Search Results (3,165)

This study presents a combined experimental–numerical methodology to calibrate the mechanical behavior of an advanced high-strength steel (AHSS) with transformation-induced plasticity (TRIP) effects, incorporating both initial plastic anisotropy and ductile damage. The investigated TRIP 780 grade, widely used in the automotive industry for its exceptional strength–ductility balance, exhibits a complex deformation response that demands accurate constitutive modeling for reliable sheet metal forming simulations. The methodology minimizes the number of required specimen geometries without compromising accuracy. Three flat-sheet specimens were employed: standard uniaxial tension (UT) and two double-notched designs reproducing intermediate (ID) and plane strain (PS) modes. Experiments combined digital image correlation with finite element analysis. Hill’s 48 quadratic yield criterion captured the initial anisotropy of the TRIP 780 sheet, while the parameters of a phenomenological ductile damage model were calibrated from the experimental data. The TRIP effect under UT was quantified by X-ray diffraction, showing a decrease in retained austenite from 9.9% (as-received) to 3.2% at 21% equivalent plastic strain. Fractography revealed damage initiation dominated by void nucleation at phase boundaries. The proposed approach yielded stress–strain predictions with R² values exceeding 0.99. This simplified approach offers a cost-effective and experimentally feasible framework for constitutive modeling of AHSS grades, enabling practical applications in advanced sheet forming simulations. Full article

A growing body of scientific literature emphasizes the role of the built environment in shaping commuting behavior among adolescents. However, the comparison of the built environment on adolescents’ route choice remains underexplored. A total of 317 Spanish adolescents participated in the study, of whom 67 adolescents provided a valid GPS-identified walking route between home and school (54.5% girls; mean age = 14.4 ± 0.7 years). Built environment variables—including residential density, number of intersections, land use mix, number of services, number of visible services from the route, street width, walkability, park area, elevation gain, elevation loss, and topographic cost—were measured using 3.28.8 QGIS software. A paired-sample analysis was performed using the Wilcoxon signed-rank test and the sign test to compare the actual route with the shortest available route. The results showed a deviation of 63.96%. Comparisons between the actual routes and the shortest ones revealed a statistically significant difference in the number of intersections (p = 0.009) and topography cost (p = 0.050). Likewise, a significant trend was found with the residential density (p = 0.080). These findings suggest that in this case study, the built environment plays an important role in adolescents’ decision-making when choosing routes for commuting to school. Some urban planning and design recommendations were given to address the results from a school built-environment-oriented approach. Full article

Standardized driving cycles such as the FTP-75 fail to represent traffic conditions in cities like Guayaquil, where high congestion and varied driving behaviors are not captured by external models. This study aimed to develop representative driving cycles for the city’s most congested urban routes, covering the north, south, center, and west zones. Using the direct method, real-world trips were conducted with an M1-category vehicle equipped with an OBDLINK MX+ device, allowing real-time data collection. Driving data were processed through OBDWIZ software Version 4.30.1 and statistically analyzed using Minitab. From pilot tests, the appropriate sample size was estimated, and normality tests were applied to determine the correct measures of central tendency. The final representative cycles were constructed using a weighting criteria method. The results provided quantified evidence of variations in average speed, idle time, and acceleration patterns across the routes, which were transformed into representative driving cycles. These cycles provide a more accurate basis for emission modeling, vehicle certification, and transport policy design in congested cities such as Guayaquil, and this is the applied impact that is highlighted in our contribution. Furthermore, the developed cycles provide a foundation for future research on emission modeling and the design of sustainable transport strategies in Latin American cities. Full article

Forest tourism visitation in U.S. national forests has grown by approximately 8 percent over the past decade (from 2014 to 2022) from 147 million to 158.7 million visits per year, indicating a clear upward trajectory in demand for nature-based leisure experiences, yet the determinants of traveler satisfaction in this context remain insufficiently understood. Existing studies have primarily emphasized destination attributes, overlooking the interplay between psychological motivations, lifestyle orientations, and physical conditions. This omission is critical because it limits a holistic understanding of forest traveler’s experiences, which prevents us from fully capturing how internal dispositions, everyday life contexts, and well-being concerns interact with destination attributes to shape satisfaction. Therefore, the purpose of this study is to explore how motivation, lifestyle, and physical condition jointly shape satisfaction in forest tourism, drawing on Push–Pull Theory and environmental psychology. A dataset of 10,792 TripAdvisor reviews of U.S. national forests was analyzed using LIWC 2022 for psycholinguistic feature extraction and Ordered Logit Regression for hypothesis testing. Results show that positive emotional tone, leisure-oriented language, health references, and reward motivation significantly enhance satisfaction, while negative tone, illness, and work-related language reduce it. Curiosity and risk motivations were non-significant, and allure exerted only a marginal effect. These findings extend the Push–Pull framework by incorporating lifestyle and physical condition as moderating variables and validate emotional tone in user-generated content as a proxy for subjective evaluations. The study refines motivation theory by revealing context-specific effects of motivational dimensions. The results offer actionable insights for destination managers, service providers, marketers, and policymakers aiming to enhance forest travel experiences and promote sustainable tourism development. Full article

The promise of sustainability of ecotourism relies on comprehending the psychological mechanism that converts experience into post-visit environmental concern. This research formulates and examines a model that connects three antecedents—Perceived Trip Quality (PTQ), Aesthetic/Spiritual Experience (ASE), and Environmental Concern (EC)—with Responsible Post-Visit Behavior (RPB) through two mediators: Tourist Satisfaction (SAT) and Personal Norms (PN). Structural equation modeling based on a quantitative, cross-sectional design examined survey responses from 585 Greek ecotourists. All three precursors meaningfully predicted RPB, directly and indirectly through SAT and PN, with partial mediation on all but the direct pathway. Mediation effects also named PN a stronger channel than SAT, particularly in converting affective and moral involvement into stable intentions. Multi-group tests for gender, age, education, environmental orientation, and previous ecotourism experience revealed significant differences; younger, inexperienced, and high-orientation tourists were more sensitive to normative and affective mechanisms. The research develops environmental and tourism psychology by combining value-based and experience-based routes to post-visit action. Practical recommendations are made to policymakers, educators, and operators to develop transformational, norm-activating experiences. Full article

Light Electric Vehicles (LEVs) offer a promising response to environmental and urban mobility challenges. This study is among the first to exploratorily examine their use, user characteristics, and owner evaluations. A qualitative pre-study with four LEV owners was conducted and informed a subsequent quantitative phase involving 23 owners. Over two weeks, participants recorded all LEV trips using GPS tracking and completed two questionnaires. Findings show that LEVs are regularly used for commuting, shopping, and work-related trips. Notably, many users live outside urban centers, indicating strong potential for short-distance travel in rural and small-town contexts for our sample. This challenges the view of LEVs as primarily urban or recreational vehicles. Within our sample, usage patterns were diverse, indicating that even among early adopters there is no single typical usage profile. While cars were perceived as slightly safer, no participant reported feeling unsafe in their LEV. User satisfaction was high: 24 of 27 respondents would choose the same vehicle again. Overall, LEVs emerge as a versatile and satisfying mobility option, relevant beyond city limits. Given their wide range of uses and positive user feedback, LEVs should be more strongly considered in transport policy to promote more sustainable and needs-based mobility. Full article

This study investigates the factors that influence household trip generation across the United States using the National Household Travel Survey (NHTS) dataset. Recognizing the limits of a one-size-fits-all modeling approach, we conduct a two-stage analysis to investigate spatial heterogeneity within travel behavior. Stage one creates a benchmark analysis, comparing advanced machine learning models (CatBoost and random forest) to a traditional linear regression model. Contrary to prevailing trends in predictive modeling, the results reveal that linear regression not only delivers competitive overall performance but also emerges as the best performing model in the majority of states. Providing optimal balance between predictive accuracy and interpretability. Building on these findings, the second stage applies state specific linear models to uncover geographic differences in trip generation drivers. The findings highlight extensive spatial heterogeneity: while core demographic variables like household size and the presence of young children show consistent effects across the US, the influence of socio-economic factors such as income and vehicle ownership are highly context-dependent and spatially volatile. These findings highlight the importance of moving beyond black box modeling and instead implementing place based, context sensitive techniques in the promotion of more effective and equitable transportation plans. Full article

Network protector units (NPUs) are crucial parts of the protection of secondary networks to effectively isolate faults occurring on the primary feeders. When a fault occurs on the primary feeder, there is a path of the fault current going through the service transformers that causes a negative flow of current on the NPU connected to the faulted feeder. Conventionally, NPUs rely on the direction of current with respect to the voltage to detect faults and make a correct trip decision. However, the conventional NPU logic does not allow the reverse power flow caused by distributed energy resources installed on secondary networks. The communication-assisted direct transfer trip logic for NPUs can be used to address this challenge. However, the communication-assisted scheme is exposed to some vulnerabilities arising from the disruption or corruption of the communicated data that can endanger the reliable operation of NPUs. This paper evaluates the impact of the malfunction of the communication system on the operation of communication-assisted NPU logic. To this end, the impact of packet modification and denial-of-service cyberattacks on the communication-assisted scheme are evaluated. The evaluation was performed using a hardware-in-the-loop (HIL) co-simulation testbed that includes both real-time power system and communication network digital simulators. This paper evaluates the impact of the cyberattacks for different fault scenarios and provides a list of recommendations to improve the reliability of communication-assisted NPU protection. Full article

One of the key challenges in the provision of sustainable energy is understanding how younger generations perceive and respond to the relatively higher cost of green energy. This paper examines the attitudes of Generation Z towards paying premium for using products and services made with green power technologies. We surveyed 173 first- and second-year full-time bachelor students from Krakow University of Economics in Poland, combining contingent valuation in daily life scenarios (coffee purchase, apartment rental, travel carbon offset, environmental donation) with measures of connectedness to nature and self-reported tipping behavior. The results show that between 69% and 82% of respondents are willing to pay a premium for green energy. The size of the premium depends on the product that is bought. We find that while respondents are willing to pay a 10.5% premium for coffee prepared in a restaurant that uses only green energy, they are willing to pay just a 3.1% premium for green electricity at home. We also find that respondents are willing to pay three times more for planting a tree than to offset the carbon footprint of a train trip. A stronger emotional and cognitive bond with nature (on a CNS scale) translates into a greater willingness to financially support environmental initiatives. Full article

In this article, we study a directed version of Douglas–Rachford splitting method in real Hilbert spaces. By using new, self-contained, and simplified techniques, we prove its weak convergence. The major innovation is that we exploit the firm non-expansiveness of the Douglas–Rachford operator for the first time to derive the best possible upper bounds on direction factors, assuming that the involved factors remain constant. We give a new rare feature selection model equipped with the TripAdvisor hotel-review dataset. Numerical results confirm the user-friendliness and efficiency of directed Douglas–Rachford splitting in solving this model. Full article

Efficient multimodal transportation from a passenger’s doorstep to the airport is critical for ensuring timely arrivals, reducing travel uncertainty, and optimizing overall travel experience. However, coordinating different modes of transport—such as walking, public transit, ride-hailing, and private vehicles—poses significant challenges due to varying schedules, traffic conditions, and transfer times. Traditional route planning methods often fail to account for real-time disruptions, leading to delays and inefficiencies. As air travel demand grows, optimizing these multimodal routes becomes increasingly important to minimize delays, improve passenger convenience, and enhance transport system resilience. To address this challenge, we propose an optimization framework combining Mixed-Integer Linear Programming (MILP) and Dynamic Programming (DP) to generate optimal travel routes from a passenger’s location to the airport gate. MILP is used to model and optimize multimodal trip decisions, considering time windows, cost constraints, and transfer dependencies. Meanwhile, DP allows for adaptive, real-time adjustments based on changing conditions such as traffic congestion, transit delays, and service availability. By integrating these two techniques, our approach ensures a robust, efficient, and scalable solution for multimodal transport routing, ultimately enhancing reliability and reducing travel time variability. The results demonstrate that the MILP solver converges within 20 iterations, reducing the objective value from 15.2 to 7.1 units with an optimality gap of 8.5%; the DP-based adaptation maintains feasibility under a 2 min disruption; and the multimodal analysis yields a total travel time of 9.0 min with a fare of 3.0 units, where the bus segment accounts for 6.5 min and 2.2 units of the total. In the multimodal transport evaluation, DP adaptation reduced cumulative delays by more than half after disruptions, while route selection demonstrated balanced trade-offs between cost and time across walking, bus, and train segments. Full article

This paper presents an initial contribution to a broader research initiative focused on the aggregate calibration of travel demand sub-models using low-cost and widely accessible data. Specifically, this first phase investigates methods and algorithms for the aggregate calibration of destination choice models, with the objective of assessing the possible utilization of an external observed matrix, eventually derived from opportunistic data. It can be hypothesized that such opportunistic data may originate from processed mobile phone data or result from the application of data fusion techniques that produce an estimated observed trip matrix. The calibration problem is formulated as a simulation-based optimization task and its implementation has been tested using a small-scale network, employing an agent-based model with a nested demand structure. A range of optimization algorithms is implemented and tested in a controlled experimental environment, and the effectiveness of various objective functions is also examined as a secondary task. Three optimization techniques are evaluated: Simultaneous Perturbation Stochastic Approximation (SPSA), Particle Swarm Optimization (PSO), and Adaptive Moment Estimation (ADAM). The application of the ADAM optimizer in this context represents a novel contribution. A comparative analysis highlights the strengths and limitations of each algorithm and identifies promising avenues for further investigation. The findings demonstrate the potential of the proposed framework to advance transportation modeling research and offer practical insights for enhancing transport simulation models, particularly in data-constrained settings. Full article

Bias Temperature Instability (BTI)-induced aging of transistors is a serious concern in modern electronic circuits, yet its effects on the operation of mixed-signal circuits have not been extensively studied. In this work, initially we analyze how BTI-induced aging degradation influences the analog front end of Flash analog-to-digital converters (ADCs). BTI-induced aging leads to substantial increments in the offset voltage of the ADC comparators, which in turn affect their trip point voltage, leading to the alteration of the ADC’s performance characteristics, such as gain, full-scale error and integral nonlinearity. Thus, erroneous responses are generated. Next, we propose a low-cost BTI-induced aging mitigation technique based on a circuit reconfiguration method which periodically alters the average voltage stress on the ADC comparators’ transistors. The proposed method limits the comparators’ offset voltage development, restricting the shift in their trip point voltage. Consequently, the impact of aging on the performance characteristics of the ADC is drastically reduced, and its reliability is improved. According to our simulations, after two years of operation, the gain error is reduced by 95.43%, the full-scale error is reduced by 63.31% and the integral nonlinearity is reduced by 63.00%, with respect to operation without applying the proposed aging mitigation technique. Full article

When complex computations cannot be performed on board a mobile robot, sensory data must be transmitted to a remote station to be processed, and the resulting actions must be sent back to the robot to execute, forming a repeating cycle. This involves stochastic round-trip times in the case of non-deterministic network communications and/or non-hard real-time software. Since robots need to react within strict time constraints, modeling these round-trip times becomes essential for many tasks. Modern approaches for modeling sequences of data are mostly based on time-series forecasting techniques, which impose a computational cost that may be prohibitive for real-time operation, do not consider all the delay sources existing in the sw/hw system, or do not work fully online, i.e., within the time of the current round-trip. Marginal probabilistic models, on the other hand, often have a lower cost, since they discard temporal dependencies between successive measurements of round-trip times, a suitable approximation when regime changes are properly handled given the typically stationary nature of these round-trip times. In this paper we focus on the hypothesis tests needed for marginal modeling of the round-trip times in remotely operated robotic systems with the presence of abrupt changes in regimes. We analyze in depth three common models, namely Log-logistic, Log-normal, and Exponential, and propose some modifications of parameter estimators for them and new thresholds for well-known goodness-of-fit tests, which are aimed at the particularities of our setting. We then evaluate our proposal on a dataset gathered from a variety of networked robot scenarios, both real and simulated; through >2100 h of high-performance computer processing, we assess the statistical robustness and practical suitability of these methods for these kinds of robotic applications. Full article

Unsafe events such as slips and trips occur regularly on construction sites. Efficient identification of these events can help protect workers from accidents and improve site safety. However, current detection methods rely on subjective reporting, which has several limitations. To address these limitations, this study presents a sound-based slip and trip classification method using wearable sound sensors and machine learning. Audio signals were recorded using a smartwatch during simulated slip and trip events. Various 1D and 2D features were extracted from the processed audio signals and used to train several classifiers. Three key findings are as follows: (1) The hybrid CNN-LSTM network achieved the highest classification accuracy of 0.966 with 2D MFCC features, while GMM-HMM achieved the highest accuracy of 0.918 with 1D sound features. (2) 1D MFCC features achieved an accuracy of 0.867, outperforming time- and frequency-domain 1D features. (3) MFCC images were the best 2D features for slip and trip classification. This study presents an objective method for detecting slip and trip events, thereby providing a complementary approach to manual assessments. Practically, the findings serve as a foundation for developing automated near-miss detection systems, identification of workers constantly vulnerable to unsafe events, and detection of unsafe and hazardous areas on construction sites. Full article