Search Results (122)

Multi-vessel formation shipping demonstrates significant potential for enhancing maritime transportation efficiency and economy. However, existing route planning systems inadequately address the unique challenges of formations, where traditional methods fail to integrate global optimality, local dynamic obstacle avoidance, and formation coordination into a cohesive system. Global planning often neglects multi-ship collaborative constraints, while local methods disregard vessel maneuvering characteristics and formation stability. This paper proposes GLFM, a three-layer hierarchical framework (global optimization–local adjustment-formation collaboration module) for intelligent route planning of transport ship formations. GLFM integrates an improved multi-objective A* algorithm for global path optimization under dynamic meteorological and oceanographic (METOC) conditions and International Maritime Organization (IMO) safety regulations, with an enhanced Artificial Potential Field (APF) method incorporating ship safety domains for dynamic local obstacle avoidance. Formation, structural stability, and coordination are achieved through an improved leader–follower approach. Simulation results demonstrate that GLFM-generated trajectories significantly outperform conventional routes, reducing average risk level by 38.46% and voyage duration by 12.15%, while maintaining zero speed and period violation rates. Effective obstacle avoidance is achieved, with the leader vessel navigating optimized global waypoints and followers maintaining formation structure. The GLFM framework successfully balances global optimality with local responsiveness, enhances formation transportation efficiency and safety, and provides a comprehensive solution for intelligent route optimization in multi-constrained marine convoy operations. Full article

Medical phantoms are essential to imaging calibration, clinician training, and the validation of therapeutic procedures. However, most ultrasound phantoms prioritize acoustic realism while neglecting the viscoelastic and anisotropic properties of fibrous soft tissues. This gap limits their effectiveness in modeling realistic biomechanical behavior, especially in wave-based diagnostics and therapeutic ultrasound. Current materials like gelatine and agarose fall short in reproducing the complex interplay between the solid and fluid components found in biological tissues. To address this, we developed a soft, anisotropic composite whose dynamic mechanical properties resemble fibrous biological tissues such as skin and skeletal muscle. This material enables wave propagation and vibration studies in controllably anisotropic media, which are rare and highly valuable. We demonstrate the tunability of damping and stiffness aligned with fiber orientation, providing a versatile platform for modeling soft-tissue dynamics and validating biomechanical simulations. The phantoms achieved Young’s moduli of 7.16–11.04 MPa for skin and 0.494–1.743 MPa for muscles, shear wave speeds of 1.51–5.93 m/s, longitudinal wave speeds of 1086–1127 m/s, and sound absorption coefficients of 0.13–0.76 dB/cm/MHz, with storage, loss, and complex moduli reaching 1.035–6.652 kPa, 0.1831–0.8546 kPa, and 2.138–10.82 kPa. These values reveal anisotropic response patterns analogous to native tissues. This novel natural fibrous composite system affords sustainable, low-cost ultrasound phantoms that support both mechanical fidelity and acoustic realism. Our approach offers a route to next-gen tissue-mimicking phantoms for elastography, wave propagation studies, and dynamic calibration across diverse clinical and research applications. Full article

Background: Location and distribution decisions are key to efficient logistics network design and are often addressed in an integrated manner as Location–Routing Problems (LRPs). Today, sustainability and resilience must be considered when designing competitive networks. This systematic review examines how and at what decision level both concerns are explored in LRPs, highlighting trends and future research challenges. Methods: A search was conducted in the Scopus database on 3 January 2024. Articles not written in English or lacking a sustainability or resilience focus were excluded. The 36 most-cited articles were selected and analyzed descriptively and theoretically, considering their approaches to sustainability and resilience, as well as the decision levels at which these approaches were considered. The studies were also analyzed based on model features and solving approaches. Results: Our findings indicated that social sustainability was the most neglected. The environmental pillar was often focused on minimizing atmospheric pollution from distribution. Regarding resilience, proactive and reactive strategies were employed to minimize disruption costs and risks and maximize network reliability. Conclusions: Research on sustainable and resilient LRPs is growing, but remains fragmented. Future studies should explore the integration of social impacts, uncertainty modeling, and real-world applications. Stronger alignment with decision maker needs and more holistic evaluation frameworks are essential to support resilient and sustainable network design. Full article

Lagochilascariasis is a neglected zoonotic helminthiasis, caused by Lagochilascaris minor, characterized by a complex and not well understood transmission cycle. This parasitic disease is endemic to Latin America, particularly Brazil, and is associated with rural and forested areas, where humans may serve as accidental hosts. The southeastern region of Pará state reports the highest number of cases, highlighting its epidemiological significance. Wildlife species, especially carnivores and rodents, play crucial roles as definitive and intermediate hosts, respectively. Although lagochilascariasis can lead to severe clinical manifestations, including chronic soft tissue infections and potential central nervous system involvement, it is likely underdiagnosed due to its similarity to fungal and bacterial diseases. The anthropization of the Amazon Biome, through deforestation and habitat fragmentation, coupled with increased human–wildlife interactions, may be influencing the epidemiology of this parasitosis. This review aims to summarize current knowledge of L. minor transmission routes, the role of wildlife in maintaining its cycle, and the impact of environmental changes on disease patterns. Such insights are vital for One Health strategies, which integrate human, animal, and environmental health approaches to mitigate the disease burden. Full article

Historical districts face pressing disaster preparedness challenges due to their special spatial properties—risks compounded by static approaches that overlook dynamic fire–pedestrian interactions. This study employs an agent-based model (ABM) for fire simulations and AnyLogic pedestrian dynamics to address these gaps in Dukezong Ancient Town, Yunnan Province, China, considering diverse ignition points, seasonal temperatures, and wind conditions. Dynamic simulations of 16 scenarios reveal critical spatial impacts: within 30 min, ≥28% of streets became impassable, with central ignition points causing faster obstructions. Static models underestimate evacuation durations by up to 135%, neglecting early stage congestions and detours caused by high-temperature zones. Congestions are concentrated along main east–west arterial roads, worsening with longer warning distances. A mismatch between evacuation flows and shelter capacity is found. Thus, a three-stage interaction simplification is derived: localized detours (0–10 min), congestion-driven delays on critical roads (11–30 min), and prolonged structural damage afterward. This study challenges static approaches by highlighting the “fast alert-fast congestion” paradox, where rapid alerts overwhelm narrow pathways. Solutions prioritize multi-route guidance systems, optimized shelter access points, and real-time information dissemination to reduce bottlenecks without costly infrastructure changes. This study advances disaster modeling by bridging disaster development with dynamic evacuation, offering a replicable framework for similar environments. Full article

Cultural heritage reflects accumulated memories, generational practices, and esthetic and cultural ideologies that shape identities. The inherent diversity and uniqueness of these identities define heritage elements, which, however, remain fragile, non-renewable, irreplaceable, and vulnerable. In regions affected by the significant devastation caused by contemporary socio-economic activities, policies often neglect the intrinsic historical and heritage value(s). The historical landscapes with tourism potential that experience decline, degradation, and destruction need even more protection and policies to aid their regeneration and preservation. This study seeks to develop a comprehensive framework for preserving a highly endangered historical road, along with its invaluable monuments and cultural landscapes, as a means to stimulate regional revitalization. The methodological approach relies on observation, field work, and semi-structured interviews to provide a comprehensive historical overview of the Estrada do Paraibuna (Paraibuna Road). This region has suffered significant degradation of its historical and natural assets as a result of intensive resource exploitation. This study underscores the heritage elements with strong tourism potential along the route between Ouro Preto and Barbacena, while underlining the critical need to combat landscape degradation. This study pursues to create a shared vision rooted in promoting sustainable practices that leverage natural and cultural resources, safeguard local culture, and encourage community collaboration in regenerative efforts. Full article

Nucleopolyhedroviruses of lepidopteran larvae (Alphabaculovirus, Baculoviridae) form the basis for effective and highly selective biological insecticides for the control of caterpillar pests of greenhouse and field crops and forests. Horizontal transmission is usually achieved following the release of large quantities of viral occlusion bodies (OBs) from virus-killed insects. In the present review, I examine the evidence for productive midgut infection in different host species and the resulting transmission through the release of OBs in the feces (frass) of the host. This has been a neglected aspect of virus transmission since it was initially studied over six decades ago. The different host–virus pathosystems vary markedly in the quantity of OBs released in feces and in their ability to contaminate the host’s food plant. The release of fecal OBs tends to increase over time as the infection progresses. Although based on a small number of studies, the prevalence of transmission of fecal inoculum is comparable with that of recognized alternative routes for transmission and dissemination, such as cannibalism and interactions with predators and parasitoids. Finally, I outline a series of predictions that would affect the importance of OBs in feces as a source of inoculum in the environment and which could form the basis for future lines of research. Full article

Container drayage, as a pivotal element of door-to-door intermodal transportation, has garnered increasing attention due to its significant influence on container logistics costs. Although various types of transport requests have been defined in the literature, no comprehensive study has addressed all of them together yet, due to the lack of an efficient model and corresponding algorithms. Furthermore, existing research on container drayage often neglects the simultaneous incorporation of two trucking operation modes, two empty container repositioning strategies, and the availability of empty containers across multiple depots. To address these issues, this study proposes a comprehensive container drayage problem (CDP) and mathematically formulates it as an innovative mixed integer linear programming (MILP) model, capturing the uncertainty and unpredictability inherent in empty container allocation, truck dispatching, and route planning. Given the problem’s complexity, obtaining an exact solution for large instances is not feasible. Therefore, an improved large neighborhood search (LNS) algorithm is tailored by incorporating the “Sequential insertion” and the “Solution re-optimization” operations. Extensive numerical experiments using randomly generated instances of varying scales validate the correctness of the proposed model and demonstrate the performance of the proposed algorithm. Additionally, sensitivity analysis on the number and distribution of depots and empty containers offers valuable managerial insights for the development of an effective container drayage system. Full article

Parasitic diseases represent a severe global burden, with current treatments often limited by toxicity, drug resistance, and suboptimal efficacy in chronic infections. This review examines the emerging role of triazole-based compounds, originally developed as antifungals, in advanced antiparasitic therapy. Their unique structural properties, particularly those of 1,2,3- and 1,2,4-triazole isomers, facilitate diverse binding interactions and favorable pharmacokinetics. By leveraging innovative synthetic approaches, such as click chemistry (copper-catalyzed azide–alkyne cycloaddition) and structure-based design, researchers have repurposed and optimized triazole scaffolds to target essential parasite pathways, including sterol biosynthesis via CYP51 and other novel enzymatic routes. Preclinical studies in models of Chagas disease, leishmaniasis, malaria, and helminth infections demonstrate that derivatives like posaconazole, ravuconazole, and DSM265 exhibit potent in vitro and in vivo activity, although their primarily static effects have limited their success as monotherapies in chronic cases. Combination strategies and hybrid molecules have demonstrated the potential to enhance efficacy and mitigate drug resistance. Despite challenges in achieving complete parasite clearance and managing potential toxicity, interdisciplinary efforts across medicinal chemistry, parasitology, and clinical research highlight the significant potential of triazoles as components of next-generation, patient-friendly antiparasitic regimens. These findings support the further optimization and clinical evaluation of triazole-based agents to improve treatments for neglected parasitic diseases. Full article

Designing routing algorithms for Low Earth Orbit (LEO) satellite networks poses a significant challenge due to their high dynamics, frequent link failures, and unevenly distributed traffic. Existing studies predominantly focus on shortest-path solutions, which compute minimum-delay paths using global topology information but often neglect the impact of traffic load on routing performance and struggle to adapt to rapid link-state variations. In this regard, we propose a Multi-Agent Reinforcement Learning-Based Joint Routing (MARL-JR) algorithm, which integrates centralized and distributed routing algorithms. MARL-JR combines the accuracy of centralized methods with the responsiveness of distributed approaches in handling dynamic disruptions. In MARL-JR, ground stations initialize Q-tables and upload them to satellites, reducing onboard computational overhead while enhancing routing performance. Compared to traditional centralized algorithms, MARL-JR achieves faster link-state awareness and adaptation; compared to distributed algorithms, it delivers superior initial performance due to optimized pre-training. Experimental results demonstrate that MARL-JR outperforms both Q-Routing (QR) and DR-BM algorithms in average delay, packet loss rate, and load-balancing efficiency. Full article

Ship operability diagrams are commonly defined based on the seakeeping analysis, showing which course and speed can safely be taken at the sea state to satisfy pre-defined seakeeping limiting values. Although ship operability diagrams are inherently probabilistic, because of the random nature of the environmental loads, their outcome is deterministic, showing if the seakeeping criteria are satisfied or not for a certain combination of environmental and operational parameters. In the present study, uncertainties in seakeeping predictions and limiting values, which are usually neglected, are integrated into the ship operability analysis. This results in probabilistic operability diagrams, where the seakeeping criteria are exceeded with certain probabilities. The approach is demonstrated in the example of the passenger ship on a route in the Adriatic Sea. Semi-analytical closed-form expressions are used for seakeeping analysis, while limiting values for vertical bow acceleration, pitch, slamming, roll, and propeller emergence are analyzed. The second-order reliability method is used to calculate probabilities of the exceedance of the seakeeping criteria, and the results are presented as probabilistic operability diagrams. The method enables the determination of a new probabilistic operability index applicable to the ship design and represents a prerequisite for risk-based decision making in ship operation. It is also presented how the method can be validated for the existing shipping route using numerical wave databases. Full article

Rockfalls pose significant threats to infrastructure, transportation routes, and human safety in mountainous regions, making them a critical concern in natural hazard and risk management. Accurate prediction of rockfall behavior is essential for designing effective mitigation strategies. The normal coefficient of restitution (R_n) is a key kinematic parameter for modeling falling rock dynamics, specifically quantifying the energy retained after collision between a rock and a slope surface. While this parameter is not directly used in prevention design, it is crucial for predicting the movement and trajectory of falling rocks and can indirectly support the development of more effective hazard mitigation strategies. However, R_n is influenced by multiple factors, including slope angle, surface material, falling rock shape, and initial velocity. The interactions among these factors make a precise prediction of R_n particularly challenging. Existing theoretical and empirical formulas typically consider individual factors in isolation, often neglecting their interactions, which leads to significant discrepancies in the results. To address this gap, we conducted a series of laboratory physical model tests to investigate the interactions among highly sensitive controlling factors and improve the accuracy of R_n prediction. A self-designed release apparatus, coupled with a high-speed recording and analysis system, was used to capture full kinematic data during rockfall collisions on slopes. This study not only examined how the main controlling factors and their interactions affect R_n but also developed a multi-factor interaction regression model, which was verified using on-site test data. The results show that the effect of the main controlling factors decreases in the following order: falling rock shape, slope surface material, initial velocity, and slope angle. Considering that falling rock shape and slope surface material cannot be quantitatively evaluated, the shape factor (η) and material factor (A_slope) are proposed to represent two controlling factors, respectively. Specifically, increases in η, A_slope, initial velocity, and slope angle are negatively correlated with R_n. Highly significant interactions were observed among falling rock shape–slope surface material, falling rock shape–initial velocity, falling rock shape–slope angle, slope surface material–initial velocity, and falling rock shape–slope surface material–initial velocity. These interactions mitigate the R_n reduction, resulting in a weaker effect than the stacking effect of the individual factors. The phenomenon is primarily attributed to the fact that high-level η, A_slope, initial velocity, and slope angle diminish the effect of intersecting factors. Finally, a comparison of the multi-factor interaction model with on-site tests and empirical formulas revealed the accuracy of the proposed model. Full article

Underwater acoustic sensor networks (UASNs) play an increasingly crucial role in both civilian and military fields. However, existing routing protocols primarily rely on node position information for forwarding decisions, neglecting link quality and energy efficiency. To address these limitations, we propose a fuzzy logic reasoning adaptive forwarding (FLRAF) routing protocol for three-dimensional (3D) UASNs. First, the FLRAF method redefines a conical forwarding region to prioritize nodes with greater effective advance distance, thereby reducing path deviations and minimizing the total number of hops. Unlike traditional approaches based on pipeline or hemispherical forwarding regions, this design ensures directional consistency in multihop forwarding, which improves transmission efficiency and energy utilization. Second, we design a nested fuzzy inference system for forwarding node selection. The inner inference system evaluates link quality by integrating the signal-to-noise ratio and some metrics related to the packet reception rate. This approach enhances robustness against transient fluctuations and provides a more stable estimation of link quality trends in dynamic underwater environments. The outer inference system incorporates link quality index, residual energy, and effective advance distance to rank candidate nodes. This multimetric decision model achieves a balanced trade-off between transmission reliability and energy efficiency. Simulation results confirm that the FLRAF method outperforms existing protocols under varying node densities and mobility conditions. It achieves a higher packet delivery rate, extended network lifetime, and lower energy consumption. These results demonstrate that the FLRAF method effectively addresses the challenges of energy constraints and unreliable links in 3D UASNs, making it a promising solution for adaptive and energy-efficient underwater communication. Full article

Unmanned Aerial Vehicles (UAVs) are widely used in applications such as land assessment, surveillance, and rescue operations, where they are often required to cover multiple disjoint regions. Coverage Path Planning (CPP) aims to determine optimal paths for UAVs to cover these areas. While CPP for single regions has been extensively studied, multi-region CPP with multiple UAVs remains underexplored. Existing methods typically focus on minimizing path length, but often neglect the nonlinear variations in energy consumption during flight, limiting their practical applicability. This paper addresses the multi-UAV, multi-region CPP as a variant of the Vehicle Routing Problem (VRP) with energy estimation. We propose an approach that optimizes UAV flight speeds to minimize energy consumption, supported by an accurate energy estimation algorithm. In addition, a heuristic algorithm is developed to balance the distribution of tasks among UAVs, considering both the scanning and transit times. Experiments using real-world data from the Changsha urban area demonstrate that our approach outperforms state-of-the-art methods in computational efficiency and energy savings, highlighting its potential for practical UAV deployment. Full article

The vehicle routing problem (VRP), as one of the classic combinatorial optimization problems, has garnered widespread attention in recent years. Existing deep reinforcement learning (DRL)-based methods predominantly focus on node information, neglecting the edge information inherent in the graph structure. Moreover, the solution trajectories produced by these methods tend to exhibit limited diversity, hindering a thorough exploration of the solution space. In this work, we propose a novel Edge-Driven Multiple Trajectory Attention Model (E-MTAM) to solve VRPs with various scales. Our model is built upon the encoder–decoder architecture, incorporating an edge-driven multi-head attention (EDMHA) block within the encoder to better utilize edge information. During the decoding process, we enhance graph embeddings with visitation information, integrating dynamic updates into static graph embeddings. Additionally, we employ a multi-decoder architecture and introduce a regularization term to encourage the generation of diverse trajectories, thus promoting solution diversity. We conduct comprehensive experiments on three types of VRPs: (1) traveling salesman problem (TSP), (2) capacitated vehicle routing problem (CVRP), and (3) orienteering problem (OP). The experimental results demonstrate that our model outperforms existing DRL-based methods and most traditional heuristic approaches, while also exhibiting strong generalization across problems of different scales. Full article