Unbalanced and insufficient development is a prominent problem in China’s period of economic transformation, and an accurate grasp of the current situation and evolutionary trend of innovation and economic development is of great significance in finding a solution and promoting regional coordinated development. This paper introduces the framework of functional data analysis to the study of both innovation and economic development in China, examining its spatial dynamic distribution and convergence characteristic in greater depth. Empirical results revealed the following. (1) Significant regional differences exist in the absolute level of China’s innovation development, with a balanced improvement in the development speed and growth potential. Significant differences exist in the absolute level and speed of economic development, but not in the potential energy of development as reflected by acceleration. (2) The σ(t) function of nationwide and regional innovation development shows a downward trend. There is σ(t) convergence in economic development nationwide and in the northeastern, eastern, and western regions, but the regional disparities in economic development within the six central provinces have not yet been effectively mitigated. (3) Nationwide and regional innovation development are in a state of β(t) convergence. There is significant β(t) convergence in the nationwide economic development, with a transition point from divergence to convergence in the northeastern, eastern, and western regions. However, the β(t) function of economic development in the central region shows a region-wide divergence. The findings of this paper have important policy implications for recognizing the balance and disparities in innovation and economic development among regions in China and promoting coordinated regional development.

This study empirically tests the effect of stock risk warning of listed companies on the risk of stock price crash of non-warning companies in the same industry and its mechanism in the Chinese A-share listed companies from 2008 to 2021 as samples. The research results show that the risk warning information has a capital market governance effect on peer companies, that is, the new risk warning information in an industry will alleviate the risk of stock price crash of peer companies. This conclusion remains robust after a series of robustness and endogeneity tests. The mediating mechanism test shows that the idiosyncratic information content of the stock price mediates the process of risk warning information affecting the risk of stock price crash of peer companies. The heterogeneity test shows that the governance effect of risk warning information on the risk of stock price crash of peer companies is more significant when the problem of company agency is more serious, the competitive market position is lower, or the differences in opinion among investors are smaller. This study provides empirical evidence for the necessity of the existence of the risk warning system. Additionally, it provides a reference for the capital market risk supervision of similar transition economies such as China.

The rapid development of China’s digital economy has promoted industrial structure upgrading and further affected the quality structure of the labor force across industries. This study conducts a theoretical derivation by building a task-based theoretical model and uses panel data of 30 Chinese provinces from 2001 to 2020 to conduct empirical research on the relationship between industrial digitalization, industrial structure upgrading, and structural changes in labor quality. The study results show that industrial digitalization and industrial structure upgrade affect changes in the quality structure of China’s industrial labor. The industrial structure upgrading index plays a mediating role in influencing industrial digitalization on the educational structure of labor employment. When industrial structure upgrading is considered as the threshold variable, the impact of industrial digitalization on the employment and educational structure of different labor forces in China’s industries has diverse threshold characteristics. In the process of digital industrial development, China needs to focus on protecting labor with secondary education at different stages of digital development and accelerate the development of a skilled labor force to drive the high-quality development of China’s industrial economy.

Considering that social network provides a channel for nodes to exchange information, resources, and interests, the fundamental task of social network search is to find the best path from the source node to the target node. The search strategy based on the shortest path principle ignores the strength and direction of the social relationship between nodes in the social network, and ignores the difference of influence between nodes, so that the search results cannot meet the needs of searchers. Considering the important role of the influence of nodes and the influence intensity between nodes in social network search, this paper proposes the path optimization principle of maximizing the comprehensive influence, and constructs a new search algorithm based on this strategy by applying the modified Dijkstra algorithm to solve the optimal path between nodes. Using the data of typical real social networks, it is verified that the path optimization algorithm based on the principle of maximizing comprehensive impact is better than the optimization algorithm based on the shortest path, and the search results are better interpretable to users. This paper had proposed a new influence maximization algorithm which has more advantages for solving social network search with high costs or benefits consideration by taking the influence intensity of nodes or between nodes into account.

This study examines the effect of the degree of household registration control on labor mismatch in China. Based on Chinese urban panel data, we construct a dynamic panel model to empirically examine the effect of the degree of household registration control on labor mismatch in China. The study finds that labor mismatch relatively exists in all regions of China, which differ significantly. Moreover, liberalizing the household registration system in cities with under- and over-allocated labor force can reduce and aggravate the degree of local labor mismatch, and improve and reduce resource allocation efficiency, respectively. This study uses the labor distortion coefficient to replace the original dependent variable, and the labor mismatch coefficient. The authors further conduct a robustness test using the system generalized moment estimation method. These results are consistent. The implication of this study is that, in the context of China’s shift from high-speed to high-quality development, local governments should tailor their household registration system reform programs to the local resource allocation situation to improve the degree of labor mismatch in China, to optimize resource allocation, and to promote economic transformation.

Since its reform and opening, the Chinese economy has witnessed unprecedented development. This long-term high-speed development has significantly impacted the environment. In the process of energy development and consumption, the environment has been severely polluted, and greenhouse emissions have increased. This has resulted in environmental imbalances such as global climate change, rising sea levels, and extreme weather. Using annual energy consumption (EC), economic growth (GDP), and carbon dioxide (CO₂) emissions in China from 1981 to 2021, this study employed the maximum likelihood estimation method to estimate the parameters of the nonlinear MS-VAR model. Cointegration tests, regimes analysis methods, and impulse response function analysis methods were adopted to explore the differences or similarities in the dynamics of the three under various regimes. The research results are as following. (i) The cointegration test findings demonstrate a long-term equilibrium relationship among EC, economic growth, and CO₂ emissions. (ii) Regime analyses exhibit that there are three regimes: “low-level regime,” “medium-level regime,” and “high-level regime.” The three regimes have a mutual transfer transmission mechanism that exhibits nonlinear properties. (iii) Impulse response function analyses show that external EC and GDP shocks favorably impact the other two variables in all three regimes. EC in all three regimes is negatively impacted by external CO₂ shocks. Moreover, in the “low-level regime,” the relationship between CO₂ and GDP has an inverted U-curve, whereas in the other two regimes, the relationship has a negative association. This study can help China formulate reasonable and effective CO₂ emissions reduction and energy policies and successfully achieve the emissions reduction goal of the 14th Five Year Plan as well as dual carbon goals.

This paper presents a collaborative filtering (CF) algorithm for a recommendation system motivated by the need for higher recommendation accuracy. Cluster analysis captures information from a group of users such that users within a given cluster are more similar to each other than to those in other clusters. Therefore, clustering helps to detect similar users. However, inconsistent similarity measures have been applied during the clustering and prediction stages in the literature. Hence, this study resolves such discrepancies through the proposed CF algorithm, which uses fuzzy clustering for relational data such that a common similarity measure is applied to both the clustering and prediction stages. Experiments were conducted with ten datasets based on an artificial dataset and 40 datasets based on eight real datasets to demonstrate that the proposed algorithm could achieve a higher CF accuracy than conventional methods.

Fragility fracture of pelvis (FFP) is increasingly affecting elderly population. Although computed tomography (CT) imaging is considered superior to conventional radiographic image for diagnosing FFP, clinicians face challenges in recognizing pelvic fractures owing to imaging contrast or feature size. This study proposes a method that combines boring survey based FFP candidate extraction from CT images and a newly developed convolutional neural network model. In addition, the proposed method also visualizes the probability of fracture on 3D bone surface data. The accuracy, precision, and recall of the proposed method were found to be 79.7%, 60.0%, and 80.6%, respectively. Furthermore, the 3D view of fracture probability on the pelvic bone surface allows for qualitative assessment and can support physicians to diagnose FFP. The findings indicate that the proposed method has potential for predicting FFP.

Traffic flow detection provides significant information for intelligent transportation systems. However, as the mainstream research direction, vision-based traffic flow detection methods currently face the challenges of a trade-off between accuracy and speed. Furthermore, it is crucial that modularization be incorporated into the system design process to enhance the maintainability and flexibility of the system. To achieve this, we propose a modular design method that divides this task into three parts: vehicle detecting, vehicle tracking, and vehicle counting. As an important link of the system, vehicle detection greatly influences the accuracy and speed of the system. We therefore introduce a lightweight network called feature adaptive fusion-YOLOX, which is based on YOLOX. Specifically, in order to eliminate redundant information brought by bilinear interpolation, we propose a feature-level upsampling method called channel to spatial, which enables upsampling without additional calculations. Based on this module, we design a lightweight, multi-scale feature fusion module, feature adaptive fusion pyramid network (FAFPN). Compared with PA-FPN, FAFPN reduces FLOPs by 61% and parameters of the neck by 50% while maintaining comparable or even slightly improved performance. Through experimental tests, the traffic flow detection method proposed in this paper achieves high accuracy and adaptability in a series of traffic surveillance videos in different types of weather and perspectives and can realize traffic flow detection in real time.

Gesture recognition is a popular technology in the field of computer vision and an important technical mean of achieving human-computer interaction. To address problems such as the limited long-range feature extraction capability of existing dynamic gesture recognition networks based on convolutional operators, we propose a dynamic gesture recognition algorithm based on spatial pyramid pooling Transformer and optical flow information fusion. We take advantage of Transformer’s large receptive field to reduce model computation while improving the model’s ability to extract features at different scales by embedding spatial pyramid pooling. We use the optical flow algorithm with the global motion aggregation module to obtain an optical flow map of hand motion, and to extract the key frames based on the similarity minimization principle. We also design an adaptive feature fusion method to fuse the spatial and temporal features of the dual channels. Finally, we demonstrate the effectiveness of model components on model recognition enhancement through ablation experiments. We conduct training and validation on the SCUT-DHGA dynamic gesture dataset and on a dataset we collected, and we perform real-time dynamic gesture recognition tests using the trained model. The results show that our algorithm achieves high accuracy even while keeping the parameters balanced. It also achieves fast and accurate recognition of dynamic gestures in real-time tests.

A function f of a graph G(p,q) admits square difference labeling if there exists a bijection f:V(G)→{0,1,2,...,p-1} such that the induced function f^*:E(G)→N given by f^*(uv)=|(f(u))²-(f(v))²| ∀uv∈E(G) is distinct. A graph which admits square difference labeling is called square difference graph. The major thrust of this paper is that we have provided an algorithm to find the square difference labeling of bloom graph.

Opisthorchis viverrini and minute intestinal fluke (MIF) infections are heavily epidemic in northeastern Thailand. Their primary cause is eating raw or undercooked cyprinid fishes, and they cause health problems in the human digestive system. In cases of liver fluke, these parasites can go through the bile duct system, which may cause cholangiocarcinoma (bile duct cancer). When a medical doctor suspects that a patient is infected with parasites, they typically request a stool analysis to determine the type of egg parasites using microscopy. Both parasites have similar characteristics, thus, it is necessary for a specialist to identify the specific type of egg parasites present. Many automatic systems have been developed using deep learning to assist doctors in diagnosing the type of egg parasite. In this study, we proposed three models of deep learning architectures and created voting ensembles to analyze egg parasite images. Images of similar liver fluke eggs and MIF eggs were taken from the Parasitology Laboratory, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand. Image data augmentation is used to expand images from different perspectives and assist the system in acquiring a greater variety of images. Three models performed effectively, by employing the hard voting ensemble, the accuracy increased to 86.67%, while for the second group, the accuracies reached 68.00%, 76.00%, and 77.33%, respectively. Using the soft voting ensemble, the accuracy improved to 79.33%. These outcomes highlight the potential of ensemble deep learning in image classification. Furthermore, these results align closely with those achieved by several experts in image classification. Hence, a promising ensemble approach can aid doctors in accurately classifying images of egg parasites.

This study examines the relationship between stylistic coach leadership style, team cohesion, and team performance for young student athletes who take part in a team sport. The stylistic coach leadership style is defined as a combination of both democratic and holistic coach leadership styles. In other words, the stylistic coach leadership style is when the coaches make the final decision based on what young student athletes think while ensuring that they are content and that the team is able to be successful overall. In order to determine the effect of stylistic coach leadership style on team cohesion and team performance, a study was conducted using an online questionnaire that was split into two parts. Young student athletes in grades 6 through 12 who were taking part in a team sport at the time were eligible to participate in this study. Demographic information was collected through the first part of the questionnaire while the second part measured each of the three constructs through questions that required the athletes to rank the statements based on the 5-point Likert scale. The results highlight that the stylistic coach leadership style directly affects both team cohesion and team performance, and team cohesion acts as a partial mediator in the relationship between stylistic coach leadership style and team performance. Finally, this study also provides some practical suggestions for coaches, with the aim of aiding them to be more efficient and effective in leading those young student athletes.

Lattice based communication P (LTC-P) systems are a class of extended P systems with lattice membrane structures. This new P-system is recently proposed in our work and LTC-P systems have been shown to be computational completeness. LTC-P systems can efficiently solve some kinds of combination-optimization problems. The purpose of this paper is to investigate the computation power of LTC-P systems through comparison with Chomsky families and Lindenmayer system. The formal framework of LTC-P systems is also provided. Then, we use LTC-P systems to solve SAT and HPP in linear time. Results have shown that LTC-P systems have comparative advantage in the use of membrane numbers.

This study measured the urinary sodium/potassium (U-Na/K) ratio using a compact ion meter to verify whether the U-Na/K ratio can estimate human blood pressure (BP) and ionic balance. The U-Na/K ratio and BP were measured in five healthy participants (including one female) after urination for at least two days per person, and lifestyle information, such as exercise and diet, were recorded at the time of measurement. Analysis of the measured data at 57 points revealed no significant difference in the U-Na/K ratio and BP, but a significant decrease in the U-Na/K ratio was observed after exercise accompanied by sweating (P = 0.01). Although previous studies have demonstrated that sodium is released by sweating, its measurement is difficult. Measuring sodium/potassium ratio is useful for preventing heatstroke and may serve as an explanatory variable for sweating and exercise intensity.

Poor communication environments always lead to unstable communication in unmanned aerial vehicle swarms. To solve the problem of task assignment in poor communication environments, this study proposed an information fusion strategy (IFS) based on information integrity and authenticity. The proposed IFS was embedded into the classical sequential and the Prim assignment and its generalization (G-Prim) decentralized task assignment algorithms, and these two improved variants with the proposed IFS were denoted as sequential auction with IFS (Seq-IFS) and G-Prim-IFS, respectively. The Bernoulli and Gilbert–Elliott models, which can model communication delay and packet loss, were adopted to describe unstable communication channels. A series of test instances with different swarm sizes and levels of communication channel reliability was used to test the performances of Seq-IFS and G-Prim-IFS in their original forms. Numerical experimental results demonstrated that the proposed Seq-IFS and G-Prim-IFS significantly outperformed their original versions in most test instances, particularly in cases with low communication environments.

This paper proposes a simple knowledge graph embedding (KGE) framework that considers the entity type information without additional resources. The KGE is used to obtain vector representations of entities and relations by learning structured information in triples. The obtained vectors are used to predict the missing links in a knowledge graph (KG). Although many KGs contain entity type information, most of the existing methods ignored the potential of the entity type information for the link prediction task. The proposed framework, which is called entity and entity type composition representation learning (EETCRL), obtains vector representations of both entities and entity types, which are combined and used for link prediction. Experimental results on three datasets show that the EETCRL outperforms the baseline methods in most cases. Furthermore, the results obtained from tests with different model sizes show that the proposed framework can achieve high performance even with a small model size. This paper also discusses the effect of considering information about entity types on the link prediction task by analyzing the experimental results.

Failure in compulsory subjects such as chemistry, calculus, physics, and basic control systems could hamper the graduation process of students. Thus, students must be successful in such obligatory courses. To address this issue, this study aims to predict student performance based on their learning outcomes using data mining techniques. In particular, we utilize decision tree (DT), k-nearest neighbor (KNN), support vector machine (SVM), and naive Bayes (NB) algorithms to predict student performance. The data for this study were gathered from the learning outcomes of students in the basic control systems course and subsequently modeled using binary and nine-level classifications. The experimental results showed that DT could perform better than KNN, SVM, and NB in the binary and nine-level classifications. Interestingly, the results of DT (i.e., the prediction values) are almost similar to those of the original values of the basic control systems course.

Narcotics are a grave crime that can lead to addiction, loss of consciousness, and even death. Furthermore, narcotics can damage society’s environment. Narcotics criminal cases have been reported widely in Indonesia. The variety of narcotics cases makes it extremely difficult for judges to make decisions. Therefore, it is necessary to study and analyze the judge’s decisions from the data on the narcotics cases. In this study, we propose using a machine learning approach based on K-means clustering method for clustering and analyzing the verdicts on narcotics cases to see the trend of the verdicts on narcotics cases. In addition, we also use latent Dirichlet allocation (LDA) topic modeling to study the trend of these narcotics cases. Based on the results of the study using K=3 for clustering, there were three categories of verdicts: decisions with light sentences (less than three years), decisions with moderate sentences (three to six years), and decisions with severe sentences (more than ten years). Furthermore, using topic modeling based on the LDA method, the top three topics of narcotics cases based on the verdicts were determined, namely: the first topic refers to verdicts where narcotics perpetrators are found guilty; the second topic refers to verdicts with evidence of marijuana-type narcotics; and the third topic refers to verdicts with evidence of methamphetamine-type narcotics.

This study proposes an object detection algorithm based on the improved YOLOv5 network for the uncivilized behavior of reclining public chair, which often occurs in cities. The current object detection field is studied by a single object. For the behavior of a lying public chair, the object to be measured is composed of two parts: the chair and the human posture jointly. Furthermore, the features of the object will show a large variability under different shooting angles, so the model’s ability to extract features of the object is extremely important. This paper incorporates the Ghost module based on the YOLOv5 network to enable the model to learn more object features. The Ghost makes the neural network lighter by using linear convolution instead of nonlinear convolution, and its generated redundant features can help the model learn more object features and improve the model performance. In addition, this paper uses a new loss function EIoU to replace the original loss function CIoU. By comparison, EIoU solves the problem that CIoU fails in penalty terms under specific conditions. EIoU enables the model to converge faster and better. After experimental validation on the test set, it is shown that the improved YOLO network improves F1 by 3.5% and mAP by 4.2% compared to the original algorithm.

This paper studies the cooperative multi-area coverage problem with obstacles, which requires a group of robots to cover an area while avoiding collisions. This problem is very common in scenarios such as garbage removal, mine clearance, and regional information collection. The currently proposed algorithms usually have the problem of high redundancy and weak scene scalability. This paper designs a cooperative area coverage algorithm for multiple robots. First, a set of rules is proposed to divide the area to be covered into several small areas. Then, a genetic algorithm based on circular coding is designed to allocate these divided areas to several robots. Finally, the coverage path of the robot is designed using the zigzag method, so that the robot can cover the area allocated to it. Through computational experiments, it has been verified that this algorithm has efficiency advantages over state-of-the-art algorithms in certain scenarios and has scalability for different scenarios.

X-ray examinations are crucial for fracture diagnosis and treatment. However, some fractures do not present obvious imaging feature in early X-rays, which can result in misdiagnosis. Therefore, an ASCNet model is proposed in this study for X-ray femoral fracture classification. This model adopts the self-calibration convolution method to obtain more discriminative feature representation. This convolutional way can enable each spatial location to adaptively encode the context information of distant regions and make the model obtain some characteristic information hidden in X-ray images. Additionaly, the ASCNet model integrates the convolutional block attention module and coordinate attention module to capture different information from space and channels to fully obtain the apparent fracture features in X-ray images. Finally, the effectiveness of the proposed model is verified using the femoral fracture dataset. The final classification accuracy and AUC value of the ASCNet are 0.9286 and 0.9720, respectively. The experimental results demonstrate that the ASCNet model performs better than ResNet50 and SCNet50. Furthermore, the proposed model presents specific advantages in recognizing occult fractures in X-ray images.

Indonesia needs energy to meet its needs. Energy sourced from waste is called biomass briquettes. The manufacture of briquettes is still done in a traditional way, so there are product quality problems. A computational approach has been taken to base the quality of the product on certain characteristics so that the types of briquettes that are made can be classified or sorted. The research objective of this work is to determine the quality of briquettes. The approach uses deep learning methods and convolutional neural network (CNN) architecture. Classification is based on good and bad briquette products, and testing is based on the level of performance accuracy. The dataset formed consists of 5,280 images. As training data, 85% of data is used and 15% is used as test data using 300 epoch parameters, 32 batch sizes, and learning speed up to 0.001. The results of testing and evaluating the performance of briquette products with the general CNN architecture have a level of accuracy that is not optimal. The optimization results of testing the MobileNetV2 architecture with a ratio of 70:30 obtains predictions with 0.99 as the highest accuracy value and 0.73 as the lowest. The average predicted value of the model is 0.95. The computational approach is able to provide traditional communities with solutions for the process of making briquettes.

Facial beauty prediction is an emerging topic. The pursuit of facial beauty is the nature of human beings. As the demand for aesthetic surgery has increased significantly over the past few years, an understanding beauty is becoming increasingly important in medical settings. This work proposes a new ensemble based on the pre-trained convolutional neural network (CNN) models to identify scores for facial beauty prediction. These ensembles were originally built from the following previously trained models: DenseNet-201, Inception-v3, MobileNetV2, and EfficientNetB7. According to the SCUT-FBP5500 benchmark dataset, the proposed model obtains a Pearson coefficient of 0.9469. This reveals that the suggested EN-CNNs model can be successfully applied in a variety of face-to-face applications.

Three-dimensional (3D) surface reconstruction is used to solve the problem of the narrow field of view in laparoscopy. It can provide surgeons or computer-assisted surgery systems with real-time complete internal abdominal anatomy. However, rapid changes in image depth, less texture, and specular reflection pose a challenge for the reconstruction. It is difficult to stably complete the reconstruction process using feature-based simultaneous localization and mapping (SLAM) method. This paper proposes a robust laparoscopic 3D surface reconstruction method using SLAM, which can automatically select appropriate parameters for stereo matching and robustly find matching point pairs for laparoscope motion estimation. The changing trend of disparity maps is used to predict stereo matching parameters to improve the quality of the disparity map. Feature patch extraction and tracking are selected to replace feature point extraction and matching in motion estimation, which reduces its failure and interruption in feature-based SLAM. The proposed feature patch matching method is suitable for parallel computing, which can improve its computing speed. Evaluation results on public in vivo and ex vivo porcine abdominal video data show the efficiency and robustness of our 3D surface reconstruction approach.