Nanjing University of Science and Technology ZiJin College

Background Very little research has been done to simultaneously investigate the effects of Tai Chi (TC) on depression and heart rate variability (HRV). This study, therefore, attempted to explore the effects of TC on depression and on HRV parameters. Methods Sixty older individuals with depression score of 10 or above (the Geriatric Depression Scale, GDS) were randomly assigned into two groups: TC (n = 30) and control group (n = 30). Participants in the experimental group participated in a 24-week TC training program (three 60-min sessions per week), whereas individuals in the control group maintained their unaltered lifestyle. Depression and HRV were measured using the GDS and digital electrocardiogram at baseline and after the 24-week intervention. Results The TC had produced significant positive chances in depression and some HRV parameters (mean heart rate, RMSSD, HF, LFnorm, and HFnorm) (p < 0.05), whereas these positive results were not observed in the control group. Conclusions The results of this study indicated that TC may alleviate depression of the elderly through modulating autonomous nervous system or HRV parameters. This study adds to a growing body of research showing that TC may be effective in treating depression of the elderly. Tai Chi as a mild to moderate mind-body exercise is suitable for older individuals who suffer from depression.

With the advance of deep learning, the performance of single image super-resolution (SR) has been notably improved by convolution neural network (CNN)-based methods. However, the increasing depth of CNNs makes them more difficult to train, which hinders the SR networks from achieving greater success. To overcome this, a wide range of related mechanisms has been introduced into the SR networks recently, with the aim of helping them converge more quickly and perform better. This has resulted in many research papers that incorporated a variety of attention mechanisms into the above SR baseline from different perspectives. Thus, this survey focuses on this topic and provides a review of these recently published works by grouping them into three major categories: channel attention, spatial attention, and non-local attention. For each of the groups in the taxonomy, the basic concepts are first explained, and then we delve deep into the detailed insights and contributions. Finally, we conclude this review by highlighting the bottlenecks of the current SR attention mechanisms, and propose a new perspective that can be viewed as a potential way to make a breakthrough.

Abstract Deep learning techniques and deep networks have recently been extensively studied and widely applied to single image super‐resolution (SR). Among them, channel attention has garnered the most focus owing to its significant boost in the presentational power of a convolutional neural network. However, the original channel attention neglects the critical importance of the positional information, thus imposing performance limitations. Here, a novel perspective, namely, a coordinate attention mechanism, is explored to alleviate the aforementioned problem, and accordingly result in an enhanced SR performance. Specifically, a deep residual coordinate attention SR network (COSR) is proposed, which mainly incorporates the presented coordinate attention blocks into a deep nested residual structure. The coordinate attention captures the positional information by computing the average value vector from the two spatial directions, thus aggregating the features in different coordinates. The nested residual blocks pass low‐frequency information from the top to the end through the skip connection lines, allowing convolution filters to concentrate more on high‐frequency textures and edges, thereby reducing the difficulty of reconstruction. Extensive experiments demonstrate that our proposed COSR achieves a better performance and exceeds many state‐of‐the‐art SR methods in terms of both quantitative metrics and visual quality.

Entrepreneurship plays an active role in promoting economic and population integration and social mobility. To further promote economic and social development, the Chinese government and universities have launched entrepreneurship education courses and encouraged college students to participate in entrepreneurship competitions to enhance their entrepreneurial knowledge, entrepreneurial ability and entrepreneurial intention. However, the entrepreneurial intention of Chinese college students is still not high. Therefore, a question arises: How should entrepreneurial education be carried out? Can entrepreneurial competitions and entrepreneurial self-efficacy be an effective medium in augmenting entrepreneurial education on entrepreneurial intention? Is family income an effective moderator affecting college students’ entrepreneurial intention? To answer these questions, this study used quantitative methods to collect 351 sample data points, and a theoretical model was constructed to explain the mechanism forming entrepreneurial education and entrepreneurial intention. The results show that entrepreneurial self-efficacy plays a partial mediating role between entrepreneurial education and entrepreneurial intention, entrepreneurial competition and entrepreneurial self-efficacy play a chain mediating role and family income positively moderates the relationship between entrepreneurial education and entrepreneurial intention. The contribution of this study is to reveal the black box of the formation mechanism in college students’ entrepreneurial intentions, affirms the role of the Chinese government in promoting entrepreneurial competitions and provides empirical evidence for the effective development of entrepreneurial practise activities, as well as theoretical references for entrepreneurial policy makers.

The symmetric MEMS gyroscope is a typical representative of inertial navigation sensors in recent years. It is different from the traditional mechanical rotor gyroscope in that it structurally discards the high-speed rotor and other moving parts to extend the service life and significantly improve accuracy. The highest accuracy is achieved when the ideal mode-matching state is realized. Due to the processing limitation, this index cannot be achieved, and we can only explore ways to approach this index continuously. This paper's results of error suppression for the symmetric MEMS gyroscope are initially classified into three categories. The first category mainly introduces the processing structure and working mode of the symmetrical gyroscope. The second is mechanical tuning from the structure and the third is electrostatic tuning from the peripheral control circuit. Based on the listed results, the paper compares the two tuning modes and analyzes their advantages and disadvantages. The fourth category is the tuning means incorporating the emerging algorithm. On this basis, the elements of improvement for future high-precision symmetric MEMS gyroscopes are envisioned to provide a part of the theoretical reference for the future development direction of sensors in inertial navigation.

This paper investigates the front-wheel differential steering system (DSS) for a four-wheel independent-drive (4WID) electric vehicle (EV) with a steer-by-wire (SBW) system in case of the steering failure. The nonlinear dynamic model of differential steering vehicle (DSV) is established and the stability regions at different adhesion coefficients are determined based on the theory of phase plane. The traditional front-wheel steering vehicle is selected as the reference model. The direct yaw moment control (DYC) aiming to restore the vehicle to the stability region, and the DSS control aiming to achieve the normal steering function based on the theory of model reference sliding mode control, are researched and applied to the nonlinear dynamic model successively. The direct yaw moment and differential driving torque of the front-wheel needed for the vehicle stability and DSS are obtained respectively. The simulation results show that the proposed integrated control can simultaneously ensure the differential steering and vehicle stability of the nonlinear vehicle on different adhesion coefficient roads.

Vehicle automatic driving technology can effectively improve the safety performance of vehicle driving. This research is aimed at the needs of vehicle automatic driving. Combined with vehicle perception technology, a better target recognition algorithm is proposed. By comparing the recognition and recall rate of ION algorithm, HYPERNET algorithm, R-CNN (Regions with CNN features) algorithm, and multi-strategy region proposal network (MSRPN) algorithm, it can be seen that MSPRN algorithm has better algorithm performance and is suitable for target detection and recognition in vehicle automatic driving. Received: 5 November 2021 | Revised: 12 December 2021 | Accepted: 10 January 2022 Conflicts of Interest The author declares that he has no conflicts of interest to this work.

In the context of large-scale fire areas and complex forest environments, the task of identifying the subtle features and aspects of fire can pose a significant challenge for the deep learning model. As a result, to enhance the model’s ability to represent features and its precision in detection, this study initially introduces ConvNeXtV2 and Conv2Former to the You Only Look Once version 7 (YOLOv7) algorithm, separately, and then compares the results with the original YOLOv7 algorithm through experiments. After comprehensive comparison, the proposed ConvNeXtV2-YOLOv7 based on ConvNeXtV2 exhibits a superior performance in detecting forest fires. Additionally, in order to further focus the network on the crucial information in the task of detecting forest fires and minimize irrelevant background interference, the efficient layer aggregation network (ELAN) structure in the backbone network is enhanced by adding four attention mechanisms: the normalization-based attention module (NAM), simple attention mechanism (SimAM), global attention mechanism (GAM), and convolutional block attention module (CBAM). The experimental results, which demonstrate the suitability of ELAN combined with the CBAM module for forest fire detection, lead to the proposal of a new method for forest fire detection called CNTCB-YOLOv7. The CNTCB-YOLOv7 algorithm outperforms the YOLOv7 algorithm, with an increase in accuracy of 2.39%, recall rate of 0.73%, and average precision (AP) of 1.14%.

The urban metro system accommodates significant travel demand and alleviates traffic congestion. Improving metro operational efficiency can increase the metro operator revenue and promote the development of robust urban transportation. To achieve this goal, passenger flow forecasting is a crucial and well-recognized task in metro operation. However, passenger flow forecasting is a challenging task as there exist many unquantifiable factors in resident travel. To address this problem, we propose an innovative model named Interaction Graph Network (IG-Net) to perform passenger flow forecasting at the station level, capable of capturing the non-Euclidean relationships between stations. Three kinds of inter-station interaction graphs are developed to model these inter-station interactions: connectivity, similarity, and temporal correlation graphs. Moreover, we apply multiple channels of graph convolutional neural networks to capture interaction representations and develop a multi-task learning architecture across multiple stations. The proposed IG-Net achieved better performance than the benchmark models when forecasting passenger flow over multiple stations, based on experiments with the Suzhou metro. Finally, we identify the significant effects of interaction graph combinations and multi-task loss functions via further experimentation.

In the process of classifying fresh-cut flowers, the classification accuracy of the algorithm plays a vital role in the control of quality stability, uniformity, and price of fresh-cut flowers, while the classification speed of an algorithm determines the possibility of industrial application. Currently, research on fresh-cut flower classification focuses on the breakthrough of classification accuracy, ignoring the real-time processing speed of the terminal, which seriously affects the use of fresh-cut flower online classification technology. In this study, RGB images and depth information data for 434 rose flowers were collected using a binocular stereo depth camera. Combined with the actual production line classification environment, a set of data argumentation solutions was developed under the condition of limited samples. The architecture was established and optimized based on the ShuffleNet V2 network backbone unit, transfer learning was performed, and an appropriate attention mechanism was invoked to classify flowers of five specifications. The experimental results showed that the proposed network structure had a competitive advantage in terms of parameter quantity, classification speed, and accuracy compared with traditional networks without an attention mechanism and other lightweight networks. The classification accuracy on the 3-channel (RGB channel) flower dataset and the 4-channel (RGB and depth channel) flower datasets were 98.891% and 99.915%, respectively, and the overall prediction classification speed can reach 0.020 seconds per flower. Compared to the fresh-cut flower classification machines currently on the market, the speed of the proposed method has a great advantage. These advantages are of great significance for the design and development of fresh-cut flower classification and grading systems, and the proposed method is instructive for the identification and application of multichannel data in the future.

To meet evolving humidity monitoring needs, the development of flexible, high-performance humidity sensors is crucial. This study introduces an innovative flexible humidity sensor using a single-step laser scribing technique to fabricate a flexible in situ Co3O4 nanoparticle-embedded laser-induced graphene (Co3O4–LIG) composite electrode. Compared to conventional LIG electrodes, the Co3O4–LIG electrode exhibits improved conductivity and hydrophilicity, enhancing charge transfer and water molecule affinity. The unique two-dimensional structure and exceptional water permeability of graphene oxide (GO) combine with the rapid water response and high specific surface area of carboxylated multiwalled carbon nanotubes (MWCNTs), thereby assuming a crucial function in the modification and optimization of the performance of humidity sensors. Through the application of a homogenously blended aqueous solution comprising GO and MWCNTs in precise proportions onto the Co3O4–LIG composite electrode, an excellent humidity-responsive layer is established, culminating in the realization of a cutting-edge GO–MWCNTs@Co3O4–LIG flexible humidity sensor. Noteworthy attributes of this sensor include a heightened sensitivity [959.1% (ΔR/R0)], rapid response and recovery times (within 5 and 26 s, respectively), and a noteworthy linearity (R2 = 0.994) across a relative humidity range of 14 to 95%. The findings presented herein offer valuable insights and a practical blueprint for the design and production of flexible humidity sensors.

Image recognition is widely used in the field of computer vision today. As a kind of image recognition, digit recognition is widely used. Today, the online recognition technology in digit recognition is relatively mature while the offline recognition technology is not. This paper mainly introduces an offline recognition system for handwritten digits based on convolutional neural networks. The system uses the MINST dataset as a training sample and pre-processes the picture with the Opencv toolkit. Then it uses LeNet-5 in the convolutional neural network to extract the handwritten digit image features, repeatedly convolution pooling, and pull the result into a one-dimensional vector. And finally find the highest probability point to determine the result to achieve handwritten digit recognition with the Softmax regression model. The application of this system can greatly reduce labor costs and improve work efficiency, which is of great significance in many fields.

During the driving of the smart car, due to the complexity of the environment, a single sensor or multiple homogeneous sensors cannot fully perceive the traffic environment around the smart car. Therefore, it is necessary to study the information fusion scheme of heterogeneous sensors, use the advantages of heterogeneous sensors to make up for the shortcomings of a single sensor, to realize the function of cooperation and mutual compensation among multiple heterogeneous sensors. Therefore, this paper proposes a data fusion method based on discrete factor multi-sensor target recognition. The output data from multiple sensors acquired over too many periods and multiple regions give the discrete factor of the sensor corresponding to the target characteristic. The current weight of multi-sensor target recognition is given according to the discrete factor, and the relative consistency and the relativeness of multi-sensor target recognition is established. Weighted consistency and other functions; combined with the current weights of multi-sensor target recognition and related consistency functions, a data fusion support calculation model for multi-sensor target recognition is constructed. The test results show that the scheme is more reliable and has a certain anti-interference ability, which can make up for the shortcomings of a single sensor and improve the target recognition rate.

The influence of teachers’ inner forces and factors in professional development (PD) has been highlighted in the past decades. However, little is written about the interplay of English as a foreign language (EFL) teachers’ desires to attend PD programs and their perceived job satisfaction and organizational commitment. To fill this gap, this study used three questionnaires to inspect the relationships among these three constructs. It also aimed to showcase whether Chinese EFL teachers’ desire to attend PD programs is predicted by their job satisfaction and organizational commitment. Adopting a random sampling technique, a sample of 357 EFL teachers was recruited from different colleges and universities in China. The results of Structural Equation Modeling (SEM) and correlation analysis revealed a positive and strong correlation between teachers’ job satisfaction, organizational commitment, and their desire to attend PD programs. Moreover, it was found that both job satisfaction and organizational commitment could collectively predict around 73% of changes in teachers’ desire to attend PD programs. The results are discussed and implications for the theory and practice of second/foreign language (L2) education in light of psycho-affective factors are enlisted. FUNDING INFORMATION. This work was supported by “Jiangsu Provincial Social Science Fund of China” (Grant No.: 22ZWD001).

To evaluate the ecological niche of photovoltaic agriculture in China, an evaluation index system was constructed. Based on the presentation form of interval numbers, we used the interval entropy weight method and interval cloud model to measure the niche state value and niche role value of photovoltaic agriculture. In this way, we determined the development trend of the ecological niche of photovoltaic agriculture. The results show that Chinese photovoltaic agriculture is in a good state and plays a good, but weak, role. The ecological niche of China’s photovoltaic agriculture will undergo a four-stage evolution process: positioning, integration, leap, and symbiosis. China has completed the positioning stage and entered the integration stage. Hence, it is important to constantly improve the level of industrial integration technology and to form a new photovoltaic agriculture recycling economic ecosystem.

BACKGROUND: Leadership style and job satisfaction are currently hot issues in the field of management psychology research, especially with regard to young employees. OBJECTIVE: This study attempted to explain the mechanism of improving employees' job satisfaction by combining the key factors of work-family balance and psychological capital. METHODS: We adopted the literature method, questionnaire survey method, and statistics method to conduct the research. And we conducted structural equation modeling (SEM) analysis of 540 young university employees in China using the random sampling method for sampling. RESULTS: Based on the structural equation modeling (SEM) analysis of 540 young university employees in China, the results show that inclusive leadership has a positive impact on improving employees' job satisfaction and that work-family balance is beneficial to serving leaders in improving employees' job satisfaction. Simultaneously, psychological capital positively moderates the indirect effect of inclusive leadership on improving job satisfaction. CONCLUSION: The final model revealed an important path from inclusive leadership to job satisfaction through work-family balance. These findings not only extend and enrich the relevant research on the relationship between inclusive leadership and job satisfaction but also shed some light on university management practice.

Forest fires pose a severe threat to ecological environments and the safety of human lives and property, making real-time forest fire monitoring crucial. This study addresses challenges in forest fire image object detection, including small fire targets, sparse smoke, and difficulties in feature extraction, by proposing TFNet, a Transformer-based multi-scale feature fusion detection network. TFNet integrates several components: SRModule, CG-MSFF Encoder, Decoder and Head, and WIOU Loss. The SRModule employs a multi-branch structure to learn diverse feature representations of forest fire images, utilizing 1 × 1 convolutions to generate redundant feature maps and enhance feature diversity. The CG-MSFF Encoder introduces a context-guided attention mechanism combined with adaptive feature fusion (AFF), enabling effective multi-scale feature fusion by reweighting features across layers and extracting both local and global representations. The Decoder and Head refine the output by iteratively optimizing target queries using self- and cross-attention, improving detection accuracy. Additionally, the WIOU Loss assigns varying weights to the IoU metric for predicted versus ground truth boxes, thereby balancing positive and negative samples and improving localization accuracy. Experimental results on two publicly available datasets, D-Fire and M4SFWD, demonstrate that TFNet outperforms comparative models in terms of precision, recall, F1-Score, mAP50, and mAP50–95. Specifically, on the D-Fire dataset, TFNet achieved metrics of 81.6% precision, 74.8% recall, an F1-Score of 78.1%, mAP50 of 81.2%, and mAP50–95 of 46.8%. On the M4SFWD dataset, these metrics improved to 86.6% precision, 83.3% recall, an F1-Score of 84.9%, mAP50 of 89.2%, and mAP50–95 of 52.2%. The proposed TFNet offers technical support for developing efficient and practical forest fire monitoring systems.

Purpose The purpose of this paper is to examine the underlying mediating mechanism and contextual conditions in the relationship between dynamic capabilities and novelty-centered business model design (NCBMD). Design/methodology/approach Using data from 146 firms in China and the process conditional modeling, the authors acquire evidence supporting the hypothesized moderated mediation. Findings The authors find that interfunctional coordination plays a crucial mediator role in the relationship between dynamic capabilities and NCBMD. Environmental dynamism positively moderates the mediating effect of interfunctional coordination on the relationship of dynamic capabilities and NCBMD. Research limitations/implications First, the research setting focuses on a specific intermediary mechanism of dynamic capabilities on NCBMD. Second, dynamic capabilities are considered as an integrative construct in the study. Future research could further examine the effect mechanism of dynamic capabilities' sub-dimensions, which might provide more theoretical findings. Third, the impact of public policies, an important source of environmental dynamism, on NCBMD needs a fine-grained analysis. Fourth, the sample data restricts the popularity of the conclusion. Practical implications First, firms should be aware of the irreplaceable role of dynamic capabilities in the process of designing a novel business model. Second, firms promoting the design of business models should pay more attention to interfunctional coordination. Third, the significant moderating mediation effect reveals that the importance of interfunctional coordination for the relationship between dynamic capabilities and NCBMD under a highly dynamic environment. Originality/value First, the authors reveal how a firm's dynamic capabilities can promote NCBMD. By focusing on the influence of dynamic capabilities on NCBMD, the authors elucidate the source of value creation from the perspective of organizational capability. Second, the analysis of mediating effect delineates the bridging mechanism of dynamic capabilities and NCBMD. These findings emphasize the important role of interfunctional coordination in designing a novel business model. Third, given the context of this research, the results present implications for the role of a dynamic environment. For the methodology of theoretical research, the different findings indicate that scholars could further refine the manipulation of moderators, which contributes to elucidate new conclusions ignored in the past studies. Accordingly, this research extends both theoretical research and methodology.

Under the single-loop structure, the problem of overcurrent protection for permanent magnet synchronous motor (PMSM) is investigated in this paper. Due to the combination of speed loop and current loop, the q-axis current become a state of PMSM control system, instead of the output of current loop in the cascade control. As a consequence, the conventional controllers can not restrict the q-axis current into a safe zone. But an oversize transient current may lead to damage of hardwares and property loss. To this end, a new current-constrained controller is proposed to deal with the issue of over-current protection. Different from previous methods of state constraints, the effects of disturbance is taken into account of controller design. A reduced-order generalized proportional integral observer (RGPIO) is adopted to estimate the uncertainties and disturbances. Our method can be considered as a composition of current-constrained control plus feed-forward compensation based on RGPIO. A rigorous stability analysis for the closed-loop system is presented. Compared with the conventional PID controller, the proposed method not only limits q-axis current to a safe range, but also has a good anti-disturbance ability. The feasibility and efficiency of the proposed method is validated by both simulation and experimental results.

Recommender systems are effective in predicting the most suitable products for users, such as movies and books. To facilitate personalized recommendations, the quality of item ratings should be guaranteed. However, a few ratings might not be accurate enough due to the uncertainty of user behavior and are referred to as natural noise. In this article, we present a novel fuzzy clustering‐based method for detecting noisy ratings. The entropy of a subset of the original ratings dataset is used to indicate the data‐driven uncertainty, and evaluation metrics are adopted to represent the prediction‐driven uncertainty. After the repetition of resampling and the execution of a recommendation algorithm, the entropy and evaluation metrics vectors are obtained and are empirically categorized to identify the proportion of the potential noise. Then, the fuzzy C‐means‐based denoising (FCMD) algorithm is performed to verify the natural noise under the assumption that natural noise is primarily the result of the exceptional behavior of users. Finally, a case study is performed using two real‐world datasets. The experimental results show that our proposal outperforms previous proposals and has an advantage in dealing with natural noise.