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Single-period inventory models with uncertain demand are very well known in the business analytics community. Typically, such models are rule-based functions, or sets of functions, of one decision variable (order quantity) and one random variable (demand). In academics, the models are taught selectively and usually not completely. Students are exposed to applications of selected models and solution aspects usually within the Inventory Control or similar topics. This learning-case oriented paper attempts to provide a fuller coverage of the solution process, starting with mathematical model building, following with calculus-based solution development and ending with an alternative approach through simulation. A special emphasis is dedicated to the learning processes, providing students with a variety of tools selected from Algebra, Calculus, Probability, Statistics and Spreadsheet Technology. The students are expected to benefit from studying this paper to better understand business solving processes. All computational operations are implemented in a spreadsheet program.

Computational Thinking (CT) is pervasive in our daily lives and is useful for problem-solving. Decision-making is a crucial part of problem-solving. In the extant literature, problem-solving strategies in educational settings are often conveniently attributed to intuition; however, it is well documented that computer programmers might even have difficulty describing about their intuitive insights during problem-solving using natural language (such as English), and subsequently convert what has been described using words into software code. Hence, a more analytical approach using mathematical equations and descriptions of CT is offered in this paper as a potential form of rudimentary scaffolding, which might be useful to facilitators and learners of CT-related activities. In the present paper, the decision-making processes during an unplugged CT activity are delineated via Grey-based mathematical equations, which is useful for informing educators who may wish to explain to their learners about the various aspects of CT which are involved in the unplugged activity and simultaneously use these mathematical equations as scaffolds between the unplugged activity and computer code programming. This theoretical manuscript may serve as a base for learners, should the facilitator ask them to embark on a software programming activity that is closely associated to the unplugged CT activity.

This paper undertakes a revisit of the sunk cost fallacy, which refers to the tendency of people to persist investing resources into something, even if it is destined to have no good outcome. We emphasize that the utilities associated with different alternatives are not static for decision makers, which is exactly opposite to the traditional perspective. This paper argues that the utility of an option may change due to the choice of another option, suggesting that decisions considered irrational by the traditional analytical method, i.e., sunk cost fallacy, may be rational. We propose a novel analytical method for decision making with sunk cost when considering the utility change and validate the effectiveness of this method through mathematical modeling and computational experiments. This paper mathematically describes such decision-making problems, analyzing the impact of changes in the utilities across different alternatives on decision making with a real-world example. Furthermore, we develop a two-stage stochastic optimization model for such decision-making problems and employ the sample average approximation (SAA) method to solve them. The results from computational experiments indicate that some decisions traditionally considered irrational are, in fact, rational when the utility of an option changes as a result of choosing another option. This paper, therefore, highlights the significance of incorporating utility changes into the decision-making process and stands as a valuable addition to the literature, offering a refreshed and effective decision-making method for improved decision making. [ABSTRACT FROM AUTHOR]

Background: A person‐centred approach in the context of health services delivery implies a biopsychosocial model focusing on all factors that influence the person's health and functioning. Those wishing to monitor change should consider this perspective when they develop and use guidelines to stimulate active consideration of the person's needs, preferences and participation in goal setting, intervention selection and the use of appropriate outcome measures. Objective: To develop a position paper that promotes a person‐centred approach in guideline development and implementation. Design, setting and participants: We used three narrative discussion formats to collect data for achieving consensus: a nominal group technique for the Allied Health Steering Group, an Internet discussion board and a workshop at the annual G‐I‐N conference. We analysed the data for relevant themes to draft recommendations. Results: We built the position paper on the values of the biopsychosocial model. Four key themes for enhancing a person‐centred approach in clinical guidelines emerged: (i) use a joint definition of health‐related quality of life as an essential component of intervention goals, (ii) incorporate the International Classification of Functioning, Disability and Health (ICF) as a framework for considering all domains related to health, (iii) adopt a shared decision‐making method, and (iv) incorporate patient‐reported health outcome measures. The position statement includes 14 recommendations for guideline developers, implementers and users. Conclusion: This position paper describes essential elements for incorporating a person‐centred approach in clinical guidelines. The consensus process provided information about barriers and facilitators that might help us develop strategies for implementing person‐centred care. [ABSTRACT FROM AUTHOR]

The population in Singapore is ageing, adding pressure to community care as the health and social needs of its residents increase. This has accelerated the pace at which Regional Health Systems adopt and deliver its population health strategies from early prevention, chronic disease management, crisis care to end-of-life care. To this end, the Central Health Integrated Care Network (ICN) began its journey to develop Communities of Care (CoCs) with other health and social care partners to meet the needs of residents in the Central Zone of Singapore. This paper describes the processes and steps taken by Central Health ICN to build partnerships with other agencies and organisations to build place-based models of care in the local neighbourhoods. The faciliating factors and the barriers faced in the implementation of CoCs were described to allow sharing of such learnings on large scale change. Strategies in overcoming some of the challenges were also presented to demonstrate the iterative processes required in building integrated place-based models of care to meet the needs of the residents in different communities. [ABSTRACT FROM AUTHOR]

The definition of Fermatean fuzzy soft sets and some of its features are introduced in this study. A Fermatean fuzzy soft set is a parameterized family of Fermatean fuzzy sets and a generalization of intuitionistic and Pythagorean fuzzy soft sets. This paper presents a definition of the Fermatean fuzzy soft entropy. Also acquired are the formulae for standard distance measures such as Hamming and Euclidean distance. Other formulas have also been proposed for calculating the entropy and distance measurements of FFSSs. Even if the entropy and distance measures are defined for other set extensions, they cannot be applied directly to Fermatean fuzzy soft sets. It can be used to determine the uncertainty associated with a Fermatean fuzzy soft set, discover similarities between any two Fermatean fuzzy soft sets using the proposed distance measures, and compare it to other existing structures in the literature. Fermatean fuzzy soft set applications in decision-making and pattern recognition difficulties are also examined. Finally, comparison studies with other known equations are performed. [ABSTRACT FROM AUTHOR]

Network DEA models deal with measurements of relative efficiency of Decision-Making Units (DMUs) when the insight of their internal structures is available. In network models, sub-processes are connected by links or intermediate products. Links have the dual role of output from one division or sub-process and input to another one. Therefore, improving the efficiency score of one division by increasing its output may reduce the score of another division because of increasing its input. To address this conflict, in the present paper we proposed a new approach in Slack-Based Measure (SBM) framework which provides deeper insights regarding the sources of inefficiency. The proposed approach is a two-phase procedure in which Phase-I determine the role of intermediate measures by solving a linear program and partitions the intermediate measures into three groups of "input type", "output type" and "fixed-flows" and Phase-II measures the scores of the DMUs under evaluation. Providing a classification for intermediate products and account their excesses or shortfalls in efficiency calculation while the continuity of link flows between subunits are kept, are the advantages of the proposed approach. [ABSTRACT FROM AUTHOR]

A good quality stability model is a key factor for accurate power system operations.Inaccurate parameters of the stability models affect the decision making which paves the way for serious consequences. Thus, it is necessary to calibrate the stability model parameters in a regular manner. There are several calibration methods in the literature which are based on simultaneous estimation of the parameters and states. However, not all of the model parameters are well estimable simultaneously. Simultaneous estimation of parameters with high collinearity may result in biased calibration results. In this paper, the trajectory sensitivity method is used to detect the sensitive parameters and construct the sensitivity matrix. Then, parameters with high linear dependency are identified using the sensitivity matrix. It is shown that, despite the high sensitivity of a parameter, its estimability degrades as the collinearity with other parameters increase. In this paper an identifiability analysis that detects the collinearity among the sensitive parameters is proposed. The proposed method is validated using WSCC 9-Bus System. [ABSTRACT FROM AUTHOR]

This research is among the most relevant research on the problems of natural gas transportation via pipe networks. The increased demand for natural gas in Kazakhstan is associated with a greater level of environmental friendliness; as a result, many power-generating stations use natural gas as their main source of energy. Modernization of existing thermal power plants is necessary to improve the environmental situation in the country. There are three main groups of gas pipeline systems considered in the literature: collection, transmission, and distribution systems. In this article, we present detailed research on the transmission process and develop useful approaches. Over the past few years, a huge amount of research has been conducted on many problems of decision-making in the gas industry and, in particular, on optimizing the pipeline network. In this paper, we consider dynamical models, highlighting aspects of modelling and the most relevant solutions to date. This research can serve as a useful tool for understanding the evolution of many real-world applications and the most recent advances in solution methodologies emerging in this complex field of research. The results of this research can be used to develop technologies for automating calculations, planning, and optimizing natural gas transportation. [ABSTRACT FROM AUTHOR]

This paper argues that there is a requirement to connect project management (PM) with innovation and sustainability to address new societal needs. Societal demands have facilitated further complexity and uncertainty within the PM discipline and have led to both theoretical and practical evolutions. Key evolutions are identified, which suggests that PM is becoming more holistic and interdisciplinary in nature so it can successfully deal with this complexity and uncertainty. Furthermore, holistic thinking naturally connects with systemic thinking, which justifies the selection of system dynamics (SD) to support PM decision‐making. An extensive critical review of SD modelling with respect to identified evolutions within the PM discipline is undertaken. The contribution of this paper connects innovation and sustainability to the PM discipline by the initial development of a causal loop diagram (CLD). [ABSTRACT FROM AUTHOR]

In this paper, we use new techniques to prove Hyers-Ulam and Hyers-Ulam-Rasiass stability of Deeba, Drygas and logarithmic functional equations in non-Archimedean normed spaces. We generalize some earlier results connected with the stability of these functional equations and inequalities. In addition, we provide some examples to clarify the definitions and theorems. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the normalized Ricci-Bourguignon flow with incomplete initial metric on an open surface. We show that such a flow converges exponentially to a metric with constant Gaussian curvature if the initial metric is suitable. In particular, if the initial metric is complete, then the metrics converge to the standard hyperbolic metric. [ABSTRACT FROM AUTHOR]

In the context of project management, the attention given to project portfolio management has increased in recent years. The use of mathematical programming for portfolio management is also on the rise, because it integrates the project interactions with the multiple objectives of portfolio management into a single model. Among the possible objectives, recent studies have paid special attention to the emerging objective of future preparedness, which has not yet been incorporated into the existing mathematical models. This paper presents a mixed-integer nonlinear optimization model for portfolio selection that considers four main performance measures for project management, namely, value maximization, strategic alignment, balance, and future preparedness. Given the importance of the last measure, the purpose of this paper is to provide a more complete model that provides the marginal contribution and the best combination of projects according to the needs of the company. The model is tested using real data from two companies, one in Brazil and one in Canada, and the results obtained are coherent with their respective practices. [ABSTRACT FROM AUTHOR]

This paper mainly provides a group decision-making (GDM) method based on linguistic interval-valued intuitionistic fuzzy preference relations (LIVIFPRs), where consistency and consensus analysis is conducted. The multiplicative consistency of LIVIFPRs is first introduced, and a consistency-based model is built to ascertain the missing values of an incomplete LIVIFPR. Considering the smallest distance, an optimization model is established to repair the unacceptably multiplicatively consistent LIVIFPR to have acceptable consistency. Meanwhile, the linguistic interval-valued intuitionistic fuzzy priority weights of LIVIFPR are constructed via the optimal solutions of a programming model. Then, Algorithm I for decision-making with one incomplete LIVIFPR is presented. For the GDM problem, the weights of experts are determined before aggregating the individual LIVIFPRs. Moreover, when the consensus of an individual LIVIFPR is unacceptable, a mathematical model is utilized to reach the consensus requirement. Subsequently, Algorithm II for GDM with incomplete LIVIFPRs is proposed step-by-step. Finally, the new GDM method is used to evaluate four Chinese express companies, and the advantages of this approach are demonstrated by performing a comparison analysis. [ABSTRACT FROM AUTHOR]

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Senior managers are the customers of organizational performance measurement methods to help them make better decisions at the firm level. One of the most applicable methods is Network Data Envelopment Analysis (DEA). Network DEA models consider systems that have a network structure in which system inputs, after passing several intermediate interactions, are transformed into intermediate productions and finally leave the system as output products. However, many realworld cases do not necessarily conform to this network structure, which is related to the system outputs during multiple time periods or the same "dynamic" impacts. These structures cannot handle dynamic impacts. Therefore, this paper presents a novel structure that can consider the dynamic impacts and influences of sub-units on each other at various time periods. Besides, two models based on slack variables are proposed which can consider dynamic effects and calculate the efficiency of such networks. Using these models, the overall efficiency of networks is calculated for the whole time period. Finally, these models are applied to two examples, and the results obtained are compared with other methods. [ABSTRACT FROM AUTHOR]

The efficiency of target damage is to measure the effect of projectile attacks. Because there is antagonism at the intersection of projectiles and targets, it is very difficult to establish scientific damage assessment strategy model and a numerical calculation method. To scientifically evaluate the target damage effect when the projectile attacks the aircraft target, this paper introduces a game confrontation mechanism and proposes a numerical calculation method of aircraft target damage game strategy under the intersection confrontation. According to the theory of two-person non-cooperative games, the projectile is regarded as one of the players in the confrontation damage game, and the aircraft target is regarded as the other player. The damage gain model and gain function of the intersection confrontation of both sides are established. The effective expectation function and the minimum and maximum theorem are used to obtain the optimal Nash equilibrium solution of the game matrix strategy, and the Nash equilibrium point of the strategy space set is derived. Through calculation and comparative analysis, the results show that the optimal gain degree composed of the damage strategy selected by both the projectile and aircraft target under the Nash equilibrium solution brings the greatest gain. The proposed hybrid game strategy of damage assessment can be an effective reference for air combat decision making in the state of intersection confrontation. [ABSTRACT FROM AUTHOR]

The estimating returns to scale (RTS) and identifying the area in which the type of returns to the scale of a single decision maker unit (DMU) remains sustainable helps managers to have more accurate predictions about the size variations of a DMU. Contrary to theoretical studies and attitudinal surveys, the RTS-based econometric estimates of the panel data are derived from the time changes observed in inputs and outputs. Therefore the description of returns-to-scale based on the panel data is closer to those that are related to changes in demand and changes in the policy of managers during the sample period. it’s clear that the use of the panel data, in addition to estimating returns to scale closer to the view of the managers, leads to a significant increase in the number of inputs and outputs for each DMU. Since we know that the DEA models have computational complexity for the great practical issues, use particularly when there are large numbers of outputs and inputs reducing the number of models will greatly help to solve these issues. In most articles, the returns-to-scale estimates for decision-making units (DMU) are performed with at least two LP models. Furthermore have problems in some cases and may not be able to investigate all DMUs. In this paper, a method has been proposed which determines returns-to-scale and identifies boundary units by solving only one model. In fact, unlike some articles which explore the boundary DMUs, the assessment area in this paper is all the DMUs observed in PPS (Production Possible Set). Therefore, based on this approach, identifying boundary units and estimating their returns-to-scale will be possible by solving only one LP model. Further, according to the proposed dual model, a different perspective has been proposed to identify and evaluate boundary units and to estimate the returns-to-scale by solving a single LP model. Additionally and according to numerous observations, the dual model is able to estimate the returns-to-scale of the image point of the inefficient units in the BCC model in the input orientation. The correctness of all content has been shown theoretically as well as intuitively, with due substantiation. [ABSTRACT FROM AUTHOR]

Symmetry is a key part of the study of basic forces and particles, as well as the creation of mathematical models that help scientists in various scientific disciplines understand complex events. Scientists can figure out what a system is made of and how it works by looking at its symmetry. They can then use this information to make predictions and create new materials and technologies. Humanity has conquered many once-fatal diseases due to medical research and technological advancements. Although this progress is encouraging, there are still a great many areas that require continual human efforts. An effort is made in this article to choose the best treatment strategy to completely manage the pandemic of COVID-19 under conjunctive complex fuzzy knowledge. In this paper, the concept of conjunctive complex fuzzy relations is presented and numerous set theoretical aspects of this phenomenon are established. The investigation of this ideology is further expanded to describe different sorts of essential structural conjunctive complex fuzzy relations. Matrix and graphical representations of the formation of these newly specified relations are also provided. Moreover, this concept has been successfully employed to provide a therapy strategy for a rapid recovery from COVID-19. Furthermore, a comparative analysis is conducted to demonstrate the validity and applicability of the suggested approaches compared to existing methods. [ABSTRACT FROM AUTHOR]

While the American Physical Therapy Association has upheld a code of ethics since 1935, the philosophical underpinnings of physical therapist practice have yet to be robustly explicated. Theoretical work in the field of philosophy of medicine can be engaged to study physical therapist practice. Modifying the phenomenological and teleological framework of Edmund Pellegrino, a physician and prominent bioethicist, the purpose of this theoretical paper is to particularize Pellegrino's philosophy of medicine to construct an internal morality of physical therapy. Acknowledging that the internal morality of health care professions is founded in the relationship between a patient and a health care professional, this paper analyzes the nature and telos , or end, of physical therapy through 3 phenomena of physical therapy—the fact of disability, the act of profession, and the act of physical therapy. This paper claims that, rather than medicine's clinical truth of a good treatment decision, physical therapy's clinical truth is a good process that capacitates patients. This relational approach to an internal morality robustly underpins a philosophy and ethics of physical therapy. [ABSTRACT FROM AUTHOR]

In the paper we discuss and compare two commonly used methods of finding the shortest paths in networks, namely Dijkstra's and A* algorithms. We compare their effectiveness in terms of traversing road network in circumstances that require swift decision making in the event of dynamically changing road conditions on the basis of studies conducted for evacuation plans. To build a proper model of such a network, a method of appropriate edge-weighting is introduced, based on empirical data collected by other researchers. Then, we use the basics of the theory of quasimetric spaces to introduce a heuristic to such graphs, which is easy to calculate metric. The heuristic we obtain is both admissible and consistent, which allows us to use it efficiently in A* search algorithms. The developed application can be used in studies into evacuation from hazardous areas. In this case, optimum calculative efficiency is achievable with a simultaneous reduction of calculation time (when compared to Dijkstra's algorithm). Our application can be applied during the first stage, i.e., prior to the occurrence of a disaster, since this is an appropriate time for preparation by planning, drilling, early warning, and designating the rescue services that are to participate in the following stages. [ABSTRACT FROM AUTHOR]

Background: Non-beneficial treatment is closely tied to inappropriate treatment at the end-of-life. Understanding the interplay between how and why these situations arise in acute care settings according to the various stakeholders is pivotal to informing decision-making and best practice at end-of-life. Aim: To define and understand determinants of non-beneficial and inappropriate treatments for patients with a non-cancer diagnosis, in acute care settings at the end-of-life. Design: Systematic review of peer-reviewed studies focusing on the above and conducted in upper-middle- and high-income countries. A narrative synthesis was undertaken, guided by Realist principles. Data sources: Cochrane; PubMed; Scopus; Embase; CINAHL; and Web of Science. Results: Sixty-six studies (32 qualitative, 28 quantitative, and 6 mixed-methods) were included after screening 4,754 papers. Non-beneficial treatment was largely defined as when the burden of treatment outweighs any benefit to the patient. Inappropriate treatment at the end-of-life was similar to this, but additionally accounted for patient and family preferences. Contexts in which outcomes related to non-beneficial treatment and/or inappropriate treatment occurred were described as veiled by uncertainty, driven by organizational culture, and limited by profiles and characteristics of involved stakeholders. Mechanisms relating to 'Motivation to Address Conflict & Seek Agreement' helped to lessen uncertainty around decision-making. Establishing agreement was reliant on 'Valuing Clear Communication and Sharing of Information'. Reaching consensus was dependent on 'Choices around Timing & Documenting of end-of-life Decisions'. Conclusion: A framework mapping determinants of non-beneficial and inappropriate end-of-life treatment is developed and proposed to be potentially transferable to diverse contexts. Future studies should test and update the framework as an implementation tool. Trial registration: PROSPERO Protocol CRD42021214137. [ABSTRACT FROM AUTHOR]

The concept of (quasi) superhypergraphs as a generalization of graphs makes a relation between some sets of elements in detail and in general (in the form of parts to parts, parts to whole, and whole to whole elements of sets) and is very useful in the real world. This paper considers the novel concept of (quasi) superhypergraphs and introduces the notation of dominating set and domination number of (quasi) superhypergraphs. Especially, we have analyzed the domination number of uniform (quasi) superhypergraphs and computed their domination number on different cases. The flows (from right to left, from left to right, and two-sided) as maps play a main role in (quasi) superhypergraphs and it is proved that domination numbers of (quasi) superhypergraphs are dependent on the flows. We define the valued-star (quasi) superhypergraphs for the design of hypernetworks and compute their domination numbers. We have shown that the domination numbers of valued-star (quasi) superhypergraphs are distinct in different flow states. In final, we introduce some applications of dominating sets of (quasi) superhypergraphs in hypernetwork as computer networks and treatment networks with the optimal application. [ABSTRACT FROM AUTHOR]

One of the humanities’ most basic topics is the response time to creative problem-solving and decision-making in this field. In recent years, response time modeling by fitting an exponentially-modified normal (E-MN) probability distribution and the results obtained from this process have been widely used. The E-MN probability distribution results from the convolution of a normal probability distribution and an exponential probability distribution and contains three parameters. In this paper, a developed Bayesian (DB) estimation method is introduced to estimate the parameters of an E-MN probability distribution. This new estimation method uses the adaptive rejection Metropolis-Hastings (ARM-H) sampling method. The reason for this is that in normal mode and based on the classical Bayesian estimation method, the chosen prior probability density functions (pdfs) lead to posterior pdfs with unknown form and, they are not always logarithmically concave. Also, respectively, simulation and real data sets study have been done to demonstrate the better performance of the DB estimation method than the two other wellknown estimation methods used in this context, including the maximum likelihood (ML) estimation method and the quantile maximum likelihood (QML) estimation method. To show the better efficiency of the proposed estimation method compared with the two other estimation methods, the root mean squared error (RMSE) criterion is used. [ABSTRACT FROM AUTHOR]

In this paper the meanings of neutrosophic special n-dominating set, neutrosophic special n-domination number, inverse neutrosophic special domination set (number) and inverse neutrosophic special n-domination number are introduced and some of related results are investigated. Finally, an application of inverse neutrosophic special dominating set in decision making under ashy clauses between certainty and uncertainty is provided. In fact, we present a decision-making problem in real-world applied example which discusses the factors influencing a companys efficiency. The presented model is, in fact, a factor-based model wherein the impact score of each factor is divided into two types of direct and indirect influences. [ABSTRACT FROM AUTHOR]

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]

Security assurance (SA) is a technique that helps organizations to appraise the trust and confidence that a system can be operated correctly and securely. To foster effective SA, there must be systematic techniques to reflect the fact that the system meets its security requirements and, at the same time, is resilient against security vulnerabilities and failures. Quantitative SA evaluation applies computational and mathematical techniques for deriving a set of SA metrics to express the assurance level that a system reaches. Such metrics are intended to quantify the strength and weaknesses of the system that can be used to support improved decision making and strategic planning initiatives. Utilizing metrics to capture and evaluate a system's security posture has gained attention in recent years. However, scarce work has described how to combine SA evaluation while taking into account both SA metrics modeling and analysis. This paper aims to develop a novel approach for the modeling, calculation, and analysis of SA metrics that could ultimately enhance quantitative SA evaluation. [ABSTRACT FROM AUTHOR]

For nearly 20 years, the Test Feeder Working Group of the Distribution System Analysis Subcommittee has been developing openly available distribution test feeders for use by researchers. The purpose of these test feeders is to provide models of distribution systems that reflect the wide diversity in design and their various analytic challenges. Because of their utility and accessibility, the test feeders have been used for a wide range of research, some of which has been outside the original scope of intended uses. This paper provides an overview of the existing distribution feeder models and clarifies the specific analytic challenges that they were originally designed to examine. Additionally, this paper will provide guidance on which feeders are best suited for various types of analysis. The purpose of this paper is to provide the original intent of the Working Group and to provide the information necessary so that researchers may make an informed decision on which of the test feeders are most appropriate for their work. [ABSTRACT FROM PUBLISHER]

Decision making trial and evaluation laboratory (DEMATEL) is considered as an effective method for the identification of causeeffect chain components of a complex system. It deals with evaluating interdependent relationships among factors and finding the critical ones through a visual structural model. Over the recent decade, a large number of studies have been done on the application ofDEMATEL andmany different variants have been put forward in the literature. The objective of this study is to review systematically the methodologies and applications of the DEMATEL technique. We reviewed a total of 346 papers published from 2006 to 2016 in the international journals. According to the approaches used, these publications are grouped into five categories: classicalDEMATEL, fuzzyDEMATEL, greyDEMATEL, analytical network process- (ANP-)DEMATEL, and otherDEMATEL. All papers with respect to each category are summarized and analyzed, pointing out their implementing procedures, real applications, and crucial findings. This systematic and comprehensive review holds valuable insights for researchers and practitioners into using the DEMATEL in terms of indicating current research trends and potential directions for further research. [ABSTRACT FROM AUTHOR]

In this paper, we study the relationship between several well known classes of discrete choice models, i.e., the random utility model (RUM), the representative agent model (RAM), and the semiparametric choice model (SCM). Using a welfare-based model as an intermediate, we show that the RAM and the SCM are equivalent. Furthermore, we show that both models as well as the welfare-based model strictly subsume the RUM when there are three or more alternatives, while the four are equivalent when there are only two alternatives. Thus, this paper presents a complete picture of the relationship between these choice models. [ABSTRACT FROM AUTHOR]

The paper shows how the aspects of uncertainty in spatial harvest scheduling can be embedded into a harvest optimization model. We introduce an approach based on robust optimization that secures better scheduling schematics of the decision maker while eliminating a significant portion of uncertainty in the decisions. The robust programming approach presented in this paper was applied in a real management area of Central Europe. The basic harvest scheduling model with harvest-flow constraints was created. The uncertainty that is assessed here is due to forest inventory errors and growth prediction errors of stand volume. The modelled results were compared with randomly simulated errors of stand volume. The effects of different levels of robustness and uncertainty on harvest-flow were analyzed. The analysis confirmed that using the robust approach for harvest decisions always ensures significantly better solutions in terms of the harvested volume than the worst-case scenarios created under the same constraints. The construction of a mathematical model as well as the methodology of simulations are described in detail. The observed results confirmed obvious advantages of robust optimization. However, many problems with its application in forest management must still be solved. This study helps to address the need to develop and explore methods for decision-making under different kinds of uncertainty in forest management. [ABSTRACT FROM AUTHOR]

In this paper, the notion of picture fuzzy sub-hyperspace of a hyper vector space is introduced and some related results are investigated on the basis of some basic operations (intersection, union, Cartesian product etc.) on picture fuzzy sets. The concept of picture fuzzy linear transformation with respect to some picture fuzzy sub-hyperspace is initiated here and some important results are studied in this regard. It is shown that with respect to some pre-assumed picture fuzzy sub-hyperspace, linear combination of two picture fuzzy linear transformations is a picture fuzzy linear transformation, composition of two picture fuzzy linear transformations is a picture fuzzy linear transformation and inverse of a bijective picture fuzzy linear transformation is a picture fuzzy linear transformation. The effect of good linear transformation on picture fuzzy sub-hyperspaces is discussed here. It is shown that the image of a picture fuzzy sub-hyperspace is a picture fuzzy sub-hyperspace under bijective good linear transformation and the inverse image of a picture fuzzy sub-hyperspace is a picture fuzzy subhyperspace under good linear transformation. Some important results on picture fuzzy sub-hyperspaces in the light of (;;)-cut of picture fuzzy set are studied here. Finally, an application of picture fuzzy sub-hyperspace conditions in decision making problem is presented here. [ABSTRACT FROM AUTHOR]

Not only mathematical statistics, differential equations, and mathematical models were used to analyze and reduce data, but a rough set model is also employed in medical, engineering, and other fields to analyze and reduce them. The goal of this paper is to introduce a minimal structure concept to produce new rough set models and show that it is suitable for analyzing most real-life problems, reduction of attributes, and decision making. We examine the effectiveness of the following method in the problem of electric power generators and decision making. We also offer a comparison of our method and Pawlak's method. Finally, the variable precision model improves the accuracy of decision making. [ABSTRACT FROM AUTHOR]

The uncertain data appear in majority of real-world phenomena regarding business, engineering, manufacturing, industries, medical, and other social fields. The neutrosophic sets (NSs), soft sets (SSs), and rough sets (RSs) are distinct mathematical models to deal with different types of uncertainties in data and information. Nowadays, several hybrid models are emerging as their combination or their further generalizations. In this paper, the concept of multineutrosophic fuzzy soft rough sets (MNFSRSs) by combining multineutrosophic fuzzy soft sets (MNSSs) with RSs is presented. Certain eminent features of the proposed structure are investigated. Moreover, a general decision-making (DM) algorithm is described for DM problems to indicate the effectiveness of the presented model. [ABSTRACT FROM AUTHOR]

Introduction: Burnout is a syndrome defined by emotional exhaustion leading to depersonalization and reductions in personal accomplishments at work. The negative consequences can be negative for the efficiency of the organization, reducing productivity and quality of care. Aim: The purpose of this critical review is it to elaborate on the main theoretical models that describe and interpret professional burnout in contemporary nursing. Methods: For this paper's needs a systematic review following the Preferred Reporting Items for Systematic Reviews was conducted. The search engines used were PubMed, CINAHL, PsychInfo, Scopus, and Embase. The inclusion criteria were any primary studies examining models of burnout in nurses in peer-reviewed journals, and published in English dating back to mid-1970s until 2022. Results: Many theoretical models have been developed by researchers who attempted to interpret burnout syndrome and how it interacts with the individual and the environment. Hereafter are listed the most common models for describing and explaining burnout syndrome. Thus, our search revealed nine main models which are: Job-Demand-Control-Support; Stages of Disillusionment; Staff Burnout Model; Model of 3 Dimensions; Unfairness and Fairness Model; 12-Phase Burnout Check; Effort-Reward Imbalance Model and Job Demands Resources Model. Discussion: Many studies emphasize the relationships between burnout, job dis-satisfaction and declining mental health coupled often with cynicism. Within the theoretical analysis though, the same factors arise which are viewed either as predictors of burnout or may be also viewed as outcomes of it. This observation highlights a further characteristic of the burnout literature in nursing, namely the similarity of the findings and the cross-sectional nature of the evidence. Yet, in order to promote theoretical understanding of burnout, research needs to set priorities on the use of more routine but concrete empirical data on employee behaviors such as attrition levels, absenteeism and turnover. Conclusions: Nurses around the world need to recognize that burnout is a solid occupational hazard per se that affects not only themselves but patients, organizations, health care systems and even society in general. In this context, burnout in nursing is also associated with worsening safety and quality of care. [ABSTRACT FROM AUTHOR]

Operations research methods focus on formulating decision models, models that approximate the real work environment to mathematical models, and their optimal solution helps the decision maker to make optimal decisions that guarantee the greatest profit or the lowest cost. These models were built based on the data collected on the practical issue under study, and these data are classic values, specific values that are correct and accurate during the period in which these data are collected, which means that they remain ideal if the surrounding conditions remain the same as those in which they were collected. Data, and since the aim of this study is to make ideal decisions and to develop future plans through which we achieve the greatest profit or the least cost, It was necessary to search for a study that fits all the conditions in which the work environment passes in the present and the future. If we take data, neural values, indeterminate values - uncertain - leave nothing to chance, and take into account all conditions from worst to best. In this paper, we will present a complementary study to what we have done in previous research, the purpose of which was to reformulate some operational research methods using neutrosophic data. We will reformulate the assignment problem and one of the methods for its solution, which is the Hungarian method, using neutrosophic concepts, and we will explain the difference between using classical and neutrosophic data through examples. [ABSTRACT FROM AUTHOR]

Real-world information is imperfect and usually characterized by uncertainty and partial reliability. Given these limitations, Z-numbers is introduced as a more suitable concept for describing real-world information. In recent years, Z-numbers has received immense attention. However, the general approach for computations over Z-number is too complex. Thus a simpler mathematical model has been proposed—Two Dimensional Belief Function (TDBF). A TDBF is an ordered pair of belief functions; the first belief function is on the frame of discernment of the values that a variable can take, and the second belief function is a measure of reliability of the first belief function. However, the processing of TDBF-based information requires a new theory to be developed, together with new approaches and procedures for computation with TDBF. In this paper, we propose a general framework for computations over TDBF, comprising addition, subtraction, multiplication, division, square and square root of TDBFs. In particular, a general approach to implementing combination over two TDBFs is also given. Our proposed method is validated by a variety of numerical examples, with an application in decision making problem. [ABSTRACT FROM AUTHOR]

The most difficult problem of offline signature verification (SV) is that a signature is merely a static image missing a lot of the dynamic information associated with it. In this paper, three separate pseudo-dynamic features based on the gray level: gradient based local binary pattern (GLBP), statistical features of gray level co-occurrence matrix (SGLCM), simplified histogram of oriented gradients (SHOG) are proposed for writer-independent offline SV. These gray-level features can convey both texture information and the relative structural relationship of signature strokes. In addition, our experiments prove that the proposed features contain complementary information. Using random forests (RFs) as classifier, a fusion of the proposed features could achieve 7.42% and 0.08% average error rate (AER) for GPDS-253 and CEDAR datasets, respectively, which show the effectiveness of the proposed system. The implication of this paper is that part dynamic information could be extracted from a static gray level image. [ABSTRACT FROM AUTHOR]

Based on the data collected in Uganda, Nepal, and Ethiopia, the papers included in this supplement fill a critical gap in evidence regarding multisectoral National Nutrition Action Plans. The studies offer new data and new thinking on how and why governance, effective financial decentralization, and improved accountability all matter for nutrition actions in low-income countries. This introductory paper offers an overview of the current state of evidence and thinking on the multisectoral nutrition policy cycle, including how governance and financing support that process. It also explores the benefits of applying a systems lens to understand the dynamic, enabling processes of the policy cycle-from research to knowledge and ultimately action-and to provide more dynamic and accurate information for nutrition advocacy and evidence-based decision-making. It concludes with key findings from the 5 country-level studies included. Several important themes emerge: the egregious gap in human resources needed for effective nutrition actions in most low-income settings, the value of research on bottlenecks and successes, and the need for routine monitoring of national policies and plans to measure their effectiveness in achieving both their own stated goals and global sustainable development goals. Reviewing these studies together provides a path forward in building stronger, evidence-based multisectoral nutrition policies and supporting implementation of the nutrition activities included within them. [ABSTRACT FROM AUTHOR]

The oil industry in Thailand primarily relies on truck transportation due to the versatility of trucks. Different truck sizes can easily access many customer locations better than any other transportation mode. However, although trucks have many advantages, the transportation cost per unit is very expensive compared to railway, air, waterway, and pipe transportation modes. Previous data indicated that shipping costs were approximately 703.995 USD/month/fleet and the transportation utilization was 83.145%. Therefore, efficiencies should be identified to improve business operations. This paper proposes an oil transportation plan with transshipment points by adopting Network Flow Models (NFM) to develop a Generalized Network Flow Model (GNFM). The developed GNFM was applied to solve three different problems and compared to the existing method. The objectives of the proposed model were to minimize total cost and maximize truck efficiency. Multi-Criteria Decision Analysis (MCDA) was considered to set the optimal decision criteria for managing the oil transportation network with the transshipment sites. The proposed model with the Cluster Model (CT) provided optimal results, with a total cost of 663.539 USD/month/fleet and a truck efficiency of 88.029%. [ABSTRACT FROM AUTHOR]

Due to the recent worldwide outbreak of COVID-19, there has been an enormous change in our lifestyle and it has a severe impact in different fields like finance, education, business, travel, tourism, economy, etc., in all the affected countries. In this scenario, people must be careful and cautious about the symptoms and should act accordingly. Accurate predictions of different factors, like the end date of the pandemic, duration of lockdown and spreading trend can guide us through the pandemic and precautions can be taken accordingly. Multiple attempts have been made to model the virus transmission, but none of them has investigated it at a global level. The novelty of the proposed work lies here. In this paper, first, authors have analysed spreading of the said disease using data collected from various platforms and then, have presented a predictive mathematical model for fifteen countries from first, second and third world for probable future projections of this pandemic. The prediction can be used by planning commission, healthcare organizations and the government agencies as well for creating suitable arrangements against this pandemic. [ABSTRACT FROM AUTHOR]

The increasing availability of data from sports events has led to many new directions of research, and sports analytics can play a role in making better decisions both within a club and at the level of an individual player. The ability to objectively evaluate individual players in team sports is one aspect that may enable better decision making, but such evaluations are not straightforward to obtain. One class of ratings for individual players in team sports, known as plus-minus ratings, attempt to distribute credit for the performance of a team onto the players of that team. Such ratings have a long history, going back at least to the 1950s, but in recent years research on advanced versions of plus-minus ratings has increased noticeably. This paper presents a comprehensive review of contributions to plus-minus ratings in later years, pointing out some key developments and showing the richness of the mathematical models developed. One conclusion is that the literature on plus-minus ratings is quite fragmented, but that awareness of past contributions to the field should allow researchers to focus on some of the many open research questions related to the evaluation of individual players in team sports. [ABSTRACT FROM AUTHOR]

Invasive species are a major threat to the economy, the environment, health, and thus human well-being. The international community, including the United Nations’ Global Invasive Species Program (GISP), National Invasive Species Council (NISC), and Center for Invasive Species Management (CISM), has called for a rapid control of invaders in order to minimize their adverse impacts. The effective management of invasive species is a highly complex problem requiring the development of decision tools that help managers prioritize actions most efficiently by considering corresponding bio-economic costs, impacts on ecosystems, and benefits of control. Operations research methods, such as mathematical programming models, are powerful tools for evaluating different management strategies and providing optimal decisions for allocating limited resources to control invaders. In this paper, we summarize the mathematical models applied to optimize invasive species prevention, surveillance, and control. We first define key concepts in invasive species management (ISM) in a framework that characterizes biological invasions, associated economic and environmental costs, and their management. We then present a spatio-temporal optimization model that illustrates various biological and economic aspects of an ISM problem. Next, we classify the relevant literature with respect to modeling methods: optimal control, stochastic dynamic programming, linear programming, mixed-integer programming, simulation models, and others. We further classify the ISM models with respect to the solution method used, their focus and objectives, and the specific application considered. We discuss limitations of the existing research and provide several directions for further research in optimizing ISM planning. Our review highlights the fact that operations research could play a key role in ISM and environmental decision-making, in particular closing the gap between the decision-support needs of managers and the decision-making tools currently available to management. [ABSTRACT FROM AUTHOR]

Many studies have concentrated on fixed cost and resource allocation issues by using data envelopment analysis (DEA). The existing approaches allocate fixed cost or resource primary based on efficiency principle. They usually assume that all of the DMUs become efficient after fixed cost or resource allocation. However, due to the existing of technology heterogeneity among DMUs, it is impractical for all the DMUs to achieve a common technology level, especially when some DMUs are far from the efficient frontier. In this paper, under the centralized decision environment, we present a new approach to deal with fixed cost and resource allocation problems by considering the factor of technology heterogeneity. Specifically, the concepts of meta-efficiency and group efficiency as well as meta-technology ratio are firstly introduced to reflect the technology level of the DMUs. Then two centralized DEA models considering technology heterogeneity are proposed to allocate fixed cost and resources, respectively. Finally, two numerical examples are presented to illustrate the feasibility and superiority of the proposed approach compared with prior studies. [ABSTRACT FROM AUTHOR]

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