Cognitive computing models for estimation of reference evapotranspiration: A review

Irrigation practices can be advanced by the aid of cognitive computing models. Repeated droughts, population expansion and the impact of global warming collectively impose rigorous restrictions over irrigation practices. Reference evapotranspiration (ET₀) is a vital factor to predict the crop water requirements based on climate data. There are many techniques available for the prediction of ET₀. An efficient ET₀ prediction model plays an important role in irrigation system to increase water productivity. In the present study, a review has been carried out over cognitive computing models used for the estimation of ET₀. Review exhibits that artificial neural network (ANN) approach outperforms support vector machine (SVM) and genetic programming (GP). Second order neural network (SONN) is the most promising approach among ANN models.     

The Stochastic Goal Programming Model: Theory and Applications

Supported by a network of researchers and practitioners, the goal programming (GP) model is alive today more than ever and is continually fed with theoretical developments and new applications with resounding success. The standard formulation of the GP model was introduced in the earliest of 1960s, and since then, important extensions and numerous applications have been proposed. One of these variants is the stochastic GP model that deals with the uncertainty of some decision‐making situations by using stochastic calculus. In such a situation, the decision maker is not able to assess with certainty the different parameters. However, he or she can provide some information regarding the likelihood of occurrence of the decision‐making parameter values. The aim of this paper is to highlight the main methodological developments of the stochastic GP model and to present an overview of its applications in several domains. Copyright © 2012 John Wiley & Sons, Ltd.     

A note on the use of goal programming for the multiplicative AHP

The main aim of this paper is to identify the opportunities of utilizing goal programming (GP) in the multiplicative analytic hierarchy process (MAHP). It starts with the issue of weight derivation from judgemental matrices. The use of GP for the weight derivation problem is not new, but GP is viewed in this paper from the perspective of augmenting the capabilities of the widely used row geometric mean method (RGMM) of the logarithmic least squares technique (LLST). Different possible approaches using GP are discussed. It is shown that the formulation of the GP problem can be easily modified to provide the same weights as those of the LLST. While this proposed GP technique is not superior to the RGMM in terms of computational ease or speed, it is quite useful in solving certain other problems of the MAHP, such as interval judgements and missing judgements, which cannot be readily solved by the RGMM. The proposed technique provides extensive scope for utilizing the vast literature on non-linear programming, say, for conducting sensitivity analysis. It also has the potential to be useful to more complicated issues of the MAHP, such as group decision making and interlevel dependence, hitherto little explored areas of the MAHP. © 1997 John Wiley & Sons, Ltd.     

The operation of Lake Kariba: A multiobjective decision analysis

The paper describes a successful application of Bayesian decision analysis to the operation of the Lake Kariba hydropower system. This management problem is complicated by the high uncertainty of the inflow process, multiple and conflicting objectives and the influence of time on some of the parameters in the management task. Inflows to the reservoir are forecast through dynamic linear models. Managerial preferences are modelled through a multiattribute utility function. Since the solution of the exact model is computationally too demanding, a heuristic method is applied to find a feasible control strategy. A comparison with results obtained by methods used previously demonstrates the superiority of the methodology presented here.     

Preference Structure Modelling for Multi-objective Decision Making: A Goal–Programming Approach

We propose a new approach for modelling preference structures in multi-objective decision-making (MODM) problems. The basic idea of the approach is to first develop PROMETHEE-influenced objective functions and then to use these to reformulate the problem as a distance-based goal–programming (GP) model. Three basic functional forms are proposed and explicit expressions are developed for them. Among other things, the expressions allow for the straightforward development of an interactive framework while keeping the information requirements from the decision maker (DM) at a minimum. An ‘automatic’ piecewise linear approximation scheme is proposed for solving the GP model. © 1997 by John Wiley & Sons, Ltd. J. Multi-Crit. Decis. Anal. 6 : 150–154 (1997) No. of Figures: 0. No. of Tables: 0. No. of References: 12.     

Integer programs for one- and two-mode blockmodeling based on prespecified image matrices for structural and regular equivalence

Establishing blockmodels for one- and two-mode binary network matrices has typically been accomplished using multiple restarts of heuristic algorithms that minimize functions of inconsistency with an ideal block structure. Although these algorithms likely yield exceptional performance, they are not assured to provide blockmodels that optimize the functional indices. In this paper, we present integer programming models that, for a prespecified image matrix, can produce guaranteed optimal solutions for matrices of nontrivial size. Accordingly, analysts performing a confirmatory analysis of a prespecified blockmodel structure can apply our models directly to obtain an optimal solution. In exploratory cases where a blockmodel structure is not prespecified, we recommend a two-stage procedure, where a heuristic method is first used to identify an image matrix and the integer program is subsequently formulated and solved to identify the optimal solution for that image matrix. Although best suited for ideal block structures associated with structural equivalence, the integer programming models have the flexibility to accommodate functional indices pertaining to regular equivalence. Computational results are reported for a variety of one- and two-mode matrices from the blockmodeling literature.     

Several programming models with unascertained parameters and their applications

Several programming models are introduced with the consideration of available unascertained information. In this case, the so‐called unascertained information is some numerical values whose ranges are known but their exact values are not. These models resolve several vital weaknesses of the traditional programming methods to a certain degree. Our study includes considerations of linear and non‐linear programming models with grey parameters, grey 0–1 programming, and satisfactory and quasi‐optimal solutions of grey linear programmings. Finally, some practical applications are given in order to test the applicability of our theory. Copyright © 1999 John Wiley & Sons, Ltd.     

An interactive multicriteria decision model for multipurpose reservoir management: the Shellmouth Reservoir

Reservoir management is inherently multicriterial, since any release decision involves implicit trade-offs between various conflicting objectives. The release decision reflects concerns such as flood protection, generation of hydroelectric power, dilution of wastewater and heated effluent, supply of municipal, agricultural and industrial water, maintenance of environmental standards and satisfaction of recreational needs. This paper presents a framework for analysing trade-offs between several decision criteria and includes the dilution of heated effluents from downstream thermoelectric power generation in an optimization model for reservoir management. The model is formulated and analysed in an interactive multicriteria decision-making (MCDM) modelling framework. Rather than providing specific target levels or ad hoc constants in a goal-programming framework as proposed elsewhere, this multicriteria framework suggests a systematic way of evaluating trade-offs by progressive preference assessment. The MCDM model, based on a Chebyshev metric and a contracted cone approach, is learning-oriented and permits a natural exploration of the decision space while maintaining non-dominated decisions. A detailed case study of the Shellmouth Reservoir in Manitoba, Canada serves as an illustration of the model. © 1998 John Wiley & Sons, Ltd.     

Aspiration/reservation-based decision support—a step beyond goal programming

Real-life decision problems are usually so complex that they cannot be modelled with a single objective function, thus creating a need for clear and efficient techniques for handling multiple criteria to support the decision process. A widely used technique and one commonly taught in general OR/MS courses is goal programming, which is clear and appealing. On the other hand, goal programming is strongly criticized by multiple-criteria optimization specialists for its non-compliance with the efficiency (Pareto-optimality) principle. In this paper we show how the implementation techniques of goal programming can be used to model the reference point method and its extension, aspiration/reservation-based decision support. Thereby we show a congruence between these approaches and suggest how the GP model with relaxation of some traditional assumptions can be extended to an efficient decision support technique meeting the efficiency principle and other standards of multiobjective optimization theory.     

Optimal Least-Squares Unidimensional Scaling: Improved Branch-and-Bound Procedures and Comparison to Dynamic Programming

There are two well-known methods for obtaining a guaranteed globally optimal solution to the problem of least-squares unidimensional scaling of a symmetric dissimilarity matrix: (a) dynamic programming, and (b) branch-and-bound. Dynamic programming is generally more efficient than branch-and-bound, but the former is limited to matrices with approximately 26 or fewer objects because of computer memory limitations. We present some new branch-and-bound procedures that improve computational efficiency, and enable guaranteed globally optimal solutions to be obtained for matrices with up to 35 objects. Experimental tests were conducted to compare the relative performances of the new procedures, a previously published branch-and-bound algorithm, and a dynamic programming solution strategy. These experiments, which included both synthetic and empirical dissimilarity matrices, yielded the following findings: (a) the new branch-and-bound procedures were often drastically more efficient than the previously published branch-and-bound algorithm, (b) when computationally feasible, the dynamic programming approach was more efficient than each of the branch-and-bound procedures, and (c) the new branch-and-bound procedures require minimal computer memory and can provide optimal solutions for matrices that are too large for dynamic programming implementation.The authors gratefully acknowledge the helpful comments of three anonymous reviewers and the Editor. We especially thank Larry Hubert and one of the reviewers for providing us with the MATLAB files for optimal and heuristic least-squares unidimensional scaling methods.This revised article was published online in June 2005 with all corrections incorporated.     

Optimal trade-offs of multiple factors in transfer pricing problems

Transfer pricing problems have been extensively researched by a number of scholars. It is well recognized that transfer pricing problems have a multiple-criteria (objectives) feature and can be formulated as a model of multiple-criteria linear programming. However, few methods have the capability of dealing with all possible optimal trade-offs of multiple criteria in optimal solutions of the models. In this paper a linear multiple-factor model is developed to provide managers with a more systematic and comprehensive scenario of all possible optimal transfer prices depending on both multiple criteria and multiple constraint levels. The trade-offs of all possible optimal transfer prices can be used as a basis for managers of a corporation to make a high-quality decision in selecting their transfer pricing systems for business competition. © 1998 John Wiley & Sons, Ltd.     

Resource Allocation by Greek and Dutch Students: A Test of Three Models

Three models are presented that explain cross-cultural differences in the allocation of resources to various social categories. (1) Individualism-collectivism predicts, for all kinds of resources, a higher allocation in collectivistic than in individualistic societies to members of one's in group. (2) Kagitçibasi's model of family change makes a distinction between material and nonmaterial resources. Collectivistic value patterns should lead to higher allocation of nonmaterial resources than found in individualistic societies, but no such difference is expected for material investments. Both these models assume cross-cultural differences in value orientations at a high level of generality. (3) An alternative is proposed, predicting universal patterns of exchange with only context-specific cross-cultural variations at a low level of generality. Hypotheses derived from the three models are tested with data from Greece and The Netherlands. It is concluded that the results are most compatible with the third model.     

Search and rescue operation in flooded areas: A survey on emerging sensor networking-enabled IoT-oriented technologies and applications

The climate and weather dynamics in the past few years has driven a massive increase in the number and intensity of flood disasters, which severely claim casualties in human, goods and properties. Aimed to reduce these casualties, emerging software-defined internet protocol-based communication technologies in the form of Internet of Things (IoT) have attracted strong interests from disaster mitigation stakeholders to rapidly locate victims and acquire their relevant information, which in turn can boost up the efficiency and effectiveness of Search and Rescue (SAR) missions. In order to capture state-of-the-art development and technological challenges, this paper presents an extensive review on the flood SAR systems, highlighting some of the key emerging IoT technologies that prove or are potentially useful in improving the SAR operation by the rescuers. Furthermore, a comprehensive study on different existing communication technologies for SAR is provided, covering the system architecture, communication network compositions and applications. Based on the critical analysis of existing works, this paper puts forward a proposal on an IoT-aided integrated flood management framework to support SAR in the flood-catchment areas, leveraging upon three-domain (ground, water and air) collaborative wireless networks.     

Origins,uses of,and relations between goal programming and data envelopment analysis

Origins and uses of ‘goal programming’ and ‘data envelopment analysis’ (DEA) are identified and discussed. The purpose of this paper is not only to review some of the history of these developments, but also to show some of their uses (e.g. in statistical regression formulations) in order to suggest paths for possible further developments. Turning to how the two types of models relate to each other, the ‘additive model’ of DEA is shown to have the same structure as a goal programming model in which only ‘one‐sided deviations’ are permitted. A way for formally relating the two to each other is then provided. However, the objectives are differently oriented because goal programming is directed to future performances as part of the planning function whereas DEA is directed to evaluating past performances as part of the control function of management. Other possible ways of comparing and combining the two approaches are also noted including statistical regressions that utilize goal programming to ensure that the resulting estimates satisfy the multi‐criteria conditions that are often encountered in managerial applications. Both goal programming and DEA originated in actual applications that were successfully addressed. The research was then generalized and published. This leads to what is referred to as an ‘applications‐driven theory’ strategy for research that is also described in this paper. Copyright © 2006 John Wiley & Sons, Ltd.     

A general experiment programming system for the IBM 1800

A system is described that meets some user requirements of programming ease, general applicability, and simultaneous multiple S and laboratory operation. The system is based on an extremely flexible time-sharing real-time monitor and a user-level task-oriented programming language which together free the user from all multiple S bookkeeping programming. The conceptually simple language consists of simple commands for operations at the level of experimental procedure, such as displaying stimuli, collecting and timing responses, providing time delays, and recording data. Other commands support string manipulation, arithmetic, and disk I/O. The system is programmed only for the IBM 1800; however, it represents a successful approach to laboratory computerization.     

On portfolio management with value at risk and uncertain returns via an artificial neural network scheme

This paper focuses on the computation issue of portfolio optimization with scenario-based Value-at-Risk. The main idea is to replace the portfolio selection models with linear programming problems. According to the convex optimization theory and some concepts of ordinary differential equations, a neural network model for solving linear programming problems is presented. The equilibrium point of the proposed model is proved to be equivalent to the optimal solution of the original problem. It is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the portfolio selection problem with uncertain returns. Several illustrative examples are provided to show the feasibility and the efficiency of the proposed method in this paper.     

Differences in attentional strategies by novice and experienced operating theatre scrub nurses

This study investigated the effect of nursing experience on attention allocation and task performance during surgery. The prevention of cases of retained foreign bodies after surgery typically depends on scrub nurses, who are responsible for performing multiple tasks that impose heavy demands on the nurses' cognitive resources. However, the relationship between the level of experiences and attention allocation strategies has not been extensively studied. Eye movement data were collected from 10 novice and 10 experienced scrub nurses in the operating theater for caesarean section surgeries. Visual scanning data, analyzed by dividing the workstation into four main areas and the surgery into four stages, were compared to the optimum expected value estimated by SEEV (Salience, Effort, Expectancy, and Value) model. Both experienced and novice nurses showed significant correlations to the optimal percentage dwell time values, and significant differences were found in attention allocation optimality between experienced and novice nurses, with experienced nurses adhering significantly more to the optimal in the stages of high workload. Experienced nurses spent less time on the final count and encountered fewer interruptions during the count than novices indicating better performance in task management, whereas novice nurses switched attention between areas of interest more than experienced nurses. The results provide empirical evidence of a relationship between the application of optimal visual attention management strategies and performance, opening up possibilities to the development of visual attention and interruption training for better performance.     

The rhinal cortex: 'gatekeeper' of the declarative memory system

Almost all studies probing neural activity underlying the declarative memory system in humans have investigated either memory encoding or retrieval. Here, however, we suggest integrating encoding and retrieval operations into a single operation executed by the rhinal cortex. The more familiar an item is, the less rhinal processing it requires and the less vigorously it is encoded into memory. Given the anatomical position and the functional properties of the rhinal cortex, this operation fulfills an essential task: it optimally allocates limited encoding resources away from familiar information and towards novel information. We propose a rhinal processing stage that optimizes the declarative memory system by fully integrating encoding and retrieval operations into a single 'gatekeeper' operation.     

运用基因表达式编程的自陈量表数据建模

探讨基因表达式编程对自陈量表测量数据的建模方法。运用威廉斯创造力测验和认知需求量表获得400位中学生的测量分数,通过数据清洗,保留383个被试的分数作为建模的数据集。运用哈曼单因素检验方法没有发现共同方法偏差。采用均匀设计方法对基因表达式编程中的5个参数进行优化配置,在测试拟合度最大的试验条件下,找到了测试误差最小的模型。比较基因表达式编程和BP神经网络、支持向量回归机、多元线性回归、二次多项式回归所建模型的预测精度。研究表明,基因表达式编程能用于自陈量表测量数据的建模,该模型比传统方法所建的模型具有更高的预测精度,而且模型是稳健的。     

An "exhaustive" search for tasks that fall between the choice and simple reactions

An experimental search was made for reaction time (RT) tasks that require the processing operation of stimulus discrimination or response selection, but not both. The existence of such a task would allow the subtractive determination of the mean duration of the separate operations. Each of the three reactions that historically have been represented as possessing only one of the two operations was subjected to empirical testing. None of them met the most basic requirements. Donders' (1868/1969) contingent (c) reaction and Taylor's (1966) selection (b')reaction were not reliably faster than a compatible choice (b) reaction. Wundt's (1880) discrimination (d) reaction was not sensitive to difficulty of discrimination. The possibility is suggested that stimulus discrimination and response selection in the choice reaction are inseparable operations. The present negative results emphasize the importance of establishing validity of any use of the subtraction method through appropriate experimental methods. Some parallels of the compatibility issue are found in experiments in which RT is used as a measure of the duration of programming operations.