Search Results (1,836)

This paper presents a CFD-based method for the aerodynamic design of a high-pressure compressor rotor blisk, taking into account manufacturing constraints. Focus is placed on the influence of geometric deviations caused by the dynamic constraints of the milling machine. Special attention is given to the leading edge region of the blade, where high curvature results in increased sensitivity in both aerodynamic behavior and manufacturability. The generic blisk geometry on which this study is based is characterized by an elliptical leading edge. For the optimization, the leading edge is described by Bézier curves that transition smoothly to the suction and pressure sides with continuous curvature and a non-dimensional length ratio. In steady-state RANS parameter studies, the length ratio is systematically varied while the chord length is kept constant. For the aerodynamic evaluation of the design’s key performance parameters such as blade pressure distribution, total pressure loss and compressor efficiency are considered. To evaluate the machine dynamics for a given design, compliance with the nominal feed rate and the deviation between the planned and actual tool tip positions were used as evaluation parameters. Compared to the reference geometry with an elliptical leading edge, the purely aerodynamic optimization achieved an isentropic efficiency improvement of +0.24 percentage points in the aerodynamic design point and a profile deviation improvement of $-3$$\mu$m in the 99th quantile. The interdisciplinary optimization achieved an improvement of +0.20 percentage points and $-30$$\mathrm{\mu }$m, respectively. This comparative study illustrates the potential of multidisciplinary design approaches that balance aerodynamic performance goals with manufacturability via a novel approach for Design-to-Manufacture-to-Design. Full article

This study explores the mechanical behavior of polymer and composite specimens fabricated using fused deposition modeling (FDM), focusing on three material configurations: acrylonitrile butadiene styrene (ABS), carbon fiber-reinforced polyphthalamide (PPA/Cf), and a sandwich-structured composite. A systematic experimental plan was developed using the Box–Behnken design (BBD) to investigate the effects of material type (MT), infill pattern (IP), and printing direction (PD) on tensile and flexural strength. Experimental results showed that the PPA/Cf material with a “Cross” IP printed “Flat” yielded the highest mechanical performance, achieving a tensile strength of 75.8 MPa and a flexural strength of 102.3 MPa. In contrast, the lowest values were observed in ABS parts with a “Grid” pattern and “Upright” orientation, recording 37.8 MPa tensile and 49.5 MPa flexural strength. Analysis of variance (ANOVA) results confirmed that all three factors significantly influenced both outputs (p < 0.001), with MT being the most dominant factor. Machine learning (ML) algorithms, Bayesian linear regression (BLR), and Gaussian process regression (GPR) were employed to predict mechanical performance. GPR achieved the best overall accuracy with R² = 0.9935 and MAPE = 11.14% for tensile strength and R² = 0.9925 and MAPE = 12.96% for flexural strength. Comparatively, the traditional BBD yielded slightly lower performance with MAPE = 13.02% and R² = 0.9895 for tensile strength. Validation tests conducted on three unseen configurations clearly demonstrated the generalization capability of the models. Based on actual vs. predicted values, the GPR yielded the lowest average prediction errors, with MAPE values of 0.54% for tensile and 0.45% for flexural strength. In comparison, BLR achieved 0.79% and 0.60%, while BBD showed significantly higher errors at 1.76% and 1.32%, respectively. Full article

In the context of promoting sustainable mobility, walking and cycling have been widely recognized for their environmental and health benefits. However, a notable gap often exists between residents’ motivation to engage in these modes and their actual behavior. This study focuses on this motivation–behavior discrepancy and explores how heterogeneous information interventions—within the constraints of the existing built environment—can effectively influence residents’ travel psychology and behavior. Drawing on Protection Motivation Theory, this study aims to uncover the psychological mechanisms behind travel-mode choices and quantify the relative impacts of different types of information interventions. A travel survey was conducted in Yangzhou, China, collecting data from 1052 residents. Cluster analysis was performed using travel psychology data to categorize travel motivations and examine their alignment with actual travel behavior. A random forest model was then employed to assess the effects of individual attributes, travel characteristics, and information intervention attributes on the choice of walking and cycling. The results reveal a significant motivation–behavior gap: while 76% of surveyed residents expressed motivation to walk or cycle, only 30% actually adopted these modes. Based on this, further research shows that informational attributes exhibit a stronger effect in terms of promoting walking and cycling behavior compared to individual attributes and travel characteristics. Among these, health-related information demonstrates the maximum efficacy in areas with well-developed infrastructure. Specifically, health-related information has a greater impact on cycling (21.4%), while environmental information exerts a stronger influence on walking (7.31%). These findings suggest that leveraging information to promote walking and cycling should be more targeted. Full article

Light-gauge steel frame (LGSF) materials are inherently susceptible to stochastic imperfections arising from their design, manufacturing, and erection. These defects can compromise operational integrity and adversely impact structural stability, especially during the construction period. Consequently, a thorough investigation into the buckling characteristics of LGSF materials with such imperfections is imperative. Conventional stochastic probabilistic methods, such as Monte Carlo simulations, often fail to fully capture intrinsic material and complex structural properties, leading to discrepancies between computational predictions and actual behavior. To address these limitations, this study introduces an innovative model using the Criteria Importance Through Intercriteria Correlation (CRITIC) method to assess LGSF materials under combined defects scenarios. The CRITIC method systematically evaluates various buckling modes in LGSFs under combined defects to identify the most detrimental modal combination, representing the most unfavorable scenario. Rigorous finite element analysis is then performed on the LGSF model based on this critical scenario. Compared to conventional approaches, the proposed CRITIC-based combined defects analysis model predicts a 0%~5% reduction in the critical load factor and a 1%~3% increase in ultimate displacement at control nodes. These findings indicate that the CRITIC-based method yields a more critical combination of buckling modes, thereby enhancing the reliability and safety of the simulation results. Furthermore, this research demonstrates that, for LGSF materials, the common assumption that the first-order buckling mode is inherently the most deleterious failure pattern is inaccurate. Full article

Promoting behavioral change in mobility is essential for sustainable urban development. This study evaluates the effectiveness of gamified challenges in fostering sustainable travel behaviors among high school students and teachers within the High School Challenge (HSC) 2024 campaign in Lecco, Italy. Over a 13-week period, participants tracked their commuting habits via gamified mobile application, Play&Go, that awarded points for sustainable mobility choices and introduced weekly challenges. Using behavioral (GPS-based tracking) and self-report data, we assessed the influence of challenge types, player characteristics (HEXAD Player Types, Big Five traits), and user experience evaluations on participation, retention, and behavior change. The results show that challenges, particularly those based on walking distances and framed as intra-team goals, significantly enhanced user engagement and contributed to improved mobility behaviors during participants’ free time. Compared to the 2023 edition without challenges, the 2024 campaign achieved better retention. HEXAD Player Types were more predictive of user appreciation than Personality Traits, though these effects were more evident in subjective evaluations than actual behavior. Overall, findings highlight the importance of tailoring gamified interventions to users’ motivational profiles and structuring challenges around SMART principles. This study contributes to the design of behaviorally informed, scalable solutions for sustainable mobility transitions. Full article

This article presents an empirical evaluation of the technical and economic performance of a building-integrated photovoltaic (PV) system implemented at the Faculty of Architecture and Urbanism of the University of Cuenca, Ecuador. This study explores both stages of deployment, beginning with a 7.7 kWp pilot system and later scaling to a full 75.6 kWp configuration. This hourly monitoring of power exchanges with utility was conducted over several months using high-resolution instrumentation and cloud-based analytics platforms. A detailed comparison between projected energy output, recorded production, and real energy consumption was carried out, revealing how seasonal variability, cloud cover, and academic schedules influence system behavior. The findings also include a comparison between billed and actual electricity prices, as well as an analysis of the system’s payback period under different cost scenarios, including state-subsidized and real-cost frameworks. The results confirm that energy exports are frequent during weekends and that daily generation often exceeds on-site demand on non-working days. Although the university benefits from low electricity tariffs, the system demonstrates financial feasibility when broader public cost structures are considered. This study highlights operational outcomes under real-use conditions and provides insights for scaling distributed generation in institutional settings, with particular relevance for Andean urban contexts with similar solar profiles and tariff structures. Full article

Slope stability analysis is conventionally performed using the strength reduction method with the proportional reduction in shear strength parameters. However, during actual slope failure processes, the attenuation characteristics of rock mass cohesion ($c$) and internal friction angle ($\phi$) are often inconsistent, and their reduction paths exhibit clear nonlinearity. Relying solely on proportional reduction paths to calculate safety factors may therefore lack scientific rigor and fail to reflect true slope behavior. To address this limitation, this study proposes a novel approach that considers the non-proportional reduction of $c$ and $\phi$, without dependence on predefined reduction paths. The method begins with an analysis of slope stability states based on energy dissipation theory. A Bayesian Gaussian Mixture Model (BGMM) is employed for intelligent interpretation of the dissipated energy data, and, combined with energy mutation theory, is used to identify instability states under various reduction parameter combinations. To compute the safety factor, the concept of a “reference slope” is introduced. This reference slope represents the state at which the slope reaches limit equilibrium under strength reduction. The safety factor is then defined as the ratio of the shear strength of the target analyzed slope to that of the reference slope, providing a physically meaningful and interpretable safety index. Compared with traditional proportional reduction methods, the proposed approach offers more accurate estimation of safety factors, demonstrates superior sensitivity in identifying critical slopes, and significantly improves the reliability and precision of slope stability assessments. These advantages contribute to enhanced safety management and risk control in slope engineering practice. Full article

Solar power generation is rapidly emerging within renewable energy due to its cost-effectiveness and ease of deployment. However, improper inspection and maintenance lead to significant damage from unnoticed solar hotspots. Even with inspections, factors like shadows, dust, and shading cause localized heat, mimicking hotspot behavior. This study emphasizes interpretability and efficiency, identifying key predictive features through feature-level and What-if Analysis. It evaluates model training and inference times to assess effectiveness in resource-limited environments, aiming to balance accuracy, generalization, and efficiency. Using Unmanned Aerial Vehicle (UAV)-acquired thermal images from five datasets, the study compares five Machine Learning (ML) models and five Deep Learning (DL) models. Explainable AI (XAI) techniques guide the analysis, with a particular focus on MPEG (Moving Picture Experts Group)-7 features for hotspot discrimination, supported by statistical validation. Medium Gaussian SVM achieved the best trade-off, with 99.3% accuracy and 18 s inference time. Feature analysis revealed blue chrominance as a strong early indicator of hotspot detection. Statistical validation across datasets confirmed the discriminative strength of MPEG-7 features. This study revisits the assumption that DL models are inherently superior, presenting an interpretable alternative for hotspot detection; highlighting the potential impact of domain mismatch. Model-level insight shows that both absolute and relative temperature variations are important in solar panel inspections. The relative decrease in “blueness” provides a crucial early indication of faults, especially in low-contrast thermal images where distinguishing normal warm areas from actual hotspot is difficult. Feature-level insight highlights how subtle changes in color composition, particularly reductions in blue components, serve as early indicators of developing anomalies. Full article

In this paper, we discuss the prediction of the delivery efficiency of magnetic carriers based on their properties and field parameters. We developed a theory describing the behavior of magnetic capsules in the capillaries of living systems. A partial differential equation for the spatial distribution of magnetic capsules has been obtained. We propose to characterize the interaction between the magnetic field and the capsules using a single vector, which we call “specific magnetic force”. To test our theory, we performed experiments on a model of a capillary bed and on a living organism with two types of magnetic capsules that differ in size and amount of magnetic material. The experimental results show that the distribution of the capsules in the field correlated with the theory, but there were fewer actually accumulated capsules than predicted by the theory. In the weaker fields, the difference was more significant than in stronger ones. We proposed an explanation for this phenomenon based on the assumption that a certain level of magnetic force is needed to keep the capsules close to the capillary wall. We also suggested a formula for the relationship between the probability of capsule precipitation and the magnetic force. We found the effective value of a specific magnetic force at which all the capsules attracted by the magnet reach the capillary wall. This value can be considered as the minimum level for the field at which it is, in principle, possible to achieve a significant magnetic control effect. We demonstrated that for each type of capsule, there is a specific radius of magnet for which the effective magnetic force is achieved at the largest possible distance from the magnet’s surface. For the capsules examined in this study, the maximum distance where the effective field can be achieved does not exceed 1.5 cm. The results of the study contribute to our understanding of the behavior of magnetic particles in the capillaries of living organisms when exposed to a magnetic field. Full article

The HoReCa (Hotels, Restaurants, and Cafes) sector plays a pivotal role in the economy due to its strong connections with various other industries, including agriculture, food and beverage, construction, packaging, waste management, water, and textiles. Given its broad impact, understanding the perceptions of students—emerging consumers and future professionals—could provide valuable insights for businesses seeking to enhance sustainable practices in ways that resonate with younger generations and improve their competitiveness. However, there is still limited understanding of how students perceive and engage with sustainability in this sector. This study explores student perceptions of sustainability practices within the HoReCa sector, examining their awareness levels, expectations, and behavior. The objective is to assess how effectively current business approaches align with student values regarding sustainability initiatives and identify key factors influencing their engagement. A structured questionnaire was distributed among university students, and the collected data was analyzed using statistical techniques to identify meaningful trends and correlations. Findings revealed a notable disconnect between students’ professed sustainability values and their actual behavior. Primary obstacles included price sensitivity, skepticism toward environmental marketing claims, and insufficient access to clear sustainability information from businesses. Despite supporting sustainable initiatives in principle, students often struggle to translate their values into purchasing decisions. The research suggests that greater business transparency, enhanced sustainability education, and incentive programs could foster increased student engagement. Full article

The initiation of ductile fractures in medium-strength AW5754 and high-strength AW6082 aluminum alloys at different quasi-static strain rates and under multiaxial stress states was investigated through a series of tensile tests using various specimen geometries. The sensitivity of the stress triaxiality locus to variations in the loading rate was examined for these two aluminum alloy families. Fractographic and elemental analyses were also conducted via SEM and EDS. Numerical simulations based on the finite element method (FEM) were performed using ABAQUS/Standard to determine the actual stress triaxialities and the equivalent plastic strains at fracture. The numerical approach was validated by comparing the simulation results with the experimental findings. These simulations facilitated the generation of a stress triaxiality locus through a curve-fitting process. Among the considered fitting functions, an exponential function was selected as it provided the most accurate relation between the equivalent plastic strain at fracture and the corresponding stress state across different strain rates. The results reveal different strain rate dependencies for the two alloys within a very low strain rate range. The resulting stress triaxiality loci provide a valuable tool for predicting fracture strains and for more accurately evaluating stress states. Overall, the findings of this study significantly advance the understanding of the fracture initiation behavior of aluminum alloys under multiaxial loading conditions and their sensitivity to various quasi-static loading rates. Full article

In the actual installation of cables, inclined cable laying within covered cable trays is a relatively common method. To investigate the effects of different tilt angles on the combustion behavior of cables within covered cable trays, aluminum conductor polyethylene-insulated power cables were used as the test cables. The flame morphology, temperature distribution, and fire spread rate during the cable combustion process were analyzed for experimental scenarios for which the cable laying angles and the ignition positions changed. The results indicate that the inclination angle of the covered cable tray has a significant impact on flame propagation and temperature distribution. For the ignition located at the lowest part of the cable, the fire spread rate increases significantly with the tilt angle. In contrast, for the ignition located at the highest part of the cable, the fire spread rate initially decreases slightly and then increases, with a relatively smaller overall change in magnitude. Under both ignition positions, the flame spread rate significantly increases at 15–30°. Therefore, in actual cable installation processes, cables within covered troughs should avoid large-angle inclinations. Full article

Short videos’ increasing popularity and increased user engagement have sparked concerns about time perception. While studies have linked gaming or watching TV series to time loss, research on short videos’ temporal impact is scarce. This study aims to investigate the impact of short video use on time distortion (including perceptions of time for experimental tasks and weekly usage) through an experimental design. Fifty-six college students were randomly assigned to two time duration conditions (long-duration for 16 min 9 s or short-duration for 5 min 23 s). Participants in both conditions were instructed to watch short videos and read public articles for the same duration and then estimate the time duration of the tasks. Subsequently, participants completed a questionnaire about their estimated and actual weekly short video use and problematic short watching levels. The results showed that the impact of task duration on time perception was significant. Task type had no significant impact on time perception, with no notable difference in time estimation between conditions involving watching short videos and reading. The interaction between time duration and task type was not significant. Additionally, problematic short video watching and the estimated weekly short video use were not significantly related to time distortion. This study contributes to empirical research on time distortion while watching short videos, providing insights for expanding theoretical models of addictive behaviors and interventions for problematic short video use. Full article

The development of Advanced Driver Assistance Systems (ADASs) has reached a stage where, in addition to the traditional challenges of path planning and control, there is an increasing focus on the behavior of these systems. Assistance functions shall be personalized to deliver a full user experience. Therefore, driver modeling is a key area of research for next-generation ADASs. One of the most common tasks in everyday driving is lane keeping. Drivers are assisted by lane-keeping systems to keep their vehicle in the center of the lane. However, human drivers often deviate from the center line. It has been shown that the driver’s choice to deviate from the center line can be modeled by a linear combination of preview curvature information. This model is called the Linear Driver Model. In this paper, we fit the LDM parameters to real driving data. The drivers are then clustered based on the individual parameters. It is shown that clusters are not only formed by the numerical similarity of the driver parameters, but the drivers in a cluster actually have similar behavior in terms of path selection. Finally, an Extended Kalman Filter (EKF) is proposed to learn the model parameters at run-time. Any new driver can be classified into one of the driver type groups. This information can be used to modify the behavior of the lane-keeping system to mimic human driving, resulting in a more personalized driving experience. Full article

Against the backdrop of evolving global climate patterns, the frequency and intensity of extreme weather events have increased significantly, posing unprecedented threats to agricultural production. This change has particularly profound impacts on agricultural systems in developing countries, making the enhancement of farmers’ capacity to withstand extreme weather events a crucial component for achieving sustainable agricultural development. As an essential safeguard for agricultural production, agricultural insurance plays an indispensable role in risk management. However, a pronounced gap persists between policy aspirations and actual adoption rates among farmers in developing economies. This study employs the integrated theory of planned behavior (TPB) and protection motivation theory (PMT) to construct an analytical framework incorporating psychological, socio-cultural, and risk-perception factors. Using Jiangsu Province—a representative high-risk agricultural region in China—as a case study, we administered 608 structured questionnaires to farmers. Structural equation modeling was applied to identify determinants influencing insurance adoption decisions. The findings reveal that farmers’ agricultural insurance purchase decisions are influenced by multiple factors. At the individual level, risk perception promotes purchase intention by activating protection motivation, while cost–benefit assessment enables farmers to make rational evaluations. At the social level, subjective norms can significantly enhance farmers’ purchase intention. Further analysis indicates that perceived severity indirectly enhances purchase intention by positively influencing attitude, while response costs negatively affect purchase intention by weakening perceived behavior control. Although challenges such as cognitive gaps and product mismatch exist in the intention-behavior transition, institutional trust can effectively mitigate these issues. It not only strengthens the positive impact of psychological factors on purchase intention, but also significantly facilitates the transformation of purchase intention into actual behavior. To promote targeted policy interventions for agricultural insurance, we propose corresponding policy recommendations from the perspective of public intervention based on the research findings. Full article