Refined genetic algorithm -- Economic dispatch example

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

Sheble, G.B.; Brittig, K.

1995-02-01

A genetic-based algorithm is used to solve an economic dispatch (ED) problem. The algorithm utilizes payoff information of perspective solutions to evaluate optimality. Thus, the constraints of classical LaGrangian techniques on unit curves are eliminated. Using an economic dispatch problem as a basis for comparison, several different techniques which enhance program efficiency and accuracy, such as mutation prediction, elitism, interval approximation and penalty factors, are explored. Two unique genetic algorithms are also compared. The results are verified for a sample problem using a classical technique.

Advanced order management in ERM systems: the tic-tac-toe algorithm

NASA Astrophysics Data System (ADS)

Badell, Mariana; Fernandez, Elena; Puigjaner, Luis

2000-10-01

The concept behind improved enterprise resource planning systems (ERP) systems is the overall integration of the whole enterprise functionality into the management systems through financial links. Converting current software into real management decision tools requires crucial changes in the current approach to ERP systems. This evolution must be able to incorporate the technological achievements both properly and in time. The exploitation phase of plants needs an open web-based environment for collaborative business-engineering with on-line schedulers. Today's short lifecycles of products and processes require sharp and finely tuned management actions that must be guided by scheduling tools. Additionally, such actions must be able to keep track of money movements related to supply chain events. Thus, the necessary outputs require financial-production integration at the scheduling level as proposed in the new approach of enterprise management systems (ERM). Within this framework, the economical analysis of the due date policy and its optimization become essential to manage dynamically realistic and optimal delivery dates with price-time trade-off during the marketing activities. In this work we propose a scheduling tool with web-based interface conducted by autonomous agents when precise economic information relative to plant and business actions and their effects are provided. It aims to attain a better arrangement of the marketing and production events in order to face the bid/bargain process during e-commerce. Additionally, management systems require real time execution and an efficient transaction-oriented approach capable to dynamically adopt realistic and optimal actions to support marketing management. To this end the TicTacToe algorithm provides sequence optimization with acceptable tolerances in realistic time.

An Innovative Software Tool Suite for Power Plant Model Validation and Parameter Calibration using PMU Measurements

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

Li, Yuanyuan; Diao, Ruisheng; Huang, Renke

Maintaining good quality of power plant stability models is of critical importance to ensure the secure and economic operation and planning of today’s power grid with its increasing stochastic and dynamic behavior. According to North American Electric Reliability (NERC) standards, all generators in North America with capacities larger than 10 MVA are required to validate their models every five years. Validation is quite costly and can significantly affect the revenue of generator owners, because the traditional staged testing requires generators to be taken offline. Over the past few years, validating and calibrating parameters using online measurements including phasor measurement unitsmore » (PMUs) and digital fault recorders (DFRs) has been proven to be a cost-effective approach. In this paper, an innovative open-source tool suite is presented for validating power plant models using PPMV tool, identifying bad parameters with trajectory sensitivity analysis, and finally calibrating parameters using an ensemble Kalman filter (EnKF) based algorithm. The architectural design and the detailed procedures to run the tool suite are presented, with results of test on a realistic hydro power plant using PMU measurements for 12 different events. The calibrated parameters of machine, exciter, governor and PSS models demonstrate much better performance than the original models for all the events and show the robustness of the proposed calibration algorithm.« less

Research on prediction of agricultural machinery total power based on grey model optimized by genetic algorithm

NASA Astrophysics Data System (ADS)

Xie, Yan; Li, Mu; Zhou, Jin; Zheng, Chang-zheng

2009-07-01

Agricultural machinery total power is an important index to reflex and evaluate the level of agricultural mechanization. It is the power source of agricultural production, and is the main factors to enhance the comprehensive agricultural production capacity expand production scale and increase the income of the farmers. Its demand is affected by natural, economic, technological and social and other "grey" factors. Therefore, grey system theory can be used to analyze the development of agricultural machinery total power. A method based on genetic algorithm optimizing grey modeling process is introduced in this paper. This method makes full use of the advantages of the grey prediction model and characteristics of genetic algorithm to find global optimization. So the prediction model is more accurate. According to data from a province, the GM (1, 1) model for predicting agricultural machinery total power was given based on the grey system theories and genetic algorithm. The result indicates that the model can be used as agricultural machinery total power an effective tool for prediction.

Heat Transfer Search Algorithm for Non-convex Economic Dispatch Problems

NASA Astrophysics Data System (ADS)

Hazra, Abhik; Das, Saborni; Basu, Mousumi

2018-06-01

This paper presents Heat Transfer Search (HTS) algorithm for the non-linear economic dispatch problem. HTS algorithm is based on the law of thermodynamics and heat transfer. The proficiency of the suggested technique has been disclosed on three dissimilar complicated economic dispatch problems with valve point effect; prohibited operating zone; and multiple fuels with valve point effect. Test results acquired from the suggested technique for the economic dispatch problem have been fitted to that acquired from other stated evolutionary techniques. It has been observed that the suggested HTS carry out superior solutions.

Heat Transfer Search Algorithm for Non-convex Economic Dispatch Problems

NASA Astrophysics Data System (ADS)

Hazra, Abhik; Das, Saborni; Basu, Mousumi

2018-03-01

This paper presents Heat Transfer Search (HTS) algorithm for the non-linear economic dispatch problem. HTS algorithm is based on the law of thermodynamics and heat transfer. The proficiency of the suggested technique has been disclosed on three dissimilar complicated economic dispatch problems with valve point effect; prohibited operating zone; and multiple fuels with valve point effect. Test results acquired from the suggested technique for the economic dispatch problem have been fitted to that acquired from other stated evolutionary techniques. It has been observed that the suggested HTS carry out superior solutions.

Trends in non-stationary signal processing techniques applied to vibration analysis of wind turbine drive train - A contemporary survey

NASA Astrophysics Data System (ADS)

Uma Maheswari, R.; Umamaheswari, R.

2017-02-01

Condition Monitoring System (CMS) substantiates potential economic benefits and enables prognostic maintenance in wind turbine-generator failure prevention. Vibration Monitoring and Analysis is a powerful tool in drive train CMS, which enables the early detection of impending failure/damage. In variable speed drives such as wind turbine-generator drive trains, the vibration signal acquired is of non-stationary and non-linear. The traditional stationary signal processing techniques are inefficient to diagnose the machine faults in time varying conditions. The current research trend in CMS for drive-train focuses on developing/improving non-linear, non-stationary feature extraction and fault classification algorithms to improve fault detection/prediction sensitivity and selectivity and thereby reducing the misdetection and false alarm rates. In literature, review of stationary signal processing algorithms employed in vibration analysis is done at great extent. In this paper, an attempt is made to review the recent research advances in non-linear non-stationary signal processing algorithms particularly suited for variable speed wind turbines.

Application of genetic algorithm in integrated setup planning and operation sequencing

NASA Astrophysics Data System (ADS)

Kafashi, Sajad; Shakeri, Mohsen

2011-01-01

Process planning is an essential component for linking design and manufacturing process. Setup planning and operation sequencing is two main tasks in process planning. Many researches solved these two problems separately. Considering the fact that the two functions are complementary, it is necessary to integrate them more tightly so that performance of a manufacturing system can be improved economically and competitively. This paper present a generative system and genetic algorithm (GA) approach to process plan the given part. The proposed approach and optimization methodology analyses the TAD (tool approach direction), tolerance relation between features and feature precedence relations to generate all possible setups and operations using workshop resource database. Based on these technological constraints the GA algorithm approach, which adopts the feature-based representation, optimizes the setup plan and sequence of operations using cost indices. Case study show that the developed system can generate satisfactory results in optimizing the setup planning and operation sequencing simultaneously in feasible condition.

Optimal GENCO bidding strategy

NASA Astrophysics Data System (ADS)

Gao, Feng

Electricity industries worldwide are undergoing a period of profound upheaval. The conventional vertically integrated mechanism is being replaced by a competitive market environment. Generation companies have incentives to apply novel technologies to lower production costs, for example: Combined Cycle units. Economic dispatch with Combined Cycle units becomes a non-convex optimization problem, which is difficult if not impossible to solve by conventional methods. Several techniques are proposed here: Mixed Integer Linear Programming, a hybrid method, as well as Evolutionary Algorithms. Evolutionary Algorithms share a common mechanism, stochastic searching per generation. The stochastic property makes evolutionary algorithms robust and adaptive enough to solve a non-convex optimization problem. This research implements GA, EP, and PS algorithms for economic dispatch with Combined Cycle units, and makes a comparison with classical Mixed Integer Linear Programming. The electricity market equilibrium model not only helps Independent System Operator/Regulator analyze market performance and market power, but also provides Market Participants the ability to build optimal bidding strategies based on Microeconomics analysis. Supply Function Equilibrium (SFE) is attractive compared to traditional models. This research identifies a proper SFE model, which can be applied to a multiple period situation. The equilibrium condition using discrete time optimal control is then developed for fuel resource constraints. Finally, the research discusses the issues of multiple equilibria and mixed strategies, which are caused by the transmission network. Additionally, an advantage of the proposed model for merchant transmission planning is discussed. A market simulator is a valuable training and evaluation tool to assist sellers, buyers, and regulators to understand market performance and make better decisions. A traditional optimization model may not be enough to consider the distributed, large-scale, and complex energy market. This research compares the performance and searching paths of different artificial life techniques such as Genetic Algorithm (GA), Evolutionary Programming (EP), and Particle Swarm (PS), and look for a proper method to emulate Generation Companies' (GENCOs) bidding strategies. After deregulation, GENCOs face risk and uncertainty associated with the fast-changing market environment. A profit-based bidding decision support system is critical for GENCOs to keep a competitive position in the new environment. Most past research do not pay special attention to the piecewise staircase characteristic of generator offer curves. This research proposes an optimal bidding strategy based on Parametric Linear Programming. The proposed algorithm is able to handle actual piecewise staircase energy offer curves. The proposed method is then extended to incorporate incomplete information based on Decision Analysis. Finally, the author develops an optimal bidding tool (GenBidding) and applies it to the RTS96 test system.

Successful technical trading agents using genetic programming.

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

Othling, Andrew S.; Kelly, John A.; Pryor, Richard J.

2004-10-01

Genetic programming (GP) has proved to be a highly versatile and useful tool for identifying relationships in data for which a more precise theoretical construct is unavailable. In this project, we use a GP search to develop trading strategies for agent based economic models. These strategies use stock prices and technical indicators, such as the moving average convergence/divergence and various exponentially weighted moving averages, to generate buy and sell signals. We analyze the effect of complexity constraints on the strategies as well as the relative performance of various indicators. We also present innovations in the classical genetic programming algorithm thatmore » appear to improve convergence for this problem. Technical strategies developed by our GP algorithm can be used to control the behavior of agents in economic simulation packages, such as ASPEN-D, adding variety to the current market fundamentals approach. The exploitation of arbitrage opportunities by technical analysts may help increase the efficiency of the simulated stock market, as it does in the real world. By improving the behavior of simulated stock markets, we can better estimate the effects of shocks to the economy due to terrorism or natural disasters.« less

Method of Optimizing the Construction of Machining, Assembly and Control Devices

NASA Astrophysics Data System (ADS)

Iordache, D. M.; Costea, A.; Niţu, E. L.; Rizea, A. D.; Babă, A.

2017-10-01

Industry dynamics, driven by economic and social requirements, must generate more interest in technological optimization, capable of ensuring a steady development of advanced technical means to equip machining processes. For these reasons, the development of tools, devices, work equipment and control, as well as the modernization of machine tools, is the certain solution to modernize production systems that require considerable time and effort. This type of approach is also related to our theoretical, experimental and industrial applications of recent years, presented in this paper, which have as main objectives the elaboration and use of mathematical models, new calculation methods, optimization algorithms, new processing and control methods, as well as some structures for the construction and configuration of technological equipment with a high level of performance and substantially reduced costs..

Economic and Financial Analysis Tools | Energy Analysis | NREL

Science.gov Websites

Economic and Financial Analysis Tools Economic and Financial Analysis Tools Use these economic and . Job and Economic Development Impact (JEDI) Model Use these easy-to-use, spreadsheet-based tools to analyze the economic impacts of constructing and operating power generation and biofuel plants at the

Conceptual design of cost-effective and environmentally-friendly configurations for fuel ethanol production from sugarcane by knowledge-based process synthesis.

PubMed

Sánchez, Óscar J; Cardona, Carlos A

2012-01-01

In this work, the hierarchical decomposition methodology was used to conceptually design the production of fuel ethanol from sugarcane. The decomposition of the process into six levels of analysis was carried out. Several options of technological configurations were assessed in each level considering economic and environmental criteria. The most promising alternatives were chosen rejecting the ones with a least favorable performance. Aspen Plus was employed for simulation of each one of the technological configurations studied. Aspen Icarus was used for economic evaluation of each configuration, and WAR algorithm was utilized for calculation of the environmental criterion. The results obtained showed that the most suitable synthesized flowsheet involves the continuous cultivation of Zymomonas mobilis with cane juice as substrate and including cell recycling and the ethanol dehydration by molecular sieves. The proposed strategy demonstrated to be a powerful tool for conceptual design of biotechnological processes considering both techno-economic and environmental indicators. Copyright © 2011 Elsevier Ltd. All rights reserved.

A novel approach for medical research on lymphomas

PubMed Central

Conte, Cécile; Palmaro, Aurore; Grosclaude, Pascale; Daubisse-Marliac, Laetitia; Despas, Fabien; Lapeyre-Mestre, Maryse

2018-01-01

Abstract The use of claims database to study lymphomas in real-life conditions is a crucial issue in the future. In this way, it is essential to develop validated algorithms for the identification of lymphomas in these databases. The aim of this study was to assess the validity of diagnosis codes in the French health insurance database to identify incident cases of lymphomas according to results of a regional cancer registry, as the gold standard. Between 2010 and 2013, incident lymphomas were identified in hospital data through 2 algorithms of selection. The results of the identification process and characteristics of incident lymphomas cases were compared with data from the Tarn Cancer Registry. Each algorithm's performance was assessed by estimating sensitivity, predictive positive value, specificity (SPE), and negative predictive value. During the period, the registry recorded 476 incident cases of lymphomas, of which 52 were Hodgkin lymphomas and 424 non-Hodgkin lymphomas. For corresponding area and period, algorithm 1 provides a number of incident cases close to the Registry, whereas algorithm 2 overestimated the number of incident cases by approximately 30%. Both algorithms were highly specific (SPE = 99.9%) but moderately sensitive. The comparative analysis illustrates that similar distribution and characteristics are observed in both sources. Given these findings, the use of claims database can be consider as a pertinent and powerful tool to conduct medico-economic or pharmacoepidemiological studies in lymphomas. PMID:29480830

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.

Assessment of economic status in trauma registries: A new algorithm for generating population-specific clustering-based models of economic status for time-constrained low-resource settings.

PubMed

Eyler, Lauren; Hubbard, Alan; Juillard, Catherine

2016-10-01

Low and middle-income countries (LMICs) and the world's poor bear a disproportionate share of the global burden of injury. Data regarding disparities in injury are vital to inform injury prevention and trauma systems strengthening interventions targeted towards vulnerable populations, but are limited in LMICs. We aim to facilitate injury disparities research by generating a standardized methodology for assessing economic status in resource-limited country trauma registries where complex metrics such as income, expenditures, and wealth index are infeasible to assess. To address this need, we developed a cluster analysis-based algorithm for generating simple population-specific metrics of economic status using nationally representative Demographic and Health Surveys (DHS) household assets data. For a limited number of variables, g, our algorithm performs weighted k-medoids clustering of the population using all combinations of g asset variables and selects the combination of variables and number of clusters that maximize average silhouette width (ASW). In simulated datasets containing both randomly distributed variables and "true" population clusters defined by correlated categorical variables, the algorithm selected the correct variable combination and appropriate cluster numbers unless variable correlation was very weak. When used with 2011 Cameroonian DHS data, our algorithm identified twenty economic clusters with ASW 0.80, indicating well-defined population clusters. This economic model for assessing health disparities will be used in the new Cameroonian six-hospital centralized trauma registry. By describing our standardized methodology and algorithm for generating economic clustering models, we aim to facilitate measurement of health disparities in other trauma registries in resource-limited countries. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

The Detection of Malingering: A New Tool to Identify Made-Up Depression.

PubMed

Monaro, Merylin; Toncini, Andrea; Ferracuti, Stefano; Tessari, Gianmarco; Vaccaro, Maria G; De Fazio, Pasquale; Pigato, Giorgio; Meneghel, Tiziano; Scarpazza, Cristina; Sartori, Giuseppe

2018-01-01

Major depression is a high-prevalence mental disease with major socio-economic impact, for both the direct and the indirect costs. Major depression symptoms can be faked or exaggerated in order to obtain economic compensation from insurance companies. Critically, depression is potentially easily malingered, as the symptoms that characterize this psychiatric disorder are not difficult to emulate. Although some tools to assess malingering of psychiatric conditions are already available, they are principally based on self-reporting and are thus easily faked. In this paper, we propose a new method to automatically detect the simulation of depression, which is based on the analysis of mouse movements while the patient is engaged in a double-choice computerized task, responding to simple and complex questions about depressive symptoms. This tool clearly has a key advantage over the other tools: the kinematic movement is not consciously controllable by the subjects, and thus it is almost impossible to deceive. Two groups of subjects were recruited for the study. The first one, which was used to train different machine-learning algorithms, comprises 60 subjects (20 depressed patients and 40 healthy volunteers); the second one, which was used to test the machine-learning models, comprises 27 subjects (9 depressed patients and 18 healthy volunteers). In both groups, the healthy volunteers were randomly assigned to the liars and truth-tellers group. Machine-learning models were trained on mouse dynamics features, which were collected during the subject response, and on the number of symptoms reported by participants. Statistical results demonstrated that individuals that malingered depression reported a higher number of depressive and non-depressive symptoms than depressed participants, whereas individuals suffering from depression took more time to perform the mouse-based tasks compared to both truth-tellers and liars. Machine-learning models reached a classification accuracy up to 96% in distinguishing liars from depressed patients and truth-tellers. Despite this, the data are not conclusive, as the accuracy of the algorithm has not been compared with the accuracy of the clinicians; this study presents a possible useful method that is worth further investigation.

Real-time energy-saving metro train rescheduling with primary delay identification

PubMed Central

Li, Keping; Schonfeld, Paul

2018-01-01

This paper aims to reschedule online metro trains in delay scenarios. A graph representation and a mixed integer programming model are proposed to formulate the optimization problem. The solution approach is a two-stage optimization method. In the first stage, based on a proposed train state graph and system analysis, the primary and flow-on delays are specifically analyzed and identified with a critical path algorithm. For the second stage a hybrid genetic algorithm is designed to optimize the schedule, with the delay identification results as input. Then, based on the infrastructure data of Beijing Subway Line 4 of China, case studies are presented to demonstrate the effectiveness and efficiency of the solution approach. The results show that the algorithm can quickly and accurately identify primary delays among different types of delays. The economic cost of energy consumption and total delay is considerably reduced (by more than 10% in each case). The computation time of the Hybrid-GA is low enough for rescheduling online. Sensitivity analyses further demonstrate that the proposed approach can be used as a decision-making support tool for operators. PMID:29474471

Distributed Economic Dispatch in Microgrids Based on Cooperative Reinforcement Learning.

PubMed

Liu, Weirong; Zhuang, Peng; Liang, Hao; Peng, Jun; Huang, Zhiwu; Weirong Liu; Peng Zhuang; Hao Liang; Jun Peng; Zhiwu Huang; Liu, Weirong; Liang, Hao; Peng, Jun; Zhuang, Peng; Huang, Zhiwu

2018-06-01

Microgrids incorporated with distributed generation (DG) units and energy storage (ES) devices are expected to play more and more important roles in the future power systems. Yet, achieving efficient distributed economic dispatch in microgrids is a challenging issue due to the randomness and nonlinear characteristics of DG units and loads. This paper proposes a cooperative reinforcement learning algorithm for distributed economic dispatch in microgrids. Utilizing the learning algorithm can avoid the difficulty of stochastic modeling and high computational complexity. In the cooperative reinforcement learning algorithm, the function approximation is leveraged to deal with the large and continuous state spaces. And a diffusion strategy is incorporated to coordinate the actions of DG units and ES devices. Based on the proposed algorithm, each node in microgrids only needs to communicate with its local neighbors, without relying on any centralized controllers. Algorithm convergence is analyzed, and simulations based on real-world meteorological and load data are conducted to validate the performance of the proposed algorithm.

USING GENETIC ALGORITHMS TO DESIGN ENVIRONMENTALLY FRIENDLY PROCESSES

EPA Science Inventory

Genetic algorithm calculations are applied to the design of chemical processes to achieve improvements in environmental and economic performance. By finding the set of Pareto (i.e., non-dominated) solutions one can see how different objectives, such as environmental and economic ...

Simultaneous Scheduling of Jobs, AGVs and Tools Considering Tool Transfer Times in Multi Machine FMS By SOS Algorithm

NASA Astrophysics Data System (ADS)

Sivarami Reddy, N.; Ramamurthy, D. V., Dr.; Prahlada Rao, K., Dr.

2017-08-01

This article addresses simultaneous scheduling of machines, AGVs and tools where machines are allowed to share the tools considering transfer times of jobs and tools between machines, to generate best optimal sequences that minimize makespan in a multi-machine Flexible Manufacturing System (FMS). Performance of FMS is expected to improve by effective utilization of its resources, by proper integration and synchronization of their scheduling. Symbiotic Organisms Search (SOS) algorithm is a potent tool which is a better alternative for solving optimization problems like scheduling and proven itself. The proposed SOS algorithm is tested on 22 job sets with makespan as objective for scheduling of machines and tools where machines are allowed to share tools without considering transfer times of jobs and tools and the results are compared with the results of existing methods. The results show that the SOS has outperformed. The same SOS algorithm is used for simultaneous scheduling of machines, AGVs and tools where machines are allowed to share tools considering transfer times of jobs and tools to determine the best optimal sequences that minimize makespan.

Genetic testing in the European Union: does economic evaluation matter?

PubMed

Antoñanzas, Fernando; Rodríguez-Ibeas, R; Hutter, M F; Lorente, R; Juárez, C; Pinillos, M

2012-10-01

We review the published economic evaluation studies applied to genetic technologies in the EU to know the main diseases addressed by these studies, the ways the studies were conducted and to assess the efficiency of these new technologies. The final aim of this review was to understand the possibilities of the economic evaluations performed up to date as a tool to contribute to decision making in this area. We have reviewed a set of articles found in several databases until March 2010. Literature searches were made in the following databases: PubMed; Euronheed; Centre for Reviews and Dissemination of the University of York-Health Technology Assessment, Database of Abstracts of Reviews of Effects, NHS Economic Evaluation Database; and Scopus. The algorithm was "(screening or diagnosis) and genetic and (cost or economic) and (country EU27)". We included studies if they met the following criteria: (1) a genetic technology was analysed; (2) human DNA must be tested for; (3) the analysis was a real economic evaluation or a cost study, and (4) the articles had to be related to any EU Member State. We initially found 3,559 papers on genetic testing but only 92 articles of economic analysis referred to a wide range of genetic diseases matched the inclusion criteria. The most studied diseases were as follows: cystic fibrosis (12), breast and ovarian cancer (8), hereditary hemochromatosis (6), Down's syndrome (7), colorectal cancer (5), familial hypercholesterolaemia (5), prostate cancer (4), and thrombophilia (4). Genetic tests were mostly used for screening purposes, and cost-effectiveness analysis is the most common type of economic study. The analysed gene technologies are deemed to be efficient for some specific population groups and screening algorithms according to the values of their cost-effectiveness ratios that were below the commonly accepted threshold of 30,000€. Economic evaluation of genetic technologies matters but the number of published studies is still rather low as to be widely used for most of the decisions in different jurisdictions across the EU. Further, the decision bodies across EU27 are fragmented and the responsibilities are located at different levels of the decision process for what it is difficult to find out whether a given decision on genetic tests was somehow supported by the economic evaluation results.

Optimal Design of Passive Power Filters Based on Pseudo-parallel Genetic Algorithm

NASA Astrophysics Data System (ADS)

Li, Pei; Li, Hongbo; Gao, Nannan; Niu, Lin; Guo, Liangfeng; Pei, Ying; Zhang, Yanyan; Xu, Minmin; Chen, Kerui

2017-05-01

The economic costs together with filter efficiency are taken as targets to optimize the parameter of passive filter. Furthermore, the method of combining pseudo-parallel genetic algorithm with adaptive genetic algorithm is adopted in this paper. In the early stages pseudo-parallel genetic algorithm is introduced to increase the population diversity, and adaptive genetic algorithm is used in the late stages to reduce the workload. At the same time, the migration rate of pseudo-parallel genetic algorithm is improved to change with population diversity adaptively. Simulation results show that the filter designed by the proposed method has better filtering effect with lower economic cost, and can be used in engineering.

A dynamic scheduling algorithm for singe-arm two-cluster tools with flexible processing times

NASA Astrophysics Data System (ADS)

Li, Xin; Fung, Richard Y. K.

2018-02-01

This article presents a dynamic algorithm for job scheduling in two-cluster tools producing multi-type wafers with flexible processing times. Flexible processing times mean that the actual times for processing wafers should be within given time intervals. The objective of the work is to minimize the completion time of the newly inserted wafer. To deal with this issue, a two-cluster tool is decomposed into three reduced single-cluster tools (RCTs) in a series based on a decomposition approach proposed in this article. For each single-cluster tool, a dynamic scheduling algorithm based on temporal constraints is developed to schedule the newly inserted wafer. Three experiments have been carried out to test the dynamic scheduling algorithm proposed, comparing with the results the 'earliest starting time' heuristic (EST) adopted in previous literature. The results show that the dynamic algorithm proposed in this article is effective and practical.

Investigating the enhanced Best Performance Algorithm for Annual Crop Planning problem based on economic factors.

PubMed

Adewumi, Aderemi Oluyinka; Chetty, Sivashan

2017-01-01

The Annual Crop Planning (ACP) problem was a recently introduced problem in the literature. This study further expounds on this problem by presenting a new mathematical formulation, which is based on market economic factors. To determine solutions, a new local search metaheuristic algorithm is investigated which is called the enhanced Best Performance Algorithm (eBPA). eBPA's results are compared against two well-known local search metaheuristic algorithms; these include Tabu Search and Simulated Annealing. The results show the potential of the eBPA for continuous optimization problems.

Effects of visualization on algorithm comprehension

NASA Astrophysics Data System (ADS)

Mulvey, Matthew

Computer science students are expected to learn and apply a variety of core algorithms which are an essential part of the field. Any one of these algorithms by itself is not necessarily extremely complex, but remembering the large variety of algorithms and the differences between them is challenging. To address this challenge, we present a novel algorithm visualization tool designed to enhance students understanding of Dijkstra's algorithm by allowing them to discover the rules of the algorithm for themselves. It is hoped that a deeper understanding of the algorithm will help students correctly select, adapt and apply the appropriate algorithm when presented with a problem to solve, and that what is learned here will be applicable to the design of other visualization tools designed to teach different algorithms. Our visualization tool is currently in the prototype stage, and this thesis will discuss the pedagogical approach that informs its design, as well as the results of some initial usability testing. Finally, to clarify the direction for further development of the tool, four different variations of the prototype were implemented, and the instructional effectiveness of each was assessed by having a small sample participants use the different versions of the prototype and then take a quiz to assess their comprehension of the algorithm.

Dicotyledon Weed Quantification Algorithm for Selective Herbicide Application in Maize Crops.

PubMed

Laursen, Morten Stigaard; Jørgensen, Rasmus Nyholm; Midtiby, Henrik Skov; Jensen, Kjeld; Christiansen, Martin Peter; Giselsson, Thomas Mosgaard; Mortensen, Anders Krogh; Jensen, Peter Kryger

2016-11-04

The stricter legislation within the European Union for the regulation of herbicides that are prone to leaching causes a greater economic burden on the agricultural industry through taxation. Owing to the increased economic burden, research in reducing herbicide usage has been prompted. High-resolution images from digital cameras support the studying of plant characteristics. These images can also be utilized to analyze shape and texture characteristics for weed identification. Instead of detecting weed patches, weed density can be estimated at a sub-patch level, through which even the identification of a single plant is possible. The aim of this study is to adapt the monocot and dicot coverage ratio vision (MoDiCoVi) algorithm to estimate dicotyledon leaf cover, perform grid spraying in real time, and present initial results in terms of potential herbicide savings in maize. The authors designed and executed an automated, large-scale field trial supported by the Armadillo autonomous tool carrier robot. The field trial consisted of 299 maize plots. Half of the plots (parcels) were planned with additional seeded weeds; the other half were planned with naturally occurring weeds. The in-situ evaluation showed that, compared to conventional broadcast spraying, the proposed method can reduce herbicide usage by 65% without measurable loss in biological effect.

The addition of E (Empowerment and Economics) to the ABCD algorithm in diabetes care.

PubMed

Khazrai, Yeganeh Manon; Buzzetti, Raffaella; Del Prato, Stefano; Cahn, Avivit; Raz, Itamar; Pozzilli, Paolo

2015-01-01

The ABCD (Age, Body weight, Complications, Duration of disease) algorithm was proposed as a simple and practical tool to manage patients with type 2 diabetes. Diabetes treatment, as for all chronic diseases, relies on patients' ability to cope with daily problems concerning the management of their disease in accordance with medical recommendations. Thus, it is important that patients learn to manage and cope with their disease and gain greater control over actions and decisions affecting their health. Healthcare professionals should aim to encourage and increase patients' perception about their ability to take informed decisions about disease management and to improve patient self-esteem and feeling of self-efficacy to become agents of their own health. E for Empowerment is therefore an additional factor to take into account in the management of patients with type 2 diabetes. E stands also for Economics to be considered in diabetes care. Attention should be paid to public health policies as well as to the physician faced with the dilemma of delivering the best possible care within the problem of limited resources. The financial impact of the new treatment modalities for diabetes represents an issue that needs to be addressed at multiple strata both globally and nationally. Copyright © 2015 Elsevier Inc. All rights reserved.

Dicotyledon Weed Quantification Algorithm for Selective Herbicide Application in Maize Crops

PubMed Central

Laursen, Morten Stigaard; Jørgensen, Rasmus Nyholm; Midtiby, Henrik Skov; Jensen, Kjeld; Christiansen, Martin Peter; Giselsson, Thomas Mosgaard; Mortensen, Anders Krogh; Jensen, Peter Kryger

2016-01-01

The stricter legislation within the European Union for the regulation of herbicides that are prone to leaching causes a greater economic burden on the agricultural industry through taxation. Owing to the increased economic burden, research in reducing herbicide usage has been prompted. High-resolution images from digital cameras support the studying of plant characteristics. These images can also be utilized to analyze shape and texture characteristics for weed identification. Instead of detecting weed patches, weed density can be estimated at a sub-patch level, through which even the identification of a single plant is possible. The aim of this study is to adapt the monocot and dicot coverage ratio vision (MoDiCoVi) algorithm to estimate dicotyledon leaf cover, perform grid spraying in real time, and present initial results in terms of potential herbicide savings in maize. The authors designed and executed an automated, large-scale field trial supported by the Armadillo autonomous tool carrier robot. The field trial consisted of 299 maize plots. Half of the plots (parcels) were planned with additional seeded weeds; the other half were planned with naturally occurring weeds. The in-situ evaluation showed that, compared to conventional broadcast spraying, the proposed method can reduce herbicide usage by 65% without measurable loss in biological effect. PMID:27827908

An Empirical Derivation of the Run Time of the Bubble Sort Algorithm.

ERIC Educational Resources Information Center

Gonzales, Michael G.

1984-01-01

Suggests a moving pictorial tool to help teach principles in the bubble sort algorithm. Develops such a tool applied to an unsorted list of numbers and describes a method to derive the run time of the algorithm. The method can be modified to run the times of various other algorithms. (JN)

High-Performance Integrated Virtual Environment (HIVE) Tools and Applications for Big Data Analysis.

PubMed

Simonyan, Vahan; Mazumder, Raja

2014-09-30

The High-performance Integrated Virtual Environment (HIVE) is a high-throughput cloud-based infrastructure developed for the storage and analysis of genomic and associated biological data. HIVE consists of a web-accessible interface for authorized users to deposit, retrieve, share, annotate, compute and visualize Next-generation Sequencing (NGS) data in a scalable and highly efficient fashion. The platform contains a distributed storage library and a distributed computational powerhouse linked seamlessly. Resources available through the interface include algorithms, tools and applications developed exclusively for the HIVE platform, as well as commonly used external tools adapted to operate within the parallel architecture of the system. HIVE is composed of a flexible infrastructure, which allows for simple implementation of new algorithms and tools. Currently, available HIVE tools include sequence alignment and nucleotide variation profiling tools, metagenomic analyzers, phylogenetic tree-building tools using NGS data, clone discovery algorithms, and recombination analysis algorithms. In addition to tools, HIVE also provides knowledgebases that can be used in conjunction with the tools for NGS sequence and metadata analysis.

High-Performance Integrated Virtual Environment (HIVE) Tools and Applications for Big Data Analysis

PubMed Central

Simonyan, Vahan; Mazumder, Raja

2014-01-01

The High-performance Integrated Virtual Environment (HIVE) is a high-throughput cloud-based infrastructure developed for the storage and analysis of genomic and associated biological data. HIVE consists of a web-accessible interface for authorized users to deposit, retrieve, share, annotate, compute and visualize Next-generation Sequencing (NGS) data in a scalable and highly efficient fashion. The platform contains a distributed storage library and a distributed computational powerhouse linked seamlessly. Resources available through the interface include algorithms, tools and applications developed exclusively for the HIVE platform, as well as commonly used external tools adapted to operate within the parallel architecture of the system. HIVE is composed of a flexible infrastructure, which allows for simple implementation of new algorithms and tools. Currently, available HIVE tools include sequence alignment and nucleotide variation profiling tools, metagenomic analyzers, phylogenetic tree-building tools using NGS data, clone discovery algorithms, and recombination analysis algorithms. In addition to tools, HIVE also provides knowledgebases that can be used in conjunction with the tools for NGS sequence and metadata analysis. PMID:25271953

Cost-effectiveness of the non-laboratory based Framingham algorithm in primary prevention of cardiovascular disease: A simulated analysis of a cohort of African American adults.

PubMed

Kariuki, Jacob K; Gona, Philimon; Leveille, Suzanne G; Stuart-Shor, Eileen M; Hayman, Laura L; Cromwell, Jerry

2018-06-01

The non-lab Framingham algorithm, which substitute body mass index for lipids in the laboratory based (lab-based) Framingham algorithm, has been validated among African Americans (AAs). However, its cost-effectiveness and economic tradeoffs have not been evaluated. This study examines the incremental cost-effectiveness ratio (ICER) of two cardiovascular disease (CVD) prevention programs guided by the non-lab versus lab-based Framingham algorithm. We simulated the World Health Organization CVD prevention guidelines on a cohort of 2690 AA participants in the Atherosclerosis Risk in Communities (ARIC) cohort. Costs were estimated using Medicare fee schedules (diagnostic tests, drugs & visits), Bureau of Labor Statistics (RN wages), and estimates for managing incident CVD events. Outcomes were assumed to be true positive cases detected at a data driven treatment threshold. Both algorithms had the best balance of sensitivity/specificity at the moderate risk threshold (>10% risk). Over 12years, 82% and 77% of 401 incident CVD events were accurately predicted via the non-lab and lab-based Framingham algorithms, respectively. There were 20 fewer false negative cases in the non-lab approach translating into over $900,000 in savings over 12years. The ICER was -$57,153 for every extra CVD event prevented when using the non-lab algorithm. The approach guided by the non-lab Framingham strategy dominated the lab-based approach with respect to both costs and predictive ability. Consequently, the non-lab Framingham algorithm could potentially provide a highly effective screening tool at lower cost to address the high burden of CVD especially among AA and in resource-constrained settings where lab tests are unavailable. Copyright © 2017 Elsevier Inc. All rights reserved.

Investigating the enhanced Best Performance Algorithm for Annual Crop Planning problem based on economic factors

PubMed Central

2017-01-01

The Annual Crop Planning (ACP) problem was a recently introduced problem in the literature. This study further expounds on this problem by presenting a new mathematical formulation, which is based on market economic factors. To determine solutions, a new local search metaheuristic algorithm is investigated which is called the enhanced Best Performance Algorithm (eBPA). eBPA’s results are compared against two well-known local search metaheuristic algorithms; these include Tabu Search and Simulated Annealing. The results show the potential of the eBPA for continuous optimization problems. PMID:28792495

Decision support tool for used oil regeneration technologies assessment and selection.

PubMed

Khelifi, Olfa; Dalla Giovanna, Fabio; Vranes, Sanja; Lodolo, Andrea; Miertus, Stanislav

2006-09-01

Regeneration is the most efficient way of managing used oil. It saves money by preventing costly cleanups and liabilities that are associated with mismanagement of used oil, it helps to protect the environment and it produces a technically renewable resource by enabling an indefinite recycling potential. There are a variety of processes and licensors currently offering ways to deal with used oils. Selecting a regeneration technology for used oil involves "cross-matching" key criteria. Therefore, the first prototype of spent oil regeneration (SPORE), a decision support tool, has been developed to help decision-makers to assess the available technologies and select the preferred used oil regeneration options. The analysis is based on technical, economical and environmental criteria. These criteria are ranked to determine their relative importance for a particular used oil regeneration project. The multi-criteria decision analysis (MCDA) is the core of the SPORE using the PROMETHEE II algorithm.

Hull Form Design and Optimization Tool Development

DTIC Science & Technology

2012-07-01

global minimum. The algorithm accomplishes this by using a method known as metaheuristics which allows the algorithm to examine a large area by...further development of these tools including the implementation and testing of a new optimization algorithm , the improvement of a rapid hull form...under the 2012 Naval Research Enterprise Intern Program. 15. SUBJECT TERMS hydrodynamic, hull form, generation, optimization, algorithm

New algorithms for motion error detection of numerical control machine tool by laser tracking measurement on the basis of GPS principle.

PubMed

Wang, Jindong; Chen, Peng; Deng, Yufen; Guo, Junjie

2018-01-01

As a three-dimensional measuring instrument, the laser tracker is widely used in industrial measurement. To avoid the influence of angle measurement error on the overall measurement accuracy, the multi-station and time-sharing measurement with a laser tracker is introduced on the basis of the global positioning system (GPS) principle in this paper. For the proposed method, how to accurately determine the coordinates of each measuring point by using a large amount of measured data is a critical issue. Taking detecting motion error of a numerical control machine tool, for example, the corresponding measurement algorithms are investigated thoroughly. By establishing the mathematical model of detecting motion error of a machine tool with this method, the analytical algorithm concerning on base station calibration and measuring point determination is deduced without selecting the initial iterative value in calculation. However, when the motion area of the machine tool is in a 2D plane, the coefficient matrix of base station calibration is singular, which generates a distortion result. In order to overcome the limitation of the original algorithm, an improved analytical algorithm is also derived. Meanwhile, the calibration accuracy of the base station with the improved algorithm is compared with that with the original analytical algorithm and some iterative algorithms, such as the Gauss-Newton algorithm and Levenberg-Marquardt algorithm. The experiment further verifies the feasibility and effectiveness of the improved algorithm. In addition, the different motion areas of the machine tool have certain influence on the calibration accuracy of the base station, and the corresponding influence of measurement error on the calibration result of the base station depending on the condition number of coefficient matrix are analyzed.

New algorithms for motion error detection of numerical control machine tool by laser tracking measurement on the basis of GPS principle

NASA Astrophysics Data System (ADS)

Wang, Jindong; Chen, Peng; Deng, Yufen; Guo, Junjie

2018-01-01

As a three-dimensional measuring instrument, the laser tracker is widely used in industrial measurement. To avoid the influence of angle measurement error on the overall measurement accuracy, the multi-station and time-sharing measurement with a laser tracker is introduced on the basis of the global positioning system (GPS) principle in this paper. For the proposed method, how to accurately determine the coordinates of each measuring point by using a large amount of measured data is a critical issue. Taking detecting motion error of a numerical control machine tool, for example, the corresponding measurement algorithms are investigated thoroughly. By establishing the mathematical model of detecting motion error of a machine tool with this method, the analytical algorithm concerning on base station calibration and measuring point determination is deduced without selecting the initial iterative value in calculation. However, when the motion area of the machine tool is in a 2D plane, the coefficient matrix of base station calibration is singular, which generates a distortion result. In order to overcome the limitation of the original algorithm, an improved analytical algorithm is also derived. Meanwhile, the calibration accuracy of the base station with the improved algorithm is compared with that with the original analytical algorithm and some iterative algorithms, such as the Gauss-Newton algorithm and Levenberg-Marquardt algorithm. The experiment further verifies the feasibility and effectiveness of the improved algorithm. In addition, the different motion areas of the machine tool have certain influence on the calibration accuracy of the base station, and the corresponding influence of measurement error on the calibration result of the base station depending on the condition number of coefficient matrix are analyzed.

Response to Paper III Economics in the Civics Curriculum. A Reaction to Andrew F. Brimmer.

ERIC Educational Resources Information Center

Schug, Mark C.

According to the document, Dr. Andrew Brimmer did an excellent job of identifying emerging economic concerns. Dr. Brimmer's characterization of economics as a tool kit can help young people examine important social questions using principles of economics as the tool for analysis. One way to build an economics tool kit is by placing more stress on…

Water flow algorithm decision support tool for travelling salesman problem

NASA Astrophysics Data System (ADS)

Kamarudin, Anis Aklima; Othman, Zulaiha Ali; Sarim, Hafiz Mohd

2016-08-01

This paper discuss about the role of Decision Support Tool in Travelling Salesman Problem (TSP) for helping the researchers who doing research in same area will get the better result from the proposed algorithm. A study has been conducted and Rapid Application Development (RAD) model has been use as a methodology which includes requirement planning, user design, construction and cutover. Water Flow Algorithm (WFA) with initialization technique improvement is used as the proposed algorithm in this study for evaluating effectiveness against TSP cases. For DST evaluation will go through usability testing conducted on system use, quality of information, quality of interface and overall satisfaction. Evaluation is needed for determine whether this tool can assists user in making a decision to solve TSP problems with the proposed algorithm or not. Some statistical result shown the ability of this tool in term of helping researchers to conduct the experiments on the WFA with improvements TSP initialization.

Cost-effectiveness Analysis with Influence Diagrams.

PubMed

Arias, M; Díez, F J

2015-01-01

Cost-effectiveness analysis (CEA) is used increasingly in medicine to determine whether the health benefit of an intervention is worth the economic cost. Decision trees, the standard decision modeling technique for non-temporal domains, can only perform CEA for very small problems. To develop a method for CEA in problems involving several dozen variables. We explain how to build influence diagrams (IDs) that explicitly represent cost and effectiveness. We propose an algorithm for evaluating cost-effectiveness IDs directly, i.e., without expanding an equivalent decision tree. The evaluation of an ID returns a set of intervals for the willingness to pay - separated by cost-effectiveness thresholds - and, for each interval, the cost, the effectiveness, and the optimal intervention. The algorithm that evaluates the ID directly is in general much more efficient than the brute-force method, which is in turn more efficient than the expansion of an equivalent decision tree. Using OpenMarkov, an open-source software tool that implements this algorithm, we have been able to perform CEAs on several IDs whose equivalent decision trees contain millions of branches. IDs can perform CEA on large problems that cannot be analyzed with decision trees.

Optimization of the resources management in fighting wildfires.

PubMed

Martin-Fernández, Susana; Martínez-Falero, Eugenio; Pérez-González, J Manuel

2002-09-01

Wildfires lead to important economic, social, and environmental losses, especially in areas of Mediterranean climate where they are of a high intensity and frequency. Over the past 30 years there has been a dramatic surge in the development and use of fire spread models. However, given the chaotic nature of environmental systems, it is very difficult to develop real-time fire-extinguishing models. This article proposes a method of optimizing the performance of wildfire fighting resources such that losses are kept to a minimum. The optimization procedure includes discrete simulation algorithms and Bayesian optimization methods for discrete and continuous problems (simulated annealing and Bayesian global optimization). Fast calculus algorithms are applied to provide optimization outcomes in short periods of time such that the predictions of the model and the real behavior of the fire, combat resources, and meteorological conditions are similar. In addition, adaptive algorithms take into account the chaotic behavior of wildfire so that the system can be updated with data corresponding to the real situation to obtain a new optimum solution. The application of this method to the Northwest Forest of Madrid (Spain) is also described. This application allowed us to check that it is a helpful tool in the decision-making process.

Optimization of the Resources Management in Fighting Wildfires

NASA Astrophysics Data System (ADS)

Martin-Fernández, Susana; Martínez-Falero, Eugenio; Pérez-González, J. Manuel

2002-09-01

Wildfires lead to important economic, social, and environmental losses, especially in areas of Mediterranean climate where they are of a high intensity and frequency. Over the past 30 years there has been a dramatic surge in the development and use of fire spread models. However, given the chaotic nature of environmental systems, it is very difficult to develop real-time fire-extinguishing models. This article proposes a method of optimizing the performance of wildfire fighting resources such that losses are kept to a minimum. The optimization procedure includes discrete simulation algorithms and Bayesian optimization methods for discrete and continuous problems (simulated annealing and Bayesian global optimization). Fast calculus algorithms are applied to provide optimization outcomes in short periods of time such that the predictions of the model and the real behavior of the fire, combat resources, and meteorological conditions are similar. In addition, adaptive algorithms take into account the chaotic behavior of wildfire so that the system can be updated with data corresponding to the real situation to obtain a new optimum solution. The application of this method to the Northwest Forest of Madrid (Spain) is also described. This application allowed us to check that it is a helpful tool in the decision-making process.

Symbiotic organisms search algorithm for dynamic economic dispatch with valve-point effects

NASA Astrophysics Data System (ADS)

Sonmez, Yusuf; Kahraman, H. Tolga; Dosoglu, M. Kenan; Guvenc, Ugur; Duman, Serhat

2017-05-01

In this study, symbiotic organisms search (SOS) algorithm is proposed to solve the dynamic economic dispatch with valve-point effects problem, which is one of the most important problems of the modern power system. Some practical constraints like valve-point effects, ramp rate limits and prohibited operating zones have been considered as solutions. Proposed algorithm was tested on five different test cases in 5 units, 10 units and 13 units systems. The obtained results have been compared with other well-known metaheuristic methods reported before. Results show that proposed algorithm has a good convergence and produces better results than other methods.

Reinforcement learning techniques for controlling resources in power networks

NASA Astrophysics Data System (ADS)

Kowli, Anupama Sunil

As power grids transition towards increased reliance on renewable generation, energy storage and demand response resources, an effective control architecture is required to harness the full functionalities of these resources. There is a critical need for control techniques that recognize the unique characteristics of the different resources and exploit the flexibility afforded by them to provide ancillary services to the grid. The work presented in this dissertation addresses these needs. Specifically, new algorithms are proposed, which allow control synthesis in settings wherein the precise distribution of the uncertainty and its temporal statistics are not known. These algorithms are based on recent developments in Markov decision theory, approximate dynamic programming and reinforcement learning. They impose minimal assumptions on the system model and allow the control to be "learned" based on the actual dynamics of the system. Furthermore, they can accommodate complex constraints such as capacity and ramping limits on generation resources, state-of-charge constraints on storage resources, comfort-related limitations on demand response resources and power flow limits on transmission lines. Numerical studies demonstrating applications of these algorithms to practical control problems in power systems are discussed. Results demonstrate how the proposed control algorithms can be used to improve the performance and reduce the computational complexity of the economic dispatch mechanism in a power network. We argue that the proposed algorithms are eminently suitable to develop operational decision-making tools for large power grids with many resources and many sources of uncertainty.

Self-adaptive MOEA feature selection for classification of bankruptcy prediction data.

PubMed

Gaspar-Cunha, A; Recio, G; Costa, L; Estébanez, C

2014-01-01

Bankruptcy prediction is a vast area of finance and accounting whose importance lies in the relevance for creditors and investors in evaluating the likelihood of getting into bankrupt. As companies become complex, they develop sophisticated schemes to hide their real situation. In turn, making an estimation of the credit risks associated with counterparts or predicting bankruptcy becomes harder. Evolutionary algorithms have shown to be an excellent tool to deal with complex problems in finances and economics where a large number of irrelevant features are involved. This paper provides a methodology for feature selection in classification of bankruptcy data sets using an evolutionary multiobjective approach that simultaneously minimise the number of features and maximise the classifier quality measure (e.g., accuracy). The proposed methodology makes use of self-adaptation by applying the feature selection algorithm while simultaneously optimising the parameters of the classifier used. The methodology was applied to four different sets of data. The obtained results showed the utility of using the self-adaptation of the classifier.

Self-Adaptive MOEA Feature Selection for Classification of Bankruptcy Prediction Data

PubMed Central

Gaspar-Cunha, A.; Recio, G.; Costa, L.; Estébanez, C.

2014-01-01

Bankruptcy prediction is a vast area of finance and accounting whose importance lies in the relevance for creditors and investors in evaluating the likelihood of getting into bankrupt. As companies become complex, they develop sophisticated schemes to hide their real situation. In turn, making an estimation of the credit risks associated with counterparts or predicting bankruptcy becomes harder. Evolutionary algorithms have shown to be an excellent tool to deal with complex problems in finances and economics where a large number of irrelevant features are involved. This paper provides a methodology for feature selection in classification of bankruptcy data sets using an evolutionary multiobjective approach that simultaneously minimise the number of features and maximise the classifier quality measure (e.g., accuracy). The proposed methodology makes use of self-adaptation by applying the feature selection algorithm while simultaneously optimising the parameters of the classifier used. The methodology was applied to four different sets of data. The obtained results showed the utility of using the self-adaptation of the classifier. PMID:24707201

A step by step selection method for the location and the size of a waste-to-energy facility targeting the maximum output energy and minimization of gate fee.

PubMed

Kyriakis, Efstathios; Psomopoulos, Constantinos; Kokkotis, Panagiotis; Bourtsalas, Athanasios; Themelis, Nikolaos

2017-06-23

This study attempts the development of an algorithm in order to present a step by step selection method for the location and the size of a waste-to-energy facility targeting the maximum output energy, also considering the basic obstacle which is in many cases, the gate fee. Various parameters identified and evaluated in order to formulate the proposed decision making method in the form of an algorithm. The principle simulation input is the amount of municipal solid wastes (MSW) available for incineration and along with its net calorific value are the most important factors for the feasibility of the plant. Moreover, the research is focused both on the parameters that could increase the energy production and those that affect the R1 energy efficiency factor. Estimation of the final gate fee is achieved through the economic analysis of the entire project by investigating both expenses and revenues which are expected according to the selected site and outputs of the facility. In this point, a number of commonly revenue methods were included in the algorithm. The developed algorithm has been validated using three case studies in Greece-Athens, Thessaloniki, and Central Greece, where the cities of Larisa and Volos have been selected for the application of the proposed decision making tool. These case studies were selected based on a previous publication made by two of the authors, in which these areas where examined. Results reveal that the development of a «solid» methodological approach in selecting the site and the size of waste-to-energy (WtE) facility can be feasible. However, the maximization of the energy efficiency factor R1 requires high utilization factors while the minimization of the final gate fee requires high R1 and high metals recovery from the bottom ash as well as economic exploitation of recovered raw materials if any.

Dynamic Harmony Search with Polynomial Mutation Algorithm for Valve-Point Economic Load Dispatch

PubMed Central

Karthikeyan, M.; Sree Ranga Raja, T.

2015-01-01

Economic load dispatch (ELD) problem is an important issue in the operation and control of modern control system. The ELD problem is complex and nonlinear with equality and inequality constraints which makes it hard to be efficiently solved. This paper presents a new modification of harmony search (HS) algorithm named as dynamic harmony search with polynomial mutation (DHSPM) algorithm to solve ORPD problem. In DHSPM algorithm the key parameters of HS algorithm like harmony memory considering rate (HMCR) and pitch adjusting rate (PAR) are changed dynamically and there is no need to predefine these parameters. Additionally polynomial mutation is inserted in the updating step of HS algorithm to favor exploration and exploitation of the search space. The DHSPM algorithm is tested with three power system cases consisting of 3, 13, and 40 thermal units. The computational results show that the DHSPM algorithm is more effective in finding better solutions than other computational intelligence based methods. PMID:26491710

Dynamic Harmony Search with Polynomial Mutation Algorithm for Valve-Point Economic Load Dispatch.

PubMed

Karthikeyan, M; Raja, T Sree Ranga

2015-01-01

Economic load dispatch (ELD) problem is an important issue in the operation and control of modern control system. The ELD problem is complex and nonlinear with equality and inequality constraints which makes it hard to be efficiently solved. This paper presents a new modification of harmony search (HS) algorithm named as dynamic harmony search with polynomial mutation (DHSPM) algorithm to solve ORPD problem. In DHSPM algorithm the key parameters of HS algorithm like harmony memory considering rate (HMCR) and pitch adjusting rate (PAR) are changed dynamically and there is no need to predefine these parameters. Additionally polynomial mutation is inserted in the updating step of HS algorithm to favor exploration and exploitation of the search space. The DHSPM algorithm is tested with three power system cases consisting of 3, 13, and 40 thermal units. The computational results show that the DHSPM algorithm is more effective in finding better solutions than other computational intelligence based methods.

LibKiSAO: a Java library for Querying KiSAO.

PubMed

Zhukova, Anna; Adams, Richard; Laibe, Camille; Le Novère, Nicolas

2012-09-24

The Kinetic Simulation Algorithm Ontology (KiSAO) supplies information about existing algorithms available for the simulation of Systems Biology models, their characteristics, parameters and inter-relationships. KiSAO enables the unambiguous identification of algorithms from simulation descriptions. Information about analogous methods having similar characteristics and about algorithm parameters incorporated into KiSAO is desirable for simulation tools. To retrieve this information programmatically an application programming interface (API) for KiSAO is needed. We developed libKiSAO, a Java library to enable querying of the KiSA Ontology. It implements methods to retrieve information about simulation algorithms stored in KiSAO, their characteristics and parameters, and methods to query the algorithm hierarchy and search for similar algorithms providing comparable results for the same simulation set-up. Using libKiSAO, simulation tools can make logical inferences based on this knowledge and choose the most appropriate algorithm to perform a simulation. LibKiSAO also enables simulation tools to handle a wider range of simulation descriptions by determining which of the available methods are similar and can be used instead of the one indicated in the simulation description if that one is not implemented. LibKiSAO enables Java applications to easily access information about simulation algorithms, their characteristics and parameters stored in the OWL-encoded Kinetic Simulation Algorithm Ontology. LibKiSAO can be used by simulation description editors and simulation tools to improve reproducibility of computational simulation tasks and facilitate model re-use.

Commodities Trading: An Essential Economic Tool.

ERIC Educational Resources Information Center

Welch, Mary A., Ed.

1989-01-01

This issue focuses on commodities trading as an essential economic tool. Activities include critical thinking about marketing decisions and discussion on how futures markets and options are used as important economic tools. Discussion questions and a special student project are included. (EH)

Operation management of daily economic dispatch using novel hybrid particle swarm optimization and gravitational search algorithm with hybrid mutation strategy

NASA Astrophysics Data System (ADS)

Wang, Yan; Huang, Song; Ji, Zhicheng

2017-07-01

This paper presents a hybrid particle swarm optimization and gravitational search algorithm based on hybrid mutation strategy (HGSAPSO-M) to optimize economic dispatch (ED) including distributed generations (DGs) considering market-based energy pricing. A daily ED model was formulated and a hybrid mutation strategy was adopted in HGSAPSO-M. The hybrid mutation strategy includes two mutation operators, chaotic mutation, Gaussian mutation. The proposed algorithm was tested on IEEE-33 bus and results show that the approach is effective for this problem.

Educational Tool for Optimal Controller Tuning Using Evolutionary Strategies

ERIC Educational Resources Information Center

Carmona Morales, D.; Jimenez-Hornero, J. E.; Vazquez, F.; Morilla, F.

2012-01-01

In this paper, an optimal tuning tool is presented for control structures based on multivariable proportional-integral-derivative (PID) control, using genetic algorithms as an alternative to traditional optimization algorithms. From an educational point of view, this tool provides students with the necessary means to consolidate their knowledge on…

Unified Lambert Tool for Massively Parallel Applications in Space Situational Awareness

NASA Astrophysics Data System (ADS)

Woollands, Robyn M.; Read, Julie; Hernandez, Kevin; Probe, Austin; Junkins, John L.

2018-03-01

This paper introduces a parallel-compiled tool that combines several of our recently developed methods for solving the perturbed Lambert problem using modified Chebyshev-Picard iteration. This tool (unified Lambert tool) consists of four individual algorithms, each of which is unique and better suited for solving a particular type of orbit transfer. The first is a Keplerian Lambert solver, which is used to provide a good initial guess (warm start) for solving the perturbed problem. It is also used to determine the appropriate algorithm to call for solving the perturbed problem. The arc length or true anomaly angle spanned by the transfer trajectory is the parameter that governs the automated selection of the appropriate perturbed algorithm, and is based on the respective algorithm convergence characteristics. The second algorithm solves the perturbed Lambert problem using the modified Chebyshev-Picard iteration two-point boundary value solver. This algorithm does not require a Newton-like shooting method and is the most efficient of the perturbed solvers presented herein, however the domain of convergence is limited to about a third of an orbit and is dependent on eccentricity. The third algorithm extends the domain of convergence of the modified Chebyshev-Picard iteration two-point boundary value solver to about 90% of an orbit, through regularization with the Kustaanheimo-Stiefel transformation. This is the second most efficient of the perturbed set of algorithms. The fourth algorithm uses the method of particular solutions and the modified Chebyshev-Picard iteration initial value solver for solving multiple revolution perturbed transfers. This method does require "shooting" but differs from Newton-like shooting methods in that it does not require propagation of a state transition matrix. The unified Lambert tool makes use of the General Mission Analysis Tool and we use it to compute thousands of perturbed Lambert trajectories in parallel on the Space Situational Awareness computer cluster at the LASR Lab, Texas A&M University. We demonstrate the power of our tool by solving a highly parallel example problem, that is the generation of extremal field maps for optimal spacecraft rendezvous (and eventual orbit debris removal). In addition we demonstrate the need for including perturbative effects in simulations for satellite tracking or data association. The unified Lambert tool is ideal for but not limited to space situational awareness applications.

Algorithms for optimizing the treatment of depression: making the right decision at the right time.

PubMed

Adli, M; Rush, A J; Möller, H-J; Bauer, M

2003-11-01

Medication algorithms for the treatment of depression are designed to optimize both treatment implementation and the appropriateness of treatment strategies. Thus, they are essential tools for treating and avoiding refractory depression. Treatment algorithms are explicit treatment protocols that provide specific therapeutic pathways and decision-making tools at critical decision points throughout the treatment process. The present article provides an overview of major projects of algorithm research in the field of antidepressant therapy. The Berlin Algorithm Project and the Texas Medication Algorithm Project (TMAP) compare algorithm-guided treatments with treatment as usual. The Sequenced Treatment Alternatives to Relieve Depression Project (STAR*D) compares different treatment strategies in treatment-resistant patients.

Complex Patterns in Financial Time Series Through HIGUCHI’S Fractal Dimension

NASA Astrophysics Data System (ADS)

Grace Elizabeth Rani, T. G.; Jayalalitha, G.

2016-11-01

This paper analyzes the complexity of stock exchanges through fractal theory. Closing price indices of four stock exchanges with different industry sectors are selected. Degree of complexity is assessed through Higuchi’s fractal dimension. Various window sizes are considered in evaluating the fractal dimension. It is inferred that the data considered as a whole represents random walk for all the four indices. Analysis of financial data through windowing procedure exhibits multi-fractality. Attempts to apply moving averages to reduce noise in the data revealed lower estimates of fractal dimension, which was verified using fractional Brownian motion. A change in the normalization factor in Higuchi’s algorithm did improve the results. It is quintessential to focus on rural development to realize a standard and steady growth of economy. Tools must be devised to settle the issues in this regard. Micro level institutions are necessary for the economic growth of a country like India, which would induce a sporadic development in the present global economical scenario.

Tools for Collaboration: Using and Designing Tools in Interorganizational Economic-Crime Investigation

ERIC Educational Resources Information Center

Puonti, Anne

2004-01-01

Economic-crime investigation in Finland is in transition from hierarchically organized, sequential collaboration between authorities toward parallel, interorganizational collaboration. This article describes the tools used and developed for managing the new collaborative economic-crime-investigation process. The challenge is to find…

Evaluation of Algorithms for a Miles-in-Trail Decision Support Tool

NASA Technical Reports Server (NTRS)

Bloem, Michael; Hattaway, David; Bambos, Nicholas

2012-01-01

Four machine learning algorithms were prototyped and evaluated for use in a proposed decision support tool that would assist air traffic managers as they set Miles-in-Trail restrictions. The tool would display probabilities that each possible Miles-in-Trail value should be used in a given situation. The algorithms were evaluated with an expected Miles-in-Trail cost that assumes traffic managers set restrictions based on the tool-suggested probabilities. Basic Support Vector Machine, random forest, and decision tree algorithms were evaluated, as was a softmax regression algorithm that was modified to explicitly reduce the expected Miles-in-Trail cost. The algorithms were evaluated with data from the summer of 2011 for air traffic flows bound to the Newark Liberty International Airport (EWR) over the ARD, PENNS, and SHAFF fixes. The algorithms were provided with 18 input features that describe the weather at EWR, the runway configuration at EWR, the scheduled traffic demand at EWR and the fixes, and other traffic management initiatives in place at EWR. Features describing other traffic management initiatives at EWR and the weather at EWR achieved relatively high information gain scores, indicating that they are the most useful for estimating Miles-in-Trail. In spite of a high variance or over-fitting problem, the decision tree algorithm achieved the lowest expected Miles-in-Trail costs when the algorithms were evaluated using 10-fold cross validation with the summer 2011 data for these air traffic flows.

Problem solving with genetic algorithms and Splicer

NASA Technical Reports Server (NTRS)

Bayer, Steven E.; Wang, Lui

1991-01-01

Genetic algorithms are highly parallel, adaptive search procedures (i.e., problem-solving methods) loosely based on the processes of population genetics and Darwinian survival of the fittest. Genetic algorithms have proven useful in domains where other optimization techniques perform poorly. The main purpose of the paper is to discuss a NASA-sponsored software development project to develop a general-purpose tool for using genetic algorithms. The tool, called Splicer, can be used to solve a wide variety of optimization problems and is currently available from NASA and COSMIC. This discussion is preceded by an introduction to basic genetic algorithm concepts and a discussion of genetic algorithm applications.

Leveraging Python Interoperability Tools to Improve Sapphire's Usability

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

Gezahegne, A; Love, N S

2007-12-10

The Sapphire project at the Center for Applied Scientific Computing (CASC) develops and applies an extensive set of data mining algorithms for the analysis of large data sets. Sapphire's algorithms are currently available as a set of C++ libraries. However many users prefer higher level scripting languages such as Python for their ease of use and flexibility. In this report, we evaluate four interoperability tools for the purpose of wrapping Sapphire's core functionality with Python. Exposing Sapphire's functionality through a Python interface would increase its usability and connect its algorithms to existing Python tools.

An FMS Dynamic Production Scheduling Algorithm Considering Cutting Tool Failure and Cutting Tool Life

NASA Astrophysics Data System (ADS)

Setiawan, A.; Wangsaputra, R.; Martawirya, Y. Y.; Halim, A. H.

2016-02-01

This paper deals with Flexible Manufacturing System (FMS) production rescheduling due to unavailability of cutting tools caused either of cutting tool failure or life time limit. The FMS consists of parallel identical machines integrated with an automatic material handling system and it runs fully automatically. Each machine has a same cutting tool configuration that consists of different geometrical cutting tool types on each tool magazine. The job usually takes two stages. Each stage has sequential operations allocated to machines considering the cutting tool life. In the real situation, the cutting tool can fail before the cutting tool life is reached. The objective in this paper is to develop a dynamic scheduling algorithm when a cutting tool is broken during unmanned and a rescheduling needed. The algorithm consists of four steps. The first step is generating initial schedule, the second step is determination the cutting tool failure time, the third step is determination of system status at cutting tool failure time and the fourth step is the rescheduling for unfinished jobs. The approaches to solve the problem are complete-reactive scheduling and robust-proactive scheduling. The new schedules result differences starting time and completion time of each operations from the initial schedule.

A Distributed Algorithm for Economic Dispatch Over Time-Varying Directed Networks With Delays

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

Yang, Tao; Lu, Jie; Wu, Di

In power system operation, economic dispatch problem (EDP) is designed to minimize the total generation cost while meeting the demand and satisfying generator capacity limits. This paper proposes an algorithm based on the gradient-push method to solve the EDP in a distributed manner over communication networks potentially with time-varying topologies and communication delays. It has been shown that the proposed method is guaranteed to solve the EDP if the time-varying directed communication network is uniformly jointly strongly connected. Moreover, the proposed algorithm is also able to handle arbitrarily large but bounded time delays on communication links. Numerical simulations are usedmore » to illustrate and validate the proposed algorithm.« less

Efficient RNA structure comparison algorithms.

PubMed

Arslan, Abdullah N; Anandan, Jithendar; Fry, Eric; Monschke, Keith; Ganneboina, Nitin; Bowerman, Jason

2017-12-01

Recently proposed relative addressing-based ([Formula: see text]) RNA secondary structure representation has important features by which an RNA structure database can be stored into a suffix array. A fast substructure search algorithm has been proposed based on binary search on this suffix array. Using this substructure search algorithm, we present a fast algorithm that finds the largest common substructure of given multiple RNA structures in [Formula: see text] format. The multiple RNA structure comparison problem is NP-hard in its general formulation. We introduced a new problem for comparing multiple RNA structures. This problem has more strict similarity definition and objective, and we propose an algorithm that solves this problem efficiently. We also develop another comparison algorithm that iteratively calls this algorithm to locate nonoverlapping large common substructures in compared RNAs. With the new resulting tools, we improved the RNASSAC website (linked from http://faculty.tamuc.edu/aarslan ). This website now also includes two drawing tools: one specialized for preparing RNA substructures that can be used as input by the search tool, and another one for automatically drawing the entire RNA structure from a given structure sequence.

PCTFPeval: a web tool for benchmarking newly developed algorithms for predicting cooperative transcription factor pairs in yeast.

PubMed

Lai, Fu-Jou; Chang, Hong-Tsun; Wu, Wei-Sheng

2015-01-01

Computational identification of cooperative transcription factor (TF) pairs helps understand the combinatorial regulation of gene expression in eukaryotic cells. Many advanced algorithms have been proposed to predict cooperative TF pairs in yeast. However, it is still difficult to conduct a comprehensive and objective performance comparison of different algorithms because of lacking sufficient performance indices and adequate overall performance scores. To solve this problem, in our previous study (published in BMC Systems Biology 2014), we adopted/proposed eight performance indices and designed two overall performance scores to compare the performance of 14 existing algorithms for predicting cooperative TF pairs in yeast. Most importantly, our performance comparison framework can be applied to comprehensively and objectively evaluate the performance of a newly developed algorithm. However, to use our framework, researchers have to put a lot of effort to construct it first. To save researchers time and effort, here we develop a web tool to implement our performance comparison framework, featuring fast data processing, a comprehensive performance comparison and an easy-to-use web interface. The developed tool is called PCTFPeval (Predicted Cooperative TF Pair evaluator), written in PHP and Python programming languages. The friendly web interface allows users to input a list of predicted cooperative TF pairs from their algorithm and select (i) the compared algorithms among the 15 existing algorithms, (ii) the performance indices among the eight existing indices, and (iii) the overall performance scores from two possible choices. The comprehensive performance comparison results are then generated in tens of seconds and shown as both bar charts and tables. The original comparison results of each compared algorithm and each selected performance index can be downloaded as text files for further analyses. Allowing users to select eight existing performance indices and 15 existing algorithms for comparison, our web tool benefits researchers who are eager to comprehensively and objectively evaluate the performance of their newly developed algorithm. Thus, our tool greatly expedites the progress in the research of computational identification of cooperative TF pairs.

PCTFPeval: a web tool for benchmarking newly developed algorithms for predicting cooperative transcription factor pairs in yeast

PubMed Central

2015-01-01

Background Computational identification of cooperative transcription factor (TF) pairs helps understand the combinatorial regulation of gene expression in eukaryotic cells. Many advanced algorithms have been proposed to predict cooperative TF pairs in yeast. However, it is still difficult to conduct a comprehensive and objective performance comparison of different algorithms because of lacking sufficient performance indices and adequate overall performance scores. To solve this problem, in our previous study (published in BMC Systems Biology 2014), we adopted/proposed eight performance indices and designed two overall performance scores to compare the performance of 14 existing algorithms for predicting cooperative TF pairs in yeast. Most importantly, our performance comparison framework can be applied to comprehensively and objectively evaluate the performance of a newly developed algorithm. However, to use our framework, researchers have to put a lot of effort to construct it first. To save researchers time and effort, here we develop a web tool to implement our performance comparison framework, featuring fast data processing, a comprehensive performance comparison and an easy-to-use web interface. Results The developed tool is called PCTFPeval (Predicted Cooperative TF Pair evaluator), written in PHP and Python programming languages. The friendly web interface allows users to input a list of predicted cooperative TF pairs from their algorithm and select (i) the compared algorithms among the 15 existing algorithms, (ii) the performance indices among the eight existing indices, and (iii) the overall performance scores from two possible choices. The comprehensive performance comparison results are then generated in tens of seconds and shown as both bar charts and tables. The original comparison results of each compared algorithm and each selected performance index can be downloaded as text files for further analyses. Conclusions Allowing users to select eight existing performance indices and 15 existing algorithms for comparison, our web tool benefits researchers who are eager to comprehensively and objectively evaluate the performance of their newly developed algorithm. Thus, our tool greatly expedites the progress in the research of computational identification of cooperative TF pairs. PMID:26677932

Advancing Research in Second Language Writing through Computational Tools and Machine Learning Techniques: A Research Agenda

ERIC Educational Resources Information Center

Crossley, Scott A.

2013-01-01

This paper provides an agenda for replication studies focusing on second language (L2) writing and the use of natural language processing (NLP) tools and machine learning algorithms. Specifically, it introduces a range of the available NLP tools and machine learning algorithms and demonstrates how these could be used to replicate seminal studies…

A mobile phone based tool to identify symptoms of common childhood diseases in Ghana: development and evaluation of the integrated clinical algorithm in a cross-sectional study.

PubMed

Franke, Konstantin H; Krumkamp, Ralf; Mohammed, Aliyu; Sarpong, Nimako; Owusu-Dabo, Ellis; Brinkel, Johanna; Fobil, Julius N; Marinovic, Axel Bonacic; Asihene, Philip; Boots, Mark; May, Jürgen; Kreuels, Benno

2018-03-27

The aim of this study was the development and evaluation of an algorithm-based diagnosis-tool, applicable on mobile phones, to support guardians in providing appropriate care to sick children. The algorithm was developed on the basis of the Integrated Management of Childhood Illness (IMCI) guidelines and evaluated at a hospital in Ghana. Two hundred and thirty-seven guardians applied the tool to assess their child's symptoms. Data recorded by the tool and health records completed by a physician were compared in terms of symptom detection, disease assessment and treatment recommendation. To compare both assessments, Kappa statistics and predictive values were calculated. The tool detected the symptoms of cough, fever, diarrhoea and vomiting with good agreement to the physicians' findings (kappa = 0.64; 0.59; 0.57 and 0.42 respectively). The disease assessment barely coincided with the physicians' findings. The tool's treatment recommendation correlated with the physicians' assessments in 93 out of 237 cases (39.2% agreement, kappa = 0.11), but underestimated a child's condition in only seven cases (3.0%). The algorithm-based tool achieved reliable symptom detection and treatment recommendations were administered conformably to the physicians' assessment. Testing in domestic environment is envisaged.

Experiences on developing digital down conversion algorithms using Xilinx system generator

NASA Astrophysics Data System (ADS)

Xu, Chengfa; Yuan, Yuan; Zhao, Lizhi

2013-07-01

The Digital Down Conversion (DDC) algorithm is a classical signal processing method which is widely used in radar and communication systems. In this paper, the DDC function is implemented by Xilinx System Generator tool on FPGA. System Generator is an FPGA design tool provided by Xilinx Inc and MathWorks Inc. It is very convenient for programmers to manipulate the design and debug the function, especially for the complex algorithm. Through the developing process of DDC function based on System Generator, the results show that System Generator is a very fast and efficient tool for FPGA design.

Software Management Environment (SME): Components and algorithms

NASA Technical Reports Server (NTRS)

Hendrick, Robert; Kistler, David; Valett, Jon

1994-01-01

This document presents the components and algorithms of the Software Management Environment (SME), a management tool developed for the Software Engineering Branch (Code 552) of the Flight Dynamics Division (FDD) of the Goddard Space Flight Center (GSFC). The SME provides an integrated set of visually oriented experienced-based tools that can assist software development managers in managing and planning software development projects. This document describes and illustrates the analysis functions that underlie the SME's project monitoring, estimation, and planning tools. 'SME Components and Algorithms' is a companion reference to 'SME Concepts and Architecture' and 'Software Engineering Laboratory (SEL) Relationships, Models, and Management Rules.'

The U.S. Machine Tool Industry and the Defense Industrial Base

DTIC Science & Technology

1983-01-01

GOLD, Director, Research Program in Industrial Economics , Case Western Reserve University HAMILTON HERMAN, Management Consultant NATHANIEL S. HOWE...Traditional U.S. Machine Tool Industry ........ 8 Technological Trends Shaping the Industry ........ 18 Economic Trends .................................. 23...sustained economic recovery and aggressive steps by both government and industry, an effectively com- petitive domestic machine tool industry can emerge

A Comparative Study of Interval Management Control Law Capabilities

NASA Technical Reports Server (NTRS)

Barmore, Bryan E.; Smith, Colin L.; Palmer, Susan O.; Abbott, Terence S.

2012-01-01

This paper presents a new tool designed to allow for rapid development and testing of different control algorithms for airborne spacing. This tool, Interval Management Modeling and Spacing Tool (IM MAST), is a fast-time, low-fidelity tool created to model the approach of aircraft to a runway, with a focus on their interactions with each other. Errors can be induced between pairs of aircraft by varying initial positions, winds, speed profiles, and altitude profiles. Results to-date show that only a few of the algorithms tested had poor behavior in the arrival and approach environment. The majority of the algorithms showed only minimal variation in performance under the test conditions. Trajectory-based algorithms showed high susceptibility to wind forecast errors, while performing marginally better than the other algorithms under other conditions. Trajectory-based algorithms have a sizable advantage, however, of being able to perform relative spacing operations between aircraft on different arrival routes and flight profiles without employing ghosting. methods. This comes at the higher cost of substantially increased complexity, however. Additionally, it was shown that earlier initiation of relative spacing operations provided more time for corrections to be made without any significant problems in the spacing operation itself. Initiating spacing farther out, however, would require more of the aircraft to begin spacing before they merge onto a common route.

Scalability of Comparative Analysis, Novel Algorithms and Tools (MICW - Metagenomics Informatics Challenges Workshop: 10K Genomes at a Time)

ScienceCinema

Mavrommatis, Kostas

2017-12-22

DOE JGI's Kostas Mavrommatis, chair of the Scalability of Comparative Analysis, Novel Algorithms and Tools panel, at the Metagenomics Informatics Challenges Workshop held at the DOE JGI on October 12-13, 2011.

Use of a quality improvement tool, the prioritization matrix, to identify and prioritize triage software algorithm enhancement.

PubMed

North, Frederick; Varkey, Prathiba; Caraballo, Pedro; Vsetecka, Darlene; Bartel, Greg

2007-10-11

Complex decision support software can require significant effort in maintenance and enhancement. A quality improvement tool, the prioritization matrix, was successfully used to guide software enhancement of algorithms in a symptom assessment call center.

Decision Support from Genetic Algorithms for Ship Collision Avoidance Route Planning and Alerts

NASA Astrophysics Data System (ADS)

Tsou, Ming-Cheng; Kao, Sheng-Long; Su, Chien-Min

When an officer of the watch (OOW) faces complicated marine traffic, a suitable decision support tool could be employed in support of collision avoidance decisions, to reduce the burden and greatly improve the safety of marine traffic. Decisions on routes to avoid collisions could also consider economy as well as safety. Through simulating the biological evolution model, this research adopts the genetic algorithm used in artificial intelligence to find a theoretically safety-critical recommendation for the shortest route of collision avoidance from an economic viewpoint, combining the international regulations for preventing collisions at sea (COLREGS) and the safety domain of a ship. Based on this recommendation, an optimal safe avoidance turning angle, navigation restoration time and navigational restoration angle will also be provided. A Geographic Information System (GIS) will be used as the platform for display and operation. In order to achieve advance notice of alerts and due preparation for collision avoidance, a Vessel Traffic Services (VTS) operator and the OOW can use this system as a reference to assess collision avoidance at present location.

Improved Microseismicity Detection During Newberry EGS Stimulations

DOE Data Explorer

Templeton, Dennise

2013-10-01

Effective enhanced geothermal systems (EGS) require optimal fracture networks for efficient heat transfer between hot rock and fluid. Microseismic mapping is a key tool used to infer the subsurface fracture geometry. Traditional earthquake detection and location techniques are often employed to identify microearthquakes in geothermal regions. However, most commonly used algorithms may miss events if the seismic signal of an earthquake is small relative to the background noise level or if a microearthquake occurs within the coda of a larger event. Consequently, we have developed a set of algorithms that provide improved microearthquake detection. Our objective is to investigate the microseismicity at the DOE Newberry EGS site to better image the active regions of the underground fracture network during and immediately after the EGS stimulation. Detection of more microearthquakes during EGS stimulations will allow for better seismic delineation of the active regions of the underground fracture system. This improved knowledge of the reservoir network will improve our understanding of subsurface conditions, and allow improvement of the stimulation strategy that will optimize heat extraction and maximize economic return.

Improved Microseismicity Detection During Newberry EGS Stimulations

DOE Data Explorer

Templeton, Dennise

2013-11-01

Effective enhanced geothermal systems (EGS) require optimal fracture networks for efficient heat transfer between hot rock and fluid. Microseismic mapping is a key tool used to infer the subsurface fracture geometry. Traditional earthquake detection and location techniques are often employed to identify microearthquakes in geothermal regions. However, most commonly used algorithms may miss events if the seismic signal of an earthquake is small relative to the background noise level or if a microearthquake occurs within the coda of a larger event. Consequently, we have developed a set of algorithms that provide improved microearthquake detection. Our objective is to investigate the microseismicity at the DOE Newberry EGS site to better image the active regions of the underground fracture network during and immediately after the EGS stimulation. Detection of more microearthquakes during EGS stimulations will allow for better seismic delineation of the active regions of the underground fracture system. This improved knowledge of the reservoir network will improve our understanding of subsurface conditions, and allow improvement of the stimulation strategy that will optimize heat extraction and maximize economic return.

Decision making based on data analysis and optimization algorithm applied for cogeneration systems integration into a grid

NASA Astrophysics Data System (ADS)

Asmar, Joseph Al; Lahoud, Chawki; Brouche, Marwan

2018-05-01

Cogeneration and trigeneration systems can contribute to the reduction of primary energy consumption and greenhouse gas emissions in residential and tertiary sectors, by reducing fossil fuels demand and grid losses with respect to conventional systems. The cogeneration systems are characterized by a very high energy efficiency (80 to 90%) as well as a less polluting aspect compared to the conventional energy production. The integration of these systems into the energy network must simultaneously take into account their economic and environmental challenges. In this paper, a decision-making strategy will be introduced and is divided into two parts. The first one is a strategy based on a multi-objective optimization tool with data analysis and the second part is based on an optimization algorithm. The power dispatching of the Lebanese electricity grid is then simulated and considered as a case study in order to prove the compatibility of the cogeneration power calculated by our decision-making technique. In addition, the thermal energy produced by the cogeneration systems which capacity is selected by our technique shows compatibility with the thermal demand for district heating.

Introduction of the Tools for Economic Analysis of Patient Management Interventions in Heart Failure Costing Tool: a user-friendly spreadsheet program to estimate costs of providing patient-centered interventions.

PubMed

Reed, Shelby D; Li, Yanhong; Kamble, Shital; Polsky, Daniel; Graham, Felicia L; Bowers, Margaret T; Samsa, Gregory P; Paul, Sara; Schulman, Kevin A; Whellan, David J; Riegel, Barbara J

2012-01-01

Patient-centered health care interventions, such as heart failure disease management programs, are under increasing pressure to demonstrate good value. Variability in costing methods and assumptions in economic evaluations of such interventions limit the comparability of cost estimates across studies. Valid cost estimation is critical to conducting economic evaluations and for program budgeting and reimbursement negotiations. Using sound economic principles, we developed the Tools for Economic Analysis of Patient Management Interventions in Heart Failure (TEAM-HF) Costing Tool, a spreadsheet program that can be used by researchers and health care managers to systematically generate cost estimates for economic evaluations and to inform budgetary decisions. The tool guides users on data collection and cost assignment for associated personnel, facilities, equipment, supplies, patient incentives, miscellaneous items, and start-up activities. The tool generates estimates of total program costs, cost per patient, and cost per week and presents results using both standardized and customized unit costs for side-by-side comparisons. Results from pilot testing indicated that the tool was well-formatted, easy to use, and followed a logical order. Cost estimates of a 12-week exercise training program in patients with heart failure were generated with the costing tool and were found to be consistent with estimates published in a recent study. The TEAM-HF Costing Tool could prove to be a valuable resource for researchers and health care managers to generate comprehensive cost estimates of patient-centered interventions in heart failure or other conditions for conducting high-quality economic evaluations and making well-informed health care management decisions.

VirSSPA- a virtual reality tool for surgical planning workflow.

PubMed

Suárez, C; Acha, B; Serrano, C; Parra, C; Gómez, T

2009-03-01

A virtual reality tool, called VirSSPA, was developed to optimize the planning of surgical processes. Segmentation algorithms for Computed Tomography (CT) images: a region growing procedure was used for soft tissues and a thresholding algorithm was implemented to segment bones. The algorithms operate semiautomati- cally since they only need seed selection with the mouse on each tissue segmented by the user. The novelty of the paper is the adaptation of an enhancement method based on histogram thresholding applied to CT images for surgical planning, which simplifies subsequent segmentation. A substantial improvement of the virtual reality tool VirSSPA was obtained with these algorithms. VirSSPA was used to optimize surgical planning, to decrease the time spent on surgical planning and to improve operative results. The success rate increases due to surgeons being able to see the exact extent of the patient's ailment. This tool can decrease operating room time, thus resulting in reduced costs. Virtual simulation was effective for optimizing surgical planning, which could, consequently, result in improved outcomes with reduced costs.

Equation-free analysis of agent-based models and systematic parameter determination

NASA Astrophysics Data System (ADS)

Thomas, Spencer A.; Lloyd, David J. B.; Skeldon, Anne C.

2016-12-01

Agent based models (ABM)s are increasingly used in social science, economics, mathematics, biology and computer science to describe time dependent systems in circumstances where a description in terms of equations is difficult. Yet few tools are currently available for the systematic analysis of ABM behaviour. Numerical continuation and bifurcation analysis is a well-established tool for the study of deterministic systems. Recently, equation-free (EF) methods have been developed to extend numerical continuation techniques to systems where the dynamics are described at a microscopic scale and continuation of a macroscopic property of the system is considered. To date, the practical use of EF methods has been limited by; (1) the over-head of application-specific implementation; (2) the laborious configuration of problem-specific parameters; and (3) large ensemble sizes (potentially) leading to computationally restrictive run-times. In this paper we address these issues with our tool for the EF continuation of stochastic systems, which includes algorithms to systematically configuration problem specific parameters and enhance robustness to noise. Our tool is generic and can be applied to any 'black-box' simulator and determines the essential EF parameters prior to EF analysis. Robustness is significantly improved using our convergence-constraint with a corrector-repeat (C3R) method. This algorithm automatically detects outliers based on the dynamics of the underlying system enabling both an order of magnitude reduction in ensemble size and continuation of systems at much higher levels of noise than classical approaches. We demonstrate our method with application to several ABM models, revealing parameter dependence, bifurcation and stability analysis of these complex systems giving a deep understanding of the dynamical behaviour of the models in a way that is not otherwise easily obtainable. In each case we demonstrate our systematic parameter determination stage for configuring the system specific EF parameters.

Software For Genetic Algorithms

NASA Technical Reports Server (NTRS)

Wang, Lui; Bayer, Steve E.

1992-01-01

SPLICER computer program is genetic-algorithm software tool used to solve search and optimization problems. Provides underlying framework and structure for building genetic-algorithm application program. Written in Think C.

Identifying persistent and characteristic features in firearm tool marks on cartridge cases

NASA Astrophysics Data System (ADS)

Ott, Daniel; Soons, Johannes; Thompson, Robert; Song, John

2017-12-01

Recent concerns about subjectivity in forensic firearm identification have motivated the development of algorithms to compare firearm tool marks that are imparted on ammunition and to generate quantitative measures of similarity. In this paper, we describe an algorithm that identifies impressed tool marks on a cartridge case that are both consistent between firings and contribute strongly to a surface similarity metric. The result is a representation of the tool mark topography that emphasizes both significant and persistent features across firings. This characteristic surface map is useful for understanding the variability and persistence of the tool marks created by a firearm and can provide improved discrimination between the comparison scores of samples fired from the same firearm and the scores of samples fired from different firearms. The algorithm also provides a convenient method for visualizing areas of similarity that may be useful in providing quantitative support for visual comparisons by trained examiners.

Developing Molecular Genetic Tools to Facilitate Economic Production in Green Algae

DTIC Science & Technology

2012-09-10

Economic Production in Green Algae FA9550-10-1-0052 Georgianna, David, R Gimpel, Javier Hannon, Michael, J Mayfield, Stephen, P Prof. Stephen...Final Performance Report Project Title: Developing Molecular Genetic Tools to Facilitate Economic Production in Green Algae Award Number... ECONOMIC PRODUCTION IN GREEN ALGAE ABSTRACT It is now accepted that algae have enormous potential to generate economically viable and

Minimum-Time Consensus-Based Approach for Power System Applications

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

Yang, Tao; Wu, Di; Sun, Yannan

2016-02-01

This paper presents minimum-time consensus based distributed algorithms for power system applications, such as load shedding and economic dispatch. The proposed algorithms are capable of solving these problems in a minimum number of time steps instead of asymptotically as in most of existing studies. Moreover, these algorithms are applicable to both undirected and directed communication networks. Simulation results are used to validate the proposed algorithms.

Separation analysis, a tool for analyzing multigrid algorithms

NASA Technical Reports Server (NTRS)

Costiner, Sorin; Taasan, Shlomo

1995-01-01

The separation of vectors by multigrid (MG) algorithms is applied to the study of convergence and to the prediction of the performance of MG algorithms. The separation operator for a two level cycle algorithm is derived. It is used to analyze the efficiency of the cycle when mixing of eigenvectors occurs. In particular cases the separation analysis reduces to Fourier type analysis. The separation operator of a two level cycle for a Schridubger eigenvalue problem, is derived and analyzed in a Fourier basis. Separation analysis gives information on how to choose performance relaxations and inter-level transfers. Separation analysis is a tool for analyzing and designing algorithms, and for optimizing their performance.

Understanding the stakeholders' intention to use economic decision-support tools: A cross-sectional study with the tobacco return on investment tool.

PubMed

Cheung, Kei Long; Evers, Silvia M A A; Hiligsmann, Mickaël; Vokó, Zoltán; Pokhrel, Subhash; Jones, Teresa; Muñoz, Celia; Wolfenstetter, Silke B; Józwiak-Hagymásy, Judit; de Vries, Hein

2016-01-01

Despite an increased number of economic evaluations of tobacco control interventions, the uptake by stakeholders continues to be limited. Understanding the underlying mechanism in adopting such economic decision-support tools by stakeholders is therefore important. By applying the I-Change Model, this study aims to identify which factors determine potential uptake of an economic decision-support tool, i.e., the Return on Investment tool. Stakeholders (decision-makers, purchasers of services/pharma products, professionals/service providers, evidence generators and advocates of health promotion) were interviewed in five countries, using an I-Change based questionnaire. MANOVA's were conducted to assess differences between intenders and non-intenders regarding beliefs. A multiple regression analysis was conducted to identify the main explanatory variables of intention to use an economic decision-support tool. Ninety-three stakeholders participated. Significant differences in beliefs were found between non-intenders and intenders: risk perception, attitude, social support, and self-efficacy towards using the tool. Regression showed that demographics, pre-motivational, and motivational factors explained 69% of the variation in intention. This study is the first to provide a theoretical framework to understand differences in beliefs between stakeholders who do or do not intend to use economic decision-support tools, and empirically corroborating the framework. This contributes to our understanding of the facilitators and barriers to the uptake of these studies. Copyright © 2015 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Improving Students' Understanding of the Importance of Economic Consequences in Standard Setting: A Computerized Spreadsheet Tool.

ERIC Educational Resources Information Center

Ivancevich, Daniel M.; And Others

1996-01-01

Points out that political and economic pressures have sometimes caused the Financial Accounting Standards Board to alter standards. Presents a spreadsheet tool that demonstrates the economic consequences of adopting accounting standards. (SK)

Tools for Analyzing Computing Resource Management Strategies and Algorithms for SDR Clouds

NASA Astrophysics Data System (ADS)

Marojevic, Vuk; Gomez-Miguelez, Ismael; Gelonch, Antoni

2012-09-01

Software defined radio (SDR) clouds centralize the computing resources of base stations. The computing resource pool is shared between radio operators and dynamically loads and unloads digital signal processing chains for providing wireless communications services on demand. Each new user session request particularly requires the allocation of computing resources for executing the corresponding SDR transceivers. The huge amount of computing resources of SDR cloud data centers and the numerous session requests at certain hours of a day require an efficient computing resource management. We propose a hierarchical approach, where the data center is divided in clusters that are managed in a distributed way. This paper presents a set of computing resource management tools for analyzing computing resource management strategies and algorithms for SDR clouds. We use the tools for evaluating a different strategies and algorithms. The results show that more sophisticated algorithms can achieve higher resource occupations and that a tradeoff exists between cluster size and algorithm complexity.

Generating DEM from LIDAR data - comparison of available software tools

NASA Astrophysics Data System (ADS)

Korzeniowska, K.; Lacka, M.

2011-12-01

In recent years many software tools and applications have appeared that offer procedures, scripts and algorithms to process and visualize ALS data. This variety of software tools and of "point cloud" processing methods contributed to the aim of this study: to assess algorithms available in various software tools that are used to classify LIDAR "point cloud" data, through a careful examination of Digital Elevation Models (DEMs) generated from LIDAR data on a base of these algorithms. The works focused on the most important available software tools: both commercial and open source ones. Two sites in a mountain area were selected for the study. The area of each site is 0.645 sq km. DEMs generated with analysed software tools ware compared with a reference dataset, generated using manual methods to eliminate non ground points. Surfaces were analysed using raster analysis. Minimum, maximum and mean differences between reference DEM and DEMs generated with analysed software tools were calculated, together with Root Mean Square Error. Differences between DEMs were also examined visually using transects along the grid axes in the test sites.

Combined protein construct and synthetic gene engineering for heterologous protein expression and crystallization using Gene Composer

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

Raymond, Amy; Lovell, Scott; Lorimer, Don

2009-12-01

With the goal of improving yield and success rates of heterologous protein production for structural studies we have developed the database and algorithm software package Gene Composer. This freely available electronic tool facilitates the information-rich design of protein constructs and their engineered synthetic gene sequences, as detailed in the accompanying manuscript. In this report, we compare heterologous protein expression levels from native sequences to that of codon engineered synthetic gene constructs designed by Gene Composer. A test set of proteins including a human kinase (P38{alpha}), viral polymerase (HCV NS5B), and bacterial structural protein (FtsZ) were expressed in both E. colimore » and a cell-free wheat germ translation system. We also compare the protein expression levels in E. coli for a set of 11 different proteins with greatly varied G:C content and codon bias. The results consistently demonstrate that protein yields from codon engineered Gene Composer designs are as good as or better than those achieved from the synonymous native genes. Moreover, structure guided N- and C-terminal deletion constructs designed with the aid of Gene Composer can lead to greater success in gene to structure work as exemplified by the X-ray crystallographic structure determination of FtsZ from Bacillus subtilis. These results validate the Gene Composer algorithms, and suggest that using a combination of synthetic gene and protein construct engineering tools can improve the economics of gene to structure research.« less

The Use of Economic Impact Studies as a Service Learning Tool in Undergraduate Business Programs

ERIC Educational Resources Information Center

Misner, John M.

2004-01-01

This paper examines the use of community based economic impact studies as service learning tools for undergraduate business programs. Economic impact studies are used to measure the economic benefits of a variety of activities such as community redevelopment, tourism, and expansions of existing facilities for both private and public producers.…

A Modified Artificial Bee Colony Algorithm Application for Economic Environmental Dispatch

NASA Astrophysics Data System (ADS)

Tarafdar Hagh, M.; Baghban Orandi, Omid

2018-03-01

In conventional fossil-fuel power systems, the economic environmental dispatch (EED) problem is a major problem that optimally determines the output power of generating units in a way that cost of total production and emission level be minimized simultaneously, and at the same time all the constraints of units and system are satisfied properly. To solve EED problem which is a non-convex optimization problem, a modified artificial bee colony (MABC) algorithm is proposed in this paper. This algorithm by implementing weighted sum method is applied on two test systems, and eventually, obtained results are compared with other reported results. Comparison of results confirms superiority and efficiency of proposed method clearly.

SeqCompress: an algorithm for biological sequence compression.

PubMed

Sardaraz, Muhammad; Tahir, Muhammad; Ikram, Ataul Aziz; Bajwa, Hassan

2014-10-01

The growth of Next Generation Sequencing technologies presents significant research challenges, specifically to design bioinformatics tools that handle massive amount of data efficiently. Biological sequence data storage cost has become a noticeable proportion of total cost in the generation and analysis. Particularly increase in DNA sequencing rate is significantly outstripping the rate of increase in disk storage capacity, which may go beyond the limit of storage capacity. It is essential to develop algorithms that handle large data sets via better memory management. This article presents a DNA sequence compression algorithm SeqCompress that copes with the space complexity of biological sequences. The algorithm is based on lossless data compression and uses statistical model as well as arithmetic coding to compress DNA sequences. The proposed algorithm is compared with recent specialized compression tools for biological sequences. Experimental results show that proposed algorithm has better compression gain as compared to other existing algorithms. Copyright © 2014 Elsevier Inc. All rights reserved.

Economics of Agroforestry

Treesearch

D. Evan Mercer; Frederick W. Cubbage; Gregory E. Frey

2014-01-01

This chapter provides principles, literature and a case study about the economics of agroforestry. We examine necessary conditions for achieving efficiency in agroforestry system design and economic analysis tools for assessing efficiency and adoptability of agroforestry. The tools presented here (capital budgeting, linear progranuning, production frontier analysis...

Using support vector machine to predict eco-environment burden: a case study of Wuhan, Hubei Province, China.

PubMed

Li, Xiang-Mei; Zhou, Jing-Xuan; Yuan, Song-Hu; Zhou, Xin-Ping; Fu, Qiang

2008-02-01

The human socio-economic development depends on the planet's natural capital. Humans have had a considerable impact on the earth, such as resources depression and environment deterioration. The objective of this study was to assess the impact of socio-economic development on the ecological environment of Wuhan, Hubei Province, China, during the general planning period 2006-2020. Support vector machine (SVM) model was constructed to simulate the process of eco-economic system of Wuhan. Socio-economic factors of urban total ecological footprint (TEF) were selected by partial least squares (PLS) and leave-one-out cross validation (LOOCV). Historical data of socio-economic factors as inputs, and corresponding historical data of TEF as target outputs, were presented to identify and validate the SVM model. When predicted input data after 2005 were presented to trained model as generalization sets, TEFs of 2005, 2006,..., till 2020 were simulated as output in succession. Up to 2020, the district would have suffered an accumulative TEF of 28.374 million gha, which was over 1.5 times that of 2004 and nearly 3 times that of 1988. The per capita EF would be up to 3.019 gha in 2020. The simulation indicated that although the increase rate of GDP would be restricted in a lower level during the general planning period, urban ecological environment burden could not respond to the socio-economic circumstances promptly. SVM provides tools for dynamic assessment of regional eco-environment. However, there still exist limitations and disadvantages in the model. We believe that the next logical step in deriving better dynamic models of ecosystem is to integrate SVM and other algorithms or technologies.

Genetic algorithms

NASA Technical Reports Server (NTRS)

Wang, Lui; Bayer, Steven E.

1991-01-01

Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic genetic algorithms concepts are introduced, genetic algorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of genetic algorithm technology.

Economic evaluation of the one-hour rule-out and rule-in algorithm for acute myocardial infarction using the high-sensitivity cardiac troponin T assay in the emergency department.

PubMed

Ambavane, Apoorva; Lindahl, Bertil; Giannitsis, Evangelos; Roiz, Julie; Mendivil, Joan; Frankenstein, Lutz; Body, Richard; Christ, Michael; Bingisser, Roland; Alquezar, Aitor; Mueller, Christian

2017-01-01

The 1-hour (h) algorithm triages patients presenting with suspected acute myocardial infarction (AMI) to the emergency department (ED) towards "rule-out," "rule-in," or "observation," depending on baseline and 1-h levels of high-sensitivity cardiac troponin (hs-cTn). The economic consequences of applying the accelerated 1-h algorithm are unknown. We performed a post-hoc economic analysis in a large, diagnostic, multicenter study of hs-cTnT using central adjudication of the final diagnosis by two independent cardiologists. Length of stay (LoS), resource utilization (RU), and predicted diagnostic accuracy of the 1-h algorithm compared to standard of care (SoC) in the ED were estimated. The ED LoS, RU, and accuracy of the 1-h algorithm was compared to that achieved by the SoC at ED discharge. Expert opinion was sought to characterize clinical implementation of the 1-h algorithm, which required blood draws at ED presentation and 1h, after which "rule-in" patients were transferred for coronary angiography, "rule-out" patients underwent outpatient stress testing, and "observation" patients received SoC. Unit costs were for the United Kingdom, Switzerland, and Germany. The sensitivity and specificity for the 1-h algorithm were 87% and 96%, respectively, compared to 69% and 98% for SoC. The mean ED LoS for the 1-h algorithm was 4.3h-it was 6.5h for SoC, which is a reduction of 33%. The 1-h algorithm was associated with reductions in RU, driven largely by the shorter LoS in the ED for patients with a diagnosis other than AMI. The estimated total costs per patient were £2,480 for the 1-h algorithm compared to £4,561 for SoC, a reduction of up to 46%. The analysis shows that the use of 1-h algorithm is associated with reduction in overall AMI diagnostic costs, provided it is carefully implemented in clinical practice. These results need to be prospectively validated in the future.

Molecular beacon sequence design algorithm.

PubMed

Monroe, W Todd; Haselton, Frederick R

2003-01-01

A method based on Web-based tools is presented to design optimally functioning molecular beacons. Molecular beacons, fluorogenic hybridization probes, are a powerful tool for the rapid and specific detection of a particular nucleic acid sequence. However, their synthesis costs can be considerable. Since molecular beacon performance is based on its sequence, it is imperative to rationally design an optimal sequence before synthesis. The algorithm presented here uses simple Microsoft Excel formulas and macros to rank candidate sequences. This analysis is carried out using mfold structural predictions along with other free Web-based tools. For smaller laboratories where molecular beacons are not the focus of research, the public domain algorithm described here may be usefully employed to aid in molecular beacon design.

Moats and Drawbridges: An Isolation Primitive for Reconfigurable Hardware Based Systems

DTIC Science & Technology

2007-05-01

these systems, and after being run through an optimizing CAD tool the resulting circuit is a single entangled mess of gates and wires. To prevent the...translates MATLAB [48] algorithms into HDL, logic synthesis translates this HDL into a netlist, a synthesis tool uses a place-and-route algorithm to...Core Soft Core µ Soft P Core µP Core Hard Soft Algorithms MATLAB gcc ExecutableC Code HDL C Code Bitstream Place and Route NetlistLogic Synthesis EDK µP

An Efficient Reachability Analysis Algorithm

NASA Technical Reports Server (NTRS)

Vatan, Farrokh; Fijany, Amir

2008-01-01

A document discusses a new algorithm for generating higher-order dependencies for diagnostic and sensor placement analysis when a system is described with a causal modeling framework. This innovation will be used in diagnostic and sensor optimization and analysis tools. Fault detection, diagnosis, and prognosis are essential tasks in the operation of autonomous spacecraft, instruments, and in-situ platforms. This algorithm will serve as a power tool for technologies that satisfy a key requirement of autonomous spacecraft, including science instruments and in-situ missions.

Research on Novel Algorithms for Smart Grid Reliability Assessment and Economic Dispatch

NASA Astrophysics Data System (ADS)

Luo, Wenjin

In this dissertation, several studies of electric power system reliability and economy assessment methods are presented. To be more precise, several algorithms in evaluating power system reliability and economy are studied. Furthermore, two novel algorithms are applied to this field and their simulation results are compared with conventional results. As the electrical power system develops towards extra high voltage, remote distance, large capacity and regional networking, the application of a number of new technique equipments and the electric market system have be gradually established, and the results caused by power cut has become more and more serious. The electrical power system needs the highest possible reliability due to its complication and security. In this dissertation the Boolean logic Driven Markov Process (BDMP) method is studied and applied to evaluate power system reliability. This approach has several benefits. It allows complex dynamic models to be defined, while maintaining its easy readability as conventional methods. This method has been applied to evaluate IEEE reliability test system. The simulation results obtained are close to IEEE experimental data which means that it could be used for future study of the system reliability. Besides reliability, modern power system is expected to be more economic. This dissertation presents a novel evolutionary algorithm named as quantum evolutionary membrane algorithm (QEPS), which combines the concept and theory of quantum-inspired evolutionary algorithm and membrane computation, to solve the economic dispatch problem in renewable power system with on land and offshore wind farms. The case derived from real data is used for simulation tests. Another conventional evolutionary algorithm is also used to solve the same problem for comparison. The experimental results show that the proposed method is quick and accurate to obtain the optimal solution which is the minimum cost for electricity supplied by wind farm system.

AeroADL: applying the integration of the Suomi-NPP science algorithms with the Algorithm Development Library to the calibration and validation task

NASA Astrophysics Data System (ADS)

Houchin, J. S.

2014-09-01

A common problem for the off-line validation of the calibration algorithms and algorithm coefficients is being able to run science data through the exact same software used for on-line calibration of that data. The Joint Polar Satellite System (JPSS) program solved part of this problem by making the Algorithm Development Library (ADL) available, which allows the operational algorithm code to be compiled and run on a desktop Linux workstation using flat file input and output. However, this solved only part of the problem, as the toolkit and methods to initiate the processing of data through the algorithms were geared specifically toward the algorithm developer, not the calibration analyst. In algorithm development mode, a limited number of sets of test data are staged for the algorithm once, and then run through the algorithm over and over as the software is developed and debugged. In calibration analyst mode, we are continually running new data sets through the algorithm, which requires significant effort to stage each of those data sets for the algorithm without additional tools. AeroADL solves this second problem by providing a set of scripts that wrap the ADL tools, providing both efficient means to stage and process an input data set, to override static calibration coefficient look-up-tables (LUT) with experimental versions of those tables, and to manage a library containing multiple versions of each of the static LUT files in such a way that the correct set of LUTs required for each algorithm are automatically provided to the algorithm without analyst effort. Using AeroADL, The Aerospace Corporation's analyst team has demonstrated the ability to quickly and efficiently perform analysis tasks for both the VIIRS and OMPS sensors with minimal training on the software tools.

Phenotyping for patient safety: algorithm development for electronic health record based automated adverse event and medical error detection in neonatal intensive care.

PubMed

Li, Qi; Melton, Kristin; Lingren, Todd; Kirkendall, Eric S; Hall, Eric; Zhai, Haijun; Ni, Yizhao; Kaiser, Megan; Stoutenborough, Laura; Solti, Imre

2014-01-01

Although electronic health records (EHRs) have the potential to provide a foundation for quality and safety algorithms, few studies have measured their impact on automated adverse event (AE) and medical error (ME) detection within the neonatal intensive care unit (NICU) environment. This paper presents two phenotyping AE and ME detection algorithms (ie, IV infiltrations, narcotic medication oversedation and dosing errors) and describes manual annotation of airway management and medication/fluid AEs from NICU EHRs. From 753 NICU patient EHRs from 2011, we developed two automatic AE/ME detection algorithms, and manually annotated 11 classes of AEs in 3263 clinical notes. Performance of the automatic AE/ME detection algorithms was compared to trigger tool and voluntary incident reporting results. AEs in clinical notes were double annotated and consensus achieved under neonatologist supervision. Sensitivity, positive predictive value (PPV), and specificity are reported. Twelve severe IV infiltrates were detected. The algorithm identified one more infiltrate than the trigger tool and eight more than incident reporting. One narcotic oversedation was detected demonstrating 100% agreement with the trigger tool. Additionally, 17 narcotic medication MEs were detected, an increase of 16 cases over voluntary incident reporting. Automated AE/ME detection algorithms provide higher sensitivity and PPV than currently used trigger tools or voluntary incident-reporting systems, including identification of potential dosing and frequency errors that current methods are unequipped to detect. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

cost and benefits optimization model for fault-tolerant aircraft electronic systems

NASA Technical Reports Server (NTRS)

1983-01-01

The factors involved in economic assessment of fault tolerant systems (FTS) and fault tolerant flight control systems (FTFCS) are discussed. Algorithms for optimization and economic analysis of FTFCS are documented.

Power system security enhancement through direct non-disruptive load control

NASA Astrophysics Data System (ADS)

Ramanathan, Badri Narayanan

The transition to a competitive market structure raises significant concerns regarding reliability of the power grid. A need to build tools for security assessment that produce operating limit boundaries for both static and dynamic contingencies is recognized. Besides, an increase in overall uncertainty in operating conditions makes corrective actions at times ineffective leaving the system vulnerable to instability. The tools that are in place for stability enhancement are mostly corrective and suffer from lack of robustness to operating condition changes. They often pose serious coordination challenges. With deregulation, there have also been ownership and responsibility issues associated with stability controls. However, the changing utility business model and the developments in enabling technologies such as two-way communication, metering, and control open up several new possibilities for power system security enhancement. This research proposes preventive modulation of selected loads through direct control for power system security enhancement. Two main contributions of this research are the following: development of an analysis framework and two conceptually different analysis approaches for load modulation to enhance oscillatory stability, and the development and study of algorithms for real-time modulation of thermostatic loads. The underlying analysis framework is based on the Structured Singular Value (SSV or mu) theory. Based on the above framework, two fundamentally different approaches towards analysis of the amount of load modulation for desired stability performance have been developed. Both the approaches have been tested on two different test systems: CIGRE Nordic test system and an equivalent of the Western Electric Coordinating Council test system. This research also develops algorithms for real-time modulation of thermostatic loads that use the results of the analysis. In line with some recent load management programs executed by utilities, two different algorithms based on dynamic programming are proposed for air-conditioner loads, while a decision-tree based algorithm is proposed for water-heater loads. An optimization framework has been developed employing the above algorithms. Monte Carlo simulations have been performed using this framework with the objective of studying the impact of different parameters and constraints on the effectiveness as well as the effect of control. The conclusions drawn from this research strongly advocate direct load control for stability enhancement from the perspectives of robustness and coordination, as well as economic viability and the developments towards availability of the institutional framework for load participation in providing system reliability services.

Current challenges and future perspectives of plant and agricultural biotechnology.

PubMed

Moshelion, Menachem; Altman, Arie

2015-06-01

Advances in understanding plant biology, novel genetic resources, genome modification, and omics technologies generate new solutions for food security and novel biomaterials production under changing environmental conditions. New gene and germplasm candidates that are anticipated to lead to improved crop yields and other plant traits under stress have to pass long development phases based on trial and error using large-scale field evaluation. Therefore, quantitative, objective, and automated screening methods combined with decision-making algorithms are likely to have many advantages, enabling rapid screening of the most promising crop lines at an early stage followed by final mandatory field experiments. The combination of novel molecular tools, screening technologies, and economic evaluation should become the main goal of the plant biotechnological revolution in agriculture. Copyright © 2015 Elsevier Ltd. All rights reserved.

A NEW LOG EVALUATION METHOD TO APPRAISE MESAVERDE RE-COMPLETION OPPORTUNITIES

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

Albert Greer

2003-09-11

Artificial intelligence tools, fuzzy logic and neural networks were used to evaluate the potential of the behind pipe Mesaverde formation in BMG's Mancos formation wells. A fractal geostatistical mapping algorithm was also used to predict Mesaverde production. Additionally, a conventional geological study was conducted. To date one Mesaverde completion has been performed. The Janet No.3 Mesaverde completion was non-economic. Both the AI method and the geostatistical methods predicted the failure of the Janet No.3. The Gavilan No.1 in the Mesaverde was completed during the course of the study and was an extremely good well. This well was not included inmore » the statistical dataset. The AI method predicted very good production while the fractal map predicted a poor producer.« less

ProBiS tools (algorithm, database, and web servers) for predicting and modeling of biologically interesting proteins.

PubMed

Konc, Janez; Janežič, Dušanka

2017-09-01

ProBiS (Protein Binding Sites) Tools consist of algorithm, database, and web servers for prediction of binding sites and protein ligands based on the detection of structurally similar binding sites in the Protein Data Bank. In this article, we review the operations that ProBiS Tools perform, provide comments on the evolution of the tools, and give some implementation details. We review some of its applications to biologically interesting proteins. ProBiS Tools are freely available at http://probis.cmm.ki.si and http://probis.nih.gov. Copyright © 2017 Elsevier Ltd. All rights reserved.

Efficient greedy algorithms for economic manpower shift planning

NASA Astrophysics Data System (ADS)

Nearchou, A. C.; Giannikos, I. C.; Lagodimos, A. G.

2015-01-01

Consideration is given to the economic manpower shift planning (EMSP) problem, an NP-hard capacity planning problem appearing in various industrial settings including the packing stage of production in process industries and maintenance operations. EMSP aims to determine the manpower needed in each available workday shift of a given planning horizon so as to complete a set of independent jobs at minimum cost. Three greedy heuristics are presented for the EMSP solution. These practically constitute adaptations of an existing algorithm for a simplified version of EMSP which had shown excellent performance in terms of solution quality and speed. Experimentation shows that the new algorithms perform very well in comparison to the results obtained by both the CPLEX optimizer and an existing metaheuristic. Statistical analysis is deployed to rank the algorithms in terms of their solution quality and to identify the effects that critical planning factors may have on their relative efficiency.

On the use of genetic algorithm to optimize industrial assets lifecycle management under safety and budget constraints

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

Lonchampt, J.; Fessart, K.

2013-07-01

The purpose of this paper is to describe the method and tool dedicated to optimize investments planning for industrial assets. These investments may either be preventive maintenance tasks, asset enhancements or logistic investments such as spare parts purchases. The two methodological points to investigate in such an issue are: 1. The measure of the profitability of a portfolio of investments 2. The selection and planning of an optimal set of investments 3. The measure of the risk of a portfolio of investments The measure of the profitability of a set of investments in the IPOP tool is synthesised in themore » Net Present Value indicator. The NPV is the sum of the differences of discounted cash flows (direct costs, forced outages...) between the situations with and without a given investment. These cash flows are calculated through a pseudo-Markov reliability model representing independently the components of the industrial asset and the spare parts inventories. The component model has been widely discussed over the years but the spare part model is a new one based on some approximations that will be discussed. This model, referred as the NPV function, takes for input an investments portfolio and gives its NPV. The second issue is to optimize the NPV. If all investments were independent, this optimization would be an easy calculation, unfortunately there are two sources of dependency. The first one is introduced by the spare part model, as if components are indeed independent in their reliability model, the fact that several components use the same inventory induces a dependency. The second dependency comes from economic, technical or logistic constraints, such as a global maintenance budget limit or a safety requirement limiting the residual risk of failure of a component or group of component, making the aggregation of individual optimum not necessary feasible. The algorithm used to solve such a difficult optimization problem is a genetic algorithm. After a description of the features of the software a test case is presented showing the influence of the optimization algorithm parameters on its efficiency to find an optimal investments planning. (authors)« less

Automated Means of Identifying Landslide Deposits using LiDAR Data using the Contour Connection Method Algorithm

NASA Astrophysics Data System (ADS)

Olsen, M. J.; Leshchinsky, B. A.; Tanyu, B. F.

2014-12-01

Landslides are a global natural hazard, resulting in severe economic, environmental and social impacts every year. Often, landslides occur in areas of repeated slope instability, but despite these trends, significant residential developments and critical infrastructure are built in the shadow of past landslide deposits and marginally stable slopes. These hazards, despite their sometimes enormous scale and regional propensity, however, are difficult to detect on the ground, often due to vegetative cover. However, new developments in remote sensing technology, specifically Light Detection and Ranging mapping (LiDAR) are providing a new means of viewing our landscape. Airborne LiDAR, combined with a level of post-processing, enable the creation of spatial data representative of the earth beneath the vegetation, highlighting the scars of unstable slopes of the past. This tool presents a revolutionary technique to mapping landslide deposits and their associated regions of risk; yet, their inventorying is often done manually, an approach that can be tedious, time-consuming and subjective. However, the associated LiDAR bare earth data present the opportunity to use this remote sensing technology and typical landslide geometry to create an automated algorithm that can detect and inventory deposits on a landscape scale. This algorithm, called the Contour Connection Method (CCM), functions by first detecting steep gradients, often associated with the headscarp of a failed hillslope, and initiating a search, highlighting deposits downslope of the failure. Based on input of search gradients, CCM can assist in highlighting regions identified as landslides consistently on a landscape scale, capable of mapping more than 14,000 hectares rapidly (<30 minutes). CCM has shown preliminary agreement with manual landslide inventorying in Oregon's Coast Range, realizing almost 90% agreement with inventorying performed by a trained geologist. The global threat of landslides necessitates new and effective tools for inventorying regions of risk to protect people, infrastructure and the environment from landslide hazards. Use of the CCM algorithm combined with judgment and rapidly developing remote sensing technology may help better define these regions of risk.

MVIAeval: a web tool for comprehensively evaluating the performance of a new missing value imputation algorithm.

PubMed

Wu, Wei-Sheng; Jhou, Meng-Jhun

2017-01-13

Missing value imputation is important for microarray data analyses because microarray data with missing values would significantly degrade the performance of the downstream analyses. Although many microarray missing value imputation algorithms have been developed, an objective and comprehensive performance comparison framework is still lacking. To solve this problem, we previously proposed a framework which can perform a comprehensive performance comparison of different existing algorithms. Also the performance of a new algorithm can be evaluated by our performance comparison framework. However, constructing our framework is not an easy task for the interested researchers. To save researchers' time and efforts, here we present an easy-to-use web tool named MVIAeval (Missing Value Imputation Algorithm evaluator) which implements our performance comparison framework. MVIAeval provides a user-friendly interface allowing users to upload the R code of their new algorithm and select (i) the test datasets among 20 benchmark microarray (time series and non-time series) datasets, (ii) the compared algorithms among 12 existing algorithms, (iii) the performance indices from three existing ones, (iv) the comprehensive performance scores from two possible choices, and (v) the number of simulation runs. The comprehensive performance comparison results are then generated and shown as both figures and tables. MVIAeval is a useful tool for researchers to easily conduct a comprehensive and objective performance evaluation of their newly developed missing value imputation algorithm for microarray data or any data which can be represented as a matrix form (e.g. NGS data or proteomics data). Thus, MVIAeval will greatly expedite the progress in the research of missing value imputation algorithms.

Development of a multiobjective optimization tool for the selection and placement of best management practices for nonpoint source pollution control

NASA Astrophysics Data System (ADS)

Maringanti, Chetan; Chaubey, Indrajeet; Popp, Jennie

2009-06-01

Best management practices (BMPs) are effective in reducing the transport of agricultural nonpoint source pollutants to receiving water bodies. However, selection of BMPs for placement in a watershed requires optimization of the available resources to obtain maximum possible pollution reduction. In this study, an optimization methodology is developed to select and place BMPs in a watershed to provide solutions that are both economically and ecologically effective. This novel approach develops and utilizes a BMP tool, a database that stores the pollution reduction and cost information of different BMPs under consideration. The BMP tool replaces the dynamic linkage of the distributed parameter watershed model during optimization and therefore reduces the computation time considerably. Total pollutant load from the watershed, and net cost increase from the baseline, were the two objective functions minimized during the optimization process. The optimization model, consisting of a multiobjective genetic algorithm (NSGA-II) in combination with a watershed simulation tool (Soil Water and Assessment Tool (SWAT)), was developed and tested for nonpoint source pollution control in the L'Anguille River watershed located in eastern Arkansas. The optimized solutions provided a trade-off between the two objective functions for sediment, phosphorus, and nitrogen reduction. The results indicated that buffer strips were very effective in controlling the nonpoint source pollutants from leaving the croplands. The optimized BMP plans resulted in potential reductions of 33%, 32%, and 13% in sediment, phosphorus, and nitrogen loads, respectively, from the watershed.

DECONV-TOOL: An IDL based deconvolution software package

NASA Technical Reports Server (NTRS)

Varosi, F.; Landsman, W. B.

1992-01-01

There are a variety of algorithms for deconvolution of blurred images, each having its own criteria or statistic to be optimized in order to estimate the original image data. Using the Interactive Data Language (IDL), we have implemented the Maximum Likelihood, Maximum Entropy, Maximum Residual Likelihood, and sigma-CLEAN algorithms in a unified environment called DeConv_Tool. Most of the algorithms have as their goal the optimization of statistics such as standard deviation and mean of residuals. Shannon entropy, log-likelihood, and chi-square of the residual auto-correlation are computed by DeConv_Tool for the purpose of determining the performance and convergence of any particular method and comparisons between methods. DeConv_Tool allows interactive monitoring of the statistics and the deconvolved image during computation. The final results, and optionally, the intermediate results, are stored in a structure convenient for comparison between methods and review of the deconvolution computation. The routines comprising DeConv_Tool are available via anonymous FTP through the IDL Astronomy User's Library.

Rapid Risk Evaluation (ER2) Using MS Excel Spreadsheet: a Case Study of Fredericton (new Brunswick, Canada)

NASA Astrophysics Data System (ADS)

McGrath, H.; Stefanakis, E.; Nastev, M.

2016-06-01

Conventional knowledge of the flood hazard alone (extent and frequency) is not sufficient for informed decision-making. The public safety community needs tools and guidance to adequately undertake flood hazard risk assessment in order to estimate respective damages and social and economic losses. While many complex computer models have been developed for flood risk assessment, they require highly trained personnel to prepare the necessary input (hazard, inventory of the built environment, and vulnerabilities) and analyze model outputs. As such, tools which utilize open-source software or are built within popular desktop software programs are appealing alternatives. The recently developed Rapid Risk Evaluation (ER2) application runs scenario based loss assessment analyses in a Microsoft Excel spreadsheet. User input is limited to a handful of intuitive drop-down menus utilized to describe the building type, age, occupancy and the expected water level. In anticipation of local depth damage curves and other needed vulnerability parameters, those from the U.S. FEMA's Hazus-Flood software have been imported and temporarily accessed in conjunction with user input to display exposure and estimated economic losses related to the structure and the content of the building. Building types and occupancies representative of those most exposed to flooding in Fredericton (New Brunswick) were introduced and test flood scenarios were run. The algorithm was successfully validated against results from the Hazus-Flood model for the same building types and flood depths.

Coralline reefs classification in Banco Chinchorro, Mexico

NASA Astrophysics Data System (ADS)

Contreras-Silva, Ameris I.; López-Caloca, Alejandra A.

2009-09-01

The coralline reefs in Banco Chinchorro, Mexico, are part of the great reef belt of the western Atlantic. This reef complex is formed by an extensive coralline structure with great biological richness and diversity of species. These colonies are considered highly valuable ecologically, economically, socially and culturally, and they also inherently provide biological services. Fishing and scuba diving have been the main economic activities in this area for decades. However, in recent years, there has been a bleaching process and a decrease of the coral colonies in Quintana Roo, Mexico. This drop is caused mainly by the production activities performed in the oil platforms and the presence of hurricanes among other climatic events. The deterioration of the reef system can be analyzed synoptically using remote sensing. Thanks to this type of analysis, it is possible to have updated information of the reef conditions. In this paper, satellite imagery in Landsat TM and SPOT 5 is applied in the coralline reefs classification in the 1980- 2006 time period. Thus, an integral analysis of the optical components of the water surrounding the coralline reefs, such as on phytoplankton, sediments, yellow substance and even on the same water adjacent to the coral colonies, is performed. The use of a texture algorithm (Markov Random Field) was a key tool for their identification. This algorithm, does not limit itself to image segmentation, but also works on edge detection. In future work the multitemporal analysis of the results will determine the deterioration degree of these habitats and the conservation status of the coralline areas.

Backtracking search algorithm in CVRP models for efficient solid waste collection and route optimization.

PubMed

Akhtar, Mahmuda; Hannan, M A; Begum, R A; Basri, Hassan; Scavino, Edgar

2017-03-01

Waste collection is an important part of waste management that involves different issues, including environmental, economic, and social, among others. Waste collection optimization can reduce the waste collection budget and environmental emissions by reducing the collection route distance. This paper presents a modified Backtracking Search Algorithm (BSA) in capacitated vehicle routing problem (CVRP) models with the smart bin concept to find the best optimized waste collection route solutions. The objective function minimizes the sum of the waste collection route distances. The study introduces the concept of the threshold waste level (TWL) of waste bins to reduce the number of bins to be emptied by finding an optimal range, thus minimizing the distance. A scheduling model is also introduced to compare the feasibility of the proposed model with that of the conventional collection system in terms of travel distance, collected waste, fuel consumption, fuel cost, efficiency and CO 2 emission. The optimal TWL was found to be between 70% and 75% of the fill level of waste collection nodes and had the maximum tightness value for different problem cases. The obtained results for four days show a 36.80% distance reduction for 91.40% of the total waste collection, which eventually increases the average waste collection efficiency by 36.78% and reduces the fuel consumption, fuel cost and CO 2 emission by 50%, 47.77% and 44.68%, respectively. Thus, the proposed optimization model can be considered a viable tool for optimizing waste collection routes to reduce economic costs and environmental impacts. Copyright © 2017 Elsevier Ltd. All rights reserved.

Prediction of insemination outcomes in Holstein dairy cattle using alternative machine learning algorithms.

PubMed

Shahinfar, Saleh; Page, David; Guenther, Jerry; Cabrera, Victor; Fricke, Paul; Weigel, Kent

2014-02-01

When making the decision about whether or not to breed a given cow, knowledge about the expected outcome would have an economic impact on profitability of the breeding program and net income of the farm. The outcome of each breeding can be affected by many management and physiological features that vary between farms and interact with each other. Hence, the ability of machine learning algorithms to accommodate complex relationships in the data and missing values for explanatory variables makes these algorithms well suited for investigation of reproduction performance in dairy cattle. The objective of this study was to develop a user-friendly and intuitive on-farm tool to help farmers make reproduction management decisions. Several different machine learning algorithms were applied to predict the insemination outcomes of individual cows based on phenotypic and genotypic data. Data from 26 dairy farms in the Alta Genetics (Watertown, WI) Advantage Progeny Testing Program were used, representing a 10-yr period from 2000 to 2010. Health, reproduction, and production data were extracted from on-farm dairy management software, and estimated breeding values were downloaded from the US Department of Agriculture Agricultural Research Service Animal Improvement Programs Laboratory (Beltsville, MD) database. The edited data set consisted of 129,245 breeding records from primiparous Holstein cows and 195,128 breeding records from multiparous Holstein cows. Each data point in the final data set included 23 and 25 explanatory variables and 1 binary outcome for of 0.756 ± 0.005 and 0.736 ± 0.005 for primiparous and multiparous cows, respectively. The naïve Bayes algorithm, Bayesian network, and decision tree algorithms showed somewhat poorer classification performance. An information-based variable selection procedure identified herd average conception rate, incidence of ketosis, number of previous (failed) inseminations, days in milk at breeding, and mastitis as the most effective explanatory variables in predicting pregnancy outcome. Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Quantifying Economic and Environmental Impacts of Transportation Network Disruptions with Dynamic Traffic Simulation

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

Shekar, Venkateswaran; Fiondella, Lance; Chatterjee, Samrat

Several transportation network vulnerability models have been proposed. However, most only consider disruptions as a static snapshot in time and the impact on total travel time. These approaches cannot consider the time-varying nature of travel demand nor other undesirable outcomes that follow from transportation network disruptions. This paper proposes an algorithmic approach to assess the vulnerability of a transportation network that considers the time-varying demand with an open source dynamic transportation simulation tool. The open source nature of the tool allows us to systematically consider many disruption scenarios and quantitatively compare their relative criticality. This is far more efficient thanmore » traditional approaches which would require days or weeks of a transportation engineers time to manually set up, run, and assess these simulations. In addition to travel time, we also collect statistics on additional fuel consumed and the corresponding carbon dioxide emissions. Our approach, thus provides a more systematic approach that is both time-varying and can consider additional negative consequences of disruptions for decision makers to evaluate.« less

Top-attack modeling and automatic target detection using synthetic FLIR scenery

NASA Astrophysics Data System (ADS)

Weber, Bruce A.; Penn, Joseph A.

2004-09-01

A series of experiments have been performed to verify the utility of algorithmic tools for the modeling and analysis of cold-target signatures in synthetic, top-attack, FLIR video sequences. The tools include: MuSES/CREATION for the creation of synthetic imagery with targets, an ARL target detection algorithm to detect imbedded synthetic targets in scenes, and an ARL scoring algorithm, using Receiver-Operating-Characteristic (ROC) curve analysis, to evaluate detector performance. Cold-target detection variability was examined as a function of target emissivity, surrounding clutter type, and target placement in non-obscuring clutter locations. Detector metrics were also individually scored so as to characterize the effect of signature/clutter variations. Results show that using these tools, a detailed, physically meaningful, target detection analysis is possible and that scenario specific target detectors may be developed by selective choice and/or weighting of detector metrics. However, developing these tools into a reliable predictive capability will require the extension of these results to the modeling and analysis of a large number of data sets configured for a wide range of target and clutter conditions. Finally, these tools should also be useful for the comparison of competitive detection algorithms by providing well defined, and controllable target detection scenarios, as well as for the training and testing of expert human observers.

Flowgen: Flowchart-based documentation for C + + codes

NASA Astrophysics Data System (ADS)

Kosower, David A.; Lopez-Villarejo, J. J.

2015-11-01

We present the Flowgen tool, which generates flowcharts from annotated C + + source code. The tool generates a set of interconnected high-level UML activity diagrams, one for each function or method in the C + + sources. It provides a simple and visual overview of complex implementations of numerical algorithms. Flowgen is complementary to the widely-used Doxygen documentation tool. The ultimate aim is to render complex C + + computer codes accessible, and to enhance collaboration between programmers and algorithm or science specialists. We describe the tool and a proof-of-concept application to the VINCIA plug-in for simulating collisions at CERN's Large Hadron Collider.

Using microwave Doppler radar in automated manufacturing applications

NASA Astrophysics Data System (ADS)

Smith, Gregory C.

Since the beginning of the Industrial Revolution, manufacturers worldwide have used automation to improve productivity, gain market share, and meet growing or changing consumer demand for manufactured products. To stimulate further industrial productivity, manufacturers need more advanced automation technologies: "smart" part handling systems, automated assembly machines, CNC machine tools, and industrial robots that use new sensor technologies, advanced control systems, and intelligent decision-making algorithms to "see," "hear," "feel," and "think" at the levels needed to handle complex manufacturing tasks without human intervention. The investigator's dissertation offers three methods that could help make "smart" CNC machine tools and industrial robots possible: (1) A method for detecting acoustic emission using a microwave Doppler radar detector, (2) A method for detecting tool wear on a CNC lathe using a Doppler radar detector, and (3) An online non-contact method for detecting industrial robot position errors using a microwave Doppler radar motion detector. The dissertation studies indicate that microwave Doppler radar could be quite useful in automated manufacturing applications. In particular, the methods developed may help solve two difficult problems that hinder further progress in automating manufacturing processes: (1) Automating metal-cutting operations on CNC machine tools by providing a reliable non-contact method for detecting tool wear, and (2) Fully automating robotic manufacturing tasks by providing a reliable low-cost non-contact method for detecting on-line position errors. In addition, the studies offer a general non-contact method for detecting acoustic emission that may be useful in many other manufacturing and non-manufacturing areas, as well (e.g., monitoring and nondestructively testing structures, materials, manufacturing processes, and devices). By advancing the state of the art in manufacturing automation, the studies may help stimulate future growth in industrial productivity, which also promises to fuel economic growth and promote economic stability. The study also benefits the Department of Industrial Technology at Iowa State University and the field of Industrial Technology by contributing to the ongoing "smart" machine research program within the Department of Industrial Technology and by stimulating research into new sensor technologies within the University and within the field of Industrial Technology.

Improving Environmental Model Calibration and Prediction

DTIC Science & Technology

2011-01-18

REPORT Final Report - Improving Environmental Model Calibration and Prediction 14. ABSTRACT 16. SECURITY CLASSIFICATION OF: First, we have continued to...develop tools for efficient global optimization of environmental models. Our algorithms are hybrid algorithms that combine evolutionary strategies...toward practical hybrid optimization tools for environmental models. 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND SUBTITLE 18-01-2011 13

Automatic Tool Selection in V-bending Processes by Using an Intelligent Collision Detection Algorithm

NASA Astrophysics Data System (ADS)

Salem, A. A.

2017-09-01

V-bending is widely used to produce the sheet metal components. There are global Changes in the shape of the sheet metal component during progressive bending processes. Accordingly, collisions may be occurred between part and tool during bending. Collision-free is considered one of the feasibility conditions of V-bending process planning which the tool selection is verified by the absence of the collisions. This paper proposes an intelligent collision detection algorithm which has the ability to distinguish between 2D bent parts and the other bent parts. Due to this ability, 2D and 3D collision detection subroutines have been developed in the proposed algorithm. This division of algorithm’s subroutines could reduce the computational operations during collisions detecting.

Development of TIF based figuring algorithm for deterministic pitch tool polishing

NASA Astrophysics Data System (ADS)

Yi, Hyun-Su; Kim, Sug-Whan; Yang, Ho-Soon; Lee, Yun-Woo

2007-12-01

Pitch is perhaps the oldest material used for optical polishing, leaving superior surface texture, and has been used widely in the optics shop floor. However, for its unpredictable controllability of removal characteristics, the pitch tool polishing has been rarely analysed quantitatively and many optics shops rely heavily on optician's "feel" even today. In order to bring a degree of process controllability to the pitch tool polishing, we added motorized tool motions to the conventional Draper type polishing machine and modelled the tool path in the absolute machine coordinate. We then produced a number of Tool Influence Function (TIF) both from an analytical model and a series of experimental polishing runs using the pitch tool. The theoretical TIFs agreed well with the experimental TIFs to the profile accuracy of 79 % in terms of its shape. The surface figuring algorithm was then developed in-house utilizing both theoretical and experimental TIFs. We are currently undertaking a series of trial figuring experiments to prove the performance of the polishing algorithm, and the early results indicate that the highly deterministic material removal control with the pitch tool can be achieved to a certain level of form error. The machine renovation, TIF theory and experimental confirmation, figuring simulation results are reported together with implications to deterministic polishing.

Introduction of the TEAM-HF Costing Tool: A User-Friendly Spreadsheet Program to Estimate Costs of Providing Patient-Centered Interventions

PubMed Central

Reed, Shelby D.; Li, Yanhong; Kamble, Shital; Polsky, Daniel; Graham, Felicia L.; Bowers, Margaret T.; Samsa, Gregory P.; Paul, Sara; Schulman, Kevin A.; Whellan, David J.; Riegel, Barbara J.

2011-01-01

Background Patient-centered health care interventions, such as heart failure disease management programs, are under increasing pressure to demonstrate good value. Variability in costing methods and assumptions in economic evaluations of such interventions limit the comparability of cost estimates across studies. Valid cost estimation is critical to conducting economic evaluations and for program budgeting and reimbursement negotiations. Methods and Results Using sound economic principles, we developed the Tools for Economic Analysis of Patient Management Interventions in Heart Failure (TEAM-HF) Costing Tool, a spreadsheet program that can be used by researchers or health care managers to systematically generate cost estimates for economic evaluations and to inform budgetary decisions. The tool guides users on data collection and cost assignment for associated personnel, facilities, equipment, supplies, patient incentives, miscellaneous items, and start-up activities. The tool generates estimates of total program costs, cost per patient, and cost per week and presents results using both standardized and customized unit costs for side-by-side comparisons. Results from pilot testing indicated that the tool was well-formatted, easy to use, and followed a logical order. Cost estimates of a 12-week exercise training program in patients with heart failure were generated with the costing tool and were found to be consistent with estimates published in a recent study. Conclusions The TEAM-HF Costing Tool could prove to be a valuable resource for researchers and health care managers to generate comprehensive cost estimates of patient-centered interventions in heart failure or other conditions for conducting high-quality economic evaluations and making well-informed health care management decisions. PMID:22147884

Towards an unsupervised device for the diagnosis of childhood pneumonia in low resource settings: automatic segmentation of respiratory sounds.

PubMed

Sola, J; Braun, F; Muntane, E; Verjus, C; Bertschi, M; Hugon, F; Manzano, S; Benissa, M; Gervaix, A

2016-08-01

Pneumonia remains the worldwide leading cause of children mortality under the age of five, with every year 1.4 million deaths. Unfortunately, in low resource settings, very limited diagnostic support aids are provided to point-of-care practitioners. Current UNICEF/WHO case management algorithm relies on the use of a chronometer to manually count breath rates on pediatric patients: there is thus a major need for more sophisticated tools to diagnose pneumonia that increase sensitivity and specificity of breath-rate-based algorithms. These tools should be low cost, and adapted to practitioners with limited training. In this work, a novel concept of unsupervised tool for the diagnosis of childhood pneumonia is presented. The concept relies on the automated analysis of respiratory sounds as recorded by a point-of-care electronic stethoscope. By identifying the presence of auscultation sounds at different chest locations, this diagnostic tool is intended to estimate a pneumonia likelihood score. After presenting the overall architecture of an algorithm to estimate pneumonia scores, the importance of a robust unsupervised method to identify inspiratory and expiratory phases of a respiratory cycle is highlighted. Based on data from an on-going study involving pediatric pneumonia patients, a first algorithm to segment respiratory sounds is suggested. The unsupervised algorithm relies on a Mel-frequency filter bank, a two-step Gaussian Mixture Model (GMM) description of data, and a final Hidden Markov Model (HMM) interpretation of inspiratory-expiratory sequences. Finally, illustrative results on first recruited patients are provided. The presented algorithm opens the doors to a new family of unsupervised respiratory sound analyzers that could improve future versions of case management algorithms for the diagnosis of pneumonia in low-resources settings.

Development of modelling algorithm of technological systems by statistical tests

NASA Astrophysics Data System (ADS)

Shemshura, E. A.; Otrokov, A. V.; Chernyh, V. G.

2018-03-01

The paper tackles the problem of economic assessment of design efficiency regarding various technological systems at the stage of their operation. The modelling algorithm of a technological system was performed using statistical tests and with account of the reliability index allows estimating the level of machinery technical excellence and defining the efficiency of design reliability against its performance. Economic feasibility of its application shall be determined on the basis of service quality of a technological system with further forecasting of volumes and the range of spare parts supply.

Environmental Optimization Using the WAste Reduction Algorithm (WAR)

EPA Science Inventory

Traditionally chemical process designs were optimized using purely economic measures such as rate of return. EPA scientists developed the WAste Reduction algorithm (WAR) so that environmental impacts of designs could easily be evaluated. The goal of WAR is to reduce environme...

Development of a Genetic Algorithm to Automate Clustering of a Dependency Structure Matrix

NASA Technical Reports Server (NTRS)

Rogers, James L.; Korte, John J.; Bilardo, Vincent J.

2006-01-01

Much technology assessment and organization design data exists in Microsoft Excel spreadsheets. Tools are needed to put this data into a form that can be used by design managers to make design decisions. One need is to cluster data that is highly coupled. Tools such as the Dependency Structure Matrix (DSM) and a Genetic Algorithm (GA) can be of great benefit. However, no tool currently combines the DSM and a GA to solve the clustering problem. This paper describes a new software tool that interfaces a GA written as an Excel macro with a DSM in spreadsheet format. The results of several test cases are included to demonstrate how well this new tool works.

3D measurement by digital photogrammetry

NASA Astrophysics Data System (ADS)

Schneider, Carl T.

1993-12-01

Photogrammetry is well known in geodetic surveys as aerial photogrammetry or close range applications as architectural photogrammetry. The photogrammetric methods and algorithms combined with digital cameras and digital image processing methods are now introduced for industrial applications as automation and quality control. The presented paper will describe the photogrammetric and digital image processing algorithms and the calibration methods. These algorithms and methods were demonstrated with application examples. These applications are a digital photogrammetric workstation as a mobil multi purpose 3D measuring tool and a tube measuring system as an example for a single purpose tool.

Heuristic algorithms for solving of the tool routing problem for CNC cutting machines

NASA Astrophysics Data System (ADS)

Chentsov, P. A.; Petunin, A. A.; Sesekin, A. N.; Shipacheva, E. N.; Sholohov, A. E.

2015-11-01

The article is devoted to the problem of minimizing the path of the cutting tool to shape cutting machines began. This problem can be interpreted as a generalized traveling salesman problem. Earlier version of the dynamic programming method to solve this problem was developed. Unfortunately, this method allows to process an amount not exceeding thirty circuits. In this regard, the task of constructing quasi-optimal route becomes relevant. In this paper we propose options for quasi-optimal greedy algorithms. Comparison of the results of exact and approximate algorithms is given.

Development of a Tool for an Efficient Calibration of CORSIM Models

DOT National Transportation Integrated Search

2014-08-01

This project proposes a Memetic Algorithm (MA) for the calibration of microscopic traffic flow simulation models. The proposed MA includes a combination of genetic and simulated annealing algorithms. The genetic algorithm performs the exploration of ...

Cost Function Network-based Design of Protein-Protein Interactions: predicting changes in binding affinity.

PubMed

Viricel, Clément; de Givry, Simon; Schiex, Thomas; Barbe, Sophie

2018-02-20

Accurate and economic methods to predict change in protein binding free energy upon mutation are imperative to accelerate the design of proteins for a wide range of applications. Free energy is defined by enthalpic and entropic contributions. Following the recent progresses of Artificial Intelligence-based algorithms for guaranteed NP-hard energy optimization and partition function computation, it becomes possible to quickly compute minimum energy conformations and to reliably estimate the entropic contribution of side-chains in the change of free energy of large protein interfaces. Using guaranteed Cost Function Network algorithms, Rosetta energy functions and Dunbrack's rotamer library, we developed and assessed EasyE and JayZ, two methods for binding affinity estimation that ignore or include conformational entropic contributions on a large benchmark of binding affinity experimental measures. If both approaches outperform most established tools, we observe that side-chain conformational entropy brings little or no improvement on most systems but becomes crucial in some rare cases. as open-source Python/C ++ code at sourcesup.renater.fr/projects/easy-jayz. thomas.schiex@inra.fr and sophie.barbe@insa-toulouse.fr. Supplementary data are available at Bioinformatics online.

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

PubMed Central

Zhang, Dezhi; Li, Shuangyan

2014-01-01

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

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

PubMed

Zhang, Dezhi; Li, Shuangyan; Qin, Jin

2014-01-01

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

Cost-effective analysis of different algorithms for the diagnosis of hepatitis C virus infection.

PubMed

Barreto, A M E C; Takei, K; E C, Sabino; Bellesa, M A O; Salles, N A; Barreto, C C; Nishiya, A S; Chamone, D F

2008-02-01

We compared the cost-benefit of two algorithms, recently proposed by the Centers for Disease Control and Prevention, USA, with the conventional one, the most appropriate for the diagnosis of hepatitis C virus (HCV) infection in the Brazilian population. Serum samples were obtained from 517 ELISA-positive or -inconclusive blood donors who had returned to Fundação Pró-Sangue/Hemocentro de São Paulo to confirm previous results. Algorithm A was based on signal-to-cut-off (s/co) ratio of ELISA anti-HCV samples that show s/co ratio > or =95% concordance with immunoblot (IB) positivity. For algorithm B, reflex nucleic acid amplification testing by PCR was required for ELISA-positive or -inconclusive samples and IB for PCR-negative samples. For algorithm C, all positive or inconclusive ELISA samples were submitted to IB. We observed a similar rate of positive results with the three algorithms: 287, 287, and 285 for A, B, and C, respectively, and 283 were concordant with one another. Indeterminate results from algorithms A and C were elucidated by PCR (expanded algorithm) which detected two more positive samples. The estimated cost of algorithms A and B was US$21,299.39 and US$32,397.40, respectively, which were 43.5 and 14.0% more economic than C (US$37,673.79). The cost can vary according to the technique used. We conclude that both algorithms A and B are suitable for diagnosing HCV infection in the Brazilian population. Furthermore, algorithm A is the more practical and economical one since it requires supplemental tests for only 54% of the samples. Algorithm B provides early information about the presence of viremia.

Regularization and computational methods for precise solution of perturbed orbit transfer problems

NASA Astrophysics Data System (ADS)

Woollands, Robyn Michele

The author has developed a suite of algorithms for solving the perturbed Lambert's problem in celestial mechanics. These algorithms have been implemented as a parallel computation tool that has broad applicability. This tool is composed of four component algorithms and each provides unique benefits for solving a particular type of orbit transfer problem. The first one utilizes a Keplerian solver (a-iteration) for solving the unperturbed Lambert's problem. This algorithm not only provides a "warm start" for solving the perturbed problem but is also used to identify which of several perturbed solvers is best suited for the job. The second algorithm solves the perturbed Lambert's problem using a variant of the modified Chebyshev-Picard iteration initial value solver that solves two-point boundary value problems. This method converges over about one third of an orbit and does not require a Newton-type shooting method and thus no state transition matrix needs to be computed. The third algorithm makes use of regularization of the differential equations through the Kustaanheimo-Stiefel transformation and extends the domain of convergence over which the modified Chebyshev-Picard iteration two-point boundary value solver will converge, from about one third of an orbit to almost a full orbit. This algorithm also does not require a Newton-type shooting method. The fourth algorithm uses the method of particular solutions and the modified Chebyshev-Picard iteration initial value solver to solve the perturbed two-impulse Lambert problem over multiple revolutions. The method of particular solutions is a shooting method but differs from the Newton-type shooting methods in that it does not require integration of the state transition matrix. The mathematical developments that underlie these four algorithms are derived in the chapters of this dissertation. For each of the algorithms, some orbit transfer test cases are included to provide insight on accuracy and efficiency of these individual algorithms. Following this discussion, the combined parallel algorithm, known as the unified Lambert tool, is presented and an explanation is given as to how it automatically selects which of the three perturbed solvers to compute the perturbed solution for a particular orbit transfer. The unified Lambert tool may be used to determine a single orbit transfer or for generating of an extremal field map. A case study is presented for a mission that is required to rendezvous with two pieces of orbit debris (spent rocket boosters). The unified Lambert tool software developed in this dissertation is already being utilized by several industrial partners and we are confident that it will play a significant role in practical applications, including solution of Lambert problems that arise in the current applications focused on enhanced space situational awareness.

HUMAN HEALTH METRICS FOR ENVIRONMENTAL DECISION SUPPORT TOOLS: LESSONS FROM HEALTH ECONOMICS AND DECISION ANALYSIS: PUBLISHED REPORT

EPA Science Inventory

NRMRL-CIN-1351A Hofstetter**, P., and Hammitt, J. K. Human Health Metrics for Environmental Decision Support Tools: Lessons from Health Economics and Decision Analysis. EPA/600/R-01/104 (NTIS PB2002-102119). Decision makers using environmental decision support tools are often ...

Optical Method For Monitoring Tool Control For Green Burnishing With Using Of Algorithms With Adaptive Settings

NASA Astrophysics Data System (ADS)

Lukyanov, A. A.; Grigoriev, S. N.; Bobrovskij, I. N.; Melnikov, P. A.; Bobrovskij, N. M.

2017-05-01

With regard to the complexity of the new technology and increase its reliability requirements laboriousness of control operations in industrial quality control systems increases significantly. The importance of quality management control due to the fact that its promotes the correct use of production conditions, the relevant requirements are required. Digital image processing allows to reach a new technological level of production (new technological way). The most complicated automated interpretation of information is the basis for decision-making in the management of production processes. In the case of surface analysis of tools used for processing with the using of metalworking fluids (MWF) it is more complicated. The authors suggest new algorithm for optical inspection of the wear of the cylinder tool for burnishing, which used in surface plastic deformation without using of MWF. The main advantage of proposed algorithm is the possibility of automatic recognition of images of burnisher tool with the subsequent allocation of its boundaries, finding a working surface and automatically allocating the defects and wear area. Software that implements the algorithm was developed by the authors in Matlab programming environment, but can be implemented using other programming languages.

On the evaluation of segmentation editing tools

PubMed Central

Heckel, Frank; Moltz, Jan H.; Meine, Hans; Geisler, Benjamin; Kießling, Andreas; D’Anastasi, Melvin; dos Santos, Daniel Pinto; Theruvath, Ashok Joseph; Hahn, Horst K.

2014-01-01

Abstract. Efficient segmentation editing tools are important components in the segmentation process, as no automatic methods exist that always generate sufficient results. Evaluating segmentation editing algorithms is challenging, because their quality depends on the user’s subjective impression. So far, no established methods for an objective, comprehensive evaluation of such tools exist and, particularly, intermediate segmentation results are not taken into account. We discuss the evaluation of editing algorithms in the context of tumor segmentation in computed tomography. We propose a rating scheme to qualitatively measure the accuracy and efficiency of editing tools in user studies. In order to objectively summarize the overall quality, we propose two scores based on the subjective rating and the quantified segmentation quality over time. Finally, a simulation-based evaluation approach is discussed, which allows a more reproducible evaluation without the need for human input. This automated evaluation complements user studies, allowing a more convincing evaluation, particularly during development, where frequent user studies are not possible. The proposed methods have been used to evaluate two dedicated editing algorithms on 131 representative tumor segmentations. We show how the comparison of editing algorithms benefits from the proposed methods. Our results also show the correlation of the suggested quality score with the qualitative ratings. PMID:26158063

Algorithmic Classification of Five Characteristic Types of Paraphasias.

PubMed

Fergadiotis, Gerasimos; Gorman, Kyle; Bedrick, Steven

2016-12-01

This study was intended to evaluate a series of algorithms developed to perform automatic classification of paraphasic errors (formal, semantic, mixed, neologistic, and unrelated errors). We analyzed 7,111 paraphasias from the Moss Aphasia Psycholinguistics Project Database (Mirman et al., 2010) and evaluated the classification accuracy of 3 automated tools. First, we used frequency norms from the SUBTLEXus database (Brysbaert & New, 2009) to differentiate nonword errors and real-word productions. Then we implemented a phonological-similarity algorithm to identify phonologically related real-word errors. Last, we assessed the performance of a semantic-similarity criterion that was based on word2vec (Mikolov, Yih, & Zweig, 2013). Overall, the algorithmic classification replicated human scoring for the major categories of paraphasias studied with high accuracy. The tool that was based on the SUBTLEXus frequency norms was more than 97% accurate in making lexicality judgments. The phonological-similarity criterion was approximately 91% accurate, and the overall classification accuracy of the semantic classifier ranged from 86% to 90%. Overall, the results highlight the potential of tools from the field of natural language processing for the development of highly reliable, cost-effective diagnostic tools suitable for collecting high-quality measurement data for research and clinical purposes.

Data Mining and Optimization Tools for Developing Engine Parameters Tools

NASA Technical Reports Server (NTRS)

Dhawan, Atam P.

1998-01-01

This project was awarded for understanding the problem and developing a plan for Data Mining tools for use in designing and implementing an Engine Condition Monitoring System. Tricia Erhardt and I studied the problem domain for developing an Engine Condition Monitoring system using the sparse and non-standardized datasets to be available through a consortium at NASA Lewis Research Center. We visited NASA three times to discuss additional issues related to dataset which was not made available to us. We discussed and developed a general framework of data mining and optimization tools to extract useful information from sparse and non-standard datasets. These discussions lead to the training of Tricia Erhardt to develop Genetic Algorithm based search programs which were written in C++ and used to demonstrate the capability of GA algorithm in searching an optimal solution in noisy, datasets. From the study and discussion with NASA LeRC personnel, we then prepared a proposal, which is being submitted to NASA for future work for the development of data mining algorithms for engine conditional monitoring. The proposed set of algorithm uses wavelet processing for creating multi-resolution pyramid of tile data for GA based multi-resolution optimal search.

Dynamic Load-Balancing for Distributed Heterogeneous Computing of Parallel CFD Problems

NASA Technical Reports Server (NTRS)

Ecer, A.; Chien, Y. P.; Boenisch, T.; Akay, H. U.

2000-01-01

The developed methodology is aimed at improving the efficiency of executing block-structured algorithms on parallel, distributed, heterogeneous computers. The basic approach of these algorithms is to divide the flow domain into many sub- domains called blocks, and solve the governing equations over these blocks. Dynamic load balancing problem is defined as the efficient distribution of the blocks among the available processors over a period of several hours of computations. In environments with computers of different architecture, operating systems, CPU speed, memory size, load, and network speed, balancing the loads and managing the communication between processors becomes crucial. Load balancing software tools for mutually dependent parallel processes have been created to efficiently utilize an advanced computation environment and algorithms. These tools are dynamic in nature because of the chances in the computer environment during execution time. More recently, these tools were extended to a second operating system: NT. In this paper, the problems associated with this application will be discussed. Also, the developed algorithms were combined with the load sharing capability of LSF to efficiently utilize workstation clusters for parallel computing. Finally, results will be presented on running a NASA based code ADPAC to demonstrate the developed tools for dynamic load balancing.

A new algorithm for design, operation and cost assessment of struvite (MgNH4PO4) precipitation processes.

PubMed

Birnhack, Liat; Nir, Oded; Telzhenski, Marina; Lahav, Ori

2015-01-01

Deliberate struvite (MgNH4PO4) precipitation from wastewater streams has been the topic of extensive research in the last two decades and is expected to gather worldwide momentum in the near future as a P-reuse technique. A wide range of operational alternatives has been reported for struvite precipitation, including the application of various Mg(II) sources, two pH elevation techniques and several Mg:P ratios and pH values. The choice of each operational parameter within the struvite precipitation process affects process efficiency, the overall cost and also the choice of other operational parameters. Thus, a comprehensive simulation program that takes all these parameters into account is essential for process design. This paper introduces a systematic decision-supporting tool which accepts a wide range of possible operational parameters, including unconventional Mg(II) sources (i.e. seawater and seawater nanofiltration brines). The study is supplied with a free-of-charge computerized tool (http://tx.technion.ac.il/~agrengn/agr/Struvite_Program.zip) which links two computer platforms (Python and PHREEQC) for executing thermodynamic calculations according to predefined kinetic considerations. The model can be (inter alia) used for optimizing the struvite-fluidized bed reactor process operation with respect to P removal efficiency, struvite purity and economic feasibility of the chosen alternative. The paper describes the algorithm and its underlying assumptions, and shows results (i.e. effluent water quality, cost breakdown and P removal efficiency) of several case studies consisting of typical wastewaters treated at various operational conditions.

The Impact of Economic Policies on Poverty and Income Distribution: Evaluation Techniques and Tools.

ERIC Educational Resources Information Center

Bourguignon, Francois, Ed.; Pereira da Silva, Luiz A., Ed.

This book, a collection of articles and papers, reviews techniques and tools that can be used to evaluate the poverty and distributional impact of economic policy choices. Following are its contents: "Evaluating the Poverty and Distributional Impact of Economic Policies: A Compendium of Existing Techniques" (Francois Bourguignon and Luiz A.…

Transition Marshall Space Flight Center Wind Profiler Splicing Algorithm to Launch Services Program Upper Winds Tool

NASA Technical Reports Server (NTRS)

Bauman, William H., III

2014-01-01

NASAs LSP customers and the future SLS program rely on observations of upper-level winds for steering, loads, and trajectory calculations for the launch vehicles flight. On the day of launch, the 45th Weather Squadron (45 WS) Launch Weather Officers (LWOs) monitor the upper-level winds and provide forecasts to the launch team via the AMU-developed LSP Upper Winds tool for launches at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station. This tool displays wind speed and direction profiles from rawinsondes released during launch operations, the 45th Space Wing 915-MHz Doppler Radar Wind Profilers (DRWPs) and KSC 50-MHz DRWP, and output from numerical weather prediction models.The goal of this task was to splice the wind speed and direction profiles from the 45th Space Wing (45 SW) 915-MHz Doppler radar Wind Profilers (DRWPs) and KSC 50-MHz DRWP at altitudes where the wind profiles overlap to create a smooth profile. In the first version of the LSP Upper Winds tool, the top of the 915-MHz DRWP wind profile and the bottom of the 50-MHz DRWP were not spliced, sometimes creating a discontinuity in the profile. The Marshall Space Flight Center (MSFC) Natural Environments Branch (NE) created algorithms to splice the wind profiles from the two sensors to generate an archive of vertically complete wind profiles for the SLS program. The AMU worked with MSFC NE personnel to implement these algorithms in the LSP Upper Winds tool to provide a continuous spliced wind profile.The AMU transitioned the MSFC NE algorithms to interpolate and fill data gaps in the data, implement a Gaussian weighting function to produce 50-m altitude intervals in each sensor, and splice the data together from both DRWPs. They did so by porting the MSFC NE code written with MATLAB software into Microsoft Excel Visual Basic for Applications (VBA). After testing the new algorithms in stand-alone VBA modules, the AMU replaced the existing VBA code in the LSP Upper Winds tool with the new algorithms. They then tested the code in the LSP Upper Winds tool with archived data. The tool will be delivered to the 45 WS after the 50-MHz DRWP upgrade is complete and the tool is tested with real-time data. The 50-MHz DRWP upgrade is expected to be finished in October 2014.

Pricing Government Information.

ERIC Educational Resources Information Center

Love, James

1995-01-01

Improvements in technology have increased the social and economic value of government information. This increase, combined with changes in information storage and dissemination cost, contributes to controversy over how government information should be disseminated and priced. Discussion includes economic concepts, rules and algorithms used by…

Radiated Emissions from a Remote-Controlled Airplane-Measured in a Reverberation Chamber

NASA Technical Reports Server (NTRS)

Ely, Jay J.; Koppen, Sandra V.; Nguyen, Truong X.; Dudley, Kenneth L.; Szatkowski, George N.; Quach, Cuong C.; Vazquez, Sixto L.; Mielnik, John J.; Hogge, Edward F.; Hill, Boyd L.;

Analysis of tsunami disaster map by Geographic Information System (GIS): Aceh Singkil-Indonesia

NASA Astrophysics Data System (ADS)

Farhan, A.; Akhyar, H.

2017-02-01

Tsunami risk map is used by stakeholder as a base to decide evacuation plan and evaluates from disaster. Aceh Singkil district of Aceh- Indonesia’s disaster maps have been developed and analyzed by using GIS tool. Overlay methods through algorithms are used to produce hazard map, vulnerability, capacity and finally created disaster risk map. Spatial maps are used topographic maps, administrative map, SRTM. The parameters are social, economic, physical environmental vulnerability, a level of exposed people, parameters of houses, public building, critical facilities, productive land, population density, sex ratio, poor ratio, disability ratio, age group ratio, the protected forest, natural forest, and mangrove forest. The results show high-risk tsunami disaster at nine villages; moderate levels are seventeen villages, and other villages are shown in the low level of tsunami risk disaster.

INCORPORATING ENVIRONMENTAL AND ECONOMIC CONSIDERATIONS INTO PROCESS DESIGN: THE WASTE REDUCTION (WAR) ALGORITHM

EPA Science Inventory

A general theory known as the WAste Reduction (WASR) algorithm has been developed to describe the flow and the generation of potential environmental impact through a chemical process. This theory integrates environmental impact assessment into chemical process design Potential en...

THE WASTE REDUCTION (WAR) ALGORITHM: ENVIRONMENTAL IMPACTS, ENERGY CONSUMPTION, AND ENGINEERING ECONOMICS

EPA Science Inventory

A general theory known as the WAste Reduction (WAR) algorithm has been developed to describe the flow and the generation of potential environmental impact through a chemical process. This theory defines potential environmental impact indexes that characterize the generation and t...

Applications of an architecture design and assessment system (ADAS)

NASA Technical Reports Server (NTRS)

Gray, F. Gail; Debrunner, Linda S.; White, Tennis S.

1988-01-01

A new Architecture Design and Assessment System (ADAS) tool package is introduced, and a range of possible applications is illustrated. ADAS was used to evaluate the performance of an advanced fault-tolerant computer architecture in a modern flight control application. Bottlenecks were identified and possible solutions suggested. The tool was also used to inject faults into the architecture and evaluate the synchronization algorithm, and improvements are suggested. Finally, ADAS was used as a front end research tool to aid in the design of reconfiguration algorithms in a distributed array architecture.

A total variation diminishing finite difference algorithm for sonic boom propagation models

NASA Technical Reports Server (NTRS)

Sparrow, Victor W.

1993-01-01

It is difficult to accurately model the rise phases of sonic boom waveforms with traditional finite difference algorithms because of finite difference phase dispersion. This paper introduces the concept of a total variation diminishing (TVD) finite difference method as a tool for accurately modeling the rise phases of sonic booms. A standard second order finite difference algorithm and its TVD modified counterpart are both applied to the one-way propagation of a square pulse. The TVD method clearly outperforms the non-TVD method, showing great potential as a new computational tool in the analysis of sonic boom propagation.

Algorithms for the Computation of Debris Risk

NASA Technical Reports Server (NTRS)

Matney, Mark J.

2017-01-01

Determining the risks from space debris involve a number of statistical calculations. These calculations inevitably involve assumptions about geometry - including the physical geometry of orbits and the geometry of satellites. A number of tools have been developed in NASA’s Orbital Debris Program Office to handle these calculations; many of which have never been published before. These include algorithms that are used in NASA’s Orbital Debris Engineering Model ORDEM 3.0, as well as other tools useful for computing orbital collision rates and ground casualty risks. This paper presents an introduction to these algorithms and the assumptions upon which they are based.

Algorithms for the Computation of Debris Risks

NASA Technical Reports Server (NTRS)

Matney, Mark

2017-01-01

Determining the risks from space debris involve a number of statistical calculations. These calculations inevitably involve assumptions about geometry - including the physical geometry of orbits and the geometry of non-spherical satellites. A number of tools have been developed in NASA's Orbital Debris Program Office to handle these calculations; many of which have never been published before. These include algorithms that are used in NASA's Orbital Debris Engineering Model ORDEM 3.0, as well as other tools useful for computing orbital collision rates and ground casualty risks. This paper will present an introduction to these algorithms and the assumptions upon which they are based.

Have Economic Educators Embraced Social Media as a Teaching Tool?

ERIC Educational Resources Information Center

Al-Bahrani, Abdullah; Patel, Darshak; Sheridan, Brandon J.

2017-01-01

In this article, the authors discuss the results of a study of the perceptions of a national sample of economics faculty members from various institutions regarding the use of social media as a teaching tool in and out of the economics classroom. In the past few years, social media has become globally popular, and its use is ubiquitous among…

Nonsmooth Optimization Algorithms, System Theory, and Software Tools

DTIC Science & Technology

1993-04-13

Optimization Algorithms, System Theory , and Scftware Tools" AFOSR-90-OO68 L AUTHOR($) Elijah Polak -Professor and Principal Investigator 7. PERFORMING...NSN 754Q-01-2W0-S500 Standard Form 295 (69O104 Draft) F’wsa*W by hA Sit 230.1""V AFOSR-90-0068 NONSMO0 TH OPTIMIZA TION A L GORI THMS, SYSTEM THEORY , AND

Multi-agent modelling framework for water, energy and other resource networks

NASA Astrophysics Data System (ADS)

Knox, S.; Selby, P. D.; Meier, P.; Harou, J. J.; Yoon, J.; Lachaut, T.; Klassert, C. J. A.; Avisse, N.; Mohamed, K.; Tomlinson, J.; Khadem, M.; Tilmant, A.; Gorelick, S.

2015-12-01

Bespoke modelling tools are often needed when planning future engineered interventions in the context of various climate, socio-economic and geopolitical futures. Such tools can help improve system operating policies or assess infrastructure upgrades and their risks. A frequently used approach is to simulate and/or optimise the impact of interventions in engineered systems. Modelling complex infrastructure systems can involve incorporating multiple aspects into a single model, for example physical, economic and political. This presents the challenge of combining research from diverse areas into a single system effectively. We present the Pynsim 'Python Network Simulator' framework, a library for building simulation models capable of representing, the physical, institutional and economic aspects of an engineered resources system. Pynsim is an open source, object oriented code aiming to promote integration of different modelling processes through a single code library. We present two case studies that demonstrate important features of Pynsim's design. The first is a large interdisciplinary project of a national water system in the Middle East with modellers from fields including water resources, economics, hydrology and geography each considering different facets of a multi agent system. It includes: modelling water supply and demand for households and farms; a water tanker market with transfer of water between farms and households, and policy decisions made by government institutions at district, national and international level. This study demonstrates that a well-structured library of code can provide a hub for development and act as a catalyst for integrating models. The second focuses on optimising the location of new run-of-river hydropower plants. Using a multi-objective evolutionary algorithm, this study analyses different network configurations to identify the optimal placement of new power plants within a river network. This demonstrates that Pynsim can be used to evaluate a multitude of topologies for identifying the optimal location of infrastructure investments. Pynsim is available on GitHub or via standard python installer packages such as pip. It comes with several examples and online documentation, making it attractive for those less experienced in software engineering.

Network model of project "Lean Production"

NASA Astrophysics Data System (ADS)

Khisamova, E. D.

2018-05-01

Economical production implies primarily new approaches to culture of management and organization of production and offers a set of tools and techniques that allows reducing losses significantly and making the process cheaper and faster. Economical production tools are simple solutions that allow one to see opportunities for improvement of all aspects of the business, to reduce losses significantly, to constantly improve the whole spectrum of business processes, to increase significantly the transparency and manageability of the organization, to take advantage of the potential of each employee of the company, to increase competitiveness, and to obtain significant economic benefits without making large financial expenditures. Each of economical production tools solves a specific part of the problems, and only application of their combination will allow one to solve the problem or minimize it to acceptable values. The research of the governance process project "Lean Production" permitted studying the methods and tools of lean production and developing measures for their improvement.

Task scheduling in dataflow computer architectures

NASA Technical Reports Server (NTRS)

Katsinis, Constantine

1994-01-01

Dataflow computers provide a platform for the solution of a large class of computational problems, which includes digital signal processing and image processing. Many typical applications are represented by a set of tasks which can be repetitively executed in parallel as specified by an associated dataflow graph. Research in this area aims to model these architectures, develop scheduling procedures, and predict the transient and steady state performance. Researchers at NASA have created a model and developed associated software tools which are capable of analyzing a dataflow graph and predicting its runtime performance under various resource and timing constraints. These models and tools were extended and used in this work. Experiments using these tools revealed certain properties of such graphs that require further study. Specifically, the transient behavior at the beginning of the execution of a graph can have a significant effect on the steady state performance. Transformation and retiming of the application algorithm and its initial conditions can produce a different transient behavior and consequently different steady state performance. The effect of such transformations on the resource requirements or under resource constraints requires extensive study. Task scheduling to obtain maximum performance (based on user-defined criteria), or to satisfy a set of resource constraints, can also be significantly affected by a transformation of the application algorithm. Since task scheduling is performed by heuristic algorithms, further research is needed to determine if new scheduling heuristics can be developed that can exploit such transformations. This work has provided the initial development for further long-term research efforts. A simulation tool was completed to provide insight into the transient and steady state execution of a dataflow graph. A set of scheduling algorithms was completed which can operate in conjunction with the modeling and performance tools previously developed. Initial studies on the performance of these algorithms were done to examine the effects of application algorithm transformations as measured by such quantities as number of processors, time between outputs, time between input and output, communication time, and memory size.

Variational Trajectory Optimization Tool Set: Technical description and user's manual

NASA Technical Reports Server (NTRS)

Bless, Robert R.; Queen, Eric M.; Cavanaugh, Michael D.; Wetzel, Todd A.; Moerder, Daniel D.

1993-01-01

The algorithms that comprise the Variational Trajectory Optimization Tool Set (VTOTS) package are briefly described. The VTOTS is a software package for solving nonlinear constrained optimal control problems from a wide range of engineering and scientific disciplines. The VTOTS package was specifically designed to minimize the amount of user programming; in fact, for problems that may be expressed in terms of analytical functions, the user needs only to define the problem in terms of symbolic variables. This version of the VTOTS does not support tabular data; thus, problems must be expressed in terms of analytical functions. The VTOTS package consists of two methods for solving nonlinear optimal control problems: a time-domain finite-element algorithm and a multiple shooting algorithm. These two algorithms, under the VTOTS package, may be run independently or jointly. The finite-element algorithm generates approximate solutions, whereas the shooting algorithm provides a more accurate solution to the optimization problem. A user's manual, some examples with results, and a brief description of the individual subroutines are included.

A traveling salesman approach for predicting protein functions.

PubMed

Johnson, Olin; Liu, Jing

2006-10-12

Protein-protein interaction information can be used to predict unknown protein functions and to help study biological pathways. Here we present a new approach utilizing the classic Traveling Salesman Problem to study the protein-protein interactions and to predict protein functions in budding yeast Saccharomyces cerevisiae. We apply the global optimization tool from combinatorial optimization algorithms to cluster the yeast proteins based on the global protein interaction information. We then use this clustering information to help us predict protein functions. We use our algorithm together with the direct neighbor algorithm 1 on characterized proteins and compare the prediction accuracy of the two methods. We show our algorithm can produce better predictions than the direct neighbor algorithm, which only considers the immediate neighbors of the query protein. Our method is a promising one to be used as a general tool to predict functions of uncharacterized proteins and a successful sample of using computer science knowledge and algorithms to study biological problems.

A traveling salesman approach for predicting protein functions

PubMed Central

Johnson, Olin; Liu, Jing

2006-01-01

Background Protein-protein interaction information can be used to predict unknown protein functions and to help study biological pathways. Results Here we present a new approach utilizing the classic Traveling Salesman Problem to study the protein-protein interactions and to predict protein functions in budding yeast Saccharomyces cerevisiae. We apply the global optimization tool from combinatorial optimization algorithms to cluster the yeast proteins based on the global protein interaction information. We then use this clustering information to help us predict protein functions. We use our algorithm together with the direct neighbor algorithm [1] on characterized proteins and compare the prediction accuracy of the two methods. We show our algorithm can produce better predictions than the direct neighbor algorithm, which only considers the immediate neighbors of the query protein. Conclusion Our method is a promising one to be used as a general tool to predict functions of uncharacterized proteins and a successful sample of using computer science knowledge and algorithms to study biological problems. PMID:17147783

Algorithm for solving of two-level hierarchical minimax program control problem of final state the regional socio-economic system in the presence of risks

NASA Astrophysics Data System (ADS)

Shorikov, A. F.

2017-10-01

In this paper we study the problem of optimization of guaranteed result for program control by the final state of regional social and economic system in the presence of risks. For this problem we propose a mathematical model in the form of two-level hierarchical minimax program control problem of the final state of this process with incomplete information. For solving of its problem we constructed the common algorithm that has a form of a recurrent procedure of solving a linear programming and a finite optimization problems.

Economic environmental dispatch using BSA algorithm

NASA Astrophysics Data System (ADS)

Jihane, Kartite; Mohamed, Cherkaoui

2018-05-01

Economic environmental dispatch problem (EED) is an important issue especially in the field of fossil fuel power plant system. It allows the network manager to choose among different units the most optimized in terms of fuel costs and emission level. The objective of this paper is to minimize the fuel cost with emissions constrained; the test is conducted for two cases: six generator unit and ten generator unit for the same power demand 1200Mw. The simulation has been computed in MATLAB and the result shows the robustness of the Backtracking Search optimization Algorithm (BSA) and the impact of the load demand on the emission.

Comparison of cyclic correlation algorithm implemented in matlab and python

NASA Astrophysics Data System (ADS)

Carr, Richard; Whitney, James

Simulation is a necessary step for all engineering projects. Simulation gives the engineers an approximation of how their devices will perform under different circumstances, without hav-ing to build, or before building a physical prototype. This is especially true for space bound devices, i.e., space communication systems, where the impact of system malfunction or failure is several orders of magnitude over that of terrestrial applications. Therefore having a reliable simulation tool is key in developing these devices and systems. Math Works Matrix Laboratory (MATLAB) is a matrix based software used by scientists and engineers to solve problems and perform complex simulations. MATLAB has a number of applications in a wide variety of fields which include communications, signal processing, image processing, mathematics, eco-nomics and physics. Because of its many uses MATLAB has become the preferred software for many engineers; it is also very expensive, especially for students and startups. One alternative to MATLAB is Python. The Python is a powerful, easy to use, open source programming environment that can be used to perform many of the same functions as MATLAB. Python programming environment has been steadily gaining popularity in niche programming circles. While there are not as many function included in the software as MATLAB, there are many open source functions that have been developed that are available to be downloaded for free. This paper illustrates how Python can implement the cyclic correlation algorithm and com-pares the results to the cyclic correlation algorithm implemented in the MATLAB environment. Some of the characteristics to be compared are the accuracy and precision of the results, and the length of the programs. The paper will demonstrate that Python is capable of performing simulations of complex algorithms such cyclic correlation.

Predicting Out-of-Office Blood Pressure in the Clinic for the Diagnosis of Hypertension in Primary Care: An Economic Evaluation.

PubMed

Monahan, Mark; Jowett, Sue; Lovibond, Kate; Gill, Paramjit; Godwin, Marshall; Greenfield, Sheila; Hanley, Janet; Hobbs, F D Richard; Martin, Una; Mant, Jonathan; McKinstry, Brian; Williams, Bryan; Sheppard, James P; McManus, Richard J

2018-02-01

Clinical guidelines in the United States and United Kingdom recommend that individuals with suspected hypertension should have ambulatory blood pressure (BP) monitoring to confirm the diagnosis. This approach reduces misdiagnosis because of white coat hypertension but will not identify people with masked hypertension who may benefit from treatment. The Predicting Out-of-Office Blood Pressure (PROOF-BP) algorithm predicts masked and white coat hypertension based on patient characteristics and clinic BP, improving the accuracy of diagnosis while limiting subsequent ambulatory BP monitoring. This study assessed the cost-effectiveness of using this tool in diagnosing hypertension in primary care. A Markov cost-utility cohort model was developed to compare diagnostic strategies: the PROOF-BP approach, including those with clinic BP ≥130/80 mm Hg who receive ambulatory BP monitoring as guided by the algorithm, compared with current standard diagnostic strategies including those with clinic BP ≥140/90 mm Hg combined with further monitoring (ambulatory BP monitoring as reference, clinic, and home monitoring also assessed). The model adopted a lifetime horizon with a 3-month time cycle, taking a UK Health Service/Personal Social Services perspective. The PROOF-BP algorithm was cost-effective in screening all patients with clinic BP ≥130/80 mm Hg compared with current strategies that only screen those with clinic BP ≥140/90 mm Hg, provided healthcare providers were willing to pay up to £20 000 ($26 000)/quality-adjusted life year gained. Deterministic and probabilistic sensitivity analyses supported the base-case findings. The PROOF-BP algorithm seems to be cost-effective compared with the conventional BP diagnostic options in primary care. Its use in clinical practice is likely to lead to reduced cardiovascular disease, death, and disability. © 2017 American Heart Association, Inc.

Transculturalization of a diabetes-specific nutrition algorithm: Asian application.

PubMed

Su, Hsiu-Yueh; Tsang, Man-Wo; Huang, Shih-Yi; Mechanick, Jeffrey I; Sheu, Wayne H-H; Marchetti, Albert

2012-04-01

The prevalence of type 2 diabetes (T2D) in Asia is growing at an alarming rate, posing significant clinical and economic risk to health care stakeholders. Commonly, Asian patients with T2D manifest a distinctive combination of characteristics that include earlier disease onset, distinct pathophysiology, syndrome of complications, and shorter life expectancy. Optimizing treatment outcomes for such patients requires a coordinated inclusive care plan and knowledgeable practitioners. Comprehensive management starts with medical nutrition therapy (MNT) in a broader lifestyle modification program. Implementing diabetes-specific MNT in Asia requires high-quality and transparent clinical practice guidelines (CPGs) that are regionally adapted for cultural, ethnic, and socioeconomic factors. Respected CPGs for nutrition and diabetes therapy are available from prestigious medical societies. For cost efficiency and effectiveness, health care authorities can select these CPGs for Asian implementation following abridgement and cultural adaptation that includes: defining nutrition therapy in meaningful ways, selecting lower cutoff values for healthy body mass indices and waist circumferences (WCs), identifying the dietary composition of MNT based on regional availability and preference, and expanding nutrition therapy for concomitant hypertension, dyslipidemia, overweight/obesity, and chronic kidney disease. An international task force of respected health care professionals has contributed to this process. To date, task force members have selected appropriate evidence-based CPGs and simplified them into an algorithm for diabetes-specific nutrition therapy. Following cultural adaptation, Asian and Asian-Indian versions of this algorithmic tool have emerged. The Asian version is presented in this report.

THE WASTE REDUCTION (WAR) ALGORITHM: ENVIRONMENTAL IMPACTS, ENERGY CONSUMPTION, AND ENGINEERING ECONOMICS

EPA Science Inventory

A general theory known as the Waste Reduction (WAR) Algorithm has been developed to describe the flow and the generation of potential environmental impact through a chemical process. The theory defines indexes that characterize the generation and the output of potential environm...

Economizing Education: Assessment Algorithms and Calculative Agencies

ERIC Educational Resources Information Center

O'Keeffe, Cormac

2017-01-01

International Large Scale Assessments have been producing data about educational attainment for over 60 years. More recently however, these assessments as tests have become digitally and computationally complex and increasingly rely on the calculative work performed by algorithms. In this article I first consider the coordination of relations…

Spatial analysis of fluvial terraces in GRASS GIS accessing R functionality

NASA Astrophysics Data System (ADS)

Józsa, Edina

2017-04-01

Terrace research along the Danube is a major topic of Hungarian traditional geomorphology because of the socio-economic role of terrace surfaces and their importance in paleo-environmental reconstructions. Semi-automated mapping of fluvial landforms from a coherent digital elevation dataset allow objective analysis of hydrogeomorphic characteristics with low time and cost requirements. New results obtained with unified GIS-based algorithms can be integrated with previous findings regarding landscape evolution. The complementary functionality of GRASS GIS and R provides the possibility to develop a flexible terrain analysing tool for the delineation and quantifiable analysis of terrace remnants. Using R as an intermediate analytical environment and visualisation tool gives great added value to the algorithm, while GRASS GIS is capable of handling the large digital elevation datasets and perform the demanding computations to prepare necessary raster derivatives (Bivand, R.S. et al. 2008). The proposed terrace mapping algorithm is based on the work of Demoulin, A. et al. (2007), but it is further improved in the form of GRASS GIS script tool accessing R functionality. In the first step the hydrogeomorphic signatures of the given study site are explored and the area is divided along clearly recognizable structural-morphological boundaries.The algorithm then cuts up the subregions into parallel sections in the flow direction and determines cells potentially belonging to terrace surfaces based on local slope characteristics and a minimum area size threshold. As a result an output report is created that contains a histogram of altitudes, a swath-profile of the landscape, scatter plots to represent the relation of the relative elevations and slope values in the analysed sections and a final plot showing the longitudinal profile of the river with the determined height ranges of terrace levels. The algorithm also produces a raster map of extracted terrace remnants. From this dataset it is possible to interpolate a new digital elevation model approximating the former terraced valley surface using the Ordinary Kriging method (Troiani, F. and Della Seta, M. 2011). The applicability of the algorithm was tested on the northern foreland of Gerecse Mountains, an antecedent valley section of the Danube, with terrace remnants expected in 6 to 8 altitude ranges. Methodological issues arising from determining the optimal threshold values were explored using an artificial hillslope model, while the terrace profiles and terrace-top surfaces raster generated from the digital elevation model were validated with the previous findings of traditional geomorphological surveys. This research was supported by the Human Capacities Grant Management Office and the Hungarian Ministry of Human Capacities in the framework of the NTP-NFTÖ-16 project. References: Bivand, R.S. et al. (2008). Applied Spatial Data Analysis with R. New York: Springer. 378 p. Demoulin, A. et al. (2007). An automated method to extract fluvial terraces from digital elevation models: The Vesdre valley, a case study in eastern Belgium. - Geomorphology 91 (1-2): 51-64. Troiani, E. and Della Seta, M. (2011). Geomorphological response of fluvial and coastal terraces to Quaternary tectonics and climate as revealed by geostatistical topographic analysis. - Earth Surface Processes and Landforms 36: 1193-1208.

Two New Tools for Glycopeptide Analysis Researchers: A Glycopeptide Decoy Generator and a Large Data Set of Assigned CID Spectra of Glycopeptides.

PubMed

Lakbub, Jude C; Su, Xiaomeng; Zhu, Zhikai; Patabandige, Milani W; Hua, David; Go, Eden P; Desaire, Heather

2017-08-04

The glycopeptide analysis field is tightly constrained by a lack of effective tools that translate mass spectrometry data into meaningful chemical information, and perhaps the most challenging aspect of building effective glycopeptide analysis software is designing an accurate scoring algorithm for MS/MS data. We provide the glycoproteomics community with two tools to address this challenge. The first tool, a curated set of 100 expert-assigned CID spectra of glycopeptides, contains a diverse set of spectra from a variety of glycan types; the second tool, Glycopeptide Decoy Generator, is a new software application that generates glycopeptide decoys de novo. We developed these tools so that emerging methods of assigning glycopeptides' CID spectra could be rigorously tested. Software developers or those interested in developing skills in expert (manual) analysis can use these tools to facilitate their work. We demonstrate the tools' utility in assessing the quality of one particular glycopeptide software package, GlycoPep Grader, which assigns glycopeptides to CID spectra. We first acquired the set of 100 expert assigned CID spectra; then, we used the Decoy Generator (described herein) to generate 20 decoys per target glycopeptide. The assigned spectra and decoys were used to test the accuracy of GlycoPep Grader's scoring algorithm; new strengths and weaknesses were identified in the algorithm using this approach. Both newly developed tools are freely available. The software can be downloaded at http://glycopro.chem.ku.edu/GPJ.jar.

Collaborative workbench for cyberinfrastructure to accelerate science algorithm development

NASA Astrophysics Data System (ADS)

Ramachandran, R.; Maskey, M.; Kuo, K.; Lynnes, C.

2013-12-01

There are significant untapped resources for information and knowledge creation within the Earth Science community in the form of data, algorithms, services, analysis workflows or scripts, and the related knowledge about these resources. Despite the huge growth in social networking and collaboration platforms, these resources often reside on an investigator's workstation or laboratory and are rarely shared. A major reason for this is that there are very few scientific collaboration platforms, and those that exist typically require the use of a new set of analysis tools and paradigms to leverage the shared infrastructure. As a result, adoption of these collaborative platforms for science research is inhibited by the high cost to an individual scientist of switching from his or her own familiar environment and set of tools to a new environment and tool set. This presentation will describe an ongoing project developing an Earth Science Collaborative Workbench (CWB). The CWB approach will eliminate this barrier by augmenting a scientist's current research environment and tool set to allow him or her to easily share diverse data and algorithms. The CWB will leverage evolving technologies such as commodity computing and social networking to design an architecture for scalable collaboration that will support the emerging vision of an Earth Science Collaboratory. The CWB is being implemented on the robust and open source Eclipse framework and will be compatible with widely used scientific analysis tools such as IDL. The myScience Catalog built into CWB will capture and track metadata and provenance about data and algorithms for the researchers in a non-intrusive manner with minimal overhead. Seamless interfaces to multiple Cloud services will support sharing algorithms, data, and analysis results, as well as access to storage and computer resources. A Community Catalog will track the use of shared science artifacts and manage collaborations among researchers.

A parallel-pipelined architecture for a multi carrier demodulator

NASA Astrophysics Data System (ADS)

Kwatra, S. C.; Jamali, M. M.; Eugene, Linus P.

1991-03-01

Analog devices have been used for processing the information on board the satellites. Presently, digital devices are being used because they are economical and flexible as compared to their analog counterparts. Several schemes of digital transmission can be used depending on the data rate requirement of the user. An economical scheme of transmission for small earth stations uses single channel per carrier/frequency division multiple access (SCPC/FDMA) on the uplink and time division multiplexing (TDM) on the downlink. This is a typical communication service offered to low data rate users in commercial mass market. These channels usually pertain to either voice or data transmission. An efficient digital demodulator architecture is provided for a large number of law data rate users. A demodulator primarily consists of carrier, clock, and data recovery modules. This design uses principles of parallel processing, pipelining, and time sharing schemes to process large numbers of voice or data channels. It maintains the optimum throughput which is derived from the designed architecture and from the use of high speed components. The design is optimized for reduced power and area requirements. This is essential for satellite applications. The design is also flexible in processing a group of a varying number of channels. The algorithms that are used are verified by the use of a computer aided software engineering (CASE) tool called the Block Oriented System Simulator. The data flow, control circuitry, and interface of the hardware design is simulated in C language. Also, a multiprocessor approach is provided to map, model, and simulate the demodulation algorithms mainly from a speed view point. A hypercude based architecture implementation is provided for such a scheme of operation. The hypercube structure and the demodulation models on hypercubes are simulated in Ada.

A parallel-pipelined architecture for a multi carrier demodulator. M.S. Thesis Final Technical Report, Jan. 1989 - Aug. 1990

NASA Technical Reports Server (NTRS)

Kwatra, S. C.; Jamali, M. M.; Eugene, Linus P.

1991-01-01

Analog devices have been used for processing the information on board the satellites. Presently, digital devices are being used because they are economical and flexible as compared to their analog counterparts. Several schemes of digital transmission can be used depending on the data rate requirement of the user. An economical scheme of transmission for small earth stations uses single channel per carrier/frequency division multiple access (SCPC/FDMA) on the uplink and time division multiplexing (TDM) on the downlink. This is a typical communication service offered to low data rate users in commercial mass market. These channels usually pertain to either voice or data transmission. An efficient digital demodulator architecture is provided for a large number of law data rate users. A demodulator primarily consists of carrier, clock, and data recovery modules. This design uses principles of parallel processing, pipelining, and time sharing schemes to process large numbers of voice or data channels. It maintains the optimum throughput which is derived from the designed architecture and from the use of high speed components. The design is optimized for reduced power and area requirements. This is essential for satellite applications. The design is also flexible in processing a group of a varying number of channels. The algorithms that are used are verified by the use of a computer aided software engineering (CASE) tool called the Block Oriented System Simulator. The data flow, control circuitry, and interface of the hardware design is simulated in C language. Also, a multiprocessor approach is provided to map, model, and simulate the demodulation algorithms mainly from a speed view point. A hypercude based architecture implementation is provided for such a scheme of operation. The hypercube structure and the demodulation models on hypercubes are simulated in Ada.

Validation tools for image segmentation

NASA Astrophysics Data System (ADS)

Padfield, Dirk; Ross, James

2009-02-01

A large variety of image analysis tasks require the segmentation of various regions in an image. For example, segmentation is required to generate accurate models of brain pathology that are important components of modern diagnosis and therapy. While the manual delineation of such structures gives accurate information, the automatic segmentation of regions such as the brain and tumors from such images greatly enhances the speed and repeatability of quantifying such structures. The ubiquitous need for such algorithms has lead to a wide range of image segmentation algorithms with various assumptions, parameters, and robustness. The evaluation of such algorithms is an important step in determining their effectiveness. Therefore, rather than developing new segmentation algorithms, we here describe validation methods for segmentation algorithms. Using similarity metrics comparing the automatic to manual segmentations, we demonstrate methods for optimizing the parameter settings for individual cases and across a collection of datasets using the Design of Experiment framework. We then employ statistical analysis methods to compare the effectiveness of various algorithms. We investigate several region-growing algorithms from the Insight Toolkit and compare their accuracy to that of a separate statistical segmentation algorithm. The segmentation algorithms are used with their optimized parameters to automatically segment the brain and tumor regions in MRI images of 10 patients. The validation tools indicate that none of the ITK algorithms studied are able to outperform with statistical significance the statistical segmentation algorithm although they perform reasonably well considering their simplicity.

Optimal design of low-density SNP arrays for genomic prediction: algorithm and applications

USDA-ARS?s Scientific Manuscript database

Low-density (LD) single nucleotide polymorphism (SNP) arrays provide a cost-effective solution for genomic prediction and selection, but algorithms and computational tools are needed for their optimal design. A multiple-objective, local optimization (MOLO) algorithm was developed for design of optim...

Predictive Model of Linear Antimicrobial Peptides Active against Gram-Negative Bacteria.

PubMed

Vishnepolsky, Boris; Gabrielian, Andrei; Rosenthal, Alex; Hurt, Darrell E; Tartakovsky, Michael; Managadze, Grigol; Grigolava, Maya; Makhatadze, George I; Pirtskhalava, Malak

2018-05-29

Antimicrobial peptides (AMPs) have been identified as a potential new class of anti-infectives for drug development. There are a lot of computational methods that try to predict AMPs. Most of them can only predict if a peptide will show any antimicrobial potency, but to the best of our knowledge, there are no tools which can predict antimicrobial potency against particular strains. Here we present a predictive model of linear AMPs being active against particular Gram-negative strains relying on a semi-supervised machine-learning approach with a density-based clustering algorithm. The algorithm can well distinguish peptides active against particular strains from others which may also be active but not against the considered strain. The available AMP prediction tools cannot carry out this task. The prediction tool based on the algorithm suggested herein is available on https://dbaasp.org.

pySPACE—a signal processing and classification environment in Python

PubMed Central

Krell, Mario M.; Straube, Sirko; Seeland, Anett; Wöhrle, Hendrik; Teiwes, Johannes; Metzen, Jan H.; Kirchner, Elsa A.; Kirchner, Frank

2013-01-01

In neuroscience large amounts of data are recorded to provide insights into cerebral information processing and function. The successful extraction of the relevant signals becomes more and more challenging due to increasing complexities in acquisition techniques and questions addressed. Here, automated signal processing and machine learning tools can help to process the data, e.g., to separate signal and noise. With the presented software pySPACE (http://pyspace.github.io/pyspace), signal processing algorithms can be compared and applied automatically on time series data, either with the aim of finding a suitable preprocessing, or of training supervised algorithms to classify the data. pySPACE originally has been built to process multi-sensor windowed time series data, like event-related potentials from the electroencephalogram (EEG). The software provides automated data handling, distributed processing, modular build-up of signal processing chains and tools for visualization and performance evaluation. Included in the software are various algorithms like temporal and spatial filters, feature generation and selection, classification algorithms, and evaluation schemes. Further, interfaces to other signal processing tools are provided and, since pySPACE is a modular framework, it can be extended with new algorithms according to individual needs. In the presented work, the structural hierarchies are described. It is illustrated how users and developers can interface the software and execute offline and online modes. Configuration of pySPACE is realized with the YAML format, so that programming skills are not mandatory for usage. The concept of pySPACE is to have one comprehensive tool that can be used to perform complete signal processing and classification tasks. It further allows to define own algorithms, or to integrate and use already existing libraries. PMID:24399965

pySPACE-a signal processing and classification environment in Python.

PubMed

Krell, Mario M; Straube, Sirko; Seeland, Anett; Wöhrle, Hendrik; Teiwes, Johannes; Metzen, Jan H; Kirchner, Elsa A; Kirchner, Frank

2013-01-01

In neuroscience large amounts of data are recorded to provide insights into cerebral information processing and function. The successful extraction of the relevant signals becomes more and more challenging due to increasing complexities in acquisition techniques and questions addressed. Here, automated signal processing and machine learning tools can help to process the data, e.g., to separate signal and noise. With the presented software pySPACE (http://pyspace.github.io/pyspace), signal processing algorithms can be compared and applied automatically on time series data, either with the aim of finding a suitable preprocessing, or of training supervised algorithms to classify the data. pySPACE originally has been built to process multi-sensor windowed time series data, like event-related potentials from the electroencephalogram (EEG). The software provides automated data handling, distributed processing, modular build-up of signal processing chains and tools for visualization and performance evaluation. Included in the software are various algorithms like temporal and spatial filters, feature generation and selection, classification algorithms, and evaluation schemes. Further, interfaces to other signal processing tools are provided and, since pySPACE is a modular framework, it can be extended with new algorithms according to individual needs. In the presented work, the structural hierarchies are described. It is illustrated how users and developers can interface the software and execute offline and online modes. Configuration of pySPACE is realized with the YAML format, so that programming skills are not mandatory for usage. The concept of pySPACE is to have one comprehensive tool that can be used to perform complete signal processing and classification tasks. It further allows to define own algorithms, or to integrate and use already existing libraries.

US Policy on the South China Sea: Should the US Make Adjustments Following the Permanent Court of Arbitration Ruling

DTIC Science & Technology

2017-10-26

Government of the Philippines, and international community, a diplomatic tool to counter Chinese encroachment in the Philippines exclusive economic zone and...Philippines, and international community, a diplomatic tool to counter Chinese encroachment in the Philippines exclusive economic zone and more...nations within their exclusive economic zones (EEZs). 1 Bill Hayton, The South China Sea (New Haven

Resource-Constrained Project Scheduling Under Uncertainty: Models, Algorithms and Applications

DTIC Science & Technology

2014-11-10

Make-to-Order (MTO) Production Planning using Bayesian Updating, International Journal of Production Economics (04 2014) Norman Keith Womer, Haitao...2013) Made-to-Order Production Scheduling using Bayesian Updating, Working Paper, under second-round review in International Journal of Production Economics . VI

Classification and assessment tools for structural motif discovery algorithms.

PubMed

Badr, Ghada; Al-Turaiki, Isra; Mathkour, Hassan

2013-01-01

Motif discovery is the problem of finding recurring patterns in biological data. Patterns can be sequential, mainly when discovered in DNA sequences. They can also be structural (e.g. when discovering RNA motifs). Finding common structural patterns helps to gain a better understanding of the mechanism of action (e.g. post-transcriptional regulation). Unlike DNA motifs, which are sequentially conserved, RNA motifs exhibit conservation in structure, which may be common even if the sequences are different. Over the past few years, hundreds of algorithms have been developed to solve the sequential motif discovery problem, while less work has been done for the structural case. In this paper, we survey, classify, and compare different algorithms that solve the structural motif discovery problem, where the underlying sequences may be different. We highlight their strengths and weaknesses. We start by proposing a benchmark dataset and a measurement tool that can be used to evaluate different motif discovery approaches. Then, we proceed by proposing our experimental setup. Finally, results are obtained using the proposed benchmark to compare available tools. To the best of our knowledge, this is the first attempt to compare tools solely designed for structural motif discovery. Results show that the accuracy of discovered motifs is relatively low. The results also suggest a complementary behavior among tools where some tools perform well on simple structures, while other tools are better for complex structures. We have classified and evaluated the performance of available structural motif discovery tools. In addition, we have proposed a benchmark dataset with tools that can be used to evaluate newly developed tools.

Implementation of an Evidence-Based and Content Validated Standardized Ostomy Algorithm Tool in Home Care: A Quality Improvement Project.

PubMed

Bare, Kimberly; Drain, Jerri; Timko-Progar, Monica; Stallings, Bobbie; Smith, Kimberly; Ward, Naomi; Wright, Sandra

Many nurses have limited experience with ostomy management. We sought to provide a standardized approach to ostomy education and management to support nurses in early identification of stomal and peristomal complications, pouching problems, and provide standardized solutions for managing ostomy care in general while improving utilization of formulary products. This article describes development and testing of an ostomy algorithm tool.

A software tool for determination of breast cancer treatment methods using data mining approach.

PubMed

Cakır, Abdülkadir; Demirel, Burçin

2011-12-01

In this work, breast cancer treatment methods are determined using data mining. For this purpose, software is developed to help to oncology doctor for the suggestion of application of the treatment methods about breast cancer patients. 462 breast cancer patient data, obtained from Ankara Oncology Hospital, are used to determine treatment methods for new patients. This dataset is processed with Weka data mining tool. Classification algorithms are applied one by one for this dataset and results are compared to find proper treatment method. Developed software program called as "Treatment Assistant" uses different algorithms (IB1, Multilayer Perception and Decision Table) to find out which one is giving better result for each attribute to predict and by using Java Net beans interface. Treatment methods are determined for the post surgical operation of breast cancer patients using this developed software tool. At modeling step of data mining process, different Weka algorithms are used for output attributes. For hormonotherapy output IB1, for tamoxifen and radiotherapy outputs Multilayer Perceptron and for the chemotherapy output decision table algorithm shows best accuracy performance compare to each other. In conclusion, this work shows that data mining approach can be a useful tool for medical applications particularly at the treatment decision step. Data mining helps to the doctor to decide in a short time.

A tool for assessing the economic impact of spending on public transit.

DOT National Transportation Integrated Search

2013-07-01

In this project, an Excel-based template tool was developed for transit agencies, local governments, and other stakeholders of public transit to estimate the economic impacts of spending on public transit. Features include the following: : Uses i...

Incorporating Psychological Predictors of Treatment Response into Health Economic Simulation Models: A Case Study in Type 1 Diabetes.

PubMed

Kruger, Jen; Pollard, Daniel; Basarir, Hasan; Thokala, Praveen; Cooke, Debbie; Clark, Marie; Bond, Rod; Heller, Simon; Brennan, Alan

2015-10-01

. Health economic modeling has paid limited attention to the effects that patients' psychological characteristics have on the effectiveness of treatments. This case study tests 1) the feasibility of incorporating psychological prediction models of treatment response within an economic model of type 1 diabetes, 2) the potential value of providing treatment to a subgroup of patients, and 3) the cost-effectiveness of providing treatment to a subgroup of responders defined using 5 different algorithms. . Multiple linear regressions were used to investigate relationships between patients' psychological characteristics and treatment effectiveness. Two psychological prediction models were integrated with a patient-level simulation model of type 1 diabetes. Expected value of individualized care analysis was undertaken. Five different algorithms were used to provide treatment to a subgroup of predicted responders. A cost-effectiveness analysis compared using the algorithms to providing treatment to all patients. . The psychological prediction models had low predictive power for treatment effectiveness. Expected value of individualized care results suggested that targeting education at responders could be of value. The cost-effectiveness analysis suggested, for all 5 algorithms, that providing structured education to a subgroup of predicted responders would not be cost-effective. . The psychological prediction models tested did not have sufficient predictive power to make targeting treatment cost-effective. The psychological prediction models are simple linear models of psychological behavior. Collection of data on additional covariates could potentially increase statistical power. . By collecting data on psychological variables before an intervention, we can construct predictive models of treatment response to interventions. These predictive models can be incorporated into health economic models to investigate more complex service delivery and reimbursement strategies. © The Author(s) 2015.

A Simulation Tool for Distributed Databases.

DTIC Science & Technology

1981-09-01

11-8 . Reed’s multiversion system [RE1T8] may also be viewed aa updating only copies until the commit is made. The decision to make the changes...distributed voting, and Ellis’ ring algorithm. Other, significantly different algorithms not covered in his work include Reed’s multiversion algorithm, the

The NLS-Based Nonlinear Grey Multivariate Model for Forecasting Pollutant Emissions in China.

PubMed

Pei, Ling-Ling; Li, Qin; Wang, Zheng-Xin

2018-03-08

The relationship between pollutant discharge and economic growth has been a major research focus in environmental economics. To accurately estimate the nonlinear change law of China's pollutant discharge with economic growth, this study establishes a transformed nonlinear grey multivariable (TNGM (1, N )) model based on the nonlinear least square (NLS) method. The Gauss-Seidel iterative algorithm was used to solve the parameters of the TNGM (1, N ) model based on the NLS basic principle. This algorithm improves the precision of the model by continuous iteration and constantly approximating the optimal regression coefficient of the nonlinear model. In our empirical analysis, the traditional grey multivariate model GM (1, N ) and the NLS-based TNGM (1, N ) models were respectively adopted to forecast and analyze the relationship among wastewater discharge per capita (WDPC), and per capita emissions of SO₂ and dust, alongside GDP per capita in China during the period 1996-2015. Results indicated that the NLS algorithm is able to effectively help the grey multivariable model identify the nonlinear relationship between pollutant discharge and economic growth. The results show that the NLS-based TNGM (1, N ) model presents greater precision when forecasting WDPC, SO₂ emissions and dust emissions per capita, compared to the traditional GM (1, N ) model; WDPC indicates a growing tendency aligned with the growth of GDP, while the per capita emissions of SO₂ and dust reduce accordingly.

Design Time Optimization for Hardware Watermarking Protection of HDL Designs

PubMed Central

Castillo, E.; Morales, D. P.; García, A.; Parrilla, L.; Todorovich, E.; Meyer-Baese, U.

2015-01-01

HDL-level design offers important advantages for the application of watermarking to IP cores, but its complexity also requires tools automating these watermarking algorithms. A new tool for signature distribution through combinational logic is proposed in this work. IPP@HDL, a previously proposed high-level watermarking technique, has been employed for evaluating the tool. IPP@HDL relies on spreading the bits of a digital signature at the HDL design level using combinational logic included within the original system. The development of this new tool for the signature distribution has not only extended and eased the applicability of this IPP technique, but it has also improved the signature hosting process itself. Three algorithms were studied in order to develop this automated tool. The selection of a cost function determines the best hosting solutions in terms of area and performance penalties on the IP core to protect. An 1D-DWT core and MD5 and SHA1 digital signatures were used in order to illustrate the benefits of the new tool and its optimization related to the extraction logic resources. Among the proposed algorithms, the alternative based on simulated annealing reduces the additional resources while maintaining an acceptable computation time and also saving designer effort and time. PMID:25861681

Evaluating the utility of syndromic surveillance algorithms for screening to detect potentially clonal hospital infection outbreaks

PubMed Central

Talbot, Thomas R; Schaffner, William; Bloch, Karen C; Daniels, Titus L; Miller, Randolph A

2011-01-01

Objective The authors evaluated algorithms commonly used in syndromic surveillance for use as screening tools to detect potentially clonal outbreaks for review by infection control practitioners. Design Study phase 1 applied four aberrancy detection algorithms (CUSUM, EWMA, space-time scan statistic, and WSARE) to retrospective microbiologic culture data, producing a list of past candidate outbreak clusters. In phase 2, four infectious disease physicians categorized the phase 1 algorithm-identified clusters to ascertain algorithm performance. In phase 3, project members combined the algorithms to create a unified screening system and conducted a retrospective pilot evaluation. Measurements The study calculated recall and precision for each algorithm, and created precision-recall curves for various methods of combining the algorithms into a unified screening tool. Results Individual algorithm recall and precision ranged from 0.21 to 0.31 and from 0.053 to 0.29, respectively. Few candidate outbreak clusters were identified by more than one algorithm. The best method of combining the algorithms yielded an area under the precision-recall curve of 0.553. The phase 3 combined system detected all infection control-confirmed outbreaks during the retrospective evaluation period. Limitations Lack of phase 2 reviewers' agreement indicates that subjective expert review was an imperfect gold standard. Less conservative filtering of culture results and alternate parameter selection for each algorithm might have improved algorithm performance. Conclusion Hospital outbreak detection presents different challenges than traditional syndromic surveillance. Nevertheless, algorithms developed for syndromic surveillance have potential to form the basis of a combined system that might perform clinically useful hospital outbreak screening. PMID:21606134

Algorithmic Bricks: A Tangible Robot Programming Tool for Elementary School Students

ERIC Educational Resources Information Center

Kwon, D.-Y.; Kim, H.-S.; Shim, J.-K.; Lee, W.-G.

2012-01-01

Tangible programming tools enable children to easily learn the programming process, previously considered to be difficult for them. While various tangible programming tools have been developed, there is still a lack of available tools to help students experience the general programming process. This study therefore developed a tool called…

A high-performance spatial database based approach for pathology imaging algorithm evaluation

PubMed Central

Wang, Fusheng; Kong, Jun; Gao, Jingjing; Cooper, Lee A.D.; Kurc, Tahsin; Zhou, Zhengwen; Adler, David; Vergara-Niedermayr, Cristobal; Katigbak, Bryan; Brat, Daniel J.; Saltz, Joel H.

2013-01-01

Background: Algorithm evaluation provides a means to characterize variability across image analysis algorithms, validate algorithms by comparison with human annotations, combine results from multiple algorithms for performance improvement, and facilitate algorithm sensitivity studies. The sizes of images and image analysis results in pathology image analysis pose significant challenges in algorithm evaluation. We present an efficient parallel spatial database approach to model, normalize, manage, and query large volumes of analytical image result data. This provides an efficient platform for algorithm evaluation. Our experiments with a set of brain tumor images demonstrate the application, scalability, and effectiveness of the platform. Context: The paper describes an approach and platform for evaluation of pathology image analysis algorithms. The platform facilitates algorithm evaluation through a high-performance database built on the Pathology Analytic Imaging Standards (PAIS) data model. Aims: (1) Develop a framework to support algorithm evaluation by modeling and managing analytical results and human annotations from pathology images; (2) Create a robust data normalization tool for converting, validating, and fixing spatial data from algorithm or human annotations; (3) Develop a set of queries to support data sampling and result comparisons; (4) Achieve high performance computation capacity via a parallel data management infrastructure, parallel data loading and spatial indexing optimizations in this infrastructure. Materials and Methods: We have considered two scenarios for algorithm evaluation: (1) algorithm comparison where multiple result sets from different methods are compared and consolidated; and (2) algorithm validation where algorithm results are compared with human annotations. We have developed a spatial normalization toolkit to validate and normalize spatial boundaries produced by image analysis algorithms or human annotations. The validated data were formatted based on the PAIS data model and loaded into a spatial database. To support efficient data loading, we have implemented a parallel data loading tool that takes advantage of multi-core CPUs to accelerate data injection. The spatial database manages both geometric shapes and image features or classifications, and enables spatial sampling, result comparison, and result aggregation through expressive structured query language (SQL) queries with spatial extensions. To provide scalable and efficient query support, we have employed a shared nothing parallel database architecture, which distributes data homogenously across multiple database partitions to take advantage of parallel computation power and implements spatial indexing to achieve high I/O throughput. Results: Our work proposes a high performance, parallel spatial database platform for algorithm validation and comparison. This platform was evaluated by storing, managing, and comparing analysis results from a set of brain tumor whole slide images. The tools we develop are open source and available to download. Conclusions: Pathology image algorithm validation and comparison are essential to iterative algorithm development and refinement. One critical component is the support for queries involving spatial predicates and comparisons. In our work, we develop an efficient data model and parallel database approach to model, normalize, manage and query large volumes of analytical image result data. Our experiments demonstrate that the data partitioning strategy and the grid-based indexing result in good data distribution across database nodes and reduce I/O overhead in spatial join queries through parallel retrieval of relevant data and quick subsetting of datasets. The set of tools in the framework provide a full pipeline to normalize, load, manage and query analytical results for algorithm evaluation. PMID:23599905

Scheduling Results for the THEMIS Observation Scheduling Tool

NASA Technical Reports Server (NTRS)

Mclaren, David; Rabideau, Gregg; Chien, Steve; Knight, Russell; Anwar, Sadaat; Mehall, Greg; Christensen, Philip

2011-01-01

We describe a scheduling system intended to assist in the development of instrument data acquisitions for the THEMIS instrument, onboard the Mars Odyssey spacecraft, and compare results from multiple scheduling algorithms. This tool creates observations of both (a) targeted geographical regions of interest and (b) general mapping observations, while respecting spacecraft constraints such as data volume, observation timing, visibility, lighting, season, and science priorities. This tool therefore must address both geometric and state/timing/resource constraints. We describe a tool that maps geometric polygon overlap constraints to set covering constraints using a grid-based approach. These set covering constraints are then incorporated into a greedy optimization scheduling algorithm incorporating operations constraints to generate feasible schedules. The resultant tool generates schedules of hundreds of observations per week out of potential thousands of observations. This tool is currently under evaluation by the THEMIS observation planning team at Arizona State University.

Managing and learning with multiple models: Objectives and optimization algorithms

USGS Publications Warehouse

Probert, William J. M.; Hauser, C.E.; McDonald-Madden, E.; Runge, M.C.; Baxter, P.W.J.; Possingham, H.P.

2011-01-01

The quality of environmental decisions should be gauged according to managers' objectives. Management objectives generally seek to maximize quantifiable measures of system benefit, for instance population growth rate. Reaching these goals often requires a certain degree of learning about the system. Learning can occur by using management action in combination with a monitoring system. Furthermore, actions can be chosen strategically to obtain specific kinds of information. Formal decision making tools can choose actions to favor such learning in two ways: implicitly via the optimization algorithm that is used when there is a management objective (for instance, when using adaptive management), or explicitly by quantifying knowledge and using it as the fundamental project objective, an approach new to conservation.This paper outlines three conservation project objectives - a pure management objective, a pure learning objective, and an objective that is a weighted mixture of these two. We use eight optimization algorithms to choose actions that meet project objectives and illustrate them in a simulated conservation project. The algorithms provide a taxonomy of decision making tools in conservation management when there is uncertainty surrounding competing models of system function. The algorithms build upon each other such that their differences are highlighted and practitioners may see where their decision making tools can be improved. ?? 2010 Elsevier Ltd.

An evolutionary computation based algorithm for calculating solar differential rotation by automatic tracking of coronal bright points

NASA Astrophysics Data System (ADS)

Shahamatnia, Ehsan; Dorotovič, Ivan; Fonseca, Jose M.; Ribeiro, Rita A.

2016-03-01

Developing specialized software tools is essential to support studies of solar activity evolution. With new space missions such as Solar Dynamics Observatory (SDO), solar images are being produced in unprecedented volumes. To capitalize on that huge data availability, the scientific community needs a new generation of software tools for automatic and efficient data processing. In this paper a prototype of a modular framework for solar feature detection, characterization, and tracking is presented. To develop an efficient system capable of automatic solar feature tracking and measuring, a hybrid approach combining specialized image processing, evolutionary optimization, and soft computing algorithms is being followed. The specialized hybrid algorithm for tracking solar features allows automatic feature tracking while gathering characterization details about the tracked features. The hybrid algorithm takes advantages of the snake model, a specialized image processing algorithm widely used in applications such as boundary delineation, image segmentation, and object tracking. Further, it exploits the flexibility and efficiency of Particle Swarm Optimization (PSO), a stochastic population based optimization algorithm. PSO has been used successfully in a wide range of applications including combinatorial optimization, control, clustering, robotics, scheduling, and image processing and video analysis applications. The proposed tool, denoted PSO-Snake model, was already successfully tested in other works for tracking sunspots and coronal bright points. In this work, we discuss the application of the PSO-Snake algorithm for calculating the sidereal rotational angular velocity of the solar corona. To validate the results we compare them with published manual results performed by an expert.

Proposal of a Clinical Decision Tree Algorithm Using Factors Associated with Severe Dengue Infection.

PubMed

Tamibmaniam, Jayashamani; Hussin, Narwani; Cheah, Wee Kooi; Ng, Kee Sing; Muninathan, Prema

2016-01-01

WHO's new classification in 2009: dengue with or without warning signs and severe dengue, has necessitated large numbers of admissions to hospitals of dengue patients which in turn has been imposing a huge economical and physical burden on many hospitals around the globe, particularly South East Asia and Malaysia where the disease has seen a rapid surge in numbers in recent years. Lack of a simple tool to differentiate mild from life threatening infection has led to unnecessary hospitalization of dengue patients. We conducted a single-centre, retrospective study involving serologically confirmed dengue fever patients, admitted in a single ward, in Hospital Kuala Lumpur, Malaysia. Data was collected for 4 months from February to May 2014. Socio demography, co-morbidity, days of illness before admission, symptoms, warning signs, vital signs and laboratory result were all recorded. Descriptive statistics was tabulated and simple and multiple logistic regression analysis was done to determine significant risk factors associated with severe dengue. 657 patients with confirmed dengue were analysed, of which 59 (9.0%) had severe dengue. Overall, the commonest warning sign were vomiting (36.1%) and abdominal pain (32.1%). Previous co-morbid, vomiting, diarrhoea, pleural effusion, low systolic blood pressure, high haematocrit, low albumin and high urea were found as significant risk factors for severe dengue using simple logistic regression. However the significant risk factors for severe dengue with multiple logistic regressions were only vomiting, pleural effusion, and low systolic blood pressure. Using those 3 risk factors, we plotted an algorithm for predicting severe dengue. When compared to the classification of severe dengue based on the WHO criteria, the decision tree algorithm had a sensitivity of 0.81, specificity of 0.54, positive predictive value of 0.16 and negative predictive of 0.96. The decision tree algorithm proposed in this study showed high sensitivity and NPV in predicting patients with severe dengue that may warrant admission. This tool upon further validation study can be used to help clinicians decide on further managing a patient upon first encounter. It also will have a substantial impact on health resources as low risk patients can be managed as outpatients hence reserving the scarce hospital beds and medical resources for other patients in need.

En route Spacing Tool: Efficient Conflict-free Spacing to Flow-Restricted Airspace

NASA Technical Reports Server (NTRS)

Green, S.

1999-01-01

This paper describes the Air Traffic Management (ATM) problem within the U.S. of flow-restricted en route airspace, an assessment of its impact on airspace users, and a set of near-term tools and procedures to resolve the problem. The FAA is committed, over the next few years, to deploy the first generation of modem ATM decision support tool (DST) technology under the Free-Flight Phase-1 (FFp1) program. The associated en route tools include the User Request Evaluation Tool (URET) and the Traffic Management Advisor (TMA). URET is an initial conflict probe (ICP) capability that assists controllers with the detection and resolution of conflicts in en route airspace. TMA orchestrates arrivals transitioning into high-density terminal airspace by providing controllers with scheduled times of arrival (STA) and delay feedback advisories to assist with STA conformance. However, these FFPl capabilities do not mitigate the en route Miles-In-Trail (MIT) restrictions that are dynamically applied to mitigate airspace congestion. National statistics indicate that en route facilities (Centers) apply Miles-In-Trail (MIT) restrictions for approximately 5000 hours per month. Based on results from this study, an estimated 45,000 flights are impacted by these restrictions each month. Current-day practices for implementing these restrictions result in additional controller workload and an economic impact of which the fuel penalty alone may approach several hundred dollars per flight. To mitigate much of the impact of these restrictions on users and controller workload, a DST and procedures are presented. The DST is based on a simple derivative of FFP1 technology that is designed to introduce a set of simple tools for flow-rate (spacing) conformance and integrate them with conflict-probe capabilities. The tool and associated algorithms are described based on a concept prototype implemented within the CTAS baseline in 1995. A traffic scenario is used to illustrate the controller's use of the tool, and potential display options are presented for future controller evaluation.

Economic regionalization and choice of strategic development directions of municipalities of the Republic of Tatarstan

NASA Astrophysics Data System (ADS)

Panasyuk, M. V.

2018-01-01

This paper shows the results of economic regionalization and zoning of the Republic of Tatarstan, conducted in 2017. The latest experience of economic regionalization and zoning of the Republic of Tatarstan in 2007 - 2015 is exposed. The economic regionalization problem is solved on the basis of new method and algorithm that uses quantitative measures which characterize spatial and economic features of generated economic regions including their internal and average connectivity, homogeneity, compactness, socio-economic development level and life quality of the population. Three nodal and one homogeneous economic region in the Republic of Tatarstan were identified. The results of economic zoning within homogeneous economic region led to the conclusion about two existing economic zones. They have the potential for developing new economic growth pole and three economic centers - growth points with specialization on agro-industrial sector.

Easy and Fast Reconstruction of a 3D Avatar with an RGB-D Sensor.

PubMed

Mao, Aihua; Zhang, Hong; Liu, Yuxin; Zheng, Yinglong; Li, Guiqing; Han, Guoqiang

2017-05-12

This paper proposes a new easy and fast 3D avatar reconstruction method using an RGB-D sensor. Users can easily implement human body scanning and modeling just with a personal computer and a single RGB-D sensor such as a Microsoft Kinect within a small workspace in their home or office. To make the reconstruction of 3D avatars easy and fast, a new data capture strategy is proposed for efficient human body scanning, which captures only 18 frames from six views with a close scanning distance to fully cover the body; meanwhile, efficient alignment algorithms are presented to locally align the data frames in the single view and then globally align them in multi-views based on pairwise correspondence. In this method, we do not adopt shape priors or subdivision tools to synthesize the model, which helps to reduce modeling complexity. Experimental results indicate that this method can obtain accurate reconstructed 3D avatar models, and the running performance is faster than that of similar work. This research offers a useful tool for the manufacturers to quickly and economically create 3D avatars for products design, entertainment and online shopping.

Grasping with mechanical intelligence. M.S. Thesis

NASA Technical Reports Server (NTRS)

Ulrich, Nathan Thatcher

1988-01-01

Many robotic hands have been designed and a number have been built. Because of the difficulty of controlling and using complex hands, which usually have nine or more degrees of freedom, the simple one- or two-degree-of-freedom gripper is still the most common robotic end effector. A new category of device is presented: a medium-complexity end effector. With three to five degrees of freedom, such a tool is much easier to control and use, as well as more economical, compact and lightweight than complex hands. In order to increase the versatility, it was necessary to identify grasping primitives and to implement them in the mechanism. In addition, power and enveloping grasps are stressed over fingertip and precision grasps. The design is based upon analysis of object apprehension types, requisite characteristics for active sensing, and a determination of necessary environmental interactions. Contained are the general concepts necessary to the design of a medium-complexity end effector, an analysis of typical performance, and a computer simulation of a grasp planning algorithm specific to this type of mechanism. Finally, some details concerning the UPenn Hand-a tool designed for the research laboratory-are presented.

Networking—a statistical physics perspective

NASA Astrophysics Data System (ADS)

Yeung, Chi Ho; Saad, David

2013-03-01

Networking encompasses a variety of tasks related to the communication of information on networks; it has a substantial economic and societal impact on a broad range of areas including transportation systems, wired and wireless communications and a range of Internet applications. As transportation and communication networks become increasingly more complex, the ever increasing demand for congestion control, higher traffic capacity, quality of service, robustness and reduced energy consumption requires new tools and methods to meet these conflicting requirements. The new methodology should serve for gaining better understanding of the properties of networking systems at the macroscopic level, as well as for the development of new principled optimization and management algorithms at the microscopic level. Methods of statistical physics seem best placed to provide new approaches as they have been developed specifically to deal with nonlinear large-scale systems. This review aims at presenting an overview of tools and methods that have been developed within the statistical physics community and that can be readily applied to address the emerging problems in networking. These include diffusion processes, methods from disordered systems and polymer physics, probabilistic inference, which have direct relevance to network routing, file and frequency distribution, the exploration of network structures and vulnerability, and various other practical networking applications.

The West Midlands breast cancer screening status algorithm - methodology and use as an audit tool.

PubMed

Lawrence, Gill; Kearins, Olive; O'Sullivan, Emma; Tappenden, Nancy; Wallis, Matthew; Walton, Jackie

2005-01-01

To illustrate the ability of the West Midlands breast screening status algorithm to assign a screening status to women with malignant breast cancer, and its uses as a quality assurance and audit tool. Breast cancers diagnosed between the introduction of the National Health Service [NHS] Breast Screening Programme and 31 March 2001 were obtained from the West Midlands Cancer Intelligence Unit (WMCIU). Screen-detected tumours were identified via breast screening units, and the remaining cancers were assigned to one of eight screening status categories. Multiple primaries and recurrences were excluded. A screening status was assigned to 14,680 women (96% of the cohort examined), 110 cancers were not registered at the WMCIU and the cohort included 120 screen-detected recurrences. The West Midlands breast screening status algorithm is a robust simple tool which can be used to derive data to evaluate the efficacy and impact of the NHS Breast Screening Programme.

Orion GN and C Model Based Development: Experience and Lessons Learned

NASA Technical Reports Server (NTRS)

Jackson, Mark C.; Henry, Joel R.

2012-01-01

The Orion Guidance Navigation and Control (GN&C) team is charged with developing GN&C algorithms for the Exploration Flight Test One (EFT-1) vehicle. The GN&C team is a joint team consisting primarily of Prime Contractor (Lockheed Martin) and NASA personnel and contractors. Early in the GN&C development cycle the team selected MATLAB/Simulink as the tool for developing GN&C algorithms and Mathworks autocode tools as the means for converting GN&C algorithms to flight software (FSW). This paper provides an assessment of the successes and problems encountered by the GN&C team from the perspective of Orion GN&C developers, integrators, FSW engineers and management. The Orion GN&C approach to graphical development, including simulation tools, standards development and autocode approaches are scored for the main activities that the team has completed through the development phases of the program.

Economic Load Dispatch Using Adaptive Social Acceleration Constant Based Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Jain, N. K.; Nangia, Uma; Jain, Jyoti

2018-04-01

In this paper, an Adaptive Social Acceleration Constant based Particle Swarm Optimization (ASACPSO) has been developed which uses the best value of social acceleration constant (Csg). Three formulations of Csg have been used to search for the best value of Csg. These three formulations led to the development of three algorithms-ALDPSO, AELDPSO-I and AELDPSO-II which were implemented for Economic Load Dispatch of IEEE 5 bus, 14 bus and 30 bus systems. The best value of Csg was selected based on the minimum number of Kounts i.e. number of function evaluations required to minimize the function. This value of Csg was directly used in basic PSO algorithm which led to the development of ASACPSO algorithm. ASACPSO was found to converge faster and give more accurate results compared to BPSO for IEEE 5, 14 and 30 bus systems.

SHRP2 EconWorks : wider economic benefits analysis tools : final report.

DOT National Transportation Integrated Search

2016-01-01

CDM Smith has completed an evaluation of the EconWorks Wider Economic Benefits (W.E.B.) : Analysis Tools for Connecticut Department of Transportation (CTDOT). The intent of this : evaluation was to compare the results of the outputs of this toolkit t...

A General Event Location Algorithm with Applications to Eclipse and Station Line-of-Sight

NASA Technical Reports Server (NTRS)

Parker, Joel J. K.; Hughes, Steven P.

2011-01-01

A general-purpose algorithm for the detection and location of orbital events is developed. The proposed algorithm reduces the problem to a global root-finding problem by mapping events of interest (such as eclipses, station access events, etc.) to continuous, differentiable event functions. A stepping algorithm and a bracketing algorithm are used to detect and locate the roots. Examples of event functions and the stepping/bracketing algorithms are discussed, along with results indicating performance and accuracy in comparison to commercial tools across a variety of trajectories.

A General Event Location Algorithm with Applications to Eclispe and Station Line-of-Sight

NASA Technical Reports Server (NTRS)

Parker, Joel J. K.; Hughes, Steven P.

2011-01-01

A general-purpose algorithm for the detection and location of orbital events is developed. The proposed algorithm reduces the problem to a global root-finding problem by mapping events of interest (such as eclipses, station access events, etc.) to continuous, differentiable event functions. A stepping algorithm and a bracketing algorithm are used to detect and locate the roots. Examples of event functions and the stepping/bracketing algorithms are discussed, along with results indicating performance and accuracy in comparison to commercial tools across a variety of trajectories.

Algorithms for Scheduling and Network Problems

DTIC Science & Technology

1991-09-01

time. We already know, by Lemma 2.2.1, that WOPT = O(log( mpU )), so if we could solve this integer program optimally we would be done. However, the...Folydirat, 15:177-191, 1982. [6] I.S. Belov and Ya. N. Stolin. An algorithm in a single path operations scheduling problem. In Mathematical Economics and

Three-dimensional surface imaging system for assessing human obesity

NASA Astrophysics Data System (ADS)

Xu, Bugao; Yu, Wurong; Yao, Ming; Pepper, M. Reese; Freeland-Graves, Jeanne H.

2009-10-01

The increasing prevalence of obesity suggests a need to develop a convenient, reliable, and economical tool for assessment of this condition. Three-dimensional (3-D) body surface imaging has emerged as an exciting technology for the estimation of body composition. We present a new 3-D body imaging system, which is designed for enhanced portability, affordability, and functionality. In this system, stereo vision technology is used to satisfy the requirement for a simple hardware setup and fast image acquisition. The portability of the system is created via a two-stand configuration, and the accuracy of body volume measurements is improved by customizing stereo matching and surface reconstruction algorithms that target specific problems in 3-D body imaging. Body measurement functions dedicated to body composition assessment also are developed. The overall performance of the system is evaluated in human subjects by comparison to other conventional anthropometric methods, as well as air displacement plethysmography, for body fat assessment.

A 3D surface imaging system for assessing human obesity

NASA Astrophysics Data System (ADS)

Xu, B.; Yu, W.; Yao, M.; Yao, X.; Li, Q.; Pepper, M. R.; Freeland-Graves, J. H.

2009-08-01

The increasing prevalence of obesity suggests a need to develop a convenient, reliable and economical tool for assessment of this condition. Three-dimensional (3D) body surface imaging has emerged as an exciting technology for estimation of body composition. This paper presents a new 3D body imaging system, which was designed for enhanced portability, affordability, and functionality. In this system, stereo vision technology was used to satisfy the requirements for a simple hardware setup and fast image acquisitions. The portability of the system was created via a two-stand configuration, and the accuracy of body volume measurements was improved by customizing stereo matching and surface reconstruction algorithms that target specific problems in 3D body imaging. Body measurement functions dedicated to body composition assessment also were developed. The overall performance of the system was evaluated in human subjects by comparison to other conventional anthropometric methods, as well as air displacement plethysmography, for body fat assessment.

Inland and coastal waters

NASA Astrophysics Data System (ADS)

Mouw, Colleen; Greb, Steven

2012-09-01

Workshop for Remote Sensing of Coastal and Inland Waters;Madison, Wisconsin, 20-22 June 2012 Coastal and inland water bodies, which have great value for recreation, food supply, commerce, transportation, and human health, have been experiencing external pressure from direct human activities and climate change. Given their societal and economic value, understanding issues of water quality, water quantity, and the impact of environmental change on the ecological and biogeochemical functioning of these water bodies is of interest to a broad range of communities. Remote sensing offers one of the most spatially and temporally comprehensive tools for observing these waters. While there has been some success with remotely observing these water bodies, many challenges still remain, including algorithm performance, atmospheric correction, the relationships between optical properties and biogeochemical parameters, sufficient spatial and spectral resolution, and a lack of uncertainty estimates over the wide range of environmental conditions encountered across these coastal and inland water bodies.

BMPix and PEAK tools: New methods for automated laminae recognition and counting—Application to glacial varves from Antarctic marine sediment

NASA Astrophysics Data System (ADS)

Weber, M. E.; Reichelt, L.; Kuhn, G.; Pfeiffer, M.; Korff, B.; Thurow, J.; Ricken, W.

2010-03-01

We present tools for rapid and quantitative detection of sediment lamination. The BMPix tool extracts color and gray scale curves from images at pixel resolution. The PEAK tool uses the gray scale curve and performs, for the first time, fully automated counting of laminae based on three methods. The maximum count algorithm counts every bright peak of a couplet of two laminae (annual resolution) in a smoothed curve. The zero-crossing algorithm counts every positive and negative halfway passage of the curve through a wide moving average, separating the record into bright and dark intervals (seasonal resolution). The same is true for the frequency truncation method, which uses Fourier transformation to decompose the curve into its frequency components before counting positive and negative passages. The algorithms are available at doi:10.1594/PANGAEA.729700. We applied the new methods successfully to tree rings, to well-dated and already manually counted marine varves from Saanich Inlet, and to marine laminae from the Antarctic continental margin. In combination with AMS14C dating, we found convincing evidence that laminations in Weddell Sea sites represent varves, deposited continuously over several millennia during the last glacial maximum. The new tools offer several advantages over previous methods. The counting procedures are based on a moving average generated from gray scale curves instead of manual counting. Hence, results are highly objective and rely on reproducible mathematical criteria. Also, the PEAK tool measures the thickness of each year or season. Since all information required is displayed graphically, interactive optimization of the counting algorithms can be achieved quickly and conveniently.

3D Slicer as a tool for interactive brain tumor segmentation.

PubMed

Kikinis, Ron; Pieper, Steve

2011-01-01

User interaction is required for reliable segmentation of brain tumors in clinical practice and in clinical research. By incorporating current research tools, 3D Slicer provides a set of interactive, easy to use tools that can be efficiently used for this purpose. One of the modules of 3D Slicer is an interactive editor tool, which contains a variety of interactive segmentation effects. Use of these effects for fast and reproducible segmentation of a single glioblastoma from magnetic resonance imaging data is demonstrated. The innovation in this work lies not in the algorithm, but in the accessibility of the algorithm because of its integration into a software platform that is practical for research in a clinical setting.

Algorithms and programming tools for image processing on the MPP:3

NASA Technical Reports Server (NTRS)

Reeves, Anthony P.

1987-01-01

This is the third and final report on the work done for NASA Grant 5-403 on Algorithms and Programming Tools for Image Processing on the MPP:3. All the work done for this grant is summarized in the introduction. Work done since August 1986 is reported in detail. Research for this grant falls under the following headings: (1) fundamental algorithms for the MPP; (2) programming utilities for the MPP; (3) the Parallel Pascal Development System; and (4) performance analysis. In this report, the results of two efforts are reported: region growing, and performance analysis of important characteristic algorithms. In each case, timing results from MPP implementations are included. A paper is included in which parallel algorithms for region growing on the MPP is discussed. These algorithms permit different sized regions to be merged in parallel. Details on the implementation and peformance of several important MPP algorithms are given. These include a number of standard permutations, the FFT, convolution, arbitrary data mappings, image warping, and pyramid operations, all of which have been implemented on the MPP. The permutation and image warping functions have been included in the standard development system library.

Development of Online Cognitive and Algorithm Tests as Assessment Tools in Introductory Computer Science Courses

ERIC Educational Resources Information Center

Avancena, Aimee Theresa; Nishihara, Akinori; Vergara, John Paul

2012-01-01

This paper presents the online cognitive and algorithm tests, which were developed in order to determine if certain cognitive factors and fundamental algorithms correlate with the performance of students in their introductory computer science course. The tests were implemented among Management Information Systems majors from the Philippines and…

Techniques and Tools for Trustworthy Composition of Pre-Designed Embedded Software Components

DTIC Science & Technology

2012-07-01

following option choices. 1. A plain vanilla pi-trie algorithm set to build the entire pi-trie. 2. A pi-trie algorithm filtered for positive prime...implicates only. 3. A plain vanilla pi-trie algorithm to build the entire pi-trie, but recognize variable-disjoint subformulas. 4. A pi-trie

Quantum associative memory with linear and non-linear algorithms for the diagnosis of some tropical diseases.

PubMed

Tchapet Njafa, J-P; Nana Engo, S G

2018-01-01

This paper presents the QAMDiagnos, a model of Quantum Associative Memory (QAM) that can be a helpful tool for medical staff without experience or laboratory facilities, for the diagnosis of four tropical diseases (malaria, typhoid fever, yellow fever and dengue) which have several similar signs and symptoms. The memory can distinguish a single infection from a polyinfection. Our model is a combination of the improved versions of the original linear quantum retrieving algorithm proposed by Ventura and the non-linear quantum search algorithm of Abrams and Lloyd. From the given simulation results, it appears that the efficiency of recognition is good when particular signs and symptoms of a disease are inserted given that the linear algorithm is the main algorithm. The non-linear algorithm helps confirm or correct the diagnosis or give some advice to the medical staff for the treatment. So, our QAMDiagnos that has a friendly graphical user interface for desktop and smart-phone is a sensitive and a low-cost diagnostic tool that enables rapid and accurate diagnosis of four tropical diseases. Copyright © 2017 Elsevier Ltd. All rights reserved.

Drinking Water Consequences Tools. A Literature Review

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

Pasqualini, Donatella

2016-05-12

In support of the goals of Department of Homeland Security’s (DHS) National Protection and Programs Directorate and the Federal Emergency Management Agency, the DHS Office of Science and Technology is seeking to develop and/or modify consequence assessment tools to enable drinking water systems owner/operators to estimate the societal and economic consequences of drinking water disruption due to the threats and hazards. This work will expand the breadth of consequence estimation methods and tools using the best-available data describing water distribution infrastructure, owner/assetlevel economic losses, regional-scale economic activity, and health. In addition, this project will deploy the consequence methodology and capabilitymore » within a Web-based platform. This report is intended to support DHS effort providing a review literature review of existing assessment tools of water and wastewater systems consequences to disruptions. The review includes tools that assess water systems resilience, vulnerability, and risk. This will help to understand gaps and limitations of these tools in order to plan for the development of the next-generation consequences tool for water and waste water systems disruption.« less

Health economics in public health.

PubMed

Ammerman, Alice S; Farrelly, Matthew A; Cavallo, David N; Ickes, Scott B; Hoerger, Thomas J

2009-03-01

Economic analysis is an important tool in deciding how to allocate scarce public health resources; however, there is currently a dearth of such analysis by public health researchers. Public health researchers and practitioners were surveyed to determine their current use of health economics and to identify barriers to use as well as potential strategies to decrease those barriers in order to allow them to more effectively incorporate economic analyses into their work. Data collected from five focus groups informed survey development. The survey included a demographic section and 14 multi-part questions. Participants were recruited in 2006 from three national public health organizations through e-mail; 294 academicians, practitioners, and community representatives answered the survey. Survey data were analyzed in 2007. Despite an expressed belief in the importance of health economics, more than half of the respondents reported very little or no current use of health economics in their work. Of those using health economics, cost-benefit and cost-effectiveness analysis and determination of public health costs were cited as the measures used most frequently. The most important barriers were lack of expertise, funding, time, tools, and data, as well as discomfort with economic theory. The resource deemed most important to using health economics was collaboration with economists or those with economic training. Respondents indicated a desire to learn more about health economics and tools for performing economic analysis. Given the importance of incorporating economic analysis into public health interventions, and the desire of survey respondents for more collaboration with health economists, opportunities for such collaborations should be increased.

Optimal Load Shedding and Generation Rescheduling for Overload Suppression in Large Power Systems.

NASA Astrophysics Data System (ADS)

Moon, Young-Hyun

Ever-increasing size, complexity and operation costs in modern power systems have stimulated the intensive study of an optimal Load Shedding and Generator Rescheduling (LSGR) strategy in the sense of a secure and economic system operation. The conventional approach to LSGR has been based on the application of LP (Linear Programming) with the use of an approximately linearized model, and the LP algorithm is currently considered to be the most powerful tool for solving the LSGR problem. However, all of the LP algorithms presented in the literature essentially lead to the following disadvantages: (i) piecewise linearization involved in the LP algorithms requires the introduction of a number of new inequalities and slack variables, which creates significant burden to the computing facilities, and (ii) objective functions are not formulated in terms of the state variables of the adopted models, resulting in considerable numerical inefficiency in the process of computing the optimal solution. A new approach is presented, based on the development of a new linearized model and on the application of QP (Quadratic Programming). The changes in line flows as a result of changes to bus injection power are taken into account in the proposed model by the introduction of sensitivity coefficients, which avoids the mentioned second disadvantages. A precise method to calculate these sensitivity coefficients is given. A comprehensive review of the theory of optimization is included, in which results of the development of QP algorithms for LSGR as based on Wolfe's method and Kuhn -Tucker theory are evaluated in detail. The validity of the proposed model and QP algorithms has been verified and tested on practical power systems, showing the significant reduction of both computation time and memory requirements as well as the expected lower generation costs of the optimal solution as compared with those obtained from computing the optimal solution with LP. Finally, it is noted that an efficient reactive power compensation algorithm is developed to suppress voltage disturbances due to load sheddings, and that a new method for multiple contingency simulation is presented.

Insight into efficient image registration techniques and the demons algorithm.

PubMed

Vercauteren, Tom; Pennec, Xavier; Malis, Ezio; Perchant, Aymeric; Ayache, Nicholas

2007-01-01

As image registration becomes more and more central to many biomedical imaging applications, the efficiency of the algorithms becomes a key issue. Image registration is classically performed by optimizing a similarity criterion over a given spatial transformation space. Even if this problem is considered as almost solved for linear registration, we show in this paper that some tools that have recently been developed in the field of vision-based robot control can outperform classical solutions. The adequacy of these tools for linear image registration leads us to revisit non-linear registration and allows us to provide interesting theoretical roots to the different variants of Thirion's demons algorithm. This analysis predicts a theoretical advantage to the symmetric forces variant of the demons algorithm. We show that, on controlled experiments, this advantage is confirmed, and yields a faster convergence.

Verifying the interactive convergence clock synchronization algorithm using the Boyer-Moore theorem prover

NASA Technical Reports Server (NTRS)

Young, William D.

1992-01-01

The application of formal methods to the analysis of computing systems promises to provide higher and higher levels of assurance as the sophistication of our tools and techniques increases. Improvements in tools and techniques come about as we pit the current state of the art against new and challenging problems. A promising area for the application of formal methods is in real-time and distributed computing. Some of the algorithms in this area are both subtle and important. In response to this challenge and as part of an ongoing attempt to verify an implementation of the Interactive Convergence Clock Synchronization Algorithm (ICCSA), we decided to undertake a proof of the correctness of the algorithm using the Boyer-Moore theorem prover. This paper describes our approach to proving the ICCSA using the Boyer-Moore prover.

OPTIMIZING USABILITY OF AN ECONOMIC DECISION SUPPORT TOOL: PROTOTYPE OF THE EQUIPT TOOL.

PubMed

Cheung, Kei Long; Hiligsmann, Mickaël; Präger, Maximilian; Jones, Teresa; Józwiak-Hagymásy, Judit; Muñoz, Celia; Lester-George, Adam; Pokhrel, Subhash; López-Nicolás, Ángel; Trapero-Bertran, Marta; Evers, Silvia M A A; de Vries, Hein

2018-01-01

Economic decision-support tools can provide valuable information for tobacco control stakeholders, but their usability may impact the adoption of such tools. This study aims to illustrate a mixed-method usability evaluation of an economic decision-support tool for tobacco control, using the EQUIPT ROI tool prototype as a case study. A cross-sectional mixed methods design was used, including a heuristic evaluation, a thinking aloud approach, and a questionnaire testing and exploring the usability of the Return of Investment tool. A total of sixty-six users evaluated the tool (thinking aloud) and completed the questionnaire. For the heuristic evaluation, four experts evaluated the interface. In total twenty-one percent of the respondents perceived good usability. A total of 118 usability problems were identified, from which twenty-six problems were categorized as most severe, indicating high priority to fix them before implementation. Combining user-based and expert-based evaluation methods is recommended as these were shown to identify unique usability problems. The evaluation provides input to optimize usability of a decision-support tool, and may serve as a vantage point for other developers to conduct usability evaluations to refine similar tools before wide-scale implementation. Such studies could reduce implementation gaps by optimizing usability, enhancing in turn the research impact of such interventions.

Single-Pass Serial Scheduling Heuristic for Eglin AFB Range Services Division Schedule

DTIC Science & Technology

2009-06-01

scheduling tool for this RCPSP. Research on a schedule improvement metaheuristic and coding of the complete algorithm is required before it can be...a schedule better by applying metaheuristic improvement algorithms to a feasible schedule after it is created. 2.5.1. Greedy Algorithm The...next available position, the algorithm will not utilize all the available range time and manpower. An improvement metaheuristic is required to

Jobs and Economic Development Impact (JEDI) Model: Offshore Wind User Reference Guide

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

Lantz, E.; Goldberg, M.; Keyser, D.

2013-06-01

The Offshore Wind Jobs and Economic Development Impact (JEDI) model, developed by NREL and MRG & Associates, is a spreadsheet based input-output tool. JEDI is meant to be a user friendly and transparent tool to estimate potential economic impacts supported by the development and operation of offshore wind projects. This guide describes how to use the model as well as technical information such as methodology, limitations, and data sources.

Constructing Benchmark Databases and Protocols for Medical Image Analysis: Diabetic Retinopathy

PubMed Central

Kauppi, Tomi; Kämäräinen, Joni-Kristian; Kalesnykiene, Valentina; Sorri, Iiris; Uusitalo, Hannu; Kälviäinen, Heikki

2013-01-01

We address the performance evaluation practices for developing medical image analysis methods, in particular, how to establish and share databases of medical images with verified ground truth and solid evaluation protocols. Such databases support the development of better algorithms, execution of profound method comparisons, and, consequently, technology transfer from research laboratories to clinical practice. For this purpose, we propose a framework consisting of reusable methods and tools for the laborious task of constructing a benchmark database. We provide a software tool for medical image annotation helping to collect class label, spatial span, and expert's confidence on lesions and a method to appropriately combine the manual segmentations from multiple experts. The tool and all necessary functionality for method evaluation are provided as public software packages. As a case study, we utilized the framework and tools to establish the DiaRetDB1 V2.1 database for benchmarking diabetic retinopathy detection algorithms. The database contains a set of retinal images, ground truth based on information from multiple experts, and a baseline algorithm for the detection of retinopathy lesions. PMID:23956787

Applying a visual language for image processing as a graphical teaching tool in medical imaging

NASA Astrophysics Data System (ADS)

Birchman, James J.; Tanimoto, Steven L.; Rowberg, Alan H.; Choi, Hyung-Sik; Kim, Yongmin

1992-05-01

Typical user interaction in image processing is with command line entries, pull-down menus, or text menu selections from a list, and as such is not generally graphical in nature. Although applying these interactive methods to construct more sophisticated algorithms from a series of simple image processing steps may be clear to engineers and programmers, it may not be clear to clinicians. A solution to this problem is to implement a visual programming language using visual representations to express image processing algorithms. Visual representations promote a more natural and rapid understanding of image processing algorithms by providing more visual insight into what the algorithms do than the interactive methods mentioned above can provide. Individuals accustomed to dealing with images will be more likely to understand an algorithm that is represented visually. This is especially true of referring physicians, such as surgeons in an intensive care unit. With the increasing acceptance of picture archiving and communications system (PACS) workstations and the trend toward increasing clinical use of image processing, referring physicians will need to learn more sophisticated concepts than simply image access and display. If the procedures that they perform commonly, such as window width and window level adjustment and image enhancement using unsharp masking, are depicted visually in an interactive environment, it will be easier for them to learn and apply these concepts. The software described in this paper is a visual programming language for imaging processing which has been implemented on the NeXT computer using NeXTstep user interface development tools and other tools in an object-oriented environment. The concept is based upon the description of a visual language titled `Visualization of Vision Algorithms' (VIVA). Iconic representations of simple image processing steps are placed into a workbench screen and connected together into a dataflow path by the user. As the user creates and edits a dataflow path, more complex algorithms can be built on the screen. Once the algorithm is built, it can be executed, its results can be reviewed, and operator parameters can be interactively adjusted until an optimized output is produced. The optimized algorithm can then be saved and added to the system as a new operator. This system has been evaluated as a graphical teaching tool for window width and window level adjustment, image enhancement using unsharp masking, and other techniques.

Model Based Optimal Sensor Network Design for Condition Monitoring in an IGCC Plant

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

Kumar, Rajeeva; Kumar, Aditya; Dai, Dan

2012-12-31

This report summarizes the achievements and final results of this program. The objective of this program is to develop a general model-based sensor network design methodology and tools to address key issues in the design of an optimal sensor network configuration: the type, location and number of sensors used in a network, for online condition monitoring. In particular, the focus in this work is to develop software tools for optimal sensor placement (OSP) and use these tools to design optimal sensor network configuration for online condition monitoring of gasifier refractory wear and radiant syngas cooler (RSC) fouling. The methodology developedmore » will be applicable to sensing system design for online condition monitoring for broad range of applications. The overall approach consists of (i) defining condition monitoring requirement in terms of OSP and mapping these requirements in mathematical terms for OSP algorithm, (ii) analyzing trade-off of alternate OSP algorithms, down selecting the most relevant ones and developing them for IGCC applications (iii) enhancing the gasifier and RSC models as required by OSP algorithms, (iv) applying the developed OSP algorithm to design the optimal sensor network required for the condition monitoring of an IGCC gasifier refractory and RSC fouling. Two key requirements for OSP for condition monitoring are desired precision for the monitoring variables (e.g. refractory wear) and reliability of the proposed sensor network in the presence of expected sensor failures. The OSP problem is naturally posed within a Kalman filtering approach as an integer programming problem where the key requirements of precision and reliability are imposed as constraints. The optimization is performed over the overall network cost. Based on extensive literature survey two formulations were identified as being relevant to OSP for condition monitoring; one based on LMI formulation and the other being standard INLP formulation. Various algorithms to solve these two formulations were developed and validated. For a given OSP problem the computation efficiency largely depends on the “size” of the problem. Initially a simplified 1-D gasifier model assuming axial and azimuthal symmetry was used to test out various OSP algorithms. Finally these algorithms were used to design the optimal sensor network for condition monitoring of IGCC gasifier refractory wear and RSC fouling. The sensors type and locations obtained as solution to the OSP problem were validated using model based sensing approach. The OSP algorithm has been developed in a modular form and has been packaged as a software tool for OSP design where a designer can explore various OSP design algorithm is a user friendly way. The OSP software tool is implemented in Matlab/Simulink© in-house. The tool also uses few optimization routines that are freely available on World Wide Web. In addition a modular Extended Kalman Filter (EKF) block has also been developed in Matlab/Simulink© which can be utilized for model based sensing of important process variables that are not directly measured through combining the online sensors with model based estimation once the hardware sensor and their locations has been finalized. The OSP algorithm details and the results of applying these algorithms to obtain optimal sensor location for condition monitoring of gasifier refractory wear and RSC fouling profile are summarized in this final report.« less

U.S. - GERMAN BILATERAL WORKING GROUP WORKSHOP ON: ECONOMIC TOOLS FOR SUSTAINABLE BROWNFIELDS REDEVELOPMENT

EPA Science Inventory

This CD-ROM contains information from a two-day workshop discussing innovative brownfields financing and economic strategies in the United States and Germany. A special emphasis was given to the identification of advantages and disadvantages of different financial tools, economi...

Buffer$--An Economic Analysis Tool

Treesearch

Gary Bentrup

2007-01-01

Buffer$ is an economic spreadsheet tool for analyzing the cost-benefits of conservation buffers by resource professionals. Conservation buffers are linear strips of vegetation managed for multiple landowner and societal objectives. The Microsoft Excel based spreadsheet can calculate potential income derived from a buffer, including income from cost-share/incentive...

Multimedia Tools for Teaching Economics.

ERIC Educational Resources Information Center

Pereira-Ford, Clara V.

1998-01-01

Describes one professor's experience in researching the use of multimedia tools for teaching principles of economics. Provides a list of resources consulted, including universities and colleges, books, software, laserdiscs and VHS tapes, Web sites, and journal sources. Found the students generally to be receptive to the introduction of new tools…

Data and Tools | Energy Analysis | NREL

Science.gov Websites

and Tools Energy Analysis Data and Tools NREL develops energy analysis data and tools to assess collections. Data Products Technology and Performance Analysis Tools Energy Systems Analysis Tools Economic and Financial Analysis Tools

Strategy and gaps for modeling, simulation, and control of hybrid systems

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

Rabiti, Cristian; Garcia, Humberto E.; Hovsapian, Rob

2015-04-01

The purpose of this report is to establish a strategy for modeling and simulation of candidate hybrid energy systems. Modeling and simulation is necessary to design, evaluate, and optimize the system technical and economic performance. Accordingly, this report first establishes the simulation requirements to analysis candidate hybrid systems. Simulation fidelity levels are established based on the temporal scale, real and synthetic data availability or needs, solution accuracy, and output parameters needed to evaluate case-specific figures of merit. Accordingly, the associated computational and co-simulation resources needed are established; including physical models when needed, code assembly and integrated solutions platforms, mathematical solvers,more » and data processing. This report first attempts to describe the figures of merit, systems requirements, and constraints that are necessary and sufficient to characterize the grid and hybrid systems behavior and market interactions. Loss of Load Probability (LOLP) and effective cost of Effective Cost of Energy (ECE), as opposed to the standard Levelized Cost of Electricty (LCOE), are introduced as technical and economical indices for integrated energy system evaluations. Financial assessment methods are subsequently introduced for evaluation of non-traditional, hybrid energy systems. Algorithms for coupled and iterative evaluation of the technical and economic performance are subsequently discussed. This report further defines modeling objectives, computational tools, solution approaches, and real-time data collection and processing (in some cases using real test units) that will be required to model, co-simulate, and optimize; (a) an energy system components (e.g., power generation unit, chemical process, electricity management unit), (b) system domains (e.g., thermal, electrical or chemical energy generation, conversion, and transport), and (c) systems control modules. Co-simulation of complex, tightly coupled, dynamic energy systems requires multiple simulation tools, potentially developed in several programming languages and resolved on separate time scales. Whereas further investigation and development of hybrid concepts will provide a more complete understanding of the joint computational and physical modeling needs, this report highlights areas in which co-simulation capabilities are warranted. The current development status, quality assurance, availability and maintainability of simulation tools that are currently available for hybrid systems modeling is presented. Existing gaps in the modeling and simulation toolsets and development needs are subsequently discussed. This effort will feed into a broader Roadmap activity for designing, developing, and demonstrating hybrid energy systems.« less

Dynamic control of supplemental lighting for greenhouse

NASA Astrophysics Data System (ADS)

Wang, Yuanxv; Wei, Ruihua; Xu, Lihong

2018-04-01

The development of light-emitting diodes (LED) technology to a large extent reduce the energy consumption of greenhouse, however, the light control methods to realize the energy saving still have great potential. The aim of this paper is to develop a more efficient control method of dynamic control of the LED top-lighting (TL) intensity and the LED inter-lighting (IL) intensity for the greatest economic benefits. A dynamic lighting control algorithm (DLC) based on model is proposed, which defines the economic benefit performance criterion of the supplemental lighting control. The optimal light intensity of TL and IL is calculated in real time according to the algorithm. The simulation shows that economic benefit can be increased by up to 107.35% compared to TL on-off control. It is concluded that DLC is a feasible supplemental light control method, especially under low natural light conditions.

[Fostering of health economics in Germany].

PubMed

Ulrich, V

2012-05-01

Health economics is now well established in Germany with the aim to apply economic tools to answer problems in health and health care. After a short review of the international development of health economics and the development in Germany in particular, the article looks at selected recent topics of health economic analysis in Germany (economic evaluation, industrial economics, health and education).

EconoMe-Develop - a calculation tool for multi-risk assessment and benefit-cost-analysis

NASA Astrophysics Data System (ADS)

Bründl, M.

2012-04-01

Public money is used to finance the protection of human life, material assets and the environment against natural hazards. This limited resource should be used in a way that it achieves the maximum possible effect by minimizing as many risks as possible. Hence, decision-makers are facing the question which mitigation measures should be prioritised. Benefit-Cost-Analysis (BCA) is a recognized method for determining the economic efficiency of investments in mitigation measures. In Switzerland, the Federal Office for the Environment (FOEN) judges the benefit-cost-ratio of mitigation projects on the base of the results of the calculation tool "EconoMe" [1]. The check of the economic efficiency of mitigation projects with an investment of more than 1 million CHF (800,000 EUR) by using "EconoMe" is mandatory since 2008 in Switzerland. Within "EconoMe", most calculation parameters cannot be changed by the user allowing for comparable results. Based on the risk guideline "RIKO" [2] an extended version of the operational version of "EconoMe", called "EconoMe-Develop" was developed. "EconoMe-Develop" is able to deal with various natural hazard processes and thus allows multi-risk assessments, since all restrictions of the operational version of "EconoMe" like e.g. the number of scenarios and expositions, vulnerability, spatial probability of processes and probability of presence of objects, are not existing. Additionally, the influences of uncertainty of calculation factors, like e.g. vulnerability, on the final results can be determined. "EconoMe-Develop" offers import and export of data, e.g. results of GIS-analysis. The possibility for adapting the tool to user specific requirements makes EconoMe-Develop an easy-to-use tool for risk assessment and assessment of economic efficiency of mitigation projects for risk experts. In the paper we will present the most important features of the tool and we will illustrate the application by a practical example.

Automated and Assistive Tools for Accelerated Code migration of Scientific Computing on to Heterogeneous MultiCore Systems

DTIC Science & Technology

2017-04-13

modelling code, a parallel benchmark , and a communication avoiding version of the QR algorithm. Further, several improvements to the OmpSs model were...movement; and a port of the dynamic load balancing library to OmpSs. Finally, several updates to the tools infrastructure were accomplished, including: an...OmpSs: a basic algorithm on image processing applications, a mini application representative of an ocean modelling code, a parallel benchmark , and a

A software tool for dataflow graph scheduling

NASA Technical Reports Server (NTRS)

Jones, Robert L., III

1994-01-01

A graph-theoretic design process and software tool is presented for selecting a multiprocessing scheduling solution for a class of computational problems. The problems of interest are those that can be described using a dataflow graph and are intended to be executed repetitively on multiple processors. The dataflow paradigm is very useful in exposing the parallelism inherent in algorithms. It provides a graphical and mathematical model which describes a partial ordering of algorithm tasks based on data precedence.

Small hydropower spot prediction using SWAT and a diversion algorithm, case study: Upper Citarum Basin

NASA Astrophysics Data System (ADS)

Kardhana, Hadi; Arya, Doni Khaira; Hadihardaja, Iwan K.; Widyaningtyas, Riawan, Edi; Lubis, Atika

2017-11-01

Small-Scale Hydropower (SHP) had been important electric energy power source in Indonesia. Indonesia is vast countries, consists of more than 17.000 islands. It has large fresh water resource about 3 m of rainfall and 2 m of runoff. Much of its topography is mountainous, remote but abundant with potential energy. Millions of people do not have sufficient access to electricity, some live in the remote places. Recently, SHP development was encouraged for energy supply of the places. Development of global hydrology data provides opportunity to predict distribution of hydropower potential. In this paper, we demonstrate run-of-river type SHP spot prediction tool using SWAT and a river diversion algorithm. The use of Soil and Water Assessment Tool (SWAT) with input of CFSR (Climate Forecast System Re-analysis) of 10 years period had been implemented to predict spatially distributed flow cumulative distribution function (CDF). A simple algorithm to maximize potential head of a location by a river diversion expressing head race and penstock had been applied. Firm flow and power of the SHP were estimated from the CDF and the algorithm. The tool applied to Upper Citarum River Basin and three out of four existing hydropower locations had been well predicted. The result implies that this tool is able to support acceleration of SHP development at earlier phase.

Design tool for multiprocessor scheduling and evaluation of iterative dataflow algorithms

NASA Technical Reports Server (NTRS)

Jones, Robert L., III

1995-01-01

A graph-theoretic design process and software tool is defined for selecting a multiprocessing scheduling solution for a class of computational problems. The problems of interest are those that can be described with a dataflow graph and are intended to be executed repetitively on a set of identical processors. Typical applications include signal processing and control law problems. Graph-search algorithms and analysis techniques are introduced and shown to effectively determine performance bounds, scheduling constraints, and resource requirements. The software tool applies the design process to a given problem and includes performance optimization through the inclusion of additional precedence constraints among the schedulable tasks.

Environmental-Socio-Economic Monitoring as a Tool of Region’s Environmental-Economic System Management

NASA Astrophysics Data System (ADS)

Galanina, T. V.; Baumgarten, M. I.; Mikhailov, V. G.; Koroleva, T. G.; Mikhailov, G. S.

2017-01-01

The paper deals with the region’s environmental-economic system management through a tool such as the environmental-socio-economic monitoring. The purpose of research - is analysis and development of theoretical assumptions of environmental-socio-economic monitoring system for the effective management of geographically distributed environmental-economic system. The main elements of environmental-socio-economic monitoring are identified, taking into account the characteristics of the studied area. The main result of the research is the development of multi-functional integrated monitoring system for the evaluation of the indicators "gross domestic product" and "gross national product", taking into account the influence of environmental factors. The results of the study conducted may be recommended to the regional and federal governments to support the effective, environment-friendly management decision-making consistent with the overall development concept.

Conducting systematic reviews of economic evaluations.

PubMed

Gomersall, Judith Streak; Jadotte, Yuri Tertilus; Xue, Yifan; Lockwood, Suzi; Riddle, Dru; Preda, Alin

2015-09-01

In 2012, a working group was established to review and enhance the Joanna Briggs Institute (JBI) guidance for conducting systematic review of evidence from economic evaluations addressing a question(s) about health intervention cost-effectiveness. The objective is to present the outcomes of the working group. The group conducted three activities to inform the new guidance: review of literature on the utility/futility of systematic reviews of economic evaluations and consideration of its implications for updating the existing methodology; assessment of the critical appraisal tool in the existing guidance against criteria that promotes validity in economic evaluation research and two other commonly used tools; and a workshop. The debate in the literature on the limitations/value of systematic review of economic evidence cautions that systematic reviews of economic evaluation evidence are unlikely to generate one size fits all answers to questions about the cost-effectiveness of interventions and their comparators. Informed by this finding, the working group adjusted the framing of the objectives definition in the existing JBI methodology. The shift is away from defining the objective as to determine one cost-effectiveness measure toward summarizing study estimates of cost-effectiveness and informed by consideration of the included study characteristics (patient, setting, intervention component, etc.), identifying conditions conducive to lowering costs and maximizing health benefits. The existing critical appraisal tool was included in the new guidance. The new guidance includes the recommendation that a tool designed specifically for the purpose of appraising model-based studies be used together with the generic appraisal tool for economic evaluations assessment to evaluate model-based evaluations. The guidance produced by the group offers reviewers guidance for each step of the systematic review process, which are the same steps followed in JBI reviews of other types of evidence. The updated JBI guidance will be useful for researchers wanting to synthesize evidence about economic questions, either as stand-alone reviews or part of comprehensive or mixed method evidence reviews. Although the updated methodology produced by the work of the working group has improved the JBI guidance for systematic reviews of economic evaluations, there are areas where further work is required. These include adjusting the critical appraisal tool to separate out questions addressing intervention cost and effectiveness measurement; providing more explicit guidance for assessing generalizability of findings; and offering a more robust method for evidence synthesis that facilitates achieving the more ambitious review objectives.

Analytical and CASE study on Limited Search, ID3, CHAID, C4.5, Improved C4.5 and OVA Decision Tree Algorithms to design Decision Support System

NASA Astrophysics Data System (ADS)

Kaur, Parneet; Singh, Sukhwinder; Garg, Sushil; Harmanpreet

2010-11-01

In this paper we study about classification algorithms for farm DSS. By applying classification algorithms i.e. Limited search, ID3, CHAID, C4.5, Improved C4.5 and One VS all Decision Tree on common data set of crop with specified class, results are obtained. The tool used to derive results is SPINA. The graphical results obtained from tool are compared to suggest best technique to develop farm Decision Support System. This analysis would help to researchers to design effective and fast DSS for farmer to take decision for enhancing their yield.

Algorithm Building and Learning Programming Languages Using a New Educational Paradigm

NASA Astrophysics Data System (ADS)

Jain, Anshul K.; Singhal, Manik; Gupta, Manu Sheel

2011-08-01

This research paper presents a new concept of using a single tool to associate syntax of various programming languages, algorithms and basic coding techniques. A simple framework has been programmed in Python that helps students learn skills to develop algorithms, and implement them in various programming languages. The tool provides an innovative and a unified graphical user interface for development of multimedia objects, educational games and applications. It also aids collaborative learning amongst students and teachers through an integrated mechanism based on Remote Procedure Calls. The paper also elucidates an innovative method for code generation to enable students to learn the basics of programming languages using drag-n-drop methods for image objects.

Model Checking with Edge-Valued Decision Diagrams

NASA Technical Reports Server (NTRS)

Roux, Pierre; Siminiceanu, Radu I.

2010-01-01

We describe an algebra of Edge-Valued Decision Diagrams (EVMDDs) to encode arithmetic functions and its implementation in a model checking library. We provide efficient algorithms for manipulating EVMDDs and review the theoretical time complexity of these algorithms for all basic arithmetic and relational operators. We also demonstrate that the time complexity of the generic recursive algorithm for applying a binary operator on EVMDDs is no worse than that of Multi- Terminal Decision Diagrams. We have implemented a new symbolic model checker with the intention to represent in one formalism the best techniques available at the moment across a spectrum of existing tools. Compared to the CUDD package, our tool is several orders of magnitude faster

Introducing GEOPHIRES v2.0: Updated Geothermal Techno-Economic Simulation Tool: Preprint

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

Beckers, Koenraad J; McCabe, Kevin

This paper presents an updated version of the geothermal techno-economic simulation tool GEOPHIRES (GEOthermal Energy for Production of Heat and electricity (IR) Economically Simulated). GEOPHIRES combines reservoir, wellbore, surface plant and economic models to estimate the capital, and operation and maintenance costs, lifetime energy production, and overall levelized cost of energy of a geothermal plant. The available end-use options are electricity, direct-use heat and cogeneration. The main updates in the new version include conversion of the source code from FORTRAN to Python, the option to couple to an external reservoir simulator, updated cost correlations, and more flexibility in selecting themore » time step and number of injection and production wells. An overview of all the updates and two case-studies to illustrate the tool's new capabilities are provided in this paper.« less

NREL Suite of Tools for PV and Storage Analysis

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

Elgqvist, Emma M; Salasovich, James A

Many different factors such as the solar resource, technology costs and incentives, utility cost and consumption, space available, and financial parameters impact the technical and economic potential of a PV project. NREL has developed techno-economic modeling tools that can be used to evaluate PV projects at a site.

Economic Benefits of Predictive Models for Pest Control in Agricultural Crops

USDA-ARS?s Scientific Manuscript database

Various forms of crop models or decision making tools for managing crops have existed for many years. The potential advantage of all of these decision making tools is that more informed and economically improved crop management or decision making is accomplished. However, examination of some of thes...

Economic Valuation Tools and their Applications to Ecosystem Services

EPA Science Inventory

Many of the ecosystem services that people value are not normally bought and sold; yet, this does not mean they have no measurable economic value. Economists have developed a set of approaches and tools for valuing the full range of both market and “non-market” ecosystem goods an...

Some past and present challenges of econophysics

NASA Astrophysics Data System (ADS)

Mantegna, R. N.

2016-12-01

We discuss the cultural background that was shared by some of the first econophysicists when they started to work on economic and financial problems with methods and tools of statistical physics. In particular we discuss about the role of stylized facts and statistical physical laws in economics and statistical physics respectively. As an example of the problems and potentials associated with the interaction of different communities of scholars dealing with problems observed in economic and financial systems we briefly discuss the development and the perspectives of the use of tools and concepts of networks in econophysics, economics and finance.

Universal access to HIV treatment in developing countries: going beyond the misinterpretations of the 'cost-effectiveness' algorithm.

PubMed

Moatti, Jean Paul; Marlink, Richard; Luchini, Stephane; Kazatchkine, Michel

2008-07-01

Economic cost-effectiveness analysis (CEA) has been proposed as the appropriate tool to set priorities for resource allocation among available health interventions. Controversy remains about the way CEA should be used in the field of HIV/AIDS. This paper reviews the general literature in health economics and public economics about the use of CEA for priority setting in public health, in order better to inform current debates about resource allocation in the fight against HIV/AIDS. Theoretical and practical limitations of CEA do not raise major problems when it is applied to compare alternatives for treating the same medical condition or public health problem. Using CEA to set priorities among different health interventions by ranking them from the lowest to the highest values of their cost per life-year saved is appropriate only under the very restrictive and unrealistic assumptions that all interventions compared are discrete and finite alternatives that cannot vary in terms of size and scale. In order for CEA to inform resource allocation compared across programmes to fight the AIDS epidemic, a pragmatic interpretation of this economic approach, like that proposed by the Commission on Macroeconomics and Health, is better suited. Interventions, like a number of preventive strategies and first-line antiretroviral treatments for HIV, whose marginal costs per additional life-year saved are less than three times the gross domestic product per capita, should be considered cost-effective. Because of their empirical and theoretical limitations, results of CEA should only be one element in priority setting among interventions for HIV/AIDS, which should also be informed by explicit debates about societal and ethical preferences.

Implicit, nonswitching, vector-oriented algorithm for steady transonic flow

NASA Technical Reports Server (NTRS)

Lottati, I.

1983-01-01

A rapid computation of a sequence of transonic flow solutions has to be performed in many areas of aerodynamic technology. The employment of low-cost vector array processors makes the conduction of such calculations economically feasible. However, for a full utilization of the new hardware, the developed algorithms must take advantage of the special characteristics of the vector array processor. The present investigation has the objective to develop an efficient algorithm for solving transonic flow problems governed by mixed partial differential equations on an array processor.

The Challenge of Promoting Algorithmic Thinking of Both Sciences- and Humanities-Oriented Learners

ERIC Educational Resources Information Center

Katai, Z.

2015-01-01

The research results we present in this paper reveal that properly calibrated e-learning tools have potential to effectively promote the algorithmic thinking of both science-oriented and humanities-oriented students. After students had watched an illustration (by a folk dance choreography) and an animation of the studied sorting algorithm (bubble…

Air traffic surveillance and control using hybrid estimation and protocol-based conflict resolution

NASA Astrophysics Data System (ADS)

Hwang, Inseok

The continued growth of air travel and recent advances in new technologies for navigation, surveillance, and communication have led to proposals by the Federal Aviation Administration (FAA) to provide reliable and efficient tools to aid Air Traffic Control (ATC) in performing their tasks. In this dissertation, we address four problems frequently encountered in air traffic surveillance and control; multiple target tracking and identity management, conflict detection, conflict resolution, and safety verification. We develop a set of algorithms and tools to aid ATC; These algorithms have the provable properties of safety, computational efficiency, and convergence. Firstly, we develop a multiple-maneuvering-target tracking and identity management algorithm which can keep track of maneuvering aircraft in noisy environments and of their identities. Secondly, we propose a hybrid probabilistic conflict detection algorithm between multiple aircraft which uses flight mode estimates as well as aircraft current state estimates. Our algorithm is based on hybrid models of aircraft, which incorporate both continuous dynamics and discrete mode switching. Thirdly, we develop an algorithm for multiple (greater than two) aircraft conflict avoidance that is based on a closed-form analytic solution and thus provides guarantees of safety. Finally, we consider the problem of safety verification of control laws for safety critical systems, with application to air traffic control systems. We approach safety verification through reachability analysis, which is a computationally expensive problem. We develop an over-approximate method for reachable set computation using polytopic approximation methods and dynamic optimization. These algorithms may be used either in a fully autonomous way, or as supporting tools to increase controllers' situational awareness and to reduce their work load.

Computational mechanics analysis tools for parallel-vector supercomputers

NASA Technical Reports Server (NTRS)

Storaasli, O. O.; Nguyen, D. T.; Baddourah, M. A.; Qin, J.

1993-01-01

Computational algorithms for structural analysis on parallel-vector supercomputers are reviewed. These parallel algorithms, developed by the authors, are for the assembly of structural equations, 'out-of-core' strategies for linear equation solution, massively distributed-memory equation solution, unsymmetric equation solution, general eigen-solution, geometrically nonlinear finite element analysis, design sensitivity analysis for structural dynamics, optimization algorithm and domain decomposition. The source code for many of these algorithms is available from NASA Langley.

The NLS-Based Nonlinear Grey Multivariate Model for Forecasting Pollutant Emissions in China

PubMed Central

Pei, Ling-Ling; Li, Qin

2018-01-01

The relationship between pollutant discharge and economic growth has been a major research focus in environmental economics. To accurately estimate the nonlinear change law of China’s pollutant discharge with economic growth, this study establishes a transformed nonlinear grey multivariable (TNGM (1, N)) model based on the nonlinear least square (NLS) method. The Gauss–Seidel iterative algorithm was used to solve the parameters of the TNGM (1, N) model based on the NLS basic principle. This algorithm improves the precision of the model by continuous iteration and constantly approximating the optimal regression coefficient of the nonlinear model. In our empirical analysis, the traditional grey multivariate model GM (1, N) and the NLS-based TNGM (1, N) models were respectively adopted to forecast and analyze the relationship among wastewater discharge per capita (WDPC), and per capita emissions of SO2 and dust, alongside GDP per capita in China during the period 1996–2015. Results indicated that the NLS algorithm is able to effectively help the grey multivariable model identify the nonlinear relationship between pollutant discharge and economic growth. The results show that the NLS-based TNGM (1, N) model presents greater precision when forecasting WDPC, SO2 emissions and dust emissions per capita, compared to the traditional GM (1, N) model; WDPC indicates a growing tendency aligned with the growth of GDP, while the per capita emissions of SO2 and dust reduce accordingly. PMID:29517985

Economics and Education for Human Flourishing: Wendell Berry and the "Oikonomic" Alternative to Neoliberalism

ERIC Educational Resources Information Center

Henderson, Joseph A.; Hursh, David W.

2014-01-01

Neoliberal ideologies and policies have transformed how we think about the economy, education, and the environment. Economics is presented as objective and quantifiable, best left to distant experts who develop algorithms regarding different monetary relations in our stead. This same kind of thinking--technical, numerical, decontextualized, and…

Quantifying Oldowan Stone Tool Production at Olduvai Gorge, Tanzania

PubMed Central

Reti, Jay S.

2016-01-01

Recent research suggests that variation exists among and between Oldowan stone tool assemblages. Oldowan variation might represent differential constraints on raw materials used to produce these stone implements. Alternatively, variation among Oldowan assemblages could represent different methods that Oldowan producing hominins utilized to produce these lithic implements. Identifying differential patterns of stone tool production within the Oldowan has implications for assessing how stone tool technology evolved, how traditions of lithic production might have been culturally transmitted, and for defining the timing and scope of these evolutionary events. At present there is no null model to predict what morphological variation in the Oldowan should look like. Without such a model, quantifying whether Oldowan assemblages vary due to raw material constraints or whether they vary due to differences in production technique is not possible. This research establishes a null model for Oldowan lithic artifact morphological variation. To establish these expectations this research 1) models the expected range of variation through large scale reduction experiments, 2) develops an algorithm to categorize archaeological flakes based on how they are produced, and 3) statistically assesses the methods of production behavior used by Oldowan producing hominins at the site of DK from Olduvai Gorge, Tanzania via the experimental model. Results indicate that a subset of quartzite flakes deviate from the null expectations in a manner that demonstrates efficiency in flake manufacture, while some basalt flakes deviate from null expectations in a manner that demonstrates inefficiency in flake manufacture. The simultaneous presence of efficiency in stone tool production for one raw material (quartzite) and inefficiency in stone tool production for another raw material (basalt) suggests that Oldowan producing hominins at DK were able to mediate the economic costs associated with stone tool procurement by utilizing high-cost materials more efficiently than is expected and low-cost materials in an inefficient manner. PMID:26808429

Analysis and simulation tools for solar array power systems

NASA Astrophysics Data System (ADS)

Pongratananukul, Nattorn

This dissertation presents simulation tools developed specifically for the design of solar array power systems. Contributions are made in several aspects of the system design phases, including solar source modeling, system simulation, and controller verification. A tool to automate the study of solar array configurations using general purpose circuit simulators has been developed based on the modeling of individual solar cells. Hierarchical structure of solar cell elements, including semiconductor properties, allows simulation of electrical properties as well as the evaluation of the impact of environmental conditions. A second developed tool provides a co-simulation platform with the capability to verify the performance of an actual digital controller implemented in programmable hardware such as a DSP processor, while the entire solar array including the DC-DC power converter is modeled in software algorithms running on a computer. This "virtual plant" allows developing and debugging code for the digital controller, and also to improve the control algorithm. One important task in solar arrays is to track the maximum power point on the array in order to maximize the power that can be delivered. Digital controllers implemented with programmable processors are particularly attractive for this task because sophisticated tracking algorithms can be implemented and revised when needed to optimize their performance. The proposed co-simulation tools are thus very valuable in developing and optimizing the control algorithm, before the system is built. Examples that demonstrate the effectiveness of the proposed methodologies are presented. The proposed simulation tools are also valuable in the design of multi-channel arrays. In the specific system that we have designed and tested, the control algorithm is implemented on a single digital signal processor. In each of the channels the maximum power point is tracked individually. In the prototype we built, off-the-shelf commercial DC-DC converters were utilized. At the end, the overall performance of the entire system was evaluated using solar array simulators capable of simulating various I-V characteristics, and also by using an electronic load. Experimental results are presented.

Development of Gis Tool for the Solution of Minimum Spanning Tree Problem using Prim's Algorithm

NASA Astrophysics Data System (ADS)

Dutta, S.; Patra, D.; Shankar, H.; Alok Verma, P.

2014-11-01

minimum spanning tree (MST) of a connected, undirected and weighted network is a tree of that network consisting of all its nodes and the sum of weights of all its edges is minimum among all such possible spanning trees of the same network. In this study, we have developed a new GIS tool using most commonly known rudimentary algorithm called Prim's algorithm to construct the minimum spanning tree of a connected, undirected and weighted road network. This algorithm is based on the weight (adjacency) matrix of a weighted network and helps to solve complex network MST problem easily, efficiently and effectively. The selection of the appropriate algorithm is very essential otherwise it will be very hard to get an optimal result. In case of Road Transportation Network, it is very essential to find the optimal results by considering all the necessary points based on cost factor (time or distance). This paper is based on solving the Minimum Spanning Tree (MST) problem of a road network by finding it's minimum span by considering all the important network junction point. GIS technology is usually used to solve the network related problems like the optimal path problem, travelling salesman problem, vehicle routing problems, location-allocation problems etc. Therefore, in this study we have developed a customized GIS tool using Python script in ArcGIS software for the solution of MST problem for a Road Transportation Network of Dehradun city by considering distance and time as the impedance (cost) factors. It has a number of advantages like the users do not need a greater knowledge of the subject as the tool is user-friendly and that allows to access information varied and adapted the needs of the users. This GIS tool for MST can be applied for a nationwide plan called Prime Minister Gram Sadak Yojana in India to provide optimal all weather road connectivity to unconnected villages (points). This tool is also useful for constructing highways or railways spanning several cities optimally or connecting all cities with minimum total road length.

FOCuS: a metaheuristic algorithm for computing knockouts from genome-scale models for strain optimization.

PubMed

Mutturi, Sarma

2017-06-27

Although handful tools are available for constraint-based flux analysis to generate knockout strains, most of these are either based on bilevel-MIP or its modifications. However, metaheuristic approaches that are known for their flexibility and scalability have been less studied. Moreover, in the existing tools, sectioning of search space to find optimal knocks has not been considered. Herein, a novel computational procedure, termed as FOCuS (Flower-pOllination coupled Clonal Selection algorithm), was developed to find the optimal reaction knockouts from a metabolic network to maximize the production of specific metabolites. FOCuS derives its benefits from nature-inspired flower pollination algorithm and artificial immune system-inspired clonal selection algorithm to converge to an optimal solution. To evaluate the performance of FOCuS, reported results obtained from both MIP and other metaheuristic-based tools were compared in selected case studies. The results demonstrated the robustness of FOCuS irrespective of the size of metabolic network and number of knockouts. Moreover, sectioning of search space coupled with pooling of priority reactions based on their contribution to objective function for generating smaller search space significantly reduced the computational time.

Value Addition to Cartosat-I Imagery

NASA Astrophysics Data System (ADS)

Mohan, M.

2014-11-01

In the sector of remote sensing applications, the use of stereo data is on the steady rise. An attempt is hereby made to develop a software suite specifically for exploitation of Cartosat-I data. A few algorithms to enhance the quality of basic Cartosat-I products will be presented. The algorithms heavily exploit the Rational Function Coefficients (RPCs) that are associated with the image. The algorithms include improving the geometric positioning through Bundle Block Adjustment and producing refined RPCs; generating portable stereo views using raw / refined RPCs autonomously; orthorectification and mosaicing; registering a monoscopic image rapidly with a single seed point. The outputs of these modules (including the refined RPCs) are in standard formats for further exploitation in 3rd party software. The design focus has been on minimizing the user-interaction and to customize heavily to suit the Indian context. The core libraries are in C/C++ and some of the applications come with user-friendly GUI. Further customization to suit a specific workflow is feasible as the requisite photogrammetric tools are in place and are continuously upgraded. The paper discusses the algorithms and the design considerations of developing the tools. The value-added products so produced using these tools will also be presented.

KungFQ: a simple and powerful approach to compress fastq files.

PubMed

Grassi, Elena; Di Gregorio, Federico; Molineris, Ivan

2012-01-01

Nowadays storing data derived from deep sequencing experiments has become pivotal and standard compression algorithms do not exploit in a satisfying manner their structure. A number of reference-based compression algorithms have been developed but they are less adequate when approaching new species without fully sequenced genomes or nongenomic data. We developed a tool that takes advantages of fastq characteristics and encodes them in a binary format optimized in order to be further compressed with standard tools (such as gzip or lzma). The algorithm is straightforward and does not need any external reference file, it scans the fastq only once and has a constant memory requirement. Moreover, we added the possibility to perform lossy compression, losing some of the original information (IDs and/or qualities) but resulting in smaller files; it is also possible to define a quality cutoff under which corresponding base calls are converted to N. We achieve 2.82 to 7.77 compression ratios on various fastq files without losing information and 5.37 to 8.77 losing IDs, which are often not used in common analysis pipelines. In this paper, we compare the algorithm performance with known tools, usually obtaining higher compression levels.

Tools for Accurate and Efficient Analysis of Complex Evolutionary Mechanisms in Microbial Genomes. Final Report

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

Nakhleh, Luay

I proposed to develop computationally efficient tools for accurate detection and reconstruction of microbes' complex evolutionary mechanisms, thus enabling rapid and accurate annotation, analysis and understanding of their genomes. To achieve this goal, I proposed to address three aspects. (1) Mathematical modeling. A major challenge facing the accurate detection of HGT is that of distinguishing between these two events on the one hand and other events that have similar "effects." I proposed to develop a novel mathematical approach for distinguishing among these events. Further, I proposed to develop a set of novel optimization criteria for the evolutionary analysis of microbialmore » genomes in the presence of these complex evolutionary events. (2) Algorithm design. In this aspect of the project, I proposed to develop an array of e cient and accurate algorithms for analyzing microbial genomes based on the formulated optimization criteria. Further, I proposed to test the viability of the criteria and the accuracy of the algorithms in an experimental setting using both synthetic as well as biological data. (3) Software development. I proposed the nal outcome to be a suite of software tools which implements the mathematical models as well as the algorithms developed.« less

Software tool for data mining and its applications

NASA Astrophysics Data System (ADS)

Yang, Jie; Ye, Chenzhou; Chen, Nianyi

2002-03-01

A software tool for data mining is introduced, which integrates pattern recognition (PCA, Fisher, clustering, hyperenvelop, regression), artificial intelligence (knowledge representation, decision trees), statistical learning (rough set, support vector machine), computational intelligence (neural network, genetic algorithm, fuzzy systems). It consists of nine function models: pattern recognition, decision trees, association rule, fuzzy rule, neural network, genetic algorithm, Hyper Envelop, support vector machine, visualization. The principle and knowledge representation of some function models of data mining are described. The software tool of data mining is realized by Visual C++ under Windows 2000. Nonmonotony in data mining is dealt with by concept hierarchy and layered mining. The software tool of data mining has satisfactorily applied in the prediction of regularities of the formation of ternary intermetallic compounds in alloy systems, and diagnosis of brain glioma.

Forest economics research at the Pacific Northwest Research Station, to 2000.

Treesearch

Donald F. Flora

2003-01-01

The contributions for over 80 years by scientists at the Pacific Northwest Research Station to developments in economic theory, economic tools, policies, and economic issues are summarized. This is a story of progressive accomplishments set against a constantly changing background of economic and social events.

Integrative systems modeling and multi-objective optimization

EPA Science Inventory

This presentation presents a number of algorithms, tools, and methods for utilizing multi-objective optimization within integrated systems modeling frameworks. We first present innovative methods using a genetic algorithm to optimally calibrate the VELMA and SWAT ecohydrological ...

EAGLE: 'EAGLE'Is an' Algorithmic Graph Library for Exploration

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

2015-01-16

The Resource Description Framework (RDF) and SPARQL Protocol and RDF Query Language (SPARQL) were introduced about a decade ago to enable flexible schema-free data interchange on the Semantic Web. Today data scientists use the framework as a scalable graph representation for integrating, querying, exploring and analyzing data sets hosted at different sources. With increasing adoption, the need for graph mining capabilities for the Semantic Web has emerged. Today there is no tools to conduct "graph mining" on RDF standard data sets. We address that need through implementation of popular iterative Graph Mining algorithms (Triangle count, Connected component analysis, degree distribution,more » diversity degree, PageRank, etc.). We implement these algorithms as SPARQL queries, wrapped within Python scripts and call our software tool as EAGLE. In RDF style, EAGLE stands for "EAGLE 'Is an' algorithmic graph library for exploration. EAGLE is like 'MATLAB' for 'Linked Data.'« less

Fingerprint separation: an application of ICA

NASA Astrophysics Data System (ADS)

Singh, Meenakshi; Singh, Deepak Kumar; Kalra, Prem Kumar

2008-04-01

Among all existing biometric techniques, fingerprint-based identification is the oldest method, which has been successfully used in numerous applications. Fingerprint-based identification is the most recognized tool in biometrics because of its reliability and accuracy. Fingerprint identification is done by matching questioned and known friction skin ridge impressions from fingers, palms, and toes to determine if the impressions are from the same finger (or palm, toe, etc.). There are many fingerprint matching algorithms which automate and facilitate the job of fingerprint matching, but for any of these algorithms matching can be difficult if the fingerprints are overlapped or mixed. In this paper, we have proposed a new algorithm for separating overlapped or mixed fingerprints so that the performance of the matching algorithms will improve when they are fed with these inputs. Independent Component Analysis (ICA) has been used as a tool to separate the overlapped or mixed fingerprints.

Importing statistical measures into Artemis enhances gene identification in the Leishmania genome project.

PubMed

Aggarwal, Gautam; Worthey, E A; McDonagh, Paul D; Myler, Peter J

2003-06-07

Seattle Biomedical Research Institute (SBRI) as part of the Leishmania Genome Network (LGN) is sequencing chromosomes of the trypanosomatid protozoan species Leishmania major. At SBRI, chromosomal sequence is annotated using a combination of trained and untrained non-consensus gene-prediction algorithms with ARTEMIS, an annotation platform with rich and user-friendly interfaces. Here we describe a methodology used to import results from three different protein-coding gene-prediction algorithms (GLIMMER, TESTCODE and GENESCAN) into the ARTEMIS sequence viewer and annotation tool. Comparison of these methods, along with the CODONUSAGE algorithm built into ARTEMIS, shows the importance of combining methods to more accurately annotate the L. major genomic sequence. An improvised and powerful tool for gene prediction has been developed by importing data from widely-used algorithms into an existing annotation platform. This approach is especially fruitful in the Leishmania genome project where there is large proportion of novel genes requiring manual annotation.

Denni Algorithm An Enhanced Of SMS (Scan, Move and Sort) Algorithm

NASA Astrophysics Data System (ADS)

Aprilsyah Lubis, Denni; Salim Sitompul, Opim; Marwan; Tulus; Andri Budiman, M.

2017-12-01

Sorting has been a profound area for the algorithmic researchers, and many resources are invested to suggest a more working sorting algorithm. For this purpose many existing sorting algorithms were observed in terms of the efficiency of the algorithmic complexity. Efficient sorting is important to optimize the use of other algorithms that require sorted lists to work correctly. Sorting has been considered as a fundamental problem in the study of algorithms that due to many reasons namely, the necessary to sort information is inherent in many applications, algorithms often use sorting as a key subroutine, in algorithm design there are many essential techniques represented in the body of sorting algorithms, and many engineering issues come to the fore when implementing sorting algorithms., Many algorithms are very well known for sorting the unordered lists, and one of the well-known algorithms that make the process of sorting to be more economical and efficient is SMS (Scan, Move and Sort) algorithm, an enhancement of Quicksort invented Rami Mansi in 2010. This paper presents a new sorting algorithm called Denni-algorithm. The Denni algorithm is considered as an enhancement on the SMS algorithm in average, and worst cases. The Denni algorithm is compared with the SMS algorithm and the results were promising.

Tools of Realization of Social Responsibility of Industrial Business for Sustainable Socio-economic Development of Mining Region's Rural Territory

NASA Astrophysics Data System (ADS)

Jurzina, Tatyana; Egorova, Natalia; Zaruba, Natalia; Kosinskij, Peter

2017-11-01

Modern conditions of the Russian economy do especially relevant questions of social responsibility of industrial business of the mining region for sustainable social and economic development of rural territories that demands search of the new strategy, tools, ways for positioning and increase in competitiveness of the enterprises, which are carrying out the entrepreneurial activity in this territory. The article opens problems of an influence of the industrial enterprises on the territory of presence, reasons the theoretical base directed to the formation of practical tools (mechanism) providing realization of social responsibility of business for sustainable social and economic development of rural territories of the mining region.

Assessing the potential of economic instruments for managing drought risk at river basin scale

NASA Astrophysics Data System (ADS)

Pulido-Velazquez, M.; Lopez-Nicolas, A.; Macian-Sorribes, H.

2015-12-01

Economic instruments work as incentives to adapt individual decisions to collectively agreed goals. Different types of economic instruments have been applied to manage water resources, such as water-related taxes and charges (water pricing, environmental taxes, etc.), subsidies, markets or voluntary agreements. Hydroeconomic models (HEM) provide useful insight on optimal strategies for coping with droughts by simultaneously analysing engineering, hydrology and economics of water resources management. We use HEMs for evaluating the potential of economic instruments on managing drought risk at river basin scale, considering three criteria for assessing drought risk: reliability, resilience and vulnerability. HEMs allow to calculate water scarcity costs as the economic losses due to water deliveries below the target demands, which can be used as a vulnerability descriptor of drought risk. Two generic hydroeconomic DSS tools, SIMGAMS and OPTIGAMS ( both programmed in GAMS) have been developed to evaluate water scarcity cost at river basin scale based on simulation and optimization approaches. The simulation tool SIMGAMS allocates water according to the system priorities and operating rules, and evaluate the scarcity costs using economic demand functions. The optimization tool allocates water resources for maximizing net benefits (minimizing total water scarcity plus operating cost of water use). SIMGAS allows to simulate incentive water pricing policies based on water availability in the system (scarcity pricing), while OPTIGAMS is used to simulate the effect of ideal water markets by economic optimization. These tools have been applied to the Jucar river system (Spain), highly regulated and with high share of water use for crop irrigation (greater than 80%), where water scarcity, irregular hydrology and groundwater overdraft cause droughts to have significant economic, social and environmental consequences. An econometric model was first used to explain the variation of the production value of irrigated agriculture during droughts, assessing revenue responses to varying crop prices and water availability. Hydroeconomic approaches were then used to show the potential of economic instruments in setting incentives for a more efficient management of water resources systems.

Solution of quadratic matrix equations for free vibration analysis of structures.

NASA Technical Reports Server (NTRS)

Gupta, K. K.

1973-01-01

An efficient digital computer procedure and the related numerical algorithm are presented herein for the solution of quadratic matrix equations associated with free vibration analysis of structures. Such a procedure enables accurate and economical analysis of natural frequencies and associated modes of discretized structures. The numerically stable algorithm is based on the Sturm sequence method, which fully exploits the banded form of associated stiffness and mass matrices. The related computer program written in FORTRAN V for the JPL UNIVAC 1108 computer proves to be substantially more accurate and economical than other existing procedures of such analysis. Numerical examples are presented for two structures - a cantilever beam and a semicircular arch.

Diagnostic throughput factor analysis for en-route airspace and optimal aircraft trajectory generation based on capacity prediction and controller workload

NASA Astrophysics Data System (ADS)

Shin, Sanghyun

Today's National Airspace System (NAS) is approaching its limit to efficiently cope with the increasing air traffic demand. Next Generation Air Transportation System (NextGen) with its ambitious goals aims to make the air travel more predictable with fewer delays, less time sitting on the ground and holding in the air to improve the performance of the NAS. However, currently the performance of the NAS is mostly measured using delay-based metrics which do not capture a whole range of important factors that determine the quality and level of utilization of the NAS. The factors affecting the performance of the NAS are themselves not well defined to begin with. To address these issues, motivated by the use of throughput-based metrics in many areas such as ground transportation, wireless communication and manufacturing, this thesis identifies the different factors which majorly affect the performance of the NAS as demand (split into flight cancellation and flight rerouting), safe separation (split into conflict and metering) and weather (studied as convective weather) through careful comparison with other applications and performing empirical sensitivity analysis. Additionally, the effects of different factors on the NAS's performance are quantitatively studied using real traffic data with the Future ATM Concepts Evaluation Tool (FACET) for various sectors and centers of the NAS on different days. In this thesis we propose a diagnostic tool which can analyze the factors that have greater responsibility for regions of poor and better performances of the NAS. Based on the throughput factor analysis for en-route airspace, it was found that weather and controller workload are the major factors that decrease the efficiency of the airspace. Also, since resources such as air traffic controllers, infrastructure and airspace are limited, it is becoming increasingly important to use the available resources efficiently. To alleviate the impact of the weather and controller workload while optimally utilizing limited resources, various aircraft rerouting strategies for Air Traffic Management (ATM) have been proposed. However, the number of rerouting tools available to address these issues for the center-level and the National Airspace System (NAS) are relatively less compared with the tools for the sector-level and terminal airspace. Additionally, previous works consider the airspace containing the weather as no-fly zones instead of reduced-traffic zones and do not explicitly consider controller workload when generating aircraft trajectories to avoid the weather-affected airspace, thereby reducing the overall performance of the airspace. In this thesis, a new rerouting algorithm for the center-level airspace is proposed to address these problems by introducing a feedback loop connecting a tactical rerouting algorithm with a strategic rerouting algorithm using dynamic programming and a modified A* algorithm respectively. This helps reduce the computational cost significantly while safely handling a large number of aircraft. In summary, this thesis suggests the ways in which the NAS's performance can be further improved, thereby supporting various concepts envisioned by the Next Generation Air Transportation System (NextGen) and providing vital information which can be used for suitable economic and environmental advantages.

Towards 100,000 CPU Cycle-Scavenging by Genetic Algorithms

NASA Technical Reports Server (NTRS)

Globus, Al; Biegel, Bryan A. (Technical Monitor)

2001-01-01

We examine a web-centric design using standard tools such as web servers, web browsers, PHP, and mySQL. We also consider the applicability of Information Power Grid tools such as the Globus (no relation to the author) Toolkit. We intend to implement this architecture with JavaGenes running on at least two cycle-scavengers: Condor and United Devices. JavaGenes, a genetic algorithm code written in Java, will be used to evolve multi-species reactive molecular force field parameters.

The future of hydropower planning modeling

NASA Astrophysics Data System (ADS)

Haas, J.; Zuñiga, D.; Nowak, W.; Olivares, M. A.; Castelletti, A.; Thilmant, A.

2017-12-01

Planning the investment and operation of hydropower plants with optimization tools dates back to the 1970s. The focus used to be solely on the provision of energy. However, advances in computational capacity and solving algorithms, dynamic markets, expansion of renewable sources, and a better understanding of hydropower environmental impacts have recently led to the development of novel planning approaches. In this work, we provide a review, systematization, and trend analysis of these approaches. Further, through interviews with experts, we outline the future of hydropower planning modeling and identify the gaps towards it. We classified the found models along environmental, economic, multipurpose and technical criteria. Environmental interactions include hydropeaking mitigation, water quality protection and limiting greenhouse gas emissions from reservoirs. Economic and regulatory criteria consider uncertainties of fossil fuel prices and relicensing of water rights and power purchase agreements. Multipurpose considerations account for irrigation, tourism, flood protection and drinking water. Recently included technical details account for sedimentation in reservoirs and variable efficiencies of turbines. Additional operational considerations relate to hydrological aspects such as dynamic reservoir inflows, water losses, and climate change. Although many of the above criteria have been addressed in detail on a project-to-project basis, models remain overly simplistic for planning large power fleets. Future hydropower planning tools are expected to improve the representation of the water-energy nexus, including environmental and multipurpose criteria. Further, they will concentrate on identifying new sources of operational flexibility (e.g. through installing additional turbines and pumps) for integrating renewable energy. The operational detail will increase, potentially emphasizing variable efficiencies, storage capacity losses due to sedimentation, and the timing of inflows (which are becoming more variable under climate change). Finally, the relicensing of existing operations and planning new installations are subject to deep uncertainties that need to be captured.

Spectral mapping tools from the earth sciences applied to spectral microscopy data.

PubMed

Harris, A Thomas

2006-08-01

Spectral imaging, originating from the field of earth remote sensing, is a powerful tool that is being increasingly used in a wide variety of applications for material identification. Several workers have used techniques like linear spectral unmixing (LSU) to discriminate materials in images derived from spectral microscopy. However, many spectral analysis algorithms rely on assumptions that are often violated in microscopy applications. This study explores algorithms originally developed as improvements on early earth imaging techniques that can be easily translated for use with spectral microscopy. To best demonstrate the application of earth remote sensing spectral analysis tools to spectral microscopy data, earth imaging software was used to analyze data acquired with a Leica confocal microscope with mechanical spectral scanning. For this study, spectral training signatures (often referred to as endmembers) were selected with the ENVI (ITT Visual Information Solutions, Boulder, CO) "spectral hourglass" processing flow, a series of tools that use the spectrally over-determined nature of hyperspectral data to find the most spectrally pure (or spectrally unique) pixels within the data set. This set of endmember signatures was then used in the full range of mapping algorithms available in ENVI to determine locations, and in some cases subpixel abundances of endmembers. Mapping and abundance images showed a broad agreement between the spectral analysis algorithms, supported through visual assessment of output classification images and through statistical analysis of the distribution of pixels within each endmember class. The powerful spectral analysis algorithms available in COTS software, the result of decades of research in earth imaging, are easily translated to new sources of spectral data. Although the scale between earth imagery and spectral microscopy is radically different, the problem is the same: mapping material locations and abundances based on unique spectral signatures. (c) 2006 International Society for Analytical Cytology.

The development of a scalable parallel 3-D CFD algorithm for turbomachinery. M.S. Thesis Final Report

NASA Technical Reports Server (NTRS)

Luke, Edward Allen

1993-01-01

Two algorithms capable of computing a transonic 3-D inviscid flow field about rotating machines are considered for parallel implementation. During the study of these algorithms, a significant new method of measuring the performance of parallel algorithms is developed. The theory that supports this new method creates an empirical definition of scalable parallel algorithms that is used to produce quantifiable evidence that a scalable parallel application was developed. The implementation of the parallel application and an automated domain decomposition tool are also discussed.

The behavioral economics of health and health care.

PubMed

Rice, Thomas

2013-01-01

People often make decisions in health care that are not in their best interest, ranging from failing to enroll in health insurance to which they are entitled, to engaging in extremely harmful behaviors. Traditional economic theory provides a limited tool kit for improving behavior because it assumes that people make decisions in a rational way, have the mental capacity to deal with huge amounts of information and choice, and have tastes endemic to them and not open to manipulation. Melding economics with psychology, behavioral economics acknowledges that people often do not act rationally in the economic sense. It therefore offers a potentially richer set of tools than provided by traditional economic theory to understand and influence behaviors. Only recently, however, has it been applied to health care. This article provides an overview of behavioral economics, reviews some of its contributions, and shows how it can be used in health care to improve people's decisions and health.

Sold! The Elementary Classroom Auction as Learning Tool of Communication and Economics

ERIC Educational Resources Information Center

Boyd, Josh; Boyd, Gina

2014-01-01

An auction, though an economic tool, is essentially a performance dependent on communication (Smith, 1989). The auctioneer dictates the pace, asks for bids, and acknowledges responses; the enterprise is controlled by a voice (Boyce, 2001). Bidders must listen and respond strategically to the communication of the people around them. An auction…

Research on monocentric model of urbanization by agent-based simulation

NASA Astrophysics Data System (ADS)

Xue, Ling; Yang, Kaizhong

2008-10-01

Over the past years, GIS have been widely used for modeling urbanization from a variety of perspectives such as digital terrain representation and overlay analysis using cell-based data platform. Similarly, simulation of urban dynamics has been achieved with the use of Cellular Automata. In contrast to these approaches, agent-based simulation provides a much more powerful set of tools. This allows researchers to set up a counterpart for real environmental and urban systems in computer for experimentation and scenario analysis. This Paper basically reviews the research on the economic mechanism of urbanization and an agent-based monocentric model is setup for further understanding the urbanization process and mechanism in China. We build an endogenous growth model with dynamic interactions between spatial agglomeration and urban development by using agent-based simulation. It simulates the migration decisions of two main types of agents, namely rural and urban households between rural and urban area. The model contains multiple economic interactions that are crucial in understanding urbanization and industrial process in China. These adaptive agents can adjust their supply and demand according to the market situation by a learning algorithm. The simulation result shows this agent-based urban model is able to perform the regeneration and to produce likely-to-occur projections of reality.

Five key attributes can increase marine protected areas performance for small-scale fisheries management

NASA Astrophysics Data System (ADS)

di Franco, Antonio; Thiriet, Pierre; di Carlo, Giuseppe; Dimitriadis, Charalampos; Francour, Patrice; Gutiérrez, Nicolas L.; Jeudy de Grissac, Alain; Koutsoubas, Drosos; Milazzo, Marco; Otero, María Del Mar; Piante, Catherine; Plass-Johnson, Jeremiah; Sainz-Trapaga, Susana; Santarossa, Luca; Tudela, Sergi; Guidetti, Paolo

2016-12-01

Marine protected areas (MPAs) have largely proven to be effective tools for conserving marine ecosystem, while socio-economic benefits generated by MPAs to fisheries are still under debate. Many MPAs embed a no-take zone, aiming to preserve natural populations and ecosystems, within a buffer zone where potentially sustainable activities are allowed. Small-scale fisheries (SSF) within buffer zones can be highly beneficial by promoting local socio-economies. However, guidelines to successfully manage SSFs within MPAs, ensuring both conservation and fisheries goals, and reaching a win-win scenario, are largely unavailable. From the peer-reviewed literature, grey-literature and interviews, we assembled a unique database of ecological, social and economic attributes of SSF in 25 Mediterranean MPAs. Using random forest with Boruta algorithm we identified a set of attributes determining successful SSFs management within MPAs. We show that fish stocks are healthier, fishermen incomes are higher and the social acceptance of management practices is fostered if five attributes are present (i.e. high MPA enforcement, presence of a management plan, fishermen engagement in MPA management, fishermen representative in the MPA board, and promotion of sustainable fishing). These findings are pivotal to Mediterranean coastal communities so they can achieve conservation goals while allowing for profitable exploitation of fisheries resources.

Five key attributes can increase marine protected areas performance for small-scale fisheries management.

PubMed

Di Franco, Antonio; Thiriet, Pierre; Di Carlo, Giuseppe; Dimitriadis, Charalampos; Francour, Patrice; Gutiérrez, Nicolas L; Jeudy de Grissac, Alain; Koutsoubas, Drosos; Milazzo, Marco; Otero, María Del Mar; Piante, Catherine; Plass-Johnson, Jeremiah; Sainz-Trapaga, Susana; Santarossa, Luca; Tudela, Sergi; Guidetti, Paolo

2016-12-01

Marine protected areas (MPAs) have largely proven to be effective tools for conserving marine ecosystem, while socio-economic benefits generated by MPAs to fisheries are still under debate. Many MPAs embed a no-take zone, aiming to preserve natural populations and ecosystems, within a buffer zone where potentially sustainable activities are allowed. Small-scale fisheries (SSF) within buffer zones can be highly beneficial by promoting local socio-economies. However, guidelines to successfully manage SSFs within MPAs, ensuring both conservation and fisheries goals, and reaching a win-win scenario, are largely unavailable. From the peer-reviewed literature, grey-literature and interviews, we assembled a unique database of ecological, social and economic attributes of SSF in 25 Mediterranean MPAs. Using random forest with Boruta algorithm we identified a set of attributes determining successful SSFs management within MPAs. We show that fish stocks are healthier, fishermen incomes are higher and the social acceptance of management practices is fostered if five attributes are present (i.e. high MPA enforcement, presence of a management plan, fishermen engagement in MPA management, fishermen representative in the MPA board, and promotion of sustainable fishing). These findings are pivotal to Mediterranean coastal communities so they can achieve conservation goals while allowing for profitable exploitation of fisheries resources.

Diabetes as a case study of chronic disease management with a personalized approach: the role of a structured feedback loop.

PubMed

Ceriello, Antonio; Barkai, László; Christiansen, Jens Sandahl; Czupryniak, Leszek; Gomis, Ramon; Harno, Kari; Kulzer, Bernhard; Ludvigsson, Johnny; Némethyová, Zuzana; Owens, David; Schnell, Oliver; Tankova, Tsvetalina; Taskinen, Marja-Riitta; Vergès, Bruno; Weitgasser, Raimund; Wens, Johan

2012-10-01

As non-communicable or chronic diseases are a growing threat to human health and economic growth, political stakeholders are aiming to identify options for improved response to the challenges of prevention and management of non-communicable diseases. This paper is intended to contribute ideas on personalized chronic disease management which are based on experience with one major chronic disease, namely diabetes mellitus. Diabetes provides a pertinent case of chronic disease management with a particular focus on patient self-management. Despite advances in diabetes therapy, many people with diabetes still fail to achieve treatment targets thus remaining at risk of complications. Personalizing the management of diabetes according to the patient's individual profile can help in improving therapy adherence and treatment outcomes. This paper suggests using a six-step cycle for personalized diabetes (self-)management and collaborative use of structured blood glucose data. E-health solutions can be used to improve process efficiencies and allow remote access. Decision support tools and algorithms can help doctors in making therapeutic decisions based on individual patient profiles. Available evidence about the effectiveness of the cycle's constituting elements justifies expectations that the diabetes management cycle as a whole can generate medical and economic benefit. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Introducing GEOPHIRES v2.0: Updated Geothermal Techno-Economic Simulation Tool

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

Beckers, Koenraad J; McCabe, Kevin

This paper presents an updated version of the geothermal techno-economic simulation tool GEOPHIRES (GEOthermal energy for Production of Heat and electricity ('IR') Economically Simulated). GEOPHIRES combines engineering models of the reservoir, wellbores, and surface plant facilities of a geothermal plant with an economic model to estimate the capital and operation and maintenance costs, lifetime energy production, and overall levelized cost of energy. The available end-use options are electricity, direct-use heat, and cogeneration. The main updates in the new version include conversion of the source code from FORTRAN to Python, the option to import temperature data (e.g., measured or from stand-alonemore » reservoir simulator), updated cost correlations, and more flexibility in selecting the time step and number of injection and production wells. In this paper, we provide an overview of all the updates and two case studies to illustrate the tool's new capabilities.« less

Hydro-economic modeling of the role of forests on water resources production in Andalusia, Spain

NASA Astrophysics Data System (ADS)

Beguería, Santiago; Serrano-Notivoli, Roberto; Álvarez-Palomino, Alejandro; Campos, Pablo

2014-05-01

The development of more refined information tools is a pre-requisite for supporting decision making in the context of integrated water resources management. Among these tools, hydro-economic models are favoured because they allow integrating the ecological, hydrological, infrastructure and economic aspects into a coherent, scientifically-informed framework. We present a case study that assesses physically the water resources of forest lands of the Andalusia region in Spain and conducts an economic environmental income and asset valuation of the forest surface water yield. We show how, based on available hydrologic and economic data, we can develop a comprehensive water account for all the forest lands at the regional scale. This forest water environmental valuation is part of the larger RECAMAN project, which aims at providing a robust and easily replicable accounting tool to evaluate yearly the total income an capital generated by the forest land, encompassing all measurable sources of private and public incomes (timber and cork production, auto-consumption, recreational activities, biodiversity conservation, carbon sequestration, water production, etc.). Only a comprehensive integrated tool such as the one built within the RECAMAN project may serve as a basis for the development of integrated policies such as those internationally agreed and recommended for the management of water resources.

Methods and tools to simulate the effect of economic instruments in complex water resources systems. Application to the Jucar river basin.

NASA Astrophysics Data System (ADS)

Lopez-Nicolas, Antonio; Pulido-Velazquez, Manuel

2014-05-01

The main challenge of the BLUEPRINT to safeguard Europe's water resources (EC, 2012) is to guarantee that enough good quality water is available for people's needs, the economy and the environment. In this sense, economic policy instruments such as water pricing policies and water markets can be applied to enhance efficient use of water. This paper presents a method based on hydro-economic tools to assess the effect of economic instruments on water resource systems. Hydro-economic models allow integrated analysis of water supply, demand and infrastructure operation at the river basin scale, by simultaneously combining engineering, hydrologic and economic aspects of water resources management. The method made use of the simulation and optimization hydroeconomic tools SIMGAMS and OPTIGAMS. The simulation tool SIMGAMS allocates water resources among the users according to priorities and operating rules, and evaluate economic scarcity costs of the system by using economic demand functions. The model's objective function is designed so that the system aims to meet the operational targets (ranked according to priorities) at each month while following the system operating rules. The optimization tool OPTIGAMS allocates water resources based on an economic efficiency criterion: maximize net benefits, or alternatively, minimizing the total water scarcity and operating cost of water use. SIMGAS allows to simulate incentive water pricing policies based on marginal resource opportunity costs (MROC; Pulido-Velazquez et al., 2013). Storage-dependent step pricing functions are derived from the time series of MROC values at a certain reservoir in the system. These water pricing policies are defined based on water availability in the system (scarcity pricing), so that when water storage is high, the MROC is low, while low storage (drought periods) will be associated to high MROC and therefore, high prices. We also illustrate the use of OPTIGAMS to simulate the effect of ideal water markets by economic optimization, without considering the potential effect of transaction costs. These methods and tools have been applied to the Jucar River basin (Spain). The results show the potential of economic instruments in setting incentives for a more efficient management of water resources systems. Acknowledgments: The study has been partially supported by the European Community 7th Framework Project (GENESIS project, n. 226536), SAWARES (Plan Nacional I+D+i 2008-2011, CGL2009-13238-C02-01 and C02-02), SCARCE (Consolider-Ingenio 2010 CSD2009-00065) of the Spanish Ministry of Economy and Competitiveness; and EC 7th Framework Project ENHANCE (n. 308438) Reference: Pulido-Velazquez, M., Alvarez-Mendiola, E., and Andreu, J., 2013. Design of Efficient Water Pricing Policies Integrating Basinwide Resource Opportunity Costs. J. Water Resour. Plann. Manage., 139(5): 583-592.

Algorithms and programming tools for image processing on the MPP

NASA Technical Reports Server (NTRS)

Reeves, A. P.

1985-01-01

Topics addressed include: data mapping and rotational algorithms for the Massively Parallel Processor (MPP); Parallel Pascal language; documentation for the Parallel Pascal Development system; and a description of the Parallel Pascal language used on the MPP.

Integrated Network Decompositions and Dynamic Programming for Graph Optimization (INDDGO)

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

The INDDGO software package offers a set of tools for finding exact solutions to graph optimization problems via tree decompositions and dynamic programming algorithms. Currently the framework offers serial and parallel (distributed memory) algorithms for finding tree decompositions and solving the maximum weighted independent set problem. The parallel dynamic programming algorithm is implemented on top of the MADNESS task-based runtime.

Planning Under Uncertainty: Methods and Applications

DTIC Science & Technology

2010-06-09

begun research into fundamental algorithms for optimization and re?optimization of continuous optimization problems (such as linear and quadratic... algorithm yields a 14.3% improvement over the original design while saving 68.2 % of the simulation evaluations compared to standard sample-path...They provide tools for building and justifying computational algorithms for such problems. Year. 2010 Month: 03 Final Research under this grant

Genetic algorithms in conceptual design of a light-weight, low-noise, tilt-rotor aircraft

NASA Technical Reports Server (NTRS)

Wells, Valana L.

1996-01-01

This report outlines research accomplishments in the area of using genetic algorithms (GA) for the design and optimization of rotorcraft. It discusses the genetic algorithm as a search and optimization tool, outlines a procedure for using the GA in the conceptual design of helicopters, and applies the GA method to the acoustic design of rotors.

Modelling the role of forests on water provision services: a hydro-economic valuation approach

NASA Astrophysics Data System (ADS)

Beguería, S.; Campos, P.

2015-12-01

Hydro-economic models that allow integrating the ecological, hydrological, infrastructure, economic and social aspects into a coherent, scientifically- informed framework constitute preferred tools for supporting decision making in the context of integrated water resources management. We present a case study of water regulation and provision services of forests in the Andalusia region of Spain. Our model computes the physical water flows and conducts an economic environmental income and asset valuation of forest surface and underground water yield. Based on available hydrologic and economic data, we develop a comprehensive water account for all the forest lands at the regional scale. This forest water environmental valuation is integrated within a much larger project aiming at providing a robust and easily replicable accounting tool to evaluate yearly the total income and capital of forests, encompassing all measurable sources of private and public incomes (timber and cork production, auto-consumption, recreational activities, biodiversity conservation, carbon sequestration, water production, etc.). We also force our simulation with future socio-economic scenarios to quantify the physical and economic efects of expected trends or simulated public and private policies on future water resources. Only a comprehensive integrated tool may serve as a basis for the development of integrated policies, such as those internationally agreed and recommended for the management of water resources.

CAMPAIGN: an open-source library of GPU-accelerated data clustering algorithms.

PubMed

Kohlhoff, Kai J; Sosnick, Marc H; Hsu, William T; Pande, Vijay S; Altman, Russ B

2011-08-15

Data clustering techniques are an essential component of a good data analysis toolbox. Many current bioinformatics applications are inherently compute-intense and work with very large datasets. Sequential algorithms are inadequate for providing the necessary performance. For this reason, we have created Clustering Algorithms for Massively Parallel Architectures, Including GPU Nodes (CAMPAIGN), a central resource for data clustering algorithms and tools that are implemented specifically for execution on massively parallel processing architectures. CAMPAIGN is a library of data clustering algorithms and tools, written in 'C for CUDA' for Nvidia GPUs. The library provides up to two orders of magnitude speed-up over respective CPU-based clustering algorithms and is intended as an open-source resource. New modules from the community will be accepted into the library and the layout of it is such that it can easily be extended to promising future platforms such as OpenCL. Releases of the CAMPAIGN library are freely available for download under the LGPL from https://simtk.org/home/campaign. Source code can also be obtained through anonymous subversion access as described on https://simtk.org/scm/?group_id=453. kjk33@cantab.net.

Use of genetic algorithms to improve the solid waste collection service in an urban area.

PubMed

Buenrostro-Delgado, Otoniel; Ortega-Rodriguez, Juan Manuel; Clemitshaw, Kevin C; González-Razo, Carlos; Hernández-Paniagua, Iván Y

2015-07-01

Increasing generation of Urban Solid Waste (USW) has become a significant issue in developing countries due to unprecedented population growth and high rates of urbanisation. This issue has exceeded current plans and programs of local governments to manage and dispose of USW. In this study, a Genetic Algorithm for Rule-set Production (GARP) integrated into a Geographic Information System (GIS) was used to find areas with socio-economic conditions that are representative of the generation of USW constituents in such areas. Socio-economic data of selected variables categorised by Basic Geostatistical Areas (BGAs) were taken from the 2000 National Population Census (NPC). USW and additional socio-economic data were collected during two survey campaigns in 1998 and 2004. Areas for sampling of USW were stratified into lower, middle and upper economic strata according to income. Data on USW constituents were analysed using descriptive statistics and Multivariate Analysis. ARC View 3.2 was used to convert the USW data and socio-economic variables to spatial data. Desk-top GARP software was run to generate a spatial model to identify areas with similar socio-economic conditions to those sampled. Results showed that socio-economic variables such as monthly income and education are positively correlated with waste constituents generated. The GARP used in this study revealed BGAs with similar socio-economic conditions to those sampled, where a similar composition of waste constituents generated is expected. Our results may be useful to decrease USW management costs by improving the collection services. Copyright © 2015 Elsevier Ltd. All rights reserved.

Initial Navigation Alignment of Optical Instruments on GOES-R

NASA Technical Reports Server (NTRS)

Isaacson, Peter J.; DeLuccia, Frank J.; Reth, Alan D.; Igli, David A.; Carter, Delano R.

2016-01-01

Post-launch alignment errors for the Advanced Baseline Imager (ABI) and Geospatial Lightning Mapper (GLM) on GOES-R may be too large for the image navigation and registration (INR) processing algorithms to function without an initial adjustment to calibration parameters. We present an approach that leverages a combination of user-selected image-to-image tie points and image correlation algorithms to estimate this initial launch-induced offset and calculate adjustments to the Line of Sight Motion Compensation (LMC) parameters. We also present an approach to generate synthetic test images, to which shifts and rotations of known magnitude are applied. Results of applying the initial alignment tools to a subset of these synthetic test images are presented. The results for both ABI and GLM are within the specifications established for these tools, and indicate that application of these tools during the post-launch test (PLT) phase of GOES-R operations will enable the automated INR algorithms for both instruments to function as intended.

Data Analysis with Graphical Models: Software Tools

NASA Technical Reports Server (NTRS)

Buntine, Wray L.

1994-01-01

Probabilistic graphical models (directed and undirected Markov fields, and combined in chain graphs) are used widely in expert systems, image processing and other areas as a framework for representing and reasoning with probabilities. They come with corresponding algorithms for performing probabilistic inference. This paper discusses an extension to these models by Spiegelhalter and Gilks, plates, used to graphically model the notion of a sample. This offers a graphical specification language for representing data analysis problems. When combined with general methods for statistical inference, this also offers a unifying framework for prototyping and/or generating data analysis algorithms from graphical specifications. This paper outlines the framework and then presents some basic tools for the task: a graphical version of the Pitman-Koopman Theorem for the exponential family, problem decomposition, and the calculation of exact Bayes factors. Other tools already developed, such as automatic differentiation, Gibbs sampling, and use of the EM algorithm, make this a broad basis for the generation of data analysis software.

The efficiency of geophysical adjoint codes generated by automatic differentiation tools

NASA Astrophysics Data System (ADS)

Vlasenko, A. V.; Köhl, A.; Stammer, D.

2016-02-01

The accuracy of numerical models that describe complex physical or chemical processes depends on the choice of model parameters. Estimating an optimal set of parameters by optimization algorithms requires knowledge of the sensitivity of the process of interest to model parameters. Typically the sensitivity computation involves differentiation of the model, which can be performed by applying algorithmic differentiation (AD) tools to the underlying numerical code. However, existing AD tools differ substantially in design, legibility and computational efficiency. In this study we show that, for geophysical data assimilation problems of varying complexity, the performance of adjoint codes generated by the existing AD tools (i) Open_AD, (ii) Tapenade, (iii) NAGWare and (iv) Transformation of Algorithms in Fortran (TAF) can be vastly different. Based on simple test problems, we evaluate the efficiency of each AD tool with respect to computational speed, accuracy of the adjoint, the efficiency of memory usage, and the capability of each AD tool to handle modern FORTRAN 90-95 elements such as structures and pointers, which are new elements that either combine groups of variables or provide aliases to memory addresses, respectively. We show that, while operator overloading tools are the only ones suitable for modern codes written in object-oriented programming languages, their computational efficiency lags behind source transformation by orders of magnitude, rendering the application of these modern tools to practical assimilation problems prohibitive. In contrast, the application of source transformation tools appears to be the most efficient choice, allowing handling even large geophysical data assimilation problems. However, they can only be applied to numerical models written in earlier generations of programming languages. Our study indicates that applying existing AD tools to realistic geophysical problems faces limitations that urgently need to be solved to allow the continuous use of AD tools for solving geophysical problems on modern computer architectures.

Measurement technique for in situ characterizing aberrations of projection optics in lithographic tools.

PubMed

Wang, Fan; Wang, Xiangzhao; Ma, Mingying

2006-08-20

As the feature size decreases, degradation of image quality caused by wavefront aberrations of projection optics in lithographic tools has become a serious problem in the low-k1 process. We propose a novel measurement technique for in situ characterizing aberrations of projection optics in lithographic tools. Considering the impact of the partial coherence illumination, we introduce a novel algorithm that accurately describes the pattern displacement and focus shift induced by aberrations. Employing the algorithm, the measurement condition is extended from three-beam interference to two-, three-, and hybrid-beam interferences. The experiments are performed to measure the aberrations of projection optics in an ArF scanner.

Conducting Site and Economic Renewable Energy Project Feasibility Assessments

EPA Pesticide Factsheets

The Toolbox for Renewable Energy Project Development's Conducting Site and Economic Renewable Energy Project Feasibility Assessments page provides tools and resources to evaluate solar project feasibility and economics that influence project development.

FDT 2.0: Improving scalability of the fuzzy decision tree induction tool - integrating database storage.

PubMed

Durham, Erin-Elizabeth A; Yu, Xiaxia; Harrison, Robert W

2014-12-01

Effective machine-learning handles large datasets efficiently. One key feature of handling large data is the use of databases such as MySQL. The freeware fuzzy decision tree induction tool, FDT, is a scalable supervised-classification software tool implementing fuzzy decision trees. It is based on an optimized fuzzy ID3 (FID3) algorithm. FDT 2.0 improves upon FDT 1.0 by bridging the gap between data science and data engineering: it combines a robust decisioning tool with data retention for future decisions, so that the tool does not need to be recalibrated from scratch every time a new decision is required. In this paper we briefly review the analytical capabilities of the freeware FDT tool and its major features and functionalities; examples of large biological datasets from HIV, microRNAs and sRNAs are included. This work shows how to integrate fuzzy decision algorithms with modern database technology. In addition, we show that integrating the fuzzy decision tree induction tool with database storage allows for optimal user satisfaction in today's Data Analytics world.

Multi-Agent Task Negotiation Among UAVs to Defend Against Swarm Attacks

DTIC Science & Technology

2012-03-01

are based on economic models [39]. Auction methods of task coordination also attempt to deal with agents dealing with noisy, dynamic environments...August 2006. [34] M. Alighanbari, “ Robust and decentralized task assignment algorithms for uavs,” Ph.D. dissertation, Massachusetts Institute of Technology...Implicit Coordination . . . . . . . . . . . . . 12 2.4 Decentralized Algorithm B - Market- Based . . . . . . . . . . . . . . . . 12 2.5 Decentralized

Analysis of Bioactive Amino Acids from Fish Hydrolysates with a New Bioinformatic Intelligent System Approach.

PubMed

Elaziz, Mohamed Abd; Hemdan, Ahmed Monem; Hassanien, AboulElla; Oliva, Diego; Xiong, Shengwu

2017-09-07

The current economics of the fish protein industry demand rapid, accurate and expressive prediction algorithms at every step of protein production especially with the challenge of global climate change. This help to predict and analyze functional and nutritional quality then consequently control food allergies in hyper allergic patients. As, it is quite expensive and time-consuming to know these concentrations by the lab experimental tests, especially to conduct large-scale projects. Therefore, this paper introduced a new intelligent algorithm using adaptive neuro-fuzzy inference system based on whale optimization algorithm. This algorithm is used to predict the concentration levels of bioactive amino acids in fish protein hydrolysates at different times during the year. The whale optimization algorithm is used to determine the optimal parameters in adaptive neuro-fuzzy inference system. The results of proposed algorithm are compared with others and it is indicated the higher performance of the proposed algorithm.

Wavelet-Based Peak Detection and a New Charge Inference Procedure for MS/MS Implemented in ProteoWizard’s msConvert

PubMed Central

2015-01-01

We report the implementation of high-quality signal processing algorithms into ProteoWizard, an efficient, open-source software package designed for analyzing proteomics tandem mass spectrometry data. Specifically, a new wavelet-based peak-picker (CantWaiT) and a precursor charge determination algorithm (Turbocharger) have been implemented. These additions into ProteoWizard provide universal tools that are independent of vendor platform for tandem mass spectrometry analyses and have particular utility for intralaboratory studies requiring the advantages of different platforms convergent on a particular workflow or for interlaboratory investigations spanning multiple platforms. We compared results from these tools to those obtained using vendor and commercial software, finding that in all cases our algorithms resulted in a comparable number of identified peptides for simple and complex samples measured on Waters, Agilent, and AB SCIEX quadrupole time-of-flight and Thermo Q-Exactive mass spectrometers. The mass accuracy of matched precursor ions also compared favorably with vendor and commercial tools. Additionally, typical analysis runtimes (∼1–100 ms per MS/MS spectrum) were short enough to enable the practical use of these high-quality signal processing tools for large clinical and research data sets. PMID:25411686

Wavelet-based peak detection and a new charge inference procedure for MS/MS implemented in ProteoWizard's msConvert.

PubMed

French, William R; Zimmerman, Lisa J; Schilling, Birgit; Gibson, Bradford W; Miller, Christine A; Townsend, R Reid; Sherrod, Stacy D; Goodwin, Cody R; McLean, John A; Tabb, David L

2015-02-06

We report the implementation of high-quality signal processing algorithms into ProteoWizard, an efficient, open-source software package designed for analyzing proteomics tandem mass spectrometry data. Specifically, a new wavelet-based peak-picker (CantWaiT) and a precursor charge determination algorithm (Turbocharger) have been implemented. These additions into ProteoWizard provide universal tools that are independent of vendor platform for tandem mass spectrometry analyses and have particular utility for intralaboratory studies requiring the advantages of different platforms convergent on a particular workflow or for interlaboratory investigations spanning multiple platforms. We compared results from these tools to those obtained using vendor and commercial software, finding that in all cases our algorithms resulted in a comparable number of identified peptides for simple and complex samples measured on Waters, Agilent, and AB SCIEX quadrupole time-of-flight and Thermo Q-Exactive mass spectrometers. The mass accuracy of matched precursor ions also compared favorably with vendor and commercial tools. Additionally, typical analysis runtimes (∼1-100 ms per MS/MS spectrum) were short enough to enable the practical use of these high-quality signal processing tools for large clinical and research data sets.

A statistical approach to detection of copy number variations in PCR-enriched targeted sequencing data.

PubMed

Demidov, German; Simakova, Tamara; Vnuchkova, Julia; Bragin, Anton

2016-10-22

Multiplex polymerase chain reaction (PCR) is a common enrichment technique for targeted massive parallel sequencing (MPS) protocols. MPS is widely used in biomedical research and clinical diagnostics as the fast and accurate tool for the detection of short genetic variations. However, identification of larger variations such as structure variants and copy number variations (CNV) is still being a challenge for targeted MPS. Some approaches and tools for structural variants detection were proposed, but they have limitations and often require datasets of certain type, size and expected number of amplicons affected by CNVs. In the paper, we describe novel algorithm for high-resolution germinal CNV detection in the PCR-enriched targeted sequencing data and present accompanying tool. We have developed a machine learning algorithm for the detection of large duplications and deletions in the targeted sequencing data generated with PCR-based enrichment step. We have performed verification studies and established the algorithm's sensitivity and specificity. We have compared developed tool with other available methods applicable for the described data and revealed its higher performance. We showed that our method has high specificity and sensitivity for high-resolution copy number detection in targeted sequencing data using large cohort of samples.

Optimization process planning using hybrid genetic algorithm and intelligent search for job shop machining.

PubMed

Salehi, Mojtaba; Bahreininejad, Ardeshir

2011-08-01

Optimization of process planning is considered as the key technology for computer-aided process planning which is a rather complex and difficult procedure. A good process plan of a part is built up based on two elements: (1) the optimized sequence of the operations of the part; and (2) the optimized selection of the machine, cutting tool and Tool Access Direction (TAD) for each operation. In the present work, the process planning is divided into preliminary planning, and secondary/detailed planning. In the preliminary stage, based on the analysis of order and clustering constraints as a compulsive constraint aggregation in operation sequencing and using an intelligent searching strategy, the feasible sequences are generated. Then, in the detailed planning stage, using the genetic algorithm which prunes the initial feasible sequences, the optimized operation sequence and the optimized selection of the machine, cutting tool and TAD for each operation based on optimization constraints as an additive constraint aggregation are obtained. The main contribution of this work is the optimization of sequence of the operations of the part, and optimization of machine selection, cutting tool and TAD for each operation using the intelligent search and genetic algorithm simultaneously.

Optimization process planning using hybrid genetic algorithm and intelligent search for job shop machining

PubMed Central

Salehi, Mojtaba

2010-01-01

Optimization of process planning is considered as the key technology for computer-aided process planning which is a rather complex and difficult procedure. A good process plan of a part is built up based on two elements: (1) the optimized sequence of the operations of the part; and (2) the optimized selection of the machine, cutting tool and Tool Access Direction (TAD) for each operation. In the present work, the process planning is divided into preliminary planning, and secondary/detailed planning. In the preliminary stage, based on the analysis of order and clustering constraints as a compulsive constraint aggregation in operation sequencing and using an intelligent searching strategy, the feasible sequences are generated. Then, in the detailed planning stage, using the genetic algorithm which prunes the initial feasible sequences, the optimized operation sequence and the optimized selection of the machine, cutting tool and TAD for each operation based on optimization constraints as an additive constraint aggregation are obtained. The main contribution of this work is the optimization of sequence of the operations of the part, and optimization of machine selection, cutting tool and TAD for each operation using the intelligent search and genetic algorithm simultaneously. PMID:21845020

A Quasiphysics Intelligent Model for a Long Range Fast Tool Servo

PubMed Central

Liu, Qiang; Zhou, Xiaoqin; Lin, Jieqiong; Xu, Pengzi; Zhu, Zhiwei

2013-01-01

Accurately modeling the dynamic behaviors of fast tool servo (FTS) is one of the key issues in the ultraprecision positioning of the cutting tool. Herein, a quasiphysics intelligent model (QPIM) integrating a linear physics model (LPM) and a radial basis function (RBF) based neural model (NM) is developed to accurately describe the dynamic behaviors of a voice coil motor (VCM) actuated long range fast tool servo (LFTS). To identify the parameters of the LPM, a novel Opposition-based Self-adaptive Replacement Differential Evolution (OSaRDE) algorithm is proposed which has been proved to have a faster convergence mechanism without compromising with the quality of solution and outperform than similar evolution algorithms taken for consideration. The modeling errors of the LPM and the QPIM are investigated by experiments. The modeling error of the LPM presents an obvious trend component which is about ±1.15% of the full span range verifying the efficiency of the proposed OSaRDE algorithm for system identification. As for the QPIM, the trend component in the residual error of LPM can be well suppressed, and the error of the QPIM maintains noise level. All the results verify the efficiency and superiority of the proposed modeling and identification approaches. PMID:24163627

Economic figures in herd health programmes as motivation factors for farmers.

PubMed

Anneberg, Inger; Østergaard, Søren; Ettema, Jehan Frans; Kudahl, Anne Braad

2016-11-01

Veterinarians often express frustrations when farmers do not implement their advice, and farmers sometimes shake their heads when they receive veterinary advice which is practically unfeasible. This is the background for the development of a focused 3 page economic report created in cooperation between veterinarians, farmers, advisers and researchers. Based on herd specific key-figures for management, the report presents the short- and long-term economic effects of changes in 15 management areas. Simulations are performed by the dairy herd simulation model "SimHerd". The aim is to assist the veterinarian in identifying the economically most favorable and feasible management improvements and thereby provide more relevant and prioritised advice to the farmer. In the developing process, a prototype of the advisory tool was tested by 15 veterinarians on 55 farms. After the test period, a selection of farmers were asked to take part in a qualitative evaluation questioning them whether they had implemented the action plans suggested on basis of the advisory tool and making them explain what made them agree or disagree on the results from this new advisory tool. The aim of this process was to evaluate the farmers' receptiveness to advice based on these economic analyses. We found that the analysed advisory tool (the report) can be seen as a valuable help and support for some farmers when deciding whether to implement the action plans. However, certain reservations were recognised. The trustworthiness of the tool depends on whether the veterinarians are able to suggest to the farmer which specific management changes are needed to obtain the estimated effects and what the related expenses might be (costs). Without transparency of expenses, time-limits, work hours and so on, farmers may not be convinced by the tool. Copyright © 2016 Elsevier B.V. All rights reserved.

Dynamic model of production enterprises based on accounting registers and its identification

NASA Astrophysics Data System (ADS)

Sirazetdinov, R. T.; Samodurov, A. V.; Yenikeev, I. A.; Markov, D. S.

2016-06-01

The report focuses on the mathematical modeling of economic entities based on accounting registers. Developed the dynamic model of financial and economic activity of the enterprise as a system of differential equations. Created algorithms for identification of parameters of the dynamic model. Constructed and identified the model of Russian machine-building enterprises.

Economic impact of reduced mortality due to increased cycling.

PubMed

Rutter, Harry; Cavill, Nick; Racioppi, Francesca; Dinsdale, Hywell; Oja, Pekka; Kahlmeier, Sonja

2013-01-01

Increasing regular physical activity is a key public health goal. One strategy is to change the physical environment to encourage walking and cycling, requiring partnerships with the transport and urban planning sectors. Economic evaluation is an important factor in the decision to fund any new transport scheme, but techniques for assessing the economic value of the health benefits of cycling and walking have tended to be less sophisticated than the approaches used for assessing other benefits. This study aimed to produce a practical tool for estimating the economic impact of reduced mortality due to increased cycling. The tool was intended to be transparent, easy to use, reliable, and based on conservative assumptions and default values, which can be used in the absence of local data. It addressed the question: For a given volume of cycling within a defined population, what is the economic value of the health benefits? The authors used published estimates of relative risk of all-cause mortality among regular cyclists and applied these to levels of cycling defined by the user to produce an estimate of the number of deaths potentially averted because of regular cycling. The tool then calculates the economic value of the deaths averted using the "value of a statistical life." The outputs of the tool support decision making on cycle infrastructure or policies, or can be used as part of an integrated economic appraisal. The tool's unique contribution is that it takes a public health approach to a transport problem, addresses it in epidemiologic terms, and places the results back into the transport context. Examples of its use include its adoption by the English and Swedish departments of transport as the recommended methodologic approach for estimating the health impact of walking and cycling. Copyright © 2013 World Health Organization. Published by Elsevier Inc. All rights reserved.

Using an Experimental Approach to Improving the Selective Reenlistment Bonus Program

DTIC Science & Technology

2007-06-01

when establishing a reenlistment bonus. 12 Paul G. Keat and Philip K. Y. Young . Managerial Economics ...Journal of Labor Economics Vol. 6, No. 4, October 1988, 423-444. Keat , Paul G. and Philip K. Y. Young . Managerial Economics : Economic Tools for...alternative compensation methods, specifically auctions, signaling theory, and experimental economics ; and explains how an economic experiment might be

Hubs and authorities in the world trade network using a weighted HITS algorithm.

PubMed

Deguchi, Tsuyoshi; Takahashi, Katsuhide; Takayasu, Hideki; Takayasu, Misako

2014-01-01

We investigate the economic hubs and authorities of the world trade network (WTN) from 1992 to 2012, an era of rapid economic globalization. Using a well-defined weighted hyperlink-induced topic search (HITS) algorithm, we can calculate the values of the weighted HITS hub and authority for each country in a conjugate way. In the context of the WTN, authority values are large for countries with significant imports from large hub countries, and hub values are large for countries with significant exports to high-authority countries. The United States was the largest economic authority in the WTN from 1992 to 2012. The authority value of the United States has declined since 2001, and China has now become the largest hub in the WTN. At the same time, China's authority value has grown as China is transforming itself from the "factory of the world" to the "market of the world." European countries show a tendency to trade mostly within the European Union, which has decreased Europe's hub and authority values. Japan's authority value has increased slowly, while its hub value has declined. These changes are consistent with Japan's transition from being an export-driven economy in its high economic growth era in the latter half of the twentieth century to being a more mature, economically balanced nation.

Hubs and Authorities in the World Trade Network Using a Weighted HITS Algorithm

PubMed Central

Deguchi, Tsuyoshi; Takahashi, Katsuhide; Takayasu, Hideki; Takayasu, Misako

2014-01-01

We investigate the economic hubs and authorities of the world trade network (WTN) from to , an era of rapid economic globalization. Using a well-defined weighted hyperlink-induced topic search (HITS) algorithm, we can calculate the values of the weighted HITS hub and authority for each country in a conjugate way. In the context of the WTN, authority values are large for countries with significant imports from large hub countries, and hub values are large for countries with significant exports to high-authority countries. The United States was the largest economic authority in the WTN from to . The authority value of the United States has declined since , and China has now become the largest hub in the WTN. At the same time, China's authority value has grown as China is transforming itself from the “factory of the world” to the “market of the world.” European countries show a tendency to trade mostly within the European Union, which has decreased Europe's hub and authority values. Japan's authority value has increased slowly, while its hub value has declined. These changes are consistent with Japan's transition from being an export-driven economy in its high economic growth era in the latter half of the twentieth century to being a more mature, economically balanced nation. PMID:25050940

Effect of registration on corpus callosum population differences found with DBM analysis

NASA Astrophysics Data System (ADS)

Han, Zhaoying; Thornton-Wells, Tricia A.; Gore, John C.; Dawant, Benoit M.

2011-03-01

Deformation Based Morphometry (DBM) is a relatively new method used for characterizing anatomical differences among populations. DBM is based on the analysis of the deformation fields generated by non-rigid registration algorithms, which warp the individual volumes to one standard coordinate system. Although several studies have compared non-rigid registration algorithms for segmentation tasks, few studies have compared the effect of the registration algorithm on population differences that may be uncovered through DBM. In this study, we compared DBM results obtained with five well established non-rigid registration algorithms on the corpus callosum (CC) in thirteen subjects with Williams Syndrome (WS) and thirteen Normal Control (NC) subjects. The five non-rigid registration algorithms include: (1) The Adaptive Basis Algorithm (ABA); (2) Image Registration Toolkit (IRTK); (3) FSL Nonlinear Image Registration Tool (FSL); (4) Automatic Registration Tools (ART); and (5) the normalization algorithm available in SPM8. For each algorithm, the 3D deformation fields from all subjects to the atlas were obtained and used to calculate the Jacobian determinant (JAC) at each voxel in the mid-sagittal slice of the CC. The mean JAC maps for each group were compared quantitatively across different nonrigid registration algorithms. An ANOVA test performed on the means of the JAC over the Genu and the Splenium ROIs shows the JAC differences between nonrigid registration algorithms are statistically significant over the Genu for both groups and over the Splenium for the NC group. These results suggest that it is important to consider the effect of registration when using DBM to compute morphological differences in populations.

Family-Based Benchmarking of Copy Number Variation Detection Software.

PubMed

Nutsua, Marcel Elie; Fischer, Annegret; Nebel, Almut; Hofmann, Sylvia; Schreiber, Stefan; Krawczak, Michael; Nothnagel, Michael

2015-01-01

The analysis of structural variants, in particular of copy-number variations (CNVs), has proven valuable in unraveling the genetic basis of human diseases. Hence, a large number of algorithms have been developed for the detection of CNVs in SNP array signal intensity data. Using the European and African HapMap trio data, we undertook a comparative evaluation of six commonly used CNV detection software tools, namely Affymetrix Power Tools (APT), QuantiSNP, PennCNV, GLAD, R-gada and VEGA, and assessed their level of pair-wise prediction concordance. The tool-specific CNV prediction accuracy was assessed in silico by way of intra-familial validation. Software tools differed greatly in terms of the number and length of the CNVs predicted as well as the number of markers included in a CNV. All software tools predicted substantially more deletions than duplications. Intra-familial validation revealed consistently low levels of prediction accuracy as measured by the proportion of validated CNVs (34-60%). Moreover, up to 20% of apparent family-based validations were found to be due to chance alone. Software using Hidden Markov models (HMM) showed a trend to predict fewer CNVs than segmentation-based algorithms albeit with greater validity. PennCNV yielded the highest prediction accuracy (60.9%). Finally, the pairwise concordance of CNV prediction was found to vary widely with the software tools involved. We recommend HMM-based software, in particular PennCNV, rather than segmentation-based algorithms when validity is the primary concern of CNV detection. QuantiSNP may be used as an additional tool to detect sets of CNVs not detectable by the other tools. Our study also reemphasizes the need for laboratory-based validation, such as qPCR, of CNVs predicted in silico.

Optimization of Land Use Suitability for Agriculture Using Integrated Geospatial Model and Genetic Algorithms

NASA Astrophysics Data System (ADS)

Mansor, S. B.; Pormanafi, S.; Mahmud, A. R. B.; Pirasteh, S.

2012-08-01

In this study, a geospatial model for land use allocation was developed from the view of simulating the biological autonomous adaptability to environment and the infrastructural preference. The model was developed based on multi-agent genetic algorithm. The model was customized to accommodate the constraint set for the study area, namely the resource saving and environmental-friendly. The model was then applied to solve the practical multi-objective spatial optimization allocation problems of land use in the core region of Menderjan Basin in Iran. The first task was to study the dominant crops and economic suitability evaluation of land. Second task was to determine the fitness function for the genetic algorithms. The third objective was to optimize the land use map using economical benefits. The results has indicated that the proposed model has much better performance for solving complex multi-objective spatial optimization allocation problems and it is a promising method for generating land use alternatives for further consideration in spatial decision-making.

Automated and real-time segmentation of suspicious breast masses using convolutional neural network

PubMed Central

Gregory, Adriana; Denis, Max; Meixner, Duane D.; Bayat, Mahdi; Whaley, Dana H.; Fatemi, Mostafa; Alizad, Azra

2018-01-01

In this work, a computer-aided tool for detection was developed to segment breast masses from clinical ultrasound (US) scans. The underlying Multi U-net algorithm is based on convolutional neural networks. Under the Mayo Clinic Institutional Review Board protocol, a prospective study of the automatic segmentation of suspicious breast masses was performed. The cohort consisted of 258 female patients who were clinically identified with suspicious breast masses and underwent clinical US scan and breast biopsy. The computer-aided detection tool effectively segmented the breast masses, achieving a mean Dice coefficient of 0.82, a true positive fraction (TPF) of 0.84, and a false positive fraction (FPF) of 0.01. By avoiding positioning of an initial seed, the algorithm is able to segment images in real time (13–55 ms per image), and can have potential clinical applications. The algorithm is at par with a conventional seeded algorithm, which had a mean Dice coefficient of 0.84 and performs significantly better (P< 0.0001) than the original U-net algorithm. PMID:29768415

Model-Checking with Edge-Valued Decision Diagrams

NASA Technical Reports Server (NTRS)

Roux, Pierre; Siminiceanu, Radu I.

2010-01-01

We describe an algebra of Edge-Valued Decision Diagrams (EVMDDs) to encode arithmetic functions and its implementation in a model checking library along with state-of-the-art algorithms for building the transition relation and the state space of discrete state systems. We provide efficient algorithms for manipulating EVMDDs and give upper bounds of the theoretical time complexity of these algorithms for all basic arithmetic and relational operators. We also demonstrate that the time complexity of the generic recursive algorithm for applying a binary operator on EVMDDs is no worse than that of Multi-Terminal Decision Diagrams. We have implemented a new symbolic model checker with the intention to represent in one formalism the best techniques available at the moment across a spectrum of existing tools: EVMDDs for encoding arithmetic expressions, identity-reduced MDDs for representing the transition relation, and the saturation algorithm for reachability analysis. We compare our new symbolic model checking EVMDD library with the widely used CUDD package and show that, in many cases, our tool is several orders of magnitude faster than CUDD.

CHEMINFORMATICS TOOLS FOR TOXICANT CHARACTERIZATION

EPA Science Inventory

• Continued development of the Shape Signatures method is planed.  This effort will include further development of the Shape Signatures database of PDB-extracted ligands and of the clustering algorithm.

• In addition, we plan to develop an...

• A study on directional resistivity logging-while-drilling based on self-adaptive hp-FEM

  NASA Astrophysics Data System (ADS)

  Liu, Dejun; Li, Hui; Zhang, Yingying; Zhu, Gengxue; Ai, Qinghui

  2014-12-01

  Numerical simulation of resistivity logging-while-drilling (LWD) tool response provides guidance for designing novel logging instruments and interpreting real-time logging data. In this paper, based on self-adaptive hp-finite element method (hp-FEM) algorithm, we analyze LWD tool response against model parameters and briefly illustrate geosteering capabilities of directional resistivity LWD. Numerical simulation results indicate that the change of source spacing is of obvious influence on the investigation depth and detecting precision of resistivity LWD tool; the change of frequency can improve the resolution of low-resistivity formation and high-resistivity formation. The simulation results also indicate that the self-adaptive hp-FEM algorithm has good convergence speed and calculation accuracy to guide the geologic steering drilling and it is suitable to simulate the response of resistivity LWD tools.

• HUMAN HEALTH METRICS FOR ENVIRONMENTAL DECISION SUPPORT TOOLS: LESSONS FROM HEALTH ECONOMICS AND DECISION ANALYSIS: JOURNAL ARTICLE

  EPA Science Inventory

  NRMRL-CIN-1351 Hofstetter**, P., and Hammitt, J. K. Human Health Metrics for Environmental Decision Support Tools: Lessons from Health Economics and Decision Analysis. Risk Analysis 600/R/01/104, Available: on internet, www.epa.gov/ORD/NRMRL/Pubs/600R01104, [NET]. 03/07/2001 D...

• On several aspects and applications of the multigrid method for solving partial differential equations

  NASA Technical Reports Server (NTRS)

  Dinar, N.

  1978-01-01

  Several aspects of multigrid methods are briefly described. The main subjects include the development of very efficient multigrid algorithms for systems of elliptic equations (Cauchy-Riemann, Stokes, Navier-Stokes), as well as the development of control and prediction tools (based on local mode Fourier analysis), used to analyze, check and improve these algorithms. Preliminary research on multigrid algorithms for time dependent parabolic equations is also described. Improvements in existing multigrid processes and algorithms for elliptic equations were studied.

• Deterministic implementations of single-photon multi-qubit Deutsch-Jozsa algorithms with linear optics

  NASA Astrophysics Data System (ADS)

  Wei, Hai-Rui; Liu, Ji-Zhen

  2017-02-01

  It is very important to seek an efficient and robust quantum algorithm demanding less quantum resources. We propose one-photon three-qubit original and refined Deutsch-Jozsa algorithms with polarization and two linear momentums degrees of freedom (DOFs). Our schemes are constructed by solely using linear optics. Compared to the traditional ones with one DOF, our schemes are more economic and robust because the necessary photons are reduced from three to one. Our linear-optic schemes are working in a determinate way, and they are feasible with current experimental technology.

• Deterministic implementations of single-photon multi-qubit Deutsch–Jozsa algorithms with linear optics

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

  Wei, Hai-Rui, E-mail: hrwei@ustb.edu.cn; Liu, Ji-Zhen

  2017-02-15

  It is very important to seek an efficient and robust quantum algorithm demanding less quantum resources. We propose one-photon three-qubit original and refined Deutsch–Jozsa algorithms with polarization and two linear momentums degrees of freedom (DOFs). Our schemes are constructed by solely using linear optics. Compared to the traditional ones with one DOF, our schemes are more economic and robust because the necessary photons are reduced from three to one. Our linear-optic schemes are working in a determinate way, and they are feasible with current experimental technology.

• ADAM: analysis of discrete models of biological systems using computer algebra.

  PubMed

  Hinkelmann, Franziska; Brandon, Madison; Guang, Bonny; McNeill, Rustin; Blekherman, Grigoriy; Veliz-Cuba, Alan; Laubenbacher, Reinhard

  2011-07-20

  Many biological systems are modeled qualitatively with discrete models, such as probabilistic Boolean networks, logical models, Petri nets, and agent-based models, to gain a better understanding of them. The computational complexity to analyze the complete dynamics of these models grows exponentially in the number of variables, which impedes working with complex models. There exist software tools to analyze discrete models, but they either lack the algorithmic functionality to analyze complex models deterministically or they are inaccessible to many users as they require understanding the underlying algorithm and implementation, do not have a graphical user interface, or are hard to install. Efficient analysis methods that are accessible to modelers and easy to use are needed. We propose a method for efficiently identifying attractors and introduce the web-based tool Analysis of Dynamic Algebraic Models (ADAM), which provides this and other analysis methods for discrete models. ADAM converts several discrete model types automatically into polynomial dynamical systems and analyzes their dynamics using tools from computer algebra. Specifically, we propose a method to identify attractors of a discrete model that is equivalent to solving a system of polynomial equations, a long-studied problem in computer algebra. Based on extensive experimentation with both discrete models arising in systems biology and randomly generated networks, we found that the algebraic algorithms presented in this manuscript are fast for systems with the structure maintained by most biological systems, namely sparseness and robustness. For a large set of published complex discrete models, ADAM identified the attractors in less than one second. Discrete modeling techniques are a useful tool for analyzing complex biological systems and there is a need in the biological community for accessible efficient analysis tools. ADAM provides analysis methods based on mathematical algorithms as a web-based tool for several different input formats, and it makes analysis of complex models accessible to a larger community, as it is platform independent as a web-service and does not require understanding of the underlying mathematics.

• Machine Learning for Biological Trajectory Classification Applications

  NASA Technical Reports Server (NTRS)

  Sbalzarini, Ivo F.; Theriot, Julie; Koumoutsakos, Petros

  2002-01-01

  Machine-learning techniques, including clustering algorithms, support vector machines and hidden Markov models, are applied to the task of classifying trajectories of moving keratocyte cells. The different algorithms axe compared to each other as well as to expert and non-expert test persons, using concepts from signal-detection theory. The algorithms performed very well as compared to humans, suggesting a robust tool for trajectory classification in biological applications.

• Fuels planning: science synthesis and integration; economic uses fact sheet 03: economic impacts of fuel treatments

  Treesearch

  Rocky Mountain Research Station USDA Forest Service

  2004-01-01

  With increased interest in reducing hazardous fuels in dry inland forests of the American West, agencies and the public will want to know the economic impacts of fuel reduction treatments. This fact sheet discusses the economic impact tool, a component of My Fuel Treatment Planner, for evaluating economic impacts.

• Using Economic Impact Models as an Educational Tool in Community Economic Development Programming: Lessons from Pennsylvania and Wisconsin.

  ERIC Educational Resources Information Center

  Shields, Martin; Deller, Steven C.

  2003-01-01

  Outlines an educational process designed to help provide communities with economic, social, and political information using community economic impact modeling. Describes the process of community meetings using economic impact, community demographics, and fiscal impact modules and the local preconditions that help make the process successful. (SK)

• A Search Algorithm for Determination of Economic Order Quantity in a Two-Level Supply Chain System with Transportation Cost

  NASA Astrophysics Data System (ADS)

  Pirayesh Neghab, Mohammadali; Haji, Rasoul

  This study considers a two-level supply chain system consisting of one warehouse and a number of identical retailers. In this system, we incorporate transportation costs into inventory replenishment decisions. The transportation cost contains a fixed cost and a variable cost. We assume that the demand rate at each retailer is known and the demand is confined to a single item. First, we derive the total cost which is the sum of the holding and ordering cost at the warehouse and retailers as well as the transportation cost from the warehouse to retailers. Then, we propose a search algorithm to find the economic order quantities for the warehouse and retailers which minimize the total cost.

• A Method for Aircraft Concept Selection Using Multicriteria Interactive Genetic Algorithms

  NASA Technical Reports Server (NTRS)

  Buonanno, Michael; Mavris, Dimitri

  2005-01-01

  The problem of aircraft concept selection has become increasingly difficult in recent years as a result of a change from performance as the primary evaluation criteria of aircraft concepts to the current situation in which environmental effects, economics, and aesthetics must also be evaluated and considered in the earliest stages of the decision-making process. This has prompted a shift from design using historical data regression techniques for metric prediction to the use of physics-based analysis tools that are capable of analyzing designs outside of the historical database. The use of optimization methods with these physics-based tools, however, has proven difficult because of the tendency of optimizers to exploit assumptions present in the models and drive the design towards a solution which, while promising to the computer, may be infeasible due to factors not considered by the computer codes. In addition to this difficulty, the number of discrete options available at this stage may be unmanageable due to the combinatorial nature of the concept selection problem, leading the analyst to arbitrarily choose a sub-optimum baseline vehicle. These concept decisions such as the type of control surface scheme to use, though extremely important, are frequently made without sufficient understanding of their impact on the important system metrics because of a lack of computational resources or analysis tools. This paper describes a hybrid subjective/quantitative optimization method and its application to the concept selection of a Small Supersonic Transport. The method uses Genetic Algorithms to operate on a population of designs and promote improvement by varying more than sixty parameters governing the vehicle geometry, mission, and requirements. In addition to using computer codes for evaluation of quantitative criteria such as gross weight, expert input is also considered to account for criteria such as aeroelasticity or manufacturability which may be impossible or too computationally expensive to consider explicitly in the analysis. Results indicate that concepts resulting from the use of this method represent designs which are promising to both the computer and the analyst, and that a mapping between concepts and requirements that would not otherwise be apparent is revealed.

• Documentation for MeshKit - Reactor Geometry (&mesh) Generator

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

  Jain, Rajeev; Mahadevan, Vijay

  2015-09-30

  This report gives documentation for using MeshKit’s Reactor Geometry (and mesh) Generator (RGG) GUI and also briefly documents other algorithms and tools available in MeshKit. RGG is a program designed to aid in modeling and meshing of complex/large hexagonal and rectilinear reactor cores. RGG uses Argonne’s SIGMA interfaces, Qt and VTK to produce an intuitive user interface. By integrating a 3D view of the reactor with the meshing tools and combining them into one user interface, RGG streamlines the task of preparing a simulation mesh and enables real-time feedback that reduces accidental scripting mistakes that could waste hours of meshing.more » RGG interfaces with MeshKit tools to consolidate the meshing process, meaning that going from model to mesh is as easy as a button click. This report is designed to explain RGG v 2.0 interface and provide users with the knowledge and skills to pilot RGG successfully. Brief documentation of MeshKit source code, tools and other algorithms available are also presented for developers to extend and add new algorithms to MeshKit. RGG tools work in serial and parallel and have been used to model complex reactor core models consisting of conical pins, load pads, several thousands of axially varying material properties of instrumentation pins and other interstices meshes.« less

• Holmes: a graphical tool for development, simulation and analysis of Petri net based models of complex biological systems.

  PubMed

  Radom, Marcin; Rybarczyk, Agnieszka; Szawulak, Bartlomiej; Andrzejewski, Hubert; Chabelski, Piotr; Kozak, Adam; Formanowicz, Piotr

  2017-12-01

  Model development and its analysis is a fundamental step in systems biology. The theory of Petri nets offers a tool for such a task. Since the rapid development of computer science, a variety of tools for Petri nets emerged, offering various analytical algorithms. From this follows a problem of using different programs to analyse a single model. Many file formats and different representations of results make the analysis much harder. Especially for larger nets the ability to visualize the results in a proper form provides a huge help in the understanding of their significance. We present a new tool for Petri nets development and analysis called Holmes. Our program contains algorithms for model analysis based on different types of Petri nets, e.g. invariant generator, Maximum Common Transitions (MCT) sets and cluster modules, simulation algorithms or knockout analysis tools. A very important feature is the ability to visualize the results of almost all analytical modules. The integration of such modules into one graphical environment allows a researcher to fully devote his or her time to the model building and analysis. Available at http://www.cs.put.poznan.pl/mradom/Holmes/holmes.html. piotr@cs.put.poznan.pl. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

• Evaluating some computer exhancement algorithms that improve the visibility of cometary morphology

  NASA Technical Reports Server (NTRS)

  Larson, Stephen M.; Slaughter, Charles D.

  1992-01-01

  Digital enhancement of cometary images is a necessary tool in studying cometary morphology. Many image processing algorithms, some developed specifically for comets, have been used to enhance the subtle, low contrast coma and tail features. We compare some of the most commonly used algorithms on two different images to evaluate their strong and weak points, and conclude that there currently exists no single 'ideal' algorithm, although the radial gradient spatial filter gives the best overall result. This comparison should aid users in selecting the best algorithm to enhance particular features of interest.

• Algorithmic cooling in liquid-state nuclear magnetic resonance

  NASA Astrophysics Data System (ADS)

  Atia, Yosi; Elias, Yuval; Mor, Tal; Weinstein, Yossi

  2016-01-01

  Algorithmic cooling is a method that employs thermalization to increase qubit purification level; namely, it reduces the qubit system's entropy. We utilized gradient ascent pulse engineering, an optimal control algorithm, to implement algorithmic cooling in liquid-state nuclear magnetic resonance. Various cooling algorithms were applied onto the three qubits of C132-trichloroethylene, cooling the system beyond Shannon's entropy bound in several different ways. In particular, in one experiment a carbon qubit was cooled by a factor of 4.61. This work is a step towards potentially integrating tools of NMR quantum computing into in vivo magnetic-resonance spectroscopy.

• Asymptotic analysis of SPTA-based algorithms for no-wait flow shop scheduling problem with release dates.

  PubMed

  Ren, Tao; Zhang, Chuan; Lin, Lin; Guo, Meiting; Xie, Xionghang

  2014-01-01

  We address the scheduling problem for a no-wait flow shop to optimize total completion time with release dates. With the tool of asymptotic analysis, we prove that the objective values of two SPTA-based algorithms converge to the optimal value for sufficiently large-sized problems. To further enhance the performance of the SPTA-based algorithms, an improvement scheme based on local search is provided for moderate scale problems. New lower bound is presented for evaluating the asymptotic optimality of the algorithms. Numerical simulations demonstrate the effectiveness of the proposed algorithms.

Asymptotic Analysis of SPTA-Based Algorithms for No-Wait Flow Shop Scheduling Problem with Release Dates

PubMed Central

Ren, Tao; Zhang, Chuan; Lin, Lin; Guo, Meiting; Xie, Xionghang

2014-01-01

We address the scheduling problem for a no-wait flow shop to optimize total completion time with release dates. With the tool of asymptotic analysis, we prove that the objective values of two SPTA-based algorithms converge to the optimal value for sufficiently large-sized problems. To further enhance the performance of the SPTA-based algorithms, an improvement scheme based on local search is provided for moderate scale problems. New lower bound is presented for evaluating the asymptotic optimality of the algorithms. Numerical simulations demonstrate the effectiveness of the proposed algorithms. PMID:24764774

Development of sub-daily erosion and sediment transport algorithms in SWAT

USDA-ARS?s Scientific Manuscript database

New Soil and Water Assessment Tool (SWAT) algorithms for simulation of stormwater best management practices (BMPs) such as detention basins, wet ponds, sedimentation filtration ponds, and retention irrigation systems are under development for modeling small/urban watersheds. Modeling stormwater BMPs...

CALIBRATION, OPTIMIZATION, AND SENSITIVITY AND UNCERTAINTY ALGORITHMS APPLICATION PROGRAMMING INTERFACE (COSU-API)

EPA Science Inventory

The Application Programming Interface (API) for Uncertainty Analysis, Sensitivity Analysis, and Parameter Estimation (UA/SA/PE API) tool development, here fore referred to as the Calibration, Optimization, and Sensitivity and Uncertainty Algorithms API (COSU-API), was initially d...

Aggregation Tool to Create Curated Data albums to Support Disaster Recovery and Response

NASA Astrophysics Data System (ADS)

Ramachandran, R.; Kulkarni, A.; Maskey, M.; Li, X.; Flynn, S.

2014-12-01

Economic losses due to natural hazards are estimated to be around 6-10 billion dollars annually for the U.S. and this number keeps increasing every year. This increase has been attributed to population growth and migration to more hazard prone locations. As this trend continues, in concert with shifts in weather patterns caused by climate change, it is anticipated that losses associated with natural disasters will keep growing substantially. One of challenges disaster response and recovery analysts face is to quickly find, access and utilize a vast variety of relevant geospatial data collected by different federal agencies. More often analysts may be familiar with limited, but specific datasets and are often unaware of or unfamiliar with a large quantity of other useful resources. Finding airborne or satellite data useful to a natural disaster event often requires a time consuming search through web pages and data archives. The search process for the analyst could be made much more efficient and productive if a tool could go beyond a typical search engine and provide not just links to web sites but actual links to specific data relevant to the natural disaster, parse unstructured reports for useful information nuggets, as well as gather other related reports, summaries, news stories, and images. This presentation will describe a semantic aggregation tool developed to address similar problem for Earth Science researchers. This tool provides automated curation, and creates "Data Albums" to support case studies. The generated "Data Albums" are compiled collections of information related to a specific science topic or event, containing links to relevant data files (granules) from different instruments; tools and services for visualization and analysis; information about the event contained in news reports, and images or videos to supplement research analysis. An ontology-based relevancy-ranking algorithm drives the curation of relevant data sets for a given event. This tool is now being used to generate a catalog of case studies focusing on hurricanes and severe storms.

Multidisciplinary Design, Analysis, and Optimization Tool Development Using a Genetic Algorithm

NASA Technical Reports Server (NTRS)

Pak, Chan-gi; Li, Wesley

2009-01-01

Multidisciplinary design, analysis, and optimization using a genetic algorithm is being developed at the National Aeronautics and Space Administration Dryden Flight Research Center (Edwards, California) to automate analysis and design process by leveraging existing tools to enable true multidisciplinary optimization in the preliminary design stage of subsonic, transonic, supersonic, and hypersonic aircraft. This is a promising technology, but faces many challenges in large-scale, real-world application. This report describes current approaches, recent results, and challenges for multidisciplinary design, analysis, and optimization as demonstrated by experience with the Ikhana fire pod design.!

EDNA: Expert fault digraph analysis using CLIPS

NASA Technical Reports Server (NTRS)

Dixit, Vishweshwar V.

1990-01-01

Traditionally fault models are represented by trees. Recently, digraph models have been proposed (Sack). Digraph models closely imitate the real system dependencies and hence are easy to develop, validate and maintain. However, they can also contain directed cycles and analysis algorithms are hard to find. Available algorithms tend to be complicated and slow. On the other hand, the tree analysis (VGRH, Tayl) is well understood and rooted in vast research effort and analytical techniques. The tree analysis algorithms are sophisticated and orders of magnitude faster. Transformation of a digraph (cyclic) into trees (CLP, LP) is a viable approach to blend the advantages of the representations. Neither the digraphs nor the trees provide the ability to handle heuristic knowledge. An expert system, to capture the engineering knowledge, is essential. We propose an approach here, namely, expert network analysis. We combine the digraph representation and tree algorithms. The models are augmented by probabilistic and heuristic knowledge. CLIPS, an expert system shell from NASA-JSC will be used to develop a tool. The technique provides the ability to handle probabilities and heuristic knowledge. Mixed analysis, some nodes with probabilities, is possible. The tool provides graphics interface for input, query, and update. With the combined approach it is expected to be a valuable tool in the design process as well in the capture of final design knowledge.

Thermal-economic optimisation of a CHP gas turbine system by applying a fit-problem genetic algorithm

NASA Astrophysics Data System (ADS)

Ferreira, Ana C. M.; Teixeira, Senhorinha F. C. F.; Silva, Rui G.; Silva, Ângela M.

2018-04-01

Cogeneration allows the optimal use of the primary energy sources and significant reductions in carbon emissions. Its use has great potential for applications in the residential sector. This study aims to develop a methodology for thermal-economic optimisation of small-scale micro-gas turbine for cogeneration purposes, able to fulfil domestic energy needs with a thermal power out of 125 kW. A constrained non-linear optimisation model was built. The objective function is the maximisation of the annual worth from the combined heat and power, representing the balance between the annual incomes and the expenditures subject to physical and economic constraints. A genetic algorithm coded in the java programming language was developed. An optimal micro-gas turbine able to produce 103.5 kW of electrical power with a positive annual profit (i.e. 11,925 €/year) was disclosed. The investment can be recovered in 4 years and 9 months, which is less than half of system lifetime expectancy.

2010 Comprehensive Economic Development Strategy "Vision Hampton Roads"

DOT National Transportation Integrated Search

2010-02-19

The strategy is an economic development planning tool intended to aid : local governments in decision-making. The document provides an analysis : of regional and local economic conditions within the Hampton Roads : region, defined as including the te...

Advancing School-Based Interventions through Economic Analysis

ERIC Educational Resources Information Center

Olsson, Tina M.; Ferrer-Wreder, Laura; Eninger, Lilianne

2014-01-01

Commentators interested in school-based prevention programs point to the importance of economic issues for the future of prevention efforts. Many of the processes and aims of prevention science are dependent upon prevention resources. Although economic analysis is an essential tool for assessing resource use, the attention given economic analysis…

What Should We Be Teaching in Basic Economics Courses?

ERIC Educational Resources Information Center

Gwartney, James

2012-01-01

Advanced Placement economics leaves thousands of high school students with a misleading impression of modern economics. The courses fail to cover key sources of growth and prosperity, including private ownership, dynamic competition, and entrepreneurship. The tools of public choice economics are totally ignored. Government is modeled as a…

Intelligent estimation of noise and blur variances using ANN for the restoration of ultrasound images.

PubMed

Uddin, Muhammad Shahin; Halder, Kalyan Kumar; Tahtali, Murat; Lambert, Andrew J; Pickering, Mark R; Marchese, Margaret; Stuart, Iain

2016-11-01

Ultrasound (US) imaging is a widely used clinical diagnostic tool in medical imaging techniques. It is a comparatively safe, economical, painless, portable, and noninvasive real-time tool compared to the other imaging modalities. However, the image quality of US imaging is severely affected by the presence of speckle noise and blur during the acquisition process. In order to ensure a high-quality clinical diagnosis, US images must be restored by reducing their speckle noise and blur. In general, speckle noise is modeled as a multiplicative noise following a Rayleigh distribution and blur as a Gaussian function. Hereto, we propose an intelligent estimator based on artificial neural networks (ANNs) to estimate the variances of noise and blur, which, in turn, are used to obtain an image without discernible distortions. A set of statistical features computed from the image and its complex wavelet sub-bands are used as input to the ANN. In the proposed method, we solve the inverse Rayleigh function numerically for speckle reduction and use the Richardson-Lucy algorithm for de-blurring. The performance of this method is compared with that of the traditional methods by applying them to a synthetic, physical phantom and clinical data, which confirms better restoration results by the proposed method.

To connect or not to connect? Modelling the optimal degree of centralisation for wastewater infrastructures.

PubMed

Eggimann, Sven; Truffer, Bernhard; Maurer, Max

2015-11-01

The strong reliance of most utility services on centralised network infrastructures is becoming increasingly challenged by new technological advances in decentralised alternatives. However, not enough effort has been made to develop planning tools designed to address the implications of these new opportunities and to determine the optimal degree of centralisation of these infrastructures. We introduce a planning tool for sustainable network infrastructure planning (SNIP), a two-step techno-economic heuristic modelling approach based on shortest path-finding and hierarchical-agglomerative clustering algorithms to determine the optimal degree of centralisation in the field of wastewater management. This SNIP model optimises the distribution of wastewater treatment plants and the sewer network outlay relative to several cost and sewer-design parameters. Moreover, it allows us to construct alternative optimal wastewater system designs taking into account topography, economies of scale as well as the full size range of wastewater treatment plants. We quantify and confirm that the optimal degree of centralisation decreases with increasing terrain complexity and settlement dispersion while showing that the effect of the latter exceeds that of topography. Case study results for a Swiss community indicate that the calculated optimal degree of centralisation is substantially lower than the current level. Copyright © 2015 Elsevier Ltd. All rights reserved.

Modeling and prediction of copper removal from aqueous solutions by nZVI/rGO magnetic nanocomposites using ANN-GA and ANN-PSO.

PubMed

Fan, Mingyi; Hu, Jiwei; Cao, Rensheng; Xiong, Kangning; Wei, Xionghui

2017-12-21

Reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) magnetic nanocomposites were prepared and then applied in the Cu(II) removal from aqueous solutions. Scanning electron microscopy, transmission electron microscopy, X-ray photoelectron spectroscopy and superconduction quantum interference device magnetometer were performed to characterize the nZVI/rGO nanocomposites. In order to reduce the number of experiments and the economic cost, response surface methodology (RSM) combined with artificial intelligence (AI) techniques, such as artificial neural network (ANN), genetic algorithm (GA) and particle swarm optimization (PSO), has been utilized as a major tool that can model and optimize the removal processes, because a tremendous advance has recently been made on AI that may result in extensive applications. Based on RSM, ANN-GA and ANN-PSO were employed to model the Cu(II) removal process and optimize the operating parameters, e.g., operating temperature, initial pH, initial concentration and contact time. The ANN-PSO model was proven to be an effective tool for modeling and optimizing the Cu(II) removal with a low absolute error and a high removal efficiency. Furthermore, the isotherm, kinetic, thermodynamic studies and the XPS analysis were performed to explore the mechanisms of Cu(II) removal process.

Classifying Membrane Proteins in the Proteome by Using Artificial Neural Networks Based on the Preferential Parameters of Amino Acids

NASA Astrophysics Data System (ADS)

Bose, Subrata K.; Browne, Antony; Kazemian, Hassan; White, Kenneth

Membrane proteins (MPs) are large set of biological macromolecules that play a fundamental role in physiology and pathophysiology for survival. From a pharma-economical perspective, though it is the fact that MPs constitute ˜75% of possible targets for novel drugs but MPs are one of the most understudied groups of proteins in biochemical research. This is mainly because of the technical difficulties of obtaining structural information about trans-membrane regions (these are small sequences that crossways the bilayer lipid membrane). It is quite useful to predict the location of transmembrane segments down the sequence, since these are the elementary structural building blocks defining their topology. There have been several attempts over the last 20 years to develop tools for predicting membrane-spanning regions but current tools are far away from achieving a considerable reliability in prediction. This study aims to exploit the knowledge and current understanding in the field of artificial neural networks (ANNs) in particular data representation through the development of a system to identify and predict membrane-spanning regions by analysing primary amino acids sequence. In this paper we present a novel neural network (NNs) architecture and algorithms for predicting membrane spanning regions from primary amino acids sequences by using their preference parameters.

DNAseq Workflow in a Diagnostic Context and an Example of a User Friendly Implementation.

PubMed

Wolf, Beat; Kuonen, Pierre; Dandekar, Thomas; Atlan, David

2015-01-01

Over recent years next generation sequencing (NGS) technologies evolved from costly tools used by very few, to a much more accessible and economically viable technology. Through this recently gained popularity, its use-cases expanded from research environments into clinical settings. But the technical know-how and infrastructure required to analyze the data remain an obstacle for a wider adoption of this technology, especially in smaller laboratories. We present GensearchNGS, a commercial DNAseq software suite distributed by Phenosystems SA. The focus of GensearchNGS is the optimal usage of already existing infrastructure, while keeping its use simple. This is achieved through the integration of existing tools in a comprehensive software environment, as well as custom algorithms developed with the restrictions of limited infrastructures in mind. This includes the possibility to connect multiple computers to speed up computing intensive parts of the analysis such as sequence alignments. We present a typical DNAseq workflow for NGS data analysis and the approach GensearchNGS takes to implement it. The presented workflow goes from raw data quality control to the final variant report. This includes features such as gene panels and the integration of online databases, like Ensembl for annotations or Cafe Variome for variant sharing.

BioSurfDB: knowledge and algorithms to support biosurfactants and biodegradation studies

PubMed Central

Oliveira, Jorge S.; Araújo, Wydemberg; Lopes Sales, Ana Isabela; de Brito Guerra, Alaine; da Silva Araújo, Sinara Carla; de Vasconcelos, Ana Tereza Ribeiro; Agnez-Lima, Lucymara F.; Freitas, Ana Teresa

2015-01-01

Crude oil extraction, transportation and use provoke the contamination of countless ecosystems. Therefore, bioremediation through surfactants mobilization or biodegradation is an important subject, both economically and environmentally. Bioremediation research had a great boost with the recent advances in Metagenomics, as it enabled the sequencing of uncultured microorganisms providing new insights on surfactant-producing and/or oil-degrading bacteria. Many research studies are making available genomic data from unknown organisms obtained from metagenomics analysis of oil-contaminated environmental samples. These new datasets are presently demanding the development of new tools and data repositories tailored for the biological analysis in a context of bioremediation data analysis. This work presents BioSurfDB, www.biosurfdb.org, a curated relational information system integrating data from: (i) metagenomes; (ii) organisms; (iii) biodegradation relevant genes; proteins and their metabolic pathways; (iv) bioremediation experiments results, with specific pollutants treatment efficiencies by surfactant producing organisms; and (v) a biosurfactant-curated list, grouped by producing organism, surfactant name, class and reference. The main goal of this repository is to gather information on the characterization of biological compounds and mechanisms involved in biosurfactant production and/or biodegradation and make it available in a curated way and associated with a number of computational tools to support studies of genomic and metagenomic data. Database URL: www.biosurfdb.org PMID:25833955

Economic vulnerability of timber resources to forest fires.

PubMed

y Silva, Francisco Rodríguez; Molina, Juan Ramón; González-Cabán, Armando; Machuca, Miguel Ángel Herrera

2012-06-15

The temporal-spatial planning of activities for a territorial fire management program requires knowing the value of forest ecosystems. In this paper we extend to and apply the economic valuation principle to the concept of economic vulnerability and present a methodology for the economic valuation of the forest production ecosystems. The forest vulnerability is analyzed from criteria intrinsically associated to the forest characterization, and to the potential behavior of surface fires. Integrating a mapping process of fire potential and analytical valuation algorithms facilitates the implementation of fire prevention planning. The availability of cartography of economic vulnerability of the forest ecosystems is fundamental for budget optimization, and to help in the decision making process. Published by Elsevier Ltd.

The Scenario Approach to the Development of Regional Waste Management Systems (Implementation Experience in the Regions of Russia)

ERIC Educational Resources Information Center

Fomin, Eugene P.; Alekseev, Audrey A.; Fomina, Natalia E.; Dorozhkin, Vladimir E.

2016-01-01

The article illustrates a theoretical approach to scenario modeling of economic indicators of regional waste management system. The method includes a three-iterative algorithm that allows the executive authorities and investors to take a decision on logistics, bulk, technological and economic parameters of the formation of the regional long-term…

Accessing eSDO Solar Image Processing and Visualization through AstroGrid

NASA Astrophysics Data System (ADS)

Auden, E.; Dalla, S.

2008-08-01

The eSDO project is funded by the UK's Science and Technology Facilities Council (STFC) to integrate Solar Dynamics Observatory (SDO) data, algorithms, and visualization tools with the UK's Virtual Observatory project, AstroGrid. In preparation for the SDO launch in January 2009, the eSDO team has developed nine algorithms covering coronal behaviour, feature recognition, and global / local helioseismology. Each of these algorithms has been deployed as an AstroGrid Common Execution Architecture (CEA) application so that they can be included in complex VO workflows. In addition, the PLASTIC-enabled eSDO "Streaming Tool" online movie application allows users to search multi-instrument solar archives through AstroGrid web services and visualise the image data through galleries, an interactive movie viewing applet, and QuickTime movies generated on-the-fly.

Women Empowerment and Participation in Economic Activities: Indispensable Tools for Self-Reliance and Development of Nigerian Society

ERIC Educational Resources Information Center

E. N., Ekesionye; A. N., Okolo

2012-01-01

The objective of the study was to examine women empowerment and participation in economic activities as tools for self-reliance and development of the Nigerian society. Research questions and hypothesis were used to guide the study. Structured questionnaire was used as the major instrument for data collection. Copies of questionnaires were…

Dereplication, Aggregation and Scoring Tool (DAS Tool) v1.0

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

SIEBER, CHRISTIAN

Communities of uncultivated microbes are critical to ecosystem function and microorganism health, and a key objective of metagenomic studies is to analyze organism-specific metabolic pathways and reconstruct community interaction networks. This requires accurate assignment of genes to genomes, yet existing binning methods often fail to predict a reasonable number of genomes and report many bins of low quality and completeness. Furthermore, the performance of existing algorithms varies between samples and biotypes. Here, we present a dereplication, aggregation and scoring strategy, DAS Tool, that combines the strengths of a flexible set of established binning algorithms. DAS Tools applied to a constructedmore » community generated more accurate bins than any automated method. Further, when applied to samples of different complexity, including soil, natural oil seeps, and the human gut, DAS Tool recovered substantially more near-complete genomes than any single binning method alone. Included were three genomes from a novel lineage . The ability to reconstruct many near-complete genomes from metagenomics data will greatly advance genome-centric analyses of ecosystems.« less

Firefly algorithm versus genetic algorithm as powerful variable selection tools and their effect on different multivariate calibration models in spectroscopy: A comparative study

NASA Astrophysics Data System (ADS)

Attia, Khalid A. M.; Nassar, Mohammed W. I.; El-Zeiny, Mohamed B.; Serag, Ahmed

2017-01-01

For the first time, a new variable selection method based on swarm intelligence namely firefly algorithm is coupled with three different multivariate calibration models namely, concentration residual augmented classical least squares, artificial neural network and support vector regression in UV spectral data. A comparative study between the firefly algorithm and the well-known genetic algorithm was developed. The discussion revealed the superiority of using this new powerful algorithm over the well-known genetic algorithm. Moreover, different statistical tests were performed and no significant differences were found between all the models regarding their predictabilities. This ensures that simpler and faster models were obtained without any deterioration of the quality of the calibration.

EEG-based "serious" games and monitoring tools for pain management.

PubMed

Sourina, Olga; Wang, Qiang; Nguyen, Minh Khoa

2011-01-01

EEG-based "serious games" for medical applications attracted recently more attention from the research community and industry as wireless EEG reading devices became easily available on the market. EEG-based technology has been applied in anesthesiology, psychology, etc. In this paper, we proposed and developed EEG-based "serious" games and doctor's monitoring tools that could be used for pain management. As EEG signal is considered to have a fractal nature, we proposed and develop a novel spatio-temporal fractal based algorithm for brain state quantification. The algorithm is implemented with blobby visualization tools for patient monitoring and in EEG-based "serious" games. Such games could be used by patient even at home convenience for pain management as an alternative to traditional drug treatment.

FORTRAN tools

NASA Technical Reports Server (NTRS)

Presser, L.

1978-01-01

An integrated set of FORTRAN tools that are commercially available is described. The basic purpose of various tools is summarized and their economic impact highlighted. The areas addressed by these tools include: code auditing, error detection, program portability, program instrumentation, documentation, clerical aids, and quality assurance.

Fractal Landscape Algorithms for Environmental Simulations

NASA Astrophysics Data System (ADS)

Mao, H.; Moran, S.

2014-12-01

Natural science and geographical research are now able to take advantage of environmental simulations that more accurately test experimental hypotheses, resulting in deeper understanding. Experiments affected by the natural environment can benefit from 3D landscape simulations capable of simulating a variety of terrains and environmental phenomena. Such simulations can employ random terrain generation algorithms that dynamically simulate environments to test specific models against a variety of factors. Through the use of noise functions such as Perlin noise, Simplex noise, and diamond square algorithms, computers can generate simulations that model a variety of landscapes and ecosystems. This study shows how these algorithms work together to create realistic landscapes. By seeding values into the diamond square algorithm, one can control the shape of landscape. Perlin noise and Simplex noise are also used to simulate moisture and temperature. The smooth gradient created by coherent noise allows more realistic landscapes to be simulated. Terrain generation algorithms can be used in environmental studies and physics simulations. Potential studies that would benefit from simulations include the geophysical impact of flash floods or drought on a particular region and regional impacts on low lying area due to global warming and rising sea levels. Furthermore, terrain generation algorithms also serve as aesthetic tools to display landscapes (Google Earth), and simulate planetary landscapes. Hence, it can be used as a tool to assist science education. Algorithms used to generate these natural phenomena provide scientists a different approach in analyzing our world. The random algorithms used in terrain generation not only contribute to the generating the terrains themselves, but are also capable of simulating weather patterns.

Research on WNN Modeling for Gold Price Forecasting Based on Improved Artificial Bee Colony Algorithm

PubMed Central

2014-01-01

Gold price forecasting has been a hot issue in economics recently. In this work, wavelet neural network (WNN) combined with a novel artificial bee colony (ABC) algorithm is proposed for this gold price forecasting issue. In this improved algorithm, the conventional roulette selection strategy is discarded. Besides, the convergence statuses in a previous cycle of iteration are fully utilized as feedback messages to manipulate the searching intensity in a subsequent cycle. Experimental results confirm that this new algorithm converges faster than the conventional ABC when tested on some classical benchmark functions and is effective to improve modeling capacity of WNN regarding the gold price forecasting scheme. PMID:24744773

Current Challenges in Health Economic Modeling of Cancer Therapies: A Research Inquiry

PubMed Central

Miller, Jeffrey D.; Foley, Kathleen A.; Russell, Mason W.

2014-01-01

Background The demand for economic models that evaluate cancer treatments is increasing, as healthcare decision makers struggle for ways to manage their budgets while providing the best care possible to patients with cancer. Yet, after nearly 2 decades of cultivating and refining techniques for modeling the cost-effectiveness and budget impact of cancer therapies, serious methodologic and policy challenges have emerged that question the adequacy of economic modeling as a sound decision-making tool in oncology. Objectives We sought to explore some of the contentious issues associated with the development and use of oncology economic models as informative tools in current healthcare decision-making. Our objective was to draw attention to these complex pharmacoeconomic concerns and to promote discussion within the oncology and health economics research communities. Methods Using our combined expertise in health economics research and economic modeling, we structured our inquiry around the following 4 questions: (1) Are economic models adequately addressing questions relevant to oncology decision makers; (2) What are the methodologic limitations of oncology economic models; (3) What guidelines are followed for developing oncology economic models; and (4) Is the evolution of oncology economic modeling keeping pace with treatment innovation? Within the context of each of these questions, we discuss issues related to the technical limitations of oncology modeling, the availability of adequate data for developing models, and the problems with how modeling analyses and results are presented and interpreted. Discussion There is general acceptance that economic models are good, essential tools for decision-making, but the practice of oncology and its rapidly evolving technologies present unique challenges that make assessing and demonstrating value especially complex. There is wide latitude for improvement in oncology modeling methodologies and how model results are presented and interpreted. Conclusion Complex technical and data availability issues with oncology economic modeling pose serious concerns that need to be addressed. It is our hope that this article will provide a framework to guide future discourse on this important topic. PMID:24991399

Current challenges in health economic modeling of cancer therapies: a research inquiry.

PubMed

Miller, Jeffrey D; Foley, Kathleen A; Russell, Mason W

2014-05-01

The demand for economic models that evaluate cancer treatments is increasing, as healthcare decision makers struggle for ways to manage their budgets while providing the best care possible to patients with cancer. Yet, after nearly 2 decades of cultivating and refining techniques for modeling the cost-effectiveness and budget impact of cancer therapies, serious methodologic and policy challenges have emerged that question the adequacy of economic modeling as a sound decision-making tool in oncology. We sought to explore some of the contentious issues associated with the development and use of oncology economic models as informative tools in current healthcare decision-making. Our objective was to draw attention to these complex pharmacoeconomic concerns and to promote discussion within the oncology and health economics research communities. Using our combined expertise in health economics research and economic modeling, we structured our inquiry around the following 4 questions: (1) Are economic models adequately addressing questions relevant to oncology decision makers; (2) What are the methodologic limitations of oncology economic models; (3) What guidelines are followed for developing oncology economic models; and (4) Is the evolution of oncology economic modeling keeping pace with treatment innovation? Within the context of each of these questions, we discuss issues related to the technical limitations of oncology modeling, the availability of adequate data for developing models, and the problems with how modeling analyses and results are presented and interpreted. There is general acceptance that economic models are good, essential tools for decision-making, but the practice of oncology and its rapidly evolving technologies present unique challenges that make assessing and demonstrating value especially complex. There is wide latitude for improvement in oncology modeling methodologies and how model results are presented and interpreted. Complex technical and data availability issues with oncology economic modeling pose serious concerns that need to be addressed. It is our hope that this article will provide a framework to guide future discourse on this important topic.

Prediction of flood abnormalities for improved public safety using a modified adaptive neuro-fuzzy inference system.

PubMed

Aqil, M; Kita, I; Yano, A; Nishiyama, S

2006-01-01

It is widely accepted that an efficient flood alarm system may significantly improve public safety and mitigate economical damages caused by inundations. In this paper, a modified adaptive neuro-fuzzy system is proposed to modify the traditional neuro-fuzzy model. This new method employs a rule-correction based algorithm to replace the error back propagation algorithm that is employed by the traditional neuro-fuzzy method in backward pass calculation. The final value obtained during the backward pass calculation using the rule-correction algorithm is then considered as a mapping function of the learning mechanism of the modified neuro-fuzzy system. Effectiveness of the proposed identification technique is demonstrated through a simulation study on the flood series of the Citarum River in Indonesia. The first four-year data (1987 to 1990) was used for model training/calibration, while the other remaining data (1991 to 2002) was used for testing the model. The number of antecedent flows that should be included in the input variables was determined by two statistical methods, i.e. autocorrelation and partial autocorrelation between the variables. Performance accuracy of the model was evaluated in terms of two statistical indices, i.e. mean average percentage error and root mean square error. The algorithm was developed in a decision support system environment in order to enable users to process the data. The decision support system is found to be useful due to its interactive nature, flexibility in approach, and evolving graphical features, and can be adopted for any similar situation to predict the streamflow. The main data processing includes gauging station selection, input generation, lead-time selection/generation, and length of prediction. This program enables users to process the flood data, to train/test the model using various input options, and to visualize results. The program code consists of a set of files, which can be modified as well to match other purposes. This program may also serve as a tool for real-time flood monitoring and process control. The results indicate that the modified neuro-fuzzy model applied to the flood prediction seems to have reached encouraging results for the river basin under examination. The comparison of the modified neuro-fuzzy predictions with the observed data was satisfactory, where the error resulted from the testing period was varied between 2.632% and 5.560%. Thus, this program may also serve as a tool for real-time flood monitoring and process control.

Siting and sizing of distributed generators based on improved simulated annealing particle swarm optimization.

PubMed

Su, Hongsheng

2017-12-18

Distributed power grids generally contain multiple diverse types of distributed generators (DGs). Traditional particle swarm optimization (PSO) and simulated annealing PSO (SA-PSO) algorithms have some deficiencies in site selection and capacity determination of DGs, such as slow convergence speed and easily falling into local trap. In this paper, an improved SA-PSO (ISA-PSO) algorithm is proposed by introducing crossover and mutation operators of genetic algorithm (GA) into SA-PSO, so that the capabilities of the algorithm are well embodied in global searching and local exploration. In addition, diverse types of DGs are made equivalent to four types of nodes in flow calculation by the backward or forward sweep method, and reactive power sharing principles and allocation theory are applied to determine initial reactive power value and execute subsequent correction, thus providing the algorithm a better start to speed up the convergence. Finally, a mathematical model of the minimum economic cost is established for the siting and sizing of DGs under the location and capacity uncertainties of each single DG. Its objective function considers investment and operation cost of DGs, grid loss cost, annual purchase electricity cost, and environmental pollution cost, and the constraints include power flow, bus voltage, conductor current, and DG capacity. Through applications in an IEEE33-node distributed system, it is found that the proposed method can achieve desirable economic efficiency and safer voltage level relative to traditional PSO and SA-PSO algorithms, and is a more effective planning method for the siting and sizing of DGs in distributed power grids.

Collision of Physics and Software in the Monte Carlo Application Toolkit (MCATK)

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

Sweezy, Jeremy Ed

2016-01-21

The topic is presented in a series of slides organized as follows: MCATK overview, development strategy, available algorithms, problem modeling (sources, geometry, data, tallies), parallelism, miscellaneous tools/features, example MCATK application, recent areas of research, and summary and future work. MCATK is a C++ component-based Monte Carlo neutron-gamma transport software library with continuous energy neutron and photon transport. Designed to build specialized applications and to provide new functionality in existing general-purpose Monte Carlo codes like MCNP, it reads ACE formatted nuclear data generated by NJOY. The motivation behind MCATK was to reduce costs. MCATK physics involves continuous energy neutron & gammamore » transport with multi-temperature treatment, static eigenvalue (k eff and α) algorithms, time-dependent algorithm, and fission chain algorithms. MCATK geometry includes mesh geometries and solid body geometries. MCATK provides verified, unit-test Monte Carlo components, flexibility in Monte Carlo application development, and numerous tools such as geometry and cross section plotters.« less

24 CFR 4100.1 - Functions and activities.

Code of Federal Regulations, 2013 CFR

2013-04-01

... increased investment or restructured mortgages to improve the economic viability of the buildings and to...) Neighborhood economic development and commercial revitalization strategies. The Corporation's neighborhood economic development and commercial revitalization strategies offer NHSs a variety of tools designed to...

24 CFR 4100.1 - Functions and activities.

Code of Federal Regulations, 2014 CFR

2014-04-01

... increased investment or restructured mortgages to improve the economic viability of the buildings and to...) Neighborhood economic development and commercial revitalization strategies. The Corporation's neighborhood economic development and commercial revitalization strategies offer NHSs a variety of tools designed to...

24 CFR 4100.1 - Functions and activities.

Code of Federal Regulations, 2011 CFR

2011-04-01

... increased investment or restructured mortgages to improve the economic viability of the buildings and to...) Neighborhood economic development and commercial revitalization strategies. The Corporation's neighborhood economic development and commercial revitalization strategies offer NHSs a variety of tools designed to...

24 CFR 4100.1 - Functions and activities.

Code of Federal Regulations, 2010 CFR

2010-04-01

... increased investment or restructured mortgages to improve the economic viability of the buildings and to...) Neighborhood economic development and commercial revitalization strategies. The Corporation's neighborhood economic development and commercial revitalization strategies offer NHSs a variety of tools designed to...

24 CFR 4100.1 - Functions and activities.

Code of Federal Regulations, 2012 CFR

2012-04-01

... increased investment or restructured mortgages to improve the economic viability of the buildings and to...) Neighborhood economic development and commercial revitalization strategies. The Corporation's neighborhood economic development and commercial revitalization strategies offer NHSs a variety of tools designed to...

3D TRUMP - A GBI launch window tool

NASA Astrophysics Data System (ADS)

Karels, Steven N.; Hancock, John; Matchett, Gary

3D TRUMP is a novel GPS and communicatons-link software analysis tool developed for the SDIO's Ground-Based Interceptor (GBI) program. 3D TRUMP uses a computationally efficient analysis tool which provides key GPS-based performance measures for an entire GBI mission's reentry vehicle and interceptor trajectories. Algorithms and sample outputs are presented.

The center for causal discovery of biomedical knowledge from big data

PubMed Central

Bahar, Ivet; Becich, Michael J; Benos, Panayiotis V; Berg, Jeremy; Espino, Jeremy U; Glymour, Clark; Jacobson, Rebecca Crowley; Kienholz, Michelle; Lee, Adrian V; Lu, Xinghua; Scheines, Richard

2015-01-01

The Big Data to Knowledge (BD2K) Center for Causal Discovery is developing and disseminating an integrated set of open source tools that support causal modeling and discovery of biomedical knowledge from large and complex biomedical datasets. The Center integrates teams of biomedical and data scientists focused on the refinement of existing and the development of new constraint-based and Bayesian algorithms based on causal Bayesian networks, the optimization of software for efficient operation in a supercomputing environment, and the testing of algorithms and software developed using real data from 3 representative driving biomedical projects: cancer driver mutations, lung disease, and the functional connectome of the human brain. Associated training activities provide both biomedical and data scientists with the knowledge and skills needed to apply and extend these tools. Collaborative activities with the BD2K Consortium further advance causal discovery tools and integrate tools and resources developed by other centers. PMID:26138794

Computational mechanics analysis tools for parallel-vector supercomputers

NASA Technical Reports Server (NTRS)

Storaasli, Olaf O.; Nguyen, Duc T.; Baddourah, Majdi; Qin, Jiangning

1993-01-01

Computational algorithms for structural analysis on parallel-vector supercomputers are reviewed. These parallel algorithms, developed by the authors, are for the assembly of structural equations, 'out-of-core' strategies for linear equation solution, massively distributed-memory equation solution, unsymmetric equation solution, general eigensolution, geometrically nonlinear finite element analysis, design sensitivity analysis for structural dynamics, optimization search analysis and domain decomposition. The source code for many of these algorithms is available.

Ursgal, Universal Python Module Combining Common Bottom-Up Proteomics Tools for Large-Scale Analysis.

PubMed

Kremer, Lukas P M; Leufken, Johannes; Oyunchimeg, Purevdulam; Schulze, Stefan; Fufezan, Christian

2016-03-04

Proteomics data integration has become a broad field with a variety of programs offering innovative algorithms to analyze increasing amounts of data. Unfortunately, this software diversity leads to many problems as soon as the data is analyzed using more than one algorithm for the same task. Although it was shown that the combination of multiple peptide identification algorithms yields more robust results, it is only recently that unified approaches are emerging; however, workflows that, for example, aim to optimize search parameters or that employ cascaded style searches can only be made accessible if data analysis becomes not only unified but also and most importantly scriptable. Here we introduce Ursgal, a Python interface to many commonly used bottom-up proteomics tools and to additional auxiliary programs. Complex workflows can thus be composed using the Python scripting language using a few lines of code. Ursgal is easily extensible, and we have made several database search engines (X!Tandem, OMSSA, MS-GF+, Myrimatch, MS Amanda), statistical postprocessing algorithms (qvality, Percolator), and one algorithm that combines statistically postprocessed outputs from multiple search engines ("combined FDR") accessible as an interface in Python. Furthermore, we have implemented a new algorithm ("combined PEP") that combines multiple search engines employing elements of "combined FDR", PeptideShaker, and Bayes' theorem.

Phase retrieval based wavefront sensing experimental implementation and wavefront sensing accuracy calibration

NASA Astrophysics Data System (ADS)

Mao, Heng; Wang, Xiao; Zhao, Dazun

2009-05-01

As a wavefront sensing (WFS) tool, Baseline algorithm, which is classified as the iterative-transform algorithm of phase retrieval, estimates the phase distribution at pupil from some known PSFs at defocus planes. By using multiple phase diversities and appropriate phase unwrapping methods, this algorithm can accomplish reliable unique solution and high dynamic phase measurement. In the paper, a Baseline algorithm based wavefront sensing experiment with modification of phase unwrapping has been implemented, and corresponding Graphical User Interfaces (GUI) software has also been given. The adaptability and repeatability of Baseline algorithm have been validated in experiments. Moreover, referring to the ZYGO interferometric results, the WFS accuracy of this algorithm has been exactly calibrated.

How To Steal From Nature

DTIC Science & Technology

2004-07-23

Field Principal TRIZ Tools TRIZ offers a comprehensive series of creativity and innovation tools, methods and strategies. The main tools include...Algorithm for Inventive Problem Solving) The tools shown in red can use information from nature. Hence TRIZ can drive biomimetics by organising and...targeting information. Biomimetics can drive TRIZ with new “patents”. Lessons • It’s possible to learn from nature • Huge changes in context are

Automated Design Tools for Integrated Mixed-Signal Microsystems (NeoCAD)

DTIC Science & Technology

2005-02-01

method, Model Order Reduction (MOR) tools, system-level, mixed-signal circuit synthesis and optimization tools, and parsitic extraction tools. A unique...Mission Area: Command and Control mixed signal circuit simulation parasitic extraction time-domain simulation IC design flow model order reduction... Extraction 1.2 Overall Program Milestones CHAPTER 2 FAST TIME DOMAIN MIXED-SIGNAL CIRCUIT SIMULATION 2.1 HAARSPICE Algorithms 2.1.1 Mathematical Background

2D and 3D Method of Characteristic Tools for Complex Nozzle Development

NASA Technical Reports Server (NTRS)

Rice, Tharen

2003-01-01

This report details the development of a 2D and 3D Method of Characteristic (MOC) tool for the design of complex nozzle geometries. These tools are GUI driven and can be run on most Windows-based platforms. The report provides a user's manual for these tools as well as explains the mathematical algorithms used in the MOC solutions.

Autism spectrum disorders and fetal hypoxia in a population-based cohort: Accounting for missing exposures via Estimation-Maximization algorithm

PubMed Central

2011-01-01

Background Autism spectrum disorders (ASD) are associated with complications of pregnancy that implicate fetal hypoxia (FH); the excess of ASD in male gender is poorly understood. We tested the hypothesis that risk of ASD is related to fetal hypoxia and investigated whether this effect is greater among males. Methods Provincial delivery records (PDR) identified the cohort of all 218,890 singleton live births in the province of Alberta, Canada, between 01-01-98 and 12-31-04. These were followed-up for ASD via ICD-9 diagnostic codes assigned by physician billing until 03-31-08. Maternal and obstetric risk factors, including FH determined from blood tests of acidity (pH), were extracted from PDR. The binary FH status was missing in approximately half of subjects. Assuming that characteristics of mothers and pregnancies would be correlated with FH, we used an Estimation-Maximization algorithm to estimate HF-ASD association, allowing for both missing-at-random (MAR) and specific not-missing-at-random (NMAR) mechanisms. Results Data indicated that there was excess risk of ASD among males who were hypoxic at birth, not materially affected by adjustment for potential confounding due to birth year and socio-economic status: OR 1.13, 95%CI: 0.96, 1.33 (MAR assumption). Limiting analysis to full-term males, the adjusted OR under specific NMAR assumptions spanned 95%CI of 1.0 to 1.6. Conclusion Our results are consistent with a weak effect of fetal hypoxia on risk of ASD among males. E-M algorithm is an efficient and flexible tool for modeling missing data in the studied setting. PMID:21208442

HUMAN DECISIONS AND MACHINE PREDICTIONS.

PubMed

Kleinberg, Jon; Lakkaraju, Himabindu; Leskovec, Jure; Ludwig, Jens; Mullainathan, Sendhil

2018-02-01

Can machine learning improve human decision making? Bail decisions provide a good test case. Millions of times each year, judges make jail-or-release decisions that hinge on a prediction of what a defendant would do if released. The concreteness of the prediction task combined with the volume of data available makes this a promising machine-learning application. Yet comparing the algorithm to judges proves complicated. First, the available data are generated by prior judge decisions. We only observe crime outcomes for released defendants, not for those judges detained. This makes it hard to evaluate counterfactual decision rules based on algorithmic predictions. Second, judges may have a broader set of preferences than the variable the algorithm predicts; for instance, judges may care specifically about violent crimes or about racial inequities. We deal with these problems using different econometric strategies, such as quasi-random assignment of cases to judges. Even accounting for these concerns, our results suggest potentially large welfare gains: one policy simulation shows crime reductions up to 24.7% with no change in jailing rates, or jailing rate reductions up to 41.9% with no increase in crime rates. Moreover, all categories of crime, including violent crimes, show reductions; and these gains can be achieved while simultaneously reducing racial disparities. These results suggest that while machine learning can be valuable, realizing this value requires integrating these tools into an economic framework: being clear about the link between predictions and decisions; specifying the scope of payoff functions; and constructing unbiased decision counterfactuals. JEL Codes: C10 (Econometric and statistical methods and methodology), C55 (Large datasets: Modeling and analysis), K40 (Legal procedure, the legal system, and illegal behavior).

HUMAN DECISIONS AND MACHINE PREDICTIONS*

PubMed Central

Kleinberg, Jon; Lakkaraju, Himabindu; Leskovec, Jure; Ludwig, Jens; Mullainathan, Sendhil

2018-01-01

Can machine learning improve human decision making? Bail decisions provide a good test case. Millions of times each year, judges make jail-or-release decisions that hinge on a prediction of what a defendant would do if released. The concreteness of the prediction task combined with the volume of data available makes this a promising machine-learning application. Yet comparing the algorithm to judges proves complicated. First, the available data are generated by prior judge decisions. We only observe crime outcomes for released defendants, not for those judges detained. This makes it hard to evaluate counterfactual decision rules based on algorithmic predictions. Second, judges may have a broader set of preferences than the variable the algorithm predicts; for instance, judges may care specifically about violent crimes or about racial inequities. We deal with these problems using different econometric strategies, such as quasi-random assignment of cases to judges. Even accounting for these concerns, our results suggest potentially large welfare gains: one policy simulation shows crime reductions up to 24.7% with no change in jailing rates, or jailing rate reductions up to 41.9% with no increase in crime rates. Moreover, all categories of crime, including violent crimes, show reductions; and these gains can be achieved while simultaneously reducing racial disparities. These results suggest that while machine learning can be valuable, realizing this value requires integrating these tools into an economic framework: being clear about the link between predictions and decisions; specifying the scope of payoff functions; and constructing unbiased decision counterfactuals. JEL Codes: C10 (Econometric and statistical methods and methodology), C55 (Large datasets: Modeling and analysis), K40 (Legal procedure, the legal system, and illegal behavior) PMID:29755141

Knotty: Efficient and Accurate Prediction of Complex RNA Pseudoknot Structures.

PubMed

Jabbari, Hosna; Wark, Ian; Montemagno, Carlo; Will, Sebastian

2018-06-01

The computational prediction of RNA secondary structure by free energy minimization has become an important tool in RNA research. However in practice, energy minimization is mostly limited to pseudoknot-free structures or rather simple pseudoknots, not covering many biologically important structures such as kissing hairpins. Algorithms capable of predicting sufficiently complex pseudoknots (for sequences of length n) used to have extreme complexities, e.g. Pknots (Rivas and Eddy, 1999) has O(n6) time and O(n4) space complexity. The algorithm CCJ (Chen et al., 2009) dramatically improves the asymptotic run time for predicting complex pseudoknots (handling almost all relevant pseudoknots, while being slightly less general than Pknots), but this came at the cost of large constant factors in space and time, which strongly limited its practical application (∼200 bases already require 256GB space). We present a CCJ-type algorithm, Knotty, that handles the same comprehensive pseudoknot class of structures as CCJ with improved space complexity of Θ(n3 + Z)-due to the applied technique of sparsification, the number of "candidates", Z, appears to grow significantly slower than n4 on our benchmark set (which include pseudoknotted RNAs up to 400 nucleotides). In terms of run time over this benchmark, Knotty clearly outperforms Pknots and the original CCJ implementation, CCJ 1.0; Knotty's space consumption fundamentally improves over CCJ 1.0, being on a par with the space-economic Pknots. By comparing to CCJ 2.0, our unsparsified Knotty variant, we demonstrate the isolated effect of sparsification. Moreover, Knotty employs the state-of-the-art energy model of "HotKnots DP09", which results in superior prediction accuracy over Pknots. Our software is available at https://github.com/HosnaJabbari/Knotty. will@tbi.unvie.ac.at. Supplementary data are available at Bioinformatics online.

Data Mining and Optimization Tools for Developing Engine Parameters Tools

NASA Technical Reports Server (NTRS)

Dhawan, Atam P.

1998-01-01

This project was awarded for understanding the problem and developing a plan for Data Mining tools for use in designing and implementing an Engine Condition Monitoring System. From the total budget of $5,000, Tricia and I studied the problem domain for developing ail Engine Condition Monitoring system using the sparse and non-standardized datasets to be available through a consortium at NASA Lewis Research Center. We visited NASA three times to discuss additional issues related to dataset which was not made available to us. We discussed and developed a general framework of data mining and optimization tools to extract useful information from sparse and non-standard datasets. These discussions lead to the training of Tricia Erhardt to develop Genetic Algorithm based search programs which were written in C++ and used to demonstrate the capability of GA algorithm in searching an optimal solution in noisy datasets. From the study and discussion with NASA LERC personnel, we then prepared a proposal, which is being submitted to NASA for future work for the development of data mining algorithms for engine conditional monitoring. The proposed set of algorithm uses wavelet processing for creating multi-resolution pyramid of the data for GA based multi-resolution optimal search. Wavelet processing is proposed to create a coarse resolution representation of data providing two advantages in GA based search: 1. We will have less data to begin with to make search sub-spaces. 2. It will have robustness against the noise because at every level of wavelet based decomposition, we will be decomposing the signal into low pass and high pass filters.

The application of dynamic programming in production planning

NASA Astrophysics Data System (ADS)

Wu, Run

2017-05-01

Nowadays, with the popularity of the computers, various industries and fields are widely applying computer information technology, which brings about huge demand for a variety of application software. In order to develop software meeting various needs with most economical cost and best quality, programmers must design efficient algorithms. A superior algorithm can not only soul up one thing, but also maximize the benefits and generate the smallest overhead. As one of the common algorithms, dynamic programming algorithms are used to solving problems with some sort of optimal properties. When solving problems with a large amount of sub-problems that needs repetitive calculations, the ordinary sub-recursive method requires to consume exponential time, and dynamic programming algorithm can reduce the time complexity of the algorithm to the polynomial level, according to which we can conclude that dynamic programming algorithm is a very efficient compared to other algorithms reducing the computational complexity and enriching the computational results. In this paper, we expound the concept, basic elements, properties, core, solving steps and difficulties of the dynamic programming algorithm besides, establish the dynamic programming model of the production planning problem.

What Does the Impact Statement Say About Economic Impacts? Coping With Growth.

ERIC Educational Resources Information Center

Faas, Ronald C.

Local public officials may be confronted with the use of economic multipliers when asked to react to project proposals, to environmental impact statements, or to other studies containing economic impact analyses. Employment, income, and output multipliers are tools for estimating private sector economic impacts of a new development within a local…

Restoration and economics: A union waiting to happen?

Treesearch

Alicia S.T. Robbins; Jean M. Daniels

2012-01-01

In this article, our objective is to introduce economics as a tool for the planning, prioritization, and evaluation of restoration projects. Studies that develop economic estimates of public values for ecological restoration employ methods that may be unfamiliar to practitioners. We hope to address this knowledge gap by describing economic concepts in the context of...

About JEDI | Jobs and Economic Development Impact Models | NREL

Science.gov Websites

About JEDI About JEDI The Jobs and Economic Development Impact (JEDI) models are user-friendly screening tools that estimate the economic impacts of constructing and operating power plants, fuel from industry norms), JEDI estimates the number of jobs and economic impacts to a local area that can

Economic Evaluation of New Technologies in Higher Education. N.I.E. Report Phase 1, Volume 6 of 7.

ERIC Educational Resources Information Center

Heriot-Watt Univ., Edinburgh (Scotland). Esmee Fairbairn Economics Research Centre.

Part of a series of instructional packages for use in college level economics courses, the document contains nine microeconomics chapters. Chapter I, "Economic Concepts, Issues, and Tools," discusses scarcity and choice; preferences, resources, exchange, and economic efficiency; marginal analysis and opportunity cost; and different economic…

Energy and Economics. [Revised Edition.

ERIC Educational Resources Information Center

Walstad, William; Gleason, Joyce

This unit is designed to provide high school students with an introduction to topics of energy and economics. A basic premise of the unit is that energy issues and economics are interrelated. It is believed that the application of basic economic concepts to energy issues can provide students with the tools to improve their analysis of problems and…

2010 Comprehensive Economic Development Strategy "Vision Hampton Roads" : Executive Summary

DOT National Transportation Integrated Search

2010-02-19

The strategy is an economic development planning tool intended to aid local governments in decision-making. The document provides an analysis of regional and local economic conditions within the Hampton Roads region, defined as including the ten (10)...

Workshop: Economic Impacts of Aquatic Invasive Species Workshop (2005)

EPA Pesticide Factsheets

EPA's National Center for Environmental Economics and Office of Water jointly hosted the Economic Impacts of Aquatic Invasive Species Workshop on July 20-21, 2005 in DC. Goal to examine conceptual frameworks and tools to value invasive species impacts.

Googling DNA sequences on the World Wide Web.

PubMed

Hajibabaei, Mehrdad; Singer, Gregory A C

2009-11-10

New web-based technologies provide an excellent opportunity for sharing and accessing information and using web as a platform for interaction and collaboration. Although several specialized tools are available for analyzing DNA sequence information, conventional web-based tools have not been utilized for bioinformatics applications. We have developed a novel algorithm and implemented it for searching species-specific genomic sequences, DNA barcodes, by using popular web-based methods such as Google. We developed an alignment independent character based algorithm based on dividing a sequence library (DNA barcodes) and query sequence to words. The actual search is conducted by conventional search tools such as freely available Google Desktop Search. We implemented our algorithm in two exemplar packages. We developed pre and post-processing software to provide customized input and output services, respectively. Our analysis of all publicly available DNA barcode sequences shows a high accuracy as well as rapid results. Our method makes use of conventional web-based technologies for specialized genetic data. It provides a robust and efficient solution for sequence search on the web. The integration of our search method for large-scale sequence libraries such as DNA barcodes provides an excellent web-based tool for accessing this information and linking it to other available categories of information on the web.

Development of method for quantifying essential tremor using a small optical device.

PubMed

Chen, Kai-Hsiang; Lin, Po-Chieh; Chen, Yu-Jung; Yang, Bing-Shiang; Lin, Chin-Hsien

2016-06-15

Clinical assessment scales are the most common means used by physicians to assess tremor severity. Some scientific tools that may be able to replace these scales to objectively assess the severity, such as accelerometers, digital tablets, electromyography (EMG) measurement devices, and motion capture cameras, are currently available. However, most of the operational modes of these tools are relatively complex or are only able to capture part of the clinical information; furthermore, using these tools is sometimes time consuming. Currently, there is no tool available for automatically quantifying tremor severity in clinical environments. We aimed to develop a rapid, objective, and quantitative system for measuring the severity of finger tremor using a small portable optical device (Leap Motion). A single test took 15s to conduct, and three algorithms were proposed to quantify the severity of finger tremor. The system was tested with four patients diagnosed with essential tremor. The proposed algorithms were able to quantify different characteristics of tremor in clinical environments, and could be used as references for future clinical assessments. A portable, easy-to-use, small-sized, and noncontact device (Leap Motion) was used to clinically detect and record finger movement, and three algorithms were proposed to describe tremor amplitudes. Copyright © 2016 Elsevier B.V. All rights reserved.

Path planning and parameter optimization of uniform removal in active feed polishing

NASA Astrophysics Data System (ADS)

Liu, Jian; Wang, Shaozhi; Zhang, Chunlei; Zhang, Linghua; Chen, Huanan

2015-06-01

A high-quality ultrasmooth surface is demanded in short-wave optical systems. However, the existing polishing methods have difficulties meeting the requirement on spherical or aspheric surfaces. As a new kind of small tool polishing method, active feed polishing (AFP) could attain a surface roughness of less than 0.3 nm (RMS) on spherical elements, although AFP may magnify the residual figure error or mid-frequency error. The purpose of this work is to propose an effective algorithm to realize uniform removal of the surface in the processing. At first, the principle of the AFP and the mechanism of the polishing machine are introduced. In order to maintain the processed figure error, a variable pitch spiral path planning algorithm and the dwell time-solving model are proposed. For suppressing the possible mid-frequency error, the uniformity of the synthesis tool path, which is generated by an arbitrary point at the polishing tool bottom, is analyzed and evaluated, and the angular velocity ratio of the tool spinning motion to the revolution motion is optimized. Finally, an experiment is conducted on a convex spherical surface and an ultrasmooth surface is finally acquired. In conclusion, a high-quality ultrasmooth surface can be successfully obtained with little degradation of the figure and mid-frequency errors by the algorithm.

Developments in the CCP4 molecular-graphics project.

PubMed

Potterton, Liz; McNicholas, Stuart; Krissinel, Eugene; Gruber, Jan; Cowtan, Kevin; Emsley, Paul; Murshudov, Garib N; Cohen, Serge; Perrakis, Anastassis; Noble, Martin

2004-12-01

Progress towards structure determination that is both high-throughput and high-value is dependent on the development of integrated and automatic tools for electron-density map interpretation and for the analysis of the resulting atomic models. Advances in map-interpretation algorithms are extending the resolution regime in which fully automatic tools can work reliably, but at present human intervention is required to interpret poor regions of macromolecular electron density, particularly where crystallographic data is only available to modest resolution [for example, I/sigma(I) < 2.0 for minimum resolution 2.5 A]. In such cases, a set of manual and semi-manual model-building molecular-graphics tools is needed. At the same time, converting the knowledge encapsulated in a molecular structure into understanding is dependent upon visualization tools, which must be able to communicate that understanding to others by means of both static and dynamic representations. CCP4 mg is a program designed to meet these needs in a way that is closely integrated with the ongoing development of CCP4 as a program suite suitable for both low- and high-intervention computational structural biology. As well as providing a carefully designed user interface to advanced algorithms of model building and analysis, CCP4 mg is intended to present a graphical toolkit to developers of novel algorithms in these fields.

The potential of genetic algorithms for conceptual design of rotor systems

NASA Technical Reports Server (NTRS)

Crossley, William A.; Wells, Valana L.; Laananen, David H.

1993-01-01

The capabilities of genetic algorithms as a non-calculus based, global search method make them potentially useful in the conceptual design of rotor systems. Coupling reasonably simple analysis tools to the genetic algorithm was accomplished, and the resulting program was used to generate designs for rotor systems to match requirements similar to those of both an existing helicopter and a proposed helicopter design. This provides a comparison with the existing design and also provides insight into the potential of genetic algorithms in design of new rotors.

New Graph Models and Algorithms for Detecting Salient Structures from Cluttered Images

DTIC Science & Technology

2010-02-24

Development of graph models and algorithms to detect boundaries that show certain levels of symmetry, an important geometric property of many...Bookstein. Morphometric tools for landmark data. Cambridge University Press, 1991. [8] F. L. Bookstein. Principal warps: Thin-plate splines and the

Reliability model generator specification

NASA Technical Reports Server (NTRS)

Cohen, Gerald C.; Mccann, Catherine

1990-01-01

The Reliability Model Generator (RMG), a program which produces reliability models from block diagrams for ASSIST, the interface for the reliability evaluation tool SURE is described. An account is given of motivation for RMG and the implemented algorithms are discussed. The appendices contain the algorithms and two detailed traces of examples.

Using Genetic Algorithm and MODFLOW to Characterize Aquifer System of Northwest Florida

EPA Science Inventory

By integrating Genetic Algorithm and MODFLOW2005, an optimizing tool is developed to characterize the aquifer system of Region II, Northwest Florida. The history and the newest available observation data of the aquifer system is fitted automatically by using the numerical model c...

RNA motif search with data-driven element ordering.

PubMed

Rampášek, Ladislav; Jimenez, Randi M; Lupták, Andrej; Vinař, Tomáš; Brejová, Broňa

2016-05-18

In this paper, we study the problem of RNA motif search in long genomic sequences. This approach uses a combination of sequence and structure constraints to uncover new distant homologs of known functional RNAs. The problem is NP-hard and is traditionally solved by backtracking algorithms. We have designed a new algorithm for RNA motif search and implemented a new motif search tool RNArobo. The tool enhances the RNAbob descriptor language, allowing insertions in helices, which enables better characterization of ribozymes and aptamers. A typical RNA motif consists of multiple elements and the running time of the algorithm is highly dependent on their ordering. By approaching the element ordering problem in a principled way, we demonstrate more than 100-fold speedup of the search for complex motifs compared to previously published tools. We have developed a new method for RNA motif search that allows for a significant speedup of the search of complex motifs that include pseudoknots. Such speed improvements are crucial at a time when the rate of DNA sequencing outpaces growth in computing. RNArobo is available at http://compbio.fmph.uniba.sk/rnarobo .

H2LIFT: global navigation simulation ship tracking and WMD detection in the maritime domain

NASA Astrophysics Data System (ADS)

Wyffels, Kevin

2007-04-01

This paper presents initial results for a tracking simulation of multiple maritime vehicles for use in a data fusion program detecting Weapons of Mass Destruction (WMD). This simulation supports a fusion algorithm (H2LIFT) for collecting and analyzing data providing a heuristic analysis tool for detecting weapons of mass destruction in the maritime domain. Tools required to develop a navigational simulation fitting a set of project objectives are introduced for integration into the H2LIFT algorithm. Emphasis is placed on the specific requirements of the H2LIFT project, however the basic equations, algorithms, and methodologies can be used as tools in a variety of scenario simulations. Discussion will be focused on track modeling (e.g. position tracking of ships), navigational techniques, WMD detection, and simulation of these models using Matlab and Simulink. Initial results provide absolute ship position data for a given multi-ship maritime scenario with random generation of a given ship containing a WMD. Required coordinate systems, conversions between coordinate systems, Earth modeling techniques, and navigational conventions and techniques are introduced for development of the simulations.

A Consensus Method for the Prediction of ‘Aggregation-Prone’ Peptides in Globular Proteins

PubMed Central

Tsolis, Antonios C.; Papandreou, Nikos C.; Iconomidou, Vassiliki A.; Hamodrakas, Stavros J.

2013-01-01

The purpose of this work was to construct a consensus prediction algorithm of ‘aggregation-prone’ peptides in globular proteins, combining existing tools. This allows comparison of the different algorithms and the production of more objective and accurate results. Eleven (11) individual methods are combined and produce AMYLPRED2, a publicly, freely available web tool to academic users (http://biophysics.biol.uoa.gr/AMYLPRED2), for the consensus prediction of amyloidogenic determinants/‘aggregation-prone’ peptides in proteins, from sequence alone. The performance of AMYLPRED2 indicates that it functions better than individual aggregation-prediction algorithms, as perhaps expected. AMYLPRED2 is a useful tool for identifying amyloid-forming regions in proteins that are associated with several conformational diseases, called amyloidoses, such as Altzheimer's, Parkinson's, prion diseases and type II diabetes. It may also be useful for understanding the properties of protein folding and misfolding and for helping to the control of protein aggregation/solubility in biotechnology (recombinant proteins forming bacterial inclusion bodies) and biotherapeutics (monoclonal antibodies and biopharmaceutical proteins). PMID:23326595

Feature-based classification of amino acid substitutions outside conserved functional protein domains.

PubMed

Gemovic, Branislava; Perovic, Vladimir; Glisic, Sanja; Veljkovic, Nevena

2013-01-01

There are more than 500 amino acid substitutions in each human genome, and bioinformatics tools irreplaceably contribute to determination of their functional effects. We have developed feature-based algorithm for the detection of mutations outside conserved functional domains (CFDs) and compared its classification efficacy with the most commonly used phylogeny-based tools, PolyPhen-2 and SIFT. The new algorithm is based on the informational spectrum method (ISM), a feature-based technique, and statistical analysis. Our dataset contained neutral polymorphisms and mutations associated with myeloid malignancies from epigenetic regulators ASXL1, DNMT3A, EZH2, and TET2. PolyPhen-2 and SIFT had significantly lower accuracies in predicting the effects of amino acid substitutions outside CFDs than expected, with especially low sensitivity. On the other hand, only ISM algorithm showed statistically significant classification of these sequences. It outperformed PolyPhen-2 and SIFT by 15% and 13%, respectively. These results suggest that feature-based methods, like ISM, are more suitable for the classification of amino acid substitutions outside CFDs than phylogeny-based tools.

ContEst16S: an algorithm that identifies contaminated prokaryotic genomes using 16S RNA gene sequences.

PubMed

Lee, Imchang; Chalita, Mauricio; Ha, Sung-Min; Na, Seong-In; Yoon, Seok-Hwan; Chun, Jongsik

2017-06-01

Thanks to the recent advancement of DNA sequencing technology, the cost and time of prokaryotic genome sequencing have been dramatically decreased. It has repeatedly been reported that genome sequencing using high-throughput next-generation sequencing is prone to contaminations due to its high depth of sequencing coverage. Although a few bioinformatics tools are available to detect potential contaminations, these have inherited limitations as they only use protein-coding genes. Here we introduce a new algorithm, called ContEst16S, to detect potential contaminations using 16S rRNA genes from genome assemblies. We screened 69 745 prokaryotic genomes from the NCBI Assembly Database using ContEst16S and found that 594 were contaminated by bacteria, human and plants. Of the predicted contaminated genomes, 8 % were not predicted by the existing protein-coding gene-based tool, implying that both methods can be complementary in the detection of contaminations. A web-based service of the algorithm is available at www.ezbiocloud.net/tools/contest16s.

IDEAL: Images Across Domains, Experiments, Algorithms and Learning

NASA Astrophysics Data System (ADS)

Ushizima, Daniela M.; Bale, Hrishikesh A.; Bethel, E. Wes; Ercius, Peter; Helms, Brett A.; Krishnan, Harinarayan; Grinberg, Lea T.; Haranczyk, Maciej; Macdowell, Alastair A.; Odziomek, Katarzyna; Parkinson, Dilworth Y.; Perciano, Talita; Ritchie, Robert O.; Yang, Chao

2016-11-01

Research across science domains is increasingly reliant on image-centric data. Software tools are in high demand to uncover relevant, but hidden, information in digital images, such as those coming from faster next generation high-throughput imaging platforms. The challenge is to analyze the data torrent generated by the advanced instruments efficiently, and provide insights such as measurements for decision-making. In this paper, we overview work performed by an interdisciplinary team of computational and materials scientists, aimed at designing software applications and coordinating research efforts connecting (1) emerging algorithms for dealing with large and complex datasets; (2) data analysis methods with emphasis in pattern recognition and machine learning; and (3) advances in evolving computer architectures. Engineering tools around these efforts accelerate the analyses of image-based recordings, improve reusability and reproducibility, scale scientific procedures by reducing time between experiments, increase efficiency, and open opportunities for more users of the imaging facilities. This paper describes our algorithms and software tools, showing results across image scales, demonstrating how our framework plays a role in improving image understanding for quality control of existent materials and discovery of new compounds.

Optimization of IBF parameters based on adaptive tool-path algorithm

NASA Astrophysics Data System (ADS)

Deng, Wen Hui; Chen, Xian Hua; Jin, Hui Liang; Zhong, Bo; Hou, Jin; Li, An Qi

2018-03-01

As a kind of Computer Controlled Optical Surfacing(CCOS) technology. Ion Beam Figuring(IBF) has obvious advantages in the control of surface accuracy, surface roughness and subsurface damage. The superiority and characteristics of IBF in optical component processing are analyzed from the point of view of removal mechanism. For getting more effective and automatic tool path with the information of dwell time, a novel algorithm is proposed in this thesis. Based on the removal functions made through our IBF equipment and the adaptive tool-path, optimized parameters are obtained through analysis the residual error that would be created in the polishing process. A Φ600 mm plane reflector element was used to be a simulation instance. The simulation result shows that after four combinations of processing, the surface accuracy of PV (Peak Valley) value and the RMS (Root Mean Square) value was reduced to 4.81 nm and 0.495 nm from 110.22 nm and 13.998 nm respectively in the 98% aperture. The result shows that the algorithm and optimized parameters provide a good theoretical for high precision processing of IBF.

MLViS: A Web Tool for Machine Learning-Based Virtual Screening in Early-Phase of Drug Discovery and Development

PubMed Central

Korkmaz, Selcuk; Zararsiz, Gokmen; Goksuluk, Dincer

2015-01-01

Virtual screening is an important step in early-phase of drug discovery process. Since there are thousands of compounds, this step should be both fast and effective in order to distinguish drug-like and nondrug-like molecules. Statistical machine learning methods are widely used in drug discovery studies for classification purpose. Here, we aim to develop a new tool, which can classify molecules as drug-like and nondrug-like based on various machine learning methods, including discriminant, tree-based, kernel-based, ensemble and other algorithms. To construct this tool, first, performances of twenty-three different machine learning algorithms are compared by ten different measures, then, ten best performing algorithms have been selected based on principal component and hierarchical cluster analysis results. Besides classification, this application has also ability to create heat map and dendrogram for visual inspection of the molecules through hierarchical cluster analysis. Moreover, users can connect the PubChem database to download molecular information and to create two-dimensional structures of compounds. This application is freely available through www.biosoft.hacettepe.edu.tr/MLViS/. PMID:25928885

An Efficient Computational Framework for the Analysis of Whole Slide Images: Application to Follicular Lymphoma Immunohistochemistry

PubMed Central

Samsi, Siddharth; Krishnamurthy, Ashok K.; Gurcan, Metin N.

2012-01-01

Follicular Lymphoma (FL) is one of the most common non-Hodgkin Lymphoma in the United States. Diagnosis and grading of FL is based on the review of histopathological tissue sections under a microscope and is influenced by human factors such as fatigue and reader bias. Computer-aided image analysis tools can help improve the accuracy of diagnosis and grading and act as another tool at the pathologist’s disposal. Our group has been developing algorithms for identifying follicles in immunohistochemical images. These algorithms have been tested and validated on small images extracted from whole slide images. However, the use of these algorithms for analyzing the entire whole slide image requires significant changes to the processing methodology since the images are relatively large (on the order of 100k × 100k pixels). In this paper we discuss the challenges involved in analyzing whole slide images and propose potential computational methodologies for addressing these challenges. We discuss the use of parallel computing tools on commodity clusters and compare performance of the serial and parallel implementations of our approach. PMID:22962572

The High-Performance Computing and Communications program, the national information infrastructure and health care.

PubMed Central

Lindberg, D A; Humphreys, B L

1995-01-01

The High-Performance Computing and Communications (HPCC) program is a multiagency federal effort to advance the state of computing and communications and to provide the technologic platform on which the National Information Infrastructure (NII) can be built. The HPCC program supports the development of high-speed computers, high-speed telecommunications, related software and algorithms, education and training, and information infrastructure technology and applications. The vision of the NII is to extend access to high-performance computing and communications to virtually every U.S. citizen so that the technology can be used to improve the civil infrastructure, lifelong learning, energy management, health care, etc. Development of the NII will require resolution of complex economic and social issues, including information privacy. Health-related applications supported under the HPCC program and NII initiatives include connection of health care institutions to the Internet; enhanced access to gene sequence data; the "Visible Human" Project; and test-bed projects in telemedicine, electronic patient records, shared informatics tool development, and image systems. PMID:7614116

Scalable Algorithms for Global Scale Remote Sensing Applications

NASA Astrophysics Data System (ADS)

Vatsavai, R. R.; Bhaduri, B. L.; Singh, N.

2015-12-01

Recent decade has witnessed major changes on the Earth, for example, deforestation, varying cropping and human settlement patterns, and crippling damages due to disasters. Accurate damage assessment caused by major natural and anthropogenic disasters is becoming critical due to increases in human and economic loss. This increase in loss of life and severe damages can be attributed to the growing population, as well as human migration to the disaster prone regions of the world. Rapid assessment of these changes and dissemination of accurate information is critical for creating an effective emergency response. Change detection using high-resolution satellite images is a primary tool in assessing damages, monitoring biomass and critical infrastructures, and identifying new settlements. Existing change detection methods suffer from registration errors and often based on pixel (location) wise comparison of spectral observations from single sensor. In this paper we present a novel probabilistic change detection framework based on patch comparison and a GPU implementation that supports near real-time rapid damage exploration capability.

Microgrid Design Toolkit (MDT) Technical Documentation and Component Summaries

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

Arguello, Bryan; Gearhart, Jared Lee; Jones, Katherine A.

2015-09-01

The Microgrid Design Toolkit (MDT) is a decision support software tool for microgrid designers to use during the microgrid design process. The models that support the two main capabilities in MDT are described. The first capability, the Microgrid Sizing Capability (MSC), is used to determine the size and composition of a new microgrid in the early stages of the design process. MSC is a mixed-integer linear program that is focused on developing a microgrid that is economically viable when connected to the grid. The second capability is focused on refining a microgrid design for operation in islanded mode. This secondmore » capability relies on two models: the Technology Management Optimization (TMO) model and Performance Reliability Model (PRM). TMO uses a genetic algorithm to create and refine a collection of candidate microgrid designs. It uses PRM, a simulation based reliability model, to assess the performance of these designs. TMO produces a collection of microgrid designs that perform well with respect to one or more performance metrics.« less

Design for sustainability of industrial symbiosis based on emergy and multi-objective particle swarm optimization.

PubMed

Ren, Jingzheng; Liang, Hanwei; Dong, Liang; Sun, Lu; Gao, Zhiqiu

2016-08-15

Industrial symbiosis provides novel and practical pathway to the design for the sustainability. Decision support tool for its verification is necessary for practitioners and policy makers, while to date, quantitative research is limited. The objective of this work is to present an innovative approach for supporting decision-making in the design for the sustainability with the implementation of industrial symbiosis in chemical complex. Through incorporating the emergy theory, the model is formulated as a multi-objective approach that can optimize both the economic benefit and sustainable performance of the integrated industrial system. A set of emergy based evaluation index are designed. Multi-objective Particle Swarm Algorithm is proposed to solve the model, and the decision-makers are allowed to choose the suitable solutions form the Pareto solutions. An illustrative case has been studied by the proposed method, a few of compromises between high profitability and high sustainability can be obtained for the decision-makers/stakeholders to make decision. Copyright © 2016 Elsevier B.V. All rights reserved.

Real-time algorithm for acoustic imaging with a microphone array.

PubMed

Huang, Xun

2009-05-01

Acoustic phased array has become an important testing tool in aeroacoustic research, where the conventional beamforming algorithm has been adopted as a classical processing technique. The computation however has to be performed off-line due to the expensive cost. An innovative algorithm with real-time capability is proposed in this work. The algorithm is similar to a classical observer in the time domain while extended for the array processing to the frequency domain. The observer-based algorithm is beneficial mainly for its capability of operating over sampling blocks recursively. The expensive experimental time can therefore be reduced extensively since any defect in a testing can be corrected instantaneously.

On distribution reduction and algorithm implementation in inconsistent ordered information systems.

PubMed

Zhang, Yanqin

2014-01-01

As one part of our work in ordered information systems, distribution reduction is studied in inconsistent ordered information systems (OISs). Some important properties on distribution reduction are studied and discussed. The dominance matrix is restated for reduction acquisition in dominance relations based information systems. Matrix algorithm for distribution reduction acquisition is stepped. And program is implemented by the algorithm. The approach provides an effective tool for the theoretical research and the applications for ordered information systems in practices. For more detailed and valid illustrations, cases are employed to explain and verify the algorithm and the program which shows the effectiveness of the algorithm in complicated information systems.

Efficient computation of the joint probability of multiple inherited risk alleles from pedigree data.

PubMed

Madsen, Thomas; Braun, Danielle; Peng, Gang; Parmigiani, Giovanni; Trippa, Lorenzo

2018-06-25

The Elston-Stewart peeling algorithm enables estimation of an individual's probability of harboring germline risk alleles based on pedigree data, and serves as the computational backbone of important genetic counseling tools. However, it remains limited to the analysis of risk alleles at a small number of genetic loci because its computing time grows exponentially with the number of loci considered. We propose a novel, approximate version of this algorithm, dubbed the peeling and paring algorithm, which scales polynomially in the number of loci. This allows extending peeling-based models to include many genetic loci. The algorithm creates a trade-off between accuracy and speed, and allows the user to control this trade-off. We provide exact bounds on the approximation error and evaluate it in realistic simulations. Results show that the loss of accuracy due to the approximation is negligible in important applications. This algorithm will improve genetic counseling tools by increasing the number of pathogenic risk alleles that can be addressed. To illustrate we create an extended five genes version of BRCAPRO, a widely used model for estimating the carrier probabilities of BRCA1 and BRCA2 risk alleles and assess its computational properties. © 2018 WILEY PERIODICALS, INC.

Estimating the Regional Economic Significance of Airports

DTIC Science & Technology

1992-09-01

following three options for estimating induced impacts: the economic base model , an econometric model , and a regional input-output model . One approach to...limitations, however, the economic base model has been widely used for regional economic analysis. A second approach is to develop an econometric model of...analysis is the principal statistical tool used to estimate the economic relationships. Regional econometric models are capable of estimating a single

Incidence and management of arthralgias in breast cancer patients treated with aromatase inhibitors in an outpatient oncology clinic.

PubMed

Menas, Pamela; Merkel, Douglas; Hui, Wendy; Lawton, Jessica; Harper, Abigail; Carro, George

2012-12-01

Aromatase inhibitors (AIs) are routinely used as first-line adjuvant treatment of breast cancer in postmenopausal women with hormone receptor positive tumors. The current recommended length of treatment with an AI is 5 years. Arthralgias have been frequently cited as the primary reason for discontinuation of AI therapy. Various treatment strategies are proposed in literature, but a standardized treatment algorithm has not been established. The initial purpose of this study was to describe the incidence and management of AI-induced arthralgias in patients treated at Kellogg Cancer Center (KCC). Further evaluation led to the development and the implementation of a treatment algorithm and electronic medical record (EMR) documentation tools. The retrospective chart review included 206 adult patients with hormone receptor positive breast cancer who were receiving adjuvant therapy with an AI. A multidisciplinary treatment team consisting of pharmacists, collaborative practice nurses, and physicians met to develop a standardized treatment algorithm and corresponding EMR documentation tool. The treatment algorithm and documentation tool were developed after the study to better monitor and proactively treat patients with AI-induced arthralgias. RESULTS/ CONCLUSIONS: The overall incidence of arthralgias at KCC was 48% (n = 98/206). Of these patients, 32% were documented as having arthralgias within the first 6 months of therapy initiation. Patients who reported AI-induced arthralgias were younger than patients who did not report AI-induced arthralgias (61 vs. 65 years, p = 0.002). There was no statistical difference in the incidence of arthralgias in patients with a history of chemotherapy (including taxane therapy) compared to those who did not receive chemotherapy (p = 0.352). Of patients presenting with AI-induced arthralgias, 41% did not have physician-managed treatment documented in the EMR. A standardized treatment algorithm and electronic chart documentation tools were then developed by the multidisciplinary team.

Development and application of a modified dynamic time warping algorithm (DTW-S) to analyses of primate brain expression time series

PubMed Central

2011-01-01

Background Comparing biological time series data across different conditions, or different specimens, is a common but still challenging task. Algorithms aligning two time series represent a valuable tool for such comparisons. While many powerful computation tools for time series alignment have been developed, they do not provide significance estimates for time shift measurements. Results Here, we present an extended version of the original DTW algorithm that allows us to determine the significance of time shift estimates in time series alignments, the DTW-Significance (DTW-S) algorithm. The DTW-S combines important properties of the original algorithm and other published time series alignment tools: DTW-S calculates the optimal alignment for each time point of each gene, it uses interpolated time points for time shift estimation, and it does not require alignment of the time-series end points. As a new feature, we implement a simulation procedure based on parameters estimated from real time series data, on a series-by-series basis, allowing us to determine the false positive rate (FPR) and the significance of the estimated time shift values. We assess the performance of our method using simulation data and real expression time series from two published primate brain expression datasets. Our results show that this method can provide accurate and robust time shift estimates for each time point on a gene-by-gene basis. Using these estimates, we are able to uncover novel features of the biological processes underlying human brain development and maturation. Conclusions The DTW-S provides a convenient tool for calculating accurate and robust time shift estimates at each time point for each gene, based on time series data. The estimates can be used to uncover novel biological features of the system being studied. The DTW-S is freely available as an R package TimeShift at http://www.picb.ac.cn/Comparative/data.html. PMID:21851598

Development and application of a modified dynamic time warping algorithm (DTW-S) to analyses of primate brain expression time series.

PubMed

Yuan, Yuan; Chen, Yi-Ping Phoebe; Ni, Shengyu; Xu, Augix Guohua; Tang, Lin; Vingron, Martin; Somel, Mehmet; Khaitovich, Philipp

2011-08-18

Comparing biological time series data across different conditions, or different specimens, is a common but still challenging task. Algorithms aligning two time series represent a valuable tool for such comparisons. While many powerful computation tools for time series alignment have been developed, they do not provide significance estimates for time shift measurements. Here, we present an extended version of the original DTW algorithm that allows us to determine the significance of time shift estimates in time series alignments, the DTW-Significance (DTW-S) algorithm. The DTW-S combines important properties of the original algorithm and other published time series alignment tools: DTW-S calculates the optimal alignment for each time point of each gene, it uses interpolated time points for time shift estimation, and it does not require alignment of the time-series end points. As a new feature, we implement a simulation procedure based on parameters estimated from real time series data, on a series-by-series basis, allowing us to determine the false positive rate (FPR) and the significance of the estimated time shift values. We assess the performance of our method using simulation data and real expression time series from two published primate brain expression datasets. Our results show that this method can provide accurate and robust time shift estimates for each time point on a gene-by-gene basis. Using these estimates, we are able to uncover novel features of the biological processes underlying human brain development and maturation. The DTW-S provides a convenient tool for calculating accurate and robust time shift estimates at each time point for each gene, based on time series data. The estimates can be used to uncover novel biological features of the system being studied. The DTW-S is freely available as an R package TimeShift at http://www.picb.ac.cn/Comparative/data.html.

Using GeoRePORT to report socio-economic potential for geothermal development

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

Young, Katherine R.; Levine, Aaron

The Geothermal Resource Portfolio Optimization and Reporting Tool (GeoRePORT, http://en.openei.org/wiki/GeoRePORT) was developed for reporting resource grades and project readiness levels, providing the U.S. Department of Energy a consistent and comprehensible means of evaluating projects. The tool helps funding organizations (1) quantitatively identify barriers, (2) develop measureable goals, (3) objectively evaluate proposals, including contribution to goals, (4) monitor progress, and (5) report portfolio performance. GeoRePORT assesses three categories: geological, technical, and socio-economic. Here, we describe GeoRePORT, then focus on the socio-economic assessment and its applications for assessing deployment potential in the U.S. Socio-economic attributes include land access, permitting, transmission, and market.

Modeling and predicting abstract concept or idea introduction and propagation through geopolitical groups

NASA Astrophysics Data System (ADS)

Jaenisch, Holger M.; Handley, James W.; Hicklen, Michael L.

2007-04-01

This paper describes a novel capability for modeling known idea propagation transformations and predicting responses to new ideas from geopolitical groups. Ideas are captured using semantic words that are text based and bear cognitive definitions. We demonstrate a unique algorithm for converting these into analytical predictive equations. Using the illustrative idea of "proposing a gasoline price increase of 1 per gallon from 2" and its changing perceived impact throughout 5 demographic groups, we identify 13 cost of living Diplomatic, Information, Military, and Economic (DIME) features common across all 5 demographic groups. This enables the modeling and monitoring of Political, Military, Economic, Social, Information, and Infrastructure (PMESII) effects of each group to this idea and how their "perception" of this proposal changes. Our algorithm and results are summarized in this paper.

Clean birth kits to improve birth practices: development and testing of a country level decision support tool.

PubMed

Hundley, Vanora A; Avan, Bilal I; Ahmed, Haris; Graham, Wendy J

2012-12-19

Clean birth practices can prevent sepsis, one of the leading causes of both maternal and newborn mortality. Evidence suggests that clean birth kits (CBKs), as part of package that includes education, are associated with a reduction in newborn mortality, omphalitis, and puerperal sepsis. However, questions remain about how best to approach the introduction of CBKs in country. We set out to develop a practical decision support tool for programme managers of public health systems who are considering the potential role of CBKs in their strategy for care at birth. Development and testing of the decision support tool was a three-stage process involving an international expert group and country level testing. Stage 1, the development of the tool was undertaken by the Birth Kit Working Group and involved a review of the evidence, a consensus meeting, drafting of the proposed tool and expert review. In Stage 2 the tool was tested with users through interviews (9) and a focus group, with federal and provincial level decision makers in Pakistan. In Stage 3 the findings from the country level testing were reviewed by the expert group. The decision support tool comprised three separate algorithms to guide the policy maker or programme manager through the specific steps required in making the country level decision about whether to use CBKs. The algorithms were supported by a series of questions (that could be administered by interview, focus group or questionnaire) to help the decision maker identify the information needed. The country level testing revealed that the decision support tool was easy to follow and helpful in making decisions about the potential role of CBKs. Minor modifications were made and the final algorithms are presented. Testing of the tool with users in Pakistan suggests that the tool facilitates discussion and aids decision making. However, testing in other countries is needed to determine whether these results can be replicated and to identify how the tool can be adapted to meet country specific needs.

Research on the magnetorheological finishing of large aperture off-axis aspheric optical surfaces for zinc sulfide

NASA Astrophysics Data System (ADS)

Zhang, Yunfei; Huang, Wen; Zheng, Yongcheng; Ji, Fang; Xu, Min; Duan, Zhixin; Luo, Qing; Liu, Qian; Xiao, Hong

2016-03-01

Zinc sulfide is a kind of typical infrared optical material, commonly produced using single point diamond turning (SPDT). SPDT can efficiently produce zinc sulfide aspheric surfaces with micro-roughness and acceptable figure error. However the tool marks left by the diamond turning process cause high micro-roughness that degrades the optical performance when used in the visible region of the spectrum. Magnetorheological finishing (MRF) is a deterministic, sub-aperture polishing technology that is very helpful in improving both surface micro-roughness and surface figure.This paper mainly investigates the MRF technology of large aperture off-axis aspheric optical surfaces for zinc sulfide. The topological structure and coordinate transformation of a MRF machine tool PKC1200Q2 are analyzed and its kinematics is calculated, then the post-processing algorithm model of MRF for an optical lens is established. By taking the post-processing of off-axis aspheric surfacefor example, a post-processing algorithm that can be used for a raster tool path is deduced and the errors produced by the approximate treatment are analyzed. A polishing algorithm of trajectory planning and dwell time based on matrix equation and optimization theory is presented in this paper. Adopting this algorithm an experiment is performed to machining a large-aperture off-axis aspheric surface on the MRF machine developed by ourselves. After several times' polishing, the figure accuracy PV is proved from 3.3λ to 2.0λ and RMS from 0.451λ to 0.327λ. This algorithm is used to polish the other shapes including spheres, aspheres and prisms.

Firefly algorithm versus genetic algorithm as powerful variable selection tools and their effect on different multivariate calibration models in spectroscopy: A comparative study.

PubMed

Attia, Khalid A M; Nassar, Mohammed W I; El-Zeiny, Mohamed B; Serag, Ahmed

2017-01-05

For the first time, a new variable selection method based on swarm intelligence namely firefly algorithm is coupled with three different multivariate calibration models namely, concentration residual augmented classical least squares, artificial neural network and support vector regression in UV spectral data. A comparative study between the firefly algorithm and the well-known genetic algorithm was developed. The discussion revealed the superiority of using this new powerful algorithm over the well-known genetic algorithm. Moreover, different statistical tests were performed and no significant differences were found between all the models regarding their predictabilities. This ensures that simpler and faster models were obtained without any deterioration of the quality of the calibration. Copyright © 2016 Elsevier B.V. All rights reserved.

Differential Forms: A New Tool in Economics

NASA Astrophysics Data System (ADS)

Mimkes, Jürgen

Econophysics is the transfer of methods from natural to socio-economic sciences. This concept has first been applied to finance1, but it is now also used in various applications of economics and social sciences [2,3]. The present paper focuses on problems in macro economics and growth. 1. Neoclassical theory [4, 5] neglects the “ex post” property of income and growth. Income Y(K, L) is assumed to be a function of capital and labor. But functions cannot model the “ex post” character of income. 2. Neoclassical theory is based on a Cobb Douglas function [6] with variable elasticity α, which may be fitted to economic data. But an undefined elasticity α leads to a descriptive rather than a predictive economic theory. The present paper introduces a new tool - differential forms and path dependent integrals - to macro economics. This is a solution to the problems above: 1. The integral of not exact differential forms is path dependent and can only be calculated “ex post” like income and economic growth. 2. Not exact differential forms can be made exact by an integrating factor, this leads to a new, well defined, unique production function F and a predictive economic theory.

Confidence Sharing: An Economic Strategy for Efficient Information Flows in Animal Groups

PubMed Central

Korman, Amos; Greenwald, Efrat; Feinerman, Ofer

2014-01-01

Social animals may share information to obtain a more complete and accurate picture of their surroundings. However, physical constraints on communication limit the flow of information between interacting individuals in a way that can cause an accumulation of errors and deteriorated collective behaviors. Here, we theoretically study a general model of information sharing within animal groups. We take an algorithmic perspective to identify efficient communication schemes that are, nevertheless, economic in terms of communication, memory and individual internal computation. We present a simple and natural algorithm in which each agent compresses all information it has gathered into a single parameter that represents its confidence in its behavior. Confidence is communicated between agents by means of active signaling. We motivate this model by novel and existing empirical evidences for confidence sharing in animal groups. We rigorously show that this algorithm competes extremely well with the best possible algorithm that operates without any computational constraints. We also show that this algorithm is minimal, in the sense that further reduction in communication may significantly reduce performances. Our proofs rely on the Cramér-Rao bound and on our definition of a Fisher Channel Capacity. We use these concepts to quantify information flows within the group which are then used to obtain lower bounds on collective performance. The abstract nature of our model makes it rigorously solvable and its conclusions highly general. Indeed, our results suggest confidence sharing as a central notion in the context of animal communication. PMID:25275649

Confidence sharing: an economic strategy for efficient information flows in animal groups.

PubMed

Korman, Amos; Greenwald, Efrat; Feinerman, Ofer

2014-10-01

Social animals may share information to obtain a more complete and accurate picture of their surroundings. However, physical constraints on communication limit the flow of information between interacting individuals in a way that can cause an accumulation of errors and deteriorated collective behaviors. Here, we theoretically study a general model of information sharing within animal groups. We take an algorithmic perspective to identify efficient communication schemes that are, nevertheless, economic in terms of communication, memory and individual internal computation. We present a simple and natural algorithm in which each agent compresses all information it has gathered into a single parameter that represents its confidence in its behavior. Confidence is communicated between agents by means of active signaling. We motivate this model by novel and existing empirical evidences for confidence sharing in animal groups. We rigorously show that this algorithm competes extremely well with the best possible algorithm that operates without any computational constraints. We also show that this algorithm is minimal, in the sense that further reduction in communication may significantly reduce performances. Our proofs rely on the Cramér-Rao bound and on our definition of a Fisher Channel Capacity. We use these concepts to quantify information flows within the group which are then used to obtain lower bounds on collective performance. The abstract nature of our model makes it rigorously solvable and its conclusions highly general. Indeed, our results suggest confidence sharing as a central notion in the context of animal communication.

Enabling the extended compact genetic algorithm for real-parameter optimization by using adaptive discretization.

PubMed

Chen, Ying-ping; Chen, Chao-Hong

2010-01-01

An adaptive discretization method, called split-on-demand (SoD), enables estimation of distribution algorithms (EDAs) for discrete variables to solve continuous optimization problems. SoD randomly splits a continuous interval if the number of search points within the interval exceeds a threshold, which is decreased at every iteration. After the split operation, the nonempty intervals are assigned integer codes, and the search points are discretized accordingly. As an example of using SoD with EDAs, the integration of SoD and the extended compact genetic algorithm (ECGA) is presented and numerically examined. In this integration, we adopt a local search mechanism as an optional component of our back end optimization engine. As a result, the proposed framework can be considered as a memetic algorithm, and SoD can potentially be applied to other memetic algorithms. The numerical experiments consist of two parts: (1) a set of benchmark functions on which ECGA with SoD and ECGA with two well-known discretization methods: the fixed-height histogram (FHH) and the fixed-width histogram (FWH) are compared; (2) a real-world application, the economic dispatch problem, on which ECGA with SoD is compared to other methods. The experimental results indicate that SoD is a better discretization method to work with ECGA. Moreover, ECGA with SoD works quite well on the economic dispatch problem and delivers solutions better than the best known results obtained by other methods in existence.

Caesy: A software tool for computer-aided engineering

NASA Technical Reports Server (NTRS)

Wette, Matt

1993-01-01

A new software tool, Caesy, is described. This tool provides a strongly typed programming environment for research in the development of algorithms and software for computer-aided control system design. A description of the user language and its implementation as they currently stand are presented along with a description of work in progress and areas of future work.

A Quantitative Three-Dimensional Image Analysis Tool for Maximal Acquisition of Spatial Heterogeneity Data.

PubMed

Allenby, Mark C; Misener, Ruth; Panoskaltsis, Nicki; Mantalaris, Athanasios

2017-02-01

Three-dimensional (3D) imaging techniques provide spatial insight into environmental and cellular interactions and are implemented in various fields, including tissue engineering, but have been restricted by limited quantification tools that misrepresent or underutilize the cellular phenomena captured. This study develops image postprocessing algorithms pairing complex Euclidean metrics with Monte Carlo simulations to quantitatively assess cell and microenvironment spatial distributions while utilizing, for the first time, the entire 3D image captured. Although current methods only analyze a central fraction of presented confocal microscopy images, the proposed algorithms can utilize 210% more cells to calculate 3D spatial distributions that can span a 23-fold longer distance. These algorithms seek to leverage the high sample cost of 3D tissue imaging techniques by extracting maximal quantitative data throughout the captured image.

The Structure-Mapping Engine: Algorithm and Examples.

ERIC Educational Resources Information Center

Falkenhainer, Brian; And Others

This description of the Structure-Mapping Engine (SME), a flexible, cognitive simulation program for studying analogical processing which is based on Gentner's Structure-Mapping theory of analogy, points out that the SME provides a "tool kit" for constructing matching algorithms consistent with this theory. This report provides: (1) a…

How is WFPC flat field made

NASA Technical Reports Server (NTRS)

Hsu, J.-C.; Ritchie, C. E.

1992-01-01

An algorithm developed by the WFPC IDT to generate flat fields from Earth streak exposures is now implemented in STSDAS. We explain in detail how this algorithm works and possible deficiencies. We also present two associated tools which can be used to modify the flat field obtained from the standard procedure.

Majorization as a Tool for Optimizing a Class of Matrix Functions.

ERIC Educational Resources Information Center

Kiers, Henk A.

1990-01-01

General algorithms are presented that can be used for optimizing matrix trace functions subject to certain constraints on the parameters. The parameter set that minimizes the majorizing function also decreases the matrix trace function, providing a monotonically convergent algorithm for minimizing the matrix trace function iteratively. (SLD)

A comparison of two open source LiDAR surface classification algorithms

USDA-ARS?s Scientific Manuscript database

With the progression of LiDAR (Light Detection and Ranging) towards a mainstream resource management tool, it has become necessary to understand how best to process and analyze the data. While most ground surface identification algorithms remain proprietary and have high purchase costs; a few are op...

Using Genetic Algorithm and MODFLOW to Characterize Aquifer System of Northwest Florida (Published Proceedings)

EPA Science Inventory

By integrating Genetic Algorithm and MODFLOW2005, an optimizing tool is developed to characterize the aquifer system of Region II, Northwest Florida. The history and the newest available observation data of the aquifer system is fitted automatically by using the numerical model c...

Medical image segmentation to estimate HER2 gene status in breast cancer

NASA Astrophysics Data System (ADS)

Palacios-Navarro, Guillermo; Acirón-Pomar, José Manuel; Vilchez-Sorribas, Enrique; Zambrano, Eddie Galarza

2016-02-01

This work deals with the estimation of HER2 Gene status in breast tumour images treated with in situ hybridization techniques (ISH). We propose a simple algorithm to obtain the amplification factor of HER2 gene. The obtained results are very close to those obtained by specialists in a manual way. The developed algorithm is based on colour image segmentation and has been included in a software application tool for breast tumour analysis. The developed tool focus on the estimation of the seriousness of tumours, facilitating the work of pathologists and contributing to a better diagnosis.

Let's Stop Trying to Quantify Household Vulnerability: The Problem With Simple Scales for Targeting and Evaluating Economic Strengthening Programs.

PubMed

Moret, Whitney M

2018-03-21

Economic strengthening practitioners are increasingly seeking data collection tools that will help them target households vulnerable to HIV and poor child well-being outcomes, match households to appropriate interventions, monitor their status, and determine readiness for graduation from project support. This article discusses efforts in 3 countries to develop simple, valid tools to quantify and classify economic vulnerability status. In Côte d'Ivoire, we conducted a cross-sectional survey with 3,749 households to develop a scale based on the definition of HIV-related economic vulnerability from the U.S. President's Emergency Plan for AIDS Relief (PEPFAR) for the purpose of targeting vulnerable households for PEPFAR-funded programs for orphans and vulnerable children. The vulnerability measures examined did not cluster in ways that would allow for the creation of a small number of composite measures, and thus we were unable to develop a scale. In Uganda, we assessed the validity of a vulnerability index developed to classify households according to donor classifications of economic status by measuring its association with a validated poverty measure, finding only a modest correlation. In South Africa, we developed monitoring and evaluation tools to assess economic status of individual adolescent girls and their households. We found no significant correlation with our validation measures, which included a validated measure of girls' vulnerability to HIV, a validated poverty measure, and subjective classifications generated by the community, data collector, and respondent. Overall, none of the measures of economic vulnerability used in the 3 countries varied significantly with their proposed validation items. Our findings suggest that broad constructs of economic vulnerability cannot be readily captured using simple scales to classify households and individuals in a way that accounts for a substantial amount of variance at locally defined vulnerability levels. We recommend that researchers and implementers design monitoring and evaluation instruments to capture narrower definitions of vulnerability based on characteristics programs intend to affect. We also recommend using separate tools for targeting based on context-specific indicators with evidence-based links to negative outcomes. Policy makers and donors should avoid reliance on simplified metrics of economic vulnerability in the programs they support. © Moret.

Surface Modeling of Workpiece and Tool Trajectory Planning for Spray Painting Robot

PubMed Central

Tang, Yang; Chen, Wei

2015-01-01

Automated tool trajectory planning for spray-painting robots is still a challenging problem, especially for a large free-form surface. A grid approximation of a free-form surface is adopted in CAD modeling in this paper. A free-form surface model is approximated by a set of flat patches. We describe here an efficient and flexible tool trajectory optimization scheme using T-Bézier curves calculated in a new way from trigonometrical bases. The distance between the spray gun and the free-form surface along the normal vector is varied. Automotive body parts, which are large free-form surfaces, are used to test the scheme. The experimental results show that the trajectory planning algorithm achieves satisfactory performance. This algorithm can also be extended to other applications. PMID:25993663

Surface modeling of workpiece and tool trajectory planning for spray painting robot.

PubMed

Tang, Yang; Chen, Wei

2015-01-01

Automated tool trajectory planning for spray-painting robots is still a challenging problem, especially for a large free-form surface. A grid approximation of a free-form surface is adopted in CAD modeling in this paper. A free-form surface model is approximated by a set of flat patches. We describe here an efficient and flexible tool trajectory optimization scheme using T-Bézier curves calculated in a new way from trigonometrical bases. The distance between the spray gun and the free-form surface along the normal vector is varied. Automotive body parts, which are large free-form surfaces, are used to test the scheme. The experimental results show that the trajectory planning algorithm achieves satisfactory performance. This algorithm can also be extended to other applications.

Evaluating Twitter and Its Impact on Student Learning in Principles of Economics Courses

ERIC Educational Resources Information Center

Al-Bahrani, Abdullah; Patel, Darshak; Sheridan, Brandon J.

2017-01-01

Ever since Becker and Watts (1996) found that economic educators rely heavily on "chalk and talk" as a primary teaching method, economic educators have been seeking new ways to engage students and improve learning outcomes. Recently, the use of social media as a pedagogical tool in economics has received increasing interest. The authors…

Missing value imputation for microarray data: a comprehensive comparison study and a web tool.

PubMed

Chiu, Chia-Chun; Chan, Shih-Yao; Wang, Chung-Ching; Wu, Wei-Sheng

2013-01-01

Microarray data are usually peppered with missing values due to various reasons. However, most of the downstream analyses for microarray data require complete datasets. Therefore, accurate algorithms for missing value estimation are needed for improving the performance of microarray data analyses. Although many algorithms have been developed, there are many debates on the selection of the optimal algorithm. The studies about the performance comparison of different algorithms are still incomprehensive, especially in the number of benchmark datasets used, the number of algorithms compared, the rounds of simulation conducted, and the performance measures used. In this paper, we performed a comprehensive comparison by using (I) thirteen datasets, (II) nine algorithms, (III) 110 independent runs of simulation, and (IV) three types of measures to evaluate the performance of each imputation algorithm fairly. First, the effects of different types of microarray datasets on the performance of each imputation algorithm were evaluated. Second, we discussed whether the datasets from different species have different impact on the performance of different algorithms. To assess the performance of each algorithm fairly, all evaluations were performed using three types of measures. Our results indicate that the performance of an imputation algorithm mainly depends on the type of a dataset but not on the species where the samples come from. In addition to the statistical measure, two other measures with biological meanings are useful to reflect the impact of missing value imputation on the downstream data analyses. Our study suggests that local-least-squares-based methods are good choices to handle missing values for most of the microarray datasets. In this work, we carried out a comprehensive comparison of the algorithms for microarray missing value imputation. Based on such a comprehensive comparison, researchers could choose the optimal algorithm for their datasets easily. Moreover, new imputation algorithms could be compared with the existing algorithms using this comparison strategy as a standard protocol. In addition, to assist researchers in dealing with missing values easily, we built a web-based and easy-to-use imputation tool, MissVIA (http://cosbi.ee.ncku.edu.tw/MissVIA), which supports many imputation algorithms. Once users upload a real microarray dataset and choose the imputation algorithms, MissVIA will determine the optimal algorithm for the users' data through a series of simulations, and then the imputed results can be downloaded for the downstream data analyses.

Effect of Non-rigid Registration Algorithms on Deformation Based Morphometry: A Comparative Study with Control and Williams Syndrome Subjects

PubMed Central

Han, Zhaoying; Thornton-Wells, Tricia A.; Dykens, Elisabeth M.; Gore, John C.; Dawant, Benoit M.

2014-01-01

Deformation Based Morphometry (DBM) is a widely used method for characterizing anatomical differences across groups. DBM is based on the analysis of the deformation fields generated by non-rigid registration algorithms, which warp the individual volumes to a DBM atlas. Although several studies have compared non-rigid registration algorithms for segmentation tasks, few studies have compared the effect of the registration algorithms on group differences that may be uncovered through DBM. In this study, we compared group atlas creation and DBM results obtained with five well-established non-rigid registration algorithms using thirteen subjects with Williams Syndrome (WS) and thirteen Normal Control (NC) subjects. The five non-rigid registration algorithms include: (1) The Adaptive Bases Algorithm (ABA); (2) The Image Registration Toolkit (IRTK); (3) The FSL Nonlinear Image Registration Tool (FSL); (4) The Automatic Registration Tool (ART); and (5) the normalization algorithm available in SPM8. Results indicate that the choice of algorithm has little effect on the creation of group atlases. However, regions of differences between groups detected with DBM vary from algorithm to algorithm both qualitatively and quantitatively. The unique nature of the data set used in this study also permits comparison of visible anatomical differences between the groups and regions of difference detected by each algorithm. Results show that the interpretation of DBM results is difficult. Four out of the five algorithms we have evaluated detect bilateral differences between the two groups in the insular cortex, the basal ganglia, orbitofrontal cortex, as well as in the cerebellum. These correspond to differences that have been reported in the literature and that are visible in our samples. But our results also show that some algorithms detect regions that are not detected by the others and that the extent of the detected regions varies from algorithm to algorithm. These results suggest that using more than one algorithm when performing DBM studies would increase confidence in the results. Properties of the algorithms such as the similarity measure they maximize and the regularity of the deformation fields, as well as the location of differences detected with DBM, also need to be taken into account in the interpretation process. PMID:22459439

Integration of Irma tactical scene generator into directed-energy weapon system simulation

NASA Astrophysics Data System (ADS)

Owens, Monte A.; Cole, Madison B., III; Laine, Mark R.

2003-08-01

Integrated high-fidelity physics-based simulations that include engagement models, image generation, electro-optical hardware models and control system algorithms have previously been developed by Boeing-SVS for various tracking and pointing systems. These simulations, however, had always used images with featureless or random backgrounds and simple target geometries. With the requirement to engage tactical ground targets in the presence of cluttered backgrounds, a new type of scene generation tool was required to fully evaluate system performance in this challenging environment. To answer this need, Irma was integrated into the existing suite of Boeing-SVS simulation tools, allowing scene generation capabilities with unprecedented realism. Irma is a US Air Force research tool used for high-resolution rendering and prediction of target and background signatures. The MATLAB/Simulink-based simulation achieves closed-loop tracking by running track algorithms on the Irma-generated images, processing the track errors through optical control algorithms, and moving simulated electro-optical elements. The geometry of these elements determines the sensor orientation with respect to the Irma database containing the three-dimensional background and target models. This orientation is dynamically passed to Irma through a Simulink S-function to generate the next image. This integrated simulation provides a test-bed for development and evaluation of tracking and control algorithms against representative images including complex background environments and realistic targets calibrated using field measurements.

NASA GPM GV Science Requirements

NASA Technical Reports Server (NTRS)

Smith, E.

2003-01-01

An important scientific objective of the NASA portion of the GPM Mission is to generate quantitatively-based error characterization information along with the rainrate retrievals emanating from the GPM constellation of satellites. These data must serve four main purposes: (1) they must be of sufficient quality, uniformity, and timeliness to govern the observation weighting schemes used in the data assimilation modules of numerical weather prediction models; (2) they must extend over that portion of the globe accessible by the GPM core satellite to which the NASA GV program is focused - (approx.65 degree inclination); (3) they must have sufficient specificity to enable detection of physically-formulated microphysical and meteorological weaknesses in the standard physical level 2 rainrate algorithms to be used in the GPM Precipitation Processing System (PPS), i.e., algorithms which will have evolved from the TRMM standard physical level 2 algorithms; and (4) they must support the use of physical error modeling as a primary validation tool and as the eventual replacement of the conventional GV approach of statistically intercomparing surface rainrates fiom ground and satellite measurements. This approach to ground validation research represents a paradigm shift vis-&-vis the program developed for the TRMM mission, which conducted ground validation largely as a statistical intercomparison process between raingauge-derived or radar-derived rainrates and the TRMM satellite rainrate retrievals -- long after the original satellite retrievals were archived. This approach has been able to quantify averaged rainrate differences between the satellite algorithms and the ground instruments, but has not been able to explain causes of algorithm failures or produce error information directly compatible with the cost functions of data assimilation schemes. These schemes require periodic and near-realtime bias uncertainty (i.e., global space-time distributed conditional accuracy of the retrieved rainrates) and local error covariance structure (i.e., global space-time distributed error correlation information for the local 4-dimensional space-time domain -- or in simpler terms, the matrix form of precision error). This can only be accomplished by establishing a network of high quality-heavily instrumented supersites selectively distributed at a few oceanic, continental, and coastal sites. Economics and pragmatics dictate that the network must be made up of a relatively small number of sites (6-8) created through international cooperation. This presentation will address some of the details of the methodology behind the error characterization approach, some proposed solutions for expanding site-developed error properties to regional scales, a data processing and communications concept that would enable rapid implementation of algorithm improvement by the algorithm developers, and the likely available options for developing the supersite network.

Bank-firm credit network in Japan: an analysis of a bipartite network.

PubMed

Marotta, Luca; Miccichè, Salvatore; Fujiwara, Yoshi; Iyetomi, Hiroshi; Aoyama, Hideaki; Gallegati, Mauro; Mantegna, Rosario N

2015-01-01

We investigate the networked nature of the Japanese credit market. Our investigation is performed with tools of network science. In our investigation we perform community detection with an algorithm which is identifying communities composed of both banks and firms. We show that the communities obtained by directly working on the bipartite network carry information about the networked nature of the Japanese credit market. Our analysis is performed for each calendar year during the time period from 1980 to 2011. To investigate the time evolution of the networked structure of the credit market we introduce a new statistical method to track the time evolution of detected communities. We then characterize the time evolution of communities by detecting for each time evolving set of communities the over-expression of attributes of firms and banks. Specifically, we consider as attributes the economic sector and the geographical location of firms and the type of banks. In our 32-year-long analysis we detect a persistence of the over-expression of attributes of communities of banks and firms together with a slow dynamic of changes from some specific attributes to new ones. Our empirical observations show that the credit market in Japan is a networked market where the type of banks, geographical location of firms and banks, and economic sector of the firm play a role in shaping the credit relationships between banks and firms.

Bank-Firm Credit Network in Japan: An Analysis of a Bipartite Network

PubMed Central

Marotta, Luca; Miccichè, Salvatore; Fujiwara, Yoshi; Iyetomi, Hiroshi; Aoyama, Hideaki; Gallegati, Mauro; Mantegna, Rosario N.

2015-01-01

We investigate the networked nature of the Japanese credit market. Our investigation is performed with tools of network science. In our investigation we perform community detection with an algorithm which is identifying communities composed of both banks and firms. We show that the communities obtained by directly working on the bipartite network carry information about the networked nature of the Japanese credit market. Our analysis is performed for each calendar year during the time period from 1980 to 2011. To investigate the time evolution of the networked structure of the credit market we introduce a new statistical method to track the time evolution of detected communities. We then characterize the time evolution of communities by detecting for each time evolving set of communities the over-expression of attributes of firms and banks. Specifically, we consider as attributes the economic sector and the geographical location of firms and the type of banks. In our 32-year-long analysis we detect a persistence of the over-expression of attributes of communities of banks and firms together with a slow dynamic of changes from some specific attributes to new ones. Our empirical observations show that the credit market in Japan is a networked market where the type of banks, geographical location of firms and banks, and economic sector of the firm play a role in shaping the credit relationships between banks and firms. PMID:25933413

Input-output model for MACCS nuclear accident impacts estimation¹

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

Outkin, Alexander V.; Bixler, Nathan E.; Vargas, Vanessa N

Since the original economic model for MACCS was developed, better quality economic data (as well as the tools to gather and process it) and better computational capabilities have become available. The update of the economic impacts component of the MACCS legacy model will provide improved estimates of business disruptions through the use of Input-Output based economic impact estimation. This paper presents an updated MACCS model, bases on Input-Output methodology, in which economic impacts are calculated using the Regional Economic Accounting analysis tool (REAcct) created at Sandia National Laboratories. This new GDP-based model allows quick and consistent estimation of gross domesticmore » product (GDP) losses due to nuclear power plant accidents. This paper outlines the steps taken to combine the REAcct Input-Output-based model with the MACCS code, describes the GDP loss calculation, and discusses the parameters and modeling assumptions necessary for the estimation of long-term effects of nuclear power plant accidents.« less

2014 National Park visitor spending effects: economic contributions to local communities, states, and the nation

USGS Publications Warehouse

Cullinane Thomas, Catherine; Huber, Christopher; Koontz, Lynne

2015-01-01

New this year, results from the Visitor Spending Effects report series are available online via an interactive tool. Users can explore current year visitor spending, jobs, labor income, value added, and output effects by sector for national, state, and local economies. This interactive tool is available via the NPS Social Science Program webpage at http://www.nature.nps.gov/socialscience/economics.cfm.

Utilization of debate as an educational tool to learn health economics for dental students in Malaysia.

PubMed

Khan, Saad A; Omar, Hanan; Babar, Muneer Gohar; Toh, Chooi G

2012-12-01

Health economics, a special branch of science applying economic principles to the health delivery system, is a relatively young subdiscipline. The literature is scanty about teaching health economics in the medical and dental fields. Delivery methods of this topic vary from one university to another, with lectures, seminars, and independent learning reported as teaching/learning tools used for the topic. Ideally, debates should foster the development of logical reasoning and communication skills. Health economics in dentistry is taught under the community oral health module that constitutes part of an outcome-based dental curriculum in a private dental school in Kuala Lumpur, Malaysia. For this study, the students were divided into two groups: active participants (active debaters) and supporting participants (nonactive debaters). The debate style chosen for this activity was parliamentary style. Active and nonactive debaters' perceptions were evaluated before and after the activity through a structured questionnaire using a five-point rating scale addressing the topic and perceptions about debate as an educational tool. Cronbach's alpha coefficient was used as a measure of internal consistency for the questionnaire items. Among a total of eighty-two third-year dental students of two successive cohorts (thirty-eight students and forty-four students), seventy-three completed the questionnaire, yielding a response rate of 89 percent. Students' responses to the questionnaire were analyzed with the Kruskal-Wallis analysis of variance test. Results revealed that the students felt that their interest in debate, knowledge of the topic, and reinforcement of the previous knowledge had improved following participation in the debate. Within the limitations of this study, it can be concluded that debate was a useful tool in teaching health economics to dental students.

Glycemic penalty index for adequately assessing and comparing different blood glucose control algorithms

PubMed Central

Van Herpe, Tom; De Brabanter, Jos; Beullens, Martine; De Moor, Bart; Van den Berghe, Greet

2008-01-01

Introduction Blood glucose (BG) control performed by intensive care unit (ICU) nurses is becoming standard practice for critically ill patients. New (semi-automated) 'BG control' algorithms (or 'insulin titration' algorithms) are under development, but these require stringent validation before they can replace the currently used algorithms. Existing methods for objectively comparing different insulin titration algorithms show weaknesses. In the current study, a new approach for appropriately assessing the adequacy of different algorithms is proposed. Methods Two ICU patient populations (with different baseline characteristics) were studied, both treated with a similar 'nurse-driven' insulin titration algorithm targeting BG levels of 80 to 110 mg/dl. A new method for objectively evaluating BG deviations from normoglycemia was founded on a smooth penalty function. Next, the performance of this new evaluation tool was compared with the current standard assessment methods, on an individual as well as a population basis. Finally, the impact of four selected parameters (the average BG sampling frequency, the duration of algorithm application, the severity of disease, and the type of illness) on the performance of an insulin titration algorithm was determined by multiple regression analysis. Results The glycemic penalty index (GPI) was proposed as a tool for assessing the overall glycemic control behavior in ICU patients. The GPI of a patient is the average of all penalties that are individually assigned to each measured BG value based on the optimized smooth penalty function. The computation of this index returns a number between 0 (no penalty) and 100 (the highest penalty). For some patients, the assessment of the BG control behavior using the traditional standard evaluation methods was different from the evaluation with GPI. Two parameters were found to have a significant impact on GPI: the BG sampling frequency and the duration of algorithm application. A higher BG sampling frequency and a longer algorithm application duration resulted in an apparently better performance, as indicated by a lower GPI. Conclusion The GPI is an alternative method for evaluating the performance of BG control algorithms. The blood glucose sampling frequency and the duration of algorithm application should be similar when comparing algorithms. PMID:18302732

Economic Assessment: Stability, Security, Transition and Reconstruction Operations

DTIC Science & Technology

2007-03-13

strategy has incorporated economic stability as a central part of post-combat operations and nation building. A viable, growing economy has also been a...and prosperity. Economic stability served as a tool for reconstruction as well as the catalyst for integration as outlined in the Marshall Plan...for future SSTR operations as it relates to economic stability is “Privatization can be a prerequisite for economic growth, especially where

Web-based tool for visualization of electric field distribution in deep-seated body structures and planning of electroporation-based treatments.

PubMed

Marčan, Marija; Pavliha, Denis; Kos, Bor; Forjanič, Tadeja; Miklavčič, Damijan

2015-01-01

Treatments based on electroporation are a new and promising approach to treating tumors, especially non-resectable ones. The success of the treatment is, however, heavily dependent on coverage of the entire tumor volume with a sufficiently high electric field. Ensuring complete coverage in the case of deep-seated tumors is not trivial and can in best way be ensured by patient-specific treatment planning. The basis of the treatment planning process consists of two complex tasks: medical image segmentation, and numerical modeling and optimization. In addition to previously developed segmentation algorithms for several tissues (human liver, hepatic vessels, bone tissue and canine brain) and the algorithms for numerical modeling and optimization of treatment parameters, we developed a web-based tool to facilitate the translation of the algorithms and their application in the clinic. The developed web-based tool automatically builds a 3D model of the target tissue from the medical images uploaded by the user and then uses this 3D model to optimize treatment parameters. The tool enables the user to validate the results of the automatic segmentation and make corrections if necessary before delivering the final treatment plan. Evaluation of the tool was performed by five independent experts from four different institutions. During the evaluation, we gathered data concerning user experience and measured performance times for different components of the tool. Both user reports and performance times show significant reduction in treatment-planning complexity and time-consumption from 1-2 days to a few hours. The presented web-based tool is intended to facilitate the treatment planning process and reduce the time needed for it. It is crucial for facilitating expansion of electroporation-based treatments in the clinic and ensuring reliable treatment for the patients. The additional value of the tool is the possibility of easy upgrade and integration of modules with new functionalities as they are developed.

Web-based tool for visualization of electric field distribution in deep-seated body structures and planning of electroporation-based treatments

PubMed Central

2015-01-01

Background Treatments based on electroporation are a new and promising approach to treating tumors, especially non-resectable ones. The success of the treatment is, however, heavily dependent on coverage of the entire tumor volume with a sufficiently high electric field. Ensuring complete coverage in the case of deep-seated tumors is not trivial and can in best way be ensured by patient-specific treatment planning. The basis of the treatment planning process consists of two complex tasks: medical image segmentation, and numerical modeling and optimization. Methods In addition to previously developed segmentation algorithms for several tissues (human liver, hepatic vessels, bone tissue and canine brain) and the algorithms for numerical modeling and optimization of treatment parameters, we developed a web-based tool to facilitate the translation of the algorithms and their application in the clinic. The developed web-based tool automatically builds a 3D model of the target tissue from the medical images uploaded by the user and then uses this 3D model to optimize treatment parameters. The tool enables the user to validate the results of the automatic segmentation and make corrections if necessary before delivering the final treatment plan. Results Evaluation of the tool was performed by five independent experts from four different institutions. During the evaluation, we gathered data concerning user experience and measured performance times for different components of the tool. Both user reports and performance times show significant reduction in treatment-planning complexity and time-consumption from 1-2 days to a few hours. Conclusions The presented web-based tool is intended to facilitate the treatment planning process and reduce the time needed for it. It is crucial for facilitating expansion of electroporation-based treatments in the clinic and ensuring reliable treatment for the patients. The additional value of the tool is the possibility of easy upgrade and integration of modules with new functionalities as they are developed. PMID:26356007

Jane: a new tool for the cophylogeny reconstruction problem.

PubMed

Conow, Chris; Fielder, Daniel; Ovadia, Yaniv; Libeskind-Hadas, Ran

2010-02-03

This paper describes the theory and implementation of a new software tool, called Jane, for the study of historical associations. This problem arises in parasitology (associations of hosts and parasites), molecular systematics (associations of orderings and genes), and biogeography (associations of regions and orderings). The underlying problem is that of reconciling pairs of trees subject to biologically plausible events and costs associated with these events. Existing software tools for this problem have strengths and limitations, and the new Jane tool described here provides functionality that complements existing tools. The Jane software tool uses a polynomial time dynamic programming algorithm in conjunction with a genetic algorithm to find very good, and often optimal, solutions even for relatively large pairs of trees. The tool allows the user to provide rich timing information on both the host and parasite trees. In addition the user can limit host switch distance and specify multiple host switch costs by specifying regions in the host tree and costs for host switches between pairs of regions. Jane also provides a graphical user interface that allows the user to interactively experiment with modifications to the solutions found by the program. Jane is shown to be a useful tool for cophylogenetic reconstruction. Its functionality complements existing tools and it is therefore likely to be of use to researchers in the areas of parasitology, molecular systematics, and biogeography.

AHIMSA - Ad hoc histogram information measure sensing algorithm for feature selection in the context of histogram inspired clustering techniques

NASA Technical Reports Server (NTRS)

Dasarathy, B. V.

1976-01-01

An algorithm is proposed for dimensionality reduction in the context of clustering techniques based on histogram analysis. The approach is based on an evaluation of the hills and valleys in the unidimensional histograms along the different features and provides an economical means of assessing the significance of the features in a nonparametric unsupervised data environment. The method has relevance to remote sensing applications.

Investigation on application of genetic algorithms to optimal reactive power dispatch of power systems

NASA Astrophysics Data System (ADS)

Wu, Q. H.; Ma, J. T.

1993-09-01

A primary investigation into application of genetic algorithms in optimal reactive power dispatch and voltage control is presented. The application was achieved, based on (the United Kingdom) National Grid 48 bus network model, using a novel genetic search approach. Simulation results, compared with that obtained using nonlinear programming methods, are included to show the potential of applications of the genetic search methodology in power system economical and secure operations.

CO-Benefits Risk Assessment (COBRA) Health Impacts Screening and Mapping Tool

EPA Pesticide Factsheets

The COBRA (Co-Benefits Risk Assessment) screening tool can be used by state and local governments to estimate the health and economic benefits of clean energy policies. Find information about how to use the tool here.

A Shallow Layer Approach for Geo-flow emplacement

NASA Astrophysics Data System (ADS)

Costa, A.; Folch, A.; Mecedonio, G.

2009-04-01

Geophysical flows such as lahars or lava flows severely threat the communities located on or near the volcano flanks. Risks and damages caused by the propagation of this kind of flows require a quantitative description of this phenomenon and reliable tools for forecasting their emplacement. Computational models are a valuable tool for planning risk mitigation countermeasures, such as human intervention to force flow diversion, artificial barriers, and allow for significant economical and social benefits. A FORTRAN 90 code based on a Shallow Layer Approach for Geo-flows (SLAG) for describing transport and emplacement of diluted lahars, water and lava was developed in both serial and parallel version. Three rheological models, such as those describing i) a viscous, ii) a turbulent, and iii) a dilatant flow respectively, were implemented in order to describe transport of lavas, water and diluted lahars. The code was made user-friendly by creating some interfaces that allow the user to easily define the problem, extract and interpolate the topography of the simulation domain. Moreover SLAG outputs can be written in both GRD format (e.g., Surfer), NetCDF format, or visualized directly in GoogleEarth. In SLAG the governing equations were treated using a Godunov splitting method following George (2008) algorithm based on a Riemann solver for the shallow water equations that decomposes an augmented state variable the depth, momentum, momentum flux, and bathymetry into four propagating discontinuities or waves. For our application, the algorithm was generalized for solving the energy equation. For validating the code in simulating real geophysical flows, we performed few simulations the lava flow event of the the 3rd and 4th January 1992 Etna eruption, the July 2001 Etna lava flows, January 2002 Nyragongo lava flows and few test cases for simulating transport of diluted lahars. Ref: George, D.L. (2008), Augmented Riemann Solvers for the Shallow Water Equations over Variable Topography with Steady States and Inundation, J. Comput. Phys., 227 (6), 3089-3113, doi:10.1016/j.jcp.2007.10.027.

Anthropogenic Sulphur Dioxide Load over China as Observed from Different Satellite Sensors

NASA Technical Reports Server (NTRS)

Koukouli, M. E.; Balis, D. S.; Johannes Van Der A, Ronald; Theys, N.; Hedelt, P.; Richter, A.; Krotkov, N.; Li, Can; Taylor, M.

2016-01-01

China, with its rapid economic growth and immense exporting power, has been the focus of many studies during this previous decade quantifying its increasing emissions contribution to the Earth's atmosphere. With a population slowly shifting towards enlarged power and purchasing needs, the ceaseless inauguration of new power plants, smelters, refineries and industrial parks leads infallibly to increases in sulphur dioxide, SO2, emissions. The recent capability of next generation algorithms as well as new space-borne instruments to detect anthropogenic SO2 loads has enabled a fast advancement in this field. In the following work, algorithms providing total SO2 columns over China based on SCIAMACHY/Envisat, OMI/Aura and GOME2/MetopA observations are presented. The need for post-processing and gridding of the SO2 fields is further revealed in this work, following the path of previous publications. Further, it is demonstrated that the usage of appropriate statistical tools permits studying parts of the datasets typically excluded, such as the winter months loads. Focusing on actual point sources, such as megacities and known power plant locations, instead of entire provinces, monthly mean time series have been examined in detail. The sharp decline in SO2 emissions in more than 90% - 95% of the locations studied confirms the recent implementation of government desulphurisation legislation; however, locations with increases, even for the previous five years, are also identified. These belong to provinces with emerging economies which are in haste to install power plants and are possibly viewed leniently by the authorities, in favour of growth. The SO2 load seasonality has also been examined in detail with a novel mathematical tool, with 70% of the point sources having a statistically significant annual cycle with highs in winter and lows in summer, following the heating requirements of the Chinese population.

Modelisation et optimisation des systemes energetiques a l'aide d'algorithmes evolutifs

NASA Astrophysics Data System (ADS)

Hounkonnou, Sessinou M. William

Optimization of thermal and nuclear plant has many economics advantages as well as environmentals. Therefore new operating points research and use of new tools to achieve those kind of optimization are the subject of many studies. In this momentum, this project is intended to optimize energetic systems precisely the secondary loop of Gentilly 2 nuclear plant using both the extraction of the high and low pressure turbine as well as the extraction of the mixture coming from the steam generator. A detailed thermodynamic model of the various equipment of the secondary loop such as the feed water heaters, the moisture separator-reheater, the dearator, the condenser and the turbine is carried out. We use Matlab software (version R2007b, 2007) with the library for the thermodynamic properties of water and steam (XSteam pour Matlab, Holmgren, 2006). A model of the secondary loop is than obtained thanks to the assembly of the different equipments. A simulation of the equipment and the complete cycle enabled us to release two objectifs functions knowing as the net output and the efficiency which evolve in an opposite way according to the variation of the extractions. Due to the complexity of the problem, we use a method based on the genetic algorithms for the optimization. More precisely we used a tool which was developed at the "Institut de genie nucleaire" named BEST (Boundary Exploration Search Technique) developed in VBA* (Visual BASIC for Application) for its ability to converge more quickly and to carry out a more exhaustive search at the border of the optimal solutions. The use of the DDE (Dynamic Data Exchange) enables us to link the simulator and the optimizer. The results obtained show us that they still exists several combinations of extractions which make it possible to obtain a better point of operation for the improvement of the performance of Gentilly 2 power station secondary loop. *Trademark of Microsoft

Anthropogenic sulphur dioxide load over China as observed from different satellite sensors

NASA Astrophysics Data System (ADS)

Koukouli, M. E.; Balis, D. S.; van der A, Ronald Johannes; Theys, N.; Hedelt, P.; Richter, A.; Krotkov, N.; Li, C.; Taylor, M.

2016-11-01

China, with its rapid economic growth and immense exporting power, has been the focus of many studies during this previous decade quantifying its increasing emissions contribution to the Earth's atmosphere. With a population slowly shifting towards enlarged power and purchasing needs, the ceaseless inauguration of new power plants, smelters, refineries and industrial parks leads infallibly to increases in sulphur dioxide, SO2, emissions. The recent capability of next generation algorithms as well as new space-borne instruments to detect anthropogenic SO2 loads has enabled a fast advancement in this field. In the following work, algorithms providing total SO2 columns over China based on SCIAMACHY/Envisat, OMI/Aura and GOME2/MetopA observations are presented. The need for post-processing and gridding of the SO2 fields is further revealed in this work, following the path of previous publications. Further, it is demonstrated that the usage of appropriate statistical tools permits studying parts of the datasets typically excluded, such as the winter months loads. Focusing on actual point sources, such as megacities and known power plant locations, instead of entire provinces, monthly mean time series have been examined in detail. The sharp decline in SO2 emissions in more than 90%-95% of the locations studied confirms the recent implementation of government desulphurisation legislation; however, locations with increases, even for the previous five years, are also identified. These belong to provinces with emerging economies which are in haste to install power plants and are possibly viewed leniently by the authorities, in favour of growth. The SO2 load seasonality has also been examined in detail with a novel mathematical tool, with 70% of the point sources having a statistically significant annual cycle with highs in winter and lows in summer, following the heating requirements of the Chinese population.

ADAM: Analysis of Discrete Models of Biological Systems Using Computer Algebra

PubMed Central

2011-01-01

Background Many biological systems are modeled qualitatively with discrete models, such as probabilistic Boolean networks, logical models, Petri nets, and agent-based models, to gain a better understanding of them. The computational complexity to analyze the complete dynamics of these models grows exponentially in the number of variables, which impedes working with complex models. There exist software tools to analyze discrete models, but they either lack the algorithmic functionality to analyze complex models deterministically or they are inaccessible to many users as they require understanding the underlying algorithm and implementation, do not have a graphical user interface, or are hard to install. Efficient analysis methods that are accessible to modelers and easy to use are needed. Results We propose a method for efficiently identifying attractors and introduce the web-based tool Analysis of Dynamic Algebraic Models (ADAM), which provides this and other analysis methods for discrete models. ADAM converts several discrete model types automatically into polynomial dynamical systems and analyzes their dynamics using tools from computer algebra. Specifically, we propose a method to identify attractors of a discrete model that is equivalent to solving a system of polynomial equations, a long-studied problem in computer algebra. Based on extensive experimentation with both discrete models arising in systems biology and randomly generated networks, we found that the algebraic algorithms presented in this manuscript are fast for systems with the structure maintained by most biological systems, namely sparseness and robustness. For a large set of published complex discrete models, ADAM identified the attractors in less than one second. Conclusions Discrete modeling techniques are a useful tool for analyzing complex biological systems and there is a need in the biological community for accessible efficient analysis tools. ADAM provides analysis methods based on mathematical algorithms as a web-based tool for several different input formats, and it makes analysis of complex models accessible to a larger community, as it is platform independent as a web-service and does not require understanding of the underlying mathematics. PMID:21774817

Harmony Search as a Powerful Tool for Feature Selection in QSPR Study of the Drugs Lipophilicity.

PubMed

Bahadori, Behnoosh; Atabati, Morteza

2017-01-01

Aims & Scope: Lipophilicity represents one of the most studied and most frequently used fundamental physicochemical properties. In the present work, harmony search (HS) algorithm is suggested to feature selection in quantitative structure-property relationship (QSPR) modeling to predict lipophilicity of neutral, acidic, basic and amphotheric drugs that were determined by UHPLC. Harmony search is a music-based metaheuristic optimization algorithm. It was affected by the observation that the aim of music is to search for a perfect state of harmony. Semi-empirical quantum-chemical calculations at AM1 level were used to find the optimum 3D geometry of the studied molecules and variant descriptors (1497 descriptors) were calculated by the Dragon software. The selected descriptors by harmony search algorithm (9 descriptors) were applied for model development using multiple linear regression (MLR). In comparison with other feature selection methods such as genetic algorithm and simulated annealing, harmony search algorithm has better results. The root mean square error (RMSE) with and without leave-one out cross validation (LOOCV) were obtained 0.417 and 0.302, respectively. The results were compared with those obtained from the genetic algorithm and simulated annealing methods and it showed that the HS is a helpful tool for feature selection with fine performance. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Meta-heuristic algorithms as tools for hydrological science

NASA Astrophysics Data System (ADS)

Yoo, Do Guen; Kim, Joong Hoon

2014-12-01

In this paper, meta-heuristic optimization techniques are introduced and their applications to water resources engineering, particularly in hydrological science are introduced. In recent years, meta-heuristic optimization techniques have been introduced that can overcome the problems inherent in iterative simulations. These methods are able to find good solutions and require limited computation time and memory use without requiring complex derivatives. Simulation-based meta-heuristic methods such as Genetic algorithms (GAs) and Harmony Search (HS) have powerful searching abilities, which can occasionally overcome the several drawbacks of traditional mathematical methods. For example, HS algorithms can be conceptualized from a musical performance process and used to achieve better harmony; such optimization algorithms seek a near global optimum determined by the value of an objective function, providing a more robust determination of musical performance than can be achieved through typical aesthetic estimation. In this paper, meta-heuristic algorithms and their applications (focus on GAs and HS) in hydrological science are discussed by subject, including a review of existing literature in the field. Then, recent trends in optimization are presented and a relatively new technique such as Smallest Small World Cellular Harmony Search (SSWCHS) is briefly introduced, with a summary of promising results obtained in previous studies. As a result, previous studies have demonstrated that meta-heuristic algorithms are effective tools for the development of hydrological models and the management of water resources.

New Tools in Orthology Analysis: A Brief Review of Promising Perspectives

PubMed Central

Nichio, Bruno T. L.; Marchaukoski, Jeroniza Nunes; Raittz, Roberto Tadeu

2017-01-01

Nowadays defying homology relationships among sequences is essential for biological research. Within homology the analysis of orthologs sequences is of great importance for computational biology, annotation of genomes and for phylogenetic inference. Since 2007, with the increase in the number of new sequences being deposited in large biological databases, researchers have begun to analyse computerized methodologies and tools aimed at selecting the most promising ones in the prediction of orthologous groups. Literature in this field of research describes the problems that the majority of available tools show, such as those encountered in accuracy, time required for analysis (especially in light of the increasing volume of data being submitted, which require faster techniques) and the automatization of the process without requiring manual intervention. Conducting our search through BMC, Google Scholar, NCBI PubMed, and Expasy, we examined more than 600 articles pursuing the most recent techniques and tools developed to solve most the problems still existing in orthology detection. We listed the main computational tools created and developed between 2011 and 2017, taking into consideration the differences in the type of orthology analysis, outlining the main features of each tool and pointing to the problems that each one tries to address. We also observed that several tools still use as their main algorithm the BLAST “all-against-all” methodology, which entails some limitations, such as limited number of queries, computational cost, and high processing time to complete the analysis. However, new promising tools are being developed, like OrthoVenn (which uses the Venn diagram to show the relationship of ortholog groups generated by its algorithm); or proteinOrtho (which improves the accuracy of ortholog groups); or ReMark (tackling the integration of the pipeline to turn the entry process automatic); or OrthAgogue (using algorithms developed to minimize processing time); and proteinOrtho (developed for dealing with large amounts of biological data). We made a comparison among the main features of four tool and tested them using four for prokaryotic genomas. We hope that our review can be useful for researchers and will help them in selecting the most appropriate tool for their work in the field of orthology. PMID:29163633

New Tools in Orthology Analysis: A Brief Review of Promising Perspectives.

PubMed

Nichio, Bruno T L; Marchaukoski, Jeroniza Nunes; Raittz, Roberto Tadeu

2017-01-01

Nowadays defying homology relationships among sequences is essential for biological research. Within homology the analysis of orthologs sequences is of great importance for computational biology, annotation of genomes and for phylogenetic inference. Since 2007, with the increase in the number of new sequences being deposited in large biological databases, researchers have begun to analyse computerized methodologies and tools aimed at selecting the most promising ones in the prediction of orthologous groups. Literature in this field of research describes the problems that the majority of available tools show, such as those encountered in accuracy, time required for analysis (especially in light of the increasing volume of data being submitted, which require faster techniques) and the automatization of the process without requiring manual intervention. Conducting our search through BMC, Google Scholar, NCBI PubMed, and Expasy, we examined more than 600 articles pursuing the most recent techniques and tools developed to solve most the problems still existing in orthology detection. We listed the main computational tools created and developed between 2011 and 2017, taking into consideration the differences in the type of orthology analysis, outlining the main features of each tool and pointing to the problems that each one tries to address. We also observed that several tools still use as their main algorithm the BLAST "all-against-all" methodology, which entails some limitations, such as limited number of queries, computational cost, and high processing time to complete the analysis. However, new promising tools are being developed, like OrthoVenn (which uses the Venn diagram to show the relationship of ortholog groups generated by its algorithm); or proteinOrtho (which improves the accuracy of ortholog groups); or ReMark (tackling the integration of the pipeline to turn the entry process automatic); or OrthAgogue (using algorithms developed to minimize processing time); and proteinOrtho (developed for dealing with large amounts of biological data). We made a comparison among the main features of four tool and tested them using four for prokaryotic genomas. We hope that our review can be useful for researchers and will help them in selecting the most appropriate tool for their work in the field of orthology.

A user's guide to coping with estuarine management bureaucracy: An Estuarine Planning Support System (EPSS) tool.

PubMed

Lonsdale, Jemma; Nicholson, Rose; Weston, Keith; Elliott, Michael; Birchenough, Andrew; Sühring, Roxana

2018-02-01

Estuaries are amongst the most socio-economically and ecologically important environments however, due to competing and conflicting demands, management is often challenging with a complex legislative framework managed by multiple agencies. To facilitate the understanding of this legislative framework, we have developed a GISbased Estuarine Planning Support System tool. The tool integrates the requirements of the relevant legislation and provides a basis for assessing the current environmental state of an estuary as well as informing and assessing new plans to ensure a healthy estuarine state. The tool ensures that the information is easily accessible for regulators, managers, developers and the public. The tool is intended to be adaptable, but is assessed using the Humber Estuary, United Kingdom as a case study area. The successful application of the tool for complex socio-economic and environmental systems demonstrates that the tool can efficiently guide users through the complex requirements needed to support sustainable development. Crown Copyright © 2017. Published by Elsevier Ltd. All rights reserved.

Difet: Distributed Feature Extraction Tool for High Spatial Resolution Remote Sensing Images

NASA Astrophysics Data System (ADS)

Eken, S.; Aydın, E.; Sayar, A.

2017-11-01

In this paper, we propose distributed feature extraction tool from high spatial resolution remote sensing images. Tool is based on Apache Hadoop framework and Hadoop Image Processing Interface. Two corner detection (Harris and Shi-Tomasi) algorithms and five feature descriptors (SIFT, SURF, FAST, BRIEF, and ORB) are considered. Robustness of the tool in the task of feature extraction from LandSat-8 imageries are evaluated in terms of horizontal scalability.

Interface of the general fitting tool GENFIT2 in PandaRoot

NASA Astrophysics Data System (ADS)

Prencipe, Elisabetta; Spataro, Stefano; Stockmanns, Tobias; PANDA Collaboration

2017-10-01

\\bar{{{P}}}ANDA is a planned experiment at FAIR (Darmstadt) with a cooled antiproton beam in a range [1.5; 15] GeV/c, allowing a wide physics program in nuclear and particle physics. It is the only experiment worldwide, which combines a solenoid field (B=2T) and a dipole field (B=2Tm) in a spectrometer with a fixed target topology, in that energy regime. The tracking system of \\bar{{{P}}}ANDA involves the presence of a high performance silicon vertex detector, a GEM detector, a straw-tubes central tracker, a forward tracking system, and a luminosity monitor. The offline tracking algorithm is developed within the PandaRoot framework, which is a part of the FairRoot project. The tool here presented is based on algorithms containing the Kalman Filter equations and a deterministic annealing filter. This general fitting tool (GENFIT2) offers to users also a Runge-Kutta track representation, and interfaces with Millepede II (useful for alignment) and RAVE (vertex finder). It is independent on the detector geometry and the magnetic field map, and written in C++ object-oriented modular code. Several fitting algorithms are available with GENFIT2, with user-adjustable parameters; therefore the tool is of friendly usage. A check on the fit convergence is done by GENFIT2 as well. The Kalman-Filter-based algorithms have a wide range of applications; among those in particle physics they can perform extrapolations of track parameters and covariance matrices. The adoptions of the PandaRoot framework to connect to Genfit2 are described, and the impact of GENFIT2 on the physics simulations of \\bar{{{P}}}ANDA are shown: significant improvement is reported for those channels where a good low momentum tracking is required (pT < 400 MeV/c).

Case Study: Test Results of a Tool and Method for In-Flight, Adaptive Control System Verification on a NASA F-15 Flight Research Aircraft

NASA Technical Reports Server (NTRS)

Jacklin, Stephen A.; Schumann, Johann; Guenther, Kurt; Bosworth, John

2006-01-01

Adaptive control technologies that incorporate learning algorithms have been proposed to enable autonomous flight control and to maintain vehicle performance in the face of unknown, changing, or poorly defined operating environments [1-2]. At the present time, however, it is unknown how adaptive algorithms can be routinely verified, validated, and certified for use in safety-critical applications. Rigorous methods for adaptive software verification end validation must be developed to ensure that. the control software functions as required and is highly safe and reliable. A large gap appears to exist between the point at which control system designers feel the verification process is complete, and when FAA certification officials agree it is complete. Certification of adaptive flight control software verification is complicated by the use of learning algorithms (e.g., neural networks) and degrees of system non-determinism. Of course, analytical efforts must be made in the verification process to place guarantees on learning algorithm stability, rate of convergence, and convergence accuracy. However, to satisfy FAA certification requirements, it must be demonstrated that the adaptive flight control system is also able to fail and still allow the aircraft to be flown safely or to land, while at the same time providing a means of crew notification of the (impending) failure. It was for this purpose that the NASA Ames Confidence Tool was developed [3]. This paper presents the Confidence Tool as a means of providing in-flight software assurance monitoring of an adaptive flight control system. The paper will present the data obtained from flight testing the tool on a specially modified F-15 aircraft designed to simulate loss of flight control faces.

Flexible Energy Scheduling Tool for Integrating Variable Generation | Grid

Science.gov Websites

, security-constrained economic dispatch, and automatic generation control programs. DOWNLOAD PAPER Electric commitment, security-constrained economic dispatch, and automatic generation control sub-models. Each sub resolutions and operating strategies can be explored. FESTIV produces not only economic metrics but also

USAID-NREL Partnership | NREL

Science.gov Websites

Development Impacts (I-JEDI) Developing a transparent, market-based energy sector improves competitiveness development objectives. The International Jobs and Economic Development Impacts (I-JEDI) tool is an economic model that helps users analyze gross economic impacts of renewable energy projects (such as wind, solar

GPURFSCREEN: a GPU based virtual screening tool using random forest classifier.

PubMed

Jayaraj, P B; Ajay, Mathias K; Nufail, M; Gopakumar, G; Jaleel, U C A

2016-01-01

In-silico methods are an integral part of modern drug discovery paradigm. Virtual screening, an in-silico method, is used to refine data models and reduce the chemical space on which wet lab experiments need to be performed. Virtual screening of a ligand data model requires large scale computations, making it a highly time consuming task. This process can be speeded up by implementing parallelized algorithms on a Graphical Processing Unit (GPU). Random Forest is a robust classification algorithm that can be employed in the virtual screening. A ligand based virtual screening tool (GPURFSCREEN) that uses random forests on GPU systems has been proposed and evaluated in this paper. This tool produces optimized results at a lower execution time for large bioassay data sets. The quality of results produced by our tool on GPU is same as that on a regular serial environment. Considering the magnitude of data to be screened, the parallelized virtual screening has a significantly lower running time at high throughput. The proposed parallel tool outperforms its serial counterpart by successfully screening billions of molecules in training and prediction phases.

CNV detection method optimized for high-resolution arrayCGH by normality test.

PubMed

Ahn, Jaegyoon; Yoon, Youngmi; Park, Chihyun; Park, Sanghyun

2012-04-01

High-resolution arrayCGH platform makes it possible to detect small gains and losses which previously could not be measured. However, current CNV detection tools fitted to early low-resolution data are not applicable to larger high-resolution data. When CNV detection tools are applied to high-resolution data, they suffer from high false-positives, which increases validation cost. Existing CNV detection tools also require optimal parameter values. In most cases, obtaining these values is a difficult task. This study developed a CNV detection algorithm that is optimized for high-resolution arrayCGH data. This tool operates up to 1500 times faster than existing tools on a high-resolution arrayCGH of whole human chromosomes which has 42 million probes whose average length is 50 bases, while preserving false positive/negative rates. The algorithm also uses a normality test, thereby removing the need for optimal parameters. To our knowledge, this is the first formulation for CNV detecting problems that results in a near-linear empirical overall complexity for real high-resolution data. Copyright © 2012 Elsevier Ltd. All rights reserved.