This article asks what event-level spatial analysis adds after the concentration of urban violence is already known. Using 2,800 geolocated and timestamped armed-violence events recorded by Crossfire (Fogo Cruzado) app across 21 municipalities in metropolitan Rio de Janeiro between December 2024 and March 2026, we separate four decision-relevant objects, persistent place burden, short-window event co-occurrence, excess space–time dependence, and model-based self-excitation. The design combines hotspot persistence screening, a 5 km/72 h event graph, shuffled-time and location-permutation benchmarks, and Hawkes point-process models. Linked pairs exceed the shuffled-time null by 27.5 z=17.57 , empirical p=0.0033 ), but visually strong graph fragmentation is not unusual under the spatially grounded null and attenuates sharply as thresholds widen. Persistent hotspots and temporary event communities therefore represent different policy layers rather than interchangeable maps. The former support sustained place-based prioritization; the latter can support short-run coordination only when their threshold sensitivity and provisional status are displayed. Crossfire app records are produced through a verified multi-source civic-monitoring workflow rather than passive app-location traces, although geographically uneven ascertainment remains possible and low counts in peripheral municipalities must be treated as lower bounds. The study does not identify the causal effect of any policy implemented during the observation period. Its contribution is a transferable framework for matching spatial evidence, uncertainty, and policy time horizons without reifying temporary clusters as fixed territories.

On predefined and synthetic datasets with controlled noise, we test Borda, Copeland, Footrule, Kemeny-Young, Median Rank, PageRank, Plackett-Luce, Reciprocal Rank Fusion, and Schulze, varying numbers of alternatives and rankings to assess scalability. Positional and simple pairwise rules tend to agree and reward consistently strong options; distance-based and probabilistic models can shift winners toward items closest to the average order or with higher inferred worth. Under low disagreement, most methods yield similar consensus, permitting flexible choice; under high disagreement, rankings diverge and runtimes spread widely. Borda, Median, RRF, and Schulze scale well; Kemeny-Young and Plackett-Luce become costly. We provide a comparative, mechanism-aware view of aggregation choices, linking robustness and computational feasibility to disagreement regimes to guide practical MCDA in real decision settings. Our code is available at https://github.com/Valdecy/pyRankMCDA.

This study aims to present a new hybrid method for weighting criteria. The methodological project combines the ENTROPY and CRITIC methods with the TOPSIS method to create EC-TOPSIS. The difference lies in the use of a weight range per criterion. Each weight range has a lower limit and an upper limit, which are combined to generate random numbers, producing t sets of weights per criterion, allowing t final rankings to be obtained. The final ranking is obtained by applying the MODE statistical measure to the set of t positions of each alternative. The method was validated by ranking the companies based on social media metrics consisting of user-generated content (UGC). The result was compared with the original modeling using the CRITIC-ARAS and CRITIC-COPRAS methods, and the results were consistent and balanced, with few changes. The practical implication of the method is in reducing the uncertainties surrounding the final classification due to the random weighting process and the number of interactions sent.

Product recovery is critical in reducing costs, enhancing profitability, and improving supply chain responsiveness to customer demands. Remanufacturing returned products, as part of the circular economy, is a central strategy in achieving these goals. This study presents a model that optimizes the remanufacturing process using in-house workstations and outsourcing to maximize supply chain profitability, reduce queue lengths, and ensure machine reliability. The remanufacturing system is modeled as an M/M/m/k queuing system, considering real-world supply chain constraints such as budget limitations, station capacity, and machine reliability. Supply chain optimization is achieved by maintaining efficiency while examining different remanufacturing policies and pricing strategies. The results show that expanding remanufacturing capacity enhances supply chain profitability, even with moderate increases in queue length. We provide valuable insights for supply chain managers aiming to optimize their remanufacturing processes and balance cost, efficiency, and reliability.

Decision-making under uncertainty is inherently complex. Choosing the right company to invest in is an important decision. The study proposes a Multi-Criteria Decision Making (MCDM) approach to assess companies. Financial ratios and the final score would help the decision-maker evaluate companies. This study presents a novel three-stage hybrid approach using the fuzzy analytic hierarchy process (FAHP) and Fuzzy C-Means (FCM) for financial assessment. The period used for the study represents the downward progress of COVID-19 in Turkey. The study demonstrates the applicability of the hybrid approach by applying it to the textile sector in the stock exchange. The most important financial ratios based on expert opinions are net profit, current ratio, and cash ratio. Current financial status is considered more important than the trend in financial ratios. The suggested study introduces new criteria for future trends, which are absent in the literature. The novel model integrates FAHP for expert-based weighting, dynamic trend evaluation, and FCM for flexible classification, uniquely applied to BIST-listed textile companies during COVID-19. The study uses a hybrid approach that would advance classification techniques’ applicability to a broader area with more alternatives. Adapting to financial changes and using multiple experts would help mitigate risks.

The rising energy demand for residential heating and cooling contributes significantly to environmental pollution through carbon emissions from conventional energy sources. The present study explores vapour injection heat pump (VIHP) systems to address these challenges, evaluating their performance under varying operating conditions and examining the potential of machine learning (ML) models for rapid and accurate predictions without relying on complex engineering equations. Parametric analyses were conducted for five refrigerants (R123, R134a, R152a, R32 and R1234yf) across a range of injection mass ratios (0.02–0.24) and outdoor temperatures (−9 to 38°C). The results highlight how heating and cooling capacities, compressor power and system performance vary under different conditions. For instance, performance improvements were most pronounced for R123 with increasing IMR, while R32 showed the largest sensitivity to outdoor temperature changes. Environmental impacts were quantified, with R123 producing the highest carbon emissions and R32 the lowest. Additionally, five ML models were evaluated, revealing that AdaBoost Regressor was the least accurate, ExtraTrees regressor model provided robust predictions. These findings support the design and optimization of VIHP systems, offering a cost-effective approach to improving energy efficiency and reducing environmental impacts before undertaking costly experimental studies.

Perishable products are an essential part of commerce. Shelf-life characteristics are usually not modeled in traditional inventory models. This study proposes an inventory replenishment model for perishable products with an asymmetric cost structure for holding and stock-out costs. The modeling phase involves the shelf-life characteristics of products. Shelf life is essential due to sustainability concerns, costs, and service levels due to perished products. In contrast to classical safety stock models, where stock-out costs increase linearly, the proposed model utilizes incrementally increased fixed costs for holding costs in a conflicting cost structure. It incorporates the shelf-life of the products, calculates the probability of perishing, and formulates accurate waste and total costs using an asymmetrical cost structure. The model is applied to a real dataset to assess the performance and compare it with the traditional approach. The performance of the proposed model is better, with a total cost reduction of 45.33%. Additionally, the model demonstrated a 17.21% increase in service level. The sensitivity analysis further underlined the robustness of the proposed model across various demand scenarios and shelf-life conditions. The main research gap addressed by this study is the lack of consideration for shelf-life characteristics and asymmetric cost structures in traditional inventory models. By integrating these factors, this research provides a more accurate and cost-effective approach to inventory management for perishable products, enhancing sustainability and service levels. This study's findings can help businesses optimize inventory strategies, reduce waste, and improve operational efficiency.

Proper production planning is essential for improving productivity and lowering resource (material, energy, employees) related costs in the highly competitive business world. Dealing with the challenges of asymmetric setup times—where the time required to switch between manufacturing different products varies —makes this task much more difficult. Conventional planning techniques frequently ignore these articulations and produce sub-optimal schedules. This paper proposes a novel approach to tackle the following challenge: optimizing production planning using the Fuzzy Analytic Hierarchy Process (FAHP) with asymmetric setup times and Genetic Algorithm (GA). The proposed methodology involves a step-by-step process. The first stage defines key objectives: makespan, total waste cost, and maximum weighted tardiness. Decision-makers compare the relative importance of each criterion within its hierarchy level using fuzzy numbers. The consistency of these comparisons is assessed using fuzzy consistency ratio computations. At the same time, the overall priority weights for each production planning alternative are determined by summing fuzzy judgments across the hierarchy. In the second stage, the production plan is optimized using GA, considering sequence and lot size variables and asymmetric setup times, by applying the computed weights. The comparisons are performed using the proposed approach with the optimum solution.

Accurate prediction of photovoltaic (PV) performance under varying thermal and environmental conditions is crucial for reliable energy assessment and investment planning. Commercial software often fails to fully capture the influence of cooling strategies on electrical output. This study examines three PV configurations-uncooled, aluminum fin-cooled (PV_AF), and heat pipe-cooled (PV_HP) using experimental measurements, validated numerical simulations, and machine learning (ML) models including KNN Regressor, AdaBoost, Random Forest, Gradient Boosting, and CatBoost. The tested PV/T collector has a rated maximum power of 50 W (tolerance +/- 5 W), open-circuit voltage of 24.62 V, short-circuit current of 2.57 A, maximum power voltage of 20.84 V, and maximum power current of 2.46 A. Experiments were conducted in Gaziantep, T & uuml;rkiye (37 degrees 02 ' 16.6 '' N, 37 degrees 18 ' 51.8 '' E) during January, April, July, and October 2024 to capture diverse outdoor conditions, including solar radiation, ambient temperature, and wind speed. Results demonstrate significant performance improvements with cooling, particularly under hot conditions: PV_AF and PV_HP increased efficiency by 4.98 % and 5.83 % in January, 5.08 % and 7.50 % in April, 6.36 % and 9.28 % in July, and 4.81 % and 7.68 % in October, respectively. Among ML algorithms, CatBoost achieved the best predictive accuracy (R-2 > 0.999). The findings highlight the synergistic benefits of thermal regulation and data-driven modeling in enhancing PV performance.

The aim of this research is to improve electricity generation efficiency and contribute to reducing energy costs by positioning solar panels at the optimal tilt angle in Turkey, a country with high solar energy potential. To achieve this goal, more advanced methods such as machine learning techniques are preferred instead of traditional methods calculated by mathematical formulae. In a typical Particle Swarm Optimisation (PSO) application, mathematical equations are used as the fitness function, but such an approach can be time, labour, and performance consuming. In this study, a new method called ExtraPSO (Extra Tree with Particle Swarm Optimisation) is developed by using K-Nearest Neighbors, AdaBoost (Adaptive Boosting), Gradient Boosting, Random Forest, ExtraTrees algorithms instead of a mathematical model for the fitness function. As a result of the testing of ExtraPSO, it was observed that a powerful predictive hybrid model was obtained in addition to saving labour and time. The prediction success of the developed ExtraPSO model is determined as 99.0549% with the Accuracy metric. In addition, the optimum tilt angle and maximum exergy amount were determined for Turkey according to the seasons. Considering all seasons and annual average for Turkey, the maximum exergy amount that can be obtained for a solar panel placed at the optimum tilt angle is determined as 3.871 W/m2, 5.713 W/m2, 7.048 W/ m2, 5.272 W/m2 and 5.285 W/m2 for winter, spring, summer, fall and annual average, respectively.

A key challenge in production management and operational research is the flow shop scheduling problem, characterized by its complexity in manufacturing processes. Traditional models often assume deterministic conditions, overlooking real-world uncertainties like fluctuating demand, variable processing times, and equipment failures, significantly impacting productivity and efficiency. The increasing demand for more adaptive and robust scheduling frameworks that can handle these uncertainties effectively drives the need for research in this area. Existing methods do not adequately capture modern manufacturing environments’ dynamic and unpredictable nature, resulting in inefficiencies and higher operational costs; they do not employ a fuzzy approach to benefit from human intuition. This study successfully demonstrates the application of Hexagonal Type-2 Fuzzy Sets (HT2FS) for the accurate modeling of the importance of jobs, thereby advancing fuzzy logic applications in scheduling problems. Additionally, it employs a novel Multi-Criteria Decision-Making (MCDM) approach employing Proportional Picture Fuzzy AHP (PPF-AHP) for group decision-making in a flow shop scheduling context. The research outlines the methodology involving three stages: group weight assessment through a PPF-AHP for the objectives, weight determination using HT2FS for the jobs, and optimization via Genetic Algorithm (GA), a method that gave us the optimal solution. This study contributes significantly to operational research and production scheduling by proposing a sophisticated, hybrid model that adeptly navigates the complexities of flow shop scheduling. The integration of HT2FS and MCDM techniques, particularly PPF-AHP, offers a novel approach that enhances decision-making accuracy and paves the way for future advancements in manufacturing optimization.

In the study, actual solar radiation measurements were used to determine the solar heat gains that affect the daily heating and cooling requirements. The study investigated the advantages of the PureTerm 23 PCM in indoor temperature control using data from the 2021-2022 solar radiation records. The results show that the PCM is inefficient in meeting the heating demands in January and February. In March, it was found that the PCM can save energy by meeting 16% of the daily heating demand. In April, a 57% reduction in heating demand is achieved with PCM and in May it can provide full heating and cooling with solar gains. With the use of PCM, the cooling requirement can be reduced by 69%, 56% and 59% in June, July and August, respectively. In September, it is calculated that heating and cooling needs can be eliminated by storing solar energy gains. In October and November, the heating demand can be reduced by 49% and 3% respectively, while in December there is not enough solar gain for PCM storage. PureTerm 23 PCM shows significant potential for seasonal energy storage supporting sustainable energy management for indoor temperature control.

This paper presents a 3-stage innovative approach for company assessment, integrating financial ratios with the Fuzzy Analytic Hierarchy Process (FAHP) and using an unsupervised artificial intelligence method, Self-Organizing Maps (SOM), for classification. Addressing the challenges of decision-making in resource allocation, the study combines accurate data with robust tools essential in turbulent economic times. FAHP, known for handling complex, uncertain information, is applied to refine the traditional company assessment methods by integrating different experts' opinions and conversion to numerical values. This study presents an innovative framework by integrating financial ratios, commonly used in company evaluation methodologies, with FAHP, which is capable of processing complex and uncertain data. The integration of financial ratios into FAHP enhances the accuracy and clarity in decision-making processes for evaluating and ranking companies while also allowing for the management of the inherent uncertainties in economic data. Furthermore, SOM, an unsupervised artificial intelligence method for company classification, is used. Net Profit Margin is the financial ratio evaluated with the highest weight among financial ratios by 0.38. After the FAHP phase, financial ratios obtained from the income statements and balance sheets of companies are multiplied by the respective weights for valuation. In the final phase, a total of 6 companies listed in the Borsa Istanbul Insurance Index are divided into 3 classes. The two companies receiving the highest valuation, AGESA (Agesa Life and Pension) and ANHYT (Anadolu Life Pension Joint Stock Company), have been classified as Class A. To show the performance of the proposed model, companies registered in the Electricity Sector XELKT registered 31 companies. Classification also performed well in that set. The paper contributes to the field by providing a detailed literature review, methodology, case study results, and discussions on the practical implications of this integrated assessment method and possible areas for further research and applications.

Assessment of companies is vital for an accurate investment decision. Financial ratios are essential performance indicators. However, there is no consensus in their comparison among all financial ratios. Expert opinions are an indispensable resource for such assessment. This study uses an integrated approach to benefit from expert opinions. Clustering is an important area of unsupervised learning. Clustering, when assigned to classes, can also be used for classification. It is vital to classify data to apply for decision-making. This study applies the Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy C-Means (FCM) for clustering and classification as a part of machine learning. Financial ratios are widely used to compare different companies. This study focuses on companies under the "Textile Leather Index" registered in the Istanbul Stock Exchange (BIST) for applying the proposed model. The study employed current financial results and the positive and negative trends of the last year for classification. The results allow the decision-maker to choose the right company to invest in. Among 17 companies, 2 are classified as A class. To the best of our research, using trend values and integrating FAHP and FCM for classification is new in the literature.

Safety stock is an important method to overcome variability in inventory management. The classical approach to safety stock decisions relies on historical demand and lead time statistical data, which may not capture the uncertainty and complexity of the real world. Human knowledge and experience are valuable assets for making better decisions, especially when facing unpredictable situations. The fuzzy method is widely used for employing human intuition for decisions. When fuzzy opinions are input, decisions can be made proactively rather than reactively while benefiting from future predictions. The paper aims to integrate human intuition using Hexagonal Type-2 Fuzzy Sets (HT2FS) for safety stock management. HT2FS is a generalization of Interval Type-2 Fuzzy Sets that can represent more uncertainty in the membership functions. Predictions may be integrated into the safety stock models using human intuition. The proposed model uses novel fuzzy approaches to integrate human intuition into the traditional safety stock model. Applying fuzzy sets to safety stock management allowed experts' opinions under fuzzy logic to be integrated into decision-making. The proposed novel approach uses the centre of gravity method of Polygonal Interval Type-2 Fuzzy sets for defuzzification, which is a computationally efficient method that can handle any shape of the footprint of uncertainty. A mathematical model is developed to validate fuzzy opinions that may replace historical data. The data is received from a real-life case, and human intuition is integrated using an expert's input. After the validation, a real-life numerical example has been considered to illustrate the model and its validity compared to the classical model. The outcomes show that the proposed model may contribute to the classical models, mainly when experts' inputs offer good predictions. When expert opinion on HT2FS is used for a real-life case, the results show that the expert's better representation of future variances lowers total cost by 2.8%. The results, coupled with the sensitivity analysis, underline that the proposed approach may contribute to the literature on safety stock management.

The decision-making process is part of everyday life for people and organizations. When modeling the solutions to problems, just as important as the choice of criteria and alternatives is the definition of the weights of the criteria. This study will present a new hybrid method for weighting criteria. The technique combines the ENTROPY and CRITIC methods with the PROMETHE method to create EC-PROMETHEE. The innovation consists of using a weight range per criterion. The construction of a weight range per criterion preserves the characteristics of each technique. Each weight range includes lower and upper limits, which combine to generate random numbers, producing “t” sets of weights per criterion, allowing “t” final rankings to be obtained. The alternatives receive a value corresponding to their position with each ranking generated. At the end of the process, they are ranked in descending order, thus obtaining the final ranking. The method was applied to the decision support problem of choosing policing strategies to reduce crime. The model used a decision matrix with twenty criteria and fourteen alternatives evaluated in seven different scenarios. The results obtained after 10,000 iterations proved consistent, allowing the decision maker to see how each alternative behaved according to the weights used. The practical implication observed concerning traditional models, where a single final ranking is generated for a single set of weights, is the reversal of positions after “t” iterations compared to a single iteration. The method allows managers to make decisions with reduced uncertainty, improving the quality of their decisions. In future research, we propose creating a web tool to make this method easier to use, and propose other tools are produced in Python and R.

Purpose The purpose of this study is to investigate the moderating roles of innovation intensity and lenders' monitoring on the relation between financial slack and performance. Design/methodology/approach This study adopts an empirical method using data from firms listed in both the Compustat S&P500 and Boardex for the period 2010 to 2019 to analyze the effects of innovation intensity and lenders' monitoring on the relation between financial slack and performance. Findings The authors find that financial slack is positively related to performance, and this relation is stronger as innovation intensity increases. Furthermore, we demonstrate that lenders' monitoring strengthens the positive relationship between financial slack and performance. Research limitations/implications First, this study focuses on the effects of financial slack, research and development (R&D) intensity and lenders' monitoring on financial performance. Future research might extend this study by investigating the effects of these variables on non-financial performance. Second, the data and results do not provide insights into the reasons for firms to accumulate financial slack. Future research might conduct a longitudinal field study to understand why firms build financial slack. Finally, this study only uses R&D intensity and lenders' monitoring as the moderating variables. Future studies might incorporate other contingency variables such as firms' budgeting and budget-based compensation systems to provide useful insights into the relationship between financial slack and performance. Practical implications This study provides important insights into the value of financial slack for firms that invest heavily in R&D activities. This study also provides useful insight into the benefits of lenders' monitoring to mitigate managers' unethical behavior. Social implications This study provides useful insights for companies that invest heavily in innovation activities by showing that financial slack is beneficial for this company and lenders' monitoring is needed to discipline managers in using the slack resources. Originality/value This study is the first to investigate the moderating effects of innovation intensity and lenders' monitoring on the relation between financial slack and performance. Previous studies focus their investigations on the direct effect of financial slack and performance.

Perishable products cover a high percentage of all goods. The variability, long lead times, risk period, and high service level increase the safety stock level. An increase in safety stock will also increase the probability of perished products because of the increased probability of sales of less than stock during shelf life. This study proposes a model for calculating safety stocks of perishable products besides showing the effect of perishability on service level. The effects of long lead times, risk periods, high sales and lead-time variance, and short shelf life adversely affect perished products. The study investigates and proposes a novel model for calculating total expected waste and costs with a waste quantity constraint. A real-life example compares a proposed model with waste constraints and the traditional safety stock model based on costs and waste quantity. The case study shows the better results of the proposed models.