Search Results (3,919)

Urban road traffic congestion has become a serious issue for cold chain logistics in terms of delivery time, distribution cost, product freshness, and even organization revenue and reputation. This study focuses on the cold chain distribution path by considering road traffic congestion with transportation, real-time vehicle delivery speeds, and multiple-vehicle conditions. Therefore, a vehicle routing optimization model has been established with the objectives of minimizing costs, reducing carbon emissions, and maintaining cargo freshness, and a multi-objective hybrid genetic algorithm has been developed in combination with large neighborhood search (LNSNSGA-III) for leveraging strong local search capabilities, optimizing delivery routes, and enhancing delivery efficiency. Moreover, by reasonably adjusting departure times, product freshness can be effectively enhanced. The vehicle combination strategy performs well across multiple indicators, particularly the three-type vehicle strategy. The results show that costs and carbon emissions are influenced by environmental and refrigeration temperature factors, providing a theoretical basis for cold chain management. This study highlights the harmonious optimization of cold chain coordination, balancing multiple constraints, ensuring efficient logistic system operation, and maintaining equilibrium across all dimensions, all of which reflect the concept of symmetry. In practice, these research findings can be applied to urban traffic management, delivery optimization, and cold chain logistics control to improve delivery efficiency, minimize operational costs, reduce carbon emissions, and enhance corporate competitiveness and customer satisfaction. Future research should focus on integrating complex traffic and real-time data to enhance algorithm adaptability and explore customized delivery strategies, thereby achieving more efficient and environmentally friendly logistics solutions. Full article

The efficient and sustainable design of robotic manufacturing cells is a critical aspect of modern industrial processes, for which energy and time efficiency play significant roles in achieving sustainability goals. In industrial practice, robotic cell design often involves methods such as predefined layout templates, empirical rules for positioning, and simulation-based validation. While these approaches provide a practical starting point, they may not fully account for the complex interdependencies between robot configuration, energy consumption, and operational efficiency. Consequently, opportunities for optimizing resource usage are frequently overlooked. This paper presents a novel methodology for optimizing the deployment of industrial robots and their peripherals, focusing on minimizing energy and time costs to enhance the sustainability of industrial processes. The proposed approach, grounded in experimental measurements and simulations, was validated through an experimental model of a welding robot station. The methodology integrates the analysis of the relationship between the robot base position, trajectory, and energy consumption. The results indicate that adjusting the relative positions of robots and work points can achieve energy savings of approximately six percent. Specifically, optimization reduced energy consumption by 1.6731 Wh per work cycle, translating to an annual savings of 0.8794 MWh for a 60 s clock cycle. These findings highlight the practical applicability of the proposed methodology, demonstrating its potential to significantly improve the energy and time efficiency of robotic workplaces. Full article

This study presents a novel system for diagnosing and evaluating soccer performance using wearable inertial sensors integrated into players’ insoles. Designed to meet the needs of professional podiatrists and sports practitioners, the system focuses on three key soccer-related movements: passing, shooting, and changes of direction (CoDs). The system leverages low-power IMU sensors, Bluetooth Low Energy (BLE) communication, and a cloud-based architecture to enable real-time data analysis and performance feedback. Data were collected from nine professional players from the SD Huesca women’s team during controlled tests, and bespoke algorithms were developed to process kinematic data for precise event detection. Results indicate high accuracy rates for detecting ball-striking events and CoDs, with improvements in algorithm performance achieved through adaptive thresholds and ensemble neural network models. Compared to existing systems, this approach significantly reduces costs and enhances practicality by minimizing the number of sensors required while ensuring real-time evaluation capabilities. However, the study is limited by a small sample size, which restricts generalizability. Future research will aim to expand the dataset, include diverse sports, and integrate additional sensors for broader applications. This system offers a valuable tool for injury prevention, player rehabilitation, and performance optimization in professional soccer, bridging technical advancements with practical applications in sports science. Full article

Forest fires are among the most devastating natural disasters, causing significant environmental and economic damage. Effective early prediction mechanisms are critical for minimizing these impacts. In our previous work, we developed a smart and secure wireless sensor network (WSN) utilizing physical sensors to emulate forest fire dynamics and predict fire scenarios using machine learning. Building on this foundation, this study explores the integration of virtual sensors to enhance the prediction capabilities of the WSN. Virtual sensors were generated using polynomial regression models and incorporated into a supervector framework, effectively augmenting the data from physical sensors. The enhanced dataset was used to train a multi-layer perceptron neural network (MLP NN) to classify multiple fire scenarios, covering both early warning and advanced fire states. Our experimental results demonstrate that the addition of virtual sensors significantly improves the accuracy of fire scenario predictions, especially in complex situations like “Fire with Thundering” and “Fire with Thundering and Lightning”. The extended model’s ability to predict early warning scenarios such as lightning and smoke is particularly promising for proactive fire management strategies. This paper highlights the potential of combining physical and virtual sensors in WSNs to achieve superior prediction accuracy and scalability of the field without any extra cost. Such findings pave the way for deploying scalable (cost-effective), intelligent monitoring systems capable of addressing the growing challenges of forest fire prevention and management. We obtained significant results in specific scenarios based on the number of virtual sensors added, while in some scenarios, the results were less promising compared to using only physical sensors. However, the integration of virtual sensors enables coverage of much larger areas, making it a highly promising approach despite these variations. Future work includes further optimization of the virtual sensor generation process and expanding the system’s capability to handle large-scale forest environments. Moreover, utilizing virtual sensors will alleviate many challenges associated with the huge number of deployed physical sensors. Full article

The increased share of new energy sources in Northeast China’s power mix has strained grid stability. Energy storage technologies are essential for maintaining grid stability by addressing peak shaving and frequency regulation challenges. However, a clear quantitative assessment of the region’s energy storage needs is lacking, leading to weak grid stability and limited growth potential. This paper analyzes power supply data from Northeast China and models the stochastic characteristics of new energy generation. A joint optimization model for energy storage and thermal power is developed to optimize power allocation for peak shaving and frequency regulation at minimal cost. The empirical distribution method quantifies the relationship between storage power, capacity, and confidence levels, providing insights into the region’s future energy storage demands. The study finds that under 10 typical scenarios, the demand for peaking power at a 15 min scale is ≤500 MW, and the demand for frequency regulation at a 1 min scale is ≤1000 MW. At the 90% confidence level, the required capacity for new energy storage for peak shaving and frequency regulation is 424.13 MWh and 197.65 MWh, respectively. The required power for peak shaving and frequency regulation is 247.88 MW and 527.33 MW, respectively. The durations of peak shaving and frequency regulation are 1.71 h and 0.38 h. It also forecasts the energy storage capacity in the northeast region from 2025 to 2030 under the 5% annual incremental new energy penetration scenario. These findings provide theoretical support for energy storage policies in Northeast China during the 14th Five-Year Plan and practical guidance for accelerating energy storage industrialization. Full article

A double-layer robust optimization method for capacity configuration of shared energy storage considering cluster leasing of wind farms in a market environment is proposed based on the autonomy and profitability of shared energy storage. The feasibility of the leasing model of shared energy storage in the current market environment in China is discussed, and a commercial operation model for shared energy storage to provide leasing services and participate in spot market transactions is proposed. A robust optimization model of a master-–slave game for the capacity configuration of shared energy storage is constructed, considering output uncertainties of wind-driven generators and spot prices at multiple time scales. The upper layer of the model aims to minimize the annual cost of shared energy storage and determines the leasing prices and capacity-planning schemes for each period of shared energy storage in the scenario of an interactive game of wind farm clusters. The lower level of the model aims to minimize the assessment cost of the wind farm cluster and updates the leasing capacity for each time period by utilizing the leasing prices and the leasing demand of the wind turbine output power in the worst scenario. By comparing and analyzing multiple scenarios, the master–slave-game-formed lease improves the shared-storage lease benefit by $1.46 million compared to the fixed tariff, and the multi-timescale uncertainty promotes the shared-storage cost-effectiveness to be reduced by 8.7%, while the configuration result is more robust, providing new ideas for optimizing the capacity configuration of shared energy storage in multiple application scenarios. Full article

The Machine-Managed Bidding (MMB) system is an innovative bidding mode implemented by the Chinese government to mitigate collusive bidding behavior. Prior studies have focused minimally on the bidding mechanism and the possible collusive bidding behavior under this mode. The objectives of this study are to analyze the bidding mechanism and the dynamic evolution of collusive bidding behavior under the MMB system and provide targeted regulation countermeasures. To this end, this study develops an evolutionary game model among collusion initiators, free bidders, and regulators, explores possible scenarios for evolutionarily stable strategies, and performs sensitivity analysis of critical parameters utilizing MATLAB software (Version R2024a) based on empirical data. Results indicate that: (1) The MMB model significantly mitigates vertical collusive bidding behavior but lacks measures for governing transverse collusive bidding; (2) The game model has five evolutionarily stable strategies, with the one where the collusion initiator adopting the “non-collude” strategy, the free bidder adopting the “bid” strategy, and the regulator adopting the “negative regulate” strategy being the optimal evolutionary stable strategy; (3) Decreasing the costs associated with preparing bid documents, enhancing supervision costs, increasing the technical complexity of collusive bidding, and expanding the total number of construction enterprises with high-credit and low-credit ratings can expedite the evolution of the three participants toward the optimal evolutionarily stable strategy. This study supplements current knowledge on the regulation of collusive bidding behavior and enriches the knowledge framework of the MMB model. This study also provides insights for policymakers to guarantee the smooth implementation of the MMB. Full article

With the rapid development of e-commerce, the traditional trade mode of forest products has undergone significant changes. Logistics as a key factor to support e-commerce trade is particularly important to build a reasonable e-commerce logistics model of forest products. In the logistics service industry, the issue of cooperative profit allocation of logistics alliance has been crucial and prevalent. However, logistics alliances often face the problem of incomplete information, such as the ambiguity of transportation cost and driving distance, which makes it difficult to effectively apply many classical cooperative game solutions. Therefore, this paper introduces an improved Shapley value for cooperative games in fuzzy situations regarding unclear profit allocation in e-commerce logistics alliance of forest products. This value maximizes the satisfaction of the players by minimizing the contribution excess, according to which the trapezoidal fuzzy number least square contribution is calculated. Based on this, we replace the marginal contribution of the classical Shapley value with this least square contribution, thus creating the improved Shapley value with trapezoidal fuzzy numbers. Through the verification of actual cases, this method not only has theoretical value, but also provides effective guidance for the actual profit allocation of e-commerce logistics alliance of forest products, which helps to promote the stability and sustainable development of alliance. Full article

Background: Minimally invasive surgery (MIS) for adrenal glands is becoming increasingly developed worldwide and robotic surgery has advanced significantly. Although there are still concerns about the generalization of outcomes and the cost burden, the robotic platform shows several advantages in overcoming some laparoscopic shortcomings. Materials and Methods: A systematic review and meta-analysis were conducted using the PubMed, MEDLINE and Cochrane library databases of published articles comparing RA and LA up to January 2024. The evaluated endpoints were technical and post-operative outcomes. Dichotomous data were calculated using the odds ratio (OR), while continuous data were analyzed usingmean difference (MD) with a 95% confidence interval (95% CI). A random-effects model (REM) was applied. Results: By the inclusion of 28 studies, the meta-analysis revealed no statistically significant difference in the rates of intraoperative RBC transfusion, 30-day mortality, intraoperative and overall postoperative complications, re-admission, R1 resection margin and operating time in the RA group compared with the LA. However, the overall cost of hospitalization was significantly higher in the RA group than in the LA group, [MD USD 4101.32, (95% CI 3894.85, 4307.79) p < 0.00001]. With respect to the mean intraoperative blood loss, conversion to open surgery rate, time to first flatus and length of hospital stay, the RA group showed slightly statistically significant lower rates than the laparoscopic approach. Conclusions: To our knowledge, this is the largest and most recent meta-analysis that makes these comparisons. RA can be considered safe, feasible and comparable to LA in terms of the intraoperative and post-operative outcomes. In the near future, RA could represent a promising complementary approachto LA for benign and small malignant adrenal masses, particularly in high-volume referral centers specializing in robotic surgery. However, further studies are needed to confirm these findings. Full article

In recent years, unmanned aerial vehicles (UAVs, also known as drones) have gained widespread application in fields such as data collection and inspection, owing to their lightweight design and high mobility. However, due to limitations in battery life, UAVs are often unable to independently complete large-scale data collection tasks. To address this limitation, vehicle–drone collaborative data collection has emerged as an effective solution. Existing research, however, primarily focuses on collaborative work in static task scenarios, overlooking the complexities of dynamic environments. In dynamic scenarios, tasks may arrive during the execution of both the vehicle and UAV, and each drone has different positions and remaining endurance, creating an asymmetric state. This introduces new challenges for path planning. To tackle this challenge, we propose a 0–1 integer programming model aimed at minimizing the total task completion time. Additionally, we introduce an efficient dynamic solving algorithm, referred to as Greedy and Adaptive Memory Process-based Dynamic Algorithm (GAMPDA). This algorithm first generates an initial global data collection plan based on the initial task nodes and dynamically adjusts the current data collection scheme using a greedy approach as new task nodes arrive during execution. Through comparative experiments, it was demonstrated that GAMPDA outperforms SCAN and LKH in terms of time cost, vehicle travel distance, and drone flight distance and approaches the ideal results. GAMPDA significantly enhances task completion efficiency in dynamic scenarios, providing an effective solution for collaborative data collection tasks in such environments. Full article

Background/Objectives: Healthcare-associated infections (HAIs) and antimicrobial resistance (AMR) present significant challenges in modern healthcare, leading to increased morbidity, mortality, and healthcare costs. Examination gloves play a critical role in infection prevention by serving as a barrier to reduce the risk of cross-contamination between healthcare workers and patients. This manuscript aims to provide consensus-based guidelines for the optimal selection, use, and disposal of examination gloves in healthcare settings, addressing both infection prevention and environmental sustainability. Methods: The guidelines were developed using a multi-stage Delphi process involving healthcare experts from various disciplines. Recommendations were structured to ensure compliance with international regulations and sustainability frameworks aligned with the One Health approach and Sustainable Development Goals (SDGs). Results: Key recommendations emphasize selecting gloves based on clinical needs and compliance with EN 455 standards. Sterile gloves are recommended for surgical and invasive procedures, while non-sterile gloves are suitable for routine care involving contact with blood and other body fluids or contaminated surfaces. Proper practices include performing hand hygiene before and after glove use, avoiding glove reuse, and training healthcare providers on donning and removal techniques to minimize cross-contamination. Disposal protocols should follow local clinical waste management regulations, promoting sustainability through recyclable or biodegradable materials whenever feasible. Conclusions: These consensus-based guidelines aim to enhance infection control, improve the safety of patients and healthcare workers, and minimize environmental impact. By adhering to these evidence-based practices, grounded in European regulations, healthcare settings can establish safe and sustainable glove management systems that serve as a model for global practices. Full article

This study focuses on selecting a suitable 3D printer and defining experimental methods to gather the necessary data for determining the optimal filament material for printing components of the VEX GO and VEX IQ robotic kits. The aim is to obtain the required data to identify an appropriate filament material and set 3D printing parameters to achieve the desired mechanical properties of the parts while maintaining cost-effectiveness. Another key objective is achieving optimal operational functionality, ensuring the required part performance with minimal printing costs. It is desirable for the modeled and printed parts to exhibit the required mechanical properties while maintaining economic efficiency. Another crucial aspect is achieving optimal functionality of the produced parts with minimal printing costs. This will be assessed by analyzing the impact of key 3D printing technology parameters, focusing in this research phase on material selection. The criteria for selecting filament materials include ease of printability under the conditions of primary and secondary schools, simplicity of printing, minimal need for post-processing, and adequate mechanical properties verified through experimental measurements and destructive tests on original parts from VEX GO and VEX IQ kits. The study analyzed various filaments regarding their mechanical properties, printability, and cost-effectiveness. The most significant practical contribution of this study is selecting a suitable filament material tested through a set of destructive tests, emphasizing maintaining the mechanical properties required for the real-life application of the parts. This includes repetitive assembly and disassembly of various robotic model constructions and their activation for demonstration purposes and applications of STEM/STEAM/STREAM methods in the educational process to achieve the properties of original components. Additionally, the study aims to set up 3D printing such that even a beginner-level operator, such as a primary or secondary school student under the supervision of their teacher or a teacher with minimal knowledge and experience in 3D printing, can successfully execute it. Further ongoing research focuses on evaluating the effects of characteristic 3D printing parameters, such as infill and perimeter, on the properties of 3D-printed parts through additional measurements and analyses. Full article

Globally, fire incidents cause significant social, economic, and environmental destruction, making early detection and rapid response essential for minimizing such devastation. While various traditional machine learning and deep learning techniques have been proposed, their detection performances remain poor, particularly due to low-resolution data and ineffective feature selection methods. Therefore, this study develops a novel framework for accurate fire detection, especially in challenging environments, focusing on two distinct phases: preprocessing and model initializing. In the preprocessing phase, super-resolution is applied to input data using LapSRN to effectively enhance the data quality, aiming to achieve optimal performance. In the subsequent phase, the proposed network utilizes an attention-based deep neural network (DNN) named Xception for detailed feature selection while reducing the computational cost, followed by adaptive spatial attention (ASA) to further enhance the model’s focus on a relevant spatial feature in the training data. Additionally, we contribute a medium-scale custom fire dataset, comprising high-resolution, imbalanced, and visually similar fire/non-fire images. Moreover, this study conducts an extensive experiment by exploring various pretrained DNN networks with attention modules and compares the proposed network with several state-of-the-art techniques using both a custom dataset and a standard benchmark. The experimental results demonstrate that our network achieved optimal performance in terms of precision, recall, F1-score, and accuracy among different competitive techniques, proving its suitability for real-time deployment compared to edge devices. Full article

The grid ancillary service capability of bus swapping stations (BSSs) is significantly affected by environmental temperature fluctuations and the disorderly charging and discharging of batteries. This study addressed these challenges by developing a comprehensive optimization model for scheduling BSS operations under varying environmental conditions. First, based on real-world energy consumption and temperature data, an energy consumption coefficient correction method was proposed to improve the accuracy of energy consumption calculations. Next, a greedy strategy was utilized to match vehicle battery swapping demands with available batteries in the BSS. A two-layer multi-objective optimization model was then constructed to optimize the charging and discharging power. The upper layer minimizes the total costs, including charging/discharging costs and battery degradation costs, while the lower layer minimizes the load peak-valley difference and maximizes the charging/discharging balance among all batteries. Numerical simulations of the proposed energy consumption correction coefficient improved the accuracy of electric bus energy consumption estimation. Additionally, the proposed optimization strategy reduced the operational cost of the bus battery swapping station by 53.09% and decreased the transformer’s maximum load rate by 28.28%. Full article

This study focuses on optimizing the ozonation process in drinking water production from Lake Butoniga to ensure safe water quality while minimizing disinfection by-products (DBPs). Laboratory simulations were conducted using the Box–Behnken design to model the effects of ozone dose and treatment duration on bromate formation, trihalomethanes (THMs), haloacetic acids (HAAs) and specific UV absorption (SUVA). Two ozonation strategies were tested: Strategy 1 aimed to minimize all DBPs, while Strategy 2 focused on controlling bromate levels while keeping THMs, HAAs and SUVA below 80% of maximum contaminant levels. Results showed that Strategy 2 reduced ozone consumption while maintaining water quality within regulatory standards, providing a cost-effective and environmentally sustainable treatment approach. Seasonal and depth-dependent variations in water quality had a significant impact on treatment efficiency and required adjustments to operational settings. The study also addressed discrepancies between laboratory and real plant results and suggested recalibration methods that improved the accuracy of model predictions. These results highlight the potential for integrating predictive modelling and dynamic treatment strategies into large-scale water treatment processes. Full article