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Young adults are a high-risk population for developing smartphone addiction (SA), which bring about social issues. One theoretically and empirically supported proximal risk factor of SA is preference for smartphone-based internet applications (PSIA). However, most previous studies ignore gender difference and symptomatic heterogeneity of SA. Besides, many previous data analyses contain non-addicts, and the results derived might not be applicable to smartphone addicts. To bridging the gap, we used a symptom-level network analysis to assess gender differences in the links between preferences for 8 smartphone-based internet applications and 4 SA symptoms among young adults with high-level phone addiction (619 women and 415 men). The results showed that: (1) The relationship between the preference for video and the "loss of control" symptom was more pronounced in female addicts compared to their male counterparts. (2) Shopping app had stronger bridge centrality in women's smartphone applications-SA network, which was positively linked with more SA symptoms. (3) Our research identified marginal gender differences in smartphone addicts' psychological networks, with female addicts showing stronger links between social media/eBook preferences and withdrawal symptoms, and male addicts displaying a stronger connection between gaming/eBook and other smartphone activities. The study provides a visualized network association and network metrics for understanding the relationship between PSIA and SA. We propose adopting a selective processing hypothesis and an evolutionary psychology perspective to aid in understanding these gender differences. [ABSTRACT FROM AUTHOR]

In this study, hydrodynamic analysis of an oscillating water column (OWC) array in the presence of the variable bathymetry (seafloor depth) is performed by using a semi-analytical potential-flow solver. Within the framework of linear theory, the boundary-value problem associated with wave diffraction and radiation by an OWC array with variable bathymetry is formulated and the semi-analytical hydrodynamic model is developed using the matched eigenfunction expansion method. The fluid domain above variable bathymetry is mathematically discretized into multiple subdomains using the boundary approximation method. The Haskind relation and the energy flux conservation law are used to verify the semi-analytical model. The hydrodynamic performance of the OWC array in the presence of the ripple and coral reef bathymetries are investigated in further. Pronounced oscillations are observed for the curve of reflection coefficient Cr and hydrodynamic efficiency η for cases with ripple and coral reef bathymetries. Bragg resonance is identified as the triggering of the strong oscillations in Cr and η for case of ripple bathymetry. The primary frequency of Bragg resonance and the peaked value of Cr converge as the ripple number increases (i.e., N s > 3). Particularly, for cases with coral reef bathymetry, the wave resonance modal in open-ended basins results in a significant wave amplification at the weather side of the OWC array. [ABSTRACT FROM AUTHOR]

The accumulation of cadmium (Cd) in agricultural soils presents a significant threat to crop safety, emphasizing the critical necessity for effective monitoring and management of soil Cd levels. Despite technological advancements, accurately monitoring soil Cd concentrations using satellite hyperspectral technology remains challenging, particularly in efficiently extracting spectral information. In this study, a total of 304 soil samples were collected from agricultural soils surrounding a tungsten mine located in the Xiancha River basin, Jiangxi Province, Southern China. Leveraging hyperspectral data from the ZY1-02D satellite, this research developed a comprehensive framework that evaluates the predictive accuracy of nine spectral transformations across four modeling approaches to estimate soil Cd concentrations. The spectral transformation methods included four logarithmic and reciprocal transformations, two derivative transformations, and three baseline correction and normalization transformations. The four models utilized for predicting soil Cd were partial least squares regression (PLSR), support vector machine (SVM), bidirectional recurrent neural networks (BRNN), and random forest (RF). The results indicated that these spectral transformations markedly enhanced the absorption and reflection features of the spectral curves, accentuating key peaks and troughs. Compared to the original spectral curves, the correlation analysis between the transformed spectra and soil Cd content showed a notable improvement, particularly with derivative transformations. The combination of the first derivative (FD) transformation with the RF model yielded the highest accuracy (R2 = 0.61, RMSE = 0.37 mg/kg, MAE = 0.21 mg/kg). Furthermore, the RF model in multiple spectral transformations exhibited higher suitability for modeling soil Cd content compared to other models. Overall, this research highlights the substantial applicative potential of the ZY1-02D satellite hyperspectral data for detecting soil heavy metals and provides a framework that integrates optimal spectral transformations and modeling techniques to estimate soil Cd contents. [ABSTRACT FROM AUTHOR]

Rapid and accurate mapping of soil properties in farmlands is crucial for guiding agricultural production and maintaining food security. Traditional methods using spectral features from remote sensing prove valuable for estimating soil properties, but are restricted to short periods of bare soil occurrence within agricultural settings. Addressing the challenge of predicting soil properties under crop cover, this study proposed an improved soil modeling framework that integrates dynamic crop growth information with machine learning techniques. The methodology's robustness was tested on six key soil properties in an agricultural region of China, including soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), dissolved organic carbon (DOC), dissolved organic nitrogen (DON), and pH. Four experimental scenarios were established to assess the impact of crop growth information, represented by the normalized difference vegetation index (NDVI) and phenological parameters. Specifically, Scenario I utilized only natural factors (terrain and climate data); Scenario II added phenological parameters based on Scenario I; Scenario III incorporated time-series NDVI based on Scenario I; and Scenario IV combined all variables (traditional natural factors and crop growth information). These were evaluated using three advanced machine learning models: random forest (RF), Cubist, and Extreme Gradient Boosting (XGBoost). Results demonstrated that incorporating phenological parameters and time-series NDVI significantly improved model accuracy, enhancing predictions by up to 36% over models using only natural factors. Moreover, although both are crop growth factors, the contribution of the time-series NDVI variable to model accuracy surpassed that of the phenological variable for most soil properties. Relative importance analysis suggested that the crop growth information, derived from time-series NDVI and phenology data, collectively explained 14–45% of the spatial variation in soil properties. This study highlights the significant benefits of integrating remote sensing-based crop growth factors into soil property inversion under crop-covered conditions, providing valuable insights for digital soil mapping. [ABSTRACT FROM AUTHOR]

Collision accidents involving construction vehicles and workers frequently occur at construction sites. Computer vision (CV) technology presents an efficient solution for collision-risk pre-warning. However, CV-based methods are still relatively rare and need an enhancement of their performance. Therefore, a novel three-stage collision-risk pre-warning model for construction vehicles and workers is proposed in this paper. This model consists of an object-sensing module (OSM), a trajectory prediction module (TPM), and a collision-risk assessment module (CRAM). In the OSM, the YOLOv5 algorithm is applied to identify and locate construction vehicles and workers; meanwhile, the DeepSORT algorithm is applied to the real-time tracking of the construction vehicles and workers. As a result, the historical trajectories of vehicles and workers are sensed. The original coordinates of the data are transformed to common real-world coordinate systems for convenient subsequent data acquisition, comparison, and analysis. Subsequently, the data are provided to a second stage (TPM). In the TPM, the optimized transformer algorithm is used for a real-time trajectory prediction of the construction vehicles and workers. In this paper, we enhance the reliability of the general object detection and trajectory prediction methods in the construction environments. With the assistance afforded by the optimization of the model's hyperparameters, the prediction horizon is extended, and this gives the workers more time to take preventive measures. Finally, the prediction module indicates the possible trajectories of the vehicles and workers in the future and provides these trajectories to the CRAM. In the CRAM, the worker's collision-risk level is assessed by a multi-factor-based collision-risk assessment rule, which is innovatively proposed in the present work. The multi-factor-based assessment rule is quantitatively involved in three critical risk factors, i.e., velocity, hazardous zones, and proximity. Experiments are performed within two different construction site scenarios to evaluate the effectiveness of the collision-risk pre-warning model. The research results show that the proposed collision pre-warning model can accurately predict the collision-risk level of workers at construction sites, with good tracking and predicting effect and an efficient collision-risk pre-warning strategy. Compared to the classical models, such as social-GAN and social-LSTM, the transformer-based trajectory prediction model demonstrates a superior accuracy, with an average displacement error of 0.53 m on the construction sites. Additionally, the optimized transformer model is capable of predicting six additional time steps, which equates to approximately 1.8 s. The collision pre-warning model proposed in this paper can help improve the safety of construction vehicles and workers. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to assess the relative growth rates (RGRs) of the maxilla and mandible at varying fusion stages of the spheno‐occipital synchondrosis (SOS), thereby elucidating the potential of SOS stages in predicting maxillomandibular growth. Materials and Methods: A total of 320 subjects (171 boys and 149 girls), aged 6 to 18 years, were retrospectively included. Each subject had a minimum of two longitudinal cone‐beam computed tomography (CBCT) images, with no more than one interval of SOS fusion stage change between the two scans. Subjects were categorized based on their SOS fusion stages and genders. The RGRs of the maxilla and mandible at various SOS fusion stages were measured and compared using longitudinal CBCT images. Results: Significant statistical differences were observed in maxillomandibular RGRs across various SOS fusion stages. In girls, the sagittal growth of the maxilla remained stable and active until SOS 3, subsequently exhibited deceleration in SOS 4–5 (compared to SOS 3–4, P <.05) and continued to decrease in SOS 5–6. Whereas in boys, the sagittal growth of the maxilla remained stable until SOS 4, and a deceleration trend emerged starting from SOS 5 to 6 (P <.01 compared to SOS 4–5). Mandibular growth patterns in both genders exhibited a progression of increasing‐accelerating‐decelerating rates from SOS 2 to 6. The highest RGRs for total mandibular length were observed in SOS 3–4 and SOS 4–5. Conclusion: Spheno‐occipital synchondrosis fusion stages can serve as a valid indicator of maxillomandibular growth maturation. [ABSTRACT FROM AUTHOR]