Search

Abstract: Several authors have highlighted the role of intuition in expertise. In particular, a large amount of data has been collected about intuition in expert nursing, and intuition plays an important role in the influential theory of nursing expertise developed by Benner [1984. From Novice to Expert: Excellence and Power in Clinical Nursing Practice. Addison–Wesley, Menlo Park, CA]. We discuss this theory, and highlight both data that support it and data that challenge it. Based on this assessment, we propose a new theory of nursing expertise and intuition, which emphasizes how perception and conscious problem solving are intimately related. In the discussion, we propose that this theory opens new avenues of enquiry for research into nursing expertise. [Copyright &y& Elsevier]

Abstract: Although children partition by repeatedly halving easily and spontaneously as early as the age of 4, multiplicative thinking is difficult and develops over a long period in school. Given the apparently multiplicative character of repeated halving and doubling, it is natural to ask what role they might play in the development of multiplicative thinking. We investigated this question by examining children''s solutions to folding tasks, which involved predicting the number of equal parts created by a succession of given folds and determining a sequence of folds to create a given number of equal parts. Analyzing a combination of cross-sectional data and case studies from standardized clinical interviews, we found that children were most successful at coordinating folding sequences with multiplicative thinking when they used a conceptualization of doubling based upon recursion. This conceptualization tended to generate more sophisticated solutions. [Copyright &y& Elsevier]

Abstract: The paper of Huang, Keisler and Linkov (HKL) () has motivated us to this note about the relation between ranking procedures and Multi-Criteria Decision Analysis (MCDA) tools. The key concept in the HKL-paper is in our eyes the ‘trade-off’. A ‘trade-off’ seems necessary, when conflicting indicator values are present. HKL stress that the general application of MCDA is hampered by different terminology. We do not claim to have a solution for that problem. Nevertheless, we think that the discussion is worthwhile and this note presents some of our ideas, which basically imply the need of a broadening of the definition of MCDA. [Copyright &y& Elsevier]

The main aim of this paper is to present a new multi-attribute decision-making (MADM) approach for solving the problems under the uncertain and complex environment. The key challenges during any MADM problem are how to quantify the objective uncertainty information in the data and how to aggregate such collective information. To answer this, in this paper, we utilize the concept of the bipolar fuzzy information to mark the information in terms of the positive and negative support. To aggregate this different information, we propose some power aggregation operators based on the Aczel-Alsina operators (AAO). The AAOs are the generalized t-norm based operations with an additional parameter to analyze the influence of the expert preferences. Based on these AAO and bipolar fuzzy information, we stated bipolar fuzzy AA power weighted averaging and geometric operators and investigate their features. Later, based on these operators, we establish a MADM algorithm to solve the decision-making problems. The applicability of the stated algorithm is demonstrated through a case study related to quantum computing. The comparative studies and advantages of the study are also analyzed with the various prevailing theories. [ABSTRACT FROM AUTHOR]

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]

Multi-attribute decision-making problem (MADM) is a kind of multi-criteria decision-making (MCDM), which is often used to solve decision-making problems where the decision variables are discrete and the number of decision-making schemes is limited. This paper reviews the basic definition of multi-attribute decision making (MADM) through the performance evaluation of science and technology funds in a city, and then deduces the TODIM decision making method and TOPSIS decision making method in detail. Finally, the clue case is decided by combining these two decision making methods. This paper specifically shows the usage scenarios and limitations of the TODIM decision method and the TOPSIS decision method through the clue case, analyzes the advantages and disadvantages of the two methods through the comparison of the two methods, and puts forward suggestions for the clue case based on the results of the two schemes. [ABSTRACT FROM AUTHOR]

This paper overviews and discusses the relationship between Reinforcement Learning (RL) and the recently developed Dual Control for Exploitation and Exploration (DCEE). It is argued that there are two related but quite distinctive approaches, namely, control and machine learning, in tackling intractability arising in optimal decision making/control problems. In the control approach, the original problems (of an infinite horizon) are approximated by finite horizon problems and solved online by taking advantage of the availability of computing power. In the machine learning approach, the optimal solutions are approximated through iterations, or (offline) training through trials when models are not available. When dealing with unknown environments, DCEE as a technique developed from the control approach could potentially solve similar problems as RL while offering a number of advantages, most notably, coping with uncertainty in environment/tasks, high efficiency in learning through balancing exploitation and exploration, and potential in establishing its formal properties like stability. The links between DCEE and other relevant methods like dual control, Model Predictive Control and particularly Active Inference in neuroscience are discussed. The latter provides a strong biological endorsement for DCEE. The methods and discussions are illustrated by autonomous source search using a robot. It is concluded that DCEE provides a promising, complementary approach to RL, and more research is required to develop it as a generic theory and fully realise its potential. The relationships revealed in this paper provide insights into these relevant methods and facilitate cross fertilisation between control, machine learning and neuroscience for developing autonomous control under uncertain environments. [ABSTRACT FROM AUTHOR]

Health inequalities are a perennial concern for policymakers and in service delivery to ensure fair and equitable access and outcomes. As health inequalities are socially influenced by employment, income, and education, this impacts healthcare services among socio-economically disadvantaged groups, making it a pertinent area for investigation in seeking to promote equitable access. Researchers widely acknowledge that health equity is a multi-faceted problem requiring approaches to understand the complexity and interconnections in hospital planning as a precursor to healthcare delivery. Operations research offers the potential to develop analytical models and frameworks to aid in complex decision-making that has both a strategic and operational function in problem-solving. This paper develops a simulation-based modelling framework (SimulEQUITY) to model the complexities in addressing health inequalities at a hospital level. The model encompasses an entire hospital operation (including inpatient, outpatient, and emergency department services) using the discrete-event simulation method to simulate the behaviour and performance of real-world systems, processes, or organisations. The paper makes a sustained contribution to knowledge by challenging the existing population-level planning approaches in healthcare that often overlook individual patient needs, especially within disadvantaged groups. By holistically modelling an entire hospital, socio-economic variations in patients' pathways are developed by incorporating individual patient attributes and variables. This innovative framework facilitates the exploration of diverse scenarios, from processes to resources and environmental factors, enabling key decision-makers to evaluate what intervention strategies to adopt as well as the likely scenarios for future patterns of healthcare inequality. The paper outlines the decision-support toolkit developed and the practical application of the SimulEQUITY model through to implementation within a hospital in the UK. This moves hospital management and strategic planning to a more dynamic position where a software-based approach, incorporating complexity, is implicit in the modelling rather than simplification and generalisation arising from the use of population-based models. [ABSTRACT FROM AUTHOR]

Partial data is a common challenge in many applied problems, where we can measure only the part of the solution. This paper considers the poroelasticity problem in fractured media, where only partial measurements of fracture pressures are available. The poroelasticity models have applications in many engineering and scientific fields, such as geomechanics, hydrology, and reservoir simulation. This paper aims to develop a Bayesian decision making approach using partial data and consider its application to the poroelasticity problem in fractured media. The methodology of the proposed approach combines Proper Orthogonal Decomposition, Discrete Empirical Interpolation Method, Bayesian Inversion Approach, and Bayesian Decision Theory. It allows us to compute posterior probabilities and decide according to the optimal decision rule based on the partial data. Moreover, we apply partially explicit temporal discretization to solve forward problems efficiently. For testing the proposed approach, we consider a two-dimensional model problem. We simulate the cases when we need to make a single decision about the reservoir or decisions about the elements of the set of reservoirs. The results show that the proposed approach can efficiently compute the posterior probabilities and make a decision in the presence of only partial data for both cases. [ABSTRACT FROM AUTHOR]

Multi-criteria decision-making methods aim to solve complex problems under many factors. These methods each day are more frequently applied and improved. Many researchers try to find a reliable solution for a specific problem or group of problems. More and more often, new methods are created that certain target requirements. That is why solving the specific problem can be executed on multiple methods. In this paper, we provide three new solutions for the problem presented in the referenced paper. For this purpose, the Characteristic Object's Method (COMET), the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method and the Stable Preference Ordering Towards Ideal Solution (SPOTIS) method were used. The results were then compared using selected similarity coefficients to emphasize the correlation of the resulting rankings. The research showed that choosing a method will have an impact on results and should be considered with caution. [ABSTRACT FROM AUTHOR]

Reviews of songs play an important role in online music service platforms. Prior research shows that users can make quicker and more informed decisions when presented with meaningful song reviews. However, reviews of songs are generally long in length and most of them are non-informative for users. It is difficult for users to efficiently grasp meaningful messages for making decisions. To solve this problem, one practical strategy is to provide tips , i.e., short, concise, empathetic, and self-contained descriptions about songs. Tips are produced from song reviews and should express non-trivial insights about the songs. To the best of our knowledge, no prior studies have explored the tip generation task in music domain. In this paper, we create a dataset named MTips for the task and propose a learning-to-generate framework named GenTMS for automatically generating tips from song reviews. The dataset involves 8,003 Chinese tips/non-tips from 128 songs which are distributed in five different song genres. Experimental results show that GenTMS achieves top-10 precision at 85.56%, outperforming the baseline models by at least 3.34%. Besides, to simulate the practical usage of our proposed framework, we also experiment with previously-unseen songs, during which GenTMS also achieves the best performance with top-10 precision at 78.89% on average. The results demonstrate the effectiveness of the proposed framework in tip generation of the music domain. [ABSTRACT FROM AUTHOR]

Due to the unavailability of time series data for newly listed stocks or products, it is a challenge for investors to make rational portfolio selection under uncertain circumstances. To solve the problem, a new approach is put forward in this paper. Firstly, the problem is considered as a multi-criteria decision making (MCDM) problem based on the assumption that the assessments are given in intuitionistic fuzzy set (IFS) form, which can better describe the uncertainty. Then, the TOPSIS method, which is widely used in MCDM problem, has been modified in two aspects. On the one hand, the new definitions of Absolute Positive Ideal Solution (APIS) and Absolute Negative Ideal Solution (ANIS) are proposed to represent the best returns and the greatest cost/risk in portfolio selection. They are more meaningful than the definitions of Positive Ideal Solution (PIS) and Negative Ideal Solution (NIS), which could only express the best and the worst case of portfolio but cannot achieve a balance between returns and risk. On the other hand, the weighted closeness coefficient is refined to offer the more appropriate results that are consistent with investors' demands or preferences. In addition, based on distance measure of IFSs, several novel linear programming models with different constraints are proposed to allocate the investment ratios according to investors' demands. The models can make up for the disadvantage of TOPSIS method which only considers the ranking of investments but neglects the investment ratios. Finally, compared with the conventional TOPSIS method and the IF ELECTRE Method in a numerical example, our new approach is demonstrated to be more effective and more flexible. Particularly, it can provide a more appropriate strategy in accordance with investors' preferences and demands. [ABSTRACT FROM AUTHOR]

The Choquet integral w.r.t. a capacity is a versatile tool commonly used in decision making. Its practical identification requires, however, to solve an optimization problem with exponentially many variables and constraints. The introduction of k -additive capacities, through the use of the Möbius transform, permits to reduce the number of variables to a polynomial size, but leaves the number of constraints exponential. When k = 2 , the use of vertices of the set of 2-additive capacities permits to solve the problem as the number of vertices is polynomial. When k > 2 , this solution is no more applicable as the set of vertices of k -additive capacities is not known. We propose in this paper to use instead the set of vertices which are 0-1 valued. We show that the number of such vertices is polynomial, and we observe that the loss of generality is very small for n = 4 , k = 3 , and conjecture that this still holds for larger values of n. Also, we study the geometric properties of the convex hull of 0-1 valued k -additive capacities. [ABSTRACT FROM AUTHOR]

Recently, cross-domain recommendation systems have been very helpful in improving the quality of recommendation and solving the problem of cold start and data sparsity. Cross-domain recommender systems allow the transfer of knowledge from one domain with dense ratings to other domain with sparse ratings. Such transfer of knowledge helps in addressing the data sparseness and cold start issues in traditional recommender systems. Although cross-domain recommendations have evolved significantly, yet employment of various factors, such as time, trust, and location remains a challenge. Most of the existing approaches ignore the important fact that at what specific time the user may be interested in the recommended item. Moreover, a person's trust level and sentiments may be influenced by the variation in the location and time, thereby affecting the decision making. In this paper, we propose a cross-domain recommender system that not only takes into account the time at finer granularity levels (e.g., hours, days, weeks, etc.), but also considers a persons location, trust level, and sentiment analysis while computing recommendations. Our proposed model, named as, Trust-Aware Spatial-Temporal Activity based Denoising Autoencoder (TSTDAE), employs autoencoder-based deep-learning models to generate top-N recommendations for a given user and addresses the cold-start problem in the cross-domain scenario of 'User Overlap'. The proposed work is fivefold: i) Filter out the users' biased profiles based on sentiment analysis. ii) Learn the features using autoencoder and then perform clustering among the users of source and target domains to discover the best neighbors. iii) Compute the trust and ratio of preference bias between active user (the user to whom top-N items are recommended) and their neighbors and grade the neighbors based on unbiased preferences iv) Project the best time for recommending the items to an active user and generate the top-N recommendations. We have evaluated the proposed model on a public dataset of e-commerce retail service 'AliExpress' for the evaluation metrics: Precision, Mean Absolute Precision (MAP), Normalized Discounted Cumulative Gain (NDCG), and Hit Ratio (HR). The experimental results showed improved performance of the proposed system over the existing models. [ABSTRACT FROM AUTHOR]

The studies on complex business issues in management described by multiple characteristics (indicators), use a variety of methodological approaches. Diverse decision support methods are used to solve problems in this area (depending on the situation considered). These methods include TOPSIS and VMCM. There are methodological similarities between them, but there are also several differences. VMCM eliminates some limitations of TOPSIS regarding, inter alia, the inclusion of non-typical objects, real-world patterns, and time-varying dynamics. Methodological considerations supplemented with an exemplary application are presented in this paper. [ABSTRACT FROM AUTHOR]

Machine Learning (ML) is growing in popularity and is applied in numerous fields to solve complex problems. Opportunistic Networks are a type of Ad-hoc Network where a contemporaneous path does not always exist. So, forwarding methods that assume the availability of contemporaneous paths does not work. ML techniques are applied to resolve the fundamental problems in Opportunistic Networks, including contact probability, link prediction, making a forwarding decision, friendship strength, and dynamic topology. The paper summarises different ML techniques applied in Opportunistic Networks with their benefits, research challenges, and future opportunities. The study provides insight into the Opportunistic Networks with ML and motivates the researcher to apply ML techniques to overcome various challenges in Opportunistic Networks. [ABSTRACT FROM AUTHOR]

The pervasiveness of the Internet coupled with the advantages of cloud have almost made cloud services a necessity for organizations and individuals alike. However, it is a complex decision to choose a cloud vendor because of the large number of criteria and alternatives. Furthermore, the criteria in the case of cloud vendor selection are often interactive (synergic or opposing). To address this need, an approach is presented in this paper to select the most suitable cloud vendor while considering the nuances of human decision making, such as interactive criteria, and varying behavioural characteristics of the decision-makers (DMs). To this end, the recent Choquet logit models are deployed and generalized to add to their modelling abilities. Specifically, the following choice models are developed: generalized Choquet logit, generalized attitudinal Choquet logit, generalized Choquet mixed logit, and generalized attitudinal Choquet mixed logit models. The proposed generalized logit models have the Choquet logit, and the attitudinal Choquet logit models as their special cases. Thus the contributions of this work are two-fold: first an approach is developed to select suitable cloud vendor considering the human nuances and the actual practical aspects of the problem, and secondly a new class of powerful choice models is developed which may be useful in aiding any complex human decision-making, in which the cognitive power of the DM proves to be limited. The method is designed to consider the real nuances of the human decision making such as varying attitudes and varying degree of criteria interaction. The effectiveness of the proposed models in cloud vendor selection is tested through a real world case study from the Indian state of Tamil Nadu. In the case-study, there are 9 cloud vendors in the fray and 4 experts with different attitudes and different suggestions for the best cloud vendor. An approach is developed to solve this problem and a final ranking of the cloud vendors is determined considering experts suggestions. Apart from finding the best cloud vendor, the case study also helps in studying the role of an expert's attitudinal tendency on his preferences. Thus, concomitantly, the proposed approach also guides how to choose the best alternative based on the opinions of different experts with different attitudinal tendencies, without necessitating a forced consensus. • New probabilistic discrete choice models. • Representation of the complex decision making attitude. • Consideration of interaction among the criteria. • Variants of the proposed logit models. • Real Application. [ABSTRACT FROM AUTHOR]

Ecological compensation is an important means to solve the problem of river basin water pollution, which has been widely used at home and abroad. However, the study of ecological compensation problems from the perspective of the management by using the graph model for conflict resolution (GMCR) is still a challenge. Thus, the aim of this article is to construct the graph model with intuitionistic preferences to model and analyze real life ecological compensation conflicts. Specifically, due to the lack of information and the inherent fuzziness of human cognition in ecological compensation conflicts, the intuitionistic preference structure is incorporated into the framework of the GMCR to represent the decision makers' (DMs') uncertain preference. Based on this, two sets of intuitionistic stability definitions are defined to analyze and solve the possible resolution for conflict problems with two DMs and multiple DMs, respectively. Note that a state is called intuitionistic stability for DM in a graph model based on the given intuitionistic stability definitions if and only if there is no reward for a given state to leave the initial state, that is to say, the evaluated value of the focus DM is less than an intuitionistic satisficing threshold of the same DM. Furthermore, the intuitionistic equilibrium is considered as the possible solution to the conflict problems and is intuitionistic stable for all DMs in the graph model. Finally, the ecological compensation conflicts of the Taihu Lake basin are used to demonstrate the usefulness and correctness of the graph model with intuitionistic preferences. • The intuitionistic preference is incorporated into the graph model to describe the uncertain preference of DM. • The proposed method is useful to model and analyze the ecological compensation conflicts of the Taihu Lake basin. • The paper provides a new research perspective for the graph model with uncertainty and its application. • The paper reveals the demands of DMs in ecological compensation conflicts and provides valuable decision making information. • The proposed method lays the foundations for developing detailed computational implementation algorithms of graph model. [ABSTRACT FROM AUTHOR]

Digital university audit platform has become an important part of digital campus construction. How to combine big data analysis technology with university audit research, so as to promote the in-depth development of university audit work, is an urgent problem to be solved. Audit work in Colleges and universities is an important work at present. This paper aims to study the construction of university audit platform under the background of big data, explain the current situation of the construction of audit platform, the framework structure of audit platform and the problems and reasons of audit platform, accelerate the construction of information audit technology system, improve governance ability, and assist universities and education authorities in making major decisions. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) is a frontier analysis procedure for evaluating the relative performance of decision making units (DMUs) with multiple inputs and multiple outputs. To improve its discrimination power, an important extension is proposed as cross-efficiency, which uses peer DMUs’ optimal relative weights to evaluate the relative performance. However, the existing cross-efficiency methods show an inconsistent and unbalanced evaluation standard, since each DMU might determine a different total (or mean) efficiency value across all DMUs. The different values imply that the DMUs that have assigned larger cross-efficiency scores will have a larger effect in aggregating the ultimate cross-efficiency scores and different DMUs’ effects are unbalanced in cross-efficiency methods. In this paper, we will deal with this unbalanced cross-efficiency evaluation problem. To this end, we first suggest a practical adjustment measure to rectify the traditional cross-efficiency, which will provide a common evaluation standard for all DMUs and make each DMU dispatch an identical total efficiency score across all DMUs. Further, we propose a game-like iterative procedure to obtain the optimal balanced cross-efficiency. Finally, we present both a numerical example and an empirical study derived from the literature and a real-world problem to demonstrate the usefulness and efficacy of the new balanced cross-efficiency evaluation approach. The work presented in this paper can extend the traditional cross-efficiency approaches to situations involving unbalanced evaluation standards, and make the evaluation results more practical significance. [ABSTRACT FROM AUTHOR]

People make numerous health-related choices each day: For example, deciding to brush one's teeth or to eat well and healthy - or not to do these activities. To support complex decisions and subsequent behaviour change, both Behaviour Change Interventions (BCIs) and Patient Decision Aids (PtDAs) have been developed and evolved independently to support people in health-related decision making. In this paper, we critically review BCIs and PtDAs, examine their similarities and differences, and identify potential for integration of expertise to increase the benefits for people engaging with healthcare and health behaviours. The two approaches appear to mainly differ in terms of their (1) goals and foci, (2) theoretical basis, (3) development frameworks, (4) active ingredients and (5) effect evaluation. To facilitate the integration of scientific insights from these two fields, we recommend to (1) bring both fields together and promote interprofessional discussions, (2) train (health) professionals to recognise strengths of both approaches, (3) investigate the synergy of the two fields, (4) be prepared for and try to mitigate a culture shock when the fields start to interact. Knowledge generated by researching PtDAs could be used to facilitate decisional processes that enable patients to choose goals that are in line with their values and preferences, while insights from researching BCIs could be used to facilitate engagement with, and implementation of those goals. This integration could allow researchers and intervention providers to increase the benefits for people engaging with healthcare and health behaviours. [ABSTRACT FROM AUTHOR]

The problem-solving task of the multi-criteria decision-making (MCDM) approach involves decision makers' (DMs') interaction by incorporating their preferences to arrive at one or more preferred near Pareto-optimal solution(s). The Pareto-Race is an interactive MCDM approach that relies on finding preferred solutions as the DM navigates on the Pareto surface by selecting the preferred reference direction and uniform/varying speed using the concept of Achievement Scalarization Function (ASF) or its augmented version (AASF). Selecting a reference direction or speed is possible, algorithmically, but the effectiveness of the Pareto-Race concept relies on suitable visualization methods that present the series of past solutions and convey the current objective trade-off information to assist the DM's in decision-making task. Traditional multi-dimensional Pareto-optimal front visualization methods like parallel coordinate plots, radial visualization, and heat maps are deemed insufficient for this purpose. Therefore, the paper proposes integrating the concept of the Pareto-Race MCDM approach with a recently developed interpretable self-organizing map (iSOM) based visualization method. The iSOM maps high-dimensional data into lower-dimensional space, facilitating visual convenience for the DM. The proposed method requires a finite-size representation of non-dominated solutions near the complete Pareto Front to generate iSOM plots of objectives. We also propose visualizing the metrics such as closeness to constraint boundaries, trade-off value, and robustness using iSOM to check the quality of the preferred solutions, which is rarely considered in existing MCDM approaches. iSOM plots of objective functions, closeness to constraint, trade-off, and robustness metrics assist the DM in effectively choosing new reference direction and step size in each iteration to arrive at the most preferred solution. The proposed iSOM-enabled Pareto-Race approach is demonstrated on benchmark analytical and real-world engineering examples with 3 to 10 objectives. [Display omitted] • Interactive MCDM using iSOM-aided Pareto-Race technique. • Introducing metrics such as closeness to constraint and robustness in Pareto-Race. • Visual comprehension of goodness of preferred solution and features in each iteration. • Visualization-based selection of step size and direction of search in Pareto-Race. • Demonstration on 3 to 10-objective analytical and real-world engineering examples. [ABSTRACT FROM AUTHOR]

In the diversity of contemporary decision-making tasks, where the data is no longer static and changes over time, data stream processing has become an important issue in the field of pattern recognition. In addition, most of the real problems are not balanced, representing their classes in various improportions. Following paper proposes the Prior Imbalance Compensation method, modifying on-the-fly predictions made by the base classifier, aiming at mapping prior probability in the statistics of assigned classes. It is intended to be a less computationally complex competition for popular algorithms such as smote , solving this problem by oversampling the training set. The proposed method has been tested using computer experiments on the example of a set of various data streams, leading to promising results, suggesting its usefulness in solving this type of problems. [ABSTRACT FROM AUTHOR]

Active Disturbance Rejection Control (ADRC) emerged as a promising control solution in various engineering domains. However, increased ADRC order makes it difficult to implement and tune in practice. On the other hand, Reduced-order ADRC (RADRC) structure solves this issue with the appropriate tuning of its parameters to achieve the desired performance. This paper aims to develop analytical tuning rules for RADRC for processes approximated as First-order plus dead-time models (FOPDT). These rules meet the conflicting goals of tracking and disturbance rejection restricted by robustness specification. The tuning rules are derived based on a multi-stage approach. In the first stage, the tuning problem is formulated as a multi-objective optimization problem with appropriate constraints. A Multi-objective Quasi-Oppositional Rao-1 (MOQO-Rao-1) Algorithm solves the optimization problem to produce a collection of Pareto-optimal solutions (alternatives) in the second stage. In the third stage, using the Best-Worst based PROMETHEE method, the best one is chosen among the available options. Finally, using linear regression, analytical tuning rules are developed. Separate tuning rules are proposed for lag-dominated and dead-time dominated cases. Simulation experiments on benchmark industrial processes are performed, and the findings assess the efficacy of the suggested tuning rules relative to the methods recently published. The proposed tuning rules are experimentally validated to assess their applicability in the practical scenario. Besides, the closed-loop system's stability with the suggested tuning rules is confirmed by the small-gain theorem and the dual-locus process. • Quantitative tuning rules for RADRC are proposed for industrial processes approximated as FOPDT models. • The proposed tuning rules provides a good trade-off between tracking and disturbance rejection performance. • With the proposed tuning rules, the closed-loop system with RADRC is robustly stable. • Exhibits good repetitive tracking and better performance in stochastic environment. • Simulations and real-time experimentations reconfirm the efficacy of the proposed tuning rules. [ABSTRACT FROM AUTHOR]

Mobile phones are devices that are nowadays widely used by people. The development of technology has resulted in them being used not only for talking and messaging. They have functionalities that make them similar to personal computers. These devices are used to send and receive e-mails, browse the web, organize time, take pictures, play games, listen to music. There is also a possibility of installing various applications on them. The capabilities of these devices are closely related to their parameters, such as memory card size, internal memory, RAM, camera resolution. Price, which increases with the improvement of the parameters, also is a significant criterion of choice. Therefore, the choice of the most appropriate mobile phone model to match the consumer's expectations requires the consideration of many criteria. Choosing the best option when making a decision is challenging because many factors influence the final decision. Additionally, they can be very diverse and difficult to take into account as a whole. An excellent solution to this problem is multi-criteria decision analysis (MCDA) methods, which support choosing from a wide variety of criteria. In this paper, four methods from the MCDA group - COMET, PROMETHEE II, VIKOR and TOPSIS - were used to determine the best mobile phone model among a group of alternatives. It is a complicated issue given the number of models available on the market and the number of parameters to consider when comparing. These methods have been successfully applied in the past to solve problems of similar complexity. All alternatives were analyzed using each method. Then the results were compared to each other to evaluate their similarity. Thus, the best mobile phone was selected for every applied method, and an attempt was made to find out the effect of MCDA method selection on the final ranking. The results obtained show that the tool based on MCDA methods is useful, appropriate, and effective in supporting consumer decisions when choosing mobile phones. [ABSTRACT FROM AUTHOR]

E-commerce is currently one of the most rapidly growing branches of the economy. Increasing competition in the market for this field creates the need for continuous improvement of service quality and the need to stand out from others. More and more consumers are taking advantage of the opportunity to purchase products and services over the Internet. Many products on the market and the existence of many criteria to be taken into account create difficulties in making an objective decision when choosing the most appropriate product. Multi-criteria decision-making methods (MCDMs) are designed to help make the most appropriate decision in multi-criteria problems. Literature studies have proven that they are a widely used and suitable tool to assist choice when purchasing products and services. The possibility of their application in online sales as a consumer decision support tool seems promising both to meet customers' growing needs and strengthen the position of online outlets in the market for these services. This paper presents an approach involving applying four MCDM methods TOPSIS, VIKOR, PROMETHEE II and COMET, in solving the multi-criteria problem of selecting the most favourable model of headphones. The obtained results confirm the usefulness and adequacy of the selected set of methods in choosing purchased products and devices and indicate the great potential of MCDM methods in the field of online commerce and services. [ABSTRACT FROM AUTHOR]

Multi-Criteria Decision Analysis methods are readily used in decision support models. However, many methods in this group of methods cause a problem with choosing an appropriate method for a given problem. One of such methods is COMET (Characteristic Objects Method), which compares pairs of Characteristic Objects (COs) when calculating preference values of alternatives. In this paper, the COMET method was used to create a model that evaluates swimming athletes regarding their predisposition to compete in freestyle sprint events. Besides, to reduce the complexity of the problem, a division of the initial structure was applied, which made it possible to reduce the number of comparisons to 0.7e-8 % of their initial number. To verify the obtained results, it was decided to use selected MCDA methods to solve the same problem. The correlation between the rankings was examined using the Pearson correlation coefficient and the WS similarity coefficient. Based on the results obtained, it can be concluded that the proposed model gives results that guarantee a high correlation with the results from the other methods. Moreover, it can be useful in evaluating and selecting swimming athletes for participation in given events. [ABSTRACT FROM AUTHOR]

Knowledge-based value co-creation refers to the joint work on applying knowledge such as technologies, skills and expertise to solve problems. In this paper, we study how to make decisions dynamically when the main creator cooperates with a co-creator. We develop a theoretical model to analyze the optimal effort level and output of value. The results highlight that the co-creation is a win-win strategy for both the main creator and the co-creator. The equilibrium also indicates that creators will try their best to create value in the beginning phrase and they put less and less efforts over time. However, the output of value has a peak at a certain time instant. This study provides managerial implications by exploring the sensitivity of parameters. The monetizing ability and the output sensitivity factor have positive impacts on the optimal efforts and net incomes, while the marginal cost and the natural decay rate of value affect them in a negative way. [ABSTRACT FROM AUTHOR]

Everyday situations each day more often require us to solve sometimes even complex decisional problems. Such conditions may include the problem of choice when purchasing services, products or devices. The ever-increasing assortment of products and services makes it more and more time-consuming for customers to choose the most suitable product for them. Following their intuition or considering only some selection criteria may result in an unsatisfactory choice. Decision making in multi-criteria selection problems needs the consideration of many factors, which are often conflicting. For this purpose, more people reach for multi-criteria decision methods (MCDM). These methods provide an easy way of solving a given problem under certain circumstances. With the increasing popularity of those methods, more are developed each day. This paper presents an approach based on four MCDM methods applied to solve the problem of choosing the most favourable CPU model available on the market regarding price and performance, among other criteria. For this purpose, COMET, TOPSIS, VIKOR and PROMETHEE II methods were performed. Later, a comparative analysis of the rankings obtained using the applied methods was conducted. Rankings provided by used methods were compared using correlation coefficients. The research showed that choosing a specific method is relevant for the results and that real-life problems can be successfully solved using multi-criteria decision methods. The obtained results confirm the suitability of MCDM methods as a decision support tool for consumers' product selection. Applications based on MCDM allowing customers to perform multi-criteria analysis of a set of products could contribute to increased consumer satisfaction and growth of popularity and competitiveness of online stores providing them. [ABSTRACT FROM AUTHOR]

• Differences in conditions lead choose suitable transformation templates difficult. • Interaction between problems are critical in select suitable transformation templates. • ANP and DEMATEL can complement each other to make evaluation more accurate. • Administrative-led models are suitable for solving current problems. • Market-led models are suitable for further transformation development. China and other major economies of the world have invested considerable resources to assist resource-exhausted areas in their development. But few regions have improved the stagnant state of development. The literature suggests that the lack of assistance provided to decision-makers when choosing suitable transformation templates often results in the selection of poor templates that are not suitable for a given region. This is an important factor that contributes to the difficulties among resource-exhausted areas to achieve transformation. This paper describes an application of hybrid Multi Criteria Decision Making (MCDM) technique for the selection of optimum template for the resource-exhausted region. The proposed model, Fuzzy Analytical Network Process (Fuzzy-ANP) is integrated with Fuzzy Decision-Making Trial and Evaluation Laboratory (Fuzzy-DEMATEL) to evaluate the research object and candidate templates. Evaluation is based on problems (criteria) including the developing new industries, improving governance capabilities, enhancing the ability to attract production, reducing unemployment, restoring an ecological environment and transformation the original resource-mining industry. Fuzzy-ANP and Fuzzy-DEMATEL are used to determine the priority of these criteria for the research region and for candidate template regions from two viewpoints: the importance of each issue, and the ability of each issue to influence other issues. This paper selects Wangsheng as research region and Ruhr, Chikuho, Lorraine and Houston as templates. The obtain preference order of the templates for Fuzzy-ANP and Fuzzy-DEMATEL are Houston > Lorraine > Chikuho > Ruhr and Ruhr > Chikuho > Houston > Lorraine respectively. Combining two results, the order of the templates is Chkuho > Lorranie > Houston > Ruhr. Chikuho is selected as template for Wansheng transformation. [ABSTRACT FROM AUTHOR]

With the recent advent of risk awareness, decision-making algorithms' complexity increases, posing a severe difficulty to solve such formulations of the problem online. Our approach is centered on the distribution of the return in the challenging continuous domain under partial observability. This paper proposes a simplification framework to ease the computational burden while providing guarantees on the simplification impact. On top of this framework, we present novel stochastic bounds on the return that apply to any reward function. Further, we consider simplification's impact on decision making with risk averse objectives, which, to the best of our knowledge, has not been investigated thus far. In particular, we prove that stochastic bounds on the return yield deterministic bounds on Value at Risk. The second part of the paper focuses on the joint distribution of a pair of returns given a pair of candidate policies, thereby, for the first time, accounting for the correlation between these returns. Here, we propose a novel risk averse objective and apply our simplification paradigm. Moreover, we present a novel tool called the probabilistic loss (PLoss) to completely characterize the simplification impact for any objective operator in this setting. We provably bound the cumulative and tail distribution function of PLoss using PbLoss to provide such a characterization online using only the simplified problem. In addition, we utilize this tool to offer deterministic guarantees to the simplification in the context of our novel risk averse objective. We employ our proposed framework on a particular simplification technique - reducing the number of samples for reward calculation or belief representation within planning. Finally, we verify the advantages of our approach through extensive simulations. [ABSTRACT FROM AUTHOR]

Energy management problems associated with rapid institutional, political, technical, ecological, social and economic development have been of critical concern to both national and local governments worldwide for many decades; thus, addressing such issues is a global priority. The main of objective of this study is to provide a review on the application and use of decision making approaches in regard to energy management problems. This paper selected and reviewed 196 published papers, from 1995 to 2015 in 72 important journals related to energy management, which chosen from the “Web of Science” database and in this regard, the systematic and meta-analysis method which called “PRISMA” has been proposed. All published papers were categorized into 13 different fields: environmental impact assessment, waste management, sustainability assessment, renewable energy, energy sustainability, land management, green management topics, water resources management, climate change, strategic environmental assessment, construction and environmental management and other energy management areas. Furthermore, papers were categorized based on the authors, publication year, nationality of authors, region, technique and application, number of criteria, research purpose, gap and contribution, solution and modeling, results and findings. Hybrid MCDM and fuzzy MCDM in the integrated methods were ranked as the first methods in use. The Journal of Renewable and Sustainable Energy Review was the important journal in this paper, with 32 published papers. Finally, environmental impact assessment was ranked as the first area that applied decision making approaches. Results of this study acknowledge that decision making approaches can help decision makers and stakeholders in solving some problems under uncertainties situations in environmental decision making and these approaches have seen increasing interest among previous researchers to use these approaches in various steps of environmental decision making process. [ABSTRACT FROM AUTHOR]

The aim of this paper is to provide not only a hybrid theoretical methodology in multiple-attribute decision making problems but also a practical application in maritime transportation industry. The proposed hybrid approach integrates Analytic Hierarchy Process (AHP) method into VlseKriterijumska Optimizacijia I Kompromisno Resenje, (VIKOR) technique under interval type-2 fuzzy (IT2F) environment. While AHP and VIKOR provides a comprehensive framework to solve multiple-attribute decision making (MADM) problems in maritime transportation industry, the IT2F enables to deal with much uncertainty during linguistic assessment of decision-makers. The proposed approach is demonstrated with a hatch cover design selection problem since it has utmost importance in structure of bulk carrier ships to prevent water ingress and protect cargo form the external damages. Besides its robust theoretical insight, the paper has practical contribution to the naval engineers, classification societies and ship owners who have difficulty in deciding appropriate hatch cover type during construction of the ship. [ABSTRACT FROM AUTHOR]

Systemic Constellations are a phenomenological approach to resolving personal, professional and organizational issues. They offer a way of mapping a present reality, working at the source of the hidden dynamics and moving to a resolution. This systemic approach often delivers surprising and unexpected insights while also offering the possibility to analyze and solve organizational problems. Rational analysis provides the whole picture of the problem which often turns out to be too complex for a decision making. Systemic constellations can help to simplify and clarify the situation and inform what has to happen next [8,17] . The outcomes of systemic constellations as an additional resource for solving comprehensive technical problems have not yet been sufficiently investigated. In structural constellation work dealing with technical problems, the individuals who are involved in the problem situation are used to represent different system components, substances or fields. A moderator voices the feedback from the representatives concerning their feelings or intuitive movements, and points to possible solutions. For example, a moderator places the representatives somewhere in the room, develops a three-dimensional picture of the constellation of the analyzed situation and tries to expose the factors empowering or blocking the way towards constructive solutions [13] . This paper explores the theoretical background and practical outcomes of the systemic constellation method for technical problem solving. It presents some case study work which has been conducted in recent years, and then discusses its findings and implications. The research outlined in this paper demonstrates that the noteworthy contribution of structural constellation work for problem solving is typically the result of a combination of functional analysis and the feeling-as-information principle. The constellation work helps, at first, to reveal the subjective experiences, such as feelings, moods, emotions, and bodily sensations, and then to accept them as a source of objective information relevant to the decision making process. In accordance with the latest research [19] , the use of feelings as a source of information follows the same principles as the use of any other information. This paper provides the structures of some standard templates and types of constellation work for technical problems, and discusses the preconditions for their application. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of integrating new classes on the fly into on-line classification systems. The main focus is on visual inspection tasks, although the concepts proposed in this paper can easily be applied to any other on-line classification systems. We use evolving fuzzy classifiers (EFCs), which can adapt their structure and update their parameters in incrementally due to embedded on-line adaptable classifier learning engines. We consider two different model architectures — classical single model and an all-pairs approach that uses class information to decompose the classification problem into several smaller sub-problems. The latter technique is essential for establishing new classes quickly and efficiently in the classifier, and for reducing class imbalance. Methodological novelties are (i) making appropriate structural changes in the EFC whenever a new class appears while operating in a single-pass incremental manner and (ii) estimating the expected change in classifier accuracy on the older classes. The estimation is based on an analysis of the impact of new classes on the established decision boundaries. This is important for operators, who are already familiar with an established classifier, the accuracy of which is known. The new concepts are evaluated in a real-world visual inspection scenario, where the main task is to classify event types which may occur dynamically on micro-fluidic chips and may reduce their quality. The results show stable performance of established classifiers and efficient (low number of samples requested) as well as fast integration (steeply rising accuracy curves) of new event types (classes). [ABSTRACT FROM AUTHOR]

Negations operators have been developed and applied in many fields such as image processing, decision making, mathematical morphology, fuzzy logic, etc. One of the most effective non-monotonic operators are weak negations. This paper studies the algebraic structure and the characterization of the adjoint triples and Galois implication pairs which provides a fixed pair of weak negations. The obtained results allow the user to select the best conjunctor and implications associated with the most suitable negation to be used in the computations of the problem to be solved. [ABSTRACT FROM AUTHOR]

The energy hub (EH) is a promising concept that can accurately evaluate the performance of multi-carrier integrated energy systems (IESs), ranging from a building to a district, city, region, country, or even an international level. Multi-carrier EH-based IESs available in the literature have reached a desirable level of maturity for broad scales. However, there is confusion in the literature that misleads readers regarding multi-carrier EH-based IESs located on limited scales (e.g., buildings or neighborhood level). Furthermore, multi-carrier EH-based IESs studies that involve complexities such as discrete, continuous, or mixed decision-making variables, multiple conflicting objective functions, non-linearity, non-convexity, and discontinuity are affected by different techno-economic, environmental, and social parameters that are uncertain. Ignoring such uncertain input parameters (UIPs) in these studies leads to less adaptable results to realistic conditions. However, their integration is a challenging process intensifying these complexities during studies' modeling, optimization, and decision-making processes. Therefore, this review paper aims to fill these gaps by identifying, classifying, assessing, and prioritizing different UIPs, their analyzing techniques, and solution approaches, solvers, and software for addressing relevant optimization problems to achieve a deeper understanding of current challenges and potential future research, trend, and capacities in multi-carrier EH-based IESs studies. • Scrutinizing micro multi-carrier energy hubs (μMEHs) from different perspectives to provide a holistic understanding and classification of them by answering the following key questions: • What are the uncertainties and their analyzing techniques in μMEHs' studies? • What optimization models and solution methods are considered in μMEHs' studies? • What attractive paths can be envisioned for μMEHs' studies from the perspective of uncertainty and problem-solving process? [ABSTRACT FROM AUTHOR]

Automatic controllers work best when the system they control can be sufficiently well modelled. This is a problem for control of autonomous ships in mixed traffic situations where the autonomous ship interacts with conventional ships, as the crew on other ships can and will exert unexpected behaviour that cannot be easily modelled. This paper analyses the problem of information acquisition, situational assessment and how to predict other ship's actions for autonomous ships that need to interact with conventional ships. We identify causes for the interaction problem and classify these into a decision making model. We also identify possible measures to overcome the problems and based on an impact analysis where technical, procedural and regulatory aspects are considered, we discuss and propose some possible ways to reduce or solve this problem. The conclusion is that the most likely and effective short-term solution is to assist the autonomous ships with human operators and the best longer-term solution may be to improve the information exchange between the ships, complemented with changes in COLREGs. • Analysis of the problem of situational assessment and how to predict other ship's actions for mixed traffic situations. • Root cause classification of the interaction problem and proposals on possible ways to reduce or solve the problem. • Recommendations on the likely and most effective short term solution, and the best long term solution. [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]

This paper presents a Multi-Hypotheses Tracking (MHT) approach that allows solving ambiguities that arise with previous methods of associating targets and tracks within a highly volatile vehicular environment. The previous approach based on the Dempster–Shafer Theory assumes that associations between tracks and targets are unique; this was shown to allow the formation of ghost tracks when there was too much ambiguity or conflict for the system to take a meaningful decision. The MHT algorithm described in this paper removes this uniqueness condition, allowing the system to include ambiguity and even to prevent making any decision if available data are poor. We provide a general introduction to the Dempster–Shafer Theory and present the previously used approach. Then, we explain our MHT mechanism and provide evidence of its increased performance in reducing the amount of ghost tracks and false positive processed by the tracking system. [ABSTRACT FROM AUTHOR]

This paper considers a multi-period production system where a set of machines are arranged in parallel. The machines are unreliable and the failure rate of machine depends on the load assigned to the machine. The expected production rate of the system is considered to be a non-monotonic function of its load. Because of the machine failure rate, the total production output depends on the combination of loads assigned to different machines. We consider the integration of load distribution decisions with production planning decision. The product demands are considered to be known in advance. The objective is to minimize the sum of holding costs, backorder costs, production costs, setup costs, capacity change costs and unused capacity costs while satisfying the demand over specified time horizon. The constraint is not to exceed available repair resources required to repair the machine breakdown. The paper develops two heuristics to solve the integrated load distribution and production planning problem. The first heuristic consists of a three-phase approach, while the second one is based on tabu search metaheuristic. The efficiency of the proposed heuristics is tested through the randomly generated problem instances. [ABSTRACT FROM AUTHOR]