On Reformulating Planning as Dynamic Constraint Satisfaction

NASA Technical Reports Server (NTRS)

Frank, Jeremy; Jonsson, Ari K.; Morris, Paul; Koga, Dennis (Technical Monitor)

2000-01-01

In recent years, researchers have reformulated STRIPS planning problems as SAT problems or CSPs. In this paper, we discuss the Constraint-Based Interval Planning (CBIP) paradigm, which can represent planning problems incorporating interval time and resources. We describe how to reformulate mutual exclusion constraints for a CBIP-based system, the Extendible Uniform Remote Operations Planner Architecture (EUROPA). We show that reformulations involving dynamic variable domains restrict the algorithms which can be used to solve the resulting DCSP. We present an alternative formulation which does not employ dynamic domains, and describe the relative merits of the different reformulations.

The Complexity of Bit Retrieval

DOE PAGES

Elser, Veit

2018-09-20

Bit retrieval is the problem of reconstructing a periodic binary sequence from its periodic autocorrelation, with applications in cryptography and x-ray crystallography. After defining the problem, with and without noise, we describe and compare various algorithms for solving it. A geometrical constraint satisfaction algorithm, relaxed-reflect-reflect, is currently the best algorithm for noisy bit retrieval.

The Complexity of Bit Retrieval

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

Elser, Veit

Bit retrieval is the problem of reconstructing a periodic binary sequence from its periodic autocorrelation, with applications in cryptography and x-ray crystallography. After defining the problem, with and without noise, we describe and compare various algorithms for solving it. A geometrical constraint satisfaction algorithm, relaxed-reflect-reflect, is currently the best algorithm for noisy bit retrieval.

Solution and reasoning reuse in space planning and scheduling applications

NASA Technical Reports Server (NTRS)

Verfaillie, Gerard; Schiex, Thomas

1994-01-01

In the space domain, as in other domains, the CSP (Constraint Satisfaction Problems) techniques are increasingly used to represent and solve planning and scheduling problems. But these techniques have been developed to solve CSP's which are composed of fixed sets of variables and constraints, whereas many planning and scheduling problems are dynamic. It is therefore important to develop methods which allow a new solution to be rapidly found, as close as possible to the previous one, when some variables or constraints are added or removed. After presenting some existing approaches, this paper proposes a simple and efficient method, which has been developed on the basis of the dynamic backtracking algorithm. This method allows previous solution and reasoning to be reused in the framework of a CSP which is close to the previous one. Some experimental results on general random CSPs and on operation scheduling problems for remote sensing satellites are given.

Analog Approach to Constraint Satisfaction Enabled by Spin Orbit Torque Magnetic Tunnel Junctions.

PubMed

Wijesinghe, Parami; Liyanagedera, Chamika; Roy, Kaushik

2018-05-02

Boolean satisfiability (k-SAT) is an NP-complete (k ≥ 3) problem that constitute one of the hardest classes of constraint satisfaction problems. In this work, we provide a proof of concept hardware based analog k-SAT solver, that is built using Magnetic Tunnel Junctions (MTJs). The inherent physics of MTJs, enhanced by device level modifications, is harnessed here to emulate the intricate dynamics of an analog satisfiability (SAT) solver. In the presence of thermal noise, the MTJ based system can successfully solve Boolean satisfiability problems. Most importantly, our results exhibit that, the proposed MTJ based hardware SAT solver is capable of finding a solution to a significant fraction (at least 85%) of hard 3-SAT problems, within a time that has a polynomial relationship with the number of variables(<50).

Balancing antagonistic time and resource utilization constraints in over-subscribed scheduling problems

NASA Technical Reports Server (NTRS)

Smith, Stephen F.; Pathak, Dhiraj K.

1991-01-01

In this paper, we report work aimed at applying concepts of constraint-based problem structuring and multi-perspective scheduling to an over-subscribed scheduling problem. Previous research has demonstrated the utility of these concepts as a means for effectively balancing conflicting objectives in constraint-relaxable scheduling problems, and our goal here is to provide evidence of their similar potential in the context of HST observation scheduling. To this end, we define and experimentally assess the performance of two time-bounded heuristic scheduling strategies in balancing the tradeoff between resource setup time minimization and satisfaction of absolute time constraints. The first strategy considered is motivated by dispatch-based manufacturing scheduling research, and employs a problem decomposition that concentrates local search on minimizing resource idle time due to setup activities. The second is motivated by research in opportunistic scheduling and advocates a problem decomposition that focuses attention on the goal activities that have the tightest temporal constraints. Analysis of experimental results gives evidence of differential superiority on the part of each strategy in different problem solving circumstances. A composite strategy based on recognition of characteristics of the current problem solving state is then defined and tested to illustrate the potential benefits of constraint-based problem structuring and multi-perspective scheduling in over-subscribe scheduling problems.

The min-conflicts heuristic: Experimental and theoretical results

NASA Technical Reports Server (NTRS)

Minton, Steven; Philips, Andrew B.; Johnston, Mark D.; Laird, Philip

1991-01-01

This paper describes a simple heuristic method for solving large-scale constraint satisfaction and scheduling problems. Given an initial assignment for the variables in a problem, the method operates by searching through the space of possible repairs. The search is guided by an ordering heuristic, the min-conflicts heuristic, that attempts to minimize the number of constraint violations after each step. We demonstrate empirically that the method performs orders of magnitude better than traditional backtracking techniques on certain standard problems. For example, the one million queens problem can be solved rapidly using our approach. We also describe practical scheduling applications where the method has been successfully applied. A theoretical analysis is presented to explain why the method works so well on certain types of problems and to predict when it is likely to be most effective.

Solving Constraint Satisfaction Problems with Networks of Spiking Neurons

PubMed Central

Jonke, Zeno; Habenschuss, Stefan; Maass, Wolfgang

2016-01-01

Network of neurons in the brain apply—unlike processors in our current generation of computer hardware—an event-based processing strategy, where short pulses (spikes) are emitted sparsely by neurons to signal the occurrence of an event at a particular point in time. Such spike-based computations promise to be substantially more power-efficient than traditional clocked processing schemes. However, it turns out to be surprisingly difficult to design networks of spiking neurons that can solve difficult computational problems on the level of single spikes, rather than rates of spikes. We present here a new method for designing networks of spiking neurons via an energy function. Furthermore, we show how the energy function of a network of stochastically firing neurons can be shaped in a transparent manner by composing the networks of simple stereotypical network motifs. We show that this design approach enables networks of spiking neurons to produce approximate solutions to difficult (NP-hard) constraint satisfaction problems from the domains of planning/optimization and verification/logical inference. The resulting networks employ noise as a computational resource. Nevertheless, the timing of spikes plays an essential role in their computations. Furthermore, networks of spiking neurons carry out for the Traveling Salesman Problem a more efficient stochastic search for good solutions compared with stochastic artificial neural networks (Boltzmann machines) and Gibbs sampling. PMID:27065785

Constraint Optimization Literature Review

DTIC Science & Technology

2015-11-01

COPs. 15. SUBJECT TERMS high-performance computing, mobile ad hoc network, optimization, constraint, satisfaction 16. SECURITY CLASSIFICATION OF: 17...Optimization Problems 1 2.1 Constraint Satisfaction Problems 1 2.2 Constraint Optimization Problems 3 3. Constraint Optimization Algorithms 9 3.1...Constraint Satisfaction Algorithms 9 3.1.1 Brute-Force search 9 3.1.2 Constraint Propagation 10 3.1.3 Depth-First Search 13 3.1.4 Local Search 18

Application of constraint-based satellite mission planning model in forest fire monitoring

NASA Astrophysics Data System (ADS)

Guo, Bingjun; Wang, Hongfei; Wu, Peng

2017-10-01

In this paper, a constraint-based satellite mission planning model is established based on the thought of constraint satisfaction. It includes target, request, observation, satellite, payload and other elements, with constraints linked up. The optimization goal of the model is to make full use of time and resources, and improve the efficiency of target observation. Greedy algorithm is used in the model solving to make observation plan and data transmission plan. Two simulation experiments are designed and carried out, which are routine monitoring of global forest fire and emergency monitoring of forest fires in Australia. The simulation results proved that the model and algorithm perform well. And the model is of good emergency response capability. Efficient and reasonable plan can be worked out to meet users' needs under complex cases of multiple payloads, multiple targets and variable priorities with this model.

Approximation, abstraction and decomposition in search and optimization

NASA Technical Reports Server (NTRS)

Ellman, Thomas

1992-01-01

In this paper, I discuss four different areas of my research. One portion of my research has focused on automatic synthesis of search control heuristics for constraint satisfaction problems (CSPs). I have developed techniques for automatically synthesizing two types of heuristics for CSPs: Filtering functions are used to remove portions of a search space from consideration. Another portion of my research is focused on automatic synthesis of hierarchic algorithms for solving constraint satisfaction problems (CSPs). I have developed a technique for constructing hierarchic problem solvers based on numeric interval algebra. Another portion of my research is focused on automatic decomposition of design optimization problems. We are using the design of racing yacht hulls as a testbed domain for this research. Decomposition is especially important in the design of complex physical shapes such as yacht hulls. Another portion of my research is focused on intelligent model selection in design optimization. The model selection problem results from the difficulty of using exact models to analyze the performance of candidate designs.

Reliability-based trajectory optimization using nonintrusive polynomial chaos for Mars entry mission

NASA Astrophysics Data System (ADS)

Huang, Yuechen; Li, Haiyang

2018-06-01

This paper presents the reliability-based sequential optimization (RBSO) method to settle the trajectory optimization problem with parametric uncertainties in entry dynamics for Mars entry mission. First, the deterministic entry trajectory optimization model is reviewed, and then the reliability-based optimization model is formulated. In addition, the modified sequential optimization method, in which the nonintrusive polynomial chaos expansion (PCE) method and the most probable point (MPP) searching method are employed, is proposed to solve the reliability-based optimization problem efficiently. The nonintrusive PCE method contributes to the transformation between the stochastic optimization (SO) and the deterministic optimization (DO) and to the approximation of trajectory solution efficiently. The MPP method, which is used for assessing the reliability of constraints satisfaction only up to the necessary level, is employed to further improve the computational efficiency. The cycle including SO, reliability assessment and constraints update is repeated in the RBSO until the reliability requirements of constraints satisfaction are satisfied. Finally, the RBSO is compared with the traditional DO and the traditional sequential optimization based on Monte Carlo (MC) simulation in a specific Mars entry mission to demonstrate the effectiveness and the efficiency of the proposed method.

Learning in stochastic neural networks for constraint satisfaction problems

NASA Technical Reports Server (NTRS)

Johnston, Mark D.; Adorf, Hans-Martin

1989-01-01

Researchers describe a newly-developed artificial neural network algorithm for solving constraint satisfaction problems (CSPs) which includes a learning component that can significantly improve the performance of the network from run to run. The network, referred to as the Guarded Discrete Stochastic (GDS) network, is based on the discrete Hopfield network but differs from it primarily in that auxiliary networks (guards) are asymmetrically coupled to the main network to enforce certain types of constraints. Although the presence of asymmetric connections implies that the network may not converge, it was found that, for certain classes of problems, the network often quickly converges to find satisfactory solutions when they exist. The network can run efficiently on serial machines and can find solutions to very large problems (e.g., N-queens for N as large as 1024). One advantage of the network architecture is that network connection strengths need not be instantiated when the network is established: they are needed only when a participating neural element transitions from off to on. They have exploited this feature to devise a learning algorithm, based on consistency techniques for discrete CSPs, that updates the network biases and connection strengths and thus improves the network performance.

Implicit Motives and Men’s Perceived Constraint in Fatherhood

PubMed Central

Ruppen, Jessica; Waldvogel, Patricia; Ehlert, Ulrike

2016-01-01

Research shows that implicit motives influence social relationships. However, little is known about their role in fatherhood and, particularly, how men experience their paternal role. Therefore, this study examined the association of implicit motives and fathers’ perceived constraint due to fatherhood. Furthermore, we explored their relation to fathers’ life satisfaction. Participants were fathers with biological children (N = 276). They were asked to write picture stories, which were then coded for implicit affiliation and power motives. Perceived constraint and life satisfaction were assessed on a visual analog scale. A higher implicit need for affiliation was significantly associated with lower perceived constraint, whereas the implicit need for power had the opposite effect. Perceived constraint had a negative influence on life satisfaction. Structural equation modeling revealed significant indirect effects of implicit affiliation and power motives on life satisfaction mediated by perceived constraint. Our findings indicate that men with a higher implicit need for affiliation experience less constraint due to fatherhood, resulting in higher life satisfaction. The implicit need for power, however, results in more perceived constraint and is related to decreased life satisfaction. PMID:27933023

Nonlinear Model Predictive Control with Constraint Satisfactions for a Quadcopter

NASA Astrophysics Data System (ADS)

Wang, Ye; Ramirez-Jaime, Andres; Xu, Feng; Puig, Vicenç

2017-01-01

This paper presents a nonlinear model predictive control (NMPC) strategy combined with constraint satisfactions for a quadcopter. The full dynamics of the quadcopter describing the attitude and position are nonlinear, which are quite sensitive to changes of inputs and disturbances. By means of constraint satisfactions, partial nonlinearities and modeling errors of the control-oriented model of full dynamics can be transformed into the inequality constraints. Subsequently, the quadcopter can be controlled by an NMPC controller with the updated constraints generated by constraint satisfactions. Finally, the simulation results applied to a quadcopter simulator are provided to show the effectiveness of the proposed strategy.

Connectionism, parallel constraint satisfaction processes, and gestalt principles: (re) introducing cognitive dynamics to social psychology.

PubMed

Read, S J; Vanman, E J; Miller, L C

1997-01-01

We argue that recent work in connectionist modeling, in particular the parallel constraint satisfaction processes that are central to many of these models, has great importance for understanding issues of both historical and current concern for social psychologists. We first provide a brief description of connectionist modeling, with particular emphasis on parallel constraint satisfaction processes. Second, we examine the tremendous similarities between parallel constraint satisfaction processes and the Gestalt principles that were the foundation for much of modem social psychology. We propose that parallel constraint satisfaction processes provide a computational implementation of the principles of Gestalt psychology that were central to the work of such seminal social psychologists as Asch, Festinger, Heider, and Lewin. Third, we then describe how parallel constraint satisfaction processes have been applied to three areas that were key to the beginnings of modern social psychology and remain central today: impression formation and causal reasoning, cognitive consistency (balance and cognitive dissonance), and goal-directed behavior. We conclude by discussing implications of parallel constraint satisfaction principles for a number of broader issues in social psychology, such as the dynamics of social thought and the integration of social information within the narrow time frame of social interaction.

Solving Fuzzy Optimization Problem Using Hybrid Ls-Sa Method

NASA Astrophysics Data System (ADS)

Vasant, Pandian

2011-06-01

Fuzzy optimization problem has been one of the most and prominent topics inside the broad area of computational intelligent. It's especially relevant in the filed of fuzzy non-linear programming. It's application as well as practical realization can been seen in all the real world problems. In this paper a large scale non-linear fuzzy programming problem has been solved by hybrid optimization techniques of Line Search (LS), Simulated Annealing (SA) and Pattern Search (PS). As industrial production planning problem with cubic objective function, 8 decision variables and 29 constraints has been solved successfully using LS-SA-PS hybrid optimization techniques. The computational results for the objective function respect to vagueness factor and level of satisfaction has been provided in the form of 2D and 3D plots. The outcome is very promising and strongly suggests that the hybrid LS-SA-PS algorithm is very efficient and productive in solving the large scale non-linear fuzzy programming problem.

Learning and Parallelization Boost Constraint Search

ERIC Educational Resources Information Center

Yun, Xi

2013-01-01

Constraint satisfaction problems are a powerful way to abstract and represent academic and real-world problems from both artificial intelligence and operations research. A constraint satisfaction problem is typically addressed by a sequential constraint solver running on a single processor. Rather than construct a new, parallel solver, this work…

An Efficacious Multi-Objective Fuzzy Linear Programming Approach for Optimal Power Flow Considering Distributed Generation.

PubMed

Warid, Warid; Hizam, Hashim; Mariun, Norman; Abdul-Wahab, Noor Izzri

2016-01-01

This paper proposes a new formulation for the multi-objective optimal power flow (MOOPF) problem for meshed power networks considering distributed generation. An efficacious multi-objective fuzzy linear programming optimization (MFLP) algorithm is proposed to solve the aforementioned problem with and without considering the distributed generation (DG) effect. A variant combination of objectives is considered for simultaneous optimization, including power loss, voltage stability, and shunt capacitors MVAR reserve. Fuzzy membership functions for these objectives are designed with extreme targets, whereas the inequality constraints are treated as hard constraints. The multi-objective fuzzy optimal power flow (OPF) formulation was converted into a crisp OPF in a successive linear programming (SLP) framework and solved using an efficient interior point method (IPM). To test the efficacy of the proposed approach, simulations are performed on the IEEE 30-busand IEEE 118-bus test systems. The MFLP optimization is solved for several optimization cases. The obtained results are compared with those presented in the literature. A unique solution with a high satisfaction for the assigned targets is gained. Results demonstrate the effectiveness of the proposed MFLP technique in terms of solution optimality and rapid convergence. Moreover, the results indicate that using the optimal DG location with the MFLP algorithm provides the solution with the highest quality.

An Efficacious Multi-Objective Fuzzy Linear Programming Approach for Optimal Power Flow Considering Distributed Generation

PubMed Central

Warid, Warid; Hizam, Hashim; Mariun, Norman; Abdul-Wahab, Noor Izzri

2016-01-01

This paper proposes a new formulation for the multi-objective optimal power flow (MOOPF) problem for meshed power networks considering distributed generation. An efficacious multi-objective fuzzy linear programming optimization (MFLP) algorithm is proposed to solve the aforementioned problem with and without considering the distributed generation (DG) effect. A variant combination of objectives is considered for simultaneous optimization, including power loss, voltage stability, and shunt capacitors MVAR reserve. Fuzzy membership functions for these objectives are designed with extreme targets, whereas the inequality constraints are treated as hard constraints. The multi-objective fuzzy optimal power flow (OPF) formulation was converted into a crisp OPF in a successive linear programming (SLP) framework and solved using an efficient interior point method (IPM). To test the efficacy of the proposed approach, simulations are performed on the IEEE 30-busand IEEE 118-bus test systems. The MFLP optimization is solved for several optimization cases. The obtained results are compared with those presented in the literature. A unique solution with a high satisfaction for the assigned targets is gained. Results demonstrate the effectiveness of the proposed MFLP technique in terms of solution optimality and rapid convergence. Moreover, the results indicate that using the optimal DG location with the MFLP algorithm provides the solution with the highest quality. PMID:26954783

A neural network approach to job-shop scheduling.

PubMed

Zhou, D N; Cherkassky, V; Baldwin, T R; Olson, D E

1991-01-01

A novel analog computational network is presented for solving NP-complete constraint satisfaction problems, i.e. job-shop scheduling. In contrast to most neural approaches to combinatorial optimization based on quadratic energy cost function, the authors propose to use linear cost functions. As a result, the network complexity (number of neurons and the number of resistive interconnections) grows only linearly with problem size, and large-scale implementations become possible. The proposed approach is related to the linear programming network described by D.W. Tank and J.J. Hopfield (1985), which also uses a linear cost function for a simple optimization problem. It is shown how to map a difficult constraint-satisfaction problem onto a simple neural net in which the number of neural processors equals the number of subjobs (operations) and the number of interconnections grows linearly with the total number of operations. Simulations show that the authors' approach produces better solutions than existing neural approaches to job-shop scheduling, i.e. the traveling salesman problem-type Hopfield approach and integer linear programming approach of J.P.S. Foo and Y. Takefuji (1988), in terms of the quality of the solution and the network complexity.

Principals' Self-Efficacy: Relations with Job Autonomy, Job Satisfaction, and Contextual Constraints

ERIC Educational Resources Information Center

Federici, Roger A.

2013-01-01

The purpose of the present study was to explore relations between principals' self-efficacy, perceived job autonomy, job satisfaction, and perceived contextual constraints to autonomy. Principal self-efficacy was measured by a multidimensional scale called the Norwegian Principal Self-Efficacy Scale. Job autonomy, job satisfaction, and contextual…

Scheduling of an aircraft fleet

NASA Technical Reports Server (NTRS)

Paltrinieri, Massimo; Momigliano, Alberto; Torquati, Franco

1992-01-01

Scheduling is the task of assigning resources to operations. When the resources are mobile vehicles, they describe routes through the served stations. To emphasize such aspect, this problem is usually referred to as the routing problem. In particular, if vehicles are aircraft and stations are airports, the problem is known as aircraft routing. This paper describes the solution to such a problem developed in OMAR (Operative Management of Aircraft Routing), a system implemented by Bull HN for Alitalia. In our approach, aircraft routing is viewed as a Constraint Satisfaction Problem. The solving strategy combines network consistency and tree search techniques.

[Problem-solving strategies and marital satisfaction].

PubMed

Kriegelewicz, Olga

2006-01-01

This study investigated the relation between problem-solving strategies in the marital conflict and marital satisfaction. Four problem-solving strategies (Dialogue, Loyalty, Escalation of conflict and Withdrawal) were measured by the Problem-Solving Strategies Inventory, in two versions: self-report and report of partners' perceived behaviour. This measure refers to the concept of Rusbult, Johnson and Morrow, and meets high standards of reliability (alpha Cronbach from alpha = 0.78 to alpha = 0.94) and validity. Marital satisfaction was measured by Marriage Success Scale. The sample was composed of 147 marital couples. The study revealed that satisfied couples, in comparison with non-satisfied couples, tend to use constructive problem-solving strategies (Dialogue and Loyalty). They rarely use destructive strategies like Escalation of conflict or Withdrawal. Dialogue is the strategy connected with satisfaction in a most positive manner. These might be very important guidelines to couples' psychotherapy. Loyalty to oneself is a significant positive predictor of male satisfaction is also own Loyalty. The study shows that constructive attitudes are the most significant predictors of marriage satisfaction. It is therefore worth concentrating mostly on them in the psychotherapeutic process instead of eliminating destructive attitudes.

Emergency department overcrowding: the impact of resource scarcity on physician job satisfaction.

PubMed

Rondeau, Kent V; Francescutti, Louis H

2005-01-01

Emergency departments in most developed countries have been experiencing significant overcrowding under a regime of severe resource constraints. Physicians in emergency departments increasingly find themselves toiling in workplaces that are characterized by diminished availability of, limited access to, and decreased stability of critical resources. Severe resource constraints have the potential to greatly weaken the overall job satisfaction of emergency physicians. This article examines the impact of hospital resource constraints on the job satisfaction of a large sample of emergency physicians in Canada. After controlling for workflow and patient characteristics and for various institutional and physician characteristics, institutional resource constraints are found to be major contributors to emergency physician job dissatisfaction. Resource factors that have the greatest impact on job satisfaction include availability of emergency room physicians, access to hospital technology and emergency beds, and stability of financial (investment) resources.

Teaching People to Manage Constraints: Effects on Creative Problem-Solving

ERIC Educational Resources Information Center

Peterson, David R.; Barrett, Jamie D.; Hester, Kimberly S.; Robledo, Issac C.; Hougen, Dean F.; Day, Eric A.; Mumford, Michael D.

2013-01-01

Constraints often inhibit creative problem-solving. This study examined the impact of training strategies for managing constraints on creative problem-solving. Undergraduates, 218 in all, were asked to work through 1 to 4 self-paced instructional programs focused on constraint management strategies. The quality, originality, and elegance of…

Dynamic Constraint Satisfaction with Reasonable Global Constraints

NASA Technical Reports Server (NTRS)

Frank, Jeremy

2003-01-01

Previously studied theoretical frameworks for dynamic constraint satisfaction problems (DCSPs) employ a small set of primitive operators to modify a problem instance. They do not address the desire to model problems using sophisticated global constraints, and do not address efficiency questions related to incremental constraint enforcement. In this paper, we extend a DCSP framework to incorporate global constraints with flexible scope. A simple approach to incremental propagation after scope modification can be inefficient under some circumstances. We characterize the cases when this inefficiency can occur, and discuss two ways to alleviate this problem: adding rejection variables to the scope of flexible constraints, and adding new features to constraints that permit increased control over incremental propagation.

An integrated model to measure service management and physical constraints' effect on food consumption in assisted-living facilities.

PubMed

Huang, Hui-Chun; Shanklin, Carol W

2008-05-01

The United States is experiencing remarkable growth in the elderly population, which provides both opportunities and challenges for assisted-living facilities. The objective of this study was to explore how service management influences residents' actual food consumption in assisted-living facilities. Physical factors influencing residents' service evaluation and food consumption also were investigated. A total of 394 questionnaires were distributed to assisted-living residents in seven randomly selected facilities. The questionnaire was developed based on an in-depth literature review and pilot study. Residents' perceived quality evaluations, satisfaction, and physical constraints were measured. Residents' actual food consumption was measured using a plate waste technique. A total of 118 residents in five facilities completed both questionnaires and food consumption assessments. Descriptive, multivariate analyses and structural equation modeling techniques were employed. Service management, including food and service quality and customer satisfaction, was found to significantly influence residents' food consumption. Physical constraints associated with aging, including a decline in health status, chewing problems, sensory loss, and functional disability, also significantly influenced residents' food consumption. A significant relationship was found between physical constraints and customer satisfaction. Foodservice that provides good food and service quality increases customer satisfaction and affects residents' actual food consumption. Physical constraints also influence residents' food consumption directly, or indirectly through satisfaction. The findings suggest that food and nutrition professionals in assisted-living should consider the physical profiles of their residents to enhance residents' satisfaction and nutrient intake. Recommendations for exploring residents' perspectives are discussed.

Cognitive Dissonance Reduction as Constraint Satisfaction.

ERIC Educational Resources Information Center

Shultz, Thomas R.; Lepper, Mark R.

1996-01-01

It is argued that the reduction of cognitive dissonance can be viewed as a constraint satisfaction problem, and a computational model of the process of consonance seeking is proposed. Simulations from this model matched psychological findings from the insufficient justification and free-choice paradigms of cognitive dissonance theory. (SLD)

Nurses Returning to School: Motivators, Inhibitors and Job Satisfaction.

PubMed

Harris, Patrick W; Burman, Mary E

2016-01-01

Health care employers and national nursing organizations are placing increased emphasis on nurses earning a baccalaureate degree or higher. This study examines the impact of motivators (professional and personal motivation), inhibitors (time constraints and employer discouragement), and job satisfaction on intent to return to school. Approximately half of the employed nurses in Wyoming were surveyed using a mailed questionnaire in the summer of 2013. Perceived employer discouragement and time constraints continued to play a direct role on intent to return to school regardless of nurse motivation or job satisfaction. However, motivation and job satisfaction also contributed to a nurse's intent to return to school. These results suggest that motivation and job satisfaction are significant regarding intent to return to school but can be limited by both perceived discouragement of one's employer and perceived time constraints. In order to meet the increasing demands of a better-educated nursing workforce, a shift in workplace dynamics may be warranted. Copyright © 2016 Elsevier Inc. All rights reserved.

Trading a Problem-solving Task

NASA Astrophysics Data System (ADS)

Matsubara, Shigeo

This paper focuses on a task allocation problem, especially cases where the task is to find a solution in a search problem or a constraint satisfaction problem. If the search problem is hard to solve, a contractor may fail to find a solution. Here, the more computational resources such as the CPU time the contractor invests in solving the search problem, the more a solution is likely to be found. This brings about a new problem that a contractee has to find an appropriate level of the quality in a task achievement as well as to find an efficient allocation of a task among contractors. For example, if the contractee asks the contractor to find a solution with certainty, the payment from the contractee to the contractor may exceed the contractee's benefit from obtaining a solution, which discourages the contractee from trading a task. However, solving this problem is difficult because the contractee cannot ascertain the contractor's problem-solving ability such as the amount of available resources and knowledge (e.g. algorithms, heuristics) or monitor what amount of resources are actually invested in solving the allocated task. To solve this problem, we propose a task allocation mechanism that is able to choose an appropriate level of the quality in a task achievement and prove that this mechanism guarantees that each contractor reveals its true information. Moreover, we show that our mechanism can increase the contractee's utility compared with a simple auction mechanism by using computer simulation.

Distributed Optimization

NASA Technical Reports Server (NTRS)

Macready, William; Wolpert, David

2005-01-01

We demonstrate a new framework for analyzing and controlling distributed systems, by solving constrained optimization problems with an algorithm based on that framework. The framework is ar. information-theoretic extension of conventional full-rationality game theory to allow bounded rational agents. The associated optimization algorithm is a game in which agents control the variables of the optimization problem. They do this by jointly minimizing a Lagrangian of (the probability distribution of) their joint state. The updating of the Lagrange parameters in that Lagrangian is a form of automated annealing, one that focuses the multi-agent system on the optimal pure strategy. We present computer experiments for the k-sat constraint satisfaction problem and for unconstrained minimization of NK functions.

Insight into the ten-penny problem: guiding search by constraints and maximization.

PubMed

Öllinger, Michael; Fedor, Anna; Brodt, Svenja; Szathmáry, Eörs

2017-09-01

For a long time, insight problem solving has been either understood as nothing special or as a particular class of problem solving. The first view implicates the necessity to find efficient heuristics that restrict the search space, the second, the necessity to overcome self-imposed constraints. Recently, promising hybrid cognitive models attempt to merge both approaches. In this vein, we were interested in the interplay of constraints and heuristic search, when problem solvers were asked to solve a difficult multi-step problem, the ten-penny problem. In three experimental groups and one control group (N = 4 × 30) we aimed at revealing, what constraints drive problem difficulty in this problem, and how relaxing constraints, and providing an efficient search criterion facilitates the solution. We also investigated how the search behavior of successful problem solvers and non-solvers differ. We found that relaxing constraints was necessary but not sufficient to solve the problem. Without efficient heuristics that facilitate the restriction of the search space, and testing the progress of the problem solving process, the relaxation of constraints was not effective. Relaxing constraints and applying the search criterion are both necessary to effectively increase solution rates. We also found that successful solvers showed promising moves earlier and had a higher maximization and variation rate across solution attempts. We propose that this finding sheds light on how different strategies contribute to solving difficult problems. Finally, we speculate about the implications of our findings for insight problem solving.

Reference governors for controlled belt restraint systems

NASA Astrophysics Data System (ADS)

van der Laan, E. P.; Heemels, W. P. M. H.; Luijten, H.; Veldpaus, F. E.; Steinbuch, M.

2010-07-01

Today's restraint systems typically include a number of airbags, and a three-point seat belt with load limiter and pretensioner. For the class of real-time controlled restraint systems, the restraint actuator settings are continuously manipulated during the crash. This paper presents a novel control strategy for these systems. The control strategy developed here is based on a combination of model predictive control and reference management, in which a non-linear device - a reference governor (RG) - is added to a primal closed-loop controlled system. This RG determines an optimal setpoint in terms of injury reduction and constraint satisfaction by solving a constrained optimisation problem. Prediction of the vehicle motion, required to predict future constraint violation, is included in the design and is based on past crash data, using linear regression techniques. Simulation results with MADYMO models show that, with ideal sensors and actuators, a significant reduction (45%) of the peak chest acceleration can be achieved, without prior knowledge of the crash. Furthermore, it is shown that the algorithms are sufficiently fast to be implemented online.

Terminal altitude maximization for Mars entry considering uncertainties

NASA Astrophysics Data System (ADS)

Cui, Pingyuan; Zhao, Zeduan; Yu, Zhengshi; Dai, Juan

2018-04-01

Uncertainties present in the Mars atmospheric entry process may cause state deviations from the nominal designed values, which will lead to unexpected performance degradation if the trajectory is designed merely based on the deterministic dynamic model. In this paper, a linear covariance based entry trajectory optimization method is proposed considering the uncertainties presenting in the initial states and parameters. By extending the elements of the state covariance matrix as augmented states, the statistical behavior of the trajectory is captured to reformulate the performance metrics and path constraints. The optimization problem is solved by the GPOPS-II toolbox in MATLAB environment. Monte Carlo simulations are also conducted to demonstrate the capability of the proposed method. Primary trading performances between the nominal deployment altitude and its dispersion can be observed by modulating the weights on the dispersion penalty, and a compromised result referring to maximizing the 3σ lower bound of the terminal altitude is achieved. The resulting path constraints also show better satisfaction in a disturbed environment compared with the nominal situation.

Leisure activities following a lower limb amputation.

PubMed

Couture, Mélanie; Caron, Chantal D; Desrosiers, Johanne

2010-01-01

The aim of this study was to describe leisure activities, leisure satisfaction and constraints on participation in leisure following a unilateral lower limb amputation due to vascular disease. This study used a mixed-method approach where 15 individuals with lower limb amputation completed the individual leisure profile 2-3 months post-discharge from rehabilitation. A subsample (n = 8) also participated in semi-structured interviews analysed using the Miles and Huberman analytic method. Results show that participants were involved in 12 different leisure activities on average. Compared to before the amputation, a decrease in participation was observed in all categories of leisure activity, and especially crafts, nature and outdoor activities, mechanics, sports and physical activities. Nonetheless, overall satisfaction was high. The most important constraints on participation in leisure were lack of accessibility, material considerations, functional abilities, affective constraints and social constraints. A decrease in leisure activity participation and the presence of constraints do not automatically translate into low levels of leisure satisfaction.

Social problem-solving, perceived stress, negative life events, depression and life satisfaction in psoriasis.

PubMed

Eskin, M; Savk, E; Uslu, M; Küçükaydoğan, N

2014-11-01

Psoriasis is a chronic dermatosis which may cause significant impairment of the patient's quality of life. The purpose of this study was to investigate the social problem-solving skills, perceived stress, negative life events, depression and life satisfaction in psoriasis patients. Data were gathered by means of questionnaires and clinical evaluations from 51 psoriatic patients and 51 matched healthy controls. Average disease duration was 16.47 years and average Psoriasis Area and Severity Index score was 3.67. Compared with the controls, the patients displayed lower social problem-solving skills. They displayed higher negative problem orientation and impulsive-careless problem-solving style scores than the controls. Patients tended also to show more avoidant problem-solving style and lower life satisfaction than controls. There was no difference between psoriatic patients and controls in terms of depression, perceived stress and negative life events. Higher social problem-solving skills were associated with lower depression, perceived stress and fewer numbers of negative life events but higher level of life satisfaction. The patient group largely included mild and moderate psoriatic cases. The findings of the study suggest that problem-solving training or therapy may be a suitable option for alleviating levels of psychological distress in patients suffering from psoriasis. © 2014 European Academy of Dermatology and Venereology.

Social problem-solving, perceived stress, depression and life-satisfaction in patients suffering from tension type and migraine headaches.

PubMed

Eskin, Mehmet; Akyol, Ali; Çelik, Emine Yilmaz; Gültekin, Bülent Kadri

2013-08-01

This study aimed at investigating social problem solving, perceived stress, depression, and life-satisfaction in patients with tension type and migraine headaches. Forty-nine migraine and 42 tension type headache patients (n = 91) consenting to participate were compared to a total of 49 matched healthy control group. Participants filled in a questionnaire consisting self-report measures of problem solving, perceived stress, depression and life satisfaction. They were also asked about headache duration, frequency, pain severity, psychiatric treatment and sense of control in one's life. T-tests, chi-square, analysis of variance, logistic regression analysis and Pearson product moment correlation coefficient procedures were used to analyze the data. Tension type headache patients reported having had more frequent headaches than the migraine patients but migraine patients reported having had more intense pain than the tension type headache patients. Instances of psychiatric treatment were more common among tension type headache patients than the migraine and the control group. Compared to the healthy controls, headache patients displayed a deficiency in problem solving, higher levels of perceived stress and depression. Levels of problem solving skills in headache patients were related inversely to depression, perceived stress and the number of negative life events but problem solving skills of headache patients was related positively to life-satisfaction. The findings from this study suggested that cognitive behavioral problem solving therapy or training might be a viable option for reducing levels of stress and depression, and to increase life-satisfaction in patients suffering from primary headache. © 2013 The Scandinavian Psychological Associations.

A General Connectionist Model of Attitude Structure and Change: The ACS (Attitudes as Constraint Satisfaction) Model

ERIC Educational Resources Information Center

Monroe, Brian M.; Read, Stephen J.

2008-01-01

A localist, parallel constraint satisfaction, artificial neural network model is presented that accounts for a broad collection of attitude and attitude-change phenomena. The network represents the attitude object and cognitions and beliefs related to the attitude, as well as how to integrate a persuasive message into this network. Short-term…

A heuristic constraint programmed planner for deep space exploration problems

NASA Astrophysics Data System (ADS)

Jiang, Xiao; Xu, Rui; Cui, Pingyuan

2017-10-01

In recent years, the increasing numbers of scientific payloads and growing constraints on the probe have made constraint processing technology a hotspot in the deep space planning field. In the procedure of planning, the ordering of variables and values plays a vital role. This paper we present two heuristic ordering methods for variables and values. On this basis a graphplan-like constraint-programmed planner is proposed. In the planner we convert the traditional constraint satisfaction problem to a time-tagged form with different levels. Inspired by the most constrained first principle in constraint satisfaction problem (CSP), the variable heuristic is designed by the number of unassigned variables in the constraint and the value heuristic is designed by the completion degree of the support set. The simulation experiments show that the planner proposed is effective and its performance is competitive with other kind of planners.

Family Caregiver Social Problem-Solving Abilities and Adjustment to Caring for a Relative with Vision Loss

PubMed Central

Bambara, Jennifer K.; Owsley, Cynthia; Wadley, Virginia; Martin, Roy; Porter, Chebon; Dreer, Laura E.

2009-01-01

Purpose To examine the prevalence of persons at risk for depression among family caregivers of visually impaired persons and the extent to which social problem-solving abilities are associated with caregiver depressive symptomatology and life satisfaction. Methods Family caregivers were defined as adults who accompanied their adult relative to an appointment at a low-vision rehabilitation clinic and self-identified themselves as the primary family caregiver responsible for providing some form of assistance for their relative due to vision impairment. Demographic variables, depressive symptoms, life satisfaction, caregiver burden, and social problem-solving abilities were assessed in caregivers. The patient’s visual acuity and depressive symptoms and their relationship to the caregiver’s depressive symptoms and life satisfaction were also examined. Results Ninety-six family caregivers were enrolled. Of those, 35.4% were identified as at risk for depression. Among caregivers, dysfunctional or ineffective social problem-solving abilities were significantly associated with greater depressive symptomatology and decreased life satisfaction after adjustment for caregiver burden and demographic and medical variables for both the caregiver and the visually impaired patient. Problem orientation or motivation to solving problems was also significantly associated with caregiver depression and satisfaction with life. Conclusions A substantial number of caregivers of visually impaired adults experience psychosocial distress, particularly among those who possess poor social problem-solving abilities. These results underscore the need for routine screening and treatment of emotional distress among individuals caring for relatives with vision impairments. Future research should examine the extent to which psychosocial interventions targeting caregiver social problem-solving skills may be useful not only in improving caregiver quality of life but also in subsequently enhancing rehabilitation outcomes for the visually impaired care recipient. PMID:19060279

Family caregiver social problem-solving abilities and adjustment to caring for a relative with vision loss.

PubMed

Bambara, Jennifer K; Owsley, Cynthia; Wadley, Virginia; Martin, Roy; Porter, Chebon; Dreer, Laura E

2009-04-01

To examine the prevalence of persons at risk for depression among family caregivers of visually impaired persons and the extent to which social problem-solving abilities are associated with caregiver depressive symptomatology and life satisfaction. Family caregivers were defined as adults who accompanied their adult relative to an appointment at a low-vision rehabilitation clinic and self-identified themselves as the primary family caregiver responsible for providing some form of assistance for their relative due to vision impairment. Demographic variables, depressive symptoms, life satisfaction, caregiver burden, and social problem-solving abilities were assessed in caregivers. The patient's visual acuity and depressive symptoms and their relationship to the caregiver's depressive symptoms and life satisfaction were also examined. Ninety-six family caregivers were enrolled. Of those, 35.4% were identified as at risk for depression. Among caregivers, dysfunctional or ineffective social problem-solving abilities were significantly associated with greater depressive symptomatology and decreased life satisfaction after adjustment for caregiver burden and demographic and medical variables for both the caregiver and the visually impaired patient. Problem orientation or motivation to solving problems was also significantly associated with caregiver depression and satisfaction with life. A substantial number of caregivers of visually impaired adults experience psychosocial distress, particularly among those who possess poor social problem-solving abilities. These results underscore the need for routine screening and treatment of emotional distress among individuals caring for relatives with vision impairments. Future research should examine the extent to which psychosocial interventions targeting caregiver social problem-solving skills may be useful not only in improving caregiver quality of life but also in subsequently enhancing rehabilitation outcomes for the visually impaired care recipient.

Scaffolding Online Argumentation during Problem Solving

ERIC Educational Resources Information Center

Oh, S.; Jonassen, D. H.

2007-01-01

In this study, constraint-based argumentation scaffolding was proposed to facilitate online argumentation performance and ill-structured problem solving during online discussions. In addition, epistemological beliefs were presumed to play a role in solving ill-structured diagnosis-solution problems. Constraint-based discussion boards were…

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

Baker, Kyri; Toomey, Bridget

Evolving power systems with increasing levels of stochasticity call for a need to solve optimal power flow problems with large quantities of random variables. Weather forecasts, electricity prices, and shifting load patterns introduce higher levels of uncertainty and can yield optimization problems that are difficult to solve in an efficient manner. Solution methods for single chance constraints in optimal power flow problems have been considered in the literature, ensuring single constraints are satisfied with a prescribed probability; however, joint chance constraints, ensuring multiple constraints are simultaneously satisfied, have predominantly been solved via scenario-based approaches or by utilizing Boole's inequality asmore » an upper bound. In this paper, joint chance constraints are used to solve an AC optimal power flow problem while preventing overvoltages in distribution grids under high penetrations of photovoltaic systems. A tighter version of Boole's inequality is derived and used to provide a new upper bound on the joint chance constraint, and simulation results are shown demonstrating the benefit of the proposed upper bound. The new framework allows for a less conservative and more computationally efficient solution to considering joint chance constraints, specifically regarding preventing overvoltages.« less

Dynamic motion planning of 3D human locomotion using gradient-based optimization.

PubMed

Kim, Hyung Joo; Wang, Qian; Rahmatalla, Salam; Swan, Colby C; Arora, Jasbir S; Abdel-Malek, Karim; Assouline, Jose G

2008-06-01

Since humans can walk with an infinite variety of postures and limb movements, there is no unique solution to the modeling problem to predict human gait motions. Accordingly, we test herein the hypothesis that the redundancy of human walking mechanisms makes solving for human joint profiles and force time histories an indeterminate problem best solved by inverse dynamics and optimization methods. A new optimization-based human-modeling framework is thus described for predicting three-dimensional human gait motions on level and inclined planes. The basic unknowns in the framework are the joint motion time histories of a 25-degree-of-freedom human model and its six global degrees of freedom. The joint motion histories are calculated by minimizing an objective function such as deviation of the trunk from upright posture that relates to the human model's performance. A variety of important constraints are imposed on the optimization problem, including (1) satisfaction of dynamic equilibrium equations by requiring the model's zero moment point (ZMP) to lie within the instantaneous geometrical base of support, (2) foot collision avoidance, (3) limits on ground-foot friction, and (4) vanishing yawing moment. Analytical forms of objective and constraint functions are presented and discussed for the proposed human-modeling framework in which the resulting optimization problems are solved using gradient-based mathematical programming techniques. When the framework is applied to the modeling of bipedal locomotion on level and inclined planes, acyclic human walking motions that are smooth and realistic as opposed to less natural robotic motions are obtained. The aspects of the modeling framework requiring further investigation and refinement, as well as potential applications of the framework in biomechanics, are discussed.

Neighboring extremals of dynamic optimization problems with path equality constraints

NASA Technical Reports Server (NTRS)

Lee, A. Y.

1988-01-01

Neighboring extremals of dynamic optimization problems with path equality constraints and with an unknown parameter vector are considered in this paper. With some simplifications, the problem is reduced to solving a linear, time-varying two-point boundary-value problem with integral path equality constraints. A modified backward sweep method is used to solve this problem. Two example problems are solved to illustrate the validity and usefulness of the solution technique.

A comparison of Heuristic method and Llewellyn’s rules for identification of redundant constraints

NASA Astrophysics Data System (ADS)

Estiningsih, Y.; Farikhin; Tjahjana, R. H.

2018-03-01

Important techniques in linear programming is modelling and solving practical optimization. Redundant constraints are consider for their effects on general linear programming problems. Identification and reduce redundant constraints are for avoidance of all the calculations associated when solving an associated linear programming problems. Many researchers have been proposed for identification redundant constraints. This paper a compararison of Heuristic method and Llewellyn’s rules for identification of redundant constraints.

From Constraints to Resolution Rules Part II : chains, braids, confluence and T&E

NASA Astrophysics Data System (ADS)

Berthier, Denis

In this Part II, we apply the general theory developed in Part I to a detailed analysis of the Constraint Satisfaction Problem (CSP). We show how specific types of resolution rules can be defined. In particular, we introduce the general notions of a chain and a braid. As in Part I, these notions are illustrated in detail with the Sudoku example - a problem known to be NP-complete and which is therefore typical of a broad class of hard problems. For Sudoku, we also show how far one can go in "approximating" a CSP with a resolution theory and we give an empirical statistical analysis of how the various puzzles, corresponding to different sets of entries, can be classified along a natural scale of complexity. For any CSP, we also prove the confluence property of some Resolution Theories based on braids and we show how it can be used to define different resolution strategies. Finally, we prove that, in any CSP, braids have the same solving capacity as Trial-and-Error (T&E) with no guessing and we comment this result in the Sudoku case.

Sudden emergence of q-regular subgraphs in random graphs

NASA Astrophysics Data System (ADS)

Pretti, M.; Weigt, M.

2006-07-01

We investigate the computationally hard problem whether a random graph of finite average vertex degree has an extensively large q-regular subgraph, i.e., a subgraph with all vertices having degree equal to q. We reformulate this problem as a constraint-satisfaction problem, and solve it using the cavity method of statistical physics at zero temperature. For q = 3, we find that the first large q-regular subgraphs appear discontinuously at an average vertex degree c3 - reg simeq 3.3546 and contain immediately about 24% of all vertices in the graph. This transition is extremely close to (but different from) the well-known 3-core percolation point c3 - core simeq 3.3509. For q > 3, the q-regular subgraph percolation threshold is found to coincide with that of the q-core.

A Design of Product Collaborative Online Configuration Model

NASA Astrophysics Data System (ADS)

Wang, Xiaoguo; Zheng, Jin; Zeng, Qian

According to the actual needs of mass customization, the personalization of product and its collaborative design, the paper analyzes and studies the working mechanism of modular-based product configuration technology and puts forward an information model of modular product family. Combined with case-based reasoning techniques (CBR) and the constraint satisfaction problem solving techniques (CSP), we design and study the algorithm for product configuration, and analyze its time complexity. A car chassis is made as the application object, we provide a prototype system of online configuration. Taking advantage of this system, designers can make appropriate changes on the existing programs in accordance with the demand. This will accelerate all aspects of product development and shorten the product cycle. Also the system will provide a strong technical support for enterprises to improve their market competitiveness.

Solving constrained minimum-time robot problems using the sequential gradient restoration algorithm

NASA Technical Reports Server (NTRS)

Lee, Allan Y.

1991-01-01

Three constrained minimum-time control problems of a two-link manipulator are solved using the Sequential Gradient and Restoration Algorithm (SGRA). The inequality constraints considered are reduced via Valentine-type transformations to nondifferential path equality constraints. The SGRA is then used to solve these transformed problems with equality constraints. The results obtained indicate that at least one of the two controls is at its limits at any instant in time. The remaining control then adjusts itself so that none of the system constraints is violated. Hence, the minimum-time control is either a pure bang-bang control or a combined bang-bang/singular control.

Trimodal interpretation of constraints for planning

NASA Technical Reports Server (NTRS)

Krieger, David; Brown, Richard

1987-01-01

Constraints are used in the CAMPS knowledge based planning system to represent those propositions that must be true for a plan to be acceptable. CAMPS introduces the make-mode for interpreting a constraint. Given an unsatisfied constraint, make evaluation mode suggests planning actions which, if taken, would result in a modified plan in which the constraint in question may be satisfied. These suggested planning actions, termed delta-tuples, are the raw material of intelligent plan repair. They are used both in debugging an almost-right plan and in replanning due to changing situations. Given a defective plan in which some set of constraints are violated, a problem solving strategy selects one or more constraints as a focus of attention. These selected constraints are evaluated in the make-mode to produce delta-tuples. The problem solving strategy then reviews the delta-tuples according to its application and problem-specific criteria to find the most acceptable change in terms of success likelihood and plan disruption. Finally, the problem solving strategy makes the suggested alteration to the plan and then rechecks constraints to find any unexpected consequences.

TRACON Aircraft Arrival Planning and Optimization Through Spatial Constraint Satisfaction

NASA Technical Reports Server (NTRS)

Bergh, Christopher P.; Krzeczowski, Kenneth J.; Davis, Thomas J.; Denery, Dallas G. (Technical Monitor)

1995-01-01

A new aircraft arrival planning and optimization algorithm has been incorporated into the Final Approach Spacing Tool (FAST) in the Center-TRACON Automation System (CTAS) developed at NASA-Ames Research Center. FAST simulations have been conducted over three years involving full-proficiency, level five air traffic controllers from around the United States. From these simulations an algorithm, called Spatial Constraint Satisfaction, has been designed, coded, undergone testing, and soon will begin field evaluation at the Dallas-Fort Worth and Denver International airport facilities. The purpose of this new design is an attempt to show that the generation of efficient and conflict free aircraft arrival plans at the runway does not guarantee an operationally acceptable arrival plan upstream from the runway -information encompassing the entire arrival airspace must be used in order to create an acceptable aircraft arrival plan. This new design includes functions available previously but additionally includes necessary representations of controller preferences and workload, operationally required amounts of extra separation, and integrates aircraft conflict resolution. As a result, the Spatial Constraint Satisfaction algorithm produces an optimized aircraft arrival plan that is more acceptable in terms of arrival procedures and air traffic controller workload. This paper discusses the current Air Traffic Control arrival planning procedures, previous work in this field, the design of the Spatial Constraint Satisfaction algorithm, and the results of recent evaluations of the algorithm.

Cognitive dissonance reduction as constraint satisfaction.

PubMed

Shultz, T R; Lepper, M R

1996-04-01

A constraint satisfaction neural network model (the consonance model) simulated data from the two major cognitive dissonance paradigms of insufficient justification and free choice. In several cases, the model fit the human data better than did cognitive dissonance theory. Superior fits were due to the inclusion of constraints that were not part of dissonance theory and to the increased precision inherent to this computational approach. Predictions generated by the model for a free choice between undesirable alternatives were confirmed in a new psychological experiment. The success of the consonance model underscores important, unforeseen similarities between what had been formerly regarded as the rather exotic process of dissonance reduction and a variety of other, more mundane psychological processes. Many of these processes can be understood as the progressive application of constraints supplied by beliefs and attitudes.

The Role of Motivation, Perceived Constraints, and Constraint Negotiation Strategies in Students' Internship Selection Experience

ERIC Educational Resources Information Center

Batty, Kimberly A.

2011-01-01

The purpose of this study was to document the factors (i.e., motivation and perceived constraints) and processes (i.e., constraint negotiation) that influence students' selection of and satisfaction with their internship choice. The study was conducted using a quantitative approach, which included a focus group, a pilot study, and a…

Self-Evaluation Processes in Life Satisfaction: Uncovering Measurement Non-Equivalence and Age-Related Differences

ERIC Educational Resources Information Center

Heidemeier, Heike; Staudinger, Ursula M.

2012-01-01

This study demonstrates how self-evaluation processes explain subgroup differences in ratings of life satisfaction (population heterogeneity). Life domains differ with regard to the constraints they impose on beliefs in internal control. We hypothesized that these differences are linked with cognitive biases in ratings of life satisfaction. In…

Structure Constraints in a Constraint-Based Planner

NASA Technical Reports Server (NTRS)

Pang, Wan-Lin; Golden, Keith

2004-01-01

In this paper we report our work on a new constraint domain, where variables can take structured values. Earth-science data processing (ESDP) is a planning domain that requires the ability to represent and reason about complex constraints over structured data, such as satellite images. This paper reports on a constraint-based planner for ESDP and similar domains. We discuss our approach for translating a planning problem into a constraint satisfaction problem (CSP) and for representing and reasoning about structured objects and constraints over structures.

Revisiting the SOLVE ClOOCl and ClO measurements in consideration of the Pope et al., 2007 results.

NASA Astrophysics Data System (ADS)

Stimpfle, R. M.; Wilmouth, D. M.; Anderson, J. G.

2008-12-01

The interpretation of the SOLVE measurements of ClOOCl and ClO has recently acquired renewed interest with the publication of new ClOOCl cross section measurements (Pope et al, 2007) that are significantly smaller than expected. The SOLVE analysis showed agreement with J values based upon the JPL 2002 or Burkholder 1990 cross sections, dependent upon various values for the rate constant for dimer production. J values based upon Pope are currently not in agreement with the SOLVE observations and/or their analysis. As various hypotheses emerge to possibly rationalize the Pope results, it is worthwhile to consider two critical constraints that the SOLVE halogen data place on any newly considered Clx photochemistry. The first constraint is the lack of a detectable Cl atom signal in the observed background signal at the temperature used for thermal dissociation of ClOOCl. The second constraint is the observed SZA dependence of the partitioning of ClO and ClOOCl. Here we present evidence of the Cl atom constraint.

Parallel consistent labeling algorithms

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

Samal, A.; Henderson, T.

Mackworth and Freuder have analyzed the time complexity of several constraint satisfaction algorithms. Mohr and Henderson have given new algorithms, AC-4 and PC-3, for arc and path consistency, respectively, and have shown that the arc consistency algorithm is optimal in time complexity and of the same order space complexity as the earlier algorithms. In this paper, they give parallel algorithms for solving node and arc consistency. They show that any parallel algorithm for enforcing arc consistency in the worst case must have O(na) sequential steps, where n is number of nodes, and a is the number of labels per node.more » They give several parallel algorithms to do arc consistency. It is also shown that they all have optimal time complexity. The results of running the parallel algorithms on a BBN Butterfly multiprocessor are also presented.« less

What is adaptive about adaptive decision making? A parallel constraint satisfaction account.

PubMed

Glöckner, Andreas; Hilbig, Benjamin E; Jekel, Marc

2014-12-01

There is broad consensus that human cognition is adaptive. However, the vital question of how exactly this adaptivity is achieved has remained largely open. Herein, we contrast two frameworks which account for adaptive decision making, namely broad and general single-mechanism accounts vs. multi-strategy accounts. We propose and fully specify a single-mechanism model for decision making based on parallel constraint satisfaction processes (PCS-DM) and contrast it theoretically and empirically against a multi-strategy account. To achieve sufficiently sensitive tests, we rely on a multiple-measure methodology including choice, reaction time, and confidence data as well as eye-tracking. Results show that manipulating the environmental structure produces clear adaptive shifts in choice patterns - as both frameworks would predict. However, results on the process level (reaction time, confidence), in information acquisition (eye-tracking), and from cross-predicting choice consistently corroborate single-mechanisms accounts in general, and the proposed parallel constraint satisfaction model for decision making in particular. Copyright © 2014 Elsevier B.V. All rights reserved.

Design of optimal groundwater remediation systems under flexible environmental-standard constraints.

PubMed

Fan, Xing; He, Li; Lu, Hong-Wei; Li, Jing

2015-01-01

In developing optimal groundwater remediation strategies, limited effort has been exerted to solve the uncertainty in environmental quality standards. When such uncertainty is not considered, either over optimistic or over pessimistic optimization strategies may be developed, probably leading to the formulation of rigid remediation strategies. This study advances a mathematical programming modeling approach for optimizing groundwater remediation design. This approach not only prevents the formulation of over optimistic and over pessimistic optimization strategies but also provides a satisfaction level that indicates the degree to which the environmental quality standard is satisfied. Therefore the approach may be expected to be significantly more acknowledged by the decision maker than those who do not consider standard uncertainty. The proposed approach is applied to a petroleum-contaminated site in western Canada. Results from the case study show that (1) the peak benzene concentrations can always satisfy the environmental standard under the optimal strategy, (2) the pumping rates of all wells decrease under a relaxed standard or long-term remediation approach, (3) the pumping rates are less affected by environmental quality constraints under short-term remediation, and (4) increased flexible environmental standards have a reduced effect on the optimal remediation strategy.

A dual method for optimal control problems with initial and final boundary constraints.

NASA Technical Reports Server (NTRS)

Pironneau, O.; Polak, E.

1973-01-01

This paper presents two new algorithms belonging to the family of dual methods of centers. The first can be used for solving fixed time optimal control problems with inequality constraints on the initial and terminal states. The second one can be used for solving fixed time optimal control problems with inequality constraints on the initial and terminal states and with affine instantaneous inequality constraints on the control. Convergence is established for both algorithms. Qualitative reasoning indicates that the rate of convergence is linear.

Cooperating knowledge-based systems

NASA Technical Reports Server (NTRS)

Feigenbaum, Edward A.; Buchanan, Bruce G.

1988-01-01

This final report covers work performed under Contract NCC2-220 between NASA Ames Research Center and the Knowledge Systems Laboratory, Stanford University. The period of research was from March 1, 1987 to February 29, 1988. Topics covered were as follows: (1) concurrent architectures for knowledge-based systems; (2) methods for the solution of geometric constraint satisfaction problems, and (3) reasoning under uncertainty. The research in concurrent architectures was co-funded by DARPA, as part of that agency's Strategic Computing Program. The research has been in progress since 1985, under DARPA and NASA sponsorship. The research in geometric constraint satisfaction has been done in the context of a particular application, that of determining the 3-D structure of complex protein molecules, using the constraints inferred from NMR measurements.

Quiet planting in the locked constraints satisfaction problems

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

Zdeborova, Lenka; Krzakala, Florent

2009-01-01

We study the planted ensemble of locked constraint satisfaction problems. We describe the connection between the random and planted ensembles. The use of the cavity method is combined with arguments from reconstruction on trees and first and second moment considerations; in particular the connection with the reconstruction on trees appears to be crucial. Our main result is the location of the hard region in the planted ensemble, thus providing hard satisfiable benchmarks. In a part of that hard region instances have with high probability a single satisfying assignment.

A continuous glucose monitoring and problem-solving intervention to change physical activity behavior in women with type 2 diabetes: a pilot study.

PubMed

Allen, Nancy; Whittemore, Robin; Melkus, Gail

2011-11-01

Diabetes technology has the potential to provide useful data for theory-based behavioral counseling. The aims of this study are to evaluate the feasibility, acceptability, and preliminary efficacy of a continuous glucose monitoring and problem-solving counseling intervention to change physical activity (PA) behavior in women with type 2 diabetes. Women (n=29) with type 2 diabetes were randomly assigned to one of two treatment conditions: continuous glucose counseling and problem-solving skills or continuous glucose monitoring counseling and general diabetes education. Feasibility data were obtained on intervention dose, implementation, and satisfaction. Preliminary efficacy data were collected at baseline and 12 weeks on the following measures: PA amount and intensity, diet, problem-solving skills, self-efficacy for PA, depression, hemogoloin A1c, weight, and blood pressure. Demographic and implementation variables were described using frequency distributions and summary statistics. Satisfaction data were analyzed using Wilcoxon rank. Differences between groups were analyzed using linear mixed-modeling. Women were mostly white/non-Latina with a mean age of 53 years, a 6.5-year history of diabetes, and suboptimal glycemic control. Continuous glucose monitoring plus problem-solving group participants had significantly greater problem-solving skills and had greater, although not statistically significant, dietary adherence, moderate activity minutes, weight loss, and higher intervention satisfaction pre- to post-intervention than did participants in the continuous glucose monitoring plus education group. A continuous glucose monitoring plus problem-solving intervention was feasible and acceptable, and participants had greater problem-solving skills than continuous glucose monitoring plus education group participants.

Predictors of employer satisfaction: technical and non-technical skills.

PubMed

Danielson, Jared A; Wu, Tsui-Feng; Fales-Williams, Amanda J; Kirk, Ryan A; Preast, Vanessa A

2012-01-01

Employers of 2007-2009 graduates from Iowa State University College of Veterinary Medicine were asked to respond to a survey regarding their overall satisfaction with their new employees as well as their new employees' preparation in several technical and non-technical skill areas. Seventy-five responses contained complete data and were used in the analysis. Four technical skill areas (data collection, data interpretation, planning, and taking action) and five non-technical skill areas (interpersonal skills, ability to deal with legal issues, business skills, making referrals, and problem solving) were identified. All of the skill area subscales listed above had appropriate reliability (Cronbach's alpha>0.70) and were positively and significantly correlated with overall employer satisfaction. Results of two simultaneous regression analyses indicated that of the four technical skill areas, taking action is the most salient predictor of employer satisfaction. Of the five non-technical skill areas, interpersonal skills, business skills, making referrals, and problem solving were the most important skills in predicting employer satisfaction. Hierarchical regression analysis revealed that all technical skills explained 25% of the variation in employer satisfaction; non-technical skills explained an additional 42% of the variation in employer satisfaction.

Conflict Resolution in Chinese Adolescents' Friendship: Links with Regulatory Focus and Friendship Satisfaction.

PubMed

Gao, Qin; Bian, Ran; Liu, Ru-de; He, Yili; Oei, Tian-Po

2017-04-03

It is generally acknowledged that people adopt different resolution strategies when facing conflicts with others. However, the mechanisms of conflict resolution are still unclear and under researched, in particular within the context of Chinese adolescents' same-sex friendship relations. Thus, the present study investigated the mediator role of conflict resolution strategies in the relationship between regulatory foci and friendship satisfaction for the first time. 653 Chinese adolescents completed the regulatory foci, conflict resolution style, and friendship satisfaction measures. The results of the structure equation modeling showed that while promotion focus was positively associated with problem-solving and compliance, prevention focus was positively associated with withdrawal and conflict engagement. In addition, problem-solving mediated the relationship between promotion focus and friendship satisfaction, and conflict engagement mediated the relationship between prevention focus and friendship satisfaction. These findings contribute to understanding Chinese adolescents' use of conflict resolution strategies as well as the relationship between regulatory foci and behavioral strategies in negative situations.

Integrated case management for work-related upper-extremity disorders: impact of patient satisfaction on health and work status.

PubMed

Feuerstein, Michael; Huang, Grant D; Ortiz, Jose M; Shaw, William S; Miller, Virginia I; Wood, Patricia M

2003-08-01

An integrated case management (ICM) approach (ergonomic and problem-solving intervention) to work-related upper-extremity disorders was examined in relation to patient satisfaction, future symptom severity, function, and return to work (RTW). Federal workers with work-related upper-extremity disorder workers' compensation claims (n = 205) were randomly assigned to usual care or ICM intervention. Patient satisfaction was assessed after the 4-month intervention period. Questionnaires on clinical outcomes and ergonomic exposure were administered at baseline and at 6- and 12-months postintervention. Time from intervention to RTW was obtained from an administrative database. ICM group assignment was significantly associated with greater patient satisfaction. Regression analyses found higher patient satisfaction levels predicted decreased symptom severity and functional limitations at 6 months and a shorter RTW. At 12 months, predictors of positive outcomes included male gender, lower distress, lower levels of reported ergonomic exposure, and receipt of ICM. Findings highlight the utility of targeting workplace ergonomic and problem solving skills.

A Hybrid alldifferent-Tabu Search Algorithm for Solving Sudoku Puzzles

PubMed Central

Crawford, Broderick; Paredes, Fernando; Norero, Enrique

2015-01-01

The Sudoku problem is a well-known logic-based puzzle of combinatorial number-placement. It consists in filling a n 2 × n 2 grid, composed of n columns, n rows, and n subgrids, each one containing distinct integers from 1 to n 2. Such a puzzle belongs to the NP-complete collection of problems, to which there exist diverse exact and approximate methods able to solve it. In this paper, we propose a new hybrid algorithm that smartly combines a classic tabu search procedure with the alldifferent global constraint from the constraint programming world. The alldifferent constraint is known to be efficient for domain filtering in the presence of constraints that must be pairwise different, which are exactly the kind of constraints that Sudokus own. This ability clearly alleviates the work of the tabu search, resulting in a faster and more robust approach for solving Sudokus. We illustrate interesting experimental results where our proposed algorithm outperforms the best results previously reported by hybrids and approximate methods. PMID:26078751

A Hybrid alldifferent-Tabu Search Algorithm for Solving Sudoku Puzzles.

PubMed

Soto, Ricardo; Crawford, Broderick; Galleguillos, Cristian; Paredes, Fernando; Norero, Enrique

2015-01-01

The Sudoku problem is a well-known logic-based puzzle of combinatorial number-placement. It consists in filling a n(2) × n(2) grid, composed of n columns, n rows, and n subgrids, each one containing distinct integers from 1 to n(2). Such a puzzle belongs to the NP-complete collection of problems, to which there exist diverse exact and approximate methods able to solve it. In this paper, we propose a new hybrid algorithm that smartly combines a classic tabu search procedure with the alldifferent global constraint from the constraint programming world. The alldifferent constraint is known to be efficient for domain filtering in the presence of constraints that must be pairwise different, which are exactly the kind of constraints that Sudokus own. This ability clearly alleviates the work of the tabu search, resulting in a faster and more robust approach for solving Sudokus. We illustrate interesting experimental results where our proposed algorithm outperforms the best results previously reported by hybrids and approximate methods.

Rotational-path decomposition based recursive planning for spacecraft attitude reorientation

NASA Astrophysics Data System (ADS)

Xu, Rui; Wang, Hui; Xu, Wenming; Cui, Pingyuan; Zhu, Shengying

2018-02-01

The spacecraft reorientation is a common task in many space missions. With multiple pointing constraints, it is greatly difficult to solve the constrained spacecraft reorientation planning problem. To deal with this problem, an efficient rotational-path decomposition based recursive planning (RDRP) method is proposed in this paper. The uniform pointing-constraint-ignored attitude rotation planning process is designed to solve all rotations without considering pointing constraints. Then the whole path is checked node by node. If any pointing constraint is violated, the nearest critical increment approach will be used to generate feasible alternative nodes in the process of rotational-path decomposition. As the planning path of each subdivision may still violate pointing constraints, multiple decomposition is needed and the reorientation planning is designed as a recursive manner. Simulation results demonstrate the effectiveness of the proposed method. The proposed method has been successfully applied in two SPARK microsatellites to solve onboard constrained attitude reorientation planning problem, which were developed by the Shanghai Engineering Center for Microsatellites and launched on 22 December 2016.

Comparison of student self-debriefing versus instructor debriefing in nursing simulation: A quasi-experimental study.

PubMed

Kang, Kyungja; Yu, Mi

2018-06-01

Student self-debriefing promotes self-confidence, helps to increase clinical performance, and is a more cost-effective method than is traditional instructor-led debriefing in simulation-based learning. This study compared the effectiveness of debriefing-in terms of the problem-solving process, team effectiveness, debriefing assessment, and debriefing satisfaction-between an experimental group who received both student self-debriefing (SSD) and instructor debriefing (ID) and a control group who received only instructor debriefing. This quasi-experimental study used a pretest-posttest non-equivalent control group design. Two universities in South Korea. A convenience sample of 123 senior nursing students. The data were collected between 15 April and 9 June 2016. Differences in the problem-solving process, team effectiveness, debriefing assessment, and debriefing satisfaction between the SSD + ID group and the ID-only group were measured. The SSD + ID group showed significant improvements in the problem-solving process (t = 4.32, p < .001) and debriefing satisfaction (t = 3.19, p = .002), but not in debriefing assessment (t = 1.67, p = .097) or team effectiveness (t = 1.84, p = .069) compared to ID-only group. Specifically, as the number of student sessions increased, we observed significant differences in problem-solving ability (F = 9.44, p < .001), debriefing satisfaction (F = 7.78, p < .001), and the subdomains of debriefing assessment: 'maintains an engaging environment' (F = 3.78, p = .025), 'structures the debriefing in an organized way' (F = 4.27, p = .016), and 'helps trainees achieve or sustain future performance' (F = 3.17, p = .045). Our results can be used to develop guidelines for effective debriefing following simulation in nursing education. Specifically, combining SSD and ID in simulation debriefing and increasing the number of SSD sessions could help improve the problem-solving process and debriefing satisfaction among nursing students. Copyright © 2018 Elsevier Ltd. All rights reserved.

Constraint monitoring in TOSCA

NASA Technical Reports Server (NTRS)

Beck, Howard

1992-01-01

The Job-Shop Scheduling Problem (JSSP) deals with the allocation of resources over time to factory operations. Allocations are subject to various constraints (e.g., production precedence relationships, factory capacity constraints, and limits on the allowable number of machine setups) which must be satisfied for a schedule to be valid. The identification of constraint violations and the monitoring of constraint threats plays a vital role in schedule generation in terms of the following: (1) directing the scheduling process; and (2) informing scheduling decisions. This paper describes a general mechanism for identifying constraint violations and monitoring threats to the satisfaction of constraints throughout schedule generation.

Race and Gender as Moderator Variables in Predicting Relationship Satisfaction and Relationship Commitment in a Sample of Dating Heterosexual Couples.

ERIC Educational Resources Information Center

Sanderson, Bettie; Kurdek, Lawrence A.

1993-01-01

Examined relationship satisfaction and commitment among African-American (n=34 couples) and white (n=61 couples) dating heterosexual couples. Found that extent to which variables from interdependence, individual differences, and problem-solving models were linked to both relationship satisfaction and relationship commitment did not differ for…

Symbolic PathFinder: Symbolic Execution of Java Bytecode

NASA Technical Reports Server (NTRS)

Pasareanu, Corina S.; Rungta, Neha

2010-01-01

Symbolic Pathfinder (SPF) combines symbolic execution with model checking and constraint solving for automated test case generation and error detection in Java programs with unspecified inputs. In this tool, programs are executed on symbolic inputs representing multiple concrete inputs. Values of variables are represented as constraints generated from the analysis of Java bytecode. The constraints are solved using off-the shelf solvers to generate test inputs guaranteed to achieve complex coverage criteria. SPF has been used successfully at NASA, in academia, and in industry.

Reminiscence and adaptation to critical life events in older adults with mild to moderate depressive symptoms.

PubMed

Korte, Jojanneke; Bohlmeijer, Ernst T; Westerhof, Gerben J; Pot, Anne Margriet; Pot, Anne M

2011-07-01

The role of reminiscence as a way of adapting to critical life events and chronic medical conditions was investigated in older adults with mild to moderate depressive symptoms. Reminiscence is the (non)volitional act or process of recollecting memories of one's self in the past. 171 Dutch older adults with a mean age of 64 years (SD = 7.4) participated in this study. All of them had mild to moderate depressive symptoms. Participants completed measures on critical life events, chronic medical conditions, depressive symptoms, symptoms of anxiety and satisfaction with life. The reminiscence functions included were: identity, problem solving, bitterness revival and boredom reduction. Critical life events were positively correlated with identity and problem solving. Bitterness revival and boredom reduction were both positively correlated with depressive and anxiety symptoms, and negatively to satisfaction with life. Problem solving had a negative relation with anxiety symptoms. When all the reminiscence functions were included, problem solving was uniquely associated with symptoms of anxiety, and bitterness revival was uniquely associated with depressive symptoms and satisfaction with life. Interestingly, problem solving mediated the relation of critical life events with anxiety. This study corroborates the theory that reminiscence plays a role in coping with critical life events, and thereby maintaining mental health. Furthermore, it is recommended that therapists focus on techniques which reduce bitterness revival in people with depressive symptoms, and focus on problem-solving reminiscences among people with anxiety symptoms.

The algebraic-hyperbolic approach to the linearized gravitational constraints on a Minkowski background

NASA Astrophysics Data System (ADS)

Winicour, Jeffrey

2017-08-01

An algebraic-hyperbolic method for solving the Hamiltonian and momentum constraints has recently been shown to be well posed for general nonlinear perturbations of the initial data for a Schwarzschild black hole. This is a new approach to solving the constraints of Einstein’s equations which does not involve elliptic equations and has potential importance for the construction of binary black hole data. In order to shed light on the underpinnings of this approach, we consider its application to obtain solutions of the constraints for linearized perturbations of Minkowski space. In that case, we find the surprising result that there are no suitable Cauchy hypersurfaces in Minkowski space for which the linearized algebraic-hyperbolic constraint problem is well posed.

Does Relational Efficacy Index Interactional Behaviors Associated with Marital Satisfaction across the Transition to Parenthood?

ERIC Educational Resources Information Center

Irwin, Ruth Ann C.; And Others

Many couples suffer a decline in marital satisfaction over the transition to parenthood. Previous research indicated that a couple's relational efficacy (a measure of a couple's belief in their ability to solve problems) before the birth of their first child was one of the strongest predictors of their marital satisfaction after the birth. This…

Applying ant colony optimization metaheuristic to solve forest transportation planning problems with side constraints

Treesearch

Marco A. Contreras; Woodam Chung; Greg Jones

2008-01-01

Forest transportation planning problems (FTPP) have evolved from considering only the financial aspects of timber management to more holistic problems that also consider the environmental impacts of roads. These additional requirements have introduced side constraints, making FTPP larger and more complex. Mixed-integer programming (MIP) has been used to solve FTPP, but...

Interrelationship of motivation for and perceived constraints to physical activity participation and the well-being of senior center participants

Treesearch

Motoko Miyake; Ellen Rodgers

2009-01-01

This study investigated the relationship of motivation for and perceived constraints to physical activity (PA) participation and the well-being of senior center participants. A survey instrument made up of modified versions of the Sport Motivation and Perceived Constraints Scales, the Life Satisfaction Index-Z, and the Geriatric Depression Scale was administered at the...

Mission planning optimization of video satellite for ground multi-object staring imaging

NASA Astrophysics Data System (ADS)

Cui, Kaikai; Xiang, Junhua; Zhang, Yulin

2018-03-01

This study investigates the emergency scheduling problem of ground multi-object staring imaging for a single video satellite. In the proposed mission scenario, the ground objects require a specified duration of staring imaging by the video satellite. The planning horizon is not long, i.e., it is usually shorter than one orbit period. A binary decision variable and the imaging order are used as the design variables, and the total observation revenue combined with the influence of the total attitude maneuvering time is regarded as the optimization objective. Based on the constraints of the observation time windows, satellite attitude adjustment time, and satellite maneuverability, a constraint satisfaction mission planning model is established for ground object staring imaging by a single video satellite. Further, a modified ant colony optimization algorithm with tabu lists (Tabu-ACO) is designed to solve this problem. The proposed algorithm can fully exploit the intelligence and local search ability of ACO. Based on full consideration of the mission characteristics, the design of the tabu lists can reduce the search range of ACO and improve the algorithm efficiency significantly. The simulation results show that the proposed algorithm outperforms the conventional algorithm in terms of optimization performance, and it can obtain satisfactory scheduling results for the mission planning problem.

A Planning Approach of Engineering Characteristics Based on QFD-TRIZ Integrated

NASA Astrophysics Data System (ADS)

Liu, Shang; Shi, Dongyan; Zhang, Ying

Traditional QFD planning method compromises contradictions between engineering characteristics to achieve higher customer satisfaction. However, this compromise trade-off can not eliminate the contradictions existing among the engineering characteristics which limited the overall customer satisfaction. QFD (Quality function deployment) integrated with TRIZ (the Russian acronym of the Theory of Inventive Problem Solving) becomes hot research recently for TRIZ can be used to solve contradictions between engineering characteristics which construct the roof of HOQ (House of quality). But, the traditional QFD planning approach is not suitable for QFD integrated with TRIZ for that TRIZ requires emphasizing the contradictions between engineering characteristics at problem definition stage instead of compromising trade-off. So, a new planning approach based on QFD / TRIZ integration is proposed in this paper, which based on the consideration of the correlation matrix of engineering characteristics and customer satisfaction on the basis of cost. The proposed approach suggests that TRIZ should be applied to solve contradictions at the first step, and the correlation matrix of engineering characteristics should be amended at the second step, and at next step IFR (ideal final result) must be validated, then planning execute. An example is used to illustrate the proposed approach. The application indicated that higher customer satisfaction can be met and the contradictions between the characteristic parameters are eliminated.

A systematic linear space approach to solving partially described inverse eigenvalue problems

NASA Astrophysics Data System (ADS)

Hu, Sau-Lon James; Li, Haujun

2008-06-01

Most applications of the inverse eigenvalue problem (IEP), which concerns the reconstruction of a matrix from prescribed spectral data, are associated with special classes of structured matrices. Solving the IEP requires one to satisfy both the spectral constraint and the structural constraint. If the spectral constraint consists of only one or few prescribed eigenpairs, this kind of inverse problem has been referred to as the partially described inverse eigenvalue problem (PDIEP). This paper develops an efficient, general and systematic approach to solve the PDIEP. Basically, the approach, applicable to various structured matrices, converts the PDIEP into an ordinary inverse problem that is formulated as a set of simultaneous linear equations. While solving simultaneous linear equations for model parameters, the singular value decomposition method is applied. Because of the conversion to an ordinary inverse problem, other constraints associated with the model parameters can be easily incorporated into the solution procedure. The detailed derivation and numerical examples to implement the newly developed approach to symmetric Toeplitz and quadratic pencil (including mass, damping and stiffness matrices of a linear dynamic system) PDIEPs are presented. Excellent numerical results for both kinds of problem are achieved under the situations that have either unique or infinitely many solutions.

Alternative Constraint Handling Technique for Four-Bar Linkage Path Generation

NASA Astrophysics Data System (ADS)

Sleesongsom, S.; Bureerat, S.

2018-03-01

This paper proposes an extension of a new concept for path generation from our previous work by adding a new constraint handling technique. The propose technique was initially designed for problems without prescribed timing by avoiding the timing constraint, while remain constraints are solving with a new constraint handling technique. The technique is one kind of penalty technique. The comparative study is optimisation of path generation problems are solved using self-adaptive population size teaching-learning based optimization (SAP-TLBO) and original TLBO. In this study, two traditional path generation test problem are used to test the proposed technique. The results show that the new technique can be applied with the path generation problem without prescribed timing and gives better results than the previous technique. Furthermore, SAP-TLBO outperforms the original one.

Robust local search for spacecraft operations using adaptive noise

NASA Technical Reports Server (NTRS)

Fukunaga, Alex S.; Rabideau, Gregg; Chien, Steve

2004-01-01

Randomization is a standard technique for improving the performance of local search algorithms for constraint satisfaction. However, it is well-known that local search algorithms are constraints satisfaction. However, it is well-known that local search algorithms are to the noise values selected. We investigate the use of an adaptive noise mechanism in an iterative repair-based planner/scheduler for spacecraft operations. Preliminary results indicate that adaptive noise makes the use of randomized repair moves safe and robust; that is, using adaptive noise makes it possible to consistently achieve, performance comparable with the best tuned noise setting without the need for manually tuning the noise parameter.

A Comparative Theoretical and Computational Study on Robust Counterpart Optimization: II. Probabilistic Guarantees on Constraint Satisfaction

PubMed Central

Li, Zukui; Floudas, Christodoulos A.

2012-01-01

Probabilistic guarantees on constraint satisfaction for robust counterpart optimization are studied in this paper. The robust counterpart optimization formulations studied are derived from box, ellipsoidal, polyhedral, “interval+ellipsoidal” and “interval+polyhedral” uncertainty sets (Li, Z., Ding, R., and Floudas, C.A., A Comparative Theoretical and Computational Study on Robust Counterpart Optimization: I. Robust Linear and Robust Mixed Integer Linear Optimization, Ind. Eng. Chem. Res, 2011, 50, 10567). For those robust counterpart optimization formulations, their corresponding probability bounds on constraint satisfaction are derived for different types of uncertainty characteristic (i.e., bounded or unbounded uncertainty, with or without detailed probability distribution information). The findings of this work extend the results in the literature and provide greater flexibility for robust optimization practitioners in choosing tighter probability bounds so as to find less conservative robust solutions. Extensive numerical studies are performed to compare the tightness of the different probability bounds and the conservatism of different robust counterpart optimization formulations. Guiding rules for the selection of robust counterpart optimization models and for the determination of the size of the uncertainty set are discussed. Applications in production planning and process scheduling problems are presented. PMID:23329868

An efficient method for generalized linear multiplicative programming problem with multiplicative constraints.

PubMed

Zhao, Yingfeng; Liu, Sanyang

2016-01-01

We present a practical branch and bound algorithm for globally solving generalized linear multiplicative programming problem with multiplicative constraints. To solve the problem, a relaxation programming problem which is equivalent to a linear programming is proposed by utilizing a new two-phase relaxation technique. In the algorithm, lower and upper bounds are simultaneously obtained by solving some linear relaxation programming problems. Global convergence has been proved and results of some sample examples and a small random experiment show that the proposed algorithm is feasible and efficient.

A sequential solution for anisotropic total variation image denoising with interval constraints

NASA Astrophysics Data System (ADS)

Xu, Jingyan; Noo, Frédéric

2017-09-01

We show that two problems involving the anisotropic total variation (TV) and interval constraints on the unknown variables admit, under some conditions, a simple sequential solution. Problem 1 is a constrained TV penalized image denoising problem; problem 2 is a constrained fused lasso signal approximator. The sequential solution entails finding first the solution to the unconstrained problem, and then applying a thresholding to satisfy the constraints. If the interval constraints are uniform, this sequential solution solves problem 1. If the interval constraints furthermore contain zero, the sequential solution solves problem 2. Here uniform interval constraints refer to all unknowns being constrained to the same interval. A typical example of application is image denoising in x-ray CT, where the image intensities are non-negative as they physically represent linear attenuation coefficient in the patient body. Our results are simple yet seem unknown; we establish them using the Karush-Kuhn-Tucker conditions for constrained convex optimization.

The Affective Meanings of Automatic Social Behaviors: Three Mechanisms that Explain Priming

ERIC Educational Resources Information Center

Schroder, Tobias; Thagard, Paul

2013-01-01

The priming of concepts has been shown to influence peoples' subsequent actions, often unconsciously. We propose 3 mechanisms (psychological, cultural, and biological) as a unified explanation of such effects. (a) Primed concepts influence holistic representations of situations by parallel constraint satisfaction. (b) The constraints among…

Focusing on the golden ball metaheuristic: an extended study on a wider set of problems.

PubMed

Osaba, E; Diaz, F; Carballedo, R; Onieva, E; Perallos, A

2014-01-01

Nowadays, the development of new metaheuristics for solving optimization problems is a topic of interest in the scientific community. In the literature, a large number of techniques of this kind can be found. Anyway, there are many recently proposed techniques, such as the artificial bee colony and imperialist competitive algorithm. This paper is focused on one recently published technique, the one called Golden Ball (GB). The GB is a multiple-population metaheuristic based on soccer concepts. Although it was designed to solve combinatorial optimization problems, until now, it has only been tested with two simple routing problems: the traveling salesman problem and the capacitated vehicle routing problem. In this paper, the GB is applied to four different combinatorial optimization problems. Two of them are routing problems, which are more complex than the previously used ones: the asymmetric traveling salesman problem and the vehicle routing problem with backhauls. Additionally, one constraint satisfaction problem (the n-queen problem) and one combinatorial design problem (the one-dimensional bin packing problem) have also been used. The outcomes obtained by GB are compared with the ones got by two different genetic algorithms and two distributed genetic algorithms. Additionally, two statistical tests are conducted to compare these results.

Focusing on the Golden Ball Metaheuristic: An Extended Study on a Wider Set of Problems

PubMed Central

Osaba, E.; Diaz, F.; Carballedo, R.; Onieva, E.; Perallos, A.

2014-01-01

Nowadays, the development of new metaheuristics for solving optimization problems is a topic of interest in the scientific community. In the literature, a large number of techniques of this kind can be found. Anyway, there are many recently proposed techniques, such as the artificial bee colony and imperialist competitive algorithm. This paper is focused on one recently published technique, the one called Golden Ball (GB). The GB is a multiple-population metaheuristic based on soccer concepts. Although it was designed to solve combinatorial optimization problems, until now, it has only been tested with two simple routing problems: the traveling salesman problem and the capacitated vehicle routing problem. In this paper, the GB is applied to four different combinatorial optimization problems. Two of them are routing problems, which are more complex than the previously used ones: the asymmetric traveling salesman problem and the vehicle routing problem with backhauls. Additionally, one constraint satisfaction problem (the n-queen problem) and one combinatorial design problem (the one-dimensional bin packing problem) have also been used. The outcomes obtained by GB are compared with the ones got by two different genetic algorithms and two distributed genetic algorithms. Additionally, two statistical tests are conducted to compare these results. PMID:25165742

Cognitive constraints on high school students' representations of real environmental problems

NASA Astrophysics Data System (ADS)

Barnes, Ervin Kenneth

One class of juniors and seniors was studied through one semester in the investigation of how students think about, learn from, and solve real environmental problems. The intention was to listen to student voices while researching the features of their representations of these problems, the beliefs they held (tenets), the cognitive processes they employed, and the principles of science, ecology, problem solving, and ethics they held as tenets. The focus was upon two self-selected groups as they perceived, engaged, analyzed, and proposed solutions for problems. Analysis of the student representations involved interpretation of the features to include both the perspective tenets and the envisioning processes. These processes included the intentive and attentive constraints as tenet acquisition and volitive and agential constraints as tenet affirmation. The perspective tenets included a variety of conceptual (basic science, ecological, ethical, and problem-solving) constraints as well as ontological, epistemological, and other cultural (role, status, power, and community) constraints. The perspective tenets were interpreted thematically including the ways populations of people cause and care about environmental problems, the magnitude of environmental problems and the science involved, the expectations and limitations students perceive for themselves, and the importance of community awareness and cooperation to addressing these problems. Some of these tenets were interpreted to be principles in that they were rules that were accepted by some people as true. The perspective tenets, along with the envisioning processes, were perceived to be the constraints that determined the environmental problems and limited the solution possibilities. The students thought about environmental problems in mature and principled ways using a repertoire of cognitive processes. They learned from them as they acquired and affirmed tenets. They solved them through personal choices and efforts to increase community awareness. The ways students think about, learn from, and solve real environmental problems were all constrained by the perspective tenets (including cultural tenets of role, status, and power) and envisioning processes. It was concluded that students need help from the community to go further in solving these real environmental problems.

Coping mediates the influence of personality on life satisfaction in patients with rheumatic diseases.

PubMed

Vollmann, Manja; Pukrop, Jörg; Salewski, Christel

2016-04-01

A rheumatic disease can severely impair a person's quality of life. The degree of impairment, however, is not closely related to objective indicators of disease severity. This study investigated the influence and the interplay of core psychological factors, i.e., personality and coping, on life satisfaction in patients with rheumatic diseases. Particularly, it was tested whether coping mediates the effects of personality on life satisfaction. In a cross-sectional design, 158 patients diagnosed with a rheumatic disease completed questionnaires assessing the Big 5 personality traits (BFI-10), several disease-related coping strategies (EFK) and life satisfaction (HSWBS). Data were analyzed using a complex multiple mediation analysis with the Big 5 personality traits as predictors, coping strategies as mediators and life satisfaction as outcome. All personality traits and seven of the nine coping strategies were associated with life satisfaction (rs > |0.16|, ps ≤ 0.05). The mediation analysis revealed that personality traits had no direct, but rather indirect effects on life satisfaction through coping. Neuroticism had a negative indirect effect on life satisfaction through less active problem solving and more depressive coping (indirect effects > -0.03, ps < 0.05). Extraversion, agreeableness, and conscientiousness had positive indirect effects on life satisfaction through more active problem solving, less depressive coping and/or a more active search for social support (indirect effects > 0.06, ps < 0.05). Personality and coping play a role in adjustment to rheumatic diseases. The interplay of these variables should be considered in psychological interventions for patients with rheumatic diseases.

A Simple Label Switching Algorithm for Semisupervised Structural SVMs.

PubMed

Balamurugan, P; Shevade, Shirish; Sundararajan, S

2015-10-01

In structured output learning, obtaining labeled data for real-world applications is usually costly, while unlabeled examples are available in abundance. Semisupervised structured classification deals with a small number of labeled examples and a large number of unlabeled structured data. In this work, we consider semisupervised structural support vector machines with domain constraints. The optimization problem, which in general is not convex, contains the loss terms associated with the labeled and unlabeled examples, along with the domain constraints. We propose a simple optimization approach that alternates between solving a supervised learning problem and a constraint matching problem. Solving the constraint matching problem is difficult for structured prediction, and we propose an efficient and effective label switching method to solve it. The alternating optimization is carried out within a deterministic annealing framework, which helps in effective constraint matching and avoiding poor local minima, which are not very useful. The algorithm is simple and easy to implement. Further, it is suitable for any structured output learning problem where exact inference is available. Experiments on benchmark sequence labeling data sets and a natural language parsing data set show that the proposed approach, though simple, achieves comparable generalization performance.

Multiagent distributed watershed management

NASA Astrophysics Data System (ADS)

Giuliani, M.; Castelletti, A.; Amigoni, F.; Cai, X.

2012-04-01

Deregulation and democratization of water along with increasing environmental awareness are challenging integrated water resources planning and management worldwide. The traditional centralized approach to water management, as described in much of water resources literature, is often unfeasible in most of the modern social and institutional contexts. Thus it should be reconsidered from a more realistic and distributed perspective, in order to account for the presence of multiple and often independent Decision Makers (DMs) and many conflicting stakeholders. Game theory based approaches are often used to study these situations of conflict (Madani, 2010), but they are limited to a descriptive perspective. Multiagent systems (see Wooldridge, 2009), instead, seem to be a more suitable paradigm because they naturally allow to represent a set of self-interested agents (DMs and/or stakeholders) acting in a distributed decision process at the agent level, resulting in a promising compromise alternative between the ideal centralized solution and the actual uncoordinated practices. Casting a water management problem in a multiagent framework allows to exploit the techniques and methods that are already available in this field for solving distributed optimization problems. In particular, in Distributed Constraint Satisfaction Problems (DCSP, see Yokoo et al., 2000), each agent controls some variables according to his own utility function but has to satisfy inter-agent constraints; while in Distributed Constraint Optimization Problems (DCOP, see Modi et al., 2005), the problem is generalized by introducing a global objective function to be optimized that requires a coordination mechanism between the agents. In this work, we apply a DCSP-DCOP based approach to model a steady state hypothetical watershed management problem (Yang et al., 2009), involving several active human agents (i.e. agents who make decisions) and reactive ecological agents (i.e. agents representing environmental interests). Different scenarios of distributed management are simulated, i.e. a situation where all the agents act independently, a situation in which a global coordination takes place and in-between solutions. The solutions are compared with the ones presented in Yang et al. (2009), aiming to present more general multiagent approaches to solve distributed management problems.

Solving and Learning Soft Temporal Constraints: Experimental Scenario and Examples

NASA Technical Reports Server (NTRS)

Rossi, F.; Venable, K. B.; Sperduti, A.; Khatib, L.; Morris, P.; Morris, R.; Koga, Dennis (Technical Monitor)

2001-01-01

Soft temporal constraint problems allow to describe in a natural way scenarios where events happen over time and preferences are associated to event distances and durations. However, sometimes such local preferences are difficult to set, and it may be easier instead to associate preferences to some complete solutions of the problem. To model everything in a uniform way via local preferences only, and also to take advantage of the existing constraint solvers which exploit only local preference use machine learning techniques which learn the local preferences from the global ones. In this paper we describe the existing framework for both solving and learning preferences in temporal constraint problems, the implemented modules, the experimental scenario, and preliminary results on some examples.

An Algorithm for Interactive Modeling of Space-Transportation Engine Simulations: A Constraint Satisfaction Approach

NASA Technical Reports Server (NTRS)

Mitra, Debasis; Thomas, Ajai; Hemminger, Joseph; Sakowski, Barbara

2001-01-01

In this research we have developed an algorithm for the purpose of constraint processing by utilizing relational algebraic operators. Van Beek and others have investigated in the past this type of constraint processing from within a relational algebraic framework, producing some unique results. Apart from providing new theoretical angles, this approach also gives the opportunity to use the existing efficient implementations of relational database management systems as the underlying data structures for any relevant algorithm. Our algorithm here enhances that framework. The algorithm is quite general in its current form. Weak heuristics (like forward checking) developed within the Constraint-satisfaction problem (CSP) area could be also plugged easily within this algorithm for further enhancements of efficiency. The algorithm as developed here is targeted toward a component-oriented modeling problem that we are currently working on, namely, the problem of interactive modeling for batch-simulation of engineering systems (IMBSES). However, it could be adopted for many other CSP problems as well. The research addresses the algorithm and many aspects of the problem IMBSES that we are currently handling.

The use of questions as problem-solving strategies during early childhood.

PubMed

Legare, Cristine H; Mills, Candice M; Souza, André L; Plummer, Leigh E; Yasskin, Rebecca

2013-01-01

This study examined the strategic use of questions to solve problems across early childhood. Participants (N=54, 4-, 5-, and 6-year-olds) engaged in two tasks: a novel problem-solving question task that required asking questions to an informant to determine which card in an array was located in a box and a cognitive flexibility task that required classifying stimuli by multiple dimensions. The results from the question task indicated that there were age differences in the types of questions asked, with 6-year-olds asking more constraint-seeking questions than 4- and 5-year-olds. The number of constraint-seeking questions asked was the only significant predictor of accuracy. Performance on the cognitive flexibility task correlated with both constraint-seeking strategy use and accuracy in the question task. In sum, our results provide evidence that the capacity to use questions to generate relevant information develops before the capacity to apply this information successfully and consistently to solve complex problems. We propose that the process of using questions as strategic tools is an ideal context for examining how children come to gain active and intentional control over problem solving. Copyright © 2012 Elsevier Inc. All rights reserved.

Occupational therapists' job satisfaction in a changing hospital organisation--a time-geography-based study.

PubMed

Bendixen, Hans Jørgen; Ellegård, Kajsa

2014-01-01

To investigate occupational therapists' job satisfaction under a changing regime by using a time-geographic approach focusing on the therapists' everyday working lives. Nine occupational therapists at the Copenhagen University Hospital, Gentofte, Denmark. A mixed-method design was employed. Occupational therapists kept time-geographic diaries, and the results from them were grounded for individual, semi-structured in-depth interviews. Individual reflections on everyday working life were recorded. Transcribed statements from the interviews were analysed to determine factors influencing job satisfaction. The nine therapists kept diaries for one day a month for a total of 70 preselected days over a period of nine months; six participated in individual interviews. Four factors constraining OT job satisfaction were revealed. Economic concerns, new professional paradigms and methods in combination with a new organisational structure for the occupational therapy service caused uncertainty. In addition, decreasing possibilities for supervision by colleagues influenced job satisfaction. Opportunities for experiencing autonomy in everyday working life were described as facilitators for job satisfaction. The time-geographic and interview methods were useful in focusing on the job satisfaction of occupational therapists, who provided individual interpretations of the balance between autonomy and three types of constraints in everyday working life. The constraints related to organisation, power relations and - not least - how the organisational project of the department fitted in with OTs' individual projects. Matching of organisational and individual projects is of crucial importance, not only for OTs but for most workplaces where individuals are employed to serve patients in the healthcare sector.

A randomized trial of teen online problem solving: efficacy in improving caregiver outcomes after brain injury.

PubMed

Wade, Shari L; Walz, Nicolay C; Carey, JoAnne; McMullen, Kendra M; Cass, Jennifer; Mark, Erin; Yeates, Keith Owen

2012-11-01

To examine the results of a randomized clinical trial (RCT) of Teen Online Problem Solving (TOPS), an online problem solving therapy model, in increasing problem-solving skills and decreasing depressive symptoms and global distress for caregivers of adolescents with traumatic brain injury (TBI). Families of adolescents aged 11-18 who sustained a moderate to severe TBI between 3 and 19 months earlier were recruited from hospital trauma registries. Participants were assigned to receive a web-based, problem-solving intervention (TOPS, n = 20), or access to online resources pertaining to TBI (Internet Resource Comparison; IRC; n = 21). Parent report of problem solving skills, depressive symptoms, global distress, utilization, and satisfaction were assessed pre- and posttreatment. Groups were compared on follow-up scores after controlling for pretreatment levels. Family income was examined as a potential moderator of treatment efficacy. Improvement in problem solving was examined as a mediator of reductions in depression and distress. Forty-one participants provided consent and completed baseline assessments, with follow-up assessments completed on 35 participants (16 TOPS and 19 IRC). Parents in both groups reported a high level of satisfaction with both interventions. Improvements in problem solving skills and depression were moderated by family income, with caregivers of lower income in TOPS reporting greater improvements. Increases in problem solving partially mediated reductions in global distress. Findings suggest that TOPS may be effective in improving problem solving skills and reducing depressive symptoms for certain subsets of caregivers in families of adolescents with TBI.

The theory of reasoned action as parallel constraint satisfaction: towards a dynamic computational model of health behavior.

PubMed

Orr, Mark G; Thrush, Roxanne; Plaut, David C

2013-01-01

The reasoned action approach, although ubiquitous in health behavior theory (e.g., Theory of Reasoned Action/Planned Behavior), does not adequately address two key dynamical aspects of health behavior: learning and the effect of immediate social context (i.e., social influence). To remedy this, we put forth a computational implementation of the Theory of Reasoned Action (TRA) using artificial-neural networks. Our model re-conceptualized behavioral intention as arising from a dynamic constraint satisfaction mechanism among a set of beliefs. In two simulations, we show that constraint satisfaction can simultaneously incorporate the effects of past experience (via learning) with the effects of immediate social context to yield behavioral intention, i.e., intention is dynamically constructed from both an individual's pre-existing belief structure and the beliefs of others in the individual's social context. In a third simulation, we illustrate the predictive ability of the model with respect to empirically derived behavioral intention. As the first known computational model of health behavior, it represents a significant advance in theory towards understanding the dynamics of health behavior. Furthermore, our approach may inform the development of population-level agent-based models of health behavior that aim to incorporate psychological theory into models of population dynamics.

The Theory of Reasoned Action as Parallel Constraint Satisfaction: Towards a Dynamic Computational Model of Health Behavior

PubMed Central

Orr, Mark G.; Thrush, Roxanne; Plaut, David C.

2013-01-01

The reasoned action approach, although ubiquitous in health behavior theory (e.g., Theory of Reasoned Action/Planned Behavior), does not adequately address two key dynamical aspects of health behavior: learning and the effect of immediate social context (i.e., social influence). To remedy this, we put forth a computational implementation of the Theory of Reasoned Action (TRA) using artificial-neural networks. Our model re-conceptualized behavioral intention as arising from a dynamic constraint satisfaction mechanism among a set of beliefs. In two simulations, we show that constraint satisfaction can simultaneously incorporate the effects of past experience (via learning) with the effects of immediate social context to yield behavioral intention, i.e., intention is dynamically constructed from both an individual’s pre-existing belief structure and the beliefs of others in the individual’s social context. In a third simulation, we illustrate the predictive ability of the model with respect to empirically derived behavioral intention. As the first known computational model of health behavior, it represents a significant advance in theory towards understanding the dynamics of health behavior. Furthermore, our approach may inform the development of population-level agent-based models of health behavior that aim to incorporate psychological theory into models of population dynamics. PMID:23671603

The artificial-free technique along the objective direction for the simplex algorithm

NASA Astrophysics Data System (ADS)

Boonperm, Aua-aree; Sinapiromsaran, Krung

2014-03-01

The simplex algorithm is a popular algorithm for solving linear programming problems. If the origin point satisfies all constraints then the simplex can be started. Otherwise, artificial variables will be introduced to start the simplex algorithm. If we can start the simplex algorithm without using artificial variables then the simplex iterate will require less time. In this paper, we present the artificial-free technique for the simplex algorithm by mapping the problem into the objective plane and splitting constraints into three groups. In the objective plane, one of variables which has a nonzero coefficient of the objective function is fixed in terms of another variable. Then it can split constraints into three groups: the positive coefficient group, the negative coefficient group and the zero coefficient group. Along the objective direction, some constraints from the positive coefficient group will form the optimal solution. If the positive coefficient group is nonempty, the algorithm starts with relaxing constraints from the negative coefficient group and the zero coefficient group. We guarantee the feasible region obtained from the positive coefficient group to be nonempty. The transformed problem is solved using the simplex algorithm. Additional constraints from the negative coefficient group and the zero coefficient group will be added to the solved problem and use the dual simplex method to determine the new optimal solution. An example shows the effectiveness of our algorithm.

An inexact log-normal distribution-based stochastic chance-constrained model for agricultural water quality management

NASA Astrophysics Data System (ADS)

Wang, Yu; Fan, Jie; Xu, Ye; Sun, Wei; Chen, Dong

2018-05-01

In this study, an inexact log-normal-based stochastic chance-constrained programming model was developed for solving the non-point source pollution issues caused by agricultural activities. Compared to the general stochastic chance-constrained programming model, the main advantage of the proposed model is that it allows random variables to be expressed as a log-normal distribution, rather than a general normal distribution. Possible deviations in solutions caused by irrational parameter assumptions were avoided. The agricultural system management in the Erhai Lake watershed was used as a case study, where critical system factors, including rainfall and runoff amounts, show characteristics of a log-normal distribution. Several interval solutions were obtained under different constraint-satisfaction levels, which were useful in evaluating the trade-off between system economy and reliability. The applied results show that the proposed model could help decision makers to design optimal production patterns under complex uncertainties. The successful application of this model is expected to provide a good example for agricultural management in many other watersheds.

Toward a Mature Science of Consciousness

PubMed Central

Wiese, Wanja

2018-01-01

In Being No One, Metzinger (2004[2003]) introduces an approach to the scientific study of consciousness that draws on theories and results from different disciplines, targeted at multiple levels of analysis. Descriptions and assumptions formulated at, for instance, the phenomenological, representationalist, and neurobiological levels of analysis provide different perspectives on the same phenomenon, which can ultimately yield necessary and sufficient conditions for applying the concept of phenomenal representation. In this way, the “method of interdisciplinary constraint satisfaction (MICS)” (as it has been called by Weisberg, 2005) promotes our understanding of consciousness. However, even more than a decade after the first publication of Being No One, we still lack a mature science of consciousness. This paper makes the following meta-theoretical contribution: It analyzes the hurdles an approach such as MICS has yet to overcome and discusses to what extent existing approaches solve the problems left open by MICS. Furthermore, it argues that a unifying theory of different features of consciousness is required to reach a mature science of consciousness. PMID:29896136

Robust ADP Design for Continuous-Time Nonlinear Systems With Output Constraints.

PubMed

Fan, Bo; Yang, Qinmin; Tang, Xiaoyu; Sun, Youxian

2018-06-01

In this paper, a novel robust adaptive dynamic programming (RADP)-based control strategy is presented for the optimal control of a class of output-constrained continuous-time unknown nonlinear systems. Our contribution includes a step forward beyond the usual optimal control result to show that the output of the plant is always within user-defined bounds. To achieve the new results, an error transformation technique is first established to generate an equivalent nonlinear system, whose asymptotic stability guarantees both the asymptotic stability and the satisfaction of the output restriction of the original system. Furthermore, RADP algorithms are developed to solve the transformed nonlinear optimal control problem with completely unknown dynamics as well as a robust design to guarantee the stability of the closed-loop systems in the presence of unavailable internal dynamic state. Via small-gain theorem, asymptotic stability of the original and transformed nonlinear system is theoretically guaranteed. Finally, comparison results demonstrate the merits of the proposed control policy.

Toward a Mature Science of Consciousness.

PubMed

Wiese, Wanja

2018-01-01

In Being No One , Metzinger (2004[2003]) introduces an approach to the scientific study of consciousness that draws on theories and results from different disciplines, targeted at multiple levels of analysis. Descriptions and assumptions formulated at, for instance, the phenomenological, representationalist, and neurobiological levels of analysis provide different perspectives on the same phenomenon, which can ultimately yield necessary and sufficient conditions for applying the concept of phenomenal representation. In this way, the "method of interdisciplinary constraint satisfaction (MICS)" (as it has been called by Weisberg, 2005) promotes our understanding of consciousness. However, even more than a decade after the first publication of Being No One , we still lack a mature science of consciousness. This paper makes the following meta-theoretical contribution: It analyzes the hurdles an approach such as MICS has yet to overcome and discusses to what extent existing approaches solve the problems left open by MICS. Furthermore, it argues that a unifying theory of different features of consciousness is required to reach a mature science of consciousness.

The Distribution of Job Satisfaction among Young European Graduates: Does the Choice of Study Field Matter?

ERIC Educational Resources Information Center

Vila, Luis E.; Garcia-Aracil, Adela; Mora, Jose-Gines

2007-01-01

A student's choice of a field of study is a personal decision that combines individual tastes, inclinations, preferences, and prospects related to the working life with a number of financial and academic constraints. Therefore, the analysis of the effects of degree field on job satisfaction should also address the unobserved heterogeneity among…

A quasi-Newton approach to optimization problems with probability density constraints. [problem solving in mathematical programming

NASA Technical Reports Server (NTRS)

Tapia, R. A.; Vanrooy, D. L.

1976-01-01

A quasi-Newton method is presented for minimizing a nonlinear function while constraining the variables to be nonnegative and sum to one. The nonnegativity constraints were eliminated by working with the squares of the variables and the resulting problem was solved using Tapia's general theory of quasi-Newton methods for constrained optimization. A user's guide for a computer program implementing this algorithm is provided.

A globally convergent LCL method for nonlinear optimization.

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

Friedlander, M. P.; Saunders, M. A.; Mathematics and Computer Science

2005-01-01

For optimization problems with nonlinear constraints, linearly constrained Lagrangian (LCL) methods solve a sequence of subproblems of the form 'minimize an augmented Lagrangian function subject to linearized constraints.' Such methods converge rapidly near a solution but may not be reliable from arbitrary starting points. Nevertheless, the well-known software package MINOS has proved effective on many large problems. Its success motivates us to derive a related LCL algorithm that possesses three important properties: it is globally convergent, the subproblem constraints are always feasible, and the subproblems may be solved inexactly. The new algorithm has been implemented in Matlab, with an optionmore » to use either MINOS or SNOPT (Fortran codes) to solve the linearly constrained subproblems. Only first derivatives are required. We present numerical results on a subset of the COPS, HS, and CUTE test problems, which include many large examples. The results demonstrate the robustness and efficiency of the stabilized LCL procedure.« less

Constraint Programming to Solve Maximal Density Still Life

NASA Astrophysics Data System (ADS)

Chu, Geoffrey; Petrie, Karen Elizabeth; Yorke-Smith, Neil

The Maximum Density Still Life problem fills a finite Game of Life board with a stable pattern of cells that has as many live cells as possible. Although simple to state, this problem is computationally challenging for any but the smallest sizes of board. Especially difficult is to prove that the maximum number of live cells has been found. Various approaches have been employed. The most successful are approaches based on Constraint Programming (CP). We describe the Maximum Density Still Life problem, introduce the concept of constraint programming, give an overview on how the problem can be modelled and solved with CP, and report on best-known results for the problem.

A hybrid genetic algorithm for solving bi-objective traveling salesman problems

NASA Astrophysics Data System (ADS)

Ma, Mei; Li, Hecheng

2017-08-01

The traveling salesman problem (TSP) is a typical combinatorial optimization problem, in a traditional TSP only tour distance is taken as a unique objective to be minimized. When more than one optimization objective arises, the problem is known as a multi-objective TSP. In the present paper, a bi-objective traveling salesman problem (BOTSP) is taken into account, where both the distance and the cost are taken as optimization objectives. In order to efficiently solve the problem, a hybrid genetic algorithm is proposed. Firstly, two satisfaction degree indices are provided for each edge by considering the influences of the distance and the cost weight. The first satisfaction degree is used to select edges in a “rough” way, while the second satisfaction degree is executed for a more “refined” choice. Secondly, two satisfaction degrees are also applied to generate new individuals in the iteration process. Finally, based on genetic algorithm framework as well as 2-opt selection strategy, a hybrid genetic algorithm is proposed. The simulation illustrates the efficiency of the proposed algorithm.

Problem Solving Teams in a Total Quality Management Environment.

ERIC Educational Resources Information Center

Towler, Constance F.

1993-01-01

Outlines the problem-solving team training process used at Harvard University (Massachusetts), including the size and formation of teams, roles, and time commitment. Components of the process are explained, including introduction to Total Quality Management (TQM), customer satisfaction, meeting management, Parker Team Player Survey, interactive…

A two-dimensional approach to relationship conflict: meta-analytic findings.

PubMed

Woodin, Erica M

2011-06-01

This meta-analysis of 64 studies (5,071 couples) used a metacoding system to categorize observed couple conflict behaviors into categories differing in terms of valence (positive to negative) and intensity (high to low) and resulting in five behavioral categories: hostility, distress, withdrawal, problem solving, and intimacy. Aggregate effect sizes indicated that women were somewhat more likely to display hostility, distress, and intimacy during conflict, whereas men were somewhat more likely to display withdrawal and problem solving. Gender differences were of a small magnitude. For both men and women, hostility was robustly associated with lower relationship satisfaction (medium effect), distress and withdrawal were somewhat associated (small effect), and intimacy and problem solving were both closely associated with relationship satisfaction (medium effect). Effect sizes were moderated in several cases by study characteristics including year of publication, developmental period of the sample, recruitment design, duration of observed conflict, method used to induce conflict, and type of coding system used. Findings from this meta-analysis suggest that high-intensity conflict behaviors of both a positive and negative nature are important correlates of relationship satisfaction and underscore the relatively small gender differences in many conflict behaviors. 2011 APA, all rights reserved

Fluence map optimization (FMO) with dose-volume constraints in IMRT using the geometric distance sorting method.

PubMed

Lan, Yihua; Li, Cunhua; Ren, Haozheng; Zhang, Yong; Min, Zhifang

2012-10-21

A new heuristic algorithm based on the so-called geometric distance sorting technique is proposed for solving the fluence map optimization with dose-volume constraints which is one of the most essential tasks for inverse planning in IMRT. The framework of the proposed method is basically an iterative process which begins with a simple linear constrained quadratic optimization model without considering any dose-volume constraints, and then the dose constraints for the voxels violating the dose-volume constraints are gradually added into the quadratic optimization model step by step until all the dose-volume constraints are satisfied. In each iteration step, an interior point method is adopted to solve each new linear constrained quadratic programming. For choosing the proper candidate voxels for the current dose constraint adding, a so-called geometric distance defined in the transformed standard quadratic form of the fluence map optimization model was used to guide the selection of the voxels. The new geometric distance sorting technique can mostly reduce the unexpected increase of the objective function value caused inevitably by the constraint adding. It can be regarded as an upgrading to the traditional dose sorting technique. The geometry explanation for the proposed method is also given and a proposition is proved to support our heuristic idea. In addition, a smart constraint adding/deleting strategy is designed to ensure a stable iteration convergence. The new algorithm is tested on four cases including head-neck, a prostate, a lung and an oropharyngeal, and compared with the algorithm based on the traditional dose sorting technique. Experimental results showed that the proposed method is more suitable for guiding the selection of new constraints than the traditional dose sorting method, especially for the cases whose target regions are in non-convex shapes. It is a more efficient optimization technique to some extent for choosing constraints than the dose sorting method. By integrating a smart constraint adding/deleting scheme within the iteration framework, the new technique builds up an improved algorithm for solving the fluence map optimization with dose-volume constraints.

Complex systems and health behavior change: insights from cognitive science.

PubMed

Orr, Mark G; Plaut, David C

2014-05-01

To provide proof-of-concept that quantum health behavior can be instantiated as a computational model that is informed by cognitive science, the Theory of Reasoned Action, and quantum health behavior theory. We conducted a synthetic review of the intersection of quantum health behavior change and cognitive science. We conducted simulations, using a computational model of quantum health behavior (a constraint satisfaction artificial neural network) and tested whether the model exhibited quantum-like behavior. The model exhibited clear signs of quantum-like behavior. Quantum health behavior can be conceptualized as constraint satisfaction: a mitigation between current behavioral state and the social contexts in which it operates. We outlined implications for moving forward with computational models of both quantum health behavior and health behavior in general.

Comparison of passive and active leisure activities and life satisfaction with aging.

PubMed

Cho, Dongwook; Post, Jay; Kim, Sung Kyeom

2018-03-01

Many older adults face limitations to participating in active leisure activities as a result of their physical constraints from aging. Passive leisure activities become alternative leisure activities for older adults as a result of limited physical capacity. The present study sought to determine whether there exists a difference in the frequency of participation in passive and active leisure activities, and the effect of participation in passive and active leisure activities on the life satisfaction level of old adults. A total of 460 participants aged 60-95 years were randomly selected from 21 sites in the USA. The Life Satisfaction Index - Z and the Meaningful Activity Participation Assessment were analyzed to examine older adults' life satisfaction and frequency of active or passive activities. The results showed that participation in passive leisure activities, such reading, talking on the telephone and watching TV/listening to the radio, is more frequent among older adults (P = 0.000). The regression coefficient found that club/organization or volunteering (P = 0.008), homemaking/maintenance (P = 0.017) and traveling (P = 0.017) for active leisure activities were statistically significant predictors of Life Satisfaction Index - Z for older adults. The current study shows that older adults spent much more times participating in passive leisure activities, such as radio/watching TV, talking on the phone and reading. The result also showed that active leisure activities, such as club/organization or volunteering, home making/maintenance and traveling, were significant predictors of life satisfaction for older adults controlling for covariates. The current study suggests marketing and programming plans to overcome the constraints that influence older adults' life satisfaction. Geriatr Gerontol Int 2018; 18: 380-386. © 2017 Japan Geriatrics Society.

Using Grey Wolf Algorithm to Solve the Capacitated Vehicle Routing Problem

NASA Astrophysics Data System (ADS)

Korayem, L.; Khorsid, M.; Kassem, S. S.

2015-05-01

The capacitated vehicle routing problem (CVRP) is a class of the vehicle routing problems (VRPs). In CVRP a set of identical vehicles having fixed capacities are required to fulfill customers' demands for a single commodity. The main objective is to minimize the total cost or distance traveled by the vehicles while satisfying a number of constraints, such as: the capacity constraint of each vehicle, logical flow constraints, etc. One of the methods employed in solving the CVRP is the cluster-first route-second method. It is a technique based on grouping of customers into a number of clusters, where each cluster is served by one vehicle. Once clusters are formed, a route determining the best sequence to visit customers is established within each cluster. The recently bio-inspired grey wolf optimizer (GWO), introduced in 2014, has proven to be efficient in solving unconstrained, as well as, constrained optimization problems. In the current research, our main contributions are: combining GWO with the traditional K-means clustering algorithm to generate the ‘K-GWO’ algorithm, deriving a capacitated version of the K-GWO algorithm by incorporating a capacity constraint into the aforementioned algorithm, and finally, developing 2 new clustering heuristics. The resulting algorithm is used in the clustering phase of the cluster-first route-second method to solve the CVR problem. The algorithm is tested on a number of benchmark problems with encouraging results.

An On-Campus Botanical Tour to Promote Student Satisfaction and Learning in a University Level Biodiversity or General Biology Course

ERIC Educational Resources Information Center

Ratnayaka, Harish H.

2017-01-01

Outdoor, hands-on and experiential learning, as opposed to instruction-based learning in classroom, increases student satisfaction and motivation leading to a deeper understanding of the subject. However, the use of outdoor exercises in undergraduate biology courses is declining due to a variety of constraints. Thus, the goal of this paper is to…

Minimization of the root of a quadratic functional under a system of affine equality constraints with application to portfolio management

NASA Astrophysics Data System (ADS)

Landsman, Zinoviy

2008-10-01

We present an explicit closed form solution of the problem of minimizing the root of a quadratic functional subject to a system of affine constraints. The result generalizes Z. Landsman, Minimization of the root of a quadratic functional under an affine equality constraint, J. Comput. Appl. Math. 2007, to appear, see , articles in press, where the optimization problem was solved under only one linear constraint. This is of interest for solving significant problems pertaining to financial economics as well as some classes of feasibility and optimization problems which frequently occur in tomography and other fields. The results are illustrated in the problem of optimal portfolio selection and the particular case when the expected return of finance portfolio is certain is discussed.

Damage tolerant design using collapse techniques

NASA Technical Reports Server (NTRS)

Haftka, R. T.

1982-01-01

A new approach to the design of structures for improved global damage tolerance is presented. In its undamaged condition the structure is designed subject to strength, displacement and buckling constraints. In the damaged condition the only constraint is that the structure will not collapse. The collapse load calculation is formulated as a maximization problem and solved by an interior extended penalty function. The design for minimum weight subject to constraints on the undamaged structure and a specified level of the collapse load is a minimization problem which is also solved by a penalty function formulation. Thus the overall problem is of a nested or multilevel optimization. Examples are presented to demonstrate the difference between the present and more traditional approaches.

Towards Solving the Mixing Problem in the Decomposition of Geophysical Time Series by Independent Component Analysis

NASA Technical Reports Server (NTRS)

Aires, Filipe; Rossow, William B.; Chedin, Alain; Hansen, James E. (Technical Monitor)

2000-01-01

The use of the Principal Component Analysis technique for the analysis of geophysical time series has been questioned in particular for its tendency to extract components that mix several physical phenomena even when the signal is just their linear sum. We demonstrate with a data simulation experiment that the Independent Component Analysis, a recently developed technique, is able to solve this problem. This new technique requires the statistical independence of components, a stronger constraint, that uses higher-order statistics, instead of the classical decorrelation a weaker constraint, that uses only second-order statistics. Furthermore, ICA does not require additional a priori information such as the localization constraint used in Rotational Techniques.

Problem Solving Under Time-Constraints.

ERIC Educational Resources Information Center

Richardson, Michael; Hunt, Earl

A model of how automated and controlled processing can be mixed in computer simulations of problem solving is proposed. It is based on previous work by Hunt and Lansman (1983), who developed a model of problem solving that could reproduce the data obtained with several attention and performance paradigms, extending production-system notation to…

Exploring direct and indirect influences of physical work environment on job satisfaction for early-career registered nurses employed in hospitals.

PubMed

Djukic, Maja; Kovner, Christine T; Brewer, Carol S; Fatehi, Farida; Greene, William H

2014-08-01

We explored direct and indirect influences of physical work environment on job satisfaction in a nationally representative sample of 1,141 early-career registered nurses. In the fully specified model, physical work environment had a non-significant direct effect on job satisfaction. The path analysis used to test multiple indirect effects showed that physical work environment had a positive indirect effect (p < .05) on job satisfaction through ten variables: negative affectivity, variety, workgroup cohesion, nurse-physician relations, quantitative workload, organizational constraints, distributive justice, promotional opportunity, local and non-local job opportunities. The findings make important contributions to the understanding of the relationship between physical work environment and job satisfaction. The results can inform health care leaders' insight about how physical work environment influences nurses' job satisfaction. © 2014 Wiley Periodicals, Inc.

High performance techniques for space mission scheduling

NASA Technical Reports Server (NTRS)

Smith, Stephen F.

1994-01-01

In this paper, we summarize current research at Carnegie Mellon University aimed at development of high performance techniques and tools for space mission scheduling. Similar to prior research in opportunistic scheduling, our approach assumes the use of dynamic analysis of problem constraints as a basis for heuristic focusing of problem solving search. This methodology, however, is grounded in representational assumptions more akin to those adopted in recent temporal planning research, and in a problem solving framework which similarly emphasizes constraint posting in an explicitly maintained solution constraint network. These more general representational assumptions are necessitated by the predominance of state-dependent constraints in space mission planning domains, and the consequent need to integrate resource allocation and plan synthesis processes. First, we review the space mission problems we have considered to date and indicate the results obtained in these application domains. Next, we summarize recent work in constraint posting scheduling procedures, which offer the promise of better future solutions to this class of problems.

Planning and Scheduling for Fleets of Earth Observing Satellites

NASA Technical Reports Server (NTRS)

Frank, Jeremy; Jonsson, Ari; Morris, Robert; Smith, David E.; Norvig, Peter (Technical Monitor)

2001-01-01

We address the problem of scheduling observations for a collection of earth observing satellites. This scheduling task is a difficult optimization problem, potentially involving many satellites, hundreds of requests, constraints on when and how to service each request, and resources such as instruments, recording devices, transmitters, and ground stations. High-fidelity models are required to ensure the validity of schedules; at the same time, the size and complexity of the problem makes it unlikely that systematic optimization search methods will be able to solve them in a reasonable time. This paper presents a constraint-based approach to solving the Earth Observing Satellites (EOS) scheduling problem, and proposes a stochastic heuristic search method for solving it.

[Personal resources and nursing work: a research on coping strategies and job satisfaction].

PubMed

Formentin, Silvia; Dallago, Lorenza; Negrisolo, Adriana

2009-01-01

The coping (ability to face a difficult situation), is an essential resource for nurses, because it increases the effective functioning in the working environment, job satisfaction and individual level of wellness. The aim of this study is to explore the relationship between coping strategies and job satisfaction. A self report questionnaire was administered to all the nurses working in health services and hospitals of Padua province and to free lance nurses members of the College of Nurses, to collect information on coping strategies and job satisfaction. 2264 questionnaires were returned (71%). Active coping strategies are adopted mainly from older nurses while avoiding strategies are prevalent among younger. An association was observed between job satisfaction and active coping strategies, aimed at problem solving: active strategies increase job satisfaction while avoiding strategies descrease it. Individual coping strategies implemented to face difficult situations are associated to job satisfaction.

Structural design using equilibrium programming formulations

NASA Technical Reports Server (NTRS)

Scotti, Stephen J.

1995-01-01

Solutions to increasingly larger structural optimization problems are desired. However, computational resources are strained to meet this need. New methods will be required to solve increasingly larger problems. The present approaches to solving large-scale problems involve approximations for the constraints of structural optimization problems and/or decomposition of the problem into multiple subproblems that can be solved in parallel. An area of game theory, equilibrium programming (also known as noncooperative game theory), can be used to unify these existing approaches from a theoretical point of view (considering the existence and optimality of solutions), and be used as a framework for the development of new methods for solving large-scale optimization problems. Equilibrium programming theory is described, and existing design techniques such as fully stressed design and constraint approximations are shown to fit within its framework. Two new structural design formulations are also derived. The first new formulation is another approximation technique which is a general updating scheme for the sensitivity derivatives of design constraints. The second new formulation uses a substructure-based decomposition of the structure for analysis and sensitivity calculations. Significant computational benefits of the new formulations compared with a conventional method are demonstrated.

Work Environment Questionnaires and Army Unit Effectiveness and Satisfaction Measures

DTIC Science & Technology

1977-09-01

satisfaction indices (retention rates and disciplinary actions) and place more emphasis on these outcomes. In balance , the work environment and...last me a year"), and that "effective" supervisors and organizations planned carefully to stay within these constraints and balance expenditures over...scale was out of balance with its higher end. Most instruments reviewed used Likert scales with numerical anchors; very few provided specific descriptive

Problem-Solving Management Training Effects on Sales Productivity and Job Satisfaction.

ERIC Educational Resources Information Center

Ross, Paul C.; And Others

Research suggests that effective organizational change must be led by line personnel rather than by outside consultants. The Performance Management Program (PMP) implemented in two Bell Telephone companies is a line-led, self-help program in which managers participate in problem-solving activities within their own jobs. Marketing and sales…

A Higher Harmonic Optimal Controller to Optimise Rotorcraft Aeromechanical Behaviour

NASA Technical Reports Server (NTRS)

Leyland, Jane Anne

1996-01-01

Three methods to optimize rotorcraft aeromechanical behavior for those cases where the rotorcraft plant can be adequately represented by a linear model system matrix were identified and implemented in a stand-alone code. These methods determine the optimal control vector which minimizes the vibration metric subject to constraints at discrete time points, and differ from the commonly used non-optimal constraint penalty methods such as those employed by conventional controllers in that the constraints are handled as actual constraints to an optimization problem rather than as just additional terms in the performance index. The first method is to use a Non-linear Programming algorithm to solve the problem directly. The second method is to solve the full set of non-linear equations which define the necessary conditions for optimality. The third method is to solve each of the possible reduced sets of equations defining the necessary conditions for optimality when the constraints are pre-selected to be either active or inactive, and then to simply select the best solution. The effects of maneuvers and aeroelasticity on the systems matrix are modelled by using a pseudo-random pseudo-row-dependency scheme to define the systems matrix. Cases run to date indicate that the first method of solution is reliable, robust, and easiest to use, and that it was superior to the conventional controllers which were considered.

Constraint-Muse: A Soft-Constraint Based System for Music Therapy

NASA Astrophysics Data System (ADS)

Hölzl, Matthias; Denker, Grit; Meier, Max; Wirsing, Martin

Monoidal soft constraints are a versatile formalism for specifying and solving multi-criteria optimization problems with dynamically changing user preferences. We have developed a prototype tool for interactive music creation, called Constraint Muse, that uses monoidal soft constraints to ensure that a dynamically generated melody harmonizes with input from other sources. Constraint Muse provides an easy to use interface based on Nintendo Wii controllers and is intended to be used in music therapy for people with Parkinson’s disease and for children with high-functioning autism or Asperger’s syndrome.

Iterative Goal Refinement for Robotics

DTIC Science & Technology

2014-06-01

Researchers have used a variety of ways to represent such constraints (e.g., as a constraint satisfaction problem ( Scala , to appear), in PDDL (Vaquro...lifecycle to recent models of replanning (Talamadupala et al., 2013) and continual planning ( Scala , to appear). We described goal reasoning in...F., & Barreiro, J. (2013). Towards deliberative control in marine robotics. In Marine Robot Autonomy (pp. 91–175). Springer. Scala , E. (to appear

Polynomial-time solution of prime factorization and NP-complete problems with digital memcomputing machines

NASA Astrophysics Data System (ADS)

Traversa, Fabio L.; Di Ventra, Massimiliano

2017-02-01

We introduce a class of digital machines, we name Digital Memcomputing Machines, (DMMs) able to solve a wide range of problems including Non-deterministic Polynomial (NP) ones with polynomial resources (in time, space, and energy). An abstract DMM with this power must satisfy a set of compatible mathematical constraints underlying its practical realization. We prove this by making a connection with the dynamical systems theory. This leads us to a set of physical constraints for poly-resource resolvability. Once the mathematical requirements have been assessed, we propose a practical scheme to solve the above class of problems based on the novel concept of self-organizing logic gates and circuits (SOLCs). These are logic gates and circuits able to accept input signals from any terminal, without distinction between conventional input and output terminals. They can solve boolean problems by self-organizing into their solution. They can be fabricated either with circuit elements with memory (such as memristors) and/or standard MOS technology. Using tools of functional analysis, we prove mathematically the following constraints for the poly-resource resolvability: (i) SOLCs possess a global attractor; (ii) their only equilibrium points are the solutions of the problems to solve; (iii) the system converges exponentially fast to the solutions; (iv) the equilibrium convergence rate scales at most polynomially with input size. We finally provide arguments that periodic orbits and strange attractors cannot coexist with equilibria. As examples, we show how to solve the prime factorization and the search version of the NP-complete subset-sum problem. Since DMMs map integers into integers, they are robust against noise and hence scalable. We finally discuss the implications of the DMM realization through SOLCs to the NP = P question related to constraints of poly-resources resolvability.

State-constrained booster trajectory solutions via finite elements and shooting

NASA Technical Reports Server (NTRS)

Bless, Robert R.; Hodges, Dewey H.; Seywald, Hans

1993-01-01

This paper presents an extension of a FEM formulation based on variational principles. A general formulation for handling internal boundary conditions and discontinuities in the state equations is presented, and the general formulation is modified for optimal control problems subject to state-variable inequality constraints. Solutions which only touch the state constraint and solutions which have a boundary arc of finite length are considered. Suitable shape and test functions are chosen for a FEM discretization. All element quadrature (equivalent to one-point Gaussian quadrature over each element) may be done in closed form. The final form of the algebraic equations is then derived. A simple state-constrained problem is solved. Then, for a practical application of the use of the FEM formulation, a launch vehicle subject to a dynamic pressure constraint (a first-order state inequality constraint) is solved. The results presented for the launch-vehicle trajectory have some interesting features, including a touch-point solution.

Conflict with Mothers-in-Law and Taiwanese Women's Marital Satisfaction: The Moderating Role of Husband Support

ERIC Educational Resources Information Center

Wu, Tsui-Feng; Yeh, Kuang-Hui; Cross, Susan E.; Larson, Lisa M.; Wang, Yi-Chao; Tsai, Yi-Lin

2010-01-01

This study applies social support theory to the question of whether four types of husband behavior (taking the wife's side, problem solving, ignoring conflict, and taking the mother's side) moderate the association between conflict with the mother-in-law and a Taiwanese woman's marital satisfaction. Data were collected from 125 married Taiwanese…

Transformational and derivational strategies in analogical problem solving.

PubMed

Schelhorn, Sven-Eric; Griego, Jacqueline; Schmid, Ute

2007-03-01

Analogical problem solving is mostly described as transfer of a source solution to a target problem based on the structural correspondences (mapping) between source and target. Derivational analogy (Carbonell, Machine learning: an artificial intelligence approach Los Altos. Morgan Kaufmann, 1986) proposes an alternative view: a target problem is solved by replaying a remembered problem-solving episode. Thus, the experience with the source problem is used to guide the search for the target solution by applying the same solution technique rather than by transferring the complete solution. We report an empirical study using the path finding problems presented in Novick and Hmelo (J Exp Psychol Learn Mem Cogn 20:1296-1321, 1994) as material. We show that both transformational and derivational analogy are problem-solving strategies realized by human problem solvers. Which strategy is evoked in a given problem-solving context depends on the constraints guiding object-to-object mapping between source and target problem. Specifically, if constraints facilitating mapping are available, subjects are more likely to employ a transformational strategy, otherwise they are more likely to use a derivational strategy.

Psychometric testing of the clinical nurse leader staff satisfaction instrument.

PubMed

Spiva, LeeAnna; Hart, Patricia L; Wesley, Mary Lou; Gallagher, Erin; McVay, Frank; Waggoner, Jessica; Jarrell, Nicole; Threatt, Jamie L

2014-01-01

Patient care is changing rapidly with increased complexity of care, patient volumes, and financial constraints with rising health care costs and limited reimbursements. In response, the clinical nurse leader (CNL) role was developed. No appropriate instrument exists to measure staff satisfaction with the CNL role. This study describes the development and testing of an instrument designed to measure staff satisfaction with implementation of the CNL role. The psychometric properties and factor structure of the Clinical Nurse Leader Staff Satisfaction (CNLSS) instrument was examined. A 2-factor solution was discovered for the CNLSS. Cronbach's alpha coefficients were acceptable for the subscales and instrument. The CNLSS is a valid and reliable instrument. Future research should focus on establishing test-retest reliability and construct validity.

Power Distribution System Planning with GIS Consideration

NASA Astrophysics Data System (ADS)

Wattanasophon, Sirichai; Eua-Arporn, Bundhit

This paper proposes a method for solving radial distribution system planning problems taking into account geographical information. The proposed method can automatically determine appropriate location and size of a substation, routing of feeders, and sizes of conductors while satisfying all constraints, i.e. technical constraints (voltage drop and thermal limit) and geographical constraints (obstacle, existing infrastructure, and high-cost passages). Sequential quadratic programming (SQP) and minimum path algorithm (MPA) are applied to solve the planning problem based on net price value (NPV) consideration. In addition this method integrates planner's experience and optimization process to achieve an appropriate practical solution. The proposed method has been tested with an actual distribution system, from which the results indicate that it can provide satisfactory plans.

Constraints on scattering amplitudes in multistate Landau-Zener theory

NASA Astrophysics Data System (ADS)

Sinitsyn, Nikolai A.; Lin, Jeffmin; Chernyak, Vladimir Y.

2017-01-01

We derive a set of constraints, which we will call hierarchy constraints, on scattering amplitudes of an arbitrary multistate Landau-Zener model (MLZM). The presence of additional symmetries can transform such constraints into nontrivial relations between elements of the transition probability matrix. This observation can be used to derive complete solutions of some MLZMs or, for models that cannot be solved completely, to reduce the number of independent elements of the transition probability matrix.

Levels of Simplification. The Use of Assumptions, Restrictions, and Constraints in Engineering Analysis.

ERIC Educational Resources Information Center

Whitaker, Stephen

1988-01-01

Describes the use of assumptions, restrictions, and constraints in solving difficult analytical problems in engineering. Uses the Navier-Stokes equations as examples to demonstrate use, derivations, advantages, and disadvantages of the technique. (RT)

Brief Report: Problem Solving Therapy in College Students with Autism Spectrum Disorders: Feasibility and Preliminary Efficacy

ERIC Educational Resources Information Center

Pugliese, Cara E.; White, Susan W.

2014-01-01

Students with autism spectrum disorder (ASD), though academically capable, can have difficulty succeeding in college. Evidence-based intervention to promote effective problem solving may improve quality of life, as well as success and satisfaction in college. This study adapted and piloted a group-based cognitive-behavioral intervention program,…

Merits and limitations of optimality criteria method for structural optimization

NASA Technical Reports Server (NTRS)

Patnaik, Surya N.; Guptill, James D.; Berke, Laszlo

1993-01-01

The merits and limitations of the optimality criteria (OC) method for the minimum weight design of structures subjected to multiple load conditions under stress, displacement, and frequency constraints were investigated by examining several numerical examples. The examples were solved utilizing the Optimality Criteria Design Code that was developed for this purpose at NASA Lewis Research Center. This OC code incorporates OC methods available in the literature with generalizations for stress constraints, fully utilized design concepts, and hybrid methods that combine both techniques. Salient features of the code include multiple choices for Lagrange multiplier and design variable update methods, design strategies for several constraint types, variable linking, displacement and integrated force method analyzers, and analytical and numerical sensitivities. The performance of the OC method, on the basis of the examples solved, was found to be satisfactory for problems with few active constraints or with small numbers of design variables. For problems with large numbers of behavior constraints and design variables, the OC method appears to follow a subset of active constraints that can result in a heavier design. The computational efficiency of OC methods appears to be similar to some mathematical programming techniques.

How Do Severe Constraints Affect the Search Ability of Multiobjective Evolutionary Algorithms in Water Resources?

NASA Astrophysics Data System (ADS)

Clarkin, T. J.; Kasprzyk, J. R.; Raseman, W. J.; Herman, J. D.

2015-12-01

This study contributes a diagnostic assessment of multiobjective evolutionary algorithm (MOEA) search on a set of water resources problem formulations with different configurations of constraints. Unlike constraints in classical optimization modeling, constraints within MOEA simulation-optimization represent limits on acceptable performance that delineate whether solutions within the search problem are feasible. Constraints are relevant because of the emergent pressures on water resources systems: increasing public awareness of their sustainability, coupled with regulatory pressures on water management agencies. In this study, we test several state-of-the-art MOEAs that utilize restricted tournament selection for constraint handling on varying configurations of water resources planning problems. For example, a problem that has no constraints on performance levels will be compared with a problem with several severe constraints, and a problem with constraints that have less severe values on the constraint thresholds. One such problem, Lower Rio Grande Valley (LRGV) portfolio planning, has been solved with a suite of constraints that ensure high reliability, low cost variability, and acceptable performance in a single year severe drought. But to date, it is unclear whether or not the constraints are negatively affecting MOEAs' ability to solve the problem effectively. Two categories of results are explored. The first category uses control maps of algorithm performance to determine if the algorithm's performance is sensitive to user-defined parameters. The second category uses run-time performance metrics to determine the time required for the algorithm to reach sufficient levels of convergence and diversity on the solution sets. Our work exploring the effect of constraints will better enable practitioners to define MOEA problem formulations for real-world systems, especially when stakeholders are concerned with achieving fixed levels of performance according to one or more metrics.

Results of the Software Process Improvement Efforts of the Early Adopters in NAVAIR 4.0

DTIC Science & Technology

2007-12-01

and customer satisfaction. AIRSpeed utilizes a structured, problem solving methodology called DMAIC (Define, Measure, Analyze, Improve, Control...widely used in business. DMAIC leads project teams through the logical steps from problem definition to problem resolution. Each phase has a specific set...costs and improving productivity and customer satisfaction. AIRSpeed utilizes the DMAIC (Define, Measure, Analyze, Improve, Control) structured problem

The results of STEM education methods for enhancing critical thinking and problem solving skill in physics the 10th grade level

NASA Astrophysics Data System (ADS)

Soros, P.; Ponkham, K.; Ekkapim, S.

2018-01-01

This research aimed to: 1) compare the critical think and problem solving skills before and after learning using STEM Education plan, 2) compare student achievement before and after learning about force and laws of motion using STEM Education plan, and 3) the satisfaction of learning by using STEM Education. The sample used were 37 students from grade 10 at Borabu School, Borabu District, Mahasarakham Province, semester 2, Academic year 2016. Tools used in this study consist of: 1) STEM Education plan about the force and laws of motion for grade 10 students of 1 schemes with total of 14 hours, 2) The test of critical think and problem solving skills with multiple-choice type of 5 options and 2 option of 30 items, 3) achievement test on force and laws of motion with multiple-choice of 4 options of 30 items, 4) satisfaction learning with 5 Rating Scale of 20 items. The statistics used in data analysis were percentage, mean, standard deviation, and t-test (Dependent). The results showed that 1) The student with learning using STEM Education plan have score of critical think and problem solving skills on post-test higher than pre-test with statistically significant level .01. 2) The student with learning using STEM Education plan have achievement score on post-test higher than pre-test with statistically significant level of .01. 3) The student'level of satisfaction toward the learning by using STEM Education plan was at a high level (X ¯ = 4.51, S.D=0.56).

Predit: A temporal predictive framework for scheduling systems

NASA Technical Reports Server (NTRS)

Paolucci, E.; Patriarca, E.; Sem, M.; Gini, G.

1992-01-01

Scheduling can be formalized as a Constraint Satisfaction Problem (CSP). Within this framework activities belonging to a plan are interconnected via temporal constraints that account for slack among them. Temporal representation must include methods for constraints propagation and provide a logic for symbolic and numerical deductions. In this paper we describe a support framework for opportunistic reasoning in constraint directed scheduling. In order to focus the attention of an incremental scheduler on critical problem aspects, some discrete temporal indexes are presented. They are also useful for the prediction of the degree of resources contention. The predictive method expressed through our indexes can be seen as a Knowledge Source for an opportunistic scheduler with a blackboard architecture.

Constraints on scattering amplitudes in multistate Landau-Zener theory

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

Sinitsyn, Nikolai A.; Lin, Jeffmin; Chernyak, Vladimir Y.

2017-01-30

Here, we derive a set of constraints, which we will call hierarchy constraints, on scattering amplitudes of an arbitrary multistate Landau-Zener model (MLZM). The presence of additional symmetries can transform such constraints into nontrivial relations between elements of the transition probability matrix. This observation can be used to derive complete solutions of some MLZMs or, for models that cannot be solved completely, to reduce the number of independent elements of the transition probability matrix.

A multiagent evolutionary algorithm for constraint satisfaction problems.

PubMed

Liu, Jing; Zhong, Weicai; Jiao, Licheng

2006-02-01

With the intrinsic properties of constraint satisfaction problems (CSPs) in mind, we divide CSPs into two types, namely, permutation CSPs and nonpermutation CSPs. According to their characteristics, several behaviors are designed for agents by making use of the ability of agents to sense and act on the environment. These behaviors are controlled by means of evolution, so that the multiagent evolutionary algorithm for constraint satisfaction problems (MAEA-CSPs) results. To overcome the disadvantages of the general encoding methods, the minimum conflict encoding is also proposed. Theoretical analyzes show that MAEA-CSPs has a linear space complexity and converges to the global optimum. The first part of the experiments uses 250 benchmark binary CSPs and 79 graph coloring problems from the DIMACS challenge to test the performance of MAEA-CSPs for nonpermutation CSPs. MAEA-CSPs is compared with six well-defined algorithms and the effect of the parameters is analyzed systematically. The second part of the experiments uses a classical CSP, n-queen problems, and a more practical case, job-shop scheduling problems (JSPs), to test the performance of MAEA-CSPs for permutation CSPs. The scalability of MAEA-CSPs along n for n-queen problems is studied with great care. The results show that MAEA-CSPs achieves good performance when n increases from 10(4) to 10(7), and has a linear time complexity. Even for 10(7)-queen problems, MAEA-CSPs finds the solutions by only 150 seconds. For JSPs, 59 benchmark problems are used, and good performance is also obtained.

ADMIT: a toolbox for guaranteed model invalidation, estimation and qualitative–quantitative modeling

PubMed Central

Streif, Stefan; Savchenko, Anton; Rumschinski, Philipp; Borchers, Steffen; Findeisen, Rolf

2012-01-01

Summary: Often competing hypotheses for biochemical networks exist in the form of different mathematical models with unknown parameters. Considering available experimental data, it is then desired to reject model hypotheses that are inconsistent with the data, or to estimate the unknown parameters. However, these tasks are complicated because experimental data are typically sparse, uncertain, and are frequently only available in form of qualitative if–then observations. ADMIT (Analysis, Design and Model Invalidation Toolbox) is a MatLabTM-based tool for guaranteed model invalidation, state and parameter estimation. The toolbox allows the integration of quantitative measurement data, a priori knowledge of parameters and states, and qualitative information on the dynamic or steady-state behavior. A constraint satisfaction problem is automatically generated and algorithms are implemented for solving the desired estimation, invalidation or analysis tasks. The implemented methods built on convex relaxation and optimization and therefore provide guaranteed estimation results and certificates for invalidity. Availability: ADMIT, tutorials and illustrative examples are available free of charge for non-commercial use at http://ifatwww.et.uni-magdeburg.de/syst/ADMIT/ Contact: stefan.streif@ovgu.de PMID:22451270

ADMIT: a toolbox for guaranteed model invalidation, estimation and qualitative-quantitative modeling.

PubMed

Streif, Stefan; Savchenko, Anton; Rumschinski, Philipp; Borchers, Steffen; Findeisen, Rolf

2012-05-01

Often competing hypotheses for biochemical networks exist in the form of different mathematical models with unknown parameters. Considering available experimental data, it is then desired to reject model hypotheses that are inconsistent with the data, or to estimate the unknown parameters. However, these tasks are complicated because experimental data are typically sparse, uncertain, and are frequently only available in form of qualitative if-then observations. ADMIT (Analysis, Design and Model Invalidation Toolbox) is a MatLab(TM)-based tool for guaranteed model invalidation, state and parameter estimation. The toolbox allows the integration of quantitative measurement data, a priori knowledge of parameters and states, and qualitative information on the dynamic or steady-state behavior. A constraint satisfaction problem is automatically generated and algorithms are implemented for solving the desired estimation, invalidation or analysis tasks. The implemented methods built on convex relaxation and optimization and therefore provide guaranteed estimation results and certificates for invalidity. ADMIT, tutorials and illustrative examples are available free of charge for non-commercial use at http://ifatwww.et.uni-magdeburg.de/syst/ADMIT/

Plasma Equilibria With Stochastic Magnetic Fields

NASA Astrophysics Data System (ADS)

Krommes, J. A.; Reiman, A. H.

2009-05-01

Plasma equilibria that include regions of stochastic magnetic fields are of interest in a variety of applications, including tokamaks with ergodic limiters and high-pressure stellarators. Such equilibria are examined theoretically, and a numerical algorithm for their construction is described.^2,3 % The balance between stochastic diffusion of magnetic lines and small effects^2 omitted from the simplest MHD description can support pressure and current profiles that need not be flattened in stochastic regions. The diffusion can be described analytically by renormalizing stochastic Langevin equations for pressure and parallel current j, with particular attention being paid to the satisfaction of the periodicity constraints in toroidal configurations with sheared magnetic fields. The equilibrium field configuration can then be constructed by coupling the prediction for j to Amp'ere's law, which is solved numerically. A. Reiman et al., Pressure-induced breaking of equilibrium flux surfaces in the W7AS stellarator, Nucl. Fusion 47, 572--8 (2007). J. A. Krommes and A. H. Reiman, Plasma equilibrium in a magnetic field with stochastic regions, submitted to Phys. Plasmas. J. A. Krommes, Fundamental statistical theories of plasma turbulence in magnetic fields, Phys. Reports 360, 1--351.

Modeling Regular Replacement for String Constraint Solving

NASA Technical Reports Server (NTRS)

Fu, Xiang; Li, Chung-Chih

2010-01-01

Bugs in user input sanitation of software systems often lead to vulnerabilities. Among them many are caused by improper use of regular replacement. This paper presents a precise modeling of various semantics of regular substitution, such as the declarative, finite, greedy, and reluctant, using finite state transducers (FST). By projecting an FST to its input/output tapes, we are able to solve atomic string constraints, which can be applied to both the forward and backward image computation in model checking and symbolic execution of text processing programs. We report several interesting discoveries, e.g., certain fragments of the general problem can be handled using less expressive deterministic FST. A compact representation of FST is implemented in SUSHI, a string constraint solver. It is applied to detecting vulnerabilities in web applications

The Effects of Experience Grouping on Achievement, Problem-Solving Discourse, and Satisfaction in Professional Technical Training

ERIC Educational Resources Information Center

Mulcahy, Robert Sean

2010-01-01

Learners inevitably enter adult technical training classrooms--indeed, in all classrooms--with different levels of expertise on the subject matter. When the diversity of expertise is wide and the course makes use of small group problem solving, instructors have a choice about how to group learners: they may distribute learners with greater…

Long range science scheduling for the Hubble Space Telescope

NASA Technical Reports Server (NTRS)

Miller, Glenn; Johnston, Mark

1991-01-01

Observations with NASA's Hubble Space Telescope (HST) are scheduled with the assistance of a long-range scheduling system (SPIKE) that was developed using artificial intelligence techniques. In earlier papers, the system architecture and the constraint representation and propagation mechanisms were described. The development of high-level automated scheduling tools, including tools based on constraint satisfaction techniques and neural networks is described. The performance of these tools in scheduling HST observations is discussed.

A two steps solution approach to solving large nonlinear models: application to a problem of conjunctive use.

PubMed

Vieira, J; Cunha, M C

2011-01-01

This article describes a solution method of solving large nonlinear problems in two steps. The two steps solution approach takes advantage of handling smaller and simpler models and having better starting points to improve solution efficiency. The set of nonlinear constraints (named as complicating constraints) which makes the solution of the model rather complex and time consuming is eliminated from step one. The complicating constraints are added only in the second step so that a solution of the complete model is then found. The solution method is applied to a large-scale problem of conjunctive use of surface water and groundwater resources. The results obtained are compared with solutions determined with the direct solve of the complete model in one single step. In all examples the two steps solution approach allowed a significant reduction of the computation time. This potential gain of efficiency of the two steps solution approach can be extremely important for work in progress and it can be particularly useful for cases where the computation time would be a critical factor for having an optimized solution in due time.

An efficient and flexible Abel-inversion method for noisy data

NASA Astrophysics Data System (ADS)

Antokhin, Igor I.

2016-12-01

We propose an efficient and flexible method for solving the Abel integral equation of the first kind, frequently appearing in many fields of astrophysics, physics, chemistry, and applied sciences. This equation represents an ill-posed problem, thus solving it requires some kind of regularization. Our method is based on solving the equation on a so-called compact set of functions and/or using Tikhonov's regularization. A priori constraints on the unknown function, defining a compact set, are very loose and can be set using simple physical considerations. Tikhonov's regularization in itself does not require any explicit a priori constraints on the unknown function and can be used independently of such constraints or in combination with them. Various target degrees of smoothness of the unknown function may be set, as required by the problem at hand. The advantage of the method, apart from its flexibility, is that it gives uniform convergence of the approximate solution to the exact solution, as the errors of input data tend to zero. The method is illustrated on several simulated models with known solutions. An example of astrophysical application of the method is also given.

Object matching using a locally affine invariant and linear programming techniques.

PubMed

Li, Hongsheng; Huang, Xiaolei; He, Lei

2013-02-01

In this paper, we introduce a new matching method based on a novel locally affine-invariant geometric constraint and linear programming techniques. To model and solve the matching problem in a linear programming formulation, all geometric constraints should be able to be exactly or approximately reformulated into a linear form. This is a major difficulty for this kind of matching algorithm. We propose a novel locally affine-invariant constraint which can be exactly linearized and requires a lot fewer auxiliary variables than other linear programming-based methods do. The key idea behind it is that each point in the template point set can be exactly represented by an affine combination of its neighboring points, whose weights can be solved easily by least squares. Errors of reconstructing each matched point using such weights are used to penalize the disagreement of geometric relationships between the template points and the matched points. The resulting overall objective function can be solved efficiently by linear programming techniques. Our experimental results on both rigid and nonrigid object matching show the effectiveness of the proposed algorithm.

An interactive approach based on a discrete differential evolution algorithm for a class of integer bilevel programming problems

NASA Astrophysics Data System (ADS)

Li, Hong; Zhang, Li; Jiao, Yong-Chang

2016-07-01

This paper presents an interactive approach based on a discrete differential evolution algorithm to solve a class of integer bilevel programming problems, in which integer decision variables are controlled by an upper-level decision maker and real-value or continuous decision variables are controlled by a lower-level decision maker. Using the Karush--Kuhn-Tucker optimality conditions in the lower-level programming, the original discrete bilevel formulation can be converted into a discrete single-level nonlinear programming problem with the complementarity constraints, and then the smoothing technique is applied to deal with the complementarity constraints. Finally, a discrete single-level nonlinear programming problem is obtained, and solved by an interactive approach. In each iteration, for each given upper-level discrete variable, a system of nonlinear equations including the lower-level variables and Lagrange multipliers is solved first, and then a discrete nonlinear programming problem only with inequality constraints is handled by using a discrete differential evolution algorithm. Simulation results show the effectiveness of the proposed approach.

Continuous Optimization on Constraint Manifolds

NASA Technical Reports Server (NTRS)

Dean, Edwin B.

1988-01-01

This paper demonstrates continuous optimization on the differentiable manifold formed by continuous constraint functions. The first order tensor geodesic differential equation is solved on the manifold in both numerical and closed analytic form for simple nonlinear programs. Advantages and disadvantages with respect to conventional optimization techniques are discussed.

Constraint Embedding for Multibody System Dynamics

NASA Technical Reports Server (NTRS)

Jain, Abhinandan

2009-01-01

This paper describes a constraint embedding approach for the handling of local closure constraints in multibody system dynamics. The approach uses spatial operator techniques to eliminate local-loop constraints from the system and effectively convert the system into tree-topology systems. This approach allows the direct derivation of recursive O(N) techniques for solving the system dynamics and avoiding the expensive steps that would otherwise be required for handling the closedchain dynamics. The approach is very effective for systems where the constraints are confined to small-subgraphs within the system topology. The paper provides background on the spatial operator O(N) algorithms, the extensions for handling embedded constraints, and concludes with some examples of such constraints.

An algorithm for the solution of dynamic linear programs

NASA Technical Reports Server (NTRS)

Psiaki, Mark L.

1989-01-01

The algorithm's objective is to efficiently solve Dynamic Linear Programs (DLP) by taking advantage of their special staircase structure. This algorithm constitutes a stepping stone to an improved algorithm for solving Dynamic Quadratic Programs, which, in turn, would make the nonlinear programming method of Successive Quadratic Programs more practical for solving trajectory optimization problems. The ultimate goal is to being trajectory optimization solution speeds into the realm of real-time control. The algorithm exploits the staircase nature of the large constraint matrix of the equality-constrained DLPs encountered when solving inequality-constrained DLPs by an active set approach. A numerically-stable, staircase QL factorization of the staircase constraint matrix is carried out starting from its last rows and columns. The resulting recursion is like the time-varying Riccati equation from multi-stage LQR theory. The resulting factorization increases the efficiency of all of the typical LP solution operations over that of a dense matrix LP code. At the same time numerical stability is ensured. The algorithm also takes advantage of dynamic programming ideas about the cost-to-go by relaxing active pseudo constraints in a backwards sweeping process. This further decreases the cost per update of the LP rank-1 updating procedure, although it may result in more changes of the active set that if pseudo constraints were relaxed in a non-stagewise fashion. The usual stability of closed-loop Linear/Quadratic optimally-controlled systems, if it carries over to strictly linear cost functions, implies that the saving due to reduced factor update effort may outweigh the cost of an increased number of updates. An aerospace example is presented in which a ground-to-ground rocket's distance is maximized. This example demonstrates the applicability of this class of algorithms to aerospace guidance. It also sheds light on the efficacy of the proposed pseudo constraint relaxation scheme.

Solving multiconstraint assignment problems using learning automata.

PubMed

Horn, Geir; Oommen, B John

2010-02-01

This paper considers the NP-hard problem of object assignment with respect to multiple constraints: assigning a set of elements (or objects) into mutually exclusive classes (or groups), where the elements which are "similar" to each other are hopefully located in the same class. The literature reports solutions in which the similarity constraint consists of a single index that is inappropriate for the type of multiconstraint problems considered here and where the constraints could simultaneously be contradictory. This feature, where we permit possibly contradictory constraints, distinguishes this paper from the state of the art. Indeed, we are aware of no learning automata (or other heuristic) solutions which solve this problem in its most general setting. Such a scenario is illustrated with the static mapping problem, which consists of distributing the processes of a parallel application onto a set of computing nodes. This is a classical and yet very important problem within the areas of parallel computing, grid computing, and cloud computing. We have developed four learning-automata (LA)-based algorithms to solve this problem: First, a fixed-structure stochastic automata algorithm is presented, where the processes try to form pairs to go onto the same node. This algorithm solves the problem, although it requires some centralized coordination. As it is desirable to avoid centralized control, we subsequently present three different variable-structure stochastic automata (VSSA) algorithms, which have superior partitioning properties in certain settings, although they forfeit some of the scalability features of the fixed-structure algorithm. All three VSSA algorithms model the processes as automata having first the hosting nodes as possible actions; second, the processes as possible actions; and, third, attempting to estimate the process communication digraph prior to probabilistically mapping the processes. This paper, which, we believe, comprehensively reports the pioneering LA solutions to this problem, unequivocally demonstrates that LA can play an important role in solving complex combinatorial and integer optimization problems.

Density functional with full exact exchange, balanced nonlocality of correlations, and constraint satisfaction

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

Tao, Jianmin; Perdew, John P; Staroverov, Viktor N

2008-01-01

We construct a nonlocal density functional approximation with full exact exchange, while preserving the constraint-satisfaction approach and justified error cancellations of simpler semilocal functionals. This is achieved by interpolating between different approximations suitable for two extreme regions of the electron density. In a 'normal' region, the exact exchange-correlation hole density around an electron is semilocal because its spatial range is reduced by correlation and because it integrates over a narrow range to -1. These regions are well described by popular semilocal approximations (many of which have been constructed nonempirically), because of proper accuracy for a slowly-varying density or because ofmore » error cancellation between exchange and correlation. 'Abnormal' regions, where non locality is unveiled, include those in which exchange can dominate correlation (one-electron, nonuniform high-density, and rapidly-varying limits), and those open subsystems of fluctuating electron number over which the exact exchange-correlation hole integrates to a value greater than -1. Regions between these extremes are described by a hybrid functional mixing exact and semi local exchange energy densities locally (i.e., with a mixing fraction that is a function of position r and a functional of the density). Because our mixing fraction tends to 1 in the high-density limit, we employ full exact exchange according to the rigorous definition of the exchange component of any exchange-correlation energy functional. Use of full exact exchange permits the satisfaction of many exact constraints, but the nonlocality of exchange also requires balanced nonlocality of correlation. We find that this nonlocality can demand at least five empirical parameters (corresponding roughly to the four kinds of abnormal regions). Our local hybrid functional is perhaps the first accurate size-consistent density functional with full exact exchange. It satisfies other known exact constraints, including exactness for all one-electron densities, and provides an excellent, fit 1.0 the 223 molecular enthalpies of formation of the G3/99 set and the 42 reaction barrier heights of the BH42/03 set, improving both (but especially the latter) over most semilocal functionals and global hybrids. Exact constraints, physical insights, and paradigm examples hopefully suppress 'overfitting'.« less

From near to eternity: Spin-glass planting, tiling puzzles, and constraint-satisfaction problems

NASA Astrophysics Data System (ADS)

Hamze, Firas; Jacob, Darryl C.; Ochoa, Andrew J.; Perera, Dilina; Wang, Wenlong; Katzgraber, Helmut G.

2018-04-01

We present a methodology for generating Ising Hamiltonians of tunable complexity and with a priori known ground states based on a decomposition of the model graph into edge-disjoint subgraphs. The idea is illustrated with a spin-glass model defined on a cubic lattice, where subproblems, whose couplers are restricted to the two values {-1 ,+1 } , are specified on unit cubes and are parametrized by their local degeneracy. The construction is shown to be equivalent to a type of three-dimensional constraint-satisfaction problem known as the tiling puzzle. By varying the proportions of subproblem types, the Hamiltonian can span a dramatic range of typical computational complexity, from fairly easy to many orders of magnitude more difficult than prototypical bimodal and Gaussian spin glasses in three space dimensions. We corroborate this behavior via experiments with different algorithms and discuss generalizations and extensions to different types of graphs.

An extended abstract: A heuristic repair method for constraint-satisfaction and scheduling problems

NASA Technical Reports Server (NTRS)

Minton, Steven; Johnston, Mark D.; Philips, Andrew B.; Laird, Philip

1992-01-01

The work described in this paper was inspired by a surprisingly effective neural network developed for scheduling astronomical observations on the Hubble Space Telescope. Our heuristic constraint satisfaction problem (CSP) method was distilled from an analysis of the network. In the process of carrying out the analysis, we discovered that the effectiveness of the network has little to do with its connectionist implementation. Furthermore, the ideas employed in the network can be implemented very efficiently within a symbolic CSP framework. The symbolic implementation is extremely simple. It also has the advantage that several different search strategies can be employed, although we have found that hill-climbing methods are particularly well-suited for the applications that we have investigated. We begin the paper with a brief review of the neural network. Following this, we describe our symbolic method for heuristic repair.

Superintendents' Group Problem-Solving Processes.

ERIC Educational Resources Information Center

Leithwood, Kenneth; And Others

Findings of a study that examined the collaborative problem-solving processes used by superintendents are presented in this paper. Based on information processing theory, the study utilizes a model composed of the following components: interpretation; goals; principles and values; constraints; solution processes; and mood. Data were derived from…

Towards lexicographic multi-objective linear programming using grossone methodology

NASA Astrophysics Data System (ADS)

Cococcioni, Marco; Pappalardo, Massimo; Sergeyev, Yaroslav D.

2016-10-01

Lexicographic Multi-Objective Linear Programming (LMOLP) problems can be solved in two ways: preemptive and nonpreemptive. The preemptive approach requires the solution of a series of LP problems, with changing constraints (each time the next objective is added, a new constraint appears). The nonpreemptive approach is based on a scalarization of the multiple objectives into a single-objective linear function by a weighted combination of the given objectives. It requires the specification of a set of weights, which is not straightforward and can be time consuming. In this work we present both mathematical and software ingredients necessary to solve LMOLP problems using a recently introduced computational methodology (allowing one to work numerically with infinities and infinitesimals) based on the concept of grossone. The ultimate goal of such an attempt is an implementation of a simplex-like algorithm, able to solve the original LMOLP problem by solving only one single-objective problem and without the need to specify finite weights. The expected advantages are therefore obvious.

Learning theoretical knowledge doesn't have to be boring.

PubMed

2014-11-29

Marta Kankofer was one of a small team that developed teaching materials for veterinary students to allow them to use their cognitive abilities to solve virtual problems. The VetVIP project promotes self-study based on solving problems and aims to increase satisfaction and motivation among second-year students, who find themselves in a theory-heavy stage of the veterinary course. British Veterinary Association.

Surgery scheduling optimization considering real life constraints and comprehensive operation cost of operating room.

PubMed

Xiang, Wei; Li, Chong

2015-01-01

Operating Room (OR) is the core sector in hospital expenditure, the operation management of which involves a complete three-stage surgery flow, multiple resources, prioritization of the various surgeries, and several real-life OR constraints. As such reasonable surgery scheduling is crucial to OR management. To optimize OR management and reduce operation cost, a short-term surgery scheduling problem is proposed and defined based on the survey of the OR operation in a typical hospital in China. The comprehensive operation cost is clearly defined considering both under-utilization and overutilization. A nested Ant Colony Optimization (nested-ACO) incorporated with several real-life OR constraints is proposed to solve such a combinatorial optimization problem. The 10-day manual surgery schedules from a hospital in China are compared with the optimized schedules solved by the nested-ACO. Comparison results show the advantage using the nested-ACO in several measurements: OR-related time, nurse-related time, variation in resources' working time, and the end time. The nested-ACO considering real-life operation constraints such as the difference between first and following case, surgeries priority, and fixed nurses in pre/post-operative stage is proposed to solve the surgery scheduling optimization problem. The results clearly show the benefit of using the nested-ACO in enhancing the OR management efficiency and minimizing the comprehensive overall operation cost.

Flight control with adaptive critic neural network

NASA Astrophysics Data System (ADS)

Han, Dongchen

2001-10-01

In this dissertation, the adaptive critic neural network technique is applied to solve complex nonlinear system control problems. Based on dynamic programming, the adaptive critic neural network can embed the optimal solution into a neural network. Though trained off-line, the neural network forms a real-time feedback controller. Because of its general interpolation properties, the neurocontroller has inherit robustness. The problems solved here are an agile missile control for U.S. Air Force and a midcourse guidance law for U.S. Navy. In the first three papers, the neural network was used to control an air-to-air agile missile to implement a minimum-time heading-reverse in a vertical plane corresponding to following conditions: a system without constraint, a system with control inequality constraint, and a system with state inequality constraint. While the agile missile is a one-dimensional problem, the midcourse guidance law is the first test-bed for multiple-dimensional problem. In the fourth paper, the neurocontroller is synthesized to guide a surface-to-air missile to a fixed final condition, and to a flexible final condition from a variable initial condition. In order to evaluate the adaptive critic neural network approach, the numerical solutions for these cases are also obtained by solving two-point boundary value problem with a shooting method. All of the results showed that the adaptive critic neural network could solve complex nonlinear system control problems.

Common-Sense Chemistry: The Use of Assumptions and Heuristics in Problem Solving

ERIC Educational Resources Information Center

Maeyer, Jenine Rachel

2013-01-01

Students experience difficulty learning and understanding chemistry at higher levels, often because of cognitive biases stemming from common sense reasoning constraints. These constraints can be divided into two categories: assumptions (beliefs held about the world around us) and heuristics (the reasoning strategies or rules used to build…

Think Inside the Box

ERIC Educational Resources Information Center

Spencer, John

2017-01-01

Besides "thinking outside the box," the creativity needed to solve problems often involves thinking differently about the box, finding a new approach or off-beat way to use the materials, conditions, and even constraints that one has. Spencer discusses creative constraint--what happens when a problem solver runs into barriers that make…

Teaching Database Design with Constraint-Based Tutors

ERIC Educational Resources Information Center

Mitrovic, Antonija; Suraweera, Pramuditha

2016-01-01

Design tasks are difficult to teach, due to large, unstructured solution spaces, underspecified problems, non-existent problem solving algorithms and stopping criteria. In this paper, we comment on our approach to develop KERMIT, a constraint-based tutor that taught database design. In later work, we re-implemented KERMIT as EER-Tutor, and…

Neural correlates of novelty and appropriateness processing in externally induced constraint relaxation.

PubMed

Huang, Furong; Tang, Shuang; Sun, Pei; Luo, Jing

2018-05-15

Novelty and appropriateness are considered the two fundamental features of creative thinking, including insight problem solving, which can be performed through chunk decomposition and constraint relaxation. Based on a previous study that separated the neural bases of novelty and appropriateness in chunk decomposition, in this study, we used event-related functional magnetic resonance imaging (fMRI) to further dissociate these mechanisms in constraint relaxation. Participants were guided to mentally represent the method of problem solving according to the externally provided solutions that were elaborately prepared in advance and systematically varied in their novelty and appropriateness for the given problem situation. The results showed that novelty processing was completed by the temporoparietal junction (TPJ) and regions in the executive system (dorsolateral prefrontal cortex [DLPFC]), whereas appropriateness processing was completed by the TPJ and regions in the episodic memory (hippocampus), emotion (amygdala), and reward systems (orbitofrontal cortex [OFC]). These results likely indicate that appropriateness processing can result in a more memorable and richer experience than novelty processing in constraint relaxation. The shared and distinct neural mechanisms of the features of novelty and appropriateness in constraint relaxation are discussed, enriching the representation of the change theory of insight. Copyright © 2018 Elsevier Inc. All rights reserved.

Final Report - Regulatory Considerations for Adaptive Systems

NASA Technical Reports Server (NTRS)

Wilkinson, Chris; Lynch, Jonathan; Bharadwaj, Raj

2013-01-01

This report documents the findings of a preliminary research study into new approaches to the software design assurance of adaptive systems. We suggest a methodology to overcome the software validation and verification difficulties posed by the underlying assumption of non-adaptive software in the requirementsbased- testing verification methods in RTCA/DO-178B and C. An analysis of the relevant RTCA/DO-178B and C objectives is presented showing the reasons for the difficulties that arise in showing satisfaction of the objectives and suggested additional means by which they could be satisfied. We suggest that the software design assurance problem for adaptive systems is principally one of developing correct and complete high level requirements and system level constraints that define the necessary system functional and safety properties to assure the safe use of adaptive systems. We show how analytical techniques such as model based design, mathematical modeling and formal or formal-like methods can be used to both validate the high level functional and safety requirements, establish necessary constraints and provide the verification evidence for the satisfaction of requirements and constraints that supplements conventional testing. Finally the report identifies the follow-on research topics needed to implement this methodology.

Pricing Resources in LTE Networks through Multiobjective Optimization

PubMed Central

Lai, Yung-Liang; Jiang, Jehn-Ruey

2014-01-01

The LTE technology offers versatile mobile services that use different numbers of resources. This enables operators to provide subscribers or users with differential quality of service (QoS) to boost their satisfaction. On one hand, LTE operators need to price the resources high for maximizing their profits. On the other hand, pricing also needs to consider user satisfaction with allocated resources and prices to avoid “user churn,” which means subscribers will unsubscribe services due to dissatisfaction with allocated resources or prices. In this paper, we study the pricing resources with profits and satisfaction optimization (PRPSO) problem in the LTE networks, considering the operator profit and subscribers' satisfaction at the same time. The problem is modelled as nonlinear multiobjective optimization with two optimal objectives: (1) maximizing operator profit and (2) maximizing user satisfaction. We propose to solve the problem based on the framework of the NSGA-II. Simulations are conducted for evaluating the proposed solution. PMID:24526889

Pricing resources in LTE networks through multiobjective optimization.

PubMed

Lai, Yung-Liang; Jiang, Jehn-Ruey

2014-01-01

The LTE technology offers versatile mobile services that use different numbers of resources. This enables operators to provide subscribers or users with differential quality of service (QoS) to boost their satisfaction. On one hand, LTE operators need to price the resources high for maximizing their profits. On the other hand, pricing also needs to consider user satisfaction with allocated resources and prices to avoid "user churn," which means subscribers will unsubscribe services due to dissatisfaction with allocated resources or prices. In this paper, we study the pricing resources with profits and satisfaction optimization (PRPSO) problem in the LTE networks, considering the operator profit and subscribers' satisfaction at the same time. The problem is modelled as nonlinear multiobjective optimization with two optimal objectives: (1) maximizing operator profit and (2) maximizing user satisfaction. We propose to solve the problem based on the framework of the NSGA-II. Simulations are conducted for evaluating the proposed solution.

SOFIA's Choice: Automating the Scheduling of Airborne Observations

NASA Technical Reports Server (NTRS)

Frank, Jeremy; Norvig, Peter (Technical Monitor)

1999-01-01

This paper describes the problem of scheduling observations for an airborne telescope. Given a set of prioritized observations to choose from, and a wide range of complex constraints governing legitimate choices and orderings, how can we efficiently and effectively create a valid flight plan which supports high priority observations? This problem is quite different from scheduling problems which are routinely solved automatically in industry. For instance, the problem requires making choices which lead to other choices later, and contains many interacting complex constraints over both discrete and continuous variables. Furthermore, new types of constraints may be added as the fundamental problem changes. As a result of these features, this problem cannot be solved by traditional scheduling techniques. The problem resembles other problems in NASA and industry, from observation scheduling for rovers and other science instruments to vehicle routing. The remainder of the paper is organized as follows. In 2 we describe the observatory in order to provide some background. In 3 we describe the problem of scheduling a single flight. In 4 we compare flight planning and other scheduling problems and argue that traditional techniques are not sufficient to solve this problem. We also mention similar complex scheduling problems which may benefit from efforts to solve this problem. In 5 we describe an approach for solving this problem based on research into a similar problem, that of scheduling observations for a space-borne probe. In 6 we discuss extensions of the flight planning problem as well as other problems which are similar to flight planning. In 7 we conclude and discuss future work.

Factors associated with work satisfaction of registered nurses.

PubMed

Kovner, Christine; Brewer, Carol; Wu, Yow-Wu; Cheng, Ying; Suzuki, Miho

2006-01-01

To examine the factors that influence the work satisfaction of a national sample of registered nurses in metropolitan statistical areas (MSAs). A cross-sectional mailed survey design was used. The sample consisted of RNs randomly selected from 40 MSAs in 29 states; 1,907 RNs responded (48%). The sample of 1,538 RNs working in nursing was used for analysis. The questionnaire included measures of work attitudes and demographic characteristics. The data were analyzed using ordinary least-squares regression. More than 40% of the variance in satisfaction was explained by the various work attitudes: supervisor support, work-group cohesion, variety of work, autonomy, organizational constraint, promotional opportunities, work and family conflict, and distributive justice. RNs who were White, self-perceived as healthy, and working in nursing education were more satisfied. RNs that were more career oriented were more satisfied. Of the benefits options, only paid time off was related to satisfaction. Work-related factors were significantly related to RNs' work satisfaction.

Optimization techniques applied to spectrum management for communications satellites

NASA Astrophysics Data System (ADS)

Ottey, H. R.; Sullivan, T. M.; Zusman, F. S.

This paper describes user requirements, algorithms and software design features for the application of optimization techniques to the management of the geostationary orbit/spectrum resource. Relevant problems include parameter sensitivity analyses, frequency and orbit position assignment coordination, and orbit position allotment planning. It is shown how integer and nonlinear programming as well as heuristic search techniques can be used to solve these problems. Formalized mathematical objective functions that define the problems are presented. Constraint functions that impart the necessary solution bounds are described. A versatile program structure is outlined, which would allow problems to be solved in stages while varying the problem space, solution resolution, objective function and constraints.

Constraint Embedding Technique for Multibody System Dynamics

NASA Technical Reports Server (NTRS)

Woo, Simon S.; Cheng, Michael K.

2011-01-01

Multibody dynamics play a critical role in simulation testbeds for space missions. There has been a considerable interest in the development of efficient computational algorithms for solving the dynamics of multibody systems. Mass matrix factorization and inversion techniques and the O(N) class of forward dynamics algorithms developed using a spatial operator algebra stand out as important breakthrough on this front. Techniques such as these provide the efficient algorithms and methods for the application and implementation of such multibody dynamics models. However, these methods are limited only to tree-topology multibody systems. Closed-chain topology systems require different techniques that are not as efficient or as broad as those for tree-topology systems. The closed-chain forward dynamics approach consists of treating the closed-chain topology as a tree-topology system subject to additional closure constraints. The resulting forward dynamics solution consists of: (a) ignoring the closure constraints and using the O(N) algorithm to solve for the free unconstrained accelerations for the system; (b) using the tree-topology solution to compute a correction force to enforce the closure constraints; and (c) correcting the unconstrained accelerations with correction accelerations resulting from the correction forces. This constraint-embedding technique shows how to use direct embedding to eliminate local closure-loops in the system and effectively convert the system back to a tree-topology system. At this point, standard tree-topology techniques can be brought to bear on the problem. The approach uses a spatial operator algebra approach to formulating the equations of motion. The operators are block-partitioned around the local body subgroups to convert them into aggregate bodies. Mass matrix operator factorization and inversion techniques are applied to the reformulated tree-topology system. Thus in essence, the new technique allows conversion of a system with closure-constraints into an equivalent tree-topology system, and thus allows one to take advantage of the host of techniques available to the latter class of systems. This technology is highly suitable for the class of multibody systems where the closure-constraints are local, i.e., where they are confined to small groupings of bodies within the system. Important examples of such local closure-constraints are constraints associated with four-bar linkages, geared motors, differential suspensions, etc. One can eliminate these closure-constraints and convert the system into a tree-topology system by embedding the constraints directly into the system dynamics and effectively replacing the body groupings with virtual aggregate bodies. Once eliminated, one can apply the well-known results and algorithms for tree-topology systems to solve the dynamics of such closed-chain system.

Navigating contextual constraints in discourse: Design explications in institutional talk

PubMed Central

Herijgers, MLC (Marloes); Maat, HLW (Henk) Pander

2017-01-01

Although institutional discourse is subject to a vast ensemble of constraints, its design is not fixed beforehand. On the contrary, optimizing the satisfaction of these constraints requires considerable discourse design skills from institutional agents. In this article, we analyze how Dutch banks’ mortgage advisors navigate their way through the consultations context. We focus on what we call discourse design explications, that is, stretches of talk in which participants refer to conflicting constraints in the discourse context, at the same time proposing particular discourse designs for dealing with these conflicts. We start by discussing three forms of design explication. Then we will examine the various resolutions they propose for constraint conflicts and show how advisors seek customer consent or cooperation for the proposed designs. Thus our analysis reveals how institutional agents, while providing services, work on demonstrating how the design of these services is optimized and tailored to customers. PMID:28781580

Relationship Satisfaction and Risk Factors for Suicide.

PubMed

Till, Benedikt; Tran, Ulrich S; Niederkrotenthaler, Thomas

2017-01-01

Previous studies suggest that troubled romantic relationships are associated with higher risk factors for mental health. However, studies examining the role of relationship satisfaction in suicide risk factors are scarce. We investigated differences in risk factors for suicide between individuals with high relationship satisfaction, individuals with low relationship satisfaction, and singles. Furthermore, we explored patterns of experiencing, and dealing with, conflicts in the relationship and examined associations with suicide risk factors. In this cross-sectional study, we assessed relationship status, relationship satisfaction, specific types of relationship conflicts, and suicide risk factors (i.e., suicidal ideation, hopelessness, depression) with questionnaires among 382 individuals in Austria. Risk factors for suicide were higher among singles than among individuals in happy relationships, but highest among those with low relationship satisfaction [corrected]. Participants reporting a high number of unsolved conflicts in their relationship had higher levels of suicidal ideation, hopelessness, and depression than individuals who tend to solve issues with their partner amicably or report no conflicts. Relationship satisfaction and relationship conflicts reflect risk factors for suicide, with higher levels of suicidal ideation, hopelessness, and depression reported by individuals who mentioned unsolved conflicts with their partner and experienced low satisfaction with their relationship.

The role of coping strategies and self-efficacy as predictors of life satisfaction in a sample of parents of children with autism spectrum disorder.

PubMed

Luque Salas, Bárbara; Yáñez Rodríguez, Virginia; Tabernero Urbieta, Carmen; Cuadrado, Esther

2017-02-01

This research aims to understand the role of coping strategies and self-efficacy expectations as predictors of life satisfaction in a sample of parents of boys and girls diagnosed with autistic spectrum disorder. A total of 129 parents (64 men and 65 women) answered a questionnaire on life-satisfaction, coping strategies and self-efficacy scales. Using a regression model, results show that the age of the child is associated with a lower level of satisfaction in parents. The results show that self-efficacy is the variable that best explains the level of satisfaction in mothers, while the use of problem solving explains a higher level of satisfaction in fathers. Men and women show similar levels of life satisfaction; however significant differences were found in coping strategies where women demonstrated higher expressing emotions and social support strategies than men. The development of functional coping strategies and of a high level of self-efficacy represents a key tool for adapting to caring for children with autism. Our results indicated the necessity of early intervention with parents to promote coping strategies, self-efficacy and high level of life satisfaction.

A constraint optimization based virtual network mapping method

NASA Astrophysics Data System (ADS)

Li, Xiaoling; Guo, Changguo; Wang, Huaimin; Li, Zhendong; Yang, Zhiwen

2013-03-01

Virtual network mapping problem, maps different virtual networks onto the substrate network is an extremely challenging work. This paper proposes a constraint optimization based mapping method for solving virtual network mapping problem. This method divides the problem into two phases, node mapping phase and link mapping phase, which are all NP-hard problems. Node mapping algorithm and link mapping algorithm are proposed for solving node mapping phase and link mapping phase, respectively. Node mapping algorithm adopts the thinking of greedy algorithm, mainly considers two factors, available resources which are supplied by the nodes and distance between the nodes. Link mapping algorithm is based on the result of node mapping phase, adopts the thinking of distributed constraint optimization method, which can guarantee to obtain the optimal mapping with the minimum network cost. Finally, simulation experiments are used to validate the method, and results show that the method performs very well.

A solid criterion based on strict LMI without invoking equality constraint for stabilization of continuous singular systems.

PubMed

Zhang, Xuefeng; Chen, YangQuan

2017-11-01

The paper considers the stabilization issue of linear continuous singular systems by dealing with strict linear matrix inequalities (LMIs) without invoking equality constraint and proposes a complete and effective solved LMIs formulation. The criterion is necessary and sufficient condition and can be directly solved the feasible solutions with LMI toolbox and is much more tractable and reliable in numerical simulation than existing results, which involve positive semi-definite LMIs with equality constraints. The most important property of the criterion proposed in the paper is that it can overcome the drawbacks of the invalidity caused by the singularity of Ω=PE T +SQ for stabilization of singular systems. Two counterexamples are presented to avoid the disadvantages of the existing condition of stabilization of continuous singular systems. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

An Appreciative Inquiry of E-Learning Operations in a Southern College of Education

ERIC Educational Resources Information Center

Major, Amanda E.

2014-01-01

Higher education, as an industry, is facing extraordinary fiscal constraints. Universities increasingly consider online education and its e-learning variants as a promise to solve these fiscal constraints while also supporting advances in pedagogy. Many universities promise quality education, yet questions loom about the quality of online learning…

Creativity from Constraints: What Can We Learn from Motherwell? From Modrian? From Klee?

ERIC Educational Resources Information Center

Stokes, Patricia D.

2008-01-01

This article presents a problem-solving model of variability and creativity built on the classic Reitman and Simon analyses of musical composition and architectural design. The model focuses on paired constraints: one precluding (or limiting search among) reliable, existing solutions, the other promoting (or directing search to) novel, often…

Efficient dynamic modeling of manipulators containing closed kinematic loops

NASA Astrophysics Data System (ADS)

Ferretti, Gianni; Rocco, Paolo

An approach to efficiently solve the forward dynamics problem for manipulators containing closed chains is proposed. The two main distinctive features of this approach are: the dynamics of the equivalent open loop tree structures (any closed loop can be in general modeled by imposing some additional kinematic constraints to a suitable tree structure) is computed through an efficient Newton Euler formulation; the constraint equations relative to the most commonly adopted closed chains in industrial manipulators are explicitly solved, thus, overcoming the redundancy of Lagrange's multipliers method while avoiding the inefficiency due to a numerical solution of the implicit constraint equations. The constraint equations considered for an explicit solution are those imposed by articulated gear mechanisms and planar closed chains (pantograph type structures). Articulated gear mechanisms are actually used in all industrial robots to transmit motion from actuators to links, while planar closed chains are usefully employed to increase the stiffness of the manipulators and their load capacity, as well to reduce the kinematic coupling of joint axes. The accuracy and the efficiency of the proposed approach are shown through a simulation test.

jFuzz: A Concolic Whitebox Fuzzer for Java

NASA Technical Reports Server (NTRS)

Jayaraman, Karthick; Harvison, David; Ganesh, Vijay; Kiezun, Adam

2009-01-01

We present jFuzz, a automatic testing tool for Java programs. jFuzz is a concolic whitebox fuzzer, built on the NASA Java PathFinder, an explicit-state Java model checker, and a framework for developing reliability and analysis tools for Java. Starting from a seed input, jFuzz automatically and systematically generates inputs that exercise new program paths. jFuzz uses a combination of concrete and symbolic execution, and constraint solving. Time spent on solving constraints can be significant. We implemented several well-known optimizations and name-independent caching, which aggressively normalizes the constraints to reduce the number of calls to the constraint solver. We present preliminary results due to the optimizations, and demonstrate the effectiveness of jFuzz in creating good test inputs. The source code of jFuzz is available as part of the NASA Java PathFinder. jFuzz is intended to be a research testbed for investigating new testing and analysis techniques based on concrete and symbolic execution. The source code of jFuzz is available as part of the NASA Java PathFinder.

Self-aligned quadruple patterning-compliant placement

NASA Astrophysics Data System (ADS)

Nakajima, Fumiharu; Kodama, Chikaaki; Nakayama, Koichi; Nojima, Shigeki; Kotani, Toshiya

2015-03-01

Self-Aligned Quadruple Patterning (SAQP) will be one of the leading candidates for sub-14nm node and beyond. However, compared with triple patterning, making a feasible standard cell placement has following problems. (1) When coloring conflicts occur between two adjoining cells, they may not be solved easily since SAQP layout has stronger coloring constraints. (2) SAQP layout cannot use stitch to solve coloring conflict. In this paper, we present a framework of SAQP-aware standard cell placement considering the above problems. When standard cell is placed, the proposed method tries to solve coloring conflicts between two cells by exchanging two of three colors. If some conflicts remain between adjoining cells, dummy space will be inserted to keep coloring constraints of SAQP. We show some examples to confirm effectiveness of the proposed framework. To our best knowledge, this is the first framework of SAQP-aware standard cell placement.

A one-layer recurrent neural network for constrained pseudoconvex optimization and its application for dynamic portfolio optimization.

PubMed

Liu, Qingshan; Guo, Zhishan; Wang, Jun

2012-02-01

In this paper, a one-layer recurrent neural network is proposed for solving pseudoconvex optimization problems subject to linear equality and bound constraints. Compared with the existing neural networks for optimization (e.g., the projection neural networks), the proposed neural network is capable of solving more general pseudoconvex optimization problems with equality and bound constraints. Moreover, it is capable of solving constrained fractional programming problems as a special case. The convergence of the state variables of the proposed neural network to achieve solution optimality is guaranteed as long as the designed parameters in the model are larger than the derived lower bounds. Numerical examples with simulation results illustrate the effectiveness and characteristics of the proposed neural network. In addition, an application for dynamic portfolio optimization is discussed. Copyright © 2011 Elsevier Ltd. All rights reserved.

Constraints in Genetic Programming

NASA Technical Reports Server (NTRS)

Janikow, Cezary Z.

1996-01-01

Genetic programming refers to a class of genetic algorithms utilizing generic representation in the form of program trees. For a particular application, one needs to provide the set of functions, whose compositions determine the space of program structures being evolved, and the set of terminals, which determine the space of specific instances of those programs. The algorithm searches the space for the best program for a given problem, applying evolutionary mechanisms borrowed from nature. Genetic algorithms have shown great capabilities in approximately solving optimization problems which could not be approximated or solved with other methods. Genetic programming extends their capabilities to deal with a broader variety of problems. However, it also extends the size of the search space, which often becomes too large to be effectively searched even by evolutionary methods. Therefore, our objective is to utilize problem constraints, if such can be identified, to restrict this space. In this publication, we propose a generic constraint specification language, powerful enough for a broad class of problem constraints. This language has two elements -- one reduces only the number of program instances, the other reduces both the space of program structures as well as their instances. With this language, we define the minimal set of complete constraints, and a set of operators guaranteeing offspring validity from valid parents. We also show that these operators are not less efficient than the standard genetic programming operators if one preprocesses the constraints - the necessary mechanisms are identified.

Enhanced Fuel-Optimal Trajectory-Generation Algorithm for Planetary Pinpoint Landing

NASA Technical Reports Server (NTRS)

Acikmese, Behcet; Blackmore, James C.; Scharf, Daniel P.

2011-01-01

An enhanced algorithm is developed that builds on a previous innovation of fuel-optimal powered-descent guidance (PDG) for planetary pinpoint landing. The PDG problem is to compute constrained, fuel-optimal trajectories to land a craft at a prescribed target on a planetary surface, starting from a parachute cut-off point and using a throttleable descent engine. The previous innovation showed the minimal-fuel PDG problem can be posed as a convex optimization problem, in particular, as a Second-Order Cone Program, which can be solved to global optimality with deterministic convergence properties, and hence is a candidate for onboard implementation. To increase the speed and robustness of this convex PDG algorithm for possible onboard implementation, the following enhancements are incorporated: 1) Fast detection of infeasibility (i.e., control authority is not sufficient for soft-landing) for subsequent fault response. 2) The use of a piecewise-linear control parameterization, providing smooth solution trajectories and increasing computational efficiency. 3) An enhanced line-search algorithm for optimal time-of-flight, providing quicker convergence and bounding the number of path-planning iterations needed. 4) An additional constraint that analytically guarantees inter-sample satisfaction of glide-slope and non-sub-surface flight constraints, allowing larger discretizations and, hence, faster optimization. 5) Explicit incorporation of Mars rotation rate into the trajectory computation for improved targeting accuracy. These enhancements allow faster convergence to the fuel-optimal solution and, more importantly, remove the need for a "human-in-the-loop," as constraints will be satisfied over the entire path-planning interval independent of step-size (as opposed to just at the discrete time points) and infeasible initial conditions are immediately detected. Finally, while the PDG stage is typically only a few minutes, ignoring the rotation rate of Mars can introduce 10s of meters of error. By incorporating it, the enhanced PDG algorithm becomes capable of pinpoint targeting.

A Comparative Study of Randomized Constraint Solvers for Random-Symbolic Testing

NASA Technical Reports Server (NTRS)

Takaki, Mitsuo; Cavalcanti, Diego; Gheyi, Rohit; Iyoda, Juliano; dAmorim, Marcelo; Prudencio, Ricardo

2009-01-01

The complexity of constraints is a major obstacle for constraint-based software verification. Automatic constraint solvers are fundamentally incomplete: input constraints often build on some undecidable theory or some theory the solver does not support. This paper proposes and evaluates several randomized solvers to address this issue. We compare the effectiveness of a symbolic solver (CVC3), a random solver, three hybrid solvers (i.e., mix of random and symbolic), and two heuristic search solvers. We evaluate the solvers on two benchmarks: one consisting of manually generated constraints and another generated with a concolic execution of 8 subjects. In addition to fully decidable constraints, the benchmarks include constraints with non-linear integer arithmetic, integer modulo and division, bitwise arithmetic, and floating-point arithmetic. As expected symbolic solving (in particular, CVC3) subsumes the other solvers for the concolic execution of subjects that only generate decidable constraints. For the remaining subjects the solvers are complementary.

Gravity with free initial conditions: A solution to the cosmological constant problem testable by CMB B -mode polarization

NASA Astrophysics Data System (ADS)

Totani, Tomonori

2017-10-01

In standard general relativity the universe cannot be started with arbitrary initial conditions, because four of the ten components of the Einstein's field equations (EFE) are constraints on initial conditions. In the previous work it was proposed to extend the gravity theory to allow free initial conditions, with a motivation to solve the cosmological constant problem. This was done by setting four constraints on metric variations in the action principle, which is reasonable because the gravity's physical degrees of freedom are at most six. However, there are two problems about this theory; the three constraints in addition to the unimodular condition were introduced without clear physical meanings, and the flat Minkowski spacetime is unstable against perturbations. Here a new set of gravitational field equations is derived by replacing the three constraints with new ones requiring that geodesic paths remain geodesic against metric variations. The instability problem is then naturally solved. Implications for the cosmological constant Λ are unchanged; the theory converges into EFE with nonzero Λ by inflation, but Λ varies on scales much larger than the present Hubble horizon. Then galaxies are formed only in small Λ regions, and the cosmological constant problem is solved by the anthropic argument. Because of the increased degrees of freedom in metric dynamics, the theory predicts new non-oscillatory modes of metric anisotropy generated by quantum fluctuation during inflation, and CMB B -mode polarization would be observed differently from the standard predictions by general relativity.

Quantum Resonance Approach to Combinatorial Optimization

NASA Technical Reports Server (NTRS)

Zak, Michail

1997-01-01

It is shown that quantum resonance can be used for combinatorial optimization. The advantage of the approach is in independence of the computing time upon the dimensionality of the problem. As an example, the solution to a constraint satisfaction problem of exponential complexity is demonstrated.

Working wonders? investigating insight with magic tricks.

PubMed

Danek, Amory H; Fraps, Thomas; von Müller, Albrecht; Grothe, Benedikt; Ollinger, Michael

2014-02-01

We propose a new approach to differentiate between insight and noninsight problem solving, by introducing magic tricks as problem solving domain. We argue that magic tricks are ideally suited to investigate representational change, the key mechanism that yields sudden insight into the solution of a problem, because in order to gain insight into the magicians' secret method, observers must overcome implicit constraints and thus change their problem representation. In Experiment 1, 50 participants were exposed to 34 different magic tricks, asking them to find out how the trick was accomplished. Upon solving a trick, participants indicated if they had reached the solution either with or without insight. Insight was reported in 41.1% of solutions. The new task domain revealed differences in solution accuracy, time course and solution confidence with insight solutions being more likely to be true, reached earlier, and obtaining higher confidence ratings. In Experiment 2, we explored which role self-imposed constraints actually play in magic tricks. 62 participants were presented with 12 magic tricks. One group received verbal cues, providing solution relevant information without giving the solution away. The control group received no informative cue. Experiment 2 showed that participants' constraints were suggestible to verbal cues, resulting in higher solution rates. Thus, magic tricks provide more detailed information about the differences between insightful and noninsightful problem solving, and the underlying mechanisms that are necessary to have an insight. Copyright © 2013 Elsevier B.V. All rights reserved.

Using genetic algorithms to determine near-optimal pricing, investment and operating strategies in the electric power industry

NASA Astrophysics Data System (ADS)

Wu, Dongjun

Network industries have technologies characterized by a spatial hierarchy, the "network," with capital-intensive interconnections and time-dependent, capacity-limited flows of products and services through the network to customers. This dissertation studies service pricing, investment and business operating strategies for the electric power network. First-best solutions for a variety of pricing and investment problems have been studied. The evaluation of genetic algorithms (GA, which are methods based on the idea of natural evolution) as a primary means of solving complicated network problems, both w.r.t. pricing: as well as w.r.t. investment and other operating decisions, has been conducted. New constraint-handling techniques in GAs have been studied and tested. The actual application of such constraint-handling techniques in solving practical non-linear optimization problems has been tested on several complex network design problems with encouraging initial results. Genetic algorithms provide solutions that are feasible and close to optimal when the optimal solution is know; in some instances, the near-optimal solutions for small problems by the proposed GA approach can only be tested by pushing the limits of currently available non-linear optimization software. The performance is far better than several commercially available GA programs, which are generally inadequate in solving any of the problems studied in this dissertation, primarily because of their poor handling of constraints. Genetic algorithms, if carefully designed, seem very promising in solving difficult problems which are intractable by traditional analytic methods.

Feed Forward Neural Network and Optimal Control Problem with Control and State Constraints

NASA Astrophysics Data System (ADS)

Kmet', Tibor; Kmet'ová, Mária

2009-09-01

A feed forward neural network based optimal control synthesis is presented for solving optimal control problems with control and state constraints. The paper extends adaptive critic neural network architecture proposed by [5] to the optimal control problems with control and state constraints. The optimal control problem is transcribed into a nonlinear programming problem which is implemented with adaptive critic neural network. The proposed simulation method is illustrated by the optimal control problem of nitrogen transformation cycle model. Results show that adaptive critic based systematic approach holds promise for obtaining the optimal control with control and state constraints.

An approximation function for frequency constrained structural optimization

NASA Technical Reports Server (NTRS)

Canfield, R. A.

1989-01-01

The purpose is to examine a function for approximating natural frequency constraints during structural optimization. The nonlinearity of frequencies has posed a barrier to constructing approximations for frequency constraints of high enough quality to facilitate efficient solutions. A new function to represent frequency constraints, called the Rayleigh Quotient Approximation (RQA), is presented. Its ability to represent the actual frequency constraint results in stable convergence with effectively no move limits. The objective of the optimization problem is to minimize structural weight subject to some minimum (or maximum) allowable frequency and perhaps subject to other constraints such as stress, displacement, and gage size, as well. A reason for constraining natural frequencies during design might be to avoid potential resonant frequencies due to machinery or actuators on the structure. Another reason might be to satisy requirements of an aircraft or spacecraft's control law. Whatever the structure supports may be sensitive to a frequency band that must be avoided. Any of these situations or others may require the designer to insure the satisfaction of frequency constraints. A further motivation for considering accurate approximations of natural frequencies is that they are fundamental to dynamic response constraints.

Relationship Functioning Moderates the Association Between Depressive Symptoms and Life Stressors

PubMed Central

Trombello, Joseph M.; Schoebi, Dominik; Bradbury, Thomas N.

2017-01-01

Data from 172 newlywed couples were collected over the first 4 years of marriage to test how behaviors demonstrated during marital interactions moderate associations between depressive symptoms and subsequent life stressors. Depressive symptoms and behaviors coded from problem-solving and social support interactions were analyzed as predictors of nonmarital stressors that were interpersonal and dependent on the participant's actions. Behavioral codes were found to moderate 3 of 16 symptom-to-life event associations for husbands. Husbands' reports of more depressive symptoms predicted greater levels of stress when husbands' positive affect and hard negative affect during problem-solving were relatively infrequent and when wives made frequent displays of positive behaviors during husbands' support topics. These effects remained after controlling for marital satisfaction. For wives, behavioral moderators did not interact with depressive symptoms to predict changes in stress, but marital satisfaction consistently interacted with depressive symptoms to predict future stressors beyond interpersonal behaviors. Specifically, for wives, stress generation was more evident when relationship satisfaction was low than when it was high. Our results, though different for men and women, suggest that relationship functioning can alter associations between depressive symptoms and life stress in the early years of marriage. PMID:21355647

Processing time tolerance-based ACO algorithm for solving job-shop scheduling problem

NASA Astrophysics Data System (ADS)

Luo, Yabo; Waden, Yongo P.

2017-06-01

Ordinarily, Job Shop Scheduling Problem (JSSP) is known as NP-hard problem which has uncertainty and complexity that cannot be handled by a linear method. Thus, currently studies on JSSP are concentrated mainly on applying different methods of improving the heuristics for optimizing the JSSP. However, there still exist many problems for efficient optimization in the JSSP, namely, low efficiency and poor reliability, which can easily trap the optimization process of JSSP into local optima. Therefore, to solve this problem, a study on Ant Colony Optimization (ACO) algorithm combined with constraint handling tactics is carried out in this paper. Further, the problem is subdivided into three parts: (1) Analysis of processing time tolerance-based constraint features in the JSSP which is performed by the constraint satisfying model; (2) Satisfying the constraints by considering the consistency technology and the constraint spreading algorithm in order to improve the performance of ACO algorithm. Hence, the JSSP model based on the improved ACO algorithm is constructed; (3) The effectiveness of the proposed method based on reliability and efficiency is shown through comparative experiments which are performed on benchmark problems. Consequently, the results obtained by the proposed method are better, and the applied technique can be used in optimizing JSSP.

Distributed Constrained Optimization with Semicoordinate Transformations

NASA Technical Reports Server (NTRS)

Macready, William; Wolpert, David

2006-01-01

Recent work has shown how information theory extends conventional full-rationality game theory to allow bounded rational agents. The associated mathematical framework can be used to solve constrained optimization problems. This is done by translating the problem into an iterated game, where each agent controls a different variable of the problem, so that the joint probability distribution across the agents moves gives an expected value of the objective function. The dynamics of the agents is designed to minimize a Lagrangian function of that joint distribution. Here we illustrate how the updating of the Lagrange parameters in the Lagrangian is a form of automated annealing, which focuses the joint distribution more and more tightly about the joint moves that optimize the objective function. We then investigate the use of "semicoordinate" variable transformations. These separate the joint state of the agents from the variables of the optimization problem, with the two connected by an onto mapping. We present experiments illustrating the ability of such transformations to facilitate optimization. We focus on the special kind of transformation in which the statistically independent states of the agents induces a mixture distribution over the optimization variables. Computer experiment illustrate this for &sat constraint satisfaction problems and for unconstrained minimization of NK functions.

Supporting Collaborative Learning and Problem-Solving in a Constraint-Based CSCL Environment for UML Class Diagrams

ERIC Educational Resources Information Center

Baghaei, Nilufar; Mitrovic, Antonija; Irwin, Warwick

2007-01-01

We present COLLECT-UML, a constraint-based intelligent tutoring system (ITS) that teaches object-oriented analysis and design using Unified Modelling Language (UML). UML is easily the most popular object-oriented modelling technology in current practice. While teaching how to design UML class diagrams, COLLECT-UML also provides feedback on…

A centre-free approach for resource allocation with lower bounds

NASA Astrophysics Data System (ADS)

Obando, Germán; Quijano, Nicanor; Rakoto-Ravalontsalama, Naly

2017-09-01

Since complexity and scale of systems are continuously increasing, there is a growing interest in developing distributed algorithms that are capable to address information constraints, specially for solving optimisation and decision-making problems. In this paper, we propose a novel method to solve distributed resource allocation problems that include lower bound constraints. The optimisation process is carried out by a set of agents that use a communication network to coordinate their decisions. Convergence and optimality of the method are guaranteed under some mild assumptions related to the convexity of the problem and the connectivity of the underlying graph. Finally, we compare our approach with other techniques reported in the literature, and we present some engineering applications.

Parallel constraint satisfaction in memory-based decisions.

PubMed

Glöckner, Andreas; Hodges, Sara D

2011-01-01

Three studies sought to investigate decision strategies in memory-based decisions and to test the predictions of the parallel constraint satisfaction (PCS) model for decision making (Glöckner & Betsch, 2008). Time pressure was manipulated and the model was compared against simple heuristics (take the best and equal weight) and a weighted additive strategy. From PCS we predicted that fast intuitive decision making is based on compensatory information integration and that decision time increases and confidence decreases with increasing inconsistency in the decision task. In line with these predictions we observed a predominant usage of compensatory strategies under all time-pressure conditions and even with decision times as short as 1.7 s. For a substantial number of participants, choices and decision times were best explained by PCS, but there was also evidence for use of simple heuristics. The time-pressure manipulation did not significantly affect decision strategies. Overall, the results highlight intuitive, automatic processes in decision making and support the idea that human information-processing capabilities are less severely bounded than often assumed.

Voices of impoverished Brazilian women: health implications of roles and resources.

PubMed

Messias, D K; Hall, J M; Meleis, A I

1996-01-01

This qualitative study was based on individual interviews with 75 Brazilian women in an impoverished peri-urban squatter community (favela) in southeastern Brazil. The purposes of the study were to describe women's role involvement in domestic and employment contexts; identify stresses and satisfactions of maternal, spousal, and employment roles; and assess personal and environmental role constraints and resources from the perspective of women's health. The analytic approach to the interview data was qualitative content analysis, through which thematic categories of maternal, spousal, and employment role satisfactions and stresses were identified by the researchers. Women's unrelenting work in the face of harsh social and economic environments was a broad theme woven throughout the women's descriptions of their lives. The confluence of role constraints affecting the participants' lives included poverty, marginalization, abuse, and lack of support and recognition by partners and society. In order to overcome great adversity and meet heavy role demands, these women relied on self, faith in God, family, and health resources. Implications for women's health promotion are discussed.

Well-Being among Older Adults with OA: Direct and Mediated Patterns of Control Beliefs, Optimism and Pessimism

PubMed Central

Sherman, Aurora M.; Cotter, Kelly A.

2013-01-01

Objectives To assess the contribution of important psychological resources (i.e., optimism, pessimism, control beliefs) to the psychological well-being of older adults with Osteoarthritis (OA); to assess the direct and mediated association of these psychosocial resources to outcomes (depressive symptoms, life satisfaction, and self-esteem). These objectives are important because OA is a significant stressor, treatments are limited, and psychological functioning is at risk for those coping with the condition, even compared to other chronic illnesses. Method A cross-sectional survey of 160 community-dwelling older adults with OA (81% women). Participants were not randomly selected, but nonetheless reflected the demographic makeup of the selection area. Results Ordinary least squares regression analyses using the PROCESS macro (Hayes, 2012) revealed that optimism and pessimism were associated with higher depressive symptoms and lower self-esteem indirectly through constraints beliefs. The analysis of life satisfaction showed that optimism and pessimism were each partially mediated through mastery and constraints beliefs. Discussion These results suggest that prior research, which has assessed these psychological resources as having singular relationships to outcomes, may have underestimated the importance of the relationship between these variables. We discuss possible points of intervention for older adults with OA who may experience increasing constraints beliefs over time. PMID:23418813

Job stress and job satisfaction: home care workers in a consumer-directed model of care.

PubMed

Delp, Linda; Wallace, Steven P; Geiger-Brown, Jeanne; Muntaner, Carles

2010-08-01

To investigate determinants of job satisfaction among home care workers in a consumer-directed model. Analysis of data collected from telephone interviews with 1,614 Los Angeles home care workers on the state payroll in 2003. Multivariate logistic regression analysis was used to determine the odds of job satisfaction using job stress model domains of demands, control, and support. Abuse from consumers, unpaid overtime hours, and caring for more than one consumer as well as work-health demands predict less satisfaction. Some physical and emotional demands of the dyadic care relationship are unexpectedly associated with greater job satisfaction. Social support and control, indicated by job security and union involvement, have a direct positive effect on job satisfaction. Policies that enhance the relational component of care may improve workers' ability to transform the demands of their job into dignified and satisfying labor. Adequate benefits and sufficient authorized hours of care can minimize the stress of unpaid overtime work, caring for multiple consumers, job insecurity, and the financial constraints to seeking health care. Results have implications for the structure of consumer-directed models of care and efforts to retain long-term care workers.

Student evaluation team focus groups increase students' satisfaction with the overall course evaluation process.

PubMed

Brandl, Katharina; Mandel, Jess; Winegarden, Babbi

2017-02-01

Most medical schools use online systems to gather student feedback on the quality of their educational programmes and services. Online data may be limiting, however, as the course directors cannot question the students about written comments, nor can students engage in mutual problem-solving dialogue with course directors. We describe the implementation of a student evaluation team (SET) process to permit course directors and students to gather shortly after courses end to engage in feedback and problem solving regarding the course and course elements. Approximately 16 students were randomly selected to participate in each SET meeting, along with the course director, academic deans and other faculty members involved in the design and delivery of the course. An objective expert facilitates the SET meetings. SETs are scheduled for each of the core courses and threads that occur within the first 2 years of medical school, resulting in approximately 29 SETs annually. SET-specific satisfaction surveys submitted by students (n = 76) and course directors (n = 16) in 2015 were used to evaluate the SET process itself. Survey data were collected from 885 students (2010-2015), which measured student satisfaction with the overall evaluation process before and after the implementation of SETs. Students and course directors valued the SET process itself as a positive experience. Students felt that SETs allowed their voices to be heard, and that the SET increased the probability of suggested changes being implemented. Students' satisfaction with the overall evaluation process significantly improved after implementation of the SET process. Our data suggest that the SET process is a valuable way to supplement online evaluation systems and to increase students' and faculty members' satisfaction with the evaluation process. © 2016 John Wiley & Sons Ltd and The Association for the Study of Medical Education.

Multi-Objective Trajectory Optimization of a Hypersonic Reconnaissance Vehicle with Temperature Constraints

NASA Astrophysics Data System (ADS)

Masternak, Tadeusz J.

This research determines temperature-constrained optimal trajectories for a scramjet-based hypersonic reconnaissance vehicle by developing an optimal control formulation and solving it using a variable order Gauss-Radau quadrature collocation method with a Non-Linear Programming (NLP) solver. The vehicle is assumed to be an air-breathing reconnaissance aircraft that has specified takeoff/landing locations, airborne refueling constraints, specified no-fly zones, and specified targets for sensor data collections. A three degree of freedom scramjet aircraft model is adapted from previous work and includes flight dynamics, aerodynamics, and thermal constraints. Vehicle control is accomplished by controlling angle of attack, roll angle, and propellant mass flow rate. This model is incorporated into an optimal control formulation that includes constraints on both the vehicle and mission parameters, such as avoidance of no-fly zones and coverage of high-value targets. To solve the optimal control formulation, a MATLAB-based package called General Pseudospectral Optimal Control Software (GPOPS-II) is used, which transcribes continuous time optimal control problems into an NLP problem. In addition, since a mission profile can have varying vehicle dynamics and en-route imposed constraints, the optimal control problem formulation can be broken up into several "phases" with differing dynamics and/or varying initial/final constraints. Optimal trajectories are developed using several different performance costs in the optimal control formulation: minimum time, minimum time with control penalties, and maximum range. The resulting analysis demonstrates that optimal trajectories that meet specified mission parameters and constraints can be quickly determined and used for larger-scale operational and campaign planning and execution.

Two-Stage Path Planning Approach for Designing Multiple Spacecraft Reconfiguration Maneuvers

NASA Technical Reports Server (NTRS)

Aoude, Georges S.; How, Jonathan P.; Garcia, Ian M.

2007-01-01

The paper presents a two-stage approach for designing optimal reconfiguration maneuvers for multiple spacecraft. These maneuvers involve well-coordinated and highly-coupled motions of the entire fleet of spacecraft while satisfying an arbitrary number of constraints. This problem is particularly difficult because of the nonlinearity of the attitude dynamics, the non-convexity of some of the constraints, and the coupling between the positions and attitudes of all spacecraft. As a result, the trajectory design must be solved as a single 6N DOF problem instead of N separate 6 DOF problems. The first stage of the solution approach quickly provides a feasible initial solution by solving a simplified version without differential constraints using a bi-directional Rapidly-exploring Random Tree (RRT) planner. A transition algorithm then augments this guess with feasible dynamics that are propagated from the beginning to the end of the trajectory. The resulting output is a feasible initial guess to the complete optimal control problem that is discretized in the second stage using a Gauss pseudospectral method (GPM) and solved using an off-the-shelf nonlinear solver. This paper also places emphasis on the importance of the initialization step in pseudospectral methods in order to decrease their computation times and enable the solution of a more complex class of problems. Several examples are presented and discussed.

Generating effective project scheduling heuristics by abstraction and reconstitution

NASA Technical Reports Server (NTRS)

Janakiraman, Bhaskar; Prieditis, Armand

1992-01-01

A project scheduling problem consists of a finite set of jobs, each with fixed integer duration, requiring one or more resources such as personnel or equipment, and each subject to a set of precedence relations, which specify allowable job orderings, and a set of mutual exclusion relations, which specify jobs that cannot overlap. No job can be interrupted once started. The objective is to minimize project duration. This objective arises in nearly every large construction project--from software to hardware to buildings. Because such project scheduling problems are NP-hard, they are typically solved by branch-and-bound algorithms. In these algorithms, lower-bound duration estimates (admissible heuristics) are used to improve efficiency. One way to obtain an admissible heuristic is to remove (abstract) all resources and mutual exclusion constraints and then obtain the minimal project duration for the abstracted problem; this minimal duration is the admissible heuristic. Although such abstracted problems can be solved efficiently, they yield inaccurate admissible heuristics precisely because those constraints that are central to solving the original problem are abstracted. This paper describes a method to reconstitute the abstracted constraints back into the solution to the abstracted problem while maintaining efficiency, thereby generating better admissible heuristics. Our results suggest that reconstitution can make good admissible heuristics even better.

Exploring a Structure for Mathematics Lessons That Foster Problem Solving and Reasoning

ERIC Educational Resources Information Center

Sullivan, Peter; Walker, Nadia; Borcek, Chris; Rennie, Mick

2015-01-01

While there is widespread agreement on the importance of incorporating problem solving and reasoning into mathematics classrooms, there is limited specific advice on how this can best happen. This is a report of an aspect of a project that is examining the opportunities and constraints in initiating learning by posing challenging mathematics tasks…

Solving intuitionistic fuzzy multi-objective nonlinear programming problem

NASA Astrophysics Data System (ADS)

Anuradha, D.; Sobana, V. E.

2017-11-01

This paper presents intuitionistic fuzzy multi-objective nonlinear programming problem (IFMONLPP). All the coefficients of the multi-objective nonlinear programming problem (MONLPP) and the constraints are taken to be intuitionistic fuzzy numbers (IFN). The IFMONLPP has been transformed into crisp one and solved by using Kuhn-Tucker condition. Numerical example is provided to illustrate the approach.

Dynamic Restructuring Of Problems In Artificial Intelligence

NASA Technical Reports Server (NTRS)

Schwuttke, Ursula M.

1992-01-01

"Dynamic tradeoff evaluation" (DTE) denotes proposed method and procedure for restructuring problem-solving strategies in artificial intelligence to satisfy need for timely responses to changing conditions. Detects situations in which optimal problem-solving strategies cannot be pursued because of real-time constraints, and effects tradeoffs among nonoptimal strategies in such way to minimize adverse effects upon performance of system.

Quality improvement--boon or boondoggle?

PubMed

Paterson, M A; Wendel, J

1994-01-01

Is quality improvement (QI) reducing healthcare costs while improving patient care? Researchers find that QI has improved employee satisfaction and morale, but it was designed to do more. One solution is to use problem-solving techniques to help teams identify the level at which they want to address a problem, whether that be the subinstitutional, institutional, or system level. If QI is to fulfill its promise, skilled managers must create effective teams capable of defining and solving complex problems.

Improved Monkey-King Genetic Algorithm for Solving Large Winner Determination in Combinatorial Auction

NASA Astrophysics Data System (ADS)

Li, Yuzhong

Using GA solve the winner determination problem (WDP) with large bids and items, run under different distribution, because the search space is large, constraint complex and it may easy to produce infeasible solution, would affect the efficiency and quality of algorithm. This paper present improved MKGA, including three operator: preprocessing, insert bid and exchange recombination, and use Monkey-king elite preservation strategy. Experimental results show that improved MKGA is better than SGA in population size and computation. The problem that traditional branch and bound algorithm hard to solve, improved MKGA can solve and achieve better effect.

Assessing the Effects of Service Quality of Government and Student Satisfaction in Education’s Field

NASA Astrophysics Data System (ADS)

Purwaningsih, D.

2017-03-01

The aim of the research is to analyze how the service quality of Indonesian government affect student’s satisfaction in the education field. Data collection was conducted in September 2016 through distributing questionnaires to 132 students at private universities in south Tangerang city. Sampling used incidental sampling method, while data analysis is descriptive, qualitative and quantitative, which were analyzed with the Importance Performance Analysis. The survey results revealed that the satisfaction level of the students of South Tangerang good enough to service of the Government in higher education sector with a value of 83.61 using Customer Satisfaction Index (CSI). Nevertheless, there are several factors that should be prioritized for immediate enhanced, namely: government’s ability to respond effectively to solve the problems in the academic world, fairness of the government in providing assistance to both state and private universities and attention of the government to higher education.

Quadratic Optimisation with One Quadratic Equality Constraint

DTIC Science & Technology

2010-06-01

This report presents a theoretical framework for minimising a quadratic objective function subject to a quadratic equality constraint. The first part of the report gives a detailed algorithm which computes the global minimiser without calling special nonlinear optimisation solvers. The second part of the report shows how the developed theory can be applied to solve the time of arrival geolocation problem.

Calculation of Pareto-optimal solutions to multiple-objective problems using threshold-of-acceptability constraints

NASA Technical Reports Server (NTRS)

Giesy, D. P.

1978-01-01

A technique is presented for the calculation of Pareto-optimal solutions to a multiple-objective constrained optimization problem by solving a series of single-objective problems. Threshold-of-acceptability constraints are placed on the objective functions at each stage to both limit the area of search and to mathematically guarantee convergence to a Pareto optimum.

A technique for locating function roots and for satisfying equality constraints in optimization

NASA Technical Reports Server (NTRS)

Sobieszczanski-Sobieski, Jaroslaw

1991-01-01

A new technique for locating simultaneous roots of a set of functions is described. The technique is based on the property of the Kreisselmeier-Steinhauser function which descends to a minimum at each root location. It is shown that the ensuing algorithm may be merged into any nonlinear programming method for solving optimization problems with equality constraints.

A technique for locating function roots and for satisfying equality constraints in optimization

NASA Technical Reports Server (NTRS)

Sobieszczanski-Sobieski, J.

1992-01-01

A new technique for locating simultaneous roots of a set of functions is described. The technique is based on the property of the Kreisselmeier-Steinhauser function which descends to a minimum at each root location. It is shown that the ensuing algorithm may be merged into any nonlinear programming method for solving optimization problems with equality constraints.

Task Assignment and Path Planning for Multiple Autonomous Underwater Vehicles Using 3D Dubins Curves †

PubMed Central

Zhang, Meiyan; Zheng, Yahong Rosa

2017-01-01

This paper investigates the task assignment and path planning problem for multiple AUVs in three dimensional (3D) underwater wireless sensor networks where nonholonomic motion constraints of underwater AUVs in 3D space are considered. The multi-target task assignment and path planning problem is modeled by the Multiple Traveling Sales Person (MTSP) problem and the Genetic Algorithm (GA) is used to solve the MTSP problem with Euclidean distance as the cost function and the Tour Hop Balance (THB) or Tour Length Balance (TLB) constraints as the stop criterion. The resulting tour sequences are mapped to 2D Dubins curves in the X−Y plane, and then interpolated linearly to obtain the Z coordinates. We demonstrate that the linear interpolation fails to achieve G1 continuity in the 3D Dubins path for multiple targets. Therefore, the interpolated 3D Dubins curves are checked against the AUV dynamics constraint and the ones satisfying the constraint are accepted to finalize the 3D Dubins curve selection. Simulation results demonstrate that the integration of the 3D Dubins curve with the MTSP model is successful and effective for solving the 3D target assignment and path planning problem. PMID:28696377

Task Assignment and Path Planning for Multiple Autonomous Underwater Vehicles Using 3D Dubins Curves †.

PubMed

Cai, Wenyu; Zhang, Meiyan; Zheng, Yahong Rosa

2017-07-11

This paper investigates the task assignment and path planning problem for multiple AUVs in three dimensional (3D) underwater wireless sensor networks where nonholonomic motion constraints of underwater AUVs in 3D space are considered. The multi-target task assignment and path planning problem is modeled by the Multiple Traveling Sales Person (MTSP) problem and the Genetic Algorithm (GA) is used to solve the MTSP problem with Euclidean distance as the cost function and the Tour Hop Balance (THB) or Tour Length Balance (TLB) constraints as the stop criterion. The resulting tour sequences are mapped to 2D Dubins curves in the X - Y plane, and then interpolated linearly to obtain the Z coordinates. We demonstrate that the linear interpolation fails to achieve G 1 continuity in the 3D Dubins path for multiple targets. Therefore, the interpolated 3D Dubins curves are checked against the AUV dynamics constraint and the ones satisfying the constraint are accepted to finalize the 3D Dubins curve selection. Simulation results demonstrate that the integration of the 3D Dubins curve with the MTSP model is successful and effective for solving the 3D target assignment and path planning problem.

A constraint logic programming approach to associate 1D and 3D structural components for large protein complexes.

PubMed

Dal Palù, Alessandro; Pontelli, Enrico; He, Jing; Lu, Yonggang

2007-01-01

The paper describes a novel framework, constructed using Constraint Logic Programming (CLP) and parallelism, to determine the association between parts of the primary sequence of a protein and alpha-helices extracted from 3D low-resolution descriptions of large protein complexes. The association is determined by extracting constraints from the 3D information, regarding length, relative position and connectivity of helices, and solving these constraints with the guidance of a secondary structure prediction algorithm. Parallelism is employed to enhance performance on large proteins. The framework provides a fast, inexpensive alternative to determine the exact tertiary structure of unknown proteins.

Observation Scheduling System

NASA Technical Reports Server (NTRS)

Chien, Steve A.; Tran, Daniel Q.; Rabideau, Gregg R.; Schaffer, Steven R.

2011-01-01

Software has been designed to schedule remote sensing with the Earth Observing One spacecraft. The software attempts to satisfy as many observation requests as possible considering each against spacecraft operation constraints such as data volume, thermal, pointing maneuvers, and others. More complex constraints such as temperature are approximated to enable efficient reasoning while keeping the spacecraft within safe limits. Other constraints are checked using an external software library. For example, an attitude control library is used to determine the feasibility of maneuvering between pairs of observations. This innovation can deal with a wide range of spacecraft constraints and solve large scale scheduling problems like hundreds of observations and thousands of combinations of observation sequences.

Trajectory planning of mobile robots using indirect solution of optimal control method in generalized point-to-point task

NASA Astrophysics Data System (ADS)

Nazemizadeh, M.; Rahimi, H. N.; Amini Khoiy, K.

2012-03-01

This paper presents an optimal control strategy for optimal trajectory planning of mobile robots by considering nonlinear dynamic model and nonholonomic constraints of the system. The nonholonomic constraints of the system are introduced by a nonintegrable set of differential equations which represent kinematic restriction on the motion. The Lagrange's principle is employed to derive the nonlinear equations of the system. Then, the optimal path planning of the mobile robot is formulated as an optimal control problem. To set up the problem, the nonlinear equations of the system are assumed as constraints, and a minimum energy objective function is defined. To solve the problem, an indirect solution of the optimal control method is employed, and conditions of the optimality derived as a set of coupled nonlinear differential equations. The optimality equations are solved numerically, and various simulations are performed for a nonholonomic mobile robot to illustrate effectiveness of the proposed method.

Symbolic Execution Enhanced System Testing

NASA Technical Reports Server (NTRS)

Davies, Misty D.; Pasareanu, Corina S.; Raman, Vishwanath

2012-01-01

We describe a testing technique that uses information computed by symbolic execution of a program unit to guide the generation of inputs to the system containing the unit, in such a way that the unit's, and hence the system's, coverage is increased. The symbolic execution computes unit constraints at run-time, along program paths obtained by system simulations. We use machine learning techniques treatment learning and function fitting to approximate the system input constraints that will lead to the satisfaction of the unit constraints. Execution of system input predictions either uncovers new code regions in the unit under analysis or provides information that can be used to improve the approximation. We have implemented the technique and we have demonstrated its effectiveness on several examples, including one from the aerospace domain.

Reliability Assessment of a Robust Design Under Uncertainty for a 3-D Flexible Wing

NASA Technical Reports Server (NTRS)

Gumbert, Clyde R.; Hou, Gene J. -W.; Newman, Perry A.

2003-01-01

The paper presents reliability assessment results for the robust designs under uncertainty of a 3-D flexible wing previously reported by the authors. Reliability assessments (additional optimization problems) of the active constraints at the various probabilistic robust design points are obtained and compared with the constraint values or target constraint probabilities specified in the robust design. In addition, reliability-based sensitivity derivatives with respect to design variable mean values are also obtained and shown to agree with finite difference values. These derivatives allow one to perform reliability based design without having to obtain second-order sensitivity derivatives. However, an inner-loop optimization problem must be solved for each active constraint to find the most probable point on that constraint failure surface.

Knowledge-based design of generate-and-patch problem solvers that solve global resource assignment problems

NASA Technical Reports Server (NTRS)

Voigt, Kerstin

1992-01-01

We present MENDER, a knowledge based system that implements software design techniques that are specialized to automatically compile generate-and-patch problem solvers that satisfy global resource assignments problems. We provide empirical evidence of the superior performance of generate-and-patch over generate-and-test: even with constrained generation, for a global constraint in the domain of '2D-floorplanning'. For a second constraint in '2D-floorplanning' we show that even when it is possible to incorporate the constraint into a constrained generator, a generate-and-patch problem solver may satisfy the constraint more rapidly. We also briefly summarize how an extended version of our system applies to a constraint in the domain of 'multiprocessor scheduling'.

The roles of constraint-based and dedication-based influences on user's continued online shopping behavior.

PubMed

Chang, Su-Chao; Chou, Chi-Min

2012-11-01

The objective of this study was to determine empirically the role of constraint-based and dedication-based influences as drivers of the intention to continue using online shopping websites. Constraint-based influences consist of two variables: trust and perceived switching costs. Dedication-based influences consist of three variables: satisfaction, perceived usefulness, and trust. The current results indicate that both constraint-based and dedication-based influences are important drivers of the intention to continue using online shopping websites. The data also shows that trust has the strongest total effect on online shoppers' intention to continue using online shopping websites. In addition, the results indicate that the antecedents of constraint-based influences, technical bonds (e.g., perceived operational competence and perceived website interactivity) and social bonds (e.g., perceived relationship investment, community building, and intimacy) have indirect positive effects on the intention to continue using online shopping websites. Based on these findings, this research suggests that online shopping websites should build constraint-based and dedication-based influences to enhance user's continued online shopping behaviors simultaneously.

Employing Sensitivity Derivatives for Robust Optimization under Uncertainty in CFD

NASA Technical Reports Server (NTRS)

Newman, Perry A.; Putko, Michele M.; Taylor, Arthur C., III

2004-01-01

A robust optimization is demonstrated on a two-dimensional inviscid airfoil problem in subsonic flow. Given uncertainties in statistically independent, random, normally distributed flow parameters (input variables), an approximate first-order statistical moment method is employed to represent the Computational Fluid Dynamics (CFD) code outputs as expected values with variances. These output quantities are used to form the objective function and constraints. The constraints are cast in probabilistic terms; that is, the probability that a constraint is satisfied is greater than or equal to some desired target probability. Gradient-based robust optimization of this stochastic problem is accomplished through use of both first and second-order sensitivity derivatives. For each robust optimization, the effect of increasing both input standard deviations and target probability of constraint satisfaction are demonstrated. This method provides a means for incorporating uncertainty when considering small deviations from input mean values.

A Multidisciplinary Initiative to Increase Inpatient Discharges Before Noon.

PubMed

Kane, Marlena; Weinacker, Ann; Arthofer, Rudolph; Seay-Morrison, Timothy; Elfman, Wesley; Ramirez, Mark; Ahuja, Neera; Pickham, David; Hereford, James; Welton, Mark

2016-12-01

The aim of this study is to evaluate the effect of 2 hospital-wide interventions on achieving a discharge-before-noon rate of 40%. A multidisciplinary team led by administrative and physician leadership developed a plan to diminish capacity constraints by minimizing late afternoon hospital discharges using 2 patient flow management techniques. The study was a preintervention/postintervention retrospective analysis observing all inpatients discharged across 19 inpatient units in a 484-bed, academic teaching hospital measuring calendar month discharge-before-noon percentage, patient satisfaction, and readmission rates. Patient satisfaction and readmission rates were used as baseline metrics. The discharge-before-noon percentage increased from 14% in the 11-month preintervention period to an average of 24% over the 11-month postintervention period, whereas patient satisfaction scores and readmission rates remained stable. Implementation of the 2 interventions successfully increased the percentage of discharges before noon yet did not achieve the goal of 40%. Patient satisfaction and readmission rates were not negatively impacted by the program.

Conic Sampling: An Efficient Method for Solving Linear and Quadratic Programming by Randomly Linking Constraints within the Interior

PubMed Central

Serang, Oliver

2012-01-01

Linear programming (LP) problems are commonly used in analysis and resource allocation, frequently surfacing as approximations to more difficult problems. Existing approaches to LP have been dominated by a small group of methods, and randomized algorithms have not enjoyed popularity in practice. This paper introduces a novel randomized method of solving LP problems by moving along the facets and within the interior of the polytope along rays randomly sampled from the polyhedral cones defined by the bounding constraints. This conic sampling method is then applied to randomly sampled LPs, and its runtime performance is shown to compare favorably to the simplex and primal affine-scaling algorithms, especially on polytopes with certain characteristics. The conic sampling method is then adapted and applied to solve a certain quadratic program, which compute a projection onto a polytope; the proposed method is shown to outperform the proprietary software Mathematica on large, sparse QP problems constructed from mass spectometry-based proteomics. PMID:22952741

Solving the MHD equations by the space time conservation element and solution element method

NASA Astrophysics Data System (ADS)

Zhang, Moujin; John Yu, S.-T.; Henry Lin, S.-C.; Chang, Sin-Chung; Blankson, Isaiah

2006-05-01

We apply the space-time conservation element and solution element (CESE) method to solve the ideal MHD equations with special emphasis on satisfying the divergence free constraint of magnetic field, i.e., ∇ · B = 0. In the setting of the CESE method, four approaches are employed: (i) the original CESE method without any additional treatment, (ii) a simple corrector procedure to update the spatial derivatives of magnetic field B after each time marching step to enforce ∇ · B = 0 at all mesh nodes, (iii) a constraint-transport method by using a special staggered mesh to calculate magnetic field B, and (iv) the projection method by solving a Poisson solver after each time marching step. To demonstrate the capabilities of these methods, two benchmark MHD flows are calculated: (i) a rotated one-dimensional MHD shock tube problem and (ii) a MHD vortex problem. The results show no differences between different approaches and all results compare favorably with previously reported data.

A Sequential Linear Quadratic Approach for Constrained Nonlinear Optimal Control with Adaptive Time Discretization and Application to Higher Elevation Mars Landing Problem

NASA Astrophysics Data System (ADS)

Sandhu, Amit

A sequential quadratic programming method is proposed for solving nonlinear optimal control problems subject to general path constraints including mixed state-control and state only constraints. The proposed algorithm further develops on the approach proposed in [1] with objective to eliminate the use of a high number of time intervals for arriving at an optimal solution. This is done by introducing an adaptive time discretization to allow formation of a desirable control profile without utilizing a lot of intervals. The use of fewer time intervals reduces the computation time considerably. This algorithm is further used in this thesis to solve a trajectory planning problem for higher elevation Mars landing.

Inflationary dynamics for matrix eigenvalue problems

PubMed Central

Heller, Eric J.; Kaplan, Lev; Pollmann, Frank

2008-01-01

Many fields of science and engineering require finding eigenvalues and eigenvectors of large matrices. The solutions can represent oscillatory modes of a bridge, a violin, the disposition of electrons around an atom or molecule, the acoustic modes of a concert hall, or hundreds of other physical quantities. Often only the few eigenpairs with the lowest or highest frequency (extremal solutions) are needed. Methods that have been developed over the past 60 years to solve such problems include the Lanczos algorithm, Jacobi–Davidson techniques, and the conjugate gradient method. Here, we present a way to solve the extremal eigenvalue/eigenvector problem, turning it into a nonlinear classical mechanical system with a modified Lagrangian constraint. The constraint induces exponential inflationary growth of the desired extremal solutions. PMID:18511564

Retrospective Analysis of an Ongoing Group-Based Modified Constraint-Induced Movement Therapy Program for Children with Acquired Brain Injury.

PubMed

Komar, Alyssa; Ashley, Kelsey; Hanna, Kelly; Lavallee, Julia; Woodhouse, Janet; Bernstein, Janet; Andres, Matthew; Reed, Nick

2016-01-01

A pretest-posttest retrospective design was used to evaluate the impact of a group-based modified constraint-induced movement therapy (mCIMT) program on upper extremity function and occupational performance. 20 children ages 3 to 18 years with hemiplegia following an acquired brain injury participated in a 2-week group mCIMT program. Upper extremity function was measured with the Assisting Hand Assessment (AHA) and subtests from the Quality of Upper Extremity Skills Test (QUEST). Occupational performance and satisfaction were assessed using the Canadian Occupational Performance Measure (COPM). Data were analyzed using a Wilcoxon signed-ranks test. Group-based analysis revealed upper extremity function and occupational performance attained statistically significant improvements from pre- to postintervention on all outcome measures (AHA: Z = -3.63, p = <.001; QUEST Grasps: Z = -3.10, p = .002; QUEST Dissociated Movement: Z = -2.51, p = .012; COPM Performance: Z = -3.64, p = <.001; COPM Satisfaction: Z = -3.64, p = <.001). Across individuals, clinically significant improvements were found in 65% of participants' AHA scores. 80% of COPM Performance scores and 70% of COPM Satisfaction scores demonstrated clinically significant improvements in at least one identified goal. This study is an initial step in evaluating and providing preliminary evidence supporting the effectiveness of a group-based mCIMT program for children with hemiplegia following an acquired brain injury.

Thinking inside the Tool Box: Creativity, Constraints, and the Colossal Portraits of Chuck Close

ERIC Educational Resources Information Center

Stokes, Patricia D.

2014-01-01

This article presents a problem-solving model to examine the often problematic relationship between expertise and creativity. The model has two premises, each the opposite of a common cliché. The first cliché asserts that creativity requires thinking outside-the-box. The first premise argues that experts can only think and problem solve inside the…

Revisiting Mathematical Problem Solving and Posing in the Digital Era: Toward Pedagogically Sound Uses of Modern Technology

ERIC Educational Resources Information Center

Abramovich, S.

2014-01-01

The availability of sophisticated computer programs such as "Wolfram Alpha" has made many problems found in the secondary mathematics curriculum somewhat obsolete for they can be easily solved by the software. Against this background, an interplay between the power of a modern tool of technology and educational constraints it presents is…

Relationship functioning moderates the association between depressive symptoms and life stressors.

PubMed

Trombello, Joseph M; Schoebi, Dominik; Bradbury, Thomas N

2011-02-01

Data from 172 newlywed couples were collected over the first 4 years of marriage to test how behaviors demonstrated during marital interactions moderate associations between depressive symptoms and subsequent life stressors. Depressive symptoms and behaviors coded from problem-solving and social support interactions were analyzed as predictors of nonmarital stressors that were interpersonal and dependent on the participant's actions. Behavioral codes were found to moderate 3 of 16 symptom-to-life event associations for husbands. Husbands' reports of more depressive symptoms predicted greater levels of stress when husbands' positive affect and hard negative affect during problem-solving were relatively infrequent and when wives made frequent displays of positive behaviors during husbands' support topics. These effects remained after controlling for marital satisfaction. For wives, behavioral moderators did not interact with depressive symptoms to predict changes in stress, but marital satisfaction consistently interacted with depressive symptoms to predict future stressors beyond interpersonal behaviors. Specifically, for wives, stress generation was more evident when relationship satisfaction was low than when it was high. Our results, though different for men and women, suggest that relationship functioning can alter associations between depressive symptoms and life stress in the early years of marriage. (PsycINFO Database Record (c) 2011 APA, all rights reserved). PsycINFO Database Record (c) 2011 APA, all rights reserved.

Optimal Power Allocation for Downstream xDSL With Per-Modem Total Power Constraints: Broadcast Channel Optimal Spectrum Balancing (BC-OSB)

NASA Astrophysics Data System (ADS)

Le Nir, Vincent; Moonen, Marc; Verlinden, Jan; Guenach, Mamoun

2009-02-01

Recently, the duality between Multiple Input Multiple Output (MIMO) Multiple Access Channels (MAC) and MIMO Broadcast Channels (BC) has been established under a total power constraint. The same set of rates for MAC can be achieved in BC exploiting the MAC-BC duality formulas while preserving the total power constraint. In this paper, we describe the BC optimal power allo- cation applying this duality in a downstream x-Digital Subscriber Lines (xDSL) context under a total power constraint for all modems over all tones. Then, a new algorithm called BC-Optimal Spectrum Balancing (BC-OSB) is devised for a more realistic power allocation under per-modem total power constraints. The capacity region of the primal BC problem under per-modem total power constraints is found by the dual optimization problem for the BC under per-modem total power constraints which can be rewritten as a dual optimization problem in the MAC by means of a precoder matrix based on the Lagrange multipliers. We show that the duality gap between the two problems is zero. The multi-user power allocation problem has been solved for interference channels and MAC using the OSB algorithm. In this paper we solve the problem of multi-user power allocation for the BC case using the OSB algorithm as well and we derive a computational efficient algorithm that will be referred to as BC-OSB. Simulation results are provided for two VDSL2 scenarios: the first one with Differential-Mode (DM) transmission only and the second one with both DM and Phantom- Mode (PM) transmissions.

Computing group cardinality constraint solutions for logistic regression problems.

PubMed

Zhang, Yong; Kwon, Dongjin; Pohl, Kilian M

2017-01-01

We derive an algorithm to directly solve logistic regression based on cardinality constraint, group sparsity and use it to classify intra-subject MRI sequences (e.g. cine MRIs) of healthy from diseased subjects. Group cardinality constraint models are often applied to medical images in order to avoid overfitting of the classifier to the training data. Solutions within these models are generally determined by relaxing the cardinality constraint to a weighted feature selection scheme. However, these solutions relate to the original sparse problem only under specific assumptions, which generally do not hold for medical image applications. In addition, inferring clinical meaning from features weighted by a classifier is an ongoing topic of discussion. Avoiding weighing features, we propose to directly solve the group cardinality constraint logistic regression problem by generalizing the Penalty Decomposition method. To do so, we assume that an intra-subject series of images represents repeated samples of the same disease patterns. We model this assumption by combining series of measurements created by a feature across time into a single group. Our algorithm then derives a solution within that model by decoupling the minimization of the logistic regression function from enforcing the group sparsity constraint. The minimum to the smooth and convex logistic regression problem is determined via gradient descent while we derive a closed form solution for finding a sparse approximation of that minimum. We apply our method to cine MRI of 38 healthy controls and 44 adult patients that received reconstructive surgery of Tetralogy of Fallot (TOF) during infancy. Our method correctly identifies regions impacted by TOF and generally obtains statistically significant higher classification accuracy than alternative solutions to this model, i.e., ones relaxing group cardinality constraints. Copyright © 2016 Elsevier B.V. All rights reserved.

The Effect of Bedside Presentations in the Emergency Department on Patient Satisfaction

PubMed Central

Schranz, Craig I.; Sobehart, Robert J.; Fallgatter, Kiva; Riffenburgh, Robert H.; Matteucci, Michael J.

2011-01-01

Background Due to increasing time constraints, the use of bedside presentations in resident education has declined. We examined whether patient satisfaction in the emergency department is affected when first-year residents present at the bedside with attendings. Methods We performed an observational, prospective, nonblinded study in the emergency department of a military teaching hospital. We alternately assigned first-year residents to present a convenience sample of 248 patients to the attending physician at the patient's bedside or away from the patient. We measured patient satisfaction by using the Patient Satisfaction Questionaire-18 (PSQ-18), a validated survey instrument that utilizes a Likert scale, and additional nonvalidated survey questions involving Likert and visual analog scales. Results While the median PSQ-18 score of 74 (95% confidence interval [CI], 72–76) was higher for patient satisfaction when residents made bedside presentations than that for standard presentations, 72 (95% CI, 70–74), the difference did not reach statistical significance (P  =  .33). Conclusion There was no significant difference in overall patient satisfaction between residents' bedside presentations and presentations to attendings away from the patient. Although not significant, the differences noted in PSQ-18 subscales of communication, general satisfaction, and interpersonal manner warrant further investigation. Patients did not appear to be uncomfortable with having their care discussed and with having subsequent resident education at the bedside. Future research on patient satisfaction after implementation of standardized bedside teaching techniques 5 help further elucidate this relationship. PMID:23205195

A framework for multi-stakeholder decision-making and conflict resolution

EPA Science Inventory

We propose a decision-making framework to compute compromise solutions that balance conflicting priorities of multiple stakeholders on multiple objectives. In our setting, we shape the stakeholder dis-satisfaction distribution by solving a conditional-value-at-risk (CVaR) minimiz...

Job Stress and Job Satisfaction: Home Care Workers in a Consumer-Directed Model of Care

PubMed Central

Delp, Linda; Wallace, Steven P; Geiger-Brown, Jeanne; Muntaner, Carles

2010-01-01

Objective To investigate determinants of job satisfaction among home care workers in a consumer-directed model. Data Sources/Setting Analysis of data collected from telephone interviews with 1,614 Los Angeles home care workers on the state payroll in 2003. Data Collection and Analysis Multivariate logistic regression analysis was used to determine the odds of job satisfaction using job stress model domains of demands, control, and support. Principal Findings Abuse from consumers, unpaid overtime hours, and caring for more than one consumer as well as work-health demands predict less satisfaction. Some physical and emotional demands of the dyadic care relationship are unexpectedly associated with greater job satisfaction. Social support and control, indicated by job security and union involvement, have a direct positive effect on job satisfaction. Conclusions Policies that enhance the relational component of care may improve workers' ability to transform the demands of their job into dignified and satisfying labor. Adequate benefits and sufficient authorized hours of care can minimize the stress of unpaid overtime work, caring for multiple consumers, job insecurity, and the financial constraints to seeking health care. Results have implications for the structure of consumer-directed models of care and efforts to retain long-term care workers. PMID:20403063

Volume of the steady-state space of financial flows in a monetary stock-flow-consistent model

NASA Astrophysics Data System (ADS)

Hazan, Aurélien

2017-05-01

We show that a steady-state stock-flow consistent macro-economic model can be represented as a Constraint Satisfaction Problem (CSP). The set of solutions is a polytope, which volume depends on the constraints applied and reveals the potential fragility of the economic circuit, with no need to study the dynamics. Several methods to compute the volume are compared, inspired by operations research methods and the analysis of metabolic networks, both exact and approximate. We also introduce a random transaction matrix, and study the particular case of linear flows with respect to money stocks.

Curvelet-domain multiple matching method combined with cubic B-spline function

NASA Astrophysics Data System (ADS)

Wang, Tong; Wang, Deli; Tian, Mi; Hu, Bin; Liu, Chengming

2018-05-01

Since the large amount of surface-related multiple existed in the marine data would influence the results of data processing and interpretation seriously, many researchers had attempted to develop effective methods to remove them. The most successful surface-related multiple elimination method was proposed based on data-driven theory. However, the elimination effect was unsatisfactory due to the existence of amplitude and phase errors. Although the subsequent curvelet-domain multiple-primary separation method achieved better results, poor computational efficiency prevented its application. In this paper, we adopt the cubic B-spline function to improve the traditional curvelet multiple matching method. First, select a little number of unknowns as the basis points of the matching coefficient; second, apply the cubic B-spline function on these basis points to reconstruct the matching array; third, build constraint solving equation based on the relationships of predicted multiple, matching coefficients, and actual data; finally, use the BFGS algorithm to iterate and realize the fast-solving sparse constraint of multiple matching algorithm. Moreover, the soft-threshold method is used to make the method perform better. With the cubic B-spline function, the differences between predicted multiple and original data diminish, which results in less processing time to obtain optimal solutions and fewer iterative loops in the solving procedure based on the L1 norm constraint. The applications to synthetic and field-derived data both validate the practicability and validity of the method.

Possibilities of the free-complement methodology for solving the Schrödinger equation of atoms and molecules

NASA Astrophysics Data System (ADS)

Nakatsuji, Hiroshi

Chemistry is a science of complex subjects that occupy this universe and biological world and that are composed of atoms and molecules. Its essence is diversity. However, surprisingly, whole of this science is governed by simple quantum principles like the Schrödinger and the Dirac equations. Therefore, if we can find a useful general method of solving these quantum principles under the fermionic and/or bosonic constraints accurately in a reasonable speed, we can replace somewhat empirical methodologies of this science with purely quantum theoretical and computational logics. This is the purpose of our series of studies - called ``exact theory'' in our laboratory. Some of our documents are cited below. The key idea was expressed as the free complement (FC) theory (originally called ICI theory) that was introduced to solve the Schrödinger and Dirac equations analytically. For extending this methodology to larger systems, order N methodologies are essential, but actually the antisymmetry constraints for electronic wave functions become big constraints. Recently, we have shown that the antisymmetry rule or `dogma' can be very much relaxed when our subjects are large molecular systems. In this talk, I want to present our recent progress in our FC methodology. The purpose is to construct ``predictive quantum chemistry'' that is useful in chemical and physical researches and developments in institutes and industries

Mathematical improvement of the Hopfield model for feasible solutions to the traveling salesman problem by a synapse dynamical system.

PubMed

Takahashi, Y

1998-01-01

It is well known that the Hopfield Model (HM) for neural networks to solve the Traveling Salesman Problem (TSP) suffers from three major drawbacks. (1) It can converge on nonoptimal locally minimum solutions. (2) It can converge on infeasible solutions. (3) Results are very sensitive to the careful tuning of its parameters. A number of methods have been proposed to overcome (a) well. In contrast, work on (b) and (c) has not been sufficient; techniques have not been generalized to more general optimization problems. Thus this paper mathematically resolves (b) and (c) to such an extent that the resolution can be applied to solving with some general network continuous optimization problems including the Hopfield version of the TSP. It first constructs an Extended HM (E-HM) that overcomes both (b) and (c). Fundamental techniques of the E-HM lie in the addition of a synapse dynamical system cooperated with the current HM unit dynamical system. It is this synapse dynamical system that makes the TSP constraint hold at any final states for whatever choices of the IIM parameters and an initial state. The paper then generalizes the E-HM further to a network that can solve a class of continuous optimization problems with a constraint equation where both of the objective function and the constraint function are nonnegative and continuously differentiable.

Image Reconstruction from Highly Undersampled (k, t)-Space Data with Joint Partial Separability and Sparsity Constraints

PubMed Central

Zhao, Bo; Haldar, Justin P.; Christodoulou, Anthony G.; Liang, Zhi-Pei

2012-01-01

Partial separability (PS) and sparsity have been previously used to enable reconstruction of dynamic images from undersampled (k, t)-space data. This paper presents a new method to use PS and sparsity constraints jointly for enhanced performance in this context. The proposed method combines the complementary advantages of PS and sparsity constraints using a unified formulation, achieving significantly better reconstruction performance than using either of these constraints individually. A globally convergent computational algorithm is described to efficiently solve the underlying optimization problem. Reconstruction results from simulated and in vivo cardiac MRI data are also shown to illustrate the performance of the proposed method. PMID:22695345

Level-Set Topology Optimization with Aeroelastic Constraints

NASA Technical Reports Server (NTRS)

Dunning, Peter D.; Stanford, Bret K.; Kim, H. Alicia

2015-01-01

Level-set topology optimization is used to design a wing considering skin buckling under static aeroelastic trim loading, as well as dynamic aeroelastic stability (flutter). The level-set function is defined over the entire 3D volume of a transport aircraft wing box. Therefore, the approach is not limited by any predefined structure and can explore novel configurations. The Sequential Linear Programming (SLP) level-set method is used to solve the constrained optimization problems. The proposed method is demonstrated using three problems with mass, linear buckling and flutter objective and/or constraints. A constraint aggregation method is used to handle multiple buckling constraints in the wing skins. A continuous flutter constraint formulation is used to handle difficulties arising from discontinuities in the design space caused by a switching of the critical flutter mode.

Nonlinear Waves and Inverse Scattering

DTIC Science & Technology

1990-09-18

to be published Proceedings: conference Chaos in Australia (February 1990). 5. On the Kadomtsev Petviashvili Equation and Associated Constraints by...Scattering Transfoni (IST). IST is a method which alows one to’solve nonlinear wave equations by solving certain related direct and inverse scattering...problems. We use these results to find solutions to nonlinear wave equations much like one uses Fourier analysis for linear problems. Moreover the

The Relationship of Servant Leadership on Teacher Satisfaction and Teacher Retention

ERIC Educational Resources Information Center

Engelhart, Elizabeth F.

2012-01-01

High performing schools with exceptional leaders are needed today in America. School leaders are expected to come to the job equipped with the ability to address curriculum issues, face dire budget constraints, and turn around school culture and climate. Financially, schools are suffering from the cost of teacher turnover. Students are suffering…

A hybrid fuzzy logic/constraint satisfaction problem approach to automatic decision making in simulation game models.

PubMed

Braathen, Sverre; Sendstad, Ole Jakob

2004-08-01

Possible techniques for representing automatic decision-making behavior approximating human experts in complex simulation model experiments are of interest. Here, fuzzy logic (FL) and constraint satisfaction problem (CSP) methods are applied in a hybrid design of automatic decision making in simulation game models. The decision processes of a military headquarters are used as a model for the FL/CSP decision agents choice of variables and rulebases. The hybrid decision agent design is applied in two different types of simulation games to test the general applicability of the design. The first application is a two-sided zero-sum sequential resource allocation game with imperfect information interpreted as an air campaign game. The second example is a network flow stochastic board game designed to capture important aspects of land manoeuvre operations. The proposed design is shown to perform well also in this complex game with a very large (billionsize) action set. Training of the automatic FL/CSP decision agents against selected performance measures is also shown and results are presented together with directions for future research.

Research on Centralized Voltage and Effective Inequality Identification Based on Circuit Analysis Method

NASA Astrophysics Data System (ADS)

Su, Yi; Wang, Feifeng; Lu, Yufeng; Huang, Huimin; Xia, Xiaofei

2017-09-01

This paper is based on affine function equation of the grid and OPF problem, discusses the equivalent of some inequality constraints variables optimizing. Further, we propose the model of injection current and set up the constraint sensitivity index of affine characteristics. The index can be used to identify the central point voltage and effective inequality of the system automatically. And then we can know how to compensate reactive power of the corresponding generator node and control the voltage to ensure the quality of the system voltage. When checking the effective inequalities we introduce cross-solving method of power flow. This provide a different idea for solving the power flow. The paper uses the results of the IEEE5 node examples to illustrate the validity and practicality of the proposed method.

Design and Evaluation of a Personal Diffusion Battery.

PubMed

Vosburgh, Donna J H; Klein, Timothy; Sheehan, Maura; Anthony, T Renee; Peters, Thomas M

A four-stage personal diffusion battery (pDB) was designed and constructed to measure submicron particle size distributions. The pDB consisted of a screen-type diffusion battery, solenoid valve system, and electronic controller. A data inversion spreadsheet was created to solve for the number median diameter (NMD), geometric standard deviation (GSD), and particle number concentration of unimodal aerosols using stage number concentrations from the pDB combined with a handheld condensation particle counter (pDB+CPC). The inversion spreadsheet included particle entry losses, theoretical penetrations across screens, the detection efficiency of the CPC, and constraints so the spreadsheet solved to values within the pDB range. Size distribution parameters (NMD, GSD, and number concentration) measured with the pDB+CPC with inversion spreadsheet were within 25% of those measured with a scanning mobility particle sizer (SMPS) for 5 of 12 polydisperse combustion aerosols. For three tests conducted with propylene torch exhaust, the pDB+CPC with inversion spreadsheet successfully identified that the NMD was smaller than the constraint value of 16 nm. The ratio of the nanoparticle portion of the aerosol compared to the reference ( R nano ) was calculated to determine the ability of pDB+CPC with inversion spreadsheet to measure the nanoparticle portion of the aerosols. The R nano ranged from 0.87 to 1.01 when the inversion solved and from 0.06 to 2.01 when the inversion solved to a constraint. The pDB combined with CPC has limited use as a personal monitor but combining the pDB with a different detector would allow for the pDB to be used as a personal monitor.

Design and Evaluation of a Personal Diffusion Battery

PubMed Central

Vosburgh, Donna J. H.; Klein, Timothy; Sheehan, Maura; Anthony, T. Renee; Peters, Thomas M.

2016-01-01

A four-stage personal diffusion battery (pDB) was designed and constructed to measure submicron particle size distributions. The pDB consisted of a screen-type diffusion battery, solenoid valve system, and electronic controller. A data inversion spreadsheet was created to solve for the number median diameter (NMD), geometric standard deviation (GSD), and particle number concentration of unimodal aerosols using stage number concentrations from the pDB combined with a handheld condensation particle counter (pDB+CPC). The inversion spreadsheet included particle entry losses, theoretical penetrations across screens, the detection efficiency of the CPC, and constraints so the spreadsheet solved to values within the pDB range. Size distribution parameters (NMD, GSD, and number concentration) measured with the pDB+CPC with inversion spreadsheet were within 25% of those measured with a scanning mobility particle sizer (SMPS) for 5 of 12 polydisperse combustion aerosols. For three tests conducted with propylene torch exhaust, the pDB+CPC with inversion spreadsheet successfully identified that the NMD was smaller than the constraint value of 16 nm. The ratio of the nanoparticle portion of the aerosol compared to the reference (R nano) was calculated to determine the ability of pDB+CPC with inversion spreadsheet to measure the nanoparticle portion of the aerosols. The R nano ranged from 0.87 to 1.01 when the inversion solved and from 0.06 to 2.01 when the inversion solved to a constraint. The pDB combined with CPC has limited use as a personal monitor but combining the pDB with a different detector would allow for the pDB to be used as a personal monitor. PMID:26900207

A quadratic-tensor model algorithm for nonlinear least-squares problems with linear constraints

NASA Technical Reports Server (NTRS)

Hanson, R. J.; Krogh, Fred T.

1992-01-01

A new algorithm for solving nonlinear least-squares and nonlinear equation problems is proposed which is based on approximating the nonlinear functions using the quadratic-tensor model by Schnabel and Frank. The algorithm uses a trust region defined by a box containing the current values of the unknowns. The algorithm is found to be effective for problems with linear constraints and dense Jacobian matrices.

Generating subtour elimination constraints for the TSP from pure integer solutions.

PubMed

Pferschy, Ulrich; Staněk, Rostislav

2017-01-01

The traveling salesman problem ( TSP ) is one of the most prominent combinatorial optimization problems. Given a complete graph [Formula: see text] and non-negative distances d for every edge, the TSP asks for a shortest tour through all vertices with respect to the distances d. The method of choice for solving the TSP to optimality is a branch and cut approach . Usually the integrality constraints are relaxed first and all separation processes to identify violated inequalities are done on fractional solutions . In our approach we try to exploit the impressive performance of current ILP-solvers and work only with integer solutions without ever interfering with fractional solutions. We stick to a very simple ILP-model and relax the subtour elimination constraints only. The resulting problem is solved to integer optimality, violated constraints (which are trivial to find) are added and the process is repeated until a feasible solution is found. In order to speed up the algorithm we pursue several attempts to find as many relevant subtours as possible. These attempts are based on the clustering of vertices with additional insights gained from empirical observations and random graph theory. Computational results are performed on test instances taken from the TSPLIB95 and on random Euclidean graphs .

Advanced Computational Methods for Security Constrained Financial Transmission Rights: Structure and Parallelism

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

Elbert, Stephen T.; Kalsi, Karanjit; Vlachopoulou, Maria

Financial Transmission Rights (FTRs) help power market participants reduce price risks associated with transmission congestion. FTRs are issued based on a process of solving a constrained optimization problem with the objective to maximize the FTR social welfare under power flow security constraints. Security constraints for different FTR categories (monthly, seasonal or annual) are usually coupled and the number of constraints increases exponentially with the number of categories. Commercial software for FTR calculation can only provide limited categories of FTRs due to the inherent computational challenges mentioned above. In this paper, a novel non-linear dynamical system (NDS) approach is proposed tomore » solve the optimization problem. The new formulation and performance of the NDS solver is benchmarked against widely used linear programming (LP) solvers like CPLEX™ and tested on large-scale systems using data from the Western Electricity Coordinating Council (WECC). The NDS is demonstrated to outperform the widely used CPLEX algorithms while exhibiting superior scalability. Furthermore, the NDS based solver can be easily parallelized which results in significant computational improvement.« less

Optimization of an auto-thermal ammonia synthesis reactor using cyclic coordinate method

NASA Astrophysics Data System (ADS)

A-N Nguyen, T.; Nguyen, T.-A.; Vu, T.-D.; Nguyen, K.-T.; K-T Dao, T.; P-H Huynh, K.

2017-06-01

The ammonia synthesis system is an important chemical process used in the manufacture of fertilizers, chemicals, explosives, fibers, plastics, refrigeration. In the literature, many works approaching the modeling, simulation and optimization of an auto-thermal ammonia synthesis reactor can be found. However, they just focus on the optimization of the reactor length while keeping the others parameters constant. In this study, the other parameters are also considered in the optimization problem such as the temperature of feed gas enters the catalyst zone, the initial nitrogen proportion. The optimal problem requires the maximization of an objective function which is multivariable function and subject to a number of equality constraints involving the solution of coupled differential equations and also inequality constraint. The cyclic coordinate search was applied to solve the multivariable-optimization problem. In each coordinate, the golden section method was applied to find the maximum value. The inequality constraints were treated using penalty method. The coupled differential equations system was solved using Runge-Kutta 4th order method. The results obtained from this study are also compared to the results from the literature.

Surveillance of a 2D Plane Area with 3D Deployed Cameras

PubMed Central

Fu, Yi-Ge; Zhou, Jie; Deng, Lei

2014-01-01

As the use of camera networks has expanded, camera placement to satisfy some quality assurance parameters (such as a good coverage ratio, an acceptable resolution constraints, an acceptable cost as low as possible, etc.) has become an important problem. The discrete camera deployment problem is NP-hard and many heuristic methods have been proposed to solve it, most of which make very simple assumptions. In this paper, we propose a probability inspired binary Particle Swarm Optimization (PI-BPSO) algorithm to solve a homogeneous camera network placement problem. We model the problem under some more realistic assumptions: (1) deploy the cameras in the 3D space while the surveillance area is restricted to a 2D ground plane; (2) deploy the minimal number of cameras to get a maximum visual coverage under more constraints, such as field of view (FOV) of the cameras and the minimum resolution constraints. We can simultaneously optimize the number and the configuration of the cameras through the introduction of a regulation item in the cost function. The simulation results showed the effectiveness of the proposed PI-BPSO algorithm. PMID:24469353

Optimal satisfaction degree in energy harvesting cognitive radio networks

NASA Astrophysics Data System (ADS)

Li, Zan; Liu, Bo-Yang; Si, Jiang-Bo; Zhou, Fu-Hui

2015-12-01

A cognitive radio (CR) network with energy harvesting (EH) is considered to improve both spectrum efficiency and energy efficiency. A hidden Markov model (HMM) is used to characterize the imperfect spectrum sensing process. In order to maximize the whole satisfaction degree (WSD) of the cognitive radio network, a tradeoff between the average throughput of the secondary user (SU) and the interference to the primary user (PU) is analyzed. We formulate the satisfaction degree optimization problem as a mixed integer nonlinear programming (MINLP) problem. The satisfaction degree optimization problem is solved by using differential evolution (DE) algorithm. The proposed optimization problem allows the network to adaptively achieve the optimal solution based on its required quality of service (Qos). Numerical results are given to verify our analysis. Project supported by the National Natural Science Foundation of China (Grant No. 61301179), the Doctorial Programs Foundation of the Ministry of Education of China (Grant No. 20110203110011), and the 111 Project (Grant No. B08038).

A Discussion of Issues in Integrity Constraint Monitoring

NASA Technical Reports Server (NTRS)

Fernandez, Francisco G.; Gates, Ann Q.; Cooke, Daniel E.

1998-01-01

In the development of large-scale software systems, analysts, designers, and programmers identify properties of data objects in the system. The ability to check those assertions during runtime is desirable as a means of verifying the integrity of the program. Typically, programmers ensure the satisfaction of such properties through the use of some form of manually embedded assertion check. The disadvantage to this approach is that these assertions become entangled within the program code. The goal of the research is to develop an integrity constraint monitoring mechanism whereby a repository of software system properties (called integrity constraints) are automatically inserted into the program by the mechanism to check for incorrect program behaviors. Such a mechanism would overcome many of the deficiencies of manually embedded assertion checks. This paper gives an overview of the preliminary work performed toward this goal. The manual instrumentation of constraint checking on a series of test programs is discussed, This review then is used as the basis for a discussion of issues to be considered in developing an automated integrity constraint monitor.

Invalid-point removal based on epipolar constraint in the structured-light method

NASA Astrophysics Data System (ADS)

Qi, Zhaoshuai; Wang, Zhao; Huang, Junhui; Xing, Chao; Gao, Jianmin

2018-06-01

In structured-light measurement, there unavoidably exist many invalid points caused by shadows, image noise and ambient light. According to the property of the epipolar constraint, because the retrieved phase of the invalid point is inaccurate, the corresponding projector image coordinate (PIC) will not satisfy the epipolar constraint. Based on this fact, a new invalid-point removal method based on the epipolar constraint is proposed in this paper. First, the fundamental matrix of the measurement system is calculated, which will be used for calculating the epipolar line. Then, according to the retrieved phase map of the captured fringes, the PICs of each pixel are retrieved. Subsequently, the epipolar line in the projector image plane of each pixel is obtained using the fundamental matrix. The distance between the corresponding PIC and the epipolar line of a pixel is defined as the invalidation criterion, which quantifies the satisfaction degree of the epipolar constraint. Finally, all pixels with a distance larger than a certain threshold are removed as invalid points. Experiments verified that the method is easy to implement and demonstrates better performance than state-of-the-art measurement systems.

Clients' reasons for terminating psychotherapy: a quantitative and qualitative inquiry.

PubMed

Roe, David; Dekel, Rachel; Harel, Galit; Fennig, Shmuel

2006-12-01

To study private-practice clients' perspective on reasons for psychotherapy termination and how these are related to demographic and treatment variables and to satisfaction with therapy. Eighty-four persons who had been in extended private-practice psychotherapy which ended in the preceding three years participated in the study. Mean number of months in treatment was 27.70 (SD = 18.70). Assessment included rating scales and open-ended questions assessing demographic variables, reasons for terminating therapy, and satisfaction with therapy. Quantitative results revealed that the most frequent reasons for termination were accomplishment of goals, circumstantial constraints and dissatisfaction with therapy, and that client satisfaction was positively related to positive reasons for termination. Qualitative results revealed two additional frequently mentioned reasons for termination: the client's need for independence and the client's involvement in new meaningful relationships. Findings suggest that psychotherapy termination may at times be required to facilitate the pursuit of personally meaningful goals.

Attitude Differences and Task Performance for Black and White Naval Recruits in Problem-Solving Groups of Differing Size and Racial Composition.

DTIC Science & Technology

A field study was made in which 288 black and white naval personnel (224 recruits and 64 squad leaders) in groups of varying size and racial composition performed two problem-solving tasks (knot tying and ship-routing). Black and white leaders, subordinates and group types (25% black tetrads, 75% black tetrads, racially balanced dyads and tetrads) were compared in measures of self - esteem , duration of speech, locus of control, job and general satisfaction, Bales IPA behavior, and performance on the tasks.

About the mechanism of ERP-system pilot test

NASA Astrophysics Data System (ADS)

Mitkov, V. V.; Zimin, V. V.

2018-05-01

In the paper the mathematical problem of defining the scope of pilot test is stated, which is a task of quadratic programming. The procedure of the problem solving includes the method of network programming based on the structurally similar network representation of the criterion and constraints and which reduces the original problem to a sequence of simpler evaluation tasks. The evaluation tasks are solved by the method of dichotomous programming.

Solving LP Relaxations of Large-Scale Precedence Constrained Problems

NASA Astrophysics Data System (ADS)

Bienstock, Daniel; Zuckerberg, Mark

We describe new algorithms for solving linear programming relaxations of very large precedence constrained production scheduling problems. We present theory that motivates a new set of algorithmic ideas that can be employed on a wide range of problems; on data sets arising in the mining industry our algorithms prove effective on problems with many millions of variables and constraints, obtaining provably optimal solutions in a few minutes of computation.

An integrated bi-level optimization model for air quality management of Beijing's energy system under uncertainty.

PubMed

Jin, S W; Li, Y P; Nie, S

2018-05-15

In this study, an interval chance-constrained bi-level programming (ICBP) method is developed for air quality management of municipal energy system under uncertainty. ICBP can deal with uncertainties presented as interval values and probability distributions as well as examine the risk of violating constraints. Besides, a leader-follower decision strategy is incorporated into the optimization process where two decision makers with different goals and preferences are involved. To solve the proposed model, a bi-level interactive algorithm based on satisfactory degree is introduced into the decision-making processes. Then, an ICBP based energy and environmental systems (ICBP-EES) model is formulated for Beijing, in which air quality index (AQI) is used for evaluating the integrated air quality of multiple pollutants. Result analysis can help different stakeholders adjust their tolerances to achieve the overall satisfaction of EES planning for the study city. Results reveal that natural gas is the main source for electricity-generation and heating that could lead to a potentially increment of imported energy for Beijing in future. Results also disclose that PM 10 is the major contributor to AQI. These findings can help decision makers to identify desired alternatives for EES planning and provide useful information for regional air quality management under uncertainty. Copyright © 2018 Elsevier B.V. All rights reserved.

Strategic cognitive sequencing: a computational cognitive neuroscience approach.

PubMed

Herd, Seth A; Krueger, Kai A; Kriete, Trenton E; Huang, Tsung-Ren; Hazy, Thomas E; O'Reilly, Randall C

2013-01-01

We address strategic cognitive sequencing, the "outer loop" of human cognition: how the brain decides what cognitive process to apply at a given moment to solve complex, multistep cognitive tasks. We argue that this topic has been neglected relative to its importance for systematic reasons but that recent work on how individual brain systems accomplish their computations has set the stage for productively addressing how brain regions coordinate over time to accomplish our most impressive thinking. We present four preliminary neural network models. The first addresses how the prefrontal cortex (PFC) and basal ganglia (BG) cooperate to perform trial-and-error learning of short sequences; the next, how several areas of PFC learn to make predictions of likely reward, and how this contributes to the BG making decisions at the level of strategies. The third models address how PFC, BG, parietal cortex, and hippocampus can work together to memorize sequences of cognitive actions from instruction (or "self-instruction"). The last shows how a constraint satisfaction process can find useful plans. The PFC maintains current and goal states and associates from both of these to find a "bridging" state, an abstract plan. We discuss how these processes could work together to produce strategic cognitive sequencing and discuss future directions in this area.

Route constraints model based on polychromatic sets

NASA Astrophysics Data System (ADS)

Yin, Xianjun; Cai, Chao; Wang, Houjun; Li, Dongwu

2018-03-01

With the development of unmanned aerial vehicle (UAV) technology, the fields of its application are constantly expanding. The mission planning of UAV is especially important, and the planning result directly influences whether the UAV can accomplish the task. In order to make the results of mission planning for unmanned aerial vehicle more realistic, it is necessary to consider not only the physical properties of the aircraft, but also the constraints among the various equipment on the UAV. However, constraints among the equipment of UAV are complex, and the equipment has strong diversity and variability, which makes these constraints difficult to be described. In order to solve the above problem, this paper, referring to the polychromatic sets theory used in the advanced manufacturing field to describe complex systems, presents a mission constraint model of UAV based on polychromatic sets.

Moderators and determinants of satisfaction with diet counseling for patients consuming a therapeutic diet.

PubMed

Trudeau, E; Dubé, L

1995-01-01

To identify moderators and key determinants of patient satisfaction with diet counseling. Survey questionnaire. A French-Canadian acute-care urban hospital. Population of eligible patients hospitalized for a minimum stay of 5 days. Patients excluded from the study were those with notable physical, cognitive, or emotional limitations; those receiving enteral and parenteral nutrition; and those from long-term-care units. Analyses were performed on 49 patients who consumed a therapeutic diet and who received diet counseling during their current hospital stay. Overall satisfaction with diet counseling, compliance intentions, and satisfaction with four components of diet counseling. Measures were taken on seven-point graphic scales and five-point semantic scales. Reliability estimates with Cronbach's alpha correlation coefficient, stepwise multiple regression analyses, t tests, and one-way analyses of variance. Facilitation skills and knowledge components of diet counseling were the key determinants of patient satisfaction. Among the moderators of patient satisfaction with diet counseling, women and patients with a good appetite were more satisfied with the knowledge components and had stronger compliance intentions. Patients who spent more than 50% of the time at rest were less satisfied than more active patients. Enhancing patient satisfaction implies having a good understanding of a patient's social and cultural context, developing problem-solving skills, and demonstrating greater flexibility and creativity about the means of providing diet counseling.

Determinants of job satisfaction for novice nurse managers employed in hospitals.

PubMed

Djukic, Maja; Jun, Jin; Kovner, Christine; Brewer, Carol; Fletcher, Jason

Numbering close to 300,000 nurse managers represent the largest segment of the health care management workforce. Their effectiveness is, in part, influenced by their job satisfaction. We examined factors associated with job satisfaction of novice frontline nurse managers. We used a cross-sectional, correlational survey design. The sample consisted of responders to the fifth wave of a multiyear study of new nurses in 2013 (N = 1,392; response rate of 69%) who reported working as managers (n = 209). The parent study sample consisted of registered nurses who were licensed for the first time by exam 6-18 months prior in 1 of 51 selected metropolitan statistical areas and 9 rural areas across 34 U.S. states and the District of Columbia. We examined bivariate correlations between job satisfaction and 31 personal and structural variables. All variables significantly related to job satisfaction in bivariate analysis were included in a multivariate linear regression model. In addition, we tested the interaction effects of procedural justice and negative affectivity, autonomy, and organizational constraints on job satisfaction. The Cronbach's alphas for all multi-item scales ranged from .74 to .96. In the multivariate analysis, negative affectivity (β = -.169; p = .006) and procedural justice (β = .210; p = .016) were significantly correlated with job satisfaction. The combination of predictors in the model accounted for half of the variability in job satisfaction ratings (R = .51, adjusted R = .47; p <. 001). Health care executives who want to cultivate an effective novice frontline nurse manager workforce can best ensure their satisfaction by creating an organization with strong procedural justice. This could be achieved by involving managers in decision-making processes and ensuring transparency about how decisions that affect nursing are made.

A New Runge-Kutta Discontinuous Galerkin Method with Conservation Constraint to Improve CFL Condition for Solving Conservation Laws

PubMed Central

Xu, Zhiliang; Chen, Xu-Yan; Liu, Yingjie

2014-01-01

We present a new formulation of the Runge-Kutta discontinuous Galerkin (RKDG) method [9, 8, 7, 6] for solving conservation Laws with increased CFL numbers. The new formulation requires the computed RKDG solution in a cell to satisfy additional conservation constraint in adjacent cells and does not increase the complexity or change the compactness of the RKDG method. Numerical computations for solving one-dimensional and two-dimensional scalar and systems of nonlinear hyperbolic conservation laws are performed with approximate solutions represented by piecewise quadratic and cubic polynomials, respectively. The hierarchical reconstruction [17, 33] is applied as a limiter to eliminate spurious oscillations in discontinuous solutions. From both numerical experiments and the analytic estimate of the CFL number of the newly formulated method, we find that: 1) this new formulation improves the CFL number over the original RKDG formulation by at least three times or more and thus reduces the overall computational cost; and 2) the new formulation essentially does not compromise the resolution of the numerical solutions of shock wave problems compared with ones computed by the RKDG method. PMID:25414520

A novel neural network for variational inequalities with linear and nonlinear constraints.

PubMed

Gao, Xing-Bao; Liao, Li-Zhi; Qi, Liqun

2005-11-01

Variational inequality is a uniform approach for many important optimization and equilibrium problems. Based on the sufficient and necessary conditions of the solution, this paper presents a novel neural network model for solving variational inequalities with linear and nonlinear constraints. Three sufficient conditions are provided to ensure that the proposed network with an asymmetric mapping is stable in the sense of Lyapunov and converges to an exact solution of the original problem. Meanwhile, the proposed network with a gradient mapping is also proved to be stable in the sense of Lyapunov and to have a finite-time convergence under some mild condition by using a new energy function. Compared with the existing neural networks, the new model can be applied to solve some nonmonotone problems, has no adjustable parameter, and has lower complexity. Thus, the structure of the proposed network is very simple. Since the proposed network can be used to solve a broad class of optimization problems, it has great application potential. The validity and transient behavior of the proposed neural network are demonstrated by several numerical examples.

An interior-point method for total variation regularized positron emission tomography image reconstruction

NASA Astrophysics Data System (ADS)

Bai, Bing

2012-03-01

There has been a lot of work on total variation (TV) regularized tomographic image reconstruction recently. Many of them use gradient-based optimization algorithms with a differentiable approximation of the TV functional. In this paper we apply TV regularization in Positron Emission Tomography (PET) image reconstruction. We reconstruct the PET image in a Bayesian framework, using Poisson noise model and TV prior functional. The original optimization problem is transformed to an equivalent problem with inequality constraints by adding auxiliary variables. Then we use an interior point method with logarithmic barrier functions to solve the constrained optimization problem. In this method, a series of points approaching the solution from inside the feasible region are found by solving a sequence of subproblems characterized by an increasing positive parameter. We use preconditioned conjugate gradient (PCG) algorithm to solve the subproblems directly. The nonnegativity constraint is enforced by bend line search. The exact expression of the TV functional is used in our calculations. Simulation results show that the algorithm converges fast and the convergence is insensitive to the values of the regularization and reconstruction parameters.

A Simple Model of Circuit Design.

DTIC Science & Technology

1980-05-01

mathematicians who discover mathematical ideas (i.cnat>, programmers who write code <Manna> <Barstow>, physicists who solve mechanics problems <de Kiecr-l...rules and shows how - they result in the design of circuits. ’l’he design rules must not only capture the purely mathematical constralints given by VICs...K VI.. *? and KCI, but also how those constraints can implement mechanism. Mathematical constraints tell us an amplifier’s input and output voltages

User satisfaction with primary health care by region in Brazil: 1st cycle of external evaluation from PMAQ-AB.

PubMed

Protasio, Ane Polline Lacerda; Gomes, Luciano Bezerra; Machado, Liliane Dos Santos; Valença, Ana Maria Gondim

2017-06-01

The National Program for Access and Quality Improvement in Primary Care (Programa Nacional de Melhoria do Acesso e da Qualidade da Atenção Básica, PMAQ-AB) aimed to improve healthcare public service quality and satisfaction of health service users. This study's objective was to identify the main factors influencing user satisfaction with primary care (PC) services by region in Brazil. Using secondary data from the 1st Cycle of PMAQ-AB, logistic regression models were developed by region, with user satisfaction as the dependent variable, as defined by cluster analysis. Based on the obtained models, the health unit's ability to solve users' problems and feeling respected by the health providers were the most important factors for user satisfaction in all regions in Brazil. However, other important factors by region included the following: the health unit's hours of operation meeting the user's needs (Northeast); providers asking about family members (North); providers asking about other health needs (Midwest); users being seen without an appointment (South); and users asking questions after the appointment (Southeast). In conclusion, the factors influencing user satisfaction with PC vary according to region and are mainly associated with access quality, meeting users' needs, and work process organization.

An Algorithm for Automatically Modifying Train Crew Schedule

NASA Astrophysics Data System (ADS)

Takahashi, Satoru; Kataoka, Kenji; Kojima, Teruhito; Asami, Masayuki

Once the break-down of the train schedule occurs, the crew schedule as well as the train schedule has to be modified as quickly as possible to restore them. In this paper, we propose an algorithm for automatically modifying a crew schedule that takes all constraints into consideration, presenting a model of the combined problem of crews and trains. The proposed algorithm builds an initial solution by relaxing some of the constraint conditions, and then uses a Taboo-search method to revise this solution in order to minimize the degree of constraint violation resulting from these relaxed conditions. Then we show not only that the algorithm can generate a constraint satisfaction solution, but also that the solution will satisfy the experts. That is, we show the proposed algorithm is capable of producing a usable solution in a short time by applying to actual cases of train-schedule break-down, and that the solution is at least as good as those produced manually, by comparing the both solutions with several point of view.

Intact Marriages in which One Partner Dis-Identifies with Experiences of Same-Sex Attraction.

ERIC Educational Resources Information Center

Yarhouse, Mark A.; Pawlowski, Lisa M.; Tan, Erica S. N.

2003-01-01

This study is of heterosexually married couples in which one partner reports having experienced same-sex attraction and both partners report satisfaction with their marriage despite facing such constraints. Analysis suggested a number of themes related to how spouses learned about their partners' experiences of same-sex attraction, motivations for…

A Comparison of Four Item-Selection Methods for Severely Constrained CATs

ERIC Educational Resources Information Center

He, Wei; Diao, Qi; Hauser, Carl

2014-01-01

This study compared four item-selection procedures developed for use with severely constrained computerized adaptive tests (CATs). Severely constrained CATs refer to those adaptive tests that seek to meet a complex set of constraints that are often not conclusive to each other (i.e., an item may contribute to the satisfaction of several…

Flipping the Classroom and Student Performance in Advanced Statistics: Evidence from a Quasi-Experiment

ERIC Educational Resources Information Center

Touchton, Michael

2015-01-01

I administer a quasi-experiment using undergraduate political science majors in statistics classes to evaluate whether "flipping the classroom" (the treatment) alters students' applied problem-solving performance and satisfaction relative to students in a traditional classroom environment (the control). I also assess whether general…

Problem-solving variability in older spouses: how is it linked to problem-, person-, and couple-characteristics?

PubMed

Hoppmann, Christiane A; Blanchard-Fields, Fredda

2011-09-01

Problem-solving does not take place in isolation and often involves social others such as spouses. Using repeated daily life assessments from 98 older spouses (M age = 72 years; M marriage length = 42 years), the present study examined theoretical notions from social-contextual models of coping regarding (a) the origins of problem-solving variability and (b) associations between problem-solving and specific problem-, person-, and couple- characteristics. Multilevel models indicate that the lion's share of variability in everyday problem-solving is located at the level of the problem situation. Importantly, participants reported more proactive emotion regulation and collaborative problem-solving for social than nonsocial problems. We also found person-specific consistencies in problem-solving. That is, older spouses high in Neuroticism reported more problems across the study period as well as less instrumental problem-solving and more passive emotion regulation than older spouses low in Neuroticism. Contrary to expectations, relationship satisfaction was unrelated to problem-solving in the present sample. Results are in line with the stress and coping literature in demonstrating that everyday problem-solving is a dynamic process that has to be viewed in the broader context in which it occurs. Our findings also complement previous laboratory-based work on everyday problem-solving by underscoring the benefits of examining everyday problem-solving as it unfolds in spouses' own environment.

Adaptive fuzzy dynamic surface control of nonlinear systems with input saturation and time-varying output constraints

NASA Astrophysics Data System (ADS)

Edalati, L.; Khaki Sedigh, A.; Aliyari Shooredeli, M.; Moarefianpour, A.

2018-02-01

This paper deals with the design of adaptive fuzzy dynamic surface control for uncertain strict-feedback nonlinear systems with asymmetric time-varying output constraints in the presence of input saturation. To approximate the unknown nonlinear functions and overcome the problem of explosion of complexity, a Fuzzy logic system is combined with the dynamic surface control in the backstepping design technique. To ensure the output constraints satisfaction, an asymmetric time-varying Barrier Lyapunov Function (BLF) is used. Moreover, by applying the minimal learning parameter technique, the number of the online parameters update for each subsystem is reduced to 2. Hence, the semi-globally uniformly ultimately boundedness (SGUUB) of all the closed-loop signals with appropriate tracking error convergence is guaranteed. The effectiveness of the proposed control is demonstrated by two simulation examples.

Radiofrequency pulse design in parallel transmission under strict temperature constraints.

PubMed

Boulant, Nicolas; Massire, Aurélien; Amadon, Alexis; Vignaud, Alexandre

2014-09-01

To gain radiofrequency (RF) pulse performance by directly addressing the temperature constraints, as opposed to the specific absorption rate (SAR) constraints, in parallel transmission at ultra-high field. The magnitude least-squares RF pulse design problem under hard SAR constraints was solved repeatedly by using the virtual observation points and an active-set algorithm. The SAR constraints were updated at each iteration based on the result of a thermal simulation. The numerical study was performed for an SAR-demanding and simplified time of flight sequence using B1 and ΔB0 maps obtained in vivo on a human brain at 7T. The proposed adjustment of the SAR constraints combined with an active-set algorithm provided higher flexibility in RF pulse design within a reasonable time. The modifications of those constraints acted directly upon the thermal response as desired. Although further confidence in the thermal models is needed, this study shows that RF pulse design under strict temperature constraints is within reach, allowing better RF pulse performance and faster acquisitions at ultra-high fields at the cost of higher sequence complexity. Copyright © 2013 Wiley Periodicals, Inc.

Robot, computer problem solving system

NASA Technical Reports Server (NTRS)

Becker, J. D.

1972-01-01

The development of a computer problem solving system is reported that considers physical problems faced by an artificial robot moving around in a complex environment. Fundamental interaction constraints with a real environment are simulated for the robot by visual scan and creation of an internal environmental model. The programming system used in constructing the problem solving system for the simulated robot and its simulated world environment is outlined together with the task that the system is capable of performing. A very general framework for understanding the relationship between an observed behavior and an adequate description of that behavior is included.

Trajectory Design to Mitigate Risk on the Transiting Exoplanet Survey Satellite (TESS) Mission

NASA Technical Reports Server (NTRS)

Dichmann, Donald

2016-01-01

The Transiting Exoplanet Survey Satellite (TESS) will employ a highly eccentric Earth orbit, in 2:1 lunar resonance, reached with a lunar flyby preceded by 3.5 phasing loops. The TESS mission has limited propellant and several orbit constraints. Based on analysis and simulation, we have designed the phasing loops to reduce delta-V and to mitigate risk due to maneuver execution errors. We have automated the trajectory design process and use distributed processing to generate and to optimize nominal trajectories, check constraint satisfaction, and finally model the effects of maneuver errors to identify trajectories that best meet the mission requirements.

Near-Field Diffraction Imaging from Multiple Detection Planes

NASA Astrophysics Data System (ADS)

Loetgering, L.; Golembusch, M.; Hammoud, R.; Wilhein, T.

2017-06-01

We present diffraction imaging results obtained from multiple near-field diffraction constraints. An iterative phase retrieval algorithm was implemented that uses data redundancy achieved by measuring near-field diffraction intensities at various sample-detector distances. The procedure allows for reconstructing the exit surface wave of a sample within a multiple constraint satisfaction framework neither making use of a priori knowledge as enforced in coherent diffraction imaging (CDI) nor exact scanning grid knowledge as required in ptychography. We also investigate the potential of the presented technique to deal with polychromatic radiation as important for potential application in diffraction imaging by means of tabletop EUV and X-ray sources.

Hybrid Optimization Parallel Search PACKage

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

2009-11-10

HOPSPACK is open source software for solving optimization problems without derivatives. Application problems may have a fully nonlinear objective function, bound constraints, and linear and nonlinear constraints. Problem variables may be continuous, integer-valued, or a mixture of both. The software provides a framework that supports any derivative-free type of solver algorithm. Through the framework, solvers request parallel function evaluation, which may use MPI (multiple machines) or multithreading (multiple processors/cores on one machine). The framework provides a Cache and Pending Cache of saved evaluations that reduces execution time and facilitates restarts. Solvers can dynamically create other algorithms to solve subproblems, amore » useful technique for handling multiple start points and integer-valued variables. HOPSPACK ships with the Generating Set Search (GSS) algorithm, developed at Sandia as part of the APPSPACK open source software project.« less

Applying Squeaky-Wheel Optimization Schedule Airborne Astronomy Observations

NASA Technical Reports Server (NTRS)

Frank, Jeremy; Kuerklue, Elif

2004-01-01

We apply the Squeaky Wheel Optimization (SWO) algorithm to the problem of scheduling astronomy observations for the Stratospheric Observatory for Infrared Astronomy, an airborne observatory. The problem contains complex constraints relating the feasibility of an astronomical observation to the position and time at which the observation begins, telescope elevation limits, special use airspace, and available fuel. Solving the problem requires making discrete choices (e.g. selection and sequencing of observations) and continuous ones (e.g. takeoff time and setting up observations by repositioning the aircraft). The problem also includes optimization criteria such as maximizing observing time while simultaneously minimizing total flight time. Previous approaches to the problem fail to scale when accounting for all constraints. We describe how to customize SWO to solve this problem, and show that it finds better flight plans, often with less computation time, than previous approaches.

Multiexponential models of (1+1)-dimensional dilaton gravity and Toda-Liouville integrable models

NASA Astrophysics Data System (ADS)

de Alfaro, V.; Filippov, A. T.

2010-01-01

We study general properties of a class of two-dimensional dilaton gravity (DG) theories with potentials containing several exponential terms. We isolate and thoroughly study a subclass of such theories in which the equations of motion reduce to Toda and Liouville equations. We show that the equation parameters must satisfy a certain constraint, which we find and solve for the most general multiexponential model. It follows from the constraint that integrable Toda equations in DG theories generally cannot appear without accompanying Liouville equations. The most difficult problem in the two-dimensional Toda-Liouville (TL) DG is to solve the energy and momentum constraints. We discuss this problem using the simplest examples and identify the main obstacles to solving it analytically. We then consider a subclass of integrable two-dimensional theories where scalar matter fields satisfy the Toda equations and the two-dimensional metric is trivial. We consider the simplest case in some detail. In this example, we show how to obtain the general solution. We also show how to simply derive wavelike solutions of general TL systems. In the DG theory, these solutions describe nonlinear waves coupled to gravity and also static states and cosmologies. For static states and cosmologies, we propose and study a more general one-dimensional TL model typically emerging in one-dimensional reductions of higher-dimensional gravity and supergravity theories. We especially attend to making the analytic structure of the solutions of the Toda equations as simple and transparent as possible.

Extension of the firefly algorithm and preference rules for solving MINLP problems

NASA Astrophysics Data System (ADS)

Costa, M. Fernanda P.; Francisco, Rogério B.; Rocha, Ana Maria A. C.; Fernandes, Edite M. G. P.

2017-07-01

An extension of the firefly algorithm (FA) for solving mixed-integer nonlinear programming (MINLP) problems is presented. Although penalty functions are nowadays frequently used to handle integrality conditions and inequality and equality constraints, this paper proposes the implementation within the FA of a simple rounded-based heuristic and four preference rules to find and converge to MINLP feasible solutions. Preliminary numerical experiments are carried out to validate the proposed methodology.

An Initial Model of Requirements Traceability an Empirical Study

DTIC Science & Technology

1992-09-22

procedures have been used extensively in the study of human problem-solving, including such areas as general problem-solving behavior, physics problem...heen doing unless you have traceability." " Humans don’t go back to the requirements enough." "Traceabi!ity should be extremely helpful with...by constraints on its usage: ("Traceability needs to be something that humans can work with, not just a whip held over people." "Traceability should

Fuzzy Multi-Objective Transportation Planning with Modified S-Curve Membership Function

NASA Astrophysics Data System (ADS)

Peidro, D.; Vasant, P.

2009-08-01

In this paper, the S-Curve membership function methodology is used in a transportation planning decision (TPD) problem. An interactive method for solving multi-objective TPD problems with fuzzy goals, available supply and forecast demand is developed. The proposed method attempts simultaneously to minimize the total production and transportation costs and the total delivery time with reference to budget constraints and available supply, machine capacities at each source, as well as forecast demand and warehouse space constraints at each destination. We compare in an industrial case the performance of S-curve membership functions, representing uncertainty goals and constraints in TPD problems, with linear membership functions.

Global Optimization of Interplanetary Trajectories in the Presence of Realistic Mission Contraints

NASA Technical Reports Server (NTRS)

Hinckley, David, Jr.; Englander, Jacob; Hitt, Darren

2015-01-01

Interplanetary missions are often subject to difficult constraints, like solar phase angle upon arrival at the destination, velocity at arrival, and altitudes for flybys. Preliminary design of such missions is often conducted by solving the unconstrained problem and then filtering away solutions which do not naturally satisfy the constraints. However this can bias the search into non-advantageous regions of the solution space, so it can be better to conduct preliminary design with the full set of constraints imposed. In this work two stochastic global search methods are developed which are well suited to the constrained global interplanetary trajectory optimization problem.

Aeroelastic Tailoring of Transport Wings Including Transonic Flutter Constraints

NASA Technical Reports Server (NTRS)

Stanford, Bret K.; Wieseman, Carol D.; Jutte, Christine V.

2015-01-01

Several minimum-mass optimization problems are solved to evaluate the effectiveness of a variety of novel tailoring schemes for subsonic transport wings. Aeroelastic stress and panel buckling constraints are imposed across several trimmed static maneuver loads, in addition to a transonic flutter margin constraint, captured with aerodynamic influence coefficient-based tools. Tailoring with metallic thickness variations, functionally graded materials, balanced or unbalanced composite laminates, curvilinear tow steering, and distributed trailing edge control effectors are all found to provide reductions in structural wing mass with varying degrees of success. The question as to whether this wing mass reduction will offset the increased manufacturing cost is left unresolved for each case.

Artificial bee colony algorithm for constrained possibilistic portfolio optimization problem

NASA Astrophysics Data System (ADS)

Chen, Wei

2015-07-01

In this paper, we discuss the portfolio optimization problem with real-world constraints under the assumption that the returns of risky assets are fuzzy numbers. A new possibilistic mean-semiabsolute deviation model is proposed, in which transaction costs, cardinality and quantity constraints are considered. Due to such constraints the proposed model becomes a mixed integer nonlinear programming problem and traditional optimization methods fail to find the optimal solution efficiently. Thus, a modified artificial bee colony (MABC) algorithm is developed to solve the corresponding optimization problem. Finally, a numerical example is given to illustrate the effectiveness of the proposed model and the corresponding algorithm.

Walking the Filament of Feasibility: Global Optimization of Highly-Constrained, Multi-Modal Interplanetary Trajectories Using a Novel Stochastic Search Technique

NASA Technical Reports Server (NTRS)

Englander, Arnold C.; Englander, Jacob A.

2017-01-01

Interplanetary trajectory optimization problems are highly complex and are characterized by a large number of decision variables and equality and inequality constraints as well as many locally optimal solutions. Stochastic global search techniques, coupled with a large-scale NLP solver, have been shown to solve such problems but are inadequately robust when the problem constraints become very complex. In this work, we present a novel search algorithm that takes advantage of the fact that equality constraints effectively collapse the solution space to lower dimensionality. This new approach walks the filament'' of feasibility to efficiently find the global optimal solution.

Fleet Assignment Using Collective Intelligence

NASA Technical Reports Server (NTRS)

Antoine, Nicolas E.; Bieniawski, Stefan R.; Kroo, Ilan M.; Wolpert, David H.

2004-01-01

Airline fleet assignment involves the allocation of aircraft to a set of flights legs in order to meet passenger demand, while satisfying a variety of constraints. Over the course of the day, the routing of each aircraft is determined in order to minimize the number of required flights for a given fleet. The associated flow continuity and aircraft count constraints have led researchers to focus on obtaining quasi-optimal solutions, especially at larger scales. In this paper, the authors propose the application of an agent-based integer optimization algorithm to a "cold start" fleet assignment problem. Results show that the optimizer can successfully solve such highly- constrained problems (129 variables, 184 constraints).

A global approach to kinematic path planning to robots with holonomic and nonholonomic constraints

NASA Technical Reports Server (NTRS)

Divelbiss, Adam; Seereeram, Sanjeev; Wen, John T.

1993-01-01

Robots in applications may be subject to holonomic or nonholonomic constraints. Examples of holonomic constraints include a manipulator constrained through the contact with the environment, e.g., inserting a part, turning a crank, etc., and multiple manipulators constrained through a common payload. Examples of nonholonomic constraints include no-slip constraints on mobile robot wheels, local normal rotation constraints for soft finger and rolling contacts in grasping, and conservation of angular momentum of in-orbit space robots. The above examples all involve equality constraints; in applications, there are usually additional inequality constraints such as robot joint limits, self collision and environment collision avoidance constraints, steering angle constraints in mobile robots, etc. The problem of finding a kinematically feasible path that satisfies a given set of holonomic and nonholonomic constraints, of both equality and inequality types is addressed. The path planning problem is first posed as a finite time nonlinear control problem. This problem is subsequently transformed to a static root finding problem in an augmented space which can then be iteratively solved. The algorithm has shown promising results in planning feasible paths for redundant arms satisfying Cartesian path following and goal endpoint specifications, and mobile vehicles with multiple trailers. In contrast to local approaches, this algorithm is less prone to problems such as singularities and local minima.

MO-FG-CAMPUS-TeP2-01: A Graph Form ADMM Algorithm for Constrained Quadratic Radiation Treatment Planning

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

Liu, X; Belcher, AH; Wiersma, R

Purpose: In radiation therapy optimization the constraints can be either hard constraints which must be satisfied or soft constraints which are included but do not need to be satisfied exactly. Currently the voxel dose constraints are viewed as soft constraints and included as a part of the objective function and approximated as an unconstrained problem. However in some treatment planning cases the constraints should be specified as hard constraints and solved by constrained optimization. The goal of this work is to present a computation efficiency graph form alternating direction method of multipliers (ADMM) algorithm for constrained quadratic treatment planning optimizationmore » and compare it with several commonly used algorithms/toolbox. Method: ADMM can be viewed as an attempt to blend the benefits of dual decomposition and augmented Lagrangian methods for constrained optimization. Various proximal operators were first constructed as applicable to quadratic IMRT constrained optimization and the problem was formulated in a graph form of ADMM. A pre-iteration operation for the projection of a point to a graph was also proposed to further accelerate the computation. Result: The graph form ADMM algorithm was tested by the Common Optimization for Radiation Therapy (CORT) dataset including TG119, prostate, liver, and head & neck cases. Both unconstrained and constrained optimization problems were formulated for comparison purposes. All optimizations were solved by LBFGS, IPOPT, Matlab built-in toolbox, CVX (implementing SeDuMi) and Mosek solvers. For unconstrained optimization, it was found that LBFGS performs the best, and it was 3–5 times faster than graph form ADMM. However, for constrained optimization, graph form ADMM was 8 – 100 times faster than the other solvers. Conclusion: A graph form ADMM can be applied to constrained quadratic IMRT optimization. It is more computationally efficient than several other commercial and noncommercial optimizers and it also used significantly less computer memory.« less

Robust fuzzy control subject to state variance and passivity constraints for perturbed nonlinear systems with multiplicative noises.

PubMed

Chang, Wen-Jer; Huang, Bo-Jyun

2014-11-01

The multi-constrained robust fuzzy control problem is investigated in this paper for perturbed continuous-time nonlinear stochastic systems. The nonlinear system considered in this paper is represented by a Takagi-Sugeno fuzzy model with perturbations and state multiplicative noises. The multiple performance constraints considered in this paper include stability, passivity and individual state variance constraints. The Lyapunov stability theory is employed to derive sufficient conditions to achieve the above performance constraints. By solving these sufficient conditions, the contribution of this paper is to develop a parallel distributed compensation based robust fuzzy control approach to satisfy multiple performance constraints for perturbed nonlinear systems with multiplicative noises. At last, a numerical example for the control of perturbed inverted pendulum system is provided to illustrate the applicability and effectiveness of the proposed multi-constrained robust fuzzy control method. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Dense motion estimation using regularization constraints on local parametric models.

PubMed

Patras, Ioannis; Worring, Marcel; van den Boomgaard, Rein

2004-11-01

This paper presents a method for dense optical flow estimation in which the motion field within patches that result from an initial intensity segmentation is parametrized with models of different order. We propose a novel formulation which introduces regularization constraints between the model parameters of neighboring patches. In this way, we provide the additional constraints for very small patches and for patches whose intensity variation cannot sufficiently constrain the estimation of their motion parameters. In order to preserve motion discontinuities, we use robust functions as a regularization mean. We adopt a three-frame approach and control the balance between the backward and forward constraints by a real-valued direction field on which regularization constraints are applied. An iterative deterministic relaxation method is employed in order to solve the corresponding optimization problem. Experimental results show that the proposed method deals successfully with motions large in magnitude, motion discontinuities, and produces accurate piecewise-smooth motion fields.

A two-level approach to large mixed-integer programs with application to cogeneration in energy-efficient buildings

DOE PAGES

Lin, Fu; Leyffer, Sven; Munson, Todd

2016-04-12

We study a two-stage mixed-integer linear program (MILP) with more than 1 million binary variables in the second stage. We develop a two-level approach by constructing a semi-coarse model that coarsens with respect to variables and a coarse model that coarsens with respect to both variables and constraints. We coarsen binary variables by selecting a small number of prespecified on/off profiles. We aggregate constraints by partitioning them into groups and taking convex combination over each group. With an appropriate choice of coarsened profiles, the semi-coarse model is guaranteed to find a feasible solution of the original problem and hence providesmore » an upper bound on the optimal solution. We show that solving a sequence of coarse models converges to the same upper bound with proven finite steps. This is achieved by adding violated constraints to coarse models until all constraints in the semi-coarse model are satisfied. We demonstrate the effectiveness of our approach in cogeneration for buildings. Here, the coarsened models allow us to obtain good approximate solutions at a fraction of the time required by solving the original problem. Extensive numerical experiments show that the two-level approach scales to large problems that are beyond the capacity of state-of-the-art commercial MILP solvers.« less

A two-level approach to large mixed-integer programs with application to cogeneration in energy-efficient buildings

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

Lin, Fu; Leyffer, Sven; Munson, Todd

We study a two-stage mixed-integer linear program (MILP) with more than 1 million binary variables in the second stage. We develop a two-level approach by constructing a semi-coarse model that coarsens with respect to variables and a coarse model that coarsens with respect to both variables and constraints. We coarsen binary variables by selecting a small number of prespecified on/off profiles. We aggregate constraints by partitioning them into groups and taking convex combination over each group. With an appropriate choice of coarsened profiles, the semi-coarse model is guaranteed to find a feasible solution of the original problem and hence providesmore » an upper bound on the optimal solution. We show that solving a sequence of coarse models converges to the same upper bound with proven finite steps. This is achieved by adding violated constraints to coarse models until all constraints in the semi-coarse model are satisfied. We demonstrate the effectiveness of our approach in cogeneration for buildings. Here, the coarsened models allow us to obtain good approximate solutions at a fraction of the time required by solving the original problem. Extensive numerical experiments show that the two-level approach scales to large problems that are beyond the capacity of state-of-the-art commercial MILP solvers.« less

A policy iteration approach to online optimal control of continuous-time constrained-input systems.

PubMed

Modares, Hamidreza; Naghibi Sistani, Mohammad-Bagher; Lewis, Frank L

2013-09-01

This paper is an effort towards developing an online learning algorithm to find the optimal control solution for continuous-time (CT) systems subject to input constraints. The proposed method is based on the policy iteration (PI) technique which has recently evolved as a major technique for solving optimal control problems. Although a number of online PI algorithms have been developed for CT systems, none of them take into account the input constraints caused by actuator saturation. In practice, however, ignoring these constraints leads to performance degradation or even system instability. In this paper, to deal with the input constraints, a suitable nonquadratic functional is employed to encode the constraints into the optimization formulation. Then, the proposed PI algorithm is implemented on an actor-critic structure to solve the Hamilton-Jacobi-Bellman (HJB) equation associated with this nonquadratic cost functional in an online fashion. That is, two coupled neural network (NN) approximators, namely an actor and a critic are tuned online and simultaneously for approximating the associated HJB solution and computing the optimal control policy. The critic is used to evaluate the cost associated with the current policy, while the actor is used to find an improved policy based on information provided by the critic. Convergence to a close approximation of the HJB solution as well as stability of the proposed feedback control law are shown. Simulation results of the proposed method on a nonlinear CT system illustrate the effectiveness of the proposed approach. Copyright © 2013 ISA. All rights reserved.

Powered Descent Guidance with General Thrust-Pointing Constraints

NASA Technical Reports Server (NTRS)

Carson, John M., III; Acikmese, Behcet; Blackmore, Lars

2013-01-01

The Powered Descent Guidance (PDG) algorithm and software for generating Mars pinpoint or precision landing guidance profiles has been enhanced to incorporate thrust-pointing constraints. Pointing constraints would typically be needed for onboard sensor and navigation systems that have specific field-of-view requirements to generate valid ground proximity and terrain-relative state measurements. The original PDG algorithm was designed to enforce both control and state constraints, including maximum and minimum thrust bounds, avoidance of the ground or descent within a glide slope cone, and maximum speed limits. The thrust-bound and thrust-pointing constraints within PDG are non-convex, which in general requires nonlinear optimization methods to generate solutions. The short duration of Mars powered descent requires guaranteed PDG convergence to a solution within a finite time; however, nonlinear optimization methods have no guarantees of convergence to the global optimal or convergence within finite computation time. A lossless convexification developed for the original PDG algorithm relaxed the non-convex thrust bound constraints. This relaxation was theoretically proven to provide valid and optimal solutions for the original, non-convex problem within a convex framework. As with the thrust bound constraint, a relaxation of the thrust-pointing constraint also provides a lossless convexification that ensures the enhanced relaxed PDG algorithm remains convex and retains validity for the original nonconvex problem. The enhanced PDG algorithm provides guidance profiles for pinpoint and precision landing that minimize fuel usage, minimize landing error to the target, and ensure satisfaction of all position and control constraints, including thrust bounds and now thrust-pointing constraints.

An algorithm for solving the system-level problem in multilevel optimization

NASA Technical Reports Server (NTRS)

Balling, R. J.; Sobieszczanski-Sobieski, J.

1994-01-01

A multilevel optimization approach which is applicable to nonhierarchic coupled systems is presented. The approach includes a general treatment of design (or behavior) constraints and coupling constraints at the discipline level through the use of norms. Three different types of norms are examined: the max norm, the Kreisselmeier-Steinhauser (KS) norm, and the 1(sub p) norm. The max norm is recommended. The approach is demonstrated on a class of hub frame structures which simulate multidisciplinary systems. The max norm is shown to produce system-level constraint functions which are non-smooth. A cutting-plane algorithm is presented which adequately deals with the resulting corners in the constraint functions. The algorithm is tested on hub frames with increasing number of members (which simulate disciplines), and the results are summarized.

A novel profit-allocation strategy for SDN enterprises

NASA Astrophysics Data System (ADS)

Hu, Wei; Hou, Ye; Tian, Longwei; Li, Yuan

2017-01-01

Aiming to solve the problem of profit allocation for supply and demand network (SDN) enterprises that ignores risk factors and generates low satisfaction, a novel profit-allocation model based on cooperative game theory and TOPSIS is proposed. This new model avoids the defect of the single-profit allocation model by introducing risk factors, compromise coefficients and high negotiation points. By measuring the Euclidean distance between the ideal solution vector and the negative ideal solution vector, every node's satisfaction problem for the SDN was resolved, and the mess phenomenon was avoided. Finally, the rationality and effectiveness of the proposed model was verified using a numerical example.

Emotional experiences and motivating factors associated with fingerprint analysis.

PubMed

Charlton, David; Fraser-Mackenzie, Peter A F; Dror, Itiel E

2010-03-01

In this study, we investigated the emotional and motivational factors involved in fingerprint analysis in day-to-day routine case work and in significant and harrowing criminal investigations. Thematic analysis was performed on interviews with 13 experienced fingerprint examiners from a variety of law enforcement agencies. The data revealed factors relating to job satisfaction and the use of skill. Individual satisfaction related to catching criminals was observed; this was most notable in solving high profile, serious, or long-running cases. There were positive emotional effects associated with matching fingerprints and apparent fear of making errors. Finally, we found evidence for a need of cognitive closure in fingerprint examiner decision-making.

The changing nature of nurses' job satisfaction: an exploration of sources of satisfaction in the 1990s.

PubMed

Tovey, E J; Adams, A E

1999-07-01

This paper focuses on the changing nature of nurses' job satisfaction. It compares the major sources of satisfaction and dissatisfaction experienced by acute ward nurses in the English National Health Service (NHS) in the early 1990s, with sources identified in previous research. In the light of findings from a pilot study, the suitability of existing research approaches and measurement tools for portraying nurses' contemporary work experiences is examined. The study comprised content analysis of a random sample of 130 nurses' comments about ward organizational issues, collected as part of a national survey. Findings suggest that new measurement tools need to be developed, because new sources of satisfaction and dissatisfaction emerged, directly associated with change arising out of the introduction of the NHS internal market. These include pressures associated with new roles, role conflict, lack of job security, 'tight' resources, using new technology, a perceived lowering of standards of patient care, coping with increased amounts of paperwork, and the experience of working in a rapidly and constantly changing environment. Findings also suggest that the nature of nurses' job satisfaction is increasingly being shaped by their position within the organization, denoted by clinical grade, and the organizational culture of individual NHS Trusts. Ward leaders experience dissatisfaction as a result of role conflict and strain, while nurses of lower clinical grades are increasingly concerned with managerial and resource constraints on their ability to provide good quality care. Nurses' satisfaction with management and morale were found to be significantly different between NHS Trusts. While findings may be specific to England, it is argued that they have relevance for the wider, international nursing community. This is because developing an understanding of the changing nature of nurses' job satisfaction may help to resolve recruitment and retention problems.

Formal verification of AI software

NASA Technical Reports Server (NTRS)

Rushby, John; Whitehurst, R. Alan

1989-01-01

The application of formal verification techniques to Artificial Intelligence (AI) software, particularly expert systems, is investigated. Constraint satisfaction and model inversion are identified as two formal specification paradigms for different classes of expert systems. A formal definition of consistency is developed, and the notion of approximate semantics is introduced. Examples are given of how these ideas can be applied in both declarative and imperative forms.

Comparing Teleworker Performance, Satisfaction, and Retention in the Joint Interoperability Test Command

ERIC Educational Resources Information Center

Hurd, Danny A.

2010-01-01

Telework is becoming one of the best options available to help organizations gain a competitive advantage. When management considers the fluctuating cost of fuel, emphasis on employee flexibility, and need to retain the most-qualified workforce, telework can be advantageous for solving problems. This quantitative comparative (non-experimental)…

Successful Strategies for Marketing School Levies. Fastback No. 310.

ERIC Educational Resources Information Center

Graham, Glenn T.; And Others

The use of modern marketing concepts to assist public school districts in the passage of school levies and bond issues is presented in this guidebook. The modern marketing concept is based on maximizing customer satisfaction and solving the problem of the consumer. Strategies are presented for conducting market analysis; financing, organizing, and…

The Mindful Worker. Learning and Working into the 21st Century. The Mindful Workforce Portfolio.

ERIC Educational Resources Information Center

Miles, Curtis

This textbook contains information and activities designed to help college students develop the following generalized competencies that are critical to job success and satisfaction in the 21st century: learns; thinks reflectively; manages time; manages stress; sets goals; solves problems; demonstrates interdependence; thinks systematically; solves…

Human Resources: Solving Work and Life Challenges

ERIC Educational Resources Information Center

Bartley, Sharon Jeffcoat

2003-01-01

Work-life issues are those problems employees have that impact their ability to perform their work and may lead to increasing levels of stress. Stress over time can lead to low employee morale, lower productivity, decreased job satisfaction and eventually to sickness and absenteeism. In extreme cases, stress can result in substance abuse or…

Bagnulo Heavy Fuel Internal Combustion Engine and Its Employment in Aviation

NASA Technical Reports Server (NTRS)

Fiore, Amedeo

1922-01-01

We see with great satisfaction that Bagnulo's studies and experiments on his high-speed, heavy-fuel engines, promise to solve not only the general problem of economical power and hence of thermal efficiency, but also all other special problems, of weight and space, and, what is still more important, range of error.

An Approximation Solution to Refinery Crude Oil Scheduling Problem with Demand Uncertainty Using Joint Constrained Programming

PubMed Central

Duan, Qianqian; Yang, Genke; Xu, Guanglin; Pan, Changchun

2014-01-01

This paper is devoted to develop an approximation method for scheduling refinery crude oil operations by taking into consideration the demand uncertainty. In the stochastic model the demand uncertainty is modeled as random variables which follow a joint multivariate distribution with a specific correlation structure. Compared to deterministic models in existing works, the stochastic model can be more practical for optimizing crude oil operations. Using joint chance constraints, the demand uncertainty is treated by specifying proximity level on the satisfaction of product demands. However, the joint chance constraints usually hold strong nonlinearity and consequently, it is still hard to handle it directly. In this paper, an approximation method combines a relax-and-tight technique to approximately transform the joint chance constraints to a serial of parameterized linear constraints so that the complicated problem can be attacked iteratively. The basic idea behind this approach is to approximate, as much as possible, nonlinear constraints by a lot of easily handled linear constraints which will lead to a well balance between the problem complexity and tractability. Case studies are conducted to demonstrate the proposed methods. Results show that the operation cost can be reduced effectively compared with the case without considering the demand correlation. PMID:24757433

An approximation solution to refinery crude oil scheduling problem with demand uncertainty using joint constrained programming.

PubMed

Duan, Qianqian; Yang, Genke; Xu, Guanglin; Pan, Changchun

2014-01-01

This paper is devoted to develop an approximation method for scheduling refinery crude oil operations by taking into consideration the demand uncertainty. In the stochastic model the demand uncertainty is modeled as random variables which follow a joint multivariate distribution with a specific correlation structure. Compared to deterministic models in existing works, the stochastic model can be more practical for optimizing crude oil operations. Using joint chance constraints, the demand uncertainty is treated by specifying proximity level on the satisfaction of product demands. However, the joint chance constraints usually hold strong nonlinearity and consequently, it is still hard to handle it directly. In this paper, an approximation method combines a relax-and-tight technique to approximately transform the joint chance constraints to a serial of parameterized linear constraints so that the complicated problem can be attacked iteratively. The basic idea behind this approach is to approximate, as much as possible, nonlinear constraints by a lot of easily handled linear constraints which will lead to a well balance between the problem complexity and tractability. Case studies are conducted to demonstrate the proposed methods. Results show that the operation cost can be reduced effectively compared with the case without considering the demand correlation.

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

Chow, Edmond

Solving sparse problems is at the core of many DOE computational science applications. We focus on the challenge of developing sparse algorithms that can fully exploit the parallelism in extreme-scale computing systems, in particular systems with massive numbers of cores per node. Our approach is to express a sparse matrix factorization as a large number of bilinear constraint equations, and then solving these equations via an asynchronous iterative method. The unknowns in these equations are the matrix entries of the factorization that is desired.

Time-domain finite elements in optimal control with application to launch-vehicle guidance. PhD. Thesis

NASA Technical Reports Server (NTRS)

Bless, Robert R.

1991-01-01

A time-domain finite element method is developed for optimal control problems. The theory derived is general enough to handle a large class of problems including optimal control problems that are continuous in the states and controls, problems with discontinuities in the states and/or system equations, problems with control inequality constraints, problems with state inequality constraints, or problems involving any combination of the above. The theory is developed in such a way that no numerical quadrature is necessary regardless of the degree of nonlinearity in the equations. Also, the same shape functions may be employed for every problem because all strong boundary conditions are transformed into natural or weak boundary conditions. In addition, the resulting nonlinear algebraic equations are very sparse. Use of sparse matrix solvers allows for the rapid and accurate solution of very difficult optimization problems. The formulation is applied to launch-vehicle trajectory optimization problems, and results show that real-time optimal guidance is realizable with this method. Finally, a general problem solving environment is created for solving a large class of optimal control problems. The algorithm uses both FORTRAN and a symbolic computation program to solve problems with a minimum of user interaction. The use of symbolic computation eliminates the need for user-written subroutines which greatly reduces the setup time for solving problems.

Connected Component Model for Multi-Object Tracking.

PubMed

He, Zhenyu; Li, Xin; You, Xinge; Tao, Dacheng; Tang, Yuan Yan

2016-08-01

In multi-object tracking, it is critical to explore the data associations by exploiting the temporal information from a sequence of frames rather than the information from the adjacent two frames. Since straightforwardly obtaining data associations from multi-frames is an NP-hard multi-dimensional assignment (MDA) problem, most existing methods solve this MDA problem by either developing complicated approximate algorithms, or simplifying MDA as a 2D assignment problem based upon the information extracted only from adjacent frames. In this paper, we show that the relation between associations of two observations is the equivalence relation in the data association problem, based on the spatial-temporal constraint that the trajectories of different objects must be disjoint. Therefore, the MDA problem can be equivalently divided into independent subproblems by equivalence partitioning. In contrast to existing works for solving the MDA problem, we develop a connected component model (CCM) by exploiting the constraints of the data association and the equivalence relation on the constraints. Based upon CCM, we can efficiently obtain the global solution of the MDA problem for multi-object tracking by optimizing a sequence of independent data association subproblems. Experiments on challenging public data sets demonstrate that our algorithm outperforms the state-of-the-art approaches.

Goldratt's thinking process applied to the budget constraints of a Texas MHMR facility.

PubMed

Taylor, Lloyd J; Churchwell, Lana

2004-01-01

Managers for years have known that the best way to run a business is to constantly be looking for ways to improve the way to do business. The barrier has been the ability to identify and solve the right problems. Eliyahu Goldratt (1992c), in his book The Goal, uses a love story format to illustrate his "Theory of Constraints." In Goldratt's (1994) next book, It's Not Luck, he further illustrates this powerful technique called "The Thinking Process" which is based on the Socratic method, using the "if ... then" reasoning process, The first step is to identify UDEs or undesirable effects within the organization and then use these UDEs to create a Current Reality Tree (CRT) which helps to identify the core problem. Next, use an Evaporating Cloud to come up with ideas and a way to break the constraint. Finally, use the injections in the Evaporating Cloud to create a Future Reality Tree, further validating the idea and making sure it does not create any negative effects. In this article, the "Thinking Process" will be used to identify and solve problems related to the General Medical Department of an MHMR State Hospital.

Decentralized Dimensionality Reduction for Distributed Tensor Data Across Sensor Networks.

PubMed

Liang, Junli; Yu, Guoyang; Chen, Badong; Zhao, Minghua

2016-11-01

This paper develops a novel decentralized dimensionality reduction algorithm for the distributed tensor data across sensor networks. The main contributions of this paper are as follows. First, conventional centralized methods, which utilize entire data to simultaneously determine all the vectors of the projection matrix along each tensor mode, are not suitable for the network environment. Here, we relax the simultaneous processing manner into the one-vector-by-one-vector (OVBOV) manner, i.e., determining the projection vectors (PVs) related to each tensor mode one by one. Second, we prove that in the OVBOV manner each PV can be determined without modifying any tensor data, which simplifies corresponding computations. Third, we cast the decentralized PV determination problem as a set of subproblems with consensus constraints, so that it can be solved in the network environment only by local computations and information communications among neighboring nodes. Fourth, we introduce the null space and transform the PV determination problem with complex orthogonality constraints into an equivalent hidden convex one without any orthogonality constraint, which can be solved by the Lagrange multiplier method. Finally, experimental results are given to show that the proposed algorithm is an effective dimensionality reduction scheme for the distributed tensor data across the sensor networks.

Adaptive nearly optimal control for a class of continuous-time nonaffine nonlinear systems with inequality constraints.

PubMed

Fan, Quan-Yong; Yang, Guang-Hong

2017-01-01

The state inequality constraints have been hardly considered in the literature on solving the nonlinear optimal control problem based the adaptive dynamic programming (ADP) method. In this paper, an actor-critic (AC) algorithm is developed to solve the optimal control problem with a discounted cost function for a class of state-constrained nonaffine nonlinear systems. To overcome the difficulties resulting from the inequality constraints and the nonaffine nonlinearities of the controlled systems, a novel transformation technique with redesigned slack functions and a pre-compensator method are introduced to convert the constrained optimal control problem into an unconstrained one for affine nonlinear systems. Then, based on the policy iteration (PI) algorithm, an online AC scheme is proposed to learn the nearly optimal control policy for the obtained affine nonlinear dynamics. Using the information of the nonlinear model, novel adaptive update laws are designed to guarantee the convergence of the neural network (NN) weights and the stability of the affine nonlinear dynamics without the requirement for the probing signal. Finally, the effectiveness of the proposed method is validated by simulation studies. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Power allocation for SWIPT in K-user interference channels using game theory

NASA Astrophysics Data System (ADS)

Wen, Zhigang; Liu, Ying; Liu, Xiaoqing; Li, Shan; Chen, Xianya

2018-12-01

A simultaneous wireless information and power transfer system in interference channels of multi-users is considered. In this system, each transmitter sends one data stream to its targeted receiver, which causes interference to other receivers. Since all transmitter-receiver links want to maximize their own average transmission rate, a power allocation problem under the transmit power constraints and the energy-harvesting constraints is developed. To solve this problem, we propose a game theory framework. Then, we convert the game into a variational inequalities problem by establishing the connection between game theory and variational inequalities and solve the variational inequalities problem. Through theoretical analysis, the existence and uniqueness of Nash equilibrium are both guaranteed by the theory of variational inequalities. A distributed iterative alternating optimization water-filling algorithm is derived, which is proved to converge. Numerical results show that the proposed algorithm reaches fast convergence and achieves a higher sum rate than the unaided scheme.

Multi-step optimization strategy for fuel-optimal orbital transfer of low-thrust spacecraft

NASA Astrophysics Data System (ADS)

Rasotto, M.; Armellin, R.; Di Lizia, P.

2016-03-01

An effective method for the design of fuel-optimal transfers in two- and three-body dynamics is presented. The optimal control problem is formulated using calculus of variation and primer vector theory. This leads to a multi-point boundary value problem (MPBVP), characterized by complex inner constraints and a discontinuous thrust profile. The first issue is addressed by embedding the MPBVP in a parametric optimization problem, thus allowing a simplification of the set of transversality constraints. The second problem is solved by representing the discontinuous control function by a smooth function depending on a continuation parameter. The resulting trajectory optimization method can deal with different intermediate conditions, and no a priori knowledge of the control structure is required. Test cases in both the two- and three-body dynamics show the capability of the method in solving complex trajectory design problems.

Partitioning problems in parallel, pipelined and distributed computing

NASA Technical Reports Server (NTRS)

Bokhari, S.

1985-01-01

The problem of optimally assigning the modules of a parallel program over the processors of a multiple computer system is addressed. A Sum-Bottleneck path algorithm is developed that permits the efficient solution of many variants of this problem under some constraints on the structure of the partitions. In particular, the following problems are solved optimally for a single-host, multiple satellite system: partitioning multiple chain structured parallel programs, multiple arbitrarily structured serial programs and single tree structured parallel programs. In addition, the problems of partitioning chain structured parallel programs across chain connected systems and across shared memory (or shared bus) systems are also solved under certain constraints. All solutions for parallel programs are equally applicable to pipelined programs. These results extend prior research in this area by explicitly taking concurrency into account and permit the efficient utilization of multiple computer architectures for a wide range of problems of practical interest.

Preconditioned alternating direction method of multipliers for inverse problems with constraints

NASA Astrophysics Data System (ADS)

Jiao, Yuling; Jin, Qinian; Lu, Xiliang; Wang, Weijie

2017-02-01

We propose a preconditioned alternating direction method of multipliers (ADMM) to solve linear inverse problems in Hilbert spaces with constraints, where the feature of the sought solution under a linear transformation is captured by a possibly non-smooth convex function. During each iteration step, our method avoids solving large linear systems by choosing a suitable preconditioning operator. In case the data is given exactly, we prove the convergence of our preconditioned ADMM without assuming the existence of a Lagrange multiplier. In case the data is corrupted by noise, we propose a stopping rule using information on noise level and show that our preconditioned ADMM is a regularization method; we also propose a heuristic rule when the information on noise level is unavailable or unreliable and give its detailed analysis. Numerical examples are presented to test the performance of the proposed method.

An effective pseudospectral method for constraint dynamic optimisation problems with characteristic times

NASA Astrophysics Data System (ADS)

Xiao, Long; Liu, Xinggao; Ma, Liang; Zhang, Zeyin

2018-03-01

Dynamic optimisation problem with characteristic times, widely existing in many areas, is one of the frontiers and hotspots of dynamic optimisation researches. This paper considers a class of dynamic optimisation problems with constraints that depend on the interior points either fixed or variable, where a novel direct pseudospectral method using Legendre-Gauss (LG) collocation points for solving these problems is presented. The formula for the state at the terminal time of each subdomain is derived, which results in a linear combination of the state at the LG points in the subdomains so as to avoid the complex nonlinear integral. The sensitivities of the state at the collocation points with respect to the variable characteristic times are derived to improve the efficiency of the method. Three well-known characteristic time dynamic optimisation problems are solved and compared in detail among the reported literature methods. The research results show the effectiveness of the proposed method.

Automatic scheduling of outages of nuclear power plants with time windows. Final report, January-December 1995

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

Gomes, C.

This report describes a successful project for transference of advanced AI technology into the domain of planning of outages of nuclear power plants as part of DOD`s dual-use program. ROMAN (Rome Lab Outage Manager) is the prototype system that was developed as a result of this project. ROMAN`s main innovation compared to the current state-of-the-art of outage management tools is its capability to automatically enforce safety constraints during the planning and scheduling phase. Another innovative aspect of ROMAN is the generation of more robust schedules that are feasible over time windows. In other words, ROMAN generates a family of schedulesmore » by assigning time intervals as start times to activities rather than single start times, without affecting the overall duration of the project. ROMAN uses a constraint satisfaction paradigm combining a global search tactic with constraint propagation. The derivation of very specialized representations for the constraints to perform efficient propagation is a key aspect for the generation of very fast schedules - constraints are compiled into the code, which is a novel aspect of our work using an automatic programming system, KIDS.« less

The algebra of supertraces for 2+1 super de Sitter gravity

NASA Technical Reports Server (NTRS)

Urrutia, L. F.; Waelbroeck, H.; Zertuche, F.

1993-01-01

The algebra of the observables for 2+1 super de Sitter gravity, for one genus of the spatial surface is calculated. The algebra turns out to be an infinite Lie algebra subject to non-linear constraints. The constraints are solved explicitly in terms of five independent complex supertraces. These variables are the true degrees of freedom of the system and their quantized algebra generates a new structure which is referred to as a 'central extension' of the quantum algebra SU(2)q.

Problem-solving therapy to improve depression scores among older hemodialysis patients: a pilot randomized trial.

PubMed

Erdley, Shiloh D; Gellis, Zvi D; Bogner, Hillary A; Kass, Darrin S; Green, Jamie A; Perkins, Robert M

2014-07-01

Depression is common among dialysis patients and is associated with adverse outcomes. Problem-solving therapy (PST) is effective for treating depression in older patients with chronic illness, but its effectiveness has never been reported in hemodialysis (HD) patients. We investigated the feasibility and satisfaction of PST and its impact on depression scores among older HD patients. Patients at least 60 years of age receiving maintenance HD at a single outpatient dialysis center were eligible for the study. Randomized patients received either 6 weeks of PST from a licensed renal social worker or usual care. This study modeled the staff-patient ratio standard of most dialysis clinics, and therefore only one social worker provided the interventions. Study outcomes included feasibility (successful completion of 6 weekly sessions) and patient satisfaction with PST as well as impact on depression scores (between-group comparison of mean Beck depression inventory (BDI) and Patient health questionnaire-9 (PHQ-9) scores at 6 weeks, and of mean change-from-baseline scores). The recruitment rate was 92% (35/38). All subjects randomized to the intervention arm (n = 17) and who initiated PST (n = 15) completed the study, and all reported overall satisfaction with the intervention. 87% reported that PST helped them to better solve problems and improved their ability to cope with their medical condition. At 6 weeks, there were no significant differences in mean BDI and PHQ scores between the usual care and the intervention group (BDI 11.3 vs. 9.3, p = 0.6; PHQ 5.7 vs. 3.3, p = 0.1). Mean change-from-baseline depression scores were significantly improved in the intervention group relative to the control group (change in BDI 6.3 vs.- 0.6, p = 0.004; change in PHQ 7.2 vs. 0.3, p < 0.001). The results demonstrate that PST is feasible in the dialysis unit setting, acceptable to patients, and may positively impact depression among maintenance hemodialysis patients.

Multi-model predictive control based on LMI: from the adaptation of the state-space model to the analytic description of the control law

NASA Astrophysics Data System (ADS)

Falugi, P.; Olaru, S.; Dumur, D.

2010-08-01

This article proposes an explicit robust predictive control solution based on linear matrix inequalities (LMIs). The considered predictive control strategy uses different local descriptions of the system dynamics and uncertainties and thus allows the handling of less conservative input constraints. The computed control law guarantees constraint satisfaction and asymptotic stability. The technique is effective for a class of nonlinear systems embedded into polytopic models. A detailed discussion of the procedures which adapt the partition of the state space is presented. For the practical implementation the construction of suitable (explicit) descriptions of the control law are described upon concrete algorithms.

Trajectory Design Enhancements to Mitigate Risk for the Transiting Exoplanet Survey Satellite (TESS)

NASA Technical Reports Server (NTRS)

Dichmann, Donald; Parker, Joel; Nickel, Craig; Lutz, Stephen

2016-01-01

The Transiting Exoplanet Survey Satellite (TESS) will employ a highly eccentric Earth orbit, in 2:1 lunar resonance, which will be reached with a lunar flyby preceded by 3.5 phasing loops. The TESS mission has limited propellant and several constraints on the science orbit and on the phasing loops. Based on analysis and simulation, we have designed the phasing loops to reduce delta-V (DV) and to mitigate risk due to maneuver execution errors. We have automated the trajectory design process and use distributed processing to generate and optimal nominal trajectories; to check constraint satisfaction; and finally to model the effects of maneuver errors to identify trajectories that best meet the mission requirements.

[Ebola crisis in Guinea: psychosocial support for patients and caregivers].

PubMed

Benifei, Sarah; Facon-Delahaye, Aline; Vautier, Virginie

2016-02-01

The experience of the French military health service in the fight against the Ebola epidemic in Guinea, highlights the importance of what favours the emergence of an institutional life in a context of care faced with numerous constraints and extraordinary challenges. The meticulous drawing up of procedures and the juxtaposition of expertise goes hand in hand with the construction of a triangular care system (caregivers-patients-families). This relational approach ensures each player in this system is able to find their place and a balance between constraints and satisfactions, losses and successes, isolation and support. This balance seems to favour individual and group resilience. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

A theory of planned behaviour-based analysis of TIMSS 2011 to determine factors influencing inquiry teaching practices in high-performing countries

NASA Astrophysics Data System (ADS)

Pongsophon, Pongprapan; Herman, Benjamin C.

2017-07-01

Given the abundance of literature describing the strong relationship between inquiry-based teaching and student achievement, more should be known about the factors impacting science teachers' classroom inquiry implementation. This study utilises the theory of planned behaviour to propose and validate a causal model of inquiry-based teaching through analysing data relating to high-performing countries retrieved from the 2011 Trends in International Mathematics and Science Study assessments. Data analysis was completed through structural equation modelling using a polychoric correlation matrix for data input and diagonally weighted least squares estimation. Adequate fit of the full model to the empirical data was realised. The model demonstrates that the extent the teachers participated in academic collaborations was positively related to their occupational satisfaction, confidence in teaching inquiry, and classroom inquiry practices. Furthermore, the teachers' confidence with implementing inquiry was positively related to their classroom inquiry implementation and occupational satisfaction. However, perceived student-generated constraints demonstrated a negative relationship with the teachers' confidence with implementing inquiry and occupational satisfaction. Implications from this study include supporting teachers through promoting collaborative opportunities that facilitate inquiry-based practices and occupational satisfaction.

Engagement, empowerment, and job satisfaction before implementing an academic model of shared governance.

PubMed

Owen, D C; Boswell, C; Opton, L; Franco, L; Meriwether, C

2018-06-01

Baseline information was obtained from a School of Nursing faculty and staff about perceptions of job satisfaction, empowerment, and engagement in the workplace before the introduction of an integrated faculty and staff shared governance system. Governance structure in schools of nursing has the potential to enhance or impose constraints on the work environment for faculty, staff, and stakeholders. RESULTS: Faculty and staff perceptions of job satisfaction and engagement in the workplace before the introduction of a new model of shared governance are presented. Statistical differences were found between faculty and staff responses on the overall or total scales and select subscales, and group patterns of relationships differed. We provided a description of the first shared governance structure derived from the perspective of shared governance as defined and operationalized in Magnet Hospital health care systems and includes administrators, faculty, and staff in decision-making councils. As academia embarks on this change in governance structure from hierarchical to a more flattened approach findings support examining levels of work engagement, structural and psychological empowerment, and job satisfaction as key monitors of the work environment. Copyright © 2018 Elsevier Inc. All rights reserved.

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

Baker, Kyri; Dall'Anese, Emiliano; Summers, Tyler

This paper outlines a data-driven, distributionally robust approach to solve chance-constrained AC optimal power flow problems in distribution networks. Uncertain forecasts for loads and power generated by photovoltaic (PV) systems are considered, with the goal of minimizing PV curtailment while meeting power flow and voltage regulation constraints. A data- driven approach is utilized to develop a distributionally robust conservative convex approximation of the chance-constraints; particularly, the mean and covariance matrix of the forecast errors are updated online, and leveraged to enforce voltage regulation with predetermined probability via Chebyshev-based bounds. By combining an accurate linear approximation of the AC power flowmore » equations with the distributionally robust chance constraint reformulation, the resulting optimization problem becomes convex and computationally tractable.« less

Structural design optimization with survivability dependent constraints application: Primary wing box of a multi-role fighter

NASA Technical Reports Server (NTRS)

Dolvin, Douglas J.

1992-01-01

The superior survivability of a multirole fighter is dependent upon balanced integration of technologies for reduced vulnerability and susceptability. The objective is to develop a methodology for structural design optimization with survivability dependent constraints. The design criteria for optimization will be survivability in a tactical laser environment. The following analyses are studied to establish a dependent design relationship between structural weight and survivability: (1) develop a physically linked global design model of survivability variables; and (2) apply conventional constraints to quantify survivability dependent design. It was not possible to develop an exact approach which would include all aspects of survivability dependent design, therefore guidelines are offered for solving similar problems.

Solving the Hamilton-Jacobi equation for general relativity

NASA Astrophysics Data System (ADS)

Parry, J.; Salopek, D. S.; Stewart, J. M.

1994-03-01

We demonstrate a systematic method for solving the Hamilton-Jacobi equation for general relativity with the inclusion of matter fields. The generating functional is expanded in a series of spatial gradients. Each term is manifestly invariant under reparametrizations of the spatial coordinates (``gauge invariant''). At each order we solve the Hamiltonian constraint using a conformal transformation of the three-metric as well as a line integral in superspace. This gives a recursion relation for the generating functional which then may be solved to arbitrary order simply by functionally differentiating previous orders. At fourth order in spatial gradients we demonstrate solutions for irrotational dust as well as for a scalar field. We explicitly evolve the three-metric to the same order. This method can be used to derive the Zel'dovich approximation for general relativity.

Web-based software tool for constraint-based design specification of synthetic biological systems.

PubMed

Oberortner, Ernst; Densmore, Douglas

2015-06-19

miniEugene provides computational support for solving combinatorial design problems, enabling users to specify and enumerate designs for novel biological systems based on sets of biological constraints. This technical note presents a brief tutorial for biologists and software engineers in the field of synthetic biology on how to use miniEugene. After reading this technical note, users should know which biological constraints are available in miniEugene, understand the syntax and semantics of these constraints, and be able to follow a step-by-step guide to specify the design of a classical synthetic biological system-the genetic toggle switch.1 We also provide links and references to more information on the miniEugene web application and the integration of the miniEugene software library into sophisticated Computer-Aided Design (CAD) tools for synthetic biology ( www.eugenecad.org ).

Bayesian Optimization Under Mixed Constraints with A Slack-Variable Augmented Lagrangian

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

Picheny, Victor; Gramacy, Robert B.; Wild, Stefan M.

An augmented Lagrangian (AL) can convert a constrained optimization problem into a sequence of simpler (e.g., unconstrained) problems, which are then usually solved with local solvers. Recently, surrogate-based Bayesian optimization (BO) sub-solvers have been successfully deployed in the AL framework for a more global search in the presence of inequality constraints; however, a drawback was that expected improvement (EI) evaluations relied on Monte Carlo. Here we introduce an alternative slack variable AL, and show that in this formulation the EI may be evaluated with library routines. The slack variables furthermore facilitate equality as well as inequality constraints, and mixtures thereof.more » We show our new slack “ALBO” compares favorably to the original. Its superiority over conventional alternatives is reinforced on several mixed constraint examples.« less

Robust Programming Problems Based on the Mean-Variance Model Including Uncertainty Factors

NASA Astrophysics Data System (ADS)

Hasuike, Takashi; Ishii, Hiroaki

2009-01-01

This paper considers robust programming problems based on the mean-variance model including uncertainty sets and fuzzy factors. Since these problems are not well-defined problems due to fuzzy factors, it is hard to solve them directly. Therefore, introducing chance constraints, fuzzy goals and possibility measures, the proposed models are transformed into the deterministic equivalent problems. Furthermore, in order to solve these equivalent problems efficiently, the solution method is constructed introducing the mean-absolute deviation and doing the equivalent transformations.

Optimality conditions for the numerical solution of optimization problems with PDE constraints :

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

Aguilo Valentin, Miguel Alejandro; Ridzal, Denis

2014-03-01

A theoretical framework for the numerical solution of partial di erential equation (PDE) constrained optimization problems is presented in this report. This theoretical framework embodies the fundamental infrastructure required to e ciently implement and solve this class of problems. Detail derivations of the optimality conditions required to accurately solve several parameter identi cation and optimal control problems are also provided in this report. This will allow the reader to further understand how the theoretical abstraction presented in this report translates to the application.

Interactive two-stage stochastic fuzzy programming for water resources management.

PubMed

Wang, S; Huang, G H

2011-08-01

In this study, an interactive two-stage stochastic fuzzy programming (ITSFP) approach has been developed through incorporating an interactive fuzzy resolution (IFR) method within an inexact two-stage stochastic programming (ITSP) framework. ITSFP can not only tackle dual uncertainties presented as fuzzy boundary intervals that exist in the objective function and the left- and right-hand sides of constraints, but also permit in-depth analyses of various policy scenarios that are associated with different levels of economic penalties when the promised policy targets are violated. A management problem in terms of water resources allocation has been studied to illustrate applicability of the proposed approach. The results indicate that a set of solutions under different feasibility degrees has been generated for planning the water resources allocation. They can help the decision makers (DMs) to conduct in-depth analyses of tradeoffs between economic efficiency and constraint-violation risk, as well as enable them to identify, in an interactive way, a desired compromise between satisfaction degree of the goal and feasibility of the constraints (i.e., risk of constraint violation). Copyright © 2011 Elsevier Ltd. All rights reserved.

What Have the Difference Scores Not Been Telling Us? A Critique of the Use of Self-Ideal Discrepancy in the Assessment of Body Image and Evaluation of an Alternative Data-Analytic Framework

ERIC Educational Resources Information Center

Cafri, Guy; van den Berg, Patricia; Brannick, Michael T.

2010-01-01

Difference scores are often used as a means of assessing body image satisfaction using silhouette scales. Unfortunately, difference scores suffer from numerous potential methodological problems, including reduced reliability, ambiguity, confounded effects, untested constraints, and dimensional reduction. In this article, the methodological…

Distribution Planning: An Integration of Constraint Satisfaction & Heuristic Search Techniques

DTIC Science & Technology

1990-01-01

Proceedings of the Symposium on Aritificial Intelligence in ~~litary Logistics, Arlington, VA: American Defense Preparedness Assoc. pp. 177-182...dynamic changes, too many variables, and lack pf planning time. The Human Engineeri n ~ Laboratory (HEL) is developing artificial intelligence (AI...first attempt. The field of artificial intelligence includes a variety of knowledge-based approaches. Most widely known are Expert Systems, that are

Shortcuts to Excellence: Techniques To Increase Personal Productivity, Job Satisfaction and Organizational Effectiveness.

ERIC Educational Resources Information Center

Brown, Michael H.

This document presents a case history of an 80-hour consulting contract undertaken in 1984 whereby a human resources consultant provided training in team building, communication skills, and creative problem solving to the management team of the Water Revenue Department (WRD) of the government of the District of Columbia. The troubled history of…

Blended Learning Improves Science Education.

PubMed

Stockwell, Brent R; Stockwell, Melissa S; Cennamo, Michael; Jiang, Elise

2015-08-27

Blended learning is an emerging paradigm for science education but has not been rigorously assessed. We performed a randomized controlled trial of blended learning. We found that in-class problem solving improved exam performance, and video assignments increased attendance and satisfaction. This validates a new model for science communication and education. Copyright © 2015 Elsevier Inc. All rights reserved.

Facilitating Students' Learning with Hybrid Instruction: A Comparison among Four Learning Styles

ERIC Educational Resources Information Center

Wichadee, Saovapa

2013-01-01

Introduction: Since a part of the instruction happens online, a hybrid course has usually been used to solve the problems of space and time. This article explores how students' learning styles influence their learning and satisfaction when certain format of a hybrid course is implemented. Methods: Participants were 122 first-year students at a…

Improved multi-objective ant colony optimization algorithm and its application in complex reasoning

NASA Astrophysics Data System (ADS)

Wang, Xinqing; Zhao, Yang; Wang, Dong; Zhu, Huijie; Zhang, Qing

2013-09-01

The problem of fault reasoning has aroused great concern in scientific and engineering fields. However, fault investigation and reasoning of complex system is not a simple reasoning decision-making problem. It has become a typical multi-constraint and multi-objective reticulate optimization decision-making problem under many influencing factors and constraints. So far, little research has been carried out in this field. This paper transforms the fault reasoning problem of complex system into a paths-searching problem starting from known symptoms to fault causes. Three optimization objectives are considered simultaneously: maximum probability of average fault, maximum average importance, and minimum average complexity of test. Under the constraints of both known symptoms and the causal relationship among different components, a multi-objective optimization mathematical model is set up, taking minimizing cost of fault reasoning as the target function. Since the problem is non-deterministic polynomial-hard(NP-hard), a modified multi-objective ant colony algorithm is proposed, in which a reachability matrix is set up to constrain the feasible search nodes of the ants and a new pseudo-random-proportional rule and a pheromone adjustment mechinism are constructed to balance conflicts between the optimization objectives. At last, a Pareto optimal set is acquired. Evaluation functions based on validity and tendency of reasoning paths are defined to optimize noninferior set, through which the final fault causes can be identified according to decision-making demands, thus realize fault reasoning of the multi-constraint and multi-objective complex system. Reasoning results demonstrate that the improved multi-objective ant colony optimization(IMACO) can realize reasoning and locating fault positions precisely by solving the multi-objective fault diagnosis model, which provides a new method to solve the problem of multi-constraint and multi-objective fault diagnosis and reasoning of complex system.

Constrained evolution in numerical relativity

NASA Astrophysics Data System (ADS)

Anderson, Matthew William

The strongest potential source of gravitational radiation for current and future detectors is the merger of binary black holes. Full numerical simulation of such mergers can provide realistic signal predictions and enhance the probability of detection. Numerical simulation of the Einstein equations, however, is fraught with difficulty. Stability even in static test cases of single black holes has proven elusive. Common to unstable simulations is the growth of constraint violations. This work examines the effect of controlling the growth of constraint violations by solving the constraints periodically during a simulation, an approach called constrained evolution. The effects of constrained evolution are contrasted with the results of unconstrained evolution, evolution where the constraints are not solved during the course of a simulation. Two different formulations of the Einstein equations are examined: the standard ADM formulation and the generalized Frittelli-Reula formulation. In most cases constrained evolution vastly improves the stability of a simulation at minimal computational cost when compared with unconstrained evolution. However, in the more demanding test cases examined, constrained evolution fails to produce simulations with long-term stability in spite of producing improvements in simulation lifetime when compared with unconstrained evolution. Constrained evolution is also examined in conjunction with a wide variety of promising numerical techniques, including mesh refinement and overlapping Cartesian and spherical computational grids. Constrained evolution in boosted black hole spacetimes is investigated using overlapping grids. Constrained evolution proves to be central to the host of innovations required in carrying out such intensive simulations.

Numerical Optimization Algorithms and Software for Systems Biology

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

Saunders, Michael

2013-02-02

The basic aims of this work are: to develop reliable algorithms for solving optimization problems involving large stoi- chiometric matrices; to investigate cyclic dependency between metabolic and macromolecular biosynthetic networks; and to quantify the significance of thermodynamic constraints on prokaryotic metabolism.

Complementary Constrains on Component based Multiphase Flow Problems, Should It Be Implemented Locally or Globally?

NASA Astrophysics Data System (ADS)

Shao, H.; Huang, Y.; Kolditz, O.

2015-12-01

Multiphase flow problems are numerically difficult to solve, as it often contains nonlinear Phase transition phenomena A conventional technique is to introduce the complementarity constraints where fluid properties such as liquid saturations are confined within a physically reasonable range. Based on such constraints, the mathematical model can be reformulated into a system of nonlinear partial differential equations coupled with variational inequalities. They can be then numerically handled by optimization algorithms. In this work, two different approaches utilizing the complementarity constraints based on persistent primary variables formulation[4] are implemented and investigated. The first approach proposed by Marchand et.al[1] is using "local complementary constraints", i.e. coupling the constraints with the local constitutive equations. The second approach[2],[3] , namely the "global complementary constrains", applies the constraints globally with the mass conservation equation. We will discuss how these two approaches are applied to solve non-isothermal componential multiphase flow problem with the phase change phenomenon. Several benchmarks will be presented for investigating the overall numerical performance of different approaches. The advantages and disadvantages of different models will also be concluded. References[1] E.Marchand, T.Mueller and P.Knabner. Fully coupled generalized hybrid-mixed finite element approximation of two-phase two-component flow in porous media. Part I: formulation and properties of the mathematical model, Computational Geosciences 17(2): 431-442, (2013). [2] A. Lauser, C. Hager, R. Helmig, B. Wohlmuth. A new approach for phase transitions in miscible multi-phase flow in porous media. Water Resour., 34,(2011), 957-966. [3] J. Jaffré, and A. Sboui. Henry's Law and Gas Phase Disappearance. Transp. Porous Media. 82, (2010), 521-526. [4] A. Bourgeat, M. Jurak and F. Smaï. Two-phase partially miscible flow and transport modeling in porous media : application to gas migration in a nuclear waste repository, Comp.Geosciences. (2009), Volume 13, Number 1, 29-42.

Stochastic Computations in Cortical Microcircuit Models

PubMed Central

Maass, Wolfgang

2013-01-01

Experimental data from neuroscience suggest that a substantial amount of knowledge is stored in the brain in the form of probability distributions over network states and trajectories of network states. We provide a theoretical foundation for this hypothesis by showing that even very detailed models for cortical microcircuits, with data-based diverse nonlinear neurons and synapses, have a stationary distribution of network states and trajectories of network states to which they converge exponentially fast from any initial state. We demonstrate that this convergence holds in spite of the non-reversibility of the stochastic dynamics of cortical microcircuits. We further show that, in the presence of background network oscillations, separate stationary distributions emerge for different phases of the oscillation, in accordance with experimentally reported phase-specific codes. We complement these theoretical results by computer simulations that investigate resulting computation times for typical probabilistic inference tasks on these internally stored distributions, such as marginalization or marginal maximum-a-posteriori estimation. Furthermore, we show that the inherent stochastic dynamics of generic cortical microcircuits enables them to quickly generate approximate solutions to difficult constraint satisfaction problems, where stored knowledge and current inputs jointly constrain possible solutions. This provides a powerful new computing paradigm for networks of spiking neurons, that also throws new light on how networks of neurons in the brain could carry out complex computational tasks such as prediction, imagination, memory recall and problem solving. PMID:24244126

Time-fixed rendezvous by impulse factoring with an intermediate timing constraint. [for transfer orbits

NASA Technical Reports Server (NTRS)

Green, R. N.; Kibler, J. F.; Young, G. R.

1974-01-01

A method is presented for factoring a two-impulse orbital transfer into a three- or four-impulse transfer which solves the rendezvous problem and satisfies an intermediate timing constraint. Both the time of rendezvous and the intermediate time of a alinement are formulated as any element of a finite sequence of times. These times are integer multiples of a constant plus an additive constant. The rendezvous condition is an equality constraint, whereas the intermediate alinement is an inequality constraint. The two timing constraints are satisfied by factoring the impulses into collinear parts that vectorially sum to the original impulse and by varying the resultant period differences and the number of revolutions in each orbit. Five different types of solutions arise by considering factoring either or both of the two impulses into two or three parts with a limit for four total impulses. The impulse-factoring technique may be applied to any two-impulse transfer which has distinct orbital periods.

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

PubMed

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

2013-02-01

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

Locating an imaging radar in Canada for identifying spaceborne objects

NASA Astrophysics Data System (ADS)

Schick, William G.

1992-12-01

This research presents a study of the maximal coverage p-median facility location problem as applied to the location of an imaging radar in Canada for imaging spaceborne objects. The classical mathematical formulation of the maximal coverage p-median problem is converted into network-flow with side constraint formulations that are developed using a scaled down version of the imaging radar location problem. Two types of network-flow with side constraint formulations are developed: a network using side constraints that simulates the gains in a generalized network; and a network resembling a multi-commodity flow problem that uses side constraints to force flow along identical arcs. These small formulations are expanded to encompass a case study using 12 candidate radar sites, and 48 satellites divided into three states. SAS/OR PROC NETFLOW was used to solve the network-flow with side constraint formulations. The case study show that potential for both formulations, although the simulated gains formulation encountered singular matrix computational difficulties as a result of the very organized nature of its side constraint matrix. The multi-commodity flow formulation, when combined with equi-distribution of flow constraints, provided solutions for various values of p, the number of facilities to be selected.

Model Based Optimal Control, Estimation, and Validation of Lithium-Ion Batteries

NASA Astrophysics Data System (ADS)

Perez, Hector Eduardo

This dissertation focuses on developing and experimentally validating model based control techniques to enhance the operation of lithium ion batteries, safely. An overview of the contributions to address the challenges that arise are provided below. Chapter 1: This chapter provides an introduction to battery fundamentals, models, and control and estimation techniques. Additionally, it provides motivation for the contributions of this dissertation. Chapter 2: This chapter examines reference governor (RG) methods for satisfying state constraints in Li-ion batteries. Mathematically, these constraints are formulated from a first principles electrochemical model. Consequently, the constraints explicitly model specific degradation mechanisms, such as lithium plating, lithium depletion, and overheating. This contrasts with the present paradigm of limiting measured voltage, current, and/or temperature. The critical challenges, however, are that (i) the electrochemical states evolve according to a system of nonlinear partial differential equations, and (ii) the states are not physically measurable. Assuming available state and parameter estimates, this chapter develops RGs for electrochemical battery models. The results demonstrate how electrochemical model state information can be utilized to ensure safe operation, while simultaneously enhancing energy capacity, power, and charge speeds in Li-ion batteries. Chapter 3: Complex multi-partial differential equation (PDE) electrochemical battery models are characterized by parameters that are often difficult to measure or identify. This parametric uncertainty influences the state estimates of electrochemical model-based observers for applications such as state-of-charge (SOC) estimation. This chapter develops two sensitivity-based interval observers that map bounded parameter uncertainty to state estimation intervals, within the context of electrochemical PDE models and SOC estimation. Theoretically, this chapter extends the notion of interval observers to PDE models using a sensitivity-based approach. Practically, this chapter quantifies the sensitivity of battery state estimates to parameter variations, enabling robust battery management schemes. The effectiveness of the proposed sensitivity-based interval observers is verified via a numerical study for the range of uncertain parameters. Chapter 4: This chapter seeks to derive insight on battery charging control using electrochemistry models. Directly using full order complex multi-partial differential equation (PDE) electrochemical battery models is difficult and sometimes impossible to implement. This chapter develops an approach for obtaining optimal charge control schemes, while ensuring safety through constraint satisfaction. An optimal charge control problem is mathematically formulated via a coupled reduced order electrochemical-thermal model which conserves key electrochemical and thermal state information. The Legendre-Gauss-Radau (LGR) pseudo-spectral method with adaptive multi-mesh-interval collocation is employed to solve the resulting nonlinear multi-state optimal control problem. Minimum time charge protocols are analyzed in detail subject to solid and electrolyte phase concentration constraints, as well as temperature constraints. The optimization scheme is examined using different input current bounds, and an insight on battery design for fast charging is provided. Experimental results are provided to compare the tradeoffs between an electrochemical-thermal model based optimal charge protocol and a traditional charge protocol. Chapter 5: Fast and safe charging protocols are crucial for enhancing the practicality of batteries, especially for mobile applications such as smartphones and electric vehicles. This chapter proposes an innovative approach to devising optimally health-conscious fast-safe charge protocols. A multi-objective optimal control problem is mathematically formulated via a coupled electro-thermal-aging battery model, where electrical and aging sub-models depend upon the core temperature captured by a two-state thermal sub-model. The Legendre-Gauss-Radau (LGR) pseudo-spectral method with adaptive multi-mesh-interval collocation is employed to solve the resulting highly nonlinear six-state optimal control problem. Charge time and health degradation are therefore optimally traded off, subject to both electrical and thermal constraints. Minimum-time, minimum-aging, and balanced charge scenarios are examined in detail. Sensitivities to the upper voltage bound, ambient temperature, and cooling convection resistance are investigated as well. Experimental results are provided to compare the tradeoffs between a balanced and traditional charge protocol. Chapter 6: This chapter provides concluding remarks on the findings of this dissertation and a discussion of future work.

Acceleration constraints in modeling and control of nonholonomic systems

NASA Astrophysics Data System (ADS)

Bajodah, Abdulrahman H.

2003-10-01

Acceleration constraints are used to enhance modeling techniques for dynamical systems. In particular, Kane's equations of motion subjected to bilateral constraints, unilateral constraints, and servo-constraints are modified by utilizing acceleration constraints for the purpose of simplifying the equations and increasing their applicability. The tangential properties of Kane's method provide relationships between the holonomic and the nonholonomic partial velocities, and hence allow one to describe nonholonomic generalized active and inertia forces in terms of their holonomic counterparts, i.e., those which correspond to the system without constraints. Therefore, based on the modeling process objectives, the holonomic and the nonholonomic vector entities in Kane's approach are used interchangeably to model holonomic and nonholonomic systems. When the holonomic partial velocities are used to model nonholonomic systems, the resulting models are full-order (also called nonminimal or unreduced) and separated in accelerations. As a consequence, they are readily integrable and can be used for generic system analysis. Other related topics are constraint forces, numerical stability of the nonminimal equations of motion, and numerical constraint stabilization. Two types of unilateral constraints considered are impulsive and friction constraints. Impulsive constraints are modeled by means of a continuous-in-velocities and impulse-momentum approaches. In controlled motion, the acceleration form of constraints is utilized with the Moore-Penrose generalized inverse of the corresponding constraint matrix to solve for the inverse dynamics of servo-constraints, and for the redundancy resolution of overactuated manipulators. If control variables are involved in the algebraic constraint equations, then these tools are used to modify the controlled equations of motion in order to facilitate control system design. An illustrative example of spacecraft stabilization is presented.

GWM-a ground-water management process for the U.S. Geological Survey modular ground-water model (MODFLOW-2000)

USGS Publications Warehouse

Ahlfeld, David P.; Barlow, Paul M.; Mulligan, Anne E.

2005-01-01

GWM is a Ground?Water Management Process for the U.S. Geological Survey modular three?dimensional ground?water model, MODFLOW?2000. GWM uses a response?matrix approach to solve several types of linear, nonlinear, and mixed?binary linear ground?water management formulations. Each management formulation consists of a set of decision variables, an objective function, and a set of constraints. Three types of decision variables are supported by GWM: flow?rate decision variables, which are withdrawal or injection rates at well sites; external decision variables, which are sources or sinks of water that are external to the flow model and do not directly affect the state variables of the simulated ground?water system (heads, streamflows, and so forth); and binary variables, which have values of 0 or 1 and are used to define the status of flow?rate or external decision variables. Flow?rate decision variables can represent wells that extend over one or more model cells and be active during one or more model stress periods; external variables also can be active during one or more stress periods. A single objective function is supported by GWM, which can be specified to either minimize or maximize the weighted sum of the three types of decision variables. Four types of constraints can be specified in a GWM formulation: upper and lower bounds on the flow?rate and external decision variables; linear summations of the three types of decision variables; hydraulic?head based constraints, including drawdowns, head differences, and head gradients; and streamflow and streamflow?depletion constraints. The Response Matrix Solution (RMS) Package of GWM uses the Ground?Water Flow Process of MODFLOW to calculate the change in head at each constraint location that results from a perturbation of a flow?rate variable; these changes are used to calculate the response coefficients. For linear management formulations, the resulting matrix of response coefficients is then combined with other components of the linear management formulation to form a complete linear formulation; the formulation is then solved by use of the simplex algorithm, which is incorporated into the RMS Package. Nonlinear formulations arise for simulated conditions that include water?table (unconfined) aquifers or head?dependent boundary conditions (such as streams, drains, or evapotranspiration from the water table). Nonlinear formulations are solved by sequential linear programming; that is, repeated linearization of the nonlinear features of the management problem. In this approach, response coefficients are recalculated for each iteration of the solution process. Mixed?binary linear (or mildly nonlinear) formulations are solved by use of the branch and bound algorithm, which is also incorporated into the RMS Package. Three sample problems are provided to demonstrate the use of GWM for typical ground?water flow management problems. These sample problems provide examples of how GWM input files are constructed to specify the decision variables, objective function, constraints, and solution process for a GWM run. The GWM Process runs with the MODFLOW?2000 Global and Ground?Water Flow Processes, but in its current form GWM cannot be used with the Observation, Sensitivity, Parameter?Estimation, or Ground?Water Transport Processes. The GWM Process is written with a modular structure so that new objective functions, constraint types, and solution algorithms can be added.

Career satisfaction among general surgeons in Canada: a qualitative study of enablers and barriers to improve recruitment and retention in general surgery.

PubMed

Ahmed, Najma; Conn, Lesley Gotlib; Chiu, Mary; Korabi, Bochra; Qureshi, Adnan; Nathens, Avery B; Kitto, Simon

2012-11-01

To understand what influences career satisfaction among general surgeons in urban and rural areas in Canada in order to improve recruitment and retention in general surgery. Semistructured interviews were conducted with 32 general surgeons in 2010 who were members of the Canadian Association of General Surgeons and who currently practice in either an urban or rural area. Interviews explored factors contributing to career satisfaction, as well as suggestions for preventive, screening, or management strategies to support general surgery practice. Findings revealed that both urban and rural general surgeons experienced the most satisfaction from their ability to resolve patient problems quickly and effectively, enhancing their sense of the meaningfulness of their clinical practice. The supportive relationships with colleagues, trainees, and patients was also cited as a key source of career satisfaction. Conversely, insufficient access to resources and a perceived disconnect between hospital administration and clinical practice priorities were raised as key "systems-level" problems. As a result, many participants felt alienated from their work by these systems-level barriers that were perceived to hinder the provision of high-quality patient care. Career satisfaction among both urban and rural general surgeons was influenced positively by the social aspects of their work, such as patient and colleague relationships, as well as a perception of an increasing amount of control and autonomy over their professional commitments. The modern general surgeon values a balance between professional obligations and personal time that may be difficult to achieve given the current system constraints.

Construct exploit constraint in crash analysis by bypassing canary

NASA Astrophysics Data System (ADS)

Huang, Ning; Huang, Shuguang; Huang, Hui; Chang, Chao

2017-08-01

Selective symbolic execution is a common program testing technology. Developed on the basis of it, some crash analysis systems are often used to test the fragility of the program by constructing exploit constraints, such as CRAX. From the study of crash analysis based on symbolic execution, this paper find that this technology cannot bypass the canary stack protection mechanisms. This paper makes the improvement uses the API hook in Linux. Experimental results show that the use of API hook can effectively solve the problem that crash analysis cannot bypass the canary protection.

Energy efficient LED layout optimization for near-uniform illumination

NASA Astrophysics Data System (ADS)

Ali, Ramy E.; Elgala, Hany

2016-09-01

In this paper, we consider the problem of designing energy efficient light emitting diodes (LEDs) layout while satisfying the illumination constraints. Towards this objective, we present a simple approach to the illumination design problem based on the concept of the virtual LED. We formulate a constrained optimization problem for minimizing the power consumption while maintaining a near-uniform illumination throughout the room. By solving the resulting constrained linear program, we obtain the number of required LEDs and the optimal output luminous intensities that achieve the desired illumination constraints.

Faddeev-Jackiw quantization of topological invariants: Euler and Pontryagin classes

NASA Astrophysics Data System (ADS)

Escalante, Alberto; Medel-Portugal, C.

2018-04-01

The symplectic analysis for the four dimensional Pontryagin and Euler invariants is performed within the Faddeev-Jackiw context. The Faddeev-Jackiw constraints and the generalized Faddeev-Jackiw brackets are reported; we show that in spite of the Pontryagin and Euler classes give rise the same equations of motion, its respective symplectic structures are different to each other. In addition, a quantum state that solves the Faddeev-Jackiw constraints is found, and we show that the quantum states for these invariants are different to each other. Finally, we present some remarks and conclusions.

Automated design optimization of supersonic airplane wing structures under dynamic constraints

NASA Technical Reports Server (NTRS)

Fox, R. L.; Miura, H.; Rao, S. S.

1972-01-01

The problems of the preliminary and first level detail design of supersonic aircraft wings are stated as mathematical programs and solved using automated optimum design techniques. The problem is approached in two phases: the first is a simplified equivalent plate model in which the envelope, planform and structural parameters are varied to produce a design, the second is a finite element model with fixed configuration in which the material distribution is varied. Constraints include flutter, aeroelastically computed stresses and deflections, natural frequency and a variety of geometric limitations.

Interior point techniques for LP and NLP

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

Evtushenko, Y.

By using surjective mapping the initial constrained optimization problem is transformed to a problem in a new space with only equality constraints. For the numerical solution of the latter problem we use the generalized gradient-projection method and Newton`s method. After inverse transformation to the initial space we obtain the family of numerical methods for solving optimization problems with equality and inequality constraints. In the linear programming case after some simplification we obtain Dikin`s algorithm, affine scaling algorithm and generalized primal dual interior point linear programming algorithm.

Smell Detection Agent Based Optimization Algorithm

NASA Astrophysics Data System (ADS)

Vinod Chandra, S. S.

2016-09-01

In this paper, a novel nature-inspired optimization algorithm has been employed and the trained behaviour of dogs in detecting smell trails is adapted into computational agents for problem solving. The algorithm involves creation of a surface with smell trails and subsequent iteration of the agents in resolving a path. This algorithm can be applied in different computational constraints that incorporate path-based problems. Implementation of the algorithm can be treated as a shortest path problem for a variety of datasets. The simulated agents have been used to evolve the shortest path between two nodes in a graph. This algorithm is useful to solve NP-hard problems that are related to path discovery. This algorithm is also useful to solve many practical optimization problems. The extensive derivation of the algorithm can be enabled to solve shortest path problems.

Bernoulli substitution in the Ramsey model: Optimal trajectories under control constraints

NASA Astrophysics Data System (ADS)

Krasovskii, A. A.; Lebedev, P. D.; Tarasyev, A. M.

2017-05-01

We consider a neoclassical (economic) growth model. A nonlinear Ramsey equation, modeling capital dynamics, in the case of Cobb-Douglas production function is reduced to the linear differential equation via a Bernoulli substitution. This considerably facilitates the search for a solution to the optimal growth problem with logarithmic preferences. The study deals with solving the corresponding infinite horizon optimal control problem. We consider a vector field of the Hamiltonian system in the Pontryagin maximum principle, taking into account control constraints. We prove the existence of two alternative steady states, depending on the constraints. A proposed algorithm for constructing growth trajectories combines methods of open-loop control and closed-loop regulatory control. For some levels of constraints and initial conditions, a closed-form solution is obtained. We also demonstrate the impact of technological change on the economic equilibrium dynamics. Results are supported by computer calculations.

Online blind source separation using incremental nonnegative matrix factorization with volume constraint.

PubMed

Zhou, Guoxu; Yang, Zuyuan; Xie, Shengli; Yang, Jun-Mei

2011-04-01

Online blind source separation (BSS) is proposed to overcome the high computational cost problem, which limits the practical applications of traditional batch BSS algorithms. However, the existing online BSS methods are mainly used to separate independent or uncorrelated sources. Recently, nonnegative matrix factorization (NMF) shows great potential to separate the correlative sources, where some constraints are often imposed to overcome the non-uniqueness of the factorization. In this paper, an incremental NMF with volume constraint is derived and utilized for solving online BSS. The volume constraint to the mixing matrix enhances the identifiability of the sources, while the incremental learning mode reduces the computational cost. The proposed method takes advantage of the natural gradient based multiplication updating rule, and it performs especially well in the recovery of dependent sources. Simulations in BSS for dual-energy X-ray images, online encrypted speech signals, and high correlative face images show the validity of the proposed method.

The helical structure of DNA facilitates binding

NASA Astrophysics Data System (ADS)

Berg, Otto G.; Mahmutovic, Anel; Marklund, Emil; Elf, Johan

2016-09-01

The helical structure of DNA imposes constraints on the rate of diffusion-limited protein binding. Here we solve the reaction-diffusion equations for DNA-like geometries and extend with simulations when necessary. We find that the helical structure can make binding to the DNA more than twice as fast compared to a case where DNA would be reactive only along one side. We also find that this rate advantage remains when the contributions from steric constraints and rotational diffusion of the DNA-binding protein are included. Furthermore, we find that the association rate is insensitive to changes in the steric constraints on the DNA in the helix geometry, while it is much more dependent on the steric constraints on the DNA-binding protein. We conclude that the helical structure of DNA facilitates the nonspecific binding of transcription factors and structural DNA-binding proteins in general.

General gauge mediation at the weak scale

DOE PAGES

Knapen, Simon; Redigolo, Diego; Shih, David

2016-03-09

We completely characterize General Gauge Mediation (GGM) at the weak scale by solving all IR constraints over the full parameter space. This is made possible through a combination of numerical and analytical methods, based on a set of algebraic relations among the IR soft masses derived from the GGM boundary conditions in the UV. We show how tensions between just a few constraints determine the boundaries of the parameter space: electroweak symmetry breaking (EWSB), the Higgs mass, slepton tachyons, and left-handed stop/sbottom tachyons. While these constraints allow the left-handed squarks to be arbitrarily light, they place strong lower bounds onmore » all of the right-handed squarks. Meanwhile, light EW superpartners are generic throughout much of the parameter space. This is especially the case at lower messenger scales, where a positive threshold correction to m h coming from light Higgsinos and winos is essential in order to satisfy the Higgs mass constraint.« less

A tool for efficient, model-independent management optimization under uncertainty

USGS Publications Warehouse

White, Jeremy; Fienen, Michael N.; Barlow, Paul M.; Welter, Dave E.

2018-01-01

To fill a need for risk-based environmental management optimization, we have developed PESTPP-OPT, a model-independent tool for resource management optimization under uncertainty. PESTPP-OPT solves a sequential linear programming (SLP) problem and also implements (optional) efficient, “on-the-fly” (without user intervention) first-order, second-moment (FOSM) uncertainty techniques to estimate model-derived constraint uncertainty. Combined with a user-specified risk value, the constraint uncertainty estimates are used to form chance-constraints for the SLP solution process, so that any optimal solution includes contributions from model input and observation uncertainty. In this way, a “single answer” that includes uncertainty is yielded from the modeling analysis. PESTPP-OPT uses the familiar PEST/PEST++ model interface protocols, which makes it widely applicable to many modeling analyses. The use of PESTPP-OPT is demonstrated with a synthetic, integrated surface-water/groundwater model. The function and implications of chance constraints for this synthetic model are discussed.

Sleep Does Not Promote Solving Classical Insight Problems and Magic Tricks

PubMed Central

Schönauer, Monika; Brodt, Svenja; Pöhlchen, Dorothee; Breßmer, Anja; Danek, Amory H.; Gais, Steffen

2018-01-01

During creative problem solving, initial solution attempts often fail because of self-imposed constraints that prevent us from thinking out of the box. In order to solve a problem successfully, the problem representation has to be restructured by combining elements of available knowledge in novel and creative ways. It has been suggested that sleep supports the reorganization of memory representations, ultimately aiding problem solving. In this study, we systematically tested the effect of sleep and time on problem solving, using classical insight tasks and magic tricks. Solving these tasks explicitly requires a restructuring of the problem representation and may be accompanied by a subjective feeling of insight. In two sessions, 77 participants had to solve classical insight problems and magic tricks. The two sessions either occurred consecutively or were spaced 3 h apart, with the time in between spent either sleeping or awake. We found that sleep affected neither general solution rates nor the number of solutions accompanied by sudden subjective insight. Our study thus adds to accumulating evidence that sleep does not provide an environment that facilitates the qualitative restructuring of memory representations and enables problem solving. PMID:29535620

FOR Allocation to Distribution Systems based on Credible Improvement Potential (CIP)

NASA Astrophysics Data System (ADS)

Tiwary, Aditya; Arya, L. D.; Arya, Rajesh; Choube, S. C.

2017-02-01

This paper describes an algorithm for forced outage rate (FOR) allocation to each section of an electrical distribution system subject to satisfaction of reliability constraints at each load point. These constraints include threshold values of basic reliability indices, for example, failure rate, interruption duration and interruption duration per year at load points. Component improvement potential measure has been used for FOR allocation. Component with greatest magnitude of credible improvement potential (CIP) measure is selected for improving reliability performance. The approach adopted is a monovariable method where one component is selected for FOR allocation and in the next iteration another component is selected for FOR allocation based on the magnitude of CIP. The developed algorithm is implemented on sample radial distribution system.

A System for Automatically Generating Scheduling Heuristics

NASA Technical Reports Server (NTRS)

Morris, Robert

1996-01-01

The goal of this research is to improve the performance of automated schedulers by designing and implementing an algorithm by automatically generating heuristics by selecting a schedule. The particular application selected by applying this method solves the problem of scheduling telescope observations, and is called the Associate Principal Astronomer. The input to the APA scheduler is a set of observation requests submitted by one or more astronomers. Each observation request specifies an observation program as well as scheduling constraints and preferences associated with the program. The scheduler employs greedy heuristic search to synthesize a schedule that satisfies all hard constraints of the domain and achieves a good score with respect to soft constraints expressed as an objective function established by an astronomer-user.

Optimal design of FIR triplet halfband filter bank and application in image coding.

PubMed

Kha, H H; Tuan, H D; Nguyen, T Q

2011-02-01

This correspondence proposes an efficient semidefinite programming (SDP) method for the design of a class of linear phase finite impulse response triplet halfband filter banks whose filters have optimal frequency selectivity for a prescribed regularity order. The design problem is formulated as the minimization of the least square error subject to peak error constraints and regularity constraints. By using the linear matrix inequality characterization of the trigonometric semi-infinite constraints, it can then be exactly cast as a SDP problem with a small number of variables and, hence, can be solved efficiently. Several design examples of the triplet halfband filter bank are provided for illustration and comparison with previous works. Finally, the image coding performance of the filter bank is presented.

The 2-D magnetotelluric inverse problem solved with optimization

NASA Astrophysics Data System (ADS)

van Beusekom, Ashley E.; Parker, Robert L.; Bank, Randolph E.; Gill, Philip E.; Constable, Steven

2011-02-01

The practical 2-D magnetotelluric inverse problem seeks to determine the shallow-Earth conductivity structure using finite and uncertain data collected on the ground surface. We present an approach based on using PLTMG (Piecewise Linear Triangular MultiGrid), a special-purpose code for optimization with second-order partial differential equation (PDE) constraints. At each frequency, the electromagnetic field and conductivity are treated as unknowns in an optimization problem in which the data misfit is minimized subject to constraints that include Maxwell's equations and the boundary conditions. Within this framework it is straightforward to accommodate upper and lower bounds or other conditions on the conductivity. In addition, as the underlying inverse problem is ill-posed, constraints may be used to apply various kinds of regularization. We discuss some of the advantages and difficulties associated with using PDE-constrained optimization as the basis for solving large-scale nonlinear geophysical inverse problems. Combined transverse electric and transverse magnetic complex admittances from the COPROD2 data are inverted. First, we invert penalizing size and roughness giving solutions that are similar to those found previously. In a second example, conventional regularization is replaced by a technique that imposes upper and lower bounds on the model. In both examples the data misfit is better than that obtained previously, without any increase in model complexity.

Advanced Computational Methods for Security Constrained Financial Transmission Rights

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

Kalsi, Karanjit; Elbert, Stephen T.; Vlachopoulou, Maria

Financial Transmission Rights (FTRs) are financial insurance tools to help power market participants reduce price risks associated with transmission congestion. FTRs are issued based on a process of solving a constrained optimization problem with the objective to maximize the FTR social welfare under power flow security constraints. Security constraints for different FTR categories (monthly, seasonal or annual) are usually coupled and the number of constraints increases exponentially with the number of categories. Commercial software for FTR calculation can only provide limited categories of FTRs due to the inherent computational challenges mentioned above. In this paper, first an innovative mathematical reformulationmore » of the FTR problem is presented which dramatically improves the computational efficiency of optimization problem. After having re-formulated the problem, a novel non-linear dynamic system (NDS) approach is proposed to solve the optimization problem. The new formulation and performance of the NDS solver is benchmarked against widely used linear programming (LP) solvers like CPLEX™ and tested on both standard IEEE test systems and large-scale systems using data from the Western Electricity Coordinating Council (WECC). The performance of the NDS is demonstrated to be comparable and in some cases is shown to outperform the widely used CPLEX algorithms. The proposed formulation and NDS based solver is also easily parallelizable enabling further computational improvement.« less

Designing optimal stimuli to control neuronal spike timing

PubMed Central

Packer, Adam M.; Yuste, Rafael; Paninski, Liam

2011-01-01

Recent advances in experimental stimulation methods have raised the following important computational question: how can we choose a stimulus that will drive a neuron to output a target spike train with optimal precision, given physiological constraints? Here we adopt an approach based on models that describe how a stimulating agent (such as an injected electrical current or a laser light interacting with caged neurotransmitters or photosensitive ion channels) affects the spiking activity of neurons. Based on these models, we solve the reverse problem of finding the best time-dependent modulation of the input, subject to hardware limitations as well as physiologically inspired safety measures, that causes the neuron to emit a spike train that with highest probability will be close to a target spike train. We adopt fast convex constrained optimization methods to solve this problem. Our methods can potentially be implemented in real time and may also be generalized to the case of many cells, suitable for neural prosthesis applications. With the use of biologically sensible parameters and constraints, our method finds stimulation patterns that generate very precise spike trains in simulated experiments. We also tested the intracellular current injection method on pyramidal cells in mouse cortical slices, quantifying the dependence of spiking reliability and timing precision on constraints imposed on the applied currents. PMID:21511704

Magnet status as a competitive strategy of hospital organizations: marketing a culture of excellence in nursing services.

PubMed

Tropello, Paula Grace Dunn

2003-01-01

With issues of patient safety, the nursing shortage, and managed care fiscal constraints, hospital organizations can strategically capture market share, while insuring best care practices, if they adopt the "Magnet Status" accreditation model. This quality indicator signifies to the consumer a culture of excellence in nursing services and fulfills the priority of customer satisfaction as a marketing strategy objective.

Parallelizing Data-Centric Programs

DTIC Science & Technology

2013-09-25

results than current techniques, such as ImageWebs [HGO+10], given the same budget of matches performed. 4.2 Scalable Parallel Similarity Search The work...algorithms. 5 Data-Driven Applications in the Cloud In this project, we investigated what happens when data-centric software is moved from expensive custom ...returns appropriate answer tuples. Figure 9 (b) shows the mutual constraint satisfaction that takes place in answering for 122. The intent is that

Direct handling of equality constraints in multilevel optimization

NASA Technical Reports Server (NTRS)

Renaud, John E.; Gabriele, Gary A.

1990-01-01

In recent years there have been several hierarchic multilevel optimization algorithms proposed and implemented in design studies. Equality constraints are often imposed between levels in these multilevel optimizations to maintain system and subsystem variable continuity. Equality constraints of this nature will be referred to as coupling equality constraints. In many implementation studies these coupling equality constraints have been handled indirectly. This indirect handling has been accomplished using the coupling equality constraints' explicit functional relations to eliminate design variables (generally at the subsystem level), with the resulting optimization taking place in a reduced design space. In one multilevel optimization study where the coupling equality constraints were handled directly, the researchers encountered numerical difficulties which prevented their multilevel optimization from reaching the same minimum found in conventional single level solutions. The researchers did not explain the exact nature of the numerical difficulties other than to associate them with the direct handling of the coupling equality constraints. The coupling equality constraints are handled directly, by employing the Generalized Reduced Gradient (GRG) method as the optimizer within a multilevel linear decomposition scheme based on the Sobieski hierarchic algorithm. Two engineering design examples are solved using this approach. The results show that the direct handling of coupling equality constraints in a multilevel optimization does not introduce any problems when the GRG method is employed as the internal optimizer. The optimums achieved are comparable to those achieved in single level solutions and in multilevel studies where the equality constraints have been handled indirectly.

Effective hybrid teaching-learning-based optimization algorithm for balancing two-sided assembly lines with multiple constraints

NASA Astrophysics Data System (ADS)

Tang, Qiuhua; Li, Zixiang; Zhang, Liping; Floudas, C. A.; Cao, Xiaojun

2015-09-01

Due to the NP-hardness of the two-sided assembly line balancing (TALB) problem, multiple constraints existing in real applications are less studied, especially when one task is involved with several constraints. In this paper, an effective hybrid algorithm is proposed to address the TALB problem with multiple constraints (TALB-MC). Considering the discrete attribute of TALB-MC and the continuous attribute of the standard teaching-learning-based optimization (TLBO) algorithm, the random-keys method is hired in task permutation representation, for the purpose of bridging the gap between them. Subsequently, a special mechanism for handling multiple constraints is developed. In the mechanism, the directions constraint of each task is ensured by the direction check and adjustment. The zoning constraints and the synchronism constraints are satisfied by teasing out the hidden correlations among constraints. The positional constraint is allowed to be violated to some extent in decoding and punished in cost function. Finally, with the TLBO seeking for the global optimum, the variable neighborhood search (VNS) is further hybridized to extend the local search space. The experimental results show that the proposed hybrid algorithm outperforms the late acceptance hill-climbing algorithm (LAHC) for TALB-MC in most cases, especially for large-size problems with multiple constraints, and demonstrates well balance between the exploration and the exploitation. This research proposes an effective and efficient algorithm for solving TALB-MC problem by hybridizing the TLBO and VNS.

Searching for quantum optimal controls under severe constraints

DOE PAGES

Riviello, Gregory; Tibbetts, Katharine Moore; Brif, Constantin; ...

2015-04-06

The success of quantum optimal control for both experimental and theoretical objectives is connected to the topology of the corresponding control landscapes, which are free from local traps if three conditions are met: (1) the quantum system is controllable, (2) the Jacobian of the map from the control field to the evolution operator is of full rank, and (3) there are no constraints on the control field. This paper investigates how the violation of assumption (3) affects gradient searches for globally optimal control fields. The satisfaction of assumptions (1) and (2) ensures that the control landscape lacks fundamental traps, butmore » certain control constraints can still prevent successful optimization of the objective. Using optimal control simulations, we show that the most severe field constraints are those that limit essential control resources, such as the number of control variables, the control duration, and the field strength. Proper management of these resources is an issue of great practical importance for optimization in the laboratory. For each resource, we show that constraints exceeding quantifiable limits can introduce artificial traps to the control landscape and prevent gradient searches from reaching a globally optimal solution. These results demonstrate that careful choice of relevant control parameters helps to eliminate artificial traps and facilitate successful optimization.« less

The Effects of a Couples-Based Health Behavior Intervention During Pregnancy on Latino Couples' Dyadic Satisfaction Postpartum.

PubMed

Coop Gordon, Kristina; Roberson, Patricia N E; Hughes, Jessica A; Khaddouma, Alexander M; Swamy, Geeta K; Noonan, Devon; Gonzalez, Alicia M; Fish, Laura; Pollak, Kathryn I

2018-03-30

Many couples tend to report steadily decreasing relationship quality following the birth of a child. However, little is known about the postpartum period for Latino couples, a rapidly growing ethnic group who are notably underserved by mental and physical health caregivers in the United States. Thus, this study investigated whether a brief couples' intervention focused on helping couples support each other while increasing healthy behaviors might improve dyadic functioning postpartum. This study presents secondary analyses of data regarding couple functioning from a larger randomized controlled trial with 348 Latino couples to promote smoking cessation. Portions of the intervention taught the couple communication and problem-solving skills to increase healthy behavior. Couples participated in four face-to-face assessments across 1 year starting at the end of the first trimester. Latent growth curve analyses revealed that the treatment group reported an increase in relationship satisfaction and constructive communication after the intervention, which diminished by 1-year follow-up, returning couples to their baseline levels of satisfaction. Results suggest that incorporating a brief couple intervention as part of a larger health intervention for Latinos may prevent postpartum decreases in relationship satisfaction. © 2018 Family Process Institute.

Working with Manchester triage -- job satisfaction in nursing.

PubMed

Forsgren, Susanne; Forsman, Berit; Carlström, Eric D

2009-10-01

This article covers nurses' job satisfaction during triage at emergency departments in Western Sweden. Data was collected from 74 triage nurses using a questionnaire containing 37 short form open questions. The answers were analyzed descriptively and by measuring the covariance. The open questions were analyzed by content analysis. The results showed a high degree of job satisfaction (88%). Triage as a method, the interesting nature of the work, and a certain freedom in connection with the triage tasks contributed to job satisfaction (R(2) = 0.40). The nurses found their work interesting and stimulating, although some reported job dissatisfaction due to a heavy workload and lack of competence. Most of the nurses thought that Manchester triage (MTS) was a clear and straightforward method but in need of development. The rational modelling structure by which the triage method is constructed is unable to distinguish all the parameters that an experienced nurse takes into account. When the model is allowed to take precedence over experience, it can be of hindrance and contribute to certain estimates not corresponding with the patient's needs. The participants requested regular exercises solving and discussing patient scenarios. They also wanted to participate on a regular basis in the development of the instrument.

Provisional-Ideal-Point-Based Multi-objective Optimization Method for Drone Delivery Problem

NASA Astrophysics Data System (ADS)

Omagari, Hiroki; Higashino, Shin-Ichiro

2018-04-01

In this paper, we proposed a new evolutionary multi-objective optimization method for solving drone delivery problems (DDP). It can be formulated as a constrained multi-objective optimization problem. In our previous research, we proposed the "aspiration-point-based method" to solve multi-objective optimization problems. However, this method needs to calculate the optimal values of each objective function value in advance. Moreover, it does not consider the constraint conditions except for the objective functions. Therefore, it cannot apply to DDP which has many constraint conditions. To solve these issues, we proposed "provisional-ideal-point-based method." The proposed method defines a "penalty value" to search for feasible solutions. It also defines a new reference solution named "provisional-ideal point" to search for the preferred solution for a decision maker. In this way, we can eliminate the preliminary calculations and its limited application scope. The results of the benchmark test problems show that the proposed method can generate the preferred solution efficiently. The usefulness of the proposed method is also demonstrated by applying it to DDP. As a result, the delivery path when combining one drone and one truck drastically reduces the traveling distance and the delivery time compared with the case of using only one truck.

Genetic algorithm parameters tuning for resource-constrained project scheduling problem

NASA Astrophysics Data System (ADS)

Tian, Xingke; Yuan, Shengrui

2018-04-01

Project Scheduling Problem (RCPSP) is a kind of important scheduling problem. To achieve a certain optimal goal such as the shortest duration, the smallest cost, the resource balance and so on, it is required to arrange the start and finish of all tasks under the condition of satisfying project timing constraints and resource constraints. In theory, the problem belongs to the NP-hard problem, and the model is abundant. Many combinatorial optimization problems are special cases of RCPSP, such as job shop scheduling, flow shop scheduling and so on. At present, the genetic algorithm (GA) has been used to deal with the classical RCPSP problem and achieved remarkable results. Vast scholars have also studied the improved genetic algorithm for the RCPSP problem, which makes it to solve the RCPSP problem more efficiently and accurately. However, for the selection of the main parameters of the genetic algorithm, there is no parameter optimization in these studies. Generally, we used the empirical method, but it cannot ensure to meet the optimal parameters. In this paper, the problem was carried out, which is the blind selection of parameters in the process of solving the RCPSP problem. We made sampling analysis, the establishment of proxy model and ultimately solved the optimal parameters.

Design and Application of Interactive Simulations in Problem-Solving in University-Level Physics Education

NASA Astrophysics Data System (ADS)

Ceberio, Mikel; Almudí, José Manuel; Franco, Ángel

2016-08-01

In recent years, interactive computer simulations have been progressively integrated in the teaching of the sciences and have contributed significant improvements in the teaching-learning process. Practicing problem-solving is a key factor in science and engineering education. The aim of this study was to design simulation-based problem-solving teaching materials and assess their effectiveness in improving students' ability to solve problems in university-level physics. Firstly, we analyze the effect of using simulation-based materials in the development of students' skills in employing procedures that are typically used in the scientific method of problem-solving. We found that a significant percentage of the experimental students used expert-type scientific procedures such as qualitative analysis of the problem, making hypotheses, and analysis of results. At the end of the course, only a minority of the students persisted with habits based solely on mathematical equations. Secondly, we compare the effectiveness in terms of problem-solving of the experimental group students with the students who are taught conventionally. We found that the implementation of the problem-solving strategy improved experimental students' results regarding obtaining a correct solution from the academic point of view, in standard textbook problems. Thirdly, we explore students' satisfaction with simulation-based problem-solving teaching materials and we found that the majority appear to be satisfied with the methodology proposed and took on a favorable attitude to learning problem-solving. The research was carried out among first-year Engineering Degree students.

In Search of Insight.

ERIC Educational Resources Information Center

Kaplan, Craig A.; Simon, Herbert A.

1990-01-01

Attaining the insight needed to solve the Mutilated Checkerboard problem, which requires discovery of an effective problem representation (EPR), is described. Performance on insight problems can be predicted from the availability of generators and constraints in the search for an EPR. Data for 23 undergraduates were analyzed. (TJH)

Effect of shear stress on cell cultures and other reactor problems

NASA Technical Reports Server (NTRS)

Schleier, H.

1981-01-01

Anchorage dependent cell cultures in fluidized beds are tested. Feasibility calculations indicate the allowed parameters and estimate the shear stresses therein. In addition, the diffusion equation with first order reaction is solved for the spherical shell (double bubble) reactor with various constraints.

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

Fath, L., E-mail: lukas.fath@kit.edu; Hochbruck, M., E-mail: marlis.hochbruck@kit.edu; Singh, C.V., E-mail: chandraveer.singh@utoronto.ca

Classical integration methods for molecular dynamics are inherently limited due to resonance phenomena occurring at certain time-step sizes. The mollified impulse method can partially avoid this problem by using appropriate filters based on averaging or projection techniques. However, existing filters are computationally expensive and tedious in implementation since they require either analytical Hessians or they need to solve nonlinear systems from constraints. In this work we follow a different approach based on corotation for the construction of a new filter for (flexible) biomolecular simulations. The main advantages of the proposed filter are its excellent stability properties and ease of implementationmore » in standard softwares without Hessians or solving constraint systems. By simulating multiple realistic examples such as peptide, protein, ice equilibrium and ice–ice friction, the new filter is shown to speed up the computations of long-range interactions by approximately 20%. The proposed filtered integrators allow step sizes as large as 10 fs while keeping the energy drift less than 1% on a 50 ps simulation.« less

Multi-task feature selection in microarray data by binary integer programming.

PubMed

Lan, Liang; Vucetic, Slobodan

2013-12-20

A major challenge in microarray classification is that the number of features is typically orders of magnitude larger than the number of examples. In this paper, we propose a novel feature filter algorithm to select the feature subset with maximal discriminative power and minimal redundancy by solving a quadratic objective function with binary integer constraints. To improve the computational efficiency, the binary integer constraints are relaxed and a low-rank approximation to the quadratic term is applied. The proposed feature selection algorithm was extended to solve multi-task microarray classification problems. We compared the single-task version of the proposed feature selection algorithm with 9 existing feature selection methods on 4 benchmark microarray data sets. The empirical results show that the proposed method achieved the most accurate predictions overall. We also evaluated the multi-task version of the proposed algorithm on 8 multi-task microarray datasets. The multi-task feature selection algorithm resulted in significantly higher accuracy than when using the single-task feature selection methods.

Working memory facilitates insight instead of hindering it: Comment on DeCaro, Van Stockum, and Wieth (2016).

PubMed

Chuderski, Adam; Jastrzębski, Jan

2017-12-01

The "nothing-special" account of insight predicts positive correlations of insight problem solving and working memory capacity (WMC), whereas the "special-process" account expects no, or even negative, correlations. In the latter vein, DeCaro, Van Stockum Jr., and Wieth (2016) have recently reported weak negative WMC correlations with 2 constraint relaxation matchstick problems and 3 insight problems, and thus they claim that WM hinders insight. Here, we report on 3 studies that investigated WMC and various matchstick and classical problems (including 1 study that precisely replicated DeCaro et al.'s procedure). All 3 studies yielded moderate positive correlations of WMC with both the constraint relaxation and the classical problems. WMC explained 10% variance in problem solving, no matter what problems were used or how they were applied. Thus, DeCaro et al.'s claim that WM hinders insight is unwarranted. The opposite is true: WM facilitates insight. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

A two-stage path planning approach for multiple car-like robots based on PH curves and a modified harmony search algorithm

NASA Astrophysics Data System (ADS)

Zeng, Wenhui; Yi, Jin; Rao, Xiao; Zheng, Yun

2017-11-01

In this article, collision-avoidance path planning for multiple car-like robots with variable motion is formulated as a two-stage objective optimization problem minimizing both the total length of all paths and the task's completion time. Accordingly, a new approach based on Pythagorean Hodograph (PH) curves and Modified Harmony Search algorithm is proposed to solve the two-stage path-planning problem subject to kinematic constraints such as velocity, acceleration, and minimum turning radius. First, a method of path planning based on PH curves for a single robot is proposed. Second, a mathematical model of the two-stage path-planning problem for multiple car-like robots with variable motion subject to kinematic constraints is constructed that the first-stage minimizes the total length of all paths and the second-stage minimizes the task's completion time. Finally, a modified harmony search algorithm is applied to solve the two-stage optimization problem. A set of experiments demonstrate the effectiveness of the proposed approach.

EUROPA2: Plan Database Services for Planning and Scheduling Applications

NASA Technical Reports Server (NTRS)

Bedrax-Weiss, Tania; Frank, Jeremy; Jonsson, Ari; McGann, Conor

2004-01-01

NASA missions require solving a wide variety of planning and scheduling problems with temporal constraints; simple resources such as robotic arms, communications antennae and cameras; complex replenishable resources such as memory, power and fuel; and complex constraints on geometry, heat and lighting angles. Planners and schedulers that solve these problems are used in ground tools as well as onboard systems. The diversity of planning problems and applications of planners and schedulers precludes a one-size fits all solution. However, many of the underlying technologies are common across planning domains and applications. We describe CAPR, a formalism for planning that is general enough to cover a wide variety of planning and scheduling domains of interest to NASA. We then describe EUROPA(sub 2), a software framework implementing CAPR. EUROPA(sub 2) provides efficient, customizable Plan Database Services that enable the integration of CAPR into a wide variety of applications. We describe the design of EUROPA(sub 2) from the perspective of both modeling, customization and application integration to different classes of NASA missions.

An iterative method for tri-level quadratic fractional programming problems using fuzzy goal programming approach

NASA Astrophysics Data System (ADS)

Kassa, Semu Mitiku; Tsegay, Teklay Hailay

2017-08-01

Tri-level optimization problems are optimization problems with three nested hierarchical structures, where in most cases conflicting objectives are set at each level of hierarchy. Such problems are common in management, engineering designs and in decision making situations in general, and are known to be strongly NP-hard. Existing solution methods lack universality in solving these types of problems. In this paper, we investigate a tri-level programming problem with quadratic fractional objective functions at each of the three levels. A solution algorithm has been proposed by applying fuzzy goal programming approach and by reformulating the fractional constraints to equivalent but non-fractional non-linear constraints. Based on the transformed formulation, an iterative procedure is developed that can yield a satisfactory solution to the tri-level problem. The numerical results on various illustrative examples demonstrated that the proposed algorithm is very much promising and it can also be used to solve larger-sized as well as n-level problems of similar structure.

Searching RNA motifs and their intermolecular contacts with constraint networks.

PubMed

Thébault, P; de Givry, S; Schiex, T; Gaspin, C

2006-09-01

Searching RNA gene occurrences in genomic sequences is a task whose importance has been renewed by the recent discovery of numerous functional RNA, often interacting with other ligands. Even if several programs exist for RNA motif search, none exists that can represent and solve the problem of searching for occurrences of RNA motifs in interaction with other molecules. We present a constraint network formulation of this problem. RNA are represented as structured motifs that can occur on more than one sequence and which are related together by possible hybridization. The implemented tool MilPat is used to search for several sRNA families in genomic sequences. Results show that MilPat allows to efficiently search for interacting motifs in large genomic sequences and offers a simple and extensible framework to solve such problems. New and known sRNA are identified as H/ACA candidates in Methanocaldococcus jannaschii. http://carlit.toulouse.inra.fr/MilPaT/MilPat.pl.

Caring for inpatient boarders in the emergency department: improving safety and patient and staff satisfaction.

PubMed

Bornemann-Shepherd, Melanie; Le-Lazar, Jamie; Makic, Mary Beth Flynn; DeVine, Deborah; McDevitt, Kelly; Paul, Marcee

2015-01-01

Hospital capacity constraints lead to large numbers of inpatients being held for extended periods in the emergency department. This creates concerns with safety, quality of care, and dissatisfaction of patients and staff. The aim of this quality-improvement project was to improve satisfaction and processes in which nurses provided care to inpatient boarders held in the emergency department. A quality-improvement project framework that included the use of a questionnaire was used to ascertain employee and patient dissatisfaction and identify opportunities for improvement. A task force was created to develop action plans related to holding and caring for inpatients in the emergency department. A questionnaire was sent to nursing staff in spring 2012, and responses from the questionnaire identified improvements that could be implemented to improve care for inpatient boarders. Situation-background-assessment-recommendation (SBAR) communications and direct observations were also used to identify specific improvements. Post-questionnaire results indicated improved satisfaction for both staff and patients. It was recognized early that the ED inpatient area would benefit from the supervision of an inpatient director, managers, and staff. Outcomes showed that creating an inpatient unit within the emergency department had a positive effect on staff and patient satisfaction. Copyright © 2015 Emergency Nurses Association. Published by Elsevier Inc. All rights reserved.

Romantic Relationship Satisfaction Moderates the Etiology of Adult Personality.

PubMed

South, Susan C; Krueger, Robert F; Elkins, Irene J; Iacono, William G; McGue, Matt

2016-01-01

The heritability of major normative domains of personality is well-established, with approximately half the proportion of variance attributed to genetic differences. In the current study, we examine the possibility of gene × environment interaction (G×E) for adult personality using the environmental context of intimate romantic relationship functioning. Personality and relationship satisfaction are significantly correlated phenotypically, but to date no research has examined how the genetic and environmental components of variance for personality differ as a function of romantic relationship satisfaction. Given the importance of personality for myriad outcomes from work productivity to psychopathology, it is vital to identify variables present in adulthood that may affect the etiology of personality. In the current study, quantitative models of G×E were used to determine whether the genetic and environmental influences on personality differ as a function of relationship satisfaction. We drew from a sample of now-adult twins followed longitudinally from adolescence through age 29. All participants completed the Multidimensional Personality Questionnaire (MPQ) and an abbreviated version of the Dyadic Adjustment Scale. Biometric moderation was found for eight of the eleven MPQ scales examined: well-being, social potency, negative emotionality, alienation, aggression, constraint, traditionalism, and absorption. The pattern of findings differed, suggesting that the ways in which relationship quality moderates the etiology of personality may depend on the personality trait.

Attributes of nursing work environment as predictors of registered nurses' job satisfaction and intention to leave.

PubMed

Choi, Sandy Pin-Pin; Cheung, Kin; Pang, Samantha Mei-Che

2013-04-01

To examine how front-line registered nurses' perception of their work environment associates with and predicts nurse outcomes in terms of job satisfaction and turnover intention. Mounting evidence has pointed to an inseparable link between attributes of the nursing work environment and nurse outcomes. However, there is a paucity of research examining nurses' perception of their work environment beyond the Western context. This cross-sectional survey involved 1271 registered nurses working in 135 inpatient units in 10 public hospitals in Hong Kong. The instrument comprised items developed from in-depth interviews with front-line nurses that explored nurses' perception of their work environment. Factor analysis identified five dimensions (professionalism, co-worker relationship, management, staffing and resources, and ward practice) of the nursing work environment. Logistic regression analysis further identified professionalism, management and ward practice as significant factors in predicting nurses' turnover intention, and staffing and resources as an additional factor in predicting their job satisfaction. Attributes of the nursing work environment have a significant bearing on nurses' job satisfaction and intention to leave. Managerial effort should focus on improving nurses' work conditions through detailed resource planning, effective management and removal of work constraints that affect nursing practice. © 2012 Blackwell Publishing Ltd.

The Proposal of the Model for Developing Dispatch System for Nationwide One-Day Integrative Planning

NASA Astrophysics Data System (ADS)

Kim, Hyun Soo; Choi, Hyung Rim; Park, Byung Kwon; Jung, Jae Un; Lee, Jin Wook

The problems of dispatch planning for container truck are classified as the pickup and delivery problems, which are highly complex issues that consider various constraints in the real world. However, in case of the current situation, it is developed by the control system so that it requires the automated planning system under the view of nationwide integrative planning. Therefore, the purpose of this study is to suggest model to develop the automated dispatch system through the constraint satisfaction problem and meta-heuristic technique-based algorithm. In the further study, the practical system is developed and evaluation is performed in aspect of various results. This study suggests model to undergo the study which promoted the complexity of the problems by considering the various constraints which were not considered in the early study. However, it is suggested that it is necessary to add the study which includes the real-time monitoring function for vehicles and cargos based on the information technology.

The effectiveness of a simulated scenario to teach nursing students how to perform a bed bath: A randomized clinical trial.

PubMed

Miranda, Renata Pinto Ribeiro; de Cássia Lopes Chaves, Érika; Silva Lima, Rogério; Braga, Cristiane Giffoni; Simões, Ivandira Anselmo Ribeiro; Fava, Silvana Maria Coelho Leite; Iunes, Denise Hollanda

2017-10-01

Simulation allows students to develop several skills during a bed bath that are difficult to teach only in traditional classroom lectures, such as problem-solving, student interactions with the simulator (patient), reasoning in clinical evaluations, evaluation of responses to interventions, teamwork, communication, security and privacy. This study aimed to evaluate the effectiveness of a simulated bed bath scenario on improving cognitive knowledge, practical performance and satisfaction among nursing students. Randomized controlled clinical trial. Nursing students that were in the fifth period from two educational institutions in Brazil. Nursing students (n=58). The data were collected using the assessments of cognitive knowledge, practical performance and satisfaction were made through a written test about bed baths, an Objective Structured Clinical Examination (OSCE) and a satisfaction questionnaire. We identified that the acquisition and assimilation of cognitive knowledge was significantly higher in the simulation group (p=0.001). The performance was similar in both groups regardless of the teaching strategy (p=0.435). At follow-up, the simulation group had significantly more satisfaction with the teaching method than the control group (p=0.007). The teaching strategy based on a simulated scenario of a bed bath proved to be effective for the acquisition of cognitive knowledge regarding bed baths in clinical practice and improved student satisfaction with the teaching process. Copyright © 2017 Elsevier Ltd. All rights reserved.

Natural Aggregation Approach based Home Energy Manage System with User Satisfaction Modelling

NASA Astrophysics Data System (ADS)

Luo, F. J.; Ranzi, G.; Dong, Z. Y.; Murata, J.

2017-07-01

With the prevalence of advanced sensing and two-way communication technologies, Home Energy Management System (HEMS) has attracted lots of attentions in recent years. This paper proposes a HEMS that optimally schedules the controllable Residential Energy Resources (RERs) in a Time-of-Use (TOU) pricing and high solar power penetrated environment. The HEMS aims to minimize the overall operational cost of the home, and the user’s satisfactions and requirements on the operation of different household appliances are modelled and considered in the HEMS. Further, a new biological self-aggregation intelligence based optimization technique previously proposed by the authors, i.e., Natural Aggregation Algorithm (NAA), is applied to solve the proposed HEMS optimization model. Simulations are conducted to validate the proposed method.

Solving a bi-objective mathematical model for location-routing problem with time windows in multi-echelon reverse logistics using metaheuristic procedure

NASA Astrophysics Data System (ADS)

Ghezavati, V. R.; Beigi, M.

2016-12-01

During the last decade, the stringent pressures from environmental and social requirements have spurred an interest in designing a reverse logistics (RL) network. The success of a logistics system may depend on the decisions of the facilities locations and vehicle routings. The location-routing problem (LRP) simultaneously locates the facilities and designs the travel routes for vehicles among established facilities and existing demand points. In this paper, the location-routing problem with time window (LRPTW) and homogeneous fleet type and designing a multi-echelon, and capacitated reverse logistics network, are considered which may arise in many real-life situations in logistics management. Our proposed RL network consists of hybrid collection/inspection centers, recovery centers and disposal centers. Here, we present a new bi-objective mathematical programming (BOMP) for LRPTW in reverse logistic. Since this type of problem is NP-hard, the non-dominated sorting genetic algorithm II (NSGA-II) is proposed to obtain the Pareto frontier for the given problem. Several numerical examples are presented to illustrate the effectiveness of the proposed model and algorithm. Also, the present work is an effort to effectively implement the ɛ-constraint method in GAMS software for producing the Pareto-optimal solutions in a BOMP. The results of the proposed algorithm have been compared with the ɛ-constraint method. The computational results show that the ɛ-constraint method is able to solve small-size instances to optimality within reasonable computing times, and for medium-to-large-sized problems, the proposed NSGA-II works better than the ɛ-constraint.

Initial conditions of inhomogeneous universe and the cosmological constant problem

NASA Astrophysics Data System (ADS)

Totani, Tomonori

2016-06-01

Deriving the Einstein field equations (EFE) with matter fluid from the action principle is not straightforward, because mass conservation must be added as an additional constraint to make rest-frame mass density variable in reaction to metric variation. This can be avoided by introducing a constraint 0δ(√-g) = to metric variations δ gμν, and then the cosmological constant Λ emerges as an integration constant. This is a removal of one of the four constraints on initial conditions forced by EFE at the birth of the universe, and it may imply that EFE are unnecessarily restrictive about initial conditions. I then adopt a principle that the theory of gravity should be able to solve time evolution starting from arbitrary inhomogeneous initial conditions about spacetime and matter. The equations of gravitational fields satisfying this principle are obtained, by setting four auxiliary constraints on δ gμν to extract six degrees of freedom for gravity. The cost of achieving this is a loss of general covariance, but these equations constitute a consistent theory if they hold in the special coordinate systems that can be uniquely specified with respect to the initial space-like hypersurface when the universe was born. This theory predicts that gravity is described by EFE with non-zero Λ in a homogeneous patch of the universe created by inflation, but Λ changes continuously across different patches. Then both the smallness and coincidence problems of the cosmological constant are solved by the anthropic argument. This is just a result of inhomogeneous initial conditions, not requiring any change of the fundamental physical laws in different patches.

Path Following in the Exact Penalty Method of Convex Programming.

PubMed

Zhou, Hua; Lange, Kenneth

2015-07-01

Classical penalty methods solve a sequence of unconstrained problems that put greater and greater stress on meeting the constraints. In the limit as the penalty constant tends to ∞, one recovers the constrained solution. In the exact penalty method, squared penalties are replaced by absolute value penalties, and the solution is recovered for a finite value of the penalty constant. In practice, the kinks in the penalty and the unknown magnitude of the penalty constant prevent wide application of the exact penalty method in nonlinear programming. In this article, we examine a strategy of path following consistent with the exact penalty method. Instead of performing optimization at a single penalty constant, we trace the solution as a continuous function of the penalty constant. Thus, path following starts at the unconstrained solution and follows the solution path as the penalty constant increases. In the process, the solution path hits, slides along, and exits from the various constraints. For quadratic programming, the solution path is piecewise linear and takes large jumps from constraint to constraint. For a general convex program, the solution path is piecewise smooth, and path following operates by numerically solving an ordinary differential equation segment by segment. Our diverse applications to a) projection onto a convex set, b) nonnegative least squares, c) quadratically constrained quadratic programming, d) geometric programming, and e) semidefinite programming illustrate the mechanics and potential of path following. The final detour to image denoising demonstrates the relevance of path following to regularized estimation in inverse problems. In regularized estimation, one follows the solution path as the penalty constant decreases from a large value.

Path Following in the Exact Penalty Method of Convex Programming

PubMed Central

Zhou, Hua; Lange, Kenneth

2015-01-01

Classical penalty methods solve a sequence of unconstrained problems that put greater and greater stress on meeting the constraints. In the limit as the penalty constant tends to ∞, one recovers the constrained solution. In the exact penalty method, squared penalties are replaced by absolute value penalties, and the solution is recovered for a finite value of the penalty constant. In practice, the kinks in the penalty and the unknown magnitude of the penalty constant prevent wide application of the exact penalty method in nonlinear programming. In this article, we examine a strategy of path following consistent with the exact penalty method. Instead of performing optimization at a single penalty constant, we trace the solution as a continuous function of the penalty constant. Thus, path following starts at the unconstrained solution and follows the solution path as the penalty constant increases. In the process, the solution path hits, slides along, and exits from the various constraints. For quadratic programming, the solution path is piecewise linear and takes large jumps from constraint to constraint. For a general convex program, the solution path is piecewise smooth, and path following operates by numerically solving an ordinary differential equation segment by segment. Our diverse applications to a) projection onto a convex set, b) nonnegative least squares, c) quadratically constrained quadratic programming, d) geometric programming, and e) semidefinite programming illustrate the mechanics and potential of path following. The final detour to image denoising demonstrates the relevance of path following to regularized estimation in inverse problems. In regularized estimation, one follows the solution path as the penalty constant decreases from a large value. PMID:26366044

Solving Fractional Programming Problems based on Swarm Intelligence

NASA Astrophysics Data System (ADS)

Raouf, Osama Abdel; Hezam, Ibrahim M.

2014-04-01

This paper presents a new approach to solve Fractional Programming Problems (FPPs) based on two different Swarm Intelligence (SI) algorithms. The two algorithms are: Particle Swarm Optimization, and Firefly Algorithm. The two algorithms are tested using several FPP benchmark examples and two selected industrial applications. The test aims to prove the capability of the SI algorithms to solve any type of FPPs. The solution results employing the SI algorithms are compared with a number of exact and metaheuristic solution methods used for handling FPPs. Swarm Intelligence can be denoted as an effective technique for solving linear or nonlinear, non-differentiable fractional objective functions. Problems with an optimal solution at a finite point and an unbounded constraint set, can be solved using the proposed approach. Numerical examples are given to show the feasibility, effectiveness, and robustness of the proposed algorithm. The results obtained using the two SI algorithms revealed the superiority of the proposed technique among others in computational time. A better accuracy was remarkably observed in the solution results of the industrial application problems.

Directed Bee Colony Optimization Algorithm to Solve the Nurse Rostering Problem.

PubMed

Rajeswari, M; Amudhavel, J; Pothula, Sujatha; Dhavachelvan, P

2017-01-01

The Nurse Rostering Problem is an NP-hard combinatorial optimization, scheduling problem for assigning a set of nurses to shifts per day by considering both hard and soft constraints. A novel metaheuristic technique is required for solving Nurse Rostering Problem (NRP). This work proposes a metaheuristic technique called Directed Bee Colony Optimization Algorithm using the Modified Nelder-Mead Method for solving the NRP. To solve the NRP, the authors used a multiobjective mathematical programming model and proposed a methodology for the adaptation of a Multiobjective Directed Bee Colony Optimization (MODBCO). MODBCO is used successfully for solving the multiobjective problem of optimizing the scheduling problems. This MODBCO is an integration of deterministic local search, multiagent particle system environment, and honey bee decision-making process. The performance of the algorithm is assessed using the standard dataset INRC2010, and it reflects many real-world cases which vary in size and complexity. The experimental analysis uses statistical tools to show the uniqueness of the algorithm on assessment criteria.

Directed Bee Colony Optimization Algorithm to Solve the Nurse Rostering Problem

PubMed Central

Amudhavel, J.; Pothula, Sujatha; Dhavachelvan, P.

2017-01-01

The Nurse Rostering Problem is an NP-hard combinatorial optimization, scheduling problem for assigning a set of nurses to shifts per day by considering both hard and soft constraints. A novel metaheuristic technique is required for solving Nurse Rostering Problem (NRP). This work proposes a metaheuristic technique called Directed Bee Colony Optimization Algorithm using the Modified Nelder-Mead Method for solving the NRP. To solve the NRP, the authors used a multiobjective mathematical programming model and proposed a methodology for the adaptation of a Multiobjective Directed Bee Colony Optimization (MODBCO). MODBCO is used successfully for solving the multiobjective problem of optimizing the scheduling problems. This MODBCO is an integration of deterministic local search, multiagent particle system environment, and honey bee decision-making process. The performance of the algorithm is assessed using the standard dataset INRC2010, and it reflects many real-world cases which vary in size and complexity. The experimental analysis uses statistical tools to show the uniqueness of the algorithm on assessment criteria. PMID:28473849

Incorporation of physical constraints in optimal surface search for renal cortex segmentation

NASA Astrophysics Data System (ADS)

Li, Xiuli; Chen, Xinjian; Yao, Jianhua; Zhang, Xing; Tian, Jie

2012-02-01

In this paper, we propose a novel approach for multiple surfaces segmentation based on the incorporation of physical constraints in optimal surface searching. We apply our new approach to solve the renal cortex segmentation problem, an important but not sufficiently researched issue. In this study, in order to better restrain the intensity proximity of the renal cortex and renal column, we extend the optimal surface search approach to allow for varying sampling distance and physical separation constraints, instead of the traditional fixed sampling distance and numerical separation constraints. The sampling distance of each vertex-column is computed according to the sparsity of the local triangular mesh. Then the physical constraint learned from a priori renal cortex thickness is applied to the inter-surface arcs as the separation constraints. Appropriate varying sampling distance and separation constraints were learnt from 6 clinical CT images. After training, the proposed approach was tested on a test set of 10 images. The manual segmentation of renal cortex was used as the reference standard. Quantitative analysis of the segmented renal cortex indicates that overall segmentation accuracy was increased after introducing the varying sampling distance and physical separation constraints (the average true positive volume fraction (TPVF) and false positive volume fraction (FPVF) were 83.96% and 2.80%, respectively, by using varying sampling distance and physical separation constraints compared to 74.10% and 0.18%, respectively, by using fixed sampling distance and numerical separation constraints). The experimental results demonstrated the effectiveness of the proposed approach.

Problem-solving by traumatically brain injured and neurologically intact subjects on an adaptation of the twenty questions test.

PubMed

Marshall, Robert C; Karow, Colleen M; Morelli, Claudia A; Iden, Kristin K; Dixon, Judith

2003-07-01

RAPS (Rapid Assessment of Problem-Solving) is a clinical measure for assessing verbal problem-solving in hard-to-test patients or those that may not be able to tolerate a longer, more detailed assessment. The design of the test is based on Mosher and Hornsby's Twenty Question test, but RAPS contains several modifications to facilitate its use with brain-injured individuals. This study used RAPS to compare the verbal problem-solving ability of subjects that were neurologically intact and subjects that had chronic traumatic brain injuries. Twenty-one adults that were neurologically intact (NI) and 21 adults that had incurred a traumatic brain injury (TBI) matched for age, gender and education took part in the study. Before being tested with RAPS, participants signed an IRB-approved consent form and completed a battery of neurocognitive measures. RAPS entailed the solving of three verbal problems. Each problem involved an array of 32 pictures of common objects (e.g. football) arranged in a 4x 8 grid. The subjects were instructed to ask yes/no questions to determine which picture the examiner was 'thinking of '. Three scores were computed for each problem solved: number of questions asked, percentage of constraint-seeking questions, and question-asking efficiency scores for the first four questions. No learning effects across the problems were found for any of the RAPS measures. Scores were averaged across the three problems to determine group effects. Groups of TBI and NI subjects did not differ significantly in the number of questions asked in solving RAPS problems. Members of the NI group asked significantly more constraint-seeking questions (e.g. Is it an animal?) than those in the TBI group, and the subjects that had incurred brain injuries did more guessing than the NI group. Over 70% of the time, guessing took place after the semantic category containing the target picture was known to the subject. Guesses took the form of pseudo-constraint questions (e.g. Is it the animal with a long neck?) rather than frank guesses (e.g. Is it the giraffe?). These trends were seen for both groups. Question-asking efficiency scores, computed for the first four questions of each problem, reflected the amount of information gained by the subjects' questions. It was anticipated that subjects' questioning strategies would target larger rather than smaller number of pictures and systematically reduce the number of total pictures under consideration. Question-asking efficiency scores were significantly higher for the group of NI subjects. Both groups increased question-asking efficiency scores across the first four questions, and there was no significant group x question interaction. Further analysis of the question-asking efficiency scores revealed that questions from the group of NI subjects tended to target multiple categories of pictures and larger single semantic categories of pictures on the 32-item problem-solving board, whereas those from the group of TBI subjects often targeted smaller categories or portions of categories. Two meta-cognitive functions, planning and strategy shifting, appeared to explain most of the differences in the verbal problem-solving performance between the groups. Both groups, however, demonstrated a range of abilities on RAPS. Until a larger normative database for RAPS is available, it behooves clinicians using the test to analyse results on an individual basis, to consider the subject's pre-morbid problem-solving ability and to weigh those factors associated with brain injury that could affect RAPS performance.

Artificial intelligence in robot control systems

NASA Astrophysics Data System (ADS)

Korikov, A.

2018-05-01

This paper analyzes modern concepts of artificial intelligence and known definitions of the term "level of intelligence". In robotics artificial intelligence system is defined as a system that works intelligently and optimally. The author proposes to use optimization methods for the design of intelligent robot control systems. The article provides the formalization of problems of robotic control system design, as a class of extremum problems with constraints. Solving these problems is rather complicated due to the high dimensionality, polymodality and a priori uncertainty. Decomposition of the extremum problems according to the method, suggested by the author, allows reducing them into a sequence of simpler problems, that can be successfully solved by modern computing technology. Several possible approaches to solving such problems are considered in the article.

Impact of Relational Coordination on Nurse Job Satisfaction, Work Engagement and Burnout: Achieving the Quadruple Aim.

PubMed

Havens, Donna Sullivan; Gittell, Jody Hoffer; Vasey, Joseph

2018-03-01

To explore how relational coordination, known to enhance quality and efficiency outcomes for patients and hospitals, impacts direct care nurse outcomes such as burnout, work engagement, and job satisfaction, addressing the "Quadruple Aim," to improve the experience of providing care. Hospitals are complex organizations in which multiple providers work interdependently, under conditions of uncertainty and time constraints, to deliver safe quality care despite differences in specialization, training, and status. Relational coordination-communicating and relating for the purpose of task integration-is known to improve quality, safety, and efficiency under these conditions, but less is known about its impact on the well-being of direct care providers themselves. Surveys measuring relational coordination among nurses and other types of providers as well as job-related outcomes in 5 acute care community hospitals were completed by direct care RNs. Relational coordination was significantly related to increased job satisfaction, increased work engagement, and reduced burnout. Relational coordination contributes to the well-being of direct care nurses, addressing the Quadruple Aim by improving the experience of providing care.

Designing under Constraints: Cell Phone Case Design Challenge

ERIC Educational Resources Information Center

Sutton, Kevin; Grubbs, Michael E.; Ernst, Jeremy

2014-01-01

Engineering design has been suggested as a viable instructional approach for Technology Education (TE) to intentionally provide students the opportunity to apply multidisciplinary concepts to solve ill-defined design challenges (Wells & Ernst, 2012; Sanders & Wells, 2010; Wicklein, 2006). Currently, the context for design challenges in TE…

Global asymptotic stabilisation of rational dynamical systems based on solving BMI

NASA Astrophysics Data System (ADS)

Esmaili, Farhad; Kamyad, A. V.; Jahed-Motlagh, Mohammad Reza; Pariz, Naser

2017-08-01

In this paper, the global asymptotic stabiliser design of rational systems is studied in detail. To develop the idea, the state equations of the system are transformed to a new coordinate via polynomial transformation and the state feedback control law. This in turn is followed by the satisfaction of the linear growth condition (i.e. Lipschitz at zero). Based on a linear matrix inequality solution, the system in the new coordinate is globally asymptotically stabilised and then, leading to the global asymptotic stabilisation of the primary system. The polynomial transformation coefficients are derived by solving the bilinear matrix inequality problem. To confirm the capability of this method, three examples are highlighted.

Solving Assembly Sequence Planning using Angle Modulated Simulated Kalman Filter

NASA Astrophysics Data System (ADS)

Mustapa, Ainizar; Yusof, Zulkifli Md.; Adam, Asrul; Muhammad, Badaruddin; Ibrahim, Zuwairie

2018-03-01

This paper presents an implementation of Simulated Kalman Filter (SKF) algorithm for optimizing an Assembly Sequence Planning (ASP) problem. The SKF search strategy contains three simple steps; predict-measure-estimate. The main objective of the ASP is to determine the sequence of component installation to shorten assembly time or save assembly costs. Initially, permutation sequence is generated to represent each agent. Each agent is then subjected to a precedence matrix constraint to produce feasible assembly sequence. Next, the Angle Modulated SKF (AMSKF) is proposed for solving ASP problem. The main idea of the angle modulated approach in solving combinatorial optimization problem is to use a function, g(x), to create a continuous signal. The performance of the proposed AMSKF is compared against previous works in solving ASP by applying BGSA, BPSO, and MSPSO. Using a case study of ASP, the results show that AMSKF outperformed all the algorithms in obtaining the best solution.

To Stay or Not to Stay: Non-Combatant Evacuation Operations and Their Impact on Host Nation/Regional Stability

DTIC Science & Technology

2014-06-13

School in partial satisfaction of the requirements of a Master of Science Degree in Joint Campaign Planning and Strategy. The contents of this...conflicted claims. Japan, Taiwan, Vietnam, Malaysia , Philippines and Brunei have continued to assert their territorial claims which have resulted in...In the United States, budget constraints and sequestration avoidance remain a concern as the administration and Congress manage the budget

Service score segmentation of diverse populations to improve patient and physician satisfaction- a multicase quality improvement study.

PubMed

Newhouse, David

2009-01-01

The changing demographics in the country require new strategies for providing culturally competent care. The Northern California Region Member Patient Survey provides detailed information for the clinician when the data is segmented into subsets by age, gender, and race/ethnicity. Any gaps identified allow for the clinician to focus on key areas for improvement in an efficient manner respecting the time constraints of a busy practice.

Finer Distinctions: Variability in Satisfied Older Couples' Problem-Solving Behaviors.

PubMed

Rauer, Amy; Williams, Leah; Jensen, Jakob

2017-06-01

This study utilized observational and self-report data from 64 maritally satisfied and stable older couples to explore if there were meaningful differences in how couples approached marital disagreements. Using a typology approach to classify couples based on their behaviors in a 15-minute problem-solving interaction, findings revealed four types of couples: (1) problem solvers (characterized by both spouses' higher problem-solving skills and warmth), (2) supporters (characterized by both spouses' notable warmth), (3) even couples (characterized by both spouses' moderate problem-solving skills and warmth), and (4) cool couples (characterized by both spouses' greater negativity and lower problem-solving skills and warmth). Despite the differences in these behaviors, all couples had relatively high marital satisfaction and functioning. However, across nearly all indices, spouses in the cool couple cluster reported poorer marital functioning, particularly when compared to the problem solvers and supporters. These findings suggest that even modest doses of negativity (e.g., eye roll) may be problematic for some satisfied couples later in life. The implications of these typologies are discussed as they pertain to practitioners' efforts to tailor their approaches to a wider swath of the population. © 2015 Family Process Institute.

Performance of subjects with and without severe mental illness on a clinical test of problem solving.

PubMed

Marshall, R C; McGurk, S R; Karow, C M; Kairy, T J; Flashman, L A

2006-06-01

Severe mental illness is associated with impairments in executive functions, such as conceptual reasoning, planning, and strategic thinking all of which impact problem solving. The present study examined the utility of a novel assessment tool for problem solving, the Rapid Assessment of Problem Solving Test (RAPS) in persons with severe mental illness. Subjects were 47 outpatients with severe mental illness and an equal number healthy controls matched for age and gender. Results confirmed all hypotheses with respect to how subjects with severe mental illness would perform on the RAPS. Specifically, the severely mentally ill subjects (1) solved fewer problems on the RAPS, (2) when they did solve problems on the test, they did so far less efficiently than their healthy counterparts, and (3) the two groups differed markedly in the types of questions asked on the RAPS. The healthy control subjects tended to take a systematic, organized, but not always optimal approach to solving problems on the RAPS. The subjects with severe mental illness used some of the problem solving strategies of the healthy controls, but their performance was less consistent and tended to deteriorate when the complexity of the problem solving task increased. This was reflected by a high degree of guessing in lieu of asking constraint questions, particularly if a category-limited question was insufficient to continue the problem solving effort.

ISACC in Operations

DTIC Science & Technology

2014-06-01

19th floor of a Hotel to overlook the entire event. Page 12 of 17 Figure 6: The SPF Operations Centre overlooking the Event Lessons...have a cheap system to help them solve their immediate operational needs. b. Medium enterprises, that need to have quick customization of the...Optimize” tools to help them advance their current operations to a higher service satisfaction level seen by the public. 32. Common across all

Optimization of flexible wing structures subject to strength and induced drag constraints

NASA Technical Reports Server (NTRS)

Haftka, R. T.

1977-01-01

An optimization procedure for designing wing structures subject to stress, strain, and drag constraints is presented. The optimization method utilizes an extended penalty function formulation for converting the constrained problem into a series of unconstrained ones. Newton's method is used to solve the unconstrained problems. An iterative analysis procedure is used to obtain the displacements of the wing structure including the effects of load redistribution due to the flexibility of the structure. The induced drag is calculated from the lift distribution. Approximate expressions for the constraints used during major portions of the optimization process enhance the efficiency of the procedure. A typical fighter wing is used to demonstrate the procedure. Aluminum and composite material designs are obtained. The tradeoff between weight savings and drag reduction is investigated.

Input and output constraints-based stabilisation of switched nonlinear systems with unstable subsystems and its application

NASA Astrophysics Data System (ADS)

Chen, Chao; Liu, Qian; Zhao, Jun

2018-01-01

This paper studies the problem of stabilisation of switched nonlinear systems with output and input constraints. We propose a recursive approach to solve this issue. None of the subsystems are assumed to be stablisable while the switched system is stabilised by dual design of controllers for subsystems and a switching law. When only dealing with bounded input, we provide nested switching controllers using an extended backstepping procedure. If both input and output constraints are taken into consideration, a Barrier Lyapunov Function is employed during operation to construct multiple Lyapunov functions for switched nonlinear system in the backstepping procedure. As a practical example, the control design of an equilibrium manifold expansion model of aero-engine is given to demonstrate the effectiveness of the proposed design method.

Distribution-Agnostic Stochastic Optimal Power Flow for Distribution Grids: Preprint

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

Baker, Kyri; Dall'Anese, Emiliano; Summers, Tyler

2016-09-01

This paper outlines a data-driven, distributionally robust approach to solve chance-constrained AC optimal power flow problems in distribution networks. Uncertain forecasts for loads and power generated by photovoltaic (PV) systems are considered, with the goal of minimizing PV curtailment while meeting power flow and voltage regulation constraints. A data- driven approach is utilized to develop a distributionally robust conservative convex approximation of the chance-constraints; particularly, the mean and covariance matrix of the forecast errors are updated online, and leveraged to enforce voltage regulation with predetermined probability via Chebyshev-based bounds. By combining an accurate linear approximation of the AC power flowmore » equations with the distributionally robust chance constraint reformulation, the resulting optimization problem becomes convex and computationally tractable.« less

Exploring the Tomlin-Varadarajan quantum constraints in U (1 )3 loop quantum gravity: Solutions and the Minkowski theorem

NASA Astrophysics Data System (ADS)

Lewandowski, Jerzy; Lin, Chun-Yen

2017-03-01

We explicitly solved the anomaly-free quantum constraints proposed by Tomlin and Varadarajan for the weak Euclidean model of canonical loop quantum gravity, in a large subspace of the model's kinematic Hilbert space, which is the space of the charge network states. In doing so, we first identified the subspace on which each of the constraints acts convergingly, and then by explicitly evaluating such actions we found the complete set of the solutions in the identified subspace. We showed that the space of solutions consists of two classes of states, with the first class having a property that involves the condition known from the Minkowski theorem on polyhedra, and the second class satisfying a weaker form of the spatial diffeomorphism invariance.

Coupling reconstruction and motion estimation for dynamic MRI through optical flow constraint

NASA Astrophysics Data System (ADS)

Zhao, Ningning; O'Connor, Daniel; Gu, Wenbo; Ruan, Dan; Basarab, Adrian; Sheng, Ke

2018-03-01

This paper addresses the problem of dynamic magnetic resonance image (DMRI) reconstruction and motion estimation jointly. Because of the inherent anatomical movements in DMRI acquisition, reconstruction of DMRI using motion estimation/compensation (ME/MC) has been explored under the compressed sensing (CS) scheme. In this paper, by embedding the intensity based optical flow (OF) constraint into the traditional CS scheme, we are able to couple the DMRI reconstruction and motion vector estimation. Moreover, the OF constraint is employed in a specific coarse resolution scale in order to reduce the computational complexity. The resulting optimization problem is then solved using a primal-dual algorithm due to its efficiency when dealing with nondifferentiable problems. Experiments on highly accelerated dynamic cardiac MRI with multiple receiver coils validate the performance of the proposed algorithm.

Constraints on the depth and geometry of the magma chamber of the Olympus Mons Volcano, Mars

NASA Technical Reports Server (NTRS)

Zuber, Maria T.; Mouginis-Mark, Peter J.

1990-01-01

The summit caldera of the Olympus Mons volcano exhibits one of the clearest examples of tectonic processes associated with shield volcanism on Mars. The radial distance from the center of the transition from concentric ridges to concentric graben within the oldest crater provides a constraint on the geometry and depth of the subsurface magmatic reservoir at the time of subsidence. Here, researchers use this constraint to investigate the size, shape, and depth of the reservoir. Their approach consists of calculating radial surface stresses corresponding to the range of subsurface pressure distributions representing an evacuating magma chamber. They then compare stress patterns to the observed radial positions of concentric ridges and graben. The problem is solved by employing the finite element approach using the program TECTON.

Safety analysis of discrete event systems using a simplified Petri net controller.

PubMed

Zareiee, Meysam; Dideban, Abbas; Asghar Orouji, Ali

2014-01-01

This paper deals with the problem of forbidden states in discrete event systems based on Petri net models. So, a method is presented to prevent the system from entering these states by constructing a small number of generalized mutual exclusion constraints. This goal is achieved by solving three types of Integer Linear Programming problems. The problems are designed to verify the constraints that some of them are related to verifying authorized states and the others are related to avoiding forbidden states. The obtained constraints can be enforced on the system using a small number of control places. Moreover, the number of arcs related to these places is small, and the controller after connecting them is maximally permissive. © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

A methodology for constraining power in finite element modeling of radiofrequency ablation.

PubMed

Jiang, Yansheng; Possebon, Ricardo; Mulier, Stefaan; Wang, Chong; Chen, Feng; Feng, Yuanbo; Xia, Qian; Liu, Yewei; Yin, Ting; Oyen, Raymond; Ni, Yicheng

2017-07-01

Radiofrequency ablation (RFA) is a minimally invasive thermal therapy for the treatment of cancer, hyperopia, and cardiac tachyarrhythmia. In RFA, the power delivered to the tissue is a key parameter. The objective of this study was to establish a methodology for the finite element modeling of RFA with constant power. Because of changes in the electric conductivity of tissue with temperature, a nonconventional boundary value problem arises in the mathematic modeling of RFA: neither the voltage (Dirichlet condition) nor the current (Neumann condition), but the power, that is, the product of voltage and current was prescribed on part of boundary. We solved the problem using Lagrange multiplier: the product of the voltage and current on the electrode surface is constrained to be equal to the Joule heating. We theoretically proved the equality between the product of the voltage and current on the surface of the electrode and the Joule heating in the domain. We also proved the well-posedness of the problem of solving the Laplace equation for the electric potential under a constant power constraint prescribed on the electrode surface. The Pennes bioheat transfer equation and the Laplace equation for electric potential augmented with the constraint of constant power were solved simultaneously using the Newton-Raphson algorithm. Three problems for validation were solved. Numerical results were compared either with an analytical solution deduced in this study or with results obtained by ANSYS or experiments. This work provides the finite element modeling of constant power RFA with a firm mathematical basis and opens pathway for achieving the optimal RFA power. Copyright © 2016 John Wiley & Sons, Ltd.

Linearized motion estimation for articulated planes.

PubMed

Datta, Ankur; Sheikh, Yaser; Kanade, Takeo

2011-04-01

In this paper, we describe the explicit application of articulation constraints for estimating the motion of a system of articulated planes. We relate articulations to the relative homography between planes and show that these articulations translate into linearized equality constraints on a linear least-squares system, which can be solved efficiently using a Karush-Kuhn-Tucker system. The articulation constraints can be applied for both gradient-based and feature-based motion estimation algorithms and to illustrate this, we describe a gradient-based motion estimation algorithm for an affine camera and a feature-based motion estimation algorithm for a projective camera that explicitly enforces articulation constraints. We show that explicit application of articulation constraints leads to numerically stable estimates of motion. The simultaneous computation of motion estimates for all of the articulated planes in a scene allows us to handle scene areas where there is limited texture information and areas that leave the field of view. Our results demonstrate the wide applicability of the algorithm in a variety of challenging real-world cases such as human body tracking, motion estimation of rigid, piecewise planar scenes, and motion estimation of triangulated meshes.

Firefly algorithm for cardinality constrained mean-variance portfolio optimization problem with entropy diversity constraint.

PubMed

Bacanin, Nebojsa; Tuba, Milan

2014-01-01

Portfolio optimization (selection) problem is an important and hard optimization problem that, with the addition of necessary realistic constraints, becomes computationally intractable. Nature-inspired metaheuristics are appropriate for solving such problems; however, literature review shows that there are very few applications of nature-inspired metaheuristics to portfolio optimization problem. This is especially true for swarm intelligence algorithms which represent the newer branch of nature-inspired algorithms. No application of any swarm intelligence metaheuristics to cardinality constrained mean-variance (CCMV) portfolio problem with entropy constraint was found in the literature. This paper introduces modified firefly algorithm (FA) for the CCMV portfolio model with entropy constraint. Firefly algorithm is one of the latest, very successful swarm intelligence algorithm; however, it exhibits some deficiencies when applied to constrained problems. To overcome lack of exploration power during early iterations, we modified the algorithm and tested it on standard portfolio benchmark data sets used in the literature. Our proposed modified firefly algorithm proved to be better than other state-of-the-art algorithms, while introduction of entropy diversity constraint further improved results.

Firefly Algorithm for Cardinality Constrained Mean-Variance Portfolio Optimization Problem with Entropy Diversity Constraint

PubMed Central

2014-01-01

Portfolio optimization (selection) problem is an important and hard optimization problem that, with the addition of necessary realistic constraints, becomes computationally intractable. Nature-inspired metaheuristics are appropriate for solving such problems; however, literature review shows that there are very few applications of nature-inspired metaheuristics to portfolio optimization problem. This is especially true for swarm intelligence algorithms which represent the newer branch of nature-inspired algorithms. No application of any swarm intelligence metaheuristics to cardinality constrained mean-variance (CCMV) portfolio problem with entropy constraint was found in the literature. This paper introduces modified firefly algorithm (FA) for the CCMV portfolio model with entropy constraint. Firefly algorithm is one of the latest, very successful swarm intelligence algorithm; however, it exhibits some deficiencies when applied to constrained problems. To overcome lack of exploration power during early iterations, we modified the algorithm and tested it on standard portfolio benchmark data sets used in the literature. Our proposed modified firefly algorithm proved to be better than other state-of-the-art algorithms, while introduction of entropy diversity constraint further improved results. PMID:24991645

Declarative Programming with Temporal Constraints, in the Language CG.

PubMed

Negreanu, Lorina

2015-01-01

Specifying and interpreting temporal constraints are key elements of knowledge representation and reasoning, with applications in temporal databases, agent programming, and ambient intelligence. We present and formally characterize the language CG, which tackles this issue. In CG, users are able to develop time-dependent programs, in a flexible and straightforward manner. Such programs can, in turn, be coupled with evolving environments, thus empowering users to control the environment's evolution. CG relies on a structure for storing temporal information, together with a dedicated query mechanism. Hence, we explore the computational complexity of our query satisfaction problem. We discuss previous implementation attempts of CG and introduce a novel prototype which relies on logic programming. Finally, we address the issue of consistency and correctness of CG program execution, using the Event-B modeling approach.

Aperiodic Robust Model Predictive Control for Constrained Continuous-Time Nonlinear Systems: An Event-Triggered Approach.

PubMed

Liu, Changxin; Gao, Jian; Li, Huiping; Xu, Demin

2018-05-01

The event-triggered control is a promising solution to cyber-physical systems, such as networked control systems, multiagent systems, and large-scale intelligent systems. In this paper, we propose an event-triggered model predictive control (MPC) scheme for constrained continuous-time nonlinear systems with bounded disturbances. First, a time-varying tightened state constraint is computed to achieve robust constraint satisfaction, and an event-triggered scheduling strategy is designed in the framework of dual-mode MPC. Second, the sufficient conditions for ensuring feasibility and closed-loop robust stability are developed, respectively. We show that robust stability can be ensured and communication load can be reduced with the proposed MPC algorithm. Finally, numerical simulations and comparison studies are performed to verify the theoretical results.

Planning and scheduling the Hubble Space Telescope: Practical application of advanced techniques

NASA Technical Reports Server (NTRS)

Miller, Glenn E.

1994-01-01

NASA's Hubble Space Telescope (HST) is a major astronomical facility that was launched in April, 1990. In late 1993, the first of several planned servicing missions refurbished the telescope, including corrections for a manufacturing flaw in the primary mirror. Orbiting above the distorting effects of the Earth's atmosphere, the HST provides an unrivaled combination of sensitivity, spectral coverage and angular resolution. The HST is arguably the most complex scientific observatory ever constructed and effective use of this valuable resource required novel approaches to astronomical observation and the development of advanced software systems including techniques to represent scheduling preferences and constraints, a constraint satisfaction problem (CSP) based scheduler and a rule based planning system. This paper presents a discussion of these systems and the lessons learned from operational experience.

Fuzzy Multi-Objective Vendor Selection Problem with Modified S-CURVE Membership Function

NASA Astrophysics Data System (ADS)

Díaz-Madroñero, Manuel; Peidro, David; Vasant, Pandian

2010-06-01

In this paper, the S-Curve membership function methodology is used in a vendor selection (VS) problem. An interactive method for solving multi-objective VS problems with fuzzy goals is developed. The proposed method attempts simultaneously to minimize the total order costs, the number of rejected items and the number of late delivered items with reference to several constraints such as meeting buyers' demand, vendors' capacity, vendors' quota flexibility, vendors' allocated budget, etc. We compare in an industrial case the performance of S-curve membership functions, representing uncertainty goals and constraints in VS problems, with linear membership functions.

Design optimization of continuous partially prestressed concrete beams

NASA Astrophysics Data System (ADS)

Al-Gahtani, A. S.; Al-Saadoun, S. S.; Abul-Feilat, E. A.

1995-04-01

An effective formulation for optimum design of two-span continuous partially prestressed concrete beams is described in this paper. Variable prestressing forces along the tendon profile, which may be jacked from one end or both ends with flexibility in the overlapping range and location, and the induced secondary effects are considered. The imposed constraints are on flexural stresses, ultimate flexural strength, cracking moment, ultimate shear strength, reinforcement limits cross-section dimensions, and cable profile geometries. These constraints are formulated in accordance with ACI (American Concrete Institute) code provisions. The capabilities of the program to solve several engineering problems are presented.

Black holes and local dark matter

NASA Technical Reports Server (NTRS)

Hegyi, D. J.; Kolb, E. W.; Olive, K. A.

1986-01-01

Two independent constraints are placed on the amount of dark matter in black holes contained in the galactic disk. First, gas accretion by black holes leads to X-ray emission which cannot exceed the observed soft X-ray background. Second, metals produced in stellar processes that lead to black hole formation cannot exceed the observed disk metal abundance. Based on these constraints, it appears unlikely that the missing disk mass could be contained in black holes. A consequence of this conclusion is that at least two different types of dark matter are needed to solve the various missing mass problems.

A Constraint-Based Planner for Data Production

NASA Technical Reports Server (NTRS)

Pang, Wanlin; Golden, Keith

2005-01-01

This paper presents a graph-based backtracking algorithm designed to support constrain-tbased planning in data production domains. This algorithm performs backtracking at two nested levels: the outer- backtracking following the structure of the planning graph to select planner subgoals and actions to achieve them and the inner-backtracking inside a subproblem associated with a selected action to find action parameter values. We show this algorithm works well in a planner applied to automating data production in an ecological forecasting system. We also discuss how the idea of multi-level backtracking may improve efficiency of solving semi-structured constraint problems.

Early Phonological and Lexical Development and Otitis Media: A Diary Study.

ERIC Educational Resources Information Center

Donahue, Mavis L.

1993-01-01

A child with chronic otitis media with effusion solved the problem of reduced and fluctuating auditory input with phonological selection and avoidance strategies that capitalized on prosodic cues. Findings illustrate the need to consider interactions among performance, input, and linguistic constraints to explain individual variation in language…

[Relevance between expectations before treatment, new symptoms and satisfaction after treatment in patients with pelvic organ prolapse].

PubMed

Wang, Yu; Han, Jinsong; Zhang, Kun; Zhu, Fuli; Yang, Junfang; Wang, Yiting

2015-09-01

To investigate the relevance between expectations before treatment, new symptoms and satisfaction after treatment of the pelvic organ prolapse (POP) patients. Made a collection of 75 cases of POP patients at Peking University Third Hospital, who were affected by the POP symptoms and came to our clinic for treatment from January to December in 2013. Prospectively investigate the patients' expectations before treatment, which were the most troubling symptoms to be solved. According to treatment we divided the patients into surgery and pessary groups. Two groups were followed up with the degree to achieve the desired goals using patient global impression of improvement (PGI-I), new symptoms and satisfaction after treatment, try to find the relevance between expectations before treatment, new symptoms and satisfaction after treatment. There were 47 (63%, 47/75) patients in the surgical group and 28 (37%, 28/75) patients in the pessary group. The top three problems for patients were friction when walking (25%, 19/75), dysuria (23%, 17/75) and the feeling of vaginal prolapse (19%, 14/75). The follow-up rate was of 93% (70/75), follow-up time was (5 ± 4) months. Satisfaction score after treatment of surgical group was higher than that of pessary group [(4.9 ± 0.4) versus (4.0 ± 1.3) scores, P < 0.01]. There was no statistically significant difference between two groups of PGI-I score [(6.7 ± 0.6) versus (6.6 ± 0.9) scores, P = 0.886]. The top three new symptoms after treatment were increased secretion, urinary incontinence and dysuria. PGI-I and satisfaction scores was relevant (P = 0.021). The availability of new symptoms and satisfaction scores was relevant (P = 0.001). When achieving higher expectations to the treatment and no more new symptoms, the satisfaction score after treatment is higher.

SU-F-T-340: Direct Editing of Dose Volume Histograms: Algorithms and a Unified Convex Formulation for Treatment Planning with Dose Constraints

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

Ungun, B; Stanford University School of Medicine, Stanford, CA; Fu, A

2016-06-15

Purpose: To develop a procedure for including dose constraints in convex programming-based approaches to treatment planning, and to support dynamic modification of such constraints during planning. Methods: We present a mathematical approach that allows mean dose, maximum dose, minimum dose and dose volume (i.e., percentile) constraints to be appended to any convex formulation of an inverse planning problem. The first three constraint types are convex and readily incorporated. Dose volume constraints are not convex, however, so we introduce a convex restriction that is related to CVaR-based approaches previously proposed in the literature. To compensate for the conservatism of this restriction,more » we propose a new two-pass algorithm that solves the restricted problem on a first pass and uses this solution to form exact constraints on a second pass. In another variant, we introduce slack variables for each dose constraint to prevent the problem from becoming infeasible when the user specifies an incompatible set of constraints. We implement the proposed methods in Python using the convex programming package cvxpy in conjunction with the open source convex solvers SCS and ECOS. Results: We show, for several cases taken from the clinic, that our proposed method meets specified constraints (often with margin) when they are feasible. Constraints are met exactly when we use the two-pass method, and infeasible constraints are replaced with the nearest feasible constraint when slacks are used. Finally, we introduce ConRad, a Python-embedded free software package for convex radiation therapy planning. ConRad implements the methods described above and offers a simple interface for specifying prescriptions and dose constraints. Conclusion: This work demonstrates the feasibility of using modifiable dose constraints in a convex formulation, making it practical to guide the treatment planning process with interactively specified dose constraints. This work was supported by the Stanford BioX Graduate Fellowship and NIH Grant 5R01CA176553.« less

Efficient solvers for coupled models in respiratory mechanics.

PubMed

Verdugo, Francesc; Roth, Christian J; Yoshihara, Lena; Wall, Wolfgang A

2017-02-01

We present efficient preconditioners for one of the most physiologically relevant pulmonary models currently available. Our underlying motivation is to enable the efficient simulation of such a lung model on high-performance computing platforms in order to assess mechanical ventilation strategies and contributing to design more protective patient-specific ventilation treatments. The system of linear equations to be solved using the proposed preconditioners is essentially the monolithic system arising in fluid-structure interaction (FSI) extended by additional algebraic constraints. The introduction of these constraints leads to a saddle point problem that cannot be solved with usual FSI preconditioners available in the literature. The key ingredient in this work is to use the idea of the semi-implicit method for pressure-linked equations (SIMPLE) for getting rid of the saddle point structure, resulting in a standard FSI problem that can be treated with available techniques. The numerical examples show that the resulting preconditioners approach the optimal performance of multigrid methods, even though the lung model is a complex multiphysics problem. Moreover, the preconditioners are robust enough to deal with physiologically relevant simulations involving complex real-world patient-specific lung geometries. The same approach is applicable to other challenging biomedical applications where coupling between flow and tissue deformations is modeled with additional algebraic constraints. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

An Algorithm for the Mixed Transportation Network Design Problem

PubMed Central

Liu, Xinyu; Chen, Qun

2016-01-01

This paper proposes an optimization algorithm, the dimension-down iterative algorithm (DDIA), for solving a mixed transportation network design problem (MNDP), which is generally expressed as a mathematical programming with equilibrium constraint (MPEC). The upper level of the MNDP aims to optimize the network performance via both the expansion of the existing links and the addition of new candidate links, whereas the lower level is a traditional Wardrop user equilibrium (UE) problem. The idea of the proposed solution algorithm (DDIA) is to reduce the dimensions of the problem. A group of variables (discrete/continuous) is fixed to optimize another group of variables (continuous/discrete) alternately; then, the problem is transformed into solving a series of CNDPs (continuous network design problems) and DNDPs (discrete network design problems) repeatedly until the problem converges to the optimal solution. The advantage of the proposed algorithm is that its solution process is very simple and easy to apply. Numerical examples show that for the MNDP without budget constraint, the optimal solution can be found within a few iterations with DDIA. For the MNDP with budget constraint, however, the result depends on the selection of initial values, which leads to different optimal solutions (i.e., different local optimal solutions). Some thoughts are given on how to derive meaningful initial values, such as by considering the budgets of new and reconstruction projects separately. PMID:27626803

Solution of underdetermined systems of equations with gridded a priori constraints.

PubMed

Stiros, Stathis C; Saltogianni, Vasso

2014-01-01

The TOPINV, Topological Inversion algorithm (or TGS, Topological Grid Search) initially developed for the inversion of highly non-linear redundant systems of equations, can solve a wide range of underdetermined systems of non-linear equations. This approach is a generalization of a previous conclusion that this algorithm can be used for the solution of certain integer ambiguity problems in Geodesy. The overall approach is based on additional (a priori) information for the unknown variables. In the past, such information was used either to linearize equations around approximate solutions, or to expand systems of observation equations solved on the basis of generalized inverses. In the proposed algorithm, the a priori additional information is used in a third way, as topological constraints to the unknown n variables, leading to an R(n) grid containing an approximation of the real solution. The TOPINV algorithm does not focus on point-solutions, but exploits the structural and topological constraints in each system of underdetermined equations in order to identify an optimal closed space in the R(n) containing the real solution. The centre of gravity of the grid points defining this space corresponds to global, minimum-norm solutions. The rationale and validity of the overall approach are demonstrated on the basis of examples and case studies, including fault modelling, in comparison with SVD solutions and true (reference) values, in an accuracy-oriented approach.

Geochemical mole-balance modeling with uncertain data

USGS Publications Warehouse

Parkhurst, David L.

1997-01-01

Geochemical mole-balance models are sets of chemical reactions that quantitatively account for changes in the chemical and isotopic composition of water along a flow path. A revised mole-balance formulation that includes an uncertainty term for each chemical and isotopic datum is derived. The revised formulation is comprised of mole-balance equations for each element or element redox state, alkalinity, electrons, solvent water, and each isotope; a charge-balance equation and an equation that relates the uncertainty terms for pH, alkalinity, and total dissolved inorganic carbon for each aqueous solution; inequality constraints on the size of the uncertainty terms; and inequality constraints on the sign of the mole transfer of reactants. The equations and inequality constraints are solved by a modification of the simplex algorithm combined with an exhaustive search for unique combinations of aqueous solutions and reactants for which the equations and inequality constraints can be solved and the uncertainty terms minimized. Additional algorithms find only the simplest mole-balance models and determine the ranges of mixing fractions for each solution and mole transfers for each reactant that are consistent with specified limits on the uncertainty terms. The revised formulation produces simpler and more robust mole-balance models and allows the significance of mixing fractions and mole transfers to be evaluated. In an example from the central Oklahoma aquifer, inclusion of up to 5% uncertainty in the chemical data can reduce the number of reactants in mole-balance models from seven or more to as few as three, these being cation exchange, dolomite dissolution, and silica precipitation. In another example from the Madison aquifer, inclusion of the charge-balance constraint requires significant increases in the mole transfers of calcite, dolomite, and organic matter, which reduce the estimated maximum carbon 14 age of the sample by about 10,000 years, from 22,700 years to 12,600 years.

Work hours and turnover intention among hospital physicians in Taiwan: does income matter?

PubMed

Tsai, Yu-Hsuan; Huang, Nicole; Chien, Li-Yin; Chiang, Jen-Huai; Chiou, Shu-Ti

2016-11-21

Physician shortage has become an urgent and critical challenge to many countries. According to the workforce dynamic model, long work hours may be one major pressure point to the attrition of physicians. Financial incentive is a common tool to human power retention. Therefore, this large-scale physician study investigated how pay satisfaction may influence the relationship between work hours and hospital physician's turnover intention. Data were obtained from a nationwide survey of full-time hospital staff members working at 100 hospitals in Taiwan. The analysis sample comprised 2423 full-time physicians. Dependent variable was degree of the physicians' turnover intention to leave the current hospital. The pay satisfaction was assessed by physicians themselves. We employed ordinal logistic regression models to analyze the association between the number of work hours and turnover intention. To consider the cluster effect of hospitals, we used the "gllamm" command in the statistical software package Stata Version 12.1. The results show that 351 (14.5%) of surveyed physicians reported strong intention to leave current hospital. The average work hours per week among hospital physicians was 59.8 h. As expected, work hours exhibited an independent relationship with turnover intention. More importantly, pay satisfaction could not effectively moderate the positive relationship between work hours and intentions to leave current hospital. The findings show that overtime work is prevalent among hospital physicians in Taiwan. Both the Taiwanese government and hospitals must take action to address the emerging problem of physician high turnover rate. Furthermore, hospitals should not consider relying solely on financial incentives to solve the problem. This study encouraged tackling work hour problem, which would lead to the possibility of solving high turnover intention among hospital physicians in Taiwan.

A Discrete Constraint for Entropy Conservation and Sound Waves in Cloud-Resolving Modeling

NASA Technical Reports Server (NTRS)

Zeng, Xi-Ping; Tao, Wei-Kuo; Simpson, Joanne

2003-01-01

Ideal cloud-resolving models contain little-accumulative errors. When their domain is so large that synoptic large-scale circulations are accommodated, they can be used for the simulation of the interaction between convective clouds and the large-scale circulations. This paper sets up a framework for the models, using moist entropy as a prognostic variable and employing conservative numerical schemes. The models possess no accumulative errors of thermodynamic variables when they comply with a discrete constraint on entropy conservation and sound waves. Alternatively speaking, the discrete constraint is related to the correct representation of the large-scale convergence and advection of moist entropy. Since air density is involved in entropy conservation and sound waves, the challenge is how to compute sound waves efficiently under the constraint. To address the challenge, a compensation method is introduced on the basis of a reference isothermal atmosphere whose governing equations are solved analytically. Stability analysis and numerical experiments show that the method allows the models to integrate efficiently with a large time step.

Against risk-benefit review of prisoner research.

PubMed

Chwang, Eric

2010-01-01

The 2006 Institute of Medicine (IOM) report, 'Ethical Considerations for Research Involving Prisoners', recommended five main changes to current US Common Rule regulations on prisoner research. Their third recommendation was to shift from a category-based to a risk-benefit approach to research review, similar to current guidelines on pediatric research. However, prisoners are not children, so risk-benefit constraints on prisoner research must be justified in a different way from those on pediatric research. In this paper I argue that additional risk-benefit constraints on prisoner research are unnecessary: the current Common Rule regulations, omitting category-based restrictions but conjoined with the IOM report's other four main recommendations, ensure that prisoner research is as ethical as non-prisoner research is. I explain why four problems which which may be more prevalent in prisons and which risk-benefit constraints may seem to address - coercion, undue inducements, exploitation, and protection from harm - are in fact not solved by adding further risk-benefit constraints on prisoner research.

A feasible DY conjugate gradient method for linear equality constraints

NASA Astrophysics Data System (ADS)

LI, Can

2017-09-01

In this paper, we propose a feasible conjugate gradient method for solving linear equality constrained optimization problem. The method is an extension of the Dai-Yuan conjugate gradient method proposed by Dai and Yuan to linear equality constrained optimization problem. It can be applied to solve large linear equality constrained problem due to lower storage requirement. An attractive property of the method is that the generated direction is always feasible and descent direction. Under mild conditions, the global convergence of the proposed method with exact line search is established. Numerical experiments are also given which show the efficiency of the method.

Advanced control concepts. [for shuttle ascent vehicles

NASA Technical Reports Server (NTRS)

Sharp, J. B.; Coppey, J. M.

1973-01-01

The problems of excess control devices and insufficient trim control capability on shuttle ascent vehicles were investigated. The trim problem is solved at all time points of interest using Lagrangian multipliers and a Simplex based iterative algorithm developed as a result of the study. This algorithm has the capability to solve any bounded linear problem with physically realizable constraints, and to minimize any piecewise differentiable cost function. Both solution methods also automatically distribute the command torques to the control devices. It is shown that trim requirements are unrealizable if only the orbiter engines and the aerodynamic surfaces are used.

A firefly algorithm for solving competitive location-design problem: a case study

NASA Astrophysics Data System (ADS)

Sadjadi, Seyed Jafar; Ashtiani, Milad Gorji; Ramezanian, Reza; Makui, Ahmad

2016-12-01

This paper aims at determining the optimal number of new facilities besides specifying both the optimal location and design level of them under the budget constraint in a competitive environment by a novel hybrid continuous and discrete firefly algorithm. A real-world application of locating new chain stores in the city of Tehran, Iran, is used and the results are analyzed. In addition, several examples have been solved to evaluate the efficiency of the proposed model and algorithm. The results demonstrate that the performed method provides good-quality results for the test problems.

Improved artificial bee colony algorithm for vehicle routing problem with time windows

PubMed Central

Yan, Qianqian; Zhang, Mengjie; Yang, Yunong

2017-01-01

This paper investigates a well-known complex combinatorial problem known as the vehicle routing problem with time windows (VRPTW). Unlike the standard vehicle routing problem, each customer in the VRPTW is served within a given time constraint. This paper solves the VRPTW using an improved artificial bee colony (IABC) algorithm. The performance of this algorithm is improved by a local optimization based on a crossover operation and a scanning strategy. Finally, the effectiveness of the IABC is evaluated on some well-known benchmarks. The results demonstrate the power of IABC algorithm in solving the VRPTW. PMID:28961252

On the Satisfaction of Modulus and Ambiguity Function Constraints in Radar Waveform Optimization for Detection

DTIC Science & Technology

2010-06-01

sense that the two waveforms are as close as possible in a Euclidean sense . Li et al. [33] later devised an algorithm that provides the optimal waveform...respectively), and the SWORD algorithm in [33]. These algorithms were designed for the problem of detecting a known signal in the presence of wide- sense ... sensing , astronomy, crystallography, signal processing, and image processing. (See references in the works cited below for examples.) In the general

Quality of Life: Literature Review and Recommendations for Measurement of Military Outcomes

DTIC Science & Technology

1994-01-01

organizational work may influence the perceived QOL as well as the objective QOL and that (2) the effect of work on QOL may be mediated by changes in the...quality of nonwork life as wcll as by changes in the quality of work life, By extrapolating from the job satisfaction literature and using it as a proxy...Bedeian, 1989), absenteeism (Keller, 1984), situational constraints (O’Connor, Peters, Pooyan, Weekley, Frank, & Erenkrantz, 1984), job attitudes

Expanding occupational and environmental health nurse resources. Using community projects to inspire volunteerism.

PubMed

Rojak, J; Fredrickson, P; Fitpold, K; Uhlken, C J

2001-03-01

1. Community involvement is becoming a natural extension of health and wellness services. Participation by occupational and environmental health nurses in community initiatives provides personal satisfaction, improves chapter cohesion, and increases visibility for the profession. 2. Variety in community projects enhances participation by appealing to the diversified interests and time constraints of members. 3. Volunteerism appeals to the basic beliefs of nurses helping to reach beyond individual health and wellness and target global issues.

Do it right this time: the role of employee service recovery performance in customer-perceived justice and customer loyalty after service failures.

PubMed

Liao, Hui

2007-03-01

Integrating justice and customer service literatures, this research examines the role of customer service employees' behaviors of handling customer complaints, or service recovery performance (SRP), in conveying a just image of service organizations and achieving desirable customer outcomes. Results from a field study and a laboratory study demonstrate that the dimensions of SRP--making an apology, problem solving, being courteous, and prompt handling--positively influenced customer satisfaction and then customer repurchase intent through the mediation of customer-perceived justice. In addition, service failure severity and repeated failures reduced the positive impact of some dimensions of SRP on customer satisfaction, and customer-perceived justice again mediated these moderated effects. (c) 2007 APA, all rights reserved.

Effects of a multidisciplinary group rehabilitation programme on participation of the visually impaired elderly: a pilot study.

PubMed

Alma, Manna A; Groothoff, Johan W; Melis-Dankers, Bart J M; Post, Marcel W M; Suurmeijer, Theo P B M; van der Mei, Sijrike F

2012-01-01

To pilot test the newly developed multidisciplinary group rehabilitation programme Visually Impaired elderly Persons Participating (VIPP). A single group pretest-posttest design pilot study included 29 visually impaired persons (≥ 55 years). The intervention (20 weekly meetings) consisted of four components (practical training; education, social interaction, counselling and training of problem-solving skills; individual and group goal setting; home-based exercise programme). Participation was assessed with the Utrecht Scale for Evaluation of Rehabilitation-Participation (USER-P) and the Impact on Participation and Autonomy (IPA) questionnaire at baseline, immediately and 6 months after the intervention. At scale level, no statistical significant changes over time were found whereas the effect size indicated small effects for restrictions and satisfaction with participation, and a medium effect for autonomy outdoors. At item level, improvements tended to occur in frequency of housekeeping, in restrictions in housekeeping and outdoor activities and in satisfaction with the partner relationship. Satisfaction with leisure indoors and autonomy regarding using leisure time tended to increase as well. The tentative conclusion of this small-scale pilot study is that the VIPP programme modestly benefits perceived restrictions in participation, satisfaction with participation and autonomy outdoors of the visually impaired elderly. These preliminary findings warrant further investigation.

Rapidly disintegrating vagina retentive cream suppositories of progesterone: development, patient satisfaction and in vitro/in vivo studies.

PubMed

Bendas, Ehab Rasmy; Basalious, Emad B

2016-01-01

Our objective was to develop novel vagina retentive cream suppositories (VRCS) of progesterone having rapid disintegration and good vaginal retention. VRCS of progesterone were prepared using oil in water (o/w) emulsion of mineral oil or theobroma oil in hard fat and compared with conventional vaginal suppositories (CVS) prepared by hard fat. VRCS formulations were tested for content uniformity, disintegration, melting range, in vitro release and stability studies. The most stable formulation (VRCS I) was subjected to scaling-up manufacturing and patients' satisfaction test. The rapid disintegration, good retentive properties are applicable through the inclusion of emulsified theobroma oil rather than hydrophilic surfactant into the hard fat bases. The release profile of progesterone from VRCS I showed a biphasic pattern due to the formation of progesterone reservoir in the emulsified theobroma oil. All volunteers involved in patients' satisfaction test showed high satisfactory response to the tested formulation (VRCS). The in vivo pharmacokinetic study suggests that VRCS of progesterone provided higher rate and extent of absorption compared to hard fat based suppositories. Our results proposed that emulsified theobroma oil could be promising to solve the problems of poor patients' satisfaction and variability of drug absorption associated with hard fat suppositories.

Projective-Dual Method for Solving Systems of Linear Equations with Nonnegative Variables

NASA Astrophysics Data System (ADS)

Ganin, B. V.; Golikov, A. I.; Evtushenko, Yu. G.

2018-02-01

In order to solve an underdetermined system of linear equations with nonnegative variables, the projection of a given point onto its solutions set is sought. The dual of this problem—the problem of unconstrained maximization of a piecewise-quadratic function—is solved by Newton's method. The problem of unconstrained optimization dual of the regularized problem of finding the projection onto the solution set of the system is considered. A connection of duality theory and Newton's method with some known algorithms of projecting onto a standard simplex is shown. On the example of taking into account the specifics of the constraints of the transport linear programming problem, the possibility to increase the efficiency of calculating the generalized Hessian matrix is demonstrated. Some examples of numerical calculations using MATLAB are presented.

A Flexible and Efficient Method for Solving Ill-Posed Linear Integral Equations of the First Kind for Noisy Data

NASA Astrophysics Data System (ADS)

Antokhin, I. I.

2017-06-01

We propose an efficient and flexible method for solving Fredholm and Abel integral equations of the first kind, frequently appearing in astrophysics. These equations present an ill-posed problem. Our method is based on solving them on a so-called compact set of functions and/or using Tikhonov's regularization. Both approaches are non-parametric and do not require any theoretic model, apart from some very loose a priori constraints on the unknown function. The two approaches can be used independently or in a combination. The advantage of the method, apart from its flexibility, is that it gives uniform convergence of the approximate solution to the exact one, as the errors of input data tend to zero. Simulated and astrophysical examples are presented.

Sequential Monte Carlo for Maximum Weight Subgraphs with Application to Solving Image Jigsaw Puzzles.

PubMed

Adluru, Nagesh; Yang, Xingwei; Latecki, Longin Jan

2015-05-01

We consider a problem of finding maximum weight subgraphs (MWS) that satisfy hard constraints in a weighted graph. The constraints specify the graph nodes that must belong to the solution as well as mutual exclusions of graph nodes, i.e., pairs of nodes that cannot belong to the same solution. Our main contribution is a novel inference approach for solving this problem in a sequential monte carlo (SMC) sampling framework. Usually in an SMC framework there is a natural ordering of the states of the samples. The order typically depends on observations about the states or on the annealing setup used. In many applications (e.g., image jigsaw puzzle problems), all observations (e.g., puzzle pieces) are given at once and it is hard to define a natural ordering. Therefore, we relax the assumption of having ordered observations about states and propose a novel SMC algorithm for obtaining maximum a posteriori estimate of a high-dimensional posterior distribution. This is achieved by exploring different orders of states and selecting the most informative permutations in each step of the sampling. Our experimental results demonstrate that the proposed inference framework significantly outperforms loopy belief propagation in solving the image jigsaw puzzle problem. In particular, our inference quadruples the accuracy of the puzzle assembly compared to that of loopy belief propagation.

Sequential Monte Carlo for Maximum Weight Subgraphs with Application to Solving Image Jigsaw Puzzles

PubMed Central

Adluru, Nagesh; Yang, Xingwei; Latecki, Longin Jan

2015-01-01

We consider a problem of finding maximum weight subgraphs (MWS) that satisfy hard constraints in a weighted graph. The constraints specify the graph nodes that must belong to the solution as well as mutual exclusions of graph nodes, i.e., pairs of nodes that cannot belong to the same solution. Our main contribution is a novel inference approach for solving this problem in a sequential monte carlo (SMC) sampling framework. Usually in an SMC framework there is a natural ordering of the states of the samples. The order typically depends on observations about the states or on the annealing setup used. In many applications (e.g., image jigsaw puzzle problems), all observations (e.g., puzzle pieces) are given at once and it is hard to define a natural ordering. Therefore, we relax the assumption of having ordered observations about states and propose a novel SMC algorithm for obtaining maximum a posteriori estimate of a high-dimensional posterior distribution. This is achieved by exploring different orders of states and selecting the most informative permutations in each step of the sampling. Our experimental results demonstrate that the proposed inference framework significantly outperforms loopy belief propagation in solving the image jigsaw puzzle problem. In particular, our inference quadruples the accuracy of the puzzle assembly compared to that of loopy belief propagation. PMID:26052182

Applying CBR to machine tool product configuration design oriented to customer requirements

NASA Astrophysics Data System (ADS)

Wang, Pengjia; Gong, Yadong; Xie, Hualong; Liu, Yongxian; Nee, Andrew Yehching

2017-01-01

Product customization is a trend in the current market-oriented manufacturing environment. However, deduction from customer requirements to design results and evaluation of design alternatives are still heavily reliant on the designer's experience and knowledge. To solve the problem of fuzziness and uncertainty of customer requirements in product configuration, an analysis method based on the grey rough model is presented. The customer requirements can be converted into technical characteristics effectively. In addition, an optimization decision model for product planning is established to help the enterprises select the key technical characteristics under the constraints of cost and time to serve the customer to maximal satisfaction. A new case retrieval approach that combines the self-organizing map and fuzzy similarity priority ratio method is proposed in case-based design. The self-organizing map can reduce the retrieval range and increase the retrieval efficiency, and the fuzzy similarity priority ratio method can evaluate the similarity of cases comprehensively. To ensure that the final case has the best overall performance, an evaluation method of similar cases based on grey correlation analysis is proposed to evaluate similar cases to select the most suitable case. Furthermore, a computer-aided system is developed using MATLAB GUI to assist the product configuration design. The actual example and result on an ETC series machine tool product show that the proposed method is effective, rapid and accurate in the process of product configuration. The proposed methodology provides a detailed instruction for the product configuration design oriented to customer requirements.

Initial conditions of inhomogeneous universe and the cosmological constant problem

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

Totani, Tomonori, E-mail: totani@astron.s.u-tokyo.ac.jp

Deriving the Einstein field equations (EFE) with matter fluid from the action principle is not straightforward, because mass conservation must be added as an additional constraint to make rest-frame mass density variable in reaction to metric variation. This can be avoided by introducing a constraint 0δ(√− g ) = to metric variations δ g {sup μν}, and then the cosmological constant Λ emerges as an integration constant. This is a removal of one of the four constraints on initial conditions forced by EFE at the birth of the universe, and it may imply that EFE are unnecessarily restrictive about initialmore » conditions. I then adopt a principle that the theory of gravity should be able to solve time evolution starting from arbitrary inhomogeneous initial conditions about spacetime and matter. The equations of gravitational fields satisfying this principle are obtained, by setting four auxiliary constraints on δ g {sup μν} to extract six degrees of freedom for gravity. The cost of achieving this is a loss of general covariance, but these equations constitute a consistent theory if they hold in the special coordinate systems that can be uniquely specified with respect to the initial space-like hypersurface when the universe was born. This theory predicts that gravity is described by EFE with non-zero Λ in a homogeneous patch of the universe created by inflation, but Λ changes continuously across different patches. Then both the smallness and coincidence problems of the cosmological constant are solved by the anthropic argument. This is just a result of inhomogeneous initial conditions, not requiring any change of the fundamental physical laws in different patches.« less

A Path Algorithm for Constrained Estimation

PubMed Central

Zhou, Hua; Lange, Kenneth

2013-01-01

Many least-square problems involve affine equality and inequality constraints. Although there are a variety of methods for solving such problems, most statisticians find constrained estimation challenging. The current article proposes a new path-following algorithm for quadratic programming that replaces hard constraints by what are called exact penalties. Similar penalties arise in l1 regularization in model selection. In the regularization setting, penalties encapsulate prior knowledge, and penalized parameter estimates represent a trade-off between the observed data and the prior knowledge. Classical penalty methods of optimization, such as the quadratic penalty method, solve a sequence of unconstrained problems that put greater and greater stress on meeting the constraints. In the limit as the penalty constant tends to ∞, one recovers the constrained solution. In the exact penalty method, squared penalties!are replaced by absolute value penalties, and the solution is recovered for a finite value of the penalty constant. The exact path-following method starts at the unconstrained solution and follows the solution path as the penalty constant increases. In the process, the solution path hits, slides along, and exits from the various constraints. Path following in Lasso penalized regression, in contrast, starts with a large value of the penalty constant and works its way downward. In both settings, inspection of the entire solution path is revealing. Just as with the Lasso and generalized Lasso, it is possible to plot the effective degrees of freedom along the solution path. For a strictly convex quadratic program, the exact penalty algorithm can be framed entirely in terms of the sweep operator of regression analysis. A few well-chosen examples illustrate the mechanics and potential of path following. This article has supplementary materials available online. PMID:24039382

Romantic Relationship Satisfaction Moderates the Etiology of Adult Personality

PubMed Central

South, Susan C.; Krueger, Robert F.; Elkins, Irene; Iacono, William G.; McGue, Matt

2015-01-01

The heritability of major normative domains of personality is well-established, with approximately half the proportion of variance attributed to genetic differences. In the current study, we examine the possibility of gene x environment interaction (GxE) for adult personality using the environmental context of intimate romantic relationship functioning. Personality and relationship satisfaction are significantly correlated phenotypically, but to date no research has examined how the genetic and environmental components of variance for personality differ as a function of romantic relationship satisfaction. Given the importance of personality for myriad outcomes from work productivity to psychopathology, it is vital to identify variables present in adulthood that may affect the etiology of personality. In the current study, quantitative models of GxE were used to determine whether the genetic and environmental influences on personality differ as a function of relationship satisfaction. We drew from a sample of now-adult twins followed longitudinally from adolescence through age 29. All participants completed the Multidimensional Personality Questionnaire (MPQ) and an abbreviated version of the Dyadic Adjustment Scale (DAS). Biometric moderation was found for eight of the eleven MPQ scales examined: Well-Being, Social Potency, Negative Emotionality, Alienation, Aggression, Constraint, Traditionalism, and Absorption. The pattern of findings differed, suggesting that the ways in which relationship quality moderates the etiology of personality may depend on the personality trait. PMID:26581694

Contingency, employment intentions, and retention of vulnerable low-wage workers: an examination of nursing assistants in nursing homes.

PubMed

Dill, Janette S; Morgan, Jennifer Craft; Marshall, Victor W; Pruchno, Rachel

2013-04-01

While theories of job turnover generally assume a strong correlation between job satisfaction, intention, and retention, such models may be limited in explaining turnover of low-wage health care workers. Low-wage workers likely have a lower ability to act on their employment intentions or plans due to a lack of resources that serve to cushion higher wage workers. In this study, we examine the relationship between job satisfaction, intention, and retention of nursing assistants in nursing homes and the role that "contingency factors" play in employment intentions and retention. We conceptualize "contingency factors" as resource-related constraints (e.g., being a single mother) that likely influence employment trajectories of individuals but can be independent of job satisfaction or intent. We use survey data from 315 nursing assistants in 18 nursing homes in a U.S. southern state to model employment intentions and retention. We find that job satisfaction and other perceived job characteristics (e.g., workload and perceived quality of care) are significant predictors of an individual's intent to stay in their job, the occupation of nursing assistant, and the field of long-term care. However, we find that job satisfaction and employment intentions are not significant predictors of retention. Instead, "contingency factors" such as being a primary breadwinner and individual characteristics (e.g., tenure and past health care experience) appear to be stronger factors in the retention of nursing assistants. Our findings have implications for understanding turnover among low-wage health care workers and the use of proxies such as employment intentions in measuring turnover.

Assessment of Mammography Experiences and Satisfaction among American Indian/Alaska Native Women

PubMed Central

Ndikum-Moffor, Florence M.; Braiuca, Stacy; Daley, Christine Makosky; Gajewski, Byron J.; Engelman, Kimberly K.

2013-01-01

BACKGROUND American Indian/Alaska Native (AI/AN) women have lower breast cancer (BCA) screening and 5-year survival rates than non-Hispanic Whites. Understanding reasons for low screening rates is important to combat later stage diagnoses. The purpose of this study was to assess mammography experiences and satisfaction among AI/AN women. METHODS Nine focus groups were held with rural (N=15) and urban (N=38) AI/AN women 40 years and older in Kansas and Kansas City, Missouri, living both near and far from Indian Health Service (IHS) and tribal facilities, to examine experiences and satisfaction with mammography. Transcripts were coded and themes identified using a community-based participatory research approach. FINDINGS Themes were classified under knowledge, communication, and awareness of breast cancer, barriers to mammography, mammogram facility size, impressions of mammogram technologist, motivations to getting a mammogram, and how to improve the mammogram experience. Participants had knowledge of prevention, but described cultural reasons for not discussing it and described better experiences in smaller facilities. Participants indicated having a mammogram technologist who was friendly, knowledgeable, respectful, competent, and explained the test was a determining factor in satisfaction. Other factors included family history, physician recommendation, and financial incentives. Barriers included transportation, cost, perceptions of prejudice, and time constraints. Participants on reservations or near IHS facilities preferred IHS over mainstream providers. Suggestions for improvement included caring technologists, better machines with less discomfort, and education. CONCLUSIONS Interventions to enhance the professionalism, empathy, and cultural awareness of mammogram technologists, reduce barriers, and provide positive expectations and incentives could improve satisfaction and compliance with screening mammography. PMID:24183414

Elasto-limited plastic analysis of structures for probabilistic conditions

NASA Astrophysics Data System (ADS)

Movahedi Rad, M.

2018-06-01

With applying plastic analysis and design methods, significant saving in material can be obtained. However, as a result of this benefit excessive plastic deformations and large residual displacements might develop, which in turn might lead to unserviceability and collapse of the structure. In this study, for deterministic problem the residual deformation of structures is limited by considering a constraint on the complementary strain energy of the residual forces. For probabilistic problem the constraint for the complementary strain energy of the residual forces is given randomly and critical stresses updated during the iteration. Limit curves are presented for the plastic limit load factors. The results show that these constraints have significant effects on the load factors. The formulations of the deterministic and probabilistic problems lead to mathematical programming which are solved by the use of nonlinear algorithm.

A Method for Scheduling Air Traffic with Uncertain En Route Capacity Constraints

NASA Technical Reports Server (NTRS)

Arneson, Heather; Bloem, Michael

2009-01-01

A method for scheduling ground delay and airborne holding for flights scheduled to fly through airspace with uncertain capacity constraints is presented. The method iteratively solves linear programs for departure rates and airborne holding as new probabilistic information about future airspace constraints becomes available. The objective function is the expected value of the weighted sum of ground and airborne delay. In order to limit operationally costly changes to departure rates, they are updated only when such an update would lead to a significant cost reduction. Simulation results show a 13% cost reduction over a rough approximation of current practices. Comparison between the proposed as needed replanning method and a similar method that uses fixed frequency replanning shows a typical cost reduction of 1% to 2%, and even up to a 20% cost reduction in some cases.