Search Results (4,056)

Tylvalosin is a macrolide antimicrobial with antibacterial activity against Gram-positive bacteria, some Gram-negative organisms, and mycoplasma. It is used to treat respiratory and enteric bacterial infections in swine and poultry. In this study, we aimed to investigate the pharmacokinetic changes in tylvalosin following its intravenous or oral administration at doses of 5, 10, and 25 mg/kg in broiler chickens. Forty-eight broiler chickens were included in the study. The plasma concentrations of tylvalosin were measured by using ultra-performance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS), and its pharmacokinetic parameters were evaluated by using both non-compartmental and compartmental analyses. The linear mixed-effects model revealed no dose proportionality within the 5–25 mg/kg range for either administration route. Based on pharmacokinetic data from a single oral dose, this study simulated a multiple-dose regimen of tylvalosin (25 mg/kg), demonstrating that a 6-hour dosing interval reaches a steady state after the fourth dose. Furthermore, the results show that the absolute bioavailability of tylvalosin after oral administration in chickens was relatively low, with values of 5.92%, 3.56%, and 3.04% for the doses of 5, 10, and 25 mg/kg, respectively. Further studies are required to significantly improve the oral bioavailability of tylvalosin and similar compounds through formulation optimization. Full article

Optimizing transportation in both natural and engineered systems, particularly within complex network environments, has become a pivotal area of research. Traditional methods for mitigating congestion primarily focus on routing strategies that utilize first-in-first-out (FIFO) queueing disciplines to determine the processing order of packets in buffer queues. However, these approaches often fail to explore the benefits of incorporating priority mechanisms directly within the routing decision-making processes, leaving significant room for improvement in congestion management. This study introduces an innovative generalized priority queueing (GPQ) strategy, specifically designed as an enhancement to existing FIFO-based routing methods. It is important to note that GPQ is not a new queue scheduling algorithm (e.g., deficit round robin (DRR) or weighted fair queuing (WFQ)), which typically manage multiple queues in broader queue management scenarios. Instead, GPQ integrates a dynamic priority-based mechanism into the routing layer, allowing the routing function to adaptively prioritize packets within a single buffer queue based on network conditions and packet attributes. By focusing on the routing strategy itself, GPQ improves the process of selecting packets for forwarding, thereby optimizing congestion management across the network. The effectiveness of the GPQ strategy is evaluated through extensive simulations on single-layer, two-layer, and dynamic networks. The results demonstrate significant improvements in key performance metrics, such as network throughput and average packet delay, when compared to traditional FIFO-based routing methods. These findings underscore the versatility and robustness of the GPQ strategy, emphasizing its capability to enhance network efficiency across diverse topologies and configurations. By addressing the inherent limitations of FIFO-based routing strategies and proposing a generalized yet scalable enhancement, this study makes a notable contribution to network optimization. The GPQ strategy provides a practical and adaptable solution for improving transportation efficiency in complex networks, bridging the gap between conventional routing techniques and emerging demands for dynamic congestion management. Full article

Currently, the disposal of waste tires is considered one of the priority environmental and public health problems worldwide. Every year, more than 1.4 billion unused tires are placed in landfills. Population growth, economic development, and a strong demand for vehicle production in the automotive industry increase this problem. In Mexico, nearly 36 million unused tires are deposited in landfills or clandestine deposits, the vast majority being burned or accumulated in the open air. The lack of regulations in the handling, disposal, and recycling of tires creates a worrying panorama for environmental care and the problems that this entails. The objective of this work is to propose a viable alternative for the final disposal and recycling of waste tires through the implementation of a company for the manufacture of products based on recycled rubber in the state of Tamaulipas, Mexico, reducing environmental pollution by these wastes and generating sources of employment through a sustainable company. For this purpose, a study was carried out in Tamaulipas, Mexico, through surveys to determine the number of tires that can be obtained and determine the feasibility of the business; subsequently, a prediction was made using simulation software to design and estimate the expected production in the manufacture of parking bumpers using two scenarios with two and four workers. Likewise, specialized software was used to optimize waste tire collection routes from the different tire stores to the company’s location. The results show that with an optimal design of the tire collection routes, up to 483 tons of waste tires can be recovered per year, representing 10% of the total unused tires in Tamaulipas. Because it is an environmental and social problem, installing a company manufacturing products based on recycled rubber is feasible and has a high probability of success for the region studied. According to the simulation, employing four workers increases productivity and decreases manufacturing costs. Through the simulation, three tire collection routes were determined considering the total number of tire stores in the city where the company is located. Full article

This manuscript provides actionable recommendations to enhance user satisfaction and address existing barriers regarding pooled rideshare (PR) in the United States. Despite PR’s intended benefits, such as reduced traffic congestion and cost savings, its adoption remains limited. To identify these actionable items, a U.S. nationwide survey with 5385 participants explored transportation preferences, barriers, and motivators for PR use in the summer of 2021. First, two factor analyses were conducted. The first factor analysis identified the five factors associated with one’s willingness to consider PR (time/cost, traffic/environment, safety, privacy, and service experience). The second factor analysis revealed the four factors related to ways to optimize one’s PR experience (comfort/ease of use, convenience, vehicle technology/accessibility, and passenger safety). Privacy concerns, for instance, were found to reduce the likelihood of PR adoption by 77%, and convenience had the potential to increase it by 156%. A structural equation model evaluated the relationships among these nine key factors influencing PR usage to develop the Pooled Rideshare Acceptance Model (PRAM). The privacy, safety, trust service, and convenience factors each had a significant large effect (Cohen’s f² > 0.35) on the model. PRAM was extended using multigroup analyses to reveal the nuanced impact of 16 demographics, including gender, generation, rideshare experience, etc., highlighting the need for tailored strategies to improve PR acceptance through the Pooled Rideshare Acceptance Model Multigroup Analyses (PRAMMAs). Multiple workshops were held with diverse audiences to translate the team’s findings to date into 84 actionable recommendations, categorized across topical areas like safety, routing, driver and passenger selection, user education, etc. These findings are a foundation for a future study to determine which items resonate with different user groups. In the meantime, the actional items serve as a user-driven resource for policymakers, transportation network companies, and researchers, offering a roadmap to potential improvements to PR services to address existing concerns with the goal of increasing the usage of PR. Full article

With the continuous advancement of 6G technology, SAGINs provide seamless coverage and efficient connectivity for future communications by integrating terrestrial, aerial, and satellite networks. Unmanned aerial vehicles (UAVs), owing to their high maneuverability and flexibility, have emerged as a critical component of the aerial layer in SAGINs. In this paper, we systematically review the key technologies, applications, and challenges of UAV-assisted SAGINs. First, the hierarchical architecture of SAGINs and their dynamic heterogeneous characteristics are elaborated on, and this is followed by an in-depth discussion of UAV communication. Subsequently, the core technologies of UAV-assisted SAGINs are comprehensively analyzed across five dimensions—routing protocols, security control, path planning, resource management, and UAV deployment—highlighting the progress and limitations of existing research. In terms of applications, UAV-assisted SAGINs demonstrate significant potential in disaster recovery, remote network coverage, smart cities, and agricultural monitoring. However, their practical deployment still faces challenges such as dynamic topology management, cross-layer protocol adaptation, energy-efficiency optimization, and security threats. Finally, we summarize the applications and challenges of UAV-assisted SAGINs and provide prospects for future research directions. Full article

The maritime inventory routing problem (MIRP) integrates vessel routing and inventory management over a planning horizon to optimize logistical operations in marine environments. While existing models predominantly address short-term planning with single vessels, this research advances the field by presenting a tightened mixed-integer linear programming (MILP) model designed for long-term planning with multiple vessels. The proposed model leverages an improved mathematical formulation and state-of-the-art optimization solvers to enhance computational performance. To demonstrate its applicability, the model was evaluated using benchmark instances from the literature and new instances derived from the logistics of Chilean scientific bases in Antarctica, a challenging and underexplored maritime environment. The results show computational time reductions of up to 98% for small to medium-sized instances, achieved through the incorporation of valid inequalities into the model and the use of advanced hardware and solvers. For larger instances, optimal or near-optimal solutions were achieved within one hour for a planning horizon of 60 time units, with optimality gaps below 24.7% for a 120-time-unit horizon. These findings highlight the potential of the model to support decision-making in complex maritime logistics scenarios, extending its application to long-term, multi-vessel operations in remote and environmentally sensitive regions. The proposed framework provides a valuable tool for enhancing the sustainability and efficiency of maritime logistics systems. Full article

Wireless sensor networks (WSNs) are vulnerable to several challenges. Congestion control, the utilization of trust to ensure security, and the incorporation of clustering schemes demand much attention. Algorithms designed to deal with congestion control fail to ensure security and address challenges faced due to congestion in the network. To resolve this issue, a Hybrid Trust-based Congestion-aware Cluster Routing (HTCCR) protocol is proposed to effectively detect attacker nodes and reduce congestion via optimal routing through clustering. In the proposed HTCCR protocol, node probability is determined based on the trust factor, queue congestion status, residual energy (RE), and distance from the mobile base station (BS) by using hybrid K-Harmonic Means (KHM) and the Enhanced Gravitational Search Algorithm (EGSA). Sensor nodes select cluster heads (CHs) with better fitness values and transmit data through them. The CH forwards data to a mobile sink once the sink comes into the range of CH. Priority-based data delivery is incorporated to effectively control packet forwarding based on priority level, thus decreasing congestion. It is evident that the propounded HTCCR protocol offers better performance in contrast to the benchmarked TBSEER, CTRF, and TAGA based on the average delay, packet delivery ratio (PDR), throughput, detection ratio, packet loss ratio (PLR), overheads, and energy through simulations. The proposed HTCCR protocol involves 2.5, 2.3, and 1.7 times less delay; an 18.1%, 12.5%, and 5.5% better detection ratio; 2.9, 2.6, and 1.8 times less energy; a 2.2, 1.9, and 1.5 times lower PLR; a 14.5%, 10.5%, and 5.2% better PDR; a 30.7%, 28.5%, and 18.4% better throughput; and 2.27, 1.91, and 1.66 times lower routing overheads in contrast to the TBSEER, CTRF, and TAGA protocols, respectively. The HTCCR protocol involves 4.1% less delay for the ‘C1’ and ‘C2’ RT packets, and the average throughput of RT is 10.4% better when compared with NRT. Full article

Pre-trained models trained with internet-scale data have achieved significant improvements in perception, interaction, and reasoning. Using them as the basis of embodied grasping methods has greatly promoted the development of robotics applications. In this paper, we provide a comprehensive review of the latest developments in this field. First, we summarize the embodied foundations, including cutting-edge embodied robots, simulation platforms, publicly available datasets, and data acquisition methods, to fully understand the research focus. Then, the embodied algorithms are introduced, starting from pre-trained models, with three main research goals: (1) embodied perception, using data captured by visual sensors to perform point cloud extraction or 3D reconstruction, combined with pre-trained models, to understand the target object and external environment and directly predict the execution of actions; (2) embodied strategy: In imitation learning, the pre-trained model is used to enhance data or as a feature extractor to enhance the generalization ability of the model. In reinforcement learning, the pre-trained model is used to obtain the optimal reward function, which improves the learning efficiency and ability of reinforcement learning; (3) embodied agent: The pre-trained model adopts hierarchical or holistic execution to achieve end-to-end robot control. Finally, the challenges of the current research are summarized, and a perspective on feasible technical routes is provided. Full article

This study develops a Green Vehicle Routing Problem (GVRP) model to address key logistics challenges, including time windows, simultaneous pickup and delivery, heterogeneous vehicle fleets, and multiple trip allocations. The model incorporates emissions-related costs, such as carbon taxes, to encourage sustainable supply chain operations. Emissions are calculated based on the total shipment weight and the travel distance of each vehicle. The objective is to minimize operational costs while balancing economic efficiency and environmental sustainability. A Genetic Algorithm (GA) is applied to optimize vehicle routing and allocation, enhancing efficiency and reducing costs. A Liquid Petroleum Gas (LPG) distribution case study in Yogyakarta, Indonesia, validates the model’s effectiveness. The results show significant cost savings compared to current route planning methods, alongside a slight increase in carbon. A sensitivity analysis was conducted by testing the model with varying numbers of stations, revealing its robustness and the impact of the station density on the solution quality. By integrating carbon taxes and detailed emission calculations into its objective function, the GVRP model offers a practical solution for real-world logistics challenges. This study provides valuable insights for achieving cost-effective operations while advancing green supply chain practices. Full article

Giardiasis and cryptosporidiosis, caused by Giardia lamblia and Cryptosporidium spp., are parasitic infections transmitted through faecal–oral routes or contaminated water. Although less common in Spain compared to developing countries, they pose a public health concern, particularly for vulnerable groups like children and immunocompromised individuals. This study aims to analyse the cases reported to the Microbiological Information System (MIS) in children between 2012 and 2021, as well as their distribution across sociodemographic variables. Proportions and infectivity rates were determined for epidemiological and sociodemographic data, and the incidence rate for giardiasis and cryptosporidiosis was calculated annually and by health sector. The variables analysed included sex, age, health sector and weather. For both diseases, there was a significant decrease in the number of cases in 2020, suggesting the importance of person-to-person transmission. Children were infected by Giardia in significantly higher proportion (p < 0.001), being the majority in age groups 5–14 years, while the proportion of boys and girls infected by Cryptosporidium was almost identical (1.4% vs. 1.3%), in children aged 2–4 years. Periodically there was a significant increase in cases of cryptosporidiosis, apparently related to the presence of torrential rains. Transmission is related to increased temperature and rainfall. Person-to-person transmission in the paediatric population needs further investigation. This study provides the foundation for future research on the evolution of cases of giardiasis and cryptosporidiosis in Spanish children. The data emphasise the need for informational campaigns on hygienic measures and efforts by public health authorities to maintain water resources in optimal condition to prevent parasite spread. Full article

Demand Responsive Transit (DRT) is gaining attention as a flexible and efficient solution for connecting urban transit hubs, but challenges such as travel time variability and punctuality remain significant barriers. This study develops a robust optimization framework with variable travel speed to address these issues, minimizing user and operator costs while reducing transfer waiting times. The framework incorporates variable travel speeds and employs a genetic algorithm to optimize routes and operations compared to many studies using constant commercial speed. Experiments conducted in Hwaseong, South Korea, analyzed scenarios with varying service rates, vehicle capacities, and detour ratios. Results show that implementing punctuality-constrained DRT reduces total travel times by 14% compared to subways and 36% compared to buses, highlighting its potential to significantly improve user convenience and operational efficiency. The findings suggest that carefully designed DRT systems with highly reliable punctuality can enhance urban mobility by integrating seamlessly with existing transit networks, providing a cost-effective and reliable alternative to traditional public transport. Full article

DTCO (Design–Technology Co-optimization) facilitates communication between the design and process flows, thereby expediting the cycle of the chip development pipeline. Within the DTCO framework, the development of a standard cell library, which entails the rapid generation of standard cell layouts, constitutes a crucial aspect in enhancing the efficiency of digital integrated circuit development. In light of the issue of the substantial time consumption associated with manual layout design prevalent in the industry, a novel method for the automatic generation of standard cell layouts, leveraging reinforcement learning for placement and the Dijkstra algorithm for routing, is proposed. Compared with traditional automatic layout algorithms, the proposed methodology exhibits enhanced adaptability. When accounting for the influence of technological node variations on design alterations, the device information and key design rule parameters are configured as adjustable variables to accommodate the migration across different processes. It is demonstrated that the proposed method can accelerate the DTCO cycle and enable the migration of the standard cell layout from the 55 nm process to the 28 nm process of a specific foundry. It is anticipated that this approach can offer novel perspectives for the advancement of EDA tools dedicated to the automatic generation of standard cell layouts. Full article

Despite the growing body of research on fuel alternatives for reducing carbon emissions in maritime shipping, there remains a lack of comprehensive cost–benefit analyses from the perspective of shipowners considering both retrofit and new construction options across multiple shipping routes. This paper carries out the optimization of carbon emission reduction investment schemes for replacement fuel ships from the perspective of the shipowners, with low-carbon fuel ships (LNG-fueled and methanol-fueled) and zero-carbon fuel ships (ammonia-fueled and hydrogen-fueled) as feasible options for shipowners to choose. Shipowners are advised to consider fuel retrofit options carefully, with methanol as a promising low-carbon fuel on certain routes and LNG for achieving both cost-effectiveness and compliance with upcoming zero-carbon regulations. The considered influencing factors include sailing distances, fuel prices, and container freight rates. A cost–benefit analysis model is proposed to conduct quantitative comparative analyses. The feasibility of various fuel options reflects both economic conditions and regulatory environments influencing operational costs and potential future carbon pricing. Under baseline conditions, our analysis reveals: For route 1, the NPV of retrofitting ships to use methanol yields the highest return among low-carbon options; for route 2, all replacement fuel options result in negative NPVs, indicating no investment value; and for route 3, retrofit options for LNG and new constructions for methanol are feasible, with LNG offering the shortest payback period. Full article

To achieve the optimal planning of grid resource storage for a large-scale distribution network (DN), a cluster partition-based zonal planning method for the DN, considering reliability, is proposed. Firstly, a comprehensive clustering partition index is proposed, which includes the modularity index, power balance index, and node affiliation index. A hybrid genetic–simulated annealing algorithm is employed to perform the cluster partition. Secondly, a three-layer joint expansion planning model based on cluster partitioning is proposed. At the upper level, a route planning model is established to optimize the routing of the cluster. At the intermediate level, a location and capacity planning model for distributed photovoltaics and energy storage is formulated, taking uncertainties into account. By introducing uncertainty parameters, the range of uncertainty for sources and loads is characterized. At the lower level, reliability indices within the clusters are calculated to ensure operational reliability while reducing the conservatism of the optimization outcomes. Finally, the proposed method is applied to a real distribution network in China, demonstrating its effectiveness in improving the economic efficiency of DN planning. Full article

With the development of the chemical industry, the threat of mercury pollution to human health is increasing. Therefore, it is necessary to develop a low-cost, convenient and efficient Hg²⁺ detection method. In this study, anthracene-based Hg²⁺ fluorescent probes AN-2S and AN-4S were synthesized by a dithioacetal reaction for the rapid and efficient detection of the Hg²⁺ concentration in water. Through molecular structure design and synthesis route optimization, the complexity and cost of the probe synthesis were greatly reduced. AN-2S and AN-4S had good water solubility, rapid response abilities and anti-interference abilities, and could specifically detect Hg²⁺ using “turn-off” or “turn-on” detection modes within 1 min. The AN-4S probe showed a wide linear response range (0~40 μmol/L) and high sensitivity (4.93 × 10⁻⁸ mol/L) to Hg²⁺ in 99% aqueous solutions, over a pH range of 5~13. The reaction mechanism between the probe and Hg²⁺ was determined using 1H NMR and FT-IR spectra and Job’s curves. It was proven that the AN-2S and AN-4S probes react with Hg²⁺ in a molar ratio of 1:1 or 1:2. The dual-detection mode enabled the probes to be used not only for the accurate quantitative detection of Hg²⁺ under a fluorescence spectrometer, but also for rapid qualitative analysis using a UV flashlight as a test strip, showing a broad practical application potential. Full article