Search Results (9,476)

Compared to commuting, grocery shopping trips, despite their profound implications for mixed land use and transportation planning, have received limited attention in travel behavior research. Drawing upon a travel diary survey conducted in a fast-growing metropolitan region of the United States, i.e., Salt Lake County, UT, this research investigated a variety of influential factors affecting mode choices associated with grocery shopping. We analyze how built environment (BE) characteristics, measured at seven spatial scales or different ways of aggregating spatial data—including straight-line buffers, network buffers, and census units—affect travel mode decisions. Key predictors of choosing walking, biking, or transit over driving include age, household size, vehicle ownership, income, land use mix, street density, and distance to the central business district (CBD). Notably, the influence of BE factors on mode choice is sensitive to different spatial aggregation methods and locations of origins and destinations. The straight-line buffer was a good indicator for the influence of store sales amount on mode choices; the network buffer was more suitable for the household built environment factors, whereas the measurement at the census block and block group levels was more effective for store-area characteristics. These findings underscore the importance of considering both the spatial analysis method and the location (home vs. store) when modeling non-work travel. A multi-scalar approach can enhance the accuracy of travel demand models and inform more effective land use and transportation planning strategies. Full article

Shandong Province is rich in geothermal resources, mainly stored in sandstone reservoirs. The setting of reasonable well spacing in the early stage of large-scale recharge has not attracted enough attention. The problem of small well spacing in geothermal engineering is particularly prominent in the sandstone thermal reservoir production area represented by Dezhou. Based on the measured data of temperature, flow, and water level, this paper constructs a typical engineering numerical model by using TOUGH2 software. It is found that when the distance between production and recharge wells is 180 m, the amount of production and recharge is 60 m³/h, and the temperature of reinjection is 30 °C, the temperature of the production well will decrease rapidly after 10 years of production and recharge. In order to solve the problem of thermal breakthrough, three optimization schemes are assumed: reducing the reinjection temperature to reduce the amount of re-injection when the amount of heat is the same, reducing the amount of production and injection when the temperature of production and injection is constant, and stopping production after the temperature of the production well decreases. However, the results show that the three schemes cannot solve the problem of thermal breakthrough or meet production demand. Therefore, it is necessary to set reasonable well spacing. Therefore, based on the strata near the Hydrological Homeland in Decheng District, the reasonable spacing of production and recharge wells is achieved by numerical simulation. Under a volumetric flux scenario ranging from 60 to 80 m³/h, the well spacing should exceed 400 m. For a volumetric flux between 80 and 140 m³/h, it is recommended that the well spacing be greater than 600 m. Full article

Rockfill particle gradation significantly influences mechanical performance in earth–rockfill dam construction, yet on-site screening is often time-consuming, labor-intensive, and structurally invasive. This study proposes a rapid and non-destructive detection method using mobile-based photography and an end-to-end image segmentation approach. An enhanced YOLOv8-seg model with an integrated dual-attention mechanism was pre-trained on laboratory images to accurately segment densely stacked particles. Transfer learning was then employed to retrain the model using a limited number of on-site images, achieving high segmentation accuracy. The proposed model attains a mAP50 of 97.8% (base dataset) and 96.1% (on-site dataset), enabling precise segmentation of adhered and overlapped particles with various sizes. A Minimum Area Rectangle algorithm was introduced to compute the gradation, closely matching the results from manual screening. This method significantly contributes to the automation of construction workflows, cutting labor costs, minimizing structural disruption, and ensuring reliable measurement quality in earth–rockfill dam projects. Full article

As a typical grain base with a dense population and high-level urbanization, the North China Plain (NCP) faces a serious threat to its sustainable development due to water shortage. Surface water area (SWA) is a key indicator for continuously measuring the trends of regional water resources and assessing their current status. Therefore, a deep understanding of its changing patterns and driving forces is essential for achieving the sustainable management of water resources. In this study, we examined the interannual variability and trends of SWA in the NCP from 1990 to 2023 using annual 30 m water body maps generated from all available Landsat imagery, a robust water mapping algorithm, and the cloud computing platform Google Earth Engine (GEE). The results showed that the SWA in the NCP has significantly increased over the past three decades. The continuous emergence of artificial reservoirs and urban lakes, along with the booming aquaculture industry, are the main factors driving the growth of SWA. Consequently, the expansion of artificial water bodies resulted in a significant increase in water evaporation (0.16 km³/yr). Moreover, the proportion of water evaporation to regional evapotranspiration (ET) gradually increased (0–0.7%/yr), indicating that the contribution of water evaporation from artificial water bodies to ET is becoming increasingly prominent. Therefore, it can be concluded that the ever-expanding artificial water bodies have become a new hidden danger affecting the water security of the NCP through evaporative loss and deserve close attention. This study not only provides us with a new perspective for deeply understanding the current status of water resources security in the NCP but also provides a typical case with great reference value for the analysis of water resources changes in other similar regions. Full article

Given the intricate operating environment of steel truss bridges, data anomalies are frequently initiated by faults in the sensor monitoring system itself during the monitoring process. This paper utilizes a steel truss bridge as a case study in engineering, with a primary focus on the deflection of the main girder. The paper establishes an Attention Mechanism-based Bidirectional Long Short-Term Memory Neural Network (ATT-BiLSTM) model, with the objective of accurately repairing abnormal monitoring data. Firstly, correlation heat maps and Gray correlation are employed to detect anomalies in key measurement point data. Subsequently, the ATT-BiLSTM and Support Vector Machine (SVR) models are established to repair the anomalous monitoring data. Finally, various evaluation indexes, including Pearson’s correlation coefficient, mean squared error, and coefficient of determination, are utilized to validate the repairing accuracy of the ATT-BiLSTM model. The findings indicate that the repair efficacy of ATT-BiLSTM on anomalous data surpasses that of SVR. The repaired data exhibited a tendency to decrease in amplitude at the anomalous position, while maintaining the prominence of the data at abrupt deflection change points, thereby preserving the characteristics of the data. The repair rate of anomalous data attained 93.88%, and the mean square error of the actual complete data was only 0.0226, leading to substantial enhancement in the integrity and reliability of the data. Full article

Neurodevelopmental disorders (NDDs) significantly impact children’s health and development. They pose a substantial burden to families and the healthcare system. Challenges in early identification, accurate and timely diagnosis, and effective treatment persist due to overlapping symptoms, lack of appropriate diagnostic biomarkers, significant stigma and discrimination, and systemic barriers. Generative Artificial Intelligence (GenAI) offers promising solutions to these challenges by enhancing screening, diagnosis, personalized treatment, and research. Although GenAI is already in use in some aspects of NDD management, effective and strategic leveraging of evolving AI tools and resources will enhance early identification and screening, reduce diagnostic processing by up to 90%, and improve clinical decision support. Proper use of GenAI will ensure individualized therapy regimens with demonstrated 36% improvement in at least one objective attention measure compared to baseline and 81–84% accuracy relative to clinician-generated plans, customize learning materials, and deliver better treatment monitoring. GenAI will also accelerate NDD-specific research and innovation with significant time savings, as well as provide tailored family support systems. Finally, it will significantly reduce the mortality and morbidity associated with NDDs. This article explores the potential of GenAI in transforming NDD management and calls for policy initiatives to integrate GenAI into NDD management systems. Full article

With the expansion of muskmelon cultivation, manual pollination is increasingly inadequate for sustaining industry development. Therefore, the development of automatic pollination robots holds significant importance in improving pollination efficiency and reducing labor dependency. Accurate flower detection and localization is a key technology for enabling automated pollination robots. In this study, the YOLO-MDL model was developed as an enhancement of YOLOv7 to achieve real-time detection and localization of muskmelon flowers. This approach adds a Coordinate Attention (CA) module to focus on relevant channel information and a Contextual Transformer (CoT) module to leverage contextual relationships among input tokens, enhancing the model’s visual representation. The pollination robot converts the 2D coordinates into 3D coordinates using a depth camera and conducts experiments on real-time detection and localization of muskmelon flowers in a greenhouse. The YOLO-MDL model was deployed in ROS to control a robotic arm for automatic pollination, verifying the accuracy of flower detection and measurement localization errors. The results indicate that the YOLO-MDL model enhances AP and F1 scores by 3.3% and 1.8%, respectively, compared to the original model. It achieves AP and F1 scores of 91.2% and 85.1%, demonstrating a clear advantage in accuracy over other models. In the localization experiments, smaller errors were revealed in all three directions. The RMSE values were 0.36 mm for the X-axis, 1.26 mm for the Y-axis, and 3.87 mm for the Z-axis. The YOLO-MDL model proposed in this study demonstrates strong performance in detecting and localizing muskmelon flowers. Based on this model, the robot can execute more precise automatic pollination and provide technical support for the future deployment of automatic pollination robots in muskmelon cultivation. Full article

Psychological distress is understood to be maintained by attention. We performed two experiments examining the impact of attention training (AT) on psychological distress symptoms. Experiment one (N = 336) investigated what effects might be detected in a simple experimental design with longitudinal measurements, while experiment two (N = 214) examined whether using a different emotional stimulus could induce an immediate anxiolytic effect in response to AT. Attentional biases were operationalized as the target search latency correlated with mood and psychological distress scores. While limited evidence of attentional biases was found in participants with higher mood distress, correlations emerged in the experimental conditions at day thirty, indicating a relationship between task latency, stress, and changes in depression (experimental one). We found no immediate between–within-group differences in outcome when including different emotional stimuli (experiment two). Despite attentional biases being less apparent in community samples, attentional training for bias modification was effective in eliciting positive biases, leading to improved mood. Notably, participants in the control condition reported the greatest mood and psychological distress improvements, whereas changes in the experimental condition primarily pertained to attentional biases. Taken together, these findings suggest that AT tasks can improve distress, but not through changes in attentional biases. Full article

This paper presents field investigations of a corrugated steel soil–steel arch structure with a span of 25.7 m and a rise of 9.0 m—currently the largest single-span structure of its kind in Europe. The structure, serving as a wildlife crossing along the DK16 expressway in northeastern Poland, was constructed using deep corrugated steel plates (500 mm× 237 mm) made from S315MC steel, without additional reinforcements such as stiffening ribs or geosynthetics. The study focused on monitoring the structural behavior during the critical backfilling phase. Displacements and strains were recorded using 34 electro-resistant strain gauges and a geodetic laser system at successive backfill levels, with particular attention to the loading stage at the crown. The measured results were compared with predictions based on the Swedish Design Method (SDM). The SDM equations did not accurately predict internal forces during backfilling. At the crown level, bending moments and axial forces were overestimated by approximately 69% and 152%, respectively. At the final backfill level, the SDM underestimated bending moments by 55% and overestimated axial forces by 90%. These findings highlight limitations of current design standards and emphasize the need for revised analytical models and long-term monitoring of large-span soil–steel structures. Full article

Soil sealing and land take are increasingly recognised as critical environmental and land use planning challenges across Europe. Although these issues have received limited attention in Baltic policymaking and the academic literature to date, available data indicate ongoing land consumption despite population decline. This study aims to analyse soil sealing patterns in Estonia, Latvia, and Lithuania between 2018 and 2021 using CLC+ Backbone data, linking them to demographic shifts and local planning frameworks. Results reveal that soil sealing increased in nearly all municipalities across the Baltic states, regardless of population trends. The analysis highlights that shrinking municipalities, constrained by limited resources and declining populations, are structurally disadvantaged in terms of land use efficiency, particularly when measured by sealed area per capita. Moreover, this study discusses emerging policy tensions, including the narrowing conceptual gap between land take and soil sealing in the proposed EU Soil Monitoring and Resilience Directive, as well as the risk of overlooking broader land artificialisation. The findings underscore the need for context-sensitive, multi-scalar approaches to land use monitoring and governance, particularly in sparsely populated and demographically imbalanced regions, such as the Baltic states. Full article

Phubbing, a relatively new phenomenon in the field of digital risks, refers to the act of ignoring someone in favor of focusing on a smartphone during face-to-face interactions. Parental phubbing, a specific form of this behavior, is a prevalent negative parenting practice that can affect parent–child relationships and child development. However, the impact of parental phubbing on the emotional and behavioral development of preschool children remains unclear. This study aims to explore the relationship between parental phubbing and preschoolers’ behavioral problems, as well as test whether parents’ mindful attention awareness (MAA) acts as a mediator between them. Method: A questionnaire was administered to 138 Italian parents (mean age = 38.5, SD = 6.2) of 138 kindergarten preschoolers (mean age = 3.9, SD = 1.03). Questionnaires included the Generic Scale of Phubbing (GSP), the Mindful Attention Awareness Scale (MAAS), and the Strengths and Difficulties Questionnaire (SDQ). Results: Analyses revealed a significant negative correlation between the MAAS and SDQ total scores, a positive correlation between the GSP total score and the SDQ total score, and a negative correlation between the GSP total score and the MAAS total score. The mediation analysis did not show a direct effect of GSP on SDQ, suggesting that parental phubbing did not directly predict children’s behavioral difficulties. Nevertheless, the indirect effect measured by bootstrapping was significant, indicating that parental MAA fully mediated the relationship between parental phubbing and preschoolers’ problematic behaviors. Conclusions: Although further research is needed, parental mindfulness may influence phubbing behaviors in parents providing valuable insights for early interventions aimed at reducing problem behaviors in young children. Full article

To address the limitations of the existing cotton seedling growth prediction methods in field environments, specifically, poor representation of spatiotemporal features and low visual fidelity in texture rendering, this paper proposes an algorithm for the prediction of cotton seedling growth from images based on FCA-STNet. The model leverages historical sequences of cotton seedling RGB images to generate an image of the predicted growth at time t + 1 and extracts 37 phenotypic traits from the predicted image. A novel STNet structure is designed to enhance the representation of spatiotemporal dependencies, while an Adaptive Fine-Grained Channel Attention (FCA) module is integrated to capture both global and local feature information. This attention mechanism focuses on individual cotton plants and their textural characteristics, effectively reducing the interference from common field-related challenges such as insufficient lighting, leaf fluttering, and wind disturbances. The experimental results demonstrate that the predicted images achieved an MSE of 0.0086, MAE of 0.0321, SSIM of 0.8339, and PSNR of 20.7011 on the test set, representing improvements of 2.27%, 0.31%, 4.73%, and 11.20%, respectively, over the baseline STNet. The method outperforms several mainstream spatiotemporal prediction models. Furthermore, the majority of the predicted phenotypic traits exhibited correlations with actual measurements with coefficients above 0.8, indicating high prediction accuracy. The proposed FCA-STNet model enables visually realistic prediction of cotton seedling growth in open-field conditions, offering a new perspective for research in growth prediction. Full article

Sources of renewable energy have attracted considerable attention. Their expanded use will have a substantial impact on both the cost of energy production and climate change. Solar energy is one efficient and safe option; however, solar energy harvesting sites, irrespective of the location, can impact the ecosystem. This experimental study explores the energy available inside and outside of novel miniature energy harvesting cages by measuring light intensity and power generated. Varying light intensity outside the cage has been utilized to study the remaining energy inside the cage of a flexible design, where the heights of the harvesting panels are parameters. Cages are built from custom photovoltaic panels arranged in a staircase manner to provide access to growing plants. The balance between power generation and biological development is investigated. Two different structures are presented to explore the variation of illumination intensity inside the cages. The experimental results show a substantial reduction in energy inside the cages. The experimental results showed up to 24% reduction in illumination inside the cages in winter. The reduction is even larger in summer, up to 57%. The results from the models provide a framework to study the possible impact on a biological system residing inside the cages, paving the way for practical farming with sustainable energy harvesting. Full article

HIV infection and hypertensive disorders of pregnancy (HDP), particularly preeclampsia (PE) with severe features, are leading causes of maternal mortality worldwide. This study investigates the role of asymmetric dimethylarginine (ADMA) and prostacyclin (PGI2) concentrations in endothelial impairment in normotensive pregnant versus PE women within an HIV endemic setting in KwaZulu-Natal Province, South Africa. The study population (n = 84) was grouped according to pregnancy type, i.e., normotensive (n = 42) and PE (n = 42), and further stratified by HIV status. Clinical factors were maternal age, weight, blood pressure (both systolic and diastolic) levels, and gestational age. Plasma concentrations of ADMA and PGI2 were measured using the enzyme-linked immunoassay (ELISA). Differences in outcomes were analyzed using the Mann–Whitney U and Kruskal–Wallis test together with Dunn’s multiple-comparison post hoc test. The non-parametric data were presented as medians and interquartile ranges. Gravidity, gestational age, and systolic and diastolic blood pressures were significantly different across the study groups where p < 0.05 was deemed significant. Furthermore, the concentration of ADMA was significantly elevated in PE HIV-positive vs. PE HIV-negative (p = 0.0174) groups. PGI2 did not show a significant difference in PE compared to normotensive pregnancies (p = 0.8826) but was significantly different across all groups (p = 0.0212). An increase in plasma ADMA levels was observed in the preeclampsia HIV-negative group compared to the normotensive HIV-negative group. This is linked to the role played by ADMA in endothelial impairment, a characteristic of PE development. PGI2 levels were decreased in PE compared to the normotensive group regardless of HIV status. These findings draw attention to the importance of endothelial indicators in pathogenesis and possibly early prediction of PE development. Full article

Originally introduced in the 1960s by DISA Elektronik as a calibration tunnel for hot-wire anemometers, the Type 55D41 has now been reengineered into a versatile and modern aerodynamic test platform. While retaining key legacy components, such as the converging nozzle and the 55D42 power unit, the upgraded system features a redesigned modular test section with optical-grade quartz windows. This enhancement enables compatibility with advanced flow diagnostics and visualization methods, including PTV, DIC, and schlieren imaging. The modernized facility maintains the precision and flow stability that made the original design widely respected, while expanding its functionality to meet the demands of contemporary experimental research. Its architecture supports the aerodynamic characterization of micro-scale static pressure probes used in aerospace, propulsion, and micro gas turbine applications. Special attention is given to assessing the influence of probe tip geometry (e.g., conical, ogive), port positioning, and stem interference on measurement accuracy. Full article