Search Results (275)

Background: The movement of endemic diseases from tropical and disadvantaged areas is gradually spreading to developed countries as well. Amoebiasis, in particular, represents a significant threat to public health, amplified by globalization and increasing contact between humans and animals or vectors. In this way, atypical diseases may emerge in our country, also through an unusual mode of transmission—likely sexual, as “sexually transmitted enteric (STE) diseases”. Objective: We report and discuss the clinic approach to a rare case of dual human infestations by ecto- and endo-parasites with multiple liver abscesses presentation in a young truck driver residing in Northern Italy. Methods: The patient underwent a comprehensive screening with laboratory and microbiological tests, and CT images. Results: In July, the patient was admitted to our hepatology unit following the ultrasound identification of two hypoechoic lesions in the right lobe of the liver. In his medical history, in April, after a seemingly harmless infestation of body lice, effectively treated, he suffered from a prolonged and debilitating episode of acute diarrhea, lasting a month. This status was also accompanied by intestinal cramps, weight loss, and recurring fever episodes. Afterwards also appeared a Quincke’s-like angioedema involving the lips and mouth mucosa, with evening time exacerbation. Conclusions: The differential diagnosis of this complex and unusual clinical picture, together with the temporal evolution of the patient’s signs and symptoms, led us to hypothesize the presence of concomitant infections. These were most likely borreliosis and a STE disease, the latter ultimately diagnosed as amebiasis with hepatic invasion, allowing appropriate treatment and a favorable clinical outcome. Full article

Background/Objectives: Women working in the informal sector are frequently exposed to multiple layers of vulnerability and trauma, yet their mental health experiences remain insufficiently explored. Methods: This qualitative study examined the mental health implications of trauma experienced by female scrap collectors in Qumbu, Eastern Cape. Guided by resilience theory, the study explored how these women navigate and cope with the challenges associated with their daily work. A total of 13 female scrap collectors were selected through convenience sampling and participated in semi-structured interviews. The data were analysed thematically. Results: The findings revealed that women engaged in scrap collection face perpetual insecurity and gendered vulnerability in informal workspaces, enduring public degradation with significant psychological consequences, experience persistent stress due to insufficient earnings and navigate informal relations with truck drivers that act as sites of economic exploitation and psychological insecurity. These experiences may contribute to chronic stress, fear, feelings of worthlessness and mental exhaustion, which significantly affect their psychological wellbeing. Conclusions: Based on these findings, the study recommends the implementation of targeted psychosocial support interventions, such as trauma-informed counselling and peer support groups, to enhance coping mechanisms and strengthen resilience. Furthermore, a multidisciplinary facilitative healthcare approach and advocacy efforts aimed at addressing the mental health needs of female scrap collectors are essential for strengthening their agency, promoting psychosocial wellbeing and reducing their exposure to harm within the informal working environment. Full article

Truck Turnaround Time (TTT), defined as the total gate-to-gate time during a truck visit to a container terminal, is a critical performance indicator of landside operations. Although machine learning has been applied to TTT prediction, existing studies typically pool import and export movements into a single model, overlooking operational heterogeneity between flows. Moreover, little attention is paid to analyzing the importance of factors that enable TTT prediction. This study develops a flow-disaggregated predictive framework for a Chilean container terminal using 754,568 export and 1,056,351 import truck visits recorded between 2017 and 2023. Four tree-based ensembles and three neural network architectures are benchmarked under a chronological train/test split. Tree-based models tend to achieve marginally lower errors, though differences are small and not uniform across flows. A pronounced asymmetry emerges: import predictions are substantially more accurate (MAE = 6.79 min; WAPE = 34.36%) than export predictions (MAE = 32.49 min; WAPE = 52.53%), and this gap persists after normalizing for differences in mean TTT. TreeSHAP analysis identifies distinct predictive structures: export TTT is primarily associated with gate congestion and maritime service activity, while import TTT is more strongly associated with intra-terminal travel distance and crane operator experience. The higher Gini concentration and bidirectionality of dominant export predictors are consistent with unobserved drivers—such as the states of inspection queues (customs, sanitary, etc.) and the off-dock truck staging area—that limit predictive accuracy beyond process variability alone. In the integrated model, flow-identifying variables rank among the most influential features, providing empirical support for flow disaggregation. These findings indicate that flow-specific modeling improves both accuracy and interpretability in operationally heterogeneous terminal processes. Full article

The present work proposes a holistic techno-economic framework for the production of green hydrogen and synthetic methane from a dedicated 18.8 MW renewable park (13.8 MW wind and 5 MW photovoltaic) in southern Italy. A Mixed-Integer Linear Programme optimises the hourly dispatch of the electrolyser, the battery, and the bi-directional grid connection over a full-year horizon (8760 h) while meeting an annual target of 600,000 kg_H2. Two downstream pathways are compared: compressed-hydrogen transport by tube trailer at 200, 400, and 600 km, and on-site catalytic methanation via the Sabatier reaction. The assessment is deliberately restricted to the cost side, so that the two carriers are compared on a robust, price-agnostic levelised cost basis. Operated in an import-free mode to guarantee hydrogen with zero operational carbon emissions, the optimisation co-determines a cost-optimal battery capacity of 18 MWh. The levelised cost of hydrogen (LCOH) ranges between 9.55 and 10.27 €/kg_H2 at the gate, rising to 11.17–12.33 €/kg_H2 with transport, whereas the levelised cost of synthetic methane is 5.67 €/kg_CH4. On an energy basis, hydrogen is more competitive (0.335–0.370 €/kWh) compared to methane (0.409 €/kWh). A structured sensitivity analysis shows that a grid connection of 10 MW or more renders the battery unnecessary and lowers the gate LCOH to 8.26 €/kg, and ranks the renewable CAPEX and the discount rate as the dominant cost drivers. The produced hydrogen is essentially carbon-free at the gate, with the only residual emissions (0.014–0.041 kgCO₂/kWh) arising from its diesel trucking. Full article

Greenhouse gas emissions from heavy-duty vehicles (HDVs) must be drastically reduced. Battery electric trucks (BETs) are the main option for low-carbon road freight transport, but they require recharging infrastructure. However, a thorough cost analysis of public charging is lacking, especially for the Megawatt Charging System (MCS). This study estimates the infrastructure-related levelised cost of megawatt charging for battery electric trucks in Europe based on simulated truck operations and techno-economic modelling. The analysis combines empirical driving data with cost assumptions for MCS infrastructure. The reported values are infrastructure-only costs and include annualised capital expenditure, installation costs, grid connection costs and operating expenditure. They exclude electricity prices, taxes, levies, land costs and operator margins. Low- and high-cost scenarios differ in assumed charger hardware and installation costs, while grid connection costs and utilisation assumptions are held constant across scenarios. The results show that utilisation is the key driver of cost reductions over time. The infrastructure-related levelised cost of MCS declines to 0.03–0.07 EUR/kWh by 2050 under the analysed cost assumptions. The total annual infrastructure costs for Europe are estimated at 6.6–10.8 billion EUR, or 2.9–4.7 EUR cents/km. The results support policy decisions on infrastructure deployment and highlight the importance of coordinated rollout and demand growth. Full article

Hydrotreated vegetable oil (HVO) is considered a promising drop-in alternative to conventional diesel fuel for reducing greenhouse gas emissions in road freight transport. However, empirical evidence on its performance under real-world operating conditions remains limited. This is particularly true for complex logistics systems such as agricultural transport. This study assesses the effect of neat HVO (HVO100) on fuel consumption using high-resolution vehicle operational data collected from three heavy-duty trucks during a full-scale sugar beet logistics campaign in Germany. Vehicle operation was recorded via a manufacturer-independent fleet management system interface and combined with satellite-based positioning data for route reconstruction. After data preprocessing and quality filtering, a total of 3353 valid transport tours were analyzed. Fuel consumption values during HVO100 operation were corrected for density-related measurement bias. The effect of fuel type was evaluated using a linear mixed-effects model. The model accounted for load status, route topography, driving speed, and their interactions. In addition, stratified pairwise comparisons were conducted across operational conditions. The results show that, in the full three-vehicle model, HVO100 was associated with a statistically significant increase in fuel consumption of 0.51 L/100 km under baseline conditions with an empty vehicle, low topographic variability, and medium driving speed, corresponding to approximately 2.3%. In a sensitivity analysis excluding the diesel-only truck, the estimated difference decreased to 0.34 L/100 km and was no longer statistically significant. Load status and topography were identified as the dominant drivers of fuel consumption with substantially larger effects than fuel choice. Overall, the findings indicate that the effect of HVO100 on fuel consumption is small relative to operational variability. Under many real-world operating conditions, operational factors outweighed the differences attributable to fuel type. These findings indicate that switching to HVO100 did not result in a substantial volumetric fuel-consumption penalty in the investigated agricultural logistics system. Full article

Advanced Driver Assistance Systems (ADAS) can exhibit perception limitations involving large or stationary vehicles, occluded downstream hazards, and the finite detection range of onboard radar. This study proposes a microwave signal conversion based support framework comprising a vehicle-mounted V2V–V2V system and a roadside I2I–I2V system. The algorithms convert and retransmit radar signals according to vehicle speed and spacing states or downstream congestion, while longitudinal responses were behaviorally represented in VISSIM-COM through conditional Desired Speed modification for equipped trucks and buses. A calibrated 12-km Seoul Tollgate corridor was evaluated using V2V–V2V market penetration rates of 0–100%, a binary I2I–I2V condition, and a combined full-deployment scenario. Run-level TTC and conflict frequency were summarized using means, standard deviations, and 95% confidence intervals, and both measures were compared using two-sided paired t-tests. Increasing V2V–V2V penetration was associated with higher TTC and fewer conflicts. Within the tollgate influence area, combined deployment increased mean TTC from 0.72 to 1.27 s and reduced mean conflict frequency from 1486 to 764; both paired comparisons were statistically significant (p < 0.001). These findings suggest potential surrogate-safety benefits from coordinating vehicle- and infrastructure-based recognition support and advance speed adjustment. Full article

Constant-speed control of hydraulic retarders is essential for improving driving safety and reducing driver workload on long downhill roads. For heavy-duty battery electric trucks (BETs), regenerative braking provides a fast-response braking source and enables energy recovery, offering the potential to improve both speed regulation and energy efficiency. This study proposes a two-mode constant-speed downhill control framework for BETs. In the retarder braking mode, a variable-argument proportional–integral–derivative (VAPID) controller is employed to regulate the hydraulic retarder, with its parameters optimized by an improved seeker optimization algorithm (ISOA). In the cooperative braking mode, a parallel dual-controller structure is adopted, where the retarder is governed by ISOA-VAPID and regenerative braking is regulated by a fuzzy-tuned PD controller according to real-time battery states. To further improve energy recovery, an optimization-based AMT gear-shifting schedule and coordinated strategy are incorporated. The proposed framework is validated through offline simulations, sensitivity analysis, and driver-in-the-loop experiments under constant-slope, variable-slope, and real-world downhill road conditions. Results show that the retarder braking mode outperforms benchmark methods in steady-state accuracy and dynamic response. In the cooperative braking mode, braking energy is effectively recovered while the battery charging load under unfavorable battery states is reduced. Moreover, AMT gear shifting improves energy recovery efficiency with negligible influence on constant-speed performance. Full article

Electric trucks play a crucial role in achieving a zero-emission future. As battery electric technology advances, electric trucks are expected to become a cost-effective and sustainable alternative to diesel trucks. Long-haul trucks have unique driving patterns that affect their charging needs, and investigating the expected load profiles is fundamental to conducting a proper assessment of the impact of truck fleet electrification on the charging infrastructure. This article presents an agent-based modeling approach to estimate high-power charging station load profiles, leveraging open data and driver decision-making patterns. The methodology is implemented in a software tool, ABChargingSim, which includes heterogeneous charging logic (distinguishing between urgent mid-shift and long-dwell off-shift charging, as well as different driver triggers to initiate charging) alongside a vehicle’s SOC-dependent power tapering charging patterns. A case study along a Norwegian highway demonstrates the framework’s applicability for evaluating grid impacts under various heavy-duty transport electrification scenarios. The findings illustrate how driver behavior and heavy-duty vehicle charging processes shape expected load profiles, emphasizing the value of such simulation frameworks as essential decision-support tools for the strategic planning and operation of future high-power charging networks. Full article

Port operations play a strategic role in global trade but are associated with significant environmental impacts due to intensive energy use, equipment operation, and cargo handling activities. In this context, Life Cycle Assessment (LCA) emerges as an essential tool to quantify these impacts and support decarbonization strategies in the maritime-port sector. This study aims to evaluate the environmental performance and energy transition implications of fertilizer import operations in a multi-cargo port by comparing semi-automated and non-automated scenarios through a Life Cycle Assessment (LCA) approach. The methodology followed the standard LCA framework, including goal and scope definition, inventory analysis, impact assessment, and interpretation. Primary data collected in situ were combined with secondary data from the Ecoinvent database, ensuring consistency and representativeness. The results indicate that post-port logistics is the main driver of environmental impacts. In the semi-automated scenario, rail transport consumes approximately 18,800 L of diesel, showing higher efficiency due to its greater load capacity. In contrast, the non-automated scenario relies on 100 trucks, each consuming about 238.75 L per trip, resulting in higher total fuel consumption and emissions. It is concluded that the non-automated system presents higher environmental impacts across all categories analyzed, highlighting the importance of modal choice and operational efficiency in reducing emissions and supporting the energy transition in the port sector. Full article

Cameras and IMUs on heavy mining trucks supply the visual signal that Advanced Driver Assistance Systems (ADASs) use in open-pit operations. Haul roads in a surface mine are unstructured and unmarked, so a perception model must be both accurate and fast. We address this with a video-based multitask pipeline for a mining Driver Support System (DSS): a single BiSeNetV1 network produces drivable-area segmentation and steering-direction classification in one forward pass. Training used only 100 frames sampled non-sequentially from in-cab recordings of a real open-pit mine; evaluation used two full onboard sequences. To exploit temporal redundancy without annotating video, we propose an Adaptive Clockwork (A-CW) inference scheme: the spatial path runs on every frame, while the context path is refreshed only on keyframes whose cadence is set by the classification output, the same signal shown to the driver as a steering hint. This classification-guided policy increases context updates on curved segments, where the scene changes more rapidly, and reduces them on straight sections, where semantic redundancy is higher. The selected A-CW configuration was evaluated on full temporal test sequences, including one route kept entirely outside the training source. On this unseen route, A-CW achieved 94.70% road-class IoU and 73.68% Top-1 Accuracy. GPU-only throughput increased from about 55 FPS with frame-by-frame inference to 168.01 FPS, and display-excluded end-to-end processing in the simulated ADAS pipeline remained at approximately 37.5 FPS. Full article

In this study, the transition of intersection coordination models was explored using a Dynamic Game framework. The performance limitations of traditional static models, which define payoffs based on fixed geometric conflicts, were also investigated to propose a novel dynamic utility function and evaluate it at each simulation step. Its important function is a continuous dynamic risk penalty derived from the immediate traffic state, allowing adaptive, risk-aware decisions to be made by vehicles. Based on the assumption of complete information, all vehicles have full knowledge of the characteristics of their rival vehicles, such as driving styles, as well as emergency vehicles like fire trucks and ambulances. Emergency vehicle priority is ensured through a high-cost penalty structure. The Pure Strategy Nash Equilibrium is solved for using the Iterated Best Response (IBR) algorithm. Through the MATLAB simulation of urban mobility, the dynamic, risk-aware framework was found to significantly improve safety metrics (e.g., near-collision events) compared to its static counterpart. Finally, the stability of the decision is analyzed by evaluating the IBR convergence rates across various driver-type compositions. Full article

Driver distraction causes accidents in mining trucks, posing significant safety risks in open-pit mining operations. Estimating the driver’s head pose is a key task for detecting distraction. However, accurate head pose estimation typically requires large amounts of high-quality annotated data. Obtaining a high-precision head pose estimation model under conditions of limited labeled data is challenging. To address the scarcity of annotated data in mining scenarios, this paper proposes a semi-supervised framework named the semi-supervised cascade head pose estimator (SemiCHPE) for driver head pose estimation. The framework adopts a two-stage cascade architecture: the first stage involves a semi-supervised head detector (HeaDet) for head detection, while the second stage comprises a semi-supervised head pose estimator (HPE) for pose estimation. Extensive experiments conducted on our proprietary dataset of mining truck drivers demonstrate that, using only 10% of the dataset, the proposed framework achieves an F1-Score of 99.3% for head detection and a mean absolute error (MAE) of 2.8° for head pose estimation. When deployed on an NVIDIA Orin NX platform within operational mining trucks, the framework attains real-time inference at 32 frames per second with an accuracy of 91.6%, validating its effectiveness for real-world deployment in intelligent mining transportation systems. Full article

Road freight transport is an important driver of global greenhouse gas (GHG) emissions. Decarbonizing this sector demands a comprehensive assessment of emerging powertrain technologies, which are currently lacking in the literature. To fill this knowledge gap, we performed a life cycle assessment (LCA) on 10 impact categories to evaluate road freight transport in the Netherlands of four truck alternatives, assuming similar performance: fuel-cell electric (FCEV), hydrogen internal combustion engine (HICEV), battery electric (BEV), and diesel internal combustion engine (DICEV). We compared locally produced green hydrogen, according to EU regulations, with electricity and diesel as alternative fuel chains, while also considering the environmental impact of road infrastructure. We found that FCEV and HICEV trucks achieve the lowest global warming impact when green hydrogen is used. We identified discrepancies between the transport alternatives, highlighting key factors influencing NO_x and particulate matter emissions. Our research also showed that water consumption (WC) for green hydrogen is strongly influenced by upstream processes, with solar-powered electricity emerging as a crucial contributor. Our results highlight the need for more exploration on the environmental impact of green hydrogen and can be used by researchers and practitioners to further understand the complexity of reducing emissions in road freight transport. Full article

Road freight transport (RFT) faces growing pressure from increasing freight demand, stricter environmental requirements, and persistent driver shortages. Automation technologies (ATes)—especially semi-autonomous driving—are increasingly viewed as a practical pathway toward improving the sustainability performance of freight operations; however, their effects depend strongly on infrastructure and operational conditions. This study evaluates the sustainability potential of autonomous and semi-autonomous trucks through an integrated framework combining (i) a structured review of technical and regulatory developments, (ii) surveys of transport enterprises (TEes) and road users (RUs), (iii) SWOT/TOWS analysis, and (iv) a cost minimization logistics model that links operational feasibility to infrastructure readiness (IR). The proposed model minimizes cost per tonne-kilometre and introduces an Infrastructure Readiness Score (IRS) to represent the share of a route that can be operated in automated mode; it also accounts for fuel savings from platooning and higher maintenance and capital costs of semi-autonomous vehicles (SAVs). Results indicate that, as IRS increases, semi-autonomous operations achieve higher daily mileage and lower unit costs, with a break-even point at approximately IRS ≈ 0.125. Beyond this threshold, unit costs decline from EUR 0.0433 to EUR 0.0348 per tonne-kilometre as IRS rises toward 0.6, after which further infrastructure improvements yield diminishing mileage gains. These cost and utilization improvements imply sustainability benefits via improved energy efficiency and reduced emissions intensity per tonne-kilometre. Nevertheless, survey evidence highlights major adoption barriers, including insufficient IR, regulatory uncertainty, technological reliability concerns, and limited public trust in fully autonomous systems. Overall, the findings support semi-autonomous trucking as the most feasible near-term stage of transition, while emphasizing that infrastructure upgrades and governance mechanisms are critical for scaling sustainability gains. Full article