Background The present study focuses on the unique role of insight and Trait Emotional Intelligence in the realm of art education in China. Insight, traditionally associated with clinical outcomes ...such as mastering symptoms, developing adaptive behaviors, and enhancing quality of life, is newly contextualized in this study within the framework of art education. The expansion of insight research into areas like Social Psychology reflects its relevance beyond clinical settings, particularly in educational environments where creativity and emotional intelligence are pivotal. Aim In Chinese art education, insight is crucial not just for personal growth but also for professional development, integrating the understanding of emotions, culture, and artistic expression. This study explores how Trait Emotional Intelligence fosters insight and engagement in art students, underscoring its transformative impact on their educational and professional journey in the art world. Methods Using a sample of Chinese art education students at University level ( N = 881), that answered a quantitative self-report questionnaire, statistical procedures are applied to test the relationships between Trait Emotional Intelligence, Insight orientation, and students’ academic engagement. Results In the structural model, the path from Trait Emotional Intelligence to Insight Orientation was significant, indicating a positive relationship. Students’ Engagement was significantly predicted by Insight Orientation and Trait Emotional Intelligence. Implications This finding corroborates theoretical assertions that individuals with higher emotional intelligence are more inclined to have enhanced insight. The findings of the present study extend beyond the field of Art education, allowing us to provide a broad spectrum of social implications for Higher Education institutions.
Idiopathic pulmonary fibrosis (IPF) is a prototype of lethal, chronic, progressive interstitial lung disease of unknown etiology. Over the past decade, macrophage has been recognized to play a ...significant role in IPF pathogenesis. Depending on the local microenvironments, macrophages can be polarized to either classically activated (M1) or alternatively activated (M2) phenotypes. In general, M1 macrophages are responsible for wound healing after alveolar epithelial injury, while M2 macrophages are designated to resolve wound healing processes or terminate inflammatory responses in the lung. IPF is a pathological consequence resulted from altered wound healing in response to persistent lung injury. In this review, we intend to summarize the current state of knowledge regarding the process of macrophage polarization and its mediators in the pathogenesis of pulmonary fibrosis. Our goal is to update the understanding of the mechanisms underlying the initiation and progression of IPF, and by which, we expect to provide help for developing effective therapeutic strategies in clinical settings.
Although emerging data demonstrated mortality of young COVID‐19 patients, no data have reported the risk factors of mortality for these young patients, and whether obesity is a risk for young ...COVID‐19 patients remains unknown. We conducted a retrospective study including 13 young patients who died of COVID‐19 and 40 matched survivors. Logistic regression was employed to characterize the risk factors of mortality in young obese COVID‐19 patients. Most of the young deceased COVID‐19 patients were mild cases at the time of admission, but the disease progressed rapidly featured by a higher severity of patchy shadows (100.00% vs 48.70%; P = .006), pleural thickening (61.50% vs 12.80%; P = .012), and mild pericardial effusion (76.90% vs 0.00%; P < .001). Most importantly, the deceased patients manifested higher body mass index (odds ratio OR = 1.354; 95% confidence interval CI = 1.075‐1.704; P = .010), inflammation‐related index C‐reactive protein (OR = 1.014; 95% CI = 1.003‐1.025; P = .014), cardiac injury biomarker hs‐cTnI (OR = 1.420; 95% CI = 1.112‐1.814; P = .005), and increased coagulation activity biomarker D‐dimer (OR = 418.7; P = .047), as compared with that of survivors. Our data support that obesity could be a risk factor associated with high mortality in young COVID‐19 patients, whereas aggravated inflammatory response, enhanced cardiac injury, and increased coagulation activity are likely to be the mechanisms contributing to the high mortality.
Learning plays an essential role in autonomous control systems. However, how to achieve learning in the nonstationary environment for nonlinear systems is a challenging problem. In this paper, we ...present learning method for a class of nth-order strict-feedback systems by adaptive dynamic surface control (DSC) technology, which achieves the human-like ability of learning by doing and doing with learned knowledge. To achieve the learning, this paper first proposes stable adaptive DSC with auxiliary first-order filters, which ensures the boundedness of all the signals in the closed-loop system and the convergence of tracking errors in a finite time. With the help of DSC, the derivative of the filter output variable is used as the neural network (NN) input instead of traditional intermediate variables. As a result, the proposed adaptive DSC method reduces greatly the dimension of NN inputs, especially for high-order systems. After the stable DSC design, we decompose the stable closed-loop system into a series of linear time-varying perturbed subsystems. Using a recursive design, the recurrent property of NN input variables is easily verified since the complexity is overcome using DSC. Subsequently, the partial persistent excitation condition of the radial basis function NN is satisfied. By combining a state transformation, accurate approximations of the closed-loop system dynamics are recursively achieved in a local region along recurrent orbits. Then, the learning control method using the learned knowledge is proposed to achieve the closed-loop stability and the improved control performance. Simulation studies are performed to demonstrate the proposed scheme can not only reuse the learned knowledge to achieve the better control performance with the faster tracking convergence rate and the smaller tracking error but also greatly alleviate the computational burden because of reducing the number and complexity of NN input variables.
The first enantioselective construction of a new class of axially chiral naphthyl‐indole skeletons has been established by organocatalytic asymmetric coupling reactions of 2‐naphthols with ...2‐indolylmethanols (up to 99 % yield, 97:3 e.r.). This approach not only affords a new type of axially chiral heterobiaryl backbone, but also provides a new catalytic enantioselective strategy for constructing axially chiral biaryl scaffolds by making use of the C3‐electrophilicity of 2‐indolylmethanols.
Axed: The title compounds have been accessed by organocatalytic asymmetric coupling reactions of 2‐naphthols with 2‐indolylmethanols. This approach not only affords a new type of axially chiral heterobiaryl backbone, but also provides a new catalytic enantioselective strategy for constructing axially chiral biaryl scaffolds by making use of the C3‐electrophilicity of 2‐indolylmethanols. CPA=chiral phosphoric acid, M.S.=molecular sieves.
This review covers some unique diazo compounds, the coupling partners to access various versatile functionalized nitrogen heterocycles, such as indoles, indolines, isoquinolines, isoquinolones, and ...so on. Different transition metals (such as Rh, Pd, Ru, Ir, Cu, and Co) are involved in these transformations, which involve consecutive processes: transition metal‐catalyzed cascade C–H activation/carbene insertion/intramolecular annulation.
Autonomous vehicles (AVs) have promised to drastically improve the convenience of driving by releasing the burden of drivers and reducing traffic accidents with more precise control. With the fast ...development of artificial intelligence and significant advancements of the Internet of Things technologies, we have witnessed the steady progress of autonomous driving over the recent years. As promising as it is, the march of autonomous driving technologies also faces new challenges, among which security is the top concern. In this article, we give a systematic study on the security threats surrounding autonomous driving, from the angles of perception, navigation, and control. In addition to the in-depth overview of these threats, we also summarize the corresponding defense strategies. Furthermore, we discuss future research directions about the new security threats, especially those related to deep-learning-based self-driving vehicles. By providing the security guidelines at this early stage, we aim to promote new techniques and designs related to AVs from both academia and industry and boost the development of secure autonomous driving.
In the field of empirical asset pricing, the challenges of high dimensionality, non-linear relationships, and interaction effects have led to the increasing popularity of machine learning (ML) ...methods. This study investigates the performance of ML methods when predicting different measures of stock returns from various factor models and investigates the feature importance and interaction effects among firm-specific variables and macroeconomic factors in this context. Our findings reveal that neural network models exhibit consistent performance across different stock return measures when they rely solely on firm-specific characteristic variables. However, the inclusion of macroeconomic factors from the financial market, real economic activities, and investor sentiment leads to substantial improvements in the model performance. Notably, the degree of improvement varies with the specific measures of stock returns under consideration. Furthermore, our analysis indicates that, after the inclusion of macroeconomic factors, there is a dissimilarity in model performance, variable importance, and interaction effects among macroeconomic and firm-specific variables, particularly concerning abnormal returns derived from the Fama–French three- and five-factor models compared with excess returns. This divergence is primarily attributed to the extent to which these factor models remove the variance associated with the macroeconomic variables. These findings collectively offer valuable insights into the efficacy of neural network models for stock return predictions and contribute to a deeper understanding of the intricate relationship between factor models, stock returns, and macroeconomic conditions in the domain of empirical asset pricing.
Ammonia nitrogen (ammonia or ammonium) is widely present in natural waters and can significantly affect aquatic flora and fauna. It plays an extremely important role in human production and health as ...well as ecosystem stability. Many detection methods, including spectrophotometric methods and ion-selective electrodes, have been developed for the analysis of ammonia nitrogen in water. Herein, recent developments in optical detection, electrochemical detection, and biological enzyme detection systems for aqueous ammonia nitrogen detection are reviewed. The detection principles are described, advantages and disadvantages of each method are highlighted, and reagent optimization, technological innovations, and novel sensitive materials to improve the detection limits and reduce interference are discussed in detail. For oceanographers and related researchers, this review will serve as a reference of promising detection techniques.
•The methods of ammonia nitrogen detection in water are summarized to provide a basis for selecting the most suitable method.•The main parameters assessed in the studies were summarized over the last 10 years.•The main detection methods are optical detection, electrochemical detection, and biological enzyme detection.
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