The relationship between childhood trauma and Internet gaming disorder among college students: A structural equation model

open access journalBackground The aim of this study was to investigate the mechanisms of Internet gaming disorder (IGD) and the associated interaction effects of childhood trauma, depression and anxiety in college students. Methods Participants were enrolled full-time as freshmen at a University in the Hunan province, China. All participants reported their socio-demographic characteristics and undertook a standardized assessment on childhood trauma, anxiety, depression and IGD. The effect of childhood trauma on university students' internet gaming behaviour mediated by anxiety and depression was analysed using structural equation modelling (SEM) using R 3.6.1. Results In total, 922 freshmen participated in the study, with an approximately even male-to-female ratio. A mediation model with anxiety and depression as the mediators between childhood trauma and internet gaming behaviour allowing anxiety and depression to be correlated was tested using SEM. The SEM analysis revealed that a standardised total effect of childhood trauma on Internet gaming was 0.18, (Z = 5.60, 95% CI [0.02, 0.05], P < 0.001), with the direct effects of childhood trauma on Internet gaming being 0.11 (Z = 3.41, 95% CI [0.01, 0.03], P = 0.001), and the indirect effects being 0.02 (Z = 2.32, 95% CI [0.00, 0.01], P = 0.020) in the pathway of childhood trauma-depression-internet gaming; and 0.05 (Z = 3.67, 95% CI [0.00, 0.02], P < 0.001) in the pathway of childhood trauma-anxiety-Internet gaming. In addition, the two mediators anxiety and depression were significantly correlated (r = 0.50, Z = 13.54, 95% CI [3.50, 5.05], P < 0.001). Conclusions The study revealed that childhood trauma had a significant impact on adolescents' Internet gaming behaviours among college students. Anxiety and depression both significantly mediated the relationship between childhood trauma and internet gaming and augmented its negative influence. Discussion of the need to understand the subtypes of childhood traumatic experience in relationship to addictive behaviours is included

CloudBrain-MRS: An Intelligent Cloud Computing Platform for in vivo Magnetic Resonance Spectroscopy Preprocessing, Quantification, and Analysis

Magnetic resonance spectroscopy (MRS) is an important clinical imaging method for diagnosis of diseases. MRS spectrum is used to observe the signal intensity of metabolites or further infer their concentrations. Although the magnetic resonance vendors commonly provide basic functions of spectra plots and metabolite quantification, the widespread clinical research of MRS is still limited due to the lack of easy-to-use processing software or platform. To address this issue, we have developed CloudBrain-MRS, a cloud-based online platform that provides powerful hardware and advanced algorithms. The platform can be accessed simply through a web browser, without the need of any program installation on the user side. CloudBrain-MRS also integrates the classic LCModel and advanced artificial intelligence algorithms and supports batch preprocessing, quantification, and analysis of MRS data from different vendors. Additionally, the platform offers useful functions: 1) Automatically statistical analysis to find biomarkers for diseases; 2) Consistency verification between the classic and artificial intelligence quantification algorithms; 3) Colorful three-dimensional visualization for easy observation of individual metabolite spectrum. Last, both healthy and mild cognitive impairment patient data are used to demonstrate the functions of the platform. To the best of our knowledge, this is the first cloud computing platform for in vivo MRS with artificial intelligence processing. We have shared our cloud platform at MRSHub, providing free access and service for two years. Please visit https://mrshub.org/software_all/#CloudBrain-MRS or https://csrc.xmu.edu.cn/CloudBrain.html.Comment: 11 pages, 12 figure

A Novel Order Analysis and Stacked Sparse Auto-Encoder Feature Learning Method for Milling Tool Wear Condition Monitoring

Milling is a main processing mode of the modern manufacturing industry, which seriously affects the quality and precision of the machined workpiece. However, it is difficult to monitor the tool wear condition in the continuous cutting process, especially under a variable speed condition. The existing tool wear condition monitoring methods only carry out analysis with a constant engine speed. Different from the general monitoring methods, this paper put forward a milling cutter wear condition monitoring method based on order analysis (OA) and stacked sparse autoencoder (SSAE). The methodology in the research include signals feature extraction and tool wear state monitoring and were designed to analyze the three-phase spindle current signals instead of the traditional force signals and vibration signals. The variable speed signals were transformed into angle domain stationary signals by order analysis, and the SSAE neural network was used to monitor the tool wear state. The proposed method was verified on the laboratory signals and the results showed a better performance than the other methods and a better applicability in actual industrial manufacturing

Temporal-spatial variation and regulatory mechanism of carbon budgets in territorial space through the lens of carbon balance: A case of the middle reaches of the Yangtze River urban agglomerations, China

As China’s largest cross-regional urban agglomerations, the middle reaches of the Yangtze River urban agglomerations (MRYRUA) possess both significant societal carbon source volume and ecological carbon sequestration capacity. Nevertheless, with the uncontrolled expansion of urban energy consumption activities and the industry migration from eastern coastal regions to inland cities, the carbon budget pattern of territorial space is increasingly unbalanced in the MRYRUA. To achieve low-carbon regulation, this study utilized land use and energy consumption data from 31 cities within the MRYRUA to establish a “carbon source-carbon sink” quantification and spatiotemporal exploration model, revealing the spatial-temporal variation of carbon budgets from 2005 to 2020. Furthermore, we developed a carbon balance indicator analysis system by employing the carbon offset rate (COR), carbon productivity (CP), Gini coefficient, ecological support coefficient (ESC), economic contribution coefficient (ECC), and functional zoning was performed. Finally, using the GM (1,1) model, we derived the carbon budget pattern for 2050 and explored the differentiated regulatory mechanisms under the carbon balance perspective. The results indicated that: (1) The MRYRUA’s territorial carbon budgets have increased annually, displaying a spatial distribution pattern with the highest values in the central region, followed by the northwest, and the lowest in the southeast near water bodies. The spatiotemporal differentiation effects manifest as an east–west axial development trend, with spatiotemporal clustering effects demonstrating a propensity for outward dispersion from the northern hot spot radiation core. (2) The MRYRUA’s COR has consistently remained below 10% and decreased annually, while the CP has shown a yearly increase at an accelerating rate. The ESC and ECC exhibit evident spatial heterogeneity among cities. In response to the carbon emission economic benefits and carbon sequestration ecological carrying capacity reflected by carbon balance indicators, each city was classified into low-carbon economic zones, carbon intensity control zones, carbon sink functional zones, and high-carbon optimization zones. (3) From 2020 to 2050, the polarization trend of the carbon budget pattern continues to intensify. Subsequently, we have established a differentiated territorial spatial carbon balance regulatory mechanism. This mechanism strengthens the leading role of low-carbon economic zones in the green low-carbon transition, moderately retains the carbon sink functional zones in the southeast with solid carbon fixation capabilities, and promotes the transition of the northern carbon intensity control zones and high-carbon optimization zones to low-carbon economic zones. The research findings provide a scientific basis for formulating territorial spatial planning policies from a carbon neutrality perspective

A Reliable Prognosis Approach for Degradation Evaluation of Rolling Bearing Using MCLSTM

Prognostics and health management technology (PHM), a measure to ensure the reliability and safety of the operation of industrial machinery, has attracted attention and application adequately. However, how to use the monitored information to evaluate the degradation of rolling bearings is a significant issue for its predictive maintenance and autonomic logistics. This work presents a reliable health prognosis approach to estimate the health indicator (HI) and remaining useful life (RUL) of rolling bearings. Firstly, to accurately capture the degradation process, a novel health index (HI) is constructed based on correlation kurtosis for different iteration periods and a Gaussian process latency variable model (GPLVM). Then, a multiple convolutional long short-term memory (MCLSTM) network is proposed to predict HI values and RUL values. Finally, we perform experimental datasets of rolling bearings, demonstrating that the presented method surpasses other state-of-the-art prognosis approaches. The results also confirm the feasibility of the presented method in industrial machinery

Identification and Characterization of Wall-Associated Kinase (WAK) and WAK-like (WAKL) Gene Family in Juglans regia and Its Wild Related Species Juglans mandshurica

Wall-associated kinase (WAK) and WAK-like kinase (WAKL) are receptor-like kinases (RLKs), which play important roles in signal transduction between the cell wall and the cytoplasm in plants. WAK/WAKLs have been studied in many plants, but were rarely studied in the important economic walnut tree. In this study, 27 and 14 WAK/WAKL genes were identified in Juglans regia and its wild related species Juglans mandshurica, respectively. We found tandem duplication might play a critical role in the expansion of WAK/WAKL gene family in J. regia, and most of the WAK/WAKL homologous pairs underwent purified selection during evolution. All WAK/WAKL proteins have the extracellular WAK domain and the cytoplasmic protein kinase domain, and the latter was more conserved than the former. Cis-acting elements analysis showed that WAK/WAKL might be involved in plant growth and development, plant response to abiotic stress and hormones. Gene expression pattern analysis further indicated that most WAK/WAKL genes in J. regia might play a role in the development of leaves and be involved in plant response to biotic stress. Our study provides a new perspective for the evolutionary analysis of gene families in tree species and also provides potential candidate genes for studying WAK/WAKL gene function in walnuts

Intrusion Detection Model for Internet of Vehicles Using GRIPCA and OWELM

With the rapid development of the Internet of Vehicles, a large amount of vehicle network data is being generated. The large amount of data presents network communication security challenges. Although intrusion detection technology can assist in safeguarding the system from malicious attacks, the substantial data generated within the vehicle network poses time-consuming detection challenges. Thus, we propose an intrusion detection model for the Internet of Vehicles, utilizing Gaussian random incremental principal component analysis (GRIPCA) and optimal weighted extreme learning machine (OWELM). First, we utilize GRIPCA to reduce data redundancy by projecting high-dimensional data into a low-dimensional space, thus reducing storage costs. Then, we utilize the dynamic inertia weight particle swarm optimization (DPSO) to optimize the parameters of the weighted extreme learning machine (WELM) to achieve the best performance. We utilize the NSL-KDD and CIC-IDS-2017 datasets to perform experiments and compare the results with other techniques. The experimental results show the excellence of the proposed model, achieving an accuracy rate of 91.02&#x0025; on the NSL KDD dataset and 94.67&#x0025; on the CIC-IDS-2017 dataset

Design, synthesis and anti-inflammatory activity study of lansiumamide analogues for treatment of acute lung injury

Acute lung injury (ALI) is an inflammation-mediated respiratory disease with a high mortality rate. Medications with anti-inflammatory small molecules have been demonstrated in phase I and II clinical trials to considerably reduce the ALI mortality. In this study, two series of lansiumamide analogues were designed, synthesized, and evaluated for anti-inflammatory activity for ALI treatment. We found that compound 8n exhibited the best anti-inflammatory activity through inhibiting LPS-induced expression of the proinflammatory cytokines interleukin-6 (IL-6) and interleukin-1β (IL-1β) in Raw264.7 cells and activating the Nrf2/HO-1 pathway. Furthermore, we discovered in a LPS-induced ALI mice model that compound 8n significantly reduced the infiltration of inflammatory cells into lung tissue to achieve the effect of protecting lung tissues and improving ALI. Additionally, our mice model study revealed that compound 8n had a good expectorant effect. These results consistently support that lansiumamide analogue 8n represents a new class of anti-inflammatory agents with potential as a lead compound for further development into a therapeutic drug for ALI treatment