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Kidney transplantation is vital for treating end-stage renal disease, impacting roughly one in a thousand Europeans. The search for a suitable deceased donor often leads to prolonged and uncertain wait times, making living donor transplants a viable alternative. However, approximately 40% of living donors are incompatible with their intended recipients. Therefore, many countries have established kidney exchange programs, allowing patients with incompatible donors to participate in "swap" arrangements, exchanging donors with other patients in similar situations. Several variants of the vertex-disjoint cycle cover problem model the above problem, which deals with different aspects of kidney exchange as required. This paper discusses several specific vertex-disjoint cycle cover variants and deals with finding the exact solution. We employ the dataless neural networks framework to establish single differentiable functions for each variant. Recent research highlights the framework's effectiveness in representing several combinatorial optimization problems. Inspired by these findings, we propose customized dataless neural networks for vertex-disjoint cycle cover variants. We derive a differentiable function for each variant and prove that the function will attain its minimum value if an exact solution is found for the corresponding problem variant. We also provide proof of the correctness of our approach. [ABSTRACT FROM AUTHOR]

This work investigates the optimal design and operation of an alkaline water electrolysis plant fed by renewable electricity. At the basis of this investigation lies a mathematical model that is developed to capture essential mechanisms regarding gas purity and temperature constraints. This model is optimized for maximized hydrogen production given varying power inputs and design variations. Resulting operational profiles are then weighted with realistic wind power data, allowing for a novel framework in which mechanistic models of different scales are combined to predict annual hydrogen production and online-factors. The highest online-factors are achieved with independent power supplies, which, for degraded plants, results in the largest annual production increase of all design variations (up to 9%). Designs facilitating flexible pressure operation show the largest improvement potential for novel plants, increasing the production rate by up to 4.5%. [Display omitted] • Development of modeling and optimization framework for alkaline electrolysis plant. • Investigation of BoP design on load range and utilization of wind power. • HTO related load limit reduced by flexible pressure or individual power supplies. • Flexible pressure operation increases production most (4.5%) for novel plant. • Individual power supplies increase production most (9%) for degraded plant. [ABSTRACT FROM AUTHOR]

Background: Previously, drug-resistant tuberculosis (DR-TB) patients were treated with long, toxic, and relatively ineffective regimens. However, in recent years, there have been major improvements made. The 2020 World Health Organization DR-TB Treatment guidelines recommended the use of a 6-months all-oral BPaL (bedaquiline, pretomanid and linezolid) regimen under operational research (OR) conditions for selected DR-TB patients. Methods: The processes, challenges, and interim results of introducing BPaL under OR conditions in 7 countries supported under the Korea International Cooperation Agency/TB Alliance-funded "Leveraging Innovation for Faster Treatment of Tuberculosis (LIFT-TB)" project are described here. The OR objectives were to explore the feasibility of introducing the BPaL regimen, and to estimate its effectiveness and safety in a select group of DR-TB patients. Results: Between November 2020 and the end of March 2023, a total of 574 patients had been enrolled. Interim treatment success stands at an encouraging 90.9% (280/308). Although adverse events of special interest (AESI) were common, they were manageable, and only 1 patient had to discontinue the complete BPaL treatment regimen. In addition, no unexpected adverse events (AE) were seen. Conclusion: With careful advocacy, frequent communication with partners, and following steps to strengthen essential aspects of the delivery system, the project's experiences show that BPaL OR was feasible across different country settings. Project documents were constantly updated. The sharing of information, experiences, and interim results had a significant positive and motivating effect within and across countries. Interim OR results show excellent patient responses and are comparable to those seen under trial conditions. Although common, the observed AEs and AESIs were manageable, and no unexpected AEs were seen. [ABSTRACT FROM AUTHOR]

Direct reduced iron (DRI) plants, where iron ore is reduced using a mixture of hydrogen and carbon monoxide generated via steam reforming, represent a cleaner and more efficient alternative to the energy- and emissions-intensive blast furnaces used in the steel industry. Reformers operate under harsh and often fluctuating conditions, and preserving the integrity of the reformer tubes is an important economic and safety concern. This work presents a novel reformer tube model to support operational analysis. The model accounts for the relevant length scales (tube, catalyst particles) and captures thermal expansion caused by temperature transients. The model is calibrated and validated using a set of industrial data, and provides accurate predictions (within less than 0.5 mole percent) of the tube outlet composition as well as unique insights on the temperature distribution within the tube. • Modeling hydrogen reformers for direct reduced iron (DRI) production. • DRI is key to decarbonizing steel industry. • New industrially-validated two-dimensional model of reformer tubes. • Model captures tube thermal expansion and reactant diffusion in catalyst particles. [ABSTRACT FROM AUTHOR]

A K-M-N on-orbit service network architecture and its mission scheduling problem are proposed and studied, in which K space service stations and M service spacecraft are scheduled to service for N target spacecraft. The service spacecraft, which are initially docked with the space service station in the geosynchronous region, set off through orbital maneuver to conduct servicing operation for one or multiple target spacecraft upon receiving the service command. A mission scheduling strategy of on-orbit service for the target GEO spacecraft is proposed based on space service stations, in which the service spacecraft are scheduled to serve all target spacecraft with the lowest cost of fuel for the orbital transfers. As the mission scheduling for such a K-M-N GEO on-orbit service network is NP-hard, a hybrid simulated annealing optimization algorithm is put forward based on a mechanism of dynamic reverse processing of encoding and decoding to solve this problem. Four numerical case studies are presented and their results are compared with those from the literature to demonstrate the effectiveness of the approach. A series of different network model simulations and Mann-Kendall tests prove that multispace service stations can significantly reduce total fuel consumption for transfers, and the initial layout is also very important. Besides, the initial distribution of the service spacecraft also has an impact on total fuel. However, the quantitative impact of the service spacecraft is uncertain and needed to be analyzed on a specific basis, sometimes increasing the quantity of service spacecraft can reduce the total fuel, sometimes it has no effect, sometimes it may even cause negative effect when the number of service spacecraft is too large and unreasonable. Practical Applications: On-orbit servicing (OOS), refers to the execution of space operations through the exterior vehicle activities of astronauts, robots or both to extend the mission life of a spacecraft or improve the ability of a spacecraft to perform on-orbit missions. With the development of relevant enabling technologies, OOS will become a business which can be implemented commercially and in a large scale, in which one or multiple space service stations, as the space resource storage warehouse of the satellites in orbit, run in the earth orbit for a long time, and the service spacecraft will provide service to the target spacecraft in turn after completing the rendezvous and docking. In the process of servicing, the service spacecraft will return to the space service station for the replenishment when its resource is insufficient. In this paper, on-orbit service network scheduling based on space service station is studied, which is NP-hard. A hybrid simulated annealing optimization algorithm is put forward based on a mechanism of dynamic reverse processing of encoding and decoding to solve this problem. The researches in this paper are of great significance to the scale design and scheduling of on-orbit services in the future, meanwhile have a certain appeal for the relevant operational research scholars. [ABSTRACT FROM AUTHOR]

The literature finds that cultural differences have a negative impact on the success of international labor migration. However, modeling cultural effects requires a variety of individual-level, firm-level and country-level data that are not sufficiently considered in the literature. Precisely, previous migration experiences are not taken into account and the culture effect is not isolated from adaptation effects that occur with any change of employer. We find that an identified culture effect is biased if such data are not considered. To take these aspects into account, we utilize soccer data with its abundance of single player information and leverage the approaches established in Operations Research to model soccer player performance. To this end, we extend a prominent mixed-effects model to fit the case of international migration and find contrary results compared to the literature: cultural differences positively affect employee value in the long term and we identify a distinct and positive culture effect in the short run for switches between industry-leading firms. Finally, we show that our results are not driven by peculiarities of soccer player data by using a reduced model without isolating general adaptation difficulties from cultural differences. In this (too) simple model, in accordance with the literature, the biased negative culture effect emerges. [ABSTRACT FROM AUTHOR]

Existence of delays and cost overruns frequently puts the project viability in jeopardy. The integrated nature of these threats brings forward project scheduling as the primary determinant of project management success. The quality of project scheduling depends highly on the way resources are assigned to activities. In the project management literature, the efficiency of resource allocation is examined closely by the phenomenon called project crashing. This study introduces traditional and genetic algorithm approaches for the project crashing events and explains their steps in achieving the most efficient resource allocation. Within this context, the project crashing event is visualized, the insights of alternative approaches are described, and their implementations are illustrated with a case study. Besides, the procedures required for adopting the genetic algorithm approach to a typical problem are expressed. The case study illustration reveals the advantages and disadvantages of the genetic algorithm approach over the traditional approach. It is observed that the genetic algorithm approach can reach the solution in a single phase while the traditional approach requires multiple phases. On the other hand, the genetic algorithm approach may not reach the optimum solution unless the toolbox options are appropriately selected. This study presents the contribution of operational research to the project management body of knowledge by demonstrating the applicability and efficiency of genetic algorithm in the project crashing events. Researchers and industry practitioners may benefit from the proposed approach by following the indicated procedures to incorporate genetic algorithm into optimization issues in different fields. [ABSTRACT FROM AUTHOR]

Underwater acoustic sensors are vital for monitoring marine environments and detecting targets, but their optimal placement presents challenges, particularly in deep-sea environments. This paper addresses the question of determining the optimal sensor placement in a specific ocean region through a principled optimization approach. While previous studies mainly utilized heuristic algorithms without exploiting problem-specific structures, this work explores leveraging the complex three-dimensional acoustic environment through principled modeling and tailored optimization. Specifically, intricate three-dimensional multi-directional acoustic maps are constructed for each sensor. Based on these maps, the sensor placement problem is then cast as an integer linear programming, allowing the study to leverage established theoretical results from operations research. Additionally, an alternative algorithm with its performance indicator is presented to find near-optimal solutions efficiently and can empirically reach over 99% coverage of the optimal solution. Experimental results using real-life data from the South China Sea demonstrate the effectiveness of the proposed approach in achieving much larger detection coverage compared to random and empirical strategies. Notably, the alternative fast algorithm approaches the optimal solution in significantly less time. Furthermore, experiments show that any further simplification of this approach leads to the performance degradation. [ABSTRACT FROM AUTHOR]

Simple Summary: The role of mosquito eggs in disease transmission is not well understood, despite reports of virus vertical transmission and their ability to survive dry periods in a dormant stage. Additionally, field evidence shows that new generations of mosquitoes often reappear weeks or months later in certain city areas. This may be because both species follow an "R strategy" reproduction pattern, laying many eggs to ensure survival in a harsh environment. This study was conducted in southern Mexico in 2023 and focused on the egg-laying activity of Ae. aegypti and Ae. albopictus mosquitoes during the dry inter-epidemic season. Ovitraps were used in the backyards of the houses to identify egg species, and further larval containers were surveyed. Most of the larval habitats were containers smaller than 1 L, commonly not treated as they were considered trash. Even though homeowners volunteer to remove them during government cleaning community-based campaigns, city waste disposal trucks transporting these containers do not take any measures to destroy the tiny eggs. We conclude with the urgent need to develop an egg-killing treatment, which could significantly enhance vector reduction and achieve a total stage strategy within the Integrated Vector Control Programs. Although integrated management and control programs implement intense control measures for adult, pupal, larval, and breeding sites during outbreaks, there is a lack of studies to understand the role of the vector egg stage in disease dynamics. This study aimed to assess the dry season quiescent Aedes aegypti and Aedes albopictus egg populations in houses and backyards in Tapachula, southern Mexico. Two hundred and fifty ovitraps were placed in 125 homes in the Las Americas neighborhood. A total of 7290 eggs were collected from 211 (84.4%) ovitraps. Only 5667 (77.7%) hatched under insectary water immersion and food supply conditions, with 4031 (71.1%) identified as Ae. aegypti, and 1636 (28.8%) as Ae. albopictus, respectively. The remaining 1623 (22.3%) did not hatch due to Delayed Hatching and/or quiescence tropical stage. Eighty-three larval containers were sampled with desiccated eggs during the dry season; most of them were described as trash waste because larvicides are only used for larger containers of 5–10 L. Evolutionary characteristics for the two species including partial egg hatching, ambient-regulated quiescence, the ability of the embryo to survive for a more extended period intra-seasonally, the egg sticking to inner container walls, demands urgent operational research to achieve successful egg-proof larval container methods. [ABSTRACT FROM AUTHOR]

The sequence leading to conflict, as specifically in July 1914, reveals the changes in each protagonist's propensity to wage war. To characterize its timing, escalation is viewed as a viability (differential) game in progress under the protagonists' actions. The chronicle of confrontations and accommodations allows for estimating a responsiveness structure at the heart of the game. As the moves, which are the controls, are observed, and not the state variables, the game reduces to the minimization of a viability criterion under dynamics. Early estimation of the responsiveness structure operationalizes the procedure for crisis management and provides a way to anticipate the consequences of possible moves in escalations to conflict. The application to July 1914 shows the strength of the method. [ABSTRACT FROM AUTHOR]

In The traveling salesman problem (TSP) is an essential and the most popular conventional combinatorial optimization network problem in operations research, in which the TSP evaluates the shortest route or path in a network. In TSP, every node has been visited only once, excluding the starting node. In TSP, edge lengths are usually expressed to indicate journey time and expenses instead of distance from a location. The exact arc length can't be predicted because journey times and expenses vary depending on the amount of payload, climate, highway conditions, and so on. As a result, the Neutrosophic numbers introduce a new tool for dealing with unpredictability in TSP. The present article addresses TSP on a neutrosophic network where the edge weight is a neutrosophic number rather than a real number. For solving the Neutrosophic TSP, an algorithmic technique based on the genetic algorithm (GA) is proposed. We created a new mutation and crossover for our suggested GA. We used mathematical examples to show the usefulness of the algorithm that we suggested. The results of experiments suggest that the proposed GA can find the shortest path in a TSP within a neutrosophic framework. This provides valuable insights for decision-makers dealing with real-world situations characterized by imprecise and incomplete data. [ABSTRACT FROM AUTHOR]

We propose a fast method for exactly enumerating a large number of all cost-bounded solutions for combinatorial problems using Zero-suppressed Binary Decision Diagrams (ZDDs). Enumerating cost-bounded solutions is an important task to know the exact ranking of any given solution, and it enables statistical p -value evaluation and quality-controlled random sampling. Our method is based on a backtracking search for a given ZDD representing the set of all feasible solutions. The key idea is to integrate "BDD operation cache" technique and "interval-memoizing" technique with a depth-first manner of backtracking procedure. The computation time of the algorithm is empirically bounded by the input and output sizes of ZDDs. It can be significantly faster than existing enumeration systems, especially when the input and output ZDDs are well-compressed and the range of the cost constraint is large. As application examples, we applied our method to a set of difficult instances of the Hamiltonian cycle/path problems. Although it is NP-hard to find any one feasible solution without cost constraints, we tried more difficult problems to enumerate all cost-bounded solutions, and succeeded in implicitly enumerating all cost-bounded solutions for many nontrivial instances which contain billions of solutions. As cost constraints often appear in many real-life situations, our method will be useful in various fields, including data mining, statistical testing, hardware/software design, and many other operations research problems. [ABSTRACT FROM AUTHOR]

This is the PREFACE to the Special Issue "Queueing-inventory: analytical and simulation modeling and classical and retrial queues and inventory". The year 2022 was the 30th anniversary of Queueing-inventory. It was in that connection that the guest editors requested Professor Endre Boros, the Editor-in-Chief of Annals of Operations Research, for permission to guest edit a special issue. This was granted by the Editorial Board of the journal. [ABSTRACT FROM AUTHOR]

The current paper highlights the need for a unified approach that considers both micro - and macro-cognition in cognitive research. It suggests that integrating these perspectives can lead to a more complete interpretation of theatrical texts. The current work hypothesizes that the model of the translation process folds into two constructs of cognitive Efficacy (CE); the first is the macrocognitive interface, and the second is the microcognitive interface to pull away conceptual and operational analysis of the translator's performance of transfer ST and ground the mind into TT. The methodological consequences and practical part are applied to the theatrical texts to follow this model of the translation process and how it affects the scope of cognitive translation studies (CTS) by (Martín and de León, 2021). The study concluded that macro-cognitive research deals with the overall performance and ecology of task execution within the cognitive system of the translator. While it focuses on invariant processes, the use of binary linguistic levels is often employed in micro - cognitive research. The emphasis on internal validity and the ability to draw causal inferences is mentioned, as well as the convenience and utility of using large samples in analyzing complex relationships between variables of cognitive modeling, mental architecture, and knowledge sharing [ABSTRACT FROM AUTHOR]

Multi-objective optimization problems (MOPs) constitute a vital component in the field of mathematical optimization and operations research. The multi-objective evolutionary algorithm based on decomposition (MOEA/D) decomposes a MOP into a set of single-objective subproblems and approximates the true Pareto front (PF) by optimizing these subproblems in a collaborative manner. However, most existing MOEA/Ds maintain population diversity by limiting the replacement region or scale, which come at the cost of decreasing convergence. To better balance convergence and diversity, we introduce auction theory into algorithm design and propose an auction-based matching (ABM) mechanism to coordinate the replacement procedure in MOEA/D. In the ABM mechanism, each subproblem can be associated with its preferred individual in a competitive manner by simulating the auction process in economic activities. The integration of ABM into MOEA/D forms the proposed MOEA/D-ABM. Furthermore, to make the appropriate distribution of weight vectors, a modified adjustment strategy is utilized to adaptively adjust the weight vectors during the evolution process, where the trigger timing is determined by the convergence activity of the population. Finally, MOEA/D-ABM is compared with six state-of-the-art multi-objective evolutionary algorithms (MOEAs) on some benchmark problems with two to ten objectives. The experimental results show the competitiveness of MOEA/D-ABM in the performance of diversity and convergence. They also demonstrate that the use of the ABM mechanism can greatly improve the convergence rate of the algorithm. [ABSTRACT FROM AUTHOR]

Fuzzy, intuitionistic, and neutrosophic sets are the primary focus of the review investigation under the extension concepts. The significance and use of triangular shape for data representation are investigated. For modelling and expressing ambiguous or complex data, the triangle shape is a useful tool due to its simplicity and computational efficiency. Further study involves the tool of the operations research technique i.e., Network Analysis that helps in implementing and developing using the fuzzy extension principle. Critical Path Method (CPM) & Project Evaluation and Review Technique (PERT) plays a major part in the field of network in various decision-making scenarios using the real-life applications. In this comprehensive study, the insights of understanding extended fuzzy using the CPM/PERT under various applications are reviewed and analyzed for the future advancements in making more accurate and optimum results. [ABSTRACT FROM AUTHOR]

In classical graph theory, the minimal spanning tree (MST) is a subgraph that lacks cycles and efficiently connects every vertex by utilizing edges with the minimum weights. The computation of a minimum spanning tree for a graph has been a pervasive problem over time. However, in practical scenarios, uncertainty often arises in the form of fuzzy edge weights, leading to the emergence of the Fuzzy Minimum Spanning Tree (FMST). This specialized approach is adept at managing the inherent uncertainty present in edge weights within a fuzzy graph, a situation commonly encountered in real-world applications. This study introduces the initial optimization approach for the Minimum Spanning Tree Problem within the context of interval-valued fermatean neutrosophic domain. The proposed solution involves the adaptation of the Dhouib-Matrix-MSTP (DM-MSTP) method, an innovative technique designed for optimal resolution. The DM-MSTP method operates by employing a column-row navigation strategy through the adjacency matrix. To the best of our knowledge, instances of this specific problem have not been addressed previously. To address this gap, a case study is generated, providing a comprehensive application of the novel DM-MSTP method with detailed insights into its functionality and efficacy. [ABSTRACT FROM AUTHOR]

Summary: This paper suggests an extendable voltage boosting gain unit (EVBGU) using switched‐capacitors SC with a single DC source. The proposed unit can be extended by a modular pattern to increase the output voltage levels and voltage‐boosting gain of the inverter. The main feature of this work is presenting a unit topology that utilizes multiple and modular subcells to achieve (double, quadruple, hexapole, octapule, etc.) voltage gain at the output by increasing the number of the subcells. All the subcells are connected only with one DC source. A positive feedback technique has been used to charge the capacitors of the unit using the accumulative charging voltage (ACV) method. Furthermore, all the capacitors are self‐balanced, so there is no need for any additional and complex control circuit. Moreover, to decrease the cost factor of the inverter, a low IGBT switch count is taken into consideration and no passive elements such as diodes are used in the proposed structure. The H‐bridge is connected to the unit to achieve bipolar voltage at the output. The operational analysis of the inverter is delineated in detail. A comparative study, with state‐of‐the‐art MLIs in different terms such as the number of components, the total standing voltage, and the gain, shows the particularity of the proposed EVBGU. The simulation of the proposed MLI is performed using PSCAD‐EMTDC software. The simulation results are validated experimentally. [ABSTRACT FROM AUTHOR]

Background: Lymphatic filariasis (LF), a mosquito-borne helminth infection, is an important cause of chronic disability globally. The World Health Organization has validated eight Pacific Island countries as having eliminated lymphatic filariasis (LF) as a public health problem, but there are limited data to support an evidence-based approach to post-validation surveillance (PVS). Tonga was validated as having eliminated LF in 2017 but no surveillance has been conducted since 2015. This paper describes a protocol for an operational research project investigating different PVS methods in Tonga to provide an evidence base for national and regional PVS strategies. Methods: Programmatic baseline surveys and Transmission Assessment Surveys conducted between 2000–2015 were reviewed to identify historically 'high-risk' and 'low-risk' schools and communities. 'High-risk' were those with LF antigen (Ag)-positive individuals recorded in more than one survey, whilst 'low-risk' were those with no recorded Ag-positives. The outcome measure for ongoing LF transmission will be Ag-positivity, diagnosed using Alere™ Filariasis Test Strips. A targeted study will be conducted in May-July 2024 including: (i) high and low-risk schools and communities, (ii) boarding schools, and (iii) patients attending a chronic-disease clinic. We estimate a total sample size of 2,010 participants. Conclusions: Our methodology for targeted surveillance of suspected 'high-risk' populations using historical survey data can be adopted by countries when designing their PVS strategies. The results of this study will allow us to understand the current status of LF in Tonga and will be used to develop the next phase of activities. [ABSTRACT FROM AUTHOR]

Group decision-making (GDM) is crucial in various components of graph theory, management science, and operations research. In particular, in an intuitionistic fuzzy group decision-making problem, the experts communicate their preferences using intuitionistic fuzzy preference relations (IFPRs). This approach is a way that decision-makers rank or select the most desirable alternatives by gathering criteria-based information to estimate the best alternatives using a wider range of knowledge and experience. This article proposes a new statistical measure in a fuzzy environment when the data is ambiguous or unreliable to solve a decision-making problem. This study uses the variation coefficient measure combined with intuitionistic fuzzy graphs (IFG) and Laplacian energy (LE) to solve a GDM problem that utilizes intuitionistic fuzzy preference relations (IFPRs) to select a reliable alliance partner. Initially, the Laplacian energy determines the weight of individual standards, and the obtained weight average further estimates the overall criterion weight vector. We establish the authority criteria weights using the variation coefficient measure and then ultimately rank the alternatives for each criterion using the same measure. We examine four distinct companies Alpha, Beta, Delta, and Zeta to conduct a realistic GDM to choose which alliance partner would be ideal. We successfully implemented the suggested technique, determining that Alpha satisfies company standards and is ranked first among other companies. Moreover, this technique is useful for all kinds of Intuitionistic fuzzy group decision-making problems to select optimal ones. [ABSTRACT FROM AUTHOR]

This study will focus on public transportation problems in terms of energy consumption, excessive travel time, and fares. The study seeks the best solutions for such concerns in the Izmir case. It aims to optimize these subjects for the tramway and bus systems, which are comparable in terms of origin and destination points. This study, also, draws attention to the integration issue of the transportation modes. The methods employed in this study are linear programming and sensitivity analysis. For this study, nine routes, which have both tramway and bus lines, were determined. The routes sharing the same transfer points were taken to investigate which transportation type would be the most convenient or useful, what their drawbacks and positive outcomes would be, and which one would be environmentally friendly, with especially the lowest cost. For linear programming, the Solver tool of Microsoft Excel® software was utilized. Initially, the objective functions were identified as minimization of energy consumption, minimization of route length and minimization of passenger costs. The data about energy consumptions, speeds, and passenger volumes were collected are constraints of optimizations. Finally, sensitivity analysis was conducted to assess the findings. The best solutions with bus and tram options were found for both passengers and operators. Optimal routes are Fahrettin Altay-Alsancak, Fahrettin Altay-Halkapınar, Mavişehir-Bostanlı and Egekent-Bostanlı. The study contributes to the literature using the time matrix besides to the transport cost matrix. Thus, this research serves not only for monetary aims but also for environmental and social aims. [ABSTRACT FROM AUTHOR]

The multi-dimensional investor or backpack problem is an important and well-known difficult constrained combinatorial optimization problem in operations research and optimization. Nowadays, algorithms inspired by nature have become extremely important in solving many mathematical problems, including the investor’s portfolio problem. In order to reach the best solutions, this research reviewed the bat algorithm and the updates that occurred to it in solving this problem. [ABSTRACT FROM AUTHOR]

Operations Research (OR) is a branch of applied mathematics that deals with optimization problems arising from different real contexts. The solving process of its problems is based on the construction and resolution of mathematical models, showing the possible connections between mathematics and the real world. Nevertheless, OR is not typically included in most curricula of higher secondary schools (i.e. Grades 9–12), but it is usually presented mainly at university level. To show how OR could be significant for these school students' education, the authors of this paper developed an educational project consisting of three teaching units. In this paper, we share the result of the teaching experiment related to the first unit, addressed to Grade 10. Qualitative and quantitative analyses show how it is appropriate to include OR and its typology of problems in regular school mathematics lectures. Second, these data also show how modelling and problem-solving skills, developed working with OR, can be fostered by implementing a collaborative way of working, also by making use of digital technologies. Last but not least, we demonstrate the positive impact such activities have on students' appreciation of OR. [ABSTRACT FROM AUTHOR]

Computer system resilience refers to the ability of a computer system to continue functioning even in the face of unexpected events or disruptions. These disruptions can be caused by a variety of factors, such as hardware failures, software glitches, cyber attacks, or even natural disasters. Modern computational environments need applications that can recover quickly from major disruptions while also being environmentally sustainable. Balancing system resilience with energy efficiency is challenging, as efforts to improve one can harm the other. This paper presents a method to enhance disaster survivability in microservice architectures, particularly those using Kubernetes in cloud-based environments, focusing on optimizing electrical energy use. Aiming to save energy, our work adopt the consolidation strategy that means grouping multiple microservices on a single host. Our aproach uses a widely adopted analytical model, the Generalized Stochastic Petri Net (GSPN). GSPN are a powerful modeling technique that is widely used in various fields, including engineering, computer science, and operations research. One of the primary advantages of GSPN is its ability to model complex systems with a high degree of accuracy. Additionally, GSPN allows for the modeling of both logical and stochastic behavior, making it ideal for systems that involve a combination of both. Our GSPN models compute a number of metrics such as: recovery time, system availability, reliability, Mean Time to Failure, and the configuration of cloud-based microservices. We compared our approach against others focusing on survivability or efficiency. Our approach aligns with Recovery Time Objectives during sudden disasters and offers the fastest recovery, requiring 9% less warning time to fully recover in cases of disaster with alert when compared to strategies with similar electrical consumption. It also saves about 27% energy compared to low consolidation strategies and 5% against high consolidation under static conditions. [ABSTRACT FROM AUTHOR]

Introduction: HIV self‐testing (HIVST) has been shown to increase the uptake of HIV testing and help achieve the UNAIDS 95‐95‐95 targets. This study assessed the acceptability, usability (ease of use and result interpretation) and the willingness to pay for HIVST kits distributed through three distribution models, namely the community‐based, PLHIV network‐led and private practitioners models, in India. Methods: This cross‐sectional study was implemented across 14 states in India between September 2021 and June 2022. All participants could choose between blood‐based or oral‐fluid‐based test kits. Participants were shown a test‐kit usage demonstration video, and pre‐ and post‐test counselling was provided for all. Participants were followed‐up after testing, and if reported reactive, were further supported for linkage to confirmatory testing and antiretroviral therapy (ART) initiation. Results: Among the 90,605 participants found eligible, 88,080 (97%) accepted an HIVST kit. Among the 87,976 who reported using an HIVST kit, 45,207 (51%) preferred a blood‐based kit, and 42,120 (48%) reported testing for the first time. For future testing, 77,064 (88%) reported preferring HIVST over other HIV testing methods. Among those who used the kit, 83,308 (95%) found the kit easy to use, and 83,237 (95%) reported that the test results were easy to interpret. Among those who preferred HIVST for future use, 52,136 (69%) were willing to pay for the kit, with 35,854 (69%) of those willing to pay less than US$ 1.20. Only one instance of social harm was reported, with a participant reporting suicidal tendencies due to discord with their partner. Out of 328 participants (0.4%) who tested reactive with HIVST, 291 (89%) were linked to confirmatory testing; of these, 254 were confirmed HIV positive, and 216 (85%) successfully initiated ART. Conclusions: Overall, we report that nearly all participants were willing to accept HIVST, found the test kits easy to use and interpret, and about two‐thirds were willing to pay for HIVST. Given the high levels of acceptance and the ability to reach a large proportion of first‐time testers, HIVST in India could contribute to achieving the UNAIDS first 95 and ending the HIV epidemic. [ABSTRACT FROM AUTHOR]

Traditional stochastic optimization in financial operations research applications consist of a two-step process: (1) calibrate parameters of the assumed stochastic processes by minimizing a loss function, and (2) optimize a decision vector by reference to the investor's reward/risk measures. Yet this approach can encounter the error maximization problem. We combine the steps in a single unified feedforward network, called end-to-end. Two variants are examined: a model-free neural network, and a model-based network in which a risk budgeting model is embedded as an implicit layer in a deep neural network. We performed computational experiments with major ETF indices and found that the model-based approach leads to robust performance out-of-sample (2017–2021) when maximizing the Sharpe ratio as the training objective, achieving Sharpe ratio of 1.16 versus 0.83 for a pure risk budgeting model. Simulation studies show statistically significant difference between model-based and model-free approaches as well. We extend the end-to-end algorithm by filtering assets with low volatility and low returns, boosting the out-of-sample Sharpe ratio to 1.24. The end-to-end approach can be readily applied to a wide variety of financial and other optimization problems. [ABSTRACT FROM AUTHOR]

Business operations have entered the digital era in which artificial intelligence (AI), machine learning (ML) and blockchain (BKC) have emerged as major disruptive forces. In AI and ML, deep learning is a critical area. In this paper, we aim to investigate how deep learning and BKC together can help improve business operations. We first examine the operations research (OR) literature related to the applications of deep learning for business operations. Then, we discuss the prior studies on using BKC for operations. After that, we explore deep learning's applications for BKC, BKC's applications for deep learning as well as how deep learning and BKC have been used together for business operations. Then, we construct a research framework and propose future research directions. [ABSTRACT FROM AUTHOR]

Quantum annealing emerges as a promising approach for tackling complex scheduling problems such as the resource-constrained project scheduling problem (RCPSP). This study represents the first application of quantum annealing to solve the RCPSP, analyzing 12 well-known mixed integer linear programming (MILP) formulations and converting the most qubit-efficient one into a quadratic unconstrained binary optimization (QUBO) model. We then solve this model using the D-wave advantage 6.3 quantum annealer, comparing its performance against classical computer solvers. Our results indicate significant potential, particularly for small to medium-sized instances. Further, we introduce time-to-target and Atos Q-score metrics to evaluate the effectiveness of quantum annealing and reverse quantum annealing. The paper also explores advanced quantum optimization techniques, such as customized anneal schedules, enhancing our understanding and application of quantum computing in operations research. [ABSTRACT FROM AUTHOR]

Quantum annealing emerges as a promising approach for tackling complex scheduling problems such as the resource-constrained project scheduling problem (RCPSP). This study represents the first application of quantum annealing to solve the RCPSP, analyzing 12 well-known mixed integer linear programming (MILP) formulations and converting the most qubit-efficient one into a quadratic unconstrained binary optimization (QUBO) model. We then solve this model using the D-wave advantage 6.3 quantum annealer, comparing its performance against classical computer solvers. Our results indicate significant potential, particularly for small to medium-sized instances. Further, we introduce time-to-target and Atos Q-score metrics to evaluate the effectiveness of quantum annealing and reverse quantum annealing. The paper also explores advanced quantum optimization techniques, such as customized anneal schedules, enhancing our understanding and application of quantum computing in operations research. [ABSTRACT FROM AUTHOR]

The National Oceanic and Atmospheric Administration's (NOAA) Center for Satellite Applications and Research (STAR) facilitates and enables societal benefits from satellite oceanography, supporting operational and experimental satellite missions, developing new and improved ocean observing capabilities, engaging users by developing and distributing fit-for-purpose data, applications, tools, and services, and curating, translating, and integrating diverse data products into information that supports informed decision making. STAR research, development, and application efforts span from passive visible, infrared, and microwave observations to active altimetry, scatterometry, and synthetic aperture radar (SAR) observations. These efforts directly support NOAA's operational geostationary (GEO) and low Earth orbit (LEO) missions with calibration/validation and retrieval algorithm development, implementation, maintenance, and anomaly resolution, as well as leverage the broader international constellation of environmental satellites for NOAA's benefit. STAR's satellite data products and services enable research, assessments, applications, and, ultimately, decision making for understanding, predicting, managing, and protecting ocean and coastal resources, as well as assessing impacts of change on the environment, ecosystems, and climate. STAR leads the NOAA Coral Reef Watch and CoastWatch/OceanWatch/PolarWatch Programs, helping people access and utilize global and regional satellite data for ocean, coastal, and ecosystem applications. [ABSTRACT FROM AUTHOR]

This document is a preface to a special issue of the journal "Mathematics" that focuses on mathematical modeling and optimization of service supply chains. The special issue includes 14 papers that cover a wide range of topics within this field. The papers provide valuable insights for researchers, students, and practitioners in supply chain management, operations research, industrial engineering, and business management. They address areas such as customer preferences, optimization of supply chain decisions, coordination mechanisms, and the effects of the external environment. The findings can assist in addressing current issues and challenges in service supply chains, ultimately enhancing performance and efficiency. [Extracted from the article]

Industrial 4.0 (I4.0) is believed to revolutionize supply chain (SC) management and the articles in this domain have experienced remarkable increments in recent years. However, the existing insights are scattered over different sub-topics and most of the existing review papers have ignored the underground decision-making process using OR methods. This paper aims to depict the current state of the art of the articles on SC optimization in I4.0 and identify the frontiers and limitations as well as the promising research avenue in this arena. In this study, the systematic literature review methodology combined with the content analysis is adopted to survey the literature between 2013 and 2022. It contributes to the literature by identifying the four OR innovations to typify the recent advances in SC optimization: new modeling conditions, new inputs, new decisions, and new algorithms. Furthermore, we recommend four promising research avenues in this interplay: (1) incorporating new decisions relevant to data-enabled SC decisions, (2) developing data-enabled modeling approaches, (3) preprocessing parameters, and (4) developing data-enabled algorithms. Scholars can take this investigation as a means to ignite collaborative research that tackles the emerging problems in business, whereas practitioners can glean a better understanding of how to employ their OR experts to support digital SC decision-making. [ABSTRACT FROM AUTHOR]

Combinatorial optimization (CO) has a broad range of applications in various fields, including operations research, computer science, and artificial intelligence. However, many of these problems are classified as nondeterministic polynomial‐time (NP)‐complete or NP‐hard problems, which are known for their computational complexity and cannot be solved in polynomial time on traditional digital computers. To address this challenge, continuous‐time Ising machine solvers have been developed, utilizing different physical principles to map CO problems to ground state finding. However, most Ising machine prototypes operate at speeds comparable to digital hardware and rely on binarizing node states, resulting in increased system complexity and further limiting operating speed. To tackle these issues, a novel device‐algorithm co‐design method is proposed for fast sub‐optimal solution finding with low hardware complexity. On the device side, a piezoelectric lithium niobate (LiNbO3) microelectromechanical system (MEMS) oscillator network‐based Ising machine without second‐harmonic injection locking (SHIL) is devised to solve Max‐cut and graph coloring problems. The LiNbO3 oscillator operates at speeds greater than 9 GHz, making it one of the fastest oscillatory Ising machines. System‐wise, an innovative grouping method is used that achieves a performance guarantee of 0.878 for Max‐cut and 0.658 for graph coloring problems, which is comparable to Ising machines that utilize binarization. [ABSTRACT FROM AUTHOR]

The World Health Organization (WHO) endorsed the use of triple-drug mass drug administration (MDA) regimen with ivermectin, diethylcarbamazine (DEC) and albendazole (commonly abbreviated as IDA) to accelerate the elimination of lymphatic filariasis (LF) as a public health problem in settings where onchocerciasis is not co-endemic. The National Programme for Elimination of LF (NPELF) in Kenya was among the first adopters of the IDA-MDA and two annual rounds were provided in 2018 and 2019 to the residents of Lamu County and Jomvu sub-County in the coast region. This study documented the feasibility of successfully delivering the two rounds of IDA-MDA. An operational research study was undertaken to determine efficient sampling strategies, indicators, and the appropriate population groups that could be used for the monitoring and evaluation of LF programs using IDA-MDA for the elimination of the disease as a public health problem. Two cross-sectional surveys were conducted at baseline in 2018 before IDA-MDA and an impact assessment 17 months after the second round of IDA-MDA. The reported epidemiological treatment coverage was at least 80% in all implementation units during each round of IDA-MDA. Blood samples were tested for filarial antigenemia using commercial Filariasis Test Strips (FTS) and any individual found to be positive was tested again at night for the presence of microfilariae in finger prick blood smears using microscopy. The overall prevalence of circulating filarial antigen (CFA) was relatively low at the baseline survey with Jomvu having 1.39% (95% CI: 0.91, 2.11) and Lamu having 0.48% (95% CI: 0.21, 1.13). Significant reduction in CFA prevalence was observed during the impact assessment after two annual rounds of treatment. The overall relative reduction (%) in CFA prevalence following the two rounds of MDA with IDA was significant in both Jomvu (52.45%, Z = -2.46, P < 0.02) and Lamu (52.71%, Z = -1.97, P < 0.05). Heterogeneity, however, was observed in the CFA prevalence reduction between random and purposive clusters, as well as between adult and child populations. The results of the impact assessment survey offered strong evidence that it was safe to stop the IDA-MDA in the two EUs because transmission appears to have been interrupted. It is also important to implement a post-treatment surveillance system which would enable efficient detection of any recrudescence of LF transmission at a sub-evaluation unit level. Our findings show that IDA-MDA may be considered for acceleration of LF elimination in other settings where onchocerciasis is not co-endemic. Author summary: Preventive chemotherapy with antifilarial drugs is currently the mainstay public health intervention recommended for the elimination of lymphatic filariasis (commonly known as elephantiasis) in endemic countries. Two-drug regimen mass drug administration of albendazole plus either diethylcarbamazine or ivermectin has been used widely in most countries since the launch of the Global Programme to Eliminate Lymphatic Filariasis by the World Health Organization in 2000. Safety and efficacy clinical studies conducted in Papua New Guinea have shown that co-administration of three antifilarial drugs namely, ivermectin, diethylcarbamazine and albendazole is superior to the two-drug regimen diethylcarbamazine and albendazole in clearance of microfilariae from the bloodstream. Consequently, the triple-drug regimen was endorsed for accelerating efforts to eliminate lymphatic filariasis as a public health problem in areas where onchocerciasis is not co-endemic. The aim of this study was to assess the feasibility of delivering mass treatment with the triple-drug regimen under programmatic conditions. An operational research study was conducted to determine the optimal sampling strategies, indicator (or set of indicators), and study populations for monitoring and evaluation of lymphatic filariasis elimination programs using the triple-drug regimen mass treatment. Two rounds of mass treatment with the triple-drug regimen were given to the residents of Lamu County (comprising of Lamu East and Lamu West sub-counties) and Jomvu sub-County (in Mombasa) in November 2018 and November 2019. The programme epidemiological treatment coverage was at least 80% in each implementation unit during the two treatment rounds. The preventive treatment resulted in significant reductions in the prevalence of the disease with the number of positive cases being fewer than the threshold recommended for areas where transmission is likely to be no longer sustainable. [ABSTRACT FROM AUTHOR]

The core of operations research activity focuses on creating and using models. These models may be linear models, non-linear models, dynamic models, and others. Linear models are considered one of the most important and most widely used operations research models due to the availability of appropriate algorithms through which we can obtain the optimal solution, which prompts us to benefit from the nature of the topic under study and the information available to us about the variables in it to transform it into linear models. In classical logic, many nonlinear programming problems have been processed and transformed into linear programming problems. In this research, we present a study of the issue of planning hydroelectric systems, where the general policy for operating this system specifies two prices for selling the produced electricity, which makes this issue a non-linear programming issue. We will turn it into a linear programming problem using linear programming concepts, and then we will use Boolean concepts. Neutrosophic studies of linear programming and non-linear programming are presented to provide a neutrosophic formulation of the issue of planning hydroelectric systems, through which we obtain a neutrosophic linear model whose optimal solution fits all the conditions that the system's operating environment may experience during the operating period. During the two operating periods. [ABSTRACT FROM AUTHOR]

Many different active ingredients and excipients are used in the production of pharmaceutical products. Selection of the most suitable active ingredients and excipients is one of the most important stages of pharmaceutical production. When choosing the active ingredients and excipients, several factors and alternatives should be considered. In this instance, it is thought that one of the multi-criteria decision-making techniques, an operational research model, the Analytical Hierarchy Process, can assist in identifying the excipients during the pre-formulation stage. Using the Analytical Hierarchy Process, the current study seeks to identify the polymers most suitable for producing oral dispersible film formulations. The problem's criteria and potential solutions were determined before establishing the goal. Then, pairwise comparison matrices were generated, and the generated matrices were sent to the Super Decisions Version 3.2 software to reach a solution/result. The study's findings showed that the forming capacity is the most crucial factor affecting the choice of polymer to be used in the pre-formulation of oral dispersible films. Hydroxypropyl methylcellulose (14.89%) was found to be the best alternative among the polymers, followed by hydroxyethyl cellulose (12.04%) and carboxymethyl cellulose (11.58%). It was revealed that the least preferred polymers were sodium alginate (5.6%) and pectin (6.8%), which are natural polymers. It is clear from the outcomes of the various pre-formulation investigations that polymer selection in oral dispersible film formulations is one of the most critical points. This study provides a new approach to selecting the most appropriate polymers in oral dispersible film formulations. [ABSTRACT FROM AUTHOR]

Purpose: To assess which partnerships were most critical during the recovery planning process following Hurricanes Maria and Irma. We discuss the roles and impact of different types of partners, barriers and facilitators to partnerships and lessons in collaboration during the development of the economic and disaster recovery plan for Puerto Rico. Design/methodology/approach: The Homeland Security Operational Analysis Center (HSOAC) was tasked with assisting the Puerto Rican government with an assessment of damages from Hurricanes Maria and Irma and the development of the Recovery Plan. During the process, a small team compiled and coded a database of meetings with non-HSOAC partners. The team was divided into sector teams that mirrored FEMA's Recovery Support Functions. Each sector completed two surveys identifying high impact partners and their roles and contributions, as well as barriers and facilitators to partnerships. Findings: A total of 1,382 engagements were recorded across all sectors over seven months. The most frequently identified high impact partners were federal and Puerto Rican governmental organizations partners. NGOs and nonprofits were noted as key partners in obtaining community perspective. Sector teams cited a lack of trust and difficulty identifying partners as barriers to partner engagement. Given the expedited nature of disaster response, establishing partnerships before disasters occur may help facilitate community input. Early networking, increased transparency and defining roles and responsibilities may increase trust and effectiveness among partnerships. Originality/value: To our knowledge, this is one of the few studies that quantifies and illustrates the partnerships formed and their contributions during recovery planning, and lessons learned. [ABSTRACT FROM AUTHOR]

In this paper, we address the issue of whether to optimize or satisfice in model-based engineering design. When dealing with operations research problems in the context of engineering design, one may encounter (i) nonlinear, nonconvex objectives and constraints, (ii) objectives with different units, and (iii) computational models that are abstractions of reality and fidelity, Seeking a single-point optimal solution that meets the necessary and sufficient Karush–Kuhn–Tucker (KKT) conditions makes it impossible to obtain a solution that satisfies all the targeted goals. Instead, a method to identify satisficing solutions that satisfies necessary KKT condition but not the sufficient condition is proposed. These solutions are relatively robust, easy to acquire, and often good enough. In this paper, we demonstrate the combined use of the compromise Decision Support Problems and the adaptive linear programming algorithm, as proposed by Mistree and co-authors. This method is appropriate in formulating design problems and obtaining solutions that satisfy only the necessary KKT condition. Further, the use of the proposed method circumvents complications associated with the use of gradient-based optimization algorithms typically used to solve optimization problems. We discuss the efficacy of our proposed method using four test problems to illustrate how the satisficing strategy outperforms the optimizing strategy in model-based engineering design. [ABSTRACT FROM AUTHOR]

The paper aims to investigate the effect of the free-flow right-turn bypass lane at single-lane roundabouts in terms of overall delay improvements. In total, 47 different cases of various entry flows per approach were examined, utilizing 189 different distribution rates in left-turn, through and right-turn movements per case, resulting thus in 8883 discrete scenarios. The multiple linear regression analysis that followed revealed the most critical parameters that affect the overall roundabout's operational performance in terms of delay when a free-flow right-turn bypass lane is introduced. The model extracted herein, which is statistically adequate to serve as a useful tool in roundabout design, at least during the planning stage, can identify whether the installation of a free-flow right-turn bypass lane can improve the total roundabout's delay. Further analysis is required to investigate in more detail potential effects of additional parameters, such as multilane roundabouts, the existence of more than one bypass lanes and, more importantly, the presence of pedestrians, which may levy further constraints. [ABSTRACT FROM AUTHOR]

The concept of possibility fuzzy soft sets is a step in a new direction towards a soft set approach that can be used to solve decision-making issues. In this piece of research, an innovative and comprehensive conceptual framework for possibility multi-fuzzy soft ordered semigroups by making use of the notions that are associated with possibility multi-fuzzy soft sets as well as ordered semigroups is introduced. Possibility multi-fuzzy soft ordered semigroups mark a newly developed theoretical avenue, and the central aim of this paper is to investigate it. The focus lies on investigating this newly developed theoretical direction, with practical examples drawn from decision-making and diagnosis practices to enhance understanding and appeal to researchers' interests. We strictly build the notions of possibility multi-fuzzy soft left (right) ideals, as well as l-idealistic and r-idealistic possibility multi-fuzzy soft ordered semigroups. Furthermore, various algebraic operations, such as union, intersection, as well as AND and OR operations are derived, while also providing a comprehensive discussion of their properties. To clarify these innovative ideas, the theoretical constructs are further reinforced with a set of demonstrative examples in order to guarantee deep and improved comprehension of the proposed framework. [ABSTRACT FROM AUTHOR]

The team orienteering problem (TOP) is a well-studied optimization challenge in the field of Operations Research, where multiple vehicles aim to maximize the total collected rewards within a given time limit by visiting a subset of nodes in a network. With the goal of including dynamic and uncertain conditions inherent in real-world transportation scenarios, we introduce a novel dynamic variant of the TOP that considers real-time changes in environmental conditions affecting reward acquisition at each node. Specifically, we model the dynamic nature of environmental factors—such as traffic congestion, weather conditions, and battery level of each vehicle—to reflect their impact on the probability of obtaining the reward when visiting each type of node in a heterogeneous network. To address this problem, a learnheuristic optimization framework is proposed. It combines a metaheuristic algorithm with Thompson sampling to make informed decisions in dynamic environments. Furthermore, we conduct empirical experiments to assess the impact of varying reward probabilities on resource allocation and route planning within the context of this dynamic TOP, where nodes might offer a different reward behavior depending upon the environmental conditions. Our numerical results indicate that the proposed learnheuristic algorithm outperforms static approaches, achieving up to 25 % better performance in highly dynamic scenarios. Our findings highlight the effectiveness of our approach in adapting to dynamic conditions and optimizing decision-making processes in transportation systems. [ABSTRACT FROM AUTHOR]

The design of fault-tolerant production and delivery systems is one of the priority areas in modern operations research. The traditional approach to modeling such systems is based on the use of stochastic models that describe the choice of a possible scenario of actions in the event of problems in a production or transportation network. Along with a number of advantages, this approach has a known drawback. The occurrence of problems of an unknown nature that can jeopardize the performance of the entire simulated system significantly complicates its use. This paper introduces the minimax problem of constructing fault-tolerant production plans (reliable production process design problem, RPPDP), the purpose of which is to ensure the uninterrupted operation of a distributed production system with minimal guaranteed cost. It is shown that the RPPDP is NP-hard in the strong sense and remains intractable under quite specific conditions. To find exact and approximate solutions with accuracy guarantees for this problem, branch-and-bound methods are developed based on the proposed compact model of the mixed integer linear program (MILP) and novel heuristic of adaptive search in large neighborhoods (adaptive large neighborhood search, ALNS) as part of extensions of the well-known Gurobi MIP solver. The high performance and complementarity of the proposed algorithms is confirmed by the results of numerical experiments carried out on a public library of benchmarking instances developed by the authors based on instances from the PCGTSPLIB library. [ABSTRACT FROM AUTHOR]

In this paper, we study unimodality conditions for distributions that describe markets with stochastic demand. Such conditions naturally emerge in the analysis of game-theoretic models of market competition (Cournot games) and supply chain coordination (Stackelberg games). We express the price elasticity of expected demand in terms of the mean residual life (MRL) function of the demand distribution and characterize optimal prices or equivalently, points of unitary elasticity, as fixed points of the MRL function. This leads to economic interpretable conditions on the demand distribution under which such fixed points exist and are unique. We find that markets with increasing price elasticity of expected demand that eventually become elastic correspond to distributions with decreasing generalized mean residual life (DGMRL) and finite second moment. DGMRL distributions strictly generalize the widely used increasing generalized failure rate (IGFR) distributions. We further elaborate on the relationship of the two classes, link their limiting behavior at infinity and examine moment and closure properties of DGMRL distributions that are important in economic applications. The DGMRL unimodality condition is useful in the analysis of optimal decisions under uncertainty in settings that are not covered by the widely-used IGFR condition; thus, it can be of broader interest to the game-theory and operations research literature. [ABSTRACT FROM AUTHOR]

Background: Female genital schistosomiasis (FGS), caused by the parasite Schistosoma haematobium (Sh), is prevalent in Sub-Saharan Africa. FGS is associated with sexual dysfunction and reproductive morbidity, and increased prevalence of HIV and cervical precancerous lesions. Lack of approved guidelines for FGS screening and diagnosis hinder accurate disease burden estimation. This study evaluated FGS burden in two Sh-endemic areas in Southern Malawi by visual and molecular diagnostic methods. Methodology/Principal findings: Women aged 15–65, sexually active, not menstruating, or pregnant, were enrolled from the MORBID study. A midwife completed a questionnaire, obtained cervicovaginal swab and lavage, and assessed FGS-associated genital lesions using hand-held colposcopy. 'Visual-FGS' was defined as specific genital lesions. 'Molecular-FGS' was defined as Sh DNA detected by real-time PCR from swabs. Microscopy detected urinary Sh egg-patent infection. In total, 950 women completed the questionnaire (median age 27, [IQR] 20–38). Visual-and molecular-FGS prevalence were 26·9% (260/967) and 8·2% (78/942), respectively. 6·5% of women with available genital and urinary samples (38/584) had egg-patent Sh infection. There was a positive significant association between molecular- and visual-FGS (AOR = 2·9, 95%CI 1·7–5·0). 'Molecular-FGS' was associated with egg-patent Sh infection (AOR = 7·5, 95% CI 3·27–17·2). Some villages had high 'molecular-FGS' prevalence, despite <10% prevalence of urinary Sh among school-age children. Conclusions/Significance: Southern Malawi carries an under-recognized FGS burden. FGS was detectable in villages not eligible for schistosomiasis control strategies, potentially leaving girls and women untreated under current WHO guidelines. Validated field-deployable methods could be considered for new control strategies. Author summary: Female genital schistosomiasis (FGS) is a neglected gynaecological disease caused by the waterborne parasite Schistosoma (S.) haematobium. Despite over 45 million women are at risk of FGS in sub-Sahara Africa (SSA), approximately only 15,000 have been screened for the disease. Diagnosis is challenging and has traditionally required high technical expertise based on visual inspection for FGS typical lesions of the genital tract using a standard colposcope, seldom available in endemic settings. Closer-to the-user and decentralized strategies for FGS screening and diagnosis should be implemented to assess disease burden and scale-up FGS surveillance. This study was nested within the larger Morbidity Operational Research for Bilharziasis Implementation Decision (MORBID) cross-sectional project, aiming to correlate schistosomiasis-related morbidity data with village level endemicity across two districts in Southern Malawi. We found a significantly moderate to high burden of FGS (between 8–27% depending on diagnostic method used), with marked age differences in diagnostic performance. Further, some villages with low schistosomiasis prevalence (which would be excluded from control strategies per new WHO guidelines), had a significantly high burden of FGS, indicating the need for formal public health interventions. Within the remit of the sustainable development goals, this study's approach and findings emphasize the need of a field-deployable strategy to FGS screening and diagnosis in endemic areas in Malawi and other similar setting. [ABSTRACT FROM AUTHOR]

One of the major challenges facing decision-makers at the present time is obtaining complete information about the issue under study, due to the unstable conditions of the work environment that are beyond the control of decision-makers, which requires them to reach an optimal decision in light of these circumstances and fluctuations and to benefit from the data that is collected. Collected by specialists to determine the appropriate probability distribution corresponding to random cases of nature, here we are faced with the issue of making a decision in the event of risk because the probability distribution is a distribution linked to the data controlled by the conditions of the work environment, which entails a great risk. Decision makers bear the responsibility of choosing the optimal decision that reduces This risk is achieved and the greatest possible profit and the least possible loss are achieved. The issue of decision-making becomes more complex as the number of events increases, and we are in dire need of an ideal study of the issue that takes into account all the circumstances of the work environment. The concept of missed opportunity is very useful in analyzing the decision making under risk, after making the decision and the occurrence of events, the decision makers may regret and wish they had chosen actions different from those they chose at the beginning. To reduce the regret of the decision makers and minimize the expected lost opportunity, researchers in the field of classical operations research presented the criterion of the expected lost opportunity through which the decision can be determined. The ideal with the least percentage of regret. In this research, we present a neutrosophic vision of the expected opportunity loss criterion by taking the data of the issue under study. neutrosophic values are ranges whose lowest limit expresses profit in the worst conditions, and only the highest represents profit in the best conditions. [ABSTRACT FROM AUTHOR]

Background: In hospital management, pinpointing steps that most enhance operating room (OR) throughput is challenging. While prior literature has utilized discrete event simulation (DES) to study specific strategies such as scheduling and resource allocation, our study examines an earlier planning phase, assessing all workflow stages to determine the most impactful steps for subsequent strategy development. Methods: DES models real‐world systems by simulating sequential events. We constructed a DES model for thoracic, gastrointestinal, and orthopedic surgeries summarized from a tertiary Chinese hospital. The model covers preoperative preparations, OR occupation, and OR preparation. Parameters were sourced from patient data and staff experience. Model outcome is OR throughput. Post‐validation, scenario analyses were conducted for each department, including: (1) improving preoperative patient preparation time; (2) increasing PACU beds; (3) improving OR preparation time; (4) use of new equipment to reduce the operative time of a selected surgery type; three levels of improvement (slight, moderate, large) were investigated. Results: The first three improvement scenarios resulted in a 1%–5% increase in OR throughput across the three departments. Large reductions in operative time of the selected surgery types led to approximately 12%, 33%, and 38% increases in gastrointestinal, thoracic, and orthopedic surgery throughput, respectively. Moderate reductions resulted in 6%–17% increases in throughput and slight reductions of 1%–7%. Conclusions: The model could reliably reflect OR workflows of the three departments. Among the options investigated, model simulations suggest that improving OR preparation time and operative time are the most effective. [ABSTRACT FROM AUTHOR]