Search Results (3,672)

Controlling pre-analytical conditions for medical biology tests, particularly during transport, is crucial for complying with the ISO 15189 standard and ensuring high-quality medical services. The use of drones, also known as unmanned aerial vehicles, to transport clinical samples is growing in scale, but requires prior validation to verify that there is no negative impact on the test results provided to doctors. This study aimed to establish a secure, high-quality solution for transporting biological samples by drone in a coastal region of France. The 80 km routes passed over several densely populated urban areas, with take-off and landing points within hospital grounds. The analytical and clinical impact of this mode of transport was compared according to two protocols: an interventional clinical trial on 30 volunteers compared to the reference transport by car, and an observational study on samples from 126 hospitalized patients compared to no transport. The system enabled samples to be transported without damage by maintaining freezing, refrigerated, and room temperatures throughout the flight, without any significant gain in travel time. Analytical variations were observed for sodium, folate, GGT, and platelet levels, with no clinical impact on the interpretation of the results. There is a risk of time-dependent alterations of blood glucose measurements in heparin tubes, which can be corrected by using fluoride tubes. This demonstrated the feasibility and security of transporting biological samples over long distances in line with the ISO 15189 standard. Controlling transport times remains crucial to assessing the quality of analyses. It is imperative to devise contingency plans for backup solutions to ensure the continuity of transportation in the event of inclement weather. Full article

Detailed geochemical modelling of the potential groundwater impacts of CO₂ geo-sequestration requires site-specific knowledge of how mobile elements are hosted within rocks. We present a simple sequential extraction procedure analogous to pH conditions produced by different partial pressures of carbon dioxide (CO₂) in contact with water. The procedure consists of three sequential steps: water at pH 7; acetic acid–ammonium acetate at pH 5 and then at pH 3, with the amounts of specific elements extracted by each step considered with respect to the whole rock total element abundance. Our purpose in developing this procedure is three-fold: (1) identify readily mobilized suites of elements for groundwater baseline and monitor bore studies; (2) provide insights regarding the mode/s of occurrence of easily extracted elements within rock samples; and (3) suggest possible mechanisms for the mobilization of rock-sourced elements into groundwater under neutral to moderately acidic pH that can inform the reactive transport modelling of carbon storage sites. In our case study, the second step extracted most of the main mobile elements of interest. Full article

This study examines consumer attitudes and experiences related to ride-sharing services in the Western Transdanubia region of Hungary. Despite the growing global popularity of shared mobility solutions, there is little empirical evidence on regional consumer acceptance patterns in the Hungarian context. Based on a structured questionnaire survey involving 500 respondents, this research explores the relationship between satisfaction with past ride-sharing experiences and the intention to reuse such services in the future. The results reveal a high willingness to reuse among those who have already tried ridesharing, yet the correlation analysis shows that satisfaction dimensions alone are not significant predictors of reuse intention. In contrast, attitudinal factors, such as preference over other transport modes, willingness to make recommendations, and perceived accessibility, exhibit strong correlations with acceptance of ride-sharing. The findings emphasise the key role of attitudes and trust in shaping consumer decisions. The paper contributes to the literature by providing regionally grounded empirical insights and offers practical and policy-level recommendations to support the diffusion of sustainable shared mobility services. Full article

Background: Since the introduction of rental e-scooters, they have become a popular mode of transportation not only in German cities but in other cities as well. However, this rapid increase in usage has coincided with a significant rise in associated injuries and accidents, outpacing those related to bicycles. A disproportionate number of these incidents involve alcohol consumption and young people under the age of 25, with a low incidence of helmet use. Following the example of driver training for children on bicycles, we carried out driver safety training with e-scooters and examined the results scientifically. Methods: The study conducted three voluntary driving safety training sessions in Berlin and Munich, with participants completing questionnaires before and after the training to measure their knowledge and skills (on a scale between 0 and 5; 0 = totally insecure and 5 = absolutely secure). The training included a technical introduction, practical exercises, and an educational component on injury data and prevention strategies. During the statistical analysis, the novice drivers (group 1) were compared to the non-novice drivers (group 2). Results: Out of 136 participants, 103 completed the training (a response rate of 75.7%). The mean age of the participants was 37.1 years, and 52.4% of them were female. A total of 59% had never used an e-scooter and were therefore assigned to group 1 (group 2 = experienced drivers). Both groups showed significant improvements in both knowledge of traffic laws and driving skills. Conclusions: The findings suggest that driving safety training potentially enhances the safe operation of e-scooters. However, the training demands a high level of time and motivation, making it less attractive for younger drivers who are most prone to accidents. Therefore, we recommend the use of digital driving safety training before the first use of e-scooters. Full article

This paper describes recent engineering designs that allow full-duplex SerDes connectivity between a number of cascaded Xilinx radio frequency system-on-chip (RF-SoC) and VCU FPGA systems. The design allows for unlimited scalability with all-to-all connectivity across FPGA systems and RF-SoCs that allow for bidirectional data transport in streaming mode at a capacity of 50 Gbps per ADC-DAC channel. A custom massively parallel systolic-array architecture supporting 8 parallel data streams from time-interleaved ADC/DACs allow real-time matrix–vector-multiplication (MVM). The MVM can be 8 × 8, 8 × 16, …, 8 × 1024 in supported matrix size, and is demonstrated in real time sustained throughput of 1 TeraMAC/second, for matrix size 8 × 512. The MVM is the building block supporting machine learning and filtering, with the computational graph split across FPGA systems using the SerDes connections. The RF data processed by the FPGA chain can be further utilized for higher-level AI workloads on an NVIDIA DGX Spark platform connected to the system. We demonstrate two platforms in which ZCU111 and ZCU1285 RF-SoC boards perform direct-RF data acquisition, while compute engines operating in real time on VCU128 and VCU129 FPGA boards showcase both digital beamforming and polyphase FIR filterbanking in a real-time bandwidth of 1.0 GHz. Full article

This study investigates the spatiotemporal characteristics and travel patterns of micro-electric vehicles (micro-EVs) by analyzing real-world trip data collected over three years from shared micro-EV services operating in three regions of South Korea. Individual trips were extracted from GPS-based trajectory data, and a network-based detour ratio was introduced to capture non-linear trip characteristics. In addition, a hierarchical clustering analysis was applied to identify heterogeneous micro-EV trip patterns. The results show that micro-EVs are predominantly used for short-distance urban trips, while a smaller but behaviorally distinct subset of trips demonstrates their capacity to support medium-distance travel under specific functional contexts. The clustering analysis identified six distinct trip pattern groups, ranging from dominant short-distance routine travel to less frequent patterns associated with adverse weather conditions and extreme detouring behavior. Overall, the findings suggest that micro-EVs function as a complementary urban mobility mode, primarily supporting localized travel while selectively accommodating extended-range and specialized trips. From a sustainability perspective, these findings highlight the role of micro-EVs as energy-efficient, low-emission alternatives to conventional passenger vehicles for short- and medium-distance urban trips. By empirically identifying heterogeneous and long-tailed micro-EV travel patterns, this study provides practical insights for sustainable urban mobility design and environmentally responsible transportation policies. Full article

To clarify how flow-channel configuration and wall spacing govern the hydrodynamic performance of an oscillating-hydrofoil biomimetic pumping device, this study conducted a systematic numerical investigation under confined-flow conditions. Using a finite-volume solver with an overset-grid technique, we compared pumping performance across three channel configurations and a range of channel–wall distances. The results showed that bidirectional-channel confinement suppresses wake deflection and irregular vorticity evolution, enabling symmetric and periodic vortex organization and thereby improving pumping efficiency by approximately 33.6% relative to the single-channel case and by 62.7% relative to the no-channel condition. Wall spacing exhibited a distinctly non-monotonic influence on performance, revealing two high-performance regimes: under extreme confinement (gap ratio $h/c=$ 1.4), the device attains peak pumping and thrust efficiencies of 19.9% and 30.7%, respectively, associated with a strongly guided jet-like transport mode; and under moderate spacing ($h/c=$ 2.2–2.6), both efficiencies remain high due to an improved balance between directional momentum transport and reduced vortex-evolution losses. These findings identify key confinement-driven mechanisms and provide practical guidance for optimizing flow-channel design in ultralow-head oscillating-hydrofoil pumping applications. Full article

One-dimensional ZnO nanowires offer significant potential for optoelectronic applications, though their controlled synthesis remains challenging. This study optimized ZnO nanowire growth via carbothermal reduction vapor transport based on the vapor–liquid–solid mechanism. Key parameters investigated were gold catalyst thickness and annealing, source temperature, system pressure, and oxygen concentration. Results show that thinner Au films promote high-density, small-diameter nanowires. An optimal source temperature window (950–1000 °C) was identified, while pressure and oxygen content critically influenced growth mode by modulating vapor supersaturation. Under optimized conditions, aligned single-crystalline ZnO nanowires with hexagonal wurtzite structure were achieved. Structural and optical characterization confirmed high crystallinity and strong near-band-edge emission, demonstrating the efficacy of the developed approach for tailored nanowire synthesis. Full article

Intelligent Transportation Systems (ITS) have progressively incorporated machine learning to optimize traffic efficiency, enhance safety, and improve real-time decision-making. However, the traditional centralized machine learning (ML) paradigm faces critical limitations regarding data privacy, scalability, and single-point vulnerabilities. This study explores FL as a decentralized alternative that preserves privacy by training local models without transferring raw data. Based on a systematic literature review encompassing 39 ITS-related studies, this work classifies applications according to their architectural detail—distinguishing systems from models—and identifies three families of federated learning (FL) frameworks: privacy-focused, integrable, and advanced infrastructure. Three representative frameworks—Federated Learning-based Gated Recurrent Unit (FedGRU), Digital Twin + Hierarchical Federated Learning (DT + HFL), and Transfer Learning with Convolutional Neural Networks (TFL-CNN)—were comparatively analyzed against a client–server baseline to assess their suitability for ITS adaptation. Our qualitative, architecture-level comparison suggests that DT + HFL and TFL-CNN, characterized by hierarchical aggregation and edge-level coordination, are conceptually better aligned with scalability and stability requirements in vehicular and traffic deployments than pure client–server baselines. FedGRU, while conceptually relevant as a meta-framework for coordinating multiple organizational models, is primarily intended as a complementary reference rather than as a standalone architecture for large-scale ITS deployment. Through application-level evaluations—including traffic prediction, accident detection, transport-mode identification, and driver profiling—this study demonstrates that FL can be effectively integrated into ITS with moderate architectural adjustments. This work does not introduce new experimental results; instead, it provides a qualitative, architecture-level comparison and adaptation guideline to support the migration of ITS applications toward federated learning. Overall, the results establish a solid methodological foundation for migrating centralized ITS architectures toward federated, privacy-preserving intelligence, in alignment with the evolution of edge and 6G infrastructures. Full article

This research simulates Aswan City’s urban transportation dynamics utilizing the Multi-Agent Transport Simulation (MATSim) framework. As a fast-expanding urban center, Aswan has many transportation difficulties that require extensive modeling toward sustainable mobility solutions. MATSim, recognized for its agent-based methodology, offers a detailed portrayal and analysis of individual travel behaviors and their interactions within the metropolitan transportation system. This study compiled and combined many databases, including demographic data, road infrastructure, public transit plans, and travel demand trends. These data are altered to produce a realistic digital clone of Aswan’s transportation system. Simulated scenarios analyze the consequences of several actions, such as increased public transit scheduling, traffic flow management, and the adoption of alternative transport modes, on minimizing congestion and boosting accessibility. Pilot findings show that MATSim effectively captures the distinct features of Aswan’s transportation network and offers practical insights for decision-makers. The results identified some opportunities to improve mobility and promote sustainable urban growth in developing cities. This study emphasized the importance of agent-based simulations in designing future transportation systems and urban infrastructure. Full article

Higher education institutions face a critical methodological challenge in pursuing net-zero commitments: Within the amount ofhe emissions related to Scope 3, including indirect emissions from water consumption, waste disposal, business travel, and mobility, employees commuting represents 50–92% of campus carbon footprints, yet reliable quantification remains elusive due to fragmented data collection and governance silos. The present research investigates how purposeful integration of the Home-to-Work Commuting Plan (HtWCP)—mandatory under Italian Decree 179/2021—into the Climate Neutrality Plan (CNP) could constitute an innovative strategy to enhance emissions accounting rigor while strengthening institutional governance. Stemming from the University of Genoa case study, we show how leveraging mandatory HtWCP survey infrastructure to collect granular mobility behavioral data (transportation mode, commuting distance, and travel frequency) directly addresses the GHG Protocol-specified distance-based methodology for Scope 3 accounting. In turn, the CNP could support the HtWCP in framing mobility actions into a wider long-term perspective, as well as suggesting a compensation mechanism and paradigm for mobility actions that are currently not included. We therefore establish a replicable model that simultaneously advances three institutional dimensions, through the operationalization of the Avoid–Shift–Improve framework within an integrated workflow: (1) methodological rigor—replacing proxy methodologies with actual behavioral data to eliminate the notorious Scope 3 data gap; (2) governance coherence—aligning voluntary and regulatory instruments to reduce fragmentation and enhance cross-functional collaboration; and (3) adaptive management—embedding biennial feedback cycles that enable continuous validation and iterative refinement of emissions reduction strategies. This framework positions universities as institutional innovators capable of modeling integrated governance approaches with potential transferability to municipal, corporate, and public administration contexts. The findings contribute novel evidence to scholarly literature on institutional sustainability, policy integration, and climate governance, whilst establishing methodological standards relevant to international harmonization efforts in carbon accounting. Full article

To enhance line capacity in high-speed railways without new infrastructure, virtual coupling train sets (VCTSs) enable reduced inter-train distances via real-time communication and cooperative control. However, unknown disturbances and model uncertainties challenge VCTS performance, often causing chattering, slow convergence, and poor disturbance rejection. This paper proposes a novel finite-time extended state observer-based nonsingular terminal sliding mode (FTESO-NTSM) control strategy. The method integrates a nonsingular terminal sliding mode surface with a hyperbolic tangent-based reaching law to ensure fast convergence and chattering suppression, while a finite-time extended state observer estimates and compensates for lumped disturbances in real time. Lyapunov analysis rigorously proves finite-time stability. Numerical simulations under different initial statuses are conducted to validate the effectiveness of the proposed method. The results show that the maximum observation error achieves 0.0087 kN. The speed chattering magnitudes reach 0.00087 km/h, 0.0017 km/h, 0.0026 km/h, and 0.0034 km/h for the leading train and three followers, respectively. Furthermore, the convergence time of the followers is 56 s, 130 s, and 76 s, respectively. The results highlight that the proposed method can significantly improve line capacity and transportation efficiency. Full article

To assess the market competitiveness of high-speed rail (HSR) express and forecast its freight volume, this paper develops an integrated framework combining strategic analysis, market forecasting, and competition assessment. A hybrid SWOT-AHP model identifies and quantifies key strategic factors, clarifying HSR express positioning. Considering macroeconomic and consumption factors, a GM(1,N) model forecasts intercity express volume. Based on a generalized cost function covering timeliness, economy, safety, and stability, an improved Logit model calculates HSR’s mode share against road and air express, deriving future HSR freight volume. Using China as a case study, results show: (1) A proactive strategy leveraging intrinsic strengths is recommended, supported by rapid intercity express growth; (2) HSR can capture over 20% mode share initially, showing strong competitiveness in medium-long distance transport; (3) Transport cost is the most sensitive factor, a 20% reduction raises mode share by 10%, while rising timeliness demands enhance long distance advantages. This study offers a quantitative basis for HSR express strategic planning. Full article

Spatial equity of healthcare services is a critical concern in social equity and spatial justice research. Despite the availability of various methods to measure this equity, few studies have integrated the supply–demand coupling perspective with the analysis of impacts of residents’ travel behaviors’ on equity. This study develops and applies a Travel Behavior-based Coupling Coordination Degree (TB-CCD) method to assess the spatial equity of healthcare services in the Xi’an region. The results show the following: (1) Traditional single-mode models may fail to accurately assess this equity, whereas the TB-CCD model provides a more realistic evaluation. (2) Public transportation and driving provide a more equitable distribution of healthcare services compared to walking and cycling modes. The spatial equity of healthcare services exhibits a distinct core–periphery pattern, where accessibility and equity levels are significantly higher in city centers than in suburban areas. (3) The distribution of inequity ‘deserts’ and ‘oases’ in healthcare services is found to be travel-mode dependent, with the walking and public transportation modes exhibiting the highest incidence of these classifications. These findings provide valuable insights for urban planners and policymakers to formulate strategies and spatial plans aimed at enhancing equity in healthcare services. Full article

With the development of manufacturing industry, traditional fixed process processing methods cannot adapt to the changes in workshop operations and the demand for small batches and multiple orders. Therefore, it is necessary to introduce multiple robots to provide a more flexible production mode. Currently, some Job Shop Scheduling Problems with Transportation (JSP-T) only consider job scheduling and vehicle task allocation, and does not focus on the problem of collision free paths between vehicles. This article proposes a novel solution framework that integrates workshop scheduling, material handling robot task allocation, and conflict free path planning between robots. With the goal of minimizing the maximum completion time (Makespan) that includes handling, this paper first establishes an extended JSP-T problem model that integrates handling time and robot paths, and provides the corresponding workshop layout map. Secondly, in the scheduling layer, an improved Deep Q-Network (DQN) method is used for dynamic scheduling to generate a feasible and optimal machining scheduling scheme. Subsequently, considering the robot’s position information, the task sequence is assigned to the robot path execution layer. Finally, at the path execution layer, the Priority Based Search (PBS) algorithm is applied to solve conflict free paths for the handling robot. The optimized solution for obtaining the maximum completion time of all jobs under the condition of conflict free path handling. The experimental results show that compared with algorithms such as PPO, the scheduling algorithm proposed in this paper has improved performance by 9.7% in Makespan, and the PBS algorithm can obtain optimized paths for multiple handling robots under conflict free conditions. The framework can handle scheduling, task allocation, and conflict-free path planning in a unified optimization process, which can adapt well to job changes and then flexible manufacturing. Full article