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.
Restorative Justice Practice: Cooperative Problem-Solving in New Zealand's Schools
ERIC Educational Resources Information Center
Drewery, Wendy
2013-01-01
This article links capability for cooperative problem-solving with socially just global development. From the perspective of the United Nations Development Programme, the work of global development, founded on a concept of global justice, is capability-building. Following Kurasawa, the article proposes that this form of global justice is enacted…
Analog Processor To Solve Optimization Problems
NASA Technical Reports Server (NTRS)
Duong, Tuan A.; Eberhardt, Silvio P.; Thakoor, Anil P.
1993-01-01
Proposed analog processor solves "traveling-salesman" problem, considered paradigm of global-optimization problems involving routing or allocation of resources. Includes electronic neural network and auxiliary circuitry based partly on concepts described in "Neural-Network Processor Would Allocate Resources" (NPO-17781) and "Neural Network Solves 'Traveling-Salesman' Problem" (NPO-17807). Processor based on highly parallel computing solves problem in significantly less time.
Addressing Society's Problems in a Global Studies Class.
ERIC Educational Resources Information Center
Pesce, Louis; And Others
1996-01-01
Describes the adaptation of the Future Problem-Solving Process (FPS) in a global studies class. The process applies state-of-the-art critical thinking and problem solving to unstable areas such as the Middle East and the former Soviet Union. Includes handouts directing the students through the process. (MJP)
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.
Electronic neural network for solving traveling salesman and similar global optimization problems
NASA Technical Reports Server (NTRS)
Thakoor, Anilkumar P. (Inventor); Moopenn, Alexander W. (Inventor); Duong, Tuan A. (Inventor); Eberhardt, Silvio P. (Inventor)
1993-01-01
This invention is a novel high-speed neural network based processor for solving the 'traveling salesman' and other global optimization problems. It comprises a novel hybrid architecture employing a binary synaptic array whose embodiment incorporates the fixed rules of the problem, such as the number of cities to be visited. The array is prompted by analog voltages representing variables such as distances. The processor incorporates two interconnected feedback networks, each of which solves part of the problem independently and simultaneously, yet which exchange information dynamically.
Glogs as Non-Routine Problem Solving Tools in Mathematics
ERIC Educational Resources Information Center
Devine, Matthew T.
2013-01-01
In mathematical problem solving, American students are falling behind their global peers because of a lack of foundational and reasoning skills. A specific area of difficulty with problem solving is working non-routine, heuristic-based problems. Many students are not provided with effective instruction and often grow frustrated and dislike math.…
Mathematical Problem Solving through Sequential Process Analysis
ERIC Educational Resources Information Center
Codina, A.; Cañadas, M. C.; Castro, E.
2015-01-01
Introduction: The macroscopic perspective is one of the frameworks for research on problem solving in mathematics education. Coming from this perspective, our study addresses the stages of thought in mathematical problem solving, offering an innovative approach because we apply sequential relations and global interrelations between the different…
Neural Network Solves "Traveling-Salesman" Problem
NASA Technical Reports Server (NTRS)
Thakoor, Anilkumar P.; Moopenn, Alexander W.
1990-01-01
Experimental electronic neural network solves "traveling-salesman" problem. Plans round trip of minimum distance among N cities, visiting every city once and only once (without backtracking). This problem is paradigm of many problems of global optimization (e.g., routing or allocation of resources) occuring in industry, business, and government. Applied to large number of cities (or resources), circuits of this kind expected to solve problem faster and more cheaply.
Toward Group Problem Solving Guidelines for 21st Century Teams
ERIC Educational Resources Information Center
Ranieri, Kathryn L.
2004-01-01
Effective problem-solving skills are critical in dealing with ambiguous and often complex issues in the present-day leaner and globally diverse organizations. Yet respected, well-established problem-solving models may be misaligned within the current work environment, particularly within a team context. Models learned from a more bureaucratic,…
ERIC Educational Resources Information Center
National Center for Education Statistics, 2017
2017-01-01
The Program for International Student Assessment (PISA) is a global education study of 15-year-old students' reading, mathematics, and science literacy and, in 2015, two optional components: financial literacy and collaborative problem solving. Fifty-one education systems administered the collaborative problem solving assessment, including 32 of…
NASA Astrophysics Data System (ADS)
Chandra, Rishabh
Partial differential equation-constrained combinatorial optimization (PDECCO) problems are a mixture of continuous and discrete optimization problems. PDECCO problems have discrete controls, but since the partial differential equations (PDE) are continuous, the optimization space is continuous as well. Such problems have several applications, such as gas/water network optimization, traffic optimization, micro-chip cooling optimization, etc. Currently, no efficient classical algorithm which guarantees a global minimum for PDECCO problems exists. A new mapping has been developed that transforms PDECCO problem, which only have linear PDEs as constraints, into quadratic unconstrained binary optimization (QUBO) problems that can be solved using an adiabatic quantum optimizer (AQO). The mapping is efficient, it scales polynomially with the size of the PDECCO problem, requires only one PDE solve to form the QUBO problem, and if the QUBO problem is solved correctly and efficiently on an AQO, guarantees a global optimal solution for the original PDECCO problem.
ERIC Educational Resources Information Center
Dondlinger, Mary Jo; McLeod, Julie K.
2015-01-01
The Global Village Playground (GVP) was a capstone learning experience designed to address institutional assessment needs while providing an integrated and authentic learning experience for students aimed at fostering complex problem solving, as well as critical and creative thinking. In the GVP, students work on simulated and real-world problems…
On a numerical solving of random generated hexamatrix games
NASA Astrophysics Data System (ADS)
Orlov, Andrei; Strekalovskiy, Alexander
2016-10-01
In this paper, we develop a global search method for finding a Nash equilibrium in a hexamatrix game (polymatrix game of three players). The method, on the one hand, is based on the equivalence theorem of the problem of finding a Nash equilibrium in the game and a special mathematical optimization problem, and, on the other hand, on the usage of Global Search Theory for solving the latter problem. The efficiency of this approach is demonstrated by the results of computational testing.
Solving SAT Problem Based on Hybrid Differential Evolution Algorithm
NASA Astrophysics Data System (ADS)
Liu, Kunqi; Zhang, Jingmin; Liu, Gang; Kang, Lishan
Satisfiability (SAT) problem is an NP-complete problem. Based on the analysis about it, SAT problem is translated equally into an optimization problem on the minimum of objective function. A hybrid differential evolution algorithm is proposed to solve the Satisfiability problem. It makes full use of strong local search capacity of hill-climbing algorithm and strong global search capability of differential evolution algorithm, which makes up their disadvantages, improves the efficiency of algorithm and avoids the stagnation phenomenon. The experiment results show that the hybrid algorithm is efficient in solving SAT problem.
A new neural network model for solving random interval linear programming problems.
Arjmandzadeh, Ziba; Safi, Mohammadreza; Nazemi, Alireza
2017-05-01
This paper presents a neural network model for solving random interval linear programming problems. The original problem involving random interval variable coefficients is first transformed into an equivalent convex second order cone programming problem. A neural network model is then constructed for solving the obtained convex second order cone problem. Employing Lyapunov function approach, it is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact satisfactory solution of the original problem. Several illustrative examples are solved in support of this technique. Copyright © 2017 Elsevier Ltd. All rights reserved.
Liu, Mengting; Amey, Rachel C; Forbes, Chad E
2017-12-01
When individuals are placed in stressful situations, they are likely to exhibit deficits in cognitive capacity over and above situational demands. Despite this, individuals may still persevere and ultimately succeed in these situations. Little is known, however, about neural network properties that instantiate success or failure in both neutral and stressful situations, particularly with respect to regions integral for problem-solving processes that are necessary for optimal performance on more complex tasks. In this study, we outline how hidden Markov modeling based on multivoxel pattern analysis can be used to quantify unique brain states underlying complex network interactions that yield either successful or unsuccessful problem solving in more neutral or stressful situations. We provide evidence that brain network stability and states underlying synchronous interactions in regions integral for problem-solving processes are key predictors of whether individuals succeed or fail in stressful situations. Findings also suggested that individuals utilize discriminate neural patterns in successfully solving problems in stressful or neutral situations. Findings overall highlight how hidden Markov modeling can provide myriad possibilities for quantifying and better understanding the role of global network interactions in the problem-solving process and how the said interactions predict success or failure in different contexts.
Wang, Lipo; Li, Sa; Tian, Fuyu; Fu, Xiuju
2004-10-01
Recently Chen and Aihara have demonstrated both experimentally and mathematically that their chaotic simulated annealing (CSA) has better search ability for solving combinatorial optimization problems compared to both the Hopfield-Tank approach and stochastic simulated annealing (SSA). However, CSA may not find a globally optimal solution no matter how slowly annealing is carried out, because the chaotic dynamics are completely deterministic. In contrast, SSA tends to settle down to a global optimum if the temperature is reduced sufficiently slowly. Here we combine the best features of both SSA and CSA, thereby proposing a new approach for solving optimization problems, i.e., stochastic chaotic simulated annealing, by using a noisy chaotic neural network. We show the effectiveness of this new approach with two difficult combinatorial optimization problems, i.e., a traveling salesman problem and a channel assignment problem for cellular mobile communications.
Complexity in Nature and Society: Complexity Management in the Age of Globalization
NASA Astrophysics Data System (ADS)
Mainzer, Klaus
The theory of nonlinear complex systems has become a proven problem-solving approach in the natural sciences from cosmic and quantum systems to cellular organisms and the brain. Even in modern engineering science self-organizing systems are developed to manage complex networks and processes. It is now recognized that many of our ecological, social, economic, and political problems are also of a global, complex, and nonlinear nature. What are the laws of sociodynamics? Is there a socio-engineering of nonlinear problem solving? What can we learn from nonlinear dynamics for complexity management in social, economic, financial and political systems? Is self-organization an acceptable strategy to handle the challenges of complexity in firms, institutions and other organizations? It is a main thesis of the talk that nature and society are basically governed by nonlinear and complex information dynamics. How computational is sociodynamics? What can we hope for social, economic and political problem solving in the age of globalization?.
2015-09-24
algorithms for solving real- world problems. Within the past five years, 2 books, 5 journal special issues, and about 60 papers have been published...Four international conferences have been organized, including the 3rd World Congress of Global Optimization. A unified methodology and algorithm have...been developed with real- world applications. This grant has been used to support and co-support three post-doctors, three PhD students, one part
Perfecting scientists’ collaboration and problem-solving skills in the virtual team environment
USDA-ARS?s Scientific Manuscript database
Perfecting Scientists’ Collaboration and Problem-Solving Skills in the Virtual Team Environment Numerous factors have contributed to the proliferation of conducting work in virtual teams at the domestic, national, and global levels: innovations in technology, critical developments in software, co-lo...
Contemporary HIV/AIDS research: Insights from knowledge management theory.
Callaghan, Chris William
2017-12-01
Knowledge management as a field is concerned with the management of knowledge, including the management of knowledge in research processes. Knowledge management theory has the potential to support research into problems such as HIV, antibiotic resistance and others, particularly in terms of aspects of scientific research related to the contribution of social science. To date, however, these challenges remain with us, and theoretical contributions that can complement natural science efforts to eradicate these problems are needed. This paper seeks to offer a theoretical contribution grounded in Kuhn's paradigm theory of innovation, and in the argument by Lakatos that scientific research can be fundamentally non-innovative, which suggests that social science aspects of knowledge creation may hold the key to more effective biomedical innovation. Given the consequences of ongoing and emerging global crises, and the failure of knowledge systems of scientific research to solve such problems outright, this paper provides a review of theory and literature arguing for a new paradigm in scientific research, based on the development of global systems to maximise research collaborations. A global systems approach effectively includes social science theory development as an important complement to the natural sciences research process. Arguably, information technology and social media technology have developed to the point at which solutions to knowledge aggregation challenges can enable solutions to knowledge problems on a scale hitherto unimaginable. Expert and non-expert crowdsourced inputs can enable problem-solving through exponentially increasing problem-solving inputs, using the 'crowd,' thereby increasing collaborations dramatically. It is argued that these developments herald a new era of participatory research, or a democratisation of research, which offers new hope for solving global social problems. This paper seeks to contribute to this end, and to the recognition of the important role of social theory in the scientific research process.
ERIC Educational Resources Information Center
Harkavy, Ira; Hartley, Matthew; Hodges, Rita Axelroth; Weeks, Joann
2016-01-01
In the rapidly accelerating global era in which we now live, human beings must solve a vast array of unprecedently complex problems. Given their proclaimed dedication to critical intelligence, and their unique constellation of formidable resources to develop it, institutions of higher education have a unique responsibility to help solve these…
An Automatic Medium to High Fidelity Low-Thrust Global Trajectory Toolchain; EMTG-GMAT
NASA Technical Reports Server (NTRS)
Beeson, Ryne T.; Englander, Jacob A.; Hughes, Steven P.; Schadegg, Maximillian
2015-01-01
Solving the global optimization, low-thrust, multiple-flyby interplanetary trajectory problem with high-fidelity dynamical models requires an unreasonable amount of computational resources. A better approach, and one that is demonstrated in this paper, is a multi-step process whereby the solution of the aforementioned problem is solved at a lower-fidelity and this solution is used as an initial guess for a higher-fidelity solver. The framework presented in this work uses two tools developed by NASA Goddard Space Flight Center: the Evolutionary Mission Trajectory Generator (EMTG) and the General Mission Analysis Tool (GMAT). EMTG is a medium to medium-high fidelity low-thrust interplanetary global optimization solver, which now has the capability to automatically generate GMAT script files for seeding a high-fidelity solution using GMAT's local optimization capabilities. A discussion of the dynamical models as well as thruster and power modeling for both EMTG and GMAT are given in this paper. Current capabilities are demonstrated with examples that highlight the toolchains ability to efficiently solve the difficult low-thrust global optimization problem with little human intervention.
Relationships among Learner Characteristics and Adolescents' Perceptions about Reading Strategy Use
ERIC Educational Resources Information Center
Cantrell, Susan Chambers; Carter, Janis C.
2009-01-01
This study investigates relationships among adolescent students' perceived use of academic reading strategies and reading achievement, age, and gender. Good readers reported using global and problem-solving strategies to a greater extent than poor readers. Surface-level problem-solving strategies were more strongly related to higher reading…
Global Public Leadership in a Technological Era
ERIC Educational Resources Information Center
Masciulli, Joseph
2011-01-01
Good (ethical and effective) global public leadership--by national politicians, intergovernmental and nongovernmental international organizational leaders, multinational corporate leaders, and technoscientists--will make a significant positive difference in our global system's capacity to solve contemporary and futuristic global problems. High…
Xia, Youshen; Sun, Changyin; Zheng, Wei Xing
2012-05-01
There is growing interest in solving linear L1 estimation problems for sparsity of the solution and robustness against non-Gaussian noise. This paper proposes a discrete-time neural network which can calculate large linear L1 estimation problems fast. The proposed neural network has a fixed computational step length and is proved to be globally convergent to an optimal solution. Then, the proposed neural network is efficiently applied to image restoration. Numerical results show that the proposed neural network is not only efficient in solving degenerate problems resulting from the nonunique solutions of the linear L1 estimation problems but also needs much less computational time than the related algorithms in solving both linear L1 estimation and image restoration problems.
WATER CONSERVATION: LOCAL SOLUTIONS TO A GLOBAL PROBLEM
Water conservation issues are discussed. Local solutions to a global problem include changing old habits relating to the usage and abuse of water resources. While the suggested behavioral changes may not solve the world's pending water crisis, they may ease the impact of the l...
Validation of the Solving Problems Scale with Teachers
ERIC Educational Resources Information Center
Ryan, Mary Elizabeth
2011-01-01
Rapid advancements in technology, global competitiveness, and an increasing demand for 21st-century skills, such as problem-solving, underscore the pivotal role teachers play to prepare our youth for an era of exponential change. Those at the forefront of education are challenged to equip students with skills and strategies necessary to think…
Students' Explanations in Complex Learning of Disciplinary Programming
ERIC Educational Resources Information Center
Vieira, Camilo
2016-01-01
Computational Science and Engineering (CSE) has been denominated as the third pillar of science and as a set of important skills to solve the problems of a global society. Along with the theoretical and the experimental approaches, computation offers a third alternative to solve complex problems that require processing large amounts of data, or…
ERIC Educational Resources Information Center
Briggs, Xavier de Souza
This paper outlines major trends affecting both the nature of urban problems and the shape of much public interest problem-solving in the urban United States. Trends affecting cities and regions include the continued suburbanization of jobs, wealth, and political power; the evolution of a skill-intensive and networked global economy in which…
Flexibility in Joint Problem Solving: The Effects of Different Points of View on Overcoming Blocks.
1986-01-01
began to assess problems in a global way that is more characteristic of expert problem solvers (Larkin et al., 1980). The cooperative situation led to...subjects report on their behavior increased their monitoring behavior. Increased monitoring and reflection on problem solving strategies led to...suggest that there may be benefits which accrue to people working together that do not accrue to individuals working 7 alone. Vygotsky (1978) discusses
Problem-Solving After Traumatic Brain Injury in Adolescence: Associations With Functional Outcomes
Wade, Shari L.; Cassedy, Amy E.; Fulks, Lauren E.; Taylor, H. Gerry; Stancin, Terry; Kirkwood, Michael W.; Yeates, Keith O.; Kurowski, Brad G.
2017-01-01
Objective To examine the association of problem-solving with functioning in youth with traumatic brain injury (TBI). Design Cross-sectional evaluation of pretreatment data from a randomized controlled trial. Setting Four children’s hospitals and 1 general hospital, with level 1 trauma units. Participants Youth, ages 11 to 18 years, who sustained moderate or severe TBI in the last 18 months (N=153). Main Outcome Measures Problem-solving skills were assessed using the Social Problem-Solving Inventory (SPSI) and the Dodge Social Information Processing Short Stories. Everyday functioning was assessed based on a structured clinical interview using the Child and Adolescent Functional Assessment Scale (CAFAS) and via adolescent ratings on the Youth Self Report (YSR). Correlations and multiple regression analyses were used to examine associations among measures. Results The TBI group endorsed lower levels of maladaptive problem-solving (negative problem orientation, careless/impulsive responding, and avoidant style) and lower levels of rational problem-solving, resulting in higher total problem-solving scores for the TBI group compared with a normative sample (P<.001). Dodge Social Information Processing Short Stories dimensions were correlated (r=.23–.37) with SPSI subscales in the anticipated direction. Although both maladaptive (P<.001) and adaptive (P=.006) problem-solving composites were associated with overall functioning on the CAFAS, only maladaptive problem-solving (P<.001) was related to the YSR total when outcomes were continuous. For the both CAFAS and YSR logistic models, maladaptive style was significantly associated with greater risk of impairment (P=.001). Conclusions Problem-solving after TBI differs from normative samples and is associated with functional impairments. The relation of problem-solving deficits after TBI with global functioning merits further investigation, with consideration of the potential effects of problem-solving interventions on functional outcomes. PMID:28389109
Problem-Solving After Traumatic Brain Injury in Adolescence: Associations With Functional Outcomes.
Wade, Shari L; Cassedy, Amy E; Fulks, Lauren E; Taylor, H Gerry; Stancin, Terry; Kirkwood, Michael W; Yeates, Keith O; Kurowski, Brad G
2017-08-01
To examine the association of problem-solving with functioning in youth with traumatic brain injury (TBI). Cross-sectional evaluation of pretreatment data from a randomized controlled trial. Four children's hospitals and 1 general hospital, with level 1 trauma units. Youth, ages 11 to 18 years, who sustained moderate or severe TBI in the last 18 months (N=153). Problem-solving skills were assessed using the Social Problem-Solving Inventory (SPSI) and the Dodge Social Information Processing Short Stories. Everyday functioning was assessed based on a structured clinical interview using the Child and Adolescent Functional Assessment Scale (CAFAS) and via adolescent ratings on the Youth Self Report (YSR). Correlations and multiple regression analyses were used to examine associations among measures. The TBI group endorsed lower levels of maladaptive problem-solving (negative problem orientation, careless/impulsive responding, and avoidant style) and lower levels of rational problem-solving, resulting in higher total problem-solving scores for the TBI group compared with a normative sample (P<.001). Dodge Social Information Processing Short Stories dimensions were correlated (r=.23-.37) with SPSI subscales in the anticipated direction. Although both maladaptive (P<.001) and adaptive (P=.006) problem-solving composites were associated with overall functioning on the CAFAS, only maladaptive problem-solving (P<.001) was related to the YSR total when outcomes were continuous. For the both CAFAS and YSR logistic models, maladaptive style was significantly associated with greater risk of impairment (P=.001). Problem-solving after TBI differs from normative samples and is associated with functional impairments. The relation of problem-solving deficits after TBI with global functioning merits further investigation, with consideration of the potential effects of problem-solving interventions on functional outcomes. Copyright © 2017 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.
Yu, Yi; Hu, Binqi; Liu, Xinglong
2018-01-01
The dispatching of hydro-thermal system is a nonlinear programming problem with multiple constraints and high dimensions and the solution techniques of the model have been a hotspot in research. Based on the advantage of that the artificial bee colony algorithm (ABC) can efficiently solve the high-dimensional problem, an improved artificial bee colony algorithm has been proposed to solve DHTS problem in this paper. The improvements of the proposed algorithm include two aspects. On one hand, local search can be guided in efficiency by the information of the global optimal solution and its gradient in each generation. The global optimal solution improves the search efficiency of the algorithm but loses diversity, while the gradient can weaken the loss of diversity caused by the global optimal solution. On the other hand, inspired by genetic algorithm, the nectar resource which has not been updated in limit generation is transformed to a new one by using selection, crossover and mutation, which can ensure individual diversity and make full use of prior information for improving the global search ability of the algorithm. The two improvements of ABC algorithm are proved to be effective via a classical numeral example at last. Among which the genetic operator for the promotion of the ABC algorithm’s performance is significant. The results are also compared with those of other state-of-the-art algorithms, the enhanced ABC algorithm has general advantages in minimum cost, average cost and maximum cost which shows its usability and effectiveness. The achievements in this paper provide a new method for solving the DHTS problems, and also offer a novel reference for the improvement of mechanism and the application of algorithms. PMID:29324743
Yu, Yi; Wu, Yonggang; Hu, Binqi; Liu, Xinglong
2018-01-01
The dispatching of hydro-thermal system is a nonlinear programming problem with multiple constraints and high dimensions and the solution techniques of the model have been a hotspot in research. Based on the advantage of that the artificial bee colony algorithm (ABC) can efficiently solve the high-dimensional problem, an improved artificial bee colony algorithm has been proposed to solve DHTS problem in this paper. The improvements of the proposed algorithm include two aspects. On one hand, local search can be guided in efficiency by the information of the global optimal solution and its gradient in each generation. The global optimal solution improves the search efficiency of the algorithm but loses diversity, while the gradient can weaken the loss of diversity caused by the global optimal solution. On the other hand, inspired by genetic algorithm, the nectar resource which has not been updated in limit generation is transformed to a new one by using selection, crossover and mutation, which can ensure individual diversity and make full use of prior information for improving the global search ability of the algorithm. The two improvements of ABC algorithm are proved to be effective via a classical numeral example at last. Among which the genetic operator for the promotion of the ABC algorithm's performance is significant. The results are also compared with those of other state-of-the-art algorithms, the enhanced ABC algorithm has general advantages in minimum cost, average cost and maximum cost which shows its usability and effectiveness. The achievements in this paper provide a new method for solving the DHTS problems, and also offer a novel reference for the improvement of mechanism and the application of algorithms.
An Enhanced Memetic Algorithm for Single-Objective Bilevel Optimization Problems.
Islam, Md Monjurul; Singh, Hemant Kumar; Ray, Tapabrata; Sinha, Ankur
2017-01-01
Bilevel optimization, as the name reflects, deals with optimization at two interconnected hierarchical levels. The aim is to identify the optimum of an upper-level leader problem, subject to the optimality of a lower-level follower problem. Several problems from the domain of engineering, logistics, economics, and transportation have an inherent nested structure which requires them to be modeled as bilevel optimization problems. Increasing size and complexity of such problems has prompted active theoretical and practical interest in the design of efficient algorithms for bilevel optimization. Given the nested nature of bilevel problems, the computational effort (number of function evaluations) required to solve them is often quite high. In this article, we explore the use of a Memetic Algorithm (MA) to solve bilevel optimization problems. While MAs have been quite successful in solving single-level optimization problems, there have been relatively few studies exploring their potential for solving bilevel optimization problems. MAs essentially attempt to combine advantages of global and local search strategies to identify optimum solutions with low computational cost (function evaluations). The approach introduced in this article is a nested Bilevel Memetic Algorithm (BLMA). At both upper and lower levels, either a global or a local search method is used during different phases of the search. The performance of BLMA is presented on twenty-five standard test problems and two real-life applications. The results are compared with other established algorithms to demonstrate the efficacy of the proposed approach.
A connectionist model for diagnostic problem solving
NASA Technical Reports Server (NTRS)
Peng, Yun; Reggia, James A.
1989-01-01
A competition-based connectionist model for solving diagnostic problems is described. The problems considered are computationally difficult in that (1) multiple disorders may occur simultaneously and (2) a global optimum in the space exponential to the total number of possible disorders is sought as a solution. The diagnostic problem is treated as a nonlinear optimization problem, and global optimization criteria are decomposed into local criteria governing node activation updating in the connectionist model. Nodes representing disorders compete with each other to account for each individual manifestation, yet complement each other to account for all manifestations through parallel node interactions. When equilibrium is reached, the network settles into a locally optimal state. Three randomly generated examples of diagnostic problems, each of which has 1024 cases, were tested, and the decomposition plus competition plus resettling approach yielded very high accuracy.
A Program for Solving the Brain Ischemia Problem
DeGracia, Donald J.
2013-01-01
Our recently described nonlinear dynamical model of cell injury is here applied to the problems of brain ischemia and neuroprotection. We discuss measurement of global brain ischemia injury dynamics by time course analysis. Solutions to proposed experiments are simulated using hypothetical values for the model parameters. The solutions solve the global brain ischemia problem in terms of “master bifurcation diagrams” that show all possible outcomes for arbitrary durations of all lethal cerebral blood flow (CBF) decrements. The global ischemia master bifurcation diagrams: (1) can map to a single focal ischemia insult, and (2) reveal all CBF decrements susceptible to neuroprotection. We simulate measuring a neuroprotectant by time course analysis, which revealed emergent nonlinear effects that set dynamical limits on neuroprotection. Using over-simplified stroke geometry, we calculate a theoretical maximum protection of approximately 50% recovery. We also calculate what is likely to be obtained in practice and obtain 38% recovery; a number close to that often reported in the literature. The hypothetical examples studied here illustrate the use of the nonlinear cell injury model as a fresh avenue of approach that has the potential, not only to solve the brain ischemia problem, but also to advance the technology of neuroprotection. PMID:24961411
Contemporary HIV/AIDS research: Insights from knowledge management theory
Callaghan, Chris William
2017-01-01
Abstract Knowledge management as a field is concerned with the management of knowledge, including the management of knowledge in research processes. Knowledge management theory has the potential to support research into problems such as HIV, antibiotic resistance and others, particularly in terms of aspects of scientific research related to the contribution of social science. To date, however, these challenges remain with us, and theoretical contributions that can complement natural science efforts to eradicate these problems are needed. This paper seeks to offer a theoretical contribution grounded in Kuhn’s paradigm theory of innovation, and in the argument by Lakatos that scientific research can be fundamentally non-innovative, which suggests that social science aspects of knowledge creation may hold the key to more effective biomedical innovation. Given the consequences of ongoing and emerging global crises, and the failure of knowledge systems of scientific research to solve such problems outright, this paper provides a review of theory and literature arguing for a new paradigm in scientific research, based on the development of global systems to maximise research collaborations. A global systems approach effectively includes social science theory development as an important complement to the natural sciences research process. Arguably, information technology and social media technology have developed to the point at which solutions to knowledge aggregation challenges can enable solutions to knowledge problems on a scale hitherto unimaginable. Expert and non-expert crowdsourced inputs can enable problem-solving through exponentially increasing problem-solving inputs, using the ‘crowd,’ thereby increasing collaborations dramatically. It is argued that these developments herald a new era of participatory research, or a democratisation of research, which offers new hope for solving global social problems. This paper seeks to contribute to this end, and to the recognition of the important role of social theory in the scientific research process. PMID:28922967
Enhancing Polyhedral Relaxations for Global Optimization
ERIC Educational Resources Information Center
Bao, Xiaowei
2009-01-01
During the last decade, global optimization has attracted a lot of attention due to the increased practical need for obtaining global solutions and the success in solving many global optimization problems that were previously considered intractable. In general, the central question of global optimization is to find an optimal solution to a given…
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
Harford, Joe B; Otero, Isabel V; Anderson, Benjamin O; Cazap, Eduardo; Gradishar, William J; Gralow, Julie R; Kane, Gabrielle M; Niëns, Laurens M; Porter, Peggy L; Reeler, Anne V; Rieger, Paula T; Shockney, Lillie D; Shulman, Lawrence N; Soldak, Tanya; Thomas, David B; Thompson, Beti; Winchester, David P; Zelle, Sten G; Badwe, Rajendra A
2011-04-01
International collaborations like the Breast Health Global Initiative (BHGI) can help low and middle income countries (LMCs) to establish or improve breast cancer control programs by providing evidence-based, resource-stratified guidelines for the management and control of breast cancer. The Problem Solving Working Group of the BHGI 2010 Global Summit met to develop a consensus statement on problem-solving strategies addressing breast cancer in LMCs. To better assess breast cancer burden in poorly studied populations, countries require accurate statistics regarding breast cancer incidence and mortality. To better identify health care system strengths and weaknesses, countries require reasonable indicators of true health system quality and capacity. Using qualitative and quantitative research methods, countries should formulate cancer control strategies to identify both system inefficiencies and patient barriers. Patient navigation programs linked to public advocacy efforts feed and strengthen functional early detection and treatment programs. Cost-effectiveness research and implementation science are tools that can guide and expand successful pilot programs. Copyright © 2011 Elsevier Ltd. All rights reserved.
Gálvez, Akemi; Iglesias, Andrés; Cabellos, Luis
2014-01-01
The problem of data fitting is very important in many theoretical and applied fields. In this paper, we consider the problem of optimizing a weighted Bayesian energy functional for data fitting by using global-support approximating curves. By global-support curves we mean curves expressed as a linear combination of basis functions whose support is the whole domain of the problem, as opposed to other common approaches in CAD/CAM and computer graphics driven by piecewise functions (such as B-splines and NURBS) that provide local control of the shape of the curve. Our method applies a powerful nature-inspired metaheuristic algorithm called cuckoo search, introduced recently to solve optimization problems. A major advantage of this method is its simplicity: cuckoo search requires only two parameters, many fewer than other metaheuristic approaches, so the parameter tuning becomes a very simple task. The paper shows that this new approach can be successfully used to solve our optimization problem. To check the performance of our approach, it has been applied to five illustrative examples of different types, including open and closed 2D and 3D curves that exhibit challenging features, such as cusps and self-intersections. Our results show that the method performs pretty well, being able to solve our minimization problem in an astonishingly straightforward way. PMID:24977175
Gálvez, Akemi; Iglesias, Andrés; Cabellos, Luis
2014-01-01
The problem of data fitting is very important in many theoretical and applied fields. In this paper, we consider the problem of optimizing a weighted Bayesian energy functional for data fitting by using global-support approximating curves. By global-support curves we mean curves expressed as a linear combination of basis functions whose support is the whole domain of the problem, as opposed to other common approaches in CAD/CAM and computer graphics driven by piecewise functions (such as B-splines and NURBS) that provide local control of the shape of the curve. Our method applies a powerful nature-inspired metaheuristic algorithm called cuckoo search, introduced recently to solve optimization problems. A major advantage of this method is its simplicity: cuckoo search requires only two parameters, many fewer than other metaheuristic approaches, so the parameter tuning becomes a very simple task. The paper shows that this new approach can be successfully used to solve our optimization problem. To check the performance of our approach, it has been applied to five illustrative examples of different types, including open and closed 2D and 3D curves that exhibit challenging features, such as cusps and self-intersections. Our results show that the method performs pretty well, being able to solve our minimization problem in an astonishingly straightforward way.
Shen, Peiping; Zhang, Tongli; Wang, Chunfeng
2017-01-01
This article presents a new approximation algorithm for globally solving a class of generalized fractional programming problems (P) whose objective functions are defined as an appropriate composition of ratios of affine functions. To solve this problem, the algorithm solves an equivalent optimization problem (Q) via an exploration of a suitably defined nonuniform grid. The main work of the algorithm involves checking the feasibility of linear programs associated with the interesting grid points. It is proved that the proposed algorithm is a fully polynomial time approximation scheme as the ratio terms are fixed in the objective function to problem (P), based on the computational complexity result. In contrast to existing results in literature, the algorithm does not require the assumptions on quasi-concavity or low-rank of the objective function to problem (P). Numerical results are given to illustrate the feasibility and effectiveness of the proposed algorithm.
A review on economic emission dispatch problems using quantum computational intelligence
NASA Astrophysics Data System (ADS)
Mahdi, Fahad Parvez; Vasant, Pandian; Kallimani, Vish; Abdullah-Al-Wadud, M.
2016-11-01
Economic emission dispatch (EED) problems are one of the most crucial problems in power systems. Growing energy demand, limitation of natural resources and global warming make this topic into the center of discussion and research. This paper reviews the use of Quantum Computational Intelligence (QCI) in solving Economic Emission Dispatch problems. QCI techniques like Quantum Genetic Algorithm (QGA) and Quantum Particle Swarm Optimization (QPSO) algorithm are discussed here. This paper will encourage the researcher to use more QCI based algorithm to get better optimal result for solving EED problems.
Overset meshing coupled with hybridizable discontinuous Galerkin finite elements
Kauffman, Justin A.; Sheldon, Jason P.; Miller, Scott T.
2017-03-01
We introduce the use of hybridizable discontinuous Galerkin (HDG) finite element methods on overlapping (overset) meshes. Overset mesh methods are advantageous for solving problems on complex geometrical domains. We also combine geometric flexibility of overset methods with the advantages of HDG methods: arbitrarily high-order accuracy, reduced size of the global discrete problem, and the ability to solve elliptic, parabolic, and/or hyperbolic problems with a unified form of discretization. This approach to developing the ‘overset HDG’ method is to couple the global solution from one mesh to the local solution on the overset mesh. We present numerical examples for steady convection–diffusionmore » and static elasticity problems. The examples demonstrate optimal order convergence in all primal fields for an arbitrary amount of overlap of the underlying meshes.« less
An approach for heterogeneous and loosely coupled geospatial data distributed computing
NASA Astrophysics Data System (ADS)
Chen, Bin; Huang, Fengru; Fang, Yu; Huang, Zhou; Lin, Hui
2010-07-01
Most GIS (Geographic Information System) applications tend to have heterogeneous and autonomous geospatial information resources, and the availability of these local resources is unpredictable and dynamic under a distributed computing environment. In order to make use of these local resources together to solve larger geospatial information processing problems that are related to an overall situation, in this paper, with the support of peer-to-peer computing technologies, we propose a geospatial data distributed computing mechanism that involves loosely coupled geospatial resource directories and a term named as Equivalent Distributed Program of global geospatial queries to solve geospatial distributed computing problems under heterogeneous GIS environments. First, a geospatial query process schema for distributed computing as well as a method for equivalent transformation from a global geospatial query to distributed local queries at SQL (Structured Query Language) level to solve the coordinating problem among heterogeneous resources are presented. Second, peer-to-peer technologies are used to maintain a loosely coupled network environment that consists of autonomous geospatial information resources, thus to achieve decentralized and consistent synchronization among global geospatial resource directories, and to carry out distributed transaction management of local queries. Finally, based on the developed prototype system, example applications of simple and complex geospatial data distributed queries are presented to illustrate the procedure of global geospatial information processing.
Global Optimal Trajectory in Chaos and NP-Hardness
NASA Astrophysics Data System (ADS)
Latorre, Vittorio; Gao, David Yang
This paper presents an unconventional theory and method for solving general nonlinear dynamical systems. Instead of the direct iterative methods, the discretized nonlinear system is first formulated as a global optimization problem via the least squares method. A newly developed canonical duality theory shows that this nonconvex minimization problem can be solved deterministically in polynomial time if a global optimality condition is satisfied. The so-called pseudo-chaos produced by linear iterative methods are mainly due to the intrinsic numerical error accumulations. Otherwise, the global optimization problem could be NP-hard and the nonlinear system can be really chaotic. A conjecture is proposed, which reveals the connection between chaos in nonlinear dynamics and NP-hardness in computer science. The methodology and the conjecture are verified by applications to the well-known logistic equation, a forced memristive circuit and the Lorenz system. Computational results show that the canonical duality theory can be used to identify chaotic systems and to obtain realistic global optimal solutions in nonlinear dynamical systems. The method and results presented in this paper should bring some new insights into nonlinear dynamical systems and NP-hardness in computational complexity theory.
New displacement-based methods for optimal truss topology design
NASA Technical Reports Server (NTRS)
Bendsoe, Martin P.; Ben-Tal, Aharon; Haftka, Raphael T.
1991-01-01
Two alternate methods for maximum stiffness truss topology design are presented. The ground structure approach is used, and the problem is formulated in terms of displacements and bar areas. This large, nonconvex optimization problem can be solved by a simultaneous analysis and design approach. Alternatively, an equivalent, unconstrained, and convex problem in the displacements only can be formulated, and this problem can be solved by a nonsmooth, steepest descent algorithm. In both methods, the explicit solving of the equilibrium equations and the assembly of the global stiffness matrix are circumvented. A large number of examples have been studied, showing the attractive features of topology design as well as exposing interesting features of optimal topologies.
ERIC Educational Resources Information Center
Carlgren, Terresa
2013-01-01
The skills of communication, critical thinking, and problem solving are essential to thriving as a citizen in the 21st century. These skills are required in order to contribute as a member of society, operate effectively in post-secondary institutions, and be competitive in the global market. Unfortunately they are not always intuitive or simple…
Motion-based prediction is sufficient to solve the aperture problem
Perrinet, Laurent U; Masson, Guillaume S
2012-01-01
In low-level sensory systems, it is still unclear how the noisy information collected locally by neurons may give rise to a coherent global percept. This is well demonstrated for the detection of motion in the aperture problem: as luminance of an elongated line is symmetrical along its axis, tangential velocity is ambiguous when measured locally. Here, we develop the hypothesis that motion-based predictive coding is sufficient to infer global motion. Our implementation is based on a context-dependent diffusion of a probabilistic representation of motion. We observe in simulations a progressive solution to the aperture problem similar to physiology and behavior. We demonstrate that this solution is the result of two underlying mechanisms. First, we demonstrate the formation of a tracking behavior favoring temporally coherent features independently of their texture. Second, we observe that incoherent features are explained away while coherent information diffuses progressively to the global scale. Most previous models included ad-hoc mechanisms such as end-stopped cells or a selection layer to track specific luminance-based features as necessary conditions to solve the aperture problem. Here, we have proved that motion-based predictive coding, as it is implemented in this functional model, is sufficient to solve the aperture problem. This solution may give insights in the role of prediction underlying a large class of sensory computations. PMID:22734489
Parameter estimation of a pulp digester model with derivative-free optimization strategies
NASA Astrophysics Data System (ADS)
Seiça, João C.; Romanenko, Andrey; Fernandes, Florbela P.; Santos, Lino O.; Fernandes, Natércia C. P.
2017-07-01
The work concerns the parameter estimation in the context of the mechanistic modelling of a pulp digester. The problem is cast as a box bounded nonlinear global optimization problem in order to minimize the mismatch between the model outputs with the experimental data observed at a real pulp and paper plant. MCSFilter and Simulated Annealing global optimization methods were used to solve the optimization problem. While the former took longer to converge to the global minimum, the latter terminated faster at a significantly higher value of the objective function and, thus, failed to find the global solution.
The Evaluation of Reflective Learning Practice: Preparing College Students for Globalization
ERIC Educational Resources Information Center
Richard, Cathleen Becnel
2010-01-01
A problem facing education today is that learning typically requires rote memorization rather than the use of higher-order thinking skills. Higher-order thinking is needed in a global society to solve real world problems, therefore students should be required to develop and practice higher-order thinking skills. The purpose of this mixed method…
A New Approach for Solving the Generalized Traveling Salesman Problem
NASA Astrophysics Data System (ADS)
Pop, P. C.; Matei, O.; Sabo, C.
The generalized traveling problem (GTSP) is an extension of the classical traveling salesman problem. The GTSP is known to be an NP-hard problem and has many interesting applications. In this paper we present a local-global approach for the generalized traveling salesman problem. Based on this approach we describe a novel hybrid metaheuristic algorithm for solving the problem using genetic algorithms. Computational results are reported for Euclidean TSPlib instances and compared with the existing ones. The obtained results point out that our hybrid algorithm is an appropriate method to explore the search space of this complex problem and leads to good solutions in a reasonable amount of time.
A dynamic model of functioning of a bank
NASA Astrophysics Data System (ADS)
Malafeyev, Oleg; Awasthi, Achal; Zaitseva, Irina; Rezenkov, Denis; Bogdanova, Svetlana
2018-04-01
In this paper, we analyze dynamic programming as a novel approach to solve the problem of maximizing the profits of a bank. The mathematical model of the problem and the description of bank's work is described in this paper. The problem is then approached using the method of dynamic programming. Dynamic programming makes sure that the solutions obtained are globally optimal and numerically stable. The optimization process is set up as a discrete multi-stage decision process and solved with the help of dynamic programming.
On unified modeling, theory, and method for solving multi-scale global optimization problems
NASA Astrophysics Data System (ADS)
Gao, David Yang
2016-10-01
A unified model is proposed for general optimization problems in multi-scale complex systems. Based on this model and necessary assumptions in physics, the canonical duality theory is presented in a precise way to include traditional duality theories and popular methods as special applications. Two conjectures on NP-hardness are proposed, which should play important roles for correctly understanding and efficiently solving challenging real-world problems. Applications are illustrated for both nonconvex continuous optimization and mixed integer nonlinear programming.
A new effective operator for the hybrid algorithm for solving global optimisation problems
NASA Astrophysics Data System (ADS)
Duc, Le Anh; Li, Kenli; Nguyen, Tien Trong; Yen, Vu Minh; Truong, Tung Khac
2018-04-01
Hybrid algorithms have been recently used to solve complex single-objective optimisation problems. The ultimate goal is to find an optimised global solution by using these algorithms. Based on the existing algorithms (HP_CRO, PSO, RCCRO), this study proposes a new hybrid algorithm called MPC (Mean-PSO-CRO), which utilises a new Mean-Search Operator. By employing this new operator, the proposed algorithm improves the search ability on areas of the solution space that the other operators of previous algorithms do not explore. Specifically, the Mean-Search Operator helps find the better solutions in comparison with other algorithms. Moreover, the authors have proposed two parameters for balancing local and global search and between various types of local search, as well. In addition, three versions of this operator, which use different constraints, are introduced. The experimental results on 23 benchmark functions, which are used in previous works, show that our framework can find better optimal or close-to-optimal solutions with faster convergence speed for most of the benchmark functions, especially the high-dimensional functions. Thus, the proposed algorithm is more effective in solving single-objective optimisation problems than the other existing algorithms.
NASA Astrophysics Data System (ADS)
Roy, Satadru
Traditional approaches to design and optimize a new system, often, use a system-centric objective and do not take into consideration how the operator will use this new system alongside of other existing systems. This "hand-off" between the design of the new system and how the new system operates alongside other systems might lead to a sub-optimal performance with respect to the operator-level objective. In other words, the system that is optimal for its system-level objective might not be best for the system-of-systems level objective of the operator. Among the few available references that describe attempts to address this hand-off, most follow an MDO-motivated subspace decomposition approach of first designing a very good system and then provide this system to the operator who decides the best way to use this new system along with the existing systems. The motivating example in this dissertation presents one such similar problem that includes aircraft design, airline operations and revenue management "subspaces". The research here develops an approach that could simultaneously solve these subspaces posed as a monolithic optimization problem. The monolithic approach makes the problem a Mixed Integer/Discrete Non-Linear Programming (MINLP/MDNLP) problem, which are extremely difficult to solve. The presence of expensive, sophisticated engineering analyses further aggravate the problem. To tackle this challenge problem, the work here presents a new optimization framework that simultaneously solves the subspaces to capture the "synergism" in the problem that the previous decomposition approaches may not have exploited, addresses mixed-integer/discrete type design variables in an efficient manner, and accounts for computationally expensive analysis tools. The framework combines concepts from efficient global optimization, Kriging partial least squares, and gradient-based optimization. This approach then demonstrates its ability to solve an 11 route airline network problem consisting of 94 decision variables including 33 integer and 61 continuous type variables. This application problem is a representation of an interacting group of systems and provides key challenges to the optimization framework to solve the MINLP problem, as reflected by the presence of a moderate number of integer and continuous type design variables and expensive analysis tool. The result indicates simultaneously solving the subspaces could lead to significant improvement in the fleet-level objective of the airline when compared to the previously developed sequential subspace decomposition approach. In developing the approach to solve the MINLP/MDNLP challenge problem, several test problems provided the ability to explore performance of the framework. While solving these test problems, the framework showed that it could solve other MDNLP problems including categorically discrete variables, indicating that the framework could have broader application than the new aircraft design-fleet allocation-revenue management problem.
NASA Astrophysics Data System (ADS)
Masuda, Kazuaki; Aiyoshi, Eitaro
We propose a method for solving optimal price decision problems for simultaneous multi-article auctions. An auction problem, originally formulated as a combinatorial problem, determines both every seller's whether or not to sell his/her article and every buyer's which article(s) to buy, so that the total utility of buyers and sellers will be maximized. Due to the duality theory, we transform it equivalently into a dual problem in which Lagrange multipliers are interpreted as articles' transaction price. As the dual problem is a continuous optimization problem with respect to the multipliers (i.e., the transaction prices), we propose a numerical method to solve it by applying heuristic global search methods. In this paper, Particle Swarm Optimization (PSO) is used to solve the dual problem, and experimental results are presented to show the validity of the proposed method.
Cognitive functioning and everyday problem solving in older adults.
Burton, Catherine L; Strauss, Esther; Hultsch, David F; Hunter, Michael A
2006-09-01
The relationship between cognitive functioning and a performance-based measure of everyday problem-solving, the Everyday Problems Test (EPT), thought to index instrumental activities of daily living (IADL), was examined in 291 community-dwelling non-demented older adults. Performance on the EPT was found to vary according to age, cognitive status, and education. Hierarchical regression analyses revealed that, after adjusting for demographic and health variables, measures of cognitive functioning accounted for 23.6% of the variance in EPT performance. In particular, measures of global cognitive status, cognitive decline, speed of processing, executive functioning, episodic memory, and verbal ability were significant predictors of EPT performance. These findings suggest that cognitive functioning along with demographic variables are important determinants of everyday problem-solving.
A Solution Framework for Environmental Characterization Problems
This paper describes experiences developing a grid-enabled framework for solving environmental inverse problems. The solution approach taken here couples environmental simulation models with global search methods and requires readily available computational resources of the grid ...
International Agreements and Cooperation in Environmental Conservation and Resource Management.
ERIC Educational Resources Information Center
Thacher, Peter S.
1991-01-01
Considerations regarding stabilizing the greenhouse effect (global warming) emphasize the difficulties in launching a comprehensive plan to deal with aspects of global change. Experience gained in dealing with atmospheric issues will help in developing a process that links management and research in solving global problems. (SLD)
ERIC Educational Resources Information Center
Wise, Mark; McTighe, Jay
2017-01-01
From global hunger to the world's water crisis, middle school students at New Jersey's West Windsor-Plainsboro Regional School District spend the last few days of the school year problem solving about the planet's most dire issues. With the Global Challenge, the school district's administrators not only want to implement an interesting and dynamic…
Principle of serviceability and gratuitousness in transplantation?
Pashkov, Vitaliy M; Golovanova, Iryna A; Noha, Petro P
the issue of commercialization of transplantation analyses in the article. Attention is paid to the importance of transplantation as a method of treatment and saving human lives. the clarify the feasibility of the introduction of donation commercialization as an avenue to solve the shortage of donor organs and means of combating with black organ market and finding alternative avenues solving these problems, which are more morally acceptable for society is the aim of this article. the experience of foreign countries has been analyses in the research. Additionally, we used data from international organizations, conclusions scientists and report of Global Financial Integrity in the research. it is impossible to solve most problems by means of paid donation. therapeutic organ and tissue cloning based on genetic technology is the best way out and solving ethical transplantation problems.
Tuo, Shouheng; Yong, Longquan; Deng, Fang’an; Li, Yanhai; Lin, Yong; Lu, Qiuju
2017-01-01
Harmony Search (HS) and Teaching-Learning-Based Optimization (TLBO) as new swarm intelligent optimization algorithms have received much attention in recent years. Both of them have shown outstanding performance for solving NP-Hard optimization problems. However, they also suffer dramatic performance degradation for some complex high-dimensional optimization problems. Through a lot of experiments, we find that the HS and TLBO have strong complementarity each other. The HS has strong global exploration power but low convergence speed. Reversely, the TLBO has much fast convergence speed but it is easily trapped into local search. In this work, we propose a hybrid search algorithm named HSTLBO that merges the two algorithms together for synergistically solving complex optimization problems using a self-adaptive selection strategy. In the HSTLBO, both HS and TLBO are modified with the aim of balancing the global exploration and exploitation abilities, where the HS aims mainly to explore the unknown regions and the TLBO aims to rapidly exploit high-precision solutions in the known regions. Our experimental results demonstrate better performance and faster speed than five state-of-the-art HS variants and show better exploration power than five good TLBO variants with similar run time, which illustrates that our method is promising in solving complex high-dimensional optimization problems. The experiment on portfolio optimization problems also demonstrate that the HSTLBO is effective in solving complex read-world application. PMID:28403224
Tuo, Shouheng; Yong, Longquan; Deng, Fang'an; Li, Yanhai; Lin, Yong; Lu, Qiuju
2017-01-01
Harmony Search (HS) and Teaching-Learning-Based Optimization (TLBO) as new swarm intelligent optimization algorithms have received much attention in recent years. Both of them have shown outstanding performance for solving NP-Hard optimization problems. However, they also suffer dramatic performance degradation for some complex high-dimensional optimization problems. Through a lot of experiments, we find that the HS and TLBO have strong complementarity each other. The HS has strong global exploration power but low convergence speed. Reversely, the TLBO has much fast convergence speed but it is easily trapped into local search. In this work, we propose a hybrid search algorithm named HSTLBO that merges the two algorithms together for synergistically solving complex optimization problems using a self-adaptive selection strategy. In the HSTLBO, both HS and TLBO are modified with the aim of balancing the global exploration and exploitation abilities, where the HS aims mainly to explore the unknown regions and the TLBO aims to rapidly exploit high-precision solutions in the known regions. Our experimental results demonstrate better performance and faster speed than five state-of-the-art HS variants and show better exploration power than five good TLBO variants with similar run time, which illustrates that our method is promising in solving complex high-dimensional optimization problems. The experiment on portfolio optimization problems also demonstrate that the HSTLBO is effective in solving complex read-world application.
Leon, Juan S; Winskell, Kate; McFarland, Deborah A; del Rio, Carlos
2015-03-01
Global health is a dynamic, emerging, and interdisciplinary field. To address current and emerging global health challenges, we need a public health workforce with adaptable and collaborative problem-solving skills. In the 2013-2014 academic year, the Hubert Department of Global Health at the Rollins School of Public Health-Emory University launched an innovative required core course for its first-year Master of Public Health students in the global health track. The course uses a case-based, problem-based learning approach to develop global health competencies. Small teams of students propose solutions to these problems by identifying learning issues and critically analyzing and synthesizing new information. We describe the course structure and logistics used to apply this approach in the context of a large class and share lessons learned.
Duarte, Belmiro P.M.; Wong, Weng Kee; Atkinson, Anthony C.
2016-01-01
T-optimum designs for model discrimination are notoriously difficult to find because of the computational difficulty involved in solving an optimization problem that involves two layers of optimization. Only a handful of analytical T-optimal designs are available for the simplest problems; the rest in the literature are found using specialized numerical procedures for a specific problem. We propose a potentially more systematic and general way for finding T-optimal designs using a Semi-Infinite Programming (SIP) approach. The strategy requires that we first reformulate the original minimax or maximin optimization problem into an equivalent semi-infinite program and solve it using an exchange-based method where lower and upper bounds produced by solving the outer and the inner programs, are iterated to convergence. A global Nonlinear Programming (NLP) solver is used to handle the subproblems, thus finding the optimal design and the least favorable parametric configuration that minimizes the residual sum of squares from the alternative or test models. We also use a nonlinear program to check the global optimality of the SIP-generated design and automate the construction of globally optimal designs. The algorithm is successfully used to produce results that coincide with several T-optimal designs reported in the literature for various types of model discrimination problems with normally distributed errors. However, our method is more general, merely requiring that the parameters of the model be estimated by a numerical optimization. PMID:27330230
Duarte, Belmiro P M; Wong, Weng Kee; Atkinson, Anthony C
2015-03-01
T-optimum designs for model discrimination are notoriously difficult to find because of the computational difficulty involved in solving an optimization problem that involves two layers of optimization. Only a handful of analytical T-optimal designs are available for the simplest problems; the rest in the literature are found using specialized numerical procedures for a specific problem. We propose a potentially more systematic and general way for finding T-optimal designs using a Semi-Infinite Programming (SIP) approach. The strategy requires that we first reformulate the original minimax or maximin optimization problem into an equivalent semi-infinite program and solve it using an exchange-based method where lower and upper bounds produced by solving the outer and the inner programs, are iterated to convergence. A global Nonlinear Programming (NLP) solver is used to handle the subproblems, thus finding the optimal design and the least favorable parametric configuration that minimizes the residual sum of squares from the alternative or test models. We also use a nonlinear program to check the global optimality of the SIP-generated design and automate the construction of globally optimal designs. The algorithm is successfully used to produce results that coincide with several T-optimal designs reported in the literature for various types of model discrimination problems with normally distributed errors. However, our method is more general, merely requiring that the parameters of the model be estimated by a numerical optimization.
Multichannel signal enhancement
Lewis, Paul S.
1990-01-01
A mixed adaptive filter is formulated for the signal processing problem where desired a priori signal information is not available. The formulation generates a least squares problem which enables the filter output to be calculated directly from an input data matrix. In one embodiment, a folded processor array enables bidirectional data flow to solve the recursive problem by back substitution without global communications. In another embodiment, a balanced processor array solves the recursive problem by forward elimination through the array. In a particular application to magnetoencephalography, the mixed adaptive filter enables an evoked response to an auditory stimulus to be identified from only a single trial.
Efficient Preconditioning for the p-Version Finite Element Method in Two Dimensions
1989-10-01
paper, we study fast parallel preconditioners for systems of equations arising from the p-version finite element method. The p-version finite element...computations and the solution of a relatively small global auxiliary problem. We study two different methods. In the first (Section 3), the global...20], will be studied in the next section. Problem (3.12) is obviously much more easily solved than the original problem ,nd the procedure is highly
A Novel Harmony Search Algorithm Based on Teaching-Learning Strategies for 0-1 Knapsack Problems
Tuo, Shouheng; Yong, Longquan; Deng, Fang'an
2014-01-01
To enhance the performance of harmony search (HS) algorithm on solving the discrete optimization problems, this paper proposes a novel harmony search algorithm based on teaching-learning (HSTL) strategies to solve 0-1 knapsack problems. In the HSTL algorithm, firstly, a method is presented to adjust dimension dynamically for selected harmony vector in optimization procedure. In addition, four strategies (harmony memory consideration, teaching-learning strategy, local pitch adjusting, and random mutation) are employed to improve the performance of HS algorithm. Another improvement in HSTL method is that the dynamic strategies are adopted to change the parameters, which maintains the proper balance effectively between global exploration power and local exploitation power. Finally, simulation experiments with 13 knapsack problems show that the HSTL algorithm can be an efficient alternative for solving 0-1 knapsack problems. PMID:24574905
A novel harmony search algorithm based on teaching-learning strategies for 0-1 knapsack problems.
Tuo, Shouheng; Yong, Longquan; Deng, Fang'an
2014-01-01
To enhance the performance of harmony search (HS) algorithm on solving the discrete optimization problems, this paper proposes a novel harmony search algorithm based on teaching-learning (HSTL) strategies to solve 0-1 knapsack problems. In the HSTL algorithm, firstly, a method is presented to adjust dimension dynamically for selected harmony vector in optimization procedure. In addition, four strategies (harmony memory consideration, teaching-learning strategy, local pitch adjusting, and random mutation) are employed to improve the performance of HS algorithm. Another improvement in HSTL method is that the dynamic strategies are adopted to change the parameters, which maintains the proper balance effectively between global exploration power and local exploitation power. Finally, simulation experiments with 13 knapsack problems show that the HSTL algorithm can be an efficient alternative for solving 0-1 knapsack problems.
Application of the gravity search algorithm to multi-reservoir operation optimization
NASA Astrophysics Data System (ADS)
Bozorg-Haddad, Omid; Janbaz, Mahdieh; Loáiciga, Hugo A.
2016-12-01
Complexities in river discharge, variable rainfall regime, and drought severity merit the use of advanced optimization tools in multi-reservoir operation. The gravity search algorithm (GSA) is an evolutionary optimization algorithm based on the law of gravity and mass interactions. This paper explores the GSA's efficacy for solving benchmark functions, single reservoir, and four-reservoir operation optimization problems. The GSA's solutions are compared with those of the well-known genetic algorithm (GA) in three optimization problems. The results show that the GSA's results are closer to the optimal solutions than the GA's results in minimizing the benchmark functions. The average values of the objective function equal 1.218 and 1.746 with the GSA and GA, respectively, in solving the single-reservoir hydropower operation problem. The global solution equals 1.213 for this same problem. The GSA converged to 99.97% of the global solution in its average-performing history, while the GA converged to 97% of the global solution of the four-reservoir problem. Requiring fewer parameters for algorithmic implementation and reaching the optimal solution in fewer number of functional evaluations are additional advantages of the GSA over the GA. The results of the three optimization problems demonstrate a superior performance of the GSA for optimizing general mathematical problems and the operation of reservoir systems.
Global Warming: Lessons from Ozone Depletion
ERIC Educational Resources Information Center
Hobson, Art
2010-01-01
My teaching and textbook have always covered many physics-related social issues, including stratospheric ozone depletion and global warming. The ozone saga is an inspiring good-news story that's instructive for solving the similar but bigger problem of global warming. Thus, as soon as students in my physics literacy course at the University of…
Preparing Teachers for a Globalized Era: An Examination of Teaching Practices in Kenya
ERIC Educational Resources Information Center
Nganga, Lydiah; Kambutu, John
2017-01-01
In an increasingly globalized 21st century, an education that is student-centered is invaluable because it supports the development of collaborative, communication and problem-solving skills (Cooke-Canitz, 2013; Kambutu & Nganga, 2009). Indeed, globalization thrives in a context of collaboration between people of different cultural persuasions…
Ozone, Climate, and Global Atmospheric Change
NASA Technical Reports Server (NTRS)
Levine, Joel S.
1992-01-01
The delicate balance of the gases that make up our atmosphere allows life to exist on Earth. Ozone depletion and global warming are related to changes in the concentrations of these gases. To solve global atmospheric problems, we need to understand the composition and chemistry of the Earth's atmosphere and the impact of human activities on them.
Examining Undergraduate Student Attitude towards Interdisciplinary Education
ERIC Educational Resources Information Center
DiBenedetto, Catherine A.; Lamm, Kevan W.; Lamm, Alexa J.; Myers, Brian E.
2016-01-01
As the global population grows, concern for a food shortage may be looming. As the next generations of agricultural and natural resource leaders are prepared to address this challenge, input throughout multiple disciplines is required to solve this dilemma. Undergraduates must be prepared to engage in problem solving and entrepreneurial thinking…
DOE Office of Scientific and Technical Information (OSTI.GOV)
Huang, Kuo -Ling; Mehrotra, Sanjay
We present a homogeneous algorithm equipped with a modified potential function for the monotone complementarity problem. We show that this potential function is reduced by at least a constant amount if a scaled Lipschitz condition (SLC) is satisfied. A practical algorithm based on this potential function is implemented in a software package named iOptimize. The implementation in iOptimize maintains global linear and polynomial time convergence properties, while achieving practical performance. It either successfully solves the problem, or concludes that the SLC is not satisfied. When compared with the mature software package MOSEK (barrier solver version 6.0.0.106), iOptimize solves convex quadraticmore » programming problems, convex quadratically constrained quadratic programming problems, and general convex programming problems in fewer iterations. Moreover, several problems for which MOSEK fails are solved to optimality. In addition, we also find that iOptimize detects infeasibility more reliably than the general nonlinear solvers Ipopt (version 3.9.2) and Knitro (version 8.0).« less
Global planning of several plants
NASA Technical Reports Server (NTRS)
Bescos, Sylvie
1992-01-01
This paper discusses an attempt to solve the problem of planning several pharmaceutical plants at a global level. The interest in planning at this level is to increase the global control over the production process, to improve its overall efficiency, and to reduce the need for interaction between production plants. In order to reduce the complexity of this problem and to make it tractable, some abstractions were made. Based on these abstractions, a prototype is being developed within the framework of the EUREKA project PROTOS, using Constraint Logic Programming techniques.
Pigache, Francois; Messine, Frédéric; Nogarede, Bertrand
2007-07-01
This paper deals with a deterministic and rational way to design piezoelectric transformers in radial mode. The proposed approach is based on the study of the inverse problem of design and on its reformulation as a mixed constrained global optimization problem. The methodology relies on the association of the analytical models for describing the corresponding optimization problem and on an exact global optimization software, named IBBA and developed by the second author to solve it. Numerical experiments are presented and compared in order to validate the proposed approach.
ERIC Educational Resources Information Center
Hay, M. Cameron
2017-01-01
Undergraduate student learning focuses on the development of disciplinary strength in majors and minors so that students gain depth in particular fields, foster individual expertise, and learn problem solving from disciplinary perspectives. However, the complexities of real-world problems do not respect disciplinary boundaries. Complex problems…
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.
Winskell, Kate; McFarland, Deborah A.; del Rio, Carlos
2015-01-01
Global health is a dynamic, emerging, and interdisciplinary field. To address current and emerging global health challenges, we need a public health workforce with adaptable and collaborative problem-solving skills. In the 2013–2014 academic year, the Hubert Department of Global Health at the Rollins School of Public Health–Emory University launched an innovative required core course for its first-year Master of Public Health students in the global health track. The course uses a case-based, problem-based learning approach to develop global health competencies. Small teams of students propose solutions to these problems by identifying learning issues and critically analyzing and synthesizing new information. We describe the course structure and logistics used to apply this approach in the context of a large class and share lessons learned. PMID:25706029
ERIC Educational Resources Information Center
van Velzen, Joke H.
2016-01-01
The mathematics curriculum often provides for relatively few mathematical thinking problems or non-routine problems that focus on a deepening of understanding mathematical concepts and the problem-solving process. To develop such problems, methods are required to evaluate their suitability. The purpose of this preliminary study was to find such an…
ERIC Educational Resources Information Center
Utsumi, Takeshi; Mogalhaes, Maria Rosa Abreu
1993-01-01
Describes accomplishments of the Global Systems Analysis and Simulation (GLOSAS) project from 1973 to the present, including a system for global peace gaming. The capabilities of interactive multimedia to link people across political and geographic boundaries for joint study, debate, research, planetary problem solving, and political action are…
Students without Borders: Global Collaborative Learning Connects School to the Real World
ERIC Educational Resources Information Center
Bickley, Mali; Carleton, Jim
2009-01-01
Kids can't help but get engaged when they're collaborating with peers across the globe to solve real-life problems. Global collaborative learning is about connecting students in communities of learners around the world so they can work together on projects that make a difference locally and globally. It is about building relationships and…
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'.
Designing a Better Experience: A Qualitative Investigation of Student Engineering Internships
ERIC Educational Resources Information Center
Paknejad, Mohammad R.
2016-01-01
Science, Technology, Engineering and Mathematics (STEM) education play a very important role in preparing students with skills necessary to obtain better jobs, solve real-world challenges, and compete in the global economy. STEM education develops critical thinking and the ability to solve complex problems. Research showed that 8 out of 10 most…
Multiple-stage ambiguity in motion perception reveals global computation of local motion directions.
Rider, Andrew T; Nishida, Shin'ya; Johnston, Alan
2016-12-01
The motion of a 1D image feature, such as a line, seen through a small aperture, or the small receptive field of a neural motion sensor, is underconstrained, and it is not possible to derive the true motion direction from a single local measurement. This is referred to as the aperture problem. How the visual system solves the aperture problem is a fundamental question in visual motion research. In the estimation of motion vectors through integration of ambiguous local motion measurements at different positions, conventional theories assume that the object motion is a rigid translation, with motion signals sharing a common motion vector within the spatial region over which the aperture problem is solved. However, this strategy fails for global rotation. Here we show that the human visual system can estimate global rotation directly through spatial pooling of locally ambiguous measurements, without an intervening step that computes local motion vectors. We designed a novel ambiguous global flow stimulus, which is globally as well as locally ambiguous. The global ambiguity implies that the stimulus is simultaneously consistent with both a global rigid translation and an infinite number of global rigid rotations. By the standard view, the motion should always be seen as a global translation, but it appears to shift from translation to rotation as observers shift fixation. This finding indicates that the visual system can estimate local vectors using a global rotation constraint, and suggests that local motion ambiguity may not be resolved until consistencies with multiple global motion patterns are assessed.
Interior search algorithm (ISA): a novel approach for global optimization.
Gandomi, Amir H
2014-07-01
This paper presents the interior search algorithm (ISA) as a novel method for solving optimization tasks. The proposed ISA is inspired by interior design and decoration. The algorithm is different from other metaheuristic algorithms and provides new insight for global optimization. The proposed method is verified using some benchmark mathematical and engineering problems commonly used in the area of optimization. ISA results are further compared with well-known optimization algorithms. The results show that the ISA is efficiently capable of solving optimization problems. The proposed algorithm can outperform the other well-known algorithms. Further, the proposed algorithm is very simple and it only has one parameter to tune. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.
Reinventing Teaching and Testing: Quality Learning for Quality Employment.
ERIC Educational Resources Information Center
Cooke, Brian P.
To succeed in today's competitive global markets, organizations are hiring responsible problem solvers and collaborative "associates" who improve productivity, assure quality service, and contribute creatively. These organizations demand employees who are skilled at learning to learn, listening, communicating, problem solving, teamwork,…
Huang, Kuo -Ling; Mehrotra, Sanjay
2016-11-08
We present a homogeneous algorithm equipped with a modified potential function for the monotone complementarity problem. We show that this potential function is reduced by at least a constant amount if a scaled Lipschitz condition (SLC) is satisfied. A practical algorithm based on this potential function is implemented in a software package named iOptimize. The implementation in iOptimize maintains global linear and polynomial time convergence properties, while achieving practical performance. It either successfully solves the problem, or concludes that the SLC is not satisfied. When compared with the mature software package MOSEK (barrier solver version 6.0.0.106), iOptimize solves convex quadraticmore » programming problems, convex quadratically constrained quadratic programming problems, and general convex programming problems in fewer iterations. Moreover, several problems for which MOSEK fails are solved to optimality. In addition, we also find that iOptimize detects infeasibility more reliably than the general nonlinear solvers Ipopt (version 3.9.2) and Knitro (version 8.0).« less
Examples of Linking Codes Within GeoFramework
NASA Astrophysics Data System (ADS)
Tan, E.; Choi, E.; Thoutireddy, P.; Aivazis, M.; Lavier, L.; Quenette, S.; Gurnis, M.
2003-12-01
Geological processes usually encompass a broad spectrum of length and time scales. Traditionally, a modeling code (solver) is written to solve a problem with specific length and time scales in mind. The utility of the solver beyond the designated purpose is usually limited. Furthermore, two distinct solvers, even if each can solve complementary parts of a new problem, are difficult to link together to solve the problem as a whole. For example, Lagrangian deformation model with visco-elastoplastic crust is used to study deformation near plate boundary. Ideally, the driving force of the deformation should be derived from underlying mantle convection, and it requires linking the Lagrangian deformation model with a Eulerian mantle convection model. As our understanding of geological processes evolves, the need of integrated modeling codes, which should reuse existing codes as much as possible, begins to surface. GeoFramework project addresses this need by developing a suite of reusable and re-combinable tools for the Earth science community. GeoFramework is based on and extends Pyre, a Python-based modeling framework, recently developed to link solid (Lagrangian) and fluid (Eulerian) models, as well as mesh generators, visualization packages, and databases, with one another for engineering applications. Under the framework, a solver is aware of the existence of other solvers and can interact with each other via exchanging information across adjacent boundary. A solver needs to conform a standard interface and provide its own implementation for exchanging boundary information. The framework also provides facilities to control the coordination between interacting solvers. We will show an example of linking two solvers within GeoFramework. CitcomS is a finite element code which solves for thermal convection within a 3D spherical shell. CitcomS can solve for problems either within a full spherical (global) domain or a restricted (regional) domain of a full sphere by using different meshers. We can embed a regional CitcomS solver within a global CitcomS solver. We not that linking instances of the same solver is conceptually equivalent to linking to different solvers. The global solver has a coarser grid and a longer stable time step than the regional solver. Therefore, a global-solver time step consists of several regional-solver time steps. The time-marching scheme is described below. First, the global solver is advanced one global-solver time step. Then, the regional solver is advanced for several regional-solver time steps until it catches up global solver. Within each regional-solver time step, the velocity field of the global solver is interpolated in time and then is imposed to the regional solver as boundary conditions. Finally, the temperature field of the regional solver is extrapolated in space and is fed back to the global. These two solvers are linked and synchronized by the time-marching scheme. An effort to embed a visco-elastoplastic representation of the crust within viscous mantle flow is underway.
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.
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.
A family of conjugate gradient methods for large-scale nonlinear equations.
Feng, Dexiang; Sun, Min; Wang, Xueyong
2017-01-01
In this paper, we present a family of conjugate gradient projection methods for solving large-scale nonlinear equations. At each iteration, it needs low storage and the subproblem can be easily solved. Compared with the existing solution methods for solving the problem, its global convergence is established without the restriction of the Lipschitz continuity on the underlying mapping. Preliminary numerical results are reported to show the efficiency of the proposed method.
Remote sensing image stitch using modified structure deformation
NASA Astrophysics Data System (ADS)
Pan, Ke-cheng; Chen, Jin-wei; Chen, Yueting; Feng, Huajun
2012-10-01
To stitch remote sensing images seamlessly without producing visual artifact which is caused by severe intensity discrepancy and structure misalignment, we modify the original structure deformation based stitching algorithm which have two main problems: Firstly, using Poisson equation to propagate deformation vectors leads to the change of the topological relationship between the key points and their surrounding pixels, which may bring in wrong image characteristics. Secondly, the diffusion area of the sparse matrix is too limited to rectify the global intensity discrepancy. To solve the first problem, we adopt Spring-Mass model and bring in external force to keep the topological relationship between key points and their surrounding pixels. We also apply tensor voting algorithm to achieve the global intensity corresponding curve of the two images to solve the second problem. Both simulated and experimental results show that our algorithm is faster and can reach better result than the original algorithm.
DQM: Decentralized Quadratically Approximated Alternating Direction Method of Multipliers
NASA Astrophysics Data System (ADS)
Mokhtari, Aryan; Shi, Wei; Ling, Qing; Ribeiro, Alejandro
2016-10-01
This paper considers decentralized consensus optimization problems where nodes of a network have access to different summands of a global objective function. Nodes cooperate to minimize the global objective by exchanging information with neighbors only. A decentralized version of the alternating directions method of multipliers (DADMM) is a common method for solving this category of problems. DADMM exhibits linear convergence rate to the optimal objective but its implementation requires solving a convex optimization problem at each iteration. This can be computationally costly and may result in large overall convergence times. The decentralized quadratically approximated ADMM algorithm (DQM), which minimizes a quadratic approximation of the objective function that DADMM minimizes at each iteration, is proposed here. The consequent reduction in computational time is shown to have minimal effect on convergence properties. Convergence still proceeds at a linear rate with a guaranteed constant that is asymptotically equivalent to the DADMM linear convergence rate constant. Numerical results demonstrate advantages of DQM relative to DADMM and other alternatives in a logistic regression problem.
ERIC Educational Resources Information Center
Quiroz Enriquez, Brissa Yazmin
2013-01-01
Global environmental change is occurring, putting our planet under pressure. Children need to understand not only why the environment is important, but also their role as citizens of a globalized society and their necessary contribution to solving global problems. Students carry their own perspectives about the environment and education, and these…
Multiscale global identification of porous structures
NASA Astrophysics Data System (ADS)
Hatłas, Marcin; Beluch, Witold
2018-01-01
The paper is devoted to the evolutionary identification of the material constants of porous structures based on measurements conducted on a macro scale. Numerical homogenization with the RVE concept is used to determine the equivalent properties of a macroscopically homogeneous material. Finite element method software is applied to solve the boundary-value problem in both scales. Global optimization methods in form of evolutionary algorithm are employed to solve the identification task. Modal analysis is performed to collect the data necessary for the identification. A numerical example presenting the effectiveness of proposed attitude is attached.
Mathematics and Global Survival.
ERIC Educational Resources Information Center
Schwartz, Richard H.
This resource was written to provide students with an awareness of critical issues facing the world today. In courses for college students, it can motivate their study of mathematics, teach them how to solve mathematical problems related to current global issues, provide coherence to mathematical studies through a focus on issues of human…
Environmental Education through Watershed Studies: Budd/Deschutes Project GREEN.
ERIC Educational Resources Information Center
Lewis, Lisa Bryce
1992-01-01
Describes the development and current status of the Global Rivers Environmental Education Network, cited as an exemplary Environmental Education program in the Pacific northwest. It is an international educational effort that provides a means for improving local and global water quality through hands-on monitoring and local problem solving for…
NASA Astrophysics Data System (ADS)
Ranaivomiarana, Narindra; Irisarri, François-Xavier; Bettebghor, Dimitri; Desmorat, Boris
2018-04-01
An optimization methodology to find concurrently material spatial distribution and material anisotropy repartition is proposed for orthotropic, linear and elastic two-dimensional membrane structures. The shape of the structure is parameterized by a density variable that determines the presence or absence of material. The polar method is used to parameterize a general orthotropic material by its elasticity tensor invariants by change of frame. A global structural stiffness maximization problem written as a compliance minimization problem is treated, and a volume constraint is applied. The compliance minimization can be put into a double minimization of complementary energy. An extension of the alternate directions algorithm is proposed to solve the double minimization problem. The algorithm iterates between local minimizations in each element of the structure and global minimizations. Thanks to the polar method, the local minimizations are solved explicitly providing analytical solutions. The global minimizations are performed with finite element calculations. The method is shown to be straightforward and efficient. Concurrent optimization of density and anisotropy distribution of a cantilever beam and a bridge are presented.
NASA Astrophysics Data System (ADS)
Fu, Junjie; Wang, Jin-zhi
2017-09-01
In this paper, we study the finite-time consensus problems with globally bounded convergence time also known as fixed-time consensus problems for multi-agent systems subject to directed communication graphs. Two new distributed control strategies are proposed such that leaderless and leader-follower consensus are achieved with convergence time independent on the initial conditions of the agents. Fixed-time formation generation and formation tracking problems are also solved as the generalizations. Simulation examples are provided to demonstrate the performance of the new controllers.
Annealing Ant Colony Optimization with Mutation Operator for Solving TSP.
Mohsen, Abdulqader M
2016-01-01
Ant Colony Optimization (ACO) has been successfully applied to solve a wide range of combinatorial optimization problems such as minimum spanning tree, traveling salesman problem, and quadratic assignment problem. Basic ACO has drawbacks of trapping into local minimum and low convergence rate. Simulated annealing (SA) and mutation operator have the jumping ability and global convergence; and local search has the ability to speed up the convergence. Therefore, this paper proposed a hybrid ACO algorithm integrating the advantages of ACO, SA, mutation operator, and local search procedure to solve the traveling salesman problem. The core of algorithm is based on the ACO. SA and mutation operator were used to increase the ants population diversity from time to time and the local search was used to exploit the current search area efficiently. The comparative experiments, using 24 TSP instances from TSPLIB, show that the proposed algorithm outperformed some well-known algorithms in the literature in terms of solution quality.
Characterizing L1-norm best-fit subspaces
NASA Astrophysics Data System (ADS)
Brooks, J. Paul; Dulá, José H.
2017-05-01
Fitting affine objects to data is the basis of many tools and methodologies in statistics, machine learning, and signal processing. The L1 norm is often employed to produce subspaces exhibiting a robustness to outliers and faulty observations. The L1-norm best-fit subspace problem is directly formulated as a nonlinear, nonconvex, and nondifferentiable optimization problem. The case when the subspace is a hyperplane can be solved to global optimality efficiently by solving a series of linear programs. The problem of finding the best-fit line has recently been shown to be NP-hard. We present necessary conditions for optimality for the best-fit subspace problem, and use them to characterize properties of optimal solutions.
Liu, Jianfeng; Laird, Carl Damon
2017-09-22
Optimal design of a gas detection systems is challenging because of the numerous sources of uncertainty, including weather and environmental conditions, leak location and characteristics, and process conditions. Rigorous CFD simulations of dispersion scenarios combined with stochastic programming techniques have been successfully applied to the problem of optimal gas detector placement; however, rigorous treatment of sensor failure and nonuniform unavailability has received less attention. To improve reliability of the design, this paper proposes a problem formulation that explicitly considers nonuniform unavailabilities and all backup detection levels. The resulting sensor placement problem is a large-scale mixed-integer nonlinear programming (MINLP) problem thatmore » requires a tailored solution approach for efficient solution. We have developed a multitree method which depends on iteratively solving a sequence of upper-bounding master problems and lower-bounding subproblems. The tailored global solution strategy is tested on a real data problem and the encouraging numerical results indicate that our solution framework is promising in solving sensor placement problems. This study was selected for the special issue in JLPPI from the 2016 International Symposium of the MKO Process Safety Center.« less
DOE Office of Scientific and Technical Information (OSTI.GOV)
Liu, Jianfeng; Laird, Carl Damon
Optimal design of a gas detection systems is challenging because of the numerous sources of uncertainty, including weather and environmental conditions, leak location and characteristics, and process conditions. Rigorous CFD simulations of dispersion scenarios combined with stochastic programming techniques have been successfully applied to the problem of optimal gas detector placement; however, rigorous treatment of sensor failure and nonuniform unavailability has received less attention. To improve reliability of the design, this paper proposes a problem formulation that explicitly considers nonuniform unavailabilities and all backup detection levels. The resulting sensor placement problem is a large-scale mixed-integer nonlinear programming (MINLP) problem thatmore » requires a tailored solution approach for efficient solution. We have developed a multitree method which depends on iteratively solving a sequence of upper-bounding master problems and lower-bounding subproblems. The tailored global solution strategy is tested on a real data problem and the encouraging numerical results indicate that our solution framework is promising in solving sensor placement problems. This study was selected for the special issue in JLPPI from the 2016 International Symposium of the MKO Process Safety Center.« less
NASA Astrophysics Data System (ADS)
Fazayeli, Saeed; Eydi, Alireza; Kamalabadi, Isa Nakhai
2017-07-01
Nowadays, organizations have to compete with different competitors in regional, national and international levels, so they have to improve their competition capabilities to survive against competitors. Undertaking activities on a global scale requires a proper distribution system which could take advantages of different transportation modes. Accordingly, the present paper addresses a location-routing problem on multimodal transportation network. The introduced problem follows four objectives simultaneously which form main contribution of the paper; determining multimodal routes between supplier and distribution centers, locating mode changing facilities, locating distribution centers, and determining product delivery tours from the distribution centers to retailers. An integer linear programming is presented for the problem, and a genetic algorithm with a new chromosome structure proposed to solve the problem. Proposed chromosome structure consists of two different parts for multimodal transportation and location-routing parts of the model. Based on published data in the literature, two numerical cases with different sizes generated and solved. Also, different cost scenarios designed to better analyze model and algorithm performance. Results show that algorithm can effectively solve large-size problems within a reasonable time which GAMS software failed to reach an optimal solution even within much longer times.
NASA Astrophysics Data System (ADS)
Fazayeli, Saeed; Eydi, Alireza; Kamalabadi, Isa Nakhai
2018-07-01
Nowadays, organizations have to compete with different competitors in regional, national and international levels, so they have to improve their competition capabilities to survive against competitors. Undertaking activities on a global scale requires a proper distribution system which could take advantages of different transportation modes. Accordingly, the present paper addresses a location-routing problem on multimodal transportation network. The introduced problem follows four objectives simultaneously which form main contribution of the paper; determining multimodal routes between supplier and distribution centers, locating mode changing facilities, locating distribution centers, and determining product delivery tours from the distribution centers to retailers. An integer linear programming is presented for the problem, and a genetic algorithm with a new chromosome structure proposed to solve the problem. Proposed chromosome structure consists of two different parts for multimodal transportation and location-routing parts of the model. Based on published data in the literature, two numerical cases with different sizes generated and solved. Also, different cost scenarios designed to better analyze model and algorithm performance. Results show that algorithm can effectively solve large-size problems within a reasonable time which GAMS software failed to reach an optimal solution even within much longer times.
Stabilisation of discrete-time polynomial fuzzy systems via a polynomial lyapunov approach
NASA Astrophysics Data System (ADS)
Nasiri, Alireza; Nguang, Sing Kiong; Swain, Akshya; Almakhles, Dhafer
2018-02-01
This paper deals with the problem of designing a controller for a class of discrete-time nonlinear systems which is represented by discrete-time polynomial fuzzy model. Most of the existing control design methods for discrete-time fuzzy polynomial systems cannot guarantee their Lyapunov function to be a radially unbounded polynomial function, hence the global stability cannot be assured. The proposed control design in this paper guarantees a radially unbounded polynomial Lyapunov functions which ensures global stability. In the proposed design, state feedback structure is considered and non-convexity problem is solved by incorporating an integrator into the controller. Sufficient conditions of stability are derived in terms of polynomial matrix inequalities which are solved via SOSTOOLS in MATLAB. A numerical example is presented to illustrate the effectiveness of the proposed controller.
NASA Astrophysics Data System (ADS)
Zhang, Langwen; Xie, Wei; Wang, Jingcheng
2017-11-01
In this work, synthesis of robust distributed model predictive control (MPC) is presented for a class of linear systems subject to structured time-varying uncertainties. By decomposing a global system into smaller dimensional subsystems, a set of distributed MPC controllers, instead of a centralised controller, are designed. To ensure the robust stability of the closed-loop system with respect to model uncertainties, distributed state feedback laws are obtained by solving a min-max optimisation problem. The design of robust distributed MPC is then transformed into solving a minimisation optimisation problem with linear matrix inequality constraints. An iterative online algorithm with adjustable maximum iteration is proposed to coordinate the distributed controllers to achieve a global performance. The simulation results show the effectiveness of the proposed robust distributed MPC algorithm.
Fat water decomposition using globally optimal surface estimation (GOOSE) algorithm.
Cui, Chen; Wu, Xiaodong; Newell, John D; Jacob, Mathews
2015-03-01
This article focuses on developing a novel noniterative fat water decomposition algorithm more robust to fat water swaps and related ambiguities. Field map estimation is reformulated as a constrained surface estimation problem to exploit the spatial smoothness of the field, thus minimizing the ambiguities in the recovery. Specifically, the differences in the field map-induced frequency shift between adjacent voxels are constrained to be in a finite range. The discretization of the above problem yields a graph optimization scheme, where each node of the graph is only connected with few other nodes. Thanks to the low graph connectivity, the problem is solved efficiently using a noniterative graph cut algorithm. The global minimum of the constrained optimization problem is guaranteed. The performance of the algorithm is compared with that of state-of-the-art schemes. Quantitative comparisons are also made against reference data. The proposed algorithm is observed to yield more robust fat water estimates with fewer fat water swaps and better quantitative results than other state-of-the-art algorithms in a range of challenging applications. The proposed algorithm is capable of considerably reducing the swaps in challenging fat water decomposition problems. The experiments demonstrate the benefit of using explicit smoothness constraints in field map estimation and solving the problem using a globally convergent graph-cut optimization algorithm. © 2014 Wiley Periodicals, Inc.
Multi-period natural gas market modeling Applications, stochastic extensions and solution approaches
NASA Astrophysics Data System (ADS)
Egging, Rudolf Gerardus
This dissertation develops deterministic and stochastic multi-period mixed complementarity problems (MCP) for the global natural gas market, as well as solution approaches for large-scale stochastic MCP. The deterministic model is unique in the combination of the level of detail of the actors in the natural gas markets and the transport options, the detailed regional and global coverage, the multi-period approach with endogenous capacity expansions for transportation and storage infrastructure, the seasonal variation in demand and the representation of market power according to Nash-Cournot theory. The model is applied to several scenarios for the natural gas market that cover the formation of a cartel by the members of the Gas Exporting Countries Forum, a low availability of unconventional gas in the United States, and cost reductions in long-distance gas transportation. 1 The results provide insights in how different regions are affected by various developments, in terms of production, consumption, traded volumes, prices and profits of market participants. The stochastic MCP is developed and applied to a global natural gas market problem with four scenarios for a time horizon until 2050 with nineteen regions and containing 78,768 variables. The scenarios vary in the possibility of a gas market cartel formation and varying depletion rates of gas reserves in the major gas importing regions. Outcomes for hedging decisions of market participants show some significant shifts in the timing and location of infrastructure investments, thereby affecting local market situations. A first application of Benders decomposition (BD) is presented to solve a large-scale stochastic MCP for the global gas market with many hundreds of first-stage capacity expansion variables and market players exerting various levels of market power. The largest problem solved successfully using BD contained 47,373 variables of which 763 first-stage variables, however using BD did not result in shorter solution times relative to solving the extensive-forms. Larger problems, up to 117,481 variables, were solved in extensive-form, but not when applying BD due to numerical issues. It is discussed how BD could significantly reduce the solution time of large-scale stochastic models, but various challenges remain and more research is needed to assess the potential of Benders decomposition for solving large-scale stochastic MCP. 1 www.gecforum.org
ERIC Educational Resources Information Center
Dowler, Lorraine
Designed so that it can be adapted to a wide range of student abilities and institutional settings, this learning module on the human dimensions of global change seeks to: actively engage students in problem solving, challenge them to think critically, invite them to participate in the process of scientific inquiry, and involve them in cooperative…
The fully actuated traffic control problem solved by global optimization and complementarity
NASA Astrophysics Data System (ADS)
Ribeiro, Isabel M.; de Lurdes de Oliveira Simões, Maria
2016-02-01
Global optimization and complementarity are used to determine the signal timing for fully actuated traffic control, regarding effective green and red times on each cycle. The average values of these parameters can be used to estimate the control delay of vehicles. In this article, a two-phase queuing system for a signalized intersection is outlined, based on the principle of minimization of the total waiting time for the vehicles. The underlying model results in a linear program with linear complementarity constraints, solved by a sequential complementarity algorithm. Departure rates of vehicles during green and yellow periods were treated as deterministic, while arrival rates of vehicles were assumed to follow a Poisson distribution. Several traffic scenarios were created and solved. The numerical results reveal that it is possible to use global optimization and complementarity over a reasonable number of cycles and determine with efficiency effective green and red times for a signalized intersection.
Comparison of penalty functions on a penalty approach to mixed-integer optimization
NASA Astrophysics Data System (ADS)
Francisco, Rogério B.; Costa, M. Fernanda P.; Rocha, Ana Maria A. C.; Fernandes, Edite M. G. P.
2016-06-01
In this paper, we present a comparative study involving several penalty functions that can be used in a penalty approach for globally solving bound mixed-integer nonlinear programming (bMIMLP) problems. The penalty approach relies on a continuous reformulation of the bMINLP problem by adding a particular penalty term to the objective function. A penalty function based on the `erf' function is proposed. The continuous nonlinear optimization problems are sequentially solved by the population-based firefly algorithm. Preliminary numerical experiments are carried out in order to analyze the quality of the produced solutions, when compared with other penalty functions available in the literature.
Global Trends in Workplace Learning
ERIC Educational Resources Information Center
Lee, Lung-Sheng; Lai, Chun-Chin
2012-01-01
The paradigm of human resource development has shifted to workplace learning and performance. Workplace can be an organization, an office, a kitchen, a shop, a farm, a website, even a home. Workplace learning is a dynamic process to solve workplace problems through learning. An identification of global trends of workplace learning can help us to…
Smoothed low rank and sparse matrix recovery by iteratively reweighted least squares minimization.
Lu, Canyi; Lin, Zhouchen; Yan, Shuicheng
2015-02-01
This paper presents a general framework for solving the low-rank and/or sparse matrix minimization problems, which may involve multiple nonsmooth terms. The iteratively reweighted least squares (IRLSs) method is a fast solver, which smooths the objective function and minimizes it by alternately updating the variables and their weights. However, the traditional IRLS can only solve a sparse only or low rank only minimization problem with squared loss or an affine constraint. This paper generalizes IRLS to solve joint/mixed low-rank and sparse minimization problems, which are essential formulations for many tasks. As a concrete example, we solve the Schatten-p norm and l2,q-norm regularized low-rank representation problem by IRLS, and theoretically prove that the derived solution is a stationary point (globally optimal if p,q ≥ 1). Our convergence proof of IRLS is more general than previous one that depends on the special properties of the Schatten-p norm and l2,q-norm. Extensive experiments on both synthetic and real data sets demonstrate that our IRLS is much more efficient.
NASA Astrophysics Data System (ADS)
Ariyarit, Atthaphon; Sugiura, Masahiko; Tanabe, Yasutada; Kanazaki, Masahiro
2018-06-01
A multi-fidelity optimization technique by an efficient global optimization process using a hybrid surrogate model is investigated for solving real-world design problems. The model constructs the local deviation using the kriging method and the global model using a radial basis function. The expected improvement is computed to decide additional samples that can improve the model. The approach was first investigated by solving mathematical test problems. The results were compared with optimization results from an ordinary kriging method and a co-kriging method, and the proposed method produced the best solution. The proposed method was also applied to aerodynamic design optimization of helicopter blades to obtain the maximum blade efficiency. The optimal shape obtained by the proposed method achieved performance almost equivalent to that obtained using the high-fidelity, evaluation-based single-fidelity optimization. Comparing all three methods, the proposed method required the lowest total number of high-fidelity evaluation runs to obtain a converged solution.
Black, D W; Allen, J; St John, D; Pfohl, B; McCormick, B; Blum, N
2009-07-01
Few predictors of treatment outcome or early discontinuation have been identified in persons with borderline personality disorder (BPD). The aim of the study was to examine the relationship between baseline clinical variables and treatment response and early discontinuation in a randomized controlled trial of System Training for Emotional Predictability and Problem Solving, a new cognitive group treatment. Improvement was rated using the Zanarini Rating Scale for BPD, the Clinical Global Impression Scale, the Global Assessment Scale and the Beck Depression Inventory. Subjects were assessed during the 20 week trial and a 1-year follow-up. Higher baseline severity was associated with greater improvement in global functioning and BPD-related symptoms. Higher impulsivity was predictive of early discontinuation. Optimal improvement was associated with attending > or = 15 sessions. Subjects likely to improve have the more severe BPD symptoms at baseline, while high levels of impulsivity are associated with early discontinuation.
Global gene expression analysis by combinatorial optimization.
Ameur, Adam; Aurell, Erik; Carlsson, Mats; Westholm, Jakub Orzechowski
2004-01-01
Generally, there is a trade-off between methods of gene expression analysis that are precise but labor-intensive, e.g. RT-PCR, and methods that scale up to global coverage but are not quite as quantitative, e.g. microarrays. In the present paper, we show how how a known method of gene expression profiling (K. Kato, Nucleic Acids Res. 23, 3685-3690 (1995)), which relies on a fairly small number of steps, can be turned into a global gene expression measurement by advanced data post-processing, with potentially little loss of accuracy. Post-processing here entails solving an ancillary combinatorial optimization problem. Validation is performed on in silico experiments generated from the FANTOM data base of full-length mouse cDNA. We present two variants of the method. One uses state-of-the-art commercial software for solving problems of this kind, the other a code developed by us specifically for this purpose, released in the public domain under GPL license.
Generating realistic images using Kray
NASA Astrophysics Data System (ADS)
Tanski, Grzegorz
2004-07-01
Kray is an application for creating realistic images. It is written in C++ programming language, has a text-based interface, solves global illumination problem using techniques such as radiosity, path tracing and photon mapping.
ERIC Educational Resources Information Center
Graedel, Thomas E.; Crutzen, Paul J.
1989-01-01
Discusses air pollution occurring due to human activity. Describes which human activities generate which emissions, including acid rain, smog, ozone depletion, and change of trace gases. Suggests global effort to solve the earth's environmental problems. (YP)
Hybrid Monte Carlo/deterministic methods for radiation shielding problems
NASA Astrophysics Data System (ADS)
Becker, Troy L.
For the past few decades, the most common type of deep-penetration (shielding) problem simulated using Monte Carlo methods has been the source-detector problem, in which a response is calculated at a single location in space. Traditionally, the nonanalog Monte Carlo methods used to solve these problems have required significant user input to generate and sufficiently optimize the biasing parameters necessary to obtain a statistically reliable solution. It has been demonstrated that this laborious task can be replaced by automated processes that rely on a deterministic adjoint solution to set the biasing parameters---the so-called hybrid methods. The increase in computational power over recent years has also led to interest in obtaining the solution in a region of space much larger than a point detector. In this thesis, we propose two methods for solving problems ranging from source-detector problems to more global calculations---weight windows and the Transform approach. These techniques employ sonic of the same biasing elements that have been used previously; however, the fundamental difference is that here the biasing techniques are used as elements of a comprehensive tool set to distribute Monte Carlo particles in a user-specified way. The weight window achieves the user-specified Monte Carlo particle distribution by imposing a particular weight window on the system, without altering the particle physics. The Transform approach introduces a transform into the neutron transport equation, which results in a complete modification of the particle physics to produce the user-specified Monte Carlo distribution. These methods are tested in a three-dimensional multigroup Monte Carlo code. For a basic shielding problem and a more realistic one, these methods adequately solved source-detector problems and more global calculations. Furthermore, they confirmed that theoretical Monte Carlo particle distributions correspond to the simulated ones, implying that these methods can be used to achieve user-specified Monte Carlo distributions. Overall, the Transform approach performed more efficiently than the weight window methods, but it performed much more efficiently for source-detector problems than for global problems.
Quantum Heterogeneous Computing for Satellite Positioning Optimization
NASA Astrophysics Data System (ADS)
Bass, G.; Kumar, V.; Dulny, J., III
2016-12-01
Hard optimization problems occur in many fields of academic study and practical situations. We present results in which quantum heterogeneous computing is used to solve a real-world optimization problem: satellite positioning. Optimization problems like this can scale very rapidly with problem size, and become unsolvable with traditional brute-force methods. Typically, such problems have been approximately solved with heuristic approaches; however, these methods can take a long time to calculate and are not guaranteed to find optimal solutions. Quantum computing offers the possibility of producing significant speed-up and improved solution quality. There are now commercially available quantum annealing (QA) devices that are designed to solve difficult optimization problems. These devices have 1000+ quantum bits, but they have significant hardware size and connectivity limitations. We present a novel heterogeneous computing stack that combines QA and classical machine learning and allows the use of QA on problems larger than the quantum hardware could solve in isolation. We begin by analyzing the satellite positioning problem with a heuristic solver, the genetic algorithm. The classical computer's comparatively large available memory can explore the full problem space and converge to a solution relatively close to the true optimum. The QA device can then evolve directly to the optimal solution within this more limited space. Preliminary experiments, using the Quantum Monte Carlo (QMC) algorithm to simulate QA hardware, have produced promising results. Working with problem instances with known global minima, we find a solution within 8% in a matter of seconds, and within 5% in a few minutes. Future studies include replacing QMC with commercially available quantum hardware and exploring more problem sets and model parameters. Our results have important implications for how heterogeneous quantum computing can be used to solve difficult optimization problems in any field.
Fast, Nonlinear, Fully Probabilistic Inversion of Large Geophysical Problems
NASA Astrophysics Data System (ADS)
Curtis, A.; Shahraeeni, M.; Trampert, J.; Meier, U.; Cho, G.
2010-12-01
Almost all Geophysical inverse problems are in reality nonlinear. Fully nonlinear inversion including non-approximated physics, and solving for probability distribution functions (pdf’s) that describe the solution uncertainty, generally requires sampling-based Monte-Carlo style methods that are computationally intractable in most large problems. In order to solve such problems, physical relationships are usually linearized leading to efficiently-solved, (possibly iterated) linear inverse problems. However, it is well known that linearization can lead to erroneous solutions, and in particular to overly optimistic uncertainty estimates. What is needed across many Geophysical disciplines is a method to invert large inverse problems (or potentially tens of thousands of small inverse problems) fully probabilistically and without linearization. This talk shows how very large nonlinear inverse problems can be solved fully probabilistically and incorporating any available prior information using mixture density networks (driven by neural network banks), provided the problem can be decomposed into many small inverse problems. In this talk I will explain the methodology, compare multi-dimensional pdf inversion results to full Monte Carlo solutions, and illustrate the method with two applications: first, inverting surface wave group and phase velocities for a fully-probabilistic global tomography model of the Earth’s crust and mantle, and second inverting industrial 3D seismic data for petrophysical properties throughout and around a subsurface hydrocarbon reservoir. The latter problem is typically decomposed into 104 to 105 individual inverse problems, each solved fully probabilistically and without linearization. The results in both cases are sufficiently close to the Monte Carlo solution to exhibit realistic uncertainty, multimodality and bias. This provides far greater confidence in the results, and in decisions made on their basis.
Zhong, Shangping; Chen, Tianshun; He, Fengying; Niu, Yuzhen
2014-09-01
For a practical pattern classification task solved by kernel methods, the computing time is mainly spent on kernel learning (or training). However, the current kernel learning approaches are based on local optimization techniques, and hard to have good time performances, especially for large datasets. Thus the existing algorithms cannot be easily extended to large-scale tasks. In this paper, we present a fast Gaussian kernel learning method by solving a specially structured global optimization (SSGO) problem. We optimize the Gaussian kernel function by using the formulated kernel target alignment criterion, which is a difference of increasing (d.i.) functions. Through using a power-transformation based convexification method, the objective criterion can be represented as a difference of convex (d.c.) functions with a fixed power-transformation parameter. And the objective programming problem can then be converted to a SSGO problem: globally minimizing a concave function over a convex set. The SSGO problem is classical and has good solvability. Thus, to find the global optimal solution efficiently, we can adopt the improved Hoffman's outer approximation method, which need not repeat the searching procedure with different starting points to locate the best local minimum. Also, the proposed method can be proven to converge to the global solution for any classification task. We evaluate the proposed method on twenty benchmark datasets, and compare it with four other Gaussian kernel learning methods. Experimental results show that the proposed method stably achieves both good time-efficiency performance and good classification performance. Copyright © 2014 Elsevier Ltd. All rights reserved.
Global Design as the Integral Person Formation Strategy
ERIC Educational Resources Information Center
Stepanov, Alexander V.; Fedorov, Vladimir A.; Vorobyeva, Julia A.; Marakulina, Ulyana ?.; Ovchinnikov, Vladislav I.
2016-01-01
The relevance of the problem under study is based on the society's need for educating an integral person who is able to solve ecumenical project tasks. Currently this problem (as natural order from the society) is emerging in the educational system and social practices but has yet to obtain substantial scientific and theoretical justification. The…
Fostering Authentic Problem Seeking: A Step toward Social Justice Engagement
ERIC Educational Resources Information Center
Bruce-Davis, Micah N.; Gilson, Cindy M.; Matthews, Michael S.
2017-01-01
Because of these learners' potential as future leaders, it is imperative that educators develop gifted students' ability to identify and solve complex social justice problems. Nourishing students' affective traits, including empathy for others, understanding of themselves, and the ability to connect to others in local and global society, will help…
Maximum Principles and Application to the Analysis of An Explicit Time Marching Algorithm
NASA Technical Reports Server (NTRS)
LeTallec, Patrick; Tidriri, Moulay D.
1996-01-01
In this paper we develop local and global estimates for the solution of convection-diffusion problems. We then study the convergence properties of a Time Marching Algorithm solving Advection-Diffusion problems on two domains using incompatible discretizations. This study is based on a De-Giorgi-Nash maximum principle.
ERIC Educational Resources Information Center
O'Neill, Eunhee Jung
2007-01-01
In today's global society, individuals with an understanding of different cultures that have the ability to apply this understanding to real world problem solving are more likely to become leaders. Preparing students for a global society is becoming a significant part of education. While many international online exchange projects have been…
Americans Need Advanced Math to Stay Globally Competitive. Math Works
ERIC Educational Resources Information Center
Achieve, Inc., 2013
2013-01-01
No student who hopes to compete in today's rapidly evolving global economy and job market can afford to graduate from high school with weak mathematical skills, which include the ability to use logic, reason, and solve problems. The benefits associated with improving the math performance of American students also extend to the larger U.S. economy.…
War: The Global Battlefield. Our Only Earth. A Curriculum for Global Problem Solving.
ERIC Educational Resources Information Center
MacRae-Campbell, Linda; McKisson, Micki
Both humanity and nature have suffered greatly from human insensitivity. Not only are the natural resources of the earth being depleted and its air, land, and water polluted, the financial resources of humanity are being wasted on destructive expenditures. The "Our Only Earth" series is an integrated science, language arts, and social studies…
NASA Astrophysics Data System (ADS)
Zhang, Kemei; Zhao, Cong-Ran; Xie, Xue-Jun
2015-12-01
This paper considers the problem of output feedback stabilisation for stochastic high-order feedforward nonlinear systems with time-varying delay. By using the homogeneous domination theory and solving several troublesome obstacles in the design and analysis, an output feedback controller is constructed to drive the closed-loop system globally asymptotically stable in probability.
Global Creativity: Introduce a Creative Element to Your Teaching through Global Collaborations
ERIC Educational Resources Information Center
Stephenson, Peter
2004-01-01
The Qualifications and Curriculum Authority (QCA) has made no secret of its desire for teachers to introduce more creativity into UK classrooms. Imaginatively minded teachers have found a number of ways to introduce creativity into the classroom. Dance, music, problem solving and role-play exercises are all well trodden routes, but it was the…
Multigrid method for stability problems
NASA Technical Reports Server (NTRS)
Ta'asan, Shlomo
1988-01-01
The problem of calculating the stability of steady state solutions of differential equations is addressed. Leading eigenvalues of large matrices that arise from discretization are calculated, and an efficient multigrid method for solving these problems is presented. The resulting grid functions are used as initial approximations for appropriate eigenvalue problems. The method employs local relaxation on all levels together with a global change on the coarsest level only, which is designed to separate the different eigenfunctions as well as to update their corresponding eigenvalues. Coarsening is done using the FAS formulation in a nonstandard way in which the right-hand side of the coarse grid equations involves unknown parameters to be solved on the coarse grid. This leads to a new multigrid method for calculating the eigenvalues of symmetric problems. Numerical experiments with a model problem are presented which demonstrate the effectiveness of the method.
Global, finite energy, weak solutions for the NLS with rough, time-dependent magnetic potentials
NASA Astrophysics Data System (ADS)
Antonelli, Paolo; Michelangeli, Alessandro; Scandone, Raffaele
2018-04-01
We prove the existence of weak solutions in the space of energy for a class of nonlinear Schrödinger equations in the presence of a external, rough, time-dependent magnetic potential. Under our assumptions, it is not possible to study the problem by means of usual arguments like resolvent techniques or Fourier integral operators, for example. We use a parabolic regularisation, and we solve the approximating Cauchy problem. This is achieved by obtaining suitable smoothing estimates for the dissipative evolution. The total mass and energy bounds allow to extend the solution globally in time. We then infer sufficient compactness properties in order to produce a global-in-time finite energy weak solution to our original problem.
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.
Fateen, Seif-Eddeen K.; Bonilla-Petriciolet, Adrian
2014-01-01
The search for reliable and efficient global optimization algorithms for solving phase stability and phase equilibrium problems in applied thermodynamics is an ongoing area of research. In this study, we evaluated and compared the reliability and efficiency of eight selected nature-inspired metaheuristic algorithms for solving difficult phase stability and phase equilibrium problems. These algorithms are the cuckoo search (CS), intelligent firefly (IFA), bat (BA), artificial bee colony (ABC), MAKHA, a hybrid between monkey algorithm and krill herd algorithm, covariance matrix adaptation evolution strategy (CMAES), magnetic charged system search (MCSS), and bare bones particle swarm optimization (BBPSO). The results clearly showed that CS is the most reliable of all methods as it successfully solved all thermodynamic problems tested in this study. CS proved to be a promising nature-inspired optimization method to perform applied thermodynamic calculations for process design. PMID:24967430
Fateen, Seif-Eddeen K; Bonilla-Petriciolet, Adrian
2014-01-01
The search for reliable and efficient global optimization algorithms for solving phase stability and phase equilibrium problems in applied thermodynamics is an ongoing area of research. In this study, we evaluated and compared the reliability and efficiency of eight selected nature-inspired metaheuristic algorithms for solving difficult phase stability and phase equilibrium problems. These algorithms are the cuckoo search (CS), intelligent firefly (IFA), bat (BA), artificial bee colony (ABC), MAKHA, a hybrid between monkey algorithm and krill herd algorithm, covariance matrix adaptation evolution strategy (CMAES), magnetic charged system search (MCSS), and bare bones particle swarm optimization (BBPSO). The results clearly showed that CS is the most reliable of all methods as it successfully solved all thermodynamic problems tested in this study. CS proved to be a promising nature-inspired optimization method to perform applied thermodynamic calculations for process design.
A review of developments in the theory of elasto-plastic flow
NASA Technical Reports Server (NTRS)
Swedlow, J. L.
1973-01-01
The theory of elasto-plastic flow is developed so that it may accommodate features such as work-hardening, anisotropy, plastic compressibility, non-continuous loading including local or global unloading, and others. A complete theory is given in quasi-linear form; as a result, many useful attributes are accessible. Several integral theorems may be written, finite deformations may be incorporated, and efficient methods for solving problems may be developed; these and other aspects are described in some detail. The theory is reduced to special forms for 2-space, and extensive experience in solving such problems is cited.
An Effective Hybrid Evolutionary Algorithm for Solving the Numerical Optimization Problems
NASA Astrophysics Data System (ADS)
Qian, Xiaohong; Wang, Xumei; Su, Yonghong; He, Liu
2018-04-01
There are many different algorithms for solving complex optimization problems. Each algorithm has been applied successfully in solving some optimization problems, but not efficiently in other problems. In this paper the Cauchy mutation and the multi-parent hybrid operator are combined to propose a hybrid evolutionary algorithm based on the communication (Mixed Evolutionary Algorithm based on Communication), hereinafter referred to as CMEA. The basic idea of the CMEA algorithm is that the initial population is divided into two subpopulations. Cauchy mutation operators and multiple paternal crossover operators are used to perform two subpopulations parallelly to evolve recursively until the downtime conditions are met. While subpopulation is reorganized, the individual is exchanged together with information. The algorithm flow is given and the performance of the algorithm is compared using a number of standard test functions. Simulation results have shown that this algorithm converges significantly faster than FEP (Fast Evolutionary Programming) algorithm, has good performance in global convergence and stability and is superior to other compared algorithms.
Annealing Ant Colony Optimization with Mutation Operator for Solving TSP
2016-01-01
Ant Colony Optimization (ACO) has been successfully applied to solve a wide range of combinatorial optimization problems such as minimum spanning tree, traveling salesman problem, and quadratic assignment problem. Basic ACO has drawbacks of trapping into local minimum and low convergence rate. Simulated annealing (SA) and mutation operator have the jumping ability and global convergence; and local search has the ability to speed up the convergence. Therefore, this paper proposed a hybrid ACO algorithm integrating the advantages of ACO, SA, mutation operator, and local search procedure to solve the traveling salesman problem. The core of algorithm is based on the ACO. SA and mutation operator were used to increase the ants population diversity from time to time and the local search was used to exploit the current search area efficiently. The comparative experiments, using 24 TSP instances from TSPLIB, show that the proposed algorithm outperformed some well-known algorithms in the literature in terms of solution quality. PMID:27999590
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.
NASA Astrophysics Data System (ADS)
Piotrowski, J.
2010-07-01
This paper presents two extensions of Kalker's algorithm Fastsim of the simplified theory of rolling contact. The first extension is for solving tangential contact problems with the coefficient of friction depending on slip velocity. Two friction laws have been considered: with and without recuperation of the static friction. According to the tribological hypothesis for metallic bodies shear failure, the friction law without recuperation of static friction is more suitable for wheel and rail than the other one. Sample results present local quantities inside the contact area (division to slip and adhesion, traction) as well as global ones (creep forces as functions of creepages and rolling velocity). For the coefficient of friction diminishing with slip, the creep forces decay after reaching the maximum and they depend on the rolling velocity. The second extension is for solving tangential contact problems with friction anisotropy characterised by a convex set of the permissible tangential tractions. The effect of the anisotropy has been shown on examples of rolling without spin and in the presence of pure spin for the elliptical set. The friction anisotropy influences tangential tractions and creep forces. Sample results present local and global quantities. Both extensions have been described with the same language of formulation and they may be merged into one, joint algorithm.
Topology-changing shape optimization with the genetic algorithm
NASA Astrophysics Data System (ADS)
Lamberson, Steven E., Jr.
The goal is to take a traditional shape optimization problem statement and modify it slightly to allow for prescribed changes in topology. This modification enables greater flexibility in the choice of parameters for the topology optimization problem, while improving the direct physical relevance of the results. This modification involves changing the optimization problem statement from a nonlinear programming problem into a form of mixed-discrete nonlinear programing problem. The present work demonstrates one possible way of using the Genetic Algorithm (GA) to solve such a problem, including the use of "masking bits" and a new modification to the bit-string affinity (BSA) termination criterion specifically designed for problems with "masking bits." A simple ten-bar truss problem proves the utility of the modified BSA for this type of problem. A more complicated two dimensional bracket problem is solved using both the proposed approach and a more traditional topology optimization approach (Solid Isotropic Microstructure with Penalization or SIMP) to enable comparison. The proposed approach is able to solve problems with both local and global constraints, which is something traditional methods cannot do. The proposed approach has a significantly higher computational burden --- on the order of 100 times larger than SIMP, although the proposed approach is able to offset this with parallel computing.
Liu, Wei; Huang, Jie
2018-03-01
This paper studies the cooperative global robust output regulation problem for a class of heterogeneous second-order nonlinear uncertain multiagent systems with jointly connected switching networks. The main contributions consist of the following three aspects. First, we generalize the result of the adaptive distributed observer from undirected jointly connected switching networks to directed jointly connected switching networks. Second, by performing a new coordinate and input transformation, we convert our problem into the cooperative global robust stabilization problem of a more complex augmented system via the distributed internal model principle. Third, we solve the stabilization problem by a distributed state feedback control law. Our result is illustrated by the leader-following consensus problem for a group of Van der Pol oscillators.
Two New PRP Conjugate Gradient Algorithms for Minimization Optimization Models.
Yuan, Gonglin; Duan, Xiabin; Liu, Wenjie; Wang, Xiaoliang; Cui, Zengru; Sheng, Zhou
2015-01-01
Two new PRP conjugate Algorithms are proposed in this paper based on two modified PRP conjugate gradient methods: the first algorithm is proposed for solving unconstrained optimization problems, and the second algorithm is proposed for solving nonlinear equations. The first method contains two aspects of information: function value and gradient value. The two methods both possess some good properties, as follows: 1) βk ≥ 0 2) the search direction has the trust region property without the use of any line search method 3) the search direction has sufficient descent property without the use of any line search method. Under some suitable conditions, we establish the global convergence of the two algorithms. We conduct numerical experiments to evaluate our algorithms. The numerical results indicate that the first algorithm is effective and competitive for solving unconstrained optimization problems and that the second algorithm is effective for solving large-scale nonlinear equations.
Two New PRP Conjugate Gradient Algorithms for Minimization Optimization Models
Yuan, Gonglin; Duan, Xiabin; Liu, Wenjie; Wang, Xiaoliang; Cui, Zengru; Sheng, Zhou
2015-01-01
Two new PRP conjugate Algorithms are proposed in this paper based on two modified PRP conjugate gradient methods: the first algorithm is proposed for solving unconstrained optimization problems, and the second algorithm is proposed for solving nonlinear equations. The first method contains two aspects of information: function value and gradient value. The two methods both possess some good properties, as follows: 1)β k ≥ 0 2) the search direction has the trust region property without the use of any line search method 3) the search direction has sufficient descent property without the use of any line search method. Under some suitable conditions, we establish the global convergence of the two algorithms. We conduct numerical experiments to evaluate our algorithms. The numerical results indicate that the first algorithm is effective and competitive for solving unconstrained optimization problems and that the second algorithm is effective for solving large-scale nonlinear equations. PMID:26502409
Hybrid Microgrid Configuration Optimization with Evolutionary Algorithms
NASA Astrophysics Data System (ADS)
Lopez, Nicolas
This dissertation explores the Renewable Energy Integration Problem, and proposes a Genetic Algorithm embedded with a Monte Carlo simulation to solve large instances of the problem that are impractical to solve via full enumeration. The Renewable Energy Integration Problem is defined as finding the optimum set of components to supply the electric demand to a hybrid microgrid. The components considered are solar panels, wind turbines, diesel generators, electric batteries, connections to the power grid and converters, which can be inverters and/or rectifiers. The methodology developed is explained as well as the combinatorial formulation. In addition, 2 case studies of a single objective optimization version of the problem are presented, in order to minimize cost and to minimize global warming potential (GWP) followed by a multi-objective implementation of the offered methodology, by utilizing a non-sorting Genetic Algorithm embedded with a monte Carlo Simulation. The method is validated by solving a small instance of the problem with known solution via a full enumeration algorithm developed by NREL in their software HOMER. The dissertation concludes that the evolutionary algorithms embedded with Monte Carlo simulation namely modified Genetic Algorithms are an efficient form of solving the problem, by finding approximate solutions in the case of single objective optimization, and by approximating the true Pareto front in the case of multiple objective optimization of the Renewable Energy Integration Problem.
ERIC Educational Resources Information Center
Cowden, Chapel D.; Santiago, Manuel F.
2016-01-01
Interdisciplinary approaches to research in the sciences have become increasingly important in solving a wide range of pressing problems at both global and local levels. It is imperative then that science majors in higher education understand the need for exploring information from a wide array of disciplines. With this in mind, interdisciplinary…
Local Responses to Global Problems: A Key to Meeting Basic Human Needs. Worldwatch Paper 17.
ERIC Educational Resources Information Center
Stokes, Bruce
The booklet maintains that the key to meeting basic human needs is the participation of individuals and communities in local problem solving. Some of the most important achievements in providing food, upgrading housing, improving human health, and tapping new energy sources, comes through local self-help projects. Proponents of local efforts at…
ERIC Educational Resources Information Center
Cakar, Bekir
2011-01-01
The situations and problems that police officers face are more complex in today's society, due in part to the increase of technology and growing complexity of globalization. Accordingly, to solve these problems and deal with the complexities, law enforcement organizations develop and apply new techniques and methods such as geographic information…
ERIC Educational Resources Information Center
Mitchell, Jerry T.; Cutter, Susan L.
This learning module aims to engage students in problem solving, critical thinking, scientific inquiry, and cooperative learning. The module is appropriate for use in any introductory or intermediate undergraduate course that focuses on human-environment relationships. The module introduces the complexities in the relationships among environmental…
PS-FW: A Hybrid Algorithm Based on Particle Swarm and Fireworks for Global Optimization
Chen, Shuangqing; Wei, Lixin; Guan, Bing
2018-01-01
Particle swarm optimization (PSO) and fireworks algorithm (FWA) are two recently developed optimization methods which have been applied in various areas due to their simplicity and efficiency. However, when being applied to high-dimensional optimization problems, PSO algorithm may be trapped in the local optima owing to the lack of powerful global exploration capability, and fireworks algorithm is difficult to converge in some cases because of its relatively low local exploitation efficiency for noncore fireworks. In this paper, a hybrid algorithm called PS-FW is presented, in which the modified operators of FWA are embedded into the solving process of PSO. In the iteration process, the abandonment and supplement mechanism is adopted to balance the exploration and exploitation ability of PS-FW, and the modified explosion operator and the novel mutation operator are proposed to speed up the global convergence and to avoid prematurity. To verify the performance of the proposed PS-FW algorithm, 22 high-dimensional benchmark functions have been employed, and it is compared with PSO, FWA, stdPSO, CPSO, CLPSO, FIPS, Frankenstein, and ALWPSO algorithms. Results show that the PS-FW algorithm is an efficient, robust, and fast converging optimization method for solving global optimization problems. PMID:29675036
ERIC Educational Resources Information Center
Woodwell, George M.
1977-01-01
Discusses contemporary challenges for biologists, environmentalists, technologists, teachers, and political supporters of science and education in solving global problems. Discussed are nation- and worldwide accumulation of toxic materials (DDT, PCB's, kepone, radioactivity, dieldrin, mirex), transformation of natural ecosystems, and instability…
Multidisciplinary optimization of a controlled space structure using 150 design variables
NASA Technical Reports Server (NTRS)
James, Benjamin B.
1993-01-01
A controls-structures interaction design method is presented. The method coordinates standard finite-element structural analysis, multivariable controls, and nonlinear programming codes and allows simultaneous optimization of the structure and control system of a spacecraft. Global sensitivity equations are used to account for coupling between the disciplines. Use of global sensitivity equations helps solve optimization problems that have a large number of design variables and a high degree of coupling between disciplines. The preliminary design of a generic geostationary platform is used to demonstrate the multidisciplinary optimization method. Design problems using 15, 63, and 150 design variables to optimize truss member sizes and feedback gain values are solved and the results are presented. The goal is to reduce the total mass of the structure and the vibration control system while satisfying constraints on vibration decay rate. Incorporation of the nonnegligible mass of actuators causes an essential coupling between structural design variables and control design variables.
Towards a global human embryonic stem cell bank.
Lott, Jason P; Savulescu, Julian
2007-08-01
An increasingly unbridgeable gap exists between the supply and demand of transplantable organs. Human embryonic stem cell technology could solve the organ shortage problem by restoring diseased or damaged tissue across a range of common conditions. However, such technology faces several largely ignored immunological challenges in delivering cell lines to large populations. We address some of these challenges and argue in favor of encouraging contribution or intentional creation of embryos from which widely immunocompatible stem cell lines could be derived. Further, we argue that current immunological constraints in tissue transplantation demand the creation of a global stem cell bank, which may hold particular promise for minority populations and other sub-groups currently marginalized from organ procurement and allocation systems. Finally, we conclude by offering a number of practical and ethically oriented recommendations for constructing a human embryonic stem cell bank that we hope will help solve the ongoing organ shortage problem.
Cognitive development in introductory physics: A research-based approach to curriculum reform
NASA Astrophysics Data System (ADS)
Teodorescu, Raluca Elena
This project describes the research on a classification of physics problems in the context of introductory physics courses. This classification, called the Taxonomy of Introductory Physics Problems (TIPP), relates physics problems to the cognitive processes required to solve them. TIPP was created for designing and clarifying educational objectives, for developing assessments that can evaluate individual component processes of the problem-solving process, and for guiding curriculum design in introductory physics courses, specifically within the context of a "thinking-skills" curriculum. TIPP relies on the following resources: (1) cognitive research findings adopted by physics education research, (2) expert-novice research discoveries acknowledged by physics education research, (3) an educational psychology taxonomy for educational objectives, and (4) various collections of physics problems created by physics education researchers or developed by textbook authors. TIPP was used in the years 2006--2008 to reform the first semester of the introductory algebra-based physics course (called Phys 11) at The George Washington University. The reform sought to transform our curriculum into a "thinking-skills" curriculum that trades "breadth for depth" by focusing on fewer topics while targeting the students' cognitive development. We employed existing research on the physics problem-solving expert-novice behavior, cognitive science and behavioral science findings, and educational psychology recommendations. Our pedagogy relies on didactic constructs such as the GW-ACCESS problem-solving protocol, learning progressions and concept maps that we have developed and implemented in our introductory physics course. These tools were designed based on TIPP. Their purpose is: (1) to help students build local and global coherent knowledge structures, (2) to develop more context-independent problem-solving abilities, (3) to gain confidence in problem solving, and (4) to establish connections between everyday phenomena and underlying physics concepts. We organize traditional and research-based physics problems such that students experience a gradual increase in complexity related to problem context, problem features and cognitive processes needed to solve the problem. The instructional environment that we designed allows for explicit monitoring, control and measurement of the cognitive processes exercised during the instruction period. It is easily adaptable to any kind of curriculum and can be readily adjusted throughout the semester. To assess the development of students' problem-solving abilities, we created rubrics that measure specific aspects of the thinking involved in physics problem solving. The Colorado Learning Attitudes about Science Survey (CLASS) was administered pre- and post-instruction to determine students' shift in dispositions towards learning physics. The Force Concept Inventory (FCI) was administered pre- and post-instruction to determine students' level of conceptual understanding. The results feature improvements in students' problem-solving abilities and in their attitudes towards learning physics.
Tien, Kai-Wen; Kulvatunyou, Boonserm; Jung, Kiwook; Prabhu, Vittaldas
2017-01-01
As cloud computing is increasingly adopted, the trend is to offer software functions as modular services and compose them into larger, more meaningful ones. The trend is attractive to analytical problems in the manufacturing system design and performance improvement domain because 1) finding a global optimization for the system is a complex problem; and 2) sub-problems are typically compartmentalized by the organizational structure. However, solving sub-problems by independent services can result in a sub-optimal solution at the system level. This paper investigates the technique called Analytical Target Cascading (ATC) to coordinate the optimization of loosely-coupled sub-problems, each may be modularly formulated by differing departments and be solved by modular analytical services. The result demonstrates that ATC is a promising method in that it offers system-level optimal solutions that can scale up by exploiting distributed and modular executions while allowing easier management of the problem formulation.
PSQP: Puzzle Solving by Quadratic Programming.
Andalo, Fernanda A; Taubin, Gabriel; Goldenstein, Siome
2017-02-01
In this article we present the first effective method based on global optimization for the reconstruction of image puzzles comprising rectangle pieces-Puzzle Solving by Quadratic Programming (PSQP). The proposed novel mathematical formulation reduces the problem to the maximization of a constrained quadratic function, which is solved via a gradient ascent approach. The proposed method is deterministic and can deal with arbitrary identical rectangular pieces. We provide experimental results showing its effectiveness when compared to state-of-the-art approaches. Although the method was developed to solve image puzzles, we also show how to apply it to the reconstruction of simulated strip-shredded documents, broadening its applicability.
On multiple crack identification by ultrasonic scanning
NASA Astrophysics Data System (ADS)
Brigante, M.; Sumbatyan, M. A.
2018-04-01
The present work develops an approach which reduces operator equations arising in the engineering problems to the problem of minimizing the discrepancy functional. For this minimization, an algorithm of random global search is proposed, which is allied to some genetic algorithms. The efficiency of the method is demonstrated by the solving problem of simultaneous identification of several linear cracks forming an array in an elastic medium by using the circular Ultrasonic scanning.
NASA Astrophysics Data System (ADS)
Chiu, Y.; Nishikawa, T.
2013-12-01
With the increasing complexity of parameter-structure identification (PSI) in groundwater modeling, there is a need for robust, fast, and accurate optimizers in the groundwater-hydrology field. For this work, PSI is defined as identifying parameter dimension, structure, and value. In this study, Voronoi tessellation and differential evolution (DE) are used to solve the optimal PSI problem. Voronoi tessellation is used for automatic parameterization, whereby stepwise regression and the error covariance matrix are used to determine the optimal parameter dimension. DE is a novel global optimizer that can be used to solve nonlinear, nondifferentiable, and multimodal optimization problems. It can be viewed as an improved version of genetic algorithms and employs a simple cycle of mutation, crossover, and selection operations. DE is used to estimate the optimal parameter structure and its associated values. A synthetic numerical experiment of continuous hydraulic conductivity distribution was conducted to demonstrate the proposed methodology. The results indicate that DE can identify the global optimum effectively and efficiently. A sensitivity analysis of the control parameters (i.e., the population size, mutation scaling factor, crossover rate, and mutation schemes) was performed to examine their influence on the objective function. The proposed DE was then applied to solve a complex parameter-estimation problem for a small desert groundwater basin in Southern California. Hydraulic conductivity, specific yield, specific storage, fault conductance, and recharge components were estimated simultaneously. Comparison of DE and a traditional gradient-based approach (PEST) shows DE to be more robust and efficient. The results of this work not only provide an alternative for PSI in groundwater models, but also extend DE applications towards solving complex, regional-scale water management optimization problems.
Global challenges as inspiration: a classroom strategy to foster social responsibility.
Vanasupa, Linda; Slivovsky, Lynn; Chen, Katherine C
2006-04-01
Social responsibility is at the heart of the Engineer's Creed embodied in the pledge that we will dedicate [our] professional knowledge and skill to the advancement and betterment of human welfare... [placing] public welfare above all other considerations. However, half century after the original creed was written, we find ourselves in a world with great technological advances and great global-scale technologically-enabled peril. These issues can be naturally integrated into the engineering curriculum in a way that enhances the development of the technological skill set. We have found that these global challenges create a natural opportunity to foster social responsibility within the engineering students whom we educate. In freshman through senior-level materials engineering courses, we used five guiding principles to shape several different classroom activities and assignments. Upon testing an initial cohort of 28 students had classroom experiences based on these five principles, we saw a shift in attitude: before the experience, 18% of the cohort viewed engineers as playing an active role in solving global problems; after the experiences, 79% recognized the engineer's role in solving global-scale problems. In this paper, we present how global issues can be used to stimulate thinking for socially-responsible engineering solutions. We set forth five guiding principles that can foster the mindset for socially responsible actions along with examples of how these principles translate into classroom activities.
Du, Shaoyi; Xu, Yiting; Wan, Teng; Hu, Huaizhong; Zhang, Sirui; Xu, Guanglin; Zhang, Xuetao
2017-01-01
The iterative closest point (ICP) algorithm is efficient and accurate for rigid registration but it needs the good initial parameters. It is easily failed when the rotation angle between two point sets is large. To deal with this problem, a new objective function is proposed by introducing a rotation invariant feature based on the Euclidean distance between each point and a global reference point, where the global reference point is a rotation invariant. After that, this optimization problem is solved by a variant of ICP algorithm, which is an iterative method. Firstly, the accurate correspondence is established by using the weighted rotation invariant feature distance and position distance together. Secondly, the rigid transformation is solved by the singular value decomposition method. Thirdly, the weight is adjusted to control the relative contribution of the positions and features. Finally this new algorithm accomplishes the registration by a coarse-to-fine way whatever the initial rotation angle is, which is demonstrated to converge monotonically. The experimental results validate that the proposed algorithm is more accurate and robust compared with the original ICP algorithm.
Du, Shaoyi; Xu, Yiting; Wan, Teng; Zhang, Sirui; Xu, Guanglin; Zhang, Xuetao
2017-01-01
The iterative closest point (ICP) algorithm is efficient and accurate for rigid registration but it needs the good initial parameters. It is easily failed when the rotation angle between two point sets is large. To deal with this problem, a new objective function is proposed by introducing a rotation invariant feature based on the Euclidean distance between each point and a global reference point, where the global reference point is a rotation invariant. After that, this optimization problem is solved by a variant of ICP algorithm, which is an iterative method. Firstly, the accurate correspondence is established by using the weighted rotation invariant feature distance and position distance together. Secondly, the rigid transformation is solved by the singular value decomposition method. Thirdly, the weight is adjusted to control the relative contribution of the positions and features. Finally this new algorithm accomplishes the registration by a coarse-to-fine way whatever the initial rotation angle is, which is demonstrated to converge monotonically. The experimental results validate that the proposed algorithm is more accurate and robust compared with the original ICP algorithm. PMID:29176780
A structural model decomposition framework for systems health management
NASA Astrophysics Data System (ADS)
Roychoudhury, I.; Daigle, M.; Bregon, A.; Pulido, B.
Systems health management (SHM) is an important set of technologies aimed at increasing system safety and reliability by detecting, isolating, and identifying faults; and predicting when the system reaches end of life (EOL), so that appropriate fault mitigation and recovery actions can be taken. Model-based SHM approaches typically make use of global, monolithic system models for online analysis, which results in a loss of scalability and efficiency for large-scale systems. Improvement in scalability and efficiency can be achieved by decomposing the system model into smaller local submodels and operating on these submodels instead. In this paper, the global system model is analyzed offline and structurally decomposed into local submodels. We define a common model decomposition framework for extracting submodels from the global model. This framework is then used to develop algorithms for solving model decomposition problems for the design of three separate SHM technologies, namely, estimation (which is useful for fault detection and identification), fault isolation, and EOL prediction. We solve these model decomposition problems using a three-tank system as a case study.
A Structural Model Decomposition Framework for Systems Health Management
NASA Technical Reports Server (NTRS)
Roychoudhury, Indranil; Daigle, Matthew J.; Bregon, Anibal; Pulido, Belamino
2013-01-01
Systems health management (SHM) is an important set of technologies aimed at increasing system safety and reliability by detecting, isolating, and identifying faults; and predicting when the system reaches end of life (EOL), so that appropriate fault mitigation and recovery actions can be taken. Model-based SHM approaches typically make use of global, monolithic system models for online analysis, which results in a loss of scalability and efficiency for large-scale systems. Improvement in scalability and efficiency can be achieved by decomposing the system model into smaller local submodels and operating on these submodels instead. In this paper, the global system model is analyzed offline and structurally decomposed into local submodels. We define a common model decomposition framework for extracting submodels from the global model. This framework is then used to develop algorithms for solving model decomposition problems for the design of three separate SHM technologies, namely, estimation (which is useful for fault detection and identification), fault isolation, and EOL prediction. We solve these model decomposition problems using a three-tank system as a case study.
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.
Global Optimization Ensemble Model for Classification Methods
Anwar, Hina; Qamar, Usman; Muzaffar Qureshi, Abdul Wahab
2014-01-01
Supervised learning is the process of data mining for deducing rules from training datasets. A broad array of supervised learning algorithms exists, every one of them with its own advantages and drawbacks. There are some basic issues that affect the accuracy of classifier while solving a supervised learning problem, like bias-variance tradeoff, dimensionality of input space, and noise in the input data space. All these problems affect the accuracy of classifier and are the reason that there is no global optimal method for classification. There is not any generalized improvement method that can increase the accuracy of any classifier while addressing all the problems stated above. This paper proposes a global optimization ensemble model for classification methods (GMC) that can improve the overall accuracy for supervised learning problems. The experimental results on various public datasets showed that the proposed model improved the accuracy of the classification models from 1% to 30% depending upon the algorithm complexity. PMID:24883382
ERIC Educational Resources Information Center
Williams, Patricia D.
2013-01-01
Learners of all ages must be able to navigate their 21st century global market place and be competitive in several aspects of their life. Learners must be able to solve complex problems, think critically, communicate, investigate, create and collaborate to thrive in a 21st century global market place. In order for this to occur in learning…
Swarm intelligence metaheuristics for enhanced data analysis and optimization.
Hanrahan, Grady
2011-09-21
The swarm intelligence (SI) computing paradigm has proven itself as a comprehensive means of solving complicated analytical chemistry problems by emulating biologically-inspired processes. As global optimum search metaheuristics, associated algorithms have been widely used in training neural networks, function optimization, prediction and classification, and in a variety of process-based analytical applications. The goal of this review is to provide readers with critical insight into the utility of swarm intelligence tools as methods for solving complex chemical problems. Consideration will be given to algorithm development, ease of implementation and model performance, detailing subsequent influences on a number of application areas in the analytical, bioanalytical and detection sciences.
Optimal spatial filtering and transfer function for SAR ocean wave spectra
NASA Technical Reports Server (NTRS)
Goldfinger, A. D.; Beal, R. C.; Tilley, D. G.
1981-01-01
The Seasat Synthetic Aperture Radar (SAR) has proved to be an instrument of great utility in the sensing of ocean conditions on a global scale. An analysis of oceanographic and atmospheric aspects of Seasat data has shown that the features observed in the imagery are linked to ocean phenomena such as storm sources and their resulting swell systems. However, there remains one central problem which has not been satisfactorily solved to date. This problem is related to the accurate measurement of wind-generated ocean wave spectra. Investigations addressing this problem are currently being conducted. The problem has two parts, including the accurate measurement of the image spectra and the inference of actual surface wave spectra from these measurements. A description is presented of the progress made towards solving the first part of the problem, taking into account a digital rather than optical computation of the image transforms.
Fast optimization of binary clusters using a novel dynamic lattice searching method.
Wu, Xia; Cheng, Wen
2014-09-28
Global optimization of binary clusters has been a difficult task despite of much effort and many efficient methods. Directing toward two types of elements (i.e., homotop problem) in binary clusters, two classes of virtual dynamic lattices are constructed and a modified dynamic lattice searching (DLS) method, i.e., binary DLS (BDLS) method, is developed. However, it was found that the BDLS can only be utilized for the optimization of binary clusters with small sizes because homotop problem is hard to be solved without atomic exchange operation. Therefore, the iterated local search (ILS) method is adopted to solve homotop problem and an efficient method based on the BDLS method and ILS, named as BDLS-ILS, is presented for global optimization of binary clusters. In order to assess the efficiency of the proposed method, binary Lennard-Jones clusters with up to 100 atoms are investigated. Results show that the method is proved to be efficient. Furthermore, the BDLS-ILS method is also adopted to study the geometrical structures of (AuPd)79 clusters with DFT-fit parameters of Gupta potential.
GLOBAL SOLUTIONS TO FOLDED CONCAVE PENALIZED NONCONVEX LEARNING
Liu, Hongcheng; Yao, Tao; Li, Runze
2015-01-01
This paper is concerned with solving nonconvex learning problems with folded concave penalty. Despite that their global solutions entail desirable statistical properties, there lack optimization techniques that guarantee global optimality in a general setting. In this paper, we show that a class of nonconvex learning problems are equivalent to general quadratic programs. This equivalence facilitates us in developing mixed integer linear programming reformulations, which admit finite algorithms that find a provably global optimal solution. We refer to this reformulation-based technique as the mixed integer programming-based global optimization (MIPGO). To our knowledge, this is the first global optimization scheme with a theoretical guarantee for folded concave penalized nonconvex learning with the SCAD penalty (Fan and Li, 2001) and the MCP penalty (Zhang, 2010). Numerical results indicate a significant outperformance of MIPGO over the state-of-the-art solution scheme, local linear approximation, and other alternative solution techniques in literature in terms of solution quality. PMID:27141126
Payá, Luis; Reinoso, Oscar; Jiménez, Luis M; Juliá, Miguel
2017-01-01
Along the past years, mobile robots have proliferated both in domestic and in industrial environments to solve some tasks such as cleaning, assistance, or material transportation. One of their advantages is the ability to operate in wide areas without the necessity of introducing changes into the existing infrastructure. Thanks to the sensors they may be equipped with and their processing systems, mobile robots constitute a versatile alternative to solve a wide range of applications. When designing the control system of a mobile robot so that it carries out a task autonomously in an unknown environment, it is expected to take decisions about its localization in the environment and about the trajectory that it has to follow in order to arrive to the target points. More concisely, the robot has to find a relatively good solution to two crucial problems: building a model of the environment, and estimating the position of the robot within this model. In this work, we propose a framework to solve these problems using only visual information. The mobile robot is equipped with a catadioptric vision sensor that provides omnidirectional images from the environment. First, the robot goes along the trajectories to include in the model and uses the visual information captured to build this model. After that, the robot is able to estimate its position and orientation with respect to the trajectory. Among the possible approaches to solve these problems, global appearance techniques are used in this work. They have emerged recently as a robust and efficient alternative compared to landmark extraction techniques. A global description method based on Radon Transform is used to design mapping and localization algorithms and a set of images captured by a mobile robot in a real environment, under realistic operation conditions, is used to test the performance of these algorithms.
ERIC Educational Resources Information Center
Abreu, Julio
2000-01-01
Four decades ago following Russia's Sputnik satellite launching, the nation embraced "new" math as part of its commitment not to fall behind its global neighbors. Issues addressed in "new-new" math include equal access to challenging learning, problem solving, reasoning and proof, communications, multiple ways to solve…
Analysis Balance Parameter of Optimal Ramp metering
NASA Astrophysics Data System (ADS)
Li, Y.; Duan, N.; Yang, X.
2018-05-01
Ramp metering is a motorway control method to avoid onset congestion through limiting the access of ramp inflows into the main road of the motorway. The optimization model of ramp metering is developed based upon cell transmission model (CTM). With the piecewise linear structure of CTM, the corresponding motorway traffic optimization problem can be formulated as a linear programming (LP) problem. It is known that LP problem can be solved by established solution algorithms such as SIMPLEX or interior-point methods for the global optimal solution. The commercial software (CPLEX) is adopted in this study to solve the LP problem within reasonable computational time. The concept is illustrated through a case study of the United Kingdom M25 Motorway. The optimal solution provides useful insights and guidances on how to manage motorway traffic in order to maximize the corresponding efficiency.
An outer approximation method for the road network design problem
2018-01-01
Best investment in the road infrastructure or the network design is perceived as a fundamental and benchmark problem in transportation. Given a set of candidate road projects with associated costs, finding the best subset with respect to a limited budget is known as a bilevel Discrete Network Design Problem (DNDP) of NP-hard computationally complexity. We engage with the complexity with a hybrid exact-heuristic methodology based on a two-stage relaxation as follows: (i) the bilevel feature is relaxed to a single-level problem by taking the network performance function of the upper level into the user equilibrium traffic assignment problem (UE-TAP) in the lower level as a constraint. It results in a mixed-integer nonlinear programming (MINLP) problem which is then solved using the Outer Approximation (OA) algorithm (ii) we further relax the multi-commodity UE-TAP to a single-commodity MILP problem, that is, the multiple OD pairs are aggregated to a single OD pair. This methodology has two main advantages: (i) the method is proven to be highly efficient to solve the DNDP for a large-sized network of Winnipeg, Canada. The results suggest that within a limited number of iterations (as termination criterion), global optimum solutions are quickly reached in most of the cases; otherwise, good solutions (close to global optimum solutions) are found in early iterations. Comparative analysis of the networks of Gao and Sioux-Falls shows that for such a non-exact method the global optimum solutions are found in fewer iterations than those found in some analytically exact algorithms in the literature. (ii) Integration of the objective function among the constraints provides a commensurate capability to tackle the multi-objective (or multi-criteria) DNDP as well. PMID:29590111
An outer approximation method for the road network design problem.
Asadi Bagloee, Saeed; Sarvi, Majid
2018-01-01
Best investment in the road infrastructure or the network design is perceived as a fundamental and benchmark problem in transportation. Given a set of candidate road projects with associated costs, finding the best subset with respect to a limited budget is known as a bilevel Discrete Network Design Problem (DNDP) of NP-hard computationally complexity. We engage with the complexity with a hybrid exact-heuristic methodology based on a two-stage relaxation as follows: (i) the bilevel feature is relaxed to a single-level problem by taking the network performance function of the upper level into the user equilibrium traffic assignment problem (UE-TAP) in the lower level as a constraint. It results in a mixed-integer nonlinear programming (MINLP) problem which is then solved using the Outer Approximation (OA) algorithm (ii) we further relax the multi-commodity UE-TAP to a single-commodity MILP problem, that is, the multiple OD pairs are aggregated to a single OD pair. This methodology has two main advantages: (i) the method is proven to be highly efficient to solve the DNDP for a large-sized network of Winnipeg, Canada. The results suggest that within a limited number of iterations (as termination criterion), global optimum solutions are quickly reached in most of the cases; otherwise, good solutions (close to global optimum solutions) are found in early iterations. Comparative analysis of the networks of Gao and Sioux-Falls shows that for such a non-exact method the global optimum solutions are found in fewer iterations than those found in some analytically exact algorithms in the literature. (ii) Integration of the objective function among the constraints provides a commensurate capability to tackle the multi-objective (or multi-criteria) DNDP as well.
A modified conjugate gradient coefficient with inexact line search for unconstrained optimization
NASA Astrophysics Data System (ADS)
Aini, Nurul; Rivaie, Mohd; Mamat, Mustafa
2016-11-01
Conjugate gradient (CG) method is a line search algorithm mostly known for its wide application in solving unconstrained optimization problems. Its low memory requirements and global convergence properties makes it one of the most preferred method in real life application such as in engineering and business. In this paper, we present a new CG method based on AMR* and CD method for solving unconstrained optimization functions. The resulting algorithm is proven to have both the sufficient descent and global convergence properties under inexact line search. Numerical tests are conducted to assess the effectiveness of the new method in comparison to some previous CG methods. The results obtained indicate that our method is indeed superior.
NASA Astrophysics Data System (ADS)
Rocha, Ana Maria A. C.; Costa, M. Fernanda P.; Fernandes, Edite M. G. P.
2016-12-01
This article presents a shifted hyperbolic penalty function and proposes an augmented Lagrangian-based algorithm for non-convex constrained global optimization problems. Convergence to an ?-global minimizer is proved. At each iteration k, the algorithm requires the ?-global minimization of a bound constrained optimization subproblem, where ?. The subproblems are solved by a stochastic population-based metaheuristic that relies on the artificial fish swarm paradigm and a two-swarm strategy. To enhance the speed of convergence, the algorithm invokes the Nelder-Mead local search with a dynamically defined probability. Numerical experiments with benchmark functions and engineering design problems are presented. The results show that the proposed shifted hyperbolic augmented Lagrangian compares favorably with other deterministic and stochastic penalty-based methods.
Globalization and Health at the United States–Mexico Border
Homedes, Núria; Ugalde, Antonio
2003-01-01
Objectives. We studied the impact of globalization on the making of health policy. Globalization is understood as economic interdependence among nations. The North American Free Trade Agreement is used as a marker to assess the effects of economic interdependence on binational health cooperation along the United States–Mexico border. Methods. We observed participants and conducted in-depth interviews with policymakers, public health specialists, representatives of professional organizations, and unions. Results. Globalization has not promoted binational health policy cooperation. Barriers that keep US and Mexican policymakers apart prevail while health problems that do not recognize international borders go unresolved. Conclusions. If international health problems are to be solved, political, cultural, and social interdependence need to be built with the same impetus by which policymakers promote international trade. PMID:14652325
Proposal of Evolutionary Simplex Method for Global Optimization Problem
NASA Astrophysics Data System (ADS)
Shimizu, Yoshiaki
To make an agile decision in a rational manner, role of optimization engineering has been notified increasingly under diversified customer demand. With this point of view, in this paper, we have proposed a new evolutionary method serving as an optimization technique in the paradigm of optimization engineering. The developed method has prospects to solve globally various complicated problem appearing in real world applications. It is evolved from the conventional method known as Nelder and Mead’s Simplex method by virtue of idea borrowed from recent meta-heuristic method such as PSO. Mentioning an algorithm to handle linear inequality constraints effectively, we have validated effectiveness of the proposed method through comparison with other methods using several benchmark problems.
Linear homotopy solution of nonlinear systems of equations in geodesy
NASA Astrophysics Data System (ADS)
Paláncz, Béla; Awange, Joseph L.; Zaletnyik, Piroska; Lewis, Robert H.
2010-01-01
A fundamental task in geodesy is solving systems of equations. Many geodetic problems are represented as systems of multivariate polynomials. A common problem in solving such systems is improper initial starting values for iterative methods, leading to convergence to solutions with no physical meaning, or to convergence that requires global methods. Though symbolic methods such as Groebner bases or resultants have been shown to be very efficient, i.e., providing solutions for determined systems such as 3-point problem of 3D affine transformation, the symbolic algebra can be very time consuming, even with special Computer Algebra Systems (CAS). This study proposes the Linear Homotopy method that can be implemented easily in high-level computer languages like C++ and Fortran that are faster than CAS by at least two orders of magnitude. Using Mathematica, the power of Homotopy is demonstrated in solving three nonlinear geodetic problems: resection, GPS positioning, and affine transformation. The method enlarging the domain of convergence is found to be efficient, less sensitive to rounding of numbers, and has lower complexity compared to other local methods like Newton-Raphson.
Malik, Suheel Abdullah; Qureshi, Ijaz Mansoor; Amir, Muhammad; Malik, Aqdas Naveed; Haq, Ihsanul
2015-01-01
In this paper, a new heuristic scheme for the approximate solution of the generalized Burgers'-Fisher equation is proposed. The scheme is based on the hybridization of Exp-function method with nature inspired algorithm. The given nonlinear partial differential equation (NPDE) through substitution is converted into a nonlinear ordinary differential equation (NODE). The travelling wave solution is approximated by the Exp-function method with unknown parameters. The unknown parameters are estimated by transforming the NODE into an equivalent global error minimization problem by using a fitness function. The popular genetic algorithm (GA) is used to solve the minimization problem, and to achieve the unknown parameters. The proposed scheme is successfully implemented to solve the generalized Burgers'-Fisher equation. The comparison of numerical results with the exact solutions, and the solutions obtained using some traditional methods, including adomian decomposition method (ADM), homotopy perturbation method (HPM), and optimal homotopy asymptotic method (OHAM), show that the suggested scheme is fairly accurate and viable for solving such problems.
Malik, Suheel Abdullah; Qureshi, Ijaz Mansoor; Amir, Muhammad; Malik, Aqdas Naveed; Haq, Ihsanul
2015-01-01
In this paper, a new heuristic scheme for the approximate solution of the generalized Burgers'-Fisher equation is proposed. The scheme is based on the hybridization of Exp-function method with nature inspired algorithm. The given nonlinear partial differential equation (NPDE) through substitution is converted into a nonlinear ordinary differential equation (NODE). The travelling wave solution is approximated by the Exp-function method with unknown parameters. The unknown parameters are estimated by transforming the NODE into an equivalent global error minimization problem by using a fitness function. The popular genetic algorithm (GA) is used to solve the minimization problem, and to achieve the unknown parameters. The proposed scheme is successfully implemented to solve the generalized Burgers'-Fisher equation. The comparison of numerical results with the exact solutions, and the solutions obtained using some traditional methods, including adomian decomposition method (ADM), homotopy perturbation method (HPM), and optimal homotopy asymptotic method (OHAM), show that the suggested scheme is fairly accurate and viable for solving such problems. PMID:25811858
NASA Astrophysics Data System (ADS)
Ushijima, T.; Yeh, W.
2013-12-01
An optimal experimental design algorithm is developed to select locations for a network of observation wells that provides the maximum information about unknown hydraulic conductivity in a confined, anisotropic aquifer. The design employs a maximal information criterion that chooses, among competing designs, the design that maximizes the sum of squared sensitivities while conforming to specified design constraints. Because that the formulated problem is non-convex and contains integer variables (necessitating a combinatorial search), for a realistically-scaled model, the problem may be difficult, if not impossible, to solve through traditional mathematical programming techniques. Genetic Algorithms (GAs) are designed to search out the global optimum; however because a GA requires a large number of calls to a groundwater model, the formulated optimization problem may still be infeasible to solve. To overcome this, Proper Orthogonal Decomposition (POD) is applied to the groundwater model to reduce its dimension. The information matrix in the full model space can then be searched without solving the full model.
On l(1): Optimal decentralized performance
NASA Technical Reports Server (NTRS)
Sourlas, Dennis; Manousiouthakis, Vasilios
1993-01-01
In this paper, the Manousiouthakis parametrization of all decentralized stabilizing controllers is employed in mathematically formulating the l(sup 1) optimal decentralized controller synthesis problem. The resulting optimization problem is infinite dimensional and therefore not directly amenable to computations. It is shown that finite dimensional optimization problems that have value arbitrarily close to the infinite dimensional one can be constructed. Based on this result, an algorithm that solves the l(sup 1) decentralized performance problems is presented. A global optimization approach to the solution of the infinite dimensional approximating problems is also discussed.
Witt, Julia
2009-11-18
This article provides a brief overview of the global health-worker shortage, which could undermine the Millennium Development Goal to halt and begin to reverse the spread of HIV/AIDS. The current situation suggests that long-term solutions to shortages can only be found by addressing the problem from a global perspective; that is, to eliminate shortages through substantial investments in training and retaining health workers in developed and developing countries, and not through policies that do not work towards solving this underlying problem, such as ones that restrict migration.
NASA Astrophysics Data System (ADS)
Malekan, Mohammad; Barros, Felício B.
2017-12-01
Generalized or extended finite element method (G/XFEM) models the crack by enriching functions of partition of unity type with discontinuous functions that represent well the physical behavior of the problem. However, this enrichment functions are not available for all problem types. Thus, one can use numerically-built (global-local) enrichment functions to have a better approximate procedure. This paper investigates the effects of micro-defects/inhomogeneities on a main crack behavior by modeling the micro-defects/inhomogeneities in the local problem using a two-scale G/XFEM. The global-local enrichment functions are influenced by the micro-defects/inhomogeneities from the local problem and thus change the approximate solution of the global problem with the main crack. This approach is presented in detail by solving three different linear elastic fracture mechanics problems for different cases: two plane stress and a Reissner-Mindlin plate problems. The numerical results obtained with the two-scale G/XFEM are compared with the reference solutions from the analytical, numerical solution using standard G/XFEM method and ABAQUS as well, and from the literature.
One Governor's Perspective of Higher Education.
ERIC Educational Resources Information Center
Wilder, L. Douglas
1990-01-01
Colleges and universities should help prepare an able, intelligent, motivated workforce, provide problem-solving services to business, and provide opportunities for individuals to better themselves. New conditions require that it also prepare for the global economy, advancing technology, an increasingly diverse population, and minority educational…
Taboo Search: An Approach to the Multiple Minima Problem
NASA Astrophysics Data System (ADS)
Cvijovic, Djurdje; Klinowski, Jacek
1995-02-01
Described here is a method, based on Glover's taboo search for discrete functions, of solving the multiple minima problem for continuous functions. As demonstrated by model calculations, the algorithm avoids entrapment in local minima and continues the search to give a near-optimal final solution. Unlike other methods of global optimization, this procedure is generally applicable, easy to implement, derivative-free, and conceptually simple.
NASA Astrophysics Data System (ADS)
Tyuleneva, Tatiana
2017-11-01
One of the problems of sustainable development of mining companies is attracting additional investment. To solve it requires access to international capital markets, in this context, enterprises need to prepare financial statements with international requirements based on the data generated by the accounting system. The article considers the basic problems of accounting in the extractive industries due to the nature of the industry, as well as evaluation of the completeness of their solution in the framework of international financial reporting standards. In addition, lists the characteristics of accounting for mining industry, due to the peculiarities of the production process that need to be considered to solve these problems. This sector is extremely important for individual countries and on a global scale.
Conflict in Context: Understanding Local to Global Security.
ERIC Educational Resources Information Center
Mertz, Gayle; Lieber, Carol Miller
This multidisciplinary guide provides middle and high school teachers and students with inquiry-based tools to support their exploration of emerging local, national, international, and transboundary security issues. Students are introduced to critical thinking, problem solving, and peacemaking strategies that will help them better understand…
Geography: Key to World Understanding.
ERIC Educational Resources Information Center
Dando, William A.
1990-01-01
Delineates the nature of applied geography, asserting that geography links the natural and social sciences. Underscores geography's role in data analysis and problem solving on a global scale. Traces the discipline's history. Maps geography's status in higher education institutions. Discusses new technologies used by geographers. Summarizes career…
Interactive Tabletops in Education
ERIC Educational Resources Information Center
Dillenbourg, Pierre; Evans, Michael
2011-01-01
Interactive tabletops are gaining increased attention from CSCL researchers. This paper analyses the relation between this technology and teaching and learning processes. At a global level, one could argue that tabletops convey a socio-constructivist flavor: they support small teams that solve problems by exploring multiple solutions. The…
ERIC Educational Resources Information Center
Peck, Kyle L.; Dorricott, Denise
1994-01-01
While businesses have been building electronic highways, education is traveling an electronic dirt road. There are 10 reasons for using technology in classrooms. Students learn and develop at different rates. Graduates must be globally aware, proficient at accessing, evaluating, and communicating information, and adept at solving complex problems.…
Global dynamic optimization approach to predict activation in metabolic pathways.
de Hijas-Liste, Gundián M; Klipp, Edda; Balsa-Canto, Eva; Banga, Julio R
2014-01-06
During the last decade, a number of authors have shown that the genetic regulation of metabolic networks may follow optimality principles. Optimal control theory has been successfully used to compute optimal enzyme profiles considering simple metabolic pathways. However, applying this optimal control framework to more general networks (e.g. branched networks, or networks incorporating enzyme production dynamics) yields problems that are analytically intractable and/or numerically very challenging. Further, these previous studies have only considered a single-objective framework. In this work we consider a more general multi-objective formulation and we present solutions based on recent developments in global dynamic optimization techniques. We illustrate the performance and capabilities of these techniques considering two sets of problems. First, we consider a set of single-objective examples of increasing complexity taken from the recent literature. We analyze the multimodal character of the associated non linear optimization problems, and we also evaluate different global optimization approaches in terms of numerical robustness, efficiency and scalability. Second, we consider generalized multi-objective formulations for several examples, and we show how this framework results in more biologically meaningful results. The proposed strategy was used to solve a set of single-objective case studies related to unbranched and branched metabolic networks of different levels of complexity. All problems were successfully solved in reasonable computation times with our global dynamic optimization approach, reaching solutions which were comparable or better than those reported in previous literature. Further, we considered, for the first time, multi-objective formulations, illustrating how activation in metabolic pathways can be explained in terms of the best trade-offs between conflicting objectives. This new methodology can be applied to metabolic networks with arbitrary topologies, non-linear dynamics and constraints.
Algorithms for Maneuvering Spacecraft Around Small Bodies
NASA Technical Reports Server (NTRS)
Acikmese, A. Bechet; Bayard, David
2006-01-01
A document describes mathematical derivations and applications of autonomous guidance algorithms for maneuvering spacecraft in the vicinities of small astronomical bodies like comets or asteroids. These algorithms compute fuel- or energy-optimal trajectories for typical maneuvers by solving the associated optimal-control problems with relevant control and state constraints. In the derivations, these problems are converted from their original continuous (infinite-dimensional) forms to finite-dimensional forms through (1) discretization of the time axis and (2) spectral discretization of control inputs via a finite number of Chebyshev basis functions. In these doubly discretized problems, the Chebyshev coefficients are the variables. These problems are, variously, either convex programming problems or programming problems that can be convexified. The resulting discrete problems are convex parameter-optimization problems; this is desirable because one can take advantage of very efficient and robust algorithms that have been developed previously and are well established for solving such problems. These algorithms are fast, do not require initial guesses, and always converge to global optima. Following the derivations, the algorithms are demonstrated by applying them to numerical examples of flyby, descent-to-hover, and ascent-from-hover maneuvers.
Xia, Yangkun; Fu, Zhuo; Tsai, Sang-Bing; Wang, Jiangtao
2018-05-10
In order to promote the development of low-carbon logistics and economize logistics distribution costs, the vehicle routing problem with split deliveries by backpack is studied. With the help of the model of classical capacitated vehicle routing problem, in this study, a form of discrete split deliveries was designed in which the customer demand can be split only by backpack. A double-objective mathematical model and the corresponding adaptive tabu search (TS) algorithm were constructed for solving this problem. By embedding the adaptive penalty mechanism, and adopting the random neighborhood selection strategy and reinitialization principle, the global optimization ability of the new algorithm was enhanced. Comparisons with the results in the literature show the effectiveness of the proposed algorithm. The proposed method can save the costs of low-carbon logistics and reduce carbon emissions, which is conducive to the sustainable development of low-carbon logistics.
Numerical optimization in Hilbert space using inexact function and gradient evaluations
NASA Technical Reports Server (NTRS)
Carter, Richard G.
1989-01-01
Trust region algorithms provide a robust iterative technique for solving non-convex unstrained optimization problems, but in many instances it is prohibitively expensive to compute high accuracy function and gradient values for the method. Of particular interest are inverse and parameter estimation problems, since function and gradient evaluations involve numerically solving large systems of differential equations. A global convergence theory is presented for trust region algorithms in which neither function nor gradient values are known exactly. The theory is formulated in a Hilbert space setting so that it can be applied to variational problems as well as the finite dimensional problems normally seen in trust region literature. The conditions concerning allowable error are remarkably relaxed: relative errors in the gradient error condition is automatically satisfied if the error is orthogonal to the gradient approximation. A technique for estimating gradient error and improving the approximation is also presented.
Howe, M L; Rabinowitz, F M; Powell, T L
1998-09-01
In the present experiment, we evaluated the effects of individual differences in reading span and variation in memory demands on class-inclusion performance. One hundred twenty college students whose reading spans ranged from low to medium to high (as indexed by a computerized version of the Daneman and Carpenter [1980] reading-span task) solved 48 class-inclusion problems. Half of the subjects had the solution information available when the problems were presented; the other half performed a detection task between solution information and problem presentation. The results from both standard statistical analyses and from a mathematical model indicated that differences in reading span and memory load had predictable, similar effects. Specifically, the sophistication of reasoning strategies declined when memory demands increased or when reading spans decreased. Surprisingly, these effects were primarily additive. The results were interpreted in terms of global resource models and findings from the developmental literature.
Parallel algorithms for boundary value problems
NASA Technical Reports Server (NTRS)
Lin, Avi
1990-01-01
A general approach to solve boundary value problems numerically in a parallel environment is discussed. The basic algorithm consists of two steps: the local step where all the P available processors work in parallel, and the global step where one processor solves a tridiagonal linear system of the order P. The main advantages of this approach are two fold. First, this suggested approach is very flexible, especially in the local step and thus the algorithm can be used with any number of processors and with any of the SIMD or MIMD machines. Secondly, the communication complexity is very small and thus can be used as easily with shared memory machines. Several examples for using this strategy are discussed.
Parallel computation using boundary elements in solid mechanics
NASA Technical Reports Server (NTRS)
Chien, L. S.; Sun, C. T.
1990-01-01
The inherent parallelism of the boundary element method is shown. The boundary element is formulated by assuming the linear variation of displacements and tractions within a line element. Moreover, MACSYMA symbolic program is employed to obtain the analytical results for influence coefficients. Three computational components are parallelized in this method to show the speedup and efficiency in computation. The global coefficient matrix is first formed concurrently. Then, the parallel Gaussian elimination solution scheme is applied to solve the resulting system of equations. Finally, and more importantly, the domain solutions of a given boundary value problem are calculated simultaneously. The linear speedups and high efficiencies are shown for solving a demonstrated problem on Sequent Symmetry S81 parallel computing system.
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.
Link-prediction to tackle the boundary specification problem in social network surveys
De Wilde, Philippe; Buarque de Lima-Neto, Fernando
2017-01-01
Diffusion processes in social networks often cause the emergence of global phenomena from individual behavior within a society. The study of those global phenomena and the simulation of those diffusion processes frequently require a good model of the global network. However, survey data and data from online sources are often restricted to single social groups or features, such as age groups, single schools, companies, or interest groups. Hence, a modeling approach is required that extrapolates the locally restricted data to a global network model. We tackle this Missing Data Problem using Link-Prediction techniques from social network research, network generation techniques from the area of Social Simulation, as well as a combination of both. We found that techniques employing less information may be more adequate to solve this problem, especially when data granularity is an issue. We validated the network models created with our techniques on a number of real-world networks, investigating degree distributions as well as the likelihood of links given the geographical distance between two nodes. PMID:28426826
Wang, Leimin; Zeng, Zhigang; Hu, Junhao; Wang, Xiaoping
2017-03-01
This paper addresses the controller design problem for global fixed-time synchronization of delayed neural networks (DNNs) with discontinuous activations. To solve this problem, adaptive control and state feedback control laws are designed. Then based on the two controllers and two lemmas, the error system is proved to be globally asymptotically stable and even fixed-time stable. Moreover, some sufficient and easy checked conditions are derived to guarantee the global synchronization of drive and response systems in fixed time. It is noted that the settling time functional for fixed-time synchronization is independent on initial conditions. Our fixed-time synchronization results contain the finite-time results as the special cases by choosing different values of the two controllers. Finally, theoretical results are supported by numerical simulations. Copyright © 2016 Elsevier Ltd. All rights reserved.
Topological defects in alternative theories to cosmic inflation and string cosmology
NASA Astrophysics Data System (ADS)
Alexander, Stephon H. S.
The physics of the Early Universe is described in terms of the inflationary paradigm, which is based on a marriage between Einstein's general theory of relativity minimally coupled to quantum field theory. Inflation was posed to solve some of the outstanding problems of the Standard Big Bang Cosmology (SBB) such as the horizon, formation of structure and monopole problems. Despite its observational and theoretical successes, inflation is plagued with fine tuning and initial singularity problems. On the other hand, superstring/M theory, a theory of quantum gravity, possesses symmetries which naturally avoid space-time singularities. This thesis investigates alternative theories to cosmic inflation for solving the initial singularity, horizon and monopole problems, making use of topological defects. It was proposed by Dvali, Liu and Vaschaspati that the monopole problem can be solved without inflation if domain walls "sweep" up the monopoles in the early universe, thus reducing their number density significantly. Necessary for this mechanism to work is the presence of an attractive force between the monopole and the domain wall as well as a channel for the monopole's unwinding. We show numerically and analytically in two field theory models that for global defects the attraction is a universal result but the unwinding is model specific. The second part of this thesis investigates a string/M theory inspired model for solving the horizon problem. It was proposed by Moffat, Albrecht and Magueijo that the horizon problem is solved with a "phase transition" associated with a varying speed of light before the surface of last scattering. We provide a string/M theory mechanism based on assuming that our space-time is a D-3 brane probing a bulk supergravity black hole bulk background. This mechanism provides the necessary time variation of the velocity of light to solve the horizon problem. We suggest a mechanism which stablilizes the speed of light on the D-3 brane. We finally address the cosmological initial singularity problem using the target space duality inherent in string/M theory. It was suggested by Brandenberger and Vafa that superstring theory can solve the singularity problem and in addition explain why only three spatial dimensions can become large. We show that under specific conditions this mechanism still persists when including the effects of D-branes.
A Memetic Algorithm for Global Optimization of Multimodal Nonseparable Problems.
Zhang, Geng; Li, Yangmin
2016-06-01
It is a big challenging issue of avoiding falling into local optimum especially when facing high-dimensional nonseparable problems where the interdependencies among vector elements are unknown. In order to improve the performance of optimization algorithm, a novel memetic algorithm (MA) called cooperative particle swarm optimizer-modified harmony search (CPSO-MHS) is proposed in this paper, where the CPSO is used for local search and the MHS for global search. The CPSO, as a local search method, uses 1-D swarm to search each dimension separately and thus converges fast. Besides, it can obtain global optimum elements according to our experimental results and analyses. MHS implements the global search by recombining different vector elements and extracting global optimum elements. The interaction between local search and global search creates a set of local search zones, where global optimum elements reside within the search space. The CPSO-MHS algorithm is tested and compared with seven other optimization algorithms on a set of 28 standard benchmarks. Meanwhile, some MAs are also compared according to the results derived directly from their corresponding references. The experimental results demonstrate a good performance of the proposed CPSO-MHS algorithm in solving multimodal nonseparable problems.
Solving geosteering inverse problems by stochastic Hybrid Monte Carlo method
Shen, Qiuyang; Wu, Xuqing; Chen, Jiefu; ...
2017-11-20
The inverse problems arise in almost all fields of science where the real-world parameters are extracted from a set of measured data. The geosteering inversion plays an essential role in the accurate prediction of oncoming strata as well as a reliable guidance to adjust the borehole position on the fly to reach one or more geological targets. This mathematical treatment is not easy to solve, which requires finding an optimum solution among a large solution space, especially when the problem is non-linear and non-convex. Nowadays, a new generation of logging-while-drilling (LWD) tools has emerged on the market. The so-called azimuthalmore » resistivity LWD tools have azimuthal sensitivity and a large depth of investigation. Hence, the associated inverse problems become much more difficult since the earth model to be inverted will have more detailed structures. The conventional deterministic methods are incapable to solve such a complicated inverse problem, where they suffer from the local minimum trap. Alternatively, stochastic optimizations are in general better at finding global optimal solutions and handling uncertainty quantification. In this article, we investigate the Hybrid Monte Carlo (HMC) based statistical inversion approach and suggest that HMC based inference is more efficient in dealing with the increased complexity and uncertainty faced by the geosteering problems.« less
Water security and the science agenda
NASA Astrophysics Data System (ADS)
Wheater, Howard S.; Gober, Patricia
2015-07-01
The freshwater environment is facing unprecedented global pressures. Unsustainable use of surface and groundwater is ubiquitous. Gross pollution is seen in developing economies, nutrient pollution is a global threat to aquatic ecosystems, and flood damage is increasing. Droughts have severe local consequences, but effects on food can be global. These current pressures are set in the context of rapid environmental change and socio-economic development, population growth, and weak and fragmented governance. We ask what should be the role of the water science community in addressing water security challenges. Deeper understanding of aquatic and terrestrial environments and their interactions with the climate system is needed, along with trans-disciplinary analysis of vulnerabilities to environmental and societal change. The human dimension must be fully integrated into water science research and viewed as an endogenous component of water system dynamics. Land and water management are inextricably linked, and thus more cross-sector coordination of research and policy is imperative. To solve real-world problems, the products of science must emerge from an iterative, collaborative, two-way exchange with management and policy communities. Science must produce knowledge that is deemed to be credible, legitimate, and salient by relevant stakeholders, and the social process of linking science to policy is thus vital to efforts to solve water problems. The paper shows how a large-scale catchment-based observatory can be used to practice trans-disciplinary science integration and address the Anthropocene's water problems.
DOE Office of Scientific and Technical Information (OSTI.GOV)
Graf, Peter; Dykes, Katherine; Scott, George
The layout of turbines in a wind farm is already a challenging nonlinear, nonconvex, nonlinearly constrained continuous global optimization problem. Here we begin to address the next generation of wind farm optimization problems by adding the complexity that there is more than one turbine type to choose from. The optimization becomes a nonlinear constrained mixed integer problem, which is a very difficult class of problems to solve. Furthermore, this document briefly summarizes the algorithm and code we have developed, the code validation steps we have performed, and the initial results for multi-turbine type and placement optimization (TTP_OPT) we have run.
Command and Control in a Complex World
2012-05-22
definition of command and control does not adequately address changes introduced through technology trends, our understanding of the global operating...processes. The current joint definition of command and control does not adequately address changes introduced through technology trends, our...the problem is actually solved. There are no definitive , objective solutions to wicked problems. For a complete definition of wicked problems, see
Piracy and Its Impact on the Economy
2010-12-01
options to supplement lost income, including 4 Catherine Zara Raymond, “Piracy and Armed Robbery in the Malacca Strait: A Problem...Africa: The Piracy Hot Spot and Its Implications for Global Security,” Mediterranean Quarterly, vol. 20 no. 3 (Summer 2009): 100. 41 Catherine Zara ...43 Catherine Zara Raymond, “Piracy and Armed Robbery in the Malacca Strait: A Problem Solved?” Naval War College Review Vol. 62 No. 3 (Summer
Organizing for Social Partnership: Higher Education in Cross-Sector Collaboration
ERIC Educational Resources Information Center
Siegel, David J.
2010-01-01
The most complex social challenges--such as post-secondary access and success for under-represented students, diversification of the workforce, poverty, environmental degradation, and global health--exceed the problem-solving capacity of single organizations or societal sectors. "Organizing for Social Partnership" provides colleges and…
Preparation for Lifelong Learning Using ePortfolios
ERIC Educational Resources Information Center
Heinrich, E.; Bhattacharya, M.; Rayudu, R.
2007-01-01
Rapid technological change, increasing globalization and a changing world of employment with multiple roles during one's professional life are necessitating a change from knowledge to learning societies. Full participation requires lifelong learning skills, meaning the ability to solve problems, work both independently and in a team, communicate…
Improving Virtual Teams through Knowledge Management: A Case Study
ERIC Educational Resources Information Center
Laughridge, James F.
2012-01-01
Within the dynamic globalized operating environment, organizations are increasingly relying on virtual teams to solve their most difficult problems, leverage their expertise and expand their presence. The use of virtual teams by organizations continues to increase greatly as the technologies supporting them evolve. Despite improvements in…
Taiwanese EFL Learners' Perceived Use of Online Reading Strategies
ERIC Educational Resources Information Center
Chen, Lisa Wen Chun
2015-01-01
Reading strategies are beneficial to learners' reading comprehension. The strategies can be divided into different categories, such as global reading strategies, problem solving strategies and support strategies. Most previous studies investigated the importance of reading strategies in the paper-based reading. However, relatively few studies…
International Field Experiences Promote Professional Development for Sustainability Leaders
ERIC Educational Resources Information Center
Hull, R. Bruce; Kimmel, Courtney; Robertson, David P.; Mortimer, Michael
2016-01-01
Purpose: This paper aims to describe, explain and evaluate a graduate education program that provides international project experiences and builds competencies related to collaborative problem-solving, cultural capacity to work globally and sustainable development. Design/methodology/approach: Qualitative analysis of survey data from 28 students…
Bifurcation analysis of eight coupled degenerate optical parametric oscillators
NASA Astrophysics Data System (ADS)
Ito, Daisuke; Ueta, Tetsushi; Aihara, Kazuyuki
2018-06-01
A degenerate optical parametric oscillator (DOPO) network realized as a coherent Ising machine can be used to solve combinatorial optimization problems. Both theoretical and experimental investigations into the performance of DOPO networks have been presented previously. However a problem remains, namely that the dynamics of the DOPO network itself can lower the search success rates of globally optimal solutions for Ising problems. This paper shows that the problem is caused by pitchfork bifurcations due to the symmetry structure of coupled DOPOs. Some two-parameter bifurcation diagrams of equilibrium points express the performance deterioration. It is shown that the emergence of non-ground states regarding local minima hampers the system from reaching the ground states corresponding to the global minimum. We then describe a parametric strategy for leading a system to the ground state by actively utilizing the bifurcation phenomena. By adjusting the parameters to break particular symmetry, we find appropriate parameter sets that allow the coherent Ising machine to obtain the globally optimal solution alone.
DOE Office of Scientific and Technical Information (OSTI.GOV)
Guruswamy, L.D.; Palmer, G.W.R. Sir; Weston, B.H.
A litany of dismal happenings - global warming, ozone layer depletion, desertification, destruction of biodiversity, acid rain, and nuclear and water accidents - are but some of the subjects covered by this book, a problem-solving casebook authored by three educators. This new book makes the obvious but important point, that environmental issues are not limited by national boundaries. The book is divided into three parts. The first three chapters of part I discuss the basic principals of traditional international law without any reference to environmental issues. Part II, comprised of seven chapters, deals with hypothetical problems that affect various aspectsmore » of the environment vis-a-vis the norms, institutions, and procedures through which the international legal system operates. The book concludes with two chapters dealing with future environmental concerns. The book focuses on issue-spotting, problem-solving, and synthesis over the assimilation and comprehension of raw, disembodied knowledge. The book helps to manage our common future on this planet, for which we will need a new global regime based essentially on the extension into international life of the rule of law, together with reliable mechanisms for accountability and enforcement that provide the basis for the effective functioning of national societies.« less
Characteristic of cognitive decline in Parkinson's disease: a 1-year follow-up.
McKinlay, Audrey; Grace, Randolph C
2011-10-01
The aim of this study was to track the evolution of cognitive decline in Parkinson's disease (PD) patients 1 year after baseline testing. Thirty-three PD patients, divided according to three previously determined subgroups based on their initial cognitive performance, and a healthy comparison group were reassessed after a 1-year interval. Participants were assessed in the following five domains: Executive Function, Problem Solving, Working Memory/Attention, Memory, and Visuospatial Ability. The PD groups differed on the domains of Executive Function, Problem Solving, and Working Memory, with the most severe deficits being evident for the group that had previously shown the greatest level of impairment. Increased cognitive problems were also associated with decreased functioning in activities of daily living. The most severely impaired group had evidence of global cognitive decline, possibly reflecting a stage of preclinical dementia.
Global computing for bioinformatics.
Loewe, Laurence
2002-12-01
Global computing, the collaboration of idle PCs via the Internet in a SETI@home style, emerges as a new way of massive parallel multiprocessing with potentially enormous CPU power. Its relations to the broader, fast-moving field of Grid computing are discussed without attempting a review of the latter. This review (i) includes a short table of milestones in global computing history, (ii) lists opportunities global computing offers for bioinformatics, (iii) describes the structure of problems well suited for such an approach, (iv) analyses the anatomy of successful projects and (v) points to existing software frameworks. Finally, an evaluation of the various costs shows that global computing indeed has merit, if the problem to be solved is already coded appropriately and a suitable global computing framework can be found. Then, either significant amounts of computing power can be recruited from the general public, or--if employed in an enterprise-wide Intranet for security reasons--idle desktop PCs can substitute for an expensive dedicated cluster.
NASA Astrophysics Data System (ADS)
Polprasert, Jirawadee; Ongsakul, Weerakorn; Dieu, Vo Ngoc
2011-06-01
This paper proposes a self-organizing hierarchical particle swarm optimization (SPSO) with time-varying acceleration coefficients (TVAC) for solving economic dispatch (ED) problem with non-smooth functions including multiple fuel options (MFO) and valve-point loading effects (VPLE). The proposed SPSO with TVAC is the new approach optimizer and good performance for solving ED problems. It can handle the premature convergence of the problem by re-initialization of velocity whenever particles are stagnated in the search space. To properly control both local and global explorations of the swarm during the optimization process, the performance of TVAC is included. The proposed method is tested in different ED problems with non-smooth cost functions and the obtained results are compared to those from many other methods in the literature. The results have revealed that the proposed SPSO with TVAC is effective in finding higher quality solutions for non-smooth ED problems than many other methods.
DOE Office of Scientific and Technical Information (OSTI.GOV)
Salinger, Andy; Evans, Katherine J; Lemieux, Jean-Francois
2011-01-01
We have implemented the Jacobian-free Newton-Krylov (JFNK) method for solving the rst-order ice sheet momentum equation in order to improve the numerical performance of the Community Ice Sheet Model (CISM), the land ice component of the Community Earth System Model (CESM). Our JFNK implementation is based on signicant re-use of existing code. For example, our physics-based preconditioner uses the original Picard linear solver in CISM. For several test cases spanning a range of geometries and boundary conditions, our JFNK implementation is 1.84-3.62 times more efficient than the standard Picard solver in CISM. Importantly, this computational gain of JFNK over themore » Picard solver increases when rening the grid. Global convergence of the JFNK solver has been signicantly improved by rescaling the equation for the basal boundary condition and through the use of an inexact Newton method. While a diverse set of test cases show that our JFNK implementation is usually robust, for some problems it may fail to converge with increasing resolution (as does the Picard solver). Globalization through parameter continuation did not remedy this problem and future work to improve robustness will explore a combination of Picard and JFNK and the use of homotopy methods.« less
A study of the performance of patients with frontal lobe lesions in a financial planning task.
Goel, V; Grafman, J; Tajik, J; Gana, S; Danto, D
1997-10-01
It has long been argued that patients with lesions in the prefrontal cortex have difficulties in decision making and problem solving in real-world, ill-structured situations, particularly problem types involving planning and look-ahead components. Recently, several researchers have questioned our ability to capture and characterize these deficits adequately using just the standard neuropsychological test batteries, and have called for tests that reflect real-world task requirements more accurately. We present data from 10 patients with focal lesions to the prefrontal cortex and 10 normal control subjects engaged in a real-world financial planning task. We also introduce a theoretical framework and methodology developed in the cognitive science literature for quantifying and analysing the complex data generated by problem-solving tasks. Our findings indicate that patient performance is impoverished at a global level but not at the local level. Patients have difficulty in organizing and structuring their problem space. Once they begin problem solving, they have difficulty in allocating adequate effort to each problem-solving phase. Patients also have difficulty dealing with the fact that there are no right or wrong answers nor official termination points in real-world planning problems. They also find it problematic to generate their own feedback. They invariably terminate the session before the details are fleshed out and all the goals satisfied. Finally, patients do not take full advantage of the fact that constraints on real-world problems are negotiable. However, it is not necessary to postulate a 'planning' deficit. It is possible to understand the patients' difficulties in real world planning tasks in terms of the following four accepted deficits: inadequate access to 'structured event complexes', difficulty in generalizing from particulars, failure to shift between 'mental sets', and poor judgment regarding adequacy and completeness of a plan.
Bringing Them in: The Experiences of Imported and Overseas-Qualified Teachers
ERIC Educational Resources Information Center
Sharplin, Elaine
2009-01-01
This qualitative multiple-site case study explores the experiences of imported and overseas-qualified teachers appointed to fill "difficult-to-staff" Western Australian rural schools. In a climate of global teacher shortages, investigation of the strategies adopted to solve this problem requires empirical examination. The study of six…
A global trend: privatization and reform of social security pension plans.
Poortvliet, W G; Laine, T P
1995-01-01
Ten years ago Chile successfully privatized its social security system, beginning a worldwide trend to solve the problem of an increasing burden on government-supported social security programs. Contributing factors include an aging population, fewer workers to support retirees, government budget deficits and the influence of politics.
NASA Astrophysics Data System (ADS)
Zhao, Liang; Huang, Shoudong; Dissanayake, Gamini
2018-07-01
This paper presents a novel hierarchical approach to solving structure-from-motion (SFM) problems. The algorithm begins with small local reconstructions based on nonlinear bundle adjustment (BA). These are then joined in a hierarchical manner using a strategy that requires solving a linear least squares optimization problem followed by a nonlinear transform. The algorithm can handle ordered monocular and stereo image sequences. Two stereo images or three monocular images are adequate for building each initial reconstruction. The bulk of the computation involves solving a linear least squares problem and, therefore, the proposed algorithm avoids three major issues associated with most of the nonlinear optimization algorithms currently used for SFM: the need for a reasonably accurate initial estimate, the need for iterations, and the possibility of being trapped in a local minimum. Also, by summarizing all the original observations into the small local reconstructions with associated information matrices, the proposed Linear SFM manages to preserve all the information contained in the observations. The paper also demonstrates that the proposed problem formulation results in a sparse structure that leads to an efficient numerical implementation. The experimental results using publicly available datasets show that the proposed algorithm yields solutions that are very close to those obtained using a global BA starting with an accurate initial estimate. The C/C++ source code of the proposed algorithm is publicly available at https://github.com/LiangZhaoPKUImperial/LinearSFM.
An International Model for Antibiotics Regulation.
Aguirre, Emilie
We face a global antibiotics resistance crisis. Antibiotic drugs are rapidly losing their effectiveness, potentially propelling us toward a post-antibiotic world. The largest use of antibiotics in the world is in food-producing animals. Food producers administer these drugs in routine, low doses—the types of doses that are incidentally the most conducive to breeding antibiotic resistance. In general, individual countries have been too slow to act in regulating misuse and overuse of antibiotics in foodproducing animals. This problem will only worsen with the significant projected growth in meat consumption and production expected in emerging economies in the near future. Although individual countries regulating antibiotics can have important effects, one country alone cannot insulate itself entirely from the effects of antibiotic resistance, nor can one country solve the crisis for itself or for the world. The global nature of the food system and the urgency of the problem require immediate global solutions. Adapting a democratic experimentalist approach at the international level can help achieve this goal. Using an international democratic experimentalist framework in conjunction with the World Organization for Animal Health (OIE) would provide for increased systematized data collection and lead to heightened, scientifically informed OIE standards, enforceable by the World Trade Organization (WTO), which could have a significant impact on the reduction of subtherapeutic use of antibiotics internationally. International democratic experimentalism addresses the global intricacy, time sensitivity, context- and culture-specificity, and knowledgeintensiveness of this problem. By encouraging more countries to experiment to solve this problem, the democratic experimentalist model would help develop a larger database of solutions to enable more meaningful cross-country comparisons across a wider range of contexts. This approach maintains democratic governance and legitimacy while maximizing data collection, efficiency, translatability, transparency, and information-sharing. Adapting democratic experimentalism internationally can enable the kind of concerted international effort required to address the pressing problem of antibiotic resistance.
NASA Technical Reports Server (NTRS)
Krasteva, Denitza T.
1998-01-01
Multidisciplinary design optimization (MDO) for large-scale engineering problems poses many challenges (e.g., the design of an efficient concurrent paradigm for global optimization based on disciplinary analyses, expensive computations over vast data sets, etc.) This work focuses on the application of distributed schemes for massively parallel architectures to MDO problems, as a tool for reducing computation time and solving larger problems. The specific problem considered here is configuration optimization of a high speed civil transport (HSCT), and the efficient parallelization of the embedded paradigm for reasonable design space identification. Two distributed dynamic load balancing techniques (random polling and global round robin with message combining) and two necessary termination detection schemes (global task count and token passing) were implemented and evaluated in terms of effectiveness and scalability to large problem sizes and a thousand processors. The effect of certain parameters on execution time was also inspected. Empirical results demonstrated stable performance and effectiveness for all schemes, and the parametric study showed that the selected algorithmic parameters have a negligible effect on performance.
The Use of Efficient Broadcast Protocols in Asynchronous Distributed Systems. Ph.D. Thesis
NASA Technical Reports Server (NTRS)
Schmuck, Frank Bernhard
1988-01-01
Reliable broadcast protocols are important tools in distributed and fault-tolerant programming. They are useful for sharing information and for maintaining replicated data in a distributed system. However, a wide range of such protocols has been proposed. These protocols differ in their fault tolerance and delivery ordering characteristics. There is a tradeoff between the cost of a broadcast protocol and how much ordering it provides. It is, therefore, desirable to employ protocols that support only a low degree of ordering whenever possible. This dissertation presents techniques for deciding how strongly ordered a protocol is necessary to solve a given application problem. It is shown that there are two distinct classes of application problems: problems that can be solved with efficient, asynchronous protocols, and problems that require global ordering. The concept of a linearization function that maps partially ordered sets of events to totally ordered histories is introduced. How to construct an asynchronous implementation that solves a given problem if a linearization function for it can be found is shown. It is proved that in general the question of whether a problem has an asynchronous solution is undecidable. Hence there exists no general algorithm that would automatically construct a suitable linearization function for a given problem. Therefore, an important subclass of problems that have certain commutativity properties are considered. Techniques for constructing asynchronous implementations for this class are presented. These techniques are useful for constructing efficient asynchronous implementations for a broad range of practical problems.
Local and global evaluation for remote sensing image segmentation
NASA Astrophysics Data System (ADS)
Su, Tengfei; Zhang, Shengwei
2017-08-01
In object-based image analysis, how to produce accurate segmentation is usually a very important issue that needs to be solved before image classification or target recognition. The study for segmentation evaluation method is key to solving this issue. Almost all of the existent evaluation strategies only focus on the global performance assessment. However, these methods are ineffective for the situation that two segmentation results with very similar overall performance have very different local error distributions. To overcome this problem, this paper presents an approach that can both locally and globally quantify segmentation incorrectness. In doing so, region-overlapping metrics are utilized to quantify each reference geo-object's over and under-segmentation error. These quantified error values are used to produce segmentation error maps which have effective illustrative power to delineate local segmentation error patterns. The error values for all of the reference geo-objects are aggregated through using area-weighted summation, so that global indicators can be derived. An experiment using two scenes of very different high resolution images showed that the global evaluation part of the proposed approach was almost as effective as other two global evaluation methods, and the local part was a useful complement to comparing different segmentation results.
On Responsibility of Scientists
NASA Astrophysics Data System (ADS)
Burdyuzha, Vladimir
The situation of modern world is analised. It is impossible for our Civilization when at least half of the World Scientists are engaged in research intended to solve military problems. Civilization cannot be called reasonable so long as it spends a huge portion of national incomes on armaments. For resolution of our global problems International Scientific Center - Brain Trust of planet must be created, the status of which should be defined and sealed by the UN organization.
Social emotion recognition, social functioning, and attempted suicide in late-life depression.
Szanto, Katalin; Dombrovski, Alexandre Y; Sahakian, Barbara J; Mulsant, Benoit H; Houck, Patricia R; Reynolds, Charles F; Clark, Luke
2012-03-01
: Lack of feeling connected and poor social problem solving have been described in suicide attempters. However, cognitive substrates of this apparent social impairment in suicide attempters remain unknown. One possible deficit, the inability to recognize others' complex emotional states has been observed not only in disorders characterized by prominent social deficits (autism-spectrum disorders and frontotemporal dementia) but also in depression and normal aging. This study assessed the relationship between social emotion recognition, problem solving, social functioning, and attempted suicide in late-life depression. : There were 90 participants: 24 older depressed suicide attempters, 38 nonsuicidal depressed elders, and 28 comparison subjects with no psychiatric history. We compared performance on the Reading the Mind in the Eyes test and measures of social networks, social support, social problem solving, and chronic interpersonal difficulties in these three groups. : Suicide attempters committed significantly more errors in social emotion recognition and showed poorer global cognitive performance than elders with no psychiatric history. Attempters had restricted social networks: they were less likely to talk to their children, had fewer close friends, and did not engage in volunteer activities, compared to nonsuicidal depressed elders and those with no psychiatric history. They also reported a pattern of struggle against others and hostility in relationships, felt a lack of social support, perceived social problems as impossible to resolve, and displayed a careless/impulsive approach to problems. : Suicide attempts in depressed elders were associated with poor social problem solving, constricted social networks, and disruptive interpersonal relationships. Impaired social emotion recognition in the suicide attempter group was related.
A General-Purpose Optimization Engine for Multi-Disciplinary Design Applications
NASA Technical Reports Server (NTRS)
Patnaik, Surya N.; Hopkins, Dale A.; Berke, Laszlo
1996-01-01
A general purpose optimization tool for multidisciplinary applications, which in the literature is known as COMETBOARDS, is being developed at NASA Lewis Research Center. The modular organization of COMETBOARDS includes several analyzers and state-of-the-art optimization algorithms along with their cascading strategy. The code structure allows quick integration of new analyzers and optimizers. The COMETBOARDS code reads input information from a number of data files, formulates a design as a set of multidisciplinary nonlinear programming problems, and then solves the resulting problems. COMETBOARDS can be used to solve a large problem which can be defined through multiple disciplines, each of which can be further broken down into several subproblems. Alternatively, a small portion of a large problem can be optimized in an effort to improve an existing system. Some of the other unique features of COMETBOARDS include design variable formulation, constraint formulation, subproblem coupling strategy, global scaling technique, analysis approximation, use of either sequential or parallel computational modes, and so forth. The special features and unique strengths of COMETBOARDS assist convergence and reduce the amount of CPU time used to solve the difficult optimization problems of aerospace industries. COMETBOARDS has been successfully used to solve a number of problems, including structural design of space station components, design of nozzle components of an air-breathing engine, configuration design of subsonic and supersonic aircraft, mixed flow turbofan engines, wave rotor topped engines, and so forth. This paper introduces the COMETBOARDS design tool and its versatility, which is illustrated by citing examples from structures, aircraft design, and air-breathing propulsion engine design.
Comedy, Yolanda L.; Gilbert, Juan E.; Pun, Suzie H.
2017-01-01
Inventors help solve all kinds of problems. The AAAS-Lemelson Invention Ambassador program celebrates inventors who have an impact on global challenges, making our communities and the globe better, one invention at a time. In this paper, we introduce two of these invention ambassadors: Dr. Suzie Pun and Dr. Juan Gilbert. Dr. Suzie Pun is the Robert F. Rushmer Professor of Bioengineering, an adjunct professor of chemical engineering, and a member of the Molecular Engineering and Sciences Institute at the University of Washington. Dr. Juan Gilbert is the Andrew Banks Family Preeminence Endowed Professor and chair of the Computer & Information Science & Engineering Department at the University of Florida. Both have a passion for solving problems and are dedicated to teaching their students to change the world. PMID:29527271
Carducci, Michael; Loscalzo, Matthew J.; Linder, John; Greasby, Tamara; Beckett, Laurel A.
2011-01-01
Abstract Context Patients on investigational clinical trials and their caregivers experience poor quality of life (QOL), which declines as the disease progresses. Objective To examine the effect of a standardized cognitive–behavioral problem-solving educational intervention on the QOL of patients enrolled on investigational clinical trials and their caregivers. Design Prospective, multi-institution, randomized trial. QOL was measured repeatedly over 6 months. Participants Patients were simultaneously enrolled onto phase 1, 2, or 3 Institutional Review Board (IRB)-approved cancer clinical trials. Intervention Intervention arm dyads participated in three conjoint educational sessions during the first month, learning the COPE problem solving model. Nonintervention arm dyads received usual care. Outcome Measures Global QOL was measured by the City of Hope Quality of Life Instruments for Patients or Caregivers; problem solving skills were measured by the Social Problem Solving Inventory-Revised. Results The results are reported using the CONSORT statement. The analytic data set included 476 dyads including 1596 patient data points and 1576 care giver data points. Patient QOL showed no significant difference in the rate of change between the intervention and usual care arms (p = 0.70). Caregiver QOL scores in the intervention arm declined, but at less than half the rate in the control arm (p = 0.02). Conclusions The COPE intervention enabled the average caregiver to come much closer to stable QOL over the 6-month follow-up. Future studies should enroll subjects much earlier in the cancer illness trajectory, a common patient/caregiver theme. The maximum effect was seen in caregivers who completed the 6-month follow-up, suggesting that the impact may increase over time. PMID:21413846
NASA Astrophysics Data System (ADS)
Freeman, S.; Kintsch, A.
2003-12-01
Boulder High School Special Education students work in teams on donated wireless computers to solve problems created by global climate change. Their text is Richard Somerville's The Forgiving Air. They utilize Wheeling Jesuit University's remote sensing web site and private computer bulletin board. Their central source for problem-based learning (PBL) is www.cotf.edu, NASA's Classroom of the Future Global Change web site. As a result, students not only improve their abilities to write, read, do math and research, speak, and work as team members, they also improve self-esteem, resilience, and willingness to take more challenging classes. Two special education students passed AP exams, Calculus and U.S. Government, last spring and Jay Matthews of Newsweek rates Boulder High as 201st of the nation's top 1000 high schools.
Hybrid DFP-CG method for solving unconstrained optimization problems
NASA Astrophysics Data System (ADS)
Osman, Wan Farah Hanan Wan; Asrul Hery Ibrahim, Mohd; Mamat, Mustafa
2017-09-01
The conjugate gradient (CG) method and quasi-Newton method are both well known method for solving unconstrained optimization method. In this paper, we proposed a new method by combining the search direction between conjugate gradient method and quasi-Newton method based on BFGS-CG method developed by Ibrahim et al. The Davidon-Fletcher-Powell (DFP) update formula is used as an approximation of Hessian for this new hybrid algorithm. Numerical result showed that the new algorithm perform well than the ordinary DFP method and proven to posses both sufficient descent and global convergence properties.
NASA Technical Reports Server (NTRS)
Bayo, Eduardo; Ledesma, Ragnar
1993-01-01
A technique is presented for solving the inverse dynamics of flexible planar multibody systems. This technique yields the non-causal joint efforts (inverse dynamics) as well as the internal states (inverse kinematics) that produce a prescribed nominal trajectory of the end effector. A non-recursive global Lagrangian approach is used in formulating the equations for motion as well as in solving the inverse dynamics equations. Contrary to the recursive method previously presented, the proposed method solves the inverse problem in a systematic and direct manner for both open-chain as well as closed-chain configurations. Numerical simulation shows that the proposed procedure provides an excellent tracking of the desired end effector trajectory.
The Cauchy problem for the Pavlov equation
NASA Astrophysics Data System (ADS)
Grinevich, P. G.; Santini, P. M.; Wu, D.
2015-10-01
Commutation of multidimensional vector fields leads to integrable nonlinear dispersionless PDEs that arise in various problems of mathematical physics and have been intensively studied in recent literature. This report aims to solve the scattering and inverse scattering problem for integrable dispersionless PDEs, recently introduced just at a formal level, concentrating on the prototypical example of the Pavlov equation, and to justify an existence theorem for global bounded solutions of the associated Cauchy problem with small data. An essential part of this work was made during the visit of the three authors to the Centro Internacional de Ciencias in Cuernavaca, Mexico in November-December 2012.
Wind Farm Turbine Type and Placement Optimization
NASA Astrophysics Data System (ADS)
Graf, Peter; Dykes, Katherine; Scott, George; Fields, Jason; Lunacek, Monte; Quick, Julian; Rethore, Pierre-Elouan
2016-09-01
The layout of turbines in a wind farm is already a challenging nonlinear, nonconvex, nonlinearly constrained continuous global optimization problem. Here we begin to address the next generation of wind farm optimization problems by adding the complexity that there is more than one turbine type to choose from. The optimization becomes a nonlinear constrained mixed integer problem, which is a very difficult class of problems to solve. This document briefly summarizes the algorithm and code we have developed, the code validation steps we have performed, and the initial results for multi-turbine type and placement optimization (TTP_OPT) we have run.
Wind farm turbine type and placement optimization
Graf, Peter; Dykes, Katherine; Scott, George; ...
2016-10-03
The layout of turbines in a wind farm is already a challenging nonlinear, nonconvex, nonlinearly constrained continuous global optimization problem. Here we begin to address the next generation of wind farm optimization problems by adding the complexity that there is more than one turbine type to choose from. The optimization becomes a nonlinear constrained mixed integer problem, which is a very difficult class of problems to solve. Furthermore, this document briefly summarizes the algorithm and code we have developed, the code validation steps we have performed, and the initial results for multi-turbine type and placement optimization (TTP_OPT) we have run.
Error behavior of multistep methods applied to unstable differential systems
NASA Technical Reports Server (NTRS)
Brown, R. L.
1977-01-01
The problem of modeling a dynamic system described by a system of ordinary differential equations which has unstable components for limited periods of time is discussed. It is shown that the global error in a multistep numerical method is the solution to a difference equation initial value problem, and the approximate solution is given for several popular multistep integration formulas. Inspection of the solution leads to the formulation of four criteria for integrators appropriate to unstable problems. A sample problem is solved numerically using three popular formulas and two different stepsizes to illustrate the appropriateness of the criteria.
The potential application of the blackboard model of problem solving to multidisciplinary design
NASA Technical Reports Server (NTRS)
Rogers, J. L.
1989-01-01
Problems associated with the sequential approach to multidisciplinary design are discussed. A blackboard model is suggested as a potential tool for implementing the multilevel decomposition approach to overcome these problems. The blackboard model serves as a global database for the solution with each discipline acting as a knowledge source for updating the solution. With this approach, it is possible for engineers to improve the coordination, communication, and cooperation in the conceptual design process, allowing them to achieve a more optimal design from an interdisciplinary standpoint.
Cooper, Andrew F; Farooq, Asif B
2015-01-01
The importance of the regional dimension of health diplomacy is only gaining slow and uneven recognition. This is in many ways surprising. As demonstrated in the work of Deacon on the ‘globalization of social policy’, global social policy has been animated and debated not only at the multilateral level but at the regional level as well. But at least in the diplomatic literature, the importance of this regional dynamic (with a focus on diverse sites and actors and the pursuit of democratic control) has been missed. The objective of this article is to explore whether health diplomacy is catching up to this larger debate re-shaping the conceptualization and practice of diplomacy more generally. In some ways, the results may be counter-productive in that this shift may encourage an increasingly fragmented process. Yet, it may also point to some breakthroughs, with diplomats, acting as ‘go to’ personnel on the front lines of operational activity, enabling actors to integrate with one another to produce effective governance. In doing so, the regional dimension is given greater recognition as a component of health diplomacy, albeit in an uneven and sometimes awkward manner. Whereas global diplomacy generally emphasizes problem solving, the regional dimension is animated by a normative orientation. PMID:26635500
Maritime Piracy: Examining the U.S. Response to a Global Threat
2010-03-01
Security Council in December 2008, as the Countering Piracy off the Horn of Africa Partnership & Action Plan ( CPAP ). In accordance with U.S. policy to...international efforts,‖43 the plan places significant importance on multilateral action to solve the problem of Somali piracy. CPAP seeks to involve a siege...an interest in maritime security.‖44 CPAP directs the U.S., in concert with a ―global partnership,‖ to address three lines of operation: 1
Tactical Synthesis Of Efficient Global Search Algorithms
NASA Technical Reports Server (NTRS)
Nedunuri, Srinivas; Smith, Douglas R.; Cook, William R.
2009-01-01
Algorithm synthesis transforms a formal specification into an efficient algorithm to solve a problem. Algorithm synthesis in Specware combines the formal specification of a problem with a high-level algorithm strategy. To derive an efficient algorithm, a developer must define operators that refine the algorithm by combining the generic operators in the algorithm with the details of the problem specification. This derivation requires skill and a deep understanding of the problem and the algorithmic strategy. In this paper we introduce two tactics to ease this process. The tactics serve a similar purpose to tactics used for determining indefinite integrals in calculus, that is suggesting possible ways to attack the problem.
Influences on Low-SES First-Generation Students' Decision to Pursue Engineering
ERIC Educational Resources Information Center
Strutz, Michele Louise
2012-01-01
"The ability of this nation to provide a growing economy, strong health and human services, and a secure and safe nation depends upon a vibrant, creative, and diverse engineering and science workforce" (Blue, et al., 2005, p.4). To contribute to technological advancements, engage in global collaboration, solve complex problems, encourage…
ERIC Educational Resources Information Center
Peters, Richard
Educators are encouraged in this document to practice a multi-disciplinary approach in the classroom to prepare students for new management styles in an interrelated society. The first section on perceptions covers the following: information processing (planning, implementing, assessing); the learning process (exploration, invention, application);…
A Case Study of 21st Century Skills Programs and Practices
ERIC Educational Resources Information Center
McLachlan, Kurt
2012-01-01
Skills, such as critical thinking, problem-solving, and innovation are necessary for the 21st Century. The economy and the forums of international business and globalization demand skilled workers. Some schools in the United States are producing such workers, however it is unclear the programs and practices these schools utilize. This study…
Relationship of Technology Education to Tech Prep.
ERIC Educational Resources Information Center
Anderson, Lowell D.
With increased global competition, it is imperative that secondary school programs be reformed so as to be able to turn out productive workers having basic skills in reading, writing, and mathematics and the ability to solve problems and learn new information. One proposed reform, tech prep, can be defined as a technical education alternative to…
Energy and the Confused Student I: Work
ERIC Educational Resources Information Center
Jewett, John W., Jr.
2008-01-01
Energy is a critical concept that is used in analyzing physical phenomena and is often an essential starting point in physics problem-solving. It is a global concept that appears throughout the physics curriculum in mechanics, thermodynamics, electromagnetism, and modern physics. Energy is also at the heart of descriptions of processes in biology,…
CTE: Educating Tomorrow's Workforce Today. Maryland Classroom. Vol. 13, No.2
ERIC Educational Resources Information Center
Mulqueen, Nan, Ed.
2008-01-01
Maryland redesigned its CTE (career and technical education) program a dozen years ago to prepare students for the 21st Century's global economy and its rapidly changing workforce needs. With 350 business and industry representatives, the state created a program whose emphasis is problem-solving and critical thinking, rather than narrow,…
Successful Technology Transfer in Colorado: A Portfolio of Technology Transfer "Success Stories."
ERIC Educational Resources Information Center
Colorado Advanced Tech. Inst., Denver.
The examples in this portfolio demonstrate how technology transfer among universities, businesses, and federal laboratories solve real-world problems, and create new goods and services. They reveal how, through strengthening the infrastructure joining private and public sectors, Colorado can better compete in the global marketplace. All of the…
DOE Office of Scientific and Technical Information (OSTI.GOV)
Mueller, Juliane
MISO is an optimization framework for solving computationally expensive mixed-integer, black-box, global optimization problems. MISO uses surrogate models to approximate the computationally expensive objective function. Hence, derivative information, which is generally unavailable for black-box simulation objective functions, is not needed. MISO allows the user to choose the initial experimental design strategy, the type of surrogate model, and the sampling strategy.
Mathematical Connections and Their Relationship to Mathematics Knowledge for Teaching Geometry
ERIC Educational Resources Information Center
Eli, Jennifer A.; Mohr-Schroeder, Margaret J.; Lee, Carl W.
2013-01-01
Effective competition in a rapidly growing global economy places demands on a society to produce individuals capable of higher-order critical thinking, creative problem solving, connection making, and innovation. We must look to our teacher education programs to help prospective middle grades teachers build the mathematical habits of mind that…
Effects of Teaching Strategies on Student Motivation to Learn in High School Mathematics Classes
ERIC Educational Resources Information Center
Toles, Ann
2010-01-01
To succeed in an increasing technological and global society, students need to develop strong mathematical and problem-solving skills. This qualitative grounded theory study examined student perceptions of the ways in which teaching strategies in high school mathematics classes affect student motivation to learn the subject. Study participants…
21st Century Skills: The Challenges Ahead
ERIC Educational Resources Information Center
Rotherham, Andrew J.; Willingham, Daniel
2009-01-01
The skills that students need for the 21st century are not really new, assert Rotherham and Willingham. Critical thinking, problem solving, information literacy, and global awareness have been important to human progress throughout history, at least among the elites in different societies. What is new is the extent to which individual and…
Grand Challenges and Chemical Engineering Curriculum--Developments at TU Dortmund University
ERIC Educational Resources Information Center
Kockmann, Norbert; Lutze, Philip; Gorak, Andrzej
2016-01-01
Chemical processing industry is progressively focusing their research activities and product placements in the areas of Grand Challenges (or Global Megatrends) such as mobility, energy, communication, or health care and food. Innovation in all these fields requires solving high complex problems, rapid product development as well as dealing with…
Guest Commentary: Global Environmental Education: A Complex Web of Reactions and Repercussions.
ERIC Educational Resources Information Center
Stapp, William B.; Mann, Lori
1985-01-01
Argues that interdependence among all living systems on earth is a necessary element in solving environmental problems, stressing similarities among international political systems and cultures. The ultimate challenge of environmental education is to bring individuals of all ages to critical understanding of the oneness of the earth. (DH)
Students' Reflections Using Visualized Learning Outcomes and E-Portfolios
ERIC Educational Resources Information Center
Narumi, Takatsune; Gotoh, Yasushi
2014-01-01
How to guarantee graduate attributes has become an urgent challenge amid the increasing progress in scientific and technological development and the globalization of economic activity. In order to solve these problems, a system is required which can visualize learning outcomes in relation to attainment targets, and store and sample records of the…
Geography Education in Asia: Samples from Different Countries and Turkey
ERIC Educational Resources Information Center
Incekara, Suleyman
2010-01-01
With the maximum use of the technology such as geographic information science (GIS), remote sensing (RS), and global positioning systems (GPSs) in geography courses, along with its integrative perspective on the social and life sciences and an emphasis on student-centered education, problem solving, and sustainable and environmental education,…
Techno Savvy: A Web 2.0 Curriculum Encouraging Critical Thinking
ERIC Educational Resources Information Center
Herro, Danielle
2014-01-01
This paper reports results from a case study focused on understanding student practices regarding production-oriented problem-solving with digital media. Thirty-seven students participated in an elective curriculum called, "Techno Savvy," a nine-week course focused on student exploration of global issues, and designed around Web 2.0…
ERIC Educational Resources Information Center
Ohio Board of Regents, Columbus.
This booklet contains 36 one-page "success stories" that reveal how the two-year colleges and the vocational and adult education system in Ohio are responding to business and industry needs with innovative problem solving and effective partnerships. Each profile includes a challenge, a solution, results, and comments from business…
PNNL’s Shared Perspectives Technology
DOE Office of Scientific and Technical Information (OSTI.GOV)
None
2015-09-25
Shared Perspectives, one of the technologies within the PNNL-developed GridOPTICS capability suite, enables neighboring organizations, such as different electric utilities, to more effectively partner to solve outages and other grid problems. Shared Perspectives provides a means for organizations to safely stream information from different organizational service areas; the technology then combines and aligns this information into a common, global view, enhancing global situation awareness that can reduce the time it takes to talk through a problem and identify solutions. The technology potentially offers applications in other areas, such as disaster response; collaboration in the monitoring/assessment of real-time events (e.g., hurricanes,more » earthquakes, and tornadoes); as well as military uses.« less
PNNLâs Shared Perspectives Technology
None
2018-01-16
Shared Perspectives, one of the technologies within the PNNL-developed GridOPTICS capability suite, enables neighboring organizations, such as different electric utilities, to more effectively partner to solve outages and other grid problems. Shared Perspectives provides a means for organizations to safely stream information from different organizational service areas; the technology then combines and aligns this information into a common, global view, enhancing global situation awareness that can reduce the time it takes to talk through a problem and identify solutions. The technology potentially offers applications in other areas, such as disaster response; collaboration in the monitoring/assessment of real-time events (e.g., hurricanes, earthquakes, and tornadoes); as well as military uses.
Dynamic optimization of chemical processes using ant colony framework.
Rajesh, J; Gupta, K; Kusumakar, H S; Jayaraman, V K; Kulkarni, B D
2001-11-01
Ant colony framework is illustrated by considering dynamic optimization of six important bench marking examples. This new computational tool is simple to implement and can tackle problems with state as well as terminal constraints in a straightforward fashion. It requires fewer grid points to reach the global optimum at relatively very low computational effort. The examples with varying degree of complexities, analyzed here, illustrate its potential for solving a large class of process optimization problems in chemical engineering.
Fu, Zhuo; Wang, Jiangtao
2018-01-01
In order to promote the development of low-carbon logistics and economize logistics distribution costs, the vehicle routing problem with split deliveries by backpack is studied. With the help of the model of classical capacitated vehicle routing problem, in this study, a form of discrete split deliveries was designed in which the customer demand can be split only by backpack. A double-objective mathematical model and the corresponding adaptive tabu search (TS) algorithm were constructed for solving this problem. By embedding the adaptive penalty mechanism, and adopting the random neighborhood selection strategy and reinitialization principle, the global optimization ability of the new algorithm was enhanced. Comparisons with the results in the literature show the effectiveness of the proposed algorithm. The proposed method can save the costs of low-carbon logistics and reduce carbon emissions, which is conducive to the sustainable development of low-carbon logistics. PMID:29747469
Hybrid General Pattern Search and Simulated Annealing for Industrail Production Planning Problems
NASA Astrophysics Data System (ADS)
Vasant, P.; Barsoum, N.
2010-06-01
In this paper, the hybridization of GPS (General Pattern Search) method and SA (Simulated Annealing) incorporated in the optimization process in order to look for the global optimal solution for the fitness function and decision variables as well as minimum computational CPU time. The real strength of SA approach been tested in this case study problem of industrial production planning. This is due to the great advantage of SA for being easily escaping from trapped in local minima by accepting up-hill move through a probabilistic procedure in the final stages of optimization process. Vasant [1] in his Ph. D thesis has provided 16 different techniques of heuristic and meta-heuristic in solving industrial production problems with non-linear cubic objective functions, eight decision variables and 29 constraints. In this paper, fuzzy technological problems have been solved using hybrid techniques of general pattern search and simulated annealing. The simulated and computational results are compared to other various evolutionary techniques.
Memoryless cooperative graph search based on the simulated annealing algorithm
NASA Astrophysics Data System (ADS)
Hou, Jian; Yan, Gang-Feng; Fan, Zhen
2011-04-01
We have studied the problem of reaching a globally optimal segment for a graph-like environment with a single or a group of autonomous mobile agents. Firstly, two efficient simulated-annealing-like algorithms are given for a single agent to solve the problem in a partially known environment and an unknown environment, respectively. It shows that under both proposed control strategies, the agent will eventually converge to a globally optimal segment with probability 1. Secondly, we use multi-agent searching to simultaneously reduce the computation complexity and accelerate convergence based on the algorithms we have given for a single agent. By exploiting graph partition, a gossip-consensus method based scheme is presented to update the key parameter—radius of the graph, ensuring that the agents spend much less time finding a globally optimal segment.
NASA Astrophysics Data System (ADS)
Kuncoro, K. S.; Junaedi, I.; Dwijanto
2018-03-01
This study aimed to reveal the effectiveness of Project Based Learning with Resource Based Learning approach computer-aided program and analyzed problem-solving abilities in terms of problem-solving steps based on Polya stages. The research method used was mixed method with sequential explanatory design. The subject of this research was the students of math semester 4. The results showed that the S-TPS (Strong Top Problem Solving) and W-TPS (Weak Top Problem Solving) had good problem-solving abilities in each problem-solving indicator. The problem-solving ability of S-MPS (Strong Middle Problem Solving) and (Weak Middle Problem Solving) in each indicator was good. The subject of S-BPS (Strong Bottom Problem Solving) had a difficulty in solving the problem with computer program, less precise in writing the final conclusion and could not reflect the problem-solving process using Polya’s step. While the Subject of W-BPS (Weak Bottom Problem Solving) had not been able to meet almost all the indicators of problem-solving. The subject of W-BPS could not precisely made the initial table of completion so that the completion phase with Polya’s step was constrained.
Multigrid method for stability problems
NASA Technical Reports Server (NTRS)
Taasan, Shlomo
1988-01-01
The problem of calculating the stability of steady state solutions of differential equations is treated. Leading eigenvalues (i.e., having maximal real part) of large matrices that arise from discretization are to be calculated. An efficient multigrid method for solving these problems is presented. The method begins by obtaining an initial approximation for the dominant subspace on a coarse level using a damped Jacobi relaxation. This proceeds until enough accuracy for the dominant subspace has been obtained. The resulting grid functions are then used as an initial approximation for appropriate eigenvalue problems. These problems are being solved first on coarse levels, followed by refinement until a desired accuracy for the eigenvalues has been achieved. The method employs local relaxation on all levels together with a global change on the coarsest level only, which is designed to separate the different eigenfunctions as well as to update their corresponding eigenvalues. Coarsening is done using the FAS formulation in a non-standard way in which the right hand side of the coarse grid equations involves unknown parameters to be solved for on the coarse grid. This in particular leads to a new multigrid method for calculating the eigenvalues of symmetric problems. Numerical experiments with a model problem demonstrate the effectiveness of the method proposed. Using an FMG algorithm a solution to the level of discretization errors is obtained in just a few work units (less than 10), where a work unit is the work involved in one Jacobi relization on the finest level.
Joint Geophysical Inversion With Multi-Objective Global Optimization Methods
NASA Astrophysics Data System (ADS)
Lelievre, P. G.; Bijani, R.; Farquharson, C. G.
2015-12-01
Pareto multi-objective global optimization (PMOGO) methods generate a suite of solutions that minimize multiple objectives (e.g. data misfits and regularization terms) in a Pareto-optimal sense. Providing a suite of models, as opposed to a single model that minimizes a weighted sum of objectives, allows a more complete assessment of the possibilities and avoids the often difficult choice of how to weight each objective. We are applying PMOGO methods to three classes of inverse problems. The first class are standard mesh-based problems where the physical property values in each cell are treated as continuous variables. The second class of problems are also mesh-based but cells can only take discrete physical property values corresponding to known or assumed rock units. In the third class we consider a fundamentally different type of inversion in which a model comprises wireframe surfaces representing contacts between rock units; the physical properties of each rock unit remain fixed while the inversion controls the position of the contact surfaces via control nodes. This third class of problem is essentially a geometry inversion, which can be used to recover the unknown geometry of a target body or to investigate the viability of a proposed Earth model. Joint inversion is greatly simplified for the latter two problem classes because no additional mathematical coupling measure is required in the objective function. PMOGO methods can solve numerically complicated problems that could not be solved with standard descent-based local minimization methods. This includes the latter two classes of problems mentioned above. There are significant increases in the computational requirements when PMOGO methods are used but these can be ameliorated using parallelization and problem dimension reduction strategies.
A Hybrid alldifferent-Tabu Search Algorithm for Solving Sudoku Puzzles
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
Application of differential evolution algorithm on self-potential data.
Li, Xiangtao; Yin, Minghao
2012-01-01
Differential evolution (DE) is a population based evolutionary algorithm widely used for solving multidimensional global optimization problems over continuous spaces, and has been successfully used to solve several kinds of problems. In this paper, differential evolution is used for quantitative interpretation of self-potential data in geophysics. Six parameters are estimated including the electrical dipole moment, the depth of the source, the distance from the origin, the polarization angle and the regional coefficients. This study considers three kinds of data from Turkey: noise-free data, contaminated synthetic data, and Field example. The differential evolution and the corresponding model parameters are constructed as regards the number of the generations. Then, we show the vibration of the parameters at the vicinity of the low misfit area. Moreover, we show how the frequency distribution of each parameter is related to the number of the DE iteration. Experimental results show the DE can be used for solving the quantitative interpretation of self-potential data efficiently compared with previous methods.
Application of Differential Evolution Algorithm on Self-Potential Data
Li, Xiangtao; Yin, Minghao
2012-01-01
Differential evolution (DE) is a population based evolutionary algorithm widely used for solving multidimensional global optimization problems over continuous spaces, and has been successfully used to solve several kinds of problems. In this paper, differential evolution is used for quantitative interpretation of self-potential data in geophysics. Six parameters are estimated including the electrical dipole moment, the depth of the source, the distance from the origin, the polarization angle and the regional coefficients. This study considers three kinds of data from Turkey: noise-free data, contaminated synthetic data, and Field example. The differential evolution and the corresponding model parameters are constructed as regards the number of the generations. Then, we show the vibration of the parameters at the vicinity of the low misfit area. Moreover, we show how the frequency distribution of each parameter is related to the number of the DE iteration. Experimental results show the DE can be used for solving the quantitative interpretation of self-potential data efficiently compared with previous methods. PMID:23240004
Evolutionary algorithm based heuristic scheme for nonlinear heat transfer equations.
Ullah, Azmat; Malik, Suheel Abdullah; Alimgeer, Khurram Saleem
2018-01-01
In this paper, a hybrid heuristic scheme based on two different basis functions i.e. Log Sigmoid and Bernstein Polynomial with unknown parameters is used for solving the nonlinear heat transfer equations efficiently. The proposed technique transforms the given nonlinear ordinary differential equation into an equivalent global error minimization problem. Trial solution for the given nonlinear differential equation is formulated using a fitness function with unknown parameters. The proposed hybrid scheme of Genetic Algorithm (GA) with Interior Point Algorithm (IPA) is opted to solve the minimization problem and to achieve the optimal values of unknown parameters. The effectiveness of the proposed scheme is validated by solving nonlinear heat transfer equations. The results obtained by the proposed scheme are compared and found in sharp agreement with both the exact solution and solution obtained by Haar Wavelet-Quasilinearization technique which witnesses the effectiveness and viability of the suggested scheme. Moreover, the statistical analysis is also conducted for investigating the stability and reliability of the presented scheme.
A Hybrid alldifferent-Tabu Search Algorithm for Solving Sudoku Puzzles.
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.
A cooperative strategy for parameter estimation in large scale systems biology models.
Villaverde, Alejandro F; Egea, Jose A; Banga, Julio R
2012-06-22
Mathematical models play a key role in systems biology: they summarize the currently available knowledge in a way that allows to make experimentally verifiable predictions. Model calibration consists of finding the parameters that give the best fit to a set of experimental data, which entails minimizing a cost function that measures the goodness of this fit. Most mathematical models in systems biology present three characteristics which make this problem very difficult to solve: they are highly non-linear, they have a large number of parameters to be estimated, and the information content of the available experimental data is frequently scarce. Hence, there is a need for global optimization methods capable of solving this problem efficiently. A new approach for parameter estimation of large scale models, called Cooperative Enhanced Scatter Search (CeSS), is presented. Its key feature is the cooperation between different programs ("threads") that run in parallel in different processors. Each thread implements a state of the art metaheuristic, the enhanced Scatter Search algorithm (eSS). Cooperation, meaning information sharing between threads, modifies the systemic properties of the algorithm and allows to speed up performance. Two parameter estimation problems involving models related with the central carbon metabolism of E. coli which include different regulatory levels (metabolic and transcriptional) are used as case studies. The performance and capabilities of the method are also evaluated using benchmark problems of large-scale global optimization, with excellent results. The cooperative CeSS strategy is a general purpose technique that can be applied to any model calibration problem. Its capability has been demonstrated by calibrating two large-scale models of different characteristics, improving the performance of previously existing methods in both cases. The cooperative metaheuristic presented here can be easily extended to incorporate other global and local search solvers and specific structural information for particular classes of problems.
A cooperative strategy for parameter estimation in large scale systems biology models
2012-01-01
Background Mathematical models play a key role in systems biology: they summarize the currently available knowledge in a way that allows to make experimentally verifiable predictions. Model calibration consists of finding the parameters that give the best fit to a set of experimental data, which entails minimizing a cost function that measures the goodness of this fit. Most mathematical models in systems biology present three characteristics which make this problem very difficult to solve: they are highly non-linear, they have a large number of parameters to be estimated, and the information content of the available experimental data is frequently scarce. Hence, there is a need for global optimization methods capable of solving this problem efficiently. Results A new approach for parameter estimation of large scale models, called Cooperative Enhanced Scatter Search (CeSS), is presented. Its key feature is the cooperation between different programs (“threads”) that run in parallel in different processors. Each thread implements a state of the art metaheuristic, the enhanced Scatter Search algorithm (eSS). Cooperation, meaning information sharing between threads, modifies the systemic properties of the algorithm and allows to speed up performance. Two parameter estimation problems involving models related with the central carbon metabolism of E. coli which include different regulatory levels (metabolic and transcriptional) are used as case studies. The performance and capabilities of the method are also evaluated using benchmark problems of large-scale global optimization, with excellent results. Conclusions The cooperative CeSS strategy is a general purpose technique that can be applied to any model calibration problem. Its capability has been demonstrated by calibrating two large-scale models of different characteristics, improving the performance of previously existing methods in both cases. The cooperative metaheuristic presented here can be easily extended to incorporate other global and local search solvers and specific structural information for particular classes of problems. PMID:22727112
Higher-Order Compact Schemes for Numerical Simulation of Incompressible Flows
NASA Technical Reports Server (NTRS)
Wilson, Robert V.; Demuren, Ayodeji O.; Carpenter, Mark
1998-01-01
A higher order accurate numerical procedure has been developed for solving incompressible Navier-Stokes equations for 2D or 3D fluid flow problems. It is based on low-storage Runge-Kutta schemes for temporal discretization and fourth and sixth order compact finite-difference schemes for spatial discretization. The particular difficulty of satisfying the divergence-free velocity field required in incompressible fluid flow is resolved by solving a Poisson equation for pressure. It is demonstrated that for consistent global accuracy, it is necessary to employ the same order of accuracy in the discretization of the Poisson equation. Special care is also required to achieve the formal temporal accuracy of the Runge-Kutta schemes. The accuracy of the present procedure is demonstrated by application to several pertinent benchmark problems.
Optimal perturbations for nonlinear systems using graph-based optimal transport
NASA Astrophysics Data System (ADS)
Grover, Piyush; Elamvazhuthi, Karthik
2018-06-01
We formulate and solve a class of finite-time transport and mixing problems in the set-oriented framework. The aim is to obtain optimal discrete-time perturbations in nonlinear dynamical systems to transport a specified initial measure on the phase space to a final measure in finite time. The measure is propagated under system dynamics in between the perturbations via the associated transfer operator. Each perturbation is described by a deterministic map in the measure space that implements a version of Monge-Kantorovich optimal transport with quadratic cost. Hence, the optimal solution minimizes a sum of quadratic costs on phase space transport due to the perturbations applied at specified times. The action of the transport map is approximated by a continuous pseudo-time flow on a graph, resulting in a tractable convex optimization problem. This problem is solved via state-of-the-art solvers to global optimality. We apply this algorithm to a problem of transport between measures supported on two disjoint almost-invariant sets in a chaotic fluid system, and to a finite-time optimal mixing problem by choosing the final measure to be uniform. In both cases, the optimal perturbations are found to exploit the phase space structures, such as lobe dynamics, leading to efficient global transport. As the time-horizon of the problem is increased, the optimal perturbations become increasingly localized. Hence, by combining the transfer operator approach with ideas from the theory of optimal mass transportation, we obtain a discrete-time graph-based algorithm for optimal transport and mixing in nonlinear systems.
An evaluation of exact methods for the multiple subset maximum cardinality selection problem.
Brusco, Michael J; Köhn, Hans-Friedrich; Steinley, Douglas
2016-05-01
The maximum cardinality subset selection problem requires finding the largest possible subset from a set of objects, such that one or more conditions are satisfied. An important extension of this problem is to extract multiple subsets, where the addition of one more object to a larger subset would always be preferred to increases in the size of one or more smaller subsets. We refer to this as the multiple subset maximum cardinality selection problem (MSMCSP). A recently published branch-and-bound algorithm solves the MSMCSP as a partitioning problem. Unfortunately, the computational requirement associated with the algorithm is often enormous, thus rendering the method infeasible from a practical standpoint. In this paper, we present an alternative approach that successively solves a series of binary integer linear programs to obtain a globally optimal solution to the MSMCSP. Computational comparisons of the methods using published similarity data for 45 food items reveal that the proposed sequential method is computationally far more efficient than the branch-and-bound approach. © 2016 The British Psychological Society.
Toward Solving the Problem of Problem Solving: An Analysis Framework
ERIC Educational Resources Information Center
Roesler, Rebecca A.
2016-01-01
Teaching is replete with problem solving. Problem solving as a skill, however, is seldom addressed directly within music teacher education curricula, and research in music education has not examined problem solving systematically. A framework detailing problem-solving component skills would provide a needed foundation. I observed problem solving…
A three-dimensional, finite element model for coastal and estuarine circulation
Walters, R.A.
1992-01-01
This paper describes the development and application of a three-dimensional model for coastal and estuarine circulation. The model uses a harmonic expansion in time and a finite element discretization in space. All nonlinear terms are retained, including quadratic bottom stress, advection and wave transport (continuity nonlinearity). The equations are solved as a global and a local problem, where the global problem is the solution of the wave equation formulation of the shallow water equations, and the local problem is the solution of the momentum equation for the vertical velocity profile. These equations are coupled to the advection-diffusion equation for salt so that density gradient forcing is included in the momentum equations. The model is applied to a study of Delaware Bay, U.S.A., where salinity intrusion is the primary focus. ?? 1991.
Connected Component Model for Multi-Object Tracking.
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.
Developing a transcultural nursing leadership institute in China.
Capitulo, Kathleen Leask
2012-09-01
Globalization has been the hallmark of the 21st century. This article focuses on developing the Transcultural Nursing Leadership Institute (TCNLI) in China. This project built a leadership program in Wenzhou, China, empowering and supporting nurses to solve problems in their own practices with evidence-based approaches and local resources using the Dreyfus International Health Foundation's method Problem Solving for Better Health (PSBH).The partnership began when I was a Visiting Professor in Wenzhou, China and established collegial relationships with the Dean of the School of Nursing and the Chief Nursing Officers of the affiliated hospitals. In contrast to previous visiting scholars who went to China to lecture on health issues, I sought to develop a sustainable program and make a lasting contribution to the nursing practice in Wenzhou. The PSBH model was the method for what became the TCNLI. The TCNLI has taught over 200 nursing leaders to develop and implement major projects and connected them to the global nursing community by facilitating joint research, publications, and education. The journeys "across the bridge" from New York to Wenzhou have taken nursing and healthcare leaders from the United States to China and reciprocally welcomed leaders from Wenzhou to the United States for professional experiences. Outcomes of our partnership include more than 200 completed change projects. International partnerships within the global healthcare community provide a vehicle to navigate the complexities of transcultural differences and ultimately a way to bridge the gap and improve global healthcare.
NASA Astrophysics Data System (ADS)
Fianti; Najwa, F. L.; Linuwih, S.
2017-04-01
Higher-order-thinking-skills can not be developed directly, except by training which is employing open-ended problems for measuring and developing critics, creativeness, and problem-solving thinking-skills of students. This study is a research and development producing open-ended problems. The purpose of this study is to measure the properness and effectiveness of the developed product and to observe the profile of higher-order-thinking-skills of students on global warming phenomenon. The result of properness test of open-ended problems according to the experts is 92,59% on the first stage and 97,53% on the second stage, so we can assume that the product isvery proper. The result of effectiveness test shows the coefficient of correlation between student’s midterm test scores and open-ended questions is 0,634 which is in the category of strong. Higher-order-thinking-skills of SMA Negeri 1 Salatiga students is in the category of good with the average achievement scores 61,28.
Hoppmann, Christiane A; Coats, Abby Heckman; Blanchard-Fields, Fredda
2008-07-01
Qualitative interviews on family and financial problems from 332 adolescents, young, middle-aged, and older adults, demonstrated that developmentally relevant goals predicted problem-solving strategy use over and above problem domain. Four focal goals concerned autonomy, generativity, maintaining good relationships with others, and changing another person. We examined both self- and other-focused problem-solving strategies. Autonomy goals were associated with self-focused instrumental problem solving and generative goals were related to other-focused instrumental problem solving in family and financial problems. Goals of changing another person were related to other-focused instrumental problem solving in the family domain only. The match between goals and strategies, an indicator of problem-solving adaptiveness, showed that young individuals displayed the greatest match between autonomy goals and self-focused problem solving, whereas older adults showed a greater match between generative goals and other-focused problem solving. Findings speak to the importance of considering goals in investigations of age-related differences in everyday problem solving.
Children's Acoustic and Linguistic Adaptations to Peers with Hearing Impairment
ERIC Educational Resources Information Center
Granlund, Sonia; Hazan, Valerie; Mahon, Merle
2018-01-01
Purpose: This study aims to examine the clear speaking strategies used by older children when interacting with a peer with hearing loss, focusing on both acoustic and linguistic adaptations in speech. Method: The Grid task, a problem-solving task developed to elicit spontaneous interactive speech, was used to obtain a range of global acoustic and…
Institutionalizing Large-Scale Curricular Change: The Top 25 Project at Miami University
ERIC Educational Resources Information Center
Hodge, David C.; Nadler, Marjorie Keeshan; Shore, Cecilia; Taylor, Beverley A. P.
2011-01-01
Now more than ever, it is urgent that colleges and universities mobilize themselves to produce graduates who are capable of being productive, creative, and responsible members of a global society. Employers want clear communicators who are strong critical thinkers and who can solve real-world problems in an ethical way. To achieve these outcomes,…
Incorporating Sustainability and 21st-Century Problem Solving into Physics Courses
ERIC Educational Resources Information Center
Rogers, Michael; Pfaff, Tom; Hamilton, Jason; Erkan, Ali
2013-01-01
As educators we are facing an unprecedented challenge to prepare our students not only for traditional careers but also for future careers that don't exist today. Many of these careers will require a firm grounding in disciplines such as physics, along with multidisciplinary, Global, and systems thinking skill sets. Our Multidisciplinary…
ERIC Educational Resources Information Center
Meade, Melinda S.; Washburn, Sarah; Holman, Jeremy T.
This learning module aims to engage students in problem solving, critical thinking, scientific inquiry, and cooperative learning. The module is appropriate for use in any introductory or intermediate undergraduate course that focuses on human-environment relationships. The module states that human health is a product of complex interactions among…
Our Troubled Skies. Our Only Earth. A Curriculum for Global Problem Solving.
ERIC Educational Resources Information Center
MacRae-Campbell, Linda; McKisson, Micki
Both humanity and nature have suffered greatly from human insensitivity. Not only are the natural resources of the earth being depleted and its air, land and water polluted, the financial resources of humanity are being wasted on destructive expenditures. The "Our Only Earth" series is an integrated science, language arts, and social studies…
The Development of Everyday Mathematics in Brazilian Children with Limited Formal Education.
ERIC Educational Resources Information Center
Guberman, Steven R.
1996-01-01
Studied the sociocultural context in which Brazilian children acquire and use everyday mathematics in terms of currency use. Participants were 105 children, ages 4 to 11, and their parents. Found decreased use of currency with increasing age. Children also used currency to aid their problem solving and progressed from global estimates to the…
ERIC Educational Resources Information Center
Liverman, Diana; Solem, Michael
This learning module aims to engage students in problem solving, critical thinking, scientific inquiry, and cooperative learning. The module is appropriate for use in any introductory or intermediate undergraduate course that focuses on human-environment relationships. The module examines the geography of human activities that produce the major…
ERIC Educational Resources Information Center
Gretter, Sarah; Yadav, Aman
2016-01-01
Developing students' 21st century skills, including creativity, critical thinking, and problem solving, has been a prevailing concern in our globalized and hyper-connected society. One of the key components for students to accomplish this is to take part in today's participatory culture, which involves becoming creators of knowledge rather than…
Turkish Social Studies Teachers' Thoughts about the Teaching of Controversial Issues
ERIC Educational Resources Information Center
Copur, Ahmet; Demirel, Muammer
2016-01-01
In today's world, one of the primary goals of education is to raise individuals as citizens equipped with the skills of communication, high-level thinking, problem solving and questioning as well as with a global viewpoint. Introducing controversial issues into the classroom environment may be among the steps to be taken to achieve these goals. In…
A Role for History and Philosophy of Biology in Exploring New Questions in Biology
ERIC Educational Resources Information Center
Daggett, Melissa A. F.
2012-01-01
A number of current reports are challenging educators of undergraduate biology students to increase the role, interactions and approaches of other disciplines. The goal stated in these reports is to produce a college graduate with the skills and competencies to solve pressing global problems such as producing ample food, fuels, and making health…
NASA Technical Reports Server (NTRS)
Nordberg, W.
1975-01-01
The use of Earth Resources Technology Satellites in solving global problems is examined. Topics discussed are: (1) management of food, water, and fiber resources; (2) exploration and management of energy and mineral resources; (3) protection of the environment; (4) protection of life and property; and (5) improvements in shipping and navigation.
Sustaining the Future: Activities for Environmental Education in U.S. History.
ERIC Educational Resources Information Center
Brown, Jeffrey L., Ed.
This volume provides methods and resources for teachers to integrate global issues and sustainable development concepts into high school U.S. history classes. The focus of the lessons is problem solving by examining development issues in U.S. history. The resource book contains two sections. Section 1 provides overview lessons on the following:…
ERIC Educational Resources Information Center
Laurillard, Diana
2016-01-01
The demographics of massive open online course (MOOC) analytics show that the great majority of learners are highly qualified professionals, and not, as originally envisaged, the global community of disadvantaged learners who have no access to good higher education. MOOC pedagogy fits well with the combination of instruction and peer community…
NASA Technical Reports Server (NTRS)
Aroeste, H.
1982-01-01
Guided Inquiry System Technique, a global approach to problem solving, was applied to the subject of Controlled Ecological Life Support Systems (CELSS). Nutrition, food processing, and the use of higher plants in a CELSS were considered by a panel of experts. Specific ideas and recommendations gleaned from discussions with panel members are presented.
ERIC Educational Resources Information Center
Patrick, Susan; Sturgis, Chris
2015-01-01
Students will face enormous challenges in the coming years--from an economy shaped by ever-advancing technologies to the impact of globalization--and need the strongest foundation of academic, technical, and problem-solving skills we can offer. In an effort to improve their educational experiences, schools across the country are exploring and…
The Ocean Crisis. Our Only Earth Series. A Curriculum for Global Problem Solving.
ERIC Educational Resources Information Center
MacRae-Campbell, Linda; And Others
Both humanity and nature have suffered greatly from human insensitivity. Not only are the natural resources of the earth being depleted and its air, land and water polluted, the financial resources of humanity are being wasted on destructive expenditures. The "Our Only Earth" series is an integrated science, language arts, and social studies…
NASA Astrophysics Data System (ADS)
Danandeh Mehr, Ali; Nourani, Vahid; Hrnjica, Bahrudin; Molajou, Amir
2017-12-01
The effectiveness of genetic programming (GP) for solving regression problems in hydrology has been recognized in recent studies. However, its capability to solve classification problems has not been sufficiently explored so far. This study develops and applies a novel classification-forecasting model, namely Binary GP (BGP), for teleconnection studies between sea surface temperature (SST) variations and maximum monthly rainfall (MMR) events. The BGP integrates certain types of data pre-processing and post-processing methods with conventional GP engine to enhance its ability to solve both regression and classification problems simultaneously. The model was trained and tested using SST series of Black Sea, Mediterranean Sea, and Red Sea as potential predictors as well as classified MMR events at two locations in Iran as predictand. Skill of the model was measured in regard to different rainfall thresholds and SST lags and compared to that of the hybrid decision tree-association rule (DTAR) model available in the literature. The results indicated that the proposed model can identify potential teleconnection signals of surrounding seas beneficial to long-term forecasting of the occurrence of the classified MMR events.
Resources in Technology: Problem-Solving.
ERIC Educational Resources Information Center
Technology Teacher, 1986
1986-01-01
This instructional module examines a key function of science and technology: problem solving. It studies the meaning of problem solving, looks at techniques for problem solving, examines case studies that exemplify the problem-solving approach, presents problems for the reader to solve, and provides a student self-quiz. (Author/CT)
Bi-material plane with interface crack for the model of semi-linear material
NASA Astrophysics Data System (ADS)
Domanskaya, T. O.; Malkov, V. M.; Malkova, Yu. V.
2018-05-01
The singular plane problems of nonlinear elasticity (plane strain and plane stress) are considered for bi-material infinite plane with interface crack. The plane is formed of two half-planes. Mechanical properties of half-planes are described by the model of semi-linear material. Using model of this harmonic material has allowed to apply the theory of complex functions and to obtain exact analytical global solutions of some nonlinear problems. Among them the problem of bi-material plane with the stresses and strains jumps at an interface is considered. As an application of the problem of jumps, the problem of interface crack is solved. The values of nominal (Piola) and Cauchy stresses and displacements are founded. Based on the global solutions the asymptotic expansions are constructed for stresses and displacements in a vicinity of crack tip. As an example the case of a free crack in bi-material plane subjected to constant stresses at infinity is studied. As a special case, the analytical solution of the problem of a crack in a homogeneous plane is obtained from the problem for bi-material plane with interface crack.
Barnett, Jason; Watson, Jean -Paul; Woodruff, David L.
2016-11-27
Progressive hedging, though an effective heuristic for solving stochastic mixed integer programs (SMIPs), is not guaranteed to converge in this case. Here, we describe BBPH, a branch and bound algorithm that uses PH at each node in the search tree such that, given sufficient time, it will always converge to a globally optimal solution. Additionally, to providing a theoretically convergent “wrapper” for PH applied to SMIPs, computational results demonstrate that for some difficult problem instances branch and bound can find improved solutions after exploring only a few nodes.
Early Design Choices: Capture, Model, Integrate, Analyze, Simulate
NASA Technical Reports Server (NTRS)
Malin, Jane T.
2004-01-01
I. Designs are constructed incrementally to meet requirements and solve problems: a) Requirements types: objectives, scenarios, constraints, ilities. etc. b) Problem/issue types: risk/safety, cost/difficulty, interaction, conflict, etc. II. Capture requirements, problems and solutions: a) Collect design and analysis products and make them accessible for integration and analysis; b) Link changes in design requirements, problems and solutions; and c) Harvest design data for design models and choice structures. III. System designs are constructed by multiple groups designing interacting subsystems a) Diverse problems, choice criteria, analysis methods and point solutions. IV. Support integration and global analysis of repercussions: a) System implications of point solutions; b) Broad analysis of interactions beyond totals of mass, cost, etc.
A Cognitive Analysis of Students’ Mathematical Problem Solving Ability on Geometry
NASA Astrophysics Data System (ADS)
Rusyda, N. A.; Kusnandi, K.; Suhendra, S.
2017-09-01
The purpose of this research is to analyze of mathematical problem solving ability of students in one of secondary school on geometry. This research was conducted by using quantitative approach with descriptive method. Population in this research was all students of that school and the sample was twenty five students that was chosen by purposive sampling technique. Data of mathematical problem solving were collected through essay test. The results showed the percentage of achievement of mathematical problem solving indicators of students were: 1) solve closed mathematical problems with context in math was 50%; 2) solve the closed mathematical problems with the context beyond mathematics was 24%; 3) solving open mathematical problems with contexts in mathematics was 35%; And 4) solving open mathematical problems with contexts outside mathematics was 44%. Based on the percentage, it can be concluded that the level of achievement of mathematical problem solving ability in geometry still low. This is because students are not used to solving problems that measure mathematical problem solving ability, weaknesses remember previous knowledge, and lack of problem solving framework. So the students’ ability of mathematical problems solving need to be improved with implement appropriate learning strategy.
A modified form of conjugate gradient method for unconstrained optimization problems
NASA Astrophysics Data System (ADS)
Ghani, Nur Hamizah Abdul; Rivaie, Mohd.; Mamat, Mustafa
2016-06-01
Conjugate gradient (CG) methods have been recognized as an interesting technique to solve optimization problems, due to the numerical efficiency, simplicity and low memory requirements. In this paper, we propose a new CG method based on the study of Rivaie et al. [7] (Comparative study of conjugate gradient coefficient for unconstrained Optimization, Aus. J. Bas. Appl. Sci. 5(2011) 947-951). Then, we show that our method satisfies sufficient descent condition and converges globally with exact line search. Numerical results show that our proposed method is efficient for given standard test problems, compare to other existing CG methods.
The Climate is A-Changin': Teaching Civic Competence for a Sustainable Climate
NASA Technical Reports Server (NTRS)
Harris, Carolyn A.; Kharecha, Pushker; Goble, Pam; Goble, Ryan
2016-01-01
A central aim of social studies curriculum is to prepare young people for making "informed and reasoned decisions for the public good" concerning consequential problems like global climate change. By developing students' "vision of a good society" and exploring what actions and policies move our society in this direction, social studies teachers have an important role in preparing students for a world undergoing enormous environmental change. This article discusses elementary curriculum connections between building students' knowledge and understanding about "their community, nation and world" and global climate change. It also suggests ideas for building civic competency and climate literacy while creating opportunities for students to practice high-value skills like "data collection and analysis, collaboration, decision-making and problem-solving."
Satellites as Sentinels for Health
NASA Technical Reports Server (NTRS)
Maynard, Nancy G.; Yland, Jan-Marcus
2001-01-01
Remotely-sensed data and observations are providing powerful new tools for addressing the human health aspects of sustainability by enabling improved understanding of the relationships and linkages between health-related environmental parameters and society as well as techniques for early warning of potential health problems. Remote sensing, geographic information systems, improved computational capabilities, and interdisciplinary research between the Earth and health science communities are being combined in rich collaborative efforts resulting in more rapid problem-solving, early warning, and prevention in global health issues. This paper provides a number of recent examples of applications of these technologies to health issues related to the following: infectious and vector-borne diseases; urban, regional and global air pollution; heat stress; UV radiation; water-borne disease; extreme weather; contaminant pathways (ocean, atmosphere, ice).
Efficient Implementation of an Optimal Interpolator for Large Spatial Data Sets
NASA Technical Reports Server (NTRS)
Memarsadeghi, Nargess; Mount, David M.
2007-01-01
Scattered data interpolation is a problem of interest in numerous areas such as electronic imaging, smooth surface modeling, and computational geometry. Our motivation arises from applications in geology and mining, which often involve large scattered data sets and a demand for high accuracy. The method of choice is ordinary kriging. This is because it is a best unbiased estimator. Unfortunately, this interpolant is computationally very expensive to compute exactly. For n scattered data points, computing the value of a single interpolant involves solving a dense linear system of size roughly n x n. This is infeasible for large n. In practice, kriging is solved approximately by local approaches that are based on considering only a relatively small'number of points that lie close to the query point. There are many problems with this local approach, however. The first is that determining the proper neighborhood size is tricky, and is usually solved by ad hoc methods such as selecting a fixed number of nearest neighbors or all the points lying within a fixed radius. Such fixed neighborhood sizes may not work well for all query points, depending on local density of the point distribution. Local methods also suffer from the problem that the resulting interpolant is not continuous. Meyer showed that while kriging produces smooth continues surfaces, it has zero order continuity along its borders. Thus, at interface boundaries where the neighborhood changes, the interpolant behaves discontinuously. Therefore, it is important to consider and solve the global system for each interpolant. However, solving such large dense systems for each query point is impractical. Recently a more principled approach to approximating kriging has been proposed based on a technique called covariance tapering. The problems arise from the fact that the covariance functions that are used in kriging have global support. Our implementations combine, utilize, and enhance a number of different approaches that have been introduced in literature for solving large linear systems for interpolation of scattered data points. For very large systems, exact methods such as Gaussian elimination are impractical since they require 0(n(exp 3)) time and 0(n(exp 2)) storage. As Billings et al. suggested, we use an iterative approach. In particular, we use the SYMMLQ method, for solving the large but sparse ordinary kriging systems that result from tapering. The main technical issue that need to be overcome in our algorithmic solution is that the points' covariance matrix for kriging should be symmetric positive definite. The goal of tapering is to obtain a sparse approximate representation of the covariance matrix while maintaining its positive definiteness. Furrer et al. used tapering to obtain a sparse linear system of the form Ax = b, where A is the tapered symmetric positive definite covariance matrix. Thus, Cholesky factorization could be used to solve their linear systems. They implemented an efficient sparse Cholesky decomposition method. They also showed if these tapers are used for a limited class of covariance models, the solution of the system converges to the solution of the original system. Matrix A in the ordinary kriging system, while symmetric, is not positive definite. Thus, their approach is not applicable to the ordinary kriging system. Therefore, we use tapering only to obtain a sparse linear system. Then, we use SYMMLQ to solve the ordinary kriging system. We show that solving large kriging systems becomes practical via tapering and iterative methods, and results in lower estimation errors compared to traditional local approaches, and significant memory savings compared to the original global system. We also developed a more efficient variant of the sparse SYMMLQ method for large ordinary kriging systems. This approach adaptively finds the correct local neighborhood for each query point in the interpolation process.
Review: Optimization methods for groundwater modeling and management
NASA Astrophysics Data System (ADS)
Yeh, William W.-G.
2015-09-01
Optimization methods have been used in groundwater modeling as well as for the planning and management of groundwater systems. This paper reviews and evaluates the various optimization methods that have been used for solving the inverse problem of parameter identification (estimation), experimental design, and groundwater planning and management. Various model selection criteria are discussed, as well as criteria used for model discrimination. The inverse problem of parameter identification concerns the optimal determination of model parameters using water-level observations. In general, the optimal experimental design seeks to find sampling strategies for the purpose of estimating the unknown model parameters. A typical objective of optimal conjunctive-use planning of surface water and groundwater is to minimize the operational costs of meeting water demand. The optimization methods include mathematical programming techniques such as linear programming, quadratic programming, dynamic programming, stochastic programming, nonlinear programming, and the global search algorithms such as genetic algorithms, simulated annealing, and tabu search. Emphasis is placed on groundwater flow problems as opposed to contaminant transport problems. A typical two-dimensional groundwater flow problem is used to explain the basic formulations and algorithms that have been used to solve the formulated optimization problems.
The Programme for Global Paediatric Research.
Zipursky, Alvin
2011-12-01
When it comes to global health, there is no 'them'... only 'us.'" - Global Health Council There is a major anomaly in child health research. The majority of pediatric research resources and expertise is located in the developed world, whereas the vast majority of childhood disease and mortality is in the developing world. This disequilibrium has been referred to as the "10/90 gap", suggesting that only 10% of global health research dollars are devoted to conditions that account for 90% of the global disease burden (Global Forum for Health Research). The Programme for Global Paediatric Research (PGPR) began as an effort to include, in a major pediatric research conference, topics dealing with diseases of children in the developing world in order to engage more interest and more research dollars. It has evolved into a program educating and linking professionals, and developing global networks of colleagues working collaboratively to solve major childhood health problems.
Neoliberal Optimism: Applying Market Techniques to Global Health.
Mei, Yuyang
2017-01-01
Global health and neoliberalism are becoming increasingly intertwined as organizations utilize markets and profit motives to solve the traditional problems of poverty and population health. I use field work conducted over 14 months in a global health technology company to explore how the promise of neoliberalism re-envisions humanitarian efforts. In this company's vaccine refrigerator project, staff members expect their investors and their market to allow them to achieve scale and develop accountability to their users in developing countries. However, the translation of neoliberal techniques to the global health sphere falls short of the ideal, as profits are meager and purchasing power remains with donor organizations. The continued optimism in market principles amidst such a non-ideal market reveals the tenacious ideological commitment to neoliberalism in these global health projects.
Broadcasting satellite service synthesis using gradient and cyclic coordinate search procedures
NASA Technical Reports Server (NTRS)
Reilly, C. H.; Mount-Campbell, C. A.; Gonsalvez, D. J.; Martin, C. H.; Levis, C. A.; Wang, C. W.
1986-01-01
Two search techniques are considered for solving satellite synthesis problems. Neither is likely to find a globally optimal solution. In order to determine which method performs better and what factors affect their performance, we design an experiment and solve the same problem under a variety of starting solution configuration-algorithm combinations. Since there is no randomization in the experiment, we present results of practical, rather than statistical, significance. Our implementation of a cyclic coordinate search procedure clearly finds better synthesis solutions than our implementation of a gradient search procedure does with our objective of maximizing the minimum C/I ratio computed at test points on the perimeters of the intended service areas. The length of the available orbital arc and the configuration of the starting solution are shown to affect the quality of the solutions found.
Broadcasting satellite service synthesis using gradient and cyclic coordinate search procedures
NASA Technical Reports Server (NTRS)
Reilly, C. H.; Mount-Campbell, C. A.; Gonsalvez, D. J.; Martin, C. H.; Levis, C. A.
1986-01-01
Two search techniques are considered for solving satellite synthesis problems. Neither is likely to find a globally optimal solution. In order to determine which method performs better and what factors affect their performance, an experiment is designed and the same problem is solved under a variety of starting solution configuration-algorithm combinations. Since there is no randomization in the experiment, results of practical, rather than statistical, significance are presented. Implementation of a cyclic coordinate search procedure clearly finds better synthesis solutions than implementation of a gradient search procedure does with the objective of maximizing the minimum C/I ratio computed at test points on the perimeters of the intended service areas. The length of the available orbital arc and the configuration of the starting solution are shown to affect the quality of the solutions found.
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.
Resource Letter RPS-1: Research in problem solving
NASA Astrophysics Data System (ADS)
Hsu, Leonardo; Brewe, Eric; Foster, Thomas M.; Harper, Kathleen A.
2004-09-01
This Resource Letter provides a guide to the literature on research in problem solving, especially in physics. The references were compiled with two audiences in mind: physicists who are (or might become) engaged in research on problem solving, and physics instructors who are interested in using research results to improve their students' learning of problem solving. In addition to general references, journal articles and books are cited for the following topics: cognitive aspects of problem solving, expert-novice problem-solver characteristics, problem solving in mathematics, alternative problem types, curricular interventions, and the use of computers in problem solving.
NASA Astrophysics Data System (ADS)
Du, Jinming; Tang, Lixin
2018-01-01
Understanding voluntary contribution in threshold public goods games has important practical implications. To improve contributions and provision frequency, free-rider problem and assurance problem should be solved. Insurance could play a significant, but largely unrecognized, role in facilitating a contribution to provision of public goods through providing insurance compensation against the losses. In this paper, we study how insurance compensation mechanism affects individuals’ decision-making under risk environments. We propose a multi-level threshold public goods game model where two kinds of public goods games (local and global) are considered. Particularly, the global public goods game involves a threshold, which is related to the safety of all the players. We theoretically probe the evolution of contributions of different levels and free-riders, and focus on the influence of the insurance on the global contribution. We explore, in both the cases, the scenarios that only global contributors could buy insurance and all the players could. It is found that with greater insurance compensation, especially under high collective risks, players are more likely to contribute globally when only global contributors are insured. On the other hand, global contribution could be promoted if a premium discount is given to global contributors when everyone buys insurance.
NASA Technical Reports Server (NTRS)
Browning, G. L.; Tzur, I.; Roble, R. G.
1987-01-01
A time-dependent model is introduced that can be used to simulate the interaction of a thunderstorm with its global electrical environment. The model solves the continuity equation of the Maxwell current, which is assumed to be composed of the conduction, displacement, and source currents. Boundary conditions which can be used in conjunction with the continuity equation to form a well-posed initial-boundary value problem are determined. Properties of various components of solutions of the initial-boundary value problem are analytically determined. The results indicate that the problem has two time scales, one determined by the background electrical conductivity and the other by the time variation of the source function. A numerical method for obtaining quantitative results is introduced, and its properties are studied. Some simulation results on the evolution of the displacement and conduction currents during the electrification of a storm are presented.
New technology and its role in enhancing global food production.
Goodman, R M
1986-09-01
The transfer in the past 3 decades of modern agricultural technology to countries of the Third World has led to a steady improvement in global food production. The results have not been evenly distributed, however, and serious problems remain. Modern biotechnology may contribute to solving some of the problems of high input costs and may also contribute to decreasing the risks associated with agriculture in developing economies. Several problems must be overcome, however. Among these are finding ways to bring the advanced technological capabilities of private companies, both large and small, to the international agricultural research network where commercial incentives are not strong or are inappropriate. Also, unless and until severe countervailing forces, such as population growth rates and deterioration of the environment, are brought under control the spread of new agricultural technology will be of little consequence in the most difficult famine-prone situations.
A new anisotropic mesh adaptation method based upon hierarchical a posteriori error estimates
NASA Astrophysics Data System (ADS)
Huang, Weizhang; Kamenski, Lennard; Lang, Jens
2010-03-01
A new anisotropic mesh adaptation strategy for finite element solution of elliptic differential equations is presented. It generates anisotropic adaptive meshes as quasi-uniform ones in some metric space, with the metric tensor being computed based on hierarchical a posteriori error estimates. A global hierarchical error estimate is employed in this study to obtain reliable directional information of the solution. Instead of solving the global error problem exactly, which is costly in general, we solve it iteratively using the symmetric Gauß-Seidel method. Numerical results show that a few GS iterations are sufficient for obtaining a reasonably good approximation to the error for use in anisotropic mesh adaptation. The new method is compared with several strategies using local error estimators or recovered Hessians. Numerical results are presented for a selection of test examples and a mathematical model for heat conduction in a thermal battery with large orthotropic jumps in the material coefficients.
Neff, Lisa A; Geers, Andrew L
2013-07-01
Do optimistic expectations facilitate or hinder adaptive responses to relationship challenges? Traditionally, optimism has been characterized as a resource that encourages positive coping efforts within relationships. Yet, some work suggests optimism can be a liability, as expecting the best may prevent individuals from taking proactive steps when confronted with difficulties. To reconcile these perspectives, the current article argues that greater attention must be given to the way in which optimistic expectancies are conceptualized. Whereas generalized dispositional optimism may predict constructive responses to relationship difficulties, more focused relationship-specific forms of optimism may predict poor coping responses. A multi-method, longitudinal study of newly married couples confirmed that spouses higher in dispositional optimism (a) reported engaging in more positive problem-solving behaviors on days in which they experienced greater relationship conflict, (b) were observed to display more constructive problem-solving behaviors when discussing important marital issues with their partner in the lab, and (c) experienced fewer declines in marital well-being over the 1st year of marriage. Conversely, spouses higher in relationship-specific optimism (a) reported engaging in fewer constructive problem-solving behaviors on high conflict days, (b) were observed to exhibit worse problem-solving behaviors in the lab-particularly when discussing marital issues of greater importance-and (c) experienced steeper declines in marital well-being over time. All findings held controlling for self-esteem and neuroticism. Together, results suggest that whereas global forms of optimism may represent a relationship asset, specific forms of optimism can place couples at risk for marital deterioration. PsycINFO Database Record (c) 2013 APA, all rights reserved.
The forest-bioenergy-carbon connection
Jay O' Laughlin
2010-01-01
Burning wood for energy is a back-to-the-future approach for solving modern problems. The burning of fossil fuels for energy and resultant carbon emissions are global concerns: âThe world needs ever increasing energy supplies to sustain economic growth and development. But energy resources are under pressure and carbon dioxide (CO2) emissions from todayâs energy use...
Recent Advances in Source Localisation Using Range Measurements
2015-10-01
Range Weighted SR- LS ............................................................................................ 5 GEOLOCATION USING SEMIDEFINITE... LS ) and the squared range least squares (SR- LS ) [3]. The R- LS -based formulation is of great interest and has been known for its optimal performance...to efficiently compute an R- LS position estimate. A number of optimization tools may be applied to globally solve the R- LS problem and are usually
ERIC Educational Resources Information Center
Senan, Divya C.
2013-01-01
The full promise of class room learning is dependent on its ability to incorporate 21st century skills in its instructional design, delivery and implementation. In this increasingly competitive global economy, it is not enough for students to acquire subject-level mastery alone. Skills like creative thinking, problem-solving, communication and…
Quadratic Optimisation with One Quadratic Equality Constraint
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.
ERIC Educational Resources Information Center
McKisson, Micki; MacRae-Campbell, Linda
Both humanity and nature have suffered greatly from human insensitivity. Not only are the natural resources of the earth being depleted and its air, land and water polluted, the financial resources of humanity are being wasted on destructive expenditures. The "Our Only Earth" series is an integrated science, language arts, and social studies…
ERIC Educational Resources Information Center
McKisson, Micki; MacRae-Campbell, Linda
Both humanity and nature have suffered greatly from human insensitivity. Not only are the natural resources of the earth being depleted and its air, land and water polluted, the financial resources of humanity are being wasted on destructive expenditures. The "Our Only Earth" series is an integrated science, language arts, and social…
Problem-Solving in Las Vegas: Students Are Building Skills and a Global Network.
ERIC Educational Resources Information Center
Budd, Gregory; Curry, Don
1995-01-01
Describes a project initiated at Silverado High School in Las Vegas, where students from Las Vegas and schools across the United States monitor the levels of radon in the atmosphere. Enables students to learn first hand about the collection, analysis, and interpretation of scientific data and to network with other students from the United States…
Energy and the Confused Student IV: A Global Approach to Energy
ERIC Educational Resources Information Center
Jewett, John W., Jr.
2008-01-01
Energy is a critical concept in physics problem-solving, but is often a major source of confusion for students if the presentation is not carefully crafted by the instructor or the textbook. In the first three articles in this series we discussed several issues related to the teaching of energy concepts. We have saved a major single issue for this…
ERIC Educational Resources Information Center
Cerulli, D.; Holbrook, J.; Mander, Ü.
2016-01-01
As global average temperatures rise, there has been an increase in the frequency and magnitude of meteorological natural hazards. To survive in the world and thrive in the work place, students need to utilize educational skills (such as creative thinking, non-routine problem solving, collaboration and systems thinking) and become independent…
Digital Libraries Creating Environmental Identity through Solving Geographical Problems
ERIC Educational Resources Information Center
Chang, Chew-Hung; Hedberg, John G.
2007-01-01
Environmental identity, or how we orient ourselves to the natural world, leads us to personalise abstract global issues and take action (or not) according to our sense of who we are. For example, are we willing to give up our luxurious cars for more fuel-efficient models even though we know that the earth is warming? In an era where web-based…
ERIC Educational Resources Information Center
McKisson, Micki; MacRae-Campbell, Linda
Both humanity and nature have suffered greatly from human insensitivity. Not only are the natural resources of the earth being depleted and its air, land and water polluted, the financial resources of humanity are being wasted on destructive expenditures. The "Our Only Earth" series is an integrated science, language arts, and social studies…
Xiao, Hu; Cui, Rongxin; Xu, Demin
2018-06-01
This paper presents a cooperative multiagent search algorithm to solve the problem of searching for a target on a 2-D plane under multiple constraints. A Bayesian framework is used to update the local probability density functions (PDFs) of the target when the agents obtain observation information. To obtain the global PDF used for decision making, a sampling-based logarithmic opinion pool algorithm is proposed to fuse the local PDFs, and a particle sampling approach is used to represent the continuous PDF. Then the Gaussian mixture model (GMM) is applied to reconstitute the global PDF from the particles, and a weighted expectation maximization algorithm is presented to estimate the parameters of the GMM. Furthermore, we propose an optimization objective which aims to guide agents to find the target with less resource consumptions, and to keep the resource consumption of each agent balanced simultaneously. To this end, a utility function-based optimization problem is put forward, and it is solved by a gradient-based approach. Several contrastive simulations demonstrate that compared with other existing approaches, the proposed one uses less overall resources and shows a better performance of balancing the resource consumption.
NASA Astrophysics Data System (ADS)
Aittokoski, Timo; Miettinen, Kaisa
2008-07-01
Solving real-life engineering problems can be difficult because they often have multiple conflicting objectives, the objective functions involved are highly nonlinear and they contain multiple local minima. Furthermore, function values are often produced via a time-consuming simulation process. These facts suggest the need for an automated optimization tool that is efficient (in terms of number of objective function evaluations) and capable of solving global and multiobjective optimization problems. In this article, the requirements on a general simulation-based optimization system are discussed and such a system is applied to optimize the performance of a two-stroke combustion engine. In the example of a simulation-based optimization problem, the dimensions and shape of the exhaust pipe of a two-stroke engine are altered, and values of three conflicting objective functions are optimized. These values are derived from power output characteristics of the engine. The optimization approach involves interactive multiobjective optimization and provides a convenient tool to balance between conflicting objectives and to find good solutions.
Hierarchical Artificial Bee Colony Algorithm for RFID Network Planning Optimization
Ma, Lianbo; Chen, Hanning; Hu, Kunyuan; Zhu, Yunlong
2014-01-01
This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP) problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness. PMID:24592200
Hierarchical artificial bee colony algorithm for RFID network planning optimization.
Ma, Lianbo; Chen, Hanning; Hu, Kunyuan; Zhu, Yunlong
2014-01-01
This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP) problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness.
A model-adaptivity method for the solution of Lennard-Jones based adhesive contact problems
NASA Astrophysics Data System (ADS)
Ben Dhia, Hachmi; Du, Shuimiao
2018-05-01
The surface micro-interaction model of Lennard-Jones (LJ) is used for adhesive contact problems (ACP). To address theoretical and numerical pitfalls of this model, a sequence of partitions of contact models is adaptively constructed to both extend and approximate the LJ model. It is formed by a combination of the LJ model with a sequence of shifted-Signorini (or, alternatively, -Linearized-LJ) models, indexed by a shift parameter field. For each model of this sequence, a weak formulation of the associated local ACP is developed. To track critical localized adhesive areas, a two-step strategy is developed: firstly, a macroscopic frictionless (as first approach) linear-elastic contact problem is solved once to detect contact separation zones. Secondly, at each shift-adaptive iteration, a micro-macro ACP is re-formulated and solved within the multiscale Arlequin framework, with significant reduction of computational costs. Comparison of our results with available analytical and numerical solutions shows the effectiveness of our global strategy.
Zhang, Bo; Duan, Haibin
2017-01-01
Three-dimension path planning of uninhabited combat aerial vehicle (UCAV) is a complicated optimal problem, which mainly focused on optimizing the flight route considering the different types of constrains under complex combating environment. A novel predator-prey pigeon-inspired optimization (PPPIO) is proposed to solve the UCAV three-dimension path planning problem in dynamic environment. Pigeon-inspired optimization (PIO) is a new bio-inspired optimization algorithm. In this algorithm, map and compass operator model and landmark operator model are used to search the best result of a function. The prey-predator concept is adopted to improve global best properties and enhance the convergence speed. The characteristics of the optimal path are presented in the form of a cost function. The comparative simulation results show that our proposed PPPIO algorithm is more efficient than the basic PIO, particle swarm optimization (PSO), and different evolution (DE) in solving UCAV three-dimensional path planning problems.
Aono, Masashi; Gunji, Yukio-Pegio
2003-10-01
The emergence derived from errors is the key importance for both novel computing and novel usage of the computer. In this paper, we propose an implementable experimental plan for the biological computing so as to elicit the emergent property of complex systems. An individual plasmodium of the true slime mold Physarum polycephalum acts in the slime mold computer. Modifying the Elementary Cellular Automaton as it entails the global synchronization problem upon the parallel computing provides the NP-complete problem solved by the slime mold computer. The possibility to solve the problem by giving neither all possible results nor explicit prescription of solution-seeking is discussed. In slime mold computing, the distributivity in the local computing logic can change dynamically, and its parallel non-distributed computing cannot be reduced into the spatial addition of multiple serial computings. The computing system based on exhaustive absence of the super-system may produce, something more than filling the vacancy.
Duan, Qian-Qian; Yang, Gen-Ke; Pan, Chang-Chun
2014-01-01
A hybrid optimization algorithm combining finite state method (FSM) and genetic algorithm (GA) is proposed to solve the crude oil scheduling problem. The FSM and GA are combined to take the advantage of each method and compensate deficiencies of individual methods. In the proposed algorithm, the finite state method makes up for the weakness of GA which is poor at local searching ability. The heuristic returned by the FSM can guide the GA algorithm towards good solutions. The idea behind this is that we can generate promising substructure or partial solution by using FSM. Furthermore, the FSM can guarantee that the entire solution space is uniformly covered. Therefore, the combination of the two algorithms has better global performance than the existing GA or FSM which is operated individually. Finally, a real-life crude oil scheduling problem from the literature is used for conducting simulation. The experimental results validate that the proposed method outperforms the state-of-art GA method. PMID:24772031
Students’ difficulties in probabilistic problem-solving
NASA Astrophysics Data System (ADS)
Arum, D. P.; Kusmayadi, T. A.; Pramudya, I.
2018-03-01
There are many errors can be identified when students solving mathematics problems, particularly in solving the probabilistic problem. This present study aims to investigate students’ difficulties in solving the probabilistic problem. It focuses on analyzing and describing students errors during solving the problem. This research used the qualitative method with case study strategy. The subjects in this research involve ten students of 9th grade that were selected by purposive sampling. Data in this research involve students’ probabilistic problem-solving result and recorded interview regarding students’ difficulties in solving the problem. Those data were analyzed descriptively using Miles and Huberman steps. The results show that students have difficulties in solving the probabilistic problem and can be divided into three categories. First difficulties relate to students’ difficulties in understanding the probabilistic problem. Second, students’ difficulties in choosing and using appropriate strategies for solving the problem. Third, students’ difficulties with the computational process in solving the problem. Based on the result seems that students still have difficulties in solving the probabilistic problem. It means that students have not able to use their knowledge and ability for responding probabilistic problem yet. Therefore, it is important for mathematics teachers to plan probabilistic learning which could optimize students probabilistic thinking ability.
Olugbara, Oludayo
2014-01-01
This paper presents an annual multiobjective crop-mix planning as a problem of concurrent maximization of net profit and maximization of crop production to determine an optimal cropping pattern. The optimal crop production in a particular planting season is a crucial decision making task from the perspectives of economic management and sustainable agriculture. A multiobjective optimal crop-mix problem is formulated and solved using the generalized differential evolution 3 (GDE3) metaheuristic to generate a globally optimal solution. The performance of the GDE3 metaheuristic is investigated by comparing its results with the results obtained using epsilon constrained and nondominated sorting genetic algorithms—being two representatives of state-of-the-art in evolutionary optimization. The performance metrics of additive epsilon, generational distance, inverted generational distance, and spacing are considered to establish the comparability. In addition, a graphical comparison with respect to the true Pareto front for the multiobjective optimal crop-mix planning problem is presented. Empirical results generally show GDE3 to be a viable alternative tool for solving a multiobjective optimal crop-mix planning problem. PMID:24883369
Optimal network modification for spectral radius dependent phase transitions
NASA Astrophysics Data System (ADS)
Rosen, Yonatan; Kirsch, Lior; Louzoun, Yoram
2016-09-01
The dynamics of contact processes on networks is often determined by the spectral radius of the networks adjacency matrices. A decrease of the spectral radius can prevent the outbreak of an epidemic, or impact the synchronization among systems of coupled oscillators. The spectral radius is thus tightly linked to network dynamics and function. As such, finding the minimal change in network structure necessary to reach the intended spectral radius is important theoretically and practically. Given contemporary big data resources such as large scale communication or social networks, this problem should be solved with a low runtime complexity. We introduce a novel method for the minimal decrease in weights of edges required to reach a given spectral radius. The problem is formulated as a convex optimization problem, where a global optimum is guaranteed. The method can be easily adjusted to an efficient discrete removal of edges. We introduce a variant of the method which finds optimal decrease with a focus on weights of vertices. The proposed algorithm is exceptionally scalable, solving the problem for real networks of tens of millions of edges in a short time.
Adekanmbi, Oluwole; Olugbara, Oludayo; Adeyemo, Josiah
2014-01-01
This paper presents an annual multiobjective crop-mix planning as a problem of concurrent maximization of net profit and maximization of crop production to determine an optimal cropping pattern. The optimal crop production in a particular planting season is a crucial decision making task from the perspectives of economic management and sustainable agriculture. A multiobjective optimal crop-mix problem is formulated and solved using the generalized differential evolution 3 (GDE3) metaheuristic to generate a globally optimal solution. The performance of the GDE3 metaheuristic is investigated by comparing its results with the results obtained using epsilon constrained and nondominated sorting genetic algorithms-being two representatives of state-of-the-art in evolutionary optimization. The performance metrics of additive epsilon, generational distance, inverted generational distance, and spacing are considered to establish the comparability. In addition, a graphical comparison with respect to the true Pareto front for the multiobjective optimal crop-mix planning problem is presented. Empirical results generally show GDE3 to be a viable alternative tool for solving a multiobjective optimal crop-mix planning problem.
Transition-Independent Decentralized Markov Decision Processes
NASA Technical Reports Server (NTRS)
Becker, Raphen; Silberstein, Shlomo; Lesser, Victor; Goldman, Claudia V.; Morris, Robert (Technical Monitor)
2003-01-01
There has been substantial progress with formal models for sequential decision making by individual agents using the Markov decision process (MDP). However, similar treatment of multi-agent systems is lacking. A recent complexity result, showing that solving decentralized MDPs is NEXP-hard, provides a partial explanation. To overcome this complexity barrier, we identify a general class of transition-independent decentralized MDPs that is widely applicable. The class consists of independent collaborating agents that are tied up by a global reward function that depends on both of their histories. We present a novel algorithm for solving this class of problems and examine its properties. The result is the first effective technique to solve optimally a class of decentralized MDPs. This lays the foundation for further work in this area on both exact and approximate solutions.
A global change data base using Thematic Mapper data - Earth Monitoring Educational System (EMES)
NASA Technical Reports Server (NTRS)
D'Antoni, Hector L.; Peterson, David L.
1992-01-01
Some of the main directions in creating an education program in earth system science aimed at combining top science and technology with high academic performance are presented. The creation of an Earth Monitoring Educational System (EMES) integrated with the research interests of the NASA Ames Research Center and one or more universities is proposed. Based on the integration of a global network of cooperators to build a global data base for assessments of global change, EMES would promote degrees at all levels in global ecology at associated universities and colleges, and extracurricular courses for multilevel audiences. EMES objectives are to: train specialists; establish a tradition of solving regional problems concerning global change in a systemic manner, using remote sensing technology as the monitoring tool; and transfer knowledge on global change to the national and world communities. South America is proposed as the pilot continent for the project.
Global existence and exponential decay of the solution for a viscoelastic wave equation with a delay
NASA Astrophysics Data System (ADS)
Dai, Qiuyi; Yang, Zhifeng
2014-10-01
In this paper, we consider initial-boundary value problem of viscoelastic wave equation with a delay term in the interior feedback. Namely, we study the following equation together with initial-boundary conditions of Dirichlet type in Ω × (0, + ∞) and prove that for arbitrary real numbers μ 1 and μ 2, the above-mentioned problem has a unique global solution under suitable assumptions on the kernel g. This improve the results of the previous literature such as Nicaise and Pignotti (SIAM J. Control Optim 45:1561-1585, 2006) and Kirane and Said-Houari (Z. Angew. Math. Phys. 62:1065-1082, 2011) by removing the restriction imposed on μ 1 and μ 2. Furthermore, we also get an exponential decay results for the energy of the concerned problem in the case μ 1 = 0 which solves an open problem proposed by Kirane and Said-Houari (Z. Angew. Math. Phys. 62:1065-1082, 2011).
NASA Astrophysics Data System (ADS)
Barajas-Solano, D. A.; Tartakovsky, A. M.
2017-12-01
We present a multiresolution method for the numerical simulation of flow and reactive transport in porous, heterogeneous media, based on the hybrid Multiscale Finite Volume (h-MsFV) algorithm. The h-MsFV algorithm allows us to couple high-resolution (fine scale) flow and transport models with lower resolution (coarse) models to locally refine both spatial resolution and transport models. The fine scale problem is decomposed into various "local'' problems solved independently in parallel and coordinated via a "global'' problem. This global problem is then coupled with the coarse model to strictly ensure domain-wide coarse-scale mass conservation. The proposed method provides an alternative to adaptive mesh refinement (AMR), due to its capacity to rapidly refine spatial resolution beyond what's possible with state-of-the-art AMR techniques, and the capability to locally swap transport models. We illustrate our method by applying it to groundwater flow and reactive transport of multiple species.
NASA Astrophysics Data System (ADS)
Adams, Wendy Kristine
The purpose of my research was to produce a problem solving evaluation tool for physics. To do this it was necessary to gain a thorough understanding of how students solve problems. Although physics educators highly value problem solving and have put extensive effort into understanding successful problem solving, there is currently no efficient way to evaluate problem solving skill. Attempts have been made in the past; however, knowledge of the principles required to solve the subject problem are so absolutely critical that they completely overshadow any other skills students may use when solving a problem. The work presented here is unique because the evaluation tool removes the requirement that the student already have a grasp of physics concepts. It is also unique because I picked a wide range of people and picked a wide range of tasks for evaluation. This is an important design feature that helps make things emerge more clearly. This dissertation includes an extensive literature review of problem solving in physics, math, education and cognitive science as well as descriptions of studies involving student use of interactive computer simulations, the design and validation of a beliefs about physics survey and finally the design of the problem solving evaluation tool. I have successfully developed and validated a problem solving evaluation tool that identifies 44 separate assets (skills) necessary for solving problems. Rigorous validation studies, including work with an independent interviewer, show these assets identified by this content-free evaluation tool are the same assets that students use to solve problems in mechanics and quantum mechanics. Understanding this set of component assets will help teachers and researchers address problem solving within the classroom.
Blanchard-Fields, Fredda; Mienaltowski, Andrew; Seay, Renee Baldi
2007-01-01
Using the Everyday Problem Solving Inventory of Cornelius and Caspi, we examined differences in problem-solving strategy endorsement and effectiveness in two domains of everyday functioning (instrumental or interpersonal, and a mixture of the two domains) and for four strategies (avoidance-denial, passive dependence, planful problem solving, and cognitive analysis). Consistent with past research, our research showed that older adults were more problem focused than young adults in their approach to solving instrumental problems, whereas older adults selected more avoidant-denial strategies than young adults when solving interpersonal problems. Overall, older adults were also more effective than young adults when solving everyday problems, in particular for interpersonal problems.
Multiple graph regularized protein domain ranking.
Wang, Jim Jing-Yan; Bensmail, Halima; Gao, Xin
2012-11-19
Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods. To tackle this problem, we have developed the Multiple Graph regularized Ranking algorithm, MultiG-Rank. Instead of using a single graph to regularize the ranking scores, MultiG-Rank approximates the intrinsic manifold of protein domain distribution by combining multiple initial graphs for the regularization. Graph weights are learned with ranking scores jointly and automatically, by alternately minimizing an objective function in an iterative algorithm. Experimental results on a subset of the ASTRAL SCOP protein domain database demonstrate that MultiG-Rank achieves a better ranking performance than single graph regularized ranking methods and pairwise similarity based ranking methods. The problem of graph model and parameter selection in graph regularized protein domain ranking can be solved effectively by combining multiple graphs. This aspect of generalization introduces a new frontier in applying multiple graphs to solving protein domain ranking applications.
Why do we need nuclear power? Energy policy in the light of history of civilization
DOE Office of Scientific and Technical Information (OSTI.GOV)
Yoda, Susumu
1996-06-01
With the population explosion as a background, economic growth needs massive consumption of energy and resources. This massive consumption of energy and resources will deteriorate the global environment. It is a complicated chain of causes and effects. The problems of economic growth, resources and energy, and environment must be solved at the same time. Here the so-called ``Trilemma`` problem emerges. To overcome the Trilemma and assure a sustainable development of the whole world, approaches and actions are needed from various viewpoints including technology, socio-economic system and civilization. From the viewpoint of energy, it will be necessary to introduce all energymore » technologies which will not deteriorate the global environment. Energy conservation and efficiency are an important part of this process. It is also important to introduce renewable energy as much as possible. Even with these efforts, the energy needed by mankind in the 21st century will be tremendous. An energy source is needed which is adequate in terms of quantity, price, and environment. It is nuclear energy that meets these requirements. Several problems must be solved before the fundamental important merit of nuclear power can be realized. These issues are discussed here. They are divided into the following categories: economic issues; technical issues; social issues; political issues; and the issues in Asia.« less
Multiple graph regularized protein domain ranking
2012-01-01
Background Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods. Results To tackle this problem, we have developed the Multiple Graph regularized Ranking algorithm, MultiG-Rank. Instead of using a single graph to regularize the ranking scores, MultiG-Rank approximates the intrinsic manifold of protein domain distribution by combining multiple initial graphs for the regularization. Graph weights are learned with ranking scores jointly and automatically, by alternately minimizing an objective function in an iterative algorithm. Experimental results on a subset of the ASTRAL SCOP protein domain database demonstrate that MultiG-Rank achieves a better ranking performance than single graph regularized ranking methods and pairwise similarity based ranking methods. Conclusion The problem of graph model and parameter selection in graph regularized protein domain ranking can be solved effectively by combining multiple graphs. This aspect of generalization introduces a new frontier in applying multiple graphs to solving protein domain ranking applications. PMID:23157331
Spontaneous gestures influence strategy choices in problem solving.
Alibali, Martha W; Spencer, Robert C; Knox, Lucy; Kita, Sotaro
2011-09-01
Do gestures merely reflect problem-solving processes, or do they play a functional role in problem solving? We hypothesized that gestures highlight and structure perceptual-motor information, and thereby make such information more likely to be used in problem solving. Participants in two experiments solved problems requiring the prediction of gear movement, either with gesture allowed or with gesture prohibited. Such problems can be correctly solved using either a perceptual-motor strategy (simulation of gear movements) or an abstract strategy (the parity strategy). Participants in the gesture-allowed condition were more likely to use perceptual-motor strategies than were participants in the gesture-prohibited condition. Gesture promoted use of perceptual-motor strategies both for participants who talked aloud while solving the problems (Experiment 1) and for participants who solved the problems silently (Experiment 2). Thus, spontaneous gestures influence strategy choices in problem solving.
Dixon-Gordon, Katherine L; Chapman, Alexander L; Lovasz, Nathalie; Walters, Kris
2011-10-01
Borderline personality disorder (BPD) is associated with poor social problem solving and problems with emotion regulation. In this study, the social problem-solving performance of undergraduates with high (n = 26), mid (n = 32), or low (n = 29) levels of BPD features was assessed with the Social Problem-Solving Inventory-Revised and using the means-ends problem-solving procedure before and after a social rejection stressor. The high-BP group, but not the low-BP group, showed a significant reduction in relevant solutions to social problems and more inappropriate solutions following the negative emotion induction. Increases in self-reported negative emotions during the emotion induction mediated the relationship between BP features and reductions in social problem-solving performance. In addition, the high-BP group demonstrated trait deficits in social problem solving on the Social Problem-Solving Inventory-Revised. These findings suggest that future research must examine social problem solving under differing emotional conditions, and that clinical interventions to improve social problem solving among persons with BP features should focus on responses to emotional contexts.
NASA Astrophysics Data System (ADS)
Chu, J. G.; Zhang, C.; Fu, G. T.; Li, Y.; Zhou, H. C.
2015-04-01
This study investigates the effectiveness of a sensitivity-informed method for multi-objective operation of reservoir systems, which uses global sensitivity analysis as a screening tool to reduce the computational demands. Sobol's method is used to screen insensitive decision variables and guide the formulation of the optimization problems with a significantly reduced number of decision variables. This sensitivity-informed problem decomposition dramatically reduces the computational demands required for attaining high quality approximations of optimal tradeoff relationships between conflicting design objectives. The search results obtained from the reduced complexity multi-objective reservoir operation problems are then used to pre-condition the full search of the original optimization problem. In two case studies, the Dahuofang reservoir and the inter-basin multi-reservoir system in Liaoning province, China, sensitivity analysis results show that reservoir performance is strongly controlled by a small proportion of decision variables. Sensitivity-informed problem decomposition and pre-conditioning are evaluated in their ability to improve the efficiency and effectiveness of multi-objective evolutionary optimization. Overall, this study illustrates the efficiency and effectiveness of the sensitivity-informed method and the use of global sensitivity analysis to inform problem decomposition when solving the complex multi-objective reservoir operation problems.
Learning Incoherent Sparse and Low-Rank Patterns from Multiple Tasks
Chen, Jianhui; Liu, Ji; Ye, Jieping
2013-01-01
We consider the problem of learning incoherent sparse and low-rank patterns from multiple tasks. Our approach is based on a linear multi-task learning formulation, in which the sparse and low-rank patterns are induced by a cardinality regularization term and a low-rank constraint, respectively. This formulation is non-convex; we convert it into its convex surrogate, which can be routinely solved via semidefinite programming for small-size problems. We propose to employ the general projected gradient scheme to efficiently solve such a convex surrogate; however, in the optimization formulation, the objective function is non-differentiable and the feasible domain is non-trivial. We present the procedures for computing the projected gradient and ensuring the global convergence of the projected gradient scheme. The computation of projected gradient involves a constrained optimization problem; we show that the optimal solution to such a problem can be obtained via solving an unconstrained optimization subproblem and an Euclidean projection subproblem. We also present two projected gradient algorithms and analyze their rates of convergence in details. In addition, we illustrate the use of the presented projected gradient algorithms for the proposed multi-task learning formulation using the least squares loss. Experimental results on a collection of real-world data sets demonstrate the effectiveness of the proposed multi-task learning formulation and the efficiency of the proposed projected gradient algorithms. PMID:24077658
Learning Incoherent Sparse and Low-Rank Patterns from Multiple Tasks.
Chen, Jianhui; Liu, Ji; Ye, Jieping
2012-02-01
We consider the problem of learning incoherent sparse and low-rank patterns from multiple tasks. Our approach is based on a linear multi-task learning formulation, in which the sparse and low-rank patterns are induced by a cardinality regularization term and a low-rank constraint, respectively. This formulation is non-convex; we convert it into its convex surrogate, which can be routinely solved via semidefinite programming for small-size problems. We propose to employ the general projected gradient scheme to efficiently solve such a convex surrogate; however, in the optimization formulation, the objective function is non-differentiable and the feasible domain is non-trivial. We present the procedures for computing the projected gradient and ensuring the global convergence of the projected gradient scheme. The computation of projected gradient involves a constrained optimization problem; we show that the optimal solution to such a problem can be obtained via solving an unconstrained optimization subproblem and an Euclidean projection subproblem. We also present two projected gradient algorithms and analyze their rates of convergence in details. In addition, we illustrate the use of the presented projected gradient algorithms for the proposed multi-task learning formulation using the least squares loss. Experimental results on a collection of real-world data sets demonstrate the effectiveness of the proposed multi-task learning formulation and the efficiency of the proposed projected gradient algorithms.
An Investigation of Secondary Teachers’ Understanding and Belief on Mathematical Problem Solving
NASA Astrophysics Data System (ADS)
Yuli Eko Siswono, Tatag; Wachidul Kohar, Ahmad; Kurniasari, Ika; Puji Astuti, Yuliani
2016-02-01
Weaknesses on problem solving of Indonesian students as reported by recent international surveys give rise to questions on how Indonesian teachers bring out idea of problem solving in mathematics lesson. An explorative study was undertaken to investigate how secondary teachers who teach mathematics at junior high school level understand and show belief toward mathematical problem solving. Participants were teachers from four cities in East Java province comprising 45 state teachers and 25 private teachers. Data was obtained through questionnaires and written test. The results of this study point out that the teachers understand pedagogical problem solving knowledge well as indicated by high score of observed teachers‘ responses showing understanding on problem solving as instruction as well as implementation of problem solving in teaching practice. However, they less understand on problem solving content knowledge such as problem solving strategies and meaning of problem itself. Regarding teacher's difficulties, teachers admitted to most frequently fail in (1) determining a precise mathematical model or strategies when carrying out problem solving steps which is supported by data of test result that revealed transformation error as the most frequently observed errors in teachers’ work and (2) choosing suitable real situation when designing context-based problem solving task. Meanwhile, analysis of teacher's beliefs on problem solving shows that teachers tend to view both mathematics and how students should learn mathematics as body static perspective, while they tend to believe to apply idea of problem solving as dynamic approach when teaching mathematics.
ERIC Educational Resources Information Center
Hayel Al-Srour, Nadia; Al-Ali, Safa M.; Al-Oweidi, Alia
2016-01-01
The present study aims to detect the impact of teacher training on creative writing and problem-solving using both Futuristic scenarios program to solve problems creatively, and creative problem solving. To achieve the objectives of the study, the sample was divided into two groups, the first consist of 20 teachers, and 23 teachers to second…
Graf, Peter A.; Billups, Stephen
2017-07-24
Computational materials design has suffered from a lack of algorithms formulated in terms of experimentally accessible variables. Here we formulate the problem of (ternary) alloy optimization at the level of choice of atoms and their composition that is normal for synthesists. Mathematically, this is a mixed integer problem where a candidate solution consists of a choice of three elements, and how much of each of them to use. This space has the natural structure of a set of equilateral triangles. We solve this problem by introducing a novel version of the DIRECT algorithm that (1) operates on equilateral triangles insteadmore » of rectangles and (2) works across multiple triangles. We demonstrate on a test case that the algorithm is both robust and efficient. Lastly, we offer an explanation of the efficacy of DIRECT -- specifically, its balance of global and local search -- by showing that 'potentially optimal rectangles' of the original algorithm are akin to the Pareto front of the 'multi-component optimization' of global and local search.« less
Local search for optimal global map generation using mid-decadal landsat images
Khatib, L.; Gasch, J.; Morris, Robert; Covington, S.
2007-01-01
NASA and the US Geological Survey (USGS) are seeking to generate a map of the entire globe using Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensor data from the "mid-decadal" period of 2004 through 2006. The global map is comprised of thousands of scene locations and, for each location, tens of different images of varying quality to chose from. Furthermore, it is desirable for images of adjacent scenes be close together in time of acquisition, to avoid obvious discontinuities due to seasonal changes. These characteristics make it desirable to formulate an automated solution to the problem of generating the complete map. This paper formulates a Global Map Generator problem as a Constraint Optimization Problem (GMG-COP) and describes an approach to solving it using local search. Preliminary results of running the algorithm on image data sets are summarized. The results suggest a significant improvement in map quality using constraint-based solutions. Copyright ?? 2007, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
DOE Office of Scientific and Technical Information (OSTI.GOV)
Graf, Peter A.; Billups, Stephen
Computational materials design has suffered from a lack of algorithms formulated in terms of experimentally accessible variables. Here we formulate the problem of (ternary) alloy optimization at the level of choice of atoms and their composition that is normal for synthesists. Mathematically, this is a mixed integer problem where a candidate solution consists of a choice of three elements, and how much of each of them to use. This space has the natural structure of a set of equilateral triangles. We solve this problem by introducing a novel version of the DIRECT algorithm that (1) operates on equilateral triangles insteadmore » of rectangles and (2) works across multiple triangles. We demonstrate on a test case that the algorithm is both robust and efficient. Lastly, we offer an explanation of the efficacy of DIRECT -- specifically, its balance of global and local search -- by showing that 'potentially optimal rectangles' of the original algorithm are akin to the Pareto front of the 'multi-component optimization' of global and local search.« less
NASA Astrophysics Data System (ADS)
Palacio-Cayetano, Joycelin
"Problem-solving through reflective thinking should be both the method and valuable outcome of science instruction in America's schools" proclaimed John Dewey (Gabel, 1995). If the development of problem-solving is a primary goal of science education, more problem-solving opportunities must be an integral part of K-16 education. To examine the effective use of technology in developing and assessing problem-solving skills, a problem-solving authoring, learning, and assessment software, the UCLA IMMEX Program-Interactive Multimedia Exercises-was investigated. This study was a twenty-week quasi-experimental study that was implemented as a control-group time series design among 120 tenth grade students. Both the experimental group (n = 60) and the control group (n = 60) participated in a problem-based learning curriculum; however, the experimental group received regular intensive experiences with IMMEX problem-solving and the control group did not. Problem-solving pretest and posttest were administered to all students. The instruments used were a 35-item Processes of Biological Inquiry Test and an IMMEX problem-solving assessment test, True Roots. Students who participated in the IMMEX Program achieved significant (p <.05) gains in problem-solving skills on both problem-solving assessment instruments. This study provided evidence that IMMEX software is highly efficient in evaluating salient elements of problem-solving. Outputs of students' problem-solving strategies revealed that unsuccessful problem solvers primarily used the following four strategies: (1) no data search strategy, students simply guessed; (2) limited data search strategy leading to insufficient data and premature closing; (3) irrelevant data search strategy, students focus in areas bearing no substantive data; and (4) extensive data search strategy with inadequate integration and analysis. On the contrary, successful problem solvers used the following strategies; (1) focused search strategy coupled with the ability to fill in knowledge gaps by accessing the appropriate resources; (2) targeted search strategy coupled with high level of analytical and integration skills; and (3) focused search strategy coupled with superior discrimination, analytical, and integration skills. The strategies of students who were successful and unsuccessful solving IMMEX problems were consistent with those of expert and novice problem solvers identified in the literature on problem-solving.
NASA Astrophysics Data System (ADS)
Ghosh, Diptesh; Chakrabarti, Anindya S.
2017-10-01
In this paper, we study a large-scale distributed coordination problem and propose efficient adaptive strategies to solve the problem. The basic problem is to allocate finite number of resources to individual agents in the absence of a central planner such that there is as little congestion as possible and the fraction of unutilized resources is reduced as far as possible. In the absence of a central planner and global information, agents can employ adaptive strategies that uses only a finite knowledge about the competitors. In this paper, we show that a combination of finite information sets and reinforcement learning can increase the utilization fraction of resources substantially.
Practical global oceanic state estimation
NASA Astrophysics Data System (ADS)
Wunsch, Carl; Heimbach, Patrick
2007-06-01
The problem of oceanographic state estimation, by means of an ocean general circulation model (GCM) and a multitude of observations, is described and contrasted with the meteorological process of data assimilation. In practice, all such methods reduce, on the computer, to forms of least-squares. The global oceanographic problem is at the present time focussed primarily on smoothing, rather than forecasting, and the data types are unlike meteorological ones. As formulated in the consortium Estimating the Circulation and Climate of the Ocean (ECCO), an automatic differentiation tool is used to calculate the so-called adjoint code of the GCM, and the method of Lagrange multipliers used to render the problem one of unconstrained least-squares minimization. Major problems today lie less with the numerical algorithms (least-squares problems can be solved by many means) than with the issues of data and model error. Results of ongoing calculations covering the period of the World Ocean Circulation Experiment, and including among other data, satellite altimetry from TOPEX/POSEIDON, Jason-1, ERS- 1/2, ENVISAT, and GFO, a global array of profiling floats from the Argo program, and satellite gravity data from the GRACE mission, suggest that the solutions are now useful for scientific purposes. Both methodology and applications are developing in a number of different directions.
NASA Astrophysics Data System (ADS)
Caldeira, K.
2013-12-01
Some carbon cycle modelers conceive of themselves as developing a representation of reality that will serve as a general purpose tool that can be used to make a wide variety of predictions. However, models are tools used to solve particular problems. If we were to ask, 'what tool is best for fastening two pieces of wood together,' depending on the circumstances that tool could be hammer, a screw driver, or perhaps some sort of glue gun. And the best kind of screw driver might depend on whether we were thinking about Philips or flat headed screws. If there is no unique answer to the question of which type of tool is best for fastening two pieces of wood together, surely there is no unique answer to the question of which type of model is best for making carbon-cycle predictions. We must first understand what problem we are trying to solve. Some modeling studies try to make the most reliable projections, considering as many processes and predicting as many observables as possible, whereas other modeling studies try to show how general trends depend on relatively few (perhaps highly aggregated) processes. This talk will look at CMIP5 carbon-cycle model results and address the issue of the extent to which the overall global-scale trends projected by these detailed models might projected by models with many fewer degrees of freedom. It should be noted that an ocean carbon-cycle model that predicts many observables at local scale is much more easily falsified (and thus in some sense is more ';scientific') than an ocean model that predicts only global scale phenomena. Nevertheless, if all that is needed is a crude estimate of global ocean CO2 uptake (say, to permit as study of the carbon-cycle on land), a simple representation of the ocean carbon cycle may suffice. This talk will take as its jumping off point two quotes: 'All models are wrong, some are useful.' - George E.P. Box 'Models should be as simple as possible but no simpler.' - Albert Einstein (likely an erroneous attribution) Potential for progress in carbon-cycle modeling rests in being clear about the problems we seek to solve, and then developing tools to solve those problems. A global carbon cycle model that represents underlying complexity in all its detail may ultimately prove useless: 'We actually made a map of the country, on the scale of a mile to the mile!' 'Have you used it much?' I enquired. 'It has never been spread out, yet,' said Mein Herr: 'the farmers objected: they said it would cover the whole country, and shut out the sunlight! So we now use the country itself, as its own map, and I assure you it does nearly as well.' - Lewis Carroll
"Thinking about a Sustainable Earth"
NASA Astrophysics Data System (ADS)
Ikeshita, Makoto
2014-05-01
1.Introduction The Course of study for Junior high school teaching was changed in 2008 in Japan. We should especially mention about this change that ESD, "Education for Sustainable Development," was written as a point of view. ESD is a kind of educations that is studied with a target for a region and that aims at reorganize of consciousness through thinking of how to be a better region. ESD's view was written for Social studies, Science, Foreign Languages, Health and Physical Education, Home Economics and Technical Arts, and the Period for Integrated Studies. Of these subjects, Social studies are the one of core subjects. Social studies for Junior high school consist of Geography, History and Civics. "Problem of us and international society" is the last part of Civics. Teacher helps students to understand international society deeply and think about the role of our country for it. Students research many problems (global environment, resources and energy, poverty etc.) and organize their thoughts on how make a better society as a part of the human family. I taught them to think about how to solve many themes like religious problems, terrorism problems, the North-South problems, and resource and energy problems. It is my practice to let them think about what they should do to solve the global warming problem. 2.The truth of my class I pointed out to the students that the length of summer time in Japan is increasing, and we can anticipate it will continue to increase in the future. After that, I explained to them that occurrence of sudden, heavy downpour of rain is increasing and helped them understand the process of this kind of downpour through some diagrams and pictures. I helped them understand the context of this increase of the length of summer time and heavy downpour within the whole earth's ecosystem. Such increases as these things are causing global warming. I asked them to think about what are the possible problems if global warming progresses. The ideas the students thought of were; a rise in the sea level because of melting ice at the north and south poles, floods, the increase of typhoons and cyclones, the increase of droughts, the progression of desertification, etc. Lastly, I asked them to think about what we can do to prevent global warming. The students suggested: saving energy to decrease carbon dioxide emissions, developing further public transportation, using bikes instead of cars, promoting recycling, and decreasing the output of garbage. 3.Conclusion It is very effective to let them think about being sustainable earth after studying Geography, History and Civics at the end of Junior high school to raise awareness concerning sustainable region on the earth, on which we live.
ERIC Educational Resources Information Center
Aljaberi, Nahil M.; Gheith, Eman
2016-01-01
This study aims to investigate the ability of pre-service class teacher at University of Petrain solving mathematical problems using Polya's Techniques, their level of problem solving skills in daily-life issues. The study also investigates the correlation between their ability to solve mathematical problems and their level of problem solving…
The Association between Motivation, Affect, and Self-regulated Learning When Solving Problems.
Baars, Martine; Wijnia, Lisette; Paas, Fred
2017-01-01
Self-regulated learning (SRL) skills are essential for learning during school years, particularly in complex problem-solving domains, such as biology and math. Although a lot of studies have focused on the cognitive resources that are needed for learning to solve problems in a self-regulated way, affective and motivational resources have received much less research attention. The current study investigated the relation between affect (i.e., Positive Affect and Negative Affect Scale), motivation (i.e., autonomous and controlled motivation), mental effort, SRL skills, and problem-solving performance when learning to solve biology problems in a self-regulated online learning environment. In the learning phase, secondary education students studied video-modeling examples of how to solve hereditary problems, solved hereditary problems which they chose themselves from a set of problems with different complexity levels (i.e., five levels). In the posttest, students solved hereditary problems, self-assessed their performance, and chose a next problem from the set of problems but did not solve these problems. The results from this study showed that negative affect, inaccurate self-assessments during the posttest, and higher perceptions of mental effort during the posttest were negatively associated with problem-solving performance after learning in a self-regulated way.
A Global Approach to the Optimal Trajectory Based on an Improved Ant Colony Algorithm for Cold Spray
NASA Astrophysics Data System (ADS)
Cai, Zhenhua; Chen, Tingyang; Zeng, Chunnian; Guo, Xueping; Lian, Huijuan; Zheng, You; Wei, Xiaoxu
2016-12-01
This paper is concerned with finding a global approach to obtain the shortest complete coverage trajectory on complex surfaces for cold spray applications. A slicing algorithm is employed to decompose the free-form complex surface into several small pieces of simple topological type. The problem of finding the optimal arrangement of the pieces is translated into a generalized traveling salesman problem (GTSP). Owing to its high searching capability and convergence performance, an improved ant colony algorithm is then used to solve the GTSP. Through off-line simulation, a robot trajectory is generated based on the optimized result. The approach is applied to coat real components with a complex surface by using the cold spray system with copper as the spraying material.
Spacecraft inertia estimation via constrained least squares
NASA Technical Reports Server (NTRS)
Keim, Jason A.; Acikmese, Behcet A.; Shields, Joel F.
2006-01-01
This paper presents a new formulation for spacecraft inertia estimation from test data. Specifically, the inertia estimation problem is formulated as a constrained least squares minimization problem with explicit bounds on the inertia matrix incorporated as LMIs [linear matrix inequalities). The resulting minimization problem is a semidefinite optimization that can be solved efficiently with guaranteed convergence to the global optimum by readily available algorithms. This method is applied to data collected from a robotic testbed consisting of a freely rotating body. The results show that the constrained least squares approach produces more accurate estimates of the inertia matrix than standard unconstrained least squares estimation methods.
Extraction of a group-pair relation: problem-solving relation from web-board documents.
Pechsiri, Chaveevan; Piriyakul, Rapepun
2016-01-01
This paper aims to extract a group-pair relation as a Problem-Solving relation, for example a DiseaseSymptom-Treatment relation and a CarProblem-Repair relation, between two event-explanation groups, a problem-concept group as a symptom/CarProblem-concept group and a solving-concept group as a treatment-concept/repair concept group from hospital-web-board and car-repair-guru-web-board documents. The Problem-Solving relation (particularly Symptom-Treatment relation) including the graphical representation benefits non-professional persons by supporting knowledge of primarily solving problems. The research contains three problems: how to identify an EDU (an Elementary Discourse Unit, which is a simple sentence) with the event concept of either a problem or a solution; how to determine a problem-concept EDU boundary and a solving-concept EDU boundary as two event-explanation groups, and how to determine the Problem-Solving relation between these two event-explanation groups. Therefore, we apply word co-occurrence to identify a problem-concept EDU and a solving-concept EDU, and machine-learning techniques to solve a problem-concept EDU boundary and a solving-concept EDU boundary. We propose using k-mean and Naïve Bayes to determine the Problem-Solving relation between the two event-explanation groups involved with clustering features. In contrast to previous works, the proposed approach enables group-pair relation extraction with high accuracy.
NASA Astrophysics Data System (ADS)
Kerner, Martin
2004-10-01
Here, we introduce an interactive communication and management system, Scope Water, which is constructed to establish a transfer of results from research work directed towards the solving of a specific problem. To proceed step by step towards this goal, the system uses a structured approach. Starting with the global exploration of knowledge, expertise, and ideas from experts, passing an objective assessment of this information and leading finally to a coopertive making up of a concept for problem solving by specialists. Scope Water has been developed on the basis of recent advances in cybernetic management experienced in team meetings and was successfully launched as a tool to gain quick access to recent results from research work on water by Strategic Science Consult Ltd. (SSC). SSC now plans to broaden the application of SCope Water by adding a platform which allows scientists on remote sensing to offer their results, knowledge and ideas as a service to help to solve specific problems on studying/monitoring aquatic systems. Single scientists, working groups and research instituitions are invited to participate in such a service metwork on remote sensing and are asked to ceclare their interest by sending an e-mail to the authors address given above.
NASA Astrophysics Data System (ADS)
Nasution, M. L.; Yerizon, Y.; Gusmiyanti, R.
2018-04-01
One of the purpose mathematic learning is to develop problem solving abilities. Problem solving is obtained through experience in questioning non-routine. Improving students’ mathematical problem-solving abilities required an appropriate strategy in learning activities one of them is models problem based learning (PBL). Thus, the purpose of this research is to determine whether the problem solving abilities of mathematical students’ who learn to use PBL better than on the ability of students’ mathematical problem solving by applying conventional learning. This research included quasi experiment with static group design and population is students class XI MIA SMAN 1 Lubuk Alung. Class experiment in the class XI MIA 5 and class control in the class XI MIA 6. The instrument of final test students’ mathematical problem solving used essay form. The result of data final test in analyzed with t-test. The result is students’ mathematical problem solving abilities with PBL better then on the ability of students’ mathematical problem solving by applying conventional learning. It’s seen from the high percentage achieved by the group of students who learn to use PBL for each indicator of students’ mathematical problem solving.
Using a general problem-solving strategy to promote transfer.
Youssef-Shalala, Amina; Ayres, Paul; Schubert, Carina; Sweller, John
2014-09-01
Cognitive load theory was used to hypothesize that a general problem-solving strategy based on a make-as-many-moves-as-possible heuristic could facilitate problem solutions for transfer problems. In four experiments, school students were required to learn about a topic through practice with a general problem-solving strategy, through a conventional problem solving strategy or by studying worked examples. In Experiments 1 and 2 using junior high school students learning geometry, low knowledge students in the general problem-solving group scored significantly higher on near or far transfer tests than the conventional problem-solving group. In Experiment 3, an advantage for a general problem-solving group over a group presented worked examples was obtained on far transfer tests using the same curriculum materials, again presented to junior high school students. No differences between conditions were found in Experiments 1, 2, or 3 using test problems similar to the acquisition problems. Experiment 4 used senior high school students studying economics and found the general problem-solving group scored significantly higher than the conventional problem-solving group on both similar and transfer tests. It was concluded that the general problem-solving strategy was helpful for novices, but not for students that had access to domain-specific knowledge. PsycINFO Database Record (c) 2014 APA, all rights reserved.
NASA Astrophysics Data System (ADS)
Hafner, Robert; Stewart, Jim
Past problem-solving research has provided a basis for helping students structure their knowledge and apply appropriate problem-solving strategies to solve problems for which their knowledge (or mental models) of scientific phenomena is adequate (model-using problem solving). This research examines how problem solving in the domain of Mendelian genetics proceeds in situations where solvers' mental models are insufficient to solve problems at hand (model-revising problem solving). Such situations require solvers to use existing models to recognize anomalous data and to revise those models to accommodate the data. The study was conducted in the context of 9-week high school genetics course and addressed: the heuristics charactenstic of successful model-revising problem solving: the nature of the model revisions, made by students as well as the nature of model development across problem types; and the basis upon which solvers decide that a revised model is sufficient (that t has both predictive and explanatory power).
Azad, Gazi F.; Kim, Mina; Marcus, Steven C.; Mandell, David S.; Sheridan, Susan M.
2016-01-01
Effective parent-teacher communication involves problem-solving concerns about students. Few studies have examined problem solving interactions between parents and teachers of children with autism spectrum disorder (ASD), with a particular focus on identifying communication barriers and strategies for improving them. This study examined the problem-solving behaviors of parents and teachers of children with ASD. Participants included 18 teachers and 39 parents of children with ASD. Parent-teacher dyads were prompted to discuss and provide a solution for a problem that a student experienced at home and at school. Parents and teachers also reported on their problem-solving behaviors. Results showed that parents and teachers displayed limited use of the core elements of problem-solving. Teachers displayed more problem-solving behaviors than parents. Both groups reported engaging in more problem-solving behaviors than they were observed to display during their discussions. Our findings suggest that teacher and parent training programs should include collaborative approaches to problem-solving. PMID:28392604
Azad, Gazi F; Kim, Mina; Marcus, Steven C; Mandell, David S; Sheridan, Susan M
2016-12-01
Effective parent-teacher communication involves problem-solving concerns about students. Few studies have examined problem solving interactions between parents and teachers of children with autism spectrum disorder (ASD), with a particular focus on identifying communication barriers and strategies for improving them. This study examined the problem-solving behaviors of parents and teachers of children with ASD. Participants included 18 teachers and 39 parents of children with ASD. Parent-teacher dyads were prompted to discuss and provide a solution for a problem that a student experienced at home and at school. Parents and teachers also reported on their problem-solving behaviors. Results showed that parents and teachers displayed limited use of the core elements of problem-solving. Teachers displayed more problem-solving behaviors than parents. Both groups reported engaging in more problem-solving behaviors than they were observed to display during their discussions. Our findings suggest that teacher and parent training programs should include collaborative approaches to problem-solving.
NASA Astrophysics Data System (ADS)
Rr Chusnul, C.; Mardiyana, S., Dewi Retno
2017-12-01
Problem solving is the basis of mathematics learning. Problem solving teaches us to clarify an issue coherently in order to avoid misunderstanding information. Sometimes there may be mistakes in problem solving due to misunderstanding the issue, choosing a wrong concept or misapplied concept. The problem-solving test was carried out after students were given treatment on learning by using cooperative learning of TTW type. The purpose of this study was to elucidate student problem regarding to problem solving errors after learning by using cooperative learning of TTW type. Newman stages were used to identify problem solving errors in this study. The new research used a descriptive method to find out problem solving errors in students. The subject in this study were students of Vocational Senior High School (SMK) in 10th grade. Test and interview was conducted for data collection. Thus, the results of this study suggested problem solving errors in students after learning by using cooperative learning of TTW type for Newman stages.
Rejection Sensitivity and Depression: Indirect Effects Through Problem Solving.
Kraines, Morganne A; Wells, Tony T
2017-01-01
Rejection sensitivity (RS) and deficits in social problem solving are risk factors for depression. Despite their relationship to depression and the potential connection between them, no studies have examined RS and social problem solving together in the context of depression. As such, we examined RS, five facets of social problem solving, and symptoms of depression in a young adult sample. A total of 180 participants completed measures of RS, social problem solving, and depressive symptoms. We used bootstrapping to examine the indirect effect of RS on depressive symptoms through problem solving. RS was positively associated with depressive symptoms. A negative problem orientation, impulsive/careless style, and avoidance style of social problem solving were positively associated with depressive symptoms, and a positive problem orientation was negatively associated with depressive symptoms. RS demonstrated an indirect effect on depressive symptoms through two social problem-solving facets: the tendency to view problems as threats to one's well-being and an avoidance problem-solving style characterized by procrastination, passivity, or overdependence on others. These results are consistent with prior research that found a positive association between RS and depression symptoms, but this is the first study to implicate specific problem-solving deficits in the relationship between RS and depression. Our results suggest that depressive symptoms in high RS individuals may result from viewing problems as threats and taking an avoidant, rather than proactive, approach to dealing with problems. These findings may have implications for problem-solving interventions for rejection sensitive individuals.
The Cyclic Nature of Problem Solving: An Emergent Multidimensional Problem-Solving Framework
ERIC Educational Resources Information Center
Carlson, Marilyn P.; Bloom, Irene
2005-01-01
This paper describes the problem-solving behaviors of 12 mathematicians as they completed four mathematical tasks. The emergent problem-solving framework draws on the large body of research, as grounded by and modified in response to our close observations of these mathematicians. The resulting "Multidimensional Problem-Solving Framework" has four…
Mathematical Problem Solving: A Review of the Literature.
ERIC Educational Resources Information Center
Funkhouser, Charles
The major perspectives on problem solving of the twentieth century are reviewed--associationism, Gestalt psychology, and cognitive science. The results of the review on teaching problem solving and the uses of computers to teach problem solving are included. Four major issues related to the teaching of problem solving are discussed: (1)…
Teaching Problem Solving Skills to Elementary Age Students with Autism
ERIC Educational Resources Information Center
Cote, Debra L.; Jones, Vita L.; Barnett, Crystal; Pavelek, Karin; Nguyen, Hoang; Sparks, Shannon L.
2014-01-01
Students with disabilities need problem-solving skills to promote their success in solving the problems of daily life. The research into problem-solving instruction has been limited for students with autism. Using a problem-solving intervention and the Self Determined Learning Model of Instruction, three elementary age students with autism were…
Learning problem-solving skills in a distance education physics course
NASA Astrophysics Data System (ADS)
Rampho, G. J.; Ramorola, M. Z.
2017-10-01
In this paper we present the results of a study on the effectiveness of combinations of delivery modes of distance education in learning problem-solving skills in a distance education introductory physics course. A problem-solving instruction with the explicit teaching of a problem-solving strategy and worked-out examples were implemented in the course. The study used the ex post facto research design with stratified sampling to investigate the effect of the learning of a problem-solving strategy on the problem-solving performance. The number of problems attempted and the mean frequency of using a strategy in solving problems in the three course presentation modes were compared. The finding of the study indicated that combining the different course presentation modes had no statistically significant effect in the learning of problem-solving skills in the distance education course.
Guided particle swarm optimization method to solve general nonlinear optimization problems
NASA Astrophysics Data System (ADS)
Abdelhalim, Alyaa; Nakata, Kazuhide; El-Alem, Mahmoud; Eltawil, Amr
2018-04-01
The development of hybrid algorithms is becoming an important topic in the global optimization research area. This article proposes a new technique in hybridizing the particle swarm optimization (PSO) algorithm and the Nelder-Mead (NM) simplex search algorithm to solve general nonlinear unconstrained optimization problems. Unlike traditional hybrid methods, the proposed method hybridizes the NM algorithm inside the PSO to improve the velocities and positions of the particles iteratively. The new hybridization considers the PSO algorithm and NM algorithm as one heuristic, not in a sequential or hierarchical manner. The NM algorithm is applied to improve the initial random solution of the PSO algorithm and iteratively in every step to improve the overall performance of the method. The performance of the proposed method was tested over 20 optimization test functions with varying dimensions. Comprehensive comparisons with other methods in the literature indicate that the proposed solution method is promising and competitive.
Perfecting Scientists' Collaboration and Problem-Solving Skills in the Virtual Team Environment
NASA Astrophysics Data System (ADS)
Jabro, A.; Jabro, J.
2012-04-01
PPerfecting Scientists' Collaboration and Problem-Solving Skills in the Virtual Team Environment Numerous factors have contributed to the proliferation of conducting work in virtual teams at the domestic, national, and global levels: innovations in technology, critical developments in software, co-located research partners and diverse funding sources, dynamic economic and political environments, and a changing workforce. Today's scientists must be prepared to not only perform work in the virtual team environment, but to work effectively and efficiently despite physical and cultural barriers. Research supports that students who have been exposed to virtual team experiences are desirable in the professional and academic arenas. Research supports establishing and maintaining established protocols for communication behavior prior to task discussion provides for successful team outcomes. Research conducted on graduate and undergraduate virtual teams' behaviors led to the development of successful pedagogic practices and assessment strategies.
"Cyber" Reading in L2: Online Reading Strategies of Students in a Philippine Public High School
ERIC Educational Resources Information Center
De Leon, John Angelo Vinuya; Tarrayo, Veronico Nogales
2014-01-01
This paper seeks to identify the online reading strategies employed by students in a Philippine Public High School. In particular, the study attempts to answer the following questions: (1) What are the online reading strategies used by the respondents (i.e., global, problem-solving, and support)?; (2) What is the frequency of use of the online…
ERIC Educational Resources Information Center
Rahman, Azman Ab.; Sahrir, Muhammad Sabri; Zainuddin, Nurkhamimi; Khafidz, Hasanah Abd.
2018-01-01
Board games have become one of the useful tools in teaching and learning. Many instructors and educators have chosen to use board games to enhance the way of delivering course contents. A board game will help students understand the education concept quickly and get involved in experiential learning, where students can manage and solve problems in…
ERIC Educational Resources Information Center
Moser, Susanne
This learning module aims to engage students in problem solving, critical thinking, scientific inquiry, and cooperative learning. The module is appropriate for use in any introductory or intermediate undergraduate course that focuses on human-environment relationships. The module explains that land use/cover change has occurred at all times in all…
In a World of Exploding Possibilities in Distance Learning, Don't Forget about the Light Bulb
ERIC Educational Resources Information Center
Bosch, Andrea; Hartenberger Toby, Lisa; Alhamzy, Abdul Rahman
2015-01-01
This article looks closely at decisions about technology choices and suggests that many are driven by markets and perceptions, and not the problems that the technology might solve. The example of market manipulation related to the commercial light bulb in the early 20th century is used to demonstrate the powerful influence of global markets to…
ERIC Educational Resources Information Center
Sankar, Chetan S.; Raju, P. K.; Alur, Ramachandriah; Venkateswaran, Rajan; Elangovan, Rajasekar
2011-01-01
The architect for the Mauritius Auditorium project sat in his office at Larsen & Toubro's headquarters in Chennai, India, pondering the phone call he had just received from the vice president, Mr. K.P. Raghavan. The polyvalent hall of the conference center was about to be used to host its first rock concert in February 2005, but during a…
ERIC Educational Resources Information Center
Omolara, Daniel Iyabode
2013-01-01
Gender inequality has been a constant refrain among those that desire social justice. To this end, a global conference on gender equality was organized by the United Nations in 1995 in Beijing, China. However, a recent study questioned the effectiveness of the conference to solve this problem as it found that women themselves are giving hegemonic…
ERIC Educational Resources Information Center
Doss, Kristy Kowalske
2018-01-01
In order to compete globally in the 21st Century, students must have the skills to design their own projects and understand how to navigate the wealth of information available at their fingertips. One of the most important tools is to be able to investigate ideas and implement a plan of action in order to answer questions that have not been…
The Association between Motivation, Affect, and Self-regulated Learning When Solving Problems
Baars, Martine; Wijnia, Lisette; Paas, Fred
2017-01-01
Self-regulated learning (SRL) skills are essential for learning during school years, particularly in complex problem-solving domains, such as biology and math. Although a lot of studies have focused on the cognitive resources that are needed for learning to solve problems in a self-regulated way, affective and motivational resources have received much less research attention. The current study investigated the relation between affect (i.e., Positive Affect and Negative Affect Scale), motivation (i.e., autonomous and controlled motivation), mental effort, SRL skills, and problem-solving performance when learning to solve biology problems in a self-regulated online learning environment. In the learning phase, secondary education students studied video-modeling examples of how to solve hereditary problems, solved hereditary problems which they chose themselves from a set of problems with different complexity levels (i.e., five levels). In the posttest, students solved hereditary problems, self-assessed their performance, and chose a next problem from the set of problems but did not solve these problems. The results from this study showed that negative affect, inaccurate self-assessments during the posttest, and higher perceptions of mental effort during the posttest were negatively associated with problem-solving performance after learning in a self-regulated way. PMID:28848467
The factor of local cultural specificity and process of globalization.
Rudnev, Viacheslav
2012-12-01
Cultural polymorphism is a difficult phenomenon, which has multiform influence on the society's life. The active interest of society to local folk knowledge in life-support activities and Nature using is one of the distinctive marks of modern time. This interest has fallen on the period of active transformations of environment as a result of industrial society's pressing on Nature, and the generating of new approaches in the studying of Nature and human activity based on the "technologies" of wild life. The success of humankind in creating artificial surroundings has led to both great success in improving the quality of peoples' lives, and also to problems with renewable resources and human health and to changing for the worse ecology. In 1992 the Unites Nations Conference on Environment and Development (Rio de Janeiro, Brazil) set fixed standards defining global violations of the environment. The zAgenda 21', adopted at this Conference, focused on the necessity of new solutions for problems of the relationships between Nature and Society, mentioning interdisciplinary research as a positive way to search for solutions to new problems, and citing as a goal a zbalance of Nature, Society and Humans'. Pre-industrial society had a different experience in using Nature and solving problems of life-support activity under a regime of sparing nature. Experience has shown that Folk knowledge and Folk technology can, in a number of instances, actually assist in solving high level problems caused by human impact on the environment, e.g., farming methods, and, as a result offering possibilities for a more sound and at the same time effective basis for long-term sustainable production at the local level. The traditional cultures of Eurasia were engaged in agricultural pursuits and had acquired unique experiences in maintaining soil fertility and a technology which limited the impact they were having on the environment. The value of Folk heritage in exploiting the environment, especially farming traditions in small-scale, non-industrial societies, has been based not only on the technologies that are "friendly" to Nature, but also (and first of all) on the perception that soil (earth) is the source of all life. This sort of perception was particularly widespread among peoples of pre-industrial societies. The problem of searching for a way to increase long-term productivity in food grain production is complicated. This problem is of global importance for today and the future. The active interest of Modern society in the Folk experiences of using the Nature to achieve sustainable economies is yet to come, but we have much to learn from these small-scale non-industrial societies. Food production needs to be increased. At the same time, the fertility of the soil must be maintained. Achieving a balance between these two necessities is the problem. Changing the present modern human outlook from its egocentric position to one that understands and respects the natural environment, based on ideas of "ecological ethics", looks especially complex, and is directly connected with the problem of forming a new culture. Actually, the global ecological crisis and related ecological problems take priority and the transition to a new model of thinking promises to be accelerated. In this context, making use of Folk heritage, Folk knowledge and experience in observing Nature and using Nature to achieve harmonious interrelations in a "Nature - Society" system, and for the elaboration of a change of attitudes is quite important for modern society on a Global level to achieve ways of Sustainability. Lucius Seneca maintains that subjugation of a Nature is possible only if obeying to Nature. Modern epoch of Globalization in economy and Financial systems creating a potential of high risks for mankind on the Global level. Special attention to local factors (local experience in Nature using, local Folk experience in Life-support activity) in context of globalization problems is important today. Actually, Glocalization can assist in adaptation process of harmonizing local and global needs to a way of Sustainability. Glocalization puts globalization problems down to the human scale. The age of Globalization has made the problem of cultural dialog extra actual, otherwise the Mankind has no chance to survive. The Glocalization is the process of creation of a harmony in Nature, Society and Humans system in the context of Sustainability.
An experience sampling study of learning, affect, and the demands control support model.
Daniels, Kevin; Boocock, Grahame; Glover, Jane; Holland, Julie; Hartley, Ruth
2009-07-01
The demands control support model (R. A. Karasek & T. Theorell, 1990) indicates that job control and social support enable workers to engage in problem solving. In turn, problem solving is thought to influence learning and well-being (e.g., anxious affect, activated pleasant affect). Two samples (N = 78, N = 106) provided data up to 4 times per day for up to 5 working days. The extent to which job control was used for problem solving was assessed by measuring the extent to which participants changed aspects of their work activities to solve problems. The extent to which social support was used to solve problems was assessed by measuring the extent to which participants discussed problems to solve problems. Learning mediated the relationship between changing aspects of work activities to solve problems and activated pleasant affect. Learning also mediated the relationship between discussing problems to solve problems and activated pleasant affect. The findings indicated that how individuals use control and support to respond to problem-solving demands is associated with organizational and individual phenomena, such as learning and affective well-being.
What Does (and Doesn't) Make Analogical Problem Solving Easy? A Complexity-Theoretic Perspective
ERIC Educational Resources Information Center
Wareham, Todd; Evans, Patricia; van Rooij, Iris
2011-01-01
Solving new problems can be made easier if one can build on experiences with other problems one has already successfully solved. The ability to exploit earlier problem-solving experiences in solving new problems seems to require several cognitive sub-abilities. Minimally, one needs to be able to retrieve relevant knowledge of earlier solved…
ERIC Educational Resources Information Center
Kamis, Arnold; Khan, Beverly K.
2009-01-01
How do we model and improve technical problem solving, such as network subnetting? This paper reports an experimental study that tested several hypotheses derived from Kolb's experiential learning cycle and Huber's problem solving model. As subjects solved a network subnetting problem, they mapped their mental processes according to Huber's…
ERIC Educational Resources Information Center
Paraschiv, Irina; Olley, J. Gregory
This paper describes the "Problem Solving for Life" training program which trains adolescents and adults with mental retardation in skills for solving social problems. The program requires group participants to solve social problems by practicing two prerequisite skills (relaxation and positive self-statements) and four problem solving steps: (1)…
Young Children's Analogical Problem Solving: Gaining Insights from Video Displays
ERIC Educational Resources Information Center
Chen, Zhe; Siegler, Robert S.
2013-01-01
This study examined how toddlers gain insights from source video displays and use the insights to solve analogous problems. Two- to 2.5-year-olds viewed a source video illustrating a problem-solving strategy and then attempted to solve analogous problems. Older but not younger toddlers extracted the problem-solving strategy depicted in the video…
Investigating Problem-Solving Perseverance Using Lesson Study
ERIC Educational Resources Information Center
Bieda, Kristen N.; Huhn, Craig
2017-01-01
Problem solving has long been a focus of research and curriculum reform (Kilpatrick 1985; Lester 1994; NCTM 1989, 2000; CCSSI 2010). The importance of problem solving is not new, but the Common Core introduced the idea of making sense of problems and persevering in solving them (CCSSI 2010, p. 6) as an aspect of problem solving. Perseverance is…
Problem-solving deficits in Iranian people with borderline personality disorder.
Akbari Dehaghi, Ashraf; Kaviani, Hossein; Tamanaeefar, Shima
2014-01-01
Interventions for people suffering from borderline personality disorder (BPD), such as dialectical behavior therapy, often include a problem-solving component. However, there is an absence of published studies examining the problem-solving abilities of this client group in Iran. The study compared inpatients and outpatients with BPD and a control group on problem-solving capabilities in an Iranian sample. It was hypothesized that patients with BPD would have more deficiencies in this area. Fifteen patients with BPD were compared to 15 healthy participants. Means-ends problem-solving task (MEPS) was used to measure problem-solving skills in both groups. BPD group reported less effective strategies in solving problems as opposed to the healthy group. Compared to the control group, participants with BPD provided empirical support for the use of problem-solving interventions with people suffering from BPD. The findings supported the idea that a problem-solving intervention can be efficiently applied either as a stand-alone therapy or in conjunction with other available psychotherapies to treat people with BPD.
Impulsivity as a mediator in the relationship between problem solving and suicidal ideation.
Gonzalez, Vivian M; Neander, Lucía L
2018-03-15
This study examined whether three facets of impulsivity previously shown to be associated with suicidal ideation and attempts (negative urgency, lack of premeditation, and lack of perseverance) help to account for the established association between problem solving deficits and suicidal ideation. Emerging adult college student drinkers with a history of at least passive suicidal ideation (N = 387) completed measures of problem solving, impulsivity, and suicidal ideation. A path analysis was conducted to examine the mediating role of impulsivity variables in the association between problem solving (rational problem solving, positive and negative problem orientation, and avoidance style) and suicidal ideation. Direct and indirect associations through impulsivity, particularly negative urgency, were found between problem solving and severity of suicidal ideation. Interventions aimed at teaching problem solving skills, as well as self-efficacy and optimism for solving life problems, may help to reduce impulsivity and suicidal ideation. © 2018 Wiley Periodicals, Inc.
Nonlinearly Activated Neural Network for Solving Time-Varying Complex Sylvester Equation.
Li, Shuai; Li, Yangming
2013-10-28
The Sylvester equation is often encountered in mathematics and control theory. For the general time-invariant Sylvester equation problem, which is defined in the domain of complex numbers, the Bartels-Stewart algorithm and its extensions are effective and widely used with an O(n³) time complexity. When applied to solving the time-varying Sylvester equation, the computation burden increases intensively with the decrease of sampling period and cannot satisfy continuous realtime calculation requirements. For the special case of the general Sylvester equation problem defined in the domain of real numbers, gradient-based recurrent neural networks are able to solve the time-varying Sylvester equation in real time, but there always exists an estimation error while a recently proposed recurrent neural network by Zhang et al [this type of neural network is called Zhang neural network (ZNN)] converges to the solution ideally. The advancements in complex-valued neural networks cast light to extend the existing real-valued ZNN for solving the time-varying real-valued Sylvester equation to its counterpart in the domain of complex numbers. In this paper, a complex-valued ZNN for solving the complex-valued Sylvester equation problem is investigated and the global convergence of the neural network is proven with the proposed nonlinear complex-valued activation functions. Moreover, a special type of activation function with a core function, called sign-bi-power function, is proven to enable the ZNN to converge in finite time, which further enhances its advantage in online processing. In this case, the upper bound of the convergence time is also derived analytically. Simulations are performed to evaluate and compare the performance of the neural network with different parameters and activation functions. Both theoretical analysis and numerical simulations validate the effectiveness of the proposed method.
Solving the flexible job shop problem by hybrid metaheuristics-based multiagent model
NASA Astrophysics Data System (ADS)
Nouri, Houssem Eddine; Belkahla Driss, Olfa; Ghédira, Khaled
2018-03-01
The flexible job shop scheduling problem (FJSP) is a generalization of the classical job shop scheduling problem that allows to process operations on one machine out of a set of alternative machines. The FJSP is an NP-hard problem consisting of two sub-problems, which are the assignment and the scheduling problems. In this paper, we propose how to solve the FJSP by hybrid metaheuristics-based clustered holonic multiagent model. First, a neighborhood-based genetic algorithm (NGA) is applied by a scheduler agent for a global exploration of the search space. Second, a local search technique is used by a set of cluster agents to guide the research in promising regions of the search space and to improve the quality of the NGA final population. The efficiency of our approach is explained by the flexible selection of the promising parts of the search space by the clustering operator after the genetic algorithm process, and by applying the intensification technique of the tabu search allowing to restart the search from a set of elite solutions to attain new dominant scheduling solutions. Computational results are presented using four sets of well-known benchmark literature instances. New upper bounds are found, showing the effectiveness of the presented approach.
Improving mathematical problem solving skills through visual media
NASA Astrophysics Data System (ADS)
Widodo, S. A.; Darhim; Ikhwanudin, T.
2018-01-01
The purpose of this article was to find out the enhancement of students’ mathematical problem solving by using visual learning media. The ability to solve mathematical problems is the ability possessed by students to solve problems encountered, one of the problem-solving model of Polya. This preliminary study was not to make a model, but it only took a conceptual approach by comparing the various literature of problem-solving skills by linking visual learning media. The results of the study indicated that the use of learning media had not been appropriated so that the ability to solve mathematical problems was not optimal. The inappropriateness of media use was due to the instructional media that was not adapted to the characteristics of the learners. Suggestions that can be given is the need to develop visual media to increase the ability to solve problems.
ERIC Educational Resources Information Center
Limin, Chen; Van Dooren, Wim; Verschaffel, Lieven
2013-01-01
The goal of the present study is to investigate the relationship between pupils' problem posing and problem solving abilities, their beliefs about problem posing and problem solving, and their general mathematics abilities, in a Chinese context. Five instruments, i.e., a problem posing test, a problem solving test, a problem posing questionnaire,…
Egea, Jose A; Henriques, David; Cokelaer, Thomas; Villaverde, Alejandro F; MacNamara, Aidan; Danciu, Diana-Patricia; Banga, Julio R; Saez-Rodriguez, Julio
2014-05-10
Optimization is the key to solving many problems in computational biology. Global optimization methods, which provide a robust methodology, and metaheuristics in particular have proven to be the most efficient methods for many applications. Despite their utility, there is a limited availability of metaheuristic tools. We present MEIGO, an R and Matlab optimization toolbox (also available in Python via a wrapper of the R version), that implements metaheuristics capable of solving diverse problems arising in systems biology and bioinformatics. The toolbox includes the enhanced scatter search method (eSS) for continuous nonlinear programming (cNLP) and mixed-integer programming (MINLP) problems, and variable neighborhood search (VNS) for Integer Programming (IP) problems. Additionally, the R version includes BayesFit for parameter estimation by Bayesian inference. The eSS and VNS methods can be run on a single-thread or in parallel using a cooperative strategy. The code is supplied under GPLv3 and is available at http://www.iim.csic.es/~gingproc/meigo.html. Documentation and examples are included. The R package has been submitted to BioConductor. We evaluate MEIGO against optimization benchmarks, and illustrate its applicability to a series of case studies in bioinformatics and systems biology where it outperforms other state-of-the-art methods. MEIGO provides a free, open-source platform for optimization that can be applied to multiple domains of systems biology and bioinformatics. It includes efficient state of the art metaheuristics, and its open and modular structure allows the addition of further methods.
2014-01-01
Background Optimization is the key to solving many problems in computational biology. Global optimization methods, which provide a robust methodology, and metaheuristics in particular have proven to be the most efficient methods for many applications. Despite their utility, there is a limited availability of metaheuristic tools. Results We present MEIGO, an R and Matlab optimization toolbox (also available in Python via a wrapper of the R version), that implements metaheuristics capable of solving diverse problems arising in systems biology and bioinformatics. The toolbox includes the enhanced scatter search method (eSS) for continuous nonlinear programming (cNLP) and mixed-integer programming (MINLP) problems, and variable neighborhood search (VNS) for Integer Programming (IP) problems. Additionally, the R version includes BayesFit for parameter estimation by Bayesian inference. The eSS and VNS methods can be run on a single-thread or in parallel using a cooperative strategy. The code is supplied under GPLv3 and is available at http://www.iim.csic.es/~gingproc/meigo.html. Documentation and examples are included. The R package has been submitted to BioConductor. We evaluate MEIGO against optimization benchmarks, and illustrate its applicability to a series of case studies in bioinformatics and systems biology where it outperforms other state-of-the-art methods. Conclusions MEIGO provides a free, open-source platform for optimization that can be applied to multiple domains of systems biology and bioinformatics. It includes efficient state of the art metaheuristics, and its open and modular structure allows the addition of further methods. PMID:24885957
ERIC Educational Resources Information Center
Higgins, Karen M.
This study investigated the effects of Oregon's Lane County "Problem Solving in Mathematics" (PSM) materials on middle-school students' attitudes, beliefs, and abilities in problem solving and mathematics. The instructional approach advocated in PSM includes: the direct teaching of five problem-solving skills, weekly challenge problems,…
NASA Astrophysics Data System (ADS)
Jin, Shan
This dissertation concerns power system expansion planning under different market mechanisms. The thesis follows a three paper format, in which each paper emphasizes a different perspective. The first paper investigates the impact of market uncertainties on a long term centralized generation expansion planning problem. The problem is modeled as a two-stage stochastic program with uncertain fuel prices and demands, which are represented as probabilistic scenario paths in a multi-period tree. Two measurements, expected cost (EC) and Conditional Value-at-Risk (CVaR), are used to minimize, respectively, the total expected cost among scenarios and the risk of incurring high costs in unfavorable scenarios. We sample paths from the scenario tree to reduce the problem scale and determine the sufficient number of scenarios by computing confidence intervals on the objective values. The second paper studies an integrated electricity supply system including generation, transmission and fuel transportation with a restructured wholesale electricity market. This integrated system expansion problem is modeled as a bi-level program in which a centralized system expansion decision is made in the upper level and the operational decisions of multiple market participants are made in the lower level. The difficulty of solving a bi-level programming problem to global optimality is discussed and three problem relaxations obtained by reformulation are explored. The third paper solves a more realistic market-based generation and transmission expansion problem. It focuses on interactions among a centralized transmission expansion decision and decentralized generation expansion decisions. It allows each generator to make its own strategic investment and operational decisions both in response to a transmission expansion decision and in anticipation of a market price settled by an Independent System Operator (ISO) market clearing problem. The model poses a complicated tri-level structure including an equilibrium problem with equilibrium constraints (EPEC) sub-problem. A hybrid iterative algorithm is proposed to solve the problem efficiently and reliably.
Hirose, Katsuhiko
2010-07-28
In the past, material innovation has changed society through new material-induced technologies, adding a new value to society. In the present world, engineers and scientists are expected to invent new materials to solve the global problem of climate change. For the transport sector, the challenge for material engineers is to change the oil-based world into a sustainable world. After witnessing the recent high oil price and its adverse impact on the global economy, it is time to accelerate our efforts towards this change. Industries are tackling global energy issues such as oil and CO2, as well as local environmental problems, such as NO(x) and particulate matter. Hydrogen is the most promising candidate to provide carbon-free, emission-free and oil-free mobility. As such, engineers are working very hard to bring this technology into the real society. This paper describes recent progress of vehicle technologies, as well as hydrogen-storage technologies to extend the cruise range and ensure the easiness of refuelling and requesting material scientists to collaborate with industry to fight against global warming.
Aquaculture and food crisis: opportunities and constraints.
Liao, I Chiu; Chao, Nai-Hsien
2009-01-01
Fish farming, now well known as aquaculture, has been well recognized since the ancient era. The first written document on fish culture was published in China in 475 BC, and the first koi pond was constructed at the Japanese Imperial Palace grounds during 71-130 AD. In recent years, aquaculture has progressively played an important role in the provision of: animal protein and gourmet cuisines, job opportunities, and foreign currency for developing countries. Asian countries produce around 91 percent of the world's total aquaculture production. Among the top ten aquaculture-producing countries, nine are from Asia. The current global population consist of more than 6.5 billion individuals; over one billion of which face hunger problem. In the highly populated Asia-Pacific region with moderately high-productivity, 642 million people are still facing hunger. Being a proficient and potential source of animal protein, aquaculture will play an increasing and important role in solving the world food problem in the future. This paper discusses both the opportunities and constraints in the aquaculture industry, specifically in the Asia-Pacific region, and its possible role in solving the current global food crisis. Strategies including promotion and adoption of traceability and HACCP systems for food safety, and marketing management for aquaculture products are also suggested. It is hoped that traditional administration of aquaculture management for survival, profit, as well as food safety will successfully match sustainability management to meet the urgent global need for food.
A global optimization algorithm inspired in the behavior of selfish herds.
Fausto, Fernando; Cuevas, Erik; Valdivia, Arturo; González, Adrián
2017-10-01
In this paper, a novel swarm optimization algorithm called the Selfish Herd Optimizer (SHO) is proposed for solving global optimization problems. SHO is based on the simulation of the widely observed selfish herd behavior manifested by individuals within a herd of animals subjected to some form of predation risk. In SHO, individuals emulate the predatory interactions between groups of prey and predators by two types of search agents: the members of a selfish herd (the prey) and a pack of hungry predators. Depending on their classification as either a prey or a predator, each individual is conducted by a set of unique evolutionary operators inspired by such prey-predator relationship. These unique traits allow SHO to improve the balance between exploration and exploitation without altering the population size. To illustrate the proficiency and robustness of the proposed method, it is compared to other well-known evolutionary optimization approaches such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Firefly Algorithm (FA), Differential Evolution (DE), Genetic Algorithms (GA), Crow Search Algorithm (CSA), Dragonfly Algorithm (DA), Moth-flame Optimization Algorithm (MOA) and Sine Cosine Algorithm (SCA). The comparison examines several standard benchmark functions, commonly considered within the literature of evolutionary algorithms. The experimental results show the remarkable performance of our proposed approach against those of the other compared methods, and as such SHO is proven to be an excellent alternative to solve global optimization problems. Copyright © 2017 Elsevier B.V. All rights reserved.
Student’s scheme in solving mathematics problems
NASA Astrophysics Data System (ADS)
Setyaningsih, Nining; Juniati, Dwi; Suwarsono
2018-03-01
The purpose of this study was to investigate students’ scheme in solving mathematics problems. Scheme are data structures for representing the concepts stored in memory. In this study, we used it in solving mathematics problems, especially ratio and proportion topics. Scheme is related to problem solving that assumes that a system is developed in the human mind by acquiring a structure in which problem solving procedures are integrated with some concepts. The data were collected by interview and students’ written works. The results of this study revealed are students’ scheme in solving the problem of ratio and proportion as follows: (1) the content scheme, where students can describe the selected components of the problem according to their prior knowledge, (2) the formal scheme, where students can explain in construct a mental model based on components that have been selected from the problem and can use existing schemes to build planning steps, create something that will be used to solve problems and (3) the language scheme, where students can identify terms, or symbols of the components of the problem.Therefore, by using the different strategies to solve the problems, the students’ scheme in solving the ratio and proportion problems will also differ.
ERIC Educational Resources Information Center
Scherer, Ronny; Tiemann, Rudiger
2012-01-01
The ability to solve complex scientific problems is regarded as one of the key competencies in science education. Until now, research on problem solving focused on the relationship between analytical and complex problem solving, but rarely took into account the structure of problem-solving processes and metacognitive aspects. This paper,…
Same Old Problem, New Name? Alerting Students to the Nature of the Problem-Solving Process
ERIC Educational Resources Information Center
Yerushalmi, Edit; Magen, Esther
2006-01-01
Students frequently misconceive the process of problem-solving, expecting the linear process required for solving an exercise, rather than the convoluted search process required to solve a genuine problem. In this paper we present an activity designed to foster in students realization and appreciation of the nature of the problem-solving process,…
ERIC Educational Resources Information Center
Gustafsson, Peter; Jonsson, Gunnar; Enghag, Margareta
2015-01-01
The problem-solving process is investigated for five groups of students when solving context-rich problems in an introductory physics course included in an engineering programme. Through transcripts of their conversation, the paths in the problem-solving process have been traced and related to a general problem-solving model. All groups exhibit…
Klein, Daniel N.; Leon, Andrew C.; Li, Chunshan; D’Zurilla, Thomas J.; Black, Sarah R.; Vivian, Dina; Dowling, Frank; Arnow, Bruce A.; Manber, Rachel; Markowitz, John C.; Kocsis, James H.
2011-01-01
Objective Depression is associated with poor social problem-solving, and psychotherapies that focus on problem-solving skills are efficacious in treating depression. We examined the associations between treatment, social problem solving, and depression in a randomized clinical trial testing the efficacy of psychotherapy augmentation for chronically depressed patients who failed to fully respond to an initial trial of pharmacotherapy (Kocsis et al., 2009). Method Participants with chronic depression (n = 491) received Cognitive Behavioral Analysis System of Psychotherapy (CBASP), which emphasizes interpersonal problem-solving, plus medication; Brief Supportive Psychotherapy (BSP) plus medication; or medication alone for 12 weeks. Results CBASP plus pharmacotherapy was associated with significantly greater improvement in social problem solving than BSP plus pharmacotherapy, and a trend for greater improvement in problem solving than pharmacotherapy alone. In addition, change in social problem solving predicted subsequent change in depressive symptoms over time. However, the magnitude of the associations between changes in social problem solving and subsequent depressive symptoms did not differ across treatment conditions. Conclusions It does not appear that improved social problem solving is a mechanism that uniquely distinguishes CBASP from other treatment approaches. PMID:21500885
Implementing thinking aloud pair and Pólya problem solving strategies in fractions
NASA Astrophysics Data System (ADS)
Simpol, N. S. H.; Shahrill, M.; Li, H.-C.; Prahmana, R. C. I.
2017-12-01
This study implemented two pedagogical strategies, the Thinking Aloud Pair Problem Solving and Pólya’s Problem Solving, to support students’ learning of fractions. The participants were 51 students (ages 11-13) from two Year 7 classes in a government secondary school in Brunei Darussalam. A mixed method design was employed in the present study, with data collected from the pre- and post-tests, problem solving behaviour questionnaire and interviews. The study aimed to explore if there were differences in the students’ problem solving behaviour before and after the implementation of the problem solving strategies. Results from the Wilcoxon Signed Rank Test revealed a significant difference in the test results regarding student problem solving behaviour, z = -3.68, p = .000, with a higher mean score for the post-test (M = 95.5, SD = 13.8) than for the pre-test (M = 88.9, SD = 15.2). This implied that there was improvement in the students’ problem solving performance from the pre-test to the post-test. Results from the questionnaire showed that more than half of the students increased scores in all four stages of the Pólya’s problem solving strategy, which provided further evidence of the students’ improvement in problem solving.
Jiang, Weili; Shang, Siyuan; Su, Yanjie
2015-01-01
People may experience an “aha” moment, when suddenly realizing a solution of a puzzling problem. This experience is called insight problem solving. Several findings suggest that catecholamine-related genes may contribute to insight problem solving, among which the catechol-O-methyltransferase (COMT) gene is the most promising candidate. The current study examined 753 healthy individuals to determine the associations between 7 candidate single nucleotide polymorphisms on the COMT gene and insight problem-solving performance, while considering gender differences. The results showed that individuals carrying A allele of rs4680 or T allele of rs4633 scored significantly higher on insight problem-solving tasks, and the COMT gene rs5993883 combined with gender interacted with correct solutions of insight problems, specifically showing that this gene only influenced insight problem-solving performance in males. This study presents the first investigation of the genetic impact on insight problem solving and provides evidence that highlights the role that the COMT gene plays in insight problem solving. PMID:26528222
Jiang, Weili; Shang, Siyuan; Su, Yanjie
2015-01-01
People may experience an "aha" moment, when suddenly realizing a solution of a puzzling problem. This experience is called insight problem solving. Several findings suggest that catecholamine-related genes may contribute to insight problem solving, among which the catechol-O-methyltransferase (COMT) gene is the most promising candidate. The current study examined 753 healthy individuals to determine the associations between 7 candidate single nucleotide polymorphisms on the COMT gene and insight problem-solving performance, while considering gender differences. The results showed that individuals carrying A allele of rs4680 or T allele of rs4633 scored significantly higher on insight problem-solving tasks, and the COMT gene rs5993883 combined with gender interacted with correct solutions of insight problems, specifically showing that this gene only influenced insight problem-solving performance in males. This study presents the first investigation of the genetic impact on insight problem solving and provides evidence that highlights the role that the COMT gene plays in insight problem solving.
Understanding Undergraduates’ Problem-Solving Processes †
Nehm, Ross H.
2010-01-01
Fostering effective problem-solving skills is one of the most longstanding and widely agreed upon goals of biology education. Nevertheless, undergraduate biology educators have yet to leverage many major findings about problem-solving processes from the educational and cognitive science research literatures. This article highlights key facets of problem-solving processes and introduces methodologies that may be used to reveal how undergraduate students perceive and represent biological problems. Overall, successful problem-solving entails a keen sensitivity to problem contexts, disciplined internal representation or modeling of the problem, and the principled management and deployment of cognitive resources. Context recognition tasks, problem representation practice, and cognitive resource management receive remarkably little emphasis in the biology curriculum, despite their central roles in problem-solving success. PMID:23653710
Thinking Process of Naive Problem Solvers to Solve Mathematical Problems
ERIC Educational Resources Information Center
Mairing, Jackson Pasini
2017-01-01
Solving problems is not only a goal of mathematical learning. Students acquire ways of thinking, habits of persistence and curiosity, and confidence in unfamiliar situations by learning to solve problems. In fact, there were students who had difficulty in solving problems. The students were naive problem solvers. This research aimed to describe…
Teaching Problem Solving without Modeling through "Thinking Aloud Pair Problem Solving."
ERIC Educational Resources Information Center
Pestel, Beverly C.
1993-01-01
Reviews research relevant to the problem of unsatisfactory student problem-solving abilities and suggests a teaching strategy that addresses the issue. Author explains how she uses teaching aloud problem solving (TAPS) in college chemistry and presents evaluation data. Among the findings are that the TAPS class got fewer problems completely right,…
Social Problem Solving, Conduct Problems, and Callous-Unemotional Traits in Children
ERIC Educational Resources Information Center
Waschbusch, Daniel A.; Walsh, Trudi M.; Andrade, Brendan F.; King, Sara; Carrey, Normand J.
2007-01-01
This study examined the association between social problem solving, conduct problems (CP), and callous-unemotional (CU) traits in elementary age children. Participants were 53 children (40 boys and 13 girls) aged 7-12 years. Social problem solving was evaluated using the Social Problem Solving Test-Revised, which requires children to produce…
Artifact-Based Transformation of IBM Global Financing
NASA Astrophysics Data System (ADS)
Chao, Tian; Cohn, David; Flatgard, Adrian; Hahn, Sandy; Linehan, Mark; Nandi, Prabir; Nigam, Anil; Pinel, Florian; Vergo, John; Wu, Frederick Y.
IBM Global Financing (IGF) is transforming its business using the Business Artifact Method, an innovative business process modeling technique that identifies key business artifacts and traces their life cycles as they are processed by the business. IGF is a complex, global business operation with many business design challenges. The Business Artifact Method is a fundamental shift in how to conceptualize, design and implement business operations. The Business Artifact Method was extended to solve the problem of designing a global standard for a complex, end-to-end process while supporting local geographic variations. Prior to employing the Business Artifact method, process decomposition, Lean and Six Sigma methods were each employed on different parts of the financing operation. Although they provided critical input to the final operational model, they proved insufficient for designing a complete, integrated, standard operation. The artifact method resulted in a business operations model that was at the right level of granularity for the problem at hand. A fully functional rapid prototype was created early in the engagement, which facilitated an improved understanding of the redesigned operations model. The resulting business operations model is being used as the basis for all aspects of business transformation in IBM Global Financing.
Multilevel algorithms for nonlinear optimization
NASA Technical Reports Server (NTRS)
Alexandrov, Natalia; Dennis, J. E., Jr.
1994-01-01
Multidisciplinary design optimization (MDO) gives rise to nonlinear optimization problems characterized by a large number of constraints that naturally occur in blocks. We propose a class of multilevel optimization methods motivated by the structure and number of constraints and by the expense of the derivative computations for MDO. The algorithms are an extension to the nonlinear programming problem of the successful class of local Brown-Brent algorithms for nonlinear equations. Our extensions allow the user to partition constraints into arbitrary blocks to fit the application, and they separately process each block and the objective function, restricted to certain subspaces. The methods use trust regions as a globalization strategy, and they have been shown to be globally convergent under reasonable assumptions. The multilevel algorithms can be applied to all classes of MDO formulations. Multilevel algorithms for solving nonlinear systems of equations are a special case of the multilevel optimization methods. In this case, they can be viewed as a trust-region globalization of the Brown-Brent class.
Hue-preserving and saturation-improved color histogram equalization algorithm.
Song, Ki Sun; Kang, Hee; Kang, Moon Gi
2016-06-01
In this paper, an algorithm is proposed to improve contrast and saturation without color degradation. The local histogram equalization (HE) method offers better performance than the global HE method, whereas the local HE method sometimes produces undesirable results due to the block-based processing. The proposed contrast-enhancement (CE) algorithm reflects the characteristics of the global HE method in the local HE method to avoid the artifacts, while global and local contrasts are enhanced. There are two ways to apply the proposed CE algorithm to color images. One is luminance processing methods, and the other one is each channel processing methods. However, these ways incur excessive or reduced saturation and color degradation problems. The proposed algorithm solves these problems by using channel adaptive equalization and similarity of ratios between the channels. Experimental results show that the proposed algorithm enhances contrast and saturation while preserving the hue and producing better performance than existing methods in terms of objective evaluation metrics.
Multidisciplinary optimization of controlled space structures with global sensitivity equations
NASA Technical Reports Server (NTRS)
Padula, Sharon L.; James, Benjamin B.; Graves, Philip C.; Woodard, Stanley E.
1991-01-01
A new method for the preliminary design of controlled space structures is presented. The method coordinates standard finite element structural analysis, multivariable controls, and nonlinear programming codes and allows simultaneous optimization of the structures and control systems of a spacecraft. Global sensitivity equations are a key feature of this method. The preliminary design of a generic geostationary platform is used to demonstrate the multidisciplinary optimization method. Fifteen design variables are used to optimize truss member sizes and feedback gain values. The goal is to reduce the total mass of the structure and the vibration control system while satisfying constraints on vibration decay rate. Incorporating the nonnegligible mass of actuators causes an essential coupling between structural design variables and control design variables. The solution of the demonstration problem is an important step toward a comprehensive preliminary design capability for structures and control systems. Use of global sensitivity equations helps solve optimization problems that have a large number of design variables and a high degree of coupling between disciplines.
Personality, problem solving, and adolescent substance use.
Jaffee, William B; D'Zurilla, Thomas J
2009-03-01
The major aim of this study was to examine the role of social problem solving in the relationship between personality and substance use in adolescents. Although a number of studies have identified a relationship between personality and substance use, the precise mechanism by which this occurs is not clear. We hypothesized that problem-solving skills could be one such mechanism. More specifically, we sought to determine whether problem solving mediates, moderates, or both mediates and moderates the relationship between different personality traits and substance use. Three hundred and seven adolescents were administered the Substance Use Profile Scale, the Social Problem-Solving Inventory-Revised, and the Personality Experiences Inventory to assess personality, social problem-solving ability, and substance use, respectively. Results showed that the dimension of rational problem solving (i.e., effective problem-solving skills) significantly mediated the relationship between hopelessness and lifetime alcohol and marijuana use. The theoretical and clinical implications of these results were discussed.
Hybrid Genetic Algorithm - Local Search Method for Ground-Water Management
NASA Astrophysics Data System (ADS)
Chiu, Y.; Nishikawa, T.; Martin, P.
2008-12-01
Ground-water management problems commonly are formulated as a mixed-integer, non-linear programming problem (MINLP). Relying only on conventional gradient-search methods to solve the management problem is computationally fast; however, the methods may become trapped in a local optimum. Global-optimization schemes can identify the global optimum, but the convergence is very slow when the optimal solution approaches the global optimum. In this study, we developed a hybrid optimization scheme, which includes a genetic algorithm and a gradient-search method, to solve the MINLP. The genetic algorithm identifies a near- optimal solution, and the gradient search uses the near optimum to identify the global optimum. Our methodology is applied to a conjunctive-use project in the Warren ground-water basin, California. Hi- Desert Water District (HDWD), the primary water-manager in the basin, plans to construct a wastewater treatment plant to reduce future septic-tank effluent from reaching the ground-water system. The treated wastewater instead will recharge the ground-water basin via percolation ponds as part of a larger conjunctive-use strategy, subject to State regulations (e.g. minimum distances and travel times). HDWD wishes to identify the least-cost conjunctive-use strategies that control ground-water levels, meet regulations, and identify new production-well locations. As formulated, the MINLP objective is to minimize water-delivery costs subject to constraints including pump capacities, available recharge water, water-supply demand, water-level constraints, and potential new-well locations. The methodology was demonstrated by an enumerative search of the entire feasible solution and comparing the optimum solution with results from the branch-and-bound algorithm. The results also indicate that the hybrid method identifies the global optimum within an affordable computation time. Sensitivity analyses, which include testing different recharge-rate scenarios, pond layouts, and water-supply constraints, indicate that the number of new wells is insensitive to water-supply constraints; however, pumping rates and patterns of the existing wells are sensitive. The locations of new wells are mildly sensitive to the pond layout.
Enhancing chemistry problem-solving achievement using problem categorization
NASA Astrophysics Data System (ADS)
Bunce, Diane M.; Gabel, Dorothy L.; Samuel, John V.
The enhancement of chemistry students' skill in problem solving through problem categorization is the focus of this study. Twenty-four students in a freshman chemistry course for health professionals are taught how to solve problems using the explicit method of problem solving (EMPS) (Bunce & Heikkinen, 1986). The EMPS is an organized approach to problem analysis which includes encoding the information given in a problem (Given, Asked For), relating this to what is already in long-term memory (Recall), and planning a solution (Overall Plan) before a mathematical solution is attempted. In addition to the EMPS training, treatment students receive three 40-minute sessions following achievement tests in which they are taught how to categorize problems. Control students use this time to review the EMPS solutions of test questions. Although problem categorization is involved in one section of the EMPS (Recall), treatment students who received specific training in problem categorization demonstrate significantly higher achievement on combination problems (those problems requiring the use of more than one chemical topic for their solution) at (p = 0.01) than their counterparts. Significantly higher achievement for treatment students is also measured on an unannounced test (p = 0.02). Analysis of interview transcripts of both treatment and control students illustrates a Rolodex approach to problem solving employed by all students in this study. The Rolodex approach involves organizing equations used to solve problems on mental index cards and flipping through them, matching units given when a new problem is to be solved. A second phenomenon observed during student interviews is the absence of a link in the conceptual understanding of the chemical concepts involved in a problem and the problem-solving skills employed to correctly solve problems. This study shows that explicit training in categorization skills and the EMPS can lead to higher achievement in complex problem-solving situations (combination problems and unannounced test). However, such achievement may be limited by the lack of linkages between students' conceptual understanding and improved problem-solving skill.
Decision-Making and Problem-Solving Approaches in Pharmacy Education
Martin, Lindsay C.; Holdford, David A.
2016-01-01
Domain 3 of the Center for the Advancement of Pharmacy Education (CAPE) 2013 Educational Outcomes recommends that pharmacy school curricula prepare students to be better problem solvers, but are silent on the type of problems they should be prepared to solve. We identified five basic approaches to problem solving in the curriculum at a pharmacy school: clinical, ethical, managerial, economic, and legal. These approaches were compared to determine a generic process that could be applied to all pharmacy decisions. Although there were similarities in the approaches, generic problem solving processes may not work for all problems. Successful problem solving requires identification of the problems faced and application of the right approach to the situation. We also advocate that the CAPE Outcomes make explicit the importance of different approaches to problem solving. Future pharmacists will need multiple approaches to problem solving to adapt to the complexity of health care. PMID:27170823
Decision-Making and Problem-Solving Approaches in Pharmacy Education.
Martin, Lindsay C; Donohoe, Krista L; Holdford, David A
2016-04-25
Domain 3 of the Center for the Advancement of Pharmacy Education (CAPE) 2013 Educational Outcomes recommends that pharmacy school curricula prepare students to be better problem solvers, but are silent on the type of problems they should be prepared to solve. We identified five basic approaches to problem solving in the curriculum at a pharmacy school: clinical, ethical, managerial, economic, and legal. These approaches were compared to determine a generic process that could be applied to all pharmacy decisions. Although there were similarities in the approaches, generic problem solving processes may not work for all problems. Successful problem solving requires identification of the problems faced and application of the right approach to the situation. We also advocate that the CAPE Outcomes make explicit the importance of different approaches to problem solving. Future pharmacists will need multiple approaches to problem solving to adapt to the complexity of health care.
Social problem-solving in Chinese baccalaureate nursing students.
Fang, Jinbo; Luo, Ying; Li, Yanhua; Huang, Wenxia
2016-11-01
To describe social problem solving in Chinese baccalaureate nursing students. A descriptive cross-sectional study was conducted with a cluster sample of 681 Chinese baccalaureate nursing students. The Chinese version of the Social Problem-Solving scale was used. Descriptive analyses, independent t-test and one-way analysis of variance were applied to analyze the data. The final year nursing students presented the highest scores of positive social problem-solving skills. Students with experiences of self-directed and problem-based learning presented significantly higher scores in Positive Problem Orientation subscale. The group with Critical thinking training experience, however, displayed higher negative problem solving scores compared with nonexperience group. Social problem solving abilities varied based upon teaching-learning strategies. Self-directed and problem-based learning may be recommended as effective way to improve social problem-solving ability. © 2016 Chinese Cochrane Center, West China Hospital of Sichuan University and John Wiley & Sons Australia, Ltd.
Problem Solving and Chemical Equilibrium: Successful versus Unsuccessful Performance.
ERIC Educational Resources Information Center
Camacho, Moises; Good, Ron
1989-01-01
Describes the problem-solving behaviors of experts and novices engaged in solving seven chemical equilibrium problems. Lists 27 behavioral tendencies of successful and unsuccessful problem solvers. Discusses several implications for a problem solving theory, think-aloud techniques, adequacy of the chemistry domain, and chemistry instruction.…
Worry and problem-solving skills and beliefs in primary school children.
Parkinson, Monika; Creswell, Cathy
2011-03-01
To examine the association between worry and problem-solving skills and beliefs (confidence and perceived control) in primary school children. Children (8-11 years) were screened using the Penn State Worry Questionnaire for Children. High (N= 27) and low (N= 30) scorers completed measures of anxiety, problem-solving skills (generating alternative solutions to problems, planfulness, and effectiveness of solutions) and problem-solving beliefs (confidence and perceived control). High and low worry groups differed significantly on measures of anxiety and problem-solving beliefs (confidence and control) but not on problem-solving skills. Consistent with findings with adults, worry in children was associated with cognitive distortions, not skills deficits. Interventions for worried children may benefit from a focus on increasing positive problem-solving beliefs. ©2010 The British Psychological Society.
Integer-ambiguity resolution in astronomy and geodesy
NASA Astrophysics Data System (ADS)
Lannes, A.; Prieur, J.-L.
2014-02-01
Recent theoretical developments in astronomical aperture synthesis have revealed the existence of integer-ambiguity problems. Those problems, which appear in the self-calibration procedures of radio imaging, have been shown to be similar to the nearest-lattice point (NLP) problems encountered in high-precision geodetic positioning and in global navigation satellite systems. In this paper we analyse the theoretical aspects of the matter and propose new methods for solving those NLP~problems. The related optimization aspects concern both the preconditioning stage, and the discrete-search stage in which the integer ambiguities are finally fixed. Our algorithms, which are described in an explicit manner, can easily be implemented. They lead to substantial gains in the processing time of both stages. Their efficiency was shown via intensive numerical tests.
NASA Astrophysics Data System (ADS)
Mushlihuddin, R.; Nurafifah; Irvan
2018-01-01
The student’s low ability in mathematics problem solving proved to the less effective of a learning process in the classroom. Effective learning was a learning that affects student’s math skills, one of which is problem-solving abilities. Problem-solving capability consisted of several stages: understanding the problem, planning the settlement, solving the problem as planned, re-examining the procedure and the outcome. The purpose of this research was to know: (1) was there any influence of PBL model in improving ability Problem solving of student math in a subject of vector analysis?; (2) was the PBL model effective in improving students’ mathematical problem-solving skills in vector analysis courses? This research was a quasi-experiment research. The data analysis techniques performed from the test stages of data description, a prerequisite test is the normality test, and hypothesis test using the ANCOVA test and Gain test. The results showed that: (1) there was an influence of PBL model in improving students’ math problem-solving abilities in vector analysis courses; (2) the PBL model was effective in improving students’ problem-solving skills in vector analysis courses with a medium category.
ERIC Educational Resources Information Center
Dufner, Hillrey A.; Alexander, Patricia A.
The differential effects of two different types of problem-solving training on the problem-solving abilities of gifted fourth graders were studied. Two successive classes of gifted fourth graders from Weslaco Independent School District (Texas) were pretested with the Coloured Progressive Matrices (CPM) and Thinking Creatively With Pictures…
Social problem-solving among adolescents treated for depression.
Becker-Weidman, Emily G; Jacobs, Rachel H; Reinecke, Mark A; Silva, Susan G; March, John S
2010-01-01
Studies suggest that deficits in social problem-solving may be associated with increased risk of depression and suicidality in children and adolescents. It is unclear, however, which specific dimensions of social problem-solving are related to depression and suicidality among youth. Moreover, rational problem-solving strategies and problem-solving motivation may moderate or predict change in depression and suicidality among children and adolescents receiving treatment. The effect of social problem-solving on acute treatment outcomes were explored in a randomized controlled trial of 439 clinically depressed adolescents enrolled in the Treatment for Adolescents with Depression Study (TADS). Measures included the Children's Depression Rating Scale-Revised (CDRS-R), the Suicidal Ideation Questionnaire--Grades 7-9 (SIQ-Jr), and the Social Problem-Solving Inventory-Revised (SPSI-R). A random coefficients regression model was conducted to examine main and interaction effects of treatment and SPSI-R subscale scores on outcomes during the 12-week acute treatment stage. Negative problem orientation, positive problem orientation, and avoidant problem-solving style were non-specific predictors of depression severity. In terms of suicidality, avoidant problem-solving style and impulsiveness/carelessness style were predictors, whereas negative problem orientation and positive problem orientation were moderators of treatment outcome. Implications of these findings, limitations, and directions for future research are discussed. Copyright 2009 Elsevier Ltd. All rights reserved.
Step by Step: Biology Undergraduates’ Problem-Solving Procedures during Multiple-Choice Assessment
Prevost, Luanna B.; Lemons, Paula P.
2016-01-01
This study uses the theoretical framework of domain-specific problem solving to explore the procedures students use to solve multiple-choice problems about biology concepts. We designed several multiple-choice problems and administered them on four exams. We trained students to produce written descriptions of how they solved the problem, and this allowed us to systematically investigate their problem-solving procedures. We identified a range of procedures and organized them as domain general, domain specific, or hybrid. We also identified domain-general and domain-specific errors made by students during problem solving. We found that students use domain-general and hybrid procedures more frequently when solving lower-order problems than higher-order problems, while they use domain-specific procedures more frequently when solving higher-order problems. Additionally, the more domain-specific procedures students used, the higher the likelihood that they would answer the problem correctly, up to five procedures. However, if students used just one domain-general procedure, they were as likely to answer the problem correctly as if they had used two to five domain-general procedures. Our findings provide a categorization scheme and framework for additional research on biology problem solving and suggest several important implications for researchers and instructors. PMID:27909021
Du, Shouqiang; Chen, Miao
2018-01-01
We consider a kind of nonsmooth optimization problems with [Formula: see text]-norm minimization, which has many applications in compressed sensing, signal reconstruction, and the related engineering problems. Using smoothing approximate techniques, this kind of nonsmooth optimization problem can be transformed into a general unconstrained optimization problem, which can be solved by the proposed smoothing modified three-term conjugate gradient method. The smoothing modified three-term conjugate gradient method is based on Polak-Ribière-Polyak conjugate gradient method. For the Polak-Ribière-Polyak conjugate gradient method has good numerical properties, the proposed method possesses the sufficient descent property without any line searches, and it is also proved to be globally convergent. Finally, the numerical experiments show the efficiency of the proposed method.
A new modified conjugate gradient coefficient for solving system of linear equations
NASA Astrophysics Data System (ADS)
Hajar, N.; ‘Aini, N.; Shapiee, N.; Abidin, Z. Z.; Khadijah, W.; Rivaie, M.; Mamat, M.
2017-09-01
Conjugate gradient (CG) method is an evolution of computational method in solving unconstrained optimization problems. This approach is easy to implement due to its simplicity and has been proven to be effective in solving real-life application. Although this field has received copious amount of attentions in recent years, some of the new approaches of CG algorithm cannot surpass the efficiency of the previous versions. Therefore, in this paper, a new CG coefficient which retains the sufficient descent and global convergence properties of the original CG methods is proposed. This new CG is tested on a set of test functions under exact line search. Its performance is then compared to that of some of the well-known previous CG methods based on number of iterations and CPU time. The results show that the new CG algorithm has the best efficiency amongst all the methods tested. This paper also includes an application of the new CG algorithm for solving large system of linear equations
NASA Astrophysics Data System (ADS)
Ushijima, Timothy T.; Yeh, William W.-G.
2013-10-01
An optimal experimental design algorithm is developed to select locations for a network of observation wells that provide maximum information about unknown groundwater pumping in a confined, anisotropic aquifer. The design uses a maximal information criterion that chooses, among competing designs, the design that maximizes the sum of squared sensitivities while conforming to specified design constraints. The formulated optimization problem is non-convex and contains integer variables necessitating a combinatorial search. Given a realistic large-scale model, the size of the combinatorial search required can make the problem difficult, if not impossible, to solve using traditional mathematical programming techniques. Genetic algorithms (GAs) can be used to perform the global search; however, because a GA requires a large number of calls to a groundwater model, the formulated optimization problem still may be infeasible to solve. As a result, proper orthogonal decomposition (POD) is applied to the groundwater model to reduce its dimensionality. Then, the information matrix in the full model space can be searched without solving the full model. Results from a small-scale test case show identical optimal solutions among the GA, integer programming, and exhaustive search methods. This demonstrates the GA's ability to determine the optimal solution. In addition, the results show that a GA with POD model reduction is several orders of magnitude faster in finding the optimal solution than a GA using the full model. The proposed experimental design algorithm is applied to a realistic, two-dimensional, large-scale groundwater problem. The GA converged to a solution for this large-scale problem.
Disciplinary Foundations for Solving Interdisciplinary Scientific Problems
ERIC Educational Resources Information Center
Zhang, Dongmei; Shen, Ji
2015-01-01
Problem-solving has been one of the major strands in science education research. But much of the problem-solving research has been conducted on discipline-based contexts; little research has been done on how students, especially individuals, solve interdisciplinary problems. To understand how individuals reason about interdisciplinary problems, we…
Engineering students' experiences and perceptions of workplace problem solving
NASA Astrophysics Data System (ADS)
Pan, Rui
In this study, I interviewed 22 engineering Co-Op students about their workplace problem solving experiences and reflections and explored: 1) Of Co-Op students who experienced workplace problem solving, what are the different ways in which students experience workplace problem solving? 2) How do students perceive a) the differences between workplace problem solving and classroom problem solving and b) in what areas are they prepared by their college education to solve workplace problems? To answer my first research question, I analyzed data through the lens of phenomenography and I conducted thematic analysis to answer my second research question. The results of this study have implications for engineering education and engineering practice. Specifically, the results reveal the different ways students experience workplace problem solving, which provide engineering educators and practicing engineers a better understanding of the nature of workplace engineering. In addition, the results indicate that there is still a gap between classroom engineering and workplace engineering. For engineering educators who aspire to prepare students to be future engineers, it is imperative to design problem solving experiences that can better prepare students with workplace competency.
Problem-Solving Deficits in Iranian People with Borderline Personality Disorder
Akbari Dehaghi, Ashraf; Kaviani, Hossein; Tamanaeefar, Shima
2014-01-01
Objective: Interventions for people suffering from borderline personality disorder (BPD), such as dialectical behavior therapy, often include a problem-solving component. However, there is an absence of published studies examining the problem-solving abilities of this client group in Iran. The study compared inpatients and outpatients with BPD and a control group on problem-solving capabilities in an Iranian sample. It was hypothesized that patients with BPD would have more deficiencies in this area. Methods: Fifteen patients with BPD were compared to 15 healthy participants. Means-ends problem-solving task (MEPS) was used to measure problem-solving skills in both groups. Results: BPD group reported less effective strategies in solving problems as opposed to the healthy group. Compared to the control group, participants with BPD provided empirical support for the use of problem-solving interventions with people suffering from BPD. Conclusions: The findings supported the idea that a problem-solving intervention can be efficiently applied either as a stand-alone therapy or in conjunction with other available psychotherapies to treat people with BPD. PMID:25798169
Enhancing memory and imagination improves problem solving among individuals with depression.
McFarland, Craig P; Primosch, Mark; Maxson, Chelsey M; Stewart, Brandon T
2017-08-01
Recent work has revealed links between memory, imagination, and problem solving, and suggests that increasing access to detailed memories can lead to improved imagination and problem-solving performance. Depression is often associated with overgeneral memory and imagination, along with problem-solving deficits. In this study, we tested the hypothesis that an interview designed to elicit detailed recollections would enhance imagination and problem solving among both depressed and nondepressed participants. In a within-subjects design, participants completed a control interview or an episodic specificity induction prior to completing memory, imagination, and problem-solving tasks. Results revealed that compared to the control interview, the episodic specificity induction fostered increased detail generation in memory and imagination and more relevant steps on the problem-solving task among depressed and nondepressed participants. This study builds on previous work by demonstrating that a brief interview can enhance problem solving among individuals with depression and supports the notion that episodic memory plays a key role in problem solving. It should be noted, however, that the results of the interview are relatively short-lived.
Gradient gravitational search: An efficient metaheuristic algorithm for global optimization.
Dash, Tirtharaj; Sahu, Prabhat K
2015-05-30
The adaptation of novel techniques developed in the field of computational chemistry to solve the concerned problems for large and flexible molecules is taking the center stage with regard to efficient algorithm, computational cost and accuracy. In this article, the gradient-based gravitational search (GGS) algorithm, using analytical gradients for a fast minimization to the next local minimum has been reported. Its efficiency as metaheuristic approach has also been compared with Gradient Tabu Search and others like: Gravitational Search, Cuckoo Search, and Back Tracking Search algorithms for global optimization. Moreover, the GGS approach has also been applied to computational chemistry problems for finding the minimal value potential energy of two-dimensional and three-dimensional off-lattice protein models. The simulation results reveal the relative stability and physical accuracy of protein models with efficient computational cost. © 2015 Wiley Periodicals, Inc.
Incremental passivity and output regulation for switched nonlinear systems
NASA Astrophysics Data System (ADS)
Pang, Hongbo; Zhao, Jun
2017-10-01
This paper studies incremental passivity and global output regulation for switched nonlinear systems, whose subsystems are not required to be incrementally passive. A concept of incremental passivity for switched systems is put forward. First, a switched system is rendered incrementally passive by the design of a state-dependent switching law. Second, the feedback incremental passification is achieved by the design of a state-dependent switching law and a set of state feedback controllers. Finally, we show that once the incremental passivity for switched nonlinear systems is assured, the output regulation problem is solved by the design of global nonlinear regulator controllers comprising two components: the steady-state control and the linear output feedback stabilising controllers, even though the problem for none of subsystems is solvable. Two examples are presented to illustrate the effectiveness of the proposed approach.
Measuring Family Problem Solving: The Family Problem Solving Diary.
ERIC Educational Resources Information Center
Kieren, Dianne K.
The development and use of the family problem-solving diary are described. The diary is one of several indicators and measures of family problem-solving behavior. It provides a record of each person's perception of day-to-day family problems (what the problem concerns, what happened, who got involved, what those involved did, how the problem…
Trumpower, David L; Goldsmith, Timothy E; Guynn, Melissa J
2004-12-01
Solving training problems with nonspecific goals (NG; i.e., solving for all possible unknown values) often results in better transfer than solving training problems with standard goals (SG; i.e., solving for one particular unknown value). In this study, we evaluated an attentional focus explanation of the goal specificity effect. According to the attentional focus view, solving NG problems causes attention to be directed to local relations among successive problem states, whereas solving SG problems causes attention to be directed to relations between the various problem states and the goal state. Attention to the former is thought to enhance structural knowledge about the problem domain and thus promote transfer. Results supported this view because structurally different transfer problems were solved faster following NG training than following SG training. Moreover, structural knowledge representations revealed more links depicting local relations following NG training and more links to the training goal following SG training. As predicted, these effects were obtained only by domain novices.
Hybrid robust predictive optimization method of power system dispatch
Chandra, Ramu Sharat [Niskayuna, NY; Liu, Yan [Ballston Lake, NY; Bose, Sumit [Niskayuna, NY; de Bedout, Juan Manuel [West Glenville, NY
2011-08-02
A method of power system dispatch control solves power system dispatch problems by integrating a larger variety of generation, load and storage assets, including without limitation, combined heat and power (CHP) units, renewable generation with forecasting, controllable loads, electric, thermal and water energy storage. The method employs a predictive algorithm to dynamically schedule different assets in order to achieve global optimization and maintain the system normal operation.
Biodegradable and bio-based polymers: future prospects of eco-friendly plastics.
Iwata, Tadahisa
2015-03-09
Currently used plastics are mostly produced from petrochemical products, but there is a growing demand for eco-friendly plastics. The use of bio-based plastics, which are produced from renewable resources, and biodegradable plastics, which are degraded in the environment, will lead to a more sustainable society and help us solve global environmental and waste management problems. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Calibration of the clock-phase biases of GNSS networks: the closure-ambiguity approach
NASA Astrophysics Data System (ADS)
Lannes, A.; Prieur, J.-L.
2013-08-01
In global navigation satellite systems (GNSS), the problem of retrieving clock-phase biases from network data has a basic rank defect. We analyse the different ways of removing this rank defect, and define a particular strategy for obtaining these phase biases in a standard form. The minimum-constrained problem to be solved in the least-squares (LS) sense depends on some integer vector which can be fixed in an arbitrary manner. We propose to solve the problem via an undifferenced approach based on the notion of closure ambiguity. We present a theoretical justification of this closure-ambiguity approach (CAA), and the main elements for a practical implementation. The links with other methods are also established. We analyse all those methods in a unified interpretative framework, and derive functional relations between the corresponding solutions and our CAA solution. This could be interesting for many GNSS applications like real-time kinematic PPP for instance. To compare the methods providing LS estimates of clock-phase biases, we define a particular solution playing the role of reference solution. For this solution, when a phase bias is estimated for the first time, its fractional part is confined to the one-cycle width interval centred on zero; the integer-ambiguity set is modified accordingly. Our theoretical study is illustrated with some simple and generic examples; it could have applications in data processing of most GNSS networks, and particularly global networks using GPS, Glonass, Galileo, or BeiDou/Compass satellites.
ERIC Educational Resources Information Center
Zhang, Yin; Chu, Samuel K. W.
2016-01-01
In recent years, a number of models concerning problem solving systems have been put forward. However, many of them stress on technology and neglect the research of problem solving itself, especially the learning mechanism related to problem solving. In this paper, we analyze the learning mechanism of problem solving, and propose that when…
Perceived problem solving, stress, and health among college students.
Largo-Wight, Erin; Peterson, P Michael; Chen, W William
2005-01-01
To study the relationships among perceived problem solving, stress, and physical health. The Perceived Stress Questionnaire (PSQ), Personal Problem solving Inventory (PSI), and a stress-related physical health symptoms checklist were used to measure perceived stress, problem solving, and health among undergraduate college students (N = 232). Perceived problem-solving ability predicted self-reported physical health symptoms (R2 = .12; P < .001) and perceived stress (R2 = .19; P < .001). Perceived problem solving was a stronger predictor of physical health and perceived stress than were physical activity, alcohol consumption, or social support. Implications for college health promotion are discussed.
NASA Astrophysics Data System (ADS)
Karthiga, S.; Chithiika Ruby, V.; Senthilvelan, M.; Lakshmanan, M.
2017-10-01
In position dependent mass (PDM) problems, the quantum dynamics of the associated systems have been understood well in the literature for particular orderings. However, no efforts seem to have been made to solve such PDM problems for general orderings to obtain a global picture. In this connection, we here consider the general ordered quantum Hamiltonian of an interesting position dependent mass problem, namely, the Mathews-Lakshmanan oscillator, and try to solve the quantum problem for all possible orderings including Hermitian and non-Hermitian ones. The other interesting point in our study is that for all possible orderings, although the Schrödinger equation of this Mathews-Lakshmanan oscillator is uniquely reduced to the associated Legendre differential equation, their eigenfunctions cannot be represented in terms of the associated Legendre polynomials with integral degree and order. Rather the eigenfunctions are represented in terms of associated Legendre polynomials with non-integral degree and order. We here explore such polynomials and represent the discrete and continuum states of the system. We also exploit the connection between associated Legendre polynomials with non-integral degree with other orthogonal polynomials such as Jacobi and Gegenbauer polynomials.
NASA Astrophysics Data System (ADS)
Hull, Michael M.; Kuo, Eric; Gupta, Ayush; Elby, Andrew
2013-06-01
Much research in engineering and physics education has focused on improving students’ problem-solving skills. This research has led to the development of step-by-step problem-solving strategies and grading rubrics to assess a student’s expertise in solving problems using these strategies. These rubrics value “communication” between the student’s qualitative description of the physical situation and the student’s formal mathematical descriptions (usually equations) at two points: when initially setting up the equations, and when evaluating the final mathematical answer for meaning and plausibility. We argue that (i) neither the rubrics nor the associated problem-solving strategies explicitly value this kind of communication during mathematical manipulations of the chosen equations, and (ii) such communication is an aspect of problem-solving expertise. To make this argument, we present a case study of two students, Alex and Pat, solving the same kinematics problem in clinical interviews. We argue that Pat’s solution, which connects manipulation of equations to their physical interpretation, is more expertlike than Alex’s solution, which uses equations more algorithmically. We then show that the types of problem-solving rubrics currently available do not discriminate between these two types of solutions. We conclude that problem-solving rubrics should be revised or repurposed to more accurately assess problem-solving expertise.
A remote sensing based vegetation classification logic for global land cover analysis
Running, Steven W.; Loveland, Thomas R.; Pierce, Lars L.; Nemani, R.R.; Hunt, E. Raymond
1995-01-01
This article proposes a simple new logic for classifying global vegetation. The critical features of this classification are that 1) it is based on simple, observable, unambiguous characteristics of vegetation structure that are important to ecosystem biogeochemistry and can be measured in the field for validation, 2) the structural characteristics are remotely sensible so that repeatable and efficient global reclassifications of existing vegetation will be possible, and 3) the defined vegetation classes directly translate into the biophysical parameters of interest by global climate and biogeochemical models. A first test of this logic for the continental United States is presented based on an existing 1 km AVHRR normalized difference vegetation index database. Procedures for solving critical remote sensing problems needed to implement the classification are discussed. Also, some inferences from this classification to advanced vegetation biophysical variables such as specific leaf area and photosynthetic capacity useful to global biogeochemical modeling are suggested.
NASA Technical Reports Server (NTRS)
Jiang, Bo-Nan; Sonnad, Vijay
1991-01-01
A p-version of the least squares finite element method, based on the velocity-pressure-vorticity formulation, is developed for solving steady state incompressible viscous flow problems. The resulting system of symmetric and positive definite linear equations can be solved satisfactorily with the conjugate gradient method. In conjunction with the use of rapid operator application which avoids the formation of either element of global matrices, it is possible to achieve a highly compact and efficient solution scheme for the incompressible Navier-Stokes equations. Numerical results are presented for two-dimensional flow over a backward facing step. The effectiveness of simple outflow boundary conditions is also demonstrated.
Examining Tasks that Facilitate the Experience of Incubation While Problem-Solving
ERIC Educational Resources Information Center
Both, Lilly; Needham, Douglas; Wood, Eileen
2004-01-01
The three studies presented here contrasted the problem-solving outcomes of university students when a break was provided or not provided during a problem-solving session. In addition, two studies explored the effect of providing hints (priming) and the placement of hints during the problem-solving session. First, the ability to solve a previously…
Implicit time-integration method for simultaneous solution of a coupled non-linear system
NASA Astrophysics Data System (ADS)
Watson, Justin Kyle
Historically large physical problems have been divided into smaller problems based on the physics involved. This is no different in reactor safety analysis. The problem of analyzing a nuclear reactor for design basis accidents is performed by a handful of computer codes each solving a portion of the problem. The reactor thermal hydraulic response to an event is determined using a system code like TRAC RELAP Advanced Computational Engine (TRACE). The core power response to the same accident scenario is determined using a core physics code like Purdue Advanced Core Simulator (PARCS). Containment response to the reactor depressurization in a Loss Of Coolant Accident (LOCA) type event is calculated by a separate code. Sub-channel analysis is performed with yet another computer code. This is just a sample of the computer codes used to solve the overall problems of nuclear reactor design basis accidents. Traditionally each of these codes operates independently from each other using only the global results from one calculation as boundary conditions to another. Industry's drive to uprate power for reactors has motivated analysts to move from a conservative approach to design basis accident towards a best estimate method. To achieve a best estimate calculation efforts have been aimed at coupling the individual physics models to improve the accuracy of the analysis and reduce margins. The current coupling techniques are sequential in nature. During a calculation time-step data is passed between the two codes. The individual codes solve their portion of the calculation and converge to a solution before the calculation is allowed to proceed to the next time-step. This thesis presents a fully implicit method of simultaneous solving the neutron balance equations, heat conduction equations and the constitutive fluid dynamics equations. It discusses the problems involved in coupling different physics phenomena within multi-physics codes and presents a solution to these problems. The thesis also outlines the basic concepts behind the nodal balance equations, heat transfer equations and the thermal hydraulic equations, which will be coupled to form a fully implicit nonlinear system of equations. The coupling of separate physics models to solve a larger problem and improve accuracy and efficiency of a calculation is not a new idea, however implementing them in an implicit manner and solving the system simultaneously is. Also the application to reactor safety codes is new and has not be done with thermal hydraulics and neutronics codes on realistic applications in the past. The coupling technique described in this thesis is applicable to other similar coupled thermal hydraulic and core physics reactor safety codes. This technique is demonstrated using coupled input decks to show that the system is solved correctly and then verified by using two derivative test problems based on international benchmark problems the OECD/NRC Three mile Island (TMI) Main Steam Line Break (MSLB) problem (representative of pressurized water reactor analysis) and the OECD/NRC Peach Bottom (PB) Turbine Trip (TT) benchmark (representative of boiling water reactor analysis).
An adaptive SVSF-SLAM algorithm to improve the success and solving the UGVs cooperation problem
NASA Astrophysics Data System (ADS)
Demim, Fethi; Nemra, Abdelkrim; Louadj, Kahina; Hamerlain, Mustapha; Bazoula, Abdelouahab
2018-05-01
This paper aims to present a Decentralised Cooperative Simultaneous Localization and Mapping (DCSLAM) solution based on 2D laser data using an Adaptive Covariance Intersection (ACI). The ACI-DCSLAM algorithm will be validated on a swarm of Unmanned Ground Vehicles (UGVs) receiving features to estimate the position and covariance of shared features before adding them to the global map. With the proposed solution, a group of (UGVs) will be able to construct a large reliable map and localise themselves within this map without any user intervention. The most popular solutions to this problem are the EKF-SLAM, Nonlinear H-infinity ? SLAM and the FAST-SLAM. The former suffers from two important problems which are the poor consistency caused by the linearization problem and the calculation of Jacobian. The second solution is the ? which is a very promising filter because it doesn't make any assumption about noise characteristics, while the latter is not suitable for real time implementation. Therefore, a new alternative solution based on the smooth variable structure filter (SVSF) is adopted. Cooperative adaptive SVSF-SLAM algorithm is proposed in this paper to solve the UGVs SLAM problem. Our main contribution consists in adapting the SVSF filter to solve the Decentralised Cooperative SLAM problem for multiple UGVs. The algorithms developed in this paper were implemented using two mobile robots Pioneer ?, equiped with 2D laser telemetry sensors. Good results are obtained by the Cooperative adaptive SVSF-SLAM algorithm compared to the Cooperative EKF/?-SLAM algorithms, especially when the noise is colored or affected by a variable bias. Simulation results confirm and show the efficiency of the proposed algorithm which is more robust, stable and adapted to real time applications.
NASA Astrophysics Data System (ADS)
Jua, S. K.; Sarwanto; Sukarmin
2018-05-01
Problem-solving skills are important skills in physics. However, according to some researchers, the problem-solving skill of Indonesian students’ problem in physics learning is categorized still low. The purpose of this study was to identify the profile of problem-solving skills of students who follow the across the interests program of physics. The subjects of the study were high school students of Social Sciences, grade X. The type of this research was descriptive research. The data which used to analyze the problem-solving skills were obtained through student questionnaires and the test results with impulse materials and collision. From the descriptive analysis results, the percentage of students’ problem-solving skill based on the test was 52.93% and indicators respectively. These results indicated that students’ problem-solving skill is categorized low.
NASA Astrophysics Data System (ADS)
Łatas, Waldemar
2018-01-01
The problem of vibrations of the beam with the attached system of translational and rotational dynamic mass dampers subjected to random excitations with peaked power spectral densities, is presented in the hereby paper. The Euler-Bernoulli beam model is applied, while for solving the equation of motion the Galerkin method and the Laplace time transform are used. The obtained transfer functions allow to determine power spectral densities of the beam deflection and other dependent variables. Numerical examples present simple optimization problems of mass dampers parameters for local and global objective functions.
Multi-Level Adaptive Techniques (MLAT) for singular-perturbation problems
NASA Technical Reports Server (NTRS)
Brandt, A.
1978-01-01
The multilevel (multigrid) adaptive technique, a general strategy of solving continuous problems by cycling between coarser and finer levels of discretization is described. It provides very fast general solvers, together with adaptive, nearly optimal discretization schemes. In the process, boundary layers are automatically either resolved or skipped, depending on a control function which expresses the computational goal. The global error decreases exponentially as a function of the overall computational work, in a uniform rate independent of the magnitude of the singular-perturbation terms. The key is high-order uniformly stable difference equations, and uniformly smoothing relaxation schemes.
An EGO-like optimization framework for sensor placement optimization in modal analysis
NASA Astrophysics Data System (ADS)
Morlier, Joseph; Basile, Aniello; Chiplunkar, Ankit; Charlotte, Miguel
2018-07-01
In aircraft design, ground/flight vibration tests are conducted to extract aircraft’s modal parameters (natural frequencies, damping ratios and mode shapes) also known as the modal basis. The main problem in aircraft modal identification is the large number of sensors needed, which increases operational time and costs. The goal of this paper is to minimize the number of sensors by optimizing their locations in order to reconstruct a truncated modal basis of N mode shapes with a high level of accuracy in the reconstruction. There are several methods to solve sensors placement optimization (SPO) problems, but for this case an original approach has been established based on an iterative process for mode shapes reconstruction through an adaptive Kriging metamodeling approach so called efficient global optimization (EGO)-SPO. The main idea in this publication is to solve an optimization problem where the sensors locations are variables and the objective function is defined by maximizing the trace of criteria so called AutoMAC. The results on a 2D wing demonstrate a reduction of sensors by 30% using our EGO-SPO strategy.
Toward multiscale modelings of grain-fluid systems
NASA Astrophysics Data System (ADS)
Chareyre, Bruno; Yuan, Chao; Montella, Eduard P.; Salager, Simon
2017-06-01
Computationally efficient methods have been developed for simulating partially saturated granular materials in the pendular regime. In contrast, one hardly avoid expensive direct resolutions of 2-phase fluid dynamics problem for mixed pendular-funicular situations or even saturated regimes. Following previous developments for single-phase flow, a pore-network approach of the coupling problems is described. The geometry and movements of phases and interfaces are described on the basis of a tetrahedrization of the pore space, introducing elementary objects such as bridge, meniscus, pore body and pore throat, together with local rules of evolution. As firmly established local rules are still missing on some aspects (entry capillary pressure and pore-scale pressure-saturation relations, forces on the grains, or kinetics of transfers in mixed situations) a multi-scale numerical framework is introduced, enhancing the pore-network approach with the help of direct simulations. Small subsets of a granular system are extracted, in which multiphase scenario are solved using the Lattice-Boltzman method (LBM). In turns, a global problem is assembled and solved at the network scale, as illustrated by a simulated primary drainage.
An Improved Hybrid Encoding Cuckoo Search Algorithm for 0-1 Knapsack Problems
Feng, Yanhong; Jia, Ke; He, Yichao
2014-01-01
Cuckoo search (CS) is a new robust swarm intelligence method that is based on the brood parasitism of some cuckoo species. In this paper, an improved hybrid encoding cuckoo search algorithm (ICS) with greedy strategy is put forward for solving 0-1 knapsack problems. First of all, for solving binary optimization problem with ICS, based on the idea of individual hybrid encoding, the cuckoo search over a continuous space is transformed into the synchronous evolution search over discrete space. Subsequently, the concept of confidence interval (CI) is introduced; hence, the new position updating is designed and genetic mutation with a small probability is introduced. The former enables the population to move towards the global best solution rapidly in every generation, and the latter can effectively prevent the ICS from trapping into the local optimum. Furthermore, the greedy transform method is used to repair the infeasible solution and optimize the feasible solution. Experiments with a large number of KP instances show the effectiveness of the proposed algorithm and its ability to achieve good quality solutions. PMID:24527026
A bat algorithm with mutation for UCAV path planning.
Wang, Gaige; Guo, Lihong; Duan, Hong; Liu, Luo; Wang, Heqi
2012-01-01
Path planning for uninhabited combat air vehicle (UCAV) is a complicated high dimension optimization problem, which mainly centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. Original bat algorithm (BA) is used to solve the UCAV path planning problem. Furthermore, a new bat algorithm with mutation (BAM) is proposed to solve the UCAV path planning problem, and a modification is applied to mutate between bats during the process of the new solutions updating. Then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic BA. The realization procedure for original BA and this improved metaheuristic approach BAM is also presented. To prove the performance of this proposed metaheuristic method, BAM is compared with BA and other population-based optimization methods, such as ACO, BBO, DE, ES, GA, PBIL, PSO, and SGA. The experiment shows that the proposed approach is more effective and feasible in UCAV path planning than the other models.
Simulated annealing two-point ray tracing
NASA Astrophysics Data System (ADS)
Velis, Danilo R.; Ulrych, Tadeusz J.
We present a new method for solving the two-point seismic ray tracing problem based on Fermat's principle. The algorithm overcomes some well known difficulties that arise in standard ray shooting and bending methods. Problems related to: (1) the selection of new take-off angles, and (2) local minima in multipathing cases, are overcome by using an efficient simulated annealing (SA) algorithm. At each iteration, the ray is propagated from the source by solving a standard initial value problem. The last portion of the raypath is then forced to pass through the receiver. Using SA, the total traveltime is then globally minimized by obtaining the initial conditions that produce the absolute minimum path. The procedure is suitable for tracing rays through 2D complex structures, although it can be extended to deal with 3D velocity media. Not only direct waves, but also reflected and head-waves can be incorporated in the scheme. One important advantage is its simplicity, in as much as any available or user-preferred initial value solver system can be used. A number of clarifying examples of multipathing in 2D media are examined.
Error analysis of multipoint flux domain decomposition methods for evolutionary diffusion problems
NASA Astrophysics Data System (ADS)
Arrarás, A.; Portero, L.; Yotov, I.
2014-01-01
We study space and time discretizations for mixed formulations of parabolic problems. The spatial approximation is based on the multipoint flux mixed finite element method, which reduces to an efficient cell-centered pressure system on general grids, including triangles, quadrilaterals, tetrahedra, and hexahedra. The time integration is performed by using a domain decomposition time-splitting technique combined with multiterm fractional step diagonally implicit Runge-Kutta methods. The resulting scheme is unconditionally stable and computationally efficient, as it reduces the global system to a collection of uncoupled subdomain problems that can be solved in parallel without the need for Schwarz-type iteration. Convergence analysis for both the semidiscrete and fully discrete schemes is presented.
Tackling the child malnutrition problem: from what and why to how much and how.
McLachlan, Milla
2006-12-01
There is strong economic evidence to invest in improving the economic status of young children, yet programs remain underresourced. Returns on investment in child nutrition in terms of improved health, better education outcomes and increased productivity are substantial, and cost estimates for effective programs are in the range of $2.8 to $5.3 billion. These amounts are modest when compared with total international development assistance or current spending on luxury goods in wealthy nations. New initiatives to redefine nutrition science and to apply innovative problem-solving technologies to the global nutrition problem suggest that steps are being taken to accelerate progress toward a malnutrition-free world.
Production scheduling with ant colony optimization
NASA Astrophysics Data System (ADS)
Chernigovskiy, A. S.; Kapulin, D. V.; Noskova, E. E.; Yamskikh, T. N.; Tsarev, R. Yu
2017-10-01
The optimum solution of the production scheduling problem for manufacturing processes at an enterprise is crucial as it allows one to obtain the required amount of production within a specified time frame. Optimum production schedule can be found using a variety of optimization algorithms or scheduling algorithms. Ant colony optimization is one of well-known techniques to solve the global multi-objective optimization problem. In the article, the authors present a solution of the production scheduling problem by means of an ant colony optimization algorithm. A case study of the algorithm efficiency estimated against some others production scheduling algorithms is presented. Advantages of the ant colony optimization algorithm and its beneficial effect on the manufacturing process are provided.
Vickers, Douglas; Lee, Michael D; Dry, Matthew; Hughes, Peter
2003-10-01
The planar Euclidean version of the traveling salesperson problem requires finding the shortest tour through a two-dimensional array of points. MacGregor and Ormerod (1996) have suggested that people solve such problems by using a global-to-local perceptual organizing process based on the convex hull of the array. We review evidence for and against this idea, before considering an alternative, local-to-global perceptual process, based on the rapid automatic identification of nearest neighbors. We compare these approaches in an experiment in which the effects of number of convex hull points and number of potential intersections on solution performance are measured. Performance worsened with more points on the convex hull and with fewer potential intersections. A measure of response uncertainty was unaffected by the number of convex hull points but increased with fewer potential intersections. We discuss a possible interpretation of these results in terms of a hierarchical solution process based on linking nearest neighbor clusters.
ERIC Educational Resources Information Center
Kiliç, Çigdem
2017-01-01
This study examined pre-service primary school teachers' performance in posing problems that require knowledge of problem-solving strategies. Quantitative and qualitative methods were combined. The 120 participants were asked to pose a problem that could be solved by using the find-a-pattern a particular problem-solving strategy. After that,…
ERIC Educational Resources Information Center
Maries, Alexandru; Singh, Chandralekha
2018-01-01
Drawing appropriate diagrams is a useful problem solving heuristic that can transform a problem into a representation that is easier to exploit for solving it. One major focus while helping introductory physics students learn effective problem solving is to help them understand that drawing diagrams can facilitate problem solution. We conducted an…
ERIC Educational Resources Information Center
Sleegers, Peter; Wassink, Hartger; van Veen, Klaas; Imants, Jeroen
2009-01-01
In addition to cognitive research on school leaders' problem solving, this study focuses on the situated and personal nature of problem framing by combining insights from cognitive research on problem solving and sense-making theory. The study reports the results of a case study of two school leaders solving problems in their daily context by…
The Place of Problem Solving in Contemporary Mathematics Curriculum Documents
ERIC Educational Resources Information Center
Stacey, Kaye
2005-01-01
This paper reviews the presentation of problem solving and process aspects of mathematics in curriculum documents from Australia, UK, USA and Singapore. The place of problem solving in the documents is reviewed and contrasted, and illustrative problems from teachers' support materials are used to demonstrate how problem solving is now more often…
Translation among Symbolic Representations in Problem-Solving. Revised.
ERIC Educational Resources Information Center
Shavelson, Richard J.; And Others
This study investigated the relationships among the symbolic representation of problems given to students to solve, the mental representations they use to solve the problems, and the accuracy of their solutions. Twenty eleventh-grade science students were asked to think aloud as they solved problems on the ideal gas laws. The problems were…
Using Students' Representations Constructed during Problem Solving to Infer Conceptual Understanding
ERIC Educational Resources Information Center
Domin, Daniel; Bodner, George
2012-01-01
The differences in the types of representations constructed during successful and unsuccessful problem-solving episodes were investigated within the context of graduate students working on problems that involve concepts from 2D-NMR. Success at problem solving was established by having the participants solve five problems relating to material just…
Errors and Understanding: The Effects of Error-Management Training on Creative Problem-Solving
ERIC Educational Resources Information Center
Robledo, Issac C.; Hester, Kimberly S.; Peterson, David R.; Barrett, Jamie D.; Day, Eric A.; Hougen, Dean P.; Mumford, Michael D.
2012-01-01
People make errors in their creative problem-solving efforts. The intent of this article was to assess whether error-management training would improve performance on creative problem-solving tasks. Undergraduates were asked to solve an educational leadership problem known to call for creative thought where problem solutions were scored for…
Encouraging Sixth-Grade Students' Problem-Solving Performance by Teaching through Problem Solving
ERIC Educational Resources Information Center
Bostic, Jonathan D.; Pape, Stephen J.; Jacobbe, Tim
2016-01-01
This teaching experiment provided students with continuous engagement in a problem-solving based instructional approach during one mathematics unit. Three sections of sixth-grade mathematics were sampled from a school in Florida, U.S.A. and one section was randomly assigned to experience teaching through problem solving. Students' problem-solving…