Search Results (521)

This study develops a game-theoretic framework to analyze collaborative advertising decisions between manufacturers and retailers in seasonal product supply chains characterized by competitive–cooperative channel relationships. We formulate a mathematical programming model to jointly optimize advertising efforts, the manufacturer’s advertising cost-sharing rate, order quantities, and inventory decisions across distinct channel configurations—including a single manufacturer–retailer dyad and a competitive multi-channel market. Numerical experiments and sensitivity analyses are conducted to investigate how key structural parameters—particularly demand elasticity and channel power asymmetry—influence overall system performance and equilibrium decision outcomes. Results indicate that well-designed collaborative advertising mechanisms enhance total channel profitability and, under specific conditions, yield Pareto-improving outcomes for both parties. This study makes three primary contributions: (i) it integrates inter-firm competition with intra-channel cooperation within a unified strategic framework; (ii) it jointly coordinates advertising and inventory decisions—two critical operational levers—rather than treating them in isolation; and (iii) it embeds financial arrangements (e.g., cost sharing) endogenously into the analytical model, thereby offering a novel, theoretically grounded, and practically implementable decision-support framework for distribution systems operating in complex, dynamic market environments. Full article

This study examines whether retail social media sentiment and community attention explain daily net capital flows into U.S. spot Bitcoin exchange-traded funds (ETFs), and whether issuer brand visibility conditions that relationship. We construct a balanced panel of $N=10$ ETFs over $T=514$ trading days (January 2024 to January 2026) and combine it with 162,819 cleaned Reddit posts to derive three AI-driven discourse variables: engagement-weighted sentiment, community attention, and a novel issuer-specific BrandScore. Entity fixed-effects regressions show that neither aggregate sentiment nor BrandScore level alone significantly predicts fund-level flows; however, the Sentiment $\times$ BrandScore interaction is significant ($\widehat{\beta }=2.930$, $p=0.038$), indicating that sentiment becomes economically meaningful only when attached to a visible issuer. This interaction survives two-way (entity + date) fixed effects ($p=0.012$) and winsorization ($p=0.004$). Panel quantile regressions reveal distributional heterogeneity in the brand-sentiment channel. Rolling 90-day window estimation confirms the mechanism is episodic, with the interaction achieving significance in 62.8% of subsample windows. These results provide suggestive evidence for a brand-filtered sentiment transmission mechanism in digital asset markets. Full article

The high-value utilization of construction and demolition waste is critical for sustainable development in the building sector. However, in construction and demolition waste (CDW) recycling supply chains, existing studies lack a systematic analysis of pricing mechanisms for such recycled CDW as substrate products, particularly regarding interest coordination and the quantification of green value. To reveal the bargaining mechanism between farmers as recyclers and processors and supermarkets as retailers under an asymmetric bargaining structure, this study applies Nash bargaining theory to construct a dynamic game model. The study revealed that (1) when the green degree of a product reaches a certain level, it can obtain a sustainable market premium and create a stable income space for both parties. (2) The relative strength of the bargaining power between the two sides significantly affects the impact of market base scale changes on profit distribution. When the bargaining power of the supermarket is lower than the threshold and the bargaining power of the farmers is higher than the threshold, the difference in profit between the farmers and the supermarket is negatively correlated with the market base scale of the CDW as a substrate. (3) The green sensitivity level of consumers affects the difference in profit of the main body with the government subsidy to farmers. This level is determined by the value of the green sensitivity coefficient of consumers and presents a differentiated adjustment effect in different value ranges, which in turn affects the transmission direction of government subsidies to profit distribution. (4) When the green sensitivity coefficient and the green communication intensity of farmers and the investment level are lower than the corresponding critical values, the difference in social welfare with or without subsidies is positively correlated with the amount of the subsidy. This study provides decision support for farmers and supermarkets in designing rational bargaining strategies and offers insights for improving coordination and sustainability in construction and demolition waste recycling supply chains. Full article

Background: Studies of fish supply chain efficiency often rely on price spreads or frontier-based measures, which do not fully capture actor-level coordination performance in heterogeneous, informal supply chains. This study addresses this gap by developing a composite Market Efficiency Index (MEI) that integrates financial performance, operational quality, service equity, and relational governance. Methods: The MEI, a multidimensional alternative to frontier-based measures, was developed and applied to data collected from 250 supply chain actors in Sri Lanka. Results: The results show a clear efficiency gradient along the supply chain, with fishers scoring the lowest (MEI = 0.44), intermediaries moderate (MEI = 0.54), and retailers the highest (MEI = 0.67), yielding an overall system efficiency of 0.55 and relational governance emerging as the weakest system-level dimension. These results indicate persistent structural differences in value distribution and in how well the fish supply chain functions as a cohesive network, driven by liquidity constraints, information asymmetry, and weak cold-chain infrastructure. Conclusions: A multidimensional supply chain assessment provides a more effective basis for diagnosing coordination constraints and enables targeted digital interventions that offer feasible pathways to improve transparency, liquidity, and inclusiveness in smallholder-dominated fish supply chains. Full article

Green investment is increasingly important in sustainable supply chain management, but it remains unclear whether the associated costs should be borne by manufacturers or retailers in competitive markets. To address this issue, this study develops a two-tier green supply chain model with one manufacturer and two competing retailers, where demand depends on retail prices and product greenness. A Stackelberg game framework is used to compare two green cost-bearing structures: manufacturer-borne green cost (MBG) and retailer-borne green cost (RBG). The results show that neither mode is universally superior. When green investment costs are low, both modes lead to the maximum feasible green level. When costs are higher, their relative performance depends on product substitutability and green cost sensitivity. Stronger substitutability increases the strategic value of greenness and may favor RBG, whereas higher green cost sensitivity tends to favor MBG because manufacturers can recover green investment through wholesale pricing. This study contributes by clarifying how green cost allocation affects pricing, demand, and profit distribution under retail competition, and it provides guidance for designing green investment arrangements in practice. Full article

Ours study focuses on the Last mile Distribution (LD) sector, which has been significantly affected by digital transformation, to examine changes in the Distribution and Logistics (DL) industry from the perspective of the national industrial system. We employed the Input–Output (IO) framework and delineated the LD and other DL industries by reconfiguring the generic IO data, which do not specify these sectors. We also constructed industrial relational networks based on the IO analysis outcomes to examine their structural properties further. Our analysis found that the forward linkages of the LD sector have been more significant than its backward linkages, and that the forward linkages tend to strengthen with digital transformation. However, the backward linkages were not strengthened by digital transformation. Network analysis also confirmed the structural hole characteristics of the LD sector and its role as a powerful authority upon which major industries depend. In particular, the latter provides structural support for the strong forward linkage effect of the LD sector. Our findings provide insights into effective policies to digitalize the DL industries. For example, if the LD sector were deregulated in an innovative manner to support the co-evolution of other industries, the national supply chain would be further enhanced. Full article

Rapid metropolitan expansion reshapes not only land-use patterns and infrastructure networks but also the spatial organization of micro-commercial systems embedded in everyday urban life. While large-scale retail restructuring has been extensively examined, the mechanisms underlying micro-commercial spatial transformation remain insufficiently theorized, particularly in rapidly urbanizing contexts. This study investigates the spatio-temporal restructuring of a representative low-threshold urban food economy in Xi’an between 2014 and 2024. Using multi-temporal point-of-interest (POI) data, kernel density estimation, and spatial Shannon entropy, we model changes in intensity gradients, distributional complexity, and zonal differentiation across morphologically distinct urban belts. The results reveal a systematic transition from centralized concentration toward polycentric embedding, characterized by the relocation of clustered micro-commercial activities along metro corridors and within emerging residential zones. Unlike classical decentralization, which implies outward diffusion, polycentric embedding reflects the infrastructural and demographic re-anchoring of clustered economic activities within newly stabilized urban territories. Entropy analysis further indicates increasing structural heterogeneity in metropolitan expansion zones, while historic cores retain symbolic concentration but exhibit declining structural dominance. These findings demonstrate that micro-commercial systems reorganize not through random dispersion, but through infrastructure-mediated embedding processes driven by metro expansion, residential aggregation, and institutional anchoring. By integrating longitudinal POI data with spatial complexity metrics, this study advances a replicable analytical framework for linking micro-scale commercial dynamics with metropolitan structural transformation. The study contributes to urban theory by reframing low-threshold economic systems as embedded infrastructures of everyday urban reproduction and provides planning insights for fostering resilient and spatially balanced commercial ecosystems under rapid metropolitan growth. Full article

Effective supply chain planning increasingly requires balancing cost-efficiency with environmental responsibility, particularly as organisations face growing pressure to reduce the carbon footprint of logistics operations. This study develops a mixed-integer linear programming model to optimise inventory and transportation decisions in a multi-echelon distribution network comprising a central warehouse, regional warehouses, and retailers. The model integrates a continuous-review (r,Q) replenishment policy, stochastic demand, safety stock requirements, transportation lead times, and stockout behaviour, enabling a detailed representation of operational dynamics under uncertainty and environmental concerns. Unlike most sustainable inventory models—which typically treat environmental impacts and replenishment control separately or rely on simplified service assumptions—this study provides an integrated framework that jointly embeds (r,Q) policies, stochastic demand, stockouts and distance-based CO₂ metrics within a unified optimisation structure. The model advances prior work by explicitly integrating continuous-review (r,Q) replenishment policies with distance-based CO₂ metrics under stochastic demand, a combination rarely addressed in sustainable multi-echelon inventory models. A multi-objective formulation captures the trade-off between economic performance and CO₂ emissions, allowing the identification of Pareto-efficient strategies that reconcile financial and environmental goals. Reducing emissions by over 90% requires an additional cost of only about 4%, demonstrating that substantial emission reductions can be achieved at relatively low additional cost. The findings offer practical insights for managers seeking to design more sustainable and cost-effective distribution policies, highlighting the value of integrated optimisation approaches in contemporary logistics systems. Full article

Salmonella is a major cause of foodborne illness worldwide, posing significant risks to public health and food safety. This study investigated the prevalence, serovar distribution, genotypic characteristics, and antimicrobial resistance (AMR) profiles of Salmonella. A total of 2515 food samples were collected from retail markets, supermarkets, and food processing facilities, and 13,670 stool samples were obtained from sentinel hospitals across 14 cities in Liaoning. The Kruskal–Wallis test was used to compare genetic features among serovars, followed by Dunn’s post hoc test for pairwise comparisons. A total of 314 Salmonella strains were identified, with raw poultry showing the highest detection rate (28.88%) among food sources and children aged 0–6 years (3.47%) the highest among the clinical age groups. Among food samples, S. Enteritidis was the most prevalent serovar (42.6%), and it was also the most common in clinical samples (35.8%); in contrast, S. 4,[5],12:i:- was dominant in pediatric clinical cases. According to AMR analysis, 90.13% of strains were resistant to at least one antibiotic and 67.83% were multidrug-resistant (MDR), with the highest resistance to ampicillin (68.47%). Analysis revealed that S. 4,[5],12:i:- harbored the ASSuT resistance module (blaTEM-1B, aph(3″)-Ib/aph(6)-Id, sul2, tet(B)). Extensive MDR phenotypes were observed in S. Indiana and S. Kentucky, associated with abundant insertion sequences (IS) and resistance genes (ARGs), including clinically critical determinants (blaNDM-9, mcr-1.1, rmtB). The highest mean virulence factor (VF) count (111.17) was observed in S. Enteritidis, contributing to its epidemiological success. Conversely, S. Indiana and S. Kentucky, predominantly food-associated serovars, exhibited reduced virulence but served as critical AMR reservoirs. These findings highlight the epidemiological characteristics and AMR risks of Salmonella in food and clinical settings, providing critical data for food safety and clinical antimicrobial stewardship. Full article

Background: Spare parts distribution networks (DNs) play a strategic role in retailers’ profitability. Among DN configuration decisions, selecting the optimal stock deployment policy—centralised, decentralised, or hybrid inventory allocation across distribution centres (DCs)—critically affects service levels and logistics costs. This decision becomes more complex with additive manufacturing (AM) as an alternative to conventional manufacturing (CM). While AM enables production with shorter lead times, its higher costs alter stock deployment cost-effectiveness. Given the complexity of joint stock deployment and manufacturing decisions, retailers require decision support systems (DSSs). Methods: To address this need, we develop a DSS through a three-step methodology: (i) a mathematical model evaluates logistics costs across different stock deployment policies and manufacturing technologies; (ii) parametric analysis tests the model across 2000 realistic scenarios; (iii) Random Forest trained on this dataset predicts optimal solutions, with SHapley Additive exPlanations (SHAP) interpreting post hoc recommendations. Results: The DSS achieves 93.4% prediction accuracy—outperforming (+16.4%) the only comparable literature DSS (77%)—while explaining recommendations. SHAP reveals that AM and CM unit costs dominate decision-making, followed by backorder costs. Conclusions: Beyond individual spare parts recommendations, the DSS provides guidelines enabling retailers to maintain cost-effective DNs aligned with evolving customer needs and to plan valuable investments in AM. Full article

As a world-renowned tourist and gaming city, Macau’s jewelry industry has formed significant spatial clustering driven by the integration of the tourism and gaming industries. However, existing research has not thoroughly explored the coupling mechanism between the agglomeration of this high-value industry and tourism potential circulation characteristics. Meanwhile, the industry confronts practical challenges, including an unbalanced layout between high-end and local brands, intense competition in core areas, and distinct service coverage blind spots in non-core areas. To fill these research gaps, this study takes the Macau Special Administrative Region as the research scope, integrates POI kernel density estimation, Voronoi diagram analysis, and space syntax to construct a three-dimensional analytical framework encompassing agglomeration intensity, service scope, and tourism flow matching, and systematically investigates the spatial clustering pattern of jewelry stores and its coupling mechanism with tourism potential circulation. The study reveals the following findings: (1) Jewelry stores exhibit a dual-segment, four-core clustering pattern. Among these, 38 high-end brands are concentrated in casino complexes and their surrounding areas, 34 comprehensive brands are evenly distributed across core and residential areas, and 300 local brands are mainly scattered in residential areas of the Macau Peninsula. (2) The service scope of jewelry stores is negatively correlated with agglomeration density. The Voronoi diagram area in core areas is 62% smaller than that in non-core areas, accompanied by a high degree of overlap—35% for high-end brands—and intense competition. In contrast, non-core areas have coverage blind spots accounting for 18% of Macau’s total land area. (3) Under a 300 m walking radius, high-integration paths identified by space syntax demonstrate an 85% matching degree with tourist routes, and the four core areas form differentiated coupling types. This study is the first to quantify the differentiated coupling mechanism between multi-level jewelry brands and tourism potential circulation. It further improves the GIS analysis framework for the coupling between commercial agglomeration and tourist behavior. The revealed negative correlation between service scope and agglomeration density, and the adaptive principle between brand spatial layout and regional functional attributes, provide universal references for similar business formats in tourist cities, including cultural and creative retail and characteristic catering. In practice, this research optimizes the spatial layout of Macau’s jewelry industry and increases the coverage rate of service blind spots to over 85%. It also provides scientific support for tourism route planning and the coordinated development of tourism and commerce in high-density tourist destinations. Full article

The rapid deployment of surveillance cameras in urban, industrial, and domestic environments has intensified the need for intelligent systems capable of analyzing video streams beyond the limitations of single-camera setups. Unlike traditional single-camera approaches, multi-camera systems expand spatial coverage, reduce blind spots, and enable consistent tracking of people and objects across non-overlapping views, thereby improving robustness against occlusions and viewpoint changes. This article presents a comprehensive review of multi-camera vision systems published between 2020 and 2025, covering application domains including public security and biometrics, intelligent transportation, smart cities and IoT, healthcare monitoring, precision agriculture, industry and robotics, pan–tilt–zoom (PTZ) camera networks, and emerging areas such as retail and forensic analysis. The review synthesizes predominant technical approaches, including deep-learning-based detection, multi-target multi-camera tracking (MTMCT), re-identification (Re-ID), spatiotemporal fusion, and edge computing architectures. Persistent challenges are identified, particularly in inter-camera data association, scalability, computational efficiency, privacy preservation, and dataset availability. Emerging trends such as distributed edge AI, cooperative camera networks, and active perception are discussed to outline future research directions toward scalable, privacy-aware, and intelligent multi-camera infrastructures. Full article

Antimicrobial-resistant bacteria associated with poultry production pose an ongoing challenge for food safety and veterinary public health. The present study evaluated the prevalence, antimicrobial resistance phenotypes, and selected resistance genes of E. coli recovered from broiler chicken carcasses and cecal content in Romania. Over a 12-month period in 2024, a total of 444 samples were collected, including 300 carcasses obtained from slaughterhouses and retail outlets and 144 cecal samples collected at slaughterhouses. Isolates were recovered using standard microbiological procedures and confirmed through biochemical and automated identification systems. Antimicrobial susceptibility was assessed using a minimum inhibitory concentration–based automated platform, and extended-spectrum β-lactamase (ESBL) production was evaluated phenotypically. Target resistance genes were investigated by PCR. E. coli was identified in 36.0% of carcass samples and 64.6% of cecal samples. High resistance rates were observed for tetracycline (82.6%), ampicillin (68.3%), and trimethoprim–sulfamethoxazole (61.2%), while multidrug resistance occurred in 34.3% of isolates. ESBL production was detected in 11.1% of carcass isolates and 11.8% of cecal isolates and was associated with the presence of blaCTX-M. Additional resistance determinants, including blaTEM, tetA, tetB, sul1, dfrA1, and aadA1, were widely distributed among isolates from both sources. The results suggest that poultry carcasses may contribute to the dissemination of resistant and ESBL-producing E. coli, reflecting intestinal carriage and contamination during processing. Strengthened antimicrobial stewardship, systematic resistance monitoring, and improved hygiene practices throughout the poultry production chain are essential to reduce the public health impact of resistant bacteria. Full article

Microplastics (MPs) have emerged as a growing environmental and food safety concern, with their presence widely reported in aquatic organisms and seafood. However, their occurrence in traditionally processed and fermented fish products remains unexplored. This study provides the first evidence of MP contamination in ethnic fermented fish products of Northeast India, namely Ngari, Hentak, and Shidal. MPs were analyzed for abundance, size distribution, morphology, color, and polymer composition using microscopic examination and Laser Raman Spectroscopy. The average MP abundance was 16.50 ± 5.18 MPs/g in Ngari, 15.73 ± 4.83 MPs/g in Shidal, and 20.50 ± 3.00 MPs/g in Hentak. Fibers and fragments were the dominant morphotypes across all products, with transparent and black particles occurring most frequently. Polymer characterization revealed polyethylene (PE) and polypropylene (PP) as the predominant polymers, followed by polyamide (PA), polyvinyl chloride (PVC), and polystyrene (PS). Size distribution analysis showed that MPs in the 101–300 µm range were most abundant in Ngari and Shidal, whereas smaller MPs (<50 µm) predominated in Hentak. The use of whole fish, including the gastrointestinal tract and gills, primary sites for MP accumulation, along with non-standardized fermentation practices and atmospheric deposition during retail, likely contributes to contamination. These findings highlight an overlooked route of human exposure to MPs through traditional fermented foods and underscore the need for improved processing practices and mitigation strategies to safeguard food safety and sustainability. Full article

In many organizations, business intelligence systems support analytical reporting and operational decision making. As data volumes grow and analytical tasks become more complex, architectures based on centralized processing pipelines increasingly face limitations related to scalability and timely response. These challenges motivate the development of alternative architectural approaches capable of operating efficiently in data-intensive environments. This study presents a modular multi-agent business intelligence framework that distributes analytical tasks across autonomous agents and applies lightweight reinforcement learning at the decision-making stage. The analytical workflow is decomposed into agents responsible for data collection, preprocessing, analytical modeling, and decision execution. Decision adaptation relies on localized policy updates driven by operational feedback, which avoids complex learning coordination and helps preserve system stability and interpretability. The proposed framework is evaluated using real-world transactional data from an electronic commerce setting. Experimental results show that the approach consistently outperforms centralized analytical pipelines and non-agent machine learning baselines in terms of processing efficiency, classification accuracy, and balanced classification performance. Threshold-independent evaluation further confirms stronger discriminative behavior across varying decision thresholds. In addition, stability analysis across repeated experimental runs indicates reduced performance variance and more predictable system behavior. These findings suggest that the proposed multi-agent business intelligence framework provides a practical and scalable alternative to centralized analytical architectures for data-intensive decision-support environments, while maintaining the robustness and transparency required in enterprise systems. The evaluation is limited to a single dataset and a classification task, and results should be interpreted within this scope. Experiments on the Online Retail dataset (UCI Machine Learning Repository) show an average accuracy of 0.89 ± 0.012 (baseline: 0.74 ± 0.029) and decision latency of 94 ± 9 ms (baseline: 137 ± 16 ms) across 10 independent runs, indicating stable behavior under repeated execution. Full article