Search Results (34)

Robotic tactile interfaces involving artificial skins often experience sliding contact conditions. At sliding interfaces, frictional loading, tangential stress, and impending slip dominate sensing behavior. This work demonstrates three-dimensional finite element (3D-FE) and Poisson–Nernst–Planck (PNP) modeling of layered ionic polymer–metal composite (IPMC) artificial skin under finger-like reciprocating sliding contact. The layered structure consists of a Nafion-based IPMC core sandwiched between thin upper and lower electrodes. A rigid acrylic slider is used to simulate reciprocating finger motion relative to the surface of the IPMC skin. A time-dependent contact mechanics model is first utilized to simulate temporal variations in normal and tangential contact fields for various coefficients of friction. Electrochemical response is then determined in COMSOL Multiphysics by coupling ion transport and electrostatics in a PNP framework to predict the output sliding current. Parametric studies are used to investigate the dependence of sensor response on the coefficient of friction, reciprocating history, layer geometry, and transport parameters. From the results, it can be noted that the resulting parameter offers a robust and physically meaningful description of the magnitude of contact-induced shear stress under multi-mode loadings, yet retaining the capability of responding to the presence of friction-induced mechanical excitation. The current model is aimed at dynamic shear sensitivity detection in sliding contacts. It is not designed for texture discrimination or fragment identification tasks. Thus, the current study demonstrates an important coupling parameter for 3D IPMC sensor models under contact and sets up a framework for enhanced electro-chemo-mechanical modeling of soft ionic tactile sensors. Full article

Emerging organic pollutants (EOPs) in aquatic environments have attracted increasing attention because many occur at trace levels, undergo transformation during environmental transport, and contribute to poorly resolved mixture risks. Traditional targeted analysis is inherently restricted to predefined compounds, whereas high-resolution mass spectrometry (HRMS)-based full-scan workflows provide broader opportunities for discovering known unknowns and previously unrecognized contaminants. This review critically synthesizes an omics-based analytical framework for aquatic environments, covering sample digitalization, instrumental analysis and acquisition modes, chemical fingerprint/non-target screening, suspect screening, effect-directed analysis, and confidence-based structural identification. Particular emphasis is placed on practical decision points and trade-offs, including dissolved versus particulate-associated analytes, LC-HRMS versus GC-HRMS coverage, hard versus soft ionization, DDA- versus DIA-type acquisition, database dependence, and the persistent difficulty of linking analytical features to toxicological relevance. The review also discusses emerging directions involving artificial intelligence, chemometrics, organometallic contaminants, and microplastic-associated chemicals. By clarifying conceptual boundaries and highlighting current limitations, this article aims to support the development of more critical, transparent, and risk-oriented workflows for the discovery and prioritization of emerging pollutants in aquatic environments. Full article

Transportation remains one of the sectors with the highest GHG emissions in urban areas, forming around a third of household footprints in affluent countries like the Nordics and being the main source of particulate matter emissions in urban areas around the world. This study focuses on the Reykjavík Capital Area in Iceland, which is known for its car-centricity and where modal shift remains a major challenge. The study examines barriers to modal shift to understand why Reykjavík residents are reluctant to change their transport modes away from private cars. The study uses softGIS survey data gathered in 2025 of 1801 respondents. The results show that mobility remains car-dominated, with even regular public and active-mode users owning a car for running errands. The main barriers for switching to public or active modes include long travel distances, high travel time need, an unreliable public transport system, and difficulties running errands. Slight differences emerged between native and non-native residents’ barriers, with the latter being more likely to be impacted by price and connectivity issues. The study further recognizes the potential impact of climate awareness and education, as people with a stronger belief in individual impact on climate were less likely to find these aspects to be a barrier. Full article

Rural destinations face a difficult challenge in balancing economic vitality with the environmental and infrastructural pressures, including congestion of car-dependent destinations. Despite growing calls for more sustainable mobility, destination management organisations (DMOs) can assume that private vehicles are vital for rural access, fearing that alternatives such as soft mobility or public transport may have an adverse effect on visitor satisfaction and spending. Yet, empirical evidence to support or challenge these assumptions remains limited. This study addresses this gap by analysing visitor survey data (N = 512) from international and domestic tourists to a rural destination in County Clare, Ireland. Using one-way and two-way ANOVA, along with chi-square and logistic regression analyses, we examine how transport mode relates to visitor satisfaction, daily expenditure, and overnight stay behaviour. Results revealed that visitor satisfaction does not significantly differ across transport modes, suggesting that sustainable mobility options (e.g., walking, cycling, public transport) do not impact the visitor experience. While transport mode had a minimal impact on spending overall, overnight visitors, regardless of how they travelled, spent significantly more than day-trippers (p < 0.001), identifying length of stay as the key economic driver. Moreover, soft mobility users (walking and cycling) had a higher likelihood of staying overnight than car users, while tour bus users were significantly less likely to do so. However, among those who did stay overnight, tour bus users reported the highest daily spending, revealing a complex relationship between mobility type and economic impact. Overall, the results question long-held assumptions linking car use with rural tourism success. Low-impact transport options, such as soft mobility and public transport, were found to sustain visitor satisfaction and spending outcomes comparable to car travel, suggesting their integration could contribute to more balanced, sustainable mobility planning. Full article

Within transport systems, train stations cover a primary role as places where access to different modes of transport must be realised effectively, providing a valuable opportunity to make rail services, public transport, and soft mobility more attractive. This research seeks to shed some light on how Italian travellers perceive the quality of train stations, and to identify priorities for action in relation to design, building, and operation that might help revitalise their attractiveness. The methodology involved designing a questionnaire capable of identifying significant correlations between attitudinal and behavioural variables via an exploratory factor analysis, reaching around 400 respondents through a snowball sampling plan. The factor “sociality and daily life” showed the importance that people place on the vitality of urban places. Travellers also consider other factors, like the overall service quality, the cleanliness and safety of a train station, the walkability of connections within the node, and the possibility of reaching the station by bicycle. The profiling of respondents using a cluster analysis based on latent factors points to specific policies, showing how actions targeting stations can have positive effects on the use of rail transport and on the propensity towards intermodality and sustainable mobility. A safe, “living” place can mitigate the risk of social degradation, while promoting walking and cycling. Full article

Hybrid electric vehicles (HEVs) have gained significant attention for their superior energy efficiency and are becoming a predominant mode of urban transportation. The DC/DC converter plays a critical role in HEV energy management systems, especially in matching the voltage levels between the battery and DC bus. This paper proposes a novel high-gain DC/DC converter with a wide input voltage range based on coupled inductors. The innovation lies in the integration of a resonant cavity and the simultaneous realization of zero-voltage switching (ZVS) and zero-current switching (ZCS), effectively reducing both voltage/current stresses on the power switches and switching losses. Compared with conventional topologies, the proposed design achieves higher voltage gain without extreme duty cycles, improved conversion efficiency, and enhanced reliability. Detailed operating principles are analyzed, and design conditions for voltage stress reduction, gain extension, and soft switching are derived. The simulation model has been conducted in a PSIM environment, and a 300 W experimental prototype, implemented using a dsPIC33FJ64GS606 digital controller, has been established and demonstrates 93% peak efficiency at a 10 times voltage gain. The performance and practical feasibility of the proposed topology have been evaluated by both simulation and experiments. Full article

Biological systems can adaptively navigate multi-terrain environments via morphological and behavioral flexibility. While robotic systems increasingly achieve locomotion versatility in one or two domains, integrating terrestrial, aquatic, and aerial mobility into a single platform remains an engineering challenge. This work tackles this by introducing a bipedal robot equipped with a reconfigurable locomotion framework, enabling seven adaptive policies: (1) thrust-assisted jumping, (2) legged crawling, (3) balanced wheeling, (4) tricycle wheeling, (5) paddling-based swimming, (6) air-propelled drifting, and (7) quadcopter flight. Field experiments and indoor statistical tests validated these capabilities. The robot achieved a 3.7-m vertical jump via thrust forces counteracting gravitational forces. A unified paddling mechanism enabled seamless transitions between crawling and swimming modes, allowing amphibious mobility in transitional environments such as riverbanks. The crawling mode demonstrated the traversal on uneven substrates (e.g., medium-density grassland, soft sand, and cobblestones) while generating sufficient push forces for object transport. In contrast, wheeling modes prioritize speed and efficiency on flat terrain. The aquatic locomotion was validated through trials in static water, an open river, and a narrow stream. The flight mode was investigated with the assistance of the jumping mechanism. By bridging terrestrial, aquatic, and aerial locomotion, this platform may have the potential for search-and-rescue and environmental monitoring applications. Full article

Modern cities are facing increasing challenges such as traffic congestion, high energy consumption, and poor air quality, making rail transit systems, known for their high capacity and low emissions, essential components of sustainable urban infrastructure. While numerous studies have examined how the built environment impacts transit ridership, the complex interactions among these factors warrant further investigation. Recent advancements in the reasoning capabilities of large language models (LLMs) offer a robust methodological foundation for analyzing the complex joint influence of multiple built environment factors. LLMs not only can comprehend the physical meaning of variables but also exhibit strong non-linear modeling and logical reasoning capabilities. This study introduces an LLM-based framework to examine how built environment factors and station characteristics shape the transit ridership dynamics by utilizing DeepSeek-R1. We develop a 4D + N variable system for a more nuanced description of the built environment of the station area which includes density, diversity, design, destination accessibility, and station characteristics, leveraging multi-source data such as points of interest (POIs), road network data, housing prices, and population data. Then, the proposed approach is validated using data from Qingdao, China, examining both single-factor and multi-factor effects on transit peak-hour ridership at the macro level (across all stations) and the meso level (specific station types). First, the variables that have a substantial effect on peak-hour transit ridership at both the macro and meso levels are discussed. Second, key and latent factor combinations are identified. Notably, some factors may appear to have limited importance at the macro level, yet they can substantially influence the peak-hour ridership when interacting with other factors. Our findings enable policymakers to formulate a balanced mix of soft and hard policies, such as integrating a flexitime policy with enhancements in active travel infrastructure to increase the attractiveness of public transit. The proposed analytical framework is adaptable across regions and applicable to various transportation modes. These insights can guide transportation managers and policymakers while optimizing Transit-Oriented Development (TOD) strategies to enhance the sustainability of the entire transportation system. Full article

Container intermodal scheduling is critical for advancing low-carbon logistics within inland port systems. However, the scheduling process faces several challenges, including the complexity of coordinating transport modes and complying with carbon emission policies. To address these issues, this study proposes a multi-objective optimization model that simultaneously considers transportation cost, carbon emissions, and time efficiency under soft time window constraints. The model is solved using an improved grey wolf–Harris hawks hybrid algorithm (IGWOHHO). This algorithm enhances population diversity through Tent chaotic mapping, balances global exploration and local exploitation with adaptive weight adjustment, and improves solution quality by incorporating an elite retention strategy. Benchmark tests show that IGWOHHO outperforms several well-established metaheuristic algorithms in terms of convergence accuracy and robustness. A case study based on an intermodal transport network further demonstrates that adjusting the objective weights flexibly provides decision support under various scenarios, achieving a dynamic balance between cost, efficiency, and environmental impact. Additionally, the analysis reveals that appropriate carbon tax pricing can encourage the adoption of greener transport modes, promoting the sustainable development of multimodal logistics systems. Full article

The transition towards a sustainable mobility model encourages an increase in the use of soft modes of transport, and thus an increase in the number of vulnerable road users, especially in urban areas. In Spain, this group of users, comprising pedestrians, cyclists, users of personal mobility vehicles and motorcyclists, accounted for 62,258 victims in road accidents in 2023, 46% of the total, with 7258 dead or seriously injured representing 65.6% of the total. Different strategies to protect vulnerable road users, including communication campaigns, are regularly developed to increase safe travel behaviour. In this context, this study analyses the campaigns issued by the Directorate General of Traffic since 1960 aimed at vulnerable road users. Only 28 campaigns met the established inclusion criteria, representing 23.5% of the total. Thus, the period 2011–2024 has seen the lowest prevalence of this type of campaign, coinciding with a context characterised by the emergence of new forms of micro-mobility that are more sustainable but also more exposed to risks. Due to this complex environment, it is recommended to increase the prevalence of campaigns targeted at vulnerable users and to maximise their effectiveness using emerging technologies such as artificial intelligence and big data, and delivered through a combination of traditional and digital media. Full article

Commuting road accidents have a direct impact on workers as well as companies. Therefore, analyzing the characteristics of commuting to and from work and workers’ perceptions of their potential risk is necessary. This study analyzes these factors in a sample of employees in a large Spanish company. A questionnaire was distributed to a total of 665 workers, assessing variables such as means of transport used and preferred in this type of commuting, perceived risks, assessment of preventive measures, involvement in a road accident during commuting, and road safety training received by the company, among others. There is a difference between preferred and used modes of transport, with factors limiting the use of soft modes. People who have experienced an accident on the way to work have a higher risk perception of this type of journey. The time and distance of the journey also have a significant influence on the perceived risk. The most highly rated preventive actions are mainly related to the accessibility and comfort of the workers’ daily commute, which helps minimize the time spent on journeys. This analysis provides relevant information on the social acceptability of different measures for the development and implementation of future actions of the prevention services that contribute to reducing this type of accident and designing strategies to promote more sustainable commuting mobility. Full article

In this study, we extend the research on the multimodal routing problem by considering flexible time window and multi-uncertainty environment. A multi-uncertainty environment includes uncertainty regarding the demand for goods, the travel speed of the transportation mode, and the transfer time between different transportation modes. This environment further results in uncertainty regarding the delivery time of goods at their destination and the earliness and lateness caused by time window violations. This study adopts triangular fuzzy numbers to model the uncertain parameters and the resulting uncertain variables. Then, a fuzzy mixed integer nonlinear programming model is established to formulate the specific problem, including both fuzzy parameters and fuzzy variables. To make the problem easily solvable, this study employs chance-constrained programming and linearization to process the proposed model to obtain an equivalent credibilistic chance-constrained linear programming reformulation with an attainable global optimum solution. A numerical case study based on a commonly used multimodal network structure is presented to demonstrate the feasibility of the proposed method. Compared to hard and soft time windows, the numerical case analysis reveals the advantages of the flexible time window in reducing the total costs, avoiding low reliability regarding timeliness, and providing confidence level-sensitive route schemes to achieve flexible routing decision-making under uncertainty. Furthermore, the numerical case analysis verifies that it is necessary to model the multi-uncertainty environment to satisfy the improved customer requirements for timeliness and enhance the flexibility of the routing, and multimodal transportation is better than unimodal transportation when routing goods in an uncertain environment. The sensitivity analysis in the numerical case study shows the conflicting relationship between the economic objective and the reliability regarding the timeliness of the routing, and the result provides a reference for the customer to find a balance between them. Full article

Soft transportation devices with high flexibility, good stability, and quick controllability have attracted increasing attention. However, a smart soft transportation device with tactile perception and a non-contact actuating mode remains a challenge. This work reports a magnetic soft pipeline (MSP) composed of sensor film, a magnetorheological elastomer (MRE) cavity pipeline, and heater film, which can not only respond well to tactile compression stimuli but also be transported by magnetic actuation. Notably, the sensor film was integrated on the upper surface of an MRE pipeline, and the relative resistance change (∆R/R₀) of the MSP was maintained at 55.8% under 2.2 mm compression displacement during 4000 loading cycles. Moreover, the heater film was integrated on the lower surface of the MRE pipeline, which endows the MSP with an electrothermal heating characteristic. The temperature of the MSP can be increased from 26.7 °C to 38.1 °C within 1 min under 0.6 V. Furthermore, the MSP was attracted and deformed under the magnetic field, and the ∆R/R₀ of the MSP reached 69.1% under application of a 165 mT magnetic field density. Benefiting from the excellent perception and magnetic deformation performances, the magnetic actuate transportation of the MSP with self-sensing was successfully achieved. This multi-functional soft pipeline integrated with in situ self-sensing, electrothermal heating, and non-contact magnetic actuating transportation performance possess high potential in smart flexible electronic devices. Full article

Ship path planning is one of the most important themes in waterway transportation, which is deemed as the cleanest mode of transportation due to its environmentally friendly and energy-efficient nature. A path-planning method that combines the soft actor-critic (SAC) and navigation risk assessment is proposed to address ship path planning in complex water environments. Specifically, a continuous environment model is established based on the Markov decision process (MDP), which considers the characteristics of the ship path-planning problem. To enhance the algorithm’s performance, an information detection strategy for restricted navigation areas is employed to improve state space, converting absolute bearing into relative bearing. Additionally, a risk penalty based on the navigation risk assessment model is introduced to ensure path safety while imposing potential energy rewards regarding navigation distance and turning angle. Finally, experimental results obtained from a navigation simulation environment verify the robustness of the proposed method. The results also demonstrate that the proposed algorithm achieves a smaller path length and sum of turning angles with safety and fuel economy improvement compared with traditional methods such as RRT (rapidly exploring random tree) and DQN (deep Q-network). Full article

Promoting public transport use is expected to contribute to reducing CO₂ emissions in the transport sector. Using Okayama City and Central Tokyo as representative case studies of regional and metropolitan areas in Japan, this study examines the impact of the evaluation of the ‘hard’ and ‘soft’ attributes of rail and bus services on the overall evaluation. This study then explores the relationship between the overall evaluation and usage frequency of rail and bus services, as well as the relationship between the usage frequency and travel-based CO₂ emissions from private transport modes. Furthermore, this study investigates whether the emissions cause differences in the evaluation of the ‘hard’ and ‘soft’ attributes of public transport services. The findings suggest prioritising an improvement in ‘hard’ rather than ‘soft’ attributes in order to reduce emissions through the use of public transport in regional areas. However, in metropolitan areas, no relationship was found between the evaluation of public transport services and emissions, presumably because of the lower ownership rate of private cars that residents can use freely and the markedly higher level of rail and bus services. This study provides a methodological reference for analysing the potential to reduce travel-based emissions from private transport modes by enhancing public transport service contents. Full article