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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]

Never has there been such focus on just one small ribbon-like ligament in the human body: the anterior cruciate ligament (ACL) is the most highly studied and highly written about ligament in the knee, and yet still, there seem to be many ongoing unknowns. Despite this, there seem to be even more so-called experts than papers, all with their own firm and didactic opinions. This review article provides a contemporary opinion on the subject of ACL tears and ACL reconstruction, with a focus on the diagnosis of ACL tears and the decision-making process, with respect to conservative versus surgical management. The review also discusses the topics of tunnel placement, graft options, internal bracing, the anterolateral ligament and, importantly, outcomes. This paper highlights that there are a number of different approaches and options, and emphasizes the importance of discussing the pros and cons of these options with your patients, thoroughly, with shared decision-making leading to fully informed consent. [ABSTRACT FROM AUTHOR]

In this study, we have extended Bulatao’s conceptual framework for selecting the determinants of method choice. Attempts have been made to capture Bulatao’s dimensions directly rather than through proxies. The results of logistic regression analysis using the 1996–97 BDHS indicate that as the number of living children increases, women prefer non-terminal efficient methods such as the pill and injectable/IUD/implant and depend less on inefficient methods such as condom and permanent methods such as sterilization. With an increase in marital duration and age, women increasingly prefer sterilization as compared with other modern methods. The results may indicate a strong preference for male children among those in the sample, as women increasingly use permanent methods to prevent pregnancy as the number of living male children increases. Condom use increases with education of both women and their husbands, compared with other modern methods, indicating that more educated couples possibly have the greater understanding necessary to use the method. The better educated are significantly less likely to use sterilization. The two indicators of contraceptive evaluation, side effect and convenience of current modern method, are very important considerations in selecting the pill, injectable/IUD/implant, and condom over other modern methods. On the basis of convenience, women are most likely to use the pill over other modern methods, while they are least likely to use sterilization. Condom is the most preferred method on the basis of side effect. On the other hand, sterilization is the least preferred of other modern methods for the same reason. Sterilization is significantly less common among the Muslims, which is likely due to religious reasons, while condom use is significantly less common among non-Muslims. The two indicators of contraceptive access, accessibility/availability of current modern method and cost of current modern method, are very important considerations in pill use, while sterilization is less preferred over other modern methods on the basis of both accessibility/availability and cost. The programmatic factor visit of family planning workers/FWAs significantly increases the likelihood of using the pill compared to other modern methods. [Copyright &y& Elsevier]

Robot sequential decision-making in the real world is a challenge because it requires the robots to simultaneously reason about the current world state and dynamics, while planning actions to accomplish complex tasks. On the one hand, declarative languages and reasoning algorithms support representing and reasoning with commonsense knowledge. But these algorithms are not good at planning actions toward maximizing cumulative reward over a long, unspecified horizon. On the other hand, probabilistic planning frameworks, such as Markov decision processes (MDPs) and partially observable MDPs (POMDPs), support planning to achieve long-term goals under uncertainty. But they are ill-equipped to represent or reason about knowledge that is not directly related to actions. In this article, we present an algorithm, called iCORPP, to simultaneously estimate the current world state, reason about world dynamics, and construct task-oriented controllers. In this process, robot decision-making problems are decomposed into two interdependent (smaller) subproblems that focus on reasoning to "understand the world" and planning to "achieve the goal" respectively. The developed algorithm has been implemented and evaluated both in simulation and on real robots using everyday service tasks, such as indoor navigation, and dialog management. Results show significant improvements in scalability, efficiency, and adaptiveness, compared to competitive baselines including handcrafted action policies. • This article formally introduces the Integrated Reasoning and Planning (IRP) problem for robots. An IRP problem has a factored state space that is specified by a set of endogenous and exogenous variables. • This article introduces a sequential decision-making algorithm, called Interleaved Commonsense Reasoning and Probabilistic Planning (iCORPP), for addressing IRP problems. • This article presents systematic evaluations of iCORPP using two tasks of mobile robot navigation, and spoken dialog systems, as demonstrated on a real robot. • This article builds on our previous research that appeared in two conference papers (Zhang and Stone, 2015; Zhang et al., 2017). This article unifies their terminology, problem statements, and algorithms. • Compared to our prior work, this article introduces a new problem statement in Section 4.1, which covers the problems addressed in both conference papers. • To address the IRP problem, we have reformulated the iCORPP algorithm (Section 4.2) into a novel form that includes the three key steps of "logical reasoning," "probabilistic reasoning over world states," and "probabilistic reasoning about world dynamics." [ABSTRACT FROM AUTHOR]

In this paper, we show the practical application of two recently proposed Multi-Criteria Decision Analysis (MCDA) methods, namely the Stable Preference Ordering Towards the Ideal Solution (SPOTIS) and the Reference Ideal Method (RIM) methods. Both of these methods can utilize the Expected Solution Point (ESP) concept, which allows it to easily reflect the decision-maker's or the customer's preferences and expectations. We show the comparison of those methods in the practical study case of laptop choice for the customer with specific needs and expectations for the hardware expressed using ESP. The comparison also includes rankings built toward an optimal solution to underline the necessity of such an approach as the Expected Solution Point. We also use the recently developed Ranking Comparison (RANCOM) method to identify criteria weights to include customer preferences in the weights. The paper also contains a broad discussion of the obtained results and limitations of the presented methods. [ABSTRACT FROM AUTHOR]

Digital data management and enterprise systems have become key to support the digitalisation of maintenance activities. With traditional maintenance activities still striving for efficiencies, platforms such as the natural language processing (NLP) are supporting industries to mine textural data, not just extracting degradation terminologies but providing the maintainer with holistic insights on the degradation process. Traditionally, the degradation analysis, the first step in maintenance, is a manual process for defect characterisation, followed by failure investigation and a remaining useful life estimation. To enable digitalisation, transfer of human cognitive decision making from the physical world to the digital world is key. This paper enables this cognitive knowledge transfer through the design of a common degradation taxonomy and extracting terminology relationships to produce degradation causality with an NLP knowledge extraction approach. Further, this paper proposes and demonstrates a framework to present the data in the form of a knowledge graph populated using an application-level ontology. Use cases in the aerospace context have been used to show the power of the NLP and conceptual journey into the digitalisation of maintenance. [ABSTRACT FROM AUTHOR]

Machine learning based decision making systems in safety critical areas place high demands on the accuracy and generalization ability of the underlying model. A common strategy to deal with uncertainties and possible mistakes is offered by learning with reject option, i.e. a model can refrain from prediction in ambiguous cases and leave the decision to a human expert. Yet, as for the models themselves, human decision-making is hampered by the fact that reject options are often implemented as black-box rules: Experts cannot readily understand the reasons for rejection. In this work, we propose a model-agnostic framework that enriches classification with reject option by explanation mechanisms. More specifically, we combine conformal prediction as a popular mathematically based technology of certainty estimation with local surrogates derived for the region of interest. This allows us to provide local explanations in terms of example-based explanation methods, including counterfactual, semi-factual, and factual methods. We demonstrate the performance of this technology through a series of benchmarks using 6 different data sets; the associated code is open source. 1 1 This article represents a substantially extended version of the conference paper Artelt et al. (2022). In addition to a more detailed explanation and derivation of the algorithms, we explore semi-factual and factual explanation methods and provide evaluations on realistic benchmark data sets. • New challenge for XAI: explain model uncertainty and reject option • Efficient realization based on counterfactual explanation of local surrogate. • Open source implementation and evaluation on benchmarks. [ABSTRACT FROM AUTHOR]

In this paper, a real-time chemi-thermomechanical pulping (CTMP) process was investigated, where opportunities were explored to increase energy consumption efficiency with a guarantee of expected pulp properties as a priority. Due to the process instrumentation limitations, such as the lack of advanced regulatory controllers and optimization units, the advanced process controllers and real-time optimization strategies can hardly be implemented in the existing CTMP processes. Therefore, a novel visualization-based process evaluation methodology is proposed to aid the mill operators in making further decisions to improve the process operations. Additionally, to determine the desirable pulp properties for production, an inferential sensor model is developed that estimates the handsheet properties from pulp properties. Also, to assess the model prediction accuracy in real-time, a reliability index model is designed. The proposed methodologies have been validated on a practical CTMP process, and the results show the satisfactory performance of the system. • Developed a strategy to evaluate the CTMP performance using visualization methodology. • A novel inferential sensor model is implemented to determine the desirable pulp properties. • A reliability index model classifying reliability levels and assessing model accuracy is developed. • The proposed model adapts to model updates, uncertainties, and computation resource limitations. [ABSTRACT FROM AUTHOR]

Media ethics is an area of inquiry that has been of great concern among journalists and public relations professionals as well as scholars across the world. Empirical research on media ethics of comparative nature has increased, providing a descriptive account on the current situations across different countries. Yet, theoretical efforts to explain global media ethics are so far inexistent. The scope of this paper is to provide a conceptual model for explaining media ethics decision-making processes that takes into account personal, professional and environment values as main influencing forces. The proposed model suggests that besides considering personal factors, such as education, background, experience, gender, etc., and country specific factors, such as political, economic, socio-cultural conditions, one should first and foremost understand the degree to which personal, professional, and environment values influence one’s judgment. This model is the first of its kind in providing an explanation of differences in global media ethics by considering the interconnectedness of different values at micro, meso, and macro levels. [ABSTRACT FROM AUTHOR]

Enterprise resource planning (ERP) adoption literature has a consensus that selecting the right ERP system is one of the most critical success factors in the ERP adoption lifecycle. While choosing a non-fitting ERP system may lead to adoption failures, however very few papers focus solely on this selection phase. Hence, given the criticality of the ERP selection phase, this paper aims to identify and review the different ERP selection methods in extant literature. This research also presents the factors and variables included in each identified selection method in ERP literature. As a result, each method identified was reviewed, analyzed, and summarized. Our main findings suggest that ERP selection is a multi-criteria decision-making (MCDM) problem, with various methods and techniques that can be utilized for such problems. Several MCDM methods have been used in literature, but often complementing more than one method combined at a time. This is since some methods excel in considering factors in uncertain environments, and other methods are best in evaluating qualitative and quantitative factors. Finally, while there are some methods that were used for cloud-ERP selections, there is no clear consensus in extant literature if some methods could best fit specifically cloud-ERP contexts in contrast to on-premises counterparts. [ABSTRACT FROM AUTHOR]

In the urban rail transportation industry, collecting various data, including the number of trips, the number of fleet breakdowns, the number of breakdowns of railway lines, etc., contributes significantly to future planning and optimization of resources and costs. We are faced with big data analysis due to the daily production of large volumes of data in the urban rail transport industry. This study aims to find a trip prediction model for Tehran Metro to do trip decision-making. This paper provides a brief introduction to knowledge discovery from failure databases and presents the data mining methodology. The relevancy of data mining for a trip prediction model will be depicted. In this sense, considering the relevance of data collected on the metro control center and intelligent trip prediction, this paper presents a functional architecture of a predictive trip model using data mining techniques. Data Mining will identify behavior patterns, allowing a more accurate early detection of faults in the daily operation. This research has investigated three situations, including delayed trips, incomplete trips, and canceled trips, using actual data recorded over the past four years, including non-programmed failures and their impact on trips. A model with more than 90% (90.48%) accuracy suggests a trip status forecast on this line. Also, the research has used Rapid Miner software and the Decision tree technique in data mining and its rules. Also, the kappa index of 0.792 is calculated for the validation model. Therefore, the proposed model enables the prediction of three types of incomplete traffic, delayed trips, and cancellation of trips due to emergency failure at the time of the operation of the Tehran Metro. [ABSTRACT FROM AUTHOR]

This paper aims to perform a literature review on the use of multi-criteria methods for assessing agricultural sustainability, focusing on the distribution of papers according to year, journal, and countries, the most productive authors, the most frequently used multi-criteria methods and their characteristics, the type of system and the type of agriculture in which these methods are being performed, the methodological approaches and assessment types, and the sustainability dimensions considered. The data collection has been carried out through the Web of Science™ platform on September 3rd, 2021. After a refinement process, 41 papers were selected. The descriptive analysis was carried out through Bibliometrix tool, while content analysis was performed using Nvivo. The descriptive analysis shows that from 2016 to 2021 the scientific production addressing multi-criteria methods to assess agricultural sustainability started to grow markedly in a very rapid matter, reaching an average of 6 papers per year. France and China are the most scientifically productive countries. The content analysis points out that the most used multi-criteria method is the AHP that was used 11 times. The outranking methods, instead, were used only 3 times. In 68% of the papers the Triple Bottom Line was used as dimensions, and in 41% of the papers the spatial applicability was the farming system. The results highlight that doesn't exist many MCDM/A methods for assessing agricultural sustainability, and most of them are compensatory. These results point out that the use of multi-criteria methods in assessing agricultural sustainability is still underexplored and can be improved. [Display omitted] • Multicriteria methods are powerful tools for assessing agricultural sustainability. • A literature review on agricultural sustainability evaluation was performed. • The number of studies has been increasing in the last five years. • Most of the approaches are based on compensatory multicriteria methods. [ABSTRACT FROM AUTHOR]

Machine Learning as a Service, MLaaS refers to cloud-based platforms that provide machine learning tools and infrastructure to users, allowing them to access and utilize machine learning capabilities without managing the underlying hardware and software. Cloud service providers offer scalable and flexible resources, enabling businesses to train and deploy machine learning models efficiently and cost-effectively. Multi-Criteria Decision making, MCDM helps individuals or organizations make choices when multiple criteria or objectives are involved. It aims to find the best alternative among a set of available options by considering various criteria or factors that are relevant to the decision. Machine learning as a service (MLaaS) and MCDM are two distinct concepts that can be combined to create powerful decision-making solutions. The present paper proposed an integrated approach based on the Analytic Hierarchy Process and TOPSIS method for choosing the best MLaaS cloud. Four alternatives for MLaaS cloud were analyzed based on seventeen shortlisted criteria. Based on the calculations, conclusions have been reached and potential study areas have been identified. [ABSTRACT FROM AUTHOR]

Nowadays, the increasing integration of artificial intelligence (AI) technologies in manufacturing processes is raising the need of users to understand and interpret the decision-making processes of complex AI systems. Traditional black-box AI models often lack transparency, making it challenging for users to comprehend the reasoning behind their outputs. In contrast, Explainable Artificial Intelligence (XAI) techniques provide interpretability by revealing the internal mechanisms of AI models, making them more trustworthy and facilitating human-AI collaboration. In order to promote XAI models' dissemination, this paper proposes a matrix-based methodology to design XAI-driven user interfaces in manufacturing contexts. It helps in mapping the users' needs and identifying the "explainability visualization types" that best fits the end users' requirements for the specific context of use. The proposed methodology was applied in the XMANAI European Project (https://ai4manufacturing.eu), aimed at creating a novel AI platform to support XAI-supported decision making in manufacturing plants. Results showed that the proposed methodology is able to guide companies in the correct implementation of XAI models, realizing the full potential of AI while ensuring human oversight and control. [ABSTRACT FROM AUTHOR]

Industry 4.0 is demanding reskilling of the workforce and, at the same time, the phenomenon of Great Resignation characterized by a wave of layoffs is threatening organizational knowledge. It is essential to build a culture where employees are engaged, more committed, and where the sharing and generation of knowledge is promoted. This paper presents a collaborative platform (co-created with 3 multi-sector organizations- 2 multinationals) to support the establishment of a Learning Organization to face the challenges of human resources in the digital paradigm. This tool integrates 5 modules: discussion forum, event creation, mentoring, suggestion system and recognition system. The 5 modules act in a combined way to achieve features that offer participation in decision making, a climate of learning opportunities, through the sharing and rise of knowledge (Learning Organization) and that encourage peer relationships and recognition at the corporate level. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) has profound economic influence in manufacturing, but its unmindful integration can also pose societal and environmental risks. This paper provides a quantified overview of manufacturing areas that are highly advanced in AI capability research, such as maintenance. Integrating Responsible AI (RAI) in further studies of those areas is essential to mitigate risks and deliver business benefits. To enable this, manufacturing specific RAI dimensions are defined to represent accountability, explainability, fairness, human-centricity, sustainability (Green AI) and privacy & security. Further, a qualitative RAI framework consisting of responsibility areas (human involvement, decision making, business focus, system design) is proposed. Practical considerations to align the framework with manufacturing requirements are made by discussing it within an AI systems lifecycle. [ABSTRACT FROM AUTHOR]

Within the paradigm of industry 5.0, manufacturing systems are seeking for human-centred production, where the operator finds high-level supervision tasks. In this context, low-level decision making should be performed by machines themselves. In this paper, a hybrid prognosis algorithm is developed to automatically inspect the cutting edges of drill-bits and to predict their Remaining Useful Life (RUL) and the associated probability density function. The solution relies on the automatic measurement of flank wear through convolutional filtering and edge detection. Prognosis exploits particle filter, which updates multi-layer perceptron with online data, to adaptively predict drill-bits RUL. The solution reduces the experimental preliminary run-to-failures needed for training standard machine learning algorithms, exploiting them in a real-time adaptive scenario, while predicting tool RUL under untested and variable cutting process operations. The algorithm uses direct wear observations, taken during set-up times (e.g., tool changes, workpiece change), thus not interfering with the process. [ABSTRACT FROM AUTHOR]

Decision analysis is a key aspect in improving slums intervention strategies utilizing a variety of tools to evaluate all relevant information that support decision-making process for dealing with the complexity of slums interventions policies. This paper presents a framework integrating Multi Criterion Decision Analysis (MCDA) with (GIS) based decision support tool to quantitatively assess land suitability for site relocation of an urban slum site utilizing Analytical Hierarchy Process (AHP), which is commonly used for fastest growing decision-analytic techniques in several disciplines for the determination of various aspects weights. Herein Batn Al Baqara region (which is located in south of Cairo) was selected as study area to be relocated as it was considered among the most difficult to deal with, three proposed relocation sites were selected and analyzed using MCDA-GIS Model. Twelve different criteria were utilized to evaluate the degree of suitability of the relocation sites. A five degree of suitability were employed according to the different criteria scores for each site. The results supported that Al Asmarat is the most suitable site for the relocation of slum since it achieved the highest evaluation score (84.92 %) when the model was applied to the relocation sites. [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]

This paper aims to construct a trader model tailored to offer individualized investment strategies for users engaged in the US stock market. The approach taken involves two key aspects: forecasting stock prices and discerning users' personality traits to formulate optimal trading recommendations. The framework is realized through the integration of neural networks and the Moving Averages algorithm. This combined approach allows forecasting upcoming changes in stock prices and generating appropriate trading signals. Complementing this, a user risk assessment is administered, and its outcomes are leveraged to furnish personalized trading advice, offering invaluable support for informed decision-making amid intricate real-time scenarios. By synergizing predictive modeling and user-specific insights, this comprehensive system contributes to enhancing trading precision and user empowerment. This work can be useful in decision making for a wide range of stock market participants, as well as for institutions specialized for this area of operation. [ABSTRACT FROM AUTHOR]

• There has been a limited scope of investigation of the contextual factors affecting driver decision-making during narrow passage interactions. • The study found that the likelihood of giving way may alter due to characteristics of the interaction partners, including their vehicle type. • It was also found that factors, external to the direct interaction partners, may affect the likelihood of giving way, such as the presence of vehicles beyond the direct interaction partners. • This paper validates previous communication findings in a UK context. Narrow passage interactions have received increased attention from academics seeking to create behavioural models of the interaction and those looking to define how autonomous vehicles (AVs) should interact with their human counterparts in a composite road system. Despite this increased attention, many factors remain unexplored in the narrow passage literature, with the literature also encompassing few driving culture contexts. To this end, this study employs an explorative survey to identify additional factors that affect driver decision-making during narrow passage interactions, as well as driver perceptions of different communications in a UK context. The study's 243 participants were presented with a range of different narrow passage scenarios and asked to indicate how likely they were to give way/yield to a vehicle approaching the narrow passage from the opposite direction. In addition, they also completed the Multidimensional Driving Style Inventory to identify their driving styles and asked to identify which signals they look for from their interaction partner during narrow passage interactions, as well as the meaning of those signals. The results of the study show that situational characteristics such as the vehicle type being interacted with, being in a rush and being followed by vehicles alter the likelihood of drivers giving way at narrow passages, whilst a person's driving style can also indicate how likely someone is to give way to another vehicle. These results highlight the factors that are considered by drivers, increasing our understanding of the factors that need to be incorporated in driver behaviour models and in AV development. [ABSTRACT FROM AUTHOR]

• The geographical factors cannot be ignored in the site selection of WTE plant. • SVNSs makes up for the weakness of traditional fuzzy sets in information description. • The weight calculation method in SVNSs background is proposed. • EDAS can reduce the adverse impact of decision makers' own reasons on the results. • The combination of GIS and MCDM can improve the precision of site selection. Waste-to-energy (WtE) plant has been attached great importance by more and more countries because of the harmless, energy reducing and recycling characteristics, and has become an important part of the city's normal operation and energy structure. Whereas, the reasonable site selection of the WtE plant is the key to ensure its sustainable development. This paper is designed to provide a two-stage model for the site selection of WtE plants. Firstly, a comprehensive assessment criteria system including environmental, economic, technical and social factors is established by selecting the reasonable influence criterias from the current relevant literature. Considering the fuzziness of the criteria and the bounded knowledge of decision makers, the single-valued neutrosophic sets (SVNSs) are introduced to describe the fuzzy information, and creatively combined with decision-making and trial evaluation laboratory-analytical network process (DANP) method to determine the influence grades of criterias and calculate the weights. After that, the relevant geographic information layers are collected and the preliminary alternatives are obtained by Geographic information system (GIS) from an overall perspective, then the extend evaluation based on distance from average solution (EDAS) method is utilized to rank the alternatives under the SVNSs environment. Finally, the model is tested to select the optimal alternative for siting the WtE plants in Beijing as an example. It is verified that the model is of great stability and feasibility by sensitivity analysis and comparative analysis. The result shows that this paper can provide a new theoretical basis for the planning of WtE plants. [ABSTRACT FROM AUTHOR]

Objective: To identify decision characteristics for which SDM authors deem SDM appropriate or not, and what arguments are used.Methods: We applied two search strategies: we included SDM models from an earlier review (strategy 1) and conducted a new search in eight databases to include papers other than describing an SDM model, such as original research, opinion papers and reviews (strategy 2).Results: From the 92 included papers, we identified 18 decision characteristics for which authors deemed SDM appropriate, including preference-sensitive, equipoise and decisions where patient commitment is needed in implementing the decision. SDM authors indicated limits to SDM, especially when there are immediate life-saving measures needed. We identified four decision characteristics on which authors of different papers disagreed on whether or not SDM is appropriate.Conclusion: The findings of this review show the broad range of decision characteristics for which authors deem SDM appropriate, the ambiguity of some, and potential limits of SDM.Practice Implications: The findings can stimulate clinicians to (re)consider pursuing SDM in situations in which they did not before. Additionally, it can inform SDM campaigns and educational programs as it shows for which decision situations SDM might be more or less challenging to practice. [ABSTRACT FROM AUTHOR]

• A new simulation methodology, intended bounded rationality methodology, is proposed. • Evaluation cost-benefit of different aspects of decision support techniques. • Classification of errors that a decision support technique can generate is proposed. • Consistent erroneous judgments and inconsistent correct judgments are possible. • A case where consistency condition in AHP provides maximum 5% of the expected return. Expert's judgments have been crucial in the development of decision theory; however, what criterion to use in the selection of experts remains an issue to address. Decision support techniques proposed to improve the quality of expert judgment decision making consider a demonstrated inconsistency of the judgments expressed by an expert as a criterion of exclusion in the decision-making process of such expert. Although consistency appears to be a desirable condition to qualify as "expert", little is known about the quality of the decisions made imposing consistency as the expert qualifying condition. This paper proposes a simulation methodology, based on an automaton programmed to make decisions in an intended but bounded rational way, to assess the cost-benefit of different aspects of decision support techniques. Within this methodology, the imposition of the consistency condition in the selection of experts is studied. In particular, the paper shows with a case study example that the Analytical hierarchy process (AHP) decision support technique expected payoff is at most 5% higher when implementing Saaty's consistency criterion of the expert's judgments than when the consistency criterion is not considered. [ABSTRACT FROM AUTHOR]

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]

Due to different users' requirements, contemporary software has become feature-rich in terms of input functions (i.e., parameters) and selections (i.e., values). Exhaustive testing on sophisticated software systems is impractical as far as testing time and cost are concerned. Various test case design strategies have been proposed in the literature, such as equivalence class partitioning, boundary value analysis and decision tables. Unlike earlier works, combinatorial t-way testing supports the detection of faults caused by two or more input parameter interactions and thus efficiently minimizes the size of the test suite. Over the past few years, metaheuristic algorithms have appeared to be the most common choice for researchers since their effectiveness proves to offer optimal/near-optimal results. However, generating a t-way test suite is an NP-hard problem, and no single t-way strategy can guarantee to show superiority to others for all types of system configurations. Hence, this paper presents a new t-way strategy based on the Gravitational Search Algorithm (GSA), known as the Gravitational Search Test Generator (GSTG). The primary contribution of this paper is that GSA has adapted for the first time to t-way test data generation. The benchmarking results showcase that GSTG obtains competitive results in most system configurations compared to other existing strategies and addresses higher combination coverage (i.e., t ≤ 10). [ABSTRACT FROM AUTHOR]

Carsharing services provide users with a new way of approaching mobility and accessing shared vehicles. Since the initial pilot studies in the early 90s, technological innovations (e.g., advances in mobile technology, increased range of electric cars) and the establishment of new business models (e.g, station-based, free-floating, peer-to-peer, packages by time and/or kilometres) helped branding carsharing as a sustainable yet flexible and personalized mobility alternative. On the other hand, the carsharing market today is extremely scattered, as it can include multiple operators, which are often in competition among each other. While this variety of operators provides the user with a variety of travel options, without proper coordination, this competition can reduce the efficiency of the carsharing market and, in extreme cases, of the entire transportation system. In this context, this paper studies the needs of travellers, local authorities, and carsharing service providers, and analyses how incentives can be used to align their goals. Taking Munich, Copenhagen, and Tel Aviv-Yafo as case studies, focus groups were used to identify thirteen different travellers' needs, which are grouped into five main categories: ownership issues, coverage area, financial aspects, vehicle settings, and integration of carsharing with other modes. Moreover, to understand the needs of service providers and public authorities, in-depth interviews were conducted. The results indicate that regulatory barriers, integration with other transport modes, and social equity issues are the most critical elements for the thriving of carsharing services. Our results also suggest that incentives can be divided into two main categories, namely direct and indirect incentives. With regards to direct incentives, parking stood out as the most important incentive. Among indirect incentives, those associated with the integration of carsharing with other transport services, were prominent. As part of the methodology, the results of the qualitative study are validated through a quantitative analysis. A bigger sample of the population answered an online survey, which was used to validate the list of potential incentives that can help aligning the goals of stakeholders and users. • Policy recommendations on how incentives can be used to support the decision making process. • A list of potential user-directed and stakeholder-directed incentives. • Incentives are classified into two different categories: direct and indirect. • The paper analyses three cities (Copenhagen - Denmark, Munich - Germany, and Tel Aviv-Yafo - Israel). • The results of the qualitative study are validated through a quantitative analysis. [ABSTRACT FROM AUTHOR]

This paper considers a strategy for identifying the decisions and bottlenecks that arise in monoclonal antibody development in the progression from a multitude of potential candidate molecules to a prioritised sub-set that are taken through development and possess a good chance of succeeding in the clinic. While the steps involved are common across the industry, the need to 'fail fast' challenges existing decision making. In any development there are numerous technical and business focused staff involved with different skill sets and areas and scales of focus. As potential products could fail in any part of the development programme, failing 'fast' requires understanding of the requirements of those in latter stages by technical and business decision makers in as early development as possible. The focus of the paper is the use of the Knowledge Acquisition Technique to elicit the expertise of numerous specialists and to develop an interpretable decision-making landscape across development to facilitate decision making. The effectiveness of the elicitation is considered and the richness of information it delivers to challenge conventional wisdom addressed. The technique is shown to deliver a broader appreciation of the requirements throughout the development supply chain. [ABSTRACT FROM AUTHOR]

For multi-attribute decision-making problems with interval numbers whose attribute weights are completely unknown, it is difficult to compare the value of interval numbers. So this paper proposes a method based on TOPSIS and the weighted parameter to deal with it. Firstly, this paper transforms the interval number matrix into two exact number matrices which reduces sorting complexity. Secondly, a parameter is given when determining the weights with entropy weight in order to reflect all the information of the interval numbers. Then TOPSIS is used to determine the order of each scheme. In addition, the average value of the ranking number is used to reflect the actual situation better. Finally, the analysis of the car purchase case shows the proposed method is feasible and practical. And the comparative analysis with the other method based on practical application dataset demonstrates the proposed method is stable and effective. [ABSTRACT FROM AUTHOR]

In this commentary paper, we discuss the use of the electrocardiogram to help clinicians make diagnostic and patient referral decisions in acute care settings. The paper discusses the factors that are likely to contribute to the variability and noise in the clinical decision making process for catheterization lab activation. These factors include the variable competence in reading ECGs, the intra/inter rater reliability, the lack of standard ECG training, the various ECG machine and filter settings, cognitive biases (such as automation bias which is the tendency to agree with the computer-aided diagnosis or AI diagnosis), the order of the information being received, tiredness or decision fatigue as well as ECG artefacts such as the signal noise or lead misplacement. We also discuss potential research questions and tools that could be used to mitigate this 'noise' and improve the quality of ECG based decision making. [ABSTRACT FROM AUTHOR]

Most Multi-Criteria Decision Analysis (MCDA) methods identify only discrete, defined sets of alternatives. Such identification does not provide an opportunity to consider a more significant number of samples acquired over long periods, which is not a desirable effect. An additional aspect of identification is the frequent application of expert knowledge, whereby a continuous model would have to be defined to consider further alternatives that would be suitable for further use. This paper considers a new approach for identifying continuous decision models named SITCOM. The purpose of this paper was to conduct a study on the accuracy of the proposed approach. The accuracy of Stochastic IdenTifiCation Of Models (SITCOM) was tested using sample evaluations obtained from fuzzy reference models (FRM). Spearman's weighted correlation coefficient was used to measure the accuracy of the created models. The experiments showed the dependence of accuracy on the number of characteristic objects. Furthermore, the obtained results prove the reliability of the obtained results, where the trends of accuracy decrease based on the number of characteristic values. [ABSTRACT FROM AUTHOR]

Machine learning is nowadays popular area of science research. As the amount of accessible data is still increasing, therefore the machine learning methods can be used for many applications. One of the more detailed topics, which is commonly analyzed in practice in regard to financial decision supporting, is the support for decision making on stock market. It has been noticed that the main focus is on the developed markets like in Asia, West Europe or USA. As the Polish stock market is also recognized as the developed market, here in opposite to Asia, West Europe or USA markets, the shortage of practical implementation of machine learning algorithms is perceptible. With this paper various learning algorithms have been used to determine its backtesting performance. The main goal of this paper is to examine the effectiveness of selected machine learning algorithms and to find the best one for stock data by comparing several selected algorithms using a backtesting environment on the same data sets and general parameters. For this purpose, experiments were carried out on one random seed and then out on 100 different seeds. [ABSTRACT FROM AUTHOR]

The objective of this paper is to analyze the role of digital planning tools implemented in the context of sustainable development in the Global South, considering that public governance has been going through an important pivotal moment since the appearance of globalization and digitalization, where collaborative participation and hybrid approaches are becoming indispensable for planning and development. In this sense, the paper will give a brief introduction of governance in the digital Era and the use of GIS-based digital planning tools, followed by the presentation of the case study of TOSCA, or Toolkit for Sustainable City Planning and Analysis, to understand the relevance of this hybridization between human and digital processes for governance and planning. TOSCA will be presented in the context of two pilot projects, with the development and deployment of the technology between 2019 and 2020, summarizing its process and outcomes from the stakeholder perspective. The discussion chapter of this paper, collects the learnings and challenges gained during this first application of the Toolkit, and points towards the future development and its application in other regions, with the aim of understanding how its conceptualization and development could be further enhanced in order to ensure its sustainable take-up under diverse contexts and use cases. [ABSTRACT FROM AUTHOR]

In contemporary healthcare systems, the selection and judicious choice of the right medical device becomes paramount consideration for accurate diagnosis, patient care, mitigate risk and cost incurred on diagnosis of disease. Moreover, medical devices are requisite indispensable tools to help healthcare professionals for effective diagnosis, selection of the right treatment amongst different options, and continuous monitoring of the patient. Thus, to select the best manufacturing company is as equally important to achieve the desired goals. This paper presents a decision making algorithm based on fuzzy theory that helps the decision maker in selecting the most suitable worldwide medical device manufacturer. Fuzzy theory is extensively used in managing databases and analyzing data to summarize information and draw meaningful conclusions. This paper utilizes the aggregation operators within the context of the complex q-rung orthopair fuzzy environment by utilizing Yager t-norm and t-conorm operations. Some aggregation operators are applied, namely, complex q-rung orthopair fuzzy Yager weighted geometric operator, complex q-rung orthopair fuzzy Yager ordered weighted geometric operator, complex q-rung orthopair fuzzy Yager weighted averaging operator and complex q-rung orthopair fuzzy Yager ordered weighted averaging operator. Additionally, the notable properties of these operators are proved. These aggregation operators are generated specifically for multi-criteria decision-making. Decision making algorithm is used to choose the most relevant medical device manufacturer (alternative) to validate its efficacy. The results demonstrate the versatility of parameters, adaptability to multi-criteria decision making, and efficiency of the proposed aggregation operators and algorithm. Test with specific criteria is also conducted to verify the legality and validity of the decision making algorithm. At last, the acquired results underwent a comparative analysis against various established methodologies, confirming the method's capacity to produce precise and accurate outcomes. • Multi criteria decision making algorithm is devised in fuzzy environment. • Aggregation operators are introduced and notable properties are investigated. • Test with specific criteria is conducted for the validity of proposed operators. • The algorithm is utilized in medical industry to choose best device manufacturer. [ABSTRACT FROM AUTHOR]

Three-way multi-attribute decision-making (3MADM) integrated with double hierarchy hesitant fuzzy linguistic term set (DHHFLTS) can not only effectively express the uncertainty of language, but also help reduce the risk of wrong decision-making. However, the existing compared methods for DHHFLTSs dismiss the variances in psychological reference points, resulting in mismatches in form and connotation for some linguistic terms. Furthermore, it is difficult or even impossible to obtain an accurate degree of difference using the existing DHHFLTS distance method for these linguistic terms. This directly affects the accuracy of obtaining conditional probabilities results in the three-way decision model. Therefore, this paper introduces the concept of the superior gradus for double hierarchy linguistic term set (DHLTS) and double hierarchy hesitant fuzzy linguistic element (DHHFLE), respectively. Then, some novel compared methods are defined that allow the identification of differences between linguistic variables. Subsequently, based on the superior gradus, a novel distance measurement is designed with a risk parameters. Through the adjustment of risk parameters, this method can respectively obtain optimistic and pessimistic results. Also, the relative loss functions designed for DHHFLTSs aim at getting more objective decision-making results. Finally, the paper proposes a novel 3MADM method under the double hierarchy hesitant fuzzy linguistic information system (DHHFLIS) and applies it to service assessment. To verify the effectiveness and rationality, the medical diagnosis data set is used, and the results are compared with other classic MAMD methods. • 3MADM with DHHFLTS reduces the risk of wrong decision-making. • Superior gradus is introduced to identify DHHFLTS's variances. • Novel distance measurement is designed with a risk parameters. • Relative loss functions for DHHFLTS enhance objective results. • Novel 3MADM method is applied in service assessment and compared with classic MAMD. [ABSTRACT FROM AUTHOR]

During the COVID-19 crisis, leaders of academic libraries had to make various decisions to retain library relevance and maintain essential services. The COVID-19 crisis raised the question of libraries' value to their universities more than ever. Libraries were facing financial difficulties but also operational challenges due to the services built around physical libraries. This paper uses a mixed methods approach to investigate academic library leaders' decision-making during the first year of the COVID-19 crisis. It combines quantitative and qualitative data from previous studies with primary data collected by the author to determine what kind of decisions university library leaders faced during the crisis and why. These studies show that leaders were concerned about the following main challenges: lack of access to physical services and collections, the safety of staff and patrons, new ways of working, and the library's role during the crisis. The results show that library leaders made decisions in smaller groups or some cases, individually due to the lack of time or information. While there have been numerous studies on libraries' response to the COVID-19 crisis in the past three years, this paper focuses mainly on academic library leaders and their decision-making to solve the crisis for their libraries. [ABSTRACT FROM AUTHOR]

A major concern with Bayesian decision making under uncertainty is the use of a single probability measure to quantify all relevant uncertainty. This paper studies prior robustness as a form of continuity of the value of a decision problem. I show that this notion of robustness is characterized by a form of stable choice over a sequence of perturbed decision problems, in which the available acts are perturbed in a precise fashion. I then introduce a choice-based measure of prior robustness and apply it to models of climate mitigation and portfolio choice. • Belief Robustness and Continuity: Beliefs are "robust" if minor changes in beliefs lead to minor changes in choices. This notion can be tested by evaluating perturbations of feasible bets. • Measure of Robustness: A comparative measure of belief robustness can quantify the sensitivity of optimal choices to parametric assumptions. • Impact on Key Economic Models: The measure shows that heavy-tailed distributions yield more robust choices in portfolio and economy-climate models, highlighting its practical importance. [ABSTRACT FROM AUTHOR]

In this paper, the problem of multiplayer hierarchical decision-making problem for non-affine systems is solved by adaptive dynamic programming. Firstly, the control dynamics are obtained according to the theory of dynamic feedback and combined with the original system dynamics to construct the affine augmented system. Thus, the non-affine multiplayer system is transformed into a general affine form. Then, the hierarchical decision problem is modeled as a Stackelberg game. In the Stackelberg game, the leader makes a decision based on the information of all followers, whereas the followers do not know each other's information and only obtain their optimal control strategy based on the leader's decision. Then, the augmented system is reconstructed by a neural network (NN) using input–output data. Moreover, a single critic NN is used to approximate the value function to obtain the optimal control strategy for each player. An extra term added to the weight update law makes the initial admissible control law no longer needed. According to the Lyapunov theory, the state of the system and the error of the weights of the NN are both uniformly ultimately bounded. Finally, the feasibility and validity of the algorithm are confirmed by simulation. [ABSTRACT FROM AUTHOR]

• Life Cycle Engineering has been demonstrated as a viable approach for space mission design. • The life cycle sustainability impacts of SmallSats have been benchmarked for the first time. • Multi-Criteria Decision Analysis is a useful technique for identifying significant hotspots. • Improvements measures are more successful with increased levels of participant interaction. The application of Life Cycle Engineering (LCE) within the concurrent engineering process presents a viable method for assessing environmental, social and economic impacts of space missions. Despite this, the novelty of the concept within space mission design has meant that the approach has not yet been widely implemented. This paper successfully demonstrates this technique for the first time and presents LCE results of three SmallSat missions designed at the University of Strathclyde using the concurrent engineering approach. The Strathclyde Space Systems Database (SSSD) was deployed to calculate the total life cycle impacts of each mission, including the identification of common design hotspots. A novel technique called Multi-Criteria Decision Analysis (MCDA) was also trialled, whereby several impact categories were converted into single scores as a test case to reduce the learning curve for engineers. Overall, the LCE results indicate that the manufacturing & production of the launcher dominate the majority of impact categories. Other common hotspots were found to relate to the use of germanium as a substrate as well as the launch event. As an additional observation, in terms of the behavioural aspects, it was clear that study participants were more open to the concept of LCE with each new concurrent engineering session, evidenced by increasing levels of interaction amongst study participants. These findings are intended to provide industrial stakeholders with a preliminary benchmark relating to the general sustainability footprint of SmallSats, whilst demonstrating the viability of integrating LCE within the concurrent engineering process of space missions. [ABSTRACT FROM AUTHOR]

The Wiener index of a strong digraph D is defined as the sum of the shortest path distances between all ordered pairs of vertices. This definition has been extended to digraphs that are not necessarily strong by defining the distance from a vertex a to a vertex b as 0 if there is no path from a to b in D. Knor et al. (2016) [9] considered (not necessarily strong) orientations of graphs with maximum Wiener index. The authors conjectured that for a given tree T , an orientation D of T of maximum Wiener index always contains a vertex v such that for every vertex u , there is either a (u , v) -path or a (v , u) -path in D. In this paper we disprove the conjecture. We also show that the problem of finding an orientation of maximum Wiener index of a given graph is NP-complete, thus answering a question by Knor et al. (2016) [8]. We briefly discuss the corresponding problem of finding an orientation of minimum Wiener index of a given graph, and show that the special case of deciding if a given graph on m edges has an orientation of Wiener index m can be solved in time quadratic in the order of the graph. [ABSTRACT FROM AUTHOR]

• Propose an end-of-use condition estimation method based on reliability and warranty policy. • Present a two-stage optimization model for the Re -X decision-making problem. • Develop a condition-based end-of-use processing policy under demand uncertainty. • Reduce lifecycle cost and environmental impact for product family. • Demonstrate the methodology with case study and result analysis. The concept of circular economy has been diffused in recent decades to promote economic growth that does not add to the burden on natural resource extraction. Re -X options (e.g., reuse, repair, refurbish, remanufacture, recycle) have been gradually adopted in the product development process and optimized to reduce or eliminate waste and pollution. Although manufacturing incorporating Re -X options can be more environmentally friendly, it involves more sources of uncertainty than traditional manufacturing since the end-of-use products can be collected from multiple origins with various quantities and qualities, and the market demand for both new and remanufactured products cannot be forecasted perfectly. Thus, there is a need to optimize the Re -X policy to alleviate the negative impacts of the higher uncertainty. One option is using the reliability information of new products to estimate the end-of-use conditions and applying multi-stage stochastic optimization to capture multiple demand scenarios. This paper develops a two-stage stochastic optimization model to optimize the quality thresholds for reuse, recycling, and remanufacturing options. Our objective is to minimize the total cost, energy consumption, and environmental impact of producing and providing warranty service for a product family. The model employs reliability information of product components to estimate the warranty service cost and the end-of-use conditions of the returned resources. A case study on a general product family is implemented to illustrate the efficacy of the optimization model. Results show that the two-stage optimization can achieve cost and environmental impact reduction for a hybrid manufacturing and remanufacturing process. [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]

As an important carrier of multi-energy coupling of regional integrated energy system, the integrated energy station integrates a variety of energy coupling devices, which can fulfill interconnection and complementarity of energy between regions and enhance the economic and environmental benefits of energy use. In the planning of integrated energy stations, it is necessary to consider the characteristics of regional resource endowments and load energy consumption. In this paper geographic information system (GIS) is introduced into the site selection planning of integrated energy stations, and a location decision model is built based on GIS & improved TOPSIS. In order to verify this for the feasibility of the model, Tianjin, China is selected as a representative case for empirical research. First, according to Chinese national standards and local planning requirements, GIS is adopted to initially screen the distribution and area of exclusion areas and viable areas for construction in the study area. Secondly, a comprehensive evaluation index system is established for nature, economy and society, in which the AHP method is used for calculate the weight of each index to obtain the final optimal alternative with the integration of the GIS geographic layer overlay function. Finally, the improved TOPSIS model is used to rank the alternatives and obtain the ranking results. Sensitivity analysis and comparative analysis is conducted on the ranking results, indicating a higher priority of selecting sites A1, A4, A5 and A11. Analysis result effectively verifies that the improved TOPSIS method can improve the accuracy of the site selection decision for integrated energy stations. This paper can provide support for the site selection and layout of integrated energy stations, effectively improve the decision-making level and work efficiency of decision-makers, and enrich the application space of GIS methods in the energy field. •Introduction of GIS method into the site selection of integrated energy stations. •Identify the best location and construction area of the integrated energy stations. •Establish a MCDM index system for siting integrated energy stations. •Construction of "GIS-AHP-improved TOPSIS" siting decision model. •Meet the integrated energy stations current and future transformation needs. [ABSTRACT FROM AUTHOR]

The MESOC Toolkit is a free and open-access online service used to aid the measurement of the societal value and impact of culture, currently under development as part of the MESOC (Measuring the Social Dimension of Culture) project. The goal of establishing transition variables that serve as impact measurement indicators is aided by an online document repository system of thematic publications (i.e., a collection of documents on the societal value and impact of cultural policies). It allows the users to input, manage, and search extensive document data based on the relevant criteria. The paper at hand paper describes the rationale behind the online document repository of the MESOC Toolkit, its implementation, and main functionalities. Furthermore, it offers an empirical evaluation of the benefits that such a system offers over non-automated procedures of document search and analysis. The results of the evaluation give way to the conclusion that there are time-saving and quality benefits for the users of the repository: efficiency in dealing with a large number of documents and quality in searching the documents using multiple criteria. [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]