Search Results (77)

Post-disaster rescue scenarios often involve complex and variable terrains, imposing heterogeneous mobility requirements on different transport modes. Single-type vehicles face challenges in independently completing comprehensive rescue tasks. This study addresses the critical problem of coordinating heterogeneous aerial and ground vehicles to collaboratively plan relay rescue routes. To tackle the NP hard multi-terrain, multi-vehicle, and multi-route path planning problem, we propose a New Adaptive Multi-Strategy Particle Swarm Optimization algorithm (AMS-PSO-NEW). The algorithm features a synergistic integration of differential evolution’s multi-strategy mutation, SHADE-based adaptive parameter control, population diversity monitoring with restart mechanisms, and multi-level local search. A sequential hybrid mechanism is designed in which DE-generated trial vectors serve as reference positions for PSO velocity updates, enabling balanced global exploration and local exploitation. By leveraging adaptive parameter tuning, success history memory, and diverse population maintenance, AMS-PSO-NEW effectively overcomes premature convergence and low accuracy issues typical in discrete combinatorial optimization using traditional PSO, achieving a balanced global exploration and local exploitation. Performance validation is conducted over six rescue scenarios varying in scale and complexity, benchmarking AMS-PSO-NEW against nine algorithms: PSO, GA, NSGA-II, GWO, DE, ABC, CS, Q-learning, and MIP. Results demonstrate superior performance across four metrics (rescue success rate, average rescue time, total cost, and fairness), with significant improvements in high-complexity environments. Full article

The Artificial Bee Colony (ABC) algorithm is a simple and effective population-based optimization method, but it may exhibit unstable convergence and weak exploitation capability in discrete and highly constrained problems. This study proposes an improved ABC framework that integrates a probabilistic Uniform crossover operator and a gene-level lock mechanism to enhance convergence stability and local refinement. The framework is applied to an integrated multi-resource allocation problem in liquid transportation, which has not previously been addressed within the ABC literature. The problem requires the simultaneous assignment of drivers, trucks, trailers, and ISO tanks under operational and regulatory constraints. Comparative analysis of different ABC configurations shows that integrating only Uniform crossover reduced the mean cost to 17.78, adding only the lock mechanism reduced it to 29.78, and combining both further decreased it to 14.94, indicating a complementary effect between the two mechanisms. The proposed configuration consistently achieved the lowest mean costs across small, medium, and large datasets. Compared with established metaheuristic algorithms and expert manual planning (34.72), the method produced lower-cost and feasible solutions, demonstrating both algorithmic robustness and practical relevance. Full article

This paper addresses the challenge of simultaneous arrival for UAV clusters and proposes a route planning method based on an enhanced Non-dominated Sorting Genetic Algorithm III (NSGA-III). Initially, the paper defines the simultaneous arrival problem and formulates the corresponding mathematical model, considering the complexity of multi-objective optimization in UAV clusters. A novel path generation framework is introduced, which incorporates multiple optimization objectives—such as time coordination, threat mitigation, and resource consumption—aimed at improving flight safety, efficiency, and resource management. To enhance the algorithm’s search performance, a hybrid approach combining the Artificial Bee Colony (ABC) algorithm with NSGA-III is proposed. This improved NSGA-III strategy overcomes the limitations of the original algorithm in managing complex constraints and multi-objective optimization problems, resulting in significant improvements in search accuracy and convergence speed. Finally, the performance of the improved algorithm is evaluated through simulations and compared with traditional methods. The results show that the proposed approach optimizes flight time, reduces resource consumption, and effectively mitigates threats, all while ensuring the simultaneous arrival of UAV clusters. Full article

The article presents a method that supports the evaluation of design manufacturability in the area of input material selection, enabling the reduction in material diversity under single-piece and small-batch production conditions. The proposed approach combines the analysis of alternative materials with activity-based costing (ABC) and data concerning actual and planned material requirements. The method enables the assessment of the impact of semi-finished product substitution on material costs, processing costs, production organisation, and material-management costs before order execution is launched. In the conducted case study, it was demonstrated that effective management of material diversity can significantly reduce the range of materials and decrease total manufacturing costs. For the analysed period, the number of material items was reduced from 32 to 19 (a 41% reduction), resulting in cost savings of approximately 11,000 PLN. In addition to total cost, the approach supports the assessment of operational benefits associated with reduced material diversity, such as a lower number of material items, fewer suppliers, reduced inbound inspection and receipt operations, and decreased inventory levels and capital tied up in stock. Material substitution may decrease or increase direct material costs and may increase machining time when larger dimensions are used; therefore, the method jointly evaluates cost and lead-time impacts prior to order release. The results confirm that integrating design, technological, and logistics data is an effective approach to rationalising material management in machinery manufacturing enterprises. Full article

The growing economic pressures on healthcare systems have heightened the need for effective and sustainable cost management strategies. This study presents a PRISMA-based systematic review of 210 peer-reviewed articles published between 1974 and 2024, retrieved from the Scopus and Web of Science databases. Following a structured selection and screening process, the articles were analysed to identify dominant cost control tools, contextual applications, and methodological trends across diverse health systems. The findings highlight a strong prevalence of Activity-Based Costing (ABC), Diagnosis-Related Groups (DRG), and benchmarking practices, predominantly in public hospital settings. However, significant thematic gaps remain, particularly concerning low-income countries, interdisciplinary integration, and the evaluation of digital technologies for financial optimisation. This review provides a comprehensive thematic synthesis of international research, consolidating knowledge in healthcare cost management and offering evidence-based recommendations to guide future empirical research, policy design, and strategic planning in health finance. Full article

Low-carbon agriculture (ABC—from the acronym in Portuguese) encompasses techniques that reduce carbon emissions while maintaining productivity and profitability. Among these, the restoration of degraded pastures is a major focus of the Brazilian ABC policy, achieved through improved pasture management or crop–livestock integration. This study analyzed the relationship between ABC credit and improvements in pasture vigor in the municipalities of Minas Gerais from 2015 to 2022, considering the carbon-mitigation potential of each region. We evaluated whether credit resources were directed toward areas with greater mitigation potential and whether this investment contributed to pasture recovery. Composite indexes were developed to represent credit investment, pasture dynamics, and theoretical carbon removal potential, followed by spatial mapping and correlation analysis. The results show that ABC credit was strongly concentrated in regions with high carbon-sequestration potential, especially Triângulo Mineiro and Alto Paranaíba, indicating a generally effective targeting of resources toward areas with greater mitigation potential. Correlation analysis also indicates a positive, although moderate, association between credit volume and pasture improvement at the municipal level. Although initial results indicated more substantial improvements in pasture vigor in lower-credit regions such as North of Minas, Jequitinhonha, and Mucuri Valley (with relative increases reaching up to 300%), an additional analysis considering the initial vigor level (baseline) revealed that these gains are strongly affected by initial pasture conditions. From a policy perspective, these findings highlight the importance of rural credit as a driver of sustainable technology adoption, while also showing that baseline conditions, technical assistance, and other public or private incentives can significantly influence restoration outcomes. Strengthening credit allocation criteria, improving technical support, and integrating carbon-mitigation indicators into regional planning could enhance environmental effectiveness. Full article

This paper presents the Circle-Mapping Transition and Weighted Red-Billed Blue Magpie Optimizer (CTWRBMO), designed to address significant challenges in 3D path planning for drones. Although the original Red-Billed Blue Magpie Optimizer (RBMO) algorithm features a simple structure, few parameters, and strong local search capability, making it well-suited for UAV path optimization, it suffers from insufficient population diversity, limited global search ability, and a tendency to fall into local optima in complex high-dimensional scenarios. To overcome these limitations, four enhancement strategies are introduced. Firstly, the Circle chaotic mapping strategy leverages the randomness and ergodicity of chaotic sequences to generate an initial population that is uniformly distributed. This enhancement improves population diversity from the beginning and provides a solid foundation for global optimization. Secondly, the ε parameter is dynamically adjusted to prioritize local refinement during the early stages of optimization. This adjustment enables rapid convergence toward potentially optimal areas. This parameter increases to enhance global search capabilities as the algorithm progresses, thereby broadening the optimization space and achieving a dynamic equilibrium. Additionally, a nonlinear dynamic weighting factor (wd) is incorporated into the position update formula. The algorithm’s ability to escape local optima is significantly improved by dynamically altering the weight ratio between historical optimal positions and the current position. Furthermore, an elite perturbation mechanism based on individual neighborhoods is implemented to generate candidate solutions using local information. This mechanism enhances the algorithm’s local exploration capabilities and improves the stability of preserving optimal solutions, supported by a greedy criterion for optimal retention. Experimental results show that the CTWRBMO algorithm significantly outperforms comparison algorithms in terms of optimization accuracy and convergence speed, demonstrating exceptional global optimization capabilities. Additional applications in UAV 3D path planning simulations evaluated paths based on length, threat avoidance efficiency, and smoothness. The results indicate that paths planned using CTWRBMO are shorter, safer, and smoother compared to those generated by the Harrier Hawks Optimization (HHO), African Vulture Optimization Algorithm (AVOA), Artificial Bee Colony (ABC) Algorithm, and the traditional Magpie Algorithm, effectively meeting practical engineering requirements for UAV 3D path planning. Full article

Background: Accurate volumetry and imaging characterization of chronic subdural hematoma (cSDH) are essential for prognostication and treatment planning, but manual assessment is time-consuming and therefore underutilized. Methods: We retrospectively analyzed preoperative non-contrast CT (NCCT) scans of 257 patients undergoing first-time surgery for uni- or bilateral cSDH. Hematoma volumes were measured manually using a semi-automated area-outlining tool on every second axial slice and compared with the volumes estimated through the ABC/2 formula. Hematoma attenuation patterns and components were categorized, and interrater reliability was assessed for volume, maximum diameter, and imaging features using intraclass correlation coefficients (ICCs) and Cohen’s κ. Results: A total of 339 hematomas were evaluated. Manual and ABC/2 volume measurements correlated strongly (R² = 0.83, ICC [3, 1] = 0.90). The interrater agreement for manual volumetry was excellent (ICC [2, 1] = 0.96). Agreement was also excellent for maximum diameter (ICC [2, 1] > 0.9) and good for midline shift assessment (κ = 0.81). Agreement was moderate for the identification of fresh clots, trabeculations, and laminations (κ = 0.62–0.72) but poor for general attenuation patterns (κ = 0.44). Conclusions: The manual volumetry of cSDH is feasible and highly reproducible between raters of different experience levels. These results provide a robust reference standard for the validation of automated volumetry tools and support the implementation of quantitative hematoma assessment in future clinical trials and routine care. Full article

Background: In the context of increasing supply chain complexity, efficient inventory management has become important in enhancing the performance of logistics systems and sustaining the competitiveness of companies. Real-time visibility, tracking, and control over stock levels ensure responsiveness, reduce waste, and support strategic decision-making. Decision support systems that integrate demand analysis with inventory policies play a pivotal role in improving operational efficiency. This paper addresses the need for more efficient stock management to optimize purchasing and inventory costs within a manufacturing environment. Methods: Production planning processes were analyzed to determine material requirements, and a representative product was selected. The study involved ABC classification based on the average annual stock value of purchased parts, complemented by an XYZ analysis to evaluate demand variability. Afterwards, stock management policies were tested, namely, continuous and periodic review models. Each item was assessed to determine the most suitable inventory management method based on its consumption profile. Results: A comparison with the company’s existing approach revealed that for 9 out of the 13 materials studied, the application of stock management models led to improvements. Conclusions: The results show a potential cost reduction of 33% for the nine materials to which stock policies were successfully applied. Full article

Background: Head and neck carcinomas represent a heterogeneous group of aggressive malignancies with often poor prognosis and high recurrence rates. In recent years, the identification and characterization of cancer stem cells (CSCs) within these tumors have profoundly reshaped our understanding of tumorigenesis, resistance mechanisms, and metastatic potential in this anatomical district. Cancer stem cells (CSCs) play a central role in therapeutic resistance, recurrence, and metastatic progression in head and neck squamous cell carcinoma (HNSCC), particularly within the anatomically complex maxillofacial region. This review has synthesized recent advances in CSC biology, including marker heterogeneity, stemness-associated pathways, and interactions with the tumor microenvironment. Methods: A narrative review of the available literature was conducted, focusing on studies dealing with cancer stem cells in head and neck carcinoma and their implications for maxillofacial surgery. Results: We have critically examined emerging systemic and locoregional CSC-targeted therapies, highlighting inhibitors of Notch, Wnt/β-catenin, Hedgehog, and Hippo/YAP pathways, ALDH and ABC transporter inhibitors, autophagy modulators, nanoparticle-based delivery systems, and CSC-directed immunotherapies. The implications of these approaches for surgical planning, resection margins, and postoperative disease control in maxillofacial oncology have been discussed. To enhance clarity and analytical value, we have incorporated two comprehensive tables summarizing CSC markers and therapeutic strategies. Collectively, the evidence indicates that integrating CSC-oriented diagnostics and therapeutics into multimodal management may improve long-term outcomes for patients with maxillofacial HNSCC. Conclusions: This review highlights the critical need for integrating CSC-focused research into clinical practice to develop more effective, personalized, and durable treatment strategies. Such an approach could enhance oncologic control, reduce recurrence, and improve functional outcomes for patients undergoing complex oncologic procedures in the maxillofacial region. Full article

The transition to electric vehicles (EVs) is pivotal for decarbonizing transport, yet the siting of EV charging stations (EVCSs) can load radial distribution networks with higher losses and more pronounced voltage drops. This study formulates the joint siting and sizing of EVCSs and distributed generators (DGs) as a constrained optimization that minimizes real and reactive losses and voltage deviation with integer bus location decisions. A novel version of the Artificial Bee Colony (ABC) algorithm known as GBest–Lévy Adaptive Differential ABC (GLAD-ABC) is introduced, combining global best guidance, differential perturbations, adaptive step sizes, Lévy-flight scouting, and periodic local refinement for finding the global optimum solution and avoiding local optima. The optimizer is coupled with a backward–forward sweep load flow and a EVCS power demand model. Validation on the IEEE-33 and IEEE-69 feeders across multiple scenarios shows that EVCS-only deployment degrades network performance, whereas optimizing EVCS and DG allocation via GLAD-ABC markedly improves voltage profiles and reduces both real and reactive losses. The proposed optimizer shows superior performance compared with other optimization algorithms reported in the literature, delivering consistently lower active losses alongside fast, stable convergence, indicating strong suitability for utility planning in EV-rich grids. Full article

In this research, an innovative, integrative method is applied, which not only links media discourse and statutory planning documents but also involves both quantitative and qualitative analysis. By going beyond the traditional extreme of either policy review or text-based SETS frameworks, this study becomes the pioneer of a dual-coded, matrix-driven approach, which is capable of measuring policy–implementation gaps and empirically revealing the impact of media framing on disaster management outcomes. The 29 October 2024 Valencia flood, which claimed over 229 lives, highlights critical shortcomings in the region’s flood management policies. This study evaluates media and institutional sources to examine how public discourse aligns with post-flood management strategies. It focuses on Valencia’s statutory flood management plan, the “Pla d’acció territorial de caràcter sectorial sobre prevenció del risc d’inundació a la Comunitat Valenciana” (“Regional Action Plan for Flood Risk Prevention,” PATRICOVA) and its limited integration with the Socio–Ecological–Technological Systems (SETS) framework, which we identify as a central weakness. By analyzing Spanish media coverage, particularly from sources such as El País, ABC, and La Vanguardia, alongside government policy documents, the study reveals a gap between theoretical flood risk planning and practical disaster response. Our keyword-based text mining of leading newspapers highlights the neglect of social, ecological, and technological interactions. While PATRICOVA emphasizes nature protection and technological infrastructure, it overlooks critical societal dimensions and climate adaptation scenarios. Media analysis reveals significant failures at the SETS interfaces, especially in early warning systems, intergovernmental coordination, and community preparedness. Full article

Transportation Management Systems (TMSs) play a pivotal role in streamlining logistics operations, yet selecting the most suitable TMS software remains a complex, multi-criteria decision-making problem. This study introduces a hybrid evaluation framework combining the Bayesian Best–Worst Method (BBWM) and TOPSIS to identify, weigh, and rank software selection criteria tailored to the logistics business. Drawing on insights from 13 logistics experts, five main criteria—technological competence, service, functionality, cost, and software developer (vendor)—and 16 detailed sub-criteria are defined to reflect business-specific needs. The core novelty of this research lies in its systematic weighting of TMS software criteria using the BBWM, offering robust and expert-driven priority insights for decision makers. Results show that functionality (26.6%), particularly load tracking (35.8%) and cost (22.7%), mainly software license cost (39.8%), are the dominant decision factors. Beyond operational optimization, this study positions TMS software selection as a strategic entry point for sustainable digital transformation in logistics. The proposed framework empowers business to align digital infrastructure choices with sustainability goals such as emissions reduction, energy efficiency, and intelligent resource planning. Applying TOPSIS to a real-world case in Türkiye, this study ranks software alternatives, with “ABC” emerging as the most favorable solution (57.2%). This paper contributes a replicable and adaptable model for TMS software evaluation, grounded in business practice and advanced decision science. Full article

This study presents a comprehensive approach to risk management and planned preventative maintenance (PPM) for cultural heritage, focusing on the hypogeum archaeological site beneath the Chamber of Commerce in Rimini, Italy. Hypogeal environments pose unique conservation challenges due to their microclimates, biological threats, and structural vulnerabilities. Applying the ABC Method—developed by ICCROM and CCI—this research systematically identifies, analyzes, and prioritizes risks associated with agents of risks. The methodology was complemented by the Nara Grid to assess the site’s authenticity and cultural value, aiding in the delineation of risk areas and informing strategic conservation priorities. The study identifies efflorescence formation, flooding risks, and lack of management guidelines as extreme threats, proposing tailored treatments and practical interventions across multiple layers of control. Through environmental monitoring, empirical analysis, and a multidisciplinary framework, the research offers a replicable model for sustainable conservation and preventive heritage management in similar subterranean contexts. Full article

This study identifies and refines the dimensions of cultural inheritance and innovative practices in industrial heritage-themed districts and develops a corresponding questionnaire scale. Based on the ABC model of attitudes, a conceptual model is constructed to examine the impact of cultural inheritance and innovation on tourist behavior, which is then empirically tested using Structural Equation Modeling (SEM). The findings reveal that the influence and mechanism of cultural inheritance and innovative practices on tourist behavior follow a continuous process in the sequence of cognition–affect–behavior tendency. All four dimensions of cultural inheritance and innovation exert a significant positive effect on tourist loyalty. Moreover, the affective component serves as a mediating factor within the chain reaction. This study constructs a new theoretical framework to explore how cultural inheritance and innovation jointly influence the formation of tourist loyalty and the underlying mechanisms, enriching the theoretical system of industrial heritage tourism and cultural management. It also provides practical theoretical support for district planning, design, and management. Full article