Search Results (56)

This study proposes a balanced truck dispatching strategy for inter-terminal transportation (ITT) in large ports, incorporating proactive demand outsourcing to address stochastic and imbalanced ITT demand. A portion of ITT tasks are intentionally outsourced to third-party public trucks at a higher cost, so that self-owned trucks can be reserved for more critical tasks. The ITT system is modeled as a closed Jackson network, in which self-owned trucks circulate among terminals and routes. An optimization model is developed to determine the optimal proactive outsourcing ratios for origin–destination terminal pairs and the appropriate fleet size of self-owned trucks, aiming to minimize total transportation costs. Reactive outsourcing is also included to handle occasional truck shortages. A mean value analysis method is used to evaluate system performance with given decisions, and a differential evolution algorithm is employed for optimization. The case study of Shanghai Yangshan Port demonstrates that the proposed strategy reduces total system cost by 9.8% compared to reactive outsourcing. The results also highlight the importance of jointly optimizing outsourcing decisions and fleet size. This study provides theoretical insights and practical guidance for ITT system management under demand uncertainty. Full article

The enclosed nature of indoor building spaces during fires creates complex fire environments and restricted evacuation routes, substantially elevating the risk of mass casualties. Traditional static evacuation routes not only overlook the complexity of fire scenarios but also fail to satisfy safety requirements for evacuation. To address this issue, this study proposes an enhanced A* algorithm to determine evacuation paths based on dynamic fire risk assessment. A dynamic fire risk assessment model is established using key fire environment parameters (e.g., temperature, visibility, and toxic gas concentration) and their corresponding personnel harm thresholds. This model quantifies fire risks within a discrete space. The A* algorithm is improved by integrating fire risk values and initial direction constraints into its heuristic function and path update strategy, thereby increasing the algorithm’s accuracy and efficiency. Using a subway station fire as a case study, the simulation results indicate that the improved algorithm can update evacuation paths in line with the dynamic evolution of fire risks. It also identifies evacuation routes by balancing fire risk, distance, and initial direction. This approach maintains the original path direction while substantially reducing path risk, achieving an approximate 70% reduction in individual evacuation path risk. This method can guide building fire safety design and the formulation of emergency evacuation plans. It also serves as a reference for path guidance during emergencies. Full article

Unmanned aerial vehicles (UAVs) have revolutionized fields such as monitoring, cargo delivery, precision farming, and emergency response, demonstrating remarkable flexibility and operational effectiveness. A fundamental aspect of UAV autonomy lies in route optimization, which determines efficient paths while considering factors like mission goals, safety, and power consumption. This article presents an extensive overview of methodologies for UAV route planning, including deterministic models, stochastic sampling techniques, biologically inspired methods, and integrated algorithmic frameworks. The discussion extends to their performance in various operational contexts, including stationary, moving, and three-dimensional settings. Innovative methods utilizing artificial intelligence, particularly machine learning and neural networks, are emphasized for their promise in facilitating adaptive responses to intricate, evolving environments. Furthermore, strategies focused on reducing energy usage and enabling coordinated operations among multiple drones are analyzed, addressing issues such as prolonged operation, distribution of assignments, and navigation around obstacles. Although notable advancements have been achieved, challenges like high computational demands and the need for immediate responsiveness persist. By consolidating the latest progress, this survey provides meaningful perspectives and guidance for the ongoing evolution of UAV route planning solutions. Full article

Airports are complex environments where efficient localization and intelligent traffic management are essential for ensuring smooth navigation and operational efficiency for both pedestrians and Autonomous Guided Vehicles (AGVs). This study presents an Artificial Intelligence (AI)-driven airport traffic management system that integrates Visible Light Communication (VLC), rerouting techniques, and adaptive reward mechanisms to optimize traffic flow, reduce congestion, and enhance safety. VLC-enabled luminaires serve as transmission points for location-specific guidance, forming a hybrid mesh network based on tetrachromatic LEDs with On-Off Keying (OOK) modulation and SiC optical receivers. AI agents, driven by Deep Reinforcement Learning (DRL), continuously analyze traffic conditions, apply adaptive rewards to improve decision-making, and dynamically reroute agents to balance traffic loads and avoid bottlenecks. Traffic states are encoded and processed through Q-learning algorithms, enabling intelligent phase activation and responsive control strategies. Simulation results confirm that the proposed system enables more balanced green time allocation, with reductions of up to 43% in vehicle-prioritized phases (e.g., Phase 1 at C1) to accommodate pedestrian flows. These adjustments lead to improved route planning, reduced halting times, and enhanced coordination between AGVs and pedestrian traffic across multiple intersections. Additionally, traffic flow responsiveness is preserved, with critical clearance phases maintaining stability or showing slight increases despite pedestrian prioritization. Simulation results confirm improved route planning, reduced halting times, and enhanced coordination between AGVs and pedestrian flows. The system also enables accurate indoor localization without relying on a Global Positioning System (GPS), supporting seamless movement and operational optimization. By combining VLC, adaptive AI models, and rerouting strategies, the proposed approach contributes to safer, more efficient, and human-centered airport mobility. Full article

Ammonia (NH₃) plays a vital role in both the agriculture and energy sectors, serving as a precursor for nitrogen fertilizers and as a promising carbon-free fuel and hydrogen carrier. However, the conventional Haber–Bosch process is highly energy-intensive, operating under elevated temperatures and pressures, and contributes significantly to global CO₂ emissions. In recent years, nonthermal plasma (NTP)-assisted ammonia synthesis has emerged as a promising alternative that enables ammonia production under mild conditions. With its ability to activate inert N₂ molecules through energetic electrons and reactive species, NTP offers a sustainable route with potential integration into renewable energy systems. This review systematically summarizes recent advances in NTP-assisted ammonia synthesis, covering reactor design, catalyst development, plasma–catalyst synergistic mechanisms, and representative reaction pathways. Particular attention is given to the influence of key plasma parameters, such as discharge power, pulse voltage, frequency, gas flow rate, and N₂/H₂ ratio, on reaction performance and energy efficiency. Additionally, comparative studies on plasma reactor configurations and materials are presented. The integration of NTP systems with green hydrogen sources and strategies to mitigate ammonia decomposition are also discussed. This review provides comprehensive insights and guidance for advancing efficient, low-carbon, and distributed ammonia production technologies. Full article

Effective route and speed decision-making can significantly reduce vessel operating costs and emissions. However, existing optimization methods developed for conventional fuel-powered vessels are inadequate for application to methanol dual-fuel ships, which represent a new energy vessel type. To address this gap, this study investigates the operational characteristics of methanol dual-fuel liners and develops a mixed-integer nonlinear programming (MINLP) model aimed at minimizing operating costs. Furthermore, an improved genetic algorithm (GA) integrated with the Nonlinear Programming Branch-and-Bound (NLP-BB) method is proposed to solve the model. The case study results demonstrate that the proposed approach can reduce operating costs by more than 15% compared to conventional route and speed strategies while also effectively decreasing emissions of CO₂, NOx, SOx, PM, and CO. Additionally, comparative experiments reveal that the designed algorithm outperforms both the GA and the Linear Interactive and General Optimizer (LINGO) solver for identifying optimal route and speed solutions. This research provides critical insights into the operational dynamics of methanol dual-fuel vessels, demonstrating that traditional route and speed optimization strategies for conventional fuel vessels are not directly applicable. This study provides critical insights into the optimization of voyage decision-making for methanol dual-fuel vessels, demonstrating that traditional route and speed optimization strategies designed for conventional fuel vessels are not directly applicable. It further elucidates the impact of methanol fuel tank capacity on voyage planning, revealing that larger tank capacities offer greater operational flexibility and improved economic performance. These findings provide valuable guidance for shipping companies in strategically planning methanol dual-fuel operations, enhancing economic efficiency while reducing vessel emissions. Full article

Interest in pulmonary/nasal routes for local delivery has significantly increased over the last decade owing to challenges faced in the delivery of molecules with poor solubility, systemic side effects, or new modalities such as biologics. This increasing interest has attracted new stakeholders to the field who have yet to explore inhaled drug product development. Contract development and manufacturing organizations (CDMOs) play a key role in supporting the development of drug products for inhalation, from early feasibility to post marketing. However, a critical gap exists for these newcomers: a clear, integrated, and a CDMO-centric roadmap for navigating the complexities of pulmonary/nasal drug product development. The purpose of this publication is to highlight the key aspects considered in the product development of inhaled dry powder products from a CDMO perspective, providing a novel and stepwise development strategy. A roadmap for the development of inhalable drug products is proposed with authors’ recommendations to facilitate the decision-making process, starting from the definition of the desired target product profile followed by dose selection in preclinical studies. The importance of understanding the nature of the API, whether a small molecule or a biologic, will be highlighted. Additionally, technical guidance on the choice of formulation (dry powder/liquid) will be provided with special focus on dry powders. Selection criteria for the particle engineering technology, mainly jet milling and spray drying, will also be discussed, including the advantages and limitations of such technologies, based on the authors’ industry expertise. Lastly, the paper will highlight the challenges and considerations for encapsulating both spray dried and jet milled powders. Unlike existing literature, this paper offers a unified framework that bridges preclinical, formulation, manufacturing, and encapsulation considerations, providing a practical tool for newcomers. Full article

Background/Objectives: The global spread of carbapenem-resistant Pseudomonas aeruginosa (CRPA) warrants collaborative action. Guidance should come from integrated One Health surveillance; however, a surveillance strategy is currently unavailable due to insufficient knowledge on the sources and transmission routes of CRPA. The aim of the SAMPAN study (“A Smart Surveillance Strategy for Carbapenem-resistant Pseudomonas aeruginosa”) is to develop a globally applicable surveillance strategy. Methods: First, an international cross-sectional study will be conducted to investigate CRPA in clinical and environmental settings in Rotterdam (The Netherlands), Rome (Italy), and Jakarta (Indonesia). Screening cultures and risk factor questionnaires will be taken from healthy individuals and patients upon hospital admission. Clinical CRPA isolates will also be included. Additionally, samples will be taken twice from wet hospital environments and monthly from the hospitals’ (drinking) water system, hospital and municipal wastewater treatment plants, and receiving rivers. Whole-genome sequencing will be performed to characterize CRPA isolates and determine the genetic relatedness among the isolates from different reservoirs. Findings from the cross-sectional study, combined with expert elicitation using a Delphi method, will serve as the input for the surveillance strategy. Conclusions: The SAMPAN study will provide a broader understanding of the sources and transmission routes of CRPA. Therewith, the development of a globally applicable smart surveillance strategy will be made possible, delivering information that is needed to guide actions against the spread of CRPA. Full article

The tourism development of industrial heritage is an effective way to activate cultural heritage and can provide new solutions for the renewal and protection of industrial heritage. This study focuses on the industrial heritage in Nanjing, aiming to explore its spatial distribution pattern, tourism development strategy, and sustainable development model. This study adopts a combination of quantitative and qualitative research methods. First, relevant information on 93 sites of industrial heritage type historic buildings in Nanjing is collected. Secondly, ArcGIS was used to visualize the evolution of industrial buildings and the spatial distribution of industrial heritage type historic buildings. Finally, the spatial analysis tools of ArcGIS and the accessibility analysis method in space syntax theory are superimposed to comprehensively analyze the spatial distribution pattern and traffic accessibility characteristics of Nanjing’s industrial heritage. The research results propose a specific plan to promote the value transformation of industrial heritage through tourism: based on the spatial distribution characteristics of Nanjing’s industrial heritage along the water system and traffic arteries, a tourist route of “multi-point, two-axis, one-center” is planned; the tourism development strategy of “point protrusion, linear links, and surface darning” is implemented; and a sustainable development model under the guidance of low-carbon environmental protection goals is explored. This study provides a reference for the protective development of industrial heritage and the expansion of tourism and opens up a new perspective for the regeneration and planning of other urban heritage. Full article

The allocation of tasks among multiple Autonomous Underwater Vehicles (AUVs) with energy constraints in underwater environments presents an NP-complete problem with far-reaching consequences for marine exploration, environmental monitoring, and underwater construction. This paper critically examines the contemporary methodologies and technologies in the task allocation for multiple AUVs, with a particular focus on strategies that optimize navigation time with energy consumption constraints. By conceptualizing the multiple AUVs task allocation issue as a Capacitated Vehicle Routing Problem (CVRP) and addressing it using the SCIP solver, this study seeks to identify effective task allocation strategies that enhance the operational efficiency and minimize the mission duration in energy-restricted underwater settings. The findings of this research provide valuable insights into efficient task allocation under energy constraints, providing useful theoretical implications and practical guidance for optimizing task planning and energy management in multiple AUVs systems. These contributions are demonstrated through the improved solution quality and computational efficiency. Full article

The melting of Arctic ice has facilitated the successful navigation of merchant ships through the Arctic route, often requiring icebreakers for assistance. To reduce the risk of accidents between merchant vessels and icebreakers stemming from human errors during operations, this paper introduces an enhanced human reliability assessment approach. This method utilizes the Dynamic Bayesian Network (DBN) model, integrated with the information, decision, and action in crew context (IDAC) framework. First, a qualitative analysis of crew maneuvering behavior in scenarios involving a collision with the preceding vessel during icebreaker assistance is conducted using the IDAC model. Second, the D–S evidence theory and cloud models are integrated to process multi-source subjective data. Finally, the human error probability of crew members is quantified using the DBN. The research results indicate that during convoy operations, the maximum probability that the officer on watch (OOW) chooses an incorrect deceleration strategy is $8.259\times {10}^{-2}$ and the collision probability is $4.129\times {10}^{-3}$. Furthermore, this study also found that the factors of Team Effectiveness and Knowledge/Abilities during convoy operations have the greatest impact on collision occurrence. This research provides important guidance and recommendations for the safe navigation of merchant ships in the Arctic waters. By reducing human errors and adopting appropriate preventive measures, the risk of collisions between merchant ships and icebreakers can be significantly decreased. Full article

As a consequence of human activity, waste generation is unavoidable, and its volume and complexity escalate with urbanization, economic progress, and the elevation of living standards in cities. Annually, the world produces about 2.01 billion tons of municipal solid waste, which often lacks environmentally safe management. The importance of solid waste management lies in its role in sustainable development, aimed at reducing the environmental harms from waste creation and disposal. With the expansion of urban populations, waste management systems grow increasingly complex, necessitating more sophisticated optimization strategies. This analysis thoroughly examines the optimization techniques used in solid waste management, assessing their application, benefits, and limitations by using PRISMA 2020. This study, reviewing the literature from 2010 to 2023, divides these techniques into three key areas: waste collection and transportation, waste treatment and disposal, and resource recovery, using tools like mathematical modeling, simulation, and artificial intelligence. It evaluates these strategies against criteria such as cost-efficiency, environmental footprint, energy usage, and social acceptability. Significant progress has been noted in optimizing waste collection and transportation through innovations in routing, bin placement, and the scheduling of vehicles. The paper also explores advancements in waste treatment and disposal, like selecting landfill sites and converting waste to energy, alongside newer methods for resource recovery, including sorting and recycling materials. In conclusion, this review identifies research gaps and suggests directions for future optimization efforts in solid waste management, emphasizing the need for cross-disciplinary collaboration, leveraging new technologies, and adopting tailored approaches to tackle the intricate challenges of managing waste. These insights offer valuable guidance for policymakers, waste management professionals, and researchers involved in crafting sustainable waste strategies. Full article

Under the influence of mountainous terrain, the spatial synergy between tourism resources and tourism service facilities has emerged as a pivotal factor affecting the overall efficiency enhancement of regional tourism destinations. In order to explore the synergistic effect of the two, taking Qimen County as the study site, this study utilizes Point of Interest (POI) data of tourism resources and tourism service facilities. It constructs a fine-scale multidimensional spatial synergy methodology based on grid vectorization to conduct scenario-based comparative analyses of altitude and population density. The objective is to elucidate the effects of fine-scale tourism development synergy and propose enhancement strategies. The findings are as follows: (1) The vertical zonation of mountains has led to a widespread, decentralized distribution of natural tourism resources in mid-to-high-altitude areas, while humanistic tourism resources in low-altitude urbanized areas exhibit a granular, clustered distribution. These contrasting scenarios manifest a polarization, making it difficult to achieve supply–demand matching of the layout pattern of tourism service facilities along transportation routes. (2) The spatial gradient effect of the synergy between the two in mountainous counties is significant, with a higher synergy level in core towns and obvious misalignment in peripheral areas. (3) Altitude and population density are critical factors influencing the supply of tourism service facilities. Through scale aggregation guidance and cost–benefit mechanisms, the spatial distribution can be classified, stratified, and optimized to better serve resource development. This study provides valuable insights into understanding laws governing development and utilization within mountainous county areas for academic research purposes. Full article

Hydrogen peroxide (H₂O₂) is one of the most environmentally friendly and versatile chemical oxidizing agents, with only O₂ and H₂O as reaction products. It is widely used in environmental protection, industrial production, and medical fields. At present, most of the industrial production of H₂O₂ adopts anthraquinone oxidation, but there are shortcomings such as pollution of the environment and large energy consumption. Covalent organic frameworks (COFs) are a class of porous crystalline materials formed by organic molecular building blocks connected by covalent bonds. The ordered conjugated structure of COFs not only facilitates the absorption of light energy but also promotes the transport of excited-state electrons. Therefore, the photochemical synthesis of H₂O₂ from water and oxygen using photocatalysts based on COFs as a green route has attracted much attention. In this review, we provide an overview of recent studies on COFs as photocatalysts and the different mechanisms involved in the photocatalytic production of hydrogen peroxide. Then, we summarize the various strategies to improve the performance. Finally, we outline the challenges and future directions of COFs in practical applications. This review highlights the potential and application prospects of COFs in the photochemical synthesis of H₂O₂, aiming to provide guidance for the design of COF-based catalysts and the optimization for photocatalytic production of H₂O₂, in order to promote scientific development and application in this field. Full article

The Sine–Levy tuna swarm optimization (SLTSO) algorithm is a novel method based on the sine strategy and Levy flight guidance. It is presented as a solution to the shortcomings of the tuna swarm optimization (TSO) algorithm, which include its tendency to reach local optima and limited capacity to search worldwide. This algorithm updates locations using the Levy flight technique and greedy approach and generates initial solutions using an elite reverse learning process. Additionally, it offers an individual location optimization method called golden sine, which enhances the algorithm’s capacity to explore widely and steer clear of local optima. To plan UAV flight paths safely and effectively in complex obstacle environments, the SLTSO algorithm considers constraints such as geographic and airspace obstacles, along with performance metrics like flight environment, flight space, flight distance, angle, altitude, and threat levels. The effectiveness of the algorithm is verified by simulation and the creation of a path planning model. Experimental results show that the SLTSO algorithm displays faster convergence rates, better optimization precision, shorter and smoother paths, and concomitant reduction in energy usage. A drone can now map its route far more effectively thanks to these improvements. Consequently, the proposed SLTSO algorithm demonstrates both efficacy and superiority in UAV route planning applications. Full article