bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis on the Reform and Development of College Physical Education Teaching Service under the Background of 5G Internet Communication
Authors: Kang Lijun , Oh Youn-poong , Li Xiaoqian
Published: June 2022

The development of the Internet of Things (IoT) made it possible for technology to communicate physical education by connecting cost-effective heterogeneous devices and digital applications to uncontrolled and accessible environments. This study explores the reforms and development of college physical education teaching services under the background of the 5th Generation Mobile Communication Technology. The data channel algorithm and the Internet of things (IoT) resource allocation algorithm for deep reinforcement learning are adopted to analyze the physical education reform in colleges. The application scenario of the IoT, the Multiple Input Multiple Output (MIMO) precoding technology, and the repeated lifting coverage of transmission time interval (TTI) in data transmission are examined based on the downlink system of the Un-narrow Band Internet of Things (U-NB-IoT). Moreover, the resource allocation problem under the access of massive IoT devices is solved through the deep reinforcement learning framework. Results show that quality of service is difficult to measure the communication network centered on the integration of teachers and students, while the quality of experience can make teachers and students feel satisfied. Moreover, quality of experience can measure the quality of service by the satisfaction of teachers and students to experience teaching resources. The resource allocation algorithm proposed can improve the experience of teachers and students using teaching resources, make the satisfaction of teachers and students reach the ideal value, further optimize the traditional problems existing in teaching, and improve the quality of students in all aspects.
Camel Milk Used as an Adjuvant Therapy to Treat Type 2 Diabetic Patients: Effects on Blood Glucose, HbA1c, Cholesterol, and TG Levels
Authors: Sboui Amel , Atig Chiraz , Khabir Abdelmajid , Hammadi Mohamed , Khorchani Touhami
Published: June 2022

The efficacy of camel milk to treat diabetes has been shown recently, especially in experimental animals and in patients with diabetes type 1 (T1DM), whereas studies on patients with type 2 diabetes mellitus (T2DM) are limited. In this clinical trial, 60 patients with T2DM who used oral antidiabetic drugs were assigned into two groups; group 1 received—in addition to the antidiabetic prescribed medicines—500 mL of raw camel milk divided equally two times/day (fasting in the morning and the night) for three months. Group 2 was treated during the same period only by the oral antidiabetic medicaments without consumption of camel milk. A significant decrease was shown in fasting blood glucose (FBG) (from 9.89 ± 0.98 to 6.13 ± 0.55 mmol/L) and postprandial glucose (PPG) (from 15.89 ± 4.34 to 7.44 ± 1.02 mmol/L) in the group 1. A significant decline (

P
<
0.05

) in HbA1c levels was observed in the group treated with camel milk (from 9.44 ± 0.16 to 6.61 ± 0.14%, with a percentage decrease of 30%). Total cholesterol and TG significantly decreased in group 1. Urea and creatinine showed no statistical differences between the two groups during the trial. Based on this study’s results, camel milk could be useful for glycemic control in T2DM patients using oral hypoglycemic agents.
Event Scene Method of Legal Domain Knowledge Map Based on Neural Network Hybrid Model
Authors: Zhou Lu
Published: June 2022

Event extraction technology is one of the important researches in the field of information extraction, which helps people accurately retrieve, find, classify, and summarize effective information from a large amount of information streams. This paper uses the neural network hybrid model to identify the trigger words and event categories of the legal domain knowledge graph events, extracts the events of interest from a large amount of free text, and displays them in a structured format. First, the original text is preprocessed, and then, the distributed semantic word vector is combined with the dependent syntactic structure and location attributes to create a semantic representation in the form of a vector. The combined deep learning model is used to extract activated words, the long-term memory loop neural network uses temporal semantics to extract deep features, and the convergent neural network completes the extraction of activated words and event categories. Finally, the experimental results show that the accuracy of event extraction of the neural network hybrid model designed in this paper has reached 77.1%, and the recall rate has reached 76.8%, which is greatly improved compared with the traditional model.
Corporate Liquidity Management in Emerging Economies under the Financial Constraints: Evidence from China
Authors: Laghari Fahmida , Chengang Ye , Chenyun Ye , Liu Yiding , Xiang Li
Published: June 2022

The main purpose of this article is to investigate the impact of the optimum level of cash holdings on corporate performance. Moreover, in this paper, the impact of financial constraints is tested as moderating factor between the relationship of cash holdings and corporate performance. The present study uses the system generalized method of moments (GMM) as the main estimation methodology. Using a sample of companies listed on stock exchanges of China, empirical pieces of evidence find that cash holdings‐corporate performance relation is a nonlinear concave and depicts similar evidence for firms with financial constraints. The financially constrained firms maintain optimal cash holding at a higher level, which corresponds to debt rationing, difficulty to access financial markets, and the high cost of external finance. Moreover, propensity‐score‐matching depicts statistically significant differences in the level of cash holdings amid financially constrained and unconstrained firms. Finally, the difference‐in‐difference estimator shows that financial crisis affects less financially constrained firms due to low reliance on external financing.
Diet and Oviposition Deprivation Effects on Survivorship, Gonotrophic Dissociation, and Mortality of Anopheles gambiae s.s.
Authors: Chisulumi Paulo S. , Nampelah Bahati , Yohana Revocatus , Philbert Anitha , Kweka Eliningaya J.
Published: June 2022

Diet quality is of paramount importance for egg batch size, longevity, and mortality of vector mosquitoes. Oviposition site presence and absence assumed to be dry season means a lot to the survivorship and mortality of most anthropophilic malaria vectors in sub-Saharan Africa. This study has assessed the effect of different diets and oviposition-site deprivation (OSD) on survivorship, longevity, and mortality of An. gambiae s.s. To determine the effect of diet and OSD on mortality, gonotrophic dissociation rates, longevity, and survivorship, six treatments were employed: Blood Fed with Oviposition (BFO), Blood Fed without oviposition (BF), Blood and Sugar Fed with Oviposition (BSFO), Blood and Sugar Fed without oviposition (BSF), Sugar Fed with Oviposition (SFO), and Sugar Fed without oviposition (SF). Mortality and gonotrophic dissociation were monitored daily. This study found that female mosquitoes offered blood meals with sugar solution and oviposition deprivation survived longer than those deprived of oviposition deprivation. Similarly, female mosquitoes fed on blood and provided with oviposition deprivation lived longer than those without oviposition deprivation. The gonotrophic dissociation rates were found to be lower in groups provided with oviposition deprivation. Our results show that OSD has a direct impact on the survivorship, gonotrophic dissociation rate, and longevity of the malaria anthropophilic vector, An. gambiae s.s., regardless of the diet.
Construction of Wireless Sensor Network Video Surveillance System for Multimedia Classroom Education and Teaching under 5G Communication Network
Authors: Wu Wenping , Lu Biao
Published: June 2022

Research on wireless sensor network video surveillance system is a hot field of media classroom education and teaching. Based on the 5G communication network theory, this paper constructs a wireless sensor network video monitoring system for multimedia classroom education and teaching, systematically analyzes the new distance education model under the condition of the integration of the two networks, and improves the interactive live classroom education model. For the classroom teaching mode, the model studies the isochronous transmission technology of data collection and video encoding and decoding, adopts hardware compression encoding, integrates key technologies such as reliable multicast and conditional access, and proposes the design of the system scheme, which solves the problem of multimedia classroom education and teaching digital issues. During the simulation process, the MATLAB software platform was used to study the effect of the number of 5G communication network nodes and node attributes (such as node position, node perception angle, instantaneous perception direction, or direction difference between neighboring nodes, node speed, etc.) on the target area or target point. The experimental results show that the performance of the joint optimization of transmission video quality and network lifetime performs well under multipath conditions. The total utility under multipath finally converges to 8.3, while the single path finally converges to 6.5, which further promotes the real-time performance of the multimedia network classroom teaching system.
Design and Implementation of Path Planning for Wheel-Track Hybrid Mobile Robot
Authors: Hong Zirong , Du Weichen , Wang Hong
Published: June 2022

Wheel-track hybrid mobile robot has been widely used because of its high autonomous mobility and its ability to adapt to complex and changeable outdoor environment. Map construction and path planning are the main conditions to achieve its autonomous mobility. Therefore, a wheel-track hybrid robot path planning has been designed and implemented. Simultaneous Localization and Mapping (SLAM) algorithms are used to process the road images captured by the robot’s camera and other sensors. A rapid raster map with Robot Operating System (ROS) was constructed by rapid recognition of key elements from the images with the help of the Oriented Fast and Rotated Brief (ORB) feature vector. The ant colony algorithm with strong optimization ability, high optimization efficiency, and flexible algorithm is used for path planning, and the problem of unfixed location of outdoor obstacles is solved at the same time. The simulation results show that the global performance and convergence speed of the improved algorithm are increased.
EEG-Based Epileptic Seizure Detection via Machine/Deep Learning Approaches: A Systematic Review
Authors: Ahmad Ijaz , Wang Xin , Zhu Mingxing , Wang Cheng , Pi Yao , Khan Javed Ali , Khan Siyab , Samuel Oluwarotimi Williams , Chen Shixiong , Li Guanglin
Published: June 2022

Epileptic seizure is one of the most chronic neurological diseases that instantaneously disrupts the lifestyle of affected individuals. Toward developing novel and efficient technology for epileptic seizure management, recent diagnostic approaches have focused on developing machine/deep learning model (ML/DL)-based electroencephalogram (EEG) methods. Importantly, EEG’s noninvasiveness and ability to offer repeated patterns of epileptic-related electrophysiological information have motivated the development of varied ML/DL algorithms for epileptic seizure diagnosis in the recent years. However, EEG’s low amplitude and nonstationary characteristics make it difficult for existing ML/DL models to achieve a consistent and satisfactory diagnosis outcome, especially in clinical settings, where environmental factors could hardly be avoided. Though several recent works have explored the use of EEG-based ML/DL methods and statistical feature for seizure diagnosis, it is unclear what the advantages and limitations of these works are, which might preclude the advancement of research and development in the field of epileptic seizure diagnosis and appropriate criteria for selecting ML/DL models and statistical feature extraction methods for EEG-based epileptic seizure diagnosis. Therefore, this paper attempts to bridge this research gap by conducting an extensive systematic review on the recent developments of EEG-based ML/DL technologies for epileptic seizure diagnosis. In the review, current development in seizure diagnosis, various statistical feature extraction methods, ML/DL models, their performances, limitations, and core challenges as applied in EEG-based epileptic seizure diagnosis were meticulously reviewed and compared. In addition, proper criteria for selecting appropriate and efficient feature extraction techniques and ML/DL models for epileptic seizure diagnosis were also discussed. Findings from this study will aid researchers in deciding the most efficient ML/DL models with optimal feature extraction methods to improve the performance of EEG-based epileptic seizure detection.
Aβ1–40 Oligomers Trigger Neutrophil Extracellular Trap Formation through TLR4- and NADPH Oxidase-Dependent Pathways in Age-Related Macular Degeneration
Authors: Chen Jinquan , Zhao Long , Ding Xuanheng , Wen Yan , Wang Lingda , Shu Qinxin , Xie Wenxi , Liu Yanyao , Peng H.
Published: June 2022

Neutrophils participate in the advancement of the human innate immune system and respond to perceived endogenous and exogenous threats. As a response mechanism, neutrophil extracellular traps (NETs) form near pathogens and surrounding tissues during an immune response. Drusen is an important marker of Age-Related Macular Degeneration (AMD) and plays an important role in the course of AMD. Aβ1-40 is the main component of drusen. However, the relationship between NETs and AMD or Aβ1-40 is unclear. Here, we found elevated levels of NETs in the serum of AMD patients and elevated levels in the serum of mouse models. We also observed the accumulation of neutrophils in the mouse retina. In addition, the production of NETs was inhibited by PAD4 inhibitors, which can alleviate chronic inflammation. Moreover, we confirmed that Aβ1-40 can induce NETs formation via the Toll-like receptor 4 (TLR4) and neutrophil NADPH oxidase (NOX) pathways. Our study confirmed that the formation of NETs is induced by Aβ1–40, and the results suggest that NETs may play a vital role in AMD pathogenesis.
The Impact of Lamina Characteristics and Types on Organic Matter Enrichment of Chang 73 Submember in Ordos Basin, NW China
Authors: Bian Congsheng , Zhao Wenzhi , Yang Tao , Liu Wei , Dai Chaocheng , Zeng Xu , Wang Kun , Li Yongxin , Xiao Di
Published: June 2022

The Chang 7 member in Ordos Basin is an important shale oil exploration layer, with new shale-oil discoveries in recent years. The Chang 73 submember is rich in organic shale, which is the main source rock of shale oil in the Yanchang Formation. In order to clarify the lamina structure, composition, types, and distribution characteristics in Chang 73 submember and its influence on organic matter enrichment, a full coring well in Chang 73 submember located in deep-lacustrine facies is selected to obtain intensive systematic core samples. Core observation, thin section identification, X-ray fluorescence element analysis, X-diffraction analysis, scanning electron microscope, electron probe, rock pyrolysis, and other techniques are performed to systematically analyze the morphology, structure, thickness, mineral compositions, and organic matter content of the shale lamina in the Chang 73 submember. Five types of lamina are identified: silty felsic lamina (SF), tuffaceous lamina (TF), organic-rich clay lamina (ORC), organic-bearing clay lamina (OBC), and homogeneous clay lamina (HC), which are further subdivided into eight subtypes. The lamina types change greatly vertically in the Chang 73 submember, in which the lower part is mainly silty felsic lamina, organic-rich clay, and tuffaceous lamina, the middle part is mainly organic rich and organic clay lamina and organic-bearing clay lamina, and the upper part is mainly homogeneous clay lamina and a small amount of silty felsic organic-bearing clay lamina and organic-bearing clay lamina. Different laminae show various organic matter types, organic matter content (TOC), and organic matter occurrence states which can be divided into four occurrence types. The TOC in organic-rich clay lamina and part of homogeneous clay lamina is high, while that of silty felsic lamina is lower. The relationship between shale lamina and organic matter enrichment is established according to the correlation analysis of laminar characteristics, mineral content, and organic matter content. Among them, the organic-rich lamina is richest of TOC and is a favorable “sweet point” for shale oil exploration.
Leakage Current Sensor and Neural Network for  MOA Monitoring
Authors: He Tao , Li Yang , Zhang Zhong , Shen Pengfei , Zhang Yu
Published: June 2022

Metal-oxide arrester (MOA) has been widely used in electric power systems. The leakage current monitoring of MOA can not only detect the MOA’s running state continuously and intelligently but also reduce the unexpected outage of the equipment, which is also beneficial to the stability of the grid. The MOA loses its protection function due to various faults caused by excessive leakage current in actual running. This article studies the monitoring method of MOA based on leakage current sensor and back propagation (BP) neural network. At first, we design a novel leakage current sensor to acquire the leakage current of MOA. Then, the leakage current measurement of MOA based on harmonic analysis is proposed. Finally, the strong training ability of the BP neural network is used to train some key parameters that can reflect the aging of MOA so as to monitor the MOA state. The experimental results show that the leakage current acquired from the simulation is close to the actual leakage current that needs to be measured. It is also shown that the proposed method has good anti-interference and can effectively monitor the aging of MOA. Through the training of the BP neural network, the experiments prove that the training method in this article is superior to other neural network training methods obviously.
A Model for Analyzing Teaching Quality Data of Sports Faculties Based on Particle Swarm Optimization Neural Network
Authors: Li Xue
Published: June 2022

In this paper, we use a particle swarm optimization neural network algorithm to analyze the teaching data of physical education faculties and evaluate the quality of teaching in physical education faculties. By studying and analyzing the optimization problem of the weight parameters of convolutional neural network training, this paper designs a hybrid algorithm combining the improved PSO algorithm and the traditional gradient descent in the framework of the BP algorithm by using the gradient information of the loss function and the principle of group cooperative search through PSO algorithm. The hybrid algorithm takes the loss function as the objective function, based on the principle of the PSO algorithm, and optimizes the objective function by combining the gradient information of the loss function of the convolutional neural network. The convergence speed and global search ability of the algorithm are effectively improved while ensuring an acceptable increase in computation. The weight values of the three-level indicators of teacher teaching behavior, student learning behavior, and teaching environment relative to the teaching quality of physical education classroom are 0.106, 0.634, and 0.260, respectively, which shows that the dimension of student learning behavior has the highest weight value in the evaluation of physical education classroom teaching quality, followed by teaching environment and finally teacher teaching behavior. Teachers’ teaching ability will affect the effect of teaching methods, and the stronger the teaching ability is, the better the selection and utilization of teaching methods can be optimized.
Comparative Study of Physical Education Teaching in Middle Schools at Home and Abroad Using Clustering Algorithm
Authors: Tan Dejun
Published: June 2022

Physical education in middle school is very important for teenagers, so it is also crucial to understand the differences between PET (Physical Education Teaching) systems in middle schools at home and abroad. The frontier and hotspot of PET research in middle schools at home and abroad are examined in this paper using citation analysis, information visualization, and cluster analysis, as well as CiteSpace software. The findings show that PET method research in China is qualitative, whereas PET method research in middle schools around the world is quantitative evaluation and empirical research. Domestic research hotspots focus on classroom instructional design, whereas foreign countries focus on load identification theory’s application in instructional design. Frontier research in the United States is dispersed and covers a wide range of topics, whereas research in other countries focuses on cognitive load theory. The classification time of this improved algorithm is reduced by 190.97 seconds when compared to the traditional KNN algorithm, and the total time is increased by more than 50%. According to the findings, nonsports or nonsports influencing factors should be given more consideration in the study of adolescent physical fitness decline in China.
Research on Terrain Sensing Method and Model Prediction for Height Adjustment of Sugarcane Harvester Base Cutter
Authors: Zhao Liang , Jiao Shengjie , Wang Chao , Zhang Jun
Published: June 2022

The sugarcane plantations in China occupy large areas of undulated terrain. During the operation of a sugarcane harvester, the operator’s vision is severely obstructed, and the height of the cutter needs to be adjusted frequently, resulting in low sugarcane harvesting efficiency and difficulty in ensuring harvesting quality. However, the current diversified non-contact methods are unable to distinguish or penetrate the interference well to provide feedback regarding the change in terrain height. Based on the proposed contact sensing method, a contact terrain sensing device is designed for application to the sugarcane harvester, and the orthogonal test is carried out with the standard orthogonal table L9(34) to study the influence of the working parameters of the device on the terrain height sensing performance. Through the analysis of the working principle of the device, three factors (moving speed, rotational torque, and preload) are tested in three levels, and index systems of the Fréchet distance and residual standard deviation are proposed. The range and variance analysis methods are used to statistically analyze the test results to determine the primary and secondary orders of operation parameters. Then, the regression model was obtained by regression analysis on the influencing factors. After analysis, the influences of moving speed and rotating torque were found to be the most significant, and the influence of preload was the smallest. The results show that the average relative errors between the experimental values and predicted model values of the Fréchet distance and residual standard deviation were 3.46% and 2.48%. According to the practical applications of the sugarcane harvester and the data of the two indexes, the optimal value ranges of moving speed (km/h) and rotating torque (Nm) are determined as (1.57, 1.75) and (0.73, 0.86), respectively, which provide a certain reference for the subsequent application of the terrain sensing device to sugarcane harvesters.
Experimental Study on Production Characteristics of Bottom Water Fractured-Vuggy Reservoir
Authors: Cheng Hong , Jiang Lin , Li Chenggang
Published: June 2022

To assess the high-pressure production characteristics of double-vug reservoirs with bottom water in the Tahe Oilfield, in this study, a high-pressure physical simulation experiment apparatus is built for double-vug reservoirs with bottom water. The high-pressure production characteristics of “vug-fracture-vug” reservoirs under different bottom water characteristics are studied experimentally, and fracture-vug relationship and oil recovery rates are explored. According to the findings, the oil recovery rate significantly affects the development effect of double-vug reservoirs with bottom water, and bottom water provides sufficient energy for reservoir development. Furthermore, considering the possible occurrence of water invasions, the production rate control must receive close attention in the development process to avoid strong water channeling. Constant-pressure bottom water has sufficient energy and can quickly replenish vug energy. Therefore, the recovery percentage and bottom water invasion and retention volume of fractured-vuggy reservoirs with constant-pressure bottom water are higher than those of fractured-vuggy reservoirs with constant-volume bottom water. Under the appropriate control of production factors, the presence of bottom water can noticeably improve the development effect of fractured-vuggy reservoirs.
An Evaluation Model of an Urban Green Finance Development Level Based on the GA-Optimized Neural Network
Authors: Zheng Yuan , Ye Xiaolan
Published: June 2022

The construction of a green financial system can promote economic transformation and development, scientifically and effectively evaluate the green environmental protection level of finance in different cities, and provide a strong impetus for its future promotion. This paper introduces a BP neural network into the evaluation system of urban green finance progress and optimizes the model through GA. According to the constructed evaluation index system, this paper makes empirical analysis of the experimental city. The optimization results show that GA can improve the training error range of the BP neural network and increase the stability of evaluation model performance and the accuracy of evaluation results. Through empirical analysis, it is concluded that the development status of the urban economy, the mode and efficiency of capital distribution, and the degree of support for the environmental protection industry in terms of policy and capital will have a great impact on the development of its environmental protection finance. The support attitude of the local government and society in energy conservation, environmental protection, and green city construction provides a broad development space for green finance to a great extent. Cities should broaden the development channels of green finance and build a sound and scientific green financial service system.
Development of a Novel Algorithm to Identify People with High Likelihood of Adult Growth Hormone Deficiency in a US Healthcare Claims Database
Authors: Yuen Kevin C. J. , Birkegard Anna Camilla , Blevins Lewis S. , Clemmons David R. , Hoffman Andrew R. , Kelepouris Nicky , Kerr Janice M. , Tarp Jens M. , Fleseriu Maria
Published: June 2022

Objective. Adult growth hormone deficiency (AGHD) is an underdiagnosed disease associated with increased morbidity and mortality. Identifying people who may benefit from growth hormone (GH) therapy can be challenging, as many AGHD symptoms resemble those of aging. We developed an algorithm to potentially help providers stratify people by their likelihood of having AGHD. Design. The algorithm was developed with, and applied to, data in the anonymized Truven Health MarketScan® claims database. Patients. A total of 135 million adults in the US aged ≥18 years with ≥6 months of data in the Truven database. Measurements. Proportion of people with high, moderate, or low likelihood of having AGHD, and differences in demographic and clinical characteristics among these groups. Results. Overall, 0.5%, 6.0%, and 93.6% of people were categorized into groups with high, moderate, or low likelihood of having AGHD, respectively. The proportions of females were 59.3%, 71.6%, and 50.4%, respectively. People in the high- and moderate-likelihood groups tended to be older than those in the low-likelihood group, with 58.3%, 49.0%, and 37.6% aged >50 years, respectively. Only 2.2% of people in the high-likelihood group received GH therapy as adults. The high-likelihood group had a higher incidence of comorbidities than the low-likelihood group, notably malignant neoplastic disease (standardized difference −0.42), malignant breast tumor (−0.27), hyperlipidemia (−0.26), hypertensive disorder (−0.25), osteoarthritis (−0.23), and heart disease (−0.22). Conclusions. This algorithm may represent a cost-effective approach to improve AGHD detection rates by identifying appropriate patients for further diagnostic testing and potential GH replacement treatment.
Prioritization of Erosion Hotspot Microwatersheds for Conservation Planning Using GIS and Remote Sensing Techniques in Antsokia-Gemiza District of North Shewa Zone, Ethiopia
Authors: Lemma Estifanos , Seneshaw Getahun Yitea , Getachew Tadesse
Published: June 2022

Soil erosion is one of the most severe forms of land degradation, which has a wide range of adverse on-site and off-site impacts in the highlands of Ethiopia in general and in the study area in particular. The objective of this study was to estimate soil erosion, identify and prioritize erosion hotspot microwatersheds in Antsokia-Gemiza district. The Revised Universal Soil Loss Equation (RUSLE) was used to estimate the potential annual soil loss. Geographic Information System (GIS) and remote sensing techniques were used to delineate the microwatersheds, produce the spatial map of all parameters and outputs, and prioritize microwatersheds. Based on the analysis, the potential soil loss of the district ranges from 0 to 240 t·ha−1year−1 with a mean annual soil loss of 43.21 t·ha−1year−1. About 12442.86 ha (33.18%) of the district falls under low and moderate severity classes, and it has a total soil loss not exceeding 11 t·ha−1year−1 (which is an acceptable or tolerable range of soil loss). The rest of the land, which covers 25046.32 ha (66.82%) of the area, falls under high to extremely severe classes (which need prime attention), with soil loss amounts ranging from 11.01 to 240 t·ha−1year−1. For prioritization purposes, the estimated potential soil loss of the district was reclassified into 12 microwatersheds. Based on the amount of soil loss across each microwatershed, MW10, MW9, and MW11 ranked 1st, 2nd, and 3rd with a percentage of 96.3%, 94.36%, and 89.28%, respectively. On the other hand, the total area covered by the existing soil and water conservation practices in the district was 5606.10 ha, of which 3808.06 ha was covered by physical conservation measures, 1305.67 ha of the area was covered by biological conservation measures and 492.37 ha was covered by area closure. Most of the existing soil and water conservation measures were implemented under high to extremely severe erosion classes. The hotspot microwatersheds with higher severity percentages will get higher priority for soil and water conservation intervention. Hence, the integrated results will provide useful information for the decision-making process concerning the erosion susceptibility of microwatersheds. Besides, GIS and remote sensing approaches in the identification and prioritization of erosion hotspot microwatersheds using RUSLE parameters are found to be more appropriate.
Resveratrol as a Promising Polyphenol in Age-Associated Cardiac Alterations
Authors: Börzsei Denise , Sebestyén Judith , Szabó Renáta , Lesi Zelma Nadin , Pálszabó Andrea , Pálszabó Patrícia , Szász András , Priksz Dániel , Juhász Béla , Veszelka Médea , Turcsán Zsolt , Deim Zoltán , Varga Csaba , Pósa Anikó
Published: June 2022

According to a widely accepted theory, oxidative stress is considered to be the number one trigger of aging-associated degenerative processes including cardiovascular diseases. In the context of aging-research, resveratrol receives special attention with its surprising number of health benefits. The aim of our study was to examine the anti-inflammatory and antioxidant effects of this dietary polyphenol in aging rat heart. 20-month-old female and male Wistar rats were divided into control (untreated) and resveratrol-treated groups. Resveratrol was administered at a dose of 0.05 mg/ml for 12 weeks dissolved in drinking water, while the control rats received ad libitum water. Cardiac level of reactive oxygen species (ROS), nuclear factor kappa B (NFκB), tumor necrosis factor alpha (TNF-α), and glutathione (GSH) parameters, as well as the activity of myeloperoxidase (MPO) and heme oxygenase (HO) enzymes were detected. Together with the biochemical measurements, hearts were isolated and used for an exposure of ischemic-reperfusion injury via Langendorff perfusion system. 12 week of resveratrol treatment suppressed the age-related inflammatory pathways including the expression of TNF-α, NFκB, and the activity of MPO while intensified the endogenous antioxidant defenses through the induction of GSH and HO system. Presumably, as a result of these processes, the necrotic area of the heart in response to an acute injury was also significantly reduced in the resveratrol-treated groups. Our findings confirmed that resveratrol has cardioprotective effects at several points by counteracting the aging-associated cellular malfunctions in the heart.
Towards a Low-Cost Teacher Orchestration Using Ubiquitous Computing Devices for Detecting Student’s Engagement
Authors: Ahmad Ibrar , Khusro Shah , Alam Iftikhar , Khan Inayat , Niazi Badam
Published: June 2022

The ubiquitous devices and technologies to support teachers and students in a learning environment include the Internet of things (IoT), learning analytics (LA), augmented or virtual reality (AR/VR), ubiquitous learning environment (ULE), and wearables. However, most of these solutions are obtrusive, with substantial infrastructure costs and pseudo-real-time results. Real-time detection of students’ activeness, participation, and activity monitoring is important, especially during a pandemic. This research study provides a low-cost teacher orchestration solution with real-time results using off-the-shelf devices. The proposed solution determines a teacher’s activeness using multimodal data (MMD) from both teacher and student’s devices. The MMD extracts different features from data, decodes them, and displays them to the instructor in real time. It allows the instructor to update their teaching methodology in real time to get more students on board and provide a more engaging learning experience. Our experimental results show that real-time feedback about the classroom’s current status helped improve learning outcomes by about 45%. Also, we investigated a 50% increase in classroom engaging experience.
Research on the Application of VR Technology in Oil and Gas Engineering Education: Taking the Well Control Emergency Rescue Robotic System as an Example
Authors: Guili Zhang , Pengxi Li , Yinglong Yu , Zhao Liang , Hanyue Zhang
Published: June 2022

Oil and gas engineering is a talent-intensive, technology-intensive, and capital-intensive field. The final teaching is insufficient and unsatisfactory, and while oil and gas engineering instruction, particularly experimental practise teaching, is hampered and restricted by many working conditions such as huge scale, large equipment, high cost, high safety risk, and enterprise production system, and so on VR technology has developed rapidly in recent years, which has brought great impacts to teaching; this study uses VR technology to build and produce a well control emergency rescue robotic system, combining knowledge points with VR technology and applying the system to the course of Drilling and Well Completion Engineering. This teaching practice makes teaching more vivid and makes the process that cannot be reproduced fully displayed in front of students’ eyes. Such an attempt has achieved such good teaching effects that students can solve problems through interactions.
ARM-Based Indoor RGB-LED Visible Light Communication System
Authors: Bao Jingjing , Mai Qiang , Fang Zhangwen , Mao Xiaowen
Published: June 2022

As a new green solid-state light source, semiconductor light-emitting diodes (LEDs) have the advantages of low power consumption, small size, long life, short response time, and good modulation performance. At the same time, the frequency band to which LED light sources belong does not require regulatory registration, thus alleviating the current problem of spectrum scarcity for wireless communications. However, white LED-based visible light communication (VLC) systems suffer from limited bandwidth and low energy efficiency. Therefore, an ARM-based indoor RGB-LED VLC system is proposed. Firstly, the three RGB colours are mixed into white light, thus obtaining a larger modulation bandwidth than normal white LEDs while illuminating normally. Secondly, the S3C6410 processor is used to modulate and demodulate the RGB-LEDs with biased light OFDM, thus obtaining a high spectrum utilisation while ensuring system transmission stability. Then, according to the characteristics of the light source of the VLC system, the leading and window functions used in the optical network transceiver module are designed to improve the communication energy efficiency of the system. Finally, functional tests were carried out on an ARM development board. The experimental results show that with a single RGB-LED light source, the maximum transmission distance is 5 cm, the maximum average delay is 68 ms, the maximum throughput is 25 Mbps, and the BER is controlled below 3.2 × 10−3, which meets the basic communication requirements.
NC-GNN: Consistent Neighbors of Nodes Help More in Graph Neural Networks
Authors: Xu Ming , Zhang Baoming , Yu Hualei , Yuan Jinliang , Wang Chongjun
Published: June 2022

Graph neural networks, as the promising methodology in data mining for graph data, currently attract much attention and are broadly applied in graph-based tasks. Existing GNN methods mostly follow the assumption of homophily, where the connected nodes are similar and share the same labels. Most graphs in the real world can satisfy the assumption. However, for the particular nodes, the situation is not always satisfied. The connections between different-labeled nodes will introduce noise in feature aggregation and result in node representation deviating in the wrong direction. In this paper, we focus on the different-labeled neighbors of labeled nodes in the graphs. By regarding aggregation among neighbors as the procedure of node feature reconstruction, we devise a novel metric neighbor consistency to measure the difference between nodes and their neighborhoods. In this way, we can evaluate the reliability of nodes after aggregation. Furthermore, we propose a novel method, Neighbor Consistent Graph Neural Networks (NC-GNN), to promote the training of graph neural networks by reweighting the influence of labeled nodes based on neighbor consistency scores. Systematic experiments are conducted on benchmark datasets, and the results demonstrate the effectiveness of our method.
A Deep Learning-Based Semantic Segmentation Architecture for Autonomous Driving Applications
Authors: Masood Sharjeel , Ahmed Fawad , Alsuhibany Suliman A. , Ghadi Yazeed Yasin , Siyal M. Y. , Kumar Harish , Khan Khyber , Ahmad Jawad
Published: June 2022

In recent years, the development of smart transportation has accelerated research on semantic segmentation as it is one of the most important problems in this area. A large receptive field has always been the center of focus when designing convolutional neural networks for semantic segmentation. A majority of recent techniques have used maxpooling to increase the receptive field of a network at an expense of decreasing its spatial resolution. Although this idea has shown improved results in object detection applications, however, when it comes to semantic segmentation, a high spatial resolution also needs to be considered. To address this issue, a new deep learning model, the M-Net is proposed in this paper which satisfies both high spatial resolution and a large enough receptive field while keeping the size of the model to a minimum. The proposed network is based on an encoder-decoder architecture. The encoder uses atrous convolution to encode the features at full resolution, and instead of using heavy transposed convolution, the decoder consists of a multipath feature extraction module that can extract multiscale context information from the encoded features. The experimental results reported in the paper demonstrate the viability of the proposed scheme.
Full Digital Workflow in the Esthetic Dental Restoration
Authors: Kongkiatkamon Suchada , Rokaya Dinesh
Published: June 2022

This case report presented a fully digital workflow in esthetic dental restoration. A 51-year-old female patient was referred to BDMS Wellness Clinic due to a fracture of the maxillary left central incisor. An immediate dental implant was planned to restore tooth 21 with esthetic crown lengthening of upper front teeth and new zirconia crowns for teeth 11, 12, 13, 21, 22, and 23. Digital impression was made using a digital scanner (PRIMESCAN®, Dentsply Sirona, Bensheim, Germany); surgical guide (Cerec Guide 3, Dentsply Sirona, Bensheim, Germany) was designed by using a designing software (Galileos Software, Dentsply Sirona, Bensheim, Germany) and was milled by using a milling machine (MCXL milling machine, Dentsply Sirona, Bensheim, Germany) to create a precise surgical guide. 3D smile design was done by using the Digital Smile Design (DSD) program, the crown lengthening guide was designed according to DSD designed by using a designing software (Inlab 19 Software, Dentsply Sirona, Bensheim, Germany), and the guide was fabricated by a 3D printer (FormLabs Form 2, Formlab, MA, USA). Provisional crowns (splinted) for teeth 11, 12, 13, 21, 22, and 23 were milled by using polymer (VIPI BLOCK TRILUX®, VIPI Industria, Pirassununga, SP, Brazil) using a milling machine (MCX5, Dentsply Sirona, Bensheim, Germany). The zirconia crowns were designed by using software (Inlab19 Software, Dentsply Sirona, Bensheim, Germany) and milled using the same milling machine. At the implant position, Ti-base is cemented by using an abutment (Multilink Hybrid Abutment, Ivoclar Vivadent, Schaan, Lichtenstein, Germany) with zirconia coping (Cercon base white shade, Dentsply Sirona, Bensheim, Germany) utilized as the abutment. The zirconia crowns (Cercon Xt, Dentsply Sirona, Bensheim, Germany) were sintered and characterized and then cemented. The patient was satisfied with the esthetic outcome of the treatment.
Efficacy of Digestive Endoscope Based on Artificial Intelligence System in Diagnosing Early Esophageal Carcinoma
Authors: Zhao Zhentao , Li Meng , Liu Ping , Yu Jingfang , Zhao Hua
Published: June 2022

Objective. To explore the efficacy of digestive endoscopy (DEN) based on artificial intelligence (AI) system in diagnosing early esophageal carcinoma. Methods. The clinical data of 300 patients with suspected esophageal carcinoma treated in our hospital from January 2018 to January 2020 were retrospectively analyzed; among them, 198 were diagnosed with esophageal carcinoma after pathological examination, and 102 had benign esophageal lesion. An AI system based on convolutional neural network (CNN) was adopted to assess the DEN images of patients with early esophageal carcinoma. A total of 200 patients (148 with early esophageal carcinoma and 52 with benign esophageal lesion) were selected as the learning group for the Inception V3 image classification system to learn; and the rest 100 patients (50 with early esophageal carcinoma and 50 with benign esophageal lesion) were included in the diagnosis group for the Inception V3 system to assist the narrow-band imaging (NBI) with diagnosis. The diagnosis results from Inception V3-assisted NBI were compared with those from imaging physicians, and the diagnostic efficacy diagram was drawn. Results. The diagnosis rate of AI-NBI was significantly faster than that of physician diagnosis (

0.02
±
0.01

vs. 5.65 ± 0.32 s (mean rate of two physicians),

P
<
0.001

); between AI-NBI diagnosis and physician diagnosis, no statistical differences in sensitivity (90.0% vs. 92.0%), specificity (92.0% vs. 94.0%), and accuracy (91.0% vs. 93.0%) were observed (

P
>
0.05

); and according to the ROC curves, AUC (95% CI) of AI-NBI

diagnosis
=
0.910

(0.845-0.975), and AUC (95% CI) of physician

diagnosis
=
0.930

(0.872-0.988). Conclusion. CNN-based AI system can assist NBI in screening early esophageal carcinoma, which has a good application prospect in the clinical diagnosis of early esophageal carcinoma.
Integration of a Tobacco Treatment Specialist into Primary Care: Perception from Multidisciplinary Team
Authors: Meyer Amanda F. , Cervenka Abby , Lammers Lacey , Furst Joseph , Ward Kenneth
Published: June 2022

Background. Tobacco continues to be on the leading cause of avoidable death. Primary care practices are ideal locations to provide tobacco cessation visits. Tobacco treatment specialists are trained individuals with expertise in providing medication and counseling management to patients to help with tobacco cessation. Purpose. The purpose of this study was to examine the integration of a tobacco treatment specialist into the primary care setting and the perception of this role from the multidisciplinary team. Method. We conducted an electronic cross-sectional survey to evaluate awareness and perception of the integration of a tobacco treatment specialist into a primary care facility that is part of a large Midwestern tertiary healthcare center. The sample for this study included all the primary clinic staff that directly work with patients and included licensed practical nurses, registered nurses, physician assistants, certified nurse practitioners, and medical doctors. Results. 55% (n = 22) of staff had utilized the tobacco treatment specialist with direct patient care. Reasons for using the specialist was for referral for follow-up tobacco cessation visit (54%), curbside consultations (21%), medication management (21%), and other reasons (5%). The majority of staff strongly agreed that utilizing the TTS was valuable. Conclusion. This study reinforced the positive impact a tobacco treatment specialist can have being integrated into the primary care practice from the perception of the multidisciplinary team.
Metformin Facilitates Osteoblastic Differentiation and M2 Macrophage Polarization by PI3K/AKT/mTOR Pathway in Human Umbilical Cord Mesenchymal Stem Cells
Authors: Shen Min , Yu Huihui , Jin Yunfeng , Mo Jiahang , Sui Jingni , Qian Xiaohan , Chen Tong
Published: June 2022

Mesenchymal stem cells (MSCs) are the most promising multipotent stem cells that can differentiate into osteoblasts, chondrocytes, and adipocytes. This cellular flexibility contributes to widespread clinical use of MSCs in tissue repair and regeneration. The immune system is a key player in regulating bone remodeling. In recent years, the association between the immune system and bone metabolism has become an increasing focus of interest. Metformin, a glucose-lowering drug, exerts powerful impact on metabolic signaling. However, whether metformin can modulate bone metabolism or whether metformin can influence immune milieu by regulation of macrophages has not been thoroughly elucidated. Herein, we specifically explored the complex interactions between macrophages and human umbilical cord mesenchymal stem cells (UC-MSCs) in the context of metformin. Our research demonstrated that metformin not only stimulated osteogenesis of UC-MSCs but also influenced the immune system via promoting M2 but reducing M1 macrophages. Mechanically, we found that metformin-treated M2 macrophages possessed more potent osteoinductive capacity in our coculture system. Molecularly, these metformin-stimulated M2 macrophages facilitated osteogenesis via activating the PI3K/AKT/mTOR pathway. As demonstrated by using PI3K-specific inhibitor LY294002, we found that the pathway inhibitor partly reversed osteoinductive activity which was activated by coculture of metformin-treated M2 macrophages. Overall, our novel research illuminated the cooperative and synergistic effects of metformin and M2 macrophages on the dynamic balance of bone metabolism.