Underwater electroacoustic transducers detect and localize targets beneath the water surface by generating acoustic waves. Due to their high power and simple structure, Tonpilz transducers are commonly used in underwater applications. To enhance data transmission speed and improve target detection capabilities using these transducers, it is necessary to increase their frequency bandwidth. One method of broadening the bandwidth is by adding damping elements to the transducer; however, this approach reduces the transmitted voltage response. In other words, increasing the frequency bandwidth comes at the cost of a reduced voltage output. To address this issue, arrays are typically used. Arrays are groups of transducers arranged together to improve performance and direct acoustic energy in a desired direction. Since accurate identification and estimation of bandwidth are critical to the performance and efficiency of a transducer—and ultimately the electroacoustic array—and given the high cost of manufacturing such transducers and arrays, the finite element method (FEM) is considered a highly desirable tool for analyzing and estimating the frequency bandwidth of electroacoustic arrays. Planar arrays are the simplest type of array. In the present study, the frequency responses of several planar arrays in square, circular, and diamond configurations have been comprehensively examined using finite element modeling. The effects of changes in array geometry, as well as variations in the number of transducers and their spacing, on the arrays’ performance have been predicted. Based on the obtained results, among three kinds of square arrays with different inter-element spacing, the array with a spacing of 0.4$\lambda$ between transducers exhibits the widest bandwidth. Additionally, among the two simulated circular arrays, the one with more elements demonstrates a higher transmitted voltage response and broader bandwidth. Furthermore, altering the array shape can reduce side lobes and help achieve the desired beam pattern. Overall, selecting the optimal array depends on the intended application, operating range, working environment, existing noise levels, and potential interference sources. Depending on these conditions, any of the examined arrays can be utilized effectively.
Selection of the optimal warehouse location represents a key strategic decision in modern logistics, particularly in the context of the rapid development of e-commerce and the increasing complexity of supply chains. The aim of this research is to identify the most favorable warehouse location within the urban area of Belgrade by applying multi-criteria decision-making (MCDM) methods. Specifically, a hybrid methodology that integrates the Step-wise Weight Assessment Ratio Analysis (SWARA) and Additive Ratio Assessment (ARAS) methods was employed to evaluate five real-world alternative locations based on eight relevant criteria. The considered criteria include: land cost, delivery time, infrastructure accessibility, labor availability, access to multiple modes of transport, site capacity, environmental conditions and regulatory compliance, as well as the competitiveness of the location itself. Criterion weights were determined through expert evaluation using the SWARA method, while the ARAS method was applied to rank the alternatives based on their normalized performance scores. The analysis indicated that the location in Batajnica (A1) is the most favorable, closely followed by the location on Pančevački Road (A3), owing to their balanced performance across economic, infrastructural, and operational dimensions. In contrast, the location in Kaluđerica/Leštane (A4) proved to be the least suitable, primarily due to poor infrastructure access and limited labor availability. The results confirm the applicability and effectiveness of combining SWARA and ARAS methods for solving complex decision-making problems involving multiple, often conflicting, criteria.
Energy remains a cornerstone of national economic development and societal advancement. However, the current trajectory of global energy production—dominated by fossil fuels and driven by escalating demand—is environmentally unsustainable. Electricity, as a versatile and high-grade form of energy, offers the advantage of being generable from both conventional and renewable sources. Nevertheless, fossil fuel–based electricity generation continues to contribute significantly to local and global environmental degradation. In response to the dual imperatives of meeting rising energy demand and reducing greenhouse gas emissions, the identification and prioritisation of sustainable electricity generation technologies have become imperative. Renewable energy sources (RES)—such as solar, wind, hydro, and biogas—offer viable alternatives, yet their relative merits must be evaluated through a rigorous and systematic approach. In this study, a multi-criteria decision-making (MCDM) framework has been employed to assess and rank RES in the Republic of Serbia. Key evaluation criteria have included construction cost, payback period, ecological impact, annual generation capacity, and potential for integration with alternative energy modes. The assessment has been conducted using the FANMA method (a novel hybrid technique named after its developers) and the Weighted Aggregated Sum Product Assessment (WASPAS) method, both of which are established tools for handling complex decision-making scenarios. The findings have provided a data-driven basis for prioritising renewable energy technologies in national energy strategies. The insights derived are expected to inform policy decisions in Serbia and offer a transferable framework for energy planning in other developing economies aiming to transition towards more sustainable power generation systems.
The increasing demand for efficient and sustainable last-mile delivery solutions has presented a significant challenge in the evolving landscape of e-commerce logistics. To address this issue, a systematic evaluation and prioritization of six alternative delivery methods—namely, home delivery, workplace delivery, delivery to a neighbor or acquaintance, staffed pickup points, unstaffed (automated) pickup points, and third-party drop-off locations—has been conducted. These alternatives have been assessed against a comprehensive set of criteria, including delivery time flexibility, accessibility, cost-efficiency, security, speed of service, and ease of product return. To capture the nuanced preferences and subjective judgements of stakeholders, the Fuzzy Factor Relationship (FARE) method has been employed to determine the relative importance of each criterion through a structured fuzzy logic framework. Subsequently, the Aggregated Decision-Making (ADAM) method has been applied to rank the delivery alternatives, integrating evaluations from key stakeholder groups—consumers, retailers, and logistics service providers. The findings reveal that unstaffed pickup points, particularly those leveraging automated systems, represent the most balanced and sustainable solution, offering superior performance in terms of cost-effectiveness, user accessibility, and operational flexibility. In contrast, while home delivery continues to be favored for its convenience, it remains constrained by elevated operational costs and limited scheduling flexibility. The methodological integration of Fuzzy FARE and ADAM ensures a robust and transparent decision-support mechanism that accounts for both qualitative and quantitative factors. These insights are expected to guide strategic decision-making in last-mile logistics (LML), contributing to service quality enhancement, operational cost reduction, and the advancement of environmentally responsible delivery systems. This evaluation framework offers practical relevance to e-commerce platforms, third-party logistics providers, and urban mobility planners seeking to implement scalable and customer-centric delivery models in complex urban environments.
To mitigate safety risks in subway shield construction within water-rich silty fine sand layers, a risk immunization strategy based on complex network theory was proposed. Safety risk factors were systematically identified through literature review and expert consultation, and their relationships were modeled as a complex network. Unlike traditional single-index analyses, this study integrated degree centrality, betweenness centrality, eigenvector centrality, and clustering coefficient centrality to comprehensively evaluate the importance of risk factors. Results indicated that targeted immunization strategies significantly outperformed random immunization, with degree centrality (DC) and betweenness centrality (BC) immunization demonstrating the best performance. Key risk sources included stratum stability, allowable surface deformation, surface settlement monitoring, and shield tunneling control. Furthermore, the optimal two-factor coupling immunization strategy was found to be the combination of DC and BC strategies, which provided the most effective risk prevention. This study is the first to apply complex network immunization simulation to safety risk management in subway shield construction, enhancing the risk index system and validating the impact of different immunization strategies on overall safety. The findings offer scientific guidance for risk management in complex geological conditions and provide theoretical support and practical insights for improving construction safety.
In recent years, frequent natural disasters and public emergencies have emphasized the importance of emergency material distribution path planning. Aiming at the problems of neglecting the differences in the urgency of the demand at the disaster-stricken points and the lack of distribution fairness in traditional research, this study proposes an emergency material distribution path planning method that integrates the priority assessment of the disaster-stricken points and multi-objective optimization. First of all, a two-level evaluation system is constructed from the dimensions of disaster degree and material demand, including the number of rescue population and other indicators, and the combined weights are calculated by combining the subjective and objective methods of hierarchical analysis (AHP) and entropy weighting, so as to quantify the urgency coefficient of the demand at each disaster site and break through the limitations of the traditional “nearby distribution” mode. On this basis, a vehicle path planning model is established with the dual objectives of minimizing the total distribution cost and vehicle load balance, and the elite strategy non-dominated sorting genetic algorithm (NSGA-II) is introduced to solve the problem. Scenario analysis is carried out with the background of public health emergencies in Jingzhou City, and the effectiveness of the model is verified based on the actual data of 64 medical material demand points. The simulation results show that the total distribution distance and vehicle load balance are optimized after optimization. Finally, it is suggested in conjunction with the current situation of emergency material distribution in China. Through the quantification of demand urgency and multi-objective collaborative optimization, this study provides theoretical basis and practical reference for improving the fairness, timeliness and resource utilization efficiency of emergency logistics, and has important application value for improving disaster relief decision-making.
The Location-Routing Problem (LRP) involves the simultaneous determination of optimal facility locations and vehicle routing strategies to fulfill customer demands while adhering to operational constraints. Traditional formulations of the LRP primarily focus on delivery-only scenarios, where goods are allocated from designated warehouses to customers through a fleet of vehicles. However, real-world logistics often necessitate the simultaneous handling of both deliveries and pickups, introducing additional complexity. Furthermore, inherent uncertainties in demand patterns make precise parameter estimation challenging, particularly regarding the quantities of goods received and dispatched by customers. To enhance the realism of the model, these demand variables are represented using fuzzy sets, capturing the uncertainty inherent in practical logistics operations. A mathematical model is developed to account for these complexities, incorporating a heterogeneous fleet of vehicles with capacity constraints. The optimization of the proposed fuzzy capacitated LRP with simultaneous pickup and delivery is conducted using a Genetic Algorithm (GA) tailored for fuzzy environments. The efficacy of the proposed approach is validated through numerical experiments, demonstrating its capability to generate high-quality solutions under uncertain conditions. The findings contribute to the advancement of location-routing optimization methodologies, providing a robust framework for decision-making in uncertain logistics environments.
This study investigates the application of numerical simulations to optimize the design and operational performance of CNC machining centers, with a focus on enhancing their structural integrity and durability. The primary objective is to identify design modifications that can mitigate the risks associated with mechanical impacts and extend the service life of the machines. Finite Element Method (FEM) simulations are conducted on actual CNC machines to examine their structural responses under a range of real-world impact scenarios. The simulations reveal critical stress concentrations and deformation patterns that occur in operational environments, providing valuable insights into the dynamic behavior of the machines. A system engineering approach is employed to simplify the analysis of the machine's response to these dynamic conditions, allowing for an efficient evaluation of potential design improvements. Linear static analyses, incorporating imposed deformation conditions, are used to gain a deeper understanding of the machine’s structural weaknesses. Several model simplifications are introduced, including modifications to geometry, contact conditions, and material properties, ensuring that the quality and accuracy of the numerical models are maintained. The results highlight the potential for targeted design modifications to reduce the likelihood of mechanical failure and enhance operational efficiency. These findings suggest that the application of advanced computational mechanics can substantially improve machine performance, ultimately contributing to the longevity and reliability of CNC machining centers.
In the context of today’s rapidly evolving automotive market, improving the reliability and efficiency of manufacturing processes remains a critical challenge for industry players. This study introduces a hybrid multi-attribute decision-making model that integrates Failure Mode and Effects Analysis (FMEA) with interval type-2 fuzzy set theory to classify and prioritize process failures. The approach enables the FMEA team to systematically identify and rank failure modes, facilitating the timely implementation of corrective actions aimed at enhancing process reliability. A key feature of the proposed model is the utilization of interval type-2 triangular fuzzy numbers (IT2TFNs), which capture the inherent uncertainty in expert assessments of risk factors (RFs). These fuzzy values are aggregated using the fuzzy harmonic mean, and the total relation matrix is derived by applying fuzzy algebraic operations, followed by defuzzification and distance calculations between fuzzy numbers. The modified Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is employed to determine the relative weights of identified RFs, while the Multi-Attributive Border Approximation Area Comparison (MABAC) technique is used to rank failure modes based on their impact on manufacturing process reliability. The model’s effectiveness is demonstrated through its application to real-world data from an automotive supply chain, highlighting its superior capability compared to conventional approaches. This research contributes to the advancement of failure management strategies, providing a comprehensive and robust framework for decision-making in complex manufacturing environments.
Multi-functional public teaching buildings, as high-density spaces, are subject to significant fire risks due to the large number of occupants and the complex nature of their design. In the event of a fire, the consequences can be catastrophic. Therefore, fire risk assessment is of paramount importance in the design and operation of such buildings. A comprehensive evaluation framework is proposed, integrating the Work Breakdown Structure (WBS) and the Risk Breakdown Structure (RBS) into a unified approach, referred to as the Integrated Work Breakdown Structure and Risk Breakdown Structure (i-WRBS) method. This framework identifies 15 key fire risk factors relevant to public school buildings. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is employed to analyze the interrelationships among these factors, while PyroSim fire simulation software is used to model the dynamics of fire smoke propagation under varying wind conditions. The diffusion of smoke in stairwells is simulated under different wind speeds and directions, and the fire risk is evaluated based on the resulting outcomes. The findings indicate that both wind speed and direction play a crucial role in determining the trajectory and velocity of smoke spread, especially within stairwells. Under low wind conditions or in the absence of wind, smoke diffusion is confined to areas close to the fire source, with stairwells located farther from the fire exhibiting comparatively lower risks. However, under higher wind speeds, the speed and range of smoke diffusion are significantly increased, with a pronounced effect in the downwind direction. The fire hazards on higher floors are found to be more sensitive to variations in wind speed, as increased wind velocity leads to more substantial fluctuations in temperature caused by the combustion process. These fluctuations are exacerbated on higher floors. The findings offer valuable insights into fire risk management, contributing to the development of fire safety strategies and the formulation of evacuation plans for large public buildings.
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