Search Results (53)

The expansion of global maritime activities threatens marine ecosystems and biodiversity. Collisions between ships and marine megafauna profoundly impact vulnerable species such as whales, who serve as keystone predators. However, the specific regions most heavily affected by shipping traffic and the multi-species facing collision risk remain poorly understood. Here, we analyzed global shipping data to assess the distribution of areas with high shipping pressure and identify global hotspots for whale–ship collisions. The results reveal that high-pressure habitats are primarily distributed within exclusive economic zones (EEZs), which are generally consistent with the distribution of collision hotspots. High-pressure habitats exhibit significant spatial mismatch: 32.9% of Marine Protected Areas endure high shipping stress and yet occupy merely 1.25% of protected ocean area. Additionally, 25.1% of collision hotspots (top 1% risk) affect four or more whale species, forming critical aggregation in regions like the Gulf of St. Lawrence and Northeast Asian marginal seas. Most of these high-risk areas lack protective measures. These findings offer actionable spatial priorities for implementing targeted conservation strategies, such as the introduction of mandatory speed restrictions and dynamic vessel routing in high-risk, multi-species hotspots. By focusing on critical aggregation areas, these strategies will help mitigate whale mortality and enhance marine biodiversity protection, supporting the sustainable coexistence of maritime activities with vulnerable marine megafauna. Full article

To investigate the influences of void defects of different sizes, molecular dynamics combined with ReaxFF-lg reactive force field was used to study the hot-spot formation mechanism and thermal decomposition behavior of 2,6-diamino-3,5-dinitropyrazine-1-oxide (LLM-105) crystals with different void defects at 2500 K. The results indicate that larger void defects are more conducive to the formation of hot-spots. The consistency of the trends in time evolution of the potential energy, species numbers, and small molecules amounts between the ideal and void-containing LLM-105 crystals demonstrates that the presence of the void defect does not alter the decomposition mechanism of the LLM-105 molecule. An increase in the size of the void defect significantly increases the degree of diffusion of the C, H, O, and N atoms in the crystals, which affects the effective collisions between the atoms and thus alters the occurrence frequency of relevant reactions and the production of relevant products. Full article

As shared micromobility grows quickly in metropolitan settings, e-scooter safety issues have become more urgent. This paper uses a Bayesian hierarchical model applied to census block groups in several Texas metropolitan areas to construct a spatial risk assessment methodology for e-scooter crashes. Based on crash statistics from 2018 to 2024, we develop a severity-weighted crash risk index and combine it with variables related to land use, transportation, demographics, economics, and other factors. The model comprises a geographically structured random effect based on a Conditional Autoregressive (CAR) model, which accounts for residual spatial clustering after capture. It also includes fixed effects for covariates such as car ownership and nightlife density, as well as regional random intercepts to account for city-level heterogeneity. Markov Chain Monte Carlo is used for model fitting; evaluation reveals robust spatial calibration and predictive ability. The following key predictors are statistically significant: a higher share of working-age residents shows a positive association with crash frequency (incidence rate ratio (IRR): ≈1.55 per +10% population aged 18–64), as does a greater proportion of car-free households (IRR ≈ 1.20). In the built environment, entertainment-related employment density is strongly linked to elevated risk (IRR ≈ 1.37), and high intersection density similarly increases crash risk (IRR ≈ 1.32). In contrast, higher residential housing density has a protective effect (IRR ≈ 0.78), correlating with fewer crashes. Additionally, a sensitivity study reveals that the risk index is responsive to policy scenarios, including reducing car ownership or increasing employment density, and is sensitive to varying crash intensity weights. Results show notable collision hotspots near entertainment venues and central areas, as well as increased baseline risk in car-oriented urban environments. The results provide practical information for targeted initiatives to lower e-scooter collision risk and safety planning. Full article

Wildlife-vehicle collisions (WVCs) threaten biodiversity, particularly in the Gulf of Mexico, where road expansion increases habitat fragmentation. This research analyzes WVC patterns in southeastern Mexico, estimating collision rates across road types and assessing environmental factors influencing roadkill frequency. Field monitoring in 2016 and 2023 recorded vertebrate roadkills along roads in Campeche, Chiapas, and Tabasco. Principal Component Analysis (PCA) and Generalized Additive Models (GAM) evaluated landscape influences on WVC occurrences. A total of 354 roadkill incidents involving 73 species of vertebrates were recorded, with mammals accounting for the highest mortality rate. Hotspots were identified along Federal Highway 259 and State Highways Balancán, Frontera-Jonuta, and Salto de Agua. Road type showed no significant effect. Land cover influenced WVCs, with cultivated forests, grasslands, and savannas showing the highest incidences. PCA identified temperature and elevation as key environmental drivers, while GAM suggested elevation had a weak but notable effect. These findings highlight the risks of road expansion in biodiversity-rich areas, where habitat fragmentation and increasing traffic intensify WVCs. Without targeted mitigation strategies, such as wildlife corridors, underpasses, and road signs, expanding infrastructure could further threaten wildlife populations by increasing roadkill rates and fragmenting habitats, particularly in ecologically sensitive landscapes like wetlands, forests, and coastal areas. Full article

The analysis of traffic crashes facilitates the identification of trends that can inform strategies to enhance road safety. This study aimed to detect high-risk zones and forecast collision patterns by integrating spatial analysis and predictive modeling. Traffic incidents along the Chone–Flavio Alfaro road segment in Manabí, Ecuador, were examined using Geographic Information Systems (GIS) and Kernel Density Estimation (KDE), based on official data from the National Traffic Agency (ANT) covering the period 2017–2023. Additionally, ARIMA, Prophet, and Long Short-Term Memory (LSTM) models were applied to predict crash occurrences. The most influential contributing factors were driver distraction, excessive speed, and adverse weather. Four main crash hotspots were identified: near Chone (PS 0–2.31), PS 2.31–7.10, PS 13.39–21.31, and PS 31.27–33.92, close to Flavio Alfaro. A total of 55 crashes were recorded, with side impacts (27.3%), pedestrian-related collisions (14.5%), and rear-end crashes (12.7%) being the most frequent types. The predictive models performed well, with Prophet achieving the highest estimated accuracy (90.8%), followed by LSTM (88.2%) and ARIMA (87.6%), based on MAE evaluations. These findings underscore the potential of intelligent transportation systems (ITSs) and predictive analytics to support proactive traffic management and resilient infrastructure development in rural regions. Full article

In recent years, the frequent collapse of bridges has underscored the severe threats posed by ship collisions and seismic forces to bridge pile foundations, particularly under scour conditions. Scour significantly increases bending moments, weakens foundation stability, and exacerbates damage under ship impacts and seismic loading. This review systematically examines the dynamic responses of bridge pile foundations subjected to multi-hazard scenarios, focusing on how scour-induced degradation exacerbates the impacts of ship collisions and seismic events. The synthesis covers experimental studies, numerical simulations, and theoretical approaches, providing a comprehensive evaluation of methodologies and findings. Advanced bibliometric tools, such as CiteSpace and VOSviewer, are employed to identify research trends, hotspots, and collaborations in this domain. Additionally, the review highlights the integration of intelligent technologies for mitigating ship collision risks and improving bridge safety management in scour-prone environments. By consolidating existing knowledge, this paper can serve as a critical reference for understanding the compounded effects of scour and other hazards on bridge pile foundations, offering guidance for future research and engineering practices. Full article

The conservation of biodiversity and the balance between ecological and societal needs are critical but often contested global issues. Wildlife-vehicle collision (WVC) on vital infrastructure, especially linear infrastructure, remains a persistent challenge from policy to practice and poses a serious life-threatening implication to humans and other non-human lives. Addressing this issue effectively requires solutions that provide win-win outcomes from both ecological and societal perspectives. This study critically analyzes a decade of roadkill incidents along Nepal’s longest East-West national highway, which passes through a biologically diverse national park in the western Terai Arc Landscape Area (TAL). Findings are drawn from field-based primary data collection of the period 2012–2022, secondary literature review, key informant interviews, and spatial analysis. The study reveals significant variations in roadkill incidence across areas and years. Despite Bardia National Park being larger and having a higher wildlife density, Banke National Park recorded higher roadkill rates. This is attributed to insufficient mitigation measures and law enforcement, more straight highway segments, and the absence of buffer zones between the core park and adjacent forest areas—only a road separates them. Wild boars (Sus scrofa) and spotted deer (Axis axis), the primary prey of Bengal tigers (Panthera tigris tigris), were the most frequently road-killed species. This may contribute to human-tiger conflicts, as observed in the study areas. Seasonal trends showed that reptiles were at higher risk during the wet season and mammals during winter. Hotspots were often located near checkpoints and water bodies, highlighting the need for targeted mitigation efforts such as wildlife crossings and provisioning wildlife requirements such as water, grassland, and shelter away from the regular traffic roads. Roadkill frequency was also influenced by forest cover and time of day, with more incidents occurring at dawn and dusk when most of the herbivores become more active in search of food, shelter, water, and their herds. The findings underscore the importance of road characteristics, animal behavior, and landscape features in roadkill occurrences. Effective mitigation strategies include wildlife crossings, speed limits, warning signs, and public education campaigns. Further research is needed to understand the factors in driving variations between parks and to assess the effectiveness of mitigation measures. Full article

The European Union (EU) is paving the way toward “Vision Zero”, a future goal of eliminating road fatalities and severe injuries. Railway level crossings are critical safety hotspots where road and rail traffic intersect and present a unique challenge in balancing the safety of both rail and road users while ensuring efficient traffic flow. Collisions at these crossings account for a significant proportion of railway-related fatalities in the EU, underscoring the need for targeted safety interventions. This article explores the impact of signal preemption strategies on the safety and operational performance of railway level crossings through a microsimulation analysis. Using VISSIM, a railway level crossing and its adjacent road intersection were modeled under existing and alternative scenarios. The preemption strategy was designed to clear vehicles from the crossing area before train arrivals, reducing conflict risks and optimizing traffic flow. Key findings reveal that the proposed preemption strategy significantly reduces queue lengths within critical safety zones, mitigating vehicle spillback and enhancing operational efficiency. The analysis highlights the importance of integrating railway operations with traffic signal systems, particularly in urban areas with limited queue storage capacity. Full article

Anthropogenic structures such as overhead powerlines pose potentially high collision risks to flying animals, particularly birds, leading to millions of fatalities each year. Studies of bird collisions with powerlines to date, however, have estimated different numbers of collision per year and per kilometer in highly variable landscapes. This study aimed to clarify the risk of bird collisions with powerlines in an average landscape, to overcome the bias towards studies in collision hotspots. We conducted experiments to determine searcher efficiency, removal, and decomposition rates of collided birds as well as searching for collision victims and recording flight movements and flight reactions towards the powerlines. Annual bird-strike rates and flight phenology were analyzed using generalized additive models (GAMs). We estimated 50.1 collision victims per powerline kilometer per year and demonstrated that pigeons (especially Wood Pigeon, Columba palumbus) accounted for the largest proportion of collision victims (approximately 65%). Our study thus offers the opportunity to estimate the number of bird collisions (and the range of species) that can be expected in areas that are not particularly rich in bird life or sensitive, especially in view of the planned intensive expansion of energy structures in the context of the green energy transition. Full article

Autonomous driving vehicles have strong path planning and obstacle avoidance capabilities, which provide great support to avoid traffic accidents. Autonomous driving has become a research hotspot worldwide. Depth estimation is a key technology in autonomous driving as it provides an important basis for accurately detecting traffic objects and avoiding collisions in advance. However, the current difficulties in depth estimation include insufficient estimation accuracy, difficulty in acquiring depth information using monocular vision, and an important challenge of fusing multiple sensors for depth estimation. To enhance depth estimation performance in complex traffic environments, this study proposes a depth estimation method in which point clouds and images obtained from MMwave radar and cameras are fused. Firstly, a residual network is established to extract the multi-scale features of the MMwave radar point clouds and the corresponding image obtained simultaneously from the same location. Correlations between the radar points and the image are established by fusing the extracted multi-scale features. A semi-dense depth estimation is achieved by assigning the depth value of the radar point to the most relevant image region. Secondly, a bidirectional feature fusion structure with additional fusion branches is designed to enhance the richness of the feature information. The information loss during the feature fusion process is reduced, and the robustness of the model is enhanced. Finally, parallel channel and position attention mechanisms are used to enhance the feature representation of the key areas in the fused feature map, the interference of irrelevant areas is suppressed, and the depth estimation accuracy is enhanced. The experimental results on the public dataset nuScenes show that, compared with the baseline model, the proposed method reduces the average absolute error (MAE) by 4.7–6.3% and the root mean square error (RMSE) by 4.2–5.2%. Full article

IEEE 802.11 is one of the most common medium access control (MAC) protocols used in wireless networks. The carrier sense multiple access with collision avoidance (CSMA/CA) mechanisms in 802.11 have been designed under the assumption that all nodes in the network are cooperative and trustworthy. However, the potential for non-cooperative nodes exists, nodes that may purposefully misbehave in order to, for example, obtain extra bandwidth, conserve their resources, or disrupt network performance. This issue is further compounded when receivers such as Wi-Fi hotspots, normally trusted by other module nodes, also misbehave. Such issues, their detection, and mitigation have, we believe, not been sufficiently addressed in the literature. This research proposes a novel trust-incorporated MAC protocol (TMAC) which detects and mitigates complex node misbehavior for distributed network environments. TMAC introduces three main features into the original IEEE 802.11 protocol. First, each node assesses a trust level for their neighbors, establishing a verifiable backoff value generation mechanism with an incorporated trust model involving senders, receivers, and common neighbors. Second, TMAC uses a collaborative penalty scheme to penalize nodes that deviate from the IEEE 802.11 protocol. This feature removes the assumption of a trusted receiver. Third, a TMAC diagnosis mechanism is carried out for each distributed node periodically, to reassess neighbor status and to reclassify each based on their trust value. Simulation results in ns2 showed that TMAC is effective in diagnosing and starving selfish or misbehaving nodes in distributed wireless networks, improving the performance of trustworthy well-behaving nodes. The significant feature of TMAC is its ability to detect sender, receiver, and colluding node misbehavior at the MAC layer with a high level of accuracy, without the need to trust any of the communicating parties. Full article

After the loss and fragmentation of habitat, vehicle collisions are one of the main threats to the long-term survival of wild koalas. Koala road strike data were analysed for a section of the Peak Downs Highway between Nebo and Spencer’s Gap, west of Mackay, Queensland, Australia. The analysis was carried out on 345 records (October 2014 to November 2023), and results suggested the spatial distribution of koala road strike followed a random pattern along this section of the highway, assuming a Poisson point pattern on a linear network. An analysis of the candidate predictors of koala vehicle collisions, including habitat and road variables, found that the amount of high-quality koala habitat (as defined by the local koalas’ tree species preference) present and the driver visibility were the only significant predictors. The relative homogeneity of landuse and vegetation across this landscape may mean that koalas do not concentrate at specific crossing points. More research, including detailed habitat mapping, is needed into this population, which currently lacks government and conservation attention, to inform mitigation efforts and reduce mortality rates for this potentially nationally significant population. Full article

Autonomous collision avoidance decision making for maritime autonomous surface ships (MASS), as one of the key technologies for MASS autonomous navigation, is a research hotspot focused on by relevant scholars in the field of navigation. In order to guarantee the rationality, efficacy, and credibility of the MASS autonomous collision avoidance scheme, it is essential to design the MASS autonomous collision avoidance algorithm under the stipulations of the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs). In order to enhance the autonomous collision avoidance decision-making capability of MASS in accordance with the relevant provisions of COLREGs, an improved NSGA-II autonomous collision avoidance decision-making algorithm based on the good point set method (GPS-NSGA-II) is proposed, which incorporates the collision hazard and the path cost of collision avoidance actions. The experimental results in the four simulation scenarios of head-on situation, overtaking situation, crossing situation, and multi-ship encounter situation demonstrate that the MASS autonomous collision avoidance decision making based on the GPS-NSGA-II algorithm under the constraints of COLREGs is capable of providing an effective collision avoidance scheme that meets the requirements of COLREGs in common encounter situations and multi-ship avoidance scenarios promptly, with a promising future application. Full article

In recent years, remotely controlling an unmanned aerial vehicle (UAV) to perform coverage search missions has become increasingly popular due to the advantages of the UAV, such as small size, high maneuverability, and low cost. However, due to the distance limitations of the remote control and endurance of a UAV, a single UAV cannot effectively perform a search mission in various and complex regions. Thus, using a group of UAVs to deal with coverage search missions has become a research hotspot in the last decade. In this paper, a differential evolution (DE)-based multi-UAV cooperative coverage algorithm is proposed to deal with the coverage tasks in different regions. In the proposed algorithm, named DECSMU, the entire coverage process is divided into many coverage stages. Before each coverage stage, every UAV automatically plans its flight path based on DE. To obtain a promising flight trajectory for a UAV, a dynamic reward function is designed to evaluate the quality of the planned path in terms of the coverage rate and the energy consumption of the UAV. In each coverage stage, an information interaction between different UAVs is carried out through a communication network, and a distributed model predictive control is used to realize the collaborative coverage of multiple UAVs. The experimental results show that the strategy can achieve high coverage and a low energy consumption index under the constraints of collision avoidance. The favorable performance in DECSMU on different regions also demonstrate that it has outstanding stability and generality. Full article

Since droplet collision with walls has become a research hotspot, scholars have conducted a large number of studies on the dynamic behavior of electrically neutral droplets colliding with dry walls. However, with the rapid development of electrostatic spray technology, there is an increasingly urgent need to study the dynamic process of collision between charged droplets and walls. In this paper, considering the actual working conditions of electrostatic spray, an electric field model is introduced based on the two-phase flow field. Through the coupling of a multiphase flow field and electric field and a multiphysics field, the dynamic numerical calculation method is used to explore the collision electrodynamic behavior of charged droplets and liquid film. The dynamic evolution process of the formation and development of the liquid crown in the collision zone was clarified, and the critical velocity and critical Weber number of the rebound, spreading, and splashing of charged droplets were tracked. The distribution characteristics of electrostatic field, pressure field, and velocity field under different working conditions are analyzed, and the dynamic mechanism of the charged droplet collision liquid film under multi-physics coupling is revealed based on the electro-viscous effect. It is confirmed that the external electric field can increase the critical velocity of droplet splashing and fragmentation and promote the spreading and fusion behavior of droplets and liquid films. The influence of the impact angle of charged droplets on the collision behavior was further explored. It was found that the charged droplets not only have a smaller critical angle for fragmentation and splashing, but also have a faster settling and fusion speed. Full article