Gender Differences in Depressive Traits among Rural and Urban Chinese Adolescent Students: Secondary Data Analysis of Nationwide Survey CFPS

Many previous studies have indicated that urban adolescents show a higher level of mental health in China compared to rural adolescents. Specifically, girls in rural areas represented a high-risk group prior to the 21st century, demonstrating more suicidal behaviour and ideation than those in the urban areas because of the severe gender inequality in rural China. However, because of the urbanisation process and centralised policy to eliminate gender inequality in recent decades, the regional and gender differences in mental health might decrease. This research aimed to probe the gender and regional differences in depressive traits among adolescent students currently in China. We adopted the national survey dataset Chinese Family Panel Studies (CFPS) conducted in 2018. Accordingly, 2173 observations from 10–15-year-old subjects were included. CFPS utilised an eight-item questionnaire to screen individuals’ depressive traits. Two dimensions of depressive traits were confirmed by CFA, namely depressed affect and anhedonia. The measurement invariance tests suggested that the two-factor model was applicable for both males and females and rural and urban students. Based on the extracted values from the CFA model, MANOVA results revealed that, compared to boys, girls experienced more depressed affect. Moreover, rural students demonstrated more anhedonia symptoms. There was no interaction between gender and region. The results suggest that, even though the gender and regional differences are small, being a female and coming from a rural area are still potential risk factors for developing depressive traits among adolescent students in China

Deep Reinforcement Learning-Assisted Federated Learning for Robust Short-term Utility Demand Forecasting in Electricity Wholesale Markets

Short-term load forecasting (STLF) plays a significant role in the operation of electricity trading markets. Considering the growing concern of data privacy, federated learning (FL) is increasingly adopted to train STLF models for utility companies (UCs) in recent research. Inspiringly, in wholesale markets, as it is not realistic for power plants (PPs) to access UCs' data directly, FL is definitely a feasible solution of obtaining an accurate STLF model for PPs. However, due to FL's distributed nature and intense competition among UCs, defects increasingly occur and lead to poor performance of the STLF model, indicating that simply adopting FL is not enough. In this paper, we propose a DRL-assisted FL approach, DEfect-AwaRe federated soft actor-critic (DearFSAC), to robustly train an accurate STLF model for PPs to forecast precise short-term utility electricity demand. Firstly. we design a STLF model based on long short-term memory (LSTM) using just historical load data and time data. Furthermore, considering the uncertainty of defects occurrence, a deep reinforcement learning (DRL) algorithm is adopted to assist FL by alleviating model degradation caused by defects. In addition, for faster convergence of FL training, an auto-encoder is designed for both dimension reduction and quality evaluation of uploaded models. In the simulations, we validate our approach on real data of Helsinki's UCs in 2019. The results show that DearFSAC outperforms all the other approaches no matter if defects occur or not

A Dimension-Augmented Physics-Informed Neural Network (DaPINN) with High Level Accuracy and Efficiency

Physics-informed neural networks (PINNs) have been widely applied in different fields due to their effectiveness in solving partial differential equations (PDEs). However, the accuracy and efficiency of PINNs need to be considerably improved for scientific and commercial use. To address this issue, we systematically propose a novel dimension-augmented physics-informed neural network (DaPINN), which simultaneously and significantly improves the accuracy and efficiency of the PINN. In the DaPINN model, we introduce inductive bias in the neural network to enhance network generalizability by adding a special regularization term to the loss function. Furthermore, we manipulate the network input dimension by inserting additional sample features and incorporating the expanded dimensionality in the loss function. Moreover, we verify the effectiveness of power series augmentation, Fourier series augmentation and replica augmentation, in both forward and backward problems. In most experiments, the error of DaPINN is 1$\sim$2 orders of magnitude lower than that of PINN. The results show that the DaPINN outperforms the original PINN in terms of both accuracy and efficiency with a reduced dependence on the number of sample points. We also discuss the complexity of the DaPINN and its compatibility with other methods.Comment: 33 pages, 12 figure

New exploration of creativity: Cross-validation analysis of the factors influencing multiteam digital creativity in the transition phase

Multiteam digital creativity (MTDC) is a new domain of creativity study that fits the new developments of the digital era, thus scholars have called for exploring MTDC in the fine-graining phase. This paper responds to this call, and adopts two studies and cross-validation analysis to explore the theoretical framework of the impact factors of MTDC in the transition phase. Study 1 adopts the qualitative analysis method of rooted theory to explore a more comprehensive impact factor and to maximize the new theory’s saturation. Study 2 adopts the CL-WG DEMATEL method, one analysis method of group decision-making and optimized concept lattice, which could cross-validation analyze the results of Study 1 and further determine the importance of the factors. The results of the studies indicate that the influencing factors of MTDC are multilevel, and the factors such as the organizational digital climate, team psychological empowerment, individual digital cognition and emotion, and leadership competence have greater impacts on MTDC. This indicates that the transition phase has a unique internal mechanism. This paper constructs a theoretical framework of factors influencing MTDC in the transition phase and provides new theoretical and practical references for how organizations could fully stimulate MTDC in the digital era. In addition, the cross-validated analytical method further enriches the study tools in the domain of organizational behavior

BAGEL: Backdoor Attacks against Federated Contrastive Learning

Federated Contrastive Learning (FCL) is an emerging privacy-preserving paradigm in distributed learning for unlabeled data. In FCL, distributed parties collaboratively learn a global encoder with unlabeled data, and the global encoder could be widely used as a feature extractor to build models for many downstream tasks. However, FCL is also vulnerable to many security threats (e.g., backdoor attacks) due to its distributed nature, which are seldom investigated in existing solutions. In this paper, we study the backdoor attack against FCL as a pioneer research, to illustrate how backdoor attacks on distributed local clients act on downstream tasks. Specifically, in our system, malicious clients can successfully inject a backdoor into the global encoder by uploading poisoned local updates, thus downstream models built with this global encoder will also inherit the backdoor. We also investigate how to inject backdoors into multiple downstream models, in terms of two different backdoor attacks, namely the \textit{centralized attack} and the \textit{decentralized attack}. Experiment results show that both the centralized and the decentralized attacks can inject backdoors into downstream models effectively with high attack success rates. Finally, we evaluate two defense methods against our proposed backdoor attacks in FCL, which indicates that the decentralized backdoor attack is more stealthy and harder to defend

A Highly Sensitive Intensity-Modulated Optical Fiber Magnetic Field Sensor Based on the Magnetic Fluid and Multimode Interference

Fiber-optic magnetic field sensing is an important method of magnetic field monitoring, which is essential for the safety of civil infrastructures, especially for power plant. We theoretically and experimentally demonstrated an optical fiber magnetic field sensor based on a single-mode-multimode-single-mode (SMS) structure immersed into the magnetic fluid (MF). The length of multimode section fiber is determined based on the self-image effect through the simulation. Due to variation characteristics of the refractive index and absorption coefficient of MF under different magnetic fields, an effective method to improve the sensitivity of SMS fiber structure is realized based on the intensity modulation method. This sensor shows a high sensitivity up to 0.097 dB/Oe and a high modulation depth up to 78% in a relatively linear range, for the no-core fiber (NCF) with the diameter of 125 μm and length of 59.8 mm as the multimode section. This optical fiber sensor possesses advantages of low cost, ease of fabrication, high sensitivity, simple structure, and compact size, with great potential applications in measuring the magnetic field

An Edge-Preserved Image Denoising Algorithm Based on Local Adaptive Regularization

Image denoising methods are often based on the minimization of an appropriately defined energy function. Many gradient dependent energy functions, such as Potts model and total variation denoising, regard image as piecewise constant function. In these methods, some important information such as edge sharpness and location is well preserved, but some detailed image feature like texture is often compromised in the process of denoising. For this reason, an image denoising method based on local adaptive regularization is proposed in this paper, which can adaptively adjust denoising degree of noisy image by adding spatial variable fidelity term, so as to better preserve fine scale features of image. Experimental results show that the proposed denoising method can achieve state-of-the-art subjective visual effect, and the signal-noise-ratio (SNR) is also objectively improved by 0.3–0.6 dB

Evolution of Publications, Subjects, and Co-authorships in Network-On-Chip Research From a Complex Network Perspective

The academia and industry have been pursuing network-on-chip (NoC) related research since two decades ago when there was an urgency to respond to the scaling and technological challenges imposed on intra-chip communication in SoC designs. Like any other research topic, NoC inevitably goes through its life cycle: A. it started up (2000-2007) and quickly gained traction in its own right; B. it then entered the phase of growth and shakeout (2008-2013) with the research outcomes peaked in 2010 and remained high for another four/five years; C. NoC research was considered mature and stable (2014-2020), with signs showing a steady slowdown. Although from time to time, excellent survey articles on different subjects/aspects of NoC appeared in the open literature, yet there is no general consensus on where we are in this NoC roadmap and where we are heading, largely due to lack of an overarching methodology and tool to assess and quantify the research outcomes and evolution. In this paper, we address this issue from the perspective of three specific complex networks, namely the citation network, the subject citation network, and the co-authorship network. The network structure parameters (e.g., modularity, diameter, etc.) and graph dynamics of the three networks are extracted and analyzed, which helps reveal and explain the reasons and the driving forces behind all the changes observed in NoC research over 20 years. Additional analyses are performed in this study to link interesting phenomena surrounding the NoC area. They include: (1) relationships between communities in citation networks and NoC subjects, (2) measure and visualization of a subject\u27s influence score and its evolution, (3) knowledge flow among the six most popular NoC subjects and their relationships, (4) evolution of various subjects in terms of number of publications, (5) collaboration patterns and cross-community collaboration among the authors in NoC research, (6) interesting observation of career lifetime and productivity among NoC researchers, and finally (7) investigation of whether or not new authors are chasing hot subjects in NoC. All these analyses have led to a prediction of publications, subjects, and co-authorship in NoC research in the near future, which is also presented in the paper

Association of triglyceride-glucose index with the prevalence of cardiovascular disease in malnourished/non-malnourished patients: a large cross-sectional study

BackgroundNumerous investigations have demonstrated a strong association between the TyG (triglyceride-glucose) index, which is derived from lipid and glucose levels in the bloodstream, and the onset and progression of cardiovascular diseases (CVD). Blood glucose and blood lipids are affected by nutritional status, and few studies have explored whether the correlation between TyG index and the risk of CVD is affected by nutritional status.AimsTo investigate the connection between TyG index and the risk of CVD among individuals with varying nutritional statuses.MethodA total of 19,847 were included in the analysis, of which 15,955 participants were non-malnourished and 3,892 patients were malnourished. According to the TyG index quartile, the patients were categorized into four groups. Logistic regression analysis and restricted cubic spline was used to study the relationship between TyG index and the risk of CVD in normal and malnourished populations.ResultsThe results of the restricted cubic spline showed that the TyG index was positively associated with the risk of CVD in the non-malnourished population. The TyG index showed a U-shaped association with the risk of CVD in malnourished people. The result is consistent with that of logistic regression (Malnutrition: Group 2: OR: 1.14; 95% CI: 0.85–1.53; Group 3: OR: 1.36; 95% CI: 1.03–1.79; Group 4: OR: 1.72; 95% CI:1.31–2.25, P for trend <0.001; Non-malnutrition: Group 2: OR: 0.82; 95% CI: 0.46–1.48; Group 3: OR: 0.88; 95% CI: 0.49–1.57; Group 4: OR: 1.45; 95% CI:0.83–2.52, P for trend =0.067).ConclusionsThe association between the TyG index and the risk of CVD varied depending on the nutritional states. When using TyG index to assess the risk of CVD, stratification combined with nutritional status helps to more accurately screen patients at high risk of CVD