Search Results (202)

The rapid development of generative artificial intelligence (AI) systems such as ChatGPT (GPT-4) could transform teaching and learning. Yet, integrating these tools requires insight into what drives students to adopt them. Research on ChatGPT acceptance has so far focused on university settings, leaving school contexts underexplored. This study addresses the gap by surveying 506 upper secondary students in Baden-Württemberg, Germany, using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Performance expectancy, habit and hedonic motivation emerged as strong predictors of behavioral intention to use ChatGPT for school purposes. Adding personality traits and personal values such as conscientiousness or preference for challenge raised the model’s explanatory power only marginally. The findings suggest that students’ readiness to employ ChatGPT reflects the anticipated learning benefits and enjoyment rather than the avoidance of effort. The original UTAUT2 is therefore sufficient to explain students’ acceptance of ChatGPT in school contexts. The results could inform educators and policy makers aiming to foster the reflective and effective use of generative AI in instruction. Full article

In this study, we extend the UTAUT2 model to explore the determinants of green purchase intention in Taiwan. By incorporating environmental concern, government support, and green trust, the model highlights how contextual and trust-based factors shape sustainable consumption. Based on 590 valid survey responses, analysis using covariance-based structural equation modeling reveals that performance expectancy, effort expectancy, social influence, price value, environmental concern, government support, and green trust all positively influence green purchase intention. Notably, green trust also mediates the relationship between the core UTAUT2 constructs and green purchase intention. In contrast, hedonic motivation and habit show no significant effects, suggesting that sustainable consumption has not become habitual or emotionally driven behavior in Taiwan. These findings emphasize the importance of rational evaluation, social context, and policy support in driving green behavior and offer practical implications for promoting sustainable consumption. Full article

Due to the complexity of the unstructured environment and the high-level requirement of smoothness when a tracked transportation vehicle is traveling, making the vehicle travel as safely and smoothly as when a skilled operator is maneuvering the vehicle is a critical issue worth studying. To this end, this study proposes a trajectory-planning method for human-like maneuvering. First, several field equipment operators are invited to manipulate the model vehicle for obstacle avoidance driving in an outdoor scene with densely distributed obstacles, and the manipulation data are collected. Then, in terms of the lateral displacement, by comparing the similarity between the data as well as the curvature change degree, the data with better smoothness are screened for processing, and a dataset of human manipulation behaviors is established for the training and testing of the trajectory-planning network. Then, using the dynamic parameters as constraints, a two-stage planning approach utilizes a modified deep network model to map trajectory points at multiple future time steps through the relationship between the spatial environment and the time series. Finally, after the experimental test and analysis with multiple methods, the root-mean-square-error and the mean-average-error indexes between the planned trajectory and the actual trajectory, as well as the trajectory-fitting situation, reveal that this study’s method is capable of planning long-step trajectory points in line with human manipulation habits, and the standard deviation of the angular acceleration and the curvature of the planned trajectory show that the trajectory planned using this study’s method has a satisfactory smoothness. Full article

PM_2.5 pollution presents a significant risk to urban habitability. The urban landscape pattern (ULP) serves as a crucial regulator that profoundly influences the spatiotemporal distribution features of PM_2.5. Analysis of the driving mechanisms of the ULP is therefore essential for optimizing urban ecological spatial planning. However, the driving mechanism is dynamic and exhibits seasonal variations. This study selected four landscape metrics and four control variables, developed a geographically and temporally weighted regression (GTWR) model, and examined the spatiotemporal and seasonal effects of ULP on PM_2.5 concentrations in the central urban area of Urumqi (CUA) from 2003 to 2023. The results show the following: (1) Over the past two decades, the four ULP metrics have shown an increasing trend in the CUA. (2) The spatial distribution of PM_2.5 concentrations follows a latitudinal gradient, with higher concentrations observed in the northern regions and lower concentrations in the southern regions, initially increasing and then declining over time. (3) The driving mechanisms of ULP on PM_2.5 exhibited significant variations across different locations and time scales. (4) Seasonal variations arise from pronounced meteorological contrasts and intensified pollution from central heating, which is particularly evident in central CUA. Full article

Understanding the drivers of green consumption behavior is crucial for promoting sustainable practices among households. This study explores the key factors influencing green consumer behavior, including environmental awareness, subjective norms, attitudes, green promotional activities, and household characteristics. By examining their interactions and the mediating role of consumer intention, this research provides a comprehensive perspective on how these elements shape household consumption choices. These findings highlight the significant impact of environmental awareness and subjective norms on shaping green consumer intentions, which, in turn, drive actual behavior. This study offers insights for policymakers and businesses to design targeted strategies that encourage sustainable consumption habits. Practical implications include the need for awareness campaigns, community engagement, and supportive policies to foster green consumer behavior. Full article

To investigate whether the skid resistance of the ramp meets the requirements of vehicle driving safety and stability, the simulation using the ideal driver model is inaccurate. Therefore, considering the driver’s driving habits, this paper proposes the use of Unmanned aerial vehicles (UAVs) for the collection and extraction of vehicle driving information. To process the collected UAV video, the Google Collaboration platform is used to modify and compile the “You Only Look Once” version 5 (YOLOv5) algorithm with Python 3.7.12, and YOLOv5 is retrained with the captured video. The results show that the precision rate P and recall rate R have satisfactory results with an F1 value of 0.86, reflecting a good P-R relationship. The loss function also stabilized at a very low level after 70 training epochs. Then, the trained YOLOv5 is used to replace the Faster R-CNN detector in the DeepSORT algorithm to improve the detection accuracy and speed and extract the vehicle driving information from the perspective of UAV. By coding, the coordinate information of the vehicle trajectory is extracted, the trajectory is smoothed, and the frame difference method is used to calculate the real-time speed information, which is convenient for the establishment of a real driver model. Full article

While the speed of the transition to battery electric vehicles (BEVs) depends on real-world driving behaviors and socioeconomic conditions, relevant predictions are often not based on real trip data. This study analyzes over 200,000 private car trips, tracked via onboard telematics across Italy, in order to assess the feasibility of replacing internal combustion engine vehicles (ICEVs) with BEVs. Given that drivers are resistant to changing their habits, we introduce the E-Private Mobility Index, which quantifies the percentage of traditional cars at present that are functionally compatible with a medium BEV, assuming home charging. Nationwide, this index reaches 30%, but only 15% of car owners would also see financial benefits. By quantifying both the potential to replace traditional cars with electric ones and the associated economic impacts, our analysis supports sustainable mobility by offering insights into the rate of penetration of sustainable and green mobility, in line with the objectives of the European Green Deal. With its unprecedented statistical significance, the study not only provides a data-driven upper threshold of BEV penetration but also offers a flexible framework for shaping future policies, allowing the adaptation of parameters and assumptions to guide a scalable transition to electric private mobility. Full article

The migration and driving factors of microplastics (MPs), as an emerging pollutant, have been reported in plateau lakes. However, whether MPs can accumulate to an extreme degree in the local aquatic organisms of plateau lakes remains unclear. Therefore, the present study mainly aims to investigate the MPs accumulated in tissues of grass carp as well as reveal their migration processes and driving factors in the Caohai watershed, a typical plateau lake in southwest China. Density flotation (saturated NaCl solution) and laser direct infrared imaging spectrometry were used to analyze the relative abundance and morphological characteristics of MPs, respectively. The results showed that the MPs’ abundance in soil, water, and sediments ranged from 1.20 × 10³ to 1.87 × 10⁴ n/kg, from 9 to 223 n/L, and from 5.00 × 10² to 1.02 × 10⁴ n/kg, respectively. The contents and composition of MPs in forestland soils were more plentiful in comparison with cultivated land soils and marshy grassland soils. Polyethylene (PE), polyvinylchloride (PVC), PA from caprolactam (PA6), and PA from hexamethylene diamine and adipic acid (PA66) were detected in grass carp, and PE was detected in all organs of grass carp. MP concentrations in the stomach, intestines, tissue, skin, and gills of grass carp ranged from 54.94 to 178.59 mg/kg. MP pollution probably mainly originated from anthropogenic factors (road traffic, farming activities, the habits of residents scattered around the study area, etc.) due to the Caohai watershed’s considerable proximity to Weining city. In addition, wind, land runoff, rivers, and atmospheric deposition in the locality directly and indirectly promoted MP migration. Our results suggested that although there is moderate MP pollution in soil, water, sediment, and grass carp in comparison with other areas, it is necessary to pay attention to PE and PVC migration via the various environmental media and the risks associated with consuming the local grass carp. The local government can make several policies to reuse and recycle agricultural film to alleviate local PE and PVC pollution. Full article

For most drivers of fuel-powered vehicles who do not have specialized eco-driving knowledge, simple and practical strategies are the most effective way to encourage eco-driving habits. By incorporating traffic light countdown timers from vehicle navigation systems, this paper develops a 0–1 integer linear programming (ILP) model to determine the optimal speed curve and further provide actionable, easy-to-implement eco-driving recommendations. Specifically, time is discretized into one-second intervals, with speed and acceleration also discretized. Pre-calculating instantaneous fuel consumption under various speed and acceleration combinations ensures the linearity of the objective function. For a specified road and a given time duration, the optimal speed profile problem for approaching intersections is transformed into a series of speed and acceleration selections. Through the analysis of multiple application scenarios, this study proposes practical and easily adoptable eco-driving strategies, which can effectively reduce vehicle fuel consumption, thereby contributing to the sustainable development of urban traffic. Full article

This study examines the impact of low-carbon city policies on urban employment using panel data from 2006 to 2021. The findings reveal that these policies significantly enhance urban employment by promoting green technological innovation, which drives economic growth and creates new job opportunities. Low-carbon policies also exhibit spatial spillover effects, benefiting neighboring cities within a 200 km radius. However, these effects vary non-linearly with distance. The key mechanisms include green technology adoption, industrial structure optimization, and the promotion of green consumption habits. These mechanisms accelerate industrial upgrading, foster the growth of tertiary and green industries, and expand job opportunities in emerging markets. Heterogeneity analysis shows more substantial employment effects in non-resource-based cities, provincial capitals, cities with high government innovation preferences, tertiary sector dominance, and higher urbanization rates. This highlights the need for policies tailored to specific urban characteristics. In conclusion, low-carbon policies integrate climate action with employment growth. Policymakers should invest in green technologies, support industrial transformation, and enhance public environmental awareness to maximize employment benefits, fostering sustainable urban development. Full article

Household savings are a long-term financial issue that can undermine the financial well-being of American families if not addressed. This study examines financial planning strategies through the Behavioral Life-Cycle (BLCH) hypothesis, focusing on long-term savings goals, financial safety nets, and foreseeable expenses. Using data from the 2022 Survey of Consumer Finances, a moderated mediation model explores how financial safety nets, self-control, and mental accounting influence saving habits. The findings show that long-term savings goals significantly mediate the relationship between financial safety nets and saving habits, while foreseeable expenses do not significantly moderate this relationship. These results highlight the importance of goal setting in promoting saving behaviors, regardless of specific financial needs. Policymakers can leverage these findings to design initiatives that encourage structured savings programs, while financial advisors should emphasize goal-setting strategies to help households improve their financial security. This research contributes to a deeper understanding of the behavioral and economic factors that drive personal savings, offering valuable insights for both policymakers and financial practitioners aiming to boost financial well-being in households. Full article

The unity of material and spiritual civilization is among the important criteria for sustainable development and modernization construction. However, defining the relationship between the two has posed a challenge to researchers. In terms of spiritual civilization, many studies on dialect maps reflect the dialect characteristics and cultural features of different regions. Regarding material civilization, changes in land use and behavior have attracted the attention of many scholars, who have extensively discussed their regional heterogeneity. However, few studies have focused on the connection between the two, and discussions on the possible bidirectional interaction between dialects and land use have been limited. Thus, in order to bridge the gap between the spiritual civilization related to language and the material civilization related to land use, this study proposes an interactive theoretical framework and conducts an in—depth analysis by taking Anhui Province in China as an example. Firstly, it comprehensively identifies the dialect types within Anhui Province and maps the dialects. This fundamental work provides a crucial basis for understanding the distribution of different dialect regions. Subsequently, a profound analysis of the spatiotemporal changes in land use in this province over time is carried out. To further explore the characteristics of land use behaviors, this study employs the Latent Dirichlet Allocation (LDA) model to mine the latent semantic topics in the land use-related data, thus enabling a more detailed understanding of the diverse patterns of land use behaviors in different regions. Finally, by uncovering the characteristics of land use changes and behavior differences in different dialect regions, this study explores the possible bidirectional interaction mechanisms. The results show that significant spatial heterogeneity in land use behavior and its driving factors can be observed within different dialect regions. Its bidirectional interaction is manifested in land use behaviors regulating people’s activities through constructing “fields” and forming habits that influence regional dialects and cultures. Meanwhile, under mobility mechanisms, new dialect systems replace indigenous languages in immigration destinations. Land use methods from emigration areas are spread through convenient communication, affecting the cultural psychology and land use behaviors of social groups in immigration destinations. This study expands the boundaries of linguistic and cultural geography, offering a new perspective for the identification of spatial differentiation and new ideas for the governance of spatial differences. Full article

The rapid evolution of intelligent vehicle technology has significantly advanced autonomous decision-making and driving safety. However, the challenge of predicting long-term trajectories in complex urban traffic persists, as traditional methodologies usually handle spatiotemporal attention mechanisms in isolation and are typically limited to short-term trajectory predictions. This paper proposes a Habit-based Temporal–Spatial Attention Long Short-Term Memory (HTSA-LSTM) network, a novel framework that integrates a dual spatiotemporal attention mechanism to capture dynamic dependencies across time and space, coupled with a driving style analysis module. The driving style analysis module employs Sparse Inverse Covariance Clustering and Spectral Clustering (SICC-SC) to extract driving primitives and cluster trajectory data, thereby revealing diverse driving behavior patterns without relying on predefined labels. By segmenting real-world driving data into fundamental behavioral units that reflect individual driving preferences, this approach enhances the model’s adaptability. These behavioral units, in conjunction with the spatiotemporal attention outputs, serve as inputs to the model, ultimately improving prediction accuracy and robustness in multi-vehicle scenarios. The model was evaluated by using the NGSIM dataset and real driving data from Wuhan, China. In comparison to benchmark models, HTSA-LSTM achieved a 20.72% reduction in the root mean square error (RMSE) and a 24.98% reduction in the negative log likelihood (NLL) for 5 s predictions of long-term trajectories. Furthermore, HTSA-LSTM achieved R² values exceeding 97.9% for 5 s predictions on highways and expressways and over 92.7% for 3 s predictions on urban roads, highlighting its excellent performance in long-term trajectory prediction and adaptability across diverse driving conditions. Full article

Background/Objectives: Taste guides the consumption of food and alcohol for both humans and rodents. Given that chronic dietary exposure to bitter and sweet foods are purported to alter the perception of bitter and sweet tastes respectively, we hypothesized that dietary habits may shape how the taste properties of ethanol are perceived and thus how it is consumed. Methods: Using C57BL/6 mice as a model, we contrasted taste behavior, morphology, and expression after a 4-week diet featuring consistent bitter, sweet, or neutral (water) stimuli. Results: Our results demonstrated that a 4-week bitter diet containing a quinine solution increased preference for ethanol, while a 4-week sweet diet consisting of a sucralose solution did not alter ethanol preference nor intake. The quinine diet also reduced the number of sweet- or umami-sensing T1R3-positive cells in the circumvallate papillae taste buds of the mice. Conclusions: Based on the behavioral changes observed with the bitter diet, it is possible that either bitter or sweet taste, or both together, drive the increase in ethanol preference. The implications of these findings for alcohol consumption are that dietary habits that do not necessarily concern alcohol may be capable of altering alcohol preference via taste habituation. Habitual intake of bitter and/or sweet foods can shift the perception of taste over time. Changes to how the taste components of alcohol are perceived may also alter how acceptable the taste of alcohol is when experienced as a whole, thereby having the unintended consequence of shifting alcohol consumption levels. Our study demonstrates another side to bitter habituation, which, thus far, has been studied in the more positive context of developing a set of dietary tactics for promoting bitter vegetable intake. Full article

Road classification, knowing whether we are driving in the city, in rural areas, or on the highway, can improve the performance of modern driver assistance systems and contribute to understanding driving habits. This study focuses on solving this problem universally using only vehicle speed data. A data logging method has been developed to assign labels to the On-board Diagnostics data. Preprocessing methods have been introduced to solve different time steps and driving lengths. A state-of-the-art conventional method was implemented as a benchmark, achieving 89.9% accuracy on our dataset. Our proposed method is a neural network-based model with an accuracy of 93% and 1.8% Type I error. As the misclassifications are not symmetric in this problem, loss function weighting has been introduced. However, this technique reduced the accuracy, so cross-validation was used to use as much data as possible during the training. Combining the two approaches resulted in a model with an accuracy of 96.21% and unwanted Type I misclassifications below 1%. Full article