Search Results (101)

This study presents a well-to-wheel life-cycle assessment (WTW-LCA) comparing battery-electric heavy-duty trucks (BEVs) with conventional diesel trucks, utilizing real-world fleet data from Southern California’s Volvo LIGHTS project. Class 7 and Class 8 vehicles were analyzed under ISO 14040/14044 standards, combining measured diesel emissions from portable emissions measurement systems (PEMSs) with BEV energy use derived from telematics and charging records. Upstream (“well-to-tank”) emissions were estimated using USLCI datasets and the 2020 Southern California Edison (SCE) power mix, with an additional scenario for BEVs powered by on-site solar energy. The analysis combines measured real-world energy consumption data from deployed battery electric trucks with on-road emission measurements from conventional diesel trucks collected by the UCR team. Environmental impacts were characterized using TRACI 2.1 across climate, air quality, toxicity, and fossil fuel depletion impact categories. The results show that BEVs reduce total WTW CO₂-equivalent emissions by approximately 75% compared to diesel. At the same time, criteria pollutants (NO_x, VOCs, SO_x, PM_2.5) decline sharply, reflecting the shift in impacts from vehicle exhaust to upstream electricity generation. Comparative analyses indicate BEV impacts range between 8% and 26% of diesel levels across most environmental indicators, with near-zero ozone-depletion effects. The main residual hotspot appears in the human-health cancer category (~35–38%), linked to upstream energy and materials, highlighting the continued need for grid decarbonization. The analysis focuses on operational WTW impacts, excluding vehicle manufacturing, battery production, and end-of-life phases. This use-phase emphasis provides a conservative yet practical basis for short-term fleet transition strategies. By integrating empirical performance data with life-cycle modeling, the study offers actionable insights to guide electrification policies and optimize upstream interventions for sustainable freight transport. These findings provide a quantitative decision-support basis for fleet operators and regulators planning near-term heavy-duty truck electrification in regions with similar grid mixes, and can serve as an empirical building block for future cradle-to-grave and dynamic LCA studies that extend beyond the operational well-to-wheels scope adopted here. Full article

Recent advances in vibration-based pavement assessment have enabled the low-cost monitoring of road conditions using inertial sensors and machine learning models. However, most studies focus on isolated tasks, such as roughness classification, without integrating statistical validation, anomaly detection, or maintenance prioritization. This study presents a unified framework for road roughness severity classification and predictive maintenance using multi-axis accelerometer data collected from urban road networks in Pretoria, South Africa. The proposed pipeline integrates ISO-referenced labeling, ensemble and deep classifiers (Random Forest, XGBoost, MLP, and 1D-CNN), McNemar’s test for model agreement validation, feature importance interpretation, and GIS-based anomaly mapping. Stratified cross-validation and hyperparameter tuning ensured robust generalization, with accuracies exceeding 99%. Statistical outlier detection enabled the early identification of deteriorated segments, supporting proactive maintenance planning. The results confirm that vertical acceleration (accel_z) is the most discriminative signal for roughness severity, validating the feasibility of lightweight single-axis sensing. The study concludes that combining supervised learning with statistical anomaly detection can provide an intelligent, scalable, and cost-effective foundation for municipal pavement management systems. The modular design further supports integration with Internet-of-Things (IoT) telematics platforms for near-real-time road condition monitoring and sustainable transport asset management. Full article

Usage-Based Insurance (UBI), also referred to as telematics-based insurance, has been experiencing a growing global diffusion. In addition to being well established in countries such as Italy, the United States, and the United Kingdom, UBI adoption is also accelerating in emerging markets such as Japan, South Africa, and Brazil. In Japan, telematics insurance has shown significant growth in recent years, with a steadily increasing subscription rate. In South Africa, UBI adoption ranks among the highest worldwide, with market penetration placing the country among the top three globally, just after the United States and Italy. In Brazil, UBI adoption is expanding, supported by government initiatives promoting road safety and innovation in the insurance sector. According to a MarketsandMarkets report of February 2025, the global UBI market is expected to grow from USD 43.38 billion in 2023 to USD 70.46 billion by 2030, with a compound annual growth rate (CAGR) of 7.2% over the forecast period. This growth is driven by the increasing adoption of both electric and internal combustion vehicles equipped with integrated telematics systems, which enable insurers to collect data on driving behavior and to tailor insurance premiums accordingly. In this paper, we analyze a large dataset consisting of trips recorded over five years from 100,000 policyholders across the Italian territory through the installation of black-box devices. Using univariate and multivariate statistical analyses, as well as Generalized Linear Models (GLMs) with Zero-Inflated Poisson distribution, we examine claims frequency and assess the relevance of various synthetic indicators of driving behavior, with the aim of identifying those that are most significant for insurance pricing. Full article

Background: Public discourse on social media plays an increasingly influential role in shaping health-related perceptions and behaviours. Individuals share experiences, concerns, and opinions beyond clinical settings around different issues. X (formerly Twitter) provides a unique lens through which to examine how different treatments are perceived, used, and debated across diverse communities over time. Objective: The study aims to (a) identify the types of ADHD medications mentioned in posts, depending on language and user type; (b) evaluate the popularity of content related to these medications, considering language and user type; (c) analyse temporal changes in the frequency of mentions between 2006 and 2022; and (d) examine the distribution of tweets across different content categories. By addressing these objectives, this study provides insights into public perceptions of ADHD medications, which may help healthcare professionals better understand online discussions and improve their communication with patients, facilitating more informed treatment decisions. Methods: An observational study was conducted analysing 254,952 tweets in Spanish and English about ADHD medications from January 2006 to December 2022. Content analysis combined inductive and deductive approaches to develop a categorisation codebook. BERTWEET and BETO models were used for machine learning classification of English and Spanish tweets, respectively. Descriptive statistical analysis was performed. Results: Overall, stimulant medications were posted more frequently and received higher engagement than non-stimulant medications. Methylphenidate, dextroamphetamine, and atomoxetine were the most commonly mentioned medications, especially by patients, who emerged as the most active users among the English tweets. Regarding medical content, tweets in English contained more than twice the number of mentions of inappropriate use compared to those in Spanish. There was a high content of online medication requests and offers in both languages. Conclusions: In this study, conducted on X, discussions on ADHD medications highlighted concerns about misuse, adherence, and trivialisation, with clear differences between English and Spanish tweets regarding focus and type of user participation. These findings suggest that monitoring social media can provide early signals about emerging trends, helping clinicians address misconceptions during consultations and informing public health strategies aimed at the safer and more responsible use of ADHD medications. Full article

The rapid development of telematics technology has greatly supported high-computing applications like autonomous driving and real-time road condition prediction. However, the limited computational resources and dynamic topology of in-vehicle terminals pose challenges such as delay, load imbalance, and bandwidth consumption. To address these, a three-layer vehicular network architecture based on cloud–edge–end collaboration was proposed, with V2X technology used for multi-hop transmission. Models for delay, energy consumption, and edge caching were designed to meet the requirements for low delay, energy efficiency, and effective caching. Additionally, a dynamic pricing model for edge resources, based on load-awareness, was proposed to balance service quality and cost-effectiveness. The enhanced NSGA-III algorithm (ADP-NSGA-III) was applied to optimize system delay, energy consumption, and system resource pricing. The experimental results (mean of 30 independent runs) indicate that, compared with the NSGA-II, NSGA-III, MOEA-D, and SPEA2 optimization schemes, the proposed scheme reduced system delay by 21.63%, 5.96%, 17.84%, and 8.30%, respectively, in a system with 55 tasks. The energy consumption was reduced by 11.87%, 7.58%, 15.59%, and 9.94%, respectively. Full article

The integration of intelligent voice-control systems represents a critical pathway for enhancing driver comfort and reducing cognitive distraction in modern vehicles. Currently, voice assistants capable of accessing real-time vehicular data (e.g., engine parameters) or controlling actuators (e.g., door locks) remain exclusive to premium brands. While aftermarket solutions like Amazon’s Echo Auto provide multimedia functionality, they lack access to critical vehicle systems. To address this gap, we develop a novel architecture leveraging the OBD-II port to enable voice-controlled telematics and actuation in mass-production vehicles. Our system interfaces with a Toyota Hilux (2020) and Mazda CX-3 SUV (2021), utilizing an MCP2515 CAN controller for engine control unit (ECU) communication, an Arduino Nano for data processing, and an ESP01 Wi-Fi module for cloud transmission. The Blynk IoT platform orchestrates data flow and provides user interfaces, while a Voiceflow-programmed Alexa skill enables natural language commands (e.g., “unlock doors”) via Alexa Auto. Experimental validation confirms the successful real-time monitoring of engine variables (coolant temperature, air–fuel ratio, ignition timing) and secure door-lock control. This work demonstrates that high-end vehicle capabilities—previously restricted to luxury segments—can be effectively implemented in series-production automobiles through standardized OBD-II protocols and IoT integration, establishing a scalable framework for next-generation in-vehicle assistants. Full article

Truck drivers are essential to global freight operations but face disproportionate safety risks due to fatigue, distraction, and demanding working conditions, all of which significantly elevate crash likelihood. This systematic review assesses how monitoring technologies have been used to improve safety among professional truck drivers, focusing on the types of technologies deployed, the variables monitored, and reported safety outcomes. Conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, the review includes 40 peer-reviewed articles published in English between 2009 and 2024, identified through systematic searches in PubMed, Scopus, Web of Science, and IEEE Xplore. Due to methodological heterogeneity, a formal risk of bias assessment was not conducted. Most studies examined wearable devices, in-vehicle cameras, telematics systems, and AI-driven platforms. These technologies monitored variables such as fatigue, stress, distraction, speed, and environmental conditions. While the findings demonstrate considerable potential to enhance safety outcomes, persistent challenges include implementation costs, privacy concerns, and variability in effectiveness. The evidence is also geographically concentrated in high-income regions, limiting broader applicability. This review highlights the urgent need for harmonized evaluation frameworks, robust validation protocols, and context-sensitive strategies to support the effective adoption of monitoring technologies in the trucking sector. Full article

This study proposes an AI-powered digital twin (DT) platform designed to support real-time traffic risk prediction, decision-making, and sustainable mobility in smart cities. The system integrates multi-source data—including static infrastructure maps, historical traffic records, telematics data, and camera feeds—into a unified cyber–physical platform. AI models are employed for data fusion, anomaly detection, and predictive analytics. In particular, the platform incorporates telematics–video fusion for enhanced trajectory accuracy and LiDAR–camera fusion for high-definition work-zone mapping. These capabilities support dynamic safety heatmaps, congestion forecasts, and scenario-based decision support. A pilot deployment on Madison’s Flex Lane corridor demonstrates real-time data processing, traffic incident reconstruction, crash-risk forecasting, and eco-driving control using a validated Vehicle-in-the-Loop setup. The modular API design enables integration with existing Advanced Traffic Management Systems (ATMSs) and supports scalable implementation. By combining predictive analytics with real-world deployment, this research offers a practical approach to improving urban traffic safety, resilience, and sustainability. Full article

Background: Post COVID-19 Condition is a recently recognized syndrome characterized by the persistence of various symptoms, including dyspnea, physical and mental fatigue, and post-exertional malaise. Currently, there is no established treatment or clear consensus on the effectiveness of rehabilitation, and given that patients could benefit from home-based rehabilitation, telerehabilitation, defined as remote rehabilitation using telematic systems, may be an option to reach more of the population with persistent COVID-19 symptoms. Therefore, it is necessary to show the efficacy of this telematic approach and the benefits of a multimodal rehabilitation strategy in these patients. Methods: Patients underwent home rehabilitation using a 12-week synchronous telerehabilitation system. The intervention included therapeutic education and physical and respiratory rehabilitation. The following variables were analyzed: Fatigue, quality of life, dyspnea, respiratory strength, aerobic capacity, and upper and lower limb strength. Conclusions: After 12 weeks, significant improvements were found in fatigue, aerobic capacity, and limb and respiratory strength. However, no improvement was found in dyspnea scores, which did not correlate with respiratory strength. Interestingly, a post-intervention correlation emerged between the distance covered in aerobic capacity and perceived fatigue, suggesting that asynchronous telerehabilitation could be a viable treatment strategy for these patients. Full article

This paper presents an integrated tunable hybrid multi-laser module designed to simultaneously generate multiple radiofrequency (RF) local oscillator (LO) signals through optical heterodyning. The device consists of five hybrid InP/Si₃N₄ integrated lasers, each incorporating an intracavity wavelength-selective optical filter formed by two micro-ring resonators. Through beating the wavelengths generated from three of these lasers, we demonstrate the simultaneous generation of two LO signals within bands crucial for satellite communications (SatCom): one in the Ka-band and the other in the V-band. The device provides an extensive wavelength tuning range across the entire C-band and exhibits exceptionally narrow optical linewidths, below 40 kHz in free-running mode. This results in ultra-wideband tunable RF signals with narrow electrical linewidths below 100 kHz. The system is compact and highly scalable, with the potential to generate up to 10 simultaneous LO signals, being a promising solution for advanced RF signal generation in high throughput satellite payloads. Full article

Background: Telematization is essential for improving port efficiency by reducing dwell times and simplifying document management. Currently, only a few ports use informatic document management tools like the Port Community System (PCS), and customs documents are produced and shared in paper format. This results in long port dwell times. Methods: A platform was developed to allow sharing of documents among port actors. The platform shares export documents of each given shipment between export and import port actors; moreover, it serves as a document management platform for ports lacking PCS. In addition, the platform helps in reorganizing the shipment in case of disruptions. Results: The platform has global validity as it allows users to share documents among all port actors worldwide. The platform is formed by the following menus: “Path”, which provides the intermodal freight path; “Shipment”, which allows one to create or change shipment data; “Send notify” to send notifies in case of disruptions; “PMIS and PCS”, which redirects to these platforms of ports involved in the project; and “Documents”, which allows one to upload and share customs documents at the global level. Conclusions: The app contributes to speeding up port operations by reducing dwell times, assists in managing shipment disruptions, and enhances intermodality in freight transportation. Full article

Vehicle-to-Everything (V2X) technology has the capability to enhance road safety by enabling wireless exchange of telematics and spatiotemporal information between connected vehicles (CVs). Effective V2X communication depends on rapid information sharing between Roadside Units (RSUs), in-vehicle On-Board Units (OBUs), and other connected infrastructure. However, there are increasing concerns with RSUs related to installation needs, reliability, and coverage, especially on rural roadways. This study aims to evaluate the benefits of augmenting CV infrastructure with satellite technology in situations where RSU access or coverage is limited while maintaining V2X security protocols and critical information exchange. The study utilizes data from over 400 personal, fleet, and commercial CVs collected during two real-world pilot deployments in the United States, one in an urban environment in Florida and one in a rural environment in Wyoming. The analysis performed shows that the delivery of critical security credential information and traveler information messages (TIMs) to CVs is dependent on a multitude of environmental and operational reliability factors. Overall, information delivery is faster with dense RSU infrastructure as compared to satellites. However, we show that by augmenting RSU infrastructure with satellite technology, the delivery of information is more robust, improving V2X system reliability, security, and overall road safety. Full article

In the contemporary era, Computational Thinking has emerged as a crucial skill for individuals to possess in order to thrive in the 21st century. In this context, there is a need to develop a methodology for cultivating these skills within a science and mathematics content education framework, particularly among pre-service teachers. This study aimed to investigate the impact of Educational Robotics on the development of Computational Thinking skills, with a particular focus on the role of gender, through a scientific and mathematical content teaching approach. A pre-experimental design with a quantitative approach was employed, and it was implemented with a total of 116 pre-service teachers, 38 males and 78 females. The results demonstrated a notable enhancement between the pre-test (8.11) and post-test (9.63) scores, emphasising specific concepts such as simple functions, while, and compound conditional. With respect to gender, statistically significant differences were identified prior to the intervention, but not following its implementation. The high level of Computational Thinking exhibited by both genders was comparable (53.85% in females and 55.26% in males) following the intervention. This indicates that the intervention is a promising approach for enhancing Computational Thinking proficiency, independent of gender and initial proficiency levels. The implementation of Educational Robotics in the teaching of science and mathematics enables the enhancement of Computational Thinking abilities among pre-service teachers, while reducing the observed gender disparity in this area of skill development. Full article

The proliferation of fake news threatens the integrity of information ecosystems, creating a pressing need for effective and interpretable detection mechanisms. Recent advances in machine learning, particularly with transformer-based models, offer promising solutions due to their superior ability to analyze complex language patterns. However, the practical implementation of these solutions often presents challenges due to their high computational costs and limited interpretability. In this work, we explore using content-based features to enhance the explainability and effectiveness of fake news detection. We propose a comprehensive feature framework encompassing characteristics related to linguistic, affective, cognitive, social, and contextual processes. This framework is evaluated across several public English datasets to identify key differences between fake and legitimate news. We assess the detection performance of these features using various traditional classifiers, including single and ensemble methods and analyze how feature reduction affects classifier performance. Our results show that, while traditional classifiers may not fully match transformer-based models, they achieve competitive results with significantly lower computational requirements. We also provide an interpretability analysis highlighting the most influential features in classification decisions. This study demonstrates the potential of interpretable features to build efficient, explainable, and accessible fake news detection systems. Full article

Lipoatrophia semicircularis is a benign pathology characterized by subcutaneous tissue atrophy that affects the skin and related structures. Its etiology remains unclear; however, in the recent few years, it has been proposed that electrostatic charges could be a potential factor. Based on this hypothesis, the aim of this work is to study the cause–effect relation between electrostatic energy and LS, providing insights into the molecular mechanisms. For this purpose, an experimental murine model was created using obese mice. One group served as a control and the other groups involved charging clothes with varying connections to the ground: through the skin, through the clothes or not connected to the ground). Skin biopsies showed that the most significant lesions, including lipophagic granulomas with inflammatory infiltrate, were found in the first group (connected to the ground through the skin). Lipophagic reactions without an inflammatory infiltrate were observed in the other groups subjected to electrical discharges. In the control mice, no histological changes were observed. Oxidative processes were also measured in lower limbs tissue. Malondialdehyde levels significantly increased in the lower limbs after electrostatic discharges. However, the presence of ground through a wire attached to highly conductive clothes around the thigh significantly reduced the effect of electrostatic charges on lipid peroxidation. To our knowledge, this is the first study in which an experimental model has been used to reproduce LS induced by electrostatic energy, suggesting a cause–effect relationship between electrostatic charge and discharge with fat tissue lesion. Full article