bluuebunny/crossref_metadata_2025 huggingface dataset:
Application of Edge Computing in Physical Education Teaching Management
Authors: Nie Dongfang , Yuan Qin
Published: August 2022

In the traditional teaching mode, teachers have limited time and energy, but the emergence of AI technology-assisted teaching has greatly facilitated students and teachers. They can study and teach anytime and anywhere and can also solve the problem of lack of professional teachers or venues. With the continuous popularization of “Internet + education” technology, the teaching mode has changed, and online courses are gradually accepted by students regardless of time and geographical constraints. This article aims to study the optimization and application of the intelligent scheduling algorithm in the physical education management system based on blockchain technology. This article proposes a combination of Edge Computing technology and intelligent algorithm software and hardware to improve the current difficulties of domestic colleges and universities. The core content of integration, system functions, course scheduling algorithm, database, and other core contents are preliminary designed, hoping to provide ideas for the specific implementation of the college course scheduling system. The experimental results in this paper show that with the support of Edge Computing in solving the CSP problem, the efficiency of various algorithms is relatively high and comparable when the filling rate is less than 90%. However, when the filling rate exceeds 90%, the execution time of the IFS CBS algorithm is relatively longer, but it is generally acceptable. The algorithm and optimization strategy are also implemented, and the performance improvement of the algorithm is compared and analyzed through experiments, which proves the feasibility of the optimization strategy.
DAFLNet: Dual Asymmetric Feature Learning Network for COVID-19 Disease Diagnosis in X-Rays
Authors: Liu Jingyao , Zhao Jiashi , Zhang Liyuan , Miao Yu , He Wei , Shi Weili , Li Yanfang , Ji Bai , Zhang Ke , Jiang Zhengang
Published: August 2022

COVID-19 has become the largest public health event worldwide since its outbreak, and early detection is a prerequisite for effective treatment. Chest X-ray images have become an important basis for screening and monitoring the disease, and deep learning has shown great potential for this task. Many studies have proposed deep learning methods for automated diagnosis of COVID-19. Although these methods have achieved excellent performance in terms of detection, most have been evaluated using limited datasets and typically use a single deep learning network to extract features. To this end, the dual asymmetric feature learning network (DAFLNet) is proposed, which is divided into two modules, DAFFM and WDFM. DAFFM mainly comprises the backbone networks EfficientNetV2 and DenseNet for feature fusion. WDFM is mainly for weighted decision-level fusion and features a new pretrained network selection algorithm (PNSA) for determination of the optimal weights. Experiments on a large dataset were conducted using two schemes, DAFLNet-1 and DAFLNet-2, and both schemes outperformed eight state-of-the-art classification techniques in terms of classification performance. DAFLNet-1 achieved an average accuracy of up to 98.56% for the triple classification of COVID-19, pneumonia, and healthy images.
[Retracted] Analysis of the Implementation Effect of College Curriculum Ideological and Political under the Background of Ecological Sustainable Development
Authors: Wu Xiaoli , Wu Dang , Ma Xiufeng
Published: August 2022

Our country is paying more and more attention to ecological issues. How to put ecological sustainable development in real life is a key problem. This article discusses the significance of integrating ecological sustainable development and college ideological political courses. Therefore, an experiment was designed to analyse the model of ecology and other courses. The final experiment showed the following: (1) The ecological sustainable development model can be well integrated into the students in their courses. Students can freely choose to study some quality assurance thought courses and reinforce their spiritual level. The mentality of this model can also self‐optimize to solve students’ troubles in choosing courses and consider the level of students intimately. (2) According to the experimental data of the figures and tables, it is concluded that the form “other professional courses” at my country is still very serious, and the popularization of ideological and political courses is not common, so it is a little troublesome to carry out work, but our system model is very characteristic. With its help, we have obtained the effect of the integration of ecological sustainable development and students’ curriculum. The state has vigorously provided economic support for their development. Under their influence, students have gradually maintained their awareness of protecting the environment. Socioeconomic and environmental conditions are also improving. However, in the future, the times will change, and we will continue to update the system model to adapt to more challenges.
An Improved Data Generalization Model for Real-Time Data Analysis
Authors: Srisaila A , Rajani D , Madhavi M V D N S , Jaya Lakshmi G , Amarendra K , Dasari Narasimha Rao
Published: August 2022

This research proposes a maximum likelihood-Weibull distribution (WD) model for the generalized data distribution family. The distribution function of the anticipated maximum likelihood-Weibull distribution is defined where the statistical properties are derived. The data distribution is capable of modelling monotonically decreasing, increasing, and constant hazard rates. The proposed maximum likelihood-Weibull distribution is used for evaluated these parameters. The experimentation is done to evaluate the potential of the maximum likelihood-Weibull distribution estimated. Here, the online available dataset is adopted for computing the anticipated maximum likelihood-Weibull distribution performance. The outcomes show that the anticipated model is well-suited for computation and compared with other distributions as it possesses maximal and least value of some statistical criteria.
QoE-Aware Video Delivery in Multimedia IoT Network with Multiple Eavesdroppers
Authors: Wu Dapeng , Xu Ruixin , Zhang Hong , Li Zhidu , Wang Ruyan , Fedotov Alexander , Badenko Vladimir
Published: August 2022

How to deal with the increasing video traffic and diverse service demands while ensuring the security of transmission is an open issue in the multimedia Internet of Things (IoT). This paper addresses this issue and studies a secure delivery scheme under a multicast scenario in the presence of multiple eavesdroppers where small base stations (SBSs) can send videos to users cooperatively. Aiming at potential eavesdroppers, a channel model including artificial noise is introduced to reduce the harm of illegal data acquisition. A network quality of experience (QoE) optimization problem is first formulated to account for video quality and delivery delay. In order to solve the nonconvex problem, the successive convex approximation (SCA) technique is applied to optimize multicast group beamforming, reduce the possibility of multicast video eavesdropping, and select video quality where a heuristic scheme is proposed to maximize the network QoE. The effectiveness of the proposed scheme is finally validated by extensive simulations in terms of algorithm convergence performance and network QoE-enhanced performance.
Fly Ash/Silica Fume Coral Concrete with Modified Polypropylene Fiber for Sustainable Building Construction
Authors: Wang Bo , Shi Wenhao , Wu Qing , Xi Xun , Yang Shangtong
Published: August 2022

Coral concrete made of coral sand/aggregates has attracted significant interest in the construction of coastal or artificial island buildings by saving significant economic and environmental costs for transporting the conventional building materials from mainland. However, the application of coral concrete is very limited due to the drawback such as low strength, brittleness, and high porosity. This paper develops a new coral concrete composite utilizing coral debris to replace sand and aggregates, sea water, supplementary cementitious waste materials, namely, fly ash and silica fume and modified polypropylene fiber (MPPF) to achieve both sustainable and economic development needs in coastal and island building and construction. The mechanical and durability of the new coral concrete are evaluated and the synergistic effects of fly ash and silica fume on the performance of MPPF coral concrete are discussed. It has been found out that the addition of fly ash and silica fume in MPPF coral concrete can significantly improve the strengths and reduce the chloride diffusion of coral concrete. Compared with those from the specimens without fly ash and silica fume, the compressive, splitting tensile, flexural strengths of MPPF concrete with 10% fly ash and 10% silica fume are improved by 31%, 33%, and 58%, respectively. The new coral concrete composite would be an ideal building material solution for coastal and artificial island applications as most of the materials come from the local wastes.
Research on Children’s Aesthetic Needs of Multinational Children’s Songs Based on Information Security
Authors: Liu Mingru
Published: August 2022

In the context of information security, the aesthetics and data privacy security of multinational children’s songs are studied. The research collects the data of foreign children’s songs, and on the basis of the collected data, the mathematical model of the children’s song text is constructed by using a random matrix. An industry consisting of various media entities that disseminate information is analyzed. In addition, vocal performance is used to improve children’s aesthetic ability and comprehensive quality. An efficient information hiding scheme based on the random matrix is proposed. Furthermore, by establishing a random matrix to optimize the information scheme hidden in the multinational children’s songs, the textual expression efficiency of children’s songs is improved. The results reveal that when the model is iterated for 200 times, the traditional algorithm has only a recognition accuracy of 0.73 for children’s songs, and the recognition accuracy of the convolution neural network (CNN) model can reach 0.76, while the proposed improved deep neural networks (DNN) algorithm model has the best recognition accuracy, which can reach 0.80. It explores how to better meet children’s aesthetic and cultural needs from the perspective of information security by optimizing the semantic indexing algorithm of random matrix mapping elements. Through the performance test of the algorithm, on account of reducing the time complexity of the algorithm, the classification accuracy of children’s song text can be effectively improved. This exploration has practical reference value for cultivating children’s comprehensive quality and aesthetic ability.
Clinical Value of Cytokine Assay in Diagnosis and Severity Assessment of Lung Cancer
Authors: Ma Jin , Zhu Shumin , Liu Zining , Mao Yafei , Li Xinyuan , Dai Lili , Zhao Xiaojie , Wei Congzhen , Liu Jinfeng , Geng Yulan
Published: August 2022

Purpose. To investigate the clinical value of interleukin 2 (IL-2), interleukin 4 (IL-4), interleukin 6 (IL-6), interleukin 10 (IL-10), tumor necrosis factor α (TNF-α), and interferon-γ (IFN-γ) in diagnosis and severity assessment of lung cancer. Methods. In this observational study, 50 physical examination healthy subjects were included in the control group and 100 lung cancer patients were included in the study group. In the study group, 53 cases with pleural effusion were subgrouped to the pleural effusion group (n = 53), while 47 patients were assigned to the nonpleural effusion group (n = 47). Plasma cytokines IL-2, IL-4, IL-6, IL-10, TNF-α, IFN-γ, and Acute Physiology and Chronic Health Evaluation II (APACHE II) scores of all eligible subjects were collected and compared. Results. The study group showed significantly higher levels of plasma cytokines IL-2, IL-4, IL-6, IL-10, TNF-α, and IFN-γ versus healthy subjects (

P
<
0.05

). Deterioration of lung cancer was associated with increased plasma cytokine levels and APACHE II scores. The combination assay of the above plasma cytokines showed significantly better diagnostic efficacy for lung cancer versus the single assay of the cytokines. Dead patients had higher plasma cytokine levels versus survived patients. The accuracy of plasma IL-2, IL-4, IL-6, IL-10, TNF-α, and IFN-γ levels in the severity assessment of lung cancer was comparable with that of the APACHE II scale. Conclusion. The plasma cytokines IL-2, IL-4, IL-6, IL-10, TNF-α, and IFN-γ are effective markers for the diagnosis of lung cancer. The combined assay contributes to the early diagnosis of lung cancer patients, and the persistent elevation of cytokines suggests an increased risk of death in lung cancer patients, so the detection of cytokine levels facilitates the severity assessment of lung cancer.
Improvement of Multiclass Classification of Pavement Objects Using Intensity and Range Images
Authors: Eslami Elham , Yun Hae-Bum
Published: August 2022

Automated recognition of road surface objects is vital for efficient and reliable road condition assessment. Despite recent advances in developing computer vision algorithms, it is still challenging to analyze road images due to the low contrast, background noises, object diversity, and variety of lighting conditions. Motivated by the need for an improved pavement objects classification, we present Dual Attention Convolutional Neural Network (DACNN) to improve the performance of multiclass classification using intensity and range images collected with 3D laser imaging devices. DACNN fuses heterogeneous information in intensity and range images to enhance distinguishing foreground from background, as well as to improve object classification in noisy images under various illumination conditions. DACNN also leverages multiscale input images by capturing contextual information for object classification with different sizes and shapes. DACNN contains an attention mechanism that (i) considers semantic interdependencies in spatial and channel dimensions and (ii) adaptively fuses scale-specific and mode-specific features so that each feature has its own level of contribution to the final decision. As a practical engineering project, dataset are collected from road surfaces using 3D laser imaging. DACNN is compared with four deep classifiers that are widely used in transportation applications. Experiments show that DACNN consistently outperforms the baselines by 22–35% on average in terms of the F-score. A comprehensive discussion is also presented regarding computational costs and how robustly the investigated classifiers perform on each road object.
Application of Deep Learning Model in Building Energy Consumption Prediction
Authors: Wang Yiqiong
Published: August 2022

In order to achieve China’s energy conservation and emission reduction goal of peaking carbon dioxide emissions around 2030, it is of great significance. An important means of building energy conservation and emission reduction is the fine management of building energy consumption, which is based on the accurate prediction of building energy consumption, so as to support the optimal management of building operation and achieve the goal of energy conservation and emission reduction. This paper puts forward the evaluation indexes of the results of the building energy consumption prediction model, uses MAPE and RMSE indexes to evaluate the accuracy of the prediction results of the model, and uses the prediction time and input parameter dimensions to evaluate the time cost of the prediction model. Then, using the three building energy consumption prediction models based on machine learning algorithm established above, the prediction of energy consumption of four types of public buildings in different seasons is completed, and the prediction results are evaluated and analyzed. According to the prediction results and the requirements of related work on the accuracy of building energy consumption prediction model, the adaptation relationship between different types of buildings and different machine learning algorithm prediction models is summarized.
Association between HBV Infection and the Prevalence of Coronary Artery Disease in the US Population
Authors: Ke Zun-Ping , Gong Miao , Zhao Gang , Geng Yue , Cheng Kuan
Published: August 2022

Aims. This study aims to investigate the association between HBV infection and coronary artery disease (CAD) prevalence in the US population. A nomogram was proposed to predict CAD based on HBV infection. Methods. 25,749 individuals were collected from the 2001-2014 National Health and Nutrition Examination Survey. Participants with hepatitis B core antibody seropositivity were identified with HBV infection, including current and previous HBV infection status. We used adjusted logistic regression and performed sensitivity analysis to investigate the association between HBV infection and the prevalence of CAD. The effect size was evaluated by odds ratio (OR) with a 95% confidence interval (CI). Then, we created a nomogram to predict coronary artery disease. Additionally, we applied the Cox regression model to assess the association between HBV infection and all-cause mortality in those with baseline CAD. Results. 1790 (6.95%) individuals were with HBV infection. In the adjusted model, individuals with HBV showed a decreased CAD risk than those without (OR, 0.81; 95% CI, 0.67-0.98). Consistently, reduced risk in self-reported angina (OR, 0.72; 95% CI, 0.52-0.98) and coronary heart disease (OR, 0.76; 95% CI, 0.58-0.98) was observed in the hepatitis B core antibody seropositivity group. The subgroup analysis showed a consistent trend in the subgroups of age (<45 or ≥45), gender (male or female), hypertension (no or yes), and diabetes (no or yes). In the testing set, the proposed predictive model showed good performance with an area under the curve of 0.85 (95% CI, 0.83-0.86). There was no significant association between HBV infection and all-cause mortality in CAD patients (adjusted

P
=
0.202

). Conclusion. Our study suggests that HBV infection was associated with lower CAD risk. The proposed nomogram showed good performance in predicting CAD. However, no significant association was observed between HBV and all-cause mortality in CAD patients.
[Retracted] Analysis of the Effect of Percutaneous Cone Shaping in Patients with Osteoporotic Vertebral Fractures
Authors: Yu Jiasheng , Fan Daobo , Bao Lei , Feng Dongqian , Zhang Bin
Published: August 2022

Objective. To study and analyze the clinical effect of percutaneous cone shaping in patients with osteoporotic vertebral fractures. Methods. A total of 100 patients with vertebral osteoporotic fracture treated in our hospital from June 2019 to September 2020 were selected, and there were 50 patients with vertebral osteoporotic fracture in each group. Patients could be divided into two groups according to different treatment plans: one group was vertebroplasty patients for group A, and the other group was nonsurgical treatment patients for group B. The incidence of complications during treatment and follow‐up was recorded in both groups. Results. According to a study, both groups of patients completed the treatment and follow‐up, no patients lost to follow‐up or death, the VAS scores and ADL scores of patients before treatment were higher, and there was no significant difference at baseline (P > 0.05). After different treatment methods, the VAS score and ADL score of group A showed good improvement after treatment compared with that before treatment, and there were certain differences within the group, with statistical significance (P < 0.05). Intragroup comparison. The VAS scores of group B at 1 week, 1 month, 3 months, and 12 months after treatment were all superior to those before treatment, and the ADL scores at 1 month, 3 months, and 12 months after treatment were all superior to those before treatment, with statistically significant differences (P < 0.05). VAS scores and ADL scores showed good improvement 1 day, 1 week, and 1 month after treatment compared with those before treatment. However, there were no significant differences in the VAS score and ADL score between the two groups at 3 and 12 months after treatment. The incidence of complications in group A is lower than that in group B. The incidence of complications in group A is as follows: there was 1 case of recurrent vertebral fracture and 1 case of urinary tract infection, and the overall incidence of complications was 4.00%. In group B, there were 1 case of recurrent fracture, 3 cases of bedsore, 2 cases of urinary tract infection, and 2 cases of pulmonary infection, and the incidence of total complications was 16.00%. After comparison between groups, there was a significant difference in the incidence of complications between the two groups, and the difference was statistically significant (P < 0.05). Conclusions. Percutaneous cone plasty can provide rapid relief of pain symptoms in patients with osteoporotic vertebral fractures and significantly improve their daily activities. However, there was no significant difference in long‐term recovery from group B. In terms of the incidence of complications, the incidence of complications in group A was lower than that in group B after certain treatment.
The Effect of Green Intellectual Capital on Green Performance in the Spanish Wine Industry: A Structural Equation Modeling Approach
Authors: Marco-Lajara Bartolomé , Zaragoza-Sáez Patrocinio , Martínez-Falcó Javier , Ruiz-Fernández Lorena
Published: August 2022

Global environmental problems, such as global warming, pollution, or deforestation, are critical issues that require a rapid and common response. In this context, companies play a decisive role in achieving environmental objectives through the ecological knowledge they can store and manage. In this context, the present research focuses its interest on analyzing how the set of green intangibles possessed by organizations, i.e., Green Intellectual Capital (GIC), affects their Green Performance (GP). Specifically, the study shows how GP is influenced by GIC through the mediating role of the Green Innovation (GI) variable. Therefore, the research questions to be answered by this study are as follows: Does GIC influence environmental performance? Does GI mediate the GIC‐GP relationship? What actions can companies take to improve their GP? There are several reasons that have led us to carry out this research. First, there is little empirical evidence of the relationships proposed in this study. Second, to the best of our knowledge, there is no previous research that has contextualized the relationships raised in the wine industry, thus representing an advance in the comprehension of the constructs studied. Third, GIC represents an incipient field of study that needs to be developed and established within the literature linked to Intellectual Capital (IC). In order to achieve the proposed objectives, data from a survey of 202 wineries in Spain were used and a quantitative approach was followed using Structural Equation Modeling (PLS‐SEM). The results of the research indicate that there is a positive and significant relationship between GIC and GP. In addition, GI partially mediates the relationship between these two variables, playing a key role in the environmental management of wineries. The theoretical and practical contributions of the study improve the understanding of the relationships raised, being a pioneering study due to its contextualization in the wine industry, as well as providing a series of guidelines for both environmental managers and winemakers to improve their GP.
[Retracted] Probiotics from Dairy Products on Intestinal Barrier Function Using Caco‐2 Cells under Microscope
Authors: Yao Peilin , Jiang Jiaxuan , Kong Zhenyang
Published: August 2022

With the continuous improvement of human living standards, people’s demand for health has become an important international research hotspot. In recent years, 41.3% of the total incidence of multiple organ failure (MOF) caused by dysfunction of the intestinal screen was found every year. The mortality rate is 62%, which is more than twice that of developed countries. This paper is aimed at observing the microscopic effects of probiotics derived from dairy products using Caco‐2 cells on intestinal barrier function. Based on the above background, the purpose of this study was to construct a Caco‐2 cell model under microscope to study the effect of probiotics on intestinal barrier function. This study first describes the background knowledge of the integration of modern microscope technology and medical field and the correlation between them. The results showed that the relative adhesion rates of Lactobacillus bulgaricus, Lactobacillus acidophilus, and Streptococcus thermophilus were 4.67 ± 0.07%, 11.53 ± 0.06%, and 18.31 ± 0.08%, respectively, which were lower than those in the normal group. The production of antibacterial substances can inhibit intestinal pathogens and adjust the balance of intestinal flora.
Determinants of Menstrual Hygiene Management Practices among Schoolgirls: A Cross-Sectional Study in the Savannah Region of Ghana
Authors: Asumah Mubarick Nungbaso , Abubakari Abdulai , Aninanya Gifty Apiung
Published: August 2022

Introduction. Menstruation is crucial in the reproductive lives of all women. The advent of menses in most settings is accompanied by physical and psychological health, religious, social, and cultural implications. The research intends to identify determinants of menstrual hygiene management (MHM) practices among adolescent girls in Junior High Schools in the West Gonja Municipality of the Savannah Region of Ghana. Methods. The study employed an analytical cross-sectional design with 430 adolescent schoolgirls selected through multistage sampling techniques. A structured questionnaire was used to collect data and analyzed using STATA version 14. A logistic regression model was run to determine the predictors of MHM practices. Results. The study discovered that 63.7% of the girls had sufficient knowledge of menstruation and menstrual hygiene. Almost all girls (97%) used some form of absorbent materials during menses, with over half of these girls (58.6%) using commercial sanitary pads, 30.5% using cloth, 3.7% using cotton, and 4.2% using tissue papers with 3.0% reported not using any absorbent material. Only 44.4% reported reusing their absorbent materials. Out of which, the majority (88.5%) of the schoolgirls cleaned their reusable absorbent material using soap and water with 77.5% drying absorbent materials in the sun. Overall, 84.9% practiced good MHM. Type of school [Adjusted Odds Ratio (AOR) =6.0; 95% Confidence Interval (CI) (2.64-13.59)], pocket money [AOR =2.5; 95% CI (1.27-4.86)], and residence [AOR =2.8 95% CI (1.55-5.18)] were the most significant determinants of menstrual hygiene management practice. Conclusion. About two-thirds of the schoolgirls are knowledgeable in menstrual hygiene but access to management materials is problematic whereas approximately half of the girls have access to sanitary pads and the rest resort to the use of cloth and cotton. Pocket money and residential status were the most important predictors of the menstrual hygiene management. The government initiative to provide schoolgirls with sanitary pads could go a long way to improve menstrual hygiene management if implemented across all schools in Ghana, particularly in rural areas.
Development of a Nomogram Model to Predict in-Hospital Survival in Patients with Multiple Trauma
Authors: Ling Lin , Zhang Wenchao , Peng Qing , Tong Jing
Published: August 2022

Background. Herein, we purposed to establish a nomogram model capable of assessing the probability of in-hospital survival in patients with multiple trauma. Methods. Our retrospective study is associated with 286 multiple trauma patients with 21 variables from 2017 to 2021 in The Second Affiliated Hospital, Hengyang Medical School, University of South China. We performed the univariate and multivariate logistic regression analyses for investigating the risk factors of multiple trauma. Further, we constructed a novel nomogram model, and this nomogram was evaluated by a calibration plot. Based on the multivariate analysis or the nomogram prediction model, we calculated the risk score of each patient for multiple trauma. Moreover, we compared the survival probability between the high-risk score and low-risk score groups. Finally, we assessed the discrimination of the risk score by using the C-index and the time-dependent receiver operating characteristics (ROC) curve. Results. Multivariate regression analysis revealed that the age and ISS scores were the independent risk factors, while the GCS score had protective effects on in-hospital survival. The high C-index and area under the curve (AUC) of the ROC curve confirmed reasonable discrimination for the multivariate analysis and the nomogram prediction model. Further, the calibration plot indicated reasonable accuracy of the nomogram predicting 30-day and 60-day survival probabilities. Conclusion. The nomogram model established here has good predictive efficacy for in-hospital survival of patients with multiple injuries.
Correlation of the Phase Angle with Muscle Ultrasound and Quality of Life in Obese Females
Authors: Primo David , Izaola Olatz , Gómez J. J. López , de Luis Daniel
Published: August 2022

Introduction. Phase angle (PhA) has been suggested to be an indicator of body cell mass and nutritional status. Clinically, the phase angle supposedly reflects body cell mass and cell membrane function, and the higher the phase angle, the better is the cell function. Muscle ultrasound (US) is an emerging nutritional assessment technique. Objective. The aim of this study was to investigate the usefulness and correlation of PhA with muscle US of quadriceps rectus femoris (QRF) in obese female subjects and the relationship with quality of life and physical performance. Material and Methods. In a total of healthy 50 obese female patients, anthropometric data by BIA, muscle mass by ultrasound at the QRF level, analytical determination, blood pressure, and quality of life were measured. Physical performance was assessed, too. Results. In total, 50 female obese patients were included with a mean age of 45.9 ± 2.4 years. The mean body mass index was 32.1 ± 1.6 kg/m2 with a mean weight of 83.5 ± 14.6 kg. Correlation analysis showed a positive correlation of PhA with all US parameters corrected by squared height (anteroposterior muscle thickness, circumference, cross-sectional area, and Echo-intensity). The correlation analysis of biochemical parameters with PhA showed a positive correlation with serum albumin and total protein levels. Physical activity and vitality scores of SF36 were correlated with PhA. Finally, PhA was positive correlated with physical performance, doing push-ups in 30 seconds (

r

 =0.42;

p

 =0.03) and doing squats in 30 seconds (

r

 =0.54;

p

 =0.02), without correlation with the time of 1.5 km walk. Conclusion. PhA was correlated with muscle area, muscle circumference, muscle echo intensity, serum protein, quality of life SF-36, and strength physical performance.
Kikuchi-Fujimoto Disease: A Differential for When It is Not Systemic Lupus Erythematosus
Authors: Kazmi Taskeen R. , Greear Emma L. , Lavallee Catherine A. , Stump Michael S. , Bankole Adegbenga A.
Published: August 2022

Kikuchi-Fujimoto disease (KFD) is a rare and benign disease process that is characterized by fever and lymphadenopathy that was first described in young Japanese women in the early 1970s. Knowledge of KFD is important as it can often mimic other causes of lymphadenopathy including systemic lupus erythematosus (SLE) or malignancies, and this can lead to invasive diagnostic testing and even treatments that can be avoided. The etiology and exact mechanism by which KFD develops is not fully understood at this time, but is thought to be an immune response of T cells and histiocytes to viral or bacterial infections. We present a 35-year-old African-American woman who was referred to the rheumatology clinic by our colleagues in the breast clinic with new onset right axillary lymphadenopathy and abnormal serologic testing with the suspicion of SLE after a malignancy had been ruled out.
Analysis of Risk Assessment of Overseas Infrastructure Projects Integrating BP-ANN Algorithm
Authors: Li Chengni , Zhou Jian
Published: August 2022

In order to improve the risk assessment effect of infrastructure projects, this paper combines the BP-ANN algorithm to conduct risk assessment and analysis of overseas infrastructure projects to avoid the problem of slow learning and speed up network learning. Moreover, this paper normalizes the input value, and redefines the concept of risk on the basis of previous research. In addition, this paper sets up to record the basic information and risk-related information of the project. In order to verify the effectiveness of the algorithm, this paper predicts the training data, test data, and verification data, respectively, to verify the effectiveness of the algorithm, and collects existing overseas infrastructure project cases as samples to perform the simulation experiments. The experimental study shows that the risk assessment model of overseas infrastructure projects that integrates the BP-ANN algorithm proposed in this paper has a good risk analysis effect.
Impact of Different Combinations of Processing Steps on Product Quality and Proximate Composition of Bêche-de-mer: A Case Study from Sri Lanka
Authors: Nishanthan G. , Kumara P. A. D. A. , Navarathne S. B. , Dissanayake D. C. T.
Published: August 2022

Bêche-de-mer (dried sea cucumbers) is considered as one of the most luxurious seafood in the world. The market demand for bêche-de-mer mainly depends on species and product quality. This study is aimed at assessing the effect of different processing methods on the product quality and nutritional composition of bêche-de-mer. Three sea cucumber species, Stichopus naso, Holothuria spinifera, and Bohadschia vitiensis, dominant in the commercial catches of Sri Lanka, were processed by following widely practiced local processing methods where 24, 48, and 24 different processing combinations were tested for each species, respectively. Bêche-de-mer satisfying the export quality were selected by considering their appearance and overall ranking made by exporters (9 individuals per each combination). The proximate composition of these products was assessed using standard methods. According to the exporters’ prioritization, very few processing methods used in this study resulted in the export-quality bêche-de-mer. Both product quality and proximate composition showed considerable variations with respect to the processing methods. It is proved that even a very small change in one processing step could lead to produce poor-quality products. Processing methods are species specific, and it is important to select the most appropriate method for each species to produce high-quality bêche-de-mer.
The RPL4P4 Pseudogene Is a Prognostic Biomarker and Is Associated with Immune Infiltration in Glioma
Authors: Wang Zengliang , Aili Yirizhati , Wang Yongxin , Maimaitiming Nuersimanguli , Qin Hu , Ji Wenyu , Fan Guofeng , Li Bo
Published: August 2022

Objective. Research over the past decade has suggested important roles for pseudogenes in gliomas. Our previous study found that the RPL4P4 pseudogene is highly expressed in gliomas. However, its biological function in gliomas remains unclear. Methods. In this study, we analyzed clinical data on patients with glioma obtained from The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), the Genotype-Tissue Expression (GTEx), and the GEPIA2 databases. We used the R language for the main analysis. Correlations among RPL4P4 expression, pathological characteristics, clinical outcome, and biological function were evaluated. In addition, the correlations of RPL4P4 expression with immune cell infiltration and glioma progression were analyzed. Finally, wound healing, Transwell, and CCK-8 assays were performed to analyze the function of RPL4P4 in glioma cells. Result. We found that RPL4P4 is highly expressed in glioma tissues and is associated with poor prognosis, IDH1 wild type, codeletion of 1p19q, and age. Multivariate analysis and the nomogram model showed that high RPL4P4 expression was an independent risk factor for glioma prognosis and had better prognostic prediction power. Moreover, high RPL4P4 expression correlated with immune cell infiltration, which showed a significant positive association with M2-type macrophages. Finally, RPL4P4 knockdown in glioma cell lines caused decreased glioma cell proliferation, invasion, and migration capacity. Conclusion. Our data suggest that RPL4P4 can function as an independent prognostic predictor of glioma. It also shows that RPL4P4 expression correlates with immune cell infiltration and that targeting RPL4P4 may be a new strategy for the treatment of glioma patients.
The Construction of Adaptive Learning for Sports Based on Aerobics Trajectory Recognition Model
Authors: Xi Chaojie
Published: August 2022

Perceiving the movement track of aerobics is a key element of learning aerobics, but the current aerobics movement is not very professional, the ability to identify the movement track is weak, and improper movement in the movement process is easy to cause physical injury. In order to improve the safety of athletes in bodybuilding training, this paper uses Kinect to hold the coach’s body contour, determine the standard level of coaches’ sports, and combine the characteristics for aerobics training, so as to improve the sports level of coaches, through data acquisition, data processing, and feature extraction to assist sports learning, as well as human posture recognition. The calculation and recognition of human skeleton joints are completed by two algorithms, which improve the human motion recognition algorithm. The aerobics data collected by Kinect device is specified and digitized, which enhances the robustness of the system and improves the performance of the algorithm and the accuracy of the motion data.
The Relationship between Burnout and Intention to Leave Work among Midwives: The Long-Lasting Impacts of COVID-19
Authors: Ahmadi Sahar , Maleki Azam
Published: August 2022

Objective. It is important to evaluate the long-term effects of the COVID-19 pandemic on the intention of midwives to leave their jobs. The study examined the relationship between burnout and the intent to leave work among midwives who worked at Ayatollah Mousavi Hospital of Zanjan, one year after the COVID-19 outbreak. Method. In a descriptive-analytical study, the intention of 88 midwives to leave their jobs was evaluated, one year after the outbreak of COVID-19 disease in 2021. The midwives were selected using convenience sampling methods. Data were collected using the Maslach burnout questionnaire and the Anticipated Turnover Scale (ATS). Data were analyzed with descriptive statistics, Chi-square test, Pearson correlation coefficient, and multiple linear regression model with the stepwise method at a 95% confidence level. Results. The mean intention to leave the job was 29.71 ± 6.75. Most of the midwives reported a moderate level of intention to leave the job (47.7%). There was a significant positive correlation between the intention to leave the job and all three components of burnout. The stepwise regression analyses indicated that emotional exhaustion (β = 0.344) and working rotational shifts (β = 0.276) were significant predictors of intent to leave the job. Conclusions. It can be concluded that the intention to leave the job of midwives was moderate. Given the relationship between emotional exhaustion and the intent to leave the job, interventions to increase the mental strength and resilience of midwives during the COVID-19 pandemic seem necessary.
A Novel Artificial Intelligence System in Formulation Dissolution Prediction
Authors: Wang Haoyu , Kwong Chiew Foong , Liu Qianyu , Liu Zhixin , Chen Zhiyuan
Published: August 2022

Artificial neural network (ANN) techniques are widely used to screen the data and predict the experimental result in pharmaceutical studies. In this study, a novel dissolution result prediction and screen system with a backpropagation network and regression methods was modeled. For this purpose, 21 groups of dissolution data were used to train and verify the ANN model. Based on the design of input data, the related data were still available to train the ANN model when the formulation composition was changed. Two regression methods, the effective data regression method (EDRM) and the reference line regression method (RLRM), make this system predict dissolution results with a high accuracy rate but use less database than the orthogonal experiment. Based on the decision tree, a data screen function is also realized in this system. This ANN model provides a novel drug prediction system with a decrease in time and cost and also easily facilitates the design of new formulation.
Calreticulin as an Adjuvant In Vivo to Promote Dendritic Cell Maturation and Enhance Antigen-Specific T Lymphocyte Responses against Melanoma
Authors: Gong Zheng , Chen Ming , Miao Jie , Han Chao-Jie , Zhong Qiao , Gong Fang-Yuan , Gao Xiao-Ming
Published: August 2022

An endoplasmic reticulum resident protein, calreticulin (CRT), participates in many cellular processes. CRT is a tumor-associated antigen with an important role in antitumor immunity. Previously, we reported that the recombinant CRT fragment 39-272 (CRT/39-272) exhibited superior immunobiological activity, activating macrophages to release cytokines and promoting dendritic cell (DC) maturation. However, the effect of CRT/39-272 in vivo, especially its adjuvant effect on in vivo antitumor immune responses, was not fully investigated. In this study, we constructed a fusion protein linking CRT/39-272 to an ovalbumin (OVA) peptide (residues 182–297, OVAp) and used the fusion protein (OVAp-CRT) to examine the adjuvant effect of CRT. We investigated whether CRT/39-272 could induce bone marrow-derived DC maturation and strongly promote the proliferation of OVA-specific T cells in vitro. Compared with OVAp, OVAp-CRT induced stronger antigen-specific T lymphocyte responses, including antigen-specific T cell proliferation, interferon-γ secretion, and cytotoxic T lymphocyte responses. OVAp-CRT-immunized mice generated significantly increased OVAp-specific antibody and CD4+/CD8+ memory T cells, which mediated long-term protective effects. OVAp-CRT upregulated CD40, CD80, and CD86 expressions in splenic conventional DCs. Furthermore, OVAp-CRT protected immunized mice against OVA-expressing B16 melanoma cells in vivo. Moreover, mice that were adoptively transferred with OVAp-CRT-pulsed DCs showed inhibited tumor growth and prolonged mouse survival. Our results demonstrate that CRT/39-272 can be used as a potential new adjuvant for tumor vaccines, and this finding may be useful in tumor vaccine development.
Bioinformatics and System Biological Approaches for the Identification of Genetic Risk Factors in the Progression of Cardiovascular Disease
Authors: Barua Joy Dip , Omit Shudeb Babu Sen , Rana Humayan Kabir , Podder Nitun Kumar , Chowdhury Utpala Nanda , Rahman Md Habibur
Published: August 2022

Background. Cardiovascular disease (CVD) is the combination of coronary heart disease, myocardial infarction, rheumatic heart disease, and peripheral vascular disease of the heart and blood vessels. It is one of the leading deadly diseases that causes one-third of the deaths yearly in the globe. Additionally, the risk factors associated with it make the situation more complex for cardiovascular patients, which lead them towards mortality, but the genetic association between CVD and its risk factors is not clearly explored in the global literature. We addressed this issue and explored the linkage between CVD and its risk factors. Methods. We developed an analytical approach to reveal the risk factors and their linkages with CVD. We used GEO microarray datasets for the CVD and other risk factors in this study. We performed several analyses including gene expression analysis, diseasome analysis, protein-protein interaction (PPI) analysis, and pathway analysis for discovering the relationship between CVD and its risk factors. We also examined the validation of our study using gold benchmark databases OMIM, dbGAP, and DisGeNET. Results. We observed that the number of 32, 17, 53, 70, and 89 differentially expressed genes (DEGs) is overlapped between CVD and its risk factors of hypertension (HTN), type 2 diabetes (T2D), hypercholesterolemia (HCL), obesity, and aging, respectively. We identified 10 major hub proteins (FPR2, TNF, CXCL8, CXCL1, IL1B, VEGFA, CYBB, PTGS2, ITGAX, and CCR5), 12 significant functional pathways, and 11 gene ontological pathways that are associated with CVD. We also found the connection of CVD with its risk factors in the gold benchmark databases. Our experimental outcomes indicate a strong association of CVD with its risk factors of HTN, T2D, HCL, obesity, and aging. Conclusions. Our computational approach explored the genetic association of CVD with its risk factors by identifying the significant DEGs, hub proteins, and signaling and ontological pathways. The outcomes of this study may be further used in the lab-based analysis for developing the effective treatment strategies of CVD.
Deep-Learning-Guided Student Classroom Action Understanding for Preschool Education
Authors: Li Xiaoli
Published: August 2022

A deep architecture for enhancing students’ action recognition is proposed to improve preschool education. This paper seamlessly combines the teaching objectives, teaching scope, teaching implementation, and breeding evaluation status of preschool breeding practice theory. We attempt to solve the problem of effective preschool teaching, based on which we propose the simple adaptation strategies. We further evaluate the practice of preschool breeding and its effectiveness. In this way, civilized and high-quality preschool talents will be cultivated, and preschool educational experiences will be promoted. In the method of promoting the preschool culture of weak-aged children, owing to the problem that the traditional action recognition algorithm can indicate the specific students’ actions, an action recognition method based on the combination of deep integration and human skeleton representation is proposed. First, the connected spatial locations and constraints are fed into a long-short-specified recall (LSTM) mode with a spatially and temporally aware algorithm which is designed to obtain spatiotemporal feature and highly separable deep joint features. Afterward, a new mechanism is introduced to resolve keyframes as well as the joints. Finally, based on the two-stream deep architecture, the effective discrimination of similar actions is achieved by integrating the color and shape features into the skeleton features by designing the deep model. Extensive experiments have demonstrated that, compared with the mainstream algorithms, this method can effectively distinguish students’ action types in the classroom of homogeneous preschool children. Thus, we can substantially improve the efficiency of preschool teaching.