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Abstract: Land-use change (LUC) estimated by economic models has sparked intense international debate. Models estimate how much LUC might be induced under prescribed scenarios and rely on assumptions to generate LUC values. It is critical to test and validate underlying assumptions with empirical evidence. Furthermore, this modeling approach cannot answer if any specific indirect effects are actually caused by biofuel policy. The best way to resolve questions of causation is via scientific methods. Kim and Dale attempt to address the question of if, rather than how much, market-induced land-use change is currently detectable based on the analysis of historic evidence, and in doing so, explore some modeling assumptions behind the drivers of change. Given that there is no accepted approach to estimate the global effects of biofuel policy on land-use change, it is critical to assess the actual effects of policies through careful analysis and interpretation of empirical data. Decision makers need a valid scientific basis for policy decisions on energy choices. [Copyright &y& Elsevier]

Abstract: The Nutrition Care Process and Model (NCPM) provides registered dietitian nutritionists (RDNs) and dietetic technicians, registered (DTRs) a framework to recognize, diagnose, and intervene upon nutrition-related health concerns. Within the NCPM, nutrition assessment is essential to develop a comprehensive evaluation of the client’s nutrition history. The application of critical thinking skills to nutrition assessment is imperative to ensure appropriate acquisition and interpretation of data. The Academy of Nutrition and Dietetics’ Career Development Guide, adapted from the Dreyfus Model of Skill Acquisition, illustrates the progression of critical thinking skills as RDNs and DTRs gain knowledge and experience with practice. The Career Development Guide is characterized by the transition through the following stages: novice, beginner, competent, proficient, and advance practice/expert. The foundation of dietetics knowledge is obtained during the novice and beginner stages. Throughout, the primary objective is introduction of the NCPM and nutrition assessment theory via dietetics education and the application of nutrition assessment in supervised practice. Next, RDNs and DTRs transition to the competent stage of practice. During this phase, entry-level knowledge and skill are applied to patient care settings, and critical thinking skills develop as RDNs and DTRs gain experience. Subsequently, RDNs and DTRs move to the proficient stage as the ability to prioritize attention, generalize, apply problem-solving skills to new scenarios, and identify innovative solutions develops. Some RDNs and DTRs may transition to the advance practice/expert stage, during which critical thinking becomes intuitive. Critical thinking skills are essential to ensure diagnostic accuracy; however, more research is needed to further describe progression of critical thinking skills among RDNs and DTRs. [Copyright &y& Elsevier]

This research paper focuses on the development of advanced mathematical models for disease diagnosis and prediction, and the subsequent creation of automated systems based on these models. These systems leverage a range of mathematical models and incorporate cutting-edge information technology achievements to provide medical professionals with valuable decision-making support. By amalgamating mathematical rigor and technological innovation, this research endeavors to enhance the accuracy and efficiency of medical diagnoses, thereby improving patient care and healthcare outcomes. This study delves into the persistent need for contemporary information systems, where information plays a crucial role in decision-making. It aims to provide an objective approach to addressing pressing medical challenges, particularly in disease diagnosis and prediction, enhancing the effectiveness of these critical tasks. Automated medical information systems, built on advanced mathematical models, significantly empower physicians. Machine diagnostics, relying on deterministic logic, the phase interval method, and information-probabilistic logic, bolster diagnostic capabilities. Functional entropy enables individuals to handle vague information, aiding decision-making. Assessing imprecision and uncertainty computationally diminishes subjectivity, while employing fuzzy set theory enhances diagnostic modeling. Mathematical models assess diagnostic indicators, and linguistic variables quantify resemblance. The diagnostic model for primary biliary cirrhosis and active hepatitis utilizes a diagnostic table and gradient projection. This comprehensive study advances medical diagnostics through mathematical models and automated systems, addressing critical healthcare challenges. [ABSTRACT FROM AUTHOR]

Background: Constrained optimization methods are already widely used in health care to solve problems that represent traditional applications of operations research methods, such as choosing the optimal location for new facilities or making the most efficient use of operating room capacity.Objectives: In this paper we illustrate the potential utility of these methods for finding optimal solutions to problems in health care delivery and policy. To do so, we selected three award-winning papers in health care delivery or policy development, reflecting a range of optimization algorithms. Two of the three papers are reviewed using the ISPOR Constrained Optimization Good Practice Checklist, adapted from the framework presented in the initial Optimization Task Force Report. The first case study illustrates application of linear programming to determine the optimal mix of screening and vaccination strategies for the prevention of cervical cancer. The second case illustrates application of the Markov Decision Process to find the optimal strategy for treating type 2 diabetes patients for hypercholesterolemia using statins. The third paper (described in Appendix 1) is used as an educational tool. The goal is to describe the characteristics of a radiation therapy optimization problem and then invite the reader to formulate the mathematical model for solving it. This example is particularly interesting because it lends itself to a range of possible models, including linear, nonlinear, and mixed-integer programming formulations. From the case studies presented, we hope the reader will develop an appreciation for the wide range of problem types that can be addressed with constrained optimization methods, as well as the variety of methods available.Conclusions: Constrained optimization methods are informative in providing insights to decision makers about optimal target solutions and the magnitude of the loss of benefit or increased costs associated with the ultimate clinical decision or policy choice. Failing to identify a mathematically superior or optimal solution represents a missed opportunity to improve economic efficiency in the delivery of care and clinical outcomes for patients. The ISPOR Optimization Methods Emerging Good Practices Task Force's first report provided an introduction to constrained optimization methods to solve important clinical and health policy problems. This report also outlined the relationship of constrained optimization methods relative to traditional health economic modeling, graphically illustrated a simple formulation, and identified some of the major variants of constrained optimization models, such as linear programming, dynamic programming, integer programming, and stochastic programming. The second report illustrates the application of constrained optimization methods in health care decision making using three case studies. The studies focus on determining optimal screening and vaccination strategies for cervical cancer, optimal statin start times for diabetes, and an educational case to invite the reader to formulate radiation therapy optimization problems. These illustrate a wide range of problem types that can be addressed with constrained optimization methods. [ABSTRACT FROM AUTHOR]

In this paper, we propose new aggregation operators for multi-criteria decision making under linguistic settings. The proposed operators are based on two sets of criteria weights. Besides the primary conventional criteria weights, we introduce a method to deduce secondary criteria weights from the criteria evaluations, which reflect the role of the different criteria in discriminating among the alternatives. The properties of the proposed operators are investigated. An approach for the application of the said operators in a group multi-criteria decision making problem is presented. Following the same, the proposed operators are applied in a case study on supplier selection. The empirical validation of the proposed operators is performed on a set of 12 real datasets. Note : All usages of he, him, his in the paper, also refer to she, and her. [ABSTRACT FROM AUTHOR]

This paper uses a mixed methods research design that combines qualitative and quantitative data from low-income parents to increase understanding of the dynamics of their child care decision-making. The paper relies on a graphically depicted conceptual model of the decision-making process. In the model, individual characteristics found in prior research to affect child care decisions are clustered into constructs: family, community, child care preferences, constraints and barriers, and financial assistance. Findings demonstrate that when controlling for other characteristics, most of the characteristics captured in the conceptual model predict type of child care selected. Study data include measures of employment constraints and a verified measure of subsidy receipt, both of which are important to understanding child care decisions of low-income parents and on which research is limited. We find both to be strong predictors of child care selection decisions. Parents' child care selection preferences emerge as the strongest predictors of the type of care selected. Parents' prioritization of support for learning and trust in the provider were the most likely to predict a specific type of care. Findings from this study have direct implications for policy and practice, especially as states implement the changes associated with the Child Care and Development Block Grant Act of 2014 with its emphasis on helping parents select arrangements that support the child's development. [ABSTRACT FROM AUTHOR]

A High Utility Itemset (HUI) mining is an important problem in the data mining literature that considers utilities of items (such as profits and margins) to discover interesting patterns from transactional databases. Several data structures, pruning strategies and algorithms have been proposed in the literature to efficiently mine high utility itemsets. Most of these works, however, do not consider itemsets with negative unit profits that provide greater flexibility to a decision maker to determine profitable itemsets. This paper aims to advance the state-of-the-art and presents a generalized high utility mining (GHUM) method that considers both positive and negative unit profits. The proposed method uses a simplified utility-list data structure for storing itemset information during the mining process. The paper also introduces a novel utility based anti-monotonic property to improve the performance of HUI mining. Furthermore, GHUM adapts key pruning strategies from the basic HUI mining literature and presents new pruning strategies to significantly improve the performance of mining. The proposed method is evaluated on a set of benchmark sparse and dense datasets and compared against a state-of-the-art method. Rigorous experimental evaluation is performed and implications of the key findings are also presented. In general, GHUM was found to deliver more than an order of magnitude improvement at a fraction of the memory over the state-of-the-art FHN method. [ABSTRACT FROM AUTHOR]

As environmental awareness grows, sustainable scheduling is attracting increasing attention. The purposes of this paper are obtain the lower bound of energy-efficient flexible job shops with machine selection, job sequencing, and machine on-off decision making via a new mathematical model and to discover more energy-efficient rules with easy implementation in real practice via an efficient Gene Expression Programming (eGEP) algorithm. This paper first formulates a novel mixed-integer linear mathematical model to achieve effective machine selection, job sequencing, and machine off-on decision making. Then for the purpose of avoiding the empirical combination, five attributes exerting direct influence on the total energy consumption are extracted and consequently involved in the evolutionary process of eGEP. Furthermore, diversified rule mining operations with multi-gene representation and self-study are designed to enhance the search space and solutions quality. And, unsupervised learning is utilized in which global best and current worst are set to guide evolution direction since the learning progress has no prior knowledge. Experimental results show that machine off-on decisions efficiently reduce the total energy consumption; and, the discovered rules reach the lower bound calculated by GAMS/CPLEX in small problems and have significant superiority over other dispatching rules in energy saving. [ABSTRACT FROM AUTHOR]

Background: As a new application technology of the Internet of Things (IoT), intelligent medical treatment has attracted the attention of both nations and industries through its promotion of medical informatisation, modernisation, and intelligentisation. Faced with a wide variety of intelligent medical terminals, consumers may be affected by various factors when making purchase decisions.Purpose: To examine and evaluate the key influential factors (and their interrelationships) of consumer adoption behavior for improving and promoting intelligent medical terminals toward achieving set aspiration level in each dimension and criterion.Method: A hybrid modified Multiple Attribute Decision-Making (MADM) model was used for this study, based on three components: (1) the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique, to build an influential network relationship map (INRM) at both 'dimensions' and 'criteria' levels; (2) the DEMATEL-based analytic network process (DANP) method, to determine the interrelationships and influential weights among the criteria and identify the source-influential factors; and (3) the modified Vlse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, to evaluate and improve for reducing the performance gaps to meet the consumers' needs for continuous improvement and sustainable products-development. First, a consensus on the influential factors affecting consumers' adoption of intelligent medical terminals was collected from experts' opinion in practical experience. Next, the interrelationships and influential weights of DANP among dimensions/criteria based on the DEMATEL technique were determined. Finally, two intelligent medicine bottles (AdhereTech, A1 alternative; and Audio/Visual Alerting Pillbox, A2 alternative) were reviewed as the terminal devices to verify the accuracy of the MADM model and evaluate its performance on each criterion for improving the total certification gaps by systematics according to the modified VIKOR method based on an INRM.Results: In this paper, the criteria and dimensions used to improve the evaluation framework are validated. The systematic evaluation in index system is constructed on the basis of five dimensions and corresponding ten criteria. Influential weights of all criteria ranges from 0.037 to 0.152, which shows the rank of criteria importance. The evaluative framework were validated synthetically and scientifically. INRM (influential network relation map) was obtained from experts' opinion through DEMATEL technique shows complex interrelationship among factors. At the dimension level, the environmental dimension influences other dimensions the most, whereas the security dimension is most influenced by others. So the improvement order of environmental dimension is prior to security dimension. The newly constructed approach was still further validated by the results of the empirical case, where performance gap improvement strategies were analyzed for decision-makers. The modified VIKOR method was especially validated for solving real-world problems in intelligent medical terminal improvement processes. For this paper, A1 performs better than A2, however, promotion mix, brand factor, and market environment are shortcomings faced by both A1 and A2. In addition, A2 should be improved in the wireless network technology, and the objective contact with a high degree of gaps.Conclusions: Based on the evaluation index system and the integrated model proposed here, decision-makers in enterprises can identify gaps when promoting intelligent medical terminals, from which they can get valuable advice to improve consumer adoption. Additionally, an INRM and the influential weights of DANP can be combined using the modified VIKOR method as integrated weightings to determine how to reduce gaps and provide the best improvement strategies for reaching set aspiration levels. [ABSTRACT FROM AUTHOR]

Mobile devices have gained popularity worldwide. The mobile device flexibility has encouraged users to turn their mobile devices into primary hubs for storing information. This paper adopts the classical CW-Lite security models as the framework and the human behavioral patterns as the context. The selected behavioral aspects refer to mobile application and mobility, deployed as major characteristics that determine security control decisions. This proposed paper requires the application of human behavioral context on mobile phones. The solution involves the novel use of behavioral aspects to improve the security of mobile phones. Two important scenarios are incorporated: analytic hierarchy process (AHP) mobile application and AHP mobility. The proposed methodology is superior because it can detect the change in the user behavior in comparison with an intruder. The applied intelligent human behavioral context on the CW-Lite model shows the advantages of AHP in detecting the changes in the user behavior and in authenticating the identity of the main user. These advantages ensure reliability and security of the phone. [ABSTRACT FROM AUTHOR]

Since the start of COVID-19, a fair amount of work has been undertaken by scholars around the world to model its progression. It became clear from the start of pandemic that its progression is affected by various factors within different communities. Subsequently, the necessary means and the range of measures used to effectively control the virus would vary from place to place. And we have been witness to different approaches adopted around the world to maintain the virus under check both in the short term and the long term. So, in this unexpected situation, it is a great challenge for the world health organization (WHO) to save the lives of COVID-19 patients. For this, several mathematical models have been made for better understanding the coronavirus contagion. Mostly, these models are based on classical integer-order derivative using real numbers which cannot capture the fading memory. Thus, in this unexpected situation, fuzzy sets (FSs) are considered due to their inherent capability to deal with uncertainty. Fuzzy sets (FSs) theory has the ability to manage uncertain situations. Thus, the goal of this research is to present newly mathematical methods based on complex Pythagorean fuzzy sets (CPyFSs) and their operators, namely complex Pythagorean fuzzy Einstein weighted geometric operator, and induced complex Pythagorean fuzzy Einstein hybrid geometric operator to reduce the spreading rate of COVID-19. At the end of the paper an illustrative example is constructed to show the effectiveness, reliability of the new techniques. [ABSTRACT FROM AUTHOR]

This paper explores the influence of digital technologies on the consumer decision-making in retail sector with two online survey-based studies. Study 1 identifies unique attributes of six digital technologies, including two current (Internet and Mobile Platform) and four emerging (Artificial Intelligence, Augmented, Mixed and Virtual Reality) technologies. Study two focuses on older consumers to understand their decision-making process when shopping for products or services using new digital technologies. We extend the AISAS (Awareness, Interest, Search, Action, and Sharing) model to show that with digital technologies, consumer decision journey is no longer linear. For example, attention can lead directly to action, without going through the interest or search stages. Similarly, purchase can lead to sharing that may lead to loyalty and psychological engagement, and reinforce attention. We found no significant difference in these effects between older and younger consumers. Besides providing useful insights about consumer decision-making process with emerging digital technologies for future academic researchers, these results also give useful ideas to marketing practitioners interested in introducing these emerging technologies to deliver superior value to their customers. • Emerging digital technologies are changing consumer decision-making process. • We explore this phenomenon in retail shopping context using two online studies. • Study one identifies and categorizes unique attributes of digital technologies. • Study two confirms non-linear consumer decision-making with digital technologies. • No influence of consumers' age on their decision-making with digital technologies. [ABSTRACT FROM AUTHOR]

Solving a decision-making problem about a brand-new product might include preferences from a high number of potential customers (e.g., followers of a company on social media) and managerial constraints (or preferences) given by corporate managers with regard to different aspects (i.e., economical, technical, environmental, etc.) over multiple criteria (e.g., weight, capacity, color, or usefulness of a product). These give us some new insights on fusing preferences given by persons having different perspectives (e.g., economical, technical, environmental, etc.), including decision-makers, and aimed to be suitable for different organizational structures (e.g., multilevel structures). Herein, a proper representation is needed to merge preferences from each perspective, enabling their propagation, throughout an organizational structure until the level in which a decision is made. This representation is presented as a decision-making unit (DMU) , and is used as the primary component of our decision-making model. In this paper, we propose a novel decision-making model that recursively merges the preferred criteria from different DMUs using the logic scoring of preference (LSP) method . An illustrative example demonstrating the applicability of the proposed model, in the context of a new product design, is included in the paper. [ABSTRACT FROM AUTHOR]

Sensitivity Analysis (SA) investigates how the variation in the output of a numerical model can be attributed to variations of its input factors. SA is increasingly being used in environmental modelling for a variety of purposes, including uncertainty assessment, model calibration and diagnostic evaluation, dominant control analysis and robust decision-making. In this paper we review the SA literature with the goal of providing: (i) a comprehensive view of SA approaches also in relation to other methodologies for model identification and application; (ii) a systematic classification of the most commonly used SA methods; (iii) practical guidelines for the application of SA. The paper aims at delivering an introduction to SA for non-specialist readers, as well as practical advice with best practice examples from the literature; and at stimulating the discussion within the community of SA developers and users regarding the setting of good practices and on defining priorities for future research. [ABSTRACT FROM AUTHOR]

Resource scheduling and planning are the strategies required to determine the sequence of activities and resource allocation during tunnel construction. Resource scheduling and planning have been implemented in a new resource model of the Decision Aids for Tunneling (DAT), which are a computer based tool used to simulate tunnel construction. Tunneling plans obtained with the new resource model of the DAT take into account the technical precedence of activities, the resource/space availability, the dynamic status of the process, and the work continuity. In particular, the new resource model of the DAT can provide the optimal tunneling plan, which produces the shortest construction time and the smallest construction cost, and satisfies the special characteristics of tunnel construction such as excavation methods, distance requirements between the headings, and preempting activities (e.g., blasting). The paper attempts to contribute to both theory and practice: Optimization of the construction process considering time, cost and resources is particularly complicated in tunneling where activities and resource availability have to be appropriately sequenced and interference has to be avoided. The paper addresses this fundamental problem with the development of different schematic tunneling plans that consider the relevant activities and optimizes them with regard to overall cost and time, also considering uncertainties. Equally important is to make the theoretical development practically useable. This is done through implementation of the resource optimization in the DAT and, very importantly, by demonstrating the practical use with an application to a real tunnel case. [ABSTRACT FROM AUTHOR]

Human factors such as distraction, fatigue, alcohol and drug use are generally ignored in car-following (CF) models. Such ignorance overestimates driver capability and leads to most CF models’ inability in realistically explaining human driving behaviors. This paper proposes a novel car-following modeling framework by introducing the difficulty of driving task measured as the dynamic interaction between driving task demand and driver capability. Task difficulty is formulated based on the famous Task–Capability Interface (TCI) model, which explains the motivations behind driver's decision making. The proposed method is applied to enhance two popular CF models: Gipps’ model and IDM, and named as TDGipps and TDIDM respectively. The behavioral soundness of TDGipps and TDIDM are discussed and their stabilities are analyzed. Moreover, the enhanced models are calibrated with the vehicle trajectory data, and validated to explain both regular and human factor influenced CF behavior (which is distraction caused by hand-held mobile phone conversation in this paper). Both the models show better performance than their predecessors, especially in presence of human factors. [ABSTRACT FROM AUTHOR]

Abstract: An artificial neural network (ANN) is a powerful mathematical framework used to either model complex relationships between inputs and outputs or find patterns in data. It is based on an interconnected group of artificial neurons, and it employs a connectionist approach to computation when processing information. ANNs have been successfully used for a great variety of applications, such as decision making, quantum chemistry, radar systems, face identification, gesture recognition, handwritten text recognition, medical diagnosis, financial applications, robotics, data mining, and e-spam filtering. In the speech community, neural architectures have been used since the beginning of the 1980s, and ANNs have been proven useful to accomplish several speech processing tasks, e.g., to extract linguistically motivated features, to perform speech detection, and to generate local scores to be used for different goals. In recent years, there has been a renewed interest in the use of ANNs for speech applications due to a major advance made in pre-training the weights in deep neural networks (DNNs). It seems that a new trend to move the speech technology forward through the use of NNs has begun, and it can therefore be instructive to review key ANN applications to automatic speech processing. In this paper, several ANN-based applications for speech processing will be presented, ranging from speech attribute extraction to phoneme estimation and/or classification. Furthermore, it will be shown that ANNs play a key role in several important speech applications, such as large vocabulary continuous speech recognition (LVCSR) and automatic language recognition. The goal of the paper is to summarize chief ANN approaches to speech processing using the experience gathered in the last seven years in our laboratories. [Copyright &y& Elsevier]

In today's business world, oil companies cannot be productive and competitive, and thus, will not survive without taking the supply chain management concepts into account. Consequently, the management of a crude oil supply chain (COSC) is increasingly receiving substantial importance. The growing number of papers and books on this topic is a further witness of this fact. To foster insight into issues intertwined with COSC problems, this paper is devoted to an extensive review of mathematical programming models in this context. The classification approach for this review is based on a taxonomy framework. In this framework, ongoing and emerging challenges surrounding the strategic and tactical decisions of COSC problems are investigated. As a main goal, the gaps of literature are analyzed to recommend possible research directions. [ABSTRACT FROM AUTHOR]

This paper studies the truckload market with carriers providing transport services between two locations. It aims to provide a modeling methodology to represent the spatial behavior of a carrier dealing with the issue of repositioning. Indeed, due to the imbalance of trade, carriers face the difficulty of finding freight for their return trips. When they operate over long distance shipments, repositioning their empty vehicles from the low-demand zone is necessary to sustain their business. Yet the mechanisms at stake by carriers to understand their repositioning decision-making process are mostly unknown and unobservable. This lack of data on carrier repositioning zone choice issues has major consequences for shipper and forwarder resource planning systems. To address this problem we develop a mathematical model to study the spatial repositioning behavior of carriers. We propose a probabilistic approach based on aggregated transport data that consists in a two-steps decision making process. The first one is the probabilistic selection of a set of repositioning candidates based on the microeconomic theory of the consumer. The second step is the choice of a region within this set through the estimation of the spatial distribution of reloading. It makes use of the graph structure of the transport data and combines a spatial interaction model and a random walk model on a graph. Using simulations, we illustrate how our methodology can be used for operational purposes to provide more transparency on carrier behavior. In conclusion, research perspectives are suggested for tackling the problem of freight demand estimation as well as rationalizing the impact of the trade imbalance on the price of a transport. • We consider a spatial choice problem from the point of view of carriers that has never been addressed in the literature. This problem could have an impact on the practical difficulties encountered by transport practitioners, particularly on the problems of information feedback and lack of visibility (especially the hidden costs due to empty repositioning). We develop a two-stage decision-making process scheme including a method for estimating the spatial reloading probability. • We define the spatial distribution of the reloading probability across the territory; an original probabilistic indicator to characterize the trade imbalance. A methodology for estimating this spatial probability is proposed. It is based on the modeling of a random walk on a bipartite graph. • We develop an extension of Huff's spatial interaction model to model conditional destination choices. This representation is particularly suitable for modeling trip-chaining. • We perform numerical experiments to demonstrate how the method could be used in practice to overcome the problem of information visibility, especially in terms of hidden costs due to repositioning. [ABSTRACT FROM AUTHOR]

Abstract: In the consensus reaching processes developed in group decision making problems we need to measure the closeness among experts’ opinions in order to obtain a consensus degree. As it is known, to achieve a full and unanimous consensus is often not reachable in practice. An alternative approach is to use softer consensus measures, which reflect better all possible partial agreements, guiding the consensus process until high agreement is achieved among individuals. Consensus models based on soft consensus measures have been widely used because these measures represent better the human perception of the essence of consensus. This paper presents an overview of consensus models based on soft consensus measures, showing the pioneering and prominent papers, the main existing approaches and the new trends and challenges. [Copyright &y& Elsevier]

Abstract: The paper focuses on the assessment of greenhouse gas emissions produced by the walls of buildings according to their lifespan. These assessments take account of the construction, maintenance and end of life. The contribution of the utilization phase must be equivalent for all technical solutions for a given usage function. In the first part of the paper, the methodology is described by (1) considering a unit area of wall (i.e. 1 square metre), (2) determining a long service life, (3) choosing technical solutions in agreement with the specifications, (4) establishing the lifespan of each technical solution according to experts, (5) finding the corresponding greenhouse gas index from an appropriate database, and finally (6) modelling the evolution of these indicators with time. Several technical solutions (concrete, brick, stone, wood, aerated concrete) are considered and lifespans range from a few years to centuries. The results of this analysis suggest and quantify the important impact of lifespan on greenhouse gas emission indicators. For example, the best technical solution for a short lifetime can be the worst on a longer duration and vice versa. In the second part, since the lifespan of a product is very difficult to determine objectively, it is considered as a variable. The numerical results presented point out the need to revisit the current life cycle tools. [Copyright &y& Elsevier]

Abstract: The decision problem considered in this paper is a hierarchical workforce scheduling problem in which a higher qualified worker can substitute for a lower qualified one, but not vice versa, labour requirements may vary, and each worker must receive n off-days a week. Within this context, five mathematical models are discussed. The first two of these five models are previously published. Both of them are for the case where the work is indivisible. The remaining three models are developed by the authors of this paper. One of these new models is for the case where the work is indivisible and the other two are for the case where the work is divisible. The three new models are proposed with the purpose of removing the shortcomings of the previously published two models. All of the five models are applied on the same illustrative example. Additionally, a total of 108 test problems are solved within the context of two computational experiments. [Copyright &y& Elsevier]

Abstract: This paper constitutes a literature review on student modeling for the last decade. The review aims at answering three basic questions on student modeling: what to model, how and why. The prevailing student modeling approaches that have been used in the past 10years are described, the aspects of students’ characteristics that were taken into consideration are presented and how a student model can be used in order to provide adaptivity and personalisation in computer-based educational software is highlighted. This paper aims to provide important information to researchers, educators and software developers of computer-based educational software ranging from e-learning and mobile learning systems to educational games including stand alone educational applications and intelligent tutoring systems. In addition, this paper can be used as a guide for making decisions about the techniques that should be adopted when designing a student model for an adaptive tutoring system. One significant conclusion is that the most preferred technique for representing the student’s mastery of knowledge is the overlay approach. Also, stereotyping seems to be ideal for modeling students’ learning styles and preferences. Furthermore, affective student modeling has had a rapid growth over the past years, while it has been noticed an increase in the adoption of fuzzy techniques and Bayesian networks in order to deal the uncertainty of student modeling. [Copyright &y& Elsevier]

Abstract: This paper presents an analysis of some selection methods used in memory consideration of Harmony search (HS) Algorithm. The selection process in memory consideration entails selecting the value of the decision variable from any solution in the Harmony memory (HM). Quite recently, there has been a tendency to adopt novel selection methods that mimic the natural phenomena of the ‘survival of the fittest’ to replace the random selection method in memory consideration. Consequently, the value of decision variable selected using memory consideration is chosen from the higher promising solutions in HM. The adopted selection methods include: proportional, tournament, linear rank, and exponential rank. It has been demonstrated that experimenting with any of these methods in memory consideration directly affects the performance of HS. However, the success of these methods is based on choosing the optimal parameter value of each. The wrong parameter settings might affect the balance between exploration and exploitation of the search space. Accordingly, this paper studies the effect of the selection method parameters in order to show their effect on HS behavior. The evaluation is conducted using standard mathematical functions used in the literature for HS adoptions. The results suggest that the optimal setting of the selection method parameters is crucial to improve the HS performance. [Copyright &y& Elsevier]

Abstract: Design, implementation and operation of solar thermal electricity plants are no more an academic task, rather they have become a necessity. In this paper, we work with power industries to formulate a multi-objective optimization model and attempt to solve the resulting problem using classical as well as evolutionary optimization techniques. On a set of four objectives having complex trade-offs, our proposed procedure first finds a set of trade-off solutions showing the entire range of optimal solutions. Thereafter, the evolutionary optimization procedure is combined with a multiple criterion decision making (MCDM) approach to focus on preferred regions of the trade-off frontier. Obtained solutions are compared with a classical generating method. Eventually, a decision-maker is involved in the process and a single preferred solution is obtained in a systematic manner. Starting with generating a wide spectrum of trade-off solutions to have a global understanding of feasible solutions, then concentrating on specific preferred regions for having a more detailed understanding of preferred solutions, and then zeroing on a single preferred solution with the help of a decision-maker demonstrates the use of multi-objective optimization and decision making methodologies in practice. As a by-product, useful properties among decision variables that are common to the obtained solutions are gathered as vital knowledge for the problem. The procedures used in this paper are ready to be used to other similar real-world problem solving tasks. [Copyright &y& Elsevier]

Abstract: On the basis of Brennan''s (1998) dynamic investment decision model and engineering management, this paper builds an investor''s learning-decision model, allowing for the uncertainty of the expected return and the investor''s learning process. In our model, the variable of learning cost is added, which modifies the condition for decision of the investor, and assumes that the investment payoff cannot be observed, if an investor does not pay the learning cost. With the above conditions, this paper derives the optimal result of a three-period decision model on the basis of an investor''s learning behavior and decision engineering. The empirical evidence verifies the result of our model in this paper. [Copyright &y& Elsevier]

Abstract: Historical commercial districts in city centres in Turkey face social, cultural, environmental and economical challenges of managing rapid urban and economical development in the last two decades. They are being subjected to dramatic physical deterioration and rapid social and economical decline. Enriching relationships between local tradesmen associations, local authorities and non-governmental organisations through a structured engagement process can deliver innovative new revitalisation approaches and design options towards sustainable futures of historical commercial districts both in Turkey and in the world. This paper presents a community engagement model, which can be seen as the revival of a guild system (Lonca), which is unique to Ottoman Turkish culture, for the sustainable future of a historical commercial district in a Turkish city, Bursa. One of the main objectives of this study is to discuss and evaluate successes and failures of this community engagement model. Other objective of the paper is to discuss the effectiveness of this civil organisation for creating scenarios about sustainable future of a historical commercial district. The key finding of this study shows community engagement models should support public decision making by developing a coherent framework to identify the sustainable future scenarios with multi- or interdisciplinary collaborations. [Copyright &y& Elsevier]

Abstract: The minimum size of subjects required for the research on human health, thermal comfort and productivity is a frequently asked question. In this paper the idea of power analysis, which helps to determine required sample size as well as to interpret research results, is introduced in order to promote good practice of power analysis in the context of human and building environment relationship research. How to calculate effect size from published article or experimental data is presented with plenty of examples. The effect sizes of several physiological and psychological measurements indicating the effect of indoor environment quality on human health, thermal comfort and productivity are presented, which could be worked as references when researchers planning their own studies. How to determine required sample size when planning a study and how to interpret the research results with power analysis are also illustrated step by step with samples. Finally how to make decisions when evaluating the study results is summarized. It is expected that these examples and the summary could help researchers to better apply power analysis in indoor environment quality (IEQ) studies. Some statistical terms used in this paper, such as power analysis, effect size, and t-test, etc., are explained in detail in the . [Copyright &y& Elsevier]

• Proposing a novel MCDM method. • Developing the BWM for considering fuzzy pairwise comparisons. • Obtaining crisp weights from a fuzzy pairwise comparison matrix. • Superiority to the two well-known previous BWMs. One of the latest multi-criteria decision-making methods is best-worst method (BWM). In the procedure of BWM, decision maker (DM) identifies the most and the least important criteria namely, best and worst. Thereafter, DM identifies the degrees which he believes the best criterion is better than the other criteria, and identifies the degrees which he believes the other criteria are better than the worst criterion. Because of uncertainties in comparisons due to using linguistic variables for pairwise comparisons and also the lack of complete information, the crisp values of pairwise comparisons cannot appropriately model the problems. This paper develops the BWM for considering fuzzy pairwise comparisons (FBWM) by proposing a new fuzzy mathematical model which yields crisp weights from a fuzzy pairwise comparison matrix. Unlike to some previous papers that obtains fuzzy weights from fuzzy pairwise comparison matrix, the crisp weights of the proposed method of this paper eliminates the supplementary aggregation of fuzzy weights and ranking procedures. Moreover, the proposed method avoids obtaining the different ranking results due to the different ranking procedures of fuzzy numbers. Another outstanding advantage of this paper is that the obtained weights of the proposed method better satisfy the initial judgments compared to previous methods, while as we know, the satisfaction of the initial judgments is essential for pairwise comparison judgments. This paper presents several numerical examples to prove the good performance and the merit of the proposed method. According to the provided numerical examples, the proposed method of this paper absolutely outperforms the two well-known previous methods. [ABSTRACT FROM AUTHOR]

Highlights • Use of outdoor microclimate maps to compare different design options at urban scale. • Tool to evaluate impact on urban comfort of the place/city garden design. • Decision-making tool to improve urban climate design considering outdoor comfort. Abstract The study is aimed at evaluating the potential effects of alternative design solutions with different green elements on outdoor microclimate with relation to a real case study application. The study has been commissioned in the framework of the follow up of a design competition, launched by the Municipality of Cesena to reshape a square in the historic city center, when a public debate raised around the arrangement of trees and green surfaces envisaged by the architectural layout. Different options were considered and the design team and the public authorities sought for evidences on the deriving benefits in the respective configurations in order to properly drive the process. Thus the scientific research approach was applied to investigate the potential impacts according to a microclimate oriented perspective. The outcomes showed that green surfaces significantly improved the outdoor comfort conditions compared to original paved ones and that a minor contribution derived by the trees arrangement. The paper reports the applied methodology according to the specific context, the interpretation of results and how they have been translated into user friendly visualizations in order to make them understandable to a broader and non technical audience. [ABSTRACT FROM AUTHOR]

Next generation wireless networks will integrate various heterogeneous technologies like WLAN, WiMax and cellular technologies etc., to support multimedia services with higher bandwidth and guaranteed quality of service (QoS). In order to keep the mobile user always connected to the best wireless network in terms of QoS parameters and user preferences, an optimal network selection technique in heterogeneous networks is required. This paper proposes a novel fuzzy-Analytic Hierarchy Process (AHP) based network selection in heterogeneous wireless networks. Triangular fuzzy numbers are used to represent the elements in the comparison matrices for voice, video and best effort applications. Deriving crisp weights from these fuzzy comparison matrices is a challenging task. When extent analysis method is applied, irrational zero weights are obtained for some attributes. Due to this, many important criteria are not considered in the decision making process. To overcome this problem, a new non-linear fuzzy optimization model for deriving crisp weights from fuzzy comparison matrices for network selection is presented. The weights obtained from this model are more consistent than the existing optimization models. Also, parameterized utility functions are used to model the different Quality of Service (QoS) attributes (bandwidth, delay, jitter, bit error rate) and user preferences (cost) for three different types of applications. Finally, scores are calculated exclusively for each network by three MADM (Multiple Attribute Decision Making) methods Simple Additive Weighting (SAW), TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) and MEW (Multiplicative Exponential Weighting). Results show that the MEW method gives more appropriate scores with utility functions than the SAW and TOPSIS methods. [ABSTRACT FROM AUTHOR]

A consensus reaching process is an iteratively developed negotiation process designed to ensure that a mutual agreement is reached by decision makers. To incorporate distribution information of hesitant fuzzy sets, probability-hesitant fuzzy sets have recently been proposed. In the context of probability-hesitant fuzzy preference relations (PHFPR), this paper aims to provide a novel consensus reaching process for group decision making problems. By means of fuzzy preference relations, an optimization based consistency improvement process is proposed to deal with the inconsistencies in a given PHFPR. Consensus measures that are developed based on the distances between the individuals are computed on three levels: an alternative pair level, an alternatives level, and a preference relations level. An algorithm that adopts a local feedback strategy is designed to improve the consensus reaching process. The feedback strategy sequentially identifies the preferences with respect to the position and the anti-ideal individuals who need to change, after which the convergence of the proposed algorithm is proven. The novelty of the proposed strategy is that it avoids the need to compute the collective preference relations and recommendations are generated for the individuals in their original domains. Finally, some numerical examples taken from the literature are given to compare the proposed approaches with existing studies. The obtained results confirm the theoretical analysis and highlight the advantages of the proposed approaches. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-demand negotiation model that takes the effect of human users’ psychological characteristics into consideration. Specifically, in our model each negotiating agent's preference over its demands can be changed, according to human users’ attitudes to risk, patience and regret, during the course of a negotiation. And the change of preference structures is determined by fuzzy logic rules, which are elicited through our psychological experiments. The applicability of our model is illustrated by using our model to solve a problem of political negotiation between two countries. Moreover, we do lots of theoretical and empirical analyses to reveal some insights into our model. In addition, to compare our model with existing ones, we make a survey on fuzzy logic based negotiation, and discuss the similarities and differences between our negotiation model and various consensus models. [ABSTRACT FROM AUTHOR]

The modeling and solving of multi-criteria decision-making (MCDM) problems under uncertainty is still a challenging topic. In real-life decision-making, using linguistic terms to represent experts’ judgments is suitable and straightforward since precise quantitative values may often be unavailable or the cost for their computation is too high. The introduction of hesitant fuzzy linguistic term sets (HFLTSs) was motivated by the limitations of prior linguistic fuzzy models and need for richer linguistic tools. However, since their introduction, comparing HFLTSs is still one of the major concerns of researchers in this area. The existing approaches in the literature commonly rely on (1) labels and intervals from the linguistic terms as the central elements of an envelope-based approach or (2) linguistic scale functions as the basis of a distance-based approach. The two approaches retain certain shortcomings resulting information distortion and loss which may inevitably degrade their credibility. In this paper, the authors are involved in the recent proposal of combining outranking approaches with HFLTSs in an MCDM context. After reviewing the existing approaches, an outranking method based on a novel knowledge-based paradigm for comparing HFLTSs is developed. Alternatively, the paradigm's foundations are the introduced concepts of fuzzy preference relations and profiles considering uncertainty degrees in decision makers’ assessments. The paradigm is then associated with a multi-criteria relational clustering (MCRC) algorithm that additionally extracts fuzzy preference relations between the resultant clusters. Last, an illustrative example is given to verify the appropriateness and efficacy of the developed approach and comparisons are made with other existing ones. [ABSTRACT FROM AUTHOR]

Abstract: We establish in this paper the optimization model of group consensus of 2-tuple linguistic preferential relations (LPRGCO Model), put forward three kinds of solutions to this model, and discover in it the convergence of group consensus. To detect the LPRGCO Model, we first build two kinds of optimal matrices as standards to measure the group consensus of 2-tuple linguistic preference relations (LPRs). And to analyze consensus deviations, we then adopt three types of measures, namely, the individual degree of consistency regarding alternative decision pairs, the deviational degree of the group consensus regarding alternative decision pairs, and the degree of group consensus regarding the original 2-tuple LPRs. On the basis of the previous analysis we not only construct an optimization model to probe into the deviation of the group consensus of 2-tuple LPRs by minimizing the weighted arithmetic average of deviation degrees of individual consistency, but also point out three feasible solutions to this optimization model: the optimal solution, satisfactory solutions, and non-inferior solutions. Accordingly, we discover different conditions in terms of the three solutions. And hence, we can from the aforementioned discussion draw a conclusion that the deviation of group consensus either decreases or stays invariant as the number of decision makers (DM) increases. To expatiate on the practical value of the model proposed, we will display in this paper numerical examples. [Copyright &y& Elsevier]

Abstract: In the current uncertain context that affects both the world economy and the energy sector, with the rapid increase in the prices of oil and gas and the very unstable political situation that affects some of the largest raw materials’ producers, there is a need for developing efficient and powerful quantitative tools that allow to model and forecast fossil fuel prices, CO2 emission allowances prices as well as electricity prices. This will improve decision making for all the agents involved in energy issues. Although there are papers focused on modelling fossil fuel prices, CO2 prices and electricity prices, the literature is scarce on attempts to consider all of them together. This paper focuses on both building a multivariate model for the aforementioned prices and comparing its results with those of univariate ones, in terms of prediction accuracy (univariate and multivariate models are compared for a large span of days, all in the first 4 months in 2011) as well as extracting common features in the volatilities of the prices of all these relevant magnitudes. The common features in volatility are extracted by means of a conditionally heteroskedastic dynamic factor model which allows to solve the curse of dimensionality problem that commonly arises when estimating multivariate GARCH models. Additionally, the common volatility factors obtained are useful for improving the forecasting intervals and have a nice economical interpretation. Besides, the results obtained and methodology proposed can be useful as a starting point for risk management or portfolio optimization under uncertainty in the current context of energy markets. [Copyright &y& Elsevier]

Abstract: This paper proposes a variant of the Non-dominated Sorting Genetic Algorithm (NSGA-II) to solve a novel mathematical model for multi-objective redundancy allocation problems (MORAP). Most researchers about redundancy allocation problem (RAP) have focused on single objective optimization, while there has been some limited research which addresses multi-objective optimization. Also all mathematical multi-objective models of general RAP assume that the type of redundancy strategy for each subsystem is predetermined and known a priori. In general, active redundancy has traditionally received greater attention; however, in practice both active and cold-standby redundancies may be used within a particular system design. The choice of redundancy strategy then becomes an additional decision variable. Thus, the proposed model and solution method are to select the best redundancy strategy, type of components, and levels of redundancy for each subsystem that maximizes the system reliability and minimize total system cost under system-level constraints. This problem belongs to the NP-hard class. This paper presents a second-generation Multiple-Objective Evolutionary Algorithm (MOEA), named NSGA-II to find the best solution for the given problem. The proposed algorithm demonstrates the ability to identify a set of optimal solutions (Pareto front), which provides the Decision Maker (DM) with a complete picture of the optimal solution space. After finding the Pareto front, a procedure is used to select the best solution from the Pareto front. Finally, the advantages of the presented multi-objective model and of the proposed algorithm are illustrated by solving test problems taken from the literature and the robustness of the proposed NSGA-II is discussed. [Copyright &y& Elsevier]

Abstract: The assessment of promotional sales with models constructed by machine learning techniques is arousing interest due, among other reasons, to the current economic situation leading to a more complex environment of simultaneous and concurrent promotional activities. An operative model diagnosis procedure was previously proposed in the companion paper, which can be readily used both for agile decision making on the architecture and implementation details of the machine learning algorithms, and for differential benchmarking among models. In this paper, a detailed example of model analysis is presented for two representative databases with different promotional behaviour, namely, a non-seasonal category (milk) and a heavily seasonal category (beer). The performance of four well-known machine learning techniques with increasing complexity is analyzed in detail here. In particular, k-Nearest Neighbours, General Regression Neural Networks, Multilayer Perceptron (MLP), and Support Vector Machines (SVM), are differentially compared. Present paper evaluates these techniques along the experiments described for both categories when applying the methodological findings obtained in the companion paper. We conclude that some elements included in the architecture are not essential for a good performance of the machine learning promotional models, such as the semiparametric nature of the kernel in SVM models, whereas other can be strongly dependent of the database, such as the convenience of multiple output models in MLP regression schemes. Additionally, the specificity of the behaviour of certain categories and product ranges determines the need to establish suitable and specific procedures for a better prediction and feature extraction. [Copyright &y& Elsevier]

Abstract: This paper presents a tour-chain-based approach to modeling urban commercial vehicle daily activity patterns. A daily tour chain refers to a sequence of daily tours made by a vehicle. Multinomial logit model results demonstrate that urban daily tour-chain choice is a result of collective decisions based on cost and shipment characteristics. This research has significant contributions to the current literature in filling the gap of understanding such critical logistics decisions as distribution channel and factors affecting tour chaining. Furthermore, the paper presents an innovative use of commercial vehicle travel survey data, and points to the urgent need for better quality data. [Copyright &y& Elsevier]

Abstract: This paper proposes an integrated approach to indeterminacy and epistemic uncertainty in order to model an intelligent agentʼs decision making about the assertability of vague statements. Initially, valuation pairs are introduced as a model of truth-gaps for propositional logic sentences. These take the form of lower and upper truth-valuations representing absolutely true and not absolutely false respectively. In particular, we consider valuation pairs based on supervaluationist principles and also on Kleeneʼs three-valued logic. The relationship between Kleene valuation pairs and supervaluation pairs is then explored in some detail with particular reference to a natural ordering on semantic precision. In the second part of the paper we extend this approach by proposing bipolar belief pairs as an integrated model combining epistemic uncertainty and indeterminacy. These comprise of lower and upper belief measures on propositional sentences, defined by a probability distribution on a finite set of possible valuation pairs. The properties of these measures are investigated together with their relationship to different types of uncertainty measure. Finally, we apply bipolar belief measures in a preliminary decision theoretic study so as to begin to understand how the use of vague expressions can help to mitigate the risk associated with making forecasts or promises. This then has potential applications to natural language generation systems. [Copyright &y& Elsevier]

Abstract: Recently Dal [Dal, F., 2011. Multiple time scale solution of an equation with quadratic and cubic nonlinearities having fractional-order derivative. Mathematical and Computational Applications 16 (1), 301–308] presented ‘a new analytical scheme’ to calculate the dynamic response of a fractionally damped nonlinear oscillator possessing both quadratic and cubic nonlinearities via the method of multiple time scales. It has been claimed that damping features are modeled via the Caputo fractional derivative. In the present paper, it is shown that both the scheme and the object of investigation are not new, and moreover, the above mentioned author''s statement is inconsistent, since under the assumptions made in the paper under consideration these two fractional-order derivatives coincide. Besides, the utilized procedure was inconsequential. It has been proved that the investigation of the dynamic response of a nonlinear viscoelastic oscillator presents the case that, with some minimal restrictions, the Riemann–Liouville and Caputo definitions produce completely equivalent mathematical models of the nonlinear viscoelastic phenomenon. [Copyright &y& Elsevier]

Abstract: This paper proposes the application of fault-tolerant control (FTC) using fuzzy predictive control. The FTC approach is based on two steps, fault detection and isolation (FDI) and fault accommodation. The fault detection is performed by a model-based approach using fuzzy modeling and fault isolation uses a fuzzy decision making approach. The information obtained on the FDI step is used to select the model to be used in fault accommodation, in a model predictive control (MPC) scheme. The fault accommodation is performed with one fuzzy model for each identified fault. The FTC scheme is used to accommodate the faults of two systems a container gantry crane and three tank benchmark system. The fuzzy FTC scheme proposed in this paper was able to detect, isolate and accommodate correctly the considered faults of both systems. [Copyright &y& Elsevier]

Abstract: This paper suggests a cause-and-effect function network (CEFN) to support technology innovation in a direct way. To support this CEFN, a cause-and-effect relationship, a function model and an ontological approach are proposed. In the CEFN, technologies from different domains can be connected because defining technologies as functions provides abstractive, representative and formal expressions of them. Using ontology guarantees linguistic disambiguation in defining or searching heterogeneous technologies. Consequently, this paper summarizes construction of a CEFN which can be used as a searching system, by which users can get results by making a query using only ‘Action–Object’ (verb–noun) combinations. The proposed system can be used both for problem solving and for discovering technological opportunity. In this paper, a method consisting of procedure and analyses to build the CEFN is suggested, and a case study is performed to demonstrate the suggested method and system. [Copyright &y& Elsevier]

Abstract: The paper refers to the importance of maintenance management to increase the vehicle fleet energy efficiency. The fleet maintenance management influences as the vehicle maintenance process itself as well as the primary transport process but also their environment. In order to increase fleet energy efficiency by means of a more efficient maintenance management, it is indispensable to observe maintenance process, transport process and the environment. Since the implementation effects of such measures can be measured by different indicators, this paper analyses the influence of indicators in all three mentioned areas on management decision-making. In this sense, appropriate indicators have been defined and subsequently used in fleet maintenance management. To determine levels and intensities of interdependence as well as relative weight of selected indicators two methods have been combined: Decision Making Trial and Evaluation Laboratory (DEMATEL) and Analytic Network Process (ANP). A model was proposed with indicators’ interdependence whose relative weights were calculated. The proposed model has been implemented in several companies with road vehicle fleets. Collected results show the perceived evaluation by company managers in view of maintenance management process influence onto their fleet energy efficiency. Besides, by proposed model implementation we have obtained equally managers’ evaluation upon effectiveness and efficiency of the maintenance management within studied companies. [Copyright &y& Elsevier]

Abstract: An adaptation phase is crucial for a good and reasonable case-based design (CBD) process, which is responsible for finding a solution to solve a new problem in the principle of k-nearest neighbors (k-NN). Statistical adaptation method is a classical method for feature-based case adaptation (FCA) because of its domain-independent and easily to be implemented, but with low adaptation accuracy. Therefore, this paper presents a new adaptation method for solution feature values of retrieved cases by introducing the adaptability value to improve the adaptation performance, called as adaptability-based FCA (AFCA). Unlike the classical statistical FCA method (SFCA) based on similarity or distance value, AFCA is performed in terms of the adaptability of old solution feature calculated by the adaptability measurement (AM) mechanism. A new AM method is studied as well in this paper, where the adaptability value for each solution feature is computed by utilizing the decision tree technique and similarity value, and the similarity is derived from the multi-algorithm-oriented hybrid SM strategy. Furthermore, to validate the feasibility and superiority of AFCA, the proposed method was applied to the power transformer design and was compared with the classical SFCAs. Empirical comparison results indicated that AFCA achieves the better adaptation performance under k-NN than other SFCAs on the basis of the adaptation accuracy. [Copyright &y& Elsevier]

Abstract Intuitionistic fuzzy sets describe information from the three aspects of membership degree, non-membership degree and hesitation degree, which has more practical significance when uncertainty pervades qualitative decision problems. In this paper, we investigate the problem of ranking intuitionistic fuzzy preference relations (IFPRs) based on various non-linear utility functions. First, we transform IFPRs into their isomorphic interval-value fuzzy preference relations (IVFPRs), and utilise non-linear utility functions, such as parabolic, S-shaped, and hyperbolic absolute risk aversion, to fit the true value of a decision-maker's judgement. Ultimately, the optimization ordering models for the membership and non-membership of IVFPRs based on utility function are constructed, with objective function aiming at minimizing the distance deviation between the multiplicative consistency ideal judgment and the actual judgment, represented by utility function, subject to the decision-maker's utility constraints. The proposed models ensure that more factual and optimal ranking of alternative is acquired, avoiding information distortion caused by the operations of intervals. Second, by introducing a non-Archimedean infinitesimal, we establish the optimization ordering model for IFPRs with the priority of utility or deviation, which realises the goal of prioritising solutions under multi-objective programming. Subsequently, we verify that a close connection exists between the ranking for membership and non-membership degree IVFPRs. Comparison analyses with existing approaches are summarized to demonstrate that the proposed models have advantage in dealing with group decision making problems with IFPRs. [ABSTRACT FROM AUTHOR]

One popular stopping rule intended to increase the rate of male births is to keep having babies until the first male is born. We show that such a stopping rule does not change the sex ratio at birth when the probability p that a birth produces a male is constant across a population. We show, however, that when p varies across couples, as evidence suggests that it does in the human population, with mean around 0.515 and standard deviation 0.05, then such a stopping rule tends to favour female births and the correct rule should be to stop procreating until the first female is born. But we also show that it does not seem that such stopping rules, even if favouring male births, can account for the dramatic increase in the rate of male births registered in some countries. Most of these conclusions have appeared in some form or other in various studies, but they are scattered across the literature and are very often presented using some heavy mathematical techniques. In this paper, we try to bring together these ideas and make them more accessible by analysing these stopping rules using the simplest mathematical tools possible. [ABSTRACT FROM AUTHOR]