In the graph model for conflict resolution (GMCR), accurately determining the preferences of decision makers (DMs) over states is crucial. DMs’ preferences are often influenced by multiple factors (criteria), which can be elicited using a multi-criteria evaluation method. Moreover, in practice, DMs prefer to utilise linguistic information to express preferences, potentially employing different linguistic evaluation formats across criteria. Given that words may convey different meanings to different individuals, DMs often exhibit personalised individual semantics (PIS) in their linguistic preferences. Therefore, a PIS-based multi-criteria evaluation method is proposed to learn preferences within GMCR. In this method, DMs express preferences using heterogeneous linguistic evaluation matrices (HLEMs) and provide preference examples in the form of relative preference relations between states. A deviation minimum-based optimisation model is designed to personalise individual semantics by minimising the deviation between preference examples and relative preference values derived from HLEMs. Then, a consistency improvement model is developed to improve the consistency between the two types of preference information by removing the minimum number of inconsistent preference examples. This is followed by obtaining the comprehensive evaluation value vectors of states from HLEMs for stability analysis. Finally, the Elmira conflict is utilised to demonstrate the application of the proposed method.

To address the issues of excessive sheet metal thinning and geometric deviation in single point incremental forming (SPIF), this paper proposed a bi-objective process parameter optimization framework for Al1060 sheet based on a multilayer perceptron (MLP) surrogate model and an improved multi-objective grey wolf optimization (IMOGWO) algorithm. Finite element simulations based on ABAQUS were conducted to generate a dataset considering variations in tool radius, initial sheet thickness, tool path strategy, step depth and forming angle. The trained MLP was used as the objective function in the optimization process to enable the rapid prediction of forming quality. The IMOGWO algorithm, enhanced by the Spm chaotic mapping initialization, an improved convergence coefficient updating mechanism and associative learning mechanism, was then employed to efficiently search for Pareto optimal solutions. For a truncated conical component case, optimal parameter sets were selected from the Pareto front via the entropy-weighted TOPSIS method for order preference by similarity to an ideal solution. Experimental verification showed close agreement with the simulated results, with relative errors of only 0.58% for the thinning rate and 3.10% for the geometric deviation. This validation demonstrates the feasibility and potential of the proposed method and its practical potential for improving the quality of SPIF forming.

Interprofessional thinking and behaviour are the key to high-quality and efficient healthcare provision. This requires specific skills which can be developed through interprofessional teaching and learning. This article sets out methods of implementation and preferences of interprofessional teaching in the education of healthcare professions from the perspective of teaching staff. Semi-structured interviews (n=11) were carried out with teaching staff from four public schools for healthcare professions in Mecklenburg-Vorpommern. The data was evaluated using the methodological approach of content-structuring qualitative content analysis. The implementation of interprofessional teaching and learning varies depending on the institution and teacher. It takes place in classrooms, in nursing skills labs, on excursions and project days, but requires a high degree of planning. Teaching staff favour case scenarios with a clear structure, which can be worked on by several professional groups or from various perspectives. Reflecting on profession-specific perspectives plays a particularly important role here. Digital formats such as blended learning, simulation labs as well as virtual and augmented reality are cited as supplementary learning formats, but often fail in their implementation due to a lack of technical equipment. In addition to political and institution-specific contexts for interprofessional teaching and learning, interprofessional educational formats are also required. These should be based on case scenarios and be modular so that individual teaching and learning content can be applied to various contexts. The integration of OER as well as digital learning formats can support both the implementation and preparation and follow-up of interprofessional learning activities.

This study presents a comprehensive performance evaluation framework for 20 leading medical laboratories worldwide using an integrated Best-Worst Method (BWM) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach. The assessment incorporates ten critical criteria encompassing clinical accuracy, operational efficiency, research output, cost-effectiveness, and technological advancement. BWM was employed to determine optimal criterion weights through systematic pairwise comparisons, followed by TOPSIS for objective laboratory ranking based on relative closeness to ideal solutions. Results indicate that Memorial Sloan Kettering Labs (USA) and MD Anderson Cancer Center Labs (USA) consistently rank highest across multiple scenarios, demonstrating superior performance in clinical accuracy and quality accreditation. The analysis reveals significant performance variations across countries and laboratory categories, with academic/research institutions generally outperforming commercial laboratories. Sensitivity analysis confirms the robustness of rankings across different weighting scenarios. This framework provides healthcare administrators, policymakers, and laboratory managers with a validated tool for benchmarking and strategic decision-making in medical laboratory services optimization.

The relevance of this study stems from the high degree of uncertainty surrounding the functioning of the logistics system for the Russian Ministry of Emergency Situations and rescue military units during emergency response. Material shortages and disruption of external communications create the risk of logistics system failure, necessitating the development of effective methods for allocating limited resources. The main results of the study include the development of a new combined method for allocating the scarce resource of the logistics system. The method integrates two key approaches: allocation based on consumer priority and proportional allocation to stated needs, taking into account the overall level of supply. To determine consumer priorities, the proposed method of relative preferences reduces subjectivity and administrative costs compared to the expert assessment method. The model includes constraints that prevent complete depletion of reserves below a critical level and ensure a minimum satisfaction of the needs of all units. The novelty of the study lies in the synthesis of the advantages of existing methods (taking into account priority and uniform distribution) in a single algorithm that adapts to the level of resource provision and allows avoiding both a complete denial of service to low-priority units and ignoring strategically important tasks.

User preference mining uses rating data, item content or comments to learn additional knowledge to support the prediction task. For the use of rating data, the usual approach is to take rating matrix as data source, and collaborative fil-tering as the algorithm to predict user preferences. Item content and comments are usually used in sentiment analysis or as auxiliary information for other al-gorithms. However, factors such as data sparsity, category diversity, and nu-merical processing requirements for aspect sentiment analysis affect model performance. This paper proposes a hybrid method, which uses the deep neural network as the basic structure, considers the complementarity of text and nu-meric data, and integrates the numeric and text embedding into the model. In the construction of text-based embedding, extracts the text summary of each text-based review, and uses the Doc2vec to convert the text summary into mul-ti-dimensional vector. Experiments on two Amazon product datasets show that the proposed model consistently outperforms other baseline models, achieving an average reduction of 15.72% in RMSE, 24.13% in MAE, and 28.91% in MSE. These results confirm the effectiveness of our proposed method for learning user preferences.

User preference mining uses rating data, item content or comments to learn additional knowledge to support the prediction task. For the use of rating data, the usual approach is to take rating matrix as data source, and collaborative filtering as the algorithm to predict user preferences. Item content and comments are usually used in sentiment analysis or as auxiliary information for other algorithms. However, factors such as data sparsity, category diversity, and numerical processing requirements for aspect sentiment analysis affect model performance. This paper proposes a hybrid method, which uses the deep neural network as the basic structure, considers the complementarity of text and numeric data, and integrates the numeric and text embedding into the model. In the construction of text-based embedding, extracts the text summary of each text-based review, and uses the Doc2vec to convert the text summary into multi-dimensional vector. Experiments on two Amazon product datasets show that the proposed model consistently outperforms other baseline models, achieving an average reduction of 15.72% in RMSE, 24.13% in MAE, and 28.91% in MSE. These results confirm the effectiveness of our proposed method for learning user preferences.

In today’s world, supply chains are expected to be better equipped to deal with sustainability issues and risks while making appropriate business decisions. In particular, the ordering policy and the design of the supply chain network (SCND) significantly contribute to the sustainability dimensions and mitigate organisational risks. Traditionally, models that determine ordering decisions (such as capacitated lot size (CLS)) and SCND problems are considered in isolation. To date, there is no model that addresses both these problems in an integrated manner under consideration of all three sustainability dimensions, risk dimensions, inventory management, and carbon footprint. To fill the existing literature gaps, the proposed study introduces a mixed-integer model that integrates CLS and SCND challenges, accounting for practical concerns like multi-period dynamics, multi-echelon structures, sustainability impacts, risk mitigation, and inventory management. This model supports crucial decisions, such as determining the optimal supply network (including the location of manufacturing units and warehouses) and calculating material flow across various supply chain network periods. The modelling approach includes: (i) assessing suppliers’ risk and social scores using Best–Worst Method (BWM) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) techniques; (ii) incorporating these scores into the model; and (iii) utilising a Lagrangian relaxation (LR) heuristic to solve the integrated model. The applicability and validity of the proposed model have been examined using a numerical case study, and sensitivity analyses have been performed to understand the robustness of the proposed modelling approach. The performance of the proposed approach was compared with that of existing solution methodologies in terms of computational efficiency and the quality of the solution obtained. The Lagrangian-based integrated model yields a smaller gap percentage (0.4%), indicating the effectiveness and accuracy of the model. The proposed model can help practitioners and managers make efficient and effective decisions when solving joint CLS-SCND problems, addressing challenging issues related to sustainability and risk.

Bundle fiber post and direct composite resin application is one of the preferred met-hods in conservative dentistry in root canal treated teeth with excessive material loss. The aim of this case series is to describe the restoration and function of maxillary central incisor and premolar teeth with bundle fiber post and composite resin application. Excessive loss of material was detected in the maxillary central and premolar teeth of two patients who pre-sented to our clinic with the complaint of fracture of old restorations. Radiographs obtained from the patients did not show any lesion on the root tip and surrounding tissues. After root canal treatment, a bundle fiber post was placed and a core structure was formed with com-posite resin. Patients were referred to the prosthesis department for the superstructures of the teeth with post core restoration. Successful results are obtained with the application of bundle fiber post systems to support the remaining root structure and restore excessive ma-terial loss.

Purpose This study aims to develop a multi-product, multi-period and multi-echelon joint capacitated lot sizing (CLS) and supply chain network design (SCND) model that addresses sustainability factors and supplier risk disruptions while effectively managing operational costs across the supply chain (SC) network. The model considers various issues, including carbon footprint, social factors, risk, inventory and multicontainer logistics. Design/methodology/approach This study is conducted in two phases. First, it calculates social and risk scores for suppliers using a synergetic application of the best-worst method (BWM) and technique for order preference by similarity to ideal solution (TOPSIS) methodologies. Second, these quantified scores are integrated into the proposed mixed integer linear programming model. To ensure the model’s robustness, a sensitivity analysis (SA) is performed by varying parameters such as social sustainability score, carbon footprint and risk. Findings In this study, gender equality has emerged as the most critical social factor, whereas fair wages rank lowest. In the risk dimensions, intellectual property risk emerges as the most significant, whereas social and political risks are found least critical. SA reveals that increasing social sustainability scores raises costs but lowers emissions and risks. Reducing carbon footprint initially decreases emissions but can increase costs beyond a threshold. Similarly, lowering risk scores results in higher costs but does not significantly impact social sustainability. The model effectively optimizes material flows, plant and warehouse selection and inventory levels while balancing economic, environmental and social objectives. Originality/value To the best of the authors’ knowledge, this is one of the few studies to propose a novel linear programming based joint CLS and SCND model that addresses sustainability (economic, social and environmental) and supplier-based risk factors simultaneously. The proposed mathematical model is relevant to policymakers and organizational managers in strategizing to mitigate various conflicting issues such as carbon footprint, social issues, risk and inventory costs while taking operational decisions.

Qualitative Choice Logic (QCL) is a non-classical logic for representing and reasoning with preferences. It adds to classical propositional logic a new connective called ordered disjunction ( × → ). x × → y intuitively means: if possible x, but if x is not possible then at least y. Different variants of QCL have been proposed. Among them, we cite Prioritised Qualitative Choice Logic, Conjunctive Choice Logic and Lexicographic Choice Logic. In this paper, we propose a method for learning preferences in the context of QCL. The method is based on an adaptation of association rules based on APRIORI algorithm. The adaptation consists of (1) generating rules that have propositional formulas in their antecedent instead of itemsets and (2) using variations of the support and confidence measures according to the semantics of QCL. We show that QCL is fully adapted for modelling experts reasoning for providing recommendations of antibiotics. Another contribution of the paper concerns a generalisation of the proposed method for learning preferences in the context of QCL variants.

This study presents a literature review of Green Human Resource Management (GHRM) practices. It synthesizes existing research to identify key GHRM practices, particularly within manufacturing organizations. The review focuses on attributes and characteristics of these practices, as discussed in the literature. It also surveys the methodologies used to assess GHRM performance, including the Best-Worst Method (BWM) and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The aim is to provide a consolidated overview of current GHRM practices and methodologies for their evaluation, offering insights for researchers and practitioners.

This work addresses the Supplier Selection and Order Allocation Problem (SSOAP) with two important evolving concepts namely viability and Industry 5.0 (I5.0) wherein sustainability plays a vital role. For this purpose, an efficient decision-making model is developed which is based on Multiple-Criteria Decision-Making (MCDM) techniques. First of all, the suppliers’ scores are measured according to the viability and I5.0 dimensions. To do so, two novel decision-making approaches of Stochastic Fuzzy Best-Worst Method (SFBWM) and Stochastic Fuzzy Technique for Order Preference by Similarity to Ideal Solution (SFTOPSIS) are developed. Then, a multi-objective mathematical model (MOMM) is proposed to choose the most appropriate suppliers and specify the number of orders in which all dimensions of the viability and I5.0 concepts are incorporated. In the next step, a Robust Stochastic-Possibilistic (RSP) technique is employed to treat the mixed uncertainty. Finally, the MOMM is treated with the help of a novel improved Goal Programming (GP) approach; i.e. Lexicographic Chebyshev Revised Multi-Choice Goal Programming (LCRMCGP). A real healthcare system is then investigated as the case study problem to represent the applicability, validity, and performance of the developed model, and eventually, render useful managerial and decision aids.

The variable flow ducted rocket (VFDR) poses significant challenges for high-precision modeling due to its complex nonlinear dynamics, harsh operational conditions, and integration of multiple physical fields. To address this challenge, this paper introduces a hybrid mechanism and data-driven modeling approach. Initially, the parameter perturbation method was employed to elucidate the interdependencies between system parameters and the VFDR's dynamic and steady-state responses. Entropy weight method (EWM) and technique for order preference by similarity to ideal solution (TOPSIS) were utilized for ranking the compensation parameters of the dynamic-state and steady-state models of the VFDR. Additionally, the throat area of the regulation valve was chosen as a compensatory parameter for the steady-state model. A data-driven residual compensation model was developed using the nonlinear autoregressive neural networks with external inputs (NARX) algorithm to enhance the steady-state mechanistic VFDR model, addressing its time-varying and high uncertainty characteristics. To mitigate dynamic response errors in the mechanistic model, a compensation strategy integrating error and similarity evolution with extreme learning machine (ELM) was implemented to generate compensation value. Simulation and ground experiment results validate the efficacy of the proposed algorithm, the experimental results indicate that, after compensation using the proposed strategy, the maximum error in a single test is reduced by 24.19%, and the average error is decreased by 17.81%.

Discrete choice experiments (DCEs) provide a method for understanding preferences for service provision and there have been limited applications to the selection of community pharmacies. The validity and accuracy of DCEs rely upon the attributes and levels used. This paper aims to describe the development of a DCE investigating New Zealanders preferences for community pharmacies. Five focus groups were conducted between August 2022 and April 2023, each representing a different demographic group. The transcripts underwent thematic analysis to develop themes and to write attributes that were important and realistic to participants. A complete survey combined choice tasks, generated through a partial factorial design, with demographic questions. It was pilot-tested using a 'think aloud' approach to ensure it was feasible and interpreted as intended. Thirty three codes were collated and refined into six attributes; location, wait time, customer service, prescription co-payments, nearby businesses, and car parking. Participants were asked to imagine they were in a new area, hence, attributes were presented as information available online. This in-depth reporting of DCE attribute development allows for robust evaluation of the validity of the processes used and identifies several differences.

PurposeWith the current wave of modernization in the dairy industry, the global dairy market has seen significant shifts. Making the most of inventory planning, machine learning (ML) maximizes the movement of commodities from one site to another. By facilitating waste reduction and quality improvement across numerous components, it reduces operational expenses. The focus of this study was to analyze existing dairy supply chain (DSC) optimization strategies and to look for ways in which DSC could be further improved. This study tends to enhance the operational excellence and continuous improvements of optimization strategies for DSC managementDesign/methodology/approachPreferred reporting items for systematic reviews and meta-analyses (PRISMA) standards for systematic reviews are served as inspiration for the study's methodology. The accepted protocol for reporting evidence in systematic reviews and meta-analyses is PRISMA. Health sciences associations and publications support the standards. For this study, the authors relied on descriptive statistics.FindingsAs a result of this modernization initiative, dairy sector has been able to boost operational efficiency by using cutting-edge optimization strategies. Historically, DSC researchers have relied on mathematical modeling tools, but recently authors have started using artificial intelligence (AI) and ML-based approaches. While mathematical modeling-based methods are still most often used, AI/ML-based methods are quickly becoming the preferred method. During the transit phase, cloud computing, shared databases and software actually transmit data to distributors, logistics companies and retailers. The company has developed comprehensive deployment, distribution and storage space selection methods as well as a supply chain road map.Practical implicationsMany sorts of environmental degradation, including large emissions of greenhouse gases that fuel climate change, are caused by the dairy industry. The industry not only harms the environment, but it also causes a great deal of animal suffering. Smaller farms struggle to make milk at the low prices that large farms, which are frequently supported by subsidies and other financial incentives, set.Originality/valueThis paper addresses a need in the dairy business by giving a primer on optimization methods and outlining how farmers and distributors may increase the efficiency of dairy processing facilities. The majority of the studies just briefly mentioned supply chain optimization.

Marketing strategy changes need to be made to face challenges in the digital era. The main factor influencing changes in marketing strategy is changes in consumer behavior. Organizations must change their methods of understanding consumer preferences and desires because consumers now have greater access to information through the internet and social media. The purpose of this study is to analyze what phenomena change marketing strategies in facing challenges in the digital era. The research method is a literature study. The results of this study are Marketing strategies must be innovative and creative because they change consumer behavior, industry problems, and assess technological advances. In the digital era, managing change is very important to overcome challenges and take advantage of opportunities. In addition, the government encourages business owners to use technology to help their company management procedures succeed in today's very tight digital market by using creative and data-based marketing methods. Organizations are expected to be able to manage change effectively, adopt new technologies, and take advantage of new possibilities. Future business success depends not only on mastery of technology, but also on the ability to innovate, adapt, and have comprehensive understanding of market dynamics.

In recent years, governments have sought to find sustainable solutions that would have a positive impact economically, environmentally, and socially. Remanufacturing is a promising solution as remanufactured products help sustainability by saving resources, like using less raw materials, cutting emissions from traditional manufacturing, lowering the amount of landfill waste, and offering a cost-effective alternative product. This paper studies the preferences of people in the Kingdom of Saudi Arabia between new and remanufactured products across three categories: electronics, car parts, and furniture. The products were evaluated based on four factors: quality, price, availability, and warranty. This research used the Best-Worst Method and Technique for Order Preference by Similarity to Ideal Solution together for the analysis. For all the product categories, the findings showed that warranty is the most weighted criteria consumers will rely on to select between the new and remanufactured products. However, consumers prefer new products over the remanufactured ones for all the product categories. Supply chain decision-makers are required to optimize the pricing of these products to increase the popularity of these products.

The vibration energy harvester beam made of laminated composite material bonded with a piezoelectric patch was studied. The harvester model was developed using finite element analysis (FEA) and validated with the results available in the literature. The prediction of the optimal harvester model was explored using response surface method (RSM) and technique for order preference by similarity to ideal solution (TOPSIS) method. The ply angle of each layer and the thickness were considered as the variables, and the frequency, stress, and voltage were the responses. The current study offers a prediction-based optimization of the energy harvester through RSM and TOPSIS.

<p>The article presents the results of a study based on the principle of a longitudinal study, which reveals the dynamics of the development of educational motivation and broad social motivation in children. The study involved 44 children aged 9-10years (27 boys and 17 girls) who studied in the 3<sup>rd</sup> grade in the 2022-2023 academic year and in the 4<sup>th</sup> grade in the 2023-2024 academic year. In the study of motivation, the methods "The level of motivation of teaching" and "Subjective position", the method of motivational preferences "Goldfish" and the method "Golden Age" were used. It is shown that children at the end of primary school age are characterized by a combination of object and subject positions in relation to education; the changes occurring at this time in educational motivation are traced– a decrease in its level from 3<sup>rd</sup> to 4<sup>th</sup> grade. The relative stability of motivation with a broader social context has been established. There are only two trends – a weakening of "children's" desires (possession of things, toys, etc.) and an increased focus on achieving emotional and material well-being. The correlations between the indicators of educational and broad social motivation are revealed. In particular, a perspective and optimal subjective position in relation to education correlates with a focus on the future, the realization of one's goals, friendship with peers and concern for the well-being of the teacher.</p>