Search Results (1,578)

To improve collision risk prediction in high-traffic coastal waters and support real-time decision-making in maritime navigation, this study proposes a regional collision risk prediction system integrating the Computed Distance at Collision (CDC) method with an Adaptive Neuro-Fuzzy Inference System (ANFIS). Unlike Distance at Closest Point of Approach (DCPA), which depends on the position of Global Positioning System (GPS) antennas, Computed Distance at Collision (CDC) directly reflects the actual hull shape and potential collision point. This enables a more realistic assessment of collision risk by accounting for the hull geometry and boundary conditions specific to different ship types. The system was designed and validated using ship motion simulations involving bulk and container ships across varying speeds and crossing angles. The CDC method was used to define collision, almost-collision, and near-collision situations based on geometric and hydrodynamic criteria. Subsequently, the FIS–CDC model was constructed using the ANFIS by learning patterns in collision time and distance under each condition. A total of four input variables—ship speed, crossing angle, remaining time, and remaining distance—were used to infer the collision risk index (CRI), allowing for a more nuanced and vessel-specific assessment than traditional CPA-based indicators. Simulation results show that the time to collision decreases with higher speeds and increases with wider crossing angles. The bulk carrier exhibited a wider collision-prone angle range and a greater sensitivity to speed changes than the container ship, highlighting differences in maneuverability and risk response. The proposed system demonstrated real-time applicability and accurate risk differentiation across scenarios. This research contributes to enhancing situational awareness and proactive risk mitigation in Maritime Autonomous Surface Ship (MASS) and Vessel Traffic System (VTS) environments. Future work will focus on real-time CDC optimization and extending the model to accommodate diverse ship types and encounter geometries. Full article

This study investigates the mechanical, morphological, and wear properties of SiO₂-filled tri-axial warp-knitted (TWK) glass fiber-reinforced vinyl ester matrix composites, with a focus on void fraction, tensile, flexural, hardness, and wear behavior. Adding SiO₂ fillers reduced void fractions, enhancing composite strength, with values ranging from 1.63% to 5.31%. Tensile tests revealed that composites with 5 wt% SiO₂ (GV1) exhibited superior tensile strength, Young’s modulus, and elongation due to enhanced fiber–matrix interaction. Conversely, composites with 10 wt% SiO₂ (GV2) showed decreased tensile performance, indicating increased brittleness. Flexural tests demonstrated that GV1 outperformed GV2, showcasing higher flexural strength, elastic modulus, and deflection, reflecting improved load-bearing capacity at optimal filler content. Shore D hardness tests confirmed that GV1 had the highest hardness among the specimens. SEM analysis revealed wear behavior under various loads and sliding distances. GV1 exhibited minimal wear loss at lower loads and distances, while higher loads caused significant matrix detachment and fiber damage. These findings highlight the importance of optimizing SiO₂ filler content to enhance epoxy composites’ mechanical and tribological performance. Full article

Background/Objectives: To assess the optical performance of two refractive premium IOLs across pupil sizes and values of corneal spherical aberration (SA). Methods: Two refractive IOLs were evaluated in this study: Tecnis Eyhance and Mini Well. The surface profiles were obtained to calculate the through-object MTF (TO MTF) curves and simulate optotype images. Entrance pupil sizes ranging from 2 to 5.5 and three corneal models were analyzed in the simulation: an average population aberrated cornea, an aberration-free cornea and a post-Lasik myopic cornea. Results: For Model 1 and pupil sizes between 3.0 and 3.5 mm, Mini Well provided acceptable visual quality from far to near distances, whereas Eyhance struggled to maintain visual quality at distances closer than intermediate. For patients with lower-than-normal corneal SA (i.e., more prolate corneas, such as post-hyperopic LASIK) both IOLs exhibited a hyperopic shift in far focus. Conversely, for patients with higher-than-normal corneal SA (i.e., more oblate corneas, such as post-myopic LASIK), the shift occurred in the myopic direction. Despite the implementation of an optimized IOL power to circumvent any shift, the TO MTF nevertheless reflected the interaction between corneal and IOL SA. Furthermore, the Mini Well demonstrated increased tolerance to less negative SA values, while Eyhance exhibited behavior consistent with a monofocal lens for more positive SA values. Conclusions: Surgeons should consider each patient’s corneal asphericity and typical pupil diameter when selecting and calculating the power of the premium IOLs studied, particularly in patients with a history of refractive surgery. Full article

The current focus of urban environmental governance is on the traffic noise pollution caused by road transportation. Elevated complex roads, defined as transportation systems comprising elevated roads and underlying ground-level roads, exhibit unique traffic noise distribution characteristics due to the presence of double-decked roads and viaducts. This study conducted noise measurements at two sections of elevated complex roads in Guangzhou, including assessing noise levels at the road boundaries and examining noise distribution at different distances from roads and building heights. The results show that the horizontal distance attenuation of noise in adjacent areas exhibits no significant difference from that of ground-level roads, but substantial discrepancies exist in vertical height distribution. The under-viaduct space experiences more severe noise pollution than areas above the viaduct height, and the installation of sound barriers alters the spatial distribution trend of traffic noise. Given that installing sound barriers solely on elevated roads is insufficient to improve the acoustic environment, systematic noise mitigation strategies should be developed for elevated composite road systems. Additionally, the study reveals that nighttime noise fluctuations are significantly greater than those during the day, further exacerbating residents’ noise annoyance. Full article

Directed grey-box fuzzers focus on testing specific target code. They have been utilized in various security applications, such as reproducing known crashes and identifying vulnerabilities resulting from incomplete patches. Distance-guided directed fuzzers calculate the distance to the target node for each node in a CFG or CG, which has always been the mainstream in this field. However, the distance can only reflect the relationship between the current node and the target node, and it does not consider the impact of the reaching sequence before the target node. To mitigate this problem, we analyzed the properties of the instrumented function’s call graph after selective instrumentation, and the complexity of reaching the target function sequence was estimated. Assisted by the sequence complexity, we proposed a two-stage function call sequence-based seed-scheduling strategy. The first stage is to select seeds with a higher probability of generating test cases that reach the target function. The second stage is to select seeds that can generate test cases that meet the conditions for triggering the vulnerability as much as possible. We implemented our approach in SEZZ based on SelectFuzz and compare it with related works. We found that SEZZ outperformed AFLGo, Beacon, WindRanger, and SelectFuzz by achieving an average improvement of 13.7×, 1.50×, 9.78×, and 2.04× faster on vulnerability exposure, respectively. Moreover, SEZZ triggered three more vulnerabilities than the other compared tools. Full article

Unbalanced accelerations occurring during tram travel have a significant impact on passenger comfort and safety, as well as on the rate of wear and tear on infrastructure and rolling stock. Ideally, these dynamic forces should be monitored continuously in real-time; however, traditional systems require high-precision accelerometers and proprietary software—investments often beyond the reach of municipally funded tram operators. To this end, as part of the research project “Accelerometer Measurements in Rail Passenger Transport Vehicles”, pilot measurement campaigns were conducted in Poland on tram lines in Gdańsk, Toruń, Bydgoszcz, and Olsztyn. Off-the-shelf smartphones equipped with MEMS accelerometers and GPS modules, running the Physics Toolbox Sensor Suite Pro app, were used. Although the research employs widely known methods, this paper addresses part of the gap in affordable real-time monitoring by demonstrating that, in the future, equipment equipped solely with consumer-grade MEMS accelerometers can deliver sufficiently accurate data in applications where high precision is not critical. This paper presents an analysis of a subset of results from the Gdańsk tram network. Lateral (x) and vertical (z) accelerations were recorded at three fixed points inside two tram models (Pesa 128NG Jazz Duo and Düwag N8C), while longitudinal accelerations were deliberately omitted at this stage due to their strong dependence on driver behavior. Raw data were exported as CSV files, processed and analyzed in R version 4.2.2, and then mapped spatially using ArcGIS cartograms. Vehicle speed was calculated both via the haversine formula—accounting for Earth’s curvature—and via a Cartesian approximation. Over the ~7 km route, both methods yielded virtually identical results, validating the simpler approach for short distances. Acceleration histograms approximated Gaussian distributions, with most values between 0.05 and 0.15 m/s², and extreme values approaching 1 m/s². The results demonstrate that low-cost mobile devices, after future calibration against certified accelerometers, can provide sufficiently rich data for ride-comfort assessment and show promise for cost-effective condition monitoring of both track and rolling stock. Future work will focus on optimizing the app’s data collection pipeline, refining standard-based analysis algorithms, and validating smartphone measurements against benchmark sensors. Full article

As distant hybridization has profound implications for evolutionary biology, aquaculture, and biodiversity conservation, this study aims to elucidate patterns of maternal inheritance, genetic divergence, and phylogenetic relationships by synthesizing mitochondrial genome (mitogenome) data from 74 distant hybrid fish species. These hybrids span diverse taxa, including 48 freshwater and 26 marine species, with a focus on Cyprinidae (n = 35) and Epinephelus (n = 14), representing the most frequently hybridized groups in freshwater and marine systems, respectively. Mitogenome lengths were highly conserved (15,973 to 17,114 bp); however, the genetic distances between hybrids and maternal species varied from 0.001 to 0.17, with 19 hybrids (25.7%) showing distances >0.02. Variable sites in these hybrids were randomly distributed but enriched in hypervariable regions, such as the D-loop and NADH dehydrogenase subunits 1, 3 and 6 (ND2, ND3, and ND6) genes, likely reflecting maternal inheritance (reported in Cyprinus carpio × Carassius auratus). Moreover, these genes were under purifying selection pressure, revealing their conserved nature. Phylogenetic reconstruction using complete mitogenomes revealed three distinct clades in hybrids: (1) Acipenseriformes, (2) a freshwater cluster dominated by Cypriniformes and Siluriformes, and (3) a marine cluster comprising Centrarchiformes, Pleuronectiformes, Scombriformes, Cichliformes, Anabantiformes, Tetraodontiformes, Perciformes, and Salmoniformes. The prevalence of Cyprinidae hybrids underscores their importance in aquaculture for hybridization, where traits such as rapid growth and disease resistance are enhanced. In contrast, marine hybrids are valued for their market value and adaptability. While mitogenome data robustly support maternal inheritance in most cases, exceptions suggest complex mechanisms, such as doubly uniparental inheritance (DUI), in distantly related crosses. Moreover, AT-skew of genes in hybrids revealed a paternal leakage of traits in mitogenomes. This study also highlights ecological risks, such as genetic swamping in native populations, emphasizing the need for responsible hybridization practices. These findings advance our understanding of the role of hybridization in fish evolution and aquaculture, providing a genomic framework and policy recommendations for optimizing breeding programs, hybrid introduction, and mitigating conservation challenges. Full article

Data over sound (DoS) is an established technique that has experienced a resurgence in recent years, finding applications in areas such as contactless payments, device pairing, authentication, presence detection, toys, and offline data transfer. This study introduces CardiaWhisper, a system that extends the DoS concept to the medical domain by using a medical data-over-sound (MDoS) framework. CardiaWhisper integrates wearable biomedical sensors with home care systems, edge or IoT gateways, and telemedical networks or cloud platforms. Using a transmitter device, vital signs such as ECG (electrocardiogram) signals, PPG (photoplethysmogram) signals, RR (respiratory rate), and ACC (acceleration/movement) are sensed, conditioned, encoded, and acoustically transmitted to a nearby receiver—typically a smartphone, tablet, or other gadget—and can be further relayed to edge and cloud infrastructures. As a case study, this paper presents the real-time transmission and processing of ECG signals. The transmitter integrates an ECG sensing module, an encoder (either a PLL-based FM modulator chip or a microcontroller), and a sound emitter in the form of a standard piezoelectric speaker. The receiver, in the form of a mobile phone, tablet, or desktop computer, captures the acoustic signal via its built-in microphone and executes software routines to decode the data. It then enables a range of control and visualization functions for both local and remote users. Emphasis is placed on describing the system architecture and its key components, as well as the software methodologies used for signal decoding on the receiver side, where several algorithms are implemented using open-source, platform-independent technologies, such as JavaScript, HTML, and CSS. While the main focus is on the transmission of analog data, digital data transmission is also illustrated. The CardiaWhisper system is evaluated across several performance parameters, including functionality, complexity, speed, noise immunity, power consumption, range, and cost-efficiency. Quantitative measurements of the signal-to-noise ratio (SNR) were performed in various realistic indoor scenarios, including different distances, obstacles, and noise environments. Preliminary results are presented, along with a discussion of design challenges, limitations, and feasible applications. Our experience demonstrates that CardiaWhisper provides a low-power, eco-friendly alternative to traditional RF or Bluetooth-based medical wearables in various applications. Full article

Background: South Asian Americans (SAA) are one of the fastest-growing immigrant groups in the U.S. and face significant health disparities, particularly regarding chronic diseases like diabetes, hypertension, and cardiovascular disease. Dietary patterns play a crucial role in these disparities, with acculturation to Western diets linked to poorer health outcomes. Despite this, the impact of food insecurity on dietary habits among SAAs remains underexplored. This study aims to examine the availability, cost, and quality of ethnic food items and how food insecurity influences dietary practices in Southern California’s SAA population. Methods: The study was conducted in San Bernardino County, California, with field data collection focused on five South Asian ethnicity-specific grocery stores and three Western grocery stores. We assessed the availability and cost of key ingredients for commonly prepared SAA dishes. Additionally, focus group interviews were held with South Asian immigrants to understand food insecurity challenges and dietary adaptations. Results: The study found significant disparities in food availability and cost between SAA-ethnic grocery stores and Western stores. SAA stores were less accessible and more widely dispersed, with an average distance of 10 miles between them. While ingredients like ginger paste and cumin powder were available in both types of stores, items such as ghee, fenugreek seeds, and black gram were harder to find in Western stores. Focus group participants noted that ethnic foods, especially vegetarian ingredients, were more expensive than Western alternatives, leading many to substitute traditional meals with cheaper, less nutritious options. Participants also raised concerns about the poor quality of items in ethnic stores, such as expired produce, which further limited their food choices. Conclusions: Food insecurity, driven by limited availability, high cost, and poor quality of ethnic foods, poses significant challenges to the SAA community’s diet and health. Addressing these barriers could improve food security and health outcomes among SAA immigrants. Full article

Rabies is a fatal but entirely vaccine-preventable disease, with the highest risk in areas where free-roaming domestic dogs are prevalent. Understanding dog owners’ knowledge, attitudes, and practices (KAP) is crucial for shaping effective rabies control strategies. This scoping review aimed to synthesize global evidence from studies evaluating dog owners’ KAP to identify behavioral factors relevant to rabies prevention and control. A systematic literature search was conducted using PubMed, Web of Science, and Scopus, covering the period from 2012 to 2025. Seventy full-text articles were included based on predefined criteria. The findings reveal substantial gaps in dog owners’ knowledge, beliefs, and behaviors regarding rabies prevention. While general awareness of rabies is high among dog owners, their knowledge about transmission, clinical signs, and the fatal nature of the disease is inconsistent, with significant variability across studies. The vaccination uptake also varied widely across studies, ranging from less than 1% to over 90%, with no study reporting full coverage. Furthermore, a strong positive correlation was found between vaccination practice and the awareness of vaccine benefits (r = 0.69, p = 0.004). Common barriers to vaccination include lack of information, vaccine accessibility, distance to clinics, and personal constraints. These insights underscore the importance of early and targeted communication about vaccination campaigns. Future research should focus on periodically evaluating KAP before and after interventions to better inform rabies control efforts. Full article

This study analyses the physical performance variables involved in different training tasks aimed at replicating the worst-case scenarios (WCSs) observed during official matches in professional football, with a focus on playing positions and occurrences within a 1 min period. Data were collected from 188 training sessions and 42 matches of a Spanish Second Division team during the 2021/2022 season. All data were reported on a per-player basis. GPS tracking devices were used to record physical variables such as total distance, high-speed running (HSR), sprints, accelerations, decelerations, and high metabolic load distance (HMLD). Players were grouped according to their match positions: central defenders, wide players, midfielders and forwards. The results showed that none of the training tasks fully replicated the physical demands of match play. However, task TYPEs 11 (Large-Sided Games) and 9 (small-sided games with orientation and transition) were the closest to match demands, particularly in terms of accelerations and decelerations. Although differences were observed across all variables, the most pronounced discrepancies were observed in sprint and HSR variables, where training tasksfailed to reach 60% of match demands. These findings highlight the need to design more specific drills that simulate the intensity of WCS, allowing for more accurate weekly training load planning. This study offers valuable contributions for optimising performance and reducing injury risk in professional footballers during the competitive period. Full article

Semantic communication has attracted considerable interest due to its potential to support emerging human-centric services, such as holographic communications, extended reality (XR), and human-machine interactions. Different from traditional communication systems that focus on minimizing the symbol-level distortion (e.g., bit error rate, signal-to-noise ratio, etc.), semantic communication targets at delivering the intended meaning at the destination user which is often quantified by various statistical divergences, often referred to as the semantic distances. Currently, there still lacks a unified framework to quantify the rate-distortion tradeoff for semantic communication with different task-specific semantic distance measures. To tackle this problem, we propose the task-specific rate-distortion theory for semantic communication where different task-specific statistic divergence metrics can be considered. To investigate the impact of different semantic distance measures on the achievable rate, we consider two popular tasks, classification and signal generation. We present the closed-form expressions of the semantic rate-distortion functions for these two different tasks and compare their performance under various scenarios. Extensive experimental results are presented to verify our theoretical results. Full article

This study presents the design and development of a structurally optimized mobile robotic system with a machine learning-based energy-efficient path planning framework. Topology optimization (TO) and finite element analysis (FEA) were applied to reduce structural weight while maintaining mechanical integrity. The optimized components were manufactured using Fused Deposition Modeling (FDM) with ABS (Acrylonitrile Butadiene Styrene) material. A custom power analysis tool was developed to compare energy consumption between the optimized and initial designs. Real-world current consumption data were collected under various terrain conditions, including inclined surfaces, vibration-inducing obstacles, gravel, and direction-altering barriers. Based on this dataset, a path planning model was developed using machine learning algorithms, capable of simultaneously optimizing both energy efficiency and path length to reach a predefined target. Unlike prior works that focus separately on structural optimization or learning-based navigation, this study integrates both domains within a single real-world robotic platform. Performance evaluations demonstrated superior results compared to traditional planning methods, which typically optimize distance or energy independently and lack real-time consumption feedback. The proposed framework reduces total energy consumption by 5.8%, cuts prototyping time by 56%, and extends mission duration by ~20%, highlighting the benefits of jointly applying TO and ML for sustainable and energy-aware robotic design. This integrated approach addresses a critical gap in the literature by demonstrating that mechanical light-weighting and intelligent path planning can be co-optimized in a deployable robotic system using empirical energy data. Full article

Corporate environmental claims often neglect the substantial ecological impact of land-use changes. This case study examines the spatial dimension of retail-driven land-use transformation by analyzing supermarket expansion in the Veneto region (northern Italy), with a focus on a large grocery retailer. We evaluated its corporate environmental claims by assessing land consumption patterns from 1983 to 2024 using Geographic Information Systems (GIS). The GIS-based methodology involved geocoding 113 Points of Sale (POS—individual retail outlets), performing photo-interpretation of historical aerial imagery, and classifying land-cover types prior to construction. We applied spatial metrics such as total converted surface area, land-cover class frequency across eight categories (e.g., agricultural, herbaceous, arboreal), and the average linear distance between afforestation sites and POS developed on previously rural land. Our findings reveal that 65.97% of the total land converted for Points of Sale development occurred in rural areas, primarily agricultural and herbaceous lands. These landscapes play a critical role in supporting urban biodiversity and providing essential ecosystem services, which are increasingly threatened by unchecked land conversion. While the corporate sustainability reports and marketing strategies emphasize afforestation efforts under their “We Love Nature” initiative, our spatial analysis uncovers no evidence of actual land-use conversion. Additionally, reforestation activities are located an average of 40.75 km from converted sites, undermining their role as effective compensatory measures. These findings raise concerns about selective disclosure and greenwashing, driving the need for more comprehensive and transparent corporate sustainability reporting. The study argues for stronger policy frameworks to incentivize urban regeneration over greenfield development and calls for the integration of land-use data into corporate sustainability disclosures. By combining geospatial methods with content analysis, the research offers new insights into the intersection of land use, business practices, and environmental sustainability in climate-vulnerable regions. Full article

Traditional collision risk warning methods primarily focus on vehicle-to-vehicle collisions, neglecting conflicts between vehicles and vulnerable road users (VRUs) such as pedestrians, while the difficulty in predicting pedestrian trajectories further limits the accuracy of collision warnings. To address this problem, this study proposes a vehicle-to-everything-based (V2X) multi-fusion vehicle–pedestrian collision warning method, aiming to enhance the traffic safety protection for VRUs. First, Unmanned Aerial Vehicle aerial imagery combined with the YOLOv7 and DeepSort algorithms is utilized to achieve target detection and tracking at unsignalized intersections, thereby constructing a vehicle–pedestrian interaction trajectory dataset. Subsequently, key foundational modules for collision warning are developed, including the vehicle trajectory module, the pedestrian trajectory module, and the risk detection module. The vehicle trajectory module is based on a kinematic model, while the pedestrian trajectory module adopts an Attention-based Social GAN (AS-GAN) model that integrates a generative adversarial network with a soft attention mechanism, enhancing prediction accuracy through a dual-discriminator strategy involving adversarial loss and displacement loss. The risk detection module applies an elliptical buffer zone algorithm to perform dynamic spatial collision determination. Finally, a collision warning framework based on the Monte Carlo (MC) method is developed. Multiple sampled pedestrian trajectories are generated by applying Gaussian perturbations to the predicted mean trajectory and combined with vehicle trajectories and collision determination results to identify potential collision targets. Furthermore, the driver perception–braking time (TTM) is incorporated to estimate the joint collision probability and assist in warning decision-making. Simulation results show that the proposed warning method achieves an accuracy of 94.5% at unsignalized intersections, outperforming traditional Time-to-Collision (TTC) and braking distance models, and effectively reducing missed and false warnings, thereby improving pedestrian traffic safety at unsignalized intersections. Full article