paper presents a reverse engineering approach for parameter estimation in spiking neural networks (SNNs). We consider the deterministic evolution of a time-discretized network with spiking neurons, where synaptic transmission has delays, modeled as a neural network of the generalized integrate and fire type. Our approach aims at by-passing the fact that the parameter estimation in SNN results in a non-deterministic polynomial-time hard problem when delays are to be considered. Here, this assumption has been reformulated as a linear programming (LP) problem in order to perform the solution in a polynomial time. Besides, the LP problem formulation makes the fact that the reverse engineering of a neural network can be performed from the observation of the spike times explicit. Furthermore, we point out how the LP adjustment mechanism is local to each neuron and has the same structure as a 'Hebbian' rule. Finally, we present a generalization of this approach to the design of input-output (I/O) transformations as a practical method to 'program' a spiking network, i.e. find a set of parameters allowing us to exactly reproduce the network output, given an input. Numerical verifications and illustrations are provided.

Covalent adaptable networks: smart, reconfigurable and responsive network systems.

PubMed

Kloxin, Christopher J; Bowman, Christopher N

2013-09-07

Covalently crosslinked materials, classically referred to as thermosets, represent a broad class of elastic materials that readily retain their shape and molecular architecture through covalent bonds that are ubiquitous throughout the network structure. These materials, in particular in their swollen gel state, have been widely used as stimuli responsive materials with their ability to change volume in response to changes in temperature, pH, or other solvent conditions and have also been used in shape memory applications. However, the existence of a permanent, unalterable shape and structure dictated by the covalently crosslinked structure has dramatically limited their abilities in this and many other areas. These materials are not generally reconfigurable, recyclable, reprocessable, and have limited ability to alter permanently their stress state, topography, topology, or structure. Recently, a new paradigm has been explored in crosslinked polymers - that of covalent adaptable networks (CANs) in which covalently crosslinked networks are formed such that triggerable, reversible chemical structures persist throughout the network. These reversible covalent bonds can be triggered through molecular triggers, light or other incident radiation, or temperature changes. Upon application of this stimulus, rather than causing a temporary shape change, the CAN structure responds by permanently adjusting its structure through either reversible addition/condensation or through reversible bond exchange mechanisms, either of which allow the material to essentially reequilibrate to its new state and condition. Here, we provide a tutorial review on these materials and their responsiveness to applied stimuli. In particular, we review the broad classification of these materials, the nature of the chemical bonds that enable the adaptable structure, how the properties of these materials depend on the reversible structure, and how the application of a stimulus causes these materials to alter their shape, topography, and properties.

Causal Interrogation of Neuronal Networks and Behavior through Virally Transduced Ivermectin Receptors.

PubMed

Obenhaus, Horst A; Rozov, Andrei; Bertocchi, Ilaria; Tang, Wannan; Kirsch, Joachim; Betz, Heinrich; Sprengel, Rolf

2016-01-01

The causal interrogation of neuronal networks involved in specific behaviors requires the spatially and temporally controlled modulation of neuronal activity. For long-term manipulation of neuronal activity, chemogenetic tools provide a reasonable alternative to short-term optogenetic approaches. Here we show that virus mediated gene transfer of the ivermectin (IVM) activated glycine receptor mutant GlyRα1 (AG) can be used for the selective and reversible silencing of specific neuronal networks in mice. In the striatum, dorsal hippocampus, and olfactory bulb, GlyRα1 (AG) promoted IVM dependent effects in representative behavioral assays. Moreover, GlyRα1 (AG) mediated silencing had a strong and reversible impact on neuronal ensemble activity and c-Fos activation in the olfactory bulb. Together our results demonstrate that long-term, reversible and re-inducible neuronal silencing via GlyRα1 (AG) is a promising tool for the interrogation of network mechanisms underlying the control of behavior and memory formation.

Reverse engineering and analysis of large genome-scale gene networks

PubMed Central

Aluru, Maneesha; Zola, Jaroslaw; Nettleton, Dan; Aluru, Srinivas

2013-01-01

Reverse engineering the whole-genome networks of complex multicellular organisms continues to remain a challenge. While simpler models easily scale to large number of genes and gene expression datasets, more accurate models are compute intensive limiting their scale of applicability. To enable fast and accurate reconstruction of large networks, we developed Tool for Inferring Network of Genes (TINGe), a parallel mutual information (MI)-based program. The novel features of our approach include: (i) B-spline-based formulation for linear-time computation of MI, (ii) a novel algorithm for direct permutation testing and (iii) development of parallel algorithms to reduce run-time and facilitate construction of large networks. We assess the quality of our method by comparison with ARACNe (Algorithm for the Reconstruction of Accurate Cellular Networks) and GeneNet and demonstrate its unique capability by reverse engineering the whole-genome network of Arabidopsis thaliana from 3137 Affymetrix ATH1 GeneChips in just 9 min on a 1024-core cluster. We further report on the development of a new software Gene Network Analyzer (GeNA) for extracting context-specific subnetworks from a given set of seed genes. Using TINGe and GeNA, we performed analysis of 241 Arabidopsis AraCyc 8.0 pathways, and the results are made available through the web. PMID:23042249

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.

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.

Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression.

PubMed

Westreich, Daniel; Lessler, Justin; Funk, Michele Jonsson

2010-08-01

Propensity scores for the analysis of observational data are typically estimated using logistic regression. Our objective in this review was to assess machine learning alternatives to logistic regression, which may accomplish the same goals but with fewer assumptions or greater accuracy. We identified alternative methods for propensity score estimation and/or classification from the public health, biostatistics, discrete mathematics, and computer science literature, and evaluated these algorithms for applicability to the problem of propensity score estimation, potential advantages over logistic regression, and ease of use. We identified four techniques as alternatives to logistic regression: neural networks, support vector machines, decision trees (classification and regression trees [CART]), and meta-classifiers (in particular, boosting). Although the assumptions of logistic regression are well understood, those assumptions are frequently ignored. All four alternatives have advantages and disadvantages compared with logistic regression. Boosting (meta-classifiers) and, to a lesser extent, decision trees (particularly CART), appear to be most promising for use in the context of propensity score analysis, but extensive simulation studies are needed to establish their utility in practice. Copyright (c) 2010 Elsevier Inc. All rights reserved.

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

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.

78 FR 57845 - Notice of Availability (NOA) for Strategic Network Optimization (SNO) Program Environmental...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-09-20

... (NOA) for Strategic Network Optimization (SNO) Program Environmental Assessment AGENCY: Defense Logistics Agency, DoD. ACTION: Notice of Availability (NOA) for Strategic Network Optimization (SNO) Program... implement the SNO initiative for improvements to material distribution network for the Department of Defense...

The Dropout Learning Algorithm

PubMed Central

Baldi, Pierre; Sadowski, Peter

2014-01-01

Dropout is a recently introduced algorithm for training neural network by randomly dropping units during training to prevent their co-adaptation. A mathematical analysis of some of the static and dynamic properties of dropout is provided using Bernoulli gating variables, general enough to accommodate dropout on units or connections, and with variable rates. The framework allows a complete analysis of the ensemble averaging properties of dropout in linear networks, which is useful to understand the non-linear case. The ensemble averaging properties of dropout in non-linear logistic networks result from three fundamental equations: (1) the approximation of the expectations of logistic functions by normalized geometric means, for which bounds and estimates are derived; (2) the algebraic equality between normalized geometric means of logistic functions with the logistic of the means, which mathematically characterizes logistic functions; and (3) the linearity of the means with respect to sums, as well as products of independent variables. The results are also extended to other classes of transfer functions, including rectified linear functions. Approximation errors tend to cancel each other and do not accumulate. Dropout can also be connected to stochastic neurons and used to predict firing rates, and to backpropagation by viewing the backward propagation as ensemble averaging in a dropout linear network. Moreover, the convergence properties of dropout can be understood in terms of stochastic gradient descent. Finally, for the regularization properties of dropout, the expectation of the dropout gradient is the gradient of the corresponding approximation ensemble, regularized by an adaptive weight decay term with a propensity for self-consistent variance minimization and sparse representations. PMID:24771879

From disaster to development: a systematic review of community-driven humanitarian logistics.

PubMed

Bealt, Jennifer; Mansouri, S Afshin

2018-01-01

A plethora of untapped resources exist within disaster-affected communities that can be used to address relief and development concerns. A systematic review of the literature relating to community participation in humanitarian logistics activities revealed that communities are able to form ad hoc networks that have the ability to meet a wide range of disaster management needs. These structures, characterised as Collaborative Aid Networks (CANs), have demonstrated efficient logistical capabilities exclusive of humanitarian organisations. This study proposes that CANs, as a result of their unique characteristics, present alternatives to established humanitarian approaches to logistics, while also mitigating the challenges commonly faced by traditional humanitarian organisations. Furthermore, CANs offer a more holistic, long-term approach to disaster management, owing to their impact on development through their involvement in humanitarian logistics. This research provides the foundation for further theoretical analysis of effective and efficient disaster management, and details opportunities for policy and practice. © 2018 The Author(s). Disasters © Overseas Development Institute, 2018.

Development and Validation of a Deep Neural Network Model for Prediction of Postoperative In-hospital Mortality.

PubMed

Lee, Christine K; Hofer, Ira; Gabel, Eilon; Baldi, Pierre; Cannesson, Maxime

2018-04-17

The authors tested the hypothesis that deep neural networks trained on intraoperative features can predict postoperative in-hospital mortality. The data used to train and validate the algorithm consists of 59,985 patients with 87 features extracted at the end of surgery. Feed-forward networks with a logistic output were trained using stochastic gradient descent with momentum. The deep neural networks were trained on 80% of the data, with 20% reserved for testing. The authors assessed improvement of the deep neural network by adding American Society of Anesthesiologists (ASA) Physical Status Classification and robustness of the deep neural network to a reduced feature set. The networks were then compared to ASA Physical Status, logistic regression, and other published clinical scores including the Surgical Apgar, Preoperative Score to Predict Postoperative Mortality, Risk Quantification Index, and the Risk Stratification Index. In-hospital mortality in the training and test sets were 0.81% and 0.73%. The deep neural network with a reduced feature set and ASA Physical Status classification had the highest area under the receiver operating characteristics curve, 0.91 (95% CI, 0.88 to 0.93). The highest logistic regression area under the curve was found with a reduced feature set and ASA Physical Status (0.90, 95% CI, 0.87 to 0.93). The Risk Stratification Index had the highest area under the receiver operating characteristics curve, at 0.97 (95% CI, 0.94 to 0.99). Deep neural networks can predict in-hospital mortality based on automatically extractable intraoperative data, but are not (yet) superior to existing methods.

Design of cold chain logistics remote monitoring system based on ZigBee and GPS location

NASA Astrophysics Data System (ADS)

Zong, Xiaoping; Shao, Heling

2017-03-01

This paper designed a remote monitoring system based on Bee Zig wireless sensor network and GPS positioning, according to the characteristics of cold chain logistics. The system consisted of the ZigBee network, gateway and monitoring center. ZigBee network temperature acquisition modules and GPS positioning acquisition module were responsible for data collection, and then send the data to the host computer through the GPRS network and Internet to realize remote monitoring of vehicle with functions of login permissions, temperature display, latitude and longitude display, historical data, real-time alarm and so on. Experiments showed that the system is stable, reliable and effective to realize the real-time remote monitoring of the vehicle in the process of cold chain transport.

Logistics Distribution Center Location Evaluation Based on Genetic Algorithm and Fuzzy Neural Network

NASA Astrophysics Data System (ADS)

Shao, Yuxiang; Chen, Qing; Wei, Zhenhua

Logistics distribution center location evaluation is a dynamic, fuzzy, open and complicated nonlinear system, which makes it difficult to evaluate the distribution center location by the traditional analysis method. The paper proposes a distribution center location evaluation system which uses the fuzzy neural network combined with the genetic algorithm. In this model, the neural network is adopted to construct the fuzzy system. By using the genetic algorithm, the parameters of the neural network are optimized and trained so as to improve the fuzzy system’s abilities of self-study and self-adaptation. At last, the sampled data are trained and tested by Matlab software. The simulation results indicate that the proposed identification model has very small errors.

Reversible Thermoset Adhesives

NASA Technical Reports Server (NTRS)

Mac Murray, Benjamin C. (Inventor); Tong, Tat H. (Inventor); Hreha, Richard D. (Inventor)

2016-01-01

Embodiments of a reversible thermoset adhesive formed by incorporating thermally-reversible cross-linking units and a method for making the reversible thermoset adhesive are provided. One approach to formulating reversible thermoset adhesives includes incorporating dienes, such as furans, and dienophiles, such as maleimides, into a polymer network as reversible covalent cross-links using Diels Alder cross-link formation between the diene and dienophile. The chemical components may be selected based on their compatibility with adhesive chemistry as well as their ability to undergo controlled, reversible cross-linking chemistry.

Quantum generalisation of feedforward neural networks

NASA Astrophysics Data System (ADS)

Wan, Kwok Ho; Dahlsten, Oscar; Kristjánsson, Hlér; Gardner, Robert; Kim, M. S.

2017-09-01

We propose a quantum generalisation of a classical neural network. The classical neurons are firstly rendered reversible by adding ancillary bits. Then they are generalised to being quantum reversible, i.e., unitary (the classical networks we generalise are called feedforward, and have step-function activation functions). The quantum network can be trained efficiently using gradient descent on a cost function to perform quantum generalisations of classical tasks. We demonstrate numerically that it can: (i) compress quantum states onto a minimal number of qubits, creating a quantum autoencoder, and (ii) discover quantum communication protocols such as teleportation. Our general recipe is theoretical and implementation-independent. The quantum neuron module can naturally be implemented photonically.

A swarm intelligence framework for reconstructing gene networks: searching for biologically plausible architectures.

PubMed

Kentzoglanakis, Kyriakos; Poole, Matthew

2012-01-01

In this paper, we investigate the problem of reverse engineering the topology of gene regulatory networks from temporal gene expression data. We adopt a computational intelligence approach comprising swarm intelligence techniques, namely particle swarm optimization (PSO) and ant colony optimization (ACO). In addition, the recurrent neural network (RNN) formalism is employed for modeling the dynamical behavior of gene regulatory systems. More specifically, ACO is used for searching the discrete space of network architectures and PSO for searching the corresponding continuous space of RNN model parameters. We propose a novel solution construction process in the context of ACO for generating biologically plausible candidate architectures. The objective is to concentrate the search effort into areas of the structure space that contain architectures which are feasible in terms of their topological resemblance to real-world networks. The proposed framework is initially applied to the reconstruction of a small artificial network that has previously been studied in the context of gene network reverse engineering. Subsequently, we consider an artificial data set with added noise for reconstructing a subnetwork of the genetic interaction network of S. cerevisiae (yeast). Finally, the framework is applied to a real-world data set for reverse engineering the SOS response system of the bacterium Escherichia coli. Results demonstrate the relative advantage of utilizing problem-specific knowledge regarding biologically plausible structural properties of gene networks over conducting a problem-agnostic search in the vast space of network architectures.

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.

Reconstruction of metabolic networks from high-throughput metabolite profiling data: in silico analysis of red blood cell metabolism.

PubMed

Nemenman, Ilya; Escola, G Sean; Hlavacek, William S; Unkefer, Pat J; Unkefer, Clifford J; Wall, Michael E

2007-12-01

We investigate the ability of algorithms developed for reverse engineering of transcriptional regulatory networks to reconstruct metabolic networks from high-throughput metabolite profiling data. For benchmarking purposes, we generate synthetic metabolic profiles based on a well-established model for red blood cell metabolism. A variety of data sets are generated, accounting for different properties of real metabolic networks, such as experimental noise, metabolite correlations, and temporal dynamics. These data sets are made available online. We use ARACNE, a mainstream algorithm for reverse engineering of transcriptional regulatory networks from gene expression data, to predict metabolic interactions from these data sets. We find that the performance of ARACNE on metabolic data is comparable to that on gene expression data.

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.

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.

Reverse Engineering of Genome-wide Gene Regulatory Networks from Gene Expression Data

PubMed Central

Liu, Zhi-Ping

2015-01-01

Transcriptional regulation plays vital roles in many fundamental biological processes. Reverse engineering of genome-wide regulatory networks from high-throughput transcriptomic data provides a promising way to characterize the global scenario of regulatory relationships between regulators and their targets. In this review, we summarize and categorize the main frameworks and methods currently available for inferring transcriptional regulatory networks from microarray gene expression profiling data. We overview each of strategies and introduce representative methods respectively. Their assumptions, advantages, shortcomings, and possible improvements and extensions are also clarified and commented. PMID:25937810

Logistic growth for the Nuzi cuneiform tablets: Analyzing family networks in ancient Mesopotamia

NASA Astrophysics Data System (ADS)

Ueda, Sumie; Makino, Kumi; Itoh, Yoshiaki; Tsuchiya, Takashi

2015-03-01

We reconstruct the published year of each cuneiform tablet of the Nuzi society in ancient Mesopotamia. The tablets are on land transaction, marriage, loan, slavery contracts, etc. The number of tablets seems to increase by logistic growth. It may show the dynamics of concentration of lands or other properties into few powerful families in a period of about sixty years and most of them are in about thirty years. We reconstruct family trees and social networks of Nuzi and estimate the published years of cuneiform tablets consistently with the trees and networks, formulating least squares problems with linear inequality constraints.

Efficient Green's Function Reaction Dynamics (GFRD) simulations for diffusion-limited, reversible reactions

NASA Astrophysics Data System (ADS)

Bashardanesh, Zahedeh; Lötstedt, Per

2018-03-01

In diffusion controlled reversible bimolecular reactions in three dimensions, a dissociation step is typically followed by multiple, rapid re-association steps slowing down the simulations of such systems. In order to improve the efficiency, we first derive an exact Green's function describing the rate at which an isolated pair of particles undergoing reversible bimolecular reactions and unimolecular decay separates beyond an arbitrarily chosen distance. Then the Green's function is used in an algorithm for particle-based stochastic reaction-diffusion simulations for prediction of the dynamics of biochemical networks. The accuracy and efficiency of the algorithm are evaluated using a reversible reaction and a push-pull chemical network. The computational work is independent of the rates of the re-associations.

When global rule reversal meets local task switching: The neural mechanisms of coordinated behavioral adaptation to instructed multi-level demand changes.

PubMed

Shi, Yiquan; Wolfensteller, Uta; Schubert, Torsten; Ruge, Hannes

2018-02-01

Cognitive flexibility is essential to cope with changing task demands and often it is necessary to adapt to combined changes in a coordinated manner. The present fMRI study examined how the brain implements such multi-level adaptation processes. Specifically, on a "local," hierarchically lower level, switching between two tasks was required across trials while the rules of each task remained unchanged for blocks of trials. On a "global" level regarding blocks of twelve trials, the task rules could reverse or remain the same. The current task was cued at the start of each trial while the current task rules were instructed before the start of a new block. We found that partly overlapping and partly segregated neural networks play different roles when coping with the combination of global rule reversal and local task switching. The fronto-parietal control network (FPN) supported the encoding of reversed rules at the time of explicit rule instruction. The same regions subsequently supported local task switching processes during actual implementation trials, irrespective of rule reversal condition. By contrast, a cortico-striatal network (CSN) including supplementary motor area and putamen was increasingly engaged across implementation trials and more so for rule reversal than for nonreversal blocks, irrespective of task switching condition. Together, these findings suggest that the brain accomplishes the coordinated adaptation to multi-level demand changes by distributing processing resources either across time (FPN for reversed rule encoding and later for task switching) or across regions (CSN for reversed rule implementation and FPN for concurrent task switching). © 2017 Wiley Periodicals, Inc.

Network modeling for reverse flows of end-of-life vehicles

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

Ene, Seval; Öztürk, Nursel

2015-04-15

Highlights: • We developed a network model for reverse flows of end-of-life vehicles. • The model considers all recovery operations for end-of-life vehicles. • A scenario-based model is used for uncertainty to improve real case applications. • The model is adequate to real case applications for end-of-life vehicles recovery. • Considerable insights are gained from the model by sensitivity analyses. - Abstract: Product recovery operations are of critical importance for the automotive industry in complying with environmental regulations concerning end-of-life products management. Manufacturers must take responsibility for their products over the entire life cycle. In this context, there is amore » need for network design methods for effectively managing recovery operations and waste. The purpose of this study is to develop a mathematical programming model for managing reverse flows in end-of-life vehicles’ recovery network. A reverse flow is the collection of used products from consumers and the transportation of these products for the purpose of recycling, reuse or disposal. The proposed model includes all operations in a product recovery and waste management network for used vehicles and reuse for vehicle parts such as collection, disassembly, refurbishing, processing (shredding), recycling, disposal and reuse of vehicle parts. The scope of the network model is to determine the numbers and locations of facilities in the network and the material flows between these facilities. The results show the performance of the model and its applicability for use in the planning of recovery operations in the automotive industry. The main objective of recovery and waste management is to maximize revenue and minimize pollution in end-of-life product operations. This study shows that with an accurate model, these activities may provide economic benefits and incentives in addition to protecting the environment.« less

Validation of long-term primary neuronal cultures and network activity through the integration of reversibly bonded microbioreactors and MEA substrates.

PubMed

Biffi, Emilia; Menegon, Andrea; Piraino, Francesco; Pedrocchi, Alessandra; Fiore, Gianfranco B; Rasponi, Marco

2012-01-01

In vitro recording of neuronal electrical activity is a widely used technique to understand brain functions and to study the effect of drugs on the central nervous system. The integration of microfluidic devices with microelectrode arrays (MEAs) enables the recording of networks activity in a controlled microenvironment. In this work, an integrated microfluidic system for neuronal cultures was developed, reversibly coupling a PDMS microfluidic device with a commercial flat MEA through magnetic forces. Neurons from mouse embryos were cultured in a 100 µm channel and their activity was followed up to 18 days in vitro. The maturation of the networks and their morphological and functional characteristics were comparable with those of networks cultured in macro-environments and described in literature. In this work, we successfully demonstrated the ability of long-term culturing of primary neuronal cells in a reversible bonded microfluidic device (based on magnetism) that will be fundamental for neuropharmacological studies. Copyright © 2011 Wiley Periodicals, Inc.

Improvement of logistics education from the point of view environmental management

NASA Astrophysics Data System (ADS)

Bányai, Á.

2009-04-01

The paper briefly presents the influence of environmental management on the improvement of the logistics education and research structure of the Department of Materials Handling and Logistics at the University of Miskolc, Hungary. The logistics, as an integrated science offers a very good possibility to demonstrate the effect of new innovative knowledge on the migration of the priorities of education and research of sciences. The importance of logistics in the field of recycling (or in wider sense in the field of environmental management) can be justified by the high proportion of logistic costs (as investment and operation costs) and these costs show that optimum logistic solutions are able to decrease the financial outcomes and lead to the establishment of a profitable system. Technological change constantly creates new demands on both education and research. The most important objective of the department is to create a unique logistics education in the country. For this reason the department offered up-to-date integrated knowledge at all level: undergraduate, master degree and PhD education. The integration of logistics means traditionally the joint use of technology of material handling, method of material flow, technology method of traffic, information technology, management sciences, production technology, marketing, market research, technology of services, mathematics and optimization, communication technology, system engineering, electronics and automation, mechatronics [1, 3]. The education and research portfolio of the department followed this tradition till 1993. The new lectures in the field of sustainability (logistics of recycling, logistics of quality management and recycling, closed loop economy, EU logistics or global logistics) became more and more important in the logistics education. The results of fast developments in closed loop economy, recycling, waste management, environmental protection are more and more used in the industry and this effected a revolutionary change in the education and research structure of logistics [2]. The European Community policy in the environment sectors aims at a high level of protection. Four principles were defined: the precautionary principle, the principle that preventive action should be taken, that environmental damages should as a priority be rectified at source and that the polluter should pay. All of these four principles have a very strong logistics background, especially in the field of import/export operations, traffic/transportation, inventory control, materials handling, fleet operations, customer service, supply chain management, distribution, strategic planning, warehousing, information systems of logistics, purchasing. These facts effect the development of different topics of logistics in each field of the education of the department: collection logistics of used products (especially WEEE), optimization of collection systems, design and control of disassembly systems, distribution of fractions of disassembled used products, design and control of recycling parks, possibilities of virtual networks in the field of recycling logistics, integration of logistics, recycling and total quality management, identification systems and recycling, etc. Within the framework of different supports our department has the opportunity to take part in European networks and research projects in the field of sustainability, environmental protection, recycling and closed loop economy. One of the biggest networks was developed within the framework of a Brite-Euram project entitled ‘Closing the loop from the product design to the end of life technologies'. The importance of logistics is certified by the fact, that this network defined the milestones of the improvement of an economically beneficial closed loop economy as quality aspects, communication and marketing, logistics and qualification. Within the frame of this project the logistics focused on the improvement of technologies (disassembly, reuse, refurbishment, remanufacturing and recycling), collection systems, and development of the concept for collection logistics and pre-disassembly, market survey in waste management. The Regional Knowledge Centre of Mechatronics and Logistics Systems was established in 2005. The overall objective of Knowledge Centre is to develop knowledge-intensive mechatronics and logistics systems in the leading edge of the world and to integrate the results in the economy and society through utilising the knowledge. The realisation of the objective requires the establishment and operation of a networking system of relations between those involved in sciences, the economy and society. The knowledge centre is a "knowledge integration tool" of the university in the field of mechanical engineering, and plays an important part in the intensification of the integration of the philosophy of sustainability into the related sciences. The program of the knowledge centre is focused on three well definable strategic fields, which are the vertical elements of the model. These are the R&D programs: world of products, materials and technologies, and integrated systems. The programs cover the implementation of seven, internationally competitive, application-oriented part tasks. These seven part tasks and the sustainability are closely related. The realisation of the part tasks through networking offers considerable results and economical-ecological benefits, forth for the participants and the region. The activities include basic and applied research, experimental development, technology transfer, as well as education and training and preparing the new scientific generation. The horizontal elements of the model are given by the utilisation of knowledge that can be interpreted in different dimensions: technical/engineering, legal, sustainability, economic, and social. The program relies on the continuation of existing relations in networks, and its regional nature is embodied in the cooperation of the higher education institutes and companies of the three counties. This publication was supported by the National Office for Research and Technology within the frame of Pázmány Péter programme. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Office for Research and Technology. Literature: [1] J. Cselényi, Gy. Fischer, J. Murvai, B. Mang: Typical models of the recycling logistics of worn out product. Proceedings of XIV. International Conference on Material Handling and Warehousing in Belgrade, 1996. pp. 138-143. [2] R. Knoth, M. Hoffmann, B. Kopacek, P. Kopacek: A logistic concept to improve the re-usability of electric and electronic equipment, Electronics and the Environment, 2001. Proceedings of the 2001 IEEE International Symposium. 2001. pp. 115 - 118. [3] L. Cser, B. Mang: Cleaner Technologies and Recycling in Hungary. Proceedings of Int. Workshop on Environmental Conscious Manufacturing in Hertogenbosch, The Netherlands, 1997. pp. 48-56.

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.

Specifications of a Simulation Model for a Local Area Network Design in Support of Stock Point Logistics Integrated Communications Environment (SPLICE).

DTIC Science & Technology

1982-10-01

class queueing system with a preemptive -resume priority service discipline, as depicted in Figure 4.2. Concerning a SPLICLAN configuration a node can...processor can be modeled as a single resource, multi-class queueing system with a preemptive -resume priority structure as the one given in Figure 4.2. An...LOCAL AREA NETWORK DESIGN IN SUPPORT OF STOCK POINT LOGISTICS INTEGRATED COMMUNICATIONS ENVIRONMENT (SPLICE) by Ioannis Th. Mastrocostopoulos October

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

Laser SRS tracker for reverse prototyping tasks

NASA Astrophysics Data System (ADS)

Kolmakov, Egor; Redka, Dmitriy; Grishkanich, Aleksandr; Tsvetkov, Konstantin

2017-10-01

According to the current great interest concerning Large-Scale Metrology applications in many different fields of manufacturing industry, technologies and techniques for dimensional measurement have recently shown a substantial improvement. Ease-of-use, logistic and economic issues, as well as metrological performance, are assuming a more and more important role among system requirements. The project is planned to conduct experimental studies aimed at identifying the impact of the application of the basic laws of chip and microlasers as radiators on the linear-angular characteristics of existing measurement systems. The project is planned to conduct experimental studies aimed at identifying the impact of the application of the basic laws of microlasers as radiators on the linear-angular characteristics of existing measurement systems. The system consists of a distributed network-based layout, whose modularity allows to fit differently sized and shaped working volumes by adequately increasing the number of sensing units. Differently from existing spatially distributed metrological instruments, the remote sensor devices are intended to provide embedded data elaboration capabilities, in order to share the overall computational load.

Detecting Anomalies in Process Control Networks

NASA Astrophysics Data System (ADS)

Rrushi, Julian; Kang, Kyoung-Don

This paper presents the estimation-inspection algorithm, a statistical algorithm for anomaly detection in process control networks. The algorithm determines if the payload of a network packet that is about to be processed by a control system is normal or abnormal based on the effect that the packet will have on a variable stored in control system memory. The estimation part of the algorithm uses logistic regression integrated with maximum likelihood estimation in an inductive machine learning process to estimate a series of statistical parameters; these parameters are used in conjunction with logistic regression formulas to form a probability mass function for each variable stored in control system memory. The inspection part of the algorithm uses the probability mass functions to estimate the normalcy probability of a specific value that a network packet writes to a variable. Experimental results demonstrate that the algorithm is very effective at detecting anomalies in process control networks.

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

Hydrophobic-Interaction-Induced Stiffening of α -Synuclein Fibril Networks

NASA Astrophysics Data System (ADS)

Semerdzhiev, Slav A.; Lindhoud, Saskia; Stefanovic, Anja; Subramaniam, Vinod; van der Schoot, Paul; Claessens, Mireille M. A. E.

2018-05-01

In water, networks of semiflexible fibrils of the protein α -synuclein stiffen significantly with increasing temperature. We make plausible that this reversible stiffening is a result of hydrophobic contacts between the fibrils that become more prominent with increasing temperature. The good agreement of our experimentally observed temperature dependence of the storage modulus of the network with a scaling theory linking network elasticity with reversible cross-linking enables us to quantify the endothermic binding enthalpy and estimate the effective size of hydrophobic patches on the fibril surface. Our findings may not only shed light on the role of amyloid deposits in disease conditions, but can also inspire new approaches for the design of thermoresponsive materials.

Hydrophobic-Interaction-Induced Stiffening of α-Synuclein Fibril Networks.

PubMed

Semerdzhiev, Slav A; Lindhoud, Saskia; Stefanovic, Anja; Subramaniam, Vinod; van der Schoot, Paul; Claessens, Mireille M A E

2018-05-18

In water, networks of semiflexible fibrils of the protein α-synuclein stiffen significantly with increasing temperature. We make plausible that this reversible stiffening is a result of hydrophobic contacts between the fibrils that become more prominent with increasing temperature. The good agreement of our experimentally observed temperature dependence of the storage modulus of the network with a scaling theory linking network elasticity with reversible cross-linking enables us to quantify the endothermic binding enthalpy and estimate the effective size of hydrophobic patches on the fibril surface. Our findings may not only shed light on the role of amyloid deposits in disease conditions, but can also inspire new approaches for the design of thermoresponsive materials.

Columbia University: Direct Reversal of Glucocorticoid Resistance by AKT inhibition in Acute Lymphoblastic Leukemia (T-ALL) | Office of Cancer Genomics

Cancer.gov

The goal of this project is to identify key druggable regulators of glucocorticoid resistance in T-ALL. To this end, a reverse-engineered T-ALL context-specific regulatory interaction network was created from a phenotypically diverse T-ALL gene expression dataset, and then this network was interrogated using master regulator analysis to find drivers of glucocorticoid resistance.

Planning the City Logistics Terminal Location by Applying the Green p-Median Model and Type-2 Neurofuzzy Network

PubMed Central

Pamučar, Dragan; Vasin, Ljubislav; Atanasković, Predrag; Miličić, Milica

2016-01-01

The paper herein presents green p-median problem (GMP) which uses the adaptive type-2 neural network for the processing of environmental and sociological parameters including costs of logistics operators and demonstrates the influence of these parameters on planning the location for the city logistics terminal (CLT) within the discrete network. CLT shows direct effects on increment of traffic volume especially in urban areas, which further results in negative environmental effects such as air pollution and noise as well as increased number of urban populations suffering from bronchitis, asthma, and similar respiratory infections. By applying the green p-median model (GMM), negative effects on environment and health in urban areas caused by delivery vehicles may be reduced to minimum. This model creates real possibilities for making the proper investment decisions so as profitable investments may be realized in the field of transport infrastructure. The paper herein also includes testing of GMM in real conditions on four CLT locations in Belgrade City zone. PMID:27195005

Planning the City Logistics Terminal Location by Applying the Green p-Median Model and Type-2 Neurofuzzy Network.

PubMed

Pamučar, Dragan; Vasin, Ljubislav; Atanasković, Predrag; Miličić, Milica

2016-01-01

The paper herein presents green p-median problem (GMP) which uses the adaptive type-2 neural network for the processing of environmental and sociological parameters including costs of logistics operators and demonstrates the influence of these parameters on planning the location for the city logistics terminal (CLT) within the discrete network. CLT shows direct effects on increment of traffic volume especially in urban areas, which further results in negative environmental effects such as air pollution and noise as well as increased number of urban populations suffering from bronchitis, asthma, and similar respiratory infections. By applying the green p-median model (GMM), negative effects on environment and health in urban areas caused by delivery vehicles may be reduced to minimum. This model creates real possibilities for making the proper investment decisions so as profitable investments may be realized in the field of transport infrastructure. The paper herein also includes testing of GMM in real conditions on four CLT locations in Belgrade City zone.

Collapse susceptibility mapping in karstified gypsum terrain (Sivas basin - Turkey) by conditional probability, logistic regression, artificial neural network models

NASA Astrophysics Data System (ADS)

Yilmaz, Isik; Keskin, Inan; Marschalko, Marian; Bednarik, Martin

2010-05-01

This study compares the GIS based collapse susceptibility mapping methods such as; conditional probability (CP), logistic regression (LR) and artificial neural networks (ANN) applied in gypsum rock masses in Sivas basin (Turkey). Digital Elevation Model (DEM) was first constructed using GIS software. Collapse-related factors, directly or indirectly related to the causes of collapse occurrence, such as distance from faults, slope angle and aspect, topographical elevation, distance from drainage, topographic wetness index- TWI, stream power index- SPI, Normalized Difference Vegetation Index (NDVI) by means of vegetation cover, distance from roads and settlements were used in the collapse susceptibility analyses. In the last stage of the analyses, collapse susceptibility maps were produced from CP, LR and ANN models, and they were then compared by means of their validations. Area Under Curve (AUC) values obtained from all three methodologies showed that the map obtained from ANN model looks like more accurate than the other models, and the results also showed that the artificial neural networks is a usefull tool in preparation of collapse susceptibility map and highly compatible with GIS operating features. Key words: Collapse; doline; susceptibility map; gypsum; GIS; conditional probability; logistic regression; artificial neural networks.

Connecting Land-Based Networks to Ships

DTIC Science & Technology

2013-06-01

multipoint wireless broadband systems, and WiMAX networks were initially deployed for fixed and nomadic (portable) applications. These standards...CAPABILITIES OF SHIP-TO-SHORE COMMUNICATIONS A. US Navy Automated Digital Network System (ADNS) The U.S. Navy’s Automated Digital Network System (ADNS...submit digitally any necessary documents to the terminal operators, contact their logistics providers, access tidal information and receive

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

Quantifying fluctuations in reversible enzymatic cycles and clocks

NASA Astrophysics Data System (ADS)

Wierenga, Harmen; ten Wolde, Pieter Rein; Becker, Nils B.

2018-04-01

Biochemical reactions are fundamentally noisy at a molecular scale. This limits the precision of reaction networks, but it also allows fluctuation measurements that may reveal the structure and dynamics of the underlying biochemical network. Here, we study nonequilibrium reaction cycles, such as the mechanochemical cycle of molecular motors, the phosphorylation cycle of circadian clock proteins, or the transition state cycle of enzymes. Fluctuations in such cycles may be measured using either of two classical definitions of the randomness parameter, which we show to be equivalent in general microscopically reversible cycles. We define a stochastic period for reversible cycles and present analytical solutions for its moments. Furthermore, we associate the two forms of the randomness parameter with the thermodynamic uncertainty relation, which sets limits on the timing precision of the cycle in terms of thermodynamic quantities. Our results should prove useful also for the study of temporal fluctuations in more general networks.

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.

Remote sensing of an agricultural soil moisture network in Walnut Creek, Iowa

USDA-ARS?s Scientific Manuscript database

The calibration and validation of soil moisture remote sensing products is complicated by the logistics of installing a soil moisture network for a long term period in an active landscape. Usually soil moisture sensors are added to existing precipitation networks which have as a singular requiremen...

The US Strategic Logistics Plan In The CBI Theater And Its Contemporary Significance

DTIC Science & Technology

2016-05-26

SUBJECT TERMS CBI Theater, Logistics, Lend-Lease Aid, LOC Network, Ledo Road, Burma Road, The Hump, AMMISCA 16. SECURITY CLASSIFICATION OF: a...19 Stilwell versus Chennault………………………………………………….………………………….......26 Efficiency of the LOC Network... LOC Line of Communication NATO North Atlantic Treaty Organization SLOC Sea Line of Communication SME Subject Matter Expert SOS Services of Supply SPOD

MARSnet: Mission-aware Autonomous Radar Sensor Network for Future Combat Systems

DTIC Science & Technology

2007-05-03

34Parameter estimation for 3-parameter log-logistic distribution (LLD3) by Porne ", Parameter estimation for 3-parameter log-logistic distribu- tion...section V we physical security, air traffic control, traffic monitoring, andvidefaconu s cribedy. video surveillance, industrial automation etc. Each

Switching "on" and "off" the adhesion in stimuli-responsive elastomers.

PubMed

Kaiser, S; Radl, S V; Manhart, J; Ayalur-Karunakaran, S; Griesser, T; Moser, A; Ganser, C; Teichert, C; Kern, W; Schlögl, S

2018-03-28

The present work aims at the preparation of dry adhesives with switchable bonding properties by using the reversible nature of the [4πs+4πs] cycloaddition of anthracenes. Photo-responsive hydrogenated carboxylated nitrile butadiene rubber with photo-responsive pendant anthracene groups is prepared by one-pot synthesis. The formation of 3D networks relies on the photodimerization of the anthracene moieties upon UV exposure (λ > 300 nm). Controlled cleavage of the crosslink sites is achieved by either deep UV exposure (λ = 254 nm) or thermal dissociation at 70 °C. The kinetics of the optical and thermal cleavage routes are compared in thin films using UV-vis spectroscopy and their influence on the reversibility of the network is detailed. Going from thin films to free standing samples the modulation of the network structure and thermo-mechanical properties over repeated crosslinking and cleavage cycles are characterized by low-field NMR spectroscopy and dynamic mechanical analysis. The applicability of the stimuli-responsive networks as adhesives with reversible bonding properties is demonstrated. The results evidence that the reversibility of the crosslinking reaction enables a controlled switching "on" and "off" of adhesion properties. The recovery of the adhesion force amounts to 75 and 80% for photo- and thermal dissociation, respectively. Spatial control of adhesion properties is evidenced by adhesion force mapping experiments of photo-patterned films.

[Treatment of acute ST Elevation myocardial infarction in a regional network ("Drip & Ship Network Rostock")].

PubMed

Schneider, Henrik; Ince, Hüseyin; Rehders, Tim; Körber, Thomas; Weber, Frank; Kische, Stephan; Chatterjee, Tuchaar; Nienaber, Christoph A

2007-12-01

Management of acute ST elevation myocardial infarction (STEMI) demands rapid and complete reperfusion of the infarct-related artery (IRA). With postinfarction prognosis depending on time delay from onset of symptoms to complete reperfusion (TIMI 3 flow) of the IRA, primary percutaneous coronary intervention (PPCI) performed by an experienced team has been shown to be superior to thrombolytic therapy with lower mortality, less frequent occurrence of nonfatal reinfarction and stroke, and thus represents the preferred treatment strategy according to the national and international guidelines. For regional implementation of PPCI, particularly in rural areas, information and transfer logistics within networks of care and direct transport of an infarction patient to a PCI hospital rather than to the closest hospital are a challenge. With successful implementation of network logistics and standardized therapeutic pathways, current guidelines and requested timelines versus thrombolysis could be met. The implemented logistics comprised 24 h/7 days stand-by services of an experienced PCI team, direct telephone hotline contact between rescue service/emergency physician and interventional cardiologist on call, and direct open access to a catheterization laboratory at any time. Within the Drip&Ship network Rostock, to date (July 2007) 1,022 consecutive patients with PCI for STEMI were documented and analyzed over 5 years; of these, 490 patients were transferred from a community hospital to the PCI center and 532 patients were admitted directly to the interventional center. In 95.1% of all transferred and in 94.8% of all directly admitted patients, PCI was successfully accomplished upon arrival. A normalized flow to the IRA after PCI was documented in 96% of both groups, no patient was subjected to thrombolytic therapy. At 12-month follow-up, there were no differences between both groups with respect to infarct size and mortality. Moreover, there was no evidence of differences in left ventricular ejection fraction between groups. Thus, transportation of STEMI patients within an established PCI network did not result in any prognostic disadvantage. Efficient network logistics with transportation for PPCI in acute STEMI ensure both safety and outcome profiles similar to patients treated by PCI in metropolitan areas.

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...

Network modeling for reverse flows of end-of-life vehicles.

PubMed

Ene, Seval; Öztürk, Nursel

2015-04-01

Product recovery operations are of critical importance for the automotive industry in complying with environmental regulations concerning end-of-life products management. Manufacturers must take responsibility for their products over the entire life cycle. In this context, there is a need for network design methods for effectively managing recovery operations and waste. The purpose of this study is to develop a mathematical programming model for managing reverse flows in end-of-life vehicles' recovery network. A reverse flow is the collection of used products from consumers and the transportation of these products for the purpose of recycling, reuse or disposal. The proposed model includes all operations in a product recovery and waste management network for used vehicles and reuse for vehicle parts such as collection, disassembly, refurbishing, processing (shredding), recycling, disposal and reuse of vehicle parts. The scope of the network model is to determine the numbers and locations of facilities in the network and the material flows between these facilities. The results show the performance of the model and its applicability for use in the planning of recovery operations in the automotive industry. The main objective of recovery and waste management is to maximize revenue and minimize pollution in end-of-life product operations. This study shows that with an accurate model, these activities may provide economic benefits and incentives in addition to protecting the environment. Copyright © 2015 Elsevier Ltd. All rights reserved.

Use of Ubiquitous Technologies in Military Logistic System in Iran

NASA Astrophysics Data System (ADS)

Jafari, P.; Sadeghi-Niaraki, A.

2013-09-01

This study is about integration and evaluation of RFID and ubiquitous technologies in military logistic system management. Firstly, supply chain management and the necessity of a revolution in logistic systems especially in military area, are explained. Secondly RFID and ubiquitous technologies and the advantages of their use in supply chain management are introduced. Lastly a system based on these technologies for controlling and increasing the speed and accuracy in military logistic system in Iran with its unique properties, is presented. The system is based on full control of military logistics (supplies) from the time of deployment to replenishment using sensor network, ubiquitous and RFID technologies.

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.

Hip fracture in the elderly: a re-analysis of the EPIDOS study with causal Bayesian networks.

PubMed

Caillet, Pascal; Klemm, Sarah; Ducher, Michel; Aussem, Alexandre; Schott, Anne-Marie

2015-01-01

Hip fractures commonly result in permanent disability, institutionalization or death in elderly. Existing hip-fracture predicting tools are underused in clinical practice, partly due to their lack of intuitive interpretation. By use of a graphical layer, Bayesian network models could increase the attractiveness of fracture prediction tools. Our aim was to study the potential contribution of a causal Bayesian network in this clinical setting. A logistic regression was performed as a standard control approach to check the robustness of the causal Bayesian network approach. EPIDOS is a multicenter study, conducted in an ambulatory care setting in five French cities between 1992 and 1996 and updated in 2010. The study included 7598 women aged 75 years or older, in which fractures were assessed quarterly during 4 years. A causal Bayesian network and a logistic regression were performed on EPIDOS data to describe major variables involved in hip fractures occurrences. Both models had similar association estimations and predictive performances. They detected gait speed and mineral bone density as variables the most involved in the fracture process. The causal Bayesian network showed that gait speed and bone mineral density were directly connected to fracture and seem to mediate the influence of all the other variables included in our model. The logistic regression approach detected multiple interactions involving psychotropic drug use, age and bone mineral density. Both approaches retrieved similar variables as predictors of hip fractures. However, Bayesian network highlighted the whole web of relation between the variables involved in the analysis, suggesting a possible mechanism leading to hip fracture. According to the latter results, intervention focusing concomitantly on gait speed and bone mineral density may be necessary for an optimal prevention of hip fracture occurrence in elderly people.

Integrated forward and reverse supply chain: A tire case study.

PubMed

Pedram, Ali; Yusoff, Nukman Bin; Udoncy, Olugu Ezutah; Mahat, Abu Bakar; Pedram, Payam; Babalola, Ayo

2017-02-01

This paper attempts to integrate both a forward and reverse supply chain to design a closed-loop supply chain network (CLSC). The problem in the design of a CLSC network is uncertainty in demand, return products and the quality of return products. Scenario analyses are generated to overcome this uncertainty. In contrast to the existing supply chain network design models, a new application of a CLSC network was studied in this paper to reduce waste. A multi-product, multi-tier mixed integer linear model is developed for a CLSC network design. The main objective is to maximize profit and provide waste management decision support in order to minimize pollution. The result shows applicability of the model in the tire industry. The model determines the number and the locations of facilities and the material flows between these facilities. Copyright © 2016 Elsevier Ltd. All rights reserved.

Applications of Time-Reversal Processing for Planetary Surface Communications

NASA Technical Reports Server (NTRS)

Barton, Richard J.

2007-01-01

Due to the power constraints imposed on wireless sensor and communication networks deployed on a planetary surface during exploration, energy efficient transfer of data becomes a critical issue. In situations where groups of nodes within a network are located in relatively close proximity, cooperative communication techniques can be utilized to improve the range, data rate, power efficiency, and lifetime of the network. In particular, if the point-to-point communication channels on the network are well modeled as frequency non-selective, distributed or cooperative beamforming can employed. For frequency-selective channels, beamforming itself is not generally appropriate, but a natural generalization of it, time-reversal communication (TRC), can still be effective. Time-reversal processing has been proposed and studied previously for other applications, including acoustical imaging, electromagnetic imaging, underwater acoustic communication, and wireless communication channels. In this paper, we study both the theoretical advantages and the experimental performance of cooperative TRC for wireless communication on planetary surfaces. We give a brief introduction to TRC and present several scenarios where TRC could be profitably employed during planetary exploration. We also present simulation results illustrating the performance of cooperative TRC employed in a complex multipath environment and discuss the optimality of cooperative TRC for data aggregation in wireless sensor networks

Reliability assessment of a multistate freight network for perishable merchandise with multiple suppliers and buyers

NASA Astrophysics Data System (ADS)

Lin, Yi-Kuei; Yeh, Cheng-Ta; Huang, Cheng-Fu

2017-01-01

This study develops a multistate freight network for single and perishable merchandise to assess the freight performance, where a node denotes a supplier, a distribution centre, or a buyer, while a logistics company providing a freight traffic service is denoted by an edge. For each logistics company, carrying capacity should be multistate since partial capacity may be reserved by some customers. The merchandise may perish or be perished during conveyance because of disadvantageous weather or collision in carrying such that the number of intact cargoes may be insufficient for the buyers. Hence, according to the perspective of supply chain management, the reliability, a probability of the network to successfully deliver the cargoes from the suppliers to the buyers subject to a budget, is proposed to be a performance index, where the suppliers and buyers are not the previous customers. An algorithm in terms of minimal paths to assess the reliability is developed. A fruit logistics case is adopted to explore the managerial implications of the reliability using sensitivity analysis.

Physical-enhanced secure strategy in an OFDM-PON.

PubMed

Zhang, Lijia; Xin, Xiangjun; Liu, Bo; Yu, Jianjun

2012-01-30

The physical layer of optical access network is vulnerable to various attacks. As the dramatic increase of users and network capacity, the issue of physical-layer security becomes more and more important. This paper proposes a physical-enhanced secure strategy for orthogonal frequency division multiplexing passive optical network (OFDM-PON) by employing frequency domain chaos scrambling. The Logistic map is adopted for the chaos mapping. The chaos scrambling strategy can dynamically allocate the scrambling matrices for different OFDM frames according to the initial condition, which enhance the confidentiality of the physical layer. A mathematical model of this secure system is derived firstly, which achieves a secure transmission at physical layer in OFDM-PON. The results from experimental implementation using Logistic mapped chaos scrambling are also given to further demonstrate the efficiency of this secure strategy. An 10.125 Gb/s 64QAM-OFDM data with Logistic mapped chaos scrambling are successfully transmitted over 25-km single mode fiber (SMF), and the experimental results show that proposed security scheme can protect the system from eavesdropper and attacker, while keep a good performance for the legal ONU.

Reverse and forward engineering of protein pattern formation.

PubMed

Kretschmer, Simon; Harrington, Leon; Schwille, Petra

2018-05-26

Living systems employ protein pattern formation to regulate important life processes in space and time. Although pattern-forming protein networks have been identified in various prokaryotes and eukaryotes, their systematic experimental characterization is challenging owing to the complex environment of living cells. In turn, cell-free systems are ideally suited for this goal, as they offer defined molecular environments that can be precisely controlled and manipulated. Towards revealing the molecular basis of protein pattern formation, we outline two complementary approaches: the biochemical reverse engineering of reconstituted networks and the de novo design, or forward engineering, of artificial self-organizing systems. We first illustrate the reverse engineering approach by the example of the Escherichia coli Min system, a model system for protein self-organization based on the reversible and energy-dependent interaction of the ATPase MinD and its activating protein MinE with a lipid membrane. By reconstituting MinE mutants impaired in ATPase stimulation, we demonstrate how large-scale Min protein patterns are modulated by MinE activity and concentration. We then provide a perspective on the de novo design of self-organizing protein networks. Tightly integrated reverse and forward engineering approaches will be key to understanding and engineering the intriguing phenomenon of protein pattern formation.This article is part of the theme issue 'Self-organization in cell biology'. © 2018 The Author(s).

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.

[Research of regional medical consumables reagent logistics system in the modern hospital].

PubMed

Wu, Jingjiong; Zhang, Yanwen; Luo, Xiaochen; Zhang, Qing; Zhu, Jianxin

2013-09-01

To explore the modern hospital and regional medical consumable reagents logistics system management. The characteristics of regional logistics, through cooperation between medical institutions within the region, and organize a wide range of special logistics activities, to make reasonable of the regional medical consumable reagents logistics. To set the regional management system, dynamic management systems, supply chain information management system, after-sales service system and assessment system. By the research of existing medical market and medical resources, to establish the regional medical supplies reagents directory and the initial data. The emphasis is centralized dispatch of medical supplies reagents, to introduce qualified logistics company for dispatching, to improve the modern hospital management efficiency, to costs down. Regional medical center and regional community health service centers constitute a regional logistics network, the introduction of medical consumable reagents logistics services, fully embodies integrity level, relevance, purpose, environmental adaptability of characteristics by the medical consumable reagents regional logistics distribution. Modern logistics distribution systems can increase the area of medical consumables reagent management efficiency and reduce costs.

Novel solutions for an old disease: diagnosis of acute appendicitis with random forest, support vector machines, and artificial neural networks.

PubMed

Hsieh, Chung-Ho; Lu, Ruey-Hwa; Lee, Nai-Hsin; Chiu, Wen-Ta; Hsu, Min-Huei; Li, Yu-Chuan Jack

2011-01-01

Diagnosing acute appendicitis clinically is still difficult. We developed random forests, support vector machines, and artificial neural network models to diagnose acute appendicitis. Between January 2006 and December 2008, patients who had a consultation session with surgeons for suspected acute appendicitis were enrolled. Seventy-five percent of the data set was used to construct models including random forest, support vector machines, artificial neural networks, and logistic regression. Twenty-five percent of the data set was withheld to evaluate model performance. The area under the receiver operating characteristic curve (AUC) was used to evaluate performance, which was compared with that of the Alvarado score. Data from a total of 180 patients were collected, 135 used for training and 45 for testing. The mean age of patients was 39.4 years (range, 16-85). Final diagnosis revealed 115 patients with and 65 without appendicitis. The AUC of random forest, support vector machines, artificial neural networks, logistic regression, and Alvarado was 0.98, 0.96, 0.91, 0.87, and 0.77, respectively. The sensitivity, specificity, positive, and negative predictive values of random forest were 94%, 100%, 100%, and 87%, respectively. Random forest performed better than artificial neural networks, logistic regression, and Alvarado. We demonstrated that random forest can predict acute appendicitis with good accuracy and, deployed appropriately, can be an effective tool in clinical decision making. Copyright © 2011 Mosby, Inc. All rights reserved.

Two-Photon Imaging of Cortical Surface Microvessels Reveals a Robust Redistribution in Blood Flow after Vascular Occlusion

PubMed Central

Schaffer, Chris B; Friedman, Beth; Nishimura, Nozomi; Schroeder, Lee F; Tsai, Philbert S; Ebner, Ford F; Lyden, Patrick D

2006-01-01

A highly interconnected network of arterioles overlies mammalian cortex to route blood to the cortical mantle. Here we test if this angioarchitecture can ensure that the supply of blood is redistributed after vascular occlusion. We use rodent parietal cortex as a model system and image the flow of red blood cells in individual microvessels. Changes in flow are quantified in response to photothrombotic occlusions to individual pial arterioles as well as to physical occlusions of the middle cerebral artery (MCA), the primary source of blood to this network. We observe that perfusion is rapidly reestablished at the first branch downstream from a photothrombotic occlusion through a reversal in flow in one vessel. More distal downstream arterioles also show reversals in flow. Further, occlusion of the MCA leads to reversals in flow through approximately half of the downstream but distant arterioles. Thus the cortical arteriolar network supports collateral flow that may mitigate the effects of vessel obstruction, as may occur secondary to neurovascular pathology. PMID:16379497

Hybrid algorithms for fuzzy reverse supply chain network design.

PubMed

Che, Z H; Chiang, Tzu-An; Kuo, Y C; Cui, Zhihua

2014-01-01

In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic Algorithm (PSO-GA), Genetic Algorithm-Simulated Annealing (GA-SA), and Particle Swarm Optimization-Simulated Annealing (PSO-SA) for solving the optimized model. During a case study of a multi-phase, multi-product reverse supply chain network, this paper explained the suitability of the optimized decision model and the applicability of the algorithms. Finally, the hybrid algorithms showed excellent solving capability when compared with original GA and PSO methods.

Hybrid Algorithms for Fuzzy Reverse Supply Chain Network Design

PubMed Central

Che, Z. H.; Chiang, Tzu-An; Kuo, Y. C.

2014-01-01

In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic Algorithm (PSO-GA), Genetic Algorithm-Simulated Annealing (GA-SA), and Particle Swarm Optimization-Simulated Annealing (PSO-SA) for solving the optimized model. During a case study of a multi-phase, multi-product reverse supply chain network, this paper explained the suitability of the optimized decision model and the applicability of the algorithms. Finally, the hybrid algorithms showed excellent solving capability when compared with original GA and PSO methods. PMID:24892057

Coding/decoding and reversibility of droplet trains in microfluidic networks.

PubMed

Fuerstman, Michael J; Garstecki, Piotr; Whitesides, George M

2007-02-09

Droplets of one liquid suspended in a second, immiscible liquid move through a microfluidic device in which a channel splits into two branches that reconnect downstream. The droplets choose a path based on the number of droplets that occupy each branch. The interaction among droplets in the channels results in complex sequences of path selection. The linearity of the flow through the microchannels, however, ensures that the behavior of the system can be reversed. This reversibility makes it possible to encrypt and decrypt signals coded in the intervals between droplets. The encoding/decoding device is a functional microfluidic system that requires droplets to navigate a network in a precise manner without the use of valves, switches, or other means of external control.

Reverse-engineering of gene networks for regulating early blood development from single-cell measurements.

PubMed

Wei, Jiangyong; Hu, Xiaohua; Zou, Xiufen; Tian, Tianhai

2017-12-28

Recent advances in omics technologies have raised great opportunities to study large-scale regulatory networks inside the cell. In addition, single-cell experiments have measured the gene and protein activities in a large number of cells under the same experimental conditions. However, a significant challenge in computational biology and bioinformatics is how to derive quantitative information from the single-cell observations and how to develop sophisticated mathematical models to describe the dynamic properties of regulatory networks using the derived quantitative information. This work designs an integrated approach to reverse-engineer gene networks for regulating early blood development based on singel-cell experimental observations. The wanderlust algorithm is initially used to develop the pseudo-trajectory for the activities of a number of genes. Since the gene expression data in the developed pseudo-trajectory show large fluctuations, we then use Gaussian process regression methods to smooth the gene express data in order to obtain pseudo-trajectories with much less fluctuations. The proposed integrated framework consists of both bioinformatics algorithms to reconstruct the regulatory network and mathematical models using differential equations to describe the dynamics of gene expression. The developed approach is applied to study the network regulating early blood cell development. A graphic model is constructed for a regulatory network with forty genes and a dynamic model using differential equations is developed for a network of nine genes. Numerical results suggests that the proposed model is able to match experimental data very well. We also examine the networks with more regulatory relations and numerical results show that more regulations may exist. We test the possibility of auto-regulation but numerical simulations do not support the positive auto-regulation. In addition, robustness is used as an importantly additional criterion to select candidate networks. The research results in this work shows that the developed approach is an efficient and effective method to reverse-engineer gene networks using single-cell experimental observations.

Cold-Chain Logistics: A Study of the Department of the Defense OCONUS Pre-Pandemic Influenza Vaccine Distribution Network

DTIC Science & Technology

2007-12-01

AUTHOR( S ) LT Daniel “Travis” Jones LT Christopher “Craig” Tecmire 5. FUNDING NUMBERS 7. PERFORMING ORGANIZATION NAME( S ...NAME( S ) AND ADDRESS(ES) N/A 10. SPONSORING/MONITORING AGENCY REPORT NUMBER 11. SUPPLEMENTARY NOTES The views expressed in this thesis are...merchandise, and not necessarily in the warehousing and the internal logistics associated with the production. The typical logistics manager of the 70’ s and

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.

Multidimensional adaptive evolution of a feed-forward network and the illusion of compensation

PubMed Central

Bullaughey, Kevin

2016-01-01

When multiple substitutions affect a trait in opposing ways, they are often assumed to be compensatory, not only with respect to the trait, but also with respect to fitness. This type of compensatory evolution has been suggested to underlie the evolution of protein structures and interactions, RNA secondary structures, and gene regulatory modules and networks. The possibility for compensatory evolution results from epistasis. Yet if epistasis is widespread, then it is also possible that the opposing substitutions are individually adaptive. I term this possibility an adaptive reversal. Although possible for arbitrary phenotype-fitness mappings, it has not yet been investigated whether such epistasis is prevalent in a biologically-realistic setting. I investigate a particular regulatory circuit, the type I coherent feed-forward loop, which is ubiquitous in natural systems and is accurately described by a simple mathematical model. I show that such reversals are common during adaptive evolution, can result solely from the topology of the fitness landscape, and can occur even when adaptation follows a modest environmental change and the network was well adapted to the original environment. The possibility of adaptive reversals warrants a systems perspective when interpreting substitution patterns in gene regulatory networks. PMID:23289561

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.

Use of neural networks to model complex immunogenetic associations of disease: human leukocyte antigen impact on the progression of human immunodeficiency virus infection.

PubMed

Ioannidis, J P; McQueen, P G; Goedert, J J; Kaslow, R A

1998-03-01

Complex immunogenetic associations of disease involving a large number of gene products are difficult to evaluate with traditional statistical methods and may require complex modeling. The authors evaluated the performance of feed-forward backpropagation neural networks in predicting rapid progression to acquired immunodeficiency syndrome (AIDS) for patients with human immunodeficiency virus (HIV) infection on the basis of major histocompatibility complex variables. Networks were trained on data from patients from the Multicenter AIDS Cohort Study (n = 139) and then validated on patients from the DC Gay cohort (n = 102). The outcome of interest was rapid disease progression, defined as progression to AIDS in <6 years from seroconversion. Human leukocyte antigen (HLA) variables were selected as network inputs with multivariate regression and a previously described algorithm selecting markers with extreme point estimates for progression risk. Network performance was compared with that of logistic regression. Networks with 15 HLA inputs and a single hidden layer of five nodes achieved a sensitivity of 87.5% and specificity of 95.6% in the training set, vs. 77.0% and 76.9%, respectively, achieved by logistic regression. When validated on the DC Gay cohort, networks averaged a sensitivity of 59.1% and specificity of 74.3%, vs. 53.1% and 61.4%, respectively, for logistic regression. Neural networks offer further support to the notion that HIV disease progression may be dependent on complex interactions between different class I and class II alleles and transporters associated with antigen processing variants. The effect in the current models is of moderate magnitude, and more data as well as other host and pathogen variables may need to be considered to improve the performance of the models. Artificial intelligence methods may complement linear statistical methods for evaluating immunogenetic associations of disease.

Coordinate measuring system based on microchip lasers for reverse prototyping

NASA Astrophysics Data System (ADS)

Iakovlev, Alexey; Grishkanich, Alexsandr S.; Redka, Dmitriy; Tsvetkov, Konstantin

2017-02-01

According to the current great interest concerning Large-Scale Metrology applications in many different fields of manufacturing industry, technologies and techniques for dimensional measurement have recently shown a substantial improvement. Ease-of-use, logistic and economic issues, as well as metrological performance, are assuming a more and more important role among system requirements. The project is planned to conduct experimental studies aimed at identifying the impact of the application of the basic laws of chip and microlasers as radiators on the linear-angular characteristics of existing measurement systems. The project is planned to conduct experimental studies aimed at identifying the impact of the application of the basic laws of microlasers as radiators on the linear-angular characteristics of existing measurement systems. The system consists of a distributed network-based layout, whose modularity allows to fit differently sized and shaped working volumes by adequately increasing the number of sensing units. Differently from existing spatially distributed metrological instruments, the remote sensor devices are intended to provide embedded data elaboration capabilities, in order to share the overall computational load.

Haughton-Mars Project Expedition 2005

NASA Technical Reports Server (NTRS)

deWeck, Olivier; Simchi-Levi, David

2006-01-01

The 2005 expedition to the Haughton-Mars Project (HMP) research station on Devon Island was part of a NASA-funded project on Space Logistics. A team of nine r&searchers from MIT went to the Canadian Arctic to participate in the annual I-IMP field campaign from July 8 to August 12, 2005. We investigated the applicability of the HMP research station as an analogue for planetary macro- and micro-logistics to the Moon and Mars, and began collecting data for modeling purposes. We also tested new technologies and procedures to enhance the ability of humans and robots to jointly explore remote environments. The expedition had four main objectives. We briefly summarize our key findings in each of these areas. 1. Classes of Supply: First, we wanted to understand what supply items existed at the HMP research station in support of planetary science and exploration research at and around the Haughton Crater. We also wanted to quantify the total amount of imported mass at HMP and compare this with predictions from existing parametric lunar base demand models. 2. Macro-Logistics Transportation Network: Our second objective was to understand the nodes, transportation routes, vehicles, capacities and crew and cargo mass flow rates required to support the HMP logistics network. 3. Agent and Asset Tracking: Since the current inventory management system on ISS relies heavily on barcodes and manual tracking, we wanted to test new automated technologies and procedures such as radio frequency identification RFID) to support exploration logistics. 4. Micro-Logistics (EVA): Finally, we wanted to understand the micro-logistical requirements of conducting both short (<1 day) and long traverses in the Mars-analog terrain on Devon Island. Micro-logistics involves the movement of surface vehicles, people and supplies from base to various exploration sites over short distances (<100 km).

Modeling Verdict Outcomes Using Social Network Measures: The Watergate and Caviar Network Cases.

PubMed

Masías, Víctor Hugo; Valle, Mauricio; Morselli, Carlo; Crespo, Fernando; Vargas, Augusto; Laengle, Sigifredo

2016-01-01

Modelling criminal trial verdict outcomes using social network measures is an emerging research area in quantitative criminology. Few studies have yet analyzed which of these measures are the most important for verdict modelling or which data classification techniques perform best for this application. To compare the performance of different techniques in classifying members of a criminal network, this article applies three different machine learning classifiers-Logistic Regression, Naïve Bayes and Random Forest-with a range of social network measures and the necessary databases to model the verdicts in two real-world cases: the U.S. Watergate Conspiracy of the 1970's and the now-defunct Canada-based international drug trafficking ring known as the Caviar Network. In both cases it was found that the Random Forest classifier did better than either Logistic Regression or Naïve Bayes, and its superior performance was statistically significant. This being so, Random Forest was used not only for classification but also to assess the importance of the measures. For the Watergate case, the most important one proved to be betweenness centrality while for the Caviar Network, it was the effective size of the network. These results are significant because they show that an approach combining machine learning with social network analysis not only can generate accurate classification models but also helps quantify the importance social network variables in modelling verdict outcomes. We conclude our analysis with a discussion and some suggestions for future work in verdict modelling using social network measures.

Design of Tactical Support Strategies in Military Logistics: Trade-offs Between Efficiency and Effectiveness. A Column and Cut Generation Modeling Methods

DTIC Science & Technology

2011-12-01

problems need to be addressed in the design of military logis- tics networks. The design problem includes strategic decisions, such as the location of...military strategic logistics [11–13]. In this study, we focus on the design of tactical logistics strategies, which achieve different optimal balances...is clear that our problem is N P−Hard 1 since it generalizes the CVPR and the BPP . Different solutions to handle the loading and routing of

Using an Adaptive Logistics Network in Africa: How Much and How Far

DTIC Science & Technology

2009-06-01

the United States. Arlington, Virginia, 9 May 2005. http://www.fas.org/irp/agency/dod/obc.pdf Coyle, John J ., Edward J . Bardi , and Robert A . Novack... J ” logistics integration (Lyden, 2008). Whereas the term “joint” in a military context signifies more than one branch of military service, Admiral...Conference and Exhibition, 13 March 2008. www.dtic.mil/ndia/2008logistics/Lyden.pdf McKinzie, Kaye LtCol. and J . Wesley Barnes. “ A Review of Strategic

Network-based regularization for matched case-control analysis of high-dimensional DNA methylation data.

PubMed

Sun, Hokeun; Wang, Shuang

2013-05-30

The matched case-control designs are commonly used to control for potential confounding factors in genetic epidemiology studies especially epigenetic studies with DNA methylation. Compared with unmatched case-control studies with high-dimensional genomic or epigenetic data, there have been few variable selection methods for matched sets. In an earlier paper, we proposed the penalized logistic regression model for the analysis of unmatched DNA methylation data using a network-based penalty. However, for popularly applied matched designs in epigenetic studies that compare DNA methylation between tumor and adjacent non-tumor tissues or between pre-treatment and post-treatment conditions, applying ordinary logistic regression ignoring matching is known to bring serious bias in estimation. In this paper, we developed a penalized conditional logistic model using the network-based penalty that encourages a grouping effect of (1) linked Cytosine-phosphate-Guanine (CpG) sites within a gene or (2) linked genes within a genetic pathway for analysis of matched DNA methylation data. In our simulation studies, we demonstrated the superiority of using conditional logistic model over unconditional logistic model in high-dimensional variable selection problems for matched case-control data. We further investigated the benefits of utilizing biological group or graph information for matched case-control data. We applied the proposed method to a genome-wide DNA methylation study on hepatocellular carcinoma (HCC) where we investigated the DNA methylation levels of tumor and adjacent non-tumor tissues from HCC patients by using the Illumina Infinium HumanMethylation27 Beadchip. Several new CpG sites and genes known to be related to HCC were identified but were missed by the standard method in the original paper. Copyright © 2012 John Wiley & Sons, Ltd.

EO-Performance relationships in Reverse Internationalization by Chinese Global Startup OEMs: Social Networks and Strategic Flexibility.

PubMed

Chin, Tachia; Tsai, Sang-Bing; Fang, Kai; Zhu, Wenzhong; Yang, Dongjin; Liu, Ren-Huai; Tsuei, Richard Ting Chang

2016-01-01

Due to the context-sensitive nature of entrepreneurial orientation (EO), it is imperative to in-depth explore the EO-performance mechanism in China at its critical, specific stage of economic reform. Under the context of "reverse internationalization" by Chinese global startup original equipment manufacturers (OEMs), this paper aims to manifest the unique links and complicated interrelationships between the individual EO dimensions and firm performance. Using structural equation modeling, we found that during reverse internationalization, proactiveness is positively related to performance; risk taking is not statistically associated with performance; innovativeness is negatively related to performance. The proactiveness-performance relationship is mediated by Strategic flexibility and moderated by social networking relationships. The dynamic and complex institutional setting, coupled with the issues of overcapacity and rising labor cost in China may explain why our distinctive results occur. This research advances the understanding of how contingent factors (social network relationships and strategic flexibility) facilitate entrepreneurial firms to break down institutional barriers and reap the most from EO. It brings new insights into how Chinese global startup OEMs draw on EO to undertake reverse internationalization, responding the calls for unraveling the heterogeneous characteristics of EO sub-dimensions and for more contextually-embedded treatment of EO-performance associations.

EO-Performance relationships in Reverse Internationalization by Chinese Global Startup OEMs: Social Networks and Strategic Flexibility

PubMed Central

Chin, Tachia; Tsai, Sang-Bing; Fang, Kai; Zhu, Wenzhong; Yang, Dongjin; Liu, Ren-huai; Tsuei, Richard Ting Chang

2016-01-01

Due to the context-sensitive nature of entrepreneurial orientation (EO), it is imperative to in-depth explore the EO-performance mechanism in China at its critical, specific stage of economic reform. Under the context of “reverse internationalization” by Chinese global startup original equipment manufacturers (OEMs), this paper aims to manifest the unique links and complicated interrelationships between the individual EO dimensions and firm performance. Using structural equation modeling, we found that during reverse internationalization, proactiveness is positively related to performance; risk taking is not statistically associated with performance; innovativeness is negatively related to performance. The proactiveness-performance relationship is mediated by Strategic flexibility and moderated by social networking relationships. The dynamic and complex institutional setting, coupled with the issues of overcapacity and rising labor cost in China may explain why our distinctive results occur. This research advances the understanding of how contingent factors (social network relationships and strategic flexibility) facilitate entrepreneurial firms to break down institutional barriers and reap the most from EO. It brings new insights into how Chinese global startup OEMs draw on EO to undertake reverse internationalization, responding the calls for unraveling the heterogeneous characteristics of EO sub-dimensions and for more contextually-embedded treatment of EO-performance associations. PMID:27631368

Post-installation activities in the Comprehensive Nuclear Test Ban Treaty (CTBT) International Monitoring System (IMS) infrasound network

NASA Astrophysics Data System (ADS)

Vivas Veloso, J. A.; Christie, D. R.; Hoffmann, T. L.; Campus, P.; Bell, M.; Langlois, A.; Martysevich, P.; Demirovik, E.; Carvalho, J.; Kramer, A.; Wu, Sean F.

2002-11-01

The provisional operation and maintenance of IMS infrasound stations after installation and subsequent certification has the objective to prepare the infrasound network for entry into force of the Comprehensive Nuclear-Test-Ban Treaty (CTBT). The goal is to maintain and fine tune the technical capabilities of the network, to repair faulty equipment, and to ensure that stations continue to meet the minimum specifications through evaluation of data quality and station recalibration. Due to the globally dispersed nature of the network, this program constitutes a significant undertaking that requires careful consideration of possible logistic approaches and their financial implications. Currently, 11 of the 60 IMS infrasound stations are transmitting data in the post-installation Testing & Evaluation mode. Another 5 stations are under provisional operation and are maintained in post-certification mode. It is expected that 20% of the infrasound network will be certified by the end of 2002. This presentation will focus on the different phases of post-installation activities of the IMS infrasound program and the logistical challenges to be tackled to ensure a cost-efficient management of the network. Specific topics will include Testing & Evaluation and Certification of Infrasound Stations, as well as Configuration Management and Network Sustainment.

Reverse engineering of TLX oncogenic transcriptional networks identifies RUNX1 as tumor suppressor in T-ALL.

PubMed

Della Gatta, Giusy; Palomero, Teresa; Perez-Garcia, Arianne; Ambesi-Impiombato, Alberto; Bansal, Mukesh; Carpenter, Zachary W; De Keersmaecker, Kim; Sole, Xavier; Xu, Luyao; Paietta, Elisabeth; Racevskis, Janis; Wiernik, Peter H; Rowe, Jacob M; Meijerink, Jules P; Califano, Andrea; Ferrando, Adolfo A

2012-02-26

The TLX1 and TLX3 transcription factor oncogenes have a key role in the pathogenesis of T cell acute lymphoblastic leukemia (T-ALL). Here we used reverse engineering of global transcriptional networks to decipher the oncogenic regulatory circuit controlled by TLX1 and TLX3. This systems biology analysis defined T cell leukemia homeobox 1 (TLX1) and TLX3 as master regulators of an oncogenic transcriptional circuit governing T-ALL. Notably, a network structure analysis of this hierarchical network identified RUNX1 as a key mediator of the T-ALL induced by TLX1 and TLX3 and predicted a tumor-suppressor role for RUNX1 in T cell transformation. Consistent with these results, we identified recurrent somatic loss-of-function mutations in RUNX1 in human T-ALL. Overall, these results place TLX1 and TLX3 at the top of an oncogenic transcriptional network controlling leukemia development, show the power of network analyses to identify key elements in the regulatory circuits governing human cancer and identify RUNX1 as a tumor-suppressor gene in T-ALL.

Life's attractors : understanding developmental systems through reverse engineering and in silico evolution.

PubMed

Jaeger, Johannes; Crombach, Anton

2012-01-01

We propose an approach to evolutionary systems biology which is based on reverse engineering of gene regulatory networks and in silico evolutionary simulations. We infer regulatory parameters for gene networks by fitting computational models to quantitative expression data. This allows us to characterize the regulatory structure and dynamical repertoire of evolving gene regulatory networks with a reasonable amount of experimental and computational effort. We use the resulting network models to identify those regulatory interactions that are conserved, and those that have diverged between different species. Moreover, we use the models obtained by data fitting as starting points for simulations of evolutionary transitions between species. These simulations enable us to investigate whether such transitions are random, or whether they show stereotypical series of regulatory changes which depend on the structure and dynamical repertoire of an evolving network. Finally, we present a case study-the gap gene network in dipterans (flies, midges, and mosquitoes)-to illustrate the practical application of the proposed methodology, and to highlight the kind of biological insights that can be gained by this approach.

Re-Purposing Google Maps Visualisation for Teaching Logistics Systems

ERIC Educational Resources Information Center

Cheong, France; Cheong, Christopher; Jie, Ferry

2012-01-01

Routing is the process of selecting appropriate paths and ordering waypoints in a network. It plays an important part in logistics and supply chain management as choosing the optimal route can minimise distribution costs. Routing optimisation, however, is a difficult problem to solve and computer software is often used to determine the best route.…

A systems biology-based investigation into the therapeutic effects of Gansui Banxia Tang on reversing the imbalanced network of hepatocellular carcinoma

NASA Astrophysics Data System (ADS)

Zhang, Yanqiong; Guo, Xiaodong; Wang, Danhua; Li, Ruisheng; Li, Xiaojuan; Xu, Ying; Liu, Zhenli; Song, Zhiqian; Lin, Ya; Li, Zhiyan; Lin, Na

2014-02-01

Several complex molecular events are involved in tumorigenesis of hepatocellular carcinoma (HCC). The interactions of these molecules may constitute the HCC imbalanced network. Gansui Banxia Tang (GSBXT), as a classic Chinese herbal formula, is a popular complementary and alternative medicine modality for treating HCC. In order to investigate the therapeutic effects and the pharmacological mechanisms of GSBXT on reversing HCC imbalanced network, we in the current study developed a comprehensive systems approach of integrating disease-specific and drug-specific networks, and successfully revealed the relationships of the ingredients in GSBXT with their putative targets, and with HCC significant molecules and HCC related pathway systems for the first time. Meanwhile, further experimental validation also demonstrated the preventive effects of GSBXT on tumor growth in mice and its regulatory effects on potential targets.

Frontal Hyperconnectivity Related to Discounting and Reversal Learning in Cocaine Subjects

PubMed Central

Camchong, Jazmin; MacDonald, Angus W; Nelson, Brent; Bell, Christopher; Mueller, Bryon A; Specker, Sheila; Lim, Kelvin O

2011-01-01

BACKGROUND Functional neuroimaging studies suggest that chronic cocaine use is associated with frontal lobe abnormalities. Functional connectivity (FC) alterations of cocaine dependent individuals (CD), however, are not yet clear. This is the first study to our knowledge that examines resting FC of anterior cingulate cortex (ACC) in CD. Because ACC is known to integrate inputs from different brain regions to regulate behavior, we hypothesize that CD will have connectivity abnormalities in ACC networks. In addition, we hypothesized that abnormalities would be associated with poor performance in delayed discounting and reversal learning tasks. METHODS Resting functional magnetic resonance imaging data were collected to look for FC differences between twenty-seven cocaine dependent individuals (CD) (5 females, age: M=39.73, SD=6.14) and twenty-four controls (5 females, age: M=39.76, SD = 7.09). Participants were assessed with delayed discounting and reversal learning tasks. Using seed-based FC measures, we examined FC in CD and controls within five ACC connectivity networks with seeds in subgenual, caudal, dorsal, rostral, and perigenual ACC. RESULTS CD showed increased FC within the perigenual ACC network in left middle frontal gyrus, ACC and middle temporal gyrus when compared to controls. FC abnormalities were significantly positively correlated with task performance in delayed discounting and reversal learning tasks in CD. CONCLUSIONS The present study shows that participants with chronic cocaine-dependency have hyperconnectivity within an ACC network known to be involved in social processing and mentalizing. In addition, FC abnormalities found in CD were associated with difficulties with delay rewards and slower adaptive learning. PMID:21371689

Architectural Guidelines for Multimedia and Hypermedia Data Interchange: Computer Aided Acquisition and Logistics Support/Concurrent Engineering (CALS/ CE) and Electronic Commerce/Electronic Data Interchange (EC/EDI)

DTIC Science & Technology

1991-09-01

other networks . 69 For example, E-mail can be sent to an SNA network through a Softswitch gateway, but at a very slow rate. As discussed in Chapter III...10 6. Communication Protocols ..................... 10 D. NEW INFRASTRUCTURES ....................... 11 1. CALS Test Network (CTN...11 2. Industrial Networks ......................... 12 3. FTS-2000 and ISDN ........................ 12 4. CALS Operational Resource

Mitigating TCP Degradation over Intermittent Link Failures using Intermediate Buffers

DTIC Science & Technology

2006-06-01

signal strength [10]. The Preemptive routing in ad hoc networks [10] attempts to predict that a route will fail by looking at the signal power of the...when the error rate is high there are non -optimal back offs in the Retransmission Timeout. And third, in the high error situation the slow start...network storage follows. In Beck et. al. [3], Logistical Networking is outlined as a means of storing data throughout the network. End to end

Spreading out of perturbations in reversible reaction networks

NASA Astrophysics Data System (ADS)

Maslov, Sergei; Sneppen, Kim; Ispolatov, I.

2007-08-01

Using an example of physical interactions between proteins, we study how a perturbation propagates in the equilibrium of a network of reversible reactions governed by the law of mass action. We introduce a matrix formalism to describe the linear response of all equilibrium concentrations to shifts in total abundances of individual reactants, and reveal its heuristic analogy to the flow of electric current in a network of resistors. Our main conclusion is that, on average, the induced changes in equilibrium concentrations decay exponentially as a function of network distance from the source of perturbation. We analyze how this decay is influenced by such factors as the topology of a network, binding strength, and correlations between concentrations of neighboring nodes. We find that the minimal branching of the network, small values of dissociation constants, and low equilibrium free (unbound) concentrations of reacting substances all decrease the decay constant and thus increase the range of propagation. Exact analytic expressions for the decay constant are obtained for the case of equally strong interactions and uniform as well as oscillating concentrations on the Bethe lattice. Our general findings are illustrated using a real network of protein-protein interactions in baker's yeast with experimentally determined protein concentrations.

Large-scale coupling dynamics of instructed reversal learning.

PubMed

Mohr, Holger; Wolfensteller, Uta; Ruge, Hannes

2018-02-15

The ability to rapidly learn from others by instruction is an important characteristic of human cognition. A recent study found that the rapid transfer from initial instructions to fluid behavior is supported by changes of functional connectivity between and within several large-scale brain networks, and particularly by the coupling of the dorsal attention network (DAN) with the cingulo-opercular network (CON). In the present study, we extended this approach to investigate how these brain networks interact when stimulus-response mappings are altered by novel instructions. We hypothesized that residual stimulus-response associations from initial practice might negatively impact the ability to implement novel instructions. Using functional imaging and large-scale connectivity analysis, we found that functional coupling between the CON and DAN was generally at a higher level during initial than reversal learning. Examining the learning-related connectivity dynamics between the CON and DAN in more detail by means of multivariate patterns analyses, we identified a specific subset of connections which showed a particularly high increase in connectivity during initial learning compared to reversal learning. This finding suggests that the CON-DAN connections can be separated into two functionally dissociable yet spatially intertwined subsystems supporting different aspects of short-term task automatization. Copyright © 2017 Elsevier Inc. All rights reserved.

Methods for inferring health-related social networks among coworkers from online communication patterns.

PubMed

Matthews, Luke J; DeWan, Peter; Rula, Elizabeth Y

2013-01-01

Studies of social networks, mapped using self-reported contacts, have demonstrated the strong influence of social connections on the propensity for individuals to adopt or maintain healthy behaviors and on their likelihood to adopt health risks such as obesity. Social network analysis may prove useful for businesses and organizations that wish to improve the health of their populations by identifying key network positions. Health traits have been shown to correlate across friendship ties, but evaluating network effects in large coworker populations presents the challenge of obtaining sufficiently comprehensive network data. The purpose of this study was to evaluate methods for using online communication data to generate comprehensive network maps that reproduce the health-associated properties of an offline social network. In this study, we examined three techniques for inferring social relationships from email traffic data in an employee population using thresholds based on: (1) the absolute number of emails exchanged, (2) logistic regression probability of an offline relationship, and (3) the highest ranked email exchange partners. As a model of the offline social network in the same population, a network map was created using social ties reported in a survey instrument. The email networks were evaluated based on the proportion of survey ties captured, comparisons of common network metrics, and autocorrelation of body mass index (BMI) across social ties. Results demonstrated that logistic regression predicted the greatest proportion of offline social ties, thresholding on number of emails exchanged produced the best match to offline network metrics, and ranked email partners demonstrated the strongest autocorrelation of BMI. Since each method had unique strengths, researchers should choose a method based on the aspects of offline behavior of interest. Ranked email partners may be particularly useful for purposes related to health traits in a social network.

Methods for Inferring Health-Related Social Networks among Coworkers from Online Communication Patterns

PubMed Central

Matthews, Luke J.; DeWan, Peter; Rula, Elizabeth Y.

2013-01-01

Studies of social networks, mapped using self-reported contacts, have demonstrated the strong influence of social connections on the propensity for individuals to adopt or maintain healthy behaviors and on their likelihood to adopt health risks such as obesity. Social network analysis may prove useful for businesses and organizations that wish to improve the health of their populations by identifying key network positions. Health traits have been shown to correlate across friendship ties, but evaluating network effects in large coworker populations presents the challenge of obtaining sufficiently comprehensive network data. The purpose of this study was to evaluate methods for using online communication data to generate comprehensive network maps that reproduce the health-associated properties of an offline social network. In this study, we examined three techniques for inferring social relationships from email traffic data in an employee population using thresholds based on: (1) the absolute number of emails exchanged, (2) logistic regression probability of an offline relationship, and (3) the highest ranked email exchange partners. As a model of the offline social network in the same population, a network map was created using social ties reported in a survey instrument. The email networks were evaluated based on the proportion of survey ties captured, comparisons of common network metrics, and autocorrelation of body mass index (BMI) across social ties. Results demonstrated that logistic regression predicted the greatest proportion of offline social ties, thresholding on number of emails exchanged produced the best match to offline network metrics, and ranked email partners demonstrated the strongest autocorrelation of BMI. Since each method had unique strengths, researchers should choose a method based on the aspects of offline behavior of interest. Ranked email partners may be particularly useful for purposes related to health traits in a social network. PMID:23418436

Three-Dimensional SnS Decorated Carbon Nano-Networks as Anode Materials for Lithium and Sodium Ion Batteries.

PubMed

Zhou, Yanli; Wang, Qi; Zhu, Xiaotao; Jiang, Fuyi

2018-02-28

The three-dimensional (3D) SnS decorated carbon nano-networks (SnS@C) were synthesized via a facile two-step method of freeze-drying combined with post-heat treatment. The lithium and sodium storage performances of above composites acting as anode materials were investigated. As anode materials for lithium ion batteries, a high reversible capacity of 780 mAh·g -1 for SnS@C composites can be obtained at 100 mA·g -1 after 100 cycles. Even cycled at a high current density of 2 A·g -1 , the reversible capacity of this composite can be maintained at 610 mAh·g -1 after 1000 cycles. The initial charge capacity for sodium ion batteries can reach 333 mAh·g -1 , and it retains a reversible capacity of 186 mAh·g -1 at 100 mA·g -1 after 100 cycles. The good lithium or sodium storage performances are likely attributed to the synergistic effects of the conductive carbon nano-networks and small SnS nanoparticles.

Prediction of Emergency Department Hospital Admission Based on Natural Language Processing and Neural Networks.

PubMed

Zhang, Xingyu; Kim, Joyce; Patzer, Rachel E; Pitts, Stephen R; Patzer, Aaron; Schrager, Justin D

2017-10-26

To describe and compare logistic regression and neural network modeling strategies to predict hospital admission or transfer following initial presentation to Emergency Department (ED) triage with and without the addition of natural language processing elements. Using data from the National Hospital Ambulatory Medical Care Survey (NHAMCS), a cross-sectional probability sample of United States EDs from 2012 and 2013 survey years, we developed several predictive models with the outcome being admission to the hospital or transfer vs. discharge home. We included patient characteristics immediately available after the patient has presented to the ED and undergone a triage process. We used this information to construct logistic regression (LR) and multilayer neural network models (MLNN) which included natural language processing (NLP) and principal component analysis from the patient's reason for visit. Ten-fold cross validation was used to test the predictive capacity of each model and receiver operating curves (AUC) were then calculated for each model. Of the 47,200 ED visits from 642 hospitals, 6,335 (13.42%) resulted in hospital admission (or transfer). A total of 48 principal components were extracted by NLP from the reason for visit fields, which explained 75% of the overall variance for hospitalization. In the model including only structured variables, the AUC was 0.824 (95% CI 0.818-0.830) for logistic regression and 0.823 (95% CI 0.817-0.829) for MLNN. Models including only free-text information generated AUC of 0.742 (95% CI 0.731- 0.753) for logistic regression and 0.753 (95% CI 0.742-0.764) for MLNN. When both structured variables and free text variables were included, the AUC reached 0.846 (95% CI 0.839-0.853) for logistic regression and 0.844 (95% CI 0.836-0.852) for MLNN. The predictive accuracy of hospital admission or transfer for patients who presented to ED triage overall was good, and was improved with the inclusion of free text data from a patient's reason for visit regardless of modeling approach. Natural language processing and neural networks that incorporate patient-reported outcome free text may increase predictive accuracy for hospital admission.

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

The response of dense dry granular material to the shear reversal

NASA Astrophysics Data System (ADS)

Zhang, Jie; Ren, Jie; Farhadi, Somayeh; Behringer, Robert

2008-11-01

We have performed two dimensional granular experiments under pure shear using bidisperse photo-elastic disks. Starting from a stress free state, a square box filled with granular particles is subject to shear. The forward shears involved various number of steps, leading to maximum strains between 0.1 and 0.3. The area is kept constant during the shear. The network of force chains gradually built up as the strain increased, leading to increased pressure and shear stress. Reverse shear was then applied to the system. Depending on the initial packing fraction and the strain at which the shear is reversed, the force chain network built prior to the shear reversal may be destroyed completely or partially destroyed. Following the force chain weakening, when the reserve shear is continuously applied to the system, there is a force chain strengthening. Following each change of the system, contact forces of individual disks were measured by applying an inverse algorithm. We also kept track of the displacement and angle of rotation of every particle from frame to frame. We present the results for the structure failure and reconstruction during shear reversals. We also present data for stresses, contact force distributions and other statistical measures.

Yield of reversible colloidal gels during flow start-up: release from kinetic arrest.

PubMed

Johnson, Lilian C; Landrum, Benjamin J; Zia, Roseanna N

2018-06-05

Yield of colloidal gels during start-up of shear flow is characterized by an overshoot in shear stress that accompanies changes in network structure. Prior studies of yield of reversible colloidal gels undergoing strong flow model the overshoot as the point at which network rupture permits fluidization. However, yield under weak flow, which is of interest in many biological and industrial fluids shows no such disintegration. The mechanics of reversible gels are influenced by bond strength and durability, where ongoing rupture and re-formation impart aging that deepens kinetic arrest [Zia et al., J. Rheol., 2014, 58, 1121], suggesting that yield be viewed as release from kinetic arrest. To explore this idea, we study reversible colloidal gels during start-up of shear flow via dynamic simulation, connecting rheological yield to detailed measurements of structure, bond dynamics, and potential energy. We find that pre-yield stress grows temporally with the changing roles of microscopic transport processes: early time behavior is set by Brownian diffusion; later, advective displacements permit relative particle motion that stretches bonds and stores energy. Stress accumulates in stretched, oriented bonds until yield, which is a tipping point to energy release, and is passed with a fully intact network, where the loss of very few bonds enables relaxation of many, easing glassy arrest. This is immediately followed by a reversal to growth in potential energy during bulk plastic deformation and condensation into larger particle domains, supporting the view that yield is an activated release from kinetic arrest. The continued condensation of dense domains and shrinkage of network surfaces, along with a decrease in the potential energy, permit the gel to evolve toward more complete phase separation, supporting our view that yield of weakly sheared gels is a 'non-equilibrium phase transition'. Our findings may be particularly useful for industrial or other coatings, where weak, slow application via shear may lead to phase separation, inhibiting smooth distribution.

Modeling Verdict Outcomes Using Social Network Measures: The Watergate and Caviar Network Cases

PubMed Central

2016-01-01

Modelling criminal trial verdict outcomes using social network measures is an emerging research area in quantitative criminology. Few studies have yet analyzed which of these measures are the most important for verdict modelling or which data classification techniques perform best for this application. To compare the performance of different techniques in classifying members of a criminal network, this article applies three different machine learning classifiers–Logistic Regression, Naïve Bayes and Random Forest–with a range of social network measures and the necessary databases to model the verdicts in two real–world cases: the U.S. Watergate Conspiracy of the 1970’s and the now–defunct Canada–based international drug trafficking ring known as the Caviar Network. In both cases it was found that the Random Forest classifier did better than either Logistic Regression or Naïve Bayes, and its superior performance was statistically significant. This being so, Random Forest was used not only for classification but also to assess the importance of the measures. For the Watergate case, the most important one proved to be betweenness centrality while for the Caviar Network, it was the effective size of the network. These results are significant because they show that an approach combining machine learning with social network analysis not only can generate accurate classification models but also helps quantify the importance social network variables in modelling verdict outcomes. We conclude our analysis with a discussion and some suggestions for future work in verdict modelling using social network measures. PMID:26824351

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.

78 FR 65975 - Notice of Availability (NOA) for Strategic Network Optimization (SNO) Environmental Assessment...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-11-04

... Impact (FONSI) AGENCY: Defense Logistics Agency, DoD. ACTION: Notice of Availability (NOA) for Strategic Network Optimization (SNO) Environmental Assessment (EA) Finding of No Significant Impact (FONSI). SUMMARY... impacts on the human environment are associated with this decision. FOR FURTHER INFORMATION CONTACT: Ann...

Artificial Neural Networks: A New Approach to Predicting Application Behavior.

ERIC Educational Resources Information Center

Gonzalez, Julie M. Byers; DesJardins, Stephen L.

2002-01-01

Applied the technique of artificial neural networks to predict which students were likely to apply to one research university. Compared the results to the traditional analysis tool, logistic regression modeling. Found that the addition of artificial intelligence models was a useful new tool for predicting student application behavior. (EV)

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.

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.

Network characteristics and patent value-Evidence from the Light-Emitting Diode industry.

PubMed

Huang, Way-Ren; Hsieh, Chia-Jen; Chang, Ke-Chiun; Kiang, Yen-Jo; Yuan, Chien-Chung; Chu, Woei-Chyn

2017-01-01

This study proposes a different angle to social network analysis that evaluates patent value and explores its influencing factors using the network centrality and network position. This study utilizes a logistic regression model to explore the relationships in the LED industry between patent value and network centrality as measured from out-degree centrality, in-degree centrality, in-closeness centrality, and network position, which is measured from effect size. The empirical result shows that out-degree centrality and in-degree centrality have significant positive effects on patent value and that effect size has a significant negative effect on patent value.

Self-Healing of Unentangled Polymer Networks with Reversible Bonds

PubMed Central

Stukalin, Evgeny B.; Cai, Li-Heng; Kumar, N. Arun; Leibler, Ludwik; Rubinstein, Michael

2013-01-01

Self-healing polymeric materials are systems that after damage can revert to their original state with full or partial recovery of mechanical strength. Using scaling theory we study a simple model of autonomic self-healing of unentangled polymer networks. In this model one of the two end monomers of each polymer chain is fixed in space mimicking dangling chains attachment to a polymer network, while the sticky monomer at the other end of each chain can form pairwise reversible bond with the sticky end of another chain. We study the reaction kinetics of reversible bonds in this simple model and analyze the different stages in the self-repair process. The formation of bridges and the recovery of the material strength across the fractured interface during the healing period occur appreciably faster after shorter waiting time, during which the fractured surfaces are kept apart. We observe the slowest formation of bridges for self-adhesion after bringing into contact two bare surfaces with equilibrium (very low) density of open stickers in comparison with self-healing. The primary role of anomalous diffusion in material self-repair for short waiting times is established, while at long waiting times the recovery of bonds across fractured interface is due to hopping diffusion of stickers between different bonded partners. Acceleration in bridge formation for self-healing compared to self-adhesion is due to excess non-equilibrium concentration of open stickers. Full recovery of reversible bonds across fractured interface (formation of bridges) occurs after appreciably longer time than the equilibration time of the concentration of reversible bonds in the bulk. PMID:24347684

Predicting human immunodeficiency virus inhibitors using multi-dimensional Bayesian network classifiers.

PubMed

Borchani, Hanen; Bielza, Concha; Toro, Carlos; Larrañaga, Pedro

2013-03-01

Our aim is to use multi-dimensional Bayesian network classifiers in order to predict the human immunodeficiency virus type 1 (HIV-1) reverse transcriptase and protease inhibitors given an input set of respective resistance mutations that an HIV patient carries. Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models especially designed to solve multi-dimensional classification problems, where each input instance in the data set has to be assigned simultaneously to multiple output class variables that are not necessarily binary. In this paper, we introduce a new method, named MB-MBC, for learning MBCs from data by determining the Markov blanket around each class variable using the HITON algorithm. Our method is applied to both reverse transcriptase and protease data sets obtained from the Stanford HIV-1 database. Regarding the prediction of antiretroviral combination therapies, the experimental study shows promising results in terms of classification accuracy compared with state-of-the-art MBC learning algorithms. For reverse transcriptase inhibitors, we get 71% and 11% in mean and global accuracy, respectively; while for protease inhibitors, we get more than 84% and 31% in mean and global accuracy, respectively. In addition, the analysis of MBC graphical structures lets us gain insight into both known and novel interactions between reverse transcriptase and protease inhibitors and their respective resistance mutations. MB-MBC algorithm is a valuable tool to analyze the HIV-1 reverse transcriptase and protease inhibitors prediction problem and to discover interactions within and between these two classes of inhibitors. Copyright © 2012 Elsevier B.V. All rights reserved.

Identifying interactions in the time and frequency domains in local and global networks - A Granger Causality Approach.

PubMed

Zou, Cunlu; Ladroue, Christophe; Guo, Shuixia; Feng, Jianfeng

2010-06-21

Reverse-engineering approaches such as Bayesian network inference, ordinary differential equations (ODEs) and information theory are widely applied to deriving causal relationships among different elements such as genes, proteins, metabolites, neurons, brain areas and so on, based upon multi-dimensional spatial and temporal data. There are several well-established reverse-engineering approaches to explore causal relationships in a dynamic network, such as ordinary differential equations (ODE), Bayesian networks, information theory and Granger Causality. Here we focused on Granger causality both in the time and frequency domain and in local and global networks, and applied our approach to experimental data (genes and proteins). For a small gene network, Granger causality outperformed all the other three approaches mentioned above. A global protein network of 812 proteins was reconstructed, using a novel approach. The obtained results fitted well with known experimental findings and predicted many experimentally testable results. In addition to interactions in the time domain, interactions in the frequency domain were also recovered. The results on the proteomic data and gene data confirm that Granger causality is a simple and accurate approach to recover the network structure. Our approach is general and can be easily applied to other types of temporal data.

A Comparison of Logistic Regression, Neural Networks, and Classification Trees Predicting Success of Actuarial Students

ERIC Educational Resources Information Center

Schumacher, Phyllis; Olinsky, Alan; Quinn, John; Smith, Richard

2010-01-01

The authors extended previous research by 2 of the authors who conducted a study designed to predict the successful completion of students enrolled in an actuarial program. They used logistic regression to determine the probability of an actuarial student graduating in the major or dropping out. They compared the results of this study with those…

Rapid Network Design

DTIC Science & Technology

2013-09-01

control GCE ground combat element LCE logistics combat element MAGTF Marine Air Ground Task Force MWCS Marine Wing Communications Squadron NPS Naval...elements: command element (CE), ground combat el- ement ( GCE ), aviation combat element (ACE), and logistics combat element (LCE). Each ele- ment...This layer provides unimpeded high-speed connectivity between remote sites and the Internet. Limited security policies are applied at this level to

Optimizing biomass feedstock logistics for forest residue processing and transportation on a tree-shaped road network

Treesearch

Hee Han; Woodam Chung; Lucas Wells; Nathaniel Anderson

2018-01-01

An important task in forest residue recovery operations is to select the most cost-efficient feedstock logistics system for a given distribution of residue piles, road access, and available machinery. Notable considerations include inaccessibility of treatment units to large chip vans and frequent, long-distance mobilization of forestry equipment required to process...

Elucidation of the transcription network governing mammalian sex determination by exploiting strain-specific susceptibility to sex reversal

PubMed Central

Munger, Steven C.; Aylor, David L.; Syed, Haider Ali; Magwene, Paul M.; Threadgill, David W.; Capel, Blanche

2009-01-01

Despite the identification of some key genes that regulate sex determination, most cases of disorders of sexual development remain unexplained. Evidence suggests that the sexual fate decision in the developing gonad depends on a complex network of interacting factors that converge on a critical threshold. To elucidate the transcriptional network underlying sex determination, we took the first expression quantitative trait loci (eQTL) approach in a developing organ. We identified reproducible differences in the transcriptome of the embryonic day 11.5 (E11.5) XY gonad between C57BL/6J (B6) and 129S1/SvImJ (129S1), indicating that the reported sensitivity of B6 to sex reversal is consistent with a higher expression of a female-like transcriptome in B6. Gene expression is highly variable in F2 XY gonads from B6 and 129S1 intercrosses, yet strong correlations emerged. We estimated the F2 coexpression network and predicted roles for genes of unknown function based on their connectivity and position within the network. A genetic analysis of the F2 population detected autosomal regions that control the expression of many sex-related genes, including Sry (sex-determining region of the Y chromosome) and Sox9 (Sry-box containing gene 9), the key regulators of male sex determination. Our results reveal the complex transcription architecture underlying sex determination, and provide a mechanism by which individuals may be sensitized for sex reversal. PMID:19884258

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.

Design of Flow Systems for Improved Networking and Reduced Noise in Biomolecular Signal Processing in Biocomputing and Biosensing Applications

PubMed Central

Verma, Arjun; Fratto, Brian E.; Privman, Vladimir; Katz, Evgeny

2016-01-01

We consider flow systems that have been utilized for small-scale biomolecular computing and digital signal processing in binary-operating biosensors. Signal measurement is optimized by designing a flow-reversal cuvette and analyzing the experimental data to theoretically extract the pulse shape, as well as reveal the level of noise it possesses. Noise reduction is then carried out numerically. We conclude that this can be accomplished physically via the addition of properly designed well-mixing flow-reversal cell(s) as an integral part of the flow system. This approach should enable improved networking capabilities and potentially not only digital but analog signal-processing in such systems. Possible applications in complex biocomputing networks and various sense-and-act systems are discussed. PMID:27399702

Self-assembly of amphiphilic molecules in organic liquids

NASA Astrophysics Data System (ADS)

Tung, Shih-Huang

2007-12-01

Amphiphilic molecules are well-known for their ability to self-assemble in water to form structures such as micelles and vesicles. In comparison, much less is known about amphiphilic self-assembly in nonpolar organic liquids. Such "reverse" self assembly can produce many of the counterparts to structures found in water. In this dissertation, we focus on the formation and dynamics of such reverse structures. We seek to obtain fundamental insight into the driving forces for reverse self-assembly processes. Three specific types of reverse structures are studied: (a) reverse wormlike micelles, i.e., long, flexible micellar chains; (b) reverse vesicles, i.e., hollow containers enclosed by reverse bilayers; and (c) organogel networks. While our focus is on the fundamentals, we note that reverse structures can be useful in a variety of applications ranging from drug delivery, controlled release, hosts for enzymatic reactions, and templates for nanomaterials synthesis. In the first part of this study, we describe a new route for forming reverse wormlike micelles in nonpolar organic liquids. This route involves the addition of trace amounts of a bile salt to solutions of the phospholipid, lecithin. We show that bile salts, due to their unique "facially amphiphilic" structure, can promote the aggregation of lecithin molecules into these reverse micellar chains. The resulting samples are viscoelastic and show interesting rheological properties. Unusual trends are seen in the temperature dependence of their rheology, which indicates the importance of hydrogen-bonding interactions in the formation of these micelles. Another remarkable feature of their rheology is the presence of strain-stiffening, where the material becomes stiffer at high deformations. Strain-stiffening has been seen before for elastic gels of biopolymers; here, we demonstrate the same properties for viscoelastic micellar solutions. The second reverse aggregate we deal with is the reverse vesicle. We present a new route for forming stable unilamellar reverse vesicles, and this involves mixing short- and long-chain lipids (lecithins) with a trace of sodium chloride. The ratio of the short to long-chain lipid controls the type and size of self-assembled structure formed, and as this ratio is increased, a transition from reverse micelles to vesicles occurs. The structural changes can be explained in terms of molecular geometry, with the sodium chloride acting as a "glue" in binding lipid headgroups together through electrostatic interactions. The final part of this dissertation focuses on organogels. The two-tailed anionic surfactant, AOT, is well-known to form spherical reverse micelles in organic solvents. We have found that trace amounts (e.g., less than 1 mM) of the dihydroxy bile salt, sodium deoxycholate (SDC) can transform these dilute micellar solutions into self-supporting, transparent organogels. The structure and rheology of these organogels is reminiscent of the self-assembled networks formed by proteins such as actin in water. The organogels are based on networks of long, rigid, cylindrical filaments, with SDC molecules stacked together in the filament core.

CTN summary of DSREDS, EDCARS, EDMICS CALS readiness testing. [Computer-aided Acquisition and Logistic Support (CALS) CALS Test Network (CTN), Digital Storage Retrieval Eng. Data System (DSREDS), Eng. Data Computer Assisted Retrieval System (EDCARS), Eng. Data Management Information and Control System (EDMICS)

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

Mitschkowetz, N.; Vickers, D.L.

This report provides a summary of the Computer-aided Acquisition and Logistic Support (CALS) Test Network (CTN) Laboratory Acceptance Test (LAT) and User Application Test (UAT) activities undertaken to evaluate the CALS capabilities being implemented as part of the Department of Defense (DOD) engineering repositories. Although the individual testing activities provided detailed reports for each repository, a synthesis of the results, conclusions, and recommendations is offered to provide a more concise presentation of the issues and the strategies, as viewed from the CTN perspective.

Water deficit-induced changes in transcription factor expression in maize seedlings

USDA-ARS?s Scientific Manuscript database

Plants tolerate water deficits by regulating gene networks controlling cellular and physiological traits to modify growth and development. Transcription factor (TFs) directed regulation of transcription within these gene networks is key to eliciting appropriate responses. In this study, reverse tran...

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.

Smart Sensing and Recognition Based on Models of Neural Networks

DTIC Science & Technology

1990-11-15

9P-o ,yY𔄃-’. AD-A230 701 University of Pensylvania Philadelphia, PA 19104-6390 SMART SENSING AND RECOGNITION BASED ON MODELS OF NEURAL NETWORKS ... networks , photonic 1 implementations, nonlinear dynamical signal processing 9 ABSTRACT (Continue on reverse if necessary and identify by block number...not develop in isolation but in synergism with sensory organs and their feature forming networks . This means that development of artificial pattern

Systems analysis reveals a transcriptional reversal of the mesenchymal phenotype induced by SNAIL-inhibitor GN-25

PubMed Central

2013-01-01

Background HMLEs (HMLE-SNAIL and Kras-HMLE, Kras-HMLE-SNAIL pairs) serve as excellent model system to interrogate the effect of SNAIL targeted agents that reverse epithelial-to-mesenchymal transition (EMT). We had earlier developed a SNAIL-p53 interaction inhibitor (GN-25) that was shown to suppress SNAIL function. In this report, using systems biology and pathway network analysis, we show that GN-25 could cause reversal of EMT leading to mesenchymal-to-epithelial transition (MET) in a well-recognized HMLE-SNAIL and Kras-HMLE-SNAIL models. Results GN-25 induced MET was found to be consistent with growth inhibition, suppression of spheroid forming capacity and induction of apoptosis. Pathway network analysis of mRNA expression using microarrays from GN-25 treated Kras-HMLE-SNAIL cells showed an orchestrated global re-organization of EMT network genes. The expression signatures were validated at the protein level (down-regulation of mesenchymal markers such as TWIST1 and TWIST2 that was concurrent with up-regulation of epithelial marker E-Cadherin), and RNAi studies validated SNAIL dependent mechanism of action of the drug. Most importantly, GN-25 modulated many major transcription factors (TFs) such as inhibition of oncogenic TFs Myc, TBX2, NR3C1 and led to enhancement in the expression of tumor suppressor TFs such as SMAD7, DD1T3, CEBPA, HOXA5, TFEB, IRF1, IRF7 and XBP1, resulting in MET as well as cell death. Conclusions Our systems and network investigations provide convincing pre-clinical evidence in support of the clinical application of GN-25 for the reversal of EMT and thereby reducing cancer cell aggressiveness. PMID:24004452

Frontal hyperconnectivity related to discounting and reversal learning in cocaine subjects.

PubMed

Camchong, Jazmin; MacDonald, Angus W; Nelson, Brent; Bell, Christopher; Mueller, Bryon A; Specker, Sheila; Lim, Kelvin O

2011-06-01

Functional neuroimaging studies suggest that chronic cocaine use is associated with frontal lobe abnormalities. Functional connectivity (FC) alterations of cocaine-dependent individuals (CD), however, are not yet clear. This is the first study to our knowledge that examines resting FC of anterior cingulate cortex (ACC) in CD. Because ACC is known to integrate inputs from different brain regions to regulate behavior, we hypothesized that CD will have connectivity abnormalities in ACC networks. In addition, we hypothesized that abnormalities would be associated with poor performance in delayed discounting and reversal learning tasks. Resting functional magnetic resonance imaging data were collected to look for FC differences between 27 CD (5 women, age: M = 39.73, SD = 6.14 years) and 24 control subjects (5 women, age: M = 39.76, SD = 7.09 years). Participants were assessed with delayed discounting and reversal learning tasks. With seed-based FC measures, we examined FC in CD and control subjects within five ACC connectivity networks with seeds in subgenual, caudal, dorsal, rostral, and perigenual ACC. The CD showed increased FC within the perigenual ACC network in left middle frontal gyrus, ACC, and middle temporal gyrus when compared with control subjects. The FC abnormalities were significantly positively correlated with task performance in delayed discounting and reversal learning tasks in CD. The present study shows that participants with chronic cocaine-dependency have hyperconnectivity within an ACC network known to be involved in social processing and "mentalizing." In addition, FC abnormalities found in CD were associated with difficulties with delay rewards and slower adaptive learning. Copyright © 2011 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Reversible stress softening of collagen based networks from the jumbo squid mantle (Dosidicus gigas).

PubMed

Torres, F G; Troncoso, O P; Rivas, E R; Gomez, C G; Lopez, D

2014-04-01

Dosidicus gigas is the largest and one of the most abundant jumbo squids in the eastern Pacific Ocean. In this paper we have studied the muscle of the mantle of D. gigas (DGM). Morphological, thermal and rheological properties were assessed by means of atomic force microscopy, scanning electron microscopy, energy-dispersive X-ray spectroscopy, differential scanning calorimetry, thermogravimetry and oscillatory rheometry. This study allowed us to assess the morphological and rheological properties of a collagen based network occurring in nature. The results showed that the DGM network displays a nonlinear effect called reversible stress softening (RSS) that has been previously described for other types of biological structures such as naturally occurring cellulose networks and actin networks. We propose that the RSS could play a key role on the way jumbo squids withstand hydrostatic pressure. The results presented here confirm that this phenomenon occurs in a wider number of materials than previously thought, all of them exhibiting different size scales as well as physical conformation. Copyright © 2013 Elsevier B.V. All rights reserved.

Validation of in situ networks via field sampling: case study in the South Fork Experimental Watershed

USDA-ARS?s Scientific Manuscript database

The calibration and validation of soil moisture remote sensing products is complicated by the logistics of installing a soil moisture network for a long term period in an active landscape. Therefore, these stations are located along field boundaries or in non-representative sites with regards to so...

Landscape-scale spatial abundance distributions discriminate core from random components of boreal lake bacterioplankton.

PubMed

Niño-García, Juan Pablo; Ruiz-González, Clara; Del Giorgio, Paul A

2016-12-01

Aquatic bacterial communities harbour thousands of coexisting taxa. To meet the challenge of discriminating between a 'core' and a sporadically occurring 'random' component of these communities, we explored the spatial abundance distribution of individual bacterioplankton taxa across 198 boreal lakes and their associated fluvial networks (188 rivers). We found that all taxa could be grouped into four distinct categories based on model statistical distributions (normal like, bimodal, logistic and lognormal). The distribution patterns across lakes and their associated river networks showed that lake communities are composed of a core of taxa whose distribution appears to be linked to in-lake environmental sorting (normal-like and bimodal categories), and a large fraction of mostly rare bacteria (94% of all taxa) whose presence appears to be largely random and linked to downstream transport in aquatic networks (logistic and lognormal categories). These rare taxa are thus likely to reflect species sorting at upstream locations, providing a perspective of the conditions prevailing in entire aquatic networks rather than only in lakes. © 2016 John Wiley & Sons Ltd/CNRS.

It's Easy To Be Wise after the Event: Concepts for Redesigning an Educational System on Logistics Derived from Reflecting Its Development and Use.

ERIC Educational Resources Information Center

Neumann, Gaby; Ziems, Dietrich; Hopner, Christian

This paper introduces a multimedia-based educational system on logistics developed at the University of Magdeburg (Germany), reports on development and implementation of the prototype, and discusses ideas for redesign. The system was tested, used, and evaluated at the university and within a European network of 24 universities, colleges, and…

Phase transition in tumor growth: I avascular development

NASA Astrophysics Data System (ADS)

Izquierdo-Kulich, E.; Rebelo, I.; Tejera, E.; Nieto-Villar, J. M.

2013-12-01

We propose a mechanism for avascular tumor growth based on a simple chemical network. This model presents a logistic behavior and shows a “second order” phase transition. We prove the fractal origin of the empirical logistics and Gompertz constant and its relation to mitosis and apoptosis rate. Finally, the thermodynamics framework developed demonstrates the entropy production rate as a Lyapunov function during avascular tumor growth.

USMC Logistics Resource Allocation Optimization Tool

DTIC Science & Technology

2015-12-01

Virtual Warehouse Concept ..........................................12  3.  New Models in Logistics Network Design and Implications for Third Party...is the smallest DD activity in terms of manpower , but due to its proximity to USMC units, stocks a much greater quantity of USMC-demanded materiel...salient conclusion to reference with respect to this thesis. 12 2. Inventory Management of Repairables in the U.S. Marine Corps— A Virtual Warehouse

Dynamic Coordination of Eu-Iminodiacetate to Control Fluorochromic Response of Polymer Hydrogels to Multistimuli.

PubMed

Weng, Gengsheng; Thanneeru, Srinivas; He, Jie

2018-03-01

New fluorochromic materials that reversibly change their emission properties in response to their environment are of interest for the development of sensors and light-emitting materials. A new design of Eu-containing polymer hydrogels showing fast self-healing and tunable fluorochromic properties in response to five different stimuli, including pH, temperature, metal ions, sonication, and force, is reported. The polymer hydrogels are fabricated using Eu-iminodiacetate (IDA) coordination in a hydrophilic poly(N,N-dimethylacrylamide) matrix. Dynamic metal-ligand coordination allows reversible formation and disruption of hydrogel networks under various stimuli which makes hydrogels self-healable and injectable. Such hydrogels show interesting switchable ON/OFF luminescence along with the sol-gel transition through the reversible formation and dissociation of Eu-IDA complexes upon various stimuli. It is demonstrated that Eu-containing hydrogels display fast and reversible mechanochromic response as well in hydrogels having interpenetrating polymer network. Those multistimuli responsive fluorochromic hydrogels illustrate a new pathway to make smart optical materials, particularly for biological sensors where multistimuli response is required. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Network characteristics and patent value—Evidence from the Light-Emitting Diode industry

PubMed Central

Huang, Way-Ren; Hsieh, Chia-Jen; Chang, Ke-Chiun; Kiang, Yen-Jo; Yuan, Chien-Chung; Chu, Woei-Chyn

2017-01-01

This study proposes a different angle to social network analysis that evaluates patent value and explores its influencing factors using the network centrality and network position. This study utilizes a logistic regression model to explore the relationships in the LED industry between patent value and network centrality as measured from out-degree centrality, in-degree centrality, in-closeness centrality, and network position, which is measured from effect size. The empirical result shows that out-degree centrality and in-degree centrality have significant positive effects on patent value and that effect size has a significant negative effect on patent value. PMID:28817587

Reverse Engineering Cellular Networks with Information Theoretic Methods

PubMed Central

Villaverde, Alejandro F.; Ross, John; Banga, Julio R.

2013-01-01

Building mathematical models of cellular networks lies at the core of systems biology. It involves, among other tasks, the reconstruction of the structure of interactions between molecular components, which is known as network inference or reverse engineering. Information theory can help in the goal of extracting as much information as possible from the available data. A large number of methods founded on these concepts have been proposed in the literature, not only in biology journals, but in a wide range of areas. Their critical comparison is difficult due to the different focuses and the adoption of different terminologies. Here we attempt to review some of the existing information theoretic methodologies for network inference, and clarify their differences. While some of these methods have achieved notable success, many challenges remain, among which we can mention dealing with incomplete measurements, noisy data, counterintuitive behaviour emerging from nonlinear relations or feedback loops, and computational burden of dealing with large data sets. PMID:24709703

Genetic Network Inference: From Co-Expression Clustering to Reverse Engineering

NASA Technical Reports Server (NTRS)

Dhaeseleer, Patrik; Liang, Shoudan; Somogyi, Roland

2000-01-01

Advances in molecular biological, analytical, and computational technologies are enabling us to systematically investigate the complex molecular processes underlying biological systems. In particular, using high-throughput gene expression assays, we are able to measure the output of the gene regulatory network. We aim here to review datamining and modeling approaches for conceptualizing and unraveling the functional relationships implicit in these datasets. Clustering of co-expression profiles allows us to infer shared regulatory inputs and functional pathways. We discuss various aspects of clustering, ranging from distance measures to clustering algorithms and multiple-duster memberships. More advanced analysis aims to infer causal connections between genes directly, i.e., who is regulating whom and how. We discuss several approaches to the problem of reverse engineering of genetic networks, from discrete Boolean networks, to continuous linear and non-linear models. We conclude that the combination of predictive modeling with systematic experimental verification will be required to gain a deeper insight into living organisms, therapeutic targeting, and bioengineering.

Tensor network method for reversible classical computation

NASA Astrophysics Data System (ADS)

Yang, Zhi-Cheng; Kourtis, Stefanos; Chamon, Claudio; Mucciolo, Eduardo R.; Ruckenstein, Andrei E.

2018-03-01

We develop a tensor network technique that can solve universal reversible classical computational problems, formulated as vertex models on a square lattice [Nat. Commun. 8, 15303 (2017), 10.1038/ncomms15303]. By encoding the truth table of each vertex constraint in a tensor, the total number of solutions compatible with partial inputs and outputs at the boundary can be represented as the full contraction of a tensor network. We introduce an iterative compression-decimation (ICD) scheme that performs this contraction efficiently. The ICD algorithm first propagates local constraints to longer ranges via repeated contraction-decomposition sweeps over all lattice bonds, thus achieving compression on a given length scale. It then decimates the lattice via coarse-graining tensor contractions. Repeated iterations of these two steps gradually collapse the tensor network and ultimately yield the exact tensor trace for large systems, without the need for manual control of tensor dimensions. Our protocol allows us to obtain the exact number of solutions for computations where a naive enumeration would take astronomically long times.

LOGISTIC NETWORK REGRESSION FOR SCALABLE ANALYSIS OF NETWORKS WITH JOINT EDGE/VERTEX DYNAMICS

PubMed Central

Almquist, Zack W.; Butts, Carter T.

2015-01-01

Change in group size and composition has long been an important area of research in the social sciences. Similarly, interest in interaction dynamics has a long history in sociology and social psychology. However, the effects of endogenous group change on interaction dynamics are a surprisingly understudied area. One way to explore these relationships is through social network models. Network dynamics may be viewed as a process of change in the edge structure of a network, in the vertex set on which edges are defined, or in both simultaneously. Although early studies of such processes were primarily descriptive, recent work on this topic has increasingly turned to formal statistical models. Although showing great promise, many of these modern dynamic models are computationally intensive and scale very poorly in the size of the network under study and/or the number of time points considered. Likewise, currently used models focus on edge dynamics, with little support for endogenously changing vertex sets. Here, the authors show how an existing approach based on logistic network regression can be extended to serve as a highly scalable framework for modeling large networks with dynamic vertex sets. The authors place this approach within a general dynamic exponential family (exponential-family random graph modeling) context, clarifying the assumptions underlying the framework (and providing a clear path for extensions), and they show how model assessment methods for cross-sectional networks can be extended to the dynamic case. Finally, the authors illustrate this approach on a classic data set involving interactions among windsurfers on a California beach. PMID:26120218

LOGISTIC NETWORK REGRESSION FOR SCALABLE ANALYSIS OF NETWORKS WITH JOINT EDGE/VERTEX DYNAMICS.

PubMed

Almquist, Zack W; Butts, Carter T

2014-08-01

Change in group size and composition has long been an important area of research in the social sciences. Similarly, interest in interaction dynamics has a long history in sociology and social psychology. However, the effects of endogenous group change on interaction dynamics are a surprisingly understudied area. One way to explore these relationships is through social network models. Network dynamics may be viewed as a process of change in the edge structure of a network, in the vertex set on which edges are defined, or in both simultaneously. Although early studies of such processes were primarily descriptive, recent work on this topic has increasingly turned to formal statistical models. Although showing great promise, many of these modern dynamic models are computationally intensive and scale very poorly in the size of the network under study and/or the number of time points considered. Likewise, currently used models focus on edge dynamics, with little support for endogenously changing vertex sets. Here, the authors show how an existing approach based on logistic network regression can be extended to serve as a highly scalable framework for modeling large networks with dynamic vertex sets. The authors place this approach within a general dynamic exponential family (exponential-family random graph modeling) context, clarifying the assumptions underlying the framework (and providing a clear path for extensions), and they show how model assessment methods for cross-sectional networks can be extended to the dynamic case. Finally, the authors illustrate this approach on a classic data set involving interactions among windsurfers on a California beach.

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

Wilson, Mark A.; Baljon, Arlette R. C.

The response of associating polymers with oscillatory shear is studied through large-scale simulations. A hybrid molecular dynamics (MD), Monte Carlo (MC) algorithm is employed. Polymer chains are modeled as a coarse-grained bead-spring system. Functionalized end groups, at both ends of the polymer chains, can form reversible bonds according to MC rules. Stress-strain curves show nonlinearities indicated by a non-ellipsoidal shape. We consider two types of nonlinearities. Type I occurs at a strain amplitude much larger than one, type II at a frequency at which the elastic storage modulus dominates the viscous loss modulus. In this last case, the network topologymore » resembles that of the system at rest. The reversible bonds are broken and chains stretch when the system moves away from the zero-strain position. For type I, the chains relax and the number of reversible bonds peaks when the system is near an extreme of the motion. During the movement to the other extreme of the cycle, first a stress overshoot occurs, then a yield accompanied by shear-banding. Lastly, the network restructures. Interestingly, the system periodically restores bonds between the same associating groups. Even though major restructuring occurs, the system remembers previous network topologies.« less

Copolymer Networks From Oligo(ε-caprolactone) and n-Butyl Acrylate Enable a Reversible Bidirectional Shape-Memory Effect at Human Body Temperature.

PubMed

Saatchi, Mersa; Behl, Marc; Nöchel, Ulrich; Lendlein, Andreas

2015-05-01

Exploiting the tremendous potential of the recently discovered reversible bidirectional shape-memory effect (rbSME) for biomedical applications requires switching temperatures in the physiological range. The recent strategy is based on the reduction of the melting temperature range (ΔT m ) of the actuating oligo(ε-caprolactone) (OCL) domains in copolymer networks from OCL and n-butyl acrylate (BA), where the reversible effect can be adjusted to the human body temperature. In addition, it is investigated whether an rbSME in the temperature range close or even above Tm,offset (end of the melting transition) can be obtained. Two series of networks having mixtures of OCLs reveal broad ΔTm s from 2 °C to 50 °C and from -10 °C to 37 °C, respectively. In cyclic, thermomechanical experiments the rbSME can be tailored to display pronounced actuation in a temperature interval between 20 °C and 37 °C. In this way, the application spectrum of the rbSME can be extended to biomedical applications. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Scale-invariance underlying the logistic equation and its social applications

NASA Astrophysics Data System (ADS)

Hernando, A.; Plastino, A.

2013-01-01

On the basis of dynamical principles we i) advance a derivation of the Logistic Equation (LE), widely employed (among multiple applications) in the simulation of population growth, and ii) demonstrate that scale-invariance and a mean-value constraint are sufficient and necessary conditions for obtaining it. We also generalize the LE to multi-component systems and show that the above dynamical mechanisms underlie a large number of scale-free processes. Examples are presented regarding city-populations, diffusion in complex networks, and popularity of technological products, all of them obeying the multi-component logistic equation in an either stochastic or deterministic way.

Research on public logistics centers of Zhenzhou city based on GIS

NASA Astrophysics Data System (ADS)

Zeng, Yuhuai; Chen, Shuisen; Tian, Zhihui; Miao, Quansheng

2008-10-01

The regional public logistics center (PLC) is the intermedium that transports goods or commodity from producer to wholesaler, retailer and end consumer through whole supply chains. According to the Central Place Theory, the PLC should be multi-centric and of more kinds of graded degrees. From the road network planning discipline, an unique index---Importance Degree, is presented to measure the capacity of a PLC. The Importance Degree selects three township criteria: total population, gross industry product and budget income as weights to calculate the weighted vectors by principle component analysis method. Finally, through the clustering analysis, we can get the graded degrees of PLCs. It proves that that this research method is very effective for the road network planning of Zhengzhou City.

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...

Comparison of De Novo Network Reverse Engineering Methods with Applications to Ecotoxicology

EPA Science Inventory

The DREAM competitions for network modeling comparisons have made several points clear: 1) incorporating knowledge beyond gene expression data may improve modeling (e.g., data from knock-out organisms), 2) most techniques do not perform better than random, and 3) more complex met...

Adaptive significance of right hemisphere activation in aphasic language comprehension

PubMed Central

Meltzer, Jed A.; Wagage, Suraji; Ryder, Jennifer; Solomon, Beth; Braun, Allen R.

2013-01-01

Aphasic patients often exhibit increased right hemisphere activity during language tasks. This may represent takeover of function by regions homologous to the left-hemisphere language networks, maladaptive interference, or adaptation of alternate compensatory strategies. To distinguish between these accounts, we tested language comprehension in 25 aphasic patients using an online sentence-picture matching paradigm while measuring brain activation with MEG. Linguistic conditions included semantically irreversible (“The boy is eating the apple”) and reversible (“The boy is pushing the girl”) sentences at three levels of syntactic complexity. As expected, patients performed well above chance on irreversible sentences, and at chance on reversible sentences of high complexity. Comprehension of reversible non-complex sentences ranged from nearly perfect to chance, and was highly correlated with offline measures of language comprehension. Lesion analysis revealed that comprehension deficits for reversible sentences were predicted by damage to the left temporal lobe. Although aphasic patients activated homologous areas in the right temporal lobe, such activation was not correlated with comprehension performance. Rather, patients with better comprehension exhibited increased activity in dorsal fronto-parietal regions. Correlations between performance and dorsal network activity occurred bilaterally during perception of sentences, and in the right hemisphere during a post-sentence memory delay. These results suggest that effortful reprocessing of perceived sentences in short-term memory can support improved comprehension in aphasia, and that strategic recruitment of alternative networks, rather than homologous takeover, may account for some findings of right hemisphere language activation in aphasia. PMID:23566891

Bile Salt Mediated Growth of Reverse Wormlike Micelles in Nonpolar Liquids

NASA Astrophysics Data System (ADS)

Tung, Shih-Huang; Huang, Yi-En; Raghavan, Srinivasa

2006-03-01

We report the growth of reverse wormlike micelles induced by the addition of a bile salt in trace amounts to solutions of the phospholipid, lecithin in nonpolar organic solvents. Previous recipes for reverse wormlike micelles have usually required the addition of water to induce reverse micellar growth; here, we show that bile salts, due to their unique ``facially amphiphilic'' structure, can play a role analogous to water and promote the longitudinal aggregation of lecithin molecules into reverse micellar chains. The formation of transient entangled networks of these reverse micelles transforms low-viscosity lecithin organosols into strongly viscoelastic fluids. The zero-shear viscosity increases by more than five orders of magnitude, and it is the molar ratio of bile salt to lecithin that controls this viscosity enhancement. The growth of reverse wormlike micelles is also confirmed by small-angle neutron scattering (SANS) experiments on these fluids.

Sense and Respond Logistics: Integrating Prediction, Responsiveness, and Control Capabilities

DTIC Science & Technology

2006-01-01

logistics SAR sense and respond SCM Supply Chain Management SCN Supply Chain Network SIDA sense, interpret, decide, act SOS source of supply TCN...commodity supply chain management ( SCM ), will have WS- SCMs that focus on integrating information for a particular MDS. 8 In the remainder of this...developed applications of ABMs for SCM .21 Applications of Agents and Agent-Based Modeling Agents have been used in telecommunications, e-commerce

Multimodal Logistics Network Design over Planning Horizon through a Hybrid Meta-Heuristic Approach

NASA Astrophysics Data System (ADS)

Shimizu, Yoshiaki; Yamazaki, Yoshihiro; Wada, Takeshi

Logistics has been acknowledged increasingly as a key issue of supply chain management to improve business efficiency under global competition and diversified customer demands. This study aims at improving a quality of strategic decision making associated with dynamic natures in logistics network optimization. Especially, noticing an importance to concern with a multimodal logistics under multiterms, we have extended a previous approach termed hybrid tabu search (HybTS). The attempt intends to deploy a strategic planning more concretely so that the strategic plan can link to an operational decision making. The idea refers to a smart extension of the HybTS to solve a dynamic mixed integer programming problem. It is a two-level iterative method composed of a sophisticated tabu search for the location problem at the upper level and a graph algorithm for the route selection at the lower level. To keep efficiency while coping with the resulting extremely large-scale problem, we invented a systematic procedure to transform the original linear program at the lower-level into a minimum cost flow problem solvable by the graph algorithm. Through numerical experiments, we verified the proposed method outperformed the commercial software. The results indicate the proposed approach can make the conventional strategic decision much more practical and is promising for real world applications.

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.

Optical interconnections and networks; Proceedings of the Meeting, The Hague, Netherlands, Mar. 14, 15, 1990

NASA Technical Reports Server (NTRS)

Bartelt, Hartmut (Editor)

1990-01-01

The conference presents papers on interconnections, clock distribution, neural networks, and components and materials. Particular attention is given to a comparison of optical and electrical data interconnections at the board and backplane levels, a wafer-level optical interconnection network layout, an analysis and simulation of photonic switch networks, and the integration of picosecond GaAs photoconductive devices with silicon circuits for optical clocking and interconnects. Consideration is also given to the optical implementation of neural networks, invariance in an optoelectronic implementation of neural networks, and the recording of reversible patterns in polymer lightguides.

Sex differences in how social networks and relationship quality influence experimental pain sensitivity.

PubMed

Vigil, Jacob M; Rowell, Lauren N; Chouteau, Simone; Chavez, Alexandre; Jaramillo, Elisa; Neal, Michael; Waid, David

2013-01-01

This is the first study to examine how both structural and functional components of individuals' social networks may moderate the association between biological sex and experimental pain sensitivity. One hundred and fifty-two healthy adults (mean age = 22yrs., 53% males) were measured for cold pressor task (CPT) pain sensitivity (i.e., intensity ratings) and core aspects of social networks (e.g., proportion of friends vs. family, affection, affirmation, and aid). Results showed consistent sex differences in how social network structures and intimate relationship functioning modulated pain sensitivity. Females showed higher pain sensitivity when their social networks consisted of a higher proportion of intimate types of relationship partners (e.g., kin vs. non kin), when they had known their network partners for a longer period of time, and when they reported higher levels of logistical support from their significant other (e.g., romantic partner). Conversely, males showed distinct patterns in the opposite direction, including an association between higher levels of logistical support from one's significant other and lower CPT pain intensity. These findings show for the first time that the direction of sex differences in exogenous pain sensitivity is likely dependent on fundamental components of the individual's social environment. The utility of a social-signaling perspective of pain behaviors for examining, comparing, and interpreting individual and group differences in experimental and clinical pain reports is discussed.

Sex Differences in How Social Networks and Relationship Quality Influence Experimental Pain Sensitivity

PubMed Central

Vigil, Jacob M.; Rowell, Lauren N.; Chouteau, Simone; Chavez, Alexandre; Jaramillo, Elisa; Neal, Michael; Waid, David

2013-01-01

This is the first study to examine how both structural and functional components of individuals’ social networks may moderate the association between biological sex and experimental pain sensitivity. One hundred and fifty-two healthy adults (mean age = 22yrs., 53% males) were measured for cold pressor task (CPT) pain sensitivity (i.e., intensity ratings) and core aspects of social networks (e.g., proportion of friends vs. family, affection, affirmation, and aid). Results showed consistent sex differences in how social network structures and intimate relationship functioning modulated pain sensitivity. Females showed higher pain sensitivity when their social networks consisted of a higher proportion of intimate types of relationship partners (e.g., kin vs. non kin), when they had known their network partners for a longer period of time, and when they reported higher levels of logistical support from their significant other (e.g., romantic partner). Conversely, males showed distinct patterns in the opposite direction, including an association between higher levels of logistical support from one’s significant other and lower CPT pain intensity. These findings show for the first time that the direction of sex differences in exogenous pain sensitivity is likely dependent on fundamental components of the individual’s social environment. The utility of a social-signaling perspective of pain behaviors for examining, comparing, and interpreting individual and group differences in experimental and clinical pain reports is discussed. PMID:24223836

Reverse-engineering the Arabidopsis thaliana transcriptional network under changing environmental conditions

PubMed Central

Carrera, Javier; Rodrigo, Guillermo; Jaramillo, Alfonso; Elena, Santiago F

2009-01-01

Background Understanding the molecular mechanisms plants have evolved to adapt their biological activities to a constantly changing environment is an intriguing question and one that requires a systems biology approach. Here we present a network analysis of genome-wide expression data combined with reverse-engineering network modeling to dissect the transcriptional control of Arabidopsis thaliana. The regulatory network is inferred by using an assembly of microarray data containing steady-state RNA expression levels from several growth conditions, developmental stages, biotic and abiotic stresses, and a variety of mutant genotypes. Results We show that the A. thaliana regulatory network has the characteristic properties of hierarchical networks. We successfully applied our quantitative network model to predict the full transcriptome of the plant for a set of microarray experiments not included in the training dataset. We also used our model to analyze the robustness in expression levels conferred by network motifs such as the coherent feed-forward loop. In addition, the meta-analysis presented here has allowed us to identify regulatory and robust genetic structures. Conclusions These data suggest that A. thaliana has evolved high connectivity in terms of transcriptional regulation among cellular functions involved in response and adaptation to changing environments, while gene networks constitutively expressed or less related to stress response are characterized by a lower connectivity. Taken together, these findings suggest conserved regulatory strategies that have been selected during the evolutionary history of this eukaryote. PMID:19754933

Empirical research on complex networks modeling of combat SoS based on data from real war-game, Part I: Statistical characteristics

NASA Astrophysics Data System (ADS)

Chen, Lei; Kou, Yingxin; Li, Zhanwu; Xu, An; Wu, Cheng

2018-01-01

We build a complex networks model of combat System-of-Systems (SoS) based on empirical data from a real war-game, this model is a combination of command & control (C2) subnetwork, sensors subnetwork, influencers subnetwork and logistical support subnetwork, each subnetwork has idiographic components and statistical characteristics. The C2 subnetwork is the core of whole combat SoS, it has a hierarchical structure with no modularity, of which robustness is strong enough to maintain normal operation after any two nodes is destroyed; the sensors subnetwork and influencers subnetwork are like sense organ and limbs of whole combat SoS, they are both flat modular networks of which degree distribution obey GEV distribution and power-law distribution respectively. The communication network is the combination of all subnetworks, it is an assortative Small-World network with core-periphery structure, the Intelligence & Communication Stations/Command Center integrated with C2 nodes in the first three level act as the hub nodes in communication network, and all the fourth-level C2 nodes, sensors, influencers and logistical support nodes have communication capability, they act as the periphery nodes in communication network, its degree distribution obeys exponential distribution in the beginning, Gaussian distribution in the middle, and power-law distribution in the end, and its path length obeys GEV distribution. The betweenness centrality distribution, closeness centrality distribution and eigenvector centrality are also been analyzed to measure the vulnerability of nodes.

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

Transmission Risks of Schistosomiasis Japonica: Extraction from Back-propagation Artificial Neural Network and Logistic Regression Model

PubMed Central

Xu, Jun-Fang; Xu, Jing; Li, Shi-Zhu; Jia, Tia-Wu; Huang, Xi-Bao; Zhang, Hua-Ming; Chen, Mei; Yang, Guo-Jing; Gao, Shu-Jing; Wang, Qing-Yun; Zhou, Xiao-Nong

2013-01-01

Background The transmission of schistosomiasis japonica in a local setting is still poorly understood in the lake regions of the People's Republic of China (P. R. China), and its transmission patterns are closely related to human, social and economic factors. Methodology/Principal Findings We aimed to apply the integrated approach of artificial neural network (ANN) and logistic regression model in assessment of transmission risks of Schistosoma japonicum with epidemiological data collected from 2339 villagers from 1247 households in six villages of Jiangling County, P.R. China. By using the back-propagation (BP) of the ANN model, 16 factors out of 27 factors were screened, and the top five factors ranked by the absolute value of mean impact value (MIV) were mainly related to human behavior, i.e. integration of water contact history and infection history, family with past infection, history of water contact, infection history, and infection times. The top five factors screened by the logistic regression model were mainly related to the social economics, i.e. village level, economic conditions of family, age group, education level, and infection times. The risk of human infection with S. japonicum is higher in the population who are at age 15 or younger, or with lower education, or with the higher infection rate of the village, or with poor family, and in the population with more than one time to be infected. Conclusion/Significance Both BP artificial neural network and logistic regression model established in a small scale suggested that individual behavior and socioeconomic status are the most important risk factors in the transmission of schistosomiasis japonica. It was reviewed that the young population (≤15) in higher-risk areas was the main target to be intervened for the disease transmission control. PMID:23556015

Predicting The Type Of Pregnancy Using Flexible Discriminate Analysis And Artificial Neural Networks: A Comparison Study

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

Hooman, A.; Mohammadzadeh, M

Some medical and epidemiological surveys have been designed to predict a nominal response variable with several levels. With regard to the type of pregnancy there are four possible states: wanted, unwanted by wife, unwanted by husband and unwanted by couple. In this paper, we have predicted the type of pregnancy, as well as the factors influencing it using three different models and comparing them. Regarding the type of pregnancy with several levels, we developed a multinomial logistic regression, a neural network and a flexible discrimination based on the data and compared their results using tow statistical indices: Surface under curvemore » (ROC) and kappa coefficient. Based on these tow indices, flexible discrimination proved to be a better fit for prediction on data in comparison to other methods. When the relations among variables are complex, one can use flexible discrimination instead of multinomial logistic regression and neural network to predict the nominal response variables with several levels in order to gain more accurate predictions.« less

Propensity score estimation: machine learning and classification methods as alternatives to logistic regression

PubMed Central

Westreich, Daniel; Lessler, Justin; Funk, Michele Jonsson

2010-01-01

Summary Objective Propensity scores for the analysis of observational data are typically estimated using logistic regression. Our objective in this Review was to assess machine learning alternatives to logistic regression which may accomplish the same goals but with fewer assumptions or greater accuracy. Study Design and Setting We identified alternative methods for propensity score estimation and/or classification from the public health, biostatistics, discrete mathematics, and computer science literature, and evaluated these algorithms for applicability to the problem of propensity score estimation, potential advantages over logistic regression, and ease of use. Results We identified four techniques as alternatives to logistic regression: neural networks, support vector machines, decision trees (CART), and meta-classifiers (in particular, boosting). Conclusion While the assumptions of logistic regression are well understood, those assumptions are frequently ignored. All four alternatives have advantages and disadvantages compared with logistic regression. Boosting (meta-classifiers) and to a lesser extent decision trees (particularly CART) appear to be most promising for use in the context of propensity score analysis, but extensive simulation studies are needed to establish their utility in practice. PMID:20630332

Proteomic analysis of three gonad types of swamp eel reveals genes differentially expressed during sex reversal.

PubMed

Sheng, Yue; Zhao, Wei; Song, Ying; Li, Zhigang; Luo, Majing; Lei, Quan; Cheng, Hanhua; Zhou, Rongjia

2015-05-18

A variety of mechanisms are engaged in sex determination in vertebrates. The teleost fish swamp eel undergoes sex reversal naturally and is an ideal model for vertebrate sexual development. However, the importance of proteome-wide scanning for gonad reversal was not previously determined. We report a 2-D electrophoresis analysis of three gonad types of proteomes during sex reversal. MS/MS analysis revealed a group of differentially expressed proteins during ovary to ovotestis to testis transformation. Cbx3 is up-regulated during gonad reversal and is likely to have a role in spermatogenesis. Rab37 is down-regulated during the reversal and is mainly associated with oogenesis. Both Cbx3 and Rab37 are linked up in a protein network. These datasets in gonadal proteomes provide a new resource for further studies in gonadal development.

Supramolecular motifs in dynamic covalent PEG-hemiaminal organogels

PubMed Central

Fox, Courtney H.; ter Hurrne, Gijs M.; Wojtecki, Rudy J.; Jones, Gavin O.; Horn, Hans W.; Meijer, E. W.; Frank, Curtis W.; Hedrick, James L.; García, Jeannette M.

2015-01-01

Dynamic covalent materials are stable materials that possess reversible behaviour triggered by stimuli such as light, redox conditions or temperature; whereas supramolecular crosslinks depend on the equilibrium constant and relative concentrations of crosslinks as a function of temperature. The combination of these two reversible chemistries can allow access to materials with unique properties. Here, we show that this combination of dynamic covalent and supramolecular chemistry can be used to prepare organogels comprising distinct networks. Two materials containing hemiaminal crosslink junctions were synthesized; one material is comprised of dynamic covalent junctions and the other contains hydrogen-bonding bis-hemiaminal moieties. Under specific network synthesis conditions, these materials exhibited self-healing behaviour. This work reports on both the molecular-level detail of hemiaminal crosslink junction formation as well as the macroscopic behaviour of hemiaminal dynamic covalent network (HDCN) elastomeric organogels. These materials have potential applications as elastomeric components in printable materials, cargo carriers and adhesives. PMID:26174864

Reversible Assembly of Graphitic Carbon Nitride 3D Network for Highly Selective Dyes Absorption and Regeneration.

PubMed

Zhang, Yuye; Zhou, Zhixin; Shen, Yanfei; Zhou, Qing; Wang, Jianhai; Liu, Anran; Liu, Songqin; Zhang, Yuanjian

2016-09-27

Responsive assembly of 2D materials is of great interest for a range of applications. In this work, interfacial functionalized carbon nitride (CN) nanofibers were synthesized by hydrolyzing bulk CN in sodium hydroxide solution. The reversible assemble and disassemble behavior of the as-prepared CN nanofibers was investigated by using CO2 as a trigger to form a hydrogel network at first. Compared to the most widespread absorbent materials such as active carbon, graphene and previously reported supramolecular gel, the proposed CN hydrogel not only exhibited a competitive absorbing capacity (maximum absorbing capacity of methylene blue up to 402 mg/g) but also overcame the typical deficiencies such as poor selectivity and high energy-consuming regeneration. This work would provide a strategy to construct a 3D CN network and open an avenue for developing smart assembly for potential applications ranging from environment to selective extraction.

Locating the source of diffusion in complex networks by time-reversal backward spreading.

PubMed

Shen, Zhesi; Cao, Shinan; Wang, Wen-Xu; Di, Zengru; Stanley, H Eugene

2016-03-01

Locating the source that triggers a dynamical process is a fundamental but challenging problem in complex networks, ranging from epidemic spreading in society and on the Internet to cancer metastasis in the human body. An accurate localization of the source is inherently limited by our ability to simultaneously access the information of all nodes in a large-scale complex network. This thus raises two critical questions: how do we locate the source from incomplete information and can we achieve full localization of sources at any possible location from a given set of observable nodes. Here we develop a time-reversal backward spreading algorithm to locate the source of a diffusion-like process efficiently and propose a general locatability condition. We test the algorithm by employing epidemic spreading and consensus dynamics as typical dynamical processes and apply it to the H1N1 pandemic in China. We find that the sources can be precisely located in arbitrary networks insofar as the locatability condition is assured. Our tools greatly improve our ability to locate the source of diffusion in complex networks based on limited accessibility of nodal information. Moreover, they have implications for controlling a variety of dynamical processes taking place on complex networks, such as inhibiting epidemics, slowing the spread of rumors, pollution control, and environmental protection.

Locating the source of diffusion in complex networks by time-reversal backward spreading

NASA Astrophysics Data System (ADS)

Shen, Zhesi; Cao, Shinan; Wang, Wen-Xu; Di, Zengru; Stanley, H. Eugene

2016-03-01

Locating the source that triggers a dynamical process is a fundamental but challenging problem in complex networks, ranging from epidemic spreading in society and on the Internet to cancer metastasis in the human body. An accurate localization of the source is inherently limited by our ability to simultaneously access the information of all nodes in a large-scale complex network. This thus raises two critical questions: how do we locate the source from incomplete information and can we achieve full localization of sources at any possible location from a given set of observable nodes. Here we develop a time-reversal backward spreading algorithm to locate the source of a diffusion-like process efficiently and propose a general locatability condition. We test the algorithm by employing epidemic spreading and consensus dynamics as typical dynamical processes and apply it to the H1N1 pandemic in China. We find that the sources can be precisely located in arbitrary networks insofar as the locatability condition is assured. Our tools greatly improve our ability to locate the source of diffusion in complex networks based on limited accessibility of nodal information. Moreover, they have implications for controlling a variety of dynamical processes taking place on complex networks, such as inhibiting epidemics, slowing the spread of rumors, pollution control, and environmental protection.

Telestroke network fundamentals.

PubMed

Meyer, Brett C; Demaerschalk, Bart M

2012-10-01

The objectives of this manuscript are to identify key components to maintaining the logistic and/or operational sustainability of a telestroke network, to identify best practices to be considered for assessment and management of acute stroke when planning for and developing a telestroke network, to show practical steps to enable progress toward implementing a telestroke solution for optimizing acute stroke care, to incorporate evidence-based practice guidelines and care pathways into a telestroke network, to emphasize technology variables and options, and to propose metrics to use when determining the performance, outcomes, and quality of a telestroke network. Copyright © 2012 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Inferring Broad Regulatory Biology from Time Course Data: Have We Reached an Upper Bound under Constraints Typical of In Vivo Studies?

PubMed Central

Craddock, Travis J. A.; Fletcher, Mary Ann; Klimas, Nancy G.

2015-01-01

There is a growing appreciation for the network biology that regulates the coordinated expression of molecular and cellular markers however questions persist regarding the identifiability of these networks. Here we explore some of the issues relevant to recovering directed regulatory networks from time course data collected under experimental constraints typical of in vivo studies. NetSim simulations of sparsely connected biological networks were used to evaluate two simple feature selection techniques used in the construction of linear Ordinary Differential Equation (ODE) models, namely truncation of terms versus latent vector projection. Performance was compared with ODE-based Time Series Network Identification (TSNI) integral, and the information-theoretic Time-Delay ARACNE (TD-ARACNE). Projection-based techniques and TSNI integral outperformed truncation-based selection and TD-ARACNE on aggregate networks with edge densities of 10-30%, i.e. transcription factor, protein-protein cliques and immune signaling networks. All were more robust to noise than truncation-based feature selection. Performance was comparable on the in silico 10-node DREAM 3 network, a 5-node Yeast synthetic network designed for In vivo Reverse-engineering and Modeling Assessment (IRMA) and a 9-node human HeLa cell cycle network of similar size and edge density. Performance was more sensitive to the number of time courses than to sample frequency and extrapolated better to larger networks by grouping experiments. In all cases performance declined rapidly in larger networks with lower edge density. Limited recovery and high false positive rates obtained overall bring into question our ability to generate informative time course data rather than the design of any particular reverse engineering algorithm. PMID:25984725

Nanoscale Control over the Mixing Behavior of Surface-Confined Bicomponent Supramolecular Networks Using an Oriented External Electric Field

PubMed Central

2017-01-01

Strong electric fields are known to influence the properties of molecules as well as materials. Here we show that by changing the orientation of an externally applied electric field, one can locally control the mixing behavior of two molecules physisorbed on a solid surface. Whether the starting two-component network evolves into an ordered two-dimensional (2D) cocrystal, yields an amorphous network where the two components phase separate, or shows preferential adsorption of only one component depends on the solution stoichiometry. The experiments are carried out by changing the orientation of the strong electric field that exists between the tip of a scanning tunneling microscope and a solid substrate. The structure of the two-component network typically changes from open porous at negative substrate bias to relatively compact when the polarity of the applied bias is reversed. The electric-field-induced mixing behavior is reversible, and the supramolecular system exhibits excellent stability and good response efficiency. When molecular guests are adsorbed in the porous networks, the field-induced switching behavior was found to be completely different. Plausible reasons behind the field-induced mixing behavior are discussed. PMID:29112378

Photo-triggered solvent-free metamorphosis of polymeric materials.

PubMed

Honda, Satoshi; Toyota, Taro

2017-09-11

Liquefaction and solidification of materials are the most fundamental changes observed during thermal phase transitions, yet the design of organic and polymeric soft materials showing isothermal reversible liquid-nonliquid conversion remains challenging. Here, we demonstrate that solvent-free repeatable molecular architectural transformation between liquid-star and nonliquid-network polymers that relies on cleavage and reformation of a covalent bond in hexaarylbiimidazole. Liquid four-armed star-shaped poly(n-butyl acrylate) and poly(dimethyl siloxane) with 2,4,5-triphenylimidazole end groups were first synthesized. Subsequent oxidation of the 2,4,5-triphenylimidazoles into 2,4,5-triphenylimidazoryl radicals and their coupling with these liquid star polymers to form hexaarylbiimidazoles afforded the corresponding nonliquid network polymers. The resulting nonliquid network polymers liquefied upon UV irradiation and produced liquid star-shaped polymers with 2,4,5-triphenylimidazoryl radical end groups that reverted to nonliquid network polymers again by recoupling of the generated 2,4,5-triphenylimidazoryl radicals immediately after terminating UV irradiation.The design of organic and polymeric soft materials showing isothermal reversible liquid-nonliquid conversion is challenging. Here, the authors show solvent-free repeatable molecular architectural transformation between liquid-star and non-liquid-network polymers by the cleavage and reformation of covalent bonds in the polymer chain.

New machine-learning algorithms for prediction of Parkinson's disease

NASA Astrophysics Data System (ADS)

Mandal, Indrajit; Sairam, N.

2014-03-01

This article presents an enhanced prediction accuracy of diagnosis of Parkinson's disease (PD) to prevent the delay and misdiagnosis of patients using the proposed robust inference system. New machine-learning methods are proposed and performance comparisons are based on specificity, sensitivity, accuracy and other measurable parameters. The robust methods of treating Parkinson's disease (PD) includes sparse multinomial logistic regression, rotation forest ensemble with support vector machines and principal components analysis, artificial neural networks, boosting methods. A new ensemble method comprising of the Bayesian network optimised by Tabu search algorithm as classifier and Haar wavelets as projection filter is used for relevant feature selection and ranking. The highest accuracy obtained by linear logistic regression and sparse multinomial logistic regression is 100% and sensitivity, specificity of 0.983 and 0.996, respectively. All the experiments are conducted over 95% and 99% confidence levels and establish the results with corrected t-tests. This work shows a high degree of advancement in software reliability and quality of the computer-aided diagnosis system and experimentally shows best results with supportive statistical inference.

Mobile Support For Logistics

DTIC Science & Technology

2016-03-01

Infrastructure to Support Mobile Devices (Takai, 2012, p. 2). The objectives needed in order to meet this goal are to: evolve spectrum management, expand... infrastructure to support wireless capabilities, and establish a mobile device security architecture (Takai, 2012, p. 2). By expanding infrastructure to...often used on Mobile Ad-Hoc Networks (MANETs). MANETS are infrastructure -less networks that include, but are not limited to, mobile devices. These

Impact of trucking network flow on preferred biorefinery locations in the southern United States

Treesearch

Timothy M. Young; Lee D. Han; James H. Perdue; Stephanie R. Hargrove; Frank M. Guess; Xia Huang; Chung-Hao Chen

2017-01-01

The impact of the trucking transportation network flow was modeled for the southern United States. The study addresses a gap in existing research by applying a Bayesian logistic regression and Geographic Information System (GIS) geospatial analysis to predict biorefinery site locations. A one-way trucking cost assuming a 128.8 km (80-mile) haul distance was estimated...

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

A Simulation Based Approach for Contingency Planning for Aircraft Turnaround Operation System Activities in Airline Hubs

NASA Technical Reports Server (NTRS)

Adeleye, Sanya; Chung, Christopher

2006-01-01

Commercial aircraft undergo a significant number of maintenance and logistical activities during the turnaround operation at the departure gate. By analyzing the sequencing of these activities, more effective turnaround contingency plans may be developed for logistical and maintenance disruptions. Turnaround contingency plans are particularly important as any kind of delay in a hub based system may cascade into further delays with subsequent connections. The contingency sequencing of the maintenance and logistical turnaround activities were analyzed using a combined network and computer simulation modeling approach. Experimental analysis of both current and alternative policies provides a framework to aid in more effective tactical decision making.

Reverse Engineering and Security Evaluation of Commercial Tags for RFID-Based IoT Applications.

PubMed

Fernández-Caramés, Tiago M; Fraga-Lamas, Paula; Suárez-Albela, Manuel; Castedo, Luis

2016-12-24

The Internet of Things (IoT) is a distributed system of physical objects that requires the seamless integration of hardware (e.g., sensors, actuators, electronics) and network communications in order to collect and exchange data. IoT smart objects need to be somehow identified to determine the origin of the data and to automatically detect the elements around us. One of the best positioned technologies to perform identification is RFID (Radio Frequency Identification), which in the last years has gained a lot of popularity in applications like access control, payment cards or logistics. Despite its popularity, RFID security has not been properly handled in numerous applications. To foster security in such applications, this article includes three main contributions. First, in order to establish the basics, a detailed review of the most common flaws found in RFID-based IoT systems is provided, including the latest attacks described in the literature. Second, a novel methodology that eases the detection and mitigation of such flaws is presented. Third, the latest RFID security tools are analyzed and the methodology proposed is applied through one of them (Proxmark 3) to validate it. Thus, the methodology is tested in different scenarios where tags are commonly used for identification. In such systems it was possible to clone transponders, extract information, and even emulate both tags and readers. Therefore, it is shown that the methodology proposed is useful for auditing security and reverse engineering RFID communications in IoT applications. It must be noted that, although this paper is aimed at fostering RFID communications security in IoT applications, the methodology can be applied to any RFID communications protocol.

Reverse Engineering and Security Evaluation of Commercial Tags for RFID-Based IoT Applications

PubMed Central

Fernández-Caramés, Tiago M.; Fraga-Lamas, Paula; Suárez-Albela, Manuel; Castedo, Luis

2016-01-01

The Internet of Things (IoT) is a distributed system of physical objects that requires the seamless integration of hardware (e.g., sensors, actuators, electronics) and network communications in order to collect and exchange data. IoT smart objects need to be somehow identified to determine the origin of the data and to automatically detect the elements around us. One of the best positioned technologies to perform identification is RFID (Radio Frequency Identification), which in the last years has gained a lot of popularity in applications like access control, payment cards or logistics. Despite its popularity, RFID security has not been properly handled in numerous applications. To foster security in such applications, this article includes three main contributions. First, in order to establish the basics, a detailed review of the most common flaws found in RFID-based IoT systems is provided, including the latest attacks described in the literature. Second, a novel methodology that eases the detection and mitigation of such flaws is presented. Third, the latest RFID security tools are analyzed and the methodology proposed is applied through one of them (Proxmark 3) to validate it. Thus, the methodology is tested in different scenarios where tags are commonly used for identification. In such systems it was possible to clone transponders, extract information, and even emulate both tags and readers. Therefore, it is shown that the methodology proposed is useful for auditing security and reverse engineering RFID communications in IoT applications. It must be noted that, although this paper is aimed at fostering RFID communications security in IoT applications, the methodology can be applied to any RFID communications protocol. PMID:28029119

Pulse power applications of silicon diodes in EML capacitive pulsers

NASA Astrophysics Data System (ADS)

Dethlefsen, Rolf; McNab, Ian; Dobbie, Clyde; Bernhardt, Tom; Puterbaugh, Robert; Levine, Frank; Coradeschi, Tom; Rinaldi, Vito

1993-01-01

Crowbar diodes are used for increasing the energy transfer from capacitive pulse forming networks. They also prevent voltage reversal on the energy storage capacitors. 52 mm diameter diodes with a 5 kV reverse blocking voltage, rated 40 kA were successfully used for the 32 MJ SSG rail gun. An uprated diode with increased current capability and a 15 kV reverse blocking voltage has been developed. Transient thermal analysis has predicted the current ratings for different pulse length. Analysis verification is obtained from destructive testing.

Association of childhood abuse with homeless women's social networks.

PubMed

Green, Harold D; Tucker, Joan S; Wenzel, Suzanne L; Golinelli, Daniela; Kennedy, David P; Ryan, Gery W; Zhou, Annie J

2012-01-01

Childhood abuse has been linked to negative sequelae for women later in life including drug and alcohol use and violence as victim or perpetrator and may also affect the development of women's social networks. Childhood abuse is prevalent among at-risk populations of women (such as the homeless) and thus may have a stronger impact on their social networks. We conducted a study to: (a) develop a typology of sheltered homeless women's social networks; (b) determine whether childhood abuse was associated with the social networks of sheltered homeless women; and (c) determine whether those associations remained after accounting for past-year substance abuse and recent intimate partner abuse. A probability sample of 428 homeless women from temporary shelter settings in Los Angeles County completed a personal network survey that provided respondent information as well as information about their network members' demographics and level of interaction with each other. Cluster analyses identified groups of women who shared specific social network characteristics. Multinomial logistic regressions revealed variables associated with group membership. We identified three groups of women with differing social network characteristics: low-risk networks, densely connected risky networks (dense, risky), and sparsely connected risky networks (sparse, risky). Multinomial logistic regressions indicated that membership in the sparse, risky network group, when compared to the low-risk group, was associated with history of childhood physical abuse (but not sexual or emotional abuse). Recent drug abuse was associated with membership in both risky network groups; however, the association of childhood physical abuse with sparse, risky network group membership remained. Although these findings support theories proposing that the experience of childhood abuse can shape women's social networks, they suggest that it may be childhood physical abuse that has the most impact among homeless women. The effects of childhood physical abuse should be more actively investigated in clinical settings, especially those frequented by homeless women, particularly with respect to the formation of social networks in social contexts that may expose these women to greater risks. Copyright © 2012. Published by Elsevier Ltd.

An analysis of respondent-driven sampling with injecting drug users in a high HIV prevalent state of India.

PubMed

Phukan, Sanjib Kumar; Medhi, Gajendra Kumar; Mahanta, Jagadish; Adhikary, Rajatashuvra; Thongamba, Gay; Paranjape, Ramesh S; Akoijam, Brogen S

2017-07-03

Personal networks are significant social spaces to spread of HIV or other blood-borne infections among hard-to-reach population, viz., injecting drug users, female sex workers, etc. Sharing of infected needles or syringes among drug users is one of the major routes of HIV transmission in Manipur, a high HIV prevalence state in India. This study was carried out to describe the network characteristics and recruitment patterns of injecting drug users and to assess the association of personal network with injecting risky behaviors in Manipur. A total of 821 injecting drug users were recruited into the study using respondent-driven sampling (RDS) from Bishnupur and Churachandpur districts of Manipur; data on demographic characteristics, HIV risk behaviors, and network size were collected from them. Transition probability matrices and homophily indices were used to describe the network characteristics, and recruitment patterns of injecting drug users. Univariate and multivariate binary logistic regression models were performed to analyze the association between the personal networks and sharing of needles or syringes. The average network size was similar in both the districts. Recruitment analysis indicates injecting drug users were mostly engaged in mixed age group setting for injecting practice. Ever married and new injectors showed lack of in-group ties. Younger injecting drug users had mainly recruited older injecting drug users from their personal network. In logistic regression analysis, higher personal network was found to be significantly associated with increased likelihood of injecting risky behaviors. Because of mixed personal network of new injectors and higher network density associated with HIV exposure, older injecting drug users may act as a link for HIV transmission or other blood-borne infections to new injectors and also to their sexual partners. The information from this study may be useful to understanding the network pattern of injecting drug users for enriching the HIV prevention in this region.

Analysis of HRCT-derived xylem network reveals reverse flow in some vessels

USDA-ARS?s Scientific Manuscript database

Flow in xylem vessels is modeled based on constructions of three dimensional xylem networks derived from High Resolution Computed Tomography (HRCT) images of grapevine (Vitis vinifera) stems. Flow in 6-14% of the vessels was found to be oriented in the opposite direction to the bulk flow under norma...

Learning Biological Networks via Bootstrapping with Optimized GO-based Gene Similarity

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

Taylor, Ronald C.; Sanfilippo, Antonio P.; McDermott, Jason E.

2010-08-02

Microarray gene expression data provide a unique information resource for learning biological networks using "reverse engineering" methods. However, there are a variety of cases in which we know which genes are involved in a given pathology of interest, but we do not have enough experimental evidence to support the use of fully-supervised/reverse-engineering learning methods. In this paper, we explore a novel semi-supervised approach in which biological networks are learned from a reference list of genes and a partial set of links for these genes extracted automatically from PubMed abstracts, using a knowledge-driven bootstrapping algorithm. We show how new relevant linksmore » across genes can be iteratively derived using a gene similarity measure based on the Gene Ontology that is optimized on the input network at each iteration. We describe an application of this approach to the TGFB pathway as a case study and show how the ensuing results prove the feasibility of the approach as an alternate or complementary technique to fully supervised methods.« less

Reverse Nearest Neighbor Search on a Protein-Protein Interaction Network to Infer Protein-Disease Associations.

PubMed

Suratanee, Apichat; Plaimas, Kitiporn

2017-01-01

The associations between proteins and diseases are crucial information for investigating pathological mechanisms. However, the number of known and reliable protein-disease associations is quite small. In this study, an analysis framework to infer associations between proteins and diseases was developed based on a large data set of a human protein-protein interaction network integrating an effective network search, namely, the reverse k -nearest neighbor (R k NN) search. The R k NN search was used to identify an impact of a protein on other proteins. Then, associations between proteins and diseases were inferred statistically. The method using the R k NN search yielded a much higher precision than a random selection, standard nearest neighbor search, or when applying the method to a random protein-protein interaction network. All protein-disease pair candidates were verified by a literature search. Supporting evidence for 596 pairs was identified. In addition, cluster analysis of these candidates revealed 10 promising groups of diseases to be further investigated experimentally. This method can be used to identify novel associations to better understand complex relationships between proteins and diseases.

An integrated molecular dynamics, principal component analysis and residue interaction network approach reveals the impact of M184V mutation on HIV reverse transcriptase resistance to lamivudine.

PubMed

Bhakat, Soumendranath; Martin, Alberto J M; Soliman, Mahmoud E S

2014-08-01

The emergence of different drug resistant strains of HIV-1 reverse transcriptase (HIV RT) remains of prime interest in relation to viral pathogenesis as well as drug development. Amongst those mutations, M184V was found to cause a complete loss of ligand fitness. In this study, we report the first account of the molecular impact of M184V mutation on HIV RT resistance to 3TC (lamivudine) using an integrated computational approach. This involved molecular dynamics simulation, binding free energy analysis, principle component analysis (PCA) and residue interaction networks (RINs). Results clearly confirmed that M184V mutation leads to steric conflict between 3TC and the beta branched side chain of valine, decreases the ligand (3TC) binding affinity by ∼7 kcal mol(-1) when compared to the wild type, changes the overall conformational landscape of the protein and distorts the native enzyme residue-residue interaction network. The comprehensive molecular insight gained from this study should be of great importance in understanding drug resistance against HIV RT as well as assisting in the design of novel reverse transcriptase inhibitors with high ligand efficacy on resistant strains.

Reverse engineering a mouse embryonic stem cell-specific transcriptional network reveals a new modulator of neuronal differentiation.

PubMed

De Cegli, Rossella; Iacobacci, Simona; Flore, Gemma; Gambardella, Gennaro; Mao, Lei; Cutillo, Luisa; Lauria, Mario; Klose, Joachim; Illingworth, Elizabeth; Banfi, Sandro; di Bernardo, Diego

2013-01-01

Gene expression profiles can be used to infer previously unknown transcriptional regulatory interaction among thousands of genes, via systems biology 'reverse engineering' approaches. We 'reverse engineered' an embryonic stem (ES)-specific transcriptional network from 171 gene expression profiles, measured in ES cells, to identify master regulators of gene expression ('hubs'). We discovered that E130012A19Rik (E13), highly expressed in mouse ES cells as compared with differentiated cells, was a central 'hub' of the network. We demonstrated that E13 is a protein-coding gene implicated in regulating the commitment towards the different neuronal subtypes and glia cells. The overexpression and knock-down of E13 in ES cell lines, undergoing differentiation into neurons and glia cells, caused a strong up-regulation of the glutamatergic neurons marker Vglut2 and a strong down-regulation of the GABAergic neurons marker GAD65 and of the radial glia marker Blbp. We confirmed E13 expression in the cerebral cortex of adult mice and during development. By immuno-based affinity purification, we characterized protein partners of E13, involved in the Polycomb complex. Our results suggest a role of E13 in regulating the division between glutamatergic projection neurons and GABAergic interneurons and glia cells possibly by epigenetic-mediated transcriptional regulation.

Microstructural Origins of Nonlinear Response in Associating Polymers under Oscillatory Shear

DOE PAGES

Wilson, Mark A.; Baljon, Arlette R. C.

2017-10-26

The response of associating polymers with oscillatory shear is studied through large-scale simulations. A hybrid molecular dynamics (MD), Monte Carlo (MC) algorithm is employed. Polymer chains are modeled as a coarse-grained bead-spring system. Functionalized end groups, at both ends of the polymer chains, can form reversible bonds according to MC rules. Stress-strain curves show nonlinearities indicated by a non-ellipsoidal shape. We consider two types of nonlinearities. Type I occurs at a strain amplitude much larger than one, type II at a frequency at which the elastic storage modulus dominates the viscous loss modulus. In this last case, the network topologymore » resembles that of the system at rest. The reversible bonds are broken and chains stretch when the system moves away from the zero-strain position. For type I, the chains relax and the number of reversible bonds peaks when the system is near an extreme of the motion. During the movement to the other extreme of the cycle, first a stress overshoot occurs, then a yield accompanied by shear-banding. Lastly, the network restructures. Interestingly, the system periodically restores bonds between the same associating groups. Even though major restructuring occurs, the system remembers previous network topologies.« less

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/

Maintenance and Logistics Support for the International Monitoring System Network of the CTBTO

NASA Astrophysics Data System (ADS)

Haslinger, F.; Brely, N.; Akrawy, M.

2007-05-01

The global network of the International Monitoring System (IMS) of the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO), once completed, will consist of 321 monitoring facilities of four different technologies: hydroacoustic, seismic, infrasonic, and radionuclide. As of today, about 65% of the installations are completed and contribute data to the products issued by the International Data Centre (IDC) of the CTBTO. In order to accomplish the task to reliably collect evidence for any potential nuclear test explosion anywhere on the planet, all stations are required to perform to very high data availability requirements (at least 98% data availability over a 12-month period). To enable reaching this requirement, a three-layer concept has been developed to allow efficient support of the IMS stations: Operations, Maintenance and Logistics, and Engineering. Within this concept Maintenance and Logistics provide second level support of the stations, whereby problems arising at the station are assigned through the IMS ticket system to Maintenance if they cannot be resolved on the Operations level. Maintenance will then activate the required resources to appropriately address and ultimately resolve the problem. These resources may be equipment support contracts, other third party contracts, or the dispatch of a maintenance team. Engineering Support will be activated if the problem requires redesign of the station or after catastrophic failures when a total rebuild of a station may be necessary. In this model, Logistics Support is responsible for parts replenishment and support contract management. Logistics Support also collects and analyzes relevant failure mode and effect information, develops supportability models, and has the responsibility for document management, obsolescence, risk & quality, and configuration management, which are key elements for efficient station support. Maintenance Support in addition is responsible for maintenance strategies, for planning and oversight of the execution of preventive maintenance programs by the Station Operators, and for review of operational troubleshooting procedures used in first level support. Particular challenges for the efficient and successful Maintenance and Logistics Support of the IMS network lie in the specific political boundary conditions regulating its implementation, in the fact that all IMS facilities and their equipment are owned by the respective host countries, and in finding the appropriate balance between outsourcing services and retaining essential in-house expertise.

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

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.

NASA Experimental Program to Stimulate Competitive Research: South Carolina

NASA Technical Reports Server (NTRS)

Sutton, Michael A.

2004-01-01

The use of an appropriate relationship model is critical for reliable prediction of future urban growth. Identification of proper variables and mathematic functions and determination of the weights or coefficients are the key tasks for building such a model. Although the conventional logistic regression model is appropriate for handing land use problems, it appears insufficient to address the issue of interdependency of the predictor variables. This study used an alternative approach to simulation and modeling urban growth using artificial neural networks. It developed an operational neural network model trained using a robust backpropagation method. The model was applied in the Myrtle Beach region of South Carolina, and tested with both global datasets and areal datasets to examine the strength of both regional models and areal models. The results indicate that the neural network model not only has many theoretic advantages over other conventional mathematic models in representing the complex urban systems, but also is practically superior to the logistic model in its capability to predict urban growth with better - accuracy and less variation. The neural network model is particularly effective in terms of successfully identifying urban patterns in the rural areas where the logistic model often falls short. It was also found from the area-based tests that there are significant intra-regional differentiations in urban growth with different rules and rates. This suggests that the global modeling approach, or one model for the entire region, may not be adequate for simulation of a urban growth at the regional scale. Future research should develop methods for identification and subdivision of these areas and use a set of area-based models to address the issues of multi-centered, intra- regionally differentiated urban growth.

Reverse engineering highlights potential principles of large gene regulatory network design and learning.

PubMed

Carré, Clément; Mas, André; Krouk, Gabriel

2017-01-01

Inferring transcriptional gene regulatory networks from transcriptomic datasets is a key challenge of systems biology, with potential impacts ranging from medicine to agronomy. There are several techniques used presently to experimentally assay transcription factors to target relationships, defining important information about real gene regulatory networks connections. These techniques include classical ChIP-seq, yeast one-hybrid, or more recently, DAP-seq or target technologies. These techniques are usually used to validate algorithm predictions. Here, we developed a reverse engineering approach based on mathematical and computer simulation to evaluate the impact that this prior knowledge on gene regulatory networks may have on training machine learning algorithms. First, we developed a gene regulatory networks-simulating engine called FRANK (Fast Randomizing Algorithm for Network Knowledge) that is able to simulate large gene regulatory networks (containing 10 4 genes) with characteristics of gene regulatory networks observed in vivo. FRANK also generates stable or oscillatory gene expression directly produced by the simulated gene regulatory networks. The development of FRANK leads to important general conclusions concerning the design of large and stable gene regulatory networks harboring scale free properties (built ex nihilo). In combination with supervised (accepting prior knowledge) support vector machine algorithm we (i) address biologically oriented questions concerning our capacity to accurately reconstruct gene regulatory networks and in particular we demonstrate that prior-knowledge structure is crucial for accurate learning, and (ii) draw conclusions to inform experimental design to performed learning able to solve gene regulatory networks in the future. By demonstrating that our predictions concerning the influence of the prior-knowledge structure on support vector machine learning capacity holds true on real data ( Escherichia coli K14 network reconstruction using network and transcriptomic data), we show that the formalism used to build FRANK can to some extent be a reasonable model for gene regulatory networks in real cells.

Natural Hazards and Supply Chain Disruptions

NASA Astrophysics Data System (ADS)

Haraguchi, M.

2016-12-01

Natural hazards distress the global economy through disruptions in supply chain networks. Moreover, despite increasing investment to infrastructure for disaster risk management, economic damages and losses caused by natural hazards are increasing. Manufacturing companies today have reduced inventories and streamlined logistics in order to maximize economic competitiveness. As a result, today's supply chains are profoundly susceptible to systemic risks, which are the risk of collapse of an entire network caused by a few node of the network. For instance, the prolonged floods in Thailand in 2011 caused supply chain disruptions in their primary industries, i.e. electronic and automotive industries, harming not only the Thai economy but also the global economy. Similar problems occurred after the Great East Japan Earthquake and Tsunami in 2011, the Mississippi River floods and droughts during 2011 - 2013, and the Earthquake in Kumamoto Japan in 2016. This study attempts to discover what kind of effective measures are available for private companies to manage supply chain disruptions caused by floods. It also proposes a method to estimate potential risks using a Bayesian network. The study uses a Bayesian network to create synthetic networks that include variables associated with the magnitude and duration of floods, major components of supply chains such as logistics, multiple layers of suppliers, warehouses, and consumer markets. Considering situations across different times, our study shows desirable data requirements for the analysis and effective measures to improve Value at Risk (VaR) for private enterprises and supply chains.

USAF Logistics Process Optimization Study for the Aircraft Asset Sustainment Process. Volume 1.

DTIC Science & Technology

1998-12-31

solely to have a record that could be matched with the CMOS receipt data. (This problem is caused by DLA systems that currently do not populate CMOS with...unable to obtain passwords to the Depot D035 systems. Figure 16 shows daily savings as of 30 September 1998 (current time frame ) and projects savings...Engineering, modeling, and systems/software development company LAN Local Area Network LFA Large Frame Aircraft LMA Logistics Management Agency LMR

Technogeopologistics: Supply Networks and Military Power in the Industrial Age

DTIC Science & Technology

2012-06-01

communication ( LOCs ) to the battlefield in ever more complex ways. Thus, logistics exhibit sensitivity to technological change. Logistics also have...War will serve as a lens for sea versus land LOCs . With the addition of the air LOC under the inter-war airpower thinkers Giulio Douhet and Billy...Mitchell, the Second World War will be a testing ground for all three domains. The interaction among sea, land, and air LOCs with both the industrial

Genetic prediction of type 2 diabetes using deep neural network.

PubMed

Kim, J; Kim, J; Kwak, M J; Bajaj, M

2018-04-01

Type 2 diabetes (T2DM) has strong heritability but genetic models to explain heritability have been challenging. We tested deep neural network (DNN) to predict T2DM using the nested case-control study of Nurses' Health Study (3326 females, 45.6% T2DM) and Health Professionals Follow-up Study (2502 males, 46.5% T2DM). We selected 96, 214, 399, and 678 single-nucleotide polymorphism (SNPs) through Fisher's exact test and L1-penalized logistic regression. We split each dataset randomly in 4:1 to train prediction models and test their performance. DNN and logistic regressions showed better area under the curve (AUC) of ROC curves than the clinical model when 399 or more SNPs included. DNN was superior than logistic regressions in AUC with 399 or more SNPs in male and 678 SNPs in female. Addition of clinical factors consistently increased AUC of DNN but failed to improve logistic regressions with 214 or more SNPs. In conclusion, we show that DNN can be a versatile tool to predict T2DM incorporating large numbers of SNPs and clinical information. Limitations include a relatively small number of the subjects mostly of European ethnicity. Further studies are warranted to confirm and improve performance of genetic prediction models using DNN in different ethnic groups. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

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.

FAST TRACK COMMUNICATION: Reversible arithmetic logic unit for quantum arithmetic

NASA Astrophysics Data System (ADS)

Kirkedal Thomsen, Michael; Glück, Robert; Axelsen, Holger Bock

2010-09-01

This communication presents the complete design of a reversible arithmetic logic unit (ALU) that can be part of a programmable reversible computing device such as a quantum computer. The presented ALU is garbage free and uses reversible updates to combine the standard reversible arithmetic and logical operations in one unit. Combined with a suitable control unit, the ALU permits the construction of an r-Turing complete computing device. The garbage-free ALU developed in this communication requires only 6n elementary reversible gates for five basic arithmetic-logical operations on two n-bit operands and does not use ancillae. This remarkable low resource consumption was achieved by generalizing the V-shape design first introduced for quantum ripple-carry adders and nesting multiple V-shapes in a novel integrated design. This communication shows that the realization of an efficient reversible ALU for a programmable computing device is possible and that the V-shape design is a very versatile approach to the design of quantum networks.

Recycling tires? Reversible crosslinking of poly(butadiene).

PubMed

Trovatti, Eliane; Lacerda, Talita M; Carvalho, Antonio J F; Gandini, Alessandro

2015-04-01

Furan-modified poly(butadiene) prepared by the thiol-ene click reaction is crosslinked with bismaleimides through the Diels-Alder reaction, giving rise to a novel recyclable elastomer. This is possible because of the thermal reversibility of the adducts responsible for the formation of the network. The use of this strategy provides the possibility to produce recyclable tires. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Predicting Engineering Student Attrition Risk Using a Probabilistic Neural Network and Comparing Results with a Backpropagation Neural Network and Logistic Regression

ERIC Educational Resources Information Center

Mason, Cindi; Twomey, Janet; Wright, David; Whitman, Lawrence

2018-01-01

As the need for engineers continues to increase, a growing focus has been placed on recruiting students into the field of engineering and retaining the students who select engineering as their field of study. As a result of this concentration on student retention, numerous studies have been conducted to identify, understand, and confirm…

Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical gaussian models and bayesian networks.

PubMed

Werhli, Adriano V; Grzegorczyk, Marco; Husmeier, Dirk

2006-10-15

An important problem in systems biology is the inference of biochemical pathways and regulatory networks from postgenomic data. Various reverse engineering methods have been proposed in the literature, and it is important to understand their relative merits and shortcomings. In the present paper, we compare the accuracy of reconstructing gene regulatory networks with three different modelling and inference paradigms: (1) Relevance networks (RNs): pairwise association scores independent of the remaining network; (2) graphical Gaussian models (GGMs): undirected graphical models with constraint-based inference, and (3) Bayesian networks (BNs): directed graphical models with score-based inference. The evaluation is carried out on the Raf pathway, a cellular signalling network describing the interaction of 11 phosphorylated proteins and phospholipids in human immune system cells. We use both laboratory data from cytometry experiments as well as data simulated from the gold-standard network. We also compare passive observations with active interventions. On Gaussian observational data, BNs and GGMs were found to outperform RNs. The difference in performance was not significant for the non-linear simulated data and the cytoflow data, though. Also, we did not observe a significant difference between BNs and GGMs on observational data in general. However, for interventional data, BNs outperform GGMs and RNs, especially when taking the edge directions rather than just the skeletons of the graphs into account. This suggests that the higher computational costs of inference with BNs over GGMs and RNs are not justified when using only passive observations, but that active interventions in the form of gene knockouts and over-expressions are required to exploit the full potential of BNs. Data, software and supplementary material are available from http://www.bioss.sari.ac.uk/staff/adriano/research.html

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.

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.

Comparison of two different artificial neural networks for prostate biopsy indication in two different patient populations.

PubMed

Stephan, Carsten; Xu, Chuanliang; Finne, Patrik; Cammann, Henning; Meyer, Hellmuth-Alexander; Lein, Michael; Jung, Klaus; Stenman, Ulf-Hakan

2007-09-01

Different artificial neural networks (ANNs) using total prostate-specific antigen (PSA) and percentage of free PSA (%fPSA) have been introduced to enhance the specificity of prostate cancer detection. The applicability of independently trained ANN and logistic regression (LR) models to different populations regarding the composition (screening versus referred) and different PSA assays has not yet been tested. Two ANN and LR models using PSA (range 4 to 10 ng/mL), %fPSA, prostate volume, digital rectal examination findings, and patient age were tested. A multilayer perceptron network (MLP) was trained on 656 screening participants (Prostatus PSA assay) and another ANN (Immulite-based ANN [iANN]) was constructed on 606 multicentric urologically referred men. These and other assay-adapted ANN models, including one new iANN-based ANN, were used. The areas under the curve for the iANN (0.736) and MLP (0.745) were equal but showed no differences to %fPSA (0.725) in the Finnish group. Only the new iANN-based ANN reached a significant larger area under the curve (0.77). At 95% sensitivity, the specificities of MLP (33%) and the new iANN-based ANN (34%) were significantly better than the iANN (23%) and %fPSA (19%). Reverse methodology using the MLP model on the referred patients revealed, in contrast, a significant improvement in the areas under the curve for iANN and MLP (each 0.83) compared with %fPSA (0.70). At 90% and 95% sensitivity, the specificities of all LR and ANN models were significantly greater than those for %fPSA. The ANNs based on different PSA assays and populations were mostly comparable, but the clearly different patient composition also allowed with assay adaptation no unbiased ANN application to the other cohort. Thus, the use of ANNs in other populations than originally built is possible, but has limitations.

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.

ARACNe-AP: Gene Network Reverse Engineering through Adaptive Partitioning inference of Mutual Information. | Office of Cancer Genomics

Cancer.gov

The accurate reconstruction of gene regulatory networks from large scale molecular profile datasets represents one of the grand challenges of Systems Biology. The Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) represents one of the most effective tools to accomplish this goal. However, the initial Fixed Bandwidth (FB) implementation is both inefficient and unable to deal with sample sets providing largely uneven coverage of the probability density space.

Petri Nets with Fuzzy Logic (PNFL): Reverse Engineering and Parametrization

PubMed Central

Küffner, Robert; Petri, Tobias; Windhager, Lukas; Zimmer, Ralf

2010-01-01

Background The recent DREAM4 blind assessment provided a particularly realistic and challenging setting for network reverse engineering methods. The in silico part of DREAM4 solicited the inference of cycle-rich gene regulatory networks from heterogeneous, noisy expression data including time courses as well as knockout, knockdown and multifactorial perturbations. Methodology and Principal Findings We inferred and parametrized simulation models based on Petri Nets with Fuzzy Logic (PNFL). This completely automated approach correctly reconstructed networks with cycles as well as oscillating network motifs. PNFL was evaluated as the best performer on DREAM4 in silico networks of size 10 with an area under the precision-recall curve (AUPR) of 81%. Besides topology, we inferred a range of additional mechanistic details with good reliability, e.g. distinguishing activation from inhibition as well as dependent from independent regulation. Our models also performed well on new experimental conditions such as double knockout mutations that were not included in the provided datasets. Conclusions The inference of biological networks substantially benefits from methods that are expressive enough to deal with diverse datasets in a unified way. At the same time, overly complex approaches could generate multiple different models that explain the data equally well. PNFL appears to strike the balance between expressive power and complexity. This also applies to the intuitive representation of PNFL models combining a straightforward graphical notation with colloquial fuzzy parameters. PMID:20862218

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.

Inferring Single Neuron Properties in Conductance Based Balanced Networks

PubMed Central

Pool, Román Rossi; Mato, Germán

2011-01-01

Balanced states in large networks are a usual hypothesis for explaining the variability of neural activity in cortical systems. In this regime the statistics of the inputs is characterized by static and dynamic fluctuations. The dynamic fluctuations have a Gaussian distribution. Such statistics allows to use reverse correlation methods, by recording synaptic inputs and the spike trains of ongoing spontaneous activity without any additional input. By using this method, properties of the single neuron dynamics that are masked by the balanced state can be quantified. To show the feasibility of this approach we apply it to large networks of conductance based neurons. The networks are classified as Type I or Type II according to the bifurcations which neurons of the different populations undergo near the firing onset. We also analyze mixed networks, in which each population has a mixture of different neuronal types. We determine under which conditions the intrinsic noise generated by the network can be used to apply reverse correlation methods. We find that under realistic conditions we can ascertain with low error the types of neurons present in the network. We also find that data from neurons with similar firing rates can be combined to perform covariance analysis. We compare the results of these methods (that do not requite any external input) to the standard procedure (that requires the injection of Gaussian noise into a single neuron). We find a good agreement between the two procedures. PMID:22016730

Comprehensive Analysis of Interaction Networks of Telomerase Reverse Transcriptase with Multiple Bioinformatic Approaches: Deep Mining the Potential Functions of Telomere and Telomerase.

PubMed

Hou, Chunyu; Wang, Fei; Liu, Xuewen; Chang, Guangming; Wang, Feng; Geng, Xin

2017-08-01

Telomerase reverse transcriptase (TERT) is the protein component of telomerase complex. Evidence has accumulated showing that the nontelomeric functions of TERT are independent of telomere elongation. However, the mechanisms governing the interaction between TERT and its target genes are not clearly revealed. The biological functions of TERT are not fully elucidated and have thus far been underestimated. To further explore these functions, we investigated TERT interaction networks using multiple bioinformatic databases, including BioGRID, STRING, DAVID, GeneCards, GeneMANIA, PANTHER, miRWalk, mirTarBase, miRNet, miRDB, and TargetScan. In addition, network diagrams were built using Cytoscape software. As competing endogenous RNAs (ceRNAs) are endogenous transcripts that compete for the binding of microRNAs (miRNAs) by using shared miRNA recognition elements, they are involved in creating widespread regulatory networks. Therefore, the ceRNA regulatory networks of TERT were also investigated in this study. Interestingly, we found that the three genes PABPC1, SLC7A11, and TP53 were present in both TERT interaction networks and ceRNAs target genes. It was predicted that TERT might play nontelomeric roles in the generation or development of some rare diseases, such as Rift Valley fever and dyscalculia. Thus, our data will help to decipher the interaction networks of TERT and reveal the unknown functions of telomerase in cancer and aging-related diseases.

Fishing in the Amazonian forest: a gendered social network puzzle

PubMed Central

Díaz-Reviriego, I.; Fernández-Llamazares, Á.; Howard, P.L; Molina, JL; Reyes-García, V

2016-01-01

We employ social network analysis (SNA) to describe the structure of subsistence fishing social networks and to explore the relation between fishers’ emic perceptions of fishing expertise and their position in networks. Participant observation and quantitative methods were employed among the Tsimane’ Amerindians of the Bolivian Amazonia. A multiple regression quadratic assignment procedure was used to explore the extent to which gender, kinship, and age homophilies influence the formation of fishing networks. Logistic regressions were performed to determine the association between the fishers’ expertise, their socio-demographic identities, and network centrality. We found that fishing networks are gendered and that there is a positive association between fishers’ expertise and centrality in networks, an association that is more striking for women than for men. We propose that a social network perspective broadens understanding of the relations that shape the intracultural distribution of fishing expertise as well as natural resource access and use. PMID:28479670

Fishing in the Amazonian forest: a gendered social network puzzle.

PubMed

Díaz-Reviriego, I; Fernández-Llamazares, Á; Howard, P L; Molina, J L; Reyes-García, V

2017-01-01

We employ social network analysis (SNA) to describe the structure of subsistence fishing social networks and to explore the relation between fishers' emic perceptions of fishing expertise and their position in networks. Participant observation and quantitative methods were employed among the Tsimane' Amerindians of the Bolivian Amazonia. A multiple regression quadratic assignment procedure was used to explore the extent to which gender, kinship, and age homophilies influence the formation of fishing networks. Logistic regressions were performed to determine the association between the fishers' expertise, their socio-demographic identities, and network centrality. We found that fishing networks are gendered and that there is a positive association between fishers' expertise and centrality in networks, an association that is more striking for women than for men. We propose that a social network perspective broadens understanding of the relations that shape the intracultural distribution of fishing expertise as well as natural resource access and use.

An exploration of the Facebook social networks of smokers and non-smokers.

PubMed

Fu, Luella; Jacobs, Megan A; Brookover, Jody; Valente, Thomas W; Cobb, Nathan K; Graham, Amanda L

2017-01-01

Social networks influence health behavior, including tobacco use and cessation. To date, little is known about whether and how the networks of online smokers and non-smokers may differ, or the potential implications of such differences with regards to intervention efforts. Understanding how social networks vary by smoking status could inform public health efforts to accelerate cessation or slow the adoption of tobacco use. These secondary analyses explore the structure of ego networks of both smokers and non-smokers collected as part of a randomized control trial conducted within Facebook. During the trial, a total of 14,010 individuals installed a Facebook smoking cessation app: 9,042 smokers who were randomized in the trial, an additional 2,881 smokers who did not meet full eligibility criteria, and 2,087 non-smokers. The ego network for all individuals was constructed out to second-degree connections. Four kinds of networks were constructed: friendship, family, photo, and group networks. From these networks we measured edges, isolates, density, mean betweenness, transitivity, and mean closeness. We also measured diameter, clustering, and modularity without ego and isolates. Logistic regressions were performed with smoking status as the response and network metrics as the primary independent variables and demographics and Facebook utilization metrics as covariates. The four networks had different characteristics, indicated by different multicollinearity issues and by logistic regression output. Among Friendship networks, the odds of smoking were higher in networks with lower betweenness (p = 0.00), lower transitivity (p = 0.00), and larger diameter (p = 0.00). Among Family networks, the odds of smoking were higher in networks with more vertices (p = .01), less transitivity (p = .04), and fewer isolates (p = .01). Among Photo networks, none of the network metrics were predictive of smoking status. Among Group networks, the odds of smoking were higher when diameter was smaller (p = .04). Together, these findings suggested that compared to non-smokers, smokers in this sample had less connected, more dispersed Facebook Friendship networks; larger but more fractured Family networks with fewer isolates; more compact Group networks; and Photo networks that were similar in network structure to those of non-smokers. This study illustrates the importance of examining structural differences in online social networks as a critical component for network-based interventions and lays the foundation for future research that examines the ways that social networks differ based on individual health behavior. Interventions that seek to target the behavior of individuals in the context of their social environment would be well served to understand social network structures of participants.

An exploration of the Facebook social networks of smokers and non-smokers

PubMed Central

2017-01-01

Background Social networks influence health behavior, including tobacco use and cessation. To date, little is known about whether and how the networks of online smokers and non-smokers may differ, or the potential implications of such differences with regards to intervention efforts. Understanding how social networks vary by smoking status could inform public health efforts to accelerate cessation or slow the adoption of tobacco use. Objectives These secondary analyses explore the structure of ego networks of both smokers and non-smokers collected as part of a randomized control trial conducted within Facebook. Methods During the trial, a total of 14,010 individuals installed a Facebook smoking cessation app: 9,042 smokers who were randomized in the trial, an additional 2,881 smokers who did not meet full eligibility criteria, and 2,087 non-smokers. The ego network for all individuals was constructed out to second-degree connections. Four kinds of networks were constructed: friendship, family, photo, and group networks. From these networks we measured edges, isolates, density, mean betweenness, transitivity, and mean closeness. We also measured diameter, clustering, and modularity without ego and isolates. Logistic regressions were performed with smoking status as the response and network metrics as the primary independent variables and demographics and Facebook utilization metrics as covariates. Results The four networks had different characteristics, indicated by different multicollinearity issues and by logistic regression output. Among Friendship networks, the odds of smoking were higher in networks with lower betweenness (p = 0.00), lower transitivity (p = 0.00), and larger diameter (p = 0.00). Among Family networks, the odds of smoking were higher in networks with more vertices (p = .01), less transitivity (p = .04), and fewer isolates (p = .01). Among Photo networks, none of the network metrics were predictive of smoking status. Among Group networks, the odds of smoking were higher when diameter was smaller (p = .04). Together, these findings suggested that compared to non-smokers, smokers in this sample had less connected, more dispersed Facebook Friendship networks; larger but more fractured Family networks with fewer isolates; more compact Group networks; and Photo networks that were similar in network structure to those of non-smokers. Conclusions This study illustrates the importance of examining structural differences in online social networks as a critical component for network-based interventions and lays the foundation for future research that examines the ways that social networks differ based on individual health behavior. Interventions that seek to target the behavior of individuals in the context of their social environment would be well served to understand social network structures of participants. PMID:29095958

NDIA 34th Annual National Logistics Forum: Logistics Innovation to Ensure Global Readiness. Held in Tampa, Florida on 15-16 May 2018

DTIC Science & Technology

2018-05-16

KEYNOTE ADDRESS BAYSHORE 1-3 Ward Heinke Vice President, Strategic Alliances, Government Markets , Forcepoint 9:45 – 10:15 am NETWORKING BREAK GALLERIA B 6...or conditions of sale (including allowances, credit terms, and warranties); allocation of markets or customers or division of territories; or refusals...Army War College. 11 WARD HEINKE Vice President, Strategic Alliances, Government Markets Forcepoint Ward is VP for Strategic Alliances, Government

A Collection of Technical Studies Completed for the Computer-Aided Acquisition and Logistic Support (CALS) Program Fiscal Year 1988. Volume 1. Text, Security and Data Management

DTIC Science & Technology

1991-03-01

management methodologies claim to be "expert systems" with security intelligence built into them to I derive a body of both facts and speculative data ... Data Administration considerations . III -21 IV. ARTIFICIAL INTELLIGENCE . .. .. .. . .. IV - 1 A. Description of Technologies . . . . . .. IV - 1 1...as intelligent gateways, wide area networks, and distributed databases for the distribution of logistics products. The integrity of CALS data and the

Hierarchical self-assembly of actin in micro-confinements using microfluidics

PubMed Central

Deshpande, Siddharth; Pfohl, Thomas

2012-01-01

We present a straightforward microfluidics system to achieve step-by-step reaction sequences in a diffusion-controlled manner in quasi two-dimensional micro-confinements. We demonstrate the hierarchical self-organization of actin (actin monomers—entangled networks of filaments—networks of bundles) in a reversible fashion by tuning the Mg2+ ion concentration in the system. We show that actin can form networks of bundles in the presence of Mg2+ without any cross-linking proteins. The properties of these networks are influenced by the confinement geometry. In square microchambers we predominantly find rectangular networks, whereas triangular meshes are predominantly found in circular chambers. PMID:24032070

Reversible logic gates based on enzyme-biocatalyzed reactions and realized in flow cells: a modular approach.

PubMed

Fratto, Brian E; Katz, Evgeny

2015-05-18

Reversible logic gates, such as the double Feynman gate, Toffoli gate and Peres gate, with 3-input/3-output channels are realized using reactions biocatalyzed with enzymes and performed in flow systems. The flow devices are constructed using a modular approach, where each flow cell is modified with one enzyme that biocatalyzes one chemical reaction. The multi-step processes mimicking the reversible logic gates are organized by combining the biocatalytic cells in different networks. This work emphasizes logical but not physical reversibility of the constructed systems. Their advantages and disadvantages are discussed and potential use in biosensing systems, rather than in computing devices, is suggested. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Rapid group-, serotype-, and vaccine strain-specific identification of poliovirus isolates by real-time reverse transcription-PCR using degenerate primers and probes containing deoxyinosine residues.

PubMed

Kilpatrick, David R; Yang, Chen-Fu; Ching, Karen; Vincent, Annelet; Iber, Jane; Campagnoli, Ray; Mandelbaum, Mark; De, Lina; Yang, Su-Ju; Nix, Allan; Kew, Olen M

2009-06-01

We have adapted our previously described poliovirus diagnostic reverse transcription-PCR (RT-PCR) assays to a real-time RT-PCR (rRT-PCR) format. Our highly specific assays and rRT-PCR reagents are designed for use in the WHO Global Polio Laboratory Network for rapid and large-scale identification of poliovirus field isolates.

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.

Going the Extra Mile: Enabling Joint Logistics for the Tactical War Fighter

DTIC Science & Technology

2010-05-04

few of the links when relocating hubs. Chains v. Networks Supply Chain Too brittle , long CPL, low clustering, simple pattern, simple control...Mass Service Perspective Efficiency Highly Optimized Brittle , Rigid Supply Chains vs Networked Cross-Service Mutual Support Cross-Enterprise...Storage and Distribution Centei\\" Army Logistician 39, no. 6 (November-December 2007): 40. 68 Glen R Dowling, "Army and Marine Joint Ammunition

Supporting Regularized Logistic Regression Privately and Efficiently.

PubMed

Li, Wenfa; Liu, Hongzhe; Yang, Peng; Xie, Wei

2016-01-01

As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Concerns over data privacy make it increasingly difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used statistical model while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluations on several studies validate the privacy guarantee, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc.

Supporting Regularized Logistic Regression Privately and Efficiently

PubMed Central

Li, Wenfa; Liu, Hongzhe; Yang, Peng; Xie, Wei

2016-01-01

As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Concerns over data privacy make it increasingly difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used statistical model while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluations on several studies validate the privacy guarantee, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc. PMID:27271738

The social network index and its relation to later-life depression among the elderly aged ≥80 years in Northern Thailand.

PubMed

Aung, Myo Nyein; Moolphate, Saiyud; Aung, Thin Nyein Nyein; Katonyoo, Chitima; Khamchai, Songyos; Wannakrairot, Pongsak

2016-01-01

Having a diverse social network is considered to be beneficial to a person's well-being. The significance, however, of social network diversity in the geriatric assessment of people aged ≥80 years has not been adequately investigated within the Southeast Asian context. This study explored the social networks belonging to the elderly aged ≥80 years and assessed the relation of social network and geriatric depression. This study was a community-based cross-sectional survey conducted in Chiang Mai Province, Northern Thailand. A representative sample of 435 community residents, aged ≥80 years, were included in a multistage sample. The participants' social network diversity was assessed by applying Cohen's social network index (SNI). The geriatric depression scale and activities of daily living measures were carried out during home visits. Descriptive analyses revealed the distribution of SNI, while the relationship between the SNI and the geriatric depression scale was examined by ordinal logistic regression models controlling possible covariants such as age, sex, and educational attainment. The median age of the sample was 83 years, with females comprising of 54.94% of the sample. The participants' children, their neighbors, and members of Buddhist temples were reported as the most frequent contacts of the study participants. Among the 435 participants, 25% were at risk of social isolation due to having a "limited" social network group (SNI 0-3), whereas 37% had a "medium" social network (SNI 4-5), and 38% had a "diverse" social network (SNI ≥6). The SNI was not different among the two sexes. Activities of daily living scores in the diverse social network group were significantly higher than those in the limited social network group. Multivariate ordinal logistic regression analysis models revealed a significant negative association between social network diversity and geriatric depression. Regular and frequent contact with various social contacts may safeguard common geriatric depression among persons aged ≥80 years. As a result, screening those at risk of social isolation is recommended to be integrated into routine primary health care-based geriatric assessment and intervention programs.

Artificial neural network, genetic algorithm, and logistic regression applications for predicting renal colic in emergency settings.

PubMed

Eken, Cenker; Bilge, Ugur; Kartal, Mutlu; Eray, Oktay

2009-06-03

Logistic regression is the most common statistical model for processing multivariate data in the medical literature. Artificial intelligence models like an artificial neural network (ANN) and genetic algorithm (GA) may also be useful to interpret medical data. The purpose of this study was to perform artificial intelligence models on a medical data sheet and compare to logistic regression. ANN, GA, and logistic regression analysis were carried out on a data sheet of a previously published article regarding patients presenting to an emergency department with flank pain suspicious for renal colic. The study population was composed of 227 patients: 176 patients had a diagnosis of urinary stone, while 51 ultimately had no calculus. The GA found two decision rules in predicting urinary stones. Rule 1 consisted of being male, pain not spreading to back, and no fever. In rule 2, pelvicaliceal dilatation on bedside ultrasonography replaced no fever. ANN, GA rule 1, GA rule 2, and logistic regression had a sensitivity of 94.9, 67.6, 56.8, and 95.5%, a specificity of 78.4, 76.47, 86.3, and 47.1%, a positive likelihood ratio of 4.4, 2.9, 4.1, and 1.8, and a negative likelihood ratio of 0.06, 0.42, 0.5, and 0.09, respectively. The area under the curve was found to be 0.867, 0.720, 0.715, and 0.713 for all applications, respectively. Data mining techniques such as ANN and GA can be used for predicting renal colic in emergency settings and to constitute clinical decision rules. They may be an alternative to conventional multivariate analysis applications used in biostatistics.

An approach for reduction of false predictions in reverse engineering of gene regulatory networks.

PubMed

Khan, Abhinandan; Saha, Goutam; Pal, Rajat Kumar

2018-05-14

A gene regulatory network discloses the regulatory interactions amongst genes, at a particular condition of the human body. The accurate reconstruction of such networks from time-series genetic expression data using computational tools offers a stiff challenge for contemporary computer scientists. This is crucial to facilitate the understanding of the proper functioning of a living organism. Unfortunately, the computational methods produce many false predictions along with the correct predictions, which is unwanted. Investigations in the domain focus on the identification of as many correct regulations as possible in the reverse engineering of gene regulatory networks to make it more reliable and biologically relevant. One way to achieve this is to reduce the number of incorrect predictions in the reconstructed networks. In the present investigation, we have proposed a novel scheme to decrease the number of false predictions by suitably combining several metaheuristic techniques. We have implemented the same using a dataset ensemble approach (i.e. combining multiple datasets) also. We have employed the proposed methodology on real-world experimental datasets of the SOS DNA Repair network of Escherichia coli and the IMRA network of Saccharomyces cerevisiae. Subsequently, we have experimented upon somewhat larger, in silico networks, namely, DREAM3 and DREAM4 Challenge networks, and 15-gene and 20-gene networks extracted from the GeneNetWeaver database. To study the effect of multiple datasets on the quality of the inferred networks, we have used four datasets in each experiment. The obtained results are encouraging enough as the proposed methodology can reduce the number of false predictions significantly, without using any supplementary prior biological information for larger gene regulatory networks. It is also observed that if a small amount of prior biological information is incorporated here, the results improve further w.r.t. the prediction of true positives. Copyright © 2018 Elsevier Ltd. All rights reserved.

Reverse engineering a mouse embryonic stem cell-specific transcriptional network reveals a new modulator of neuronal differentiation

PubMed Central

De Cegli, Rossella; Iacobacci, Simona; Flore, Gemma; Gambardella, Gennaro; Mao, Lei; Cutillo, Luisa; Lauria, Mario; Klose, Joachim; Illingworth, Elizabeth; Banfi, Sandro; di Bernardo, Diego

2013-01-01

Gene expression profiles can be used to infer previously unknown transcriptional regulatory interaction among thousands of genes, via systems biology ‘reverse engineering’ approaches. We ‘reverse engineered’ an embryonic stem (ES)-specific transcriptional network from 171 gene expression profiles, measured in ES cells, to identify master regulators of gene expression (‘hubs’). We discovered that E130012A19Rik (E13), highly expressed in mouse ES cells as compared with differentiated cells, was a central ‘hub’ of the network. We demonstrated that E13 is a protein-coding gene implicated in regulating the commitment towards the different neuronal subtypes and glia cells. The overexpression and knock-down of E13 in ES cell lines, undergoing differentiation into neurons and glia cells, caused a strong up-regulation of the glutamatergic neurons marker Vglut2 and a strong down-regulation of the GABAergic neurons marker GAD65 and of the radial glia marker Blbp. We confirmed E13 expression in the cerebral cortex of adult mice and during development. By immuno-based affinity purification, we characterized protein partners of E13, involved in the Polycomb complex. Our results suggest a role of E13 in regulating the division between glutamatergic projection neurons and GABAergic interneurons and glia cells possibly by epigenetic-mediated transcriptional regulation. PMID:23180766

Arabidopsis Ensemble Reverse-Engineered Gene Regulatory Network Discloses Interconnected Transcription Factors in Oxidative Stress[W

PubMed Central

Vermeirssen, Vanessa; De Clercq, Inge; Van Parys, Thomas; Van Breusegem, Frank; Van de Peer, Yves

2014-01-01

The abiotic stress response in plants is complex and tightly controlled by gene regulation. We present an abiotic stress gene regulatory network of 200,014 interactions for 11,938 target genes by integrating four complementary reverse-engineering solutions through average rank aggregation on an Arabidopsis thaliana microarray expression compendium. This ensemble performed the most robustly in benchmarking and greatly expands upon the availability of interactions currently reported. Besides recovering 1182 known regulatory interactions, cis-regulatory motifs and coherent functionalities of target genes corresponded with the predicted transcription factors. We provide a valuable resource of 572 abiotic stress modules of coregulated genes with functional and regulatory information, from which we deduced functional relationships for 1966 uncharacterized genes and many regulators. Using gain- and loss-of-function mutants of seven transcription factors grown under control and salt stress conditions, we experimentally validated 141 out of 271 predictions (52% precision) for 102 selected genes and mapped 148 additional transcription factor-gene regulatory interactions (49% recall). We identified an intricate core oxidative stress regulatory network where NAC13, NAC053, ERF6, WRKY6, and NAC032 transcription factors interconnect and function in detoxification. Our work shows that ensemble reverse-engineering can generate robust biological hypotheses of gene regulation in a multicellular eukaryote that can be tested by medium-throughput experimental validation. PMID:25549671

Arabidopsis ensemble reverse-engineered gene regulatory network discloses interconnected transcription factors in oxidative stress.

PubMed

Vermeirssen, Vanessa; De Clercq, Inge; Van Parys, Thomas; Van Breusegem, Frank; Van de Peer, Yves

2014-12-01

The abiotic stress response in plants is complex and tightly controlled by gene regulation. We present an abiotic stress gene regulatory network of 200,014 interactions for 11,938 target genes by integrating four complementary reverse-engineering solutions through average rank aggregation on an Arabidopsis thaliana microarray expression compendium. This ensemble performed the most robustly in benchmarking and greatly expands upon the availability of interactions currently reported. Besides recovering 1182 known regulatory interactions, cis-regulatory motifs and coherent functionalities of target genes corresponded with the predicted transcription factors. We provide a valuable resource of 572 abiotic stress modules of coregulated genes with functional and regulatory information, from which we deduced functional relationships for 1966 uncharacterized genes and many regulators. Using gain- and loss-of-function mutants of seven transcription factors grown under control and salt stress conditions, we experimentally validated 141 out of 271 predictions (52% precision) for 102 selected genes and mapped 148 additional transcription factor-gene regulatory interactions (49% recall). We identified an intricate core oxidative stress regulatory network where NAC13, NAC053, ERF6, WRKY6, and NAC032 transcription factors interconnect and function in detoxification. Our work shows that ensemble reverse-engineering can generate robust biological hypotheses of gene regulation in a multicellular eukaryote that can be tested by medium-throughput experimental validation. © 2014 American Society of Plant Biologists. All rights reserved.

Allosteric mechanism of quinoline inhibitors for HIV RT-associated RNase with MD simulation and dynamics fluctuation network.

PubMed

Cai, Yi; Liu, Hao; Chen, Haifeng

2018-03-01

The human immunodeficiency virus (HIV) is a retrovirus which infects T lymphocyte of human body and causes immunodeficiency. Reverse transcriptase inhibitors (RTIs) can inhibit some functions of RT, preventing virus synthesis (double-stranded DNA), so that HIV virus replication can be reduced. Experimental results indicate a series of benzimidazole-based inhibitors which target HIV RT-associated RNase to inhibit the reverse transcription of HIV virus. However, the allosteric mechanism is still unclear. Here, molecular dynamics simulations and dynamics fluctuation network analysis were used to reveal the binding mode between the inhibitors and RT-associated RNase. The most active molecule has more hydrophobic and electrostatic interactions than the less active inhibitor. Dynamics correlation network analysis indicates that the most active inhibitor perturbs the network of RT-associated RNase and decreases the correlation of nodes. 3D-QSAR model suggests that two robust and reliable models were constructed and validated by independent test set. 3D-QSAR model also shows that bulky negatively charged or hydrophilic substituent is favorable to bioactivity. These results reveal the allosteric mechanism of quinoline inhibitors and help to improve the bioactivity. © 2017 John Wiley & Sons A/S.

Dopamine Attenuates Ketamine-Induced Neuronal Apoptosis in the Developing Rat Retina Independent of Early Synchronized Spontaneous Network Activity.

PubMed

Dong, Jing; Gao, Lingqi; Han, Junde; Zhang, Junjie; Zheng, Jijian

2017-07-01

Deprivation of spontaneous rhythmic electrical activity in early development by anesthesia administration, among other interventions, induces neuronal apoptosis. However, it is unclear whether enhancement of neuronal electrical activity attenuates neuronal apoptosis in either normal development or after anesthesia exposure. The present study investigated the effects of dopamine, an enhancer of spontaneous rhythmic electrical activity, on ketamine-induced neuronal apoptosis in the developing rat retina. TUNEL and immunohistochemical assays indicated that ketamine time- and dose-dependently aggravated physiological and ketamine-induced apoptosis and inhibited early-synchronized spontaneous network activity. Dopamine administration reversed ketamine-induced neuronal apoptosis, but did not reverse the inhibitory effects of ketamine on early synchronized spontaneous network activity despite enhancing it in controls. Blockade of D1, D2, and A2A receptors and inhibition of cAMP/PKA signaling partially antagonized the protective effect of dopamine against ketamine-induced apoptosis. Together, these data indicate that dopamine attenuates ketamine-induced neuronal apoptosis in the developing rat retina by activating the D1, D2, and A2A receptors, and upregulating cAMP/PKA signaling, rather than through modulation of early synchronized spontaneous network activity.

Systems and methods for modeling and analyzing networks

DOEpatents

Hill, Colin C; Church, Bruce W; McDonagh, Paul D; Khalil, Iya G; Neyarapally, Thomas A; Pitluk, Zachary W

2013-10-29

The systems and methods described herein utilize a probabilistic modeling framework for reverse engineering an ensemble of causal models, from data and then forward simulating the ensemble of models to analyze and predict the behavior of the network. In certain embodiments, the systems and methods described herein include data-driven techniques for developing causal models for biological networks. Causal network models include computational representations of the causal relationships between independent variables such as a compound of interest and dependent variables such as measured DNA alterations, changes in mRNA, protein, and metabolites to phenotypic readouts of efficacy and toxicity.

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

Comparing models for quantitative risk assessment: an application to the European Registry of foreign body injuries in children.

PubMed

Berchialla, Paola; Scarinzi, Cecilia; Snidero, Silvia; Gregori, Dario

2016-08-01

Risk Assessment is the systematic study of decisions subject to uncertain consequences. An increasing interest has been focused on modeling techniques like Bayesian Networks since their capability of (1) combining in the probabilistic framework different type of evidence including both expert judgments and objective data; (2) overturning previous beliefs in the light of the new information being received and (3) making predictions even with incomplete data. In this work, we proposed a comparison among Bayesian Networks and other classical Quantitative Risk Assessment techniques such as Neural Networks, Classification Trees, Random Forests and Logistic Regression models. Hybrid approaches, combining both Classification Trees and Bayesian Networks, were also considered. Among Bayesian Networks, a clear distinction between purely data-driven approach and combination of expert knowledge with objective data is made. The aim of this paper consists in evaluating among this models which best can be applied, in the framework of Quantitative Risk Assessment, to assess the safety of children who are exposed to the risk of inhalation/insertion/aspiration of consumer products. The issue of preventing injuries in children is of paramount importance, in particular where product design is involved: quantifying the risk associated to product characteristics can be of great usefulness in addressing the product safety design regulation. Data of the European Registry of Foreign Bodies Injuries formed the starting evidence for risk assessment. Results showed that Bayesian Networks appeared to have both the ease of interpretability and accuracy in making prediction, even if simpler models like logistic regression still performed well. © The Author(s) 2013.

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.

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

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.

Decision support system for the optimal location of electrical and electronic waste treatment plants: a case study in greece.

PubMed

Achillas, Ch; Vlachokostas, Ch; Moussiopoulos, Nu; Banias, G

2010-05-01

Environmentally sound end-of-life management of Electrical and Electronic Equipment has been realised as a top priority issue internationally, both due to the waste stream's continuously increasing quantities, as well as its content in valuable and also hazardous materials. In an effort to manage Waste Electrical and Electronic Equipment (WEEE), adequate infrastructure in treatment and recycling facilities is considered a prerequisite. A critical number of such plants are mandatory to be installed in order: (i) to accommodate legislative needs, (ii) decrease transportation cost, and (iii) expand reverse logistics network and cover more areas. However, WEEE recycling infrastructures require high expenditures and therefore the decision maker need to be most precautious. In this context, special care should be given on the viability of infrastructure which is heavily dependent on facilities' location. To this end, a methodology aiming towards optimal location of Units of Treatment and Recycling is developed, taking into consideration economical together with social criteria, in an effort to interlace local acceptance and financial viability. For the decision support system's needs, ELECTRE III is adopted as a multicriteria analysis technique. The methodology's applicability is demonstrated with a real-world case study in Greece. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

Perceptual reversals during binocular rivalry: ERP components and their concomitant source differences.

PubMed

Britz, Juliane; Pitts, Michael A

2011-11-01

We used an intermittent stimulus presentation to investigate event-related potential (ERP) components associated with perceptual reversals during binocular rivalry. The combination of spatiotemporal ERP analysis with source imaging and statistical parametric mapping of the concomitant source differences yielded differences in three time windows: reversals showed increased activity in early visual (∼120 ms) and in inferior frontal and anterior temporal areas (∼400-600 ms) and decreased activity in the ventral stream (∼250-350 ms). The combination of source imaging and statistical parametric mapping suggests that these differences were due to differences in generator strength and not generator configuration, unlike the initiation of reversals in right inferior parietal areas. These results are discussed within the context of the extensive network of brain areas that has been implicated in the initiation, implementation, and appraisal of bistable perceptual reversals. Copyright © 2011 Society for Psychophysiological Research.

Reverse Ecology: from systems to environments and back.

PubMed

Levy, Roie; Borenstein, Elhanan

2012-01-01

The structure of complex biological systems reflects not only their function but also the environments in which they evolved and are adapted to. Reverse Ecology-an emerging new frontier in Evolutionary Systems Biology-aims to extract this information and to obtain novel insights into an organism's ecology. The Reverse Ecology framework facilitates the translation of high-throughput genomic data into large-scale ecological data, and has the potential to transform ecology into a high-throughput field. In this chapter, we describe some of the pioneering work in Reverse Ecology, demonstrating how system-level analysis of complex biological networks can be used to predict the natural habitats of poorly characterized microbial species, their interactions with other species, and universal patterns governing the adaptation of organisms to their environments. We further present several studies that applied Reverse Ecology to elucidate various aspects of microbial ecology, and lay out exciting future directions and potential future applications in biotechnology, biomedicine, and ecological engineering.

Reversible optical switching memristors with tunable STDP synaptic plasticity: a route to hierarchical control in artificial intelligent systems.

PubMed

Jaafar, Ayoub H; Gray, Robert J; Verrelli, Emanuele; O'Neill, Mary; Kelly, Stephen M; Kemp, Neil T

2017-11-09

Optical control of memristors opens the route to new applications in optoelectronic switching and neuromorphic computing. Motivated by the need for reversible and latched optical switching we report on the development of a memristor with electronic properties tunable and switchable by wavelength and polarization specific light. The device consists of an optically active azobenzene polymer, poly(disperse red 1 acrylate), overlaying a forest of vertically aligned ZnO nanorods. Illumination induces trans-cis isomerization of the azobenzene molecules, which expands or contracts the polymer layer and alters the resistance of the off/on states, their ratio and retention time. The reversible optical effect enables dynamic control of a memristor's learning properties including control of synaptic potentiation and depression, optical switching between short-term and long-term memory and optical modulation of the synaptic efficacy via spike timing dependent plasticity. The work opens the route to the dynamic patterning of memristor networks both spatially and temporally by light, thus allowing the development of new optically reconfigurable neural networks and adaptive electronic circuits.

3 CFR 8675 - Proclamation 8675 of May 13, 2011. National Defense Transportation Day and National...

Code of Federal Regulations, 2012 CFR

2012-01-01

... ourselves for the next revolutionary breakthroughs in transportation technology. We must provide increased... world-class logistics network, create new jobs, and win the future for our children. In recognition of...

Modeling brook trout presence and absence from landscape variables using four different analytical methods

USGS Publications Warehouse

Steen, Paul J.; Passino-Reader, Dora R.; Wiley, Michael J.

2006-01-01

As a part of the Great Lakes Regional Aquatic Gap Analysis Project, we evaluated methodologies for modeling associations between fish species and habitat characteristics at a landscape scale. To do this, we created brook trout Salvelinus fontinalis presence and absence models based on four different techniques: multiple linear regression, logistic regression, neural networks, and classification trees. The models were tested in two ways: by application to an independent validation database and cross-validation using the training data, and by visual comparison of statewide distribution maps with historically recorded occurrences from the Michigan Fish Atlas. Although differences in the accuracy of our models were slight, the logistic regression model predicted with the least error, followed by multiple regression, then classification trees, then the neural networks. These models will provide natural resource managers a way to identify habitats requiring protection for the conservation of fish species.

Design of shared unit-dose drug distribution network using multi-level particle swarm optimization.

PubMed

Chen, Linjie; Monteiro, Thibaud; Wang, Tao; Marcon, Eric

2018-03-01

Unit-dose drug distribution systems provide optimal choices in terms of medication security and efficiency for organizing the drug-use process in large hospitals. As small hospitals have to share such automatic systems for economic reasons, the structure of their logistic organization becomes a very sensitive issue. In the research reported here, we develop a generalized multi-level optimization method - multi-level particle swarm optimization (MLPSO) - to design a shared unit-dose drug distribution network. Structurally, the problem studied can be considered as a type of capacitated location-routing problem (CLRP) with new constraints related to specific production planning. This kind of problem implies that a multi-level optimization should be performed in order to minimize logistic operating costs. Our results show that with the proposed algorithm, a more suitable modeling framework, as well as computational time savings and better optimization performance are obtained than that reported in the literature on this subject.

Solid state switch

DOEpatents

Merritt, Bernard T.; Dreifuerst, Gary R.

1994-01-01

A solid state switch, with reverse conducting thyristors, is designed to operate at 20 kV hold-off voltage, 1500 A peak, 1.0 .mu.s pulsewidth, and 4500 pps, to replace thyratrons. The solid state switch is more reliable, more economical, and more easily repaired. The switch includes a stack of circuit card assemblies, a magnetic assist and a trigger chassis. Each circuit card assembly contains a reverse conducting thyristor, a resistor capacitor network, and triggering circuitry.

A new method for constructing networks from binary data

NASA Astrophysics Data System (ADS)

van Borkulo, Claudia D.; Borsboom, Denny; Epskamp, Sacha; Blanken, Tessa F.; Boschloo, Lynn; Schoevers, Robert A.; Waldorp, Lourens J.

2014-08-01

Network analysis is entering fields where network structures are unknown, such as psychology and the educational sciences. A crucial step in the application of network models lies in the assessment of network structure. Current methods either have serious drawbacks or are only suitable for Gaussian data. In the present paper, we present a method for assessing network structures from binary data. Although models for binary data are infamous for their computational intractability, we present a computationally efficient model for estimating network structures. The approach, which is based on Ising models as used in physics, combines logistic regression with model selection based on a Goodness-of-Fit measure to identify relevant relationships between variables that define connections in a network. A validation study shows that this method succeeds in revealing the most relevant features of a network for realistic sample sizes. We apply our proposed method to estimate the network of depression and anxiety symptoms from symptom scores of 1108 subjects. Possible extensions of the model are discussed.

Enhanced recycling network for spent e-bicycle batteries: A case study in Xuzhou, China.

PubMed

Chen, Fu; Yang, Baodan; Zhang, Wangyuan; Ma, Jing; Lv, Jie; Yang, Yongjun

2017-02-01

Electric bicycles (e-bicycles) are a primary means of commuting in China because of their light weight, speed, and low maintenance costs. Owing to short service life and environmental pollution hazards, recycling and reuse of e-bicycle batteries has always been a focus of industry and academia. As a typical case of both production and use of large electric bicycles, 113 major sellers, 378 corporate and individual buyers, 147 large e-bicycle repair centers, and 1317 e-bicycle owners in Xuzhou City were investigated in order to understand the sales, use, recycling, and disposal of spent e-bicycle batteries. The findings show that the existing distempered recycling system is the main limitation of spent battery recovery, and the actual recovery rate of spent batteries is lower than the estimated output (QW) for the years 2011-2014. Electric bicycle sellers play a fundamental role in the collection of spent batteries in Xuzhou, accounting for 42.3±8.3% of all batteries recovered. The widespread use of lithium batteries in recent years has resulted in a reduction in spent battery recycling because of lower battery prices. Furthermore, consumer preferences are another important factor affecting the actual recovery rate according to survey results evaluated using canonical correspondence analysis. In this paper, we suggest that a reverse logistics network system for spent battery recycling should be established in the future; in addition, enhancing producer responsibility, increasing publicity, raising of public awareness, developing green public transport, and reducing dependence on e-bicycles also should be pursued. This study seeks to provide guidance for planning construction and management policies for an effective spent battery recycling system in China and other developing countries. Copyright © 2016 Elsevier Ltd. All rights reserved.

Dynamics of social balance on networks

NASA Astrophysics Data System (ADS)

Antal, T.; Krapivsky, P. L.; Redner, S.

2005-09-01

We study the evolution of social networks that contain both friendly and unfriendly pairwise links between individual nodes. The network is endowed with dynamics in which the sense of a link in an imbalanced triad—a triangular loop with one or three unfriendly links—is reversed to make the triad balanced. With this dynamics, an infinite network undergoes a dynamic phase transition from a steady state to “paradise”—all links are friendly—as the propensity p for friendly links in an update event passes through 1/2 . A finite network always falls into a socially balanced absorbing state where no imbalanced triads remain. If the additional constraint that the number of imbalanced triads in the network not increase in an update is imposed, then the network quickly reaches a balanced final state.

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.

An improved advertising CTR prediction approach based on the fuzzy deep neural network

PubMed Central

Gao, Shu; Li, Mingjiang

2018-01-01

Combining a deep neural network with fuzzy theory, this paper proposes an advertising click-through rate (CTR) prediction approach based on a fuzzy deep neural network (FDNN). In this approach, fuzzy Gaussian-Bernoulli restricted Boltzmann machine (FGBRBM) is first applied to input raw data from advertising datasets. Next, fuzzy restricted Boltzmann machine (FRBM) is used to construct the fuzzy deep belief network (FDBN) with the unsupervised method layer by layer. Finally, fuzzy logistic regression (FLR) is utilized for modeling the CTR. The experimental results show that the proposed FDNN model outperforms several baseline models in terms of both data representation capability and robustness in advertising click log datasets with noise. PMID:29727443

Information and material flows in complex networks

NASA Astrophysics Data System (ADS)

Helbing, Dirk; Armbruster, Dieter; Mikhailov, Alexander S.; Lefeber, Erjen

2006-04-01

In this special issue, an overview of the Thematic Institute (TI) on Information and Material Flows in Complex Systems is given. The TI was carried out within EXYSTENCE, the first EU Network of Excellence in the area of complex systems. Its motivation, research approach and subjects are presented here. Among the various methods used are many-particle and statistical physics, nonlinear dynamics, as well as complex systems, network and control theory. The contributions are relevant for complex systems as diverse as vehicle and data traffic in networks, logistics, production, and material flows in biological systems. The key disciplines involved are socio-, econo-, traffic- and bio-physics, and a new research area that could be called “biologistics”.

An improved advertising CTR prediction approach based on the fuzzy deep neural network.

PubMed

Jiang, Zilong; Gao, Shu; Li, Mingjiang

2018-01-01

Combining a deep neural network with fuzzy theory, this paper proposes an advertising click-through rate (CTR) prediction approach based on a fuzzy deep neural network (FDNN). In this approach, fuzzy Gaussian-Bernoulli restricted Boltzmann machine (FGBRBM) is first applied to input raw data from advertising datasets. Next, fuzzy restricted Boltzmann machine (FRBM) is used to construct the fuzzy deep belief network (FDBN) with the unsupervised method layer by layer. Finally, fuzzy logistic regression (FLR) is utilized for modeling the CTR. The experimental results show that the proposed FDNN model outperforms several baseline models in terms of both data representation capability and robustness in advertising click log datasets with noise.

Depression and Chronic Health Conditions Among Latinos: The Role of Social Networks.

PubMed

Soto, Sandra; Arredondo, Elva M; Villodas, Miguel T; Elder, John P; Quintanar, Elena; Madanat, Hala

2016-12-01

The purpose of this study was to examine the "buffering hypothesis" of social network characteristics in the association between chronic conditions and depression among Latinos. Cross-sectional self-report data from the San Diego Prevention Research Center's community survey of Latinos were used (n = 393). Separate multiple logistic regression models tested the role of chronic conditions and social network characteristics in the likelihood of moderate-to-severe depressive symptoms. Having a greater proportion of the network comprised of friends increased the likelihood of depression among those with high cholesterol. Having a greater proportion of women in the social network was directly related to the increased likelihood of depression, regardless of the presence of chronic health conditions. Findings suggest that network characteristics may play a role in the link between chronic conditions and depression among Latinos. Future research should explore strategies targeting the social networks of Latinos to improve health outcomes.

Scaling of global input-output networks

NASA Astrophysics Data System (ADS)

Liang, Sai; Qi, Zhengling; Qu, Shen; Zhu, Ji; Chiu, Anthony S. F.; Jia, Xiaoping; Xu, Ming

2016-06-01

Examining scaling patterns of networks can help understand how structural features relate to the behavior of the networks. Input-output networks consist of industries as nodes and inter-industrial exchanges of products as links. Previous studies consider limited measures for node strengths and link weights, and also ignore the impact of dataset choice. We consider a comprehensive set of indicators in this study that are important in economic analysis, and also examine the impact of dataset choice, by studying input-output networks in individual countries and the entire world. Results show that Burr, Log-Logistic, Log-normal, and Weibull distributions can better describe scaling patterns of global input-output networks. We also find that dataset choice has limited impacts on the observed scaling patterns. Our findings can help examine the quality of economic statistics, estimate missing data in economic statistics, and identify key nodes and links in input-output networks to support economic policymaking.

An Expert System for Processing Uncorrelated Satellite Tracks

DTIC Science & Technology

1992-12-17

earthworms with much intellect e\\en though they routinely carry out this same function. One definition given artificial intelligence is "the study of mental...Networks: Benchmarking Studies ," Proceedings from the IEEE International Conference on Neural Networkv. pp. 64-65, 1988. 229 Lyddane, R., "Small...reverse if necessary and rdenqtl_ by block number, Field Group Subgroup Artificial Intelligence, Expert Systems, Neural Networks. Orbital Mechanics

The Reverse Thermal Effect in Epoxy Resins and Moisture Absorption in Semi-Interpenetrating Polymer Networks.

NASA Astrophysics Data System (ADS)

El-Sa'Ad, Leila

1989-12-01

Available from UMI in association with The British Library. Requires signed TDF. Epoxy resins exhibit many desirable properties which make them ideal subjects for use as matrices of composite materials in many commercial, military and space applications. However, due to their high cross-link density they are often brittle. Epoxy resin networks have been modified by incorporating tough, ductile thermoplastics. Such systems are referred to as Semi-Interpenetrating Polymer Networks (Semi-IPN). Systematic modification to the thermoplastics backbone allowed the morphology of the blend to be controlled from a homogeneous one-phase structure to fully separated structures. The moisture absorption by composites in humid environments has been found to lead to a deterioration in the physical and mechanical properties of the matrix. Therefore, in order to utilize composites to their full potential, their response to hot/wet environments must be known. The aims of this investigation were two-fold. Firstly, to study the effect of varying the temperature of exposure at different stages in the absorption process on the water absorption behaviour of a TGDDM/DDS epoxy resin system. Secondly, to study water absorption characteristics, under isothermal conditions, of Semi-Interpenetrating Polymer Networks possessing different morphologies, and develop a theoretical model to evaluate the diffusion coefficients of the two-phase structures. The mathematical treatment used in this analysis was based on Fick's second law of diffusion. Tests were performed on specimens immersed in water at 10 ^circ, 40^circ and 70^circC, their absorption behaviour and swelling behaviour, as a consequence of water absorption, were investigated. The absorption results of the variable temperature absorption tests indicated a saturation dependence on the absorption behaviour. Specimens saturated at a high temperature will undergo further absorption when transferred to a lower temperature. This behaviour was termed the "reverse thermal effect". The swelling results suggested that it is more tightly bound water in the polymer which takes part in the reverse thermal effect. The absorption results for the Semi-Interpenetrating Polymer Networks suggested that the two key parameters which affected the moisture uptake were the morphology of the network and the percentage of epoxy resin in the system.

Decision-making tools for distribution networks in disaster relief.

DOT National Transportation Integrated Search

2011-08-05

The devastation caused by the 2010 earthquake in Haiti was compounded by the significant logistical : challenges of distributing relief to those in need. Unfortunately this is the case with many disasters. : Rapid and efficient distribution of water,...

HiDi: an efficient reverse engineering schema for large-scale dynamic regulatory network reconstruction using adaptive differentiation.

PubMed

Deng, Yue; Zenil, Hector; Tegnér, Jesper; Kiani, Narsis A

2017-12-15

The use of differential equations (ODE) is one of the most promising approaches to network inference. The success of ODE-based approaches has, however, been limited, due to the difficulty in estimating parameters and by their lack of scalability. Here, we introduce a novel method and pipeline to reverse engineer gene regulatory networks from gene expression of time series and perturbation data based upon an improvement on the calculation scheme of the derivatives and a pre-filtration step to reduce the number of possible links. The method introduces a linear differential equation model with adaptive numerical differentiation that is scalable to extremely large regulatory networks. We demonstrate the ability of this method to outperform current state-of-the-art methods applied to experimental and synthetic data using test data from the DREAM4 and DREAM5 challenges. Our method displays greater accuracy and scalability. We benchmark the performance of the pipeline with respect to dataset size and levels of noise. We show that the computation time is linear over various network sizes. The Matlab code of the HiDi implementation is available at: www.complexitycalculator.com/HiDiScript.zip. hzenilc@gmail.com or narsis.kiani@ki.se. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Trauma Hemostasis and Oxygenation Research Network position paper on the role of hypotensive resuscitation as part of remote damage control resuscitation.

PubMed

Woolley, Thomas; Thompson, Patrick; Kirkman, Emrys; Reed, Richard; Ausset, Sylvain; Beckett, Andrew; Bjerkvig, Christopher; Cap, Andrew P; Coats, Tim; Cohen, Mitchell; Despasquale, Marc; Dorlac, Warren; Doughty, Heidi; Dutton, Richard; Eastridge, Brian; Glassberg, Elon; Hudson, Anthony; Jenkins, Donald; Keenan, Sean; Martinaud, Christophe; Miles, Ethan; Moore, Ernest; Nordmann, Giles; Prat, Nicolas; Rappold, Joseph; Reade, Michael C; Rees, Paul; Rickard, Rory; Schreiber, Martin; Shackelford, Stacy; Skogran Eliassen, Håkon; Smith, Jason; Smith, Mike; Spinella, Philip; Strandenes, Geir; Ward, Kevin; Watts, Sarah; White, Nathan; Williams, Steve

2018-06-01

The Trauma Hemostasis and Oxygenation Research (THOR) Network has developed a consensus statement on the role of permissive hypotension in remote damage control resuscitation (RDCR). A summary of the evidence on permissive hypotension follows the THOR Network position on the topic. In RDCR, the burden of time in the care of the patients suffering from noncompressible hemorrhage affects outcomes. Despite the lack of published evidence, and based on clinical experience and expertise, it is the THOR Network's opinion that the increase in prehospital time leads to an increased burden of shock, which poses a greater risk to the patient than the risk of rebleeding due to slightly increased blood pressure, especially when blood products are available as part of prehospital resuscitation.The THOR Network's consensus statement is, "In a casualty with life-threatening hemorrhage, shock should be reversed as soon as possible using a blood-based HR fluid. Whole blood is preferred to blood components. As a part of this HR, the initial systolic blood pressure target should be 100 mm Hg. In RDCR, it is vital for higher echelon care providers to receive a casualty with sufficient physiologic reserve to survive definitive surgical hemostasis and aggressive resuscitation. The combined use of blood-based resuscitation and limiting systolic blood pressure is believed to be effective in promoting hemostasis and reversing shock".

Reveal, A General Reverse Engineering Algorithm for Inference of Genetic Network Architectures

NASA Technical Reports Server (NTRS)

Liang, Shoudan; Fuhrman, Stefanie; Somogyi, Roland

1998-01-01

Given the immanent gene expression mapping covering whole genomes during development, health and disease, we seek computational methods to maximize functional inference from such large data sets. Is it possible, in principle, to completely infer a complex regulatory network architecture from input/output patterns of its variables? We investigated this possibility using binary models of genetic networks. Trajectories, or state transition tables of Boolean nets, resemble time series of gene expression. By systematically analyzing the mutual information between input states and output states, one is able to infer the sets of input elements controlling each element or gene in the network. This process is unequivocal and exact for complete state transition tables. We implemented this REVerse Engineering ALgorithm (REVEAL) in a C program, and found the problem to be tractable within the conditions tested so far. For n = 50 (elements) and k = 3 (inputs per element), the analysis of incomplete state transition tables (100 state transition pairs out of a possible 10(exp 15)) reliably produced the original rule and wiring sets. While this study is limited to synchronous Boolean networks, the algorithm is generalizable to include multi-state models, essentially allowing direct application to realistic biological data sets. The ability to adequately solve the inverse problem may enable in-depth analysis of complex dynamic systems in biology and other fields.

Sulfophenyl-Functionalized Reduced Graphene Oxide Networks on Electrospun 3D Scaffold for Ultrasensitive NO₂ Gas Sensor.

PubMed

Zou, Bin; Guo, Yunlong; Shen, Nannan; Xiao, Anshan; Li, Mingjun; Zhu, Liang; Wan, Pengbo; Sun, Xiaoming

2017-12-19

Ultrasensitive room temperature real-time NO₂ sensors are highly desirable due to potential threats on environmental security and personal respiratory. Traditional NO₂ gas sensors with highly operated temperatures (200-600 °C) and limited reversibility are mainly constructed from semiconducting oxide-deposited ceramic tubes or inter-finger probes. Herein, we report the functionalized graphene network film sensors assembled on an electrospun three-dimensional (3D) nanonetwork skeleton for ultrasensitive NO₂ sensing. The functional 3D scaffold was prepared by electrospinning interconnected polyacrylonitrile (PAN) nanofibers onto a nylon window screen to provide a 3D nanonetwork skeleton. Then, the sulfophenyl-functionalized reduced graphene oxide (SFRGO) was assembled on the electrospun 3D nanonetwork skeleton to form SFRGO network films. The assembled functionalized graphene network film sensors exhibit excellent NO₂ sensing performance (10 ppb to 20 ppm) at room temperature, reliable reversibility, good selectivity, and better sensing cycle stability. These improvements can be ascribed to the functionalization of graphene with electron-withdrawing sulfophenyl groups, the high surface-to-volume ratio, and the effective sensing channels from SFRGO wrapping onto the interconnected 3D scaffold. The SFRGO network-sensing film has the advantages of simple preparation, low cost, good processability, and ultrasensitive NO₂ sensing, all advantages that can be utilized for potential integration into smart windows and wearable electronic devices for real-time household gas sensors.

Peculiarities of solving the problems of modern logistics in high-rise construction and industrial production

NASA Astrophysics Data System (ADS)

Rubtsov, Anatoliy E.; Ushakova, Elena V.; Chirkova, Tamara V.

2018-03-01

Basing on the analysis of the enterprise (construction organization) structure and infrastructure of the entire logistics system in which this enterprise (construction organization) operates, this article proposes an approach to solve the problems of structural optimization and a set of calculation tasks, based on customer orders as well as on the required levels of insurance stocks, transit stocks and other types of stocks in the distribution network, modes of operation of the in-company transport and storage complex and a number of other factors.

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

Reverse engineering time discrete finite dynamical systems: a feasible undertaking?

PubMed

Delgado-Eckert, Edgar

2009-01-01

With the advent of high-throughput profiling methods, interest in reverse engineering the structure and dynamics of biochemical networks is high. Recently an algorithm for reverse engineering of biochemical networks was developed by Laubenbacher and Stigler. It is a top-down approach using time discrete dynamical systems. One of its key steps includes the choice of a term order, a technicality imposed by the use of Gröbner-bases calculations. The aim of this paper is to identify minimal requirements on data sets to be used with this algorithm and to characterize optimal data sets. We found minimal requirements on a data set based on how many terms the functions to be reverse engineered display. Furthermore, we identified optimal data sets, which we characterized using a geometric property called "general position". Moreover, we developed a constructive method to generate optimal data sets, provided a codimensional condition is fulfilled. In addition, we present a generalization of their algorithm that does not depend on the choice of a term order. For this method we derived a formula for the probability of finding the correct model, provided the data set used is optimal. We analyzed the asymptotic behavior of the probability formula for a growing number of variables n (i.e. interacting chemicals). Unfortunately, this formula converges to zero as fast as , where and . Therefore, even if an optimal data set is used and the restrictions in using term orders are overcome, the reverse engineering problem remains unfeasible, unless prodigious amounts of data are available. Such large data sets are experimentally impossible to generate with today's technologies.

Selective aspiration or neurotoxic lesions of orbital frontal areas 11 and 13 spared monkeys’ performance on the object discrimination reversal task

PubMed Central

Kazama, Andy; Bachevalier, Jocelyne

2009-01-01

Damage to the orbital frontal cortex (OFC) has long been associated with reversal learning deficits in several species. In monkeys, this impairment follows lesions that include several OFC subfields. However, the different connectional patterns of OFC subfields together with neuroimaging data in humans have suggested that specific OFC areas play distinctive roles in processing information necessary to guide behavior (Kringelbach and Rolls, 2004; Barbas, 2007; Price, 2007). More specifically, areas 11 and 13 contribute to a sensory network, whereas medial areas 10, 14, and 25 are heavily connected to a visceromotor network. To examine the contribution of areas 11 and 13 to reversal learning, we tested monkeys with selective damage to these two OFC areas on two versions of the ODR task using either 1 or 5 discrimination problems. We compared their performance with that of sham-operated controls and of animals with neurotoxic amygdala lesions, which served as operated controls. Neither damage to areas 11 and 13 nor damage to the amygdala affected performance on the ODR tasks. The results indicate that areas 11 and 13 do not critically contribute to reversal learning and that adjacent damage to OFC subfields (10, 12, 14 and 25) could account for the ODR deficits found in earlier lesion studies. This sparing of reversal learning will be discussed in relation to deficits found in the same animals on tasks that measure behavioral modulation when relative value of affective (positive and negative) stimuli was manipulated. PMID:19261875

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

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

Harmony search optimization algorithm for a novel transportation problem in a consolidation network

NASA Astrophysics Data System (ADS)

Davod Hosseini, Seyed; Akbarpour Shirazi, Mohsen; Taghi Fatemi Ghomi, Seyed Mohammad

2014-11-01

This article presents a new harmony search optimization algorithm to solve a novel integer programming model developed for a consolidation network. In this network, a set of vehicles is used to transport goods from suppliers to their corresponding customers via two transportation systems: direct shipment and milk run logistics. The objective of this problem is to minimize the total shipping cost in the network, so it tries to reduce the number of required vehicles using an efficient vehicle routing strategy in the solution approach. Solving several numerical examples confirms that the proposed solution approach based on the harmony search algorithm performs much better than CPLEX in reducing both the shipping cost in the network and computational time requirement, especially for realistic size problem instances.

Application of two neural network paradigms to the study of voluntary employee turnover.

PubMed

Somers, M J

1999-04-01

Two neural network paradigms--multilayer perceptron and learning vector quantization--were used to study voluntary employee turnover with a sample of 577 hospital employees. The objectives of the study were twofold. The 1st was to assess whether neural computing techniques offered greater predictive accuracy than did conventional turnover methodologies. The 2nd was to explore whether computer models of turnover based on neural network technologies offered new insights into turnover processes. When compared with logistic regression analysis, both neural network paradigms provided considerably more accurate predictions of turnover behavior, particularly with respect to the correct classification of leavers. In addition, these neural network paradigms captured nonlinear relationships that are relevant for theory development. Results are discussed in terms of their implications for future research.

Neural network classifications and correlation analysis of EEG and MEG activity accompanying spontaneous reversals of the Necker cube.

PubMed

Gaetz, M; Weinberg, H; Rzempoluck, E; Jantzen, K J

1998-04-01

It has recently been suggested that reentrant connections are essential in systems that process complex information [A. Damasio, H. Damasio, Cortical systems for the retrieval of concrete knowledge: the convergence zone framework, in: C. Koch, J.L. Davis (Eds.), Large Scale Neuronal Theories of the Brain, The MIT Press, Cambridge, 1995, pp. 61-74; G. Edelman, The Remembered Present, Basic Books, New York, 1989; M.I. Posner, M. Rothbart, Constructing neuronal theories of mind, in: C. Koch, J.L. Davis (Eds.), Large Scale Neuronal Theories of the Brain, The MIT Press, Cambridge, 1995, pp. 183-199; C. von der Malsburg, W. Schneider, A neuronal cocktail party processor, Biol. Cybem., 54 (1986) 29-40]. Reentry is not feedback, but parallel signalling in the time domain between spatially distributed maps, similar to a process of correlation between distributed systems. Accordingly, it was expected that during spontaneous reversals of the Necker cube, complex patterns of correlations between distributed systems would be present in the cortex. The present study included EEG (n=4) and MEG recordings (n=5). Two experimental questions were posed: (1) Can distributed cortical patterns present during perceptual reversals be classified differently using a generalised regression neural network (GRNN) compared to processing of a two-dimensional figure? (2) Does correlated cortical activity increase significantly during perception of a Necker cube reversal? One-second duration single trials of EEG and MEG data were analysed using the GRNN. Electrode/sensor pairings based on cortico-cortical connections were selected to assess correlated activity in each condition. The GRNN significantly classified single trials recorded during Necker cube reversals as different from single trials recorded during perception of a two-dimensional figure for both EEG and MEG. In addition, correlated cortical activity increased significantly in the Necker cube reversal condition for EEG and MEG compared to the perception of a non-reversing stimulus. Coherent MEG activity observed over occipital, parietal and temporal regions is believed to represent neural systems related to the perception of Necker cube reversals. Copyright 1998 Elsevier Science B.V.

Solid state switch

DOEpatents

Merritt, B.T.; Dreifuerst, G.R.

1994-07-19

A solid state switch, with reverse conducting thyristors, is designed to operate at 20 kV hold-off voltage, 1,500 A peak, 1.0 [mu]s pulsewidth, and 4,500 pps, to replace thyratrons. The solid state switch is more reliable, more economical, and more easily repaired. The switch includes a stack of circuit card assemblies, a magnetic assist and a trigger chassis. Each circuit card assembly contains a reverse conducting thyristor, a resistor capacitor network, and triggering circuitry. 6 figs.

Multiple network-constrained regressions expand insights into influenza vaccination responses.

PubMed

Avey, Stefan; Mohanty, Subhasis; Wilson, Jean; Zapata, Heidi; Joshi, Samit R; Siconolfi, Barbara; Tsang, Sui; Shaw, Albert C; Kleinstein, Steven H

2017-07-15

Systems immunology leverages recent technological advancements that enable broad profiling of the immune system to better understand the response to infection and vaccination, as well as the dysregulation that occurs in disease. An increasingly common approach to gain insights from these large-scale profiling experiments involves the application of statistical learning methods to predict disease states or the immune response to perturbations. However, the goal of many systems studies is not to maximize accuracy, but rather to gain biological insights. The predictors identified using current approaches can be biologically uninterpretable or present only one of many equally predictive models, leading to a narrow understanding of the underlying biology. Here we show that incorporating prior biological knowledge within a logistic modeling framework by using network-level constraints on transcriptional profiling data significantly improves interpretability. Moreover, incorporating different types of biological knowledge produces models that highlight distinct aspects of the underlying biology, while maintaining predictive accuracy. We propose a new framework, Logistic Multiple Network-constrained Regression (LogMiNeR), and apply it to understand the mechanisms underlying differential responses to influenza vaccination. Although standard logistic regression approaches were predictive, they were minimally interpretable. Incorporating prior knowledge using LogMiNeR led to models that were equally predictive yet highly interpretable. In this context, B cell-specific genes and mTOR signaling were associated with an effective vaccination response in young adults. Overall, our results demonstrate a new paradigm for analyzing high-dimensional immune profiling data in which multiple networks encoding prior knowledge are incorporated to improve model interpretability. The R source code described in this article is publicly available at https://bitbucket.org/kleinstein/logminer . steven.kleinstein@yale.edu or stefan.avey@yale.edu. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Dense module enumeration in biological networks

NASA Astrophysics Data System (ADS)

Tsuda, Koji; Georgii, Elisabeth

2009-12-01

Analysis of large networks is a central topic in various research fields including biology, sociology, and web mining. Detection of dense modules (a.k.a. clusters) is an important step to analyze the networks. Though numerous methods have been proposed to this aim, they often lack mathematical rigorousness. Namely, there is no guarantee that all dense modules are detected. Here, we present a novel reverse-search-based method for enumerating all dense modules. Furthermore, constraints from additional data sources such as gene expression profiles or customer profiles can be integrated, so that we can systematically detect dense modules with interesting profiles. We report successful applications in human protein interaction network analyses.

On understanding nuclear reaction network flows with branchings on directed graphs

NASA Astrophysics Data System (ADS)

Meyer, Bradley S.

2018-04-01

Nuclear reaction network flow diagrams are useful for understanding which reactions are governing the abundance changes at a particular time during nucleosynthesis. This is especially true when the flows are largely unidirectional, such as during the s-process of nucleosynthesis. In explosive nucleosynthesis, when reaction flows are large, and when forward reactions are nearly balanced by their reverses, reaction flows no longer give a clear picture of the abundance evolution in the network. This paper presents a way of understanding network evolution in terms of sums of branchings on a directed graph, which extends the concept of reaction flows to allow for multiple reaction pathways.

Stretched exponential dynamics of coupled logistic maps on a small-world network

NASA Astrophysics Data System (ADS)

Mahajan, Ashwini V.; Gade, Prashant M.

2018-02-01

We investigate the dynamic phase transition from partially or fully arrested state to spatiotemporal chaos in coupled logistic maps on a small-world network. Persistence of local variables in a coarse grained sense acts as an excellent order parameter to study this transition. We investigate the phase diagram by varying coupling strength and small-world rewiring probability p of nonlocal connections. The persistent region is a compact region bounded by two critical lines where band-merging crisis occurs. On one critical line, the persistent sites shows a nonexponential (stretched exponential) decay for all p while for another one, it shows crossover from nonexponential to exponential behavior as p → 1 . With an effectively antiferromagnetic coupling, coupling to two neighbors on either side leads to exchange frustration. Apart from exchange frustration, non-bipartite topology and nonlocal couplings in a small-world network could be a reason for anomalous relaxation. The distribution of trap times in asymptotic regime has a long tail as well. The dependence of temporal evolution of persistence on initial conditions is studied and a scaling form for persistence after waiting time is proposed. We present a simple possible model for this behavior.

An assessment of the barriers to the consumers' uptake of genetically modified foods: a neural network analysis.

PubMed

Rodríguez-Entrena, Macario; Salazar-Ordóñez, Melania; Becerra-Alonso, David

2016-03-30

This paper studies which of the attitudinal, cognitive and socio-economic factors determine the willingness to purchase genetically modified (GM) food, enabling the forecasting of consumers' behaviour in Andalusia, southern Spain. This classification has been made by a standard multilayer perceptron neural network trained with extreme learning machine. Later, an ordered logistic regression was applied to determine whether the neural network can outperform this traditional econometric approach. The results show that the highest relative contributions lie in the variables related to perceived risks of GM food, while the perceived benefits have a lower influence. In addition, an innovative attitude towards food presents a strong link, as does the perception of food safety. The variables with the least relative contribution are subjective knowledge about GM food and the consumers' age. The neural network approach outperforms the correct classification percentage from the ordered logistic regression. The perceived risks must be considered as a critical factor. A strategy to improve the GM food acceptance is to develop a transparent and balanced information framework that makes the potential risk understandable by society, and make them aware of the risk assessments for GM food in the EU. For its success, it is essential to improve the trust in EU institutions and scientific regulatory authorities. © 2015 Society of Chemical Industry.

Social network and individual correlates of sexual risk behavior among homeless young men who have sex with men.

PubMed

Tucker, Joan S; Hu, Jianhui; Golinelli, Daniela; Kennedy, David P; Green, Harold D; Wenzel, Suzanne L

2012-10-01

There is growing interest in network-based interventions to reduce HIV sexual risk behavior among both homeless youth and men who have sex with men. The goal of this study was to better understand the social network and individual correlates of sexual risk behavior among homeless young men who have sex with men (YMSM) to inform these HIV prevention efforts. A multistage sampling design was used to recruit a probability sample of 121 homeless YMSM (ages: 16-24 years) from shelters, drop-in centers, and street venues in Los Angeles County. Face-to-face interviews were conducted. Because of the different distributions of the three outcome variables, three distinct regression models were needed: ordinal logistic regression for unprotected sex, zero-truncated Poisson regression for number of sex partners, and logistic regression for any sex trade. Homeless YMSM were less likely to engage in unprotected sex and had fewer sex partners if their networks included platonic ties to peers who regularly attended school, and had fewer sex partners if most of their network members were not heavy drinkers. Most other aspects of network composition were unrelated to sexual risk behavior. Individual predictors of sexual risk behavior included older age, Hispanic ethnicity, lower education, depressive symptoms, less positive condom attitudes, and sleeping outdoors because of nowhere else to stay. HIV prevention programs for homeless YMSM may warrant a multipronged approach that helps these youth strengthen their ties to prosocial peers, develop more positive condom attitudes, and access needed mental health and housing services. Copyright © 2012 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Metamodeling and the Critic-based approach to multi-level optimization.

PubMed

Werbos, Ludmilla; Kozma, Robert; Silva-Lugo, Rodrigo; Pazienza, Giovanni E; Werbos, Paul J

2012-08-01

Large-scale networks with hundreds of thousands of variables and constraints are becoming more and more common in logistics, communications, and distribution domains. Traditionally, the utility functions defined on such networks are optimized using some variation of Linear Programming, such as Mixed Integer Programming (MIP). Despite enormous progress both in hardware (multiprocessor systems and specialized processors) and software (Gurobi) we are reaching the limits of what these tools can handle in real time. Modern logistic problems, for example, call for expanding the problem both vertically (from one day up to several days) and horizontally (combining separate solution stages into an integrated model). The complexity of such integrated models calls for alternative methods of solution, such as Approximate Dynamic Programming (ADP), which provide a further increase in the performance necessary for the daily operation. In this paper, we present the theoretical basis and related experiments for solving the multistage decision problems based on the results obtained for shorter periods, as building blocks for the models and the solution, via Critic-Model-Action cycles, where various types of neural networks are combined with traditional MIP models in a unified optimization system. In this system architecture, fast and simple feed-forward networks are trained to reasonably initialize more complicated recurrent networks, which serve as approximators of the value function (Critic). The combination of interrelated neural networks and optimization modules allows for multiple queries for the same system, providing flexibility and optimizing performance for large-scale real-life problems. A MATLAB implementation of our solution procedure for a realistic set of data and constraints shows promising results, compared to the iterative MIP approach. Copyright © 2012 Elsevier Ltd. All rights reserved.

Logistics in the Computer Lab.

ERIC Educational Resources Information Center

Cowles, Jim

1989-01-01

Discusses ways to provide good computer laboratory facilities for elementary and secondary schools. Topics discussed include establishing the computer lab and selecting hardware; types of software; physical layout of the room; printers; networking possibilities; considerations relating to the physical environment; and scheduling methods. (LRW)

32 CFR 161.5 - Responsibilities.

Code of Federal Regulations, 2014 CFR

2014-07-01

... telephone center support, and telecommunications engineering and network control center assistance. (7) In... Acquisition, Technology, and Logistics (USD(AT&L)), and the DoD Chief Information Officer (DoD CIO) establish..., printer consumables, and electromagnetically opaque sleeves to Defense Manpower Data Center (DMDC). (7...

Effect of Biodiesel on Diesel Engine Nitrogen Oxide and Other Regulated Emissions

DTIC Science & Technology

2006-05-01

DENIX Defense Environmental Network & Information Exchange DLA Defense Logistics Agency DNPH Dinitrophenylhydrazine DoD Department of... Dinitrophenylhydrazine (DNPH) cartridges and analyzed using a high-performance liquid chromatograph with ultraviolet detection, as per an AO/AQIRP method (Reference

Crowdsourcing for large-scale mosquito (Diptera: Culicidae) sampling

USDA-ARS?s Scientific Manuscript database

Sampling a cosmopolitan mosquito (Diptera: Culicidae) species throughout its range is logistically challenging and extremely resource intensive. Mosquito control programmes and regional networks operate at the local level and often conduct sampling activities across much of North America. A method f...

A general framework for the use of logistic regression models in meta-analysis.

PubMed

Simmonds, Mark C; Higgins, Julian Pt

2016-12-01

Where individual participant data are available for every randomised trial in a meta-analysis of dichotomous event outcomes, "one-stage" random-effects logistic regression models have been proposed as a way to analyse these data. Such models can also be used even when individual participant data are not available and we have only summary contingency table data. One benefit of this one-stage regression model over conventional meta-analysis methods is that it maximises the correct binomial likelihood for the data and so does not require the common assumption that effect estimates are normally distributed. A second benefit of using this model is that it may be applied, with only minor modification, in a range of meta-analytic scenarios, including meta-regression, network meta-analyses and meta-analyses of diagnostic test accuracy. This single model can potentially replace the variety of often complex methods used in these areas. This paper considers, with a range of meta-analysis examples, how random-effects logistic regression models may be used in a number of different types of meta-analyses. This one-stage approach is compared with widely used meta-analysis methods including Bayesian network meta-analysis and the bivariate and hierarchical summary receiver operating characteristic (ROC) models for meta-analyses of diagnostic test accuracy. © The Author(s) 2014.

Resetting the transcription factor network reverses terminal chronic hepatic failure

PubMed Central

Nishikawa, Taichiro; Bell, Aaron; Brooks, Jenna M.; Setoyama, Kentaro; Melis, Marta; Han, Bing; Fukumitsu, Ken; Handa, Kan; Tian, Jianmin; Kaestner, Klaus H.; Vodovotz, Yoram; Locker, Joseph; Soto-Gutierrez, Alejandro; Fox, Ira J.

2015-01-01

The cause of organ failure is enigmatic for many degenerative diseases, including end-stage liver disease. Here, using a CCl4-induced rat model of irreversible and fatal hepatic failure, which also exhibits terminal changes in the extracellular matrix, we demonstrated that chronic injury stably reprograms the critical balance of transcription factors and that diseased and dedifferentiated cells can be returned to normal function by re-expression of critical transcription factors, a process similar to the type of reprogramming that induces somatic cells to become pluripotent or to change their cell lineage. Forced re-expression of the transcription factor HNF4α induced expression of the other hepatocyte-expressed transcription factors; restored functionality in terminally diseased hepatocytes isolated from CCl4-treated rats; and rapidly reversed fatal liver failure in CCl4-treated animals by restoring diseased hepatocytes rather than replacing them with new hepatocytes or stem cells. Together, the results of our study indicate that disruption of the transcription factor network and cellular dedifferentiation likely mediate terminal liver failure and suggest reinstatement of this network has therapeutic potential for correcting organ failure without cell replacement. PMID:25774505

A Reverse Localization Scheme for Underwater Acoustic Sensor Networks

PubMed Central

Moradi, Marjan; Rezazadeh, Javad; Ismail, Abdul Samad

2012-01-01

Underwater Wireless Sensor Networks (UWSNs) provide new opportunities to observe and predict the behavior of aquatic environments. In some applications like target tracking or disaster prevention, sensed data is meaningless without location information. In this paper, we propose a novel 3D centralized, localization scheme for mobile underwater wireless sensor network, named Reverse Localization Scheme or RLS in short. RLS is an event-driven localization method triggered by detector sensors for launching localization process. RLS is suitable for surveillance applications that require very fast reactions to events and could report the location of the occurrence. In this method, mobile sensor nodes report the event toward the surface anchors as soon as they detect it. They do not require waiting to receive location information from anchors. Simulation results confirm that the proposed scheme improves the energy efficiency and reduces significantly localization response time with a proper level of accuracy in terms of mobility model of water currents. Major contributions of this method lie on reducing the numbers of message exchange for localization, saving the energy and decreasing the average localization response time. PMID:22666034

A reverse localization scheme for underwater acoustic sensor networks.

PubMed

Moradi, Marjan; Rezazadeh, Javad; Ismail, Abdul Samad

2012-01-01

Underwater Wireless Sensor Networks (UWSNs) provide new opportunities to observe and predict the behavior of aquatic environments. In some applications like target tracking or disaster prevention, sensed data is meaningless without location information. In this paper, we propose a novel 3D centralized, localization scheme for mobile underwater wireless sensor network, named Reverse Localization Scheme or RLS in short. RLS is an event-driven localization method triggered by detector sensors for launching localization process. RLS is suitable for surveillance applications that require very fast reactions to events and could report the location of the occurrence. In this method, mobile sensor nodes report the event toward the surface anchors as soon as they detect it. They do not require waiting to receive location information from anchors. Simulation results confirm that the proposed scheme improves the energy efficiency and reduces significantly localization response time with a proper level of accuracy in terms of mobility model of water currents. Major contributions of this method lie on reducing the numbers of message exchange for localization, saving the energy and decreasing the average localization response time.

Application studies of RFID technology in the process of coal logistics transport

NASA Astrophysics Data System (ADS)

Qiao, Bingqin; Chang, Xiaoming; Hao, Meiyan; Kong, Dejin

2012-04-01

For quality control problems in coal transport, RFID technology has been proposed to be applied to coal transportation process. The whole process RFID traceability system from coal production to consumption has been designed and coal supply chain logistics tracking system integration platform has been built, to form the coal supply chain traceability and transport tracking system and providing more and more transparent tracking and monitoring of coal quality information for consumers of coal. Currently direct transport and combined transport are the main forms of coal transportation in China. The means of transport are cars, trains and ships. In the booming networking environment of RFID technology, the RFID technology will be applied to coal logistics and provide opportunity for the coal transportation tracking in the process transportation.

A Microcomputer-Based Network Optimization Package.

DTIC Science & Technology

1981-09-01

from either cases a or c as Truncated-Newton directions. It can be shown [Ref. 27] that the TNCG algorithm is globally convergent and capable of...nonzero values of LGB indicate bounds at which arcs are fixed or reversed. Fixed arcs have negative T ( ) while free arcs have positive T ( ) values...Solution of Generalized Network Problems," Working Paper, Department of Finance and Business Economics , School of Business , University of Southern

Conservation of Information: Reverse Engineering Dark Social Systems

DTIC Science & Technology

2010-01-01

Factbook, retrieved 5/20/10 from www.cia.gov). 25 For example, Air- America , a politically liberal talk radio network, said that it would cease operations...attractive targets than mid- America ; see Reding, 2009). Obama Clinton Clinton Obama Lawless et al.: Conservation of Information 13 approached it...indefinitely sustain the control of the Communist Party--i.e., censoring Google--is increasingly at odds with Network China, which is thriving by

Statistical Inference and Reverse Engineering of Gene Regulatory Networks from Observational Expression Data

PubMed Central

Emmert-Streib, Frank; Glazko, Galina V.; Altay, Gökmen; de Matos Simoes, Ricardo

2012-01-01

In this paper, we present a systematic and conceptual overview of methods for inferring gene regulatory networks from observational gene expression data. Further, we discuss two classic approaches to infer causal structures and compare them with contemporary methods by providing a conceptual categorization thereof. We complement the above by surveying global and local evaluation measures for assessing the performance of inference algorithms. PMID:22408642

Nonvolatile Ionic Two-Terminal Memory Device

NASA Technical Reports Server (NTRS)

Williams, Roger M.

1990-01-01

Conceptual solid-state memory device nonvolatile and erasable and has only two terminals. Proposed device based on two effects: thermal phase transition and reversible intercalation of ions. Transfer of sodium ions between source of ions and electrical switching element increases or decreases electrical conductance of element, turning switch "on" or "off". Used in digital computers and neural-network computers. In neural networks, many small, densely packed switches function as erasable, nonvolatile synaptic elements.

High-risk sexual activity in the House and Ball community: influence of social networks.

PubMed

Schrager, Sheree M; Latkin, Carl A; Weiss, George; Kubicek, Katrina; Kipke, Michele D

2014-02-01

We investigated the roles of House membership and the influence of social and sexual network members on the sexual risk behavior of men in the Los Angeles House and Ball community. From February 2009 to January 2010, male participants (n = 233) completed interviewer-assisted surveys during a House meeting or Ball event. We used logistic regression to model the effects of sexual network size, influence of sexual network members, House membership status, and their interactions on high-risk sex. Significant predictors of high-risk sex included number of sexual partners in the nominated social network, multiethnicity, and previous diagnosis of sexually transmitted infection. House membership was protective against high-risk sex. Additionally, a 3-way interaction emerged between number of sexual partners in the network, influence, and network members' House membership. Future research should assess network members' attitudes and behavior in detail to provide a greater understanding of the dynamics of social influence and to identify additional avenues for intervention.

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.

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.

Engineering and Evolution of Molecular Chaperones and Protein Disaggregases with Enhanced Activity

PubMed Central

Mack, Korrie L.; Shorter, James

2016-01-01

Cells have evolved a sophisticated proteostasis network to ensure that proteins acquire and retain their native structure and function. Critical components of this network include molecular chaperones and protein disaggregases, which function to prevent and reverse deleterious protein misfolding. Nevertheless, proteostasis networks have limits, which when exceeded can have fatal consequences as in various neurodegenerative disorders, including Parkinson's disease and amyotrophic lateral sclerosis. A promising strategy is to engineer proteostasis networks to counter challenges presented by specific diseases or specific proteins. Here, we review efforts to enhance the activity of individual molecular chaperones or protein disaggregases via engineering and directed evolution. Remarkably, enhanced global activity or altered substrate specificity of various molecular chaperones, including GroEL, Hsp70, ClpX, and Spy, can be achieved by minor changes in primary sequence and often a single missense mutation. Likewise, small changes in the primary sequence of Hsp104 yield potentiated protein disaggregases that reverse the aggregation and buffer toxicity of various neurodegenerative disease proteins, including α-synuclein, TDP-43, and FUS. Collectively, these advances have revealed key mechanistic and functional insights into chaperone and disaggregase biology. They also suggest that enhanced chaperones and disaggregases could have important applications in treating human disease as well as in the purification of valuable proteins in the pharmaceutical sector. PMID:27014702

Conducting-insulating transition in adiabatic memristive networks

NASA Astrophysics Data System (ADS)

Sheldon, Forrest C.; Di Ventra, Massimiliano

2017-01-01

The development of neuromorphic systems based on memristive elements—resistors with memory—requires a fundamental understanding of their collective dynamics when organized in networks. Here, we study an experimentally inspired model of two-dimensional disordered memristive networks subject to a slowly ramped voltage and show that they undergo a discontinuous transition in the conductivity for sufficiently high values of memory, as quantified by the memristive ON-OFF ratio. We investigate the consequences of this transition for the memristive current-voltage characteristics both through simulation and theory, and demonstrate the role of current-voltage duality in relating forward and reverse switching processes. Our work sheds considerable light on the statistical properties of memristive networks that are presently studied both for unconventional computing and as models of neural networks.

The Effects of Peer Group Network Properties on Drug Use Among Homeless Youth

PubMed Central

Rice, Eric; Milburn, Norweeta G.; Rotheram-Borus, Mary Jane; Mallett, Shelley; Rosenthal, Doreen

2010-01-01

The authors examine how the properties of peer networks affect amphetamine, cocaine, and injection drug use over 3 months among newly homeless adolescents, aged 12 to 20 in Los Angeles (n = 217; 83% retention at 3 months) and Melbourne (n = 119; 72% retention at 3 months). Several hypotheses regarding the effects of social network properties on the peer influence process are developed. Multivariate logistic regression analyses show that higher concentrations of homeless peers in networks at recruitment were associated with increased likelihood of amphetamine and cocaine use at 3-month follow-up. Higher concentrations of injecting peers were associated with increased risk of injection drug use 3 months later. Change in network structure over time toward increased concentrations of homeless peers was associated with increased risk of cocaine use and injecting. Higher density networks at baseline were positively associated with increased likelihood of cocaine and amphetamine use at 3 months. PMID:20539820

Dynamic origin of segment magnetization reversal in thin-film Penrose tilings

NASA Astrophysics Data System (ADS)

Montoncello, F.; Giovannini, L.; Farmer, B.; De Long, L.

2017-02-01

We investigate the low-frequency spin wave dynamics involved in the magnetization reversal of a Penrose P2 tiling using the dynamical matrix method. This system consists of a two-dimensional, connected wire network of elongated thin-film segments, whose complete reversal occurs as a cascade of successive local segment reversals. Using soft mode theory, we interpret the reversal of an individual segment as a first order magnetic transition, in which magnetization curve of the system suffers a small discontinuity. Near this discontinuity a specific mode of the spin wave spectrum goes soft (i.e., its frequency goes to zero), triggering a local instability of the magnetization. We show that this mode is localized, and is at the origin of the local reversal. We discuss the correlation of the mode spatial profile with the ;reversal mechanism;, which is the passage of a domain wall through the segment. This process differs from reversal in periodic square or honeycomb artificial spin ices, where a cascade of reversing segments (e.g., ;Dirac string;) follows an extended (though irregular) path across the sample; here the spatial distribution of successive segment reversals is discontinuous, but strictly associated with the area where a soft mode is localized. The migration of the localization area across the P2 tiling (during reversal in decreasing applied fields) depends on changes in the internal effective field map. We discuss these results in the context of spin wave localization due to the unique topology of the P2 tiling.

Reverse osmosis water purification system

NASA Technical Reports Server (NTRS)

Ahlstrom, H. G.; Hames, P. S.; Menninger, F. J.

1986-01-01

A reverse osmosis water purification system, which uses a programmable controller (PC) as the control system, was designed and built to maintain the cleanliness and level of water for various systems of a 64-m antenna. The installation operates with other equipment of the antenna at the Goldstone Deep Space Communication Complex. The reverse osmosis system was designed to be fully automatic; with the PC, many complex sequential and timed logic networks were easily implemented and are modified. The PC monitors water levels, pressures, flows, control panel requests, and set points on analog meters; with this information various processes are initiated, monitored, modified, halted, or eliminated as required by the equipment being supplied pure water.

Fuzzy-driven energy storage system for mitigating voltage unbalance factor on distribution network with photovoltaic system

NASA Astrophysics Data System (ADS)

Wong, Jianhui; Lim, Yun Seng; Morris, Stella; Morris, Ezra; Chua, Kein Huat

2017-04-01

The amount of small-scaled renewable energy sources is anticipated to increase on the low-voltage distribution networks for the improvement of energy efficiency and reduction of greenhouse gas emission. The growth of the PV systems on the low-voltage distribution networks can create voltage unbalance, voltage rise, and reverse-power flow. Usually these issues happen with little fluctuation. However, it tends to fluctuate severely as Malaysia is a region with low clear sky index. A large amount of clouds often passes over the country, hence making the solar irradiance to be highly scattered. Therefore, the PV power output fluctuates substantially. These issues can lead to the malfunction of the electronic based equipment, reduction in the network efficiency and improper operation of the power protection system. At the current practice, the amount of PV system installed on the distribution network is constraint by the utility company. As a result, this can limit the reduction of carbon footprint. Therefore, energy storage system is proposed as a solution for these power quality issues. To ensure an effective operation of the distribution network with PV system, a fuzzy control system is developed and implemented to govern the operation of an energy storage system. The fuzzy driven energy storage system is able to mitigate the fluctuating voltage rise and voltage unbalance on the electrical grid by actively manipulates the flow of real power between the grid and the batteries. To verify the effectiveness of the proposed fuzzy driven energy storage system, an experimental network integrated with 7.2kWp PV system was setup. Several case studies are performed to evaluate the response of the proposed solution to mitigate voltage rises, voltage unbalance and reduce the amount of reverse power flow under highly intermittent PV power output.

Artificial neural network analysis based on genetic algorithm to predict the performance characteristics of a cross flow cooling tower

NASA Astrophysics Data System (ADS)

Wu, Jiasheng; Cao, Lin; Zhang, Guoqiang

2018-02-01

Cooling tower of air conditioning has been widely used as cooling equipment, and there will be broad application prospect if it can be reversibly used as heat source under heat pump heating operation condition. In view of the complex non-linear relationship of each parameter in the process of heat and mass transfer inside tower, In this paper, the BP neural network model based on genetic algorithm optimization (GABP neural network model) is established for the reverse use of cross flow cooling tower. The model adopts the structure of 6 inputs, 13 hidden nodes and 8 outputs. With this model, the outlet air dry bulb temperature, wet bulb temperature, water temperature, heat, sensible heat ratio and heat absorbing efficiency, Lewis number, a total of 8 the proportion of main performance parameters were predicted. Furthermore, the established network model is used to predict the water temperature and heat absorption of the tower at different inlet temperatures. The mean relative error MRE between BP predicted value and experimental value are 4.47%, 3.63%, 2.38%, 3.71%, 6.35%,3.14%, 13.95% and 6.80% respectively; the mean relative error MRE between GABP predicted value and experimental value are 2.66%, 3.04%, 2.27%, 3.02%, 6.89%, 3.17%, 11.50% and 6.57% respectively. The results show that the prediction results of GABP network model are better than that of BP network model; the simulation results are basically consistent with the actual situation. The GABP network model can well predict the heat and mass transfer performance of the cross flow cooling tower.

Recurrence Density Enhanced Complex Networks for Nonlinear Time Series Analysis

NASA Astrophysics Data System (ADS)

Costa, Diego G. De B.; Reis, Barbara M. Da F.; Zou, Yong; Quiles, Marcos G.; Macau, Elbert E. N.

We introduce a new method, which is entitled Recurrence Density Enhanced Complex Network (RDE-CN), to properly analyze nonlinear time series. Our method first transforms a recurrence plot into a figure of a reduced number of points yet preserving the main and fundamental recurrence properties of the original plot. This resulting figure is then reinterpreted as a complex network, which is further characterized by network statistical measures. We illustrate the computational power of RDE-CN approach by time series by both the logistic map and experimental fluid flows, which show that our method distinguishes different dynamics sufficiently well as the traditional recurrence analysis. Therefore, the proposed methodology characterizes the recurrence matrix adequately, while using a reduced set of points from the original recurrence plots.

Reliability of a Bayesian network to predict an elevated aldosterone-to-renin ratio.

PubMed

Ducher, Michel; Mounier-Véhier, Claire; Lantelme, Pierre; Vaisse, Bernard; Baguet, Jean-Philippe; Fauvel, Jean-Pierre

2015-05-01

Resistant hypertension is common, mainly idiopathic, but sometimes related to primary aldosteronism. Thus, most hypertension specialists recommend screening for primary aldosteronism. To optimize the selection of patients whose aldosterone-to-renin ratio (ARR) is elevated from simple clinical and biological characteristics. Data from consecutive patients referred between 1 June 2008 and 30 May 2009 were collected retrospectively from five French 'European excellence hypertension centres' institutional registers. Patients were included if they had at least one of: onset of hypertension before age 40 years, resistant hypertension, history of hypokalaemia, efficient treatment by spironolactone, and potassium supplementation. An ARR>32 ng/L and aldosterone>160 ng/L in patients treated without agents altering the renin-angiotensin system was considered as elevated. Bayesian network and stepwise logistic regression were used to predict an elevated ARR. Of 334 patients, 89 were excluded (31 for incomplete data, 32 for taking agents that alter the renin-angiotensin system and 26 for other reasons). Among 245 included patients, 110 had an elevated ARR. Sensitivity reached 100% or 63.3% using Bayesian network or logistic regression, respectively, and specificity reached 89.6% or 67.2%, respectively. The area under the receiver-operating-characteristic curve obtained with the Bayesian network was significantly higher than that obtained by stepwise regression (0.93±0.02 vs. 0.70±0.03; P<0.001). In hypertension centres, Bayesian network efficiently detected patients with an elevated ARR. An external validation study is required before use in primary clinical settings. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

Integrated disaster relief logistics: a stepping stone towards viable civil-military networks?

PubMed

Tatham, Peter; Rietjens, Sebastiaan Bas

2016-01-01

The twenty-first century has seen a significant rise in all forms of disasters and this has resulted in military and humanitarian organisations becoming more frequently engaged in the provision of support to those affected. Achieving an efficient and effective logistic preparation and response is one of the key elements in mitigating the impact of such events, but the establishment of mechanisms to deliver an appropriately integrated civil-military approach remains elusive. Not least because of the high percentage of assistance budgets spent on logistics, this area is considered to represent fertile ground for developing improved processes and understanding. In practice, the demands placed on civilian and military logisticians are broadly similar, as is the solution space. Speaking a common language and using common concepts, it is argued, therefore, that the logistic profession should be in the vanguard of the development of an improved civil-military interface. © 2016 The Author(s). Disasters © Overseas Development Institute, 2016.

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 comparative study on entrepreneurial attitudes modeled with logistic regression and Bayes nets.

PubMed

López Puga, Jorge; García García, Juan

2012-11-01

Entrepreneurship research is receiving increasing attention in our context, as entrepreneurs are key social agents involved in economic development. We compare the success of the dichotomic logistic regression model and the Bayes simple classifier to predict entrepreneurship, after manipulating the percentage of missing data and the level of categorization in predictors. A sample of undergraduate university students (N = 1230) completed five scales (motivation, attitude towards business creation, obstacles, deficiencies, and training needs) and we found that each of them predicted different aspects of the tendency to business creation. Additionally, our results show that the receiver operating characteristic (ROC) curve is affected by the rate of missing data in both techniques, but logistic regression seems to be more vulnerable when faced with missing data, whereas Bayes nets underperform slightly when categorization has been manipulated. Our study sheds light on the potential entrepreneur profile and we propose to use Bayesian networks as an additional alternative to overcome the weaknesses of logistic regression when missing data are present in applied research.

The humanitarian common logistic operating picture: a solution to the inter-agency coordination challenge.

PubMed

Tatham, Peter; Spens, Karen; Kovács, Gyöngyi

2017-01-01

Although significant progress has been made in developing the practice of humanitarian logistics, further improvements in efficiency and effectiveness have the potential to save lives and reduce suffering. This paper explores how the military/emergency services' concept of a common operating picture (COP) can be adapted to the humanitarian logistics context, and analyses a practical and proven approach to addressing the key challenge of inter-agency coordination and decision-making. Successful adaptation could provide the mechanism through which predicted and actual demands, together with the location and status of material in transit, are captured, evaluated, and presented in real time as the basis for enhanced decision-making between actors in the humanitarian supply network. Through the introduction of a humanitarian logistics COP and its linkages to national disaster management systems, local communities and countries affected by disasters and emergencies will be better placed to oversee and manage their response activities. © 2017 The Author(s). Disasters © Overseas Development Institute, 2017.

Two Superintendents, One Home.

ERIC Educational Resources Information Center

Pardini, Priscilla

2000-01-01

Spouses working as superintendents confront agonizing logistics while establishing ground rules for dinner talk. Couples sharing the same career risk eclipsing their personal lives with professional issues. Having one's personal support network under the same roof can be mutually beneficial and synergistic. A married superintendents roster is…

Optimal Design for Hetero-Associative Memory: Hippocampal CA1 Phase Response Curve and Spike-Timing-Dependent Plasticity

PubMed Central

Miyata, Ryota; Ota, Keisuke; Aonishi, Toru

2013-01-01

Recently reported experimental findings suggest that the hippocampal CA1 network stores spatio-temporal spike patterns and retrieves temporally reversed and spread-out patterns. In this paper, we explore the idea that the properties of the neural interactions and the synaptic plasticity rule in the CA1 network enable it to function as a hetero-associative memory recalling such reversed and spread-out spike patterns. In line with Lengyel’s speculation (Lengyel et al., 2005), we firstly derive optimally designed spike-timing-dependent plasticity (STDP) rules that are matched to neural interactions formalized in terms of phase response curves (PRCs) for performing the hetero-associative memory function. By maximizing object functions formulated in terms of mutual information for evaluating memory retrieval performance, we search for STDP window functions that are optimal for retrieval of normal and doubly spread-out patterns under the constraint that the PRCs are those of CA1 pyramidal neurons. The system, which can retrieve normal and doubly spread-out patterns, can also retrieve reversed patterns with the same quality. Finally, we demonstrate that purposely designed STDP window functions qualitatively conform to typical ones found in CA1 pyramidal neurons. PMID:24204822

Embedding memories in colloidal gels though oscillatory shear

NASA Astrophysics Data System (ADS)

Schwen, Eric; Ramaswamay, Meera; Jan, Linda; Cheng, Chieh-Min; Cohen, Itai

While gels are ubiquitous in applications from food products to filtration, their mechanical properties are usually determined by self-assembly. We use oscillatory shear to train colloidal gels, embedding memories of the training protocol in rheological responses such as the yield strain and the gel network structures. When our gels undergo shear, the particles are forced to rearrange until they organize into structures that can locally undergo reversible shear cycles. We utilize a high-speed confocal microscope and a shear cell to image a colloidal gel while simultaneously straining the gel and measuring its shear stresses. By comparing stroboscopic images of the gel, we quantify the decrease in particle rearrangement as the gel develops reversible structures. We analyze and construct a model for the rates at which different regions in the gel approach reversible configurations. Through characterizing the gel network, we determine the structural origins of these shear training memories in gels. These results may allow us to use shear training protocols to produce gels with controllable yield strains and to better understand changes in the microstructure and rheology of gels that undergo repeated shear through mixing or flowing. This research was supported in part by NSF CBET 1509308 and Xerox Corporation.

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.

A Selfish Constraint Satisfaction Genetic Algorithms for Planning a Long-Distance Transportation Network

NASA Astrophysics Data System (ADS)

Onoyama, Takashi; Maekawa, Takuya; Kubota, Sen; Tsuruta, Setuso; Komoda, Norihisa

To build a cooperative logistics network covering multiple enterprises, a planning method that can build a long-distance transportation network is required. Many strict constraints are imposed on this type of problem. To solve these strict-constraint problems, a selfish constraint satisfaction genetic algorithm (GA) is proposed. In this GA, each gene of an individual satisfies only its constraint selfishly, disregarding the constraints of other genes in the same individuals. Moreover, a constraint pre-checking method is also applied to improve the GA convergence speed. The experimental result shows the proposed method can obtain an accurate solution in a practical response time.

A Channel Network Evolution Model with Subsurface Saturation Mechanism and Analysis of the Chaotic Behavior of the Model

DTIC Science & Technology

1990-09-01

between basin shapes and hydrologic responses is fundamental for the purpose of hydrologic predictions , especially in ungaged basins. Another goal is...47] studied this model and showed analitically how very small differences in the c field generated completely different leaf vein network structures... predictability impossible. Complexity is by no means a requirement in order for a system to exhibit SIC. A system as simple as the logistic equation x,,,,=ax,,(l

Structural diversity effect on hashtag adoption in Twitter

NASA Astrophysics Data System (ADS)

Zhang, Aihua; Zheng, Mingxing; Pang, Bowen

2018-03-01

With online social network developing rapidly these years, user' behavior in online social network has attracted a lot of attentions to it. In this paper, we study Twitter user's behavior of hashtag adoption from the perspective of social contagion and focus on "structure diversity" effect on individual's behavior in Twitter. We achieve data through Twitter's API by crawling and build a users' network to carry on empirical research. The Girvan-Newman (G-N) algorithm is used to analyze the structural diversity of user's ego network, and Logistic regression model is adopted to examine the hypothesis. The findings of our empirical study indicate that user' behavior in online social network is indeed influenced by his friends and his decision is significantly affected by the number of groups that these friends belong to, which we call structural diversity.

Maximizing synchronizability of duplex networks

NASA Astrophysics Data System (ADS)

Wei, Xiang; Emenheiser, Jeffrey; Wu, Xiaoqun; Lu, Jun-an; D'Souza, Raissa M.

2018-01-01

We study the synchronizability of duplex networks formed by two randomly generated network layers with different patterns of interlayer node connections. According to the master stability function, we use the smallest nonzero eigenvalue and the eigenratio between the largest and the second smallest eigenvalues of supra-Laplacian matrices to characterize synchronizability on various duplexes. We find that the interlayer linking weight and linking fraction have a profound impact on synchronizability of duplex networks. The increasingly large inter-layer coupling weight is found to cause either decreasing or constant synchronizability for different classes of network dynamics. In addition, negative node degree correlation across interlayer links outperforms positive degree correlation when most interlayer links are present. The reverse is true when a few interlayer links are present. The numerical results and understanding based on these representative duplex networks are illustrative and instructive for building insights into maximizing synchronizability of more realistic multiplex networks.

Computational Analysis of Molecular Interaction Networks Underlying Change of HIV-1 Resistance to Selected Reverse Transcriptase Inhibitors

PubMed Central

Kierczak, Marcin; Dramiński, Michał; Koronacki, Jacek; Komorowski, Jan

2010-01-01

Motivation Despite more than two decades of research, HIV resistance to drugs remains a serious obstacle in developing efficient AIDS treatments. Several computational methods have been developed to predict resistance level from the sequence of viral proteins such as reverse transcriptase (RT) or protease. These methods, while powerful and accurate, give very little insight into the molecular interactions that underly acquisition of drug resistance/hypersusceptibility. Here, we attempt at filling this gap by using our Monte Carlo feature selection and interdependency discovery method (MCFS-ID) to elucidate molecular interaction networks that characterize viral strains with altered drug resistance levels. Results We analyzed a number of HIV-1 RT sequences annotated with drug resistance level using the MCFS-ID method. This let us expound interdependency networks that characterize change of drug resistance to six selected RT inhibitors: Abacavir, Lamivudine, Stavudine, Zidovudine, Tenofovir and Nevirapine. The networks consider interdependencies at the level of physicochemical properties of mutating amino acids, eg,: polarity. We mapped each network on the 3D structure of RT in attempt to understand the molecular meaning of interacting pairs. The discovered interactions describe several known drug resistance mechanisms and, importantly, some previously unidentified ones. Our approach can be easily applied to a whole range of problems from the domain of protein engineering. Availability A portable Java implementation of our MCFS-ID method is freely available for academic users and can be obtained at: http://www.ipipan.eu/staff/m.draminski/software.htm. PMID:21234299

Computational Analysis of Molecular Interaction Networks Underlying Change of HIV-1 Resistance to Selected Reverse Transcriptase Inhibitors.

PubMed

Kierczak, Marcin; Dramiński, Michał; Koronacki, Jacek; Komorowski, Jan

2010-12-12

Despite more than two decades of research, HIV resistance to drugs remains a serious obstacle in developing efficient AIDS treatments. Several computational methods have been developed to predict resistance level from the sequence of viral proteins such as reverse transcriptase (RT) or protease. These methods, while powerful and accurate, give very little insight into the molecular interactions that underly acquisition of drug resistance/hypersusceptibility. Here, we attempt at filling this gap by using our Monte Carlo feature selection and interdependency discovery method (MCFS-ID) to elucidate molecular interaction networks that characterize viral strains with altered drug resistance levels. We analyzed a number of HIV-1 RT sequences annotated with drug resistance level using the MCFS-ID method. This let us expound interdependency networks that characterize change of drug resistance to six selected RT inhibitors: Abacavir, Lamivudine, Stavudine, Zidovudine, Tenofovir and Nevirapine. The networks consider interdependencies at the level of physicochemical properties of mutating amino acids, eg,: polarity. We mapped each network on the 3D structure of RT in attempt to understand the molecular meaning of interacting pairs. The discovered interactions describe several known drug resistance mechanisms and, importantly, some previously unidentified ones. Our approach can be easily applied to a whole range of problems from the domain of protein engineering. A portable Java implementation of our MCFS-ID method is freely available for academic users and can be obtained at: http://www.ipipan.eu/staff/m.draminski/software.htm.

The Dynamics of Networks of Identical Theta Neurons.

PubMed

Laing, Carlo R

2018-02-05

We consider finite and infinite all-to-all coupled networks of identical theta neurons. Two types of synaptic interactions are investigated: instantaneous and delayed (via first-order synaptic processing). Extensive use is made of the Watanabe/Strogatz (WS) ansatz for reducing the dimension of networks of identical sinusoidally-coupled oscillators. As well as the degeneracy associated with the constants of motion of the WS ansatz, we also find continuous families of solutions for instantaneously coupled neurons, resulting from the reversibility of the reduced model and the form of the synaptic input. We also investigate a number of similar related models. We conclude that the dynamics of networks of all-to-all coupled identical neurons can be surprisingly complicated.

Optimal Mass Transport for Statistical Estimation, Image Analysis, Information Geometry, and Control

DTIC Science & Technology

2017-01-10

Metric Uncertainty for Spectral Estimation based on Nevanlinna-Pick Interpolation, (with J. Karlsson) Intern. Symp. on the Math . Theory of Networks and...Systems, Melbourne 2012. 22. Geometric tools for the estimation of structured covariances, (with L. Ning, X. Jiang) Intern. Symposium on the Math . Theory...estimation and the reversibility of stochastic processes, (with Y. Chen, J. Karlsson) Proc. Int. Symp. on Math . Theory of Networks and Syst., July

Reversible Control of Anisotropic Electrical Conductivity using Colloidal Microfluidic Networks

DTIC Science & Technology

2007-04-17

field with the induced charges on each electrode result in AC electroosmotic force and steady fluid flow (nonzero time averaged) with a velocity...direction of the AC electroosmotic force (flow is unidirectional). From the work of Green and co- workers, we can write the particle displacement due to... AC voltage-frequency phase space allows us to probe a wide range of colloidal configurations that resemble “capacitive” and “resistive” networks in

Reconfigurable RF Systems Using Commercially Available Digital Capacitor Arrays

DTIC Science & Technology

2013-03-01

for changing antenna loading. Note that for the receiver circuitry, the path through the FEM is reversed and the wideband RF engine is given...Network A tunable impedance-matching network is commonly used to match variable antenna impedance to the transmitter output or receiver input [1...2]. There are multiple utilities for this device. In one, the so-called static mode, the antenna can be matched to the rest of the system before

A π-π 3D network of tetranuclear μ2/μ3-carbonato Dy(III) bis-pyrazolylpyridine clusters showing single molecule magnetism features.

PubMed

Gass, Ian A; Moubaraki, Boujemaa; Langley, Stuart K; Batten, Stuart R; Murray, Keith S

2012-02-18

2,6-Di(pyrazole-3-yl)pyridine, 3-bpp, forms a porous (4(9)·6(6)) π-π mediated 3D network of trigonal pyramidal [Dy(III)(4)] carbonato-bridged complexes, with hexagonal channels comprising 54% of the unit cell volume, the material displaying slow magnetisation reversal. This journal is © The Royal Society of Chemistry 2012

First Annual Workshop on Space Operations Automation and Robotics (SOAR 87)

NASA Technical Reports Server (NTRS)

Griffin, Sandy (Editor)

1987-01-01

Several topics relative to automation and robotics technology are discussed. Automation of checkout, ground support, and logistics; automated software development; man-machine interfaces; neural networks; systems engineering and distributed/parallel processing architectures; and artificial intelligence/expert systems are among the topics covered.

Designing a capacitated multi-configuration logistics network under disturbances and parameter uncertainty: a real-world case of a drug supply chain

NASA Astrophysics Data System (ADS)

Shishebori, Davood; Babadi, Abolghasem Yousefi

2018-03-01

This study investigates the reliable multi-configuration capacitated logistics network design problem (RMCLNDP) under system disturbances, which relates to locating facilities, establishing transportation links, and also allocating their limited capacities to the customers conducive to provide their demand on the minimum expected total cost (including locating costs, link constructing costs, and also expected costs in normal and disturbance conditions). In addition, two types of risks are considered; (I) uncertain environment, (II) system disturbances. A two-level mathematical model is proposed for formulating of the mentioned problem. Also, because of the uncertain parameters of the model, an efficacious possibilistic robust optimization approach is utilized. To evaluate the model, a drug supply chain design (SCN) is studied. Finally, an extensive sensitivity analysis was done on the critical parameters. The obtained results show that the efficiency of the proposed approach is suitable and is worthwhile for analyzing the real practical problems.

Exploring the evolution of London's street network in the information space: A dual approach

NASA Astrophysics Data System (ADS)

Masucci, A. Paolo; Stanilov, Kiril; Batty, Michael

2014-01-01

We study the growth of London's street network in its dual representation, as the city has evolved over the past 224 years. The dual representation of a planar graph is a content-based network, where each node is a set of edges of the planar graph and represents a transportation unit in the so-called information space, i.e., the space where information is handled in order to navigate through the city. First, we discuss a novel hybrid technique to extract dual graphs from planar graphs, called the hierarchical intersection continuity negotiation principle. Then we show that the growth of the network can be analytically described by logistic laws and that the topological properties of the network are governed by robust log-normal distributions characterizing the network's connectivity and small-world properties that are consistent over time. Moreover, we find that the double-Pareto-like distributions for the connectivity emerge for major roads and can be modeled via a stochastic content-based network model using simple space-filling principles.

The moderating role of social networks in the relationship between alcohol consumption and treatment utilization for alcohol-related problems

PubMed Central

Mowbray, Orion

2014-01-01

Many individuals wait until alcohol use becomes severe before treatment is sought. However, social networks, or the number of social groups an individual belongs to, may play a moderating role in this relationship. Logistic regression examined the interaction of alcohol consumption and social networks as a predictor of treatment utilization while adjusting for sociodemographic and clinical variables among 1,433 lifetime alcohol-dependent respondents from wave 2 of the National Epidemiologic Survey on Alcohol Related Conditions (NESARC). Results showed that social networks moderate the relationship between alcohol consumption and treatment utilization such that for individuals with few network ties, the relationship between alcohol consumption and treatment utilization was diminished, compared to the relationship between alcohol consumption and treatment utilization for individuals with many network ties. Findings offer insight into how social networks, at times, can influence individuals to pursue treatment, while at other times, influence individuals to stay out of treatment, or seek treatment substitutes. PMID:24462223

Social support network characteristics and sexual risk taking among a racially/ethnically diverse sample of young, urban men who have sex with men.

PubMed

Kapadia, F; Siconolfi, D E; Barton, S; Olivieri, B; Lombardo, L; Halkitis, P N

2013-06-01

Associations between social support network characteristics and sexual risk among racially/ethnically diverse young men who have sex with men (YMSM) were examined using egocentric network data from a prospective cohort study of YMSM (n = 501) recruited in New York City. Bivariate and multivariable logistic regression analyses examined associations between social support network characteristics and sexual risk taking behaviors in Black, Hispanic/Latino, and White YMSM. Bivariate analyses indicated key differences in network size, composition, communication frequency and average relationship duration by race/ethnicity. In multivariable analyses, controlling for individual level sociodemographic, psychosocial and relationship factors, having a sexual partner in one's social support network was associated with unprotected sexual behavior for both Hispanic/Latino (AOR = 3.90) and White YMSM (AOR = 4.93). Further examination of key network characteristics across racial/ethnic groups are warranted in order to better understand the extant mechanisms for provision of HIV prevention programming to racially/ethnically diverse YMSM at risk for HIV.

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.

Network structure of subway passenger flows

NASA Astrophysics Data System (ADS)

Xu, Q.; Mao, B. H.; Bai, Y.

2016-03-01

The results of transportation infrastructure network analyses have been used to analyze complex networks in a topological context. However, most modeling approaches, including those based on complex network theory, do not fully account for real-life traffic patterns and may provide an incomplete view of network functions. This study utilizes trip data obtained from the Beijing Subway System to characterize individual passenger movement patterns. A directed weighted passenger flow network was constructed from the subway infrastructure network topology by incorporating trip data. The passenger flow networks exhibit several properties that can be characterized by power-law distributions based on flow size, and log-logistic distributions based on the fraction of boarding and departing passengers. The study also characterizes the temporal patterns of in-transit and waiting passengers and provides a hierarchical clustering structure for passenger flows. This hierarchical flow organization varies in the spatial domain. Ten cluster groups were identified, indicating a hierarchical urban polycentric structure composed of large concentrated flows at urban activity centers. These empirical findings provide insights regarding urban human mobility patterns within a large subway network.

Reconstruction of network topology using status-time-series data

NASA Astrophysics Data System (ADS)

Pandey, Pradumn Kumar; Badarla, Venkataramana

2018-01-01

Uncovering the heterogeneous connection pattern of a networked system from the available status-time-series (STS) data of a dynamical process on the network is of great interest in network science and known as a reverse engineering problem. Dynamical processes on a network are affected by the structure of the network. The dependency between the diffusion dynamics and structure of the network can be utilized to retrieve the connection pattern from the diffusion data. Information of the network structure can help to devise the control of dynamics on the network. In this paper, we consider the problem of network reconstruction from the available status-time-series (STS) data using matrix analysis. The proposed method of network reconstruction from the STS data is tested successfully under susceptible-infected-susceptible (SIS) diffusion dynamics on real-world and computer-generated benchmark networks. High accuracy and efficiency of the proposed reconstruction procedure from the status-time-series data define the novelty of the method. Our proposed method outperforms compressed sensing theory (CST) based method of network reconstruction using STS data. Further, the same procedure of network reconstruction is applied to the weighted networks. The ordering of the edges in the weighted networks is identified with high accuracy.

Behavioral networks as a model for intelligent agents

NASA Technical Reports Server (NTRS)

Sliwa, Nancy E.

1990-01-01

On-going work at NASA Langley Research Center in the development and demonstration of a paradigm called behavioral networks as an architecture for intelligent agents is described. This work focuses on the need to identify a methodology for smoothly integrating the characteristics of low-level robotic behavior, including actuation and sensing, with intelligent activities such as planning, scheduling, and learning. This work assumes that all these needs can be met within a single methodology, and attempts to formalize this methodology in a connectionist architecture called behavioral networks. Behavioral networks are networks of task processes arranged in a task decomposition hierarchy. These processes are connected by both command/feedback data flow, and by the forward and reverse propagation of weights which measure the dynamic utility of actions and beliefs.

Time reversal of arbitrary photonic temporal modes via nonlinear optical frequency conversion

NASA Astrophysics Data System (ADS)

Raymer, Michael G.; Reddy, Dileep V.; van Enk, Steven J.; McKinstrie, Colin J.

2018-05-01

Single-photon wave packets can carry quantum information between nodes of a quantum network. An important general operation in photon-based quantum information systems is ‘blind’ reversal of a photon’s temporal wave packet envelope, that is, the ability to reverse an envelope without knowing the temporal state of the photon. We present an all-optical means for doing so, using nonlinear-optical frequency conversion driven by a short pump pulse. The process used may be sum-frequency generation or four-wave Bragg scattering. This scheme allows for quantum operations such as a temporal-mode parity sorter. We also verify that the scheme works for arbitrary states (not only single-photon ones) of an unknown wave packet.

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.

Representativeness-based sampling network design for the State of Alaska

Treesearch

Forrest M. Hoffman; Jitendra Kumar; Richard T. Mills; William W. Hargrove

2013-01-01

Resource and logistical constraints limit the frequency and extent of environmental observations, particularly in the Arctic, necessitating the development of a systematic sampling strategy to maximize coverage and objectively represent environmental variability at desired scales. A quantitative methodology for stratifying sampling domains, informing site selection,...

Some Initiatives in a Business Forecasting Course

ERIC Educational Resources Information Center

Chu, Singfat

2007-01-01

The paper reports some initiatives to freshen up the typical undergraduate business forecasting course. These include (1) students doing research and presentations on contemporary tools and industry practices such as neural networks and collaborative forecasting (2) insertion of Logistic Regression in the curriculum (3) productive use of applets…

Riparian Sediment Delivery Ratio: Stiff Diagrams and Artifical Neural Networks

EPA Science Inventory

Various methods are used to estimate sediment transport through riparian buffers and grass jilters with the sediment delivery ratio having been the most widely applied. The U.S. Forest Service developed a sediment delivery ratio using the stiff diagram and a logistic curve to int...

Data Mining in Child Welfare.

ERIC Educational Resources Information Center

Schoech, Dick; Quinn, Andrew; Rycraft, Joan R.

2000-01-01

Examines the historical and larger context of data mining and describes data mining processes, techniques, and tools. Illustrates these using a child welfare dataset concerning the employee turnover that is mined, using logistic regression and a Bayesian neural network. Discusses the data mining process, the resulting models, their predictive…

Hazards of New Media: Youth’s Exposure to Tobacco Ads/Promotions

PubMed Central

2014-01-01

Background: A gap in knowledge exists about the youth’s exposure to protobacco campaigns via new electronic media outlets. In response, we use national data to delineate the associations between tobacco ads/promotions delivered through new media outlets (i.e., social network sites and text messages) and youth attitudes/beliefs about tobacco and intent to use (among youth who had not yet used tobacco). Methods: Data were derived from the 2011 National Youth Tobacco Survey, a nationally representative sample of U.S. youth enrolled in both public and private schools (N = 15,673). Logistic regression models were used to examine associations between demographic characteristics and reported exposure to tobacco ads/promotions via social networking sites and text messages. Logistic regression models were also used to investigate associations between exposure tobacco ads/promotions and attitudes toward tobacco. Results: We found that highly susceptible youth (i.e., minorities, very young youth, and youth who have not yet used tobacco) have observed tobacco ads/promotions on social networking sites and text messages. These youth are more likely to have favorable attitudes toward tobacco, including the intention to use tobacco among those who had not yet used tobacco. Conclusions: Our findings underscore the need for policy strategies to more effectively monitor and regulate tobacco advertising via new media outlets. PMID:24163285

Hazards of new media: youth's exposure to tobacco Ads/promotions.

PubMed

Cavazos-Rehg, Patricia A; Krauss, Melissa J; Spitznagel, Edward L; Grucza, Richard A; Bierut, Laura Jean

2014-04-01

A gap in knowledge exists about the youth's exposure to protobacco campaigns via new electronic media outlets. In response, we use national data to delineate the associations between tobacco ads/promotions delivered through new media outlets (i.e., social network sites and text messages) and youth attitudes/beliefs about tobacco and intent to use (among youth who had not yet used tobacco). Data were derived from the 2011 National Youth Tobacco Survey, a nationally representative sample of U.S. youth enrolled in both public and private schools (N = 15,673). Logistic regression models were used to examine associations between demographic characteristics and reported exposure to tobacco ads/promotions via social networking sites and text messages. Logistic regression models were also used to investigate associations between exposure tobacco ads/promotions and attitudes toward tobacco. We found that highly susceptible youth (i.e., minorities, very young youth, and youth who have not yet used tobacco) have observed tobacco ads/promotions on social networking sites and text messages. These youth are more likely to have favorable attitudes toward tobacco, including the intention to use tobacco among those who had not yet used tobacco. Our findings underscore the need for policy strategies to more effectively monitor and regulate tobacco advertising via new media outlets.

The effects of social networks on tobacco use among high-school adolescents in Mexico.

PubMed

Ramírez-Ortiz, Guadalupe; Caballero-Hoyos, Ramiro; Ramírez-López, Guadalupe; Valente, Thomas W

2012-01-01

To identify the effect of centrality in social network positions on tobacco-use among high-school adolescents in Tonala, Jalisco, Mexico. Longitudinal sociometric social network data were collected among 486 high-school adolescents in 2003 and 399 in 2004. The survey included: social network components, smoking and sociodemographic characteristics. Social network measures of centrality were calculated and multivariate logistic regression was used. Ever used tobacco (OR= 44.98), marginalized-low stratum (OR= 2.16) and in-degree (OR=1.10) predicted tobacco use. Out-degree (OR= 0 .89) and out-in-degree (OR= 0.90) protected against tobacco use. Nominating more friends rather than receiving such nominations was protective for tobacco use. Popular students, those receiving many nominations, were at higher risk for tobacco use. Involvement of leaders with capacity to influence might be an efficient strategy for dissemination of preventive messages.

Exploring the spiral of silence in adjustable social networks

NASA Astrophysics Data System (ADS)

Wu, Yue; Du, Ya-Jun; Li, Xian-Yong; Chen, Xiao-Liang

2015-03-01

This study extends the understanding of the spiral of silence theory by taking into account four factors, including the topology of networks, the time factor of information transmission, the node degree of individuals and the freedom of expression. Simulation experiments analyze the silencers, public opinion in steady state and relaxation time in small-world networks, scale-free networks and community-structured networks by adjusting the initial conditions. Results highlight that individuals are easier to keep silent in scale-free network, especially when the individual with big degree and minority opinion starts the discussion. Conversely, there are only a few individuals keep silent in the community-structured network when the two communities hold opposite opinions. Moreover, the number of silencers grows as the degree of coupling increases, and it decreases as the freedom of expression goes up. By analyzing the public opinion evolution, we also find some important conditions, such as the network topology, the potential public opinion distribution, and the status and sides of the first speaker, can drive the minority reversal.

Bottlebrush-Guided Polymer Crystallization Resulting in Supersoft and Reversibly Moldable Physical Networks

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

Daniel, William F. M.; Xie, Guojun; Vatankhah Varnoosfaderani, Mohammad

The goal of this study is to use ABA triblock copolymers with central bottlebrush B segments and crystalline linear chain A segments to demonstrate the effect of side chains on the formation and mechanical properties of physical networks cross-linked by crystallites. For this purpose, a series of bottlebrush copolymers was synthesized consisting of central amorphous bottlebrush polymer segments with a varying degree of polymerization (DP) of poly(n-butyl acrylate) (PnBA) side chains and linear tail blocks of crystallizable poly(octadecyl acrylate-stat-docosyl acrylate) (poly(ODA-stat-DA)). The materials were generated by sequential atom transfer radical polymerization (ATRP) steps starting with a series of bifunctional macroinitiatorsmore » followed by the growth of two ODA-stat-DA linear-chain tails and eventually growing poly(nBA) side chains with increasing DPs. Crystallization of the poly(ODA-stat-DA) tails resulted in a series of reversible physical networks with bottlebrush strands bridging crystalline cross-links. They displayed very low moduli of elasticity of the order of 10 3–10 4 Pa. These distinct properties are due to the bottlebrush architecture, wherein densely grafted side chains play a dual role by facilitating disentanglement of the network strands and confining crystallization of the linear-chain tails. This combination leads to physical cross-linking of supersoft networks without percolation of the crystalline phase. The cross-link density was effectively controlled by the DP of the side chains with respect to the DP of the linear tails (n A). Furthermore, shorter side chains allowed for crystallization of the linear tails of neighboring bottlebrushes, while steric repulsion between longer side chains hindered the phase separation and crystallization process and prevented network formation.« less

Bottlebrush-Guided Polymer Crystallization Resulting in Supersoft and Reversibly Moldable Physical Networks

DOE PAGES

Daniel, William F. M.; Xie, Guojun; Vatankhah Varnoosfaderani, Mohammad; ...

2017-02-24

The goal of this study is to use ABA triblock copolymers with central bottlebrush B segments and crystalline linear chain A segments to demonstrate the effect of side chains on the formation and mechanical properties of physical networks cross-linked by crystallites. For this purpose, a series of bottlebrush copolymers was synthesized consisting of central amorphous bottlebrush polymer segments with a varying degree of polymerization (DP) of poly(n-butyl acrylate) (PnBA) side chains and linear tail blocks of crystallizable poly(octadecyl acrylate-stat-docosyl acrylate) (poly(ODA-stat-DA)). The materials were generated by sequential atom transfer radical polymerization (ATRP) steps starting with a series of bifunctional macroinitiatorsmore » followed by the growth of two ODA-stat-DA linear-chain tails and eventually growing poly(nBA) side chains with increasing DPs. Crystallization of the poly(ODA-stat-DA) tails resulted in a series of reversible physical networks with bottlebrush strands bridging crystalline cross-links. They displayed very low moduli of elasticity of the order of 10 3–10 4 Pa. These distinct properties are due to the bottlebrush architecture, wherein densely grafted side chains play a dual role by facilitating disentanglement of the network strands and confining crystallization of the linear-chain tails. This combination leads to physical cross-linking of supersoft networks without percolation of the crystalline phase. The cross-link density was effectively controlled by the DP of the side chains with respect to the DP of the linear tails (n A). Furthermore, shorter side chains allowed for crystallization of the linear tails of neighboring bottlebrushes, while steric repulsion between longer side chains hindered the phase separation and crystallization process and prevented network formation.« less

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.

Self-Healing Natural Rubber with Tailorable Mechanical Properties Based on Ionic Supramolecular Hybrid Network.

PubMed

Xu, Chuanhui; Cao, Liming; Huang, Xunhui; Chen, Yukun; Lin, Baofeng; Fu, Lihua

2017-08-30

In most cases, the strength of self-healing supramolecular rubber based on noncovalent bonds is in the order of KPa, which is a challenge for their further applications. Incorporation of conventional fillers can effectively enhance the strength of rubbers, but usually accompanied by a sacrifice of self-healing capability due to that the filler system is independent of the reversible supramolecular network. In the present work, in situ reaction of methacrylic acid (MAA) and excess zinc oxide (ZnO) was realized in natural rubber (NR). Ionic cross-links in NR matrix were obtained by limiting the covalent cross-linking of NR molecules and allowing the in situ polymerization of MAA/ZnO. Because of the natural affinity between Zn 2+ ion-rich domains and ZnO, the residual nano ZnO participated in formation of a reversible ionic supramolecular hybrid network, thus having little obstructions on the reconstruction of ionic cross-links. Meanwhile, the well dispersed residual ZnO could tailor the mechanical properties of NR by changing the MAA/ZnO molar ratios. The present study thus provides a simple method to fabricate a new self-healing NR with tailorable mechanical properties that may have more potential applications.

Elastic Coupling of Nascent apCAM Adhesions to Flowing Actin Networks

PubMed Central

Mejean, Cecile O.; Schaefer, Andrew W.; Buck, Kenneth B.; Kress, Holger; Shundrovsky, Alla; Merrill, Jason W.; Dufresne, Eric R.; Forscher, Paul

2013-01-01

Adhesions are multi-molecular complexes that transmit forces generated by a cell’s acto-myosin networks to external substrates. While the physical properties of some of the individual components of adhesions have been carefully characterized, the mechanics of the coupling between the cytoskeleton and the adhesion site as a whole are just beginning to be revealed. We characterized the mechanics of nascent adhesions mediated by the immunoglobulin-family cell adhesion molecule apCAM, which is known to interact with actin filaments. Using simultaneous visualization of actin flow and quantification of forces transmitted to apCAM-coated beads restrained with an optical trap, we found that adhesions are dynamic structures capable of transmitting a wide range of forces. For forces in the picoNewton scale, the nascent adhesions’ mechanical properties are dominated by an elastic structure which can be reversibly deformed by up to 1 µm. Large reversible deformations rule out an interface between substrate and cytoskeleton that is dominated by a number of stiff molecular springs in parallel, and favor a compliant cross-linked network. Such a compliant structure may increase the lifetime of a nascent adhesion, facilitating signaling and reinforcement. PMID:24039928

Implementation of Motivational Interviewing in Substance Use Disorder Treatment: Research Network Participation and Organizational Compatibility.

PubMed

Rieckmann, Traci R; Abraham, Amanda J; Bride, Brian E

Despite considerable empirical evidence that psychosocial interventions improve addiction treatment outcomes across populations, implementation remains problematic. A small body of research points to the importance of research network participation as a facilitator of implementation; however, studies examined limited numbers of evidence-based practices. To address this gap, the present study examined factors impacting implementation of motivational interviewing (MI). This study used data from a national sample of privately funded treatment programs (n = 345) and programs participating in the National Drug Abuse Treatment Clinical Trials Network (CTN) (n = 156). Data were collected via face-to-face interviews with program administrators and clinical directors (2007-2009). Analysis included bivariate t tests and chi-square tests to compare private and CTN programs, and multivariable logistic regression of MI implementation. A majority (68.0%) of treatment programs reported use of MI. Treatment programs participating in the CTN (88.9%) were significantly more likely to report use of MI compared with non-CTN programs (58.5%; P < 0.01). CTN programs (82.1%) also were more likely to use trainers from the Motivational Interviewing Network of Trainers as compared with private programs (56.1%; P < 0.05). Multivariable logistic regression models reveal that CTN-affiliated programs and programs with a psychiatrist on staff were more likely to use MI. Programs that used the Stages of Change Readiness and Treatment Eagerness Scale assessment tool were more likely to use MI, whereas programs placing greater emphasis on confrontational group therapy were less likely to use MI. Findings suggest the critical role of research network participation, access to psychiatrists, and organizational compatibility in adoption and sustained use of MI.

Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set.

PubMed

Lenselink, Eelke B; Ten Dijke, Niels; Bongers, Brandon; Papadatos, George; van Vlijmen, Herman W T; Kowalczyk, Wojtek; IJzerman, Adriaan P; van Westen, Gerard J P

2017-08-14

The increase of publicly available bioactivity data in recent years has fueled and catalyzed research in chemogenomics, data mining, and modeling approaches. As a direct result, over the past few years a multitude of different methods have been reported and evaluated, such as target fishing, nearest neighbor similarity-based methods, and Quantitative Structure Activity Relationship (QSAR)-based protocols. However, such studies are typically conducted on different datasets, using different validation strategies, and different metrics. In this study, different methods were compared using one single standardized dataset obtained from ChEMBL, which is made available to the public, using standardized metrics (BEDROC and Matthews Correlation Coefficient). Specifically, the performance of Naïve Bayes, Random Forests, Support Vector Machines, Logistic Regression, and Deep Neural Networks was assessed using QSAR and proteochemometric (PCM) methods. All methods were validated using both a random split validation and a temporal validation, with the latter being a more realistic benchmark of expected prospective execution. Deep Neural Networks are the top performing classifiers, highlighting the added value of Deep Neural Networks over other more conventional methods. Moreover, the best method ('DNN_PCM') performed significantly better at almost one standard deviation higher than the mean performance. Furthermore, Multi-task and PCM implementations were shown to improve performance over single task Deep Neural Networks. Conversely, target prediction performed almost two standard deviations under the mean performance. Random Forests, Support Vector Machines, and Logistic Regression performed around mean performance. Finally, using an ensemble of DNNs, alongside additional tuning, enhanced the relative performance by another 27% (compared with unoptimized 'DNN_PCM'). Here, a standardized set to test and evaluate different machine learning algorithms in the context of multi-task learning is offered by providing the data and the protocols. Graphical Abstract .

Unraveling gene regulatory networks from time-resolved gene expression data -- a measures comparison study

PubMed Central

2011-01-01

Background Inferring regulatory interactions between genes from transcriptomics time-resolved data, yielding reverse engineered gene regulatory networks, is of paramount importance to systems biology and bioinformatics studies. Accurate methods to address this problem can ultimately provide a deeper insight into the complexity, behavior, and functions of the underlying biological systems. However, the large number of interacting genes coupled with short and often noisy time-resolved read-outs of the system renders the reverse engineering a challenging task. Therefore, the development and assessment of methods which are computationally efficient, robust against noise, applicable to short time series data, and preferably capable of reconstructing the directionality of the regulatory interactions remains a pressing research problem with valuable applications. Results Here we perform the largest systematic analysis of a set of similarity measures and scoring schemes within the scope of the relevance network approach which are commonly used for gene regulatory network reconstruction from time series data. In addition, we define and analyze several novel measures and schemes which are particularly suitable for short transcriptomics time series. We also compare the considered 21 measures and 6 scoring schemes according to their ability to correctly reconstruct such networks from short time series data by calculating summary statistics based on the corresponding specificity and sensitivity. Our results demonstrate that rank and symbol based measures have the highest performance in inferring regulatory interactions. In addition, the proposed scoring scheme by asymmetric weighting has shown to be valuable in reducing the number of false positive interactions. On the other hand, Granger causality as well as information-theoretic measures, frequently used in inference of regulatory networks, show low performance on the short time series analyzed in this study. Conclusions Our study is intended to serve as a guide for choosing a particular combination of similarity measures and scoring schemes suitable for reconstruction of gene regulatory networks from short time series data. We show that further improvement of algorithms for reverse engineering can be obtained if one considers measures that are rooted in the study of symbolic dynamics or ranks, in contrast to the application of common similarity measures which do not consider the temporal character of the employed data. Moreover, we establish that the asymmetric weighting scoring scheme together with symbol based measures (for low noise level) and rank based measures (for high noise level) are the most suitable choices. PMID:21771321

Intergenerational Social Networks and Health Behaviors Among Children Living in Public Housing.

PubMed

Kennedy-Hendricks, Alene; Schwartz, Heather; Thornton, Rachel Johnson; Griffin, Beth Ann; Green, Harold D; Kennedy, David P; Burkhauser, Susan; Pollack, Craig Evan

2015-11-01

In a survey of families living in public housing, we investigated whether caretakers' social networks are linked with children's health status. In 2011, 209 children and their caretakers living in public housing in suburban Montgomery County, Maryland, were surveyed regarding their health and social networks. We used logistic regression models to examine the associations between the perceived health composition of caretaker social networks and corresponding child health characteristics (e.g., exercise, diet). With each 10% increase in the proportion of the caretaker's social network that exercised regularly, the child's odds of exercising increased by 34% (adjusted odds ratio = 1.34; 95% confidence interval = 1.07, 1.69) after the caretaker's own exercise behavior and the composition of the child's peer network had been taken into account. Although children's overweight or obese status was associated with caretakers' social networks, the results were no longer significant after adjustment for caretakers' own weight status. We found that caretaker social networks are independently associated with certain aspects of child health, suggesting the importance of the broader social environment for low-income children's health.

Modelling reverse characteristics of power LEDs with thermal phenomena taken into account

NASA Astrophysics Data System (ADS)

Ptak, Przemysław; Górecki, Krzysztof

2016-01-01

This paper refers to modelling characteristics of power LEDs with a particular reference to thermal phenomena. Special attention is paid to modelling characteristics of the circuit protecting the considered device against the excessive value of the reverse voltage and to the description of the temperature influence on optical power. The network form of the worked out model is presented and some results of experimental verification of this model for the selected diodes operating at different cooling conditions are described. The very good agreement between the calculated and measured characteristics is obtained.

A reverse engineering approach to optimize experiments for the construction of biological regulatory networks.

PubMed

Zhang, Xiaomeng; Shao, Bin; Wu, Yangle; Qi, Ouyang

2013-01-01

One of the major objectives in systems biology is to understand the relation between the topological structures and the dynamics of biological regulatory networks. In this context, various mathematical tools have been developed to deduct structures of regulatory networks from microarray expression data. In general, from a single data set, one cannot deduct the whole network structure; additional expression data are usually needed. Thus how to design a microarray expression experiment in order to get the most information is a practical problem in systems biology. Here we propose three methods, namely, maximum distance method, trajectory entropy method, and sampling method, to derive the optimal initial conditions for experiments. The performance of these methods is tested and evaluated in three well-known regulatory networks (budding yeast cell cycle, fission yeast cell cycle, and E. coli. SOS network). Based on the evaluation, we propose an efficient strategy for the design of microarray expression experiments.

Unveiling the molecular mechanism of self-healing in a telechelic, supramolecular polymer network

PubMed Central

Yan, Tingzi; Schröter, Klaus; Herbst, Florian; Binder, Wolfgang H.; Thurn-Albrecht, Thomas

2016-01-01

Reversible polymeric networks can show self-healing properties due to their ability to reassemble after application of stress and fracture, but typically the relation between equilibrium molecular dynamics and self-healing kinetics has been difficult to disentangle. Here we present a well-characterized, self-assembled bulk network based on supramolecular assemblies, that allows a clear distinction between chain dynamics and network relaxation. Small angle x-ray scattering and rheological measurements provide evidence for a structurally well-defined, dense network of interconnected aggregates giving mechanical strength to the material. Different from a covalent network, the dynamic character of the supramolecular bonds enables macroscopic flow on a longer time scale and the establishment of an equilibrium structure. A combination of linear and nonlinear rheological measurements clearly identifies the terminal relaxation process as being responsible for the process of self-healing. PMID:27581380

Finite state model and compatibility theory - New analysis tools for permutation networks

NASA Technical Reports Server (NTRS)

Huang, S.-T.; Tripathi, S. K.

1986-01-01

A simple model to describe the fundamental operation theory of shuffle-exchange-type permutation networks, the finite permutation machine (FPM), is described, and theorems which transform the control matrix result to a continuous compatible vector result are developed. It is found that only 2n-1 shuffle exchange passes are necessary, and that 3n-3 passes are sufficient, to realize all permutations, reducing the sufficient number of passes by two from previous results. The flexibility of the approach is demonstrated by the description of a stack permutation machine (SPM) which can realize all permutations, and by showing that the FPM corresponding to the Benes (1965) network belongs to the SPM. The FPM corresponding to the network with two cascaded reverse-exchange networks is found to realize all permutations, and a simple mechanism to verify several equivalence relationships of various permutation networks is discussed.

Measuring Networking as an Outcome Variable in Undergraduate Research Experiences

PubMed Central

Hanauer, David I.; Hatfull, Graham

2015-01-01

The aim of this paper is to propose, present, and validate a simple survey instrument to measure student conversational networking. The tool consists of five items that cover personal and professional social networks, and its basic principle is the self-reporting of degrees of conversation, with a range of specific discussion partners. The networking instrument was validated in three studies. The basic psychometric characteristics of the scales were established by conducting a factor analysis and evaluating internal consistency using Cronbach’s alpha. The second study used a known-groups comparison and involved comparing outcomes for networking scales between two different undergraduate laboratory courses (one involving a specific effort to enhance networking). The final study looked at potential relationships between specific networking items and the established psychosocial variable of project ownership through a series of binary logistic regressions. Overall, the data from the three studies indicate that the networking scales have high internal consistency (α = 0.88), consist of a unitary dimension, can significantly differentiate between research experiences with low and high networking designs, and are related to project ownership scales. The ramifications of the networking instrument for student retention, the enhancement of public scientific literacy, and the differentiation of laboratory courses are discussed. PMID:26538387

Culture, economic development, social-network type, and mortality: Evidence from Chinese older adults.

PubMed

Li, Ting; Yang, Yang Claire; Zhang, Yanlong

2018-05-01

This study examined the patterns of social-network types and their relative survival benefits among Chinese older adults. We examined how macro-level social factors such as cultural norms and unbalanced regional economic development shaped older people's network behaviors, and whether these factors could moderate the association between network types and mortality. Using data from the Chinese Longitudinal Healthy Longevity Survey (2008-2014), we identified four network types-diverse, friend-focused, family-focused, and restricted-based on individuals' social network measures. Multinomial logistic analyses revealed that older people situated in an area with a deeply rooted family culture or a more advanced economy tend to be less likely to enroll in a diverse network than a family-focused one. This prevents them from achieving the best adaptive survival, as Cox regression analyses showed that the family-focused network type was less beneficial than the diverse one for the survival of older adults. Furthermore, while the survival advantage of the diverse-network type over the family-focused type did not change with cultural contexts, economic development attenuated the deleterious effects of the friend-focused network type. Copyright © 2018 Elsevier Ltd. All rights reserved.

Computational Systems Toxicology: recapitulating the logistical dynamics of cellular response networks in virtual tissue models (Eurotox_2017)

EPA Science Inventory

Translating in vitro data and biological information into a predictive model for human toxicity poses a significant challenge. This is especially true for complex adaptive systems such as the embryo where cellular dynamics are precisely orchestrated in space and time. Computer ce...

The Radius of Trust: Religion, Social Embeddedness and Trust in Strangers

ERIC Educational Resources Information Center

Welch, Michael R.; Sikkink, David; Loveland, Matthew T.

2007-01-01

Data from the 2002 Religion and Public Activism Survey were used to examine relationships among measures of religious orientation, embeddedness in social networks and the level of trust individuals direct toward others. Results from ordered logistic regression analysis demonstrate that Catholics and members of other denominations show…

Using a novel flood prediction model and GIS automation to measure the valley and channel morphology of large river networks

EPA Science Inventory

Traditional methods for measuring river valley and channel morphology require intensive ground-based surveys which are often expensive, time consuming, and logistically difficult to implement. The number of surveys required to assess the hydrogeomorphic structure of large river n...

75 FR 28181 - National Defense Transportation Day and National Transportation Week, 2010

Federal Register 2010, 2011, 2012, 2013, 2014

2010-05-20

... ensure America has a world-class logistics and transportation system to support our military readiness... the President of the United States of America A Proclamation The transportation networks of early America connected our rapidly growing Nation with natural waterways and dirt roads, making travel...

It Takes a Village: Network Effects on Rural Education in Afghanistan. PRGS Dissertation

ERIC Educational Resources Information Center

Hoover, Matthew Amos

2014-01-01

Often, development organizations confront a tradeoff between program priorities and operational constraints. These constraints may be financial, capacity, or logistical; regardless, the tradeoff often requires sacrificing portions of a program. This work is concerned with figuring out how, when constrained, an organization or program manager can…

Network Skewness Measures Resilience in Lake Ecosystems

NASA Astrophysics Data System (ADS)

Langdon, P. G.; Wang, R.; Dearing, J.; Zhang, E.; Doncaster, P.; Yang, X.; Yang, H.; Dong, X.; Hu, Z.; Xu, M.; Yanjie, Z.; Shen, J.

2017-12-01

Changes in ecosystem resilience defy straightforward quantification from biodiversity metrics, which ignore influences of community structure. Naturally self-organized network structures show positive skewness in the distribution of node connections. Here we test for skewness reduction in lake diatom communities facing anthropogenic stressors, across a network of 273 lakes in China containing 452 diatom species. Species connections show positively skewed distributions in little-impacted lakes, switching to negative skewness in lakes associated with human settlement, surrounding land-use change, and higher phosphorus concentration. Dated sediment cores reveal a down-shifting of network skewness as human impacts intensify, and reversal with recovery from disturbance. The appearance and degree of negative skew presents a new diagnostic for quantifying system resilience and impacts from exogenous forcing on ecosystem communities.

Shallow Water MCM and ASW Using Off-Board, Autonomous Sensor Networks and Multistatic, Time-Reversal Acoustics

DTIC Science & Technology

2005-09-30

generalizations of auto-focusing and track - before - detect (TBD) algorithms. Another issue concerns the stability and coherence of surface and seabed multiples and their potential use in advanced medium-frequency sonar concepts.

Stellar Parameter Determination With J-Plus Using Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Whitten, Devin D.

2017-10-01

The J-PLUS narrow-band filter system provides a unique opportunity for the determination of stellar parameters and chemical abundances from photometry alone. Mapping stellar magnitudes to estimates of surface temperature, [Fe/H], and [C/Fe] is an excellent application of machine learning and in particular, artificial neural networks (ANN). The logistics and performance of this ANN methodology is explored with the J-PLUS Early Data Release, as well as the potential impact of stellar parameters from J-PLUS on the field of Galactic chemical evolution.

Link prediction measures considering different neighbors’ effects and application in social networks

NASA Astrophysics Data System (ADS)

Luo, Peng; Wu, Chong; Li, Yongli

Link prediction measures have been attracted particular attention in the field of mathematical physics. In this paper, we consider the different effects of neighbors in link prediction and focus on four different situations: only consider the individual’s own effects; consider the effects of individual, neighbors and neighbors’ neighbors; consider the effects of individual, neighbors, neighbors’ neighbors, neighbors’ neighbors’ neighbors and neighbors’ neighbors’ neighbors’ neighbors; consider the whole network participants’ effects. Then, according to the four situations, we present our link prediction models which also take the effects of social characteristics into consideration. An artificial network is adopted to illustrate the parameter estimation based on logistic regression. Furthermore, we compare our methods with the some other link prediction methods (LPMs) to examine the validity of our proposed model in online social networks. The results show the superior of our proposed link prediction methods compared with others. In the application part, our models are applied to study the social network evolution and used to recommend friends and cooperators in social networks.

Data on synthesis and thermo-mechanical properties of stimuli-responsive rubber materials bearing pendant anthracene groups.

PubMed

Manhart, Jakob; Ayalur-Karunakaran, Santhosh; Radl, Simone; Oesterreicher, Andreas; Moser, Andreas; Ganser, Christian; Teichert, Christian; Pinter, Gerald; Kern, Wolfgang; Griesser, Thomas; Schlögl, Sandra

2016-12-01

The photo-reversible [4πs+4πs] cycloaddition reaction of pendant anthracene moieties represents a convenient strategy to impart wavelength dependent properties into hydrogenated carboxylated nitrile butadiene rubber (HXNBR) networks. The present article provides the 1 H NMR data on the reaction kinetics of the side chain functionalization of HXNBR. 2-(Anthracene-9-yl)oxirane with reactive epoxy groups is covalently attached to the polymer side chain of HXNBR via ring opening reaction between the epoxy and the carboxylic groups. Along with the identification, 1 H NMR data on the quantification of the attached functional groups are shown in dependence on reaction time and concentration of 2-(anthracene-9-yl)oxirane. Changes in the modification yield are reflected in the mechanical properties and DMA data of photo-responsive elastomers are illustrated in dependence on the number of attached anthracene groups. DMA curves over repeated cycles of UV induced crosslinking ( λ >300 nm) and UV induced cleavage ( λ =254 nm) are further depicted, demonstrating the photo-reversibility of the thermo-mechanical properties. Interpretation and discussion of the data are provided in "Design and application of photo-reversible elastomer networks by using the [4πs+4πs] cycloaddition reaction of pendant anthracene groups" (Manhart et al., 2016) [1].

Kainate and metabolic perturbation mimicking spinal injury differentially contribute to early damage of locomotor networks in the in vitro neonatal rat spinal cord.

PubMed

Taccola, G; Margaryan, G; Mladinic, M; Nistri, A

2008-08-13

Acute spinal cord injury evolves rapidly to produce secondary damage even to initially spared areas. The result is loss of locomotion, rarely reversible in man. It is, therefore, important to understand the early pathophysiological processes which affect spinal locomotor networks. Regardless of their etiology, spinal lesions are believed to include combinatorial effects of excitotoxicity and severe stroke-like metabolic perturbations. To clarify the relative contribution by excitotoxicity and toxic metabolites to dysfunction of locomotor networks, spinal reflexes and intrinsic network rhythmicity, we used, as a model, the in vitro thoraco-lumbar spinal cord of the neonatal rat treated (1 h) with either kainate or a pathological medium (containing free radicals and hypoxic/aglycemic conditions), or their combination. After washout, electrophysiological responses were monitored for 24 h and cell damage analyzed histologically. Kainate suppressed fictive locomotion irreversibly, while it reversibly blocked neuronal excitability and intrinsic bursting induced by synaptic inhibition block. This result was associated with significant neuronal loss around the central canal. Combining kainate with the pathological medium evoked extensive, irreversible damage to the spinal cord. The pathological medium alone slowed down fictive locomotion and intrinsic bursting: these oscillatory patterns remained throughout without regaining their control properties. This phenomenon was associated with polysynaptic reflex depression and preferential damage to glial cells, while neurons were comparatively spared. Our model suggests distinct roles of excitotoxicity and metabolic dysfunction in the acute damage of locomotor networks, indicating that different strategies might be necessary to treat the various early components of acute spinal cord lesion.

Classifying machinery condition using oil samples and binary logistic regression

NASA Astrophysics Data System (ADS)

Phillips, J.; Cripps, E.; Lau, John W.; Hodkiewicz, M. R.

2015-08-01

The era of big data has resulted in an explosion of condition monitoring information. The result is an increasing motivation to automate the costly and time consuming human elements involved in the classification of machine health. When working with industry it is important to build an understanding and hence some trust in the classification scheme for those who use the analysis to initiate maintenance tasks. Typically "black box" approaches such as artificial neural networks (ANN) and support vector machines (SVM) can be difficult to provide ease of interpretability. In contrast, this paper argues that logistic regression offers easy interpretability to industry experts, providing insight to the drivers of the human classification process and to the ramifications of potential misclassification. Of course, accuracy is of foremost importance in any automated classification scheme, so we also provide a comparative study based on predictive performance of logistic regression, ANN and SVM. A real world oil analysis data set from engines on mining trucks is presented and using cross-validation we demonstrate that logistic regression out-performs the ANN and SVM approaches in terms of prediction for healthy/not healthy engines.

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.

A support network typology for application in older populations with a preponderance of multigenerational households.

PubMed

Burholt, Vanessa; Dobbs, Christine

2014-08-01

This paper considers the support networks of older people in populations with a preponderance of multigenerational households and examines the most vulnerable network types in terms of loneliness and isolation. Current common typologies of support networks may not be sensitive to differences within and between different cultures. This paper uses cross-sectional data drawn from 590 elders (Gujaratis, Punjabis and Sylhetis) living in the United Kingdom and South Asia. Six variables were used in K-means cluster analysis to establish a new network typology. Two logistic regression models using loneliness and isolation as dependent variables assessed the contribution of the new network type to wellbeing. Four support networks were identified: 'Multigenerational Households: Older Integrated Networks', 'Multigenerational Households: Younger Family Networks', 'Family and Friends Integrated Networks' and 'Non-kin Restricted Networks'. Older South Asians with 'Non-kin Restricted Networks' were more likely to be lonely and isolated compared to others. Using network typologies developed with individualistically oriented cultures, distributions are skewed towards more robust network types and could underestimate the support needs of older people from familistic cultures, who may be isolated and lonely and with limited informal sources of help. The new typology identifies different network types within multigenerational households, identifies a greater proportion of older people with vulnerable networks and could positively contribute to service planning.

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.

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.

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.

Computation by symmetry operations in a structured model of the brain: Recognition of rotational invariance and time reversal

NASA Astrophysics Data System (ADS)

McGrann, John V.; Shaw, Gordon L.; Shenoy, Krishna V.; Leng, Xiaodan; Mathews, Robert B.

1994-06-01

Symmetries have long been recognized as a vital component of physical and biological systems. What we propose here is that symmetry operations are an important feature of higher brain function and result from the spatial and temporal modularity of the cortex. These symmetry operations arise naturally in the trion model of the cortex. The trion model is a highly structured mathematical realization of the Mountcastle organizational principle [Mountcastle, in The Mindful Brain (MIT, Cambridge, 1978)] in which the cortical column is the basic neural network of the cortex and is comprised of subunit minicolumns, which are idealized as trions with three levels of firing. A columnar network of a small number of trions has a large repertoire of quasistable, periodic spatial-temporal firing magic patterns (MP's), which can be excited. The MP's are related by specific symmetries: Spatial rotation, parity, ``spin'' reversal, and time reversal as well as other ``global'' symmetry operations in this abstract internal language of the brain. These MP's can be readily enhanced (as well as inherent categories of MP's) by only a small change in connection strengths via a Hebb learning rule. Learning introduces small breaking of the symmetries in the connectivities which enables a symmetry in the patterns to be recognized in the Monte Carlo evolution of the MP's. Examples of the recognition of rotational invariance and of a time-reversed pattern are presented. We propose the possibility of building a logic device from the hardware implementation of a higher level architecture of trion cortical columns.

Adaptable Hydrogel Networks with Reversible Linkages for Tissue Engineering

PubMed Central

Wang, Huiyuan

2015-01-01

Adaptable hydrogels have recently emerged as a promising platform for three-dimensional (3D) cell encapsulation and culture. In conventional, covalently crosslinked hydrogels, degradation is typically required to allow complex cellular functions to occur, leading to bulk material degradation. In contrast, adaptable hydrogels are formed by reversible crosslinks. Through breaking and re-forming of the reversible linkages, adaptable hydrogels can be locally modified to permit complex cellular functions while maintaining their long-term integrity. In addition, these adaptable materials can have biomimetic viscoelastic properties that make them well suited for several biotechnology and medical applications. In this review, adaptable hydrogel design considerations and linkage selections are overviewed, with a focus on various cell compatible crosslinking mechanisms that can be exploited to form adaptable hydrogels for tissue engineering. PMID:25989348

Linking Individual and Collective Behavior in Adaptive Social Networks

NASA Astrophysics Data System (ADS)

Pinheiro, Flávio L.; Santos, Francisco C.; Pacheco, Jorge M.

2016-03-01

Adaptive social structures are known to promote the evolution of cooperation. However, up to now the characterization of the collective, population-wide dynamics resulting from the self-organization of individual strategies on a coevolving, adaptive network has remained unfeasible. Here we establish a (reversible) link between individual (micro)behavior and collective (macro)behavior for coevolutionary processes. We demonstrate that an adaptive network transforms a two-person social dilemma locally faced by individuals into a collective dynamics that resembles that associated with an N -person coordination game, whose characterization depends sensitively on the relative time scales between the entangled behavioral and network evolutions. In particular, we show that the faster the relative rate of adaptation of the network, the smaller the critical fraction of cooperators required for cooperation to prevail, thus establishing a direct link between network adaptation and the evolution of cooperation. The framework developed here is general and may be readily applied to other dynamical processes occurring on adaptive networks, notably, the spreading of contagious diseases or the diffusion of innovations.

Dynamic network reconstruction from gene expression data applied to immune response during bacterial infection.

PubMed

Guthke, Reinhard; Möller, Ulrich; Hoffmann, Martin; Thies, Frank; Töpfer, Susanne

2005-04-15

The immune response to bacterial infection represents a complex network of dynamic gene and protein interactions. We present an optimized reverse engineering strategy aimed at a reconstruction of this kind of interaction networks. The proposed approach is based on both microarray data and available biological knowledge. The main kinetics of the immune response were identified by fuzzy clustering of gene expression profiles (time series). The number of clusters was optimized using various evaluation criteria. For each cluster a representative gene with a high fuzzy-membership was chosen in accordance with available physiological knowledge. Then hypothetical network structures were identified by seeking systems of ordinary differential equations, whose simulated kinetics could fit the gene expression profiles of the cluster-representative genes. For the construction of hypothetical network structures singular value decomposition (SVD) based methods and a newly introduced heuristic Network Generation Method here were compared. It turned out that the proposed novel method could find sparser networks and gave better fits to the experimental data. Reinhard.Guthke@hki-jena.de.

76 FR 64330 - Advanced Scientific Computing Advisory Committee

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-18

... talks on HPC Reliability, Diffusion on Complex Networks, and Reversible Software Execution Systems Report from Applied Math Workshop on Mathematics for the Analysis, Simulation, and Optimization of Complex Systems Report from ASCR-BES Workshop on Data Challenges from Next Generation Facilities Public...

Expectation propagation for large scale Bayesian inference of non-linear molecular networks from perturbation data.

PubMed

Narimani, Zahra; Beigy, Hamid; Ahmad, Ashar; Masoudi-Nejad, Ali; Fröhlich, Holger

2017-01-01

Inferring the structure of molecular networks from time series protein or gene expression data provides valuable information about the complex biological processes of the cell. Causal network structure inference has been approached using different methods in the past. Most causal network inference techniques, such as Dynamic Bayesian Networks and ordinary differential equations, are limited by their computational complexity and thus make large scale inference infeasible. This is specifically true if a Bayesian framework is applied in order to deal with the unavoidable uncertainty about the correct model. We devise a novel Bayesian network reverse engineering approach using ordinary differential equations with the ability to include non-linearity. Besides modeling arbitrary, possibly combinatorial and time dependent perturbations with unknown targets, one of our main contributions is the use of Expectation Propagation, an algorithm for approximate Bayesian inference over large scale network structures in short computation time. We further explore the possibility of integrating prior knowledge into network inference. We evaluate the proposed model on DREAM4 and DREAM8 data and find it competitive against several state-of-the-art existing network inference methods.

Bayesian network prior: network analysis of biological data using external knowledge

PubMed Central

Isci, Senol; Dogan, Haluk; Ozturk, Cengizhan; Otu, Hasan H.

2014-01-01

Motivation: Reverse engineering GI networks from experimental data is a challenging task due to the complex nature of the networks and the noise inherent in the data. One way to overcome these hurdles would be incorporating the vast amounts of external biological knowledge when building interaction networks. We propose a framework where GI networks are learned from experimental data using Bayesian networks (BNs) and the incorporation of external knowledge is also done via a BN that we call Bayesian Network Prior (BNP). BNP depicts the relation between various evidence types that contribute to the event ‘gene interaction’ and is used to calculate the probability of a candidate graph (G) in the structure learning process. Results: Our simulation results on synthetic, simulated and real biological data show that the proposed approach can identify the underlying interaction network with high accuracy even when the prior information is distorted and outperforms existing methods. Availability: Accompanying BNP software package is freely available for academic use at http://bioe.bilgi.edu.tr/BNP. Contact: hasan.otu@bilgi.edu.tr Supplementary Information: Supplementary data are available at Bioinformatics online. PMID:24215027

The positive role of social networks and social networking technology in the condom-using behaviors of homeless young people.

PubMed

Rice, Eric

2010-01-01

To examine the impact of condom-using peers in the social networks of homeless young people, differences in behaviors were assessed based on the social location of ties (home-based vs. street-based) and how those ties are maintained (face-to-face vs. via social networking technology). "Ego-centric" social network data were collected from 103 currently sexually active homeless young people aged 16-26 years in Los Angeles, California. Associations between condom use and the condom-using behaviors of social network influences were assessed using standard logistic regression. About 52% of respondents had a street-based peer who was a condom user. Having such a peer was associated with a 70% reduction in the odds of having unprotected sex at last intercourse. About 22% of respondents had a condom-using, home-based peer with whom they communicated only via social networking technology. Having such a peer was associated with a 90% reduction in risky sexual behavior and a 3.5 times increase in safer sex behavior. The study revealed several implications for new human immunodeficiency virus-prevention interventions that mobilize these networks and social networking technologies.

The influence of self-exempting beliefs and social networks on daily smoking: a mediation relationship explored.

PubMed

Yang, Xiaozhao Y; Kelly, Brian C; Yang, Tingzhong

2014-09-01

The decision to initiate, maintain, or quit cigarette smoking is structured by both social networks and health beliefs. Self-exempting beliefs affect people's decisions in favor of a behavior even when they recognize the harm associated with it. This study incorporated the literatures on social networks and self-exempting beliefs to study the problem of daily smoking by exploring their mediatory relationships and the mechanisms of how smoking behavior is developed and maintained. Specifically, this article hypothesizes that social networks affect daily smoking directly as well as indirectly by facilitating the formation of self-exempting beliefs. The sample comes from urban male residents in Hangzhou, China randomly selected and interviewed through multistage sampling in 2011. Using binary mediation analysis with logistic regression to test the hypotheses, the authors found that (a) daily smoking is associated with having smokers in several social network arenas and (b) self-exempting beliefs about smoking mediate the association between coworker network and daily smoking, but not for family network and friend network. The role of social network at work place in the creation and maintenance of self-exempting beliefs should be considered by policymakers, prevention experts, and interventionists.

Analysis of logistic distribution performance of good supply from PT. Mentari Trans Nusantara distribution center to branches using Smart PLS 3.0

NASA Astrophysics Data System (ADS)

Endrawati, Titin; Siregar, M. Tirtana

2018-03-01

PT Mentari Trans Nusantara is a company engaged in the distribution of goods from the manufacture of the product to the distributor branch of the customer so that the product distribution must be controlled directly from the PT Mentari Trans Nusantara Center for faster delivery process. Problems often occur on the expedition company which in charge in sending the goods although it has quite extensive networking. The company is less control over logistics management. Meanwhile, logistics distribution management control policy will affect the company's performance in distributing products to customer distributor branches and managing product inventory in distribution center. PT Mentari Trans Nusantara is an expedition company which engaged in good delivery, including in Jakarta. Logistics management performance is very important due to its related to the supply of goods from the central activities to the branches based oncustomer demand. Supply chain management performance is obviously depends on the location of both the distribution center and branches, the smoothness of transportation in the distribution and the availability of the product in the distribution center to meet the demand in order to avoid losing sales. This study concluded that the company could be more efficient and effective in minimizing the risks of loses by improve its logistic management.

Reverse engineering gene regulatory networks from measurement with missing values.

PubMed

Ogundijo, Oyetunji E; Elmas, Abdulkadir; Wang, Xiaodong

2016-12-01

Gene expression time series data are usually in the form of high-dimensional arrays. Unfortunately, the data may sometimes contain missing values: for either the expression values of some genes at some time points or the entire expression values of a single time point or some sets of consecutive time points. This significantly affects the performance of many algorithms for gene expression analysis that take as an input, the complete matrix of gene expression measurement. For instance, previous works have shown that gene regulatory interactions can be estimated from the complete matrix of gene expression measurement. Yet, till date, few algorithms have been proposed for the inference of gene regulatory network from gene expression data with missing values. We describe a nonlinear dynamic stochastic model for the evolution of gene expression. The model captures the structural, dynamical, and the nonlinear natures of the underlying biomolecular systems. We present point-based Gaussian approximation (PBGA) filters for joint state and parameter estimation of the system with one-step or two-step missing measurements . The PBGA filters use Gaussian approximation and various quadrature rules, such as the unscented transform (UT), the third-degree cubature rule and the central difference rule for computing the related posteriors. The proposed algorithm is evaluated with satisfying results for synthetic networks, in silico networks released as a part of the DREAM project, and the real biological network, the in vivo reverse engineering and modeling assessment (IRMA) network of yeast Saccharomyces cerevisiae . PBGA filters are proposed to elucidate the underlying gene regulatory network (GRN) from time series gene expression data that contain missing values. In our state-space model, we proposed a measurement model that incorporates the effect of the missing data points into the sequential algorithm. This approach produces a better inference of the model parameters and hence, more accurate prediction of the underlying GRN compared to when using the conventional Gaussian approximation (GA) filters ignoring the missing data points.

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.

Social support network characteristics and sexual risk taking among a racially/ethnically diverse sample of young, urban men who have sex with men

PubMed Central

Kapadia, F; Siconolfi, DE; Barton, S; Olivieri, B; Lombardo, L; Halkitis, PN

2013-01-01

Associations between social support network characteristics and sexual risk among racially/ethnically diverse young men who have sex with men (YMSM) were examined using egocentric network data from a prospective cohort study of YMSM (n=501) recruited in New York City. Bivariate and multivariable logistic regression analyses examined associations between social support network characteristics and sexual risk taking behaviors in Black, Hispanic/Latino, and White YMSM. Bivariate analyses indicated key differences in network size, composition, communication frequency and average relationship duration by race/ethnicity. In multivariable analyses, controlling for individual level sociodemographic, psychosocial and relationship factors, having a sexual partner in one’s social support network was associated with unprotected sexual behavior for both Hispanic/Latino (AOR=3.90) and White YMSM (AOR=4.93). Further examination of key network characteristics across racial/ethnic groups are warranted in order to better understand the extant mechanisms for provision of HIV prevention programming to racially/ethnically diverse YMSM at risk for HIV. PMID:23553346

Reverse engineering a gene network using an asynchronous parallel evolution strategy

PubMed Central

2010-01-01

Background The use of reverse engineering methods to infer gene regulatory networks by fitting mathematical models to gene expression data is becoming increasingly popular and successful. However, increasing model complexity means that more powerful global optimisation techniques are required for model fitting. The parallel Lam Simulated Annealing (pLSA) algorithm has been used in such approaches, but recent research has shown that island Evolutionary Strategies can produce faster, more reliable results. However, no parallel island Evolutionary Strategy (piES) has yet been demonstrated to be effective for this task. Results Here, we present synchronous and asynchronous versions of the piES algorithm, and apply them to a real reverse engineering problem: inferring parameters in the gap gene network. We find that the asynchronous piES exhibits very little communication overhead, and shows significant speed-up for up to 50 nodes: the piES running on 50 nodes is nearly 10 times faster than the best serial algorithm. We compare the asynchronous piES to pLSA on the same test problem, measuring the time required to reach particular levels of residual error, and show that it shows much faster convergence than pLSA across all optimisation conditions tested. Conclusions Our results demonstrate that the piES is consistently faster and more reliable than the pLSA algorithm on this problem, and scales better with increasing numbers of nodes. In addition, the piES is especially well suited to further improvements and adaptations: Firstly, the algorithm's fast initial descent speed and high reliability make it a good candidate for being used as part of a global/local search hybrid algorithm. Secondly, it has the potential to be used as part of a hierarchical evolutionary algorithm, which takes advantage of modern multi-core computing architectures. PMID:20196855

NASA Goddard Space Flight Center

NASA Technical Reports Server (NTRS)

Carter, David; Wetzel, Scott

2000-01-01

The NASA SLR Operational Center is responsible for: 1) NASA SLR network control, sustaining engineering, and logistics; 2) ILRS mission operations; and 3) ILRS and NASA SLR data operations. NASA SLR network control and sustaining engineering tasks include technical support, daily system performance monitoring, system scheduling, operator training, station status reporting, system relocation, logistics and support of the ILRS Networks and Engineering Working Group. These activities ensure the NASA SLR systems are meeting ILRS and NASA mission support requirements. ILRS mission operations tasks include mission planning, mission analysis, mission coordination, development of mission support plans, and support of the ILRS Missions Working Group. These activities ensure than new mission and campaign requirements are coordinated with the ILRS. Global Normal Points (NP) data, NASA SLR FullRate (FR) data, and satellite predictions are managed as part of data operations. Part of this operation includes supporting the ILRS Data Formats and Procedures Working Group. Global NP data operations consist of receipt, format and data integrity verification, archiving and merging. This activity culminates in the daily electronic transmission of NP files to the CDDIS. Currently of all these functions are automated. However, to ensure the timely and accurate flow of data, regular monitoring and maintenance of the operational software systems, computer systems and computer networking are performed. Tracking statistics between the stations and the data centers are compared periodically to eliminate lost data. Future activities in this area include sub-daily (i.e., hourly) NP data management, more stringent data integrity tests, and automatic station notification of format and data integrity issues.

Individual and Network Interventions With Injection Drug Users in 5 Ukraine Cities

PubMed Central

Lehman, Wayne E. K.; Latkin, Carl A.; Dvoryak, Sergey; Brewster, John T.; Royer, Mark S.; Sinitsyna, Larisa

2011-01-01

Objectives. We evaluated the effects of an individual intervention versus a network intervention on HIV-related injection and sexual risk behaviors among street-recruited opiate injection drug users in 5 Ukraine cities. Methods. Between 2004 and 2006, 722 opiate injection drug users were recruited to participate in interventions that were either individually based or based on a social network model in which peer educators intervened with their network members. Audio computer-assisted self-interview techniques were used to interview participants at baseline and follow-up. Results. Multiple logistic analyses controlling for baseline injection and sexual risks revealed that both peer educators and network members in the network intervention reduced injection-related risk behaviors significantly more than did those in the individually based intervention and that peer educators increased condom use significantly more than did those in the individual intervention. Individual intervention participants, however, showed significantly greater improvements than did network members with respect to reductions in sexual risk behaviors. Conclusions. Social network interventions may be more effective than individually based interventions in changing injection risk behaviors among both peer educators and network members. The effectiveness of network interventions in changing sexual risk behaviors is less clear, probably owing to network composition and inhibitions regarding discussing sexual risk behaviors. PMID:20395584

Social networks and alcohol use disorders: findings from a nationally representative sample

PubMed Central

Mowbray, Orion; Quinn, Adam; Cranford, James A.

2014-01-01

Background While some argue that social network ties of individuals with alcohol use disorders (AUD) are robust, there is evidence to suggest that individuals with AUDs have few social network ties, which are a known risk factor for health and wellness. Objectives Social network ties to friends, family, co-workers and communities of individuals are compared among individuals with a past-year diagnosis of alcohol dependence or alcohol abuse to individuals with no lifetime diagnosis of AUD. Method Respondents from Wave 2 of the National Epidemiologic Survey on Alcohol Related Conditions (NESARC) were assessed for the presence of past-year alcohol dependence or past-year alcohol abuse, social network ties, sociodemographics and clinical characteristics. Results Bivariate analyses showed that both social network size and social network diversity was significantly smaller among individuals with alcohol dependence, compared to individuals with alcohol abuse or no AUD. When social and clinical factors related to AUD status were controlled, multinomial logistic models showed that social network diversity remained a significant predictor of AUD status, while social network size did not differ among AUD groups. Conclusion Social networks of individuals with AUD may be different than individuals with no AUD, but this claim is dependent on specific AUD diagnosis and how social networks are measured. PMID:24405256

Myxobacteria, Polarity, and Multicellular Morphogenesis

PubMed Central

Kaiser, Dale; Robinson, Mark; Kroos, Lee

2010-01-01

Myxobacteria are renowned for the ability to sporulate within fruiting bodies whose shapes are species-specific. The capacity to build those multicellular structures arises from the ability of M. xanthus to organize high cell-density swarms, in which the cells tend to be aligned with each other while constantly in motion. The intrinsic polarity of rod-shaped cells lays the foundation, and each cell uses two polar engines for gliding on surfaces. It sprouts retractile type IV pili from the leading cell pole and secretes capsular polysaccharide through nozzles from the trailing pole. Regularly periodic reversal of the gliding direction was found to be required for swarming. Those reversals are generated by a G-protein switch which is driven by a sharply tuned oscillator. Starvation induces fruiting body development, and systematic reductions in the reversal frequency are necessary for the cells to aggregate rather than continue to swarm. Developmental gene expression is regulated by a network that is connected to the suppression of reversals. PMID:20610548