bluuebunny/crossref_metadata_2025 huggingface dataset:
Non-Invasive Grading and Sorting of Mango (Mangifera indica L.) Using Antlion Optimizer-Based Artificial Neural Networks
Authors: Gill Jasmeen , Singh Ravinder Pal
Published: June 2022

Mango is an imperative commercial fruit in terms of market value and volume of production. In addition, it is grown in more than ninety nations around the globe. Consequently, the demand for effective grading and sorting has increased, ever since. This communication describes a non-invasive mango fruit grading and sorting model that utilizes hybrid soft computing approach. Artificial neural networks (ANN), optimized with Antlion optimizer (ALO), are used as a classification tool. The quality of mangoes is evaluated according to four grading parameters: size (volume and morphology), maturity (ripe/unripe), defect (defective/healthy) and variety (cultivar). Besides, a comparison of proposed grading system with state-of-the-art models is performed. The system showed an overall classification rate of 95.8% and outperformed the other models. Results demonstrate the effectiveness of proposed model in fruit grading and sorting applications.
DEVELOPMENT AND ANALYSIS OF PROSTHETIC FOOT BY A NEW IDEA
Authors: MOHAMMED HASAN SAAD
Published: June 2022

The procedure involved in evolving a compound that was utilized for producing prosthetic foot with mechanical characteristics and a sensible price is presented. Compound’s characteristics such as Charpy impact strength and tensile were investigated using mechanical tests. During this procedure 10% linear low density polyethylene (LLDPE) was added to 90% high-density polyethylene (HDPE). This reduced the energy impact as well as ultimate yield stress whilst elongated at break from 27% in comparison to those of pure HDPE. Prostheses properties were further studied in comparison to human foot. The analytical part indicates the static analysis outcomes by experimental as well as finite element methods (ANSYS Workbench 14). Thus, the foot was formed, and dorsiflexion was measured. Overall, the nonarticulated (HDPE and LLDPE) foot was compared to SACH foot by cost and weight. It was found that the nonarticulated foot’s weight was lighter by 2% and its cost was lower than SACH foot by approximately 60%. The nonarticulated foot has great properties, for instance a life of foot cycles (9∘ and 2,193,228) as well as good dorsiflexion in comparison to SACH foot with 6.4[Formula: see text] and 896,213 life of foot cycles.
Surveillance Too Cheap to Meter
Authors: Kamp Poul-Henning
Published: June 2022

IT nerds tend to find technological solutions for all sorts of problems?economic, political, sociological, and so on. Most of the time, these solutions don't make the problems that much worse, but when a problem is of a purely economic nature, only solutions that affect the economics of the situation can possibly work. Neither cryptography nor smart programming will be able to move the needle even a little bit when the fundamental problem is that surveillance is too cheap to meter.
Structural Biology of Cilia and Intraflagellar Transport
Authors: Klena Nikolai , Pigino Gaia
Published: June 2022

Cilia are ubiquitous microtubule-based eukaryotic organelles that project from the cell to generate motility or function in cellular signaling. Motile cilia or flagella contain axonemal dynein motors and other complexes to achieve beating. Primary cilia are immotile and act as signaling hubs, with receptors shuttling between the cytoplasm and ciliary compartment. In both cilia types, an intraflagellar transport (IFT) system powered by unique kinesin and dynein motors functions to deliver the molecules required to build cilia and maintain their functions. Cryo-electron tomography has helped to reveal the organization of protein complex arrangement along the axoneme and the structure of anterograde IFT trains as well as the structure of primary cilia. Only recently, single-particle analysis (SPA) cryo-electron microscopy has provided molecular details of the protein organization of ciliary components, helping us to understand how they bind to microtubule doublets and how mechanical force propagated by dynein conformational changes is converted into ciliary beating. Here we highlight recent structural advances that are leading to greater knowledge of ciliary function.
Optimization of a Logistics Transportation Network Based on a Genetic Algorithm
Authors: Liu He , Zhan Pengbin , Zhou Meng
Published: June 2022

In order to solve the problem of genetic algorithm, the author proposes a research on optimization of a logistics transportation network. First, combined with the distribution characteristics of logistics, starting from the relevant research theories of vehicle routing problems, the vehicle travel distance is considered in the basic model of vehicle routing optimization, a vehicle routing model with soft time window is established to minimize the total cost, respectively, and a vehicle routing model with multiple distribution centers. Second, on this basis, according to the actual problems and model needs of the research, using the basic principle of genetic algorithm, the solution algorithm design of the built model is carried out and we use MATLAB for programming, so that the solution of the built model is finally realized. We set T = 100, S = 50, Pc = 0.95, Pm = 0.1. β1 = 0.005, β2 = 1, and β3 = 0.005. Finally, the actual distribution data of the G supermarket chain in the region are used as the experimental data; by picking two shipping instances, the two constructed models and the designed genetic algorithm are verified, respectively.
An Enhanced Ant Colony Optimization Mechanism for the Classification of Depressive Disorders
Authors: Saif Alghawli Abed , Taloba Ahmed I.
Published: June 2022

Bipolar disorder is marked by mood swings that alternate between mania and depression. The stages of bipolar disorder (BD), as one of the most common mental conditions, are often misdiagnosed as major depressive disorder (MDD), resulting in ineffective treatment and a poor prognosis. As a result, distinguishing MDD from BD at an earlier phase of the disease may aid in more efficient and targeted treatments. In this research, an enhanced ACO (IACO) technique biologically inspired by and following the required ant colony optimization (ACO) was utilized to minimize the number of features by deleting unrelated or redundant feature data. To distinguish MDD and BD individuals, the selected features were loaded into a support vector machine (SVM), a sophisticated mathematical technique for classification process, regression, functional estimates, and modeling operations. In respect of classifications efficiency and frequency of features extracted, the performance of the IACO method was linked to that of regular ACO, particle swarm optimization (PSO), and genetic algorithm (GA) techniques. The validation was performed using a nested cross-validation (CV) approach to produce nearly reliable estimates of classification error.
Progress in the Preparation of Large-Size High-Performance CuCr Alloys
Authors: Han Jin-Ru , Dou Zhi-He , Zhang Ting-An , An Wang
Published: June 2022

CuCr contact material, whose mass fraction of Cr is generally in the range of 25% to 50%, is one of the most desirable contact materials for medium- and high-voltage vacuum switches. The homogeneity and denseness of CuCr alloy are the keys to its application. The infiltration and powder sintering have defects, such as uneven distribution of Cr phase, and great difficulties in preparing high-performance CuCr alloys with low gas content; the equipment investment in the vacuum consumable arc melting method is large and the production cost is high, while mechanical alloying, vacuum induction melting, and laser-selective melting as new technologies are still in the laboratory stage. From the perspective of surface modification treatment and multicomponent alloying, refinement of Cr phase grains can improve the properties of CuCr alloy. The new process of aluminothermic reduction-electromagnetic coupling refining-water and gas composite rapid cooling takes oxides as raw material and obtains a fully miscible CuCr alloy melt by aluminothermic reduction theoretically at 2848K, followed by secondary refining under electromagnetic field coupling and water-gas composite cooling and casting, which can obtain alloy ingots of 50 mm in diameter and 10 mm in height. The density and hardness of CuCr alloy prepared by this process as vacuum contact material are better than the requirements of GB/T 26867-2011, so this process has the potential to become a new-generation preparation method for large-size homogeneous high-performance CuCr alloy.
Consumer Group Identification Algorithm for Ice and Snow Sports
Authors: Zhang Ting , Wang Wei
Published: June 2022

As an important part of the modern sports industry system, the quality and level of its development are related to whether China’s sports industry can successfully become a pillar industry of the national economy. Therefore, the development of the ice and snow sports industry is to promote the expansion of China’s sports industry scale high quality development of the national economy and an important way to build sports power. Participative sports consumption is the most important part of sports consumption and the development of the sports industry. The sports industry separated from participative sports consumption is water without source and tree without roots, while participative sports consumption demand is the power source of participative sports consumption. At present, there is no systematic and complete research on participation sports consumption demand. In order to understand the causes and demand state of residents’ participation sports consumption demand and provide entry points for enterprises to formulate marketing strategies, this study constructs an organic system with participation sports service products as consumption objects, centering on the demanding state of participation sports consumers. In the system, on the theory of supply and demand, under the guidance of consumption economics theory, adhere to the combination of theoretical research and empirical analysis, the combination of macroplanning and microdesign, the combination of qualitative analysis and quantitative analysis, through the empirical investigation and receipt collection of residents’ participation sports consumption demand, the use of systematic analysis, literature method, and survey method, through mathematical analysis, and other research methods, the paper explores the main causes and demand conditions of residents’ participation sports consumption demand in different consumption states and excavates the main causes and demand conditions of participating sports consumption demand in different consumption states under different sports levels.
Light-Weighted Deep Learning Model to Detect Fault in IoT-Based Industrial Equipment
Authors: Soni Mukesh , Khan Ihtiram Raza , Basir Sameer , Chadha Raman , Alguno Arnold C. , Bhowmik Tapas
Published: June 2022

Industry 4.0, with the widespread use of IoT, is a significant opportunity to improve the reliability of industrial equipment through problem detection. It is difficult to utilize a unified model to depict the working condition of devices in real-world industrial scenarios because of the complex and dynamic relationship between devices. The scope of this research is that it can detect equipment defects and deploys them in a natural production environment. The proposed research is describing an online detection method for system failures based on long short-term memory neural networks. In recent years, deep learning technology has taken over as the primary method for detecting faults. A neural network with a long short-term memory is used to develop an online defect detection model. Feature extraction from sensor data is done using the curve alignment method. Based on long-term memory neural networks, the fault detection model is built (LSTM). In the end, sliding window technology is used to identify and fix the problem: the model’s online detection and update. The method’s efficacy is demonstrated by experiments based on real data from power plant sensors.
Tuberculosis Exposure from a Healthcare Worker to Patients in a Neonatal Intensive Care Unit (NICU)
Authors: Casalino Maria , Jegathesan Thivia , Sgro Michael , Rea Elizabeth , Muller Matthew , Campbell Douglas M.
Published: June 2022

The nosocomial spread of Mycobacterium tuberculosis from a healthcare worker with infectious pulmonary tuberculosis disease to patients remains a risk in the healthcare environment, including neonatal intensive care units. In this paper, we outlined a protocol for neonates exposed to tuberculosis in a neonatal intensive care unit that includes skin testing, chest X-ray imaging, and prophylactic isoniazid. Neonatal patients were followed up with tuberculosis skin testing at both three months corrected age and two months postexposure. To our knowledge, this is the first Canadian study to illustrate a protocol following tuberculosis exposure in a neonatal intensive care unit for exposed neonates.
Visual Network Analysis Based on Stereo Vision and Feature Matching Algorithm
Authors: Shi Lili
Published: June 2022

Functions such as Internet browsing and online shopping have a great impact on people’s lives. The footprints of people browsing various information and news on the web page are also increasing year by year. More and more people begin to pay attention to the visual communication in web design. Network information is widely loved by people because of its convenience, quickness, and simplicity. In order to study the visual problem in network information, this paper proposes to use a feature matching algorithm to study the visual information transmission in web design. Using stereo vision and feature matching algorithm, the target recognition function in the visual communication of web design is realized. The content of web design is defined from the perspective of visual beauty and overall harmony. By extracting the feature points in the data, the two-dimensional vision is transformed into a three-dimensional vision. It not only can accurately extract feature points from data but also can convert 2D vision to 3D vision. Finally, in order to optimize the feature matching speed of web design images, the epipolar constraint algorithm is used to optimize the feature matching function. The experimental results show that the content of this paper can truly show the virtual effect in web design and can intuitively upload and transmit simple and rich information content. This paper not only meets the aesthetic needs of the public for web design but also improves the problem of fuzzy information in the process of visual information transmission.
circ_0072464 Shuttled by Bone Mesenchymal Stem Cell-Secreted Extracellular Vesicles Inhibits Nucleus Pulposus Cell Ferroptosis to Relieve Intervertebral Disc Degeneration
Authors: Yu Xiaojun , Xu Haoran , Liu Qikun , Wang Yingguang , Wang Shanxi , Lu Rui , Jiang Yongqiao , Kang Hao , Hu Weihua
Published: June 2022

Ferroptosis, as an iron-dependent form of necrotic cell death, has been reported to affect activities of nucleus pulposus cells (NPCs). However, its role in the pathogenesis of intervertebral disc degeneration (IDD) is largely unknown. Notably, our bioinformatics analysis predicted that circ_0072464 was downregulated in nucleus pulposus of IDD mice. Therefore, this study is aimed at clarifying the mechanisms of extracellular vesicle- (EV-) encapsulated circ_0072464 derived from bone marrow mesenchymal stem cells (BMSCs) in NPC ferroptosis in IDD. EVs were extracted from mouse BMSCs (BMSC-EVs) and then cocultured with IL-1β-induced NPCs, followed by detection of matrix synthesis, proliferation, and ferroptosis of NPCs based on gain- or loss-of-function experiments. It was found that the uptake of BMSC-EVs by NPCs alleviated IDD. circ_0072464 and NRF2 were downregulated, and miR-431 was upregulated in IDD. Mechanistically, circ_0072464 competitively bound to miR-431, which targeted and inhibited NRF2 expression. BMSC-derived EVs carrying circ_0072464 inhibited NPC ferroptosis to promote matrix synthesis and proliferation of NPCs by inhibiting miR-431 and upregulating NRF2. Besides, in vivo experiments also confirmed that BMSC-EVs alleviated intervertebral disc lesions in mice with IDD through the circ_0072464/miR-431/NRF2 axis. Collectively, BMSC-EV-loaded circ_0072464 inhibited NPC ferroptosis to relieve IDD via upregulation of miR-431-mediated NRF2, therefore providing a potential therapeutic target against IDD.
Maslinic Acid Inhibits the Growth of Malignant Gliomas by Inducing Apoptosis via MAPK Signaling
Authors: Wang Yongqiang , Zhang Hewei , Ye Zusen , Ye Qiang , Yang Xuezhi , Mao Wei , Xu Ruoting , Zhang Yanlei
Published: June 2022

Background. Gliomas are primary malignant brain tumors. Despite recent advances in surgery and clinical neuro-oncology, the prognosis of patients with glioma is still poor. Therefore, there is an urgent need to find new therapeutic drugs. Methods. Here, we have studied the anticancer effect of maslinic acid in glioma and explored its potential molecular mechanism. CCK-8, Ki67 immunofluorescence, and colony formation tests are used to detect the proliferation of glioma cells. Transwell and migration experiments are used to detect the function of cell invasion and migration, and RNA-seq was performed to identify differentially expressed genes. Western blot analysis helps us identify important signaling pathways. Finally, the anticancer effect of maslinic acid was confirmed in vivo through tumor xenografting experiments. Results. Our experiments obtained high-throughput data on the treatment of maslinic acid in glioma. We found that maslinic acid significantly inhibits the proliferation, invasion, and migration of glioma cells and promotes the apoptosis of glioma cells via suppressing MAPK signaling. Conclusions. This is the first time to analyze the mechanism of maslinic acid against glioma based on transcription. Our experiments show that maslinic acid may be a useful natural product for the treatment of glioma.
Triglyceride-Glucose Index for the Diagnosis of Metabolic Syndrome: A Cross-Sectional Study of 298,652 Individuals Receiving a Health Check-Up in China
Authors: Jiang Mingfei , Li Xiaoran , Wu Huan , Su Fan , Cao Lei , Ren Xia , Hu Jian , Tatenda Grace , Cheng Mingjia , Wen Yufeng
Published: June 2022

Objective. We herein aim to explore the relationship between the triglyceride-glucose (TyG) index and metabolic syndrome (MS). Methods. We enrolled 298,652 individuals with an average age of 47.08 ± 12.94 years and who underwent health check-ups at the First Affiliated Hospital of Wuhu Wannan Medical College in this cross-sectional study from 2014 to 2016. We enlisted 125,025 women (41.86%) and 173,627 men (58.14%). The survey information included a questionnaire survey, a physical examination, and a laboratory examination. Results. The prevalence of MS increased gradually in the TyG-index subgroups (Q1, TyG <8.30; Q2, 8.30≤ TyG <8.83; and Q3, TyG ≥8.83). We noted significant differences in hypertension, hyperlipidemia, hyperglycemia, sex, age, body mass index (BMI), smoking and drinking habits, and estimated glomerular filtration rate between the TyG-index subgroups. Multiclass logistic regression analysis showed that the group with TyG <8.30 was the reference group, and the 8.30≤ TyG <8.83 and the TyG ≥8.83 groups exhibited a higher TyG index with MS, and a lower TyG index without MS disease. In the linear curve analysis of the TyG index and MS components, BMI, systolic blood pressure, and diastolic blood pressure showed upward trends, while high-density lipoprotein cholesterol showed no obvious trend in the TyG index at a range of 7.8–11.0. Receiver operating characteristic analysis was used to evaluate the predictive value of the TyG index, triglycerides, and fasting blood glucose for MS, and we found that the area under the TyG index curve was the largest (AUC = 0.89). Conclusion. There were associations between the TyG index and MS and its components, and the TyG index is therefore of great value in the early diagnosis of MS.
Research on Energy Supply System Applied to Autonomous Underwater Observation Vehicles
Authors: Wang Chunjie , Chai Yugeng , Cui Lin
Published: June 2022

The discharge characteristics of seawater batteries can be used to prolong the working hours of autonomous underwater observation vehicles. However, the output voltage level of the seawater battery is low, so it cannot directly be used to power supply underwater observation vehicles. In this paper, a boost converter is proposed based on the seawater batteries to power supply autonomous underwater observation vehicles. The proposed converter greatly increases the voltage level of the seawater battery and directly supplies the underwater observation vehicles. Compared with the traditional boost converter, the voltage gain of the proposed converter is more than ten times higher than it under the same duty ratio condition. Its structure is asymmetric interleaved parallel, which can enable power devices to obtain lower voltage stress and ensure that the input current ripple is low. The operating principle and homeostasis property of the proposed converter are analyzed in detail. Finally, a prototype is built. Through simulation and experiment, the correctness of the theoretical design is verified and experimental results are analyzed.
A Sampling Load Frequency Control Scheme for Power Systems with Time Delays
Authors: Sriraman R. , Younis Jihad A. , Lim C. P. , Hammachukiattikul P. , Rajchakit G. , Boonsatit N.
Published: June 2022

In this study, we investigate the effectiveness of a robust sampled‐data H∞ load frequency control (LFC) scheme for power systems with randomly occurring time‐varying delays. By using the input‐delay technique, the sampled‐data LFC model is reformulated as a continuous time‐delay representation. Then, Bernoulli‐distributed white noise sequences are used to describe randomly occurring time‐varying delays in the sampled‐data LFC model. Some less conservative conditions are achieved by utilizing the Lyapunov–Krasovskii functional (LKF) and employing Jensen inequality and reciprocal convex combination lemma to ensure the considered power system has mean‐square asymptotic stability under the designed control scheme. The derived results are based on linear matrix inequalities (LMIs) that can readily be solved using the MATLAB LMI toolbox. The criteria obtained are used to analyze the upper bounds for time delays, and a comparison study to validate the efficacy of the presented method is presented.
Evaluation of the Antiulcer Activity of Methanolic Extract and Solvent Fractions of the Leaves of Calpurnia aurea (Ait.) Benth. (Fabaceae) in Rats
Authors: Andargie Yared , Sisay Woretaw , Molla Mulugeta , Norahun Alefe , Singh Pradeep
Published: June 2022

Introduction. In Ethiopia, traditionally, the leaves of Calpurnia aurea have been utilized to treat peptic ulcer disease. Therefore, the objective of the present study was to examine the antiulcer activity of Calpurnia aurea hydromethanolic leaf extract and solvent fractions in rats. Methods. The ulcer-healing potential of the crude test extract was assessed in rats by adopting pyloric ligation-, acidified ethanol-, and acetic acid-induced ulcer methods; while, in solvent fractions, the acidified ethanol-induced ulcer model was used. In all models, three serial test doses (100, 200, and 400 mg/kg) were given and the antiulcer activity was investigated. Standard drugs like sucralfate (100 mg/kg), omeprazole (20 mg/kg), and cimetidine (100 mg/kg) have been used as a positive control; whereas distilled water (10 mL/kg) was used as the negative control. Parameters like ulcer index, total acidity, pH, gastric volume, and gastric mucin level were all measured. Results. In an acute toxicity study, the test extract at the limit test dose (2 g/kg) was safe following a single dose administration. In pyloric ligation-induced ulcers, the plant extract at 200 and 400 mg/kg significantly reduced the ulcer index, the volume of stomach secretion, and total acidity while raising gastric pH and mucus content (P<0.05). Likewise, in the acidified ethanol- and acetic acid-induced ulcer models, the extract at both test doses (200 and 400 mg/kg) also displayed a substantial reduction (P<0.05) in ulcer index. Among the fractions, the ethyl acetate fraction revealed remarkable cytoprotective activity at all test doses and the aqueous fraction at 400 mg/kg (P<0.05). In contrast, the effect of chloroform fraction was found to be negligible. The peak ulcer inhibition was noted at 400 mg/kg of ethyl acetate fraction (52.4%). Conclusion. The study showed that the crude extract and solvent fractions possess remarkable antiulcer activity.
Multisensor Human Resource Data Fusion and Its Application in Industrial Distribution
Authors: Jiang Sheng , Zheng ChuanZhen
Published: June 2022

Over the past 30 years of reform and opening up, China has undergone tremendous changes. With the continuous growth of China’s economy, the core competitiveness of Chinese enterprises is also increasing day by day. After China joined the WTO in 2001, economic globalization hit Chinese enterprises. Under such circumstances, a large number of foreign multinational companies have entered China to establish factories, which has caused huge pressure on Chinese companies. Facing this unprecedented pressure, Chinese enterprises need to fundamentally improve management and operation problems. Companies need to change aggressively to respond to the shock of foreign companies. The development of multisensor technology has brought great changes to enterprise human resource management. The real-time changes in this technology allow the continuous optimization of human resource management. Human resource management technology also needs to adapt to the modern social environment and constantly improve and quickly existing enterprise employee management methods and methods. These technical contents need to be optimized, and the working methods need to be improved. At the current stage, enterprise human resource management has not fully understood multisensor technology. Enterprises have poor adaptability in the application of this technology, and have not carried out comprehensive planning and clear optimization of detailed rules from the macro level. On this basis, the company has also failed to establish a relatively excellent team of professional management talents. There are three issues involved in talent team management. This article firstly analyzes the effect of human resource management in contemporary enterprises based on multisensor technology. Enterprises need to improve their awareness and application of sensor technology. It also needs to establish the overall strategy of human resource management and specific means of implementation. Through the in-depth application of these technologies, enterprises can enhance the comprehensive capabilities of human resource management teams. The application of this technology also provides practical and effective help for modern enterprise human resource management.
Design and Protection Strategy of Distributed Intrusion Detection System in Big Data Environment
Authors: Chen Rong
Published: June 2022

One of the important research topics is protecting the host from threats by developing a reliable and accurate intrusion detection system. However, since the amount of data has grown fast due to the emergence of big data, the performance of traditional systems designed to identify breaches has suffered several flaws. One of them, for example, is known as single-point failure; low adaptability and a high false alarm rate are also typical. Hadoop is used to detect intrusions to tackle these difficulties. The Java system is used to create a framework with a significant data flow that detects intrusions when a distributed system is built. The proposed solution employs a distributed operating system for data collection, storage, and analysis. The results indicate that external distributed denial of service (DDoS) attacks are recognized quickly. The single-point failure issue is overcome, alleviating the bottleneck problem of data processing ability.
Construction of College Innovation and Entrepreneurship Information-Sharing Platform under Big Data Analysis
Authors: Ma Shiliang
Published: June 2022

Achieving outcomes in today’s colleges will need a creative attitude to be associated with higher necessary reforms to ensure a long-term future. The goal of this study is to enhance college students’ creativity and entrepreneurship by providing them with enough innovation and entrepreneurship information (IEI). This exploration is to build a college IEI sharing platform under big data analysis (BDA). First, the existing problems of IEI sharing in colleges are analyzed, and the construction principles of IEI sharing platform in colleges are put forward. Then, the platform’s structure is designed, and its operation mechanism is explained. Next, the IEI sharing platform of colleges is designed and constructed through BDA. At the source layer, the platform checks the big data collection goal, confirms the scope and substance of the collected big data via the open access mechanism, and communicates the extracted target big data to the data layer via the data collector. In the data layer, the data table is streamlined, and the data processing method is built. They gathered a large amount of classified and preserved data, and then communicated to the analysis layer via the classified storage mechanism via the open data sharing interface. The analysis layer analyzes and processes large data using a BDA algorithm, allowing for adaptive scheduling and information access. The performance content may eventually alter with the demands by integrating the big data received through the integration and sharing method. The results of the experiments indicate that the platform’s data mining range is pretty consistent. When the number of data operation requests exceeds 10,000, the platform’s processing speed reaches 45000. Furthermore, the time cost remains constant within 2 s as the data scale rises. As the number of repetitions grows, the recall rate rises to 98%. The findings indicate that the platform offers significant data mining, processing, and scheduling skills, as well as the ability to distribute IEI in real time among universities. This study helps the promotion of entrepreneurship among college students by improving the present college IEI sharing platform.
Intelligent Analysis of Exercise Health Big Data Based on Deep Convolutional Neural Network
Authors: Cui Cui
Published: June 2022

In this paper, the algorithm of the deep convolutional neural network is used to conduct in-depth research and analysis of sports health big data, and an intelligent analysis system is designed for the practical process. A convolutional neural network is one of the most popular methods of deep learning today. The convolutional neural network has the feature of local perception, which allows a complete image to be divided into several small parts, by learning the characteristic features of each local part and then merging the local information at the high level to get the full representation information. In this paper, we first apply a convolutional neural network for four classifications of brainwave data and analyze the accuracy and recall of the model. The model is then further optimized to improve its accuracy and is compared with other models to confirm its effectiveness. A demonstration platform of emotional fatigue detection with multimodal data feature fusion was established to realize data acquisition, emotional fatigue detection, and emotion feedback functions. The emotional fatigue detection platform was tested to verify that the proposed model can be used for time-series data feature learning. According to the platform requirement analysis and detailed functional design, the development of each functional module of the platform was completed and system testing was conducted. The big data platform constructed in this study can meet the basic needs of health monitoring for data analysis, which is conducive to the formation of a good situation of orderly and effective interaction among multiple subjects, thus improving the information service level of health monitoring and promoting comprehensive health development.
Huayu Sanjie Enema Liquid Relieves Pain in Endometriosis Model Rats by Inhibiting Inflammation, Peripheral Sensitization, and Pelvic Adhesion
Authors: Zong Chunxiao , Sun Lu , Xu Xin , Xue Xiaoou
Published: June 2022

The objective of this study is to observe the effect of relieving pain of Huayu Sanjie enema liquid (HYSJ-EL) on endometriosis model rats and to explore its mechanism of action. Of 24 female Sprague Dawley rats, six were randomly selected as the sham operation group (normal control group). The remaining rats were used to establish rat models of endometriosis through autologous endometrial transplantation combined with estrogen injection. Successfully modeled rats were randomly divided into the model, indomethacin (Western medicine group), and HYSJ-EL (Chinese herbs group) treatment groups. The thermal pain threshold of rats was measured, and hematoxylin and eosin staining was used to observe pathological changes after sampling. Serum levels of prostaglandin E2 (PGE2), interleukin-6 (IL-6), macrophage inflammatory protein-2 (MIP-2), plasminogen activator inhibitor-1 (PAI-1), and transforming growth factor-β (TGF-β) were measured using an enzyme-linked immunosorbent assay (ELISA). Furthermore, the protein and mRNA expression levels of transient receptor potential vanilloid-1 (TRPV1) and tumor necrosis factor-α (TNF-α) in the endometrium and endometriotic lesions were measured using Western blotting and quantitative real-time PCR assays, respectively. Compared to the model group, the heat pain threshold of rats in the HYSJ-EL group was significantly increased (

P
<
0.01

), and the serum levels of PGE2, IL-6, MIP-2, PAI-1, and TGF-β were significantly decreased (

P
<
0.01

), as well as the expression of TRPV1 and TNF-α protein and mRNA in the tissue of the ectopic lesion was significantly decreased (

P
<
0.05

). These results indicate that the Huayu Sanjie enema liquid exerts analgesic effects on endometriosis by inhibiting inflammation, peripheral nerve sensitization, and pelvic adhesion.
Multiple Impossible Differential Attacks for ForkAES
Authors: Jiang Zilong , Jin Chenhui
Published: June 2022

To yield a highly efficient authentication encryption design for very short messages, the tweakable forkcipher is proposed, which is a tweakable block cipher that uses forking construction to produce two output blocks. The designers also presented ForkAES, a forkcipher that is based on the round function of AES and the tweakable variant of KIASU. Therefore, the security of ForkAES is found on the block ciphers AES and KIASU. However, from the perspective of new forking construction, attackers may obtain some unique properties. Impossible differential attacks are widely used on block ciphers. This paper studies the security of ForkAES against multiple impossible differential cryptanalysis. Based on the property of the forking construction, two types of impossible differential distinguishers have been constructed. We first use the tweak with different truncated differences to build more attack trails. Then, we first propose the multiple impossible differential attack for ForkAES-

∗

-5-4. Thus, only a single round would remain as a security margin. Utilizing multiple attack trails, our attack scenario obtains more subkey bytes and enhances the subkey’s sieving efficiency without increasing complexity. Furthermore, we carefully consider the process of recovering the master key, which can efficiently reject wrong candidate keys. In reconstruction queries, our attack reaches the longest number of rounds for ForkAES in impossible differential analysis, with

2

100.8

lookups,

2

85.8

chosen ciphertexts, and

2

92.7

memory blocks to store AES states. In encryption queries, we improve the previous attacks on ForkAES by attacking one more round (i.e., ForkAES-

∗

-5-4), with

2

118.2

lookups,

2

111.4

plaintexts, and

2

92.7

memory blocks to store AES states.
Power-Sensitive Early Warning Data Conversion Method Combined with Multiobjective Differential Evolution Algorithm
Authors: Gao Xianzhou , Yu Pengfei , Liang Fei , Yang Ruxia , Jia Aiqi
Published: June 2022

Aiming at the problems of economy, security, and reliability of power system operation, the multiobjective gap evolutionary algorithm is introduced into power-sensitive early warning data conversion. A power-sensitive early warning data conversion (PSEWDC) method based on multiobjective gap evolutionary algorithm (MOGEA) is proposed. According to the type of equipment with problems in the operation of the power system and the rapid diagnosis of defects and problems, the method quickly locates the problems and analyzes the causes, replaces and rearranges the original data by using the substitution data method to eliminate the nonlinear autocorrelation, and then, according to the convergence of the long-range correlation index, identifies the abnormal values that have no impact on the overall fluctuation of the original sequence, judges that the current sequence reaches the critical point of extreme events, and provides a reference threshold for real-time risk early warning. Finally, the example analysis shows that this method can finally determine the risk early warning threshold of power peak and valley load by analyzing the convergence of long-range correlation index, so as to avoid the blindness of subjective experience, provide a theoretical basis for power load risk early warning, effectively solve the problems of subjectivity, lack of dynamics, and lack of theoretical basis for setting risk threshold in traditional power risk early warning research, and have an intelligent insight into the security situation of power big data.
A Novel Model for Calculating Uplink-Downlink Spectral Efficiency in Cooperative Communication MIMO Systems
Authors: Pouryeganeh Mohammad Reza , Fard Azim , Tavakoli Mohammad Bagher
Published: June 2022

Cooperative communication that enables the use of relays between a base station (BS) and end users is an effective technique against fading to improve network performance, especially in increasing spectral efficiency (SE) and network coverage. However, systems that use cooperative communication have weaknesses in the structure, such as resistance to latency and an increase in the bandwidth of substitute users (SU), which can be improved by optimizing the relay selection method effectively. The system due to insufficient spacing between antennas and insufficient scattering in the channel has spatially faded that leads to spatial correlation. We use Kronecker statistical model correlated multiantenna channels to implement detection techniques and eliminate interference in cooperative communication. The validity of the Kronecker model lies in the fact that correlation coefficients of transmission are independent of the receiving antennas. In other words, the spatial correlation model separates both ends of the communication link. Therefore, using the minimum mean-squared-error (MMSE) technique, we have removed the need for optimal elimination of interference between SU and relay service provider or primary user (PU). Regardless of BS performance, the scope of this work is restricted to the layer after the BS in the interaction between the service providers and substitute users. The primary purpose of the presented method is to improve the spectral gain through the elimination of interference. Simulation results show about 10% of SE improvement along with considerable traffic gain.
Principal Component Research of the Teaching Model Based on Multimodal Neural Network Algorithm
Authors: Yang Guang , Liang Xiaodong , Deng Shanshan , Chen Xiao
Published: June 2022

With the deepening and improvement of the contemporary English educating reform, the lookup on the satisfactory English training has attracted greater and extra attention. The key to enhance the English training is to enhance good teaching, and English teaching model is the key measure to enhance good schooling and teaching. Based on a single neural network, it can solely describe the randomness and irregularity of English education quality and cannot describe the whole exchange traits of English education model, which makes the impact deviation of teaching model larger. Based on the in-depth learning of the contemporary state of affairs and traits of English education model, blended with the traits of neural network, this paper constructs an English teaching model primarily based on multimodal neural network algorithm. The experimental results show that the convergence speed of multimodal neural network model is 76% higher than that of single network model, the sum of squares of average error is 79%, and the average evaluation accuracy is 13.99% and 6.42% higher than that of convolution neural network model and radial basis function neural network model, respectively. It is demonstrated that the multimodal neural network model does not accelerate the convergence speed of the network or improve the prediction accuracy of the model and can quickly realize the ability of global optimization. It shows the effectiveness and accuracy of using multimodal neural network algorithm to model English teaching quality and provides a feasible solution for teaching quality model.
Stabilizers Effects Comprehensive Assessment on the Physical and Chemical Properties of Soft Clays
Authors: Pourebrahim Farzad , Zolfegharifar Sayyed Yaghoub
Published: June 2022

Deep soil mixing (DSM) is one of the practical methods of soil improvement in the world, which produces cement columns and modification by injecting stabilizing materials such as cement or lime using a mechanically hollow shaft drill in the soil. In this method, the soil characteristics are improved by mixing the soil with cementitious materials by deep mixers and creating cement soil columns. In addition, by overlapping the columns before full setting, continuous walls can be constructed below the ground. The aim of this study was to investigate the effect of the additive type on the strength of the samples. For soil samples stabilized with lime, the maximum value of uniaxial resistance was 247 kPa and for the mixture of 10% lime and 4% sodium silicate was obtained.
Analysis of the Effect of Urban Residents’ Sports Consumption on GDP Growth Based on Deep Learning
Authors: Gao Heng , Zhang Yawen , Zhao Yinhong , Ma Junjie , Yuan XinGuo
Published: June 2022

Nowadays, emerging industries are emerging, and the sports industry has become a remarkable new economic growth point. Vigorously tapping the potential of residents’ sports consumption has important theoretical and practical significance for promoting the development of the sports industry, improving people’s living standards, and stimulating economic growth. In this paper, a deep learning model is constructed, and the random forest and random network models in the deep learning network are used to analyze the pulling effect of urban residents’ sports consumption on economic growth. Since the consumption level of urban residents is much higher than that of rural residents, urban residents are in a dominant position in sports consumption, so this paper takes urban residents’ sports consumption as the core to explore the pulling effect of urban residents’ sports consumption on economic growth. The research theme of this paper is the pulling effect of urban residents’ sports consumption on economic growth, so this paper sets the explanatory variable as the added value of GDP, expressed by GDP. Sports consumption has the characteristics of inevitability, gradualness, and diversity. With the continuous change of people’s living standards, sports consumption also presents several stages of relative consumption pattern changes. Research shows that sports consumption has a positive role in promoting economic growth and the transformation and upgrading of economic development mode. Every one percentage point change in sports consumption leads to an economic growth of 0.186 percentage points, and with the increase of the lag period, urban residents’ sports consumption will gradually increase the driving effect of economic growth. This effect can be analyzed at the micro- and macrolevels and enhanced by a causal cumulative cycle mechanism.
Application of Mobile Information System in Quality Education of Research Activities
Authors: Liu Yonglang , Ding Sanqing
Published: June 2022

In order to solve the problems of uneven local educational resources, imperfect comprehensive practice systems, and insufficient teachers in rural schools, this paper proposes a research activity based on mobile information system. The research activities proposed in this paper take STEAM (the acronym of Science, Technology, Engineering, Art, and Mathematics) as the educational concept, carry out local research-based learning (research-based learning, hereinafter referred to as “research-based learning”), and create STEAM research activities suitable for local teaching in combination with the requirements of national education reform to improve students’ scientific literacy such as innovation, scientific, and technological level, and independent practice and exploration. The results are as follows: 92% of the students in the experimental group think that STEAM research and learning activities are very interesting and recognize the research and learning process; STEAM research activities have a strong role in promoting the cultivation of students’ three abilities and the training of core skills, and the scores are increased by about 50%; the excellent, good, qualified, and unqualified students in the experimental group accounted for 15%, 73%, 12%, and 0%, respectively, while the control group accounted for 6%, 55%, 32%, and 7%, respectively. Through the participation of STEAM research activities, the students’ learning attitude has been greatly improved. The practice of STEAM research activities proposed in this paper in the research and implementation institute has achieved remarkable results, which provides experience for Western schools in the follow-up stage of compulsory education to carry out STEAM research activities.
Contact and Infiltration Behavior of Al Melt and Al-B4C Composite
Authors: Shi Wei , Yu Xincheng , Zhang Pengchao , Shang Zhenguo , Yao Kunpeng , Yu Yingshui
Published: June 2022

In the present study, the authors used sessile drop method to simulate the contacting and infiltration behavior between Al melt and Al–B4C composite. In order to track the penetration distance of the metal melt infiltrate into the Al-B4C composite matrix when the infiltration is not obvious, element Si was added into the metal melt as the tracer agent. The Al-10%Si melt was dropped onto the Al-B4C composite substrate at 913K. The changes of droplet bottom size, height, and contact angle during the spreading and penetration with Al-B4C composite substrate surface are investigated by infiltration experiment equipment. The process of the droplet infiltrating into the Al-B4C composite can be divided into two stages: (a) a slow infiltration stage that the contact angle changes slowly; (b) a quick infiltration stage with the rapidly-changed contact angle. The content of B4C and the pores in the composite plays an important role on the infiltration of the droplet. The higher the density, the easier the infiltration proceeds.