bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimization of Environmental Parameters of Ice and Snow Sports Venues Based on the BP Neural Network and Wireless Communication Technology
Authors: Li Jiangtao , An Jing , Yao Xiaolin , Ai Zhenguo
Published: May 2022

Ice and snow sports have become a scene of attraction nowadays. It not only encourages sports but also earns more revenue through tourism. Tourists love and admire this kind of sport because of its uniqueness, unlike other sports. The Olympic Games also conduct ice and snow sports under the Winter Olympic Sports. This kind of sport is played in ice rinks. Ice rinks are of two types, namely, natural and artificial. Natural ice rinks are formed in snowy regions, whereas artificial ice rinks can be set up anywhere with the help of technology. An artificial or natural ice rink, a sports venue, or a stadium is constructed accordingly. This research focuses on the optimization of environmental parameters of the ice and snow sports venue based on the BP neural network and wireless communication technology. There are various factors to be monitored when an indoor ice and snow sports venue is being considered. They are temperature monitoring, health monitoring of the audience, natural disaster detection and alarm in the case of natural ice rinks, and building safety concerning the climatic conditions. The proposed methodology uses BP neural networks and wireless communication technology to optimize the environmental parameters of the ice and snow sports venue. It has been demonstrated to have numerous advantages over traditional ice and snow sports methods. In this research, the convolutional BP neural network algorithm was implemented to analyse the environmental parameters of the chosen sport. The proposed algorithm is compared with the existing weighted centroid model. The results show that the proposed model has achieved an accuracy of 99.45%.
Early Postoperative Pain Control and Inflammation for Total Knee Arthroplasty: A Retrospective Comparison of Continuous Adductor Canal Block versus Single-Shot Adductor Canal Block Combined with Patient-Controlled Intravenous Analgesia
Authors: Yang Xiaojuan , Dong Jun , Xiong Wei , Huang Fusen
Published: May 2022

Objective. The aim of this study was to compare pain control and inflammation among patients who received a continuous adductor canal block (CACB) versus single-shot adductor canal block (SACB) combined with patient-controlled intravenous analgesia (PCIA) for total knee arthroplasty (TKA) analgesia in the first two days after surgery. Design. Matched cohort retrospective study. Setting. University hospital. Patients. One hundred fifty-six patient charts were included in this study: 78 patients with CACB in Group A and 78 patients with SACB combined with PCIA in Group B. Patients were matched according to age, body mass index, and American Society of Anesthesiologists class. Measurements. The primary outcome of the study was Visual Analogue Scale (VAS) pain scores before operation (Pre) and at postoperative 6 (POH6), 12 (POH12), 24 (POH24), 30 (POH30), 36 (POH36), and 48 hours (POH48). Secondary outcomes included patient-controlled bolus, time of first postoperative ambulation, range of knee flexion and extension, inflammation cytokines on Pre and POH48, percentage of remedial analgesics treatment, incidence of adverse events and complications, hospital stay and cost, and Numerical Rating Scale (NRS) satisfaction scores at discharge. Main Results. Mean VAS scores at rest and with motion were lower in Group B than in Group A on all postoperative hours. At POH30, compared with Group A (1.1 ± 0.6), mean VAS scores at rest in Group B (0.9 ± 0.4) were lower (P=0.048), and compared with Group A (2.6 ± 0.7), mean VAS scores with motion in Group B (2.2 ± 0.8) were lower (P=0.001). The number of patient-controlled bolus was 4.3 ± 1.6 (95% CI 3.9–4.6) in Group A and 3.1 ± 1.3 (95% CI 2.8–3.4) in Group B, respectively (P<0.001). Patients in Group B displayed better functional recovery and inflammation results at POH48 than Group A with respect to range of knee flexion and extension (117.8 ± 10.9° vs. 125.2 ± 9.4°,P<0.001) and inflammation cytokines, including erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), and interleukin-6 (IL-6) ((43.8 ± 16.1) vs. (36.8 ± 13.2),P=0.003; (34.9 ± 9.4 mg/L) vs. (29.6 ± 10.6 mg/L),P=0.001; (21.3 ± 8.7 pg/ml) vs. (14.0 ± 7.0 pg/ml),P<0.001)). Conclusion. SACB combined with PCIA in the first two days of patients undergoing TKA has better analgesic and beneficial effects on functional recovery and inflammation.
Multipoint Ecological Environment Monitoring System with Internet of Things
Authors: Chen Chaoying
Published: May 2022

Science and technology is a double-edged sword; while it brings convenience to people, it also threatens the environment of human. The pollution of the ecological environment has reached a point that cannot be ignored. Traditional methods and measures to protect or monitor the environment can no longer meet the increasing environmental pollution. Therefore, we should use the latest scientific and technological achievements to improve our environment. Through high-tech means to monitor the changes in the ecological environment pollution, we always remind people to protect environment. With continuous innovation of sensing, communication, and embedded technology, the monitoring system also needs to be updated accordingly, which has great research significance. With Internet of Things technology, this work has researched and designed a multipoint ecological environment monitoring system. It can realize intelligent monitoring for ecological environment, achieving the goal of integrating the measurement parameters of the ecological environment monitoring, the system modularization, the real-time data transmission, and the integration of monitoring services.
An Empirical Investigation in Analysing the Critical Factors of Artificial Intelligence in Influencing the Food Processing Industry: A Multivariate Analysis of Variance (MANOVA) Approach
Authors: Raghavendra G. S. , Mary S. Shyni Carmel , Acharjee Purnendu Bikash , Varun V. L , Bukhari Syed Nisar Hussain , Dutta Chiranjit , Samori Issah Abubakari
Published: May 2022

In the era of digital technology, where innovation and digitization are transforming the business functions, the field of customer relationship management has witnessed sea change in the recent decade. The application of artificial intelligence in food processing has enabled enhancing the availability of food in an effective manner for all the individuals. It has been regarded that the application of labor force tends to play a crucial aspect for the overall execution of things in the different domains related to food manufacturing and processing, which are related to the enhanced involvement of individuals in the processing of food and related products, the industry could not able to meet the growing demand from the customers. So, in order to overcome these critical issues, it is noted that the application of technology such as automation and artificial intelligence is implemented for enhanced processing and enable delivering quality products to the customers at lower cost. The impact of AI in the current business world is becoming more indispensable as companies have started to unleash its potential. The role of AI in food processing is fast changing the manner in which the customer queries are addressed, enabling analysing the needs and requirements, and focus on creating improved packaging, high quality, and better shelf life. This empirical investigation is focusing on analysing the critical factors related to AI in influencing the food processing industry. The researchers intend to apply quantitative analysis using IBM SPSS package, and the results are stated in detail based on the analysis.
The Integrability of a New Fractional Soliton Hierarchy and Its Application
Authors: Zhu Xiao-ming , Zhang Jian-bing
Published: May 2022

Two fractional soliton equations are presented generated from the same spectral problem involved in a fractional potential by the zero-curvature representations. They are a kind of special reductions of the famous AKNS system. The two equations are integrable for they both possess explicit soliton solutions constructed by the

N
−

fold Darboux transformation. As an application of the obtained solutions, new soliton solutions of the classic

2
+
1

-dimensional Kadometsev-Petviashvili (KP) equation are soughed out by a cubic polynomial relation. Dynamic properties are analyzed in detail.
Evaluation of Chlorogenic Acid Supplementation in Koi (Cyprinus carpio) Diet: Growth Performance, Body Color, Antioxidant Activity, Serum Biochemical Parameters, and Immune Response
Authors: Xu Guanling , Xing Wei , Yu Huanhuan , Jiang Na , Ma Zhihong , Luo Lin , Li Tieliang
Published: May 2022

A ten-week feeding trial was performed to evaluate the effects of chlorogenic acid (CGA) on the growth, body color, antioxidant activity, serum biochemical parameters, and immune response of koi. Five diets were formulated to contain 0 (control), 200 (CGA-2), 400 (CGA-4), 600 (CGA-6), and 800 (CGA-8) mg/kg CGA for the rearing of experimental fish. By means of an analysis by polynomial orthogonal contrasts, it was determined that dietary supplementation with CGA had no significant effects on the growth performance of koi (

P
>
0.05

), while body color indices (redness, yellowness, and lightness) showed a linear order polynomial (

P
<
0.05

). A quadratic response was found for the relationship between total carotenoid in scale of koi and dietary CGA levels, with an optimum level of 381.84 mg/kg. Dietary CGA levels and lysozyme activity showed a cubic relationship polynomial model, with an optimum level of 383.89-975.73 mg/kg. TGF-β expression in the head-kidney of koi was obviously improved by 600 mg/kg CGA diet (

P
<
0.05

). And in regards to effect in serum, SOD and CAT activities was best explicated by a linear order polynomial, while the quadratic and cubic relationship polynomial models represented the GSH and GSH-Px activities, which the optimal supplementation level of CGA was estimated at 550.78 and 231.74-782.66 mg/kg, respectively. The relationship between ALT activity and CGA supplementation was best explicated by quadratic relationship polynomial, with an optimum level of 522.78 mg/kg. Based on the model observed, 522.78-600 mg/kg CGA was the recommended supplementation in diet that improved the body color, antioxidant activity, biochemical parameters, and immune response of koi.
FCN Network-Based Weed and Crop Segmentation for IoT-Aided Agriculture Applications
Authors: Kamal Shoaib , Shende Vaishali Gajendra , Swaroopa Korla , Bindhu Madhavi P. , Akram Patan Saleem , Pant Kumud , Patil Shantala Devi , Sahile Kibebe
Published: May 2022

The main purpose of the work is to evaluate the deep machine learning algorithms used for the distinction between weeds and crop plants using the open database of images of the carrot garden. Precision farming methods are highly prevalent in the agricultural environment and can embed intelligent methods in drones and ground vehicles for real-time operation. In this work, the accuracy of the weed and crop segment is analyzed using two different frameworks of deep learning for the semantic segment: the fully convolutional network and the ResNet. An open database with images of 40 plants and weeds was used for the case study. The results show a global accuracy of more than 90% in the verification package for both structures. In the second experiment, new FCN networks were trained to evaluate the impact of these processes on different image preprocessing and separation performance by different training/testing rates of the dataset.
Emotion Analysis for Foreign Language Learning under Scenarios of Network
Authors: Zheng Jingzhi , Xu Fang
Published: May 2022

With the development of the Internet, modern educational technology is also making continuous progress based on the Internet. Internet technology provides us with abundant online learning resources and creates a brand new teaching mode. Changes in the field of education bring new opportunities and challenges, which affect students' mental and physical health. This study focuses on the psychological problems of students under the background of network teaching. Through data analysis, it can be concluded that emotions not only affect students' interest in learning but also affect their learning process. The results show that emotion plays an important role in students' learning, and different sources of learning emotion and emotional experience have different effects on students' academic performance. Teachers must scientifically use modern educational technology to establish a good learning environment and, at the same time, help students to have a healthy psychology. Only having a healthy body and mind can improve one's comprehensive ability.
A Software Vulnerability Detection Method Based on Complex Network Community
Authors: Shan Chun , Gong Yinghui , Xiong Ling , Liao Shuyan , Wang Yuyang
Published: May 2022

To find out whether there is any vulnerability in software programs where conditional judgment is ignored, this article proposes a software vulnerability detection method based on complex network community. First, the method abstracts the software system into a directed weighted graph by using the software algebraic component model and then preprocesses the directed weighted graph to get a complex network graph. Then, by using the partition algorithm, the complex network graph is divided into the communities, and the key nodes in communities are found by nRank algorithm. Finally, the graph of the key nodes with high influence is matched with the complex network graph that has been preprocessed. In order to evaluate the effectiveness of the community partition algorithm and the nRank algorithm, comparative experiments are carried out on two datasets. The experimental results show that the community partition algorithm is better than the comparison algorithm in precision, recall, and comprehensive evaluation index, and the nRank algorithm is closer to the result of degree centrality measurement index than the PageRank algorithm and the LeaderRank algorithm. The spring-shiro-training project is used to verify the vulnerability detection method based on complex network community, and the results show that the method is effective.
Construction of a Public Service Cloud Platform for Disabled People Based on the Big Data Management Model of the Internet of Things
Authors: Liu Xiaoyan , Chang Guiyang
Published: May 2022

With the continuous development of social economy and the continuous improvement of people’s ideological level, the state and social people have given more and more care and assistance to the disadvantaged group of disabled persons. The construction of a public service cloud platform for the disabled is one of the important measures to protect the human rights of the disabled and improve the lives of the disabled. According to the statistics of the China Disabled Persons’ Federation, since the twentieth century, with the acceleration of the equalization of basic public services and the establishment of urban and rural social security systems, a practical and effective public service system for the disabled has made leaps and bounds. The construction of the cloud platform for public services for the disabled is an important branch of the construction of the public service system for the disabled. However, due to the late development of the construction of the cloud platform for public services for the disabled in China, it is not perfect, and there are still some problems to be solved. This paper mainly studies the public service cloud platform for the disabled based on the big data management model of the Internet of Things. Based on this research theme, this paper conducts a service response efficiency test for the disabled provided by the disabled public service cloud platform based on the big data management model of the Internet of Things. It concluded that the public service cloud platform for the disabled based on the big data management model of the Internet of Things has improved the response efficiency of providing services for the disabled by 57%.
Design and Implementation of Smart Tourism Service Platform from the Perspective of Artificial Intelligence
Authors: Li Qi , Zhang Yi
Published: May 2022

With the advancement of science and technology and the further improvement of people’s living standards, tourism has become a hot topic in today’s society. This paper is aimed at studying the development of a smart tourism service platform using artificial intelligence recognition technology and automatic program design. This paper proposes functions such as online ticket booking through the design of intelligent access control in scenic spots (combined with intelligent voice, face recognition, and other technologies), scenic miniprograms, and WeChat public accounts. Tourists can collect tickets automatically through the intelligent assistant designed by the intelligent service system. This allows tourists to consult and interact with intelligent machine assistants, reducing human-to-human contact during travel, thereby reducing the probability of epidemic infection (mainly spread by coughing or sneezing, close contact with sick people, touching contaminated surfaces, etc.). Finally, the smart tourism service platform is promoted, and an online questionnaire survey is conducted on 2,000 tourists who use the service platform to understand the actual use of the system. The results of the questionnaire survey showed that among the 2,000 tourists’ evaluations, 55.15% were satisfied with the system, 41.5% were very satisfied, 3.1% felt average, 0.25% felt dissatisfied, and 0% were very dissatisfied. At the same time, a large number of positive evaluation words were received in the feedback results of tourists, which shows that the smart service system designed in this paper is very popular with tourists. Meanwhile, a great deal of positive evaluation words were received in the feedback results of tourists (such as full of technology, special convenience, and special safety). This shows that the intelligent service system designed in this paper is very popular with tourists.
Energy-Efficient Markov-Based Lifetime Enhancement Approach for Underwater Acoustic Sensor Network
Authors: Sivakumar V. , Kanagachidambaresan G. R. , Dhilip kumar V. , Arif Muhammad , Jackson Christy , Arulkumaran G.
Published: May 2022

The multihop underwater acoustic sensor network (M-UASN) collects oceanographic data at different depths. Due to the harsh underwater environment, the route is a major research problem. In this article, the routing path from source to sink is adapted by the vector-based forwarding (VBF) protocol. In VBF, based on the vector size, the packets are transmitted within the pipe from hop to hop. The limitation is that every node inside the pipe vector receives the same packets. That results in a waste of battery energy and, in turn, reduces the lifetime of the acoustic node. To enhance, in this article, it is divided into two parts. The first part is that the first hop nodes from the source are optimally divided into subsets such that all the second hop nodes will receive packets from each subset. This optimal route cover subset is identified with an evolutionary memetic algorithm. The election of subset is done through a voltage reference model, and the battery voltage is modeled mathematically and the role of the nodes is given based on the voltage profile and Markov probability approach. This method enhances the lifetime of the underwater acoustic network when compared with the VBF algorithm. The proposed model also provides improved throughput and equal load sharing. The results are compared with VBF, quality-of-service aware evolutionary routing protocol (QERP), and multiobjective optimized opportunistic routing (BMOOR).
Individual and Catalytic Co‐Pyrolysis of Agricultural Outcomes and Polymeric Materials over Nano‐HZSM‐5 Zeolite: Synergistic Effects and Yield Analysis for Heating Applications
Authors: Dhanalakshmi C. Sowmya , Ahalya N. , Vidhyalakshmi P. , Krishnaraj C. , Selvam N. , Patil Pravin P. , Kalippan S. , Prabhakar S.
Published: May 2022

The catalytic effect of nano‐HZSM‐5 zeolite on co‐pyrolysis of cotton shell (CS) and municipal plastic wastes (MPW) was studied. The influence of reaction temperature during individual pyrolysis, blending ratio, and catalytic effects was studied by applying constant heating rate. The experiments were conducted in a fixed bed batch type reactor. The hindering effect during catalytic decomposition of MPW was carried out and its positive synergistic effect on liquid oil yield was analysed. The reaction temperature for all the experiments are fixed based on the decomposition rate obtained from thermogravimetric study. The experimental outcomes revealed that during co‐pyrolysis, the formation of char was reduced to 7.2 wt% with increased liquid oil yield of 66.5 wt%. Furthermore, adding catalyst for co‐pyrolysis process improved the reaction by decreasing char formation. During catalytic process, the maximum liquid oil output was 69.3 wt% at 500°C temperature, CS/MPW ratio of 1 : 2. When compared to co‐pyrolysis process, the catalytic co‐pyrolysis showed 4.21 wt% higher liquid oil yield. The physical analysis of the oil shows maximum hating value of 34.6 MJ/kg. The FTIR study on catalytic co‐pyrolysis oil shows the presence of aliphatic and aromatic hydrocarbons.
Application of Image Denoising Algorithm and Data Mining in Psychological Teaching Quality Evaluation
Authors: Li Lingling
Published: May 2022

In order to improve the psychological teaching quality of college students, this paper combines image denoising algorithm and data mining algorithm to construct a psychological teaching quality evaluation system. The denoising algorithm is mainly used to identify the behavior and expressions of the students so as to explore the psychological state of the students. Moreover, this paper combines the data mining algorithm to carry out the quantitative analysis of students’ psychological state and analyzes and introduces the calibration principle of Dirckx method in detail to improve it and then forms a new calibration method. In addition, based on the arctangent function, this paper proposes a new two-frame random phase-shift fringe image phase shift extraction algorithm. The research results show that the image denoising algorithm and data mining proposed in this paper have a good effect in the evaluation method of psychological teaching quality and can play a good role in the improvement of students’ mental health.
Effect of Pile-Soil Relative Stiffness on Deformation Characteristics of the Laterally Loaded Pile
Authors: Yuan Bingxiang , Chen Minjie , Chen Weijie , Luo Qingzi , Li Hongzhong
Published: May 2022

Laterally loaded piles are widely used in structural foundations under horizontal loads such as wave load, wind load, seismic load, and traffic load. Based on the complexity of pile-soil interaction, a test simulation of single pile through the indoor 1 g model test was conducted, in order to study the influence of different loading heights and relative stiffness of pile and soil on the bending moment, soil resistance, and horizontal displacement of pile. It is found that the shear modulus of soil from large to small is arranged as dry fine sand, dry coarse sand, saturated fine sand, and saturated coarse sand. The shear modulus of dry fine sand, dry coarse sand, and saturated fine sand are close under the same loading height and horizontal displacement of pile top, while the shear modulus of saturated coarse sand decreases sharply. Under the condition of water saturation, the particle size has a significant effect on the shear modulus of soil. The relative stiffness of pile soil is found to be the main influencing condition of pile horizontal displacement. Compared with the displacement and bending moment of the pile, the loading height has more influence on the soil resistance, and the main influence area is the soil resistance of the pile toe.
Multimedia Vision Improvement and Simulation in Consideration of Virtual Reality Reconstruction Algorithms
Authors: He Jing
Published: May 2022

Due to the large noise and many discrete points of the image in the traditional image reconstruction process, the reconstruction quality of the image deviates greatly from the actual target. In this study, the virtual reality reconstruction algorithm is applied to multimedia vision, the virtual reality image is corrected by using the binocular offset positioning method, the denoising process is performed in the image reconstruction process, and the high-pass filter matrix is used to improve the image reproduction. At the same time, the three-dimensional reconstruction algorithm is used to perform correlation retrieval, the ensemble point set and the discrete point set are obtained, the maximum and minimum reconstruction degree areas are clarified, and the deviation reconstruction and peak relocation can be performed. Finally, the experimental test results show that the algorithm in this study can enhance the authenticity of image reconstruction, improve the accuracy of image corner detection, and effectively reduce the noise interference in the process of reconstructing the image.
Exploration of Black Boxes of Supervised Machine Learning Models: A Demonstration on Development of Predictive Heart Risk Score
Authors: Sajid Mirza Rizwan , Khan Arshad Ali , Albar Haitham M. , Muhammad Noryanti , Sami Waqas , Bukhari Syed Ahmad Chan , Wajahat Iram
Published: May 2022

Machine learning (ML) often provides applicable high-performance models to facilitate decision-makers in various fields. However, this high performance is achieved at the expense of the interpretability of these models, which has been criticized by practitioners and has become a significant hindrance in their application. Therefore, in highly sensitive decisions, black boxes of ML models are not recommended. We proposed a novel methodology that uses complex supervised ML models and transforms them into simple, interpretable, transparent statistical models. This methodology is like stacking ensemble ML in which the best ML models are used as a base learner to compute relative feature weights. The index of these weights is further used as a single covariate in the simple logistic regression model to estimate the likelihood of an event. We tested this methodology on the primary dataset related to cardiovascular diseases (CVDs), the leading cause of mortalities in recent times. Therefore, early risk assessment is an important dimension that can potentially reduce the burden of CVDs and their related mortality through accurate but interpretable risk prediction models. We developed an artificial neural network and support vector machines based on ML models and transformed them into a simple statistical model and heart risk scores. These simplified models were found transparent, reliable, valid, interpretable, and approximate in predictions. The findings of this study suggest that complex supervised ML models can be efficiently transformed into simple statistical models that can also be validated.
Effect of the Passive Stabilizer Bar on the Vehicle’s Stability
Authors: Nguyen Duc Ngoc , Dang Ngoc Duyen , Tran Thi Thu Huong , Hoang Thang Binh , Nguyen Tuan Anh
Published: May 2022

The phenomenon of vehicle rollover usually occurs when the driver suddenly steers at high speed. The centrifugal force will appear and cause the vehicle’s body to tilt. To overcome this situation, the solution of using the stabilizer bar is proposed. The passive stabilizer bar has a simple structure, low cost, and long service life. As a result, it is now a standard on most vehicles. This paper has established a dynamic model to describe a vehicle’s oscillation. As a result of the study, the maximum roll angle of the vehicle was reduced from 9.0° to 8.2° when the stabilizer bar was used. Besides, the minimum value of the vertical force at the wheel reached 485(N) and 1162(N), respectively, corresponding to the two survey cases. The movement trajectory of the vehicle when using the stabilizer bar does not have a big difference compared to the case when the vehicle does not use the stabilizer bar. So, the vehicle’s stability and safety can be effectively improved. This is the basis for further developing complex stable bar patterns in the future.
Influence of Zn in the CdS Crystal Lattice and Its Impact over the Catalytic Activity in the H2 Production
Authors: Morelos-Medina Luis Fernando , Nava-Mendoza Rufino , Cortés-Romero Carlos Martín , Velázquez-Castillo Rodrigo Rafael , Santos-Cruz J. , Cuán-Hernandez María de los Ángeles
Published: May 2022

Solid solutions ZnxCd1−xS were compared by coprecipitation and sonochemistry methods, where

x
=
0.2

. This rate is the one that presents the best photocatalytic activity and the best performance for the production of hydrogen from a water source, according to our previous results. The influence of the synthesis method on the morphology and its electronic properties of the CdS and Zn0.2Cd0.8S systems was investigated, as well as the effect of the incorporation of Zn in the crystal lattice and its effect on the production of hydrogen from the division of water. X-ray diffraction characterization confirmed the formation of a solid solution corresponding to a face-centered cubic crystalline phase with preferential growth of (111), (220), and (311) planes and hierarchical morphology according to SEM and TEM microscopy, formed by aggregated nanoparticles of ZnxCd1−xS compound. The crystallite size decreased with the Zn incorporation in the crystal lattice from 8.61 nm by a coprecipitation method to 4.37 nm when the synthesis was assisted with sonochemistry, and the crystallite sizes range from 7.92 nm to 3.80 nm for the case of CdS pure. Other characterization techniques were also used, such as photoluminescence (PL), Raman microscopy, and N2 adsorption and desorption. The PL results show tunability of band edge emission as a function of zinc concentration in the Zn0.2Cd0.8S nanoparticles. For the water splitting to hydrogen production, the separation of the charge carriers promoted by the incorporation of Zn in addition to the shallow trap emissions generated defects in the catalyst structure; such processes are important factors found which increased with the sonochemical method, as well as the potential matched positions.
Information Collection, Analysis, and Monitoring System of Children’s Physical Training Based on Multisensor
Authors: Zhang Zhonglin , Xu Jiajia , Peng Chenggen , Chen Yuping
Published: May 2022

In order to obtain more children’s physical training information and improve the accuracy of children’s physical training monitoring, a multisensor-based children’s physical training information collection, analysis, and monitoring system is proposed. In the process of physical training and sports training, people’s physical training information collection is directly related to the level and effectiveness of physical training. With the combination of multisensor concept and sports training information collection, it can collect the key index data of sports mobilization in real time with the help of multiple sensors and information technology. Taking children’s physical training as the object, this paper designs a multisensor physical training data information acquisition terminal, collects different training characteristic data with the help of multisensor equipment, and then comprehensively analyzes and monitors the physical information with the help of certain fusion technology, so as to construct a human posture recognition algorithm based on children’s physical training information acquisition. Support vector machine and decision tree are used to classify children’s different physical exercise states, and a relatively perfect algorithm architecture of human posture recognition is constructed. The results show that for two decision trees, each decision tree is trained with a total of 675 groups of data, and a total of 342 groups of data are verified and pruned. The two decision trees take 7.17 s and 7.32 s to complete the training process, respectively. It can be seen that when the number of training groups is equal, the training time of the two placement methods is close, so it can be considered that the two placement methods have little effect on the training speed of decision tree. The experimental data show that the design of children’s physical training monitoring system in this paper has a certain market value.
An Effective Video Summarization Framework Based on the Object of Interest Using Deep Learning
Authors: Ul Haq Hafiz Burhan , Asif Muhammad , Ahmad Maaz Bin , Ashraf Rehan , Mahmood Toqeer
Published: May 2022

The advancements in digital video technology have empowered video surveillance to play a vital role in ensuring security and safety. Public and private enterprises use surveillance systems to monitor and analyze daily activities. Consequently, a massive volume of data is generated in videos that require further processing to achieve security protocol. Analyzing video content is tedious and a time-consuming task. Moreover, it also requires high-speed computing hardware. The video summarization concept has emerged to overcome these limitations. This paper presents a customized video summarization framework based on deep learning. The proposed framework enables a user to summarize the videos according to the Object of Interest (OoI), for example, person, airplane, mobile phone, bike, and car. Various experiments are conducted to evaluate the performance of the proposed framework on the video summarization (VSUMM) dataset, title-based video summarization (TVSum) dataset, and own dataset. The accuracy of VSUMM, TVSum, and own dataset is 99.6%, 99.9%, and 99.2%, respectively. A desktop application is also developed to help the user summarize the video based on the OoI.
Photocatalytic Behavior of Supported Copper Double Salt: The Role of Graphene Oxide
Authors: Shabestari Marjan E. , Baselga Juan , Martin Olga
Published: May 2022

The design of a photocatalyst that may work efficiently with sunlight is a fundamental concern to fight against environmental pollution and electrochemical hydrogen storage devices. In this work, it has been found that the green microwave-assisted decoration of graphene by copper double salt (DS) enhances visible sunlight photocatalysis efficiency. Nanohybrids of graphene oxide decorated with Cu(I) and Cu(II) oxides and copper hydroxy nitrate double salt were selected as photocatalysts for the degradation of rhodamine B in aqueous solution to study the effect of the graphene oxide support. The photodegradation process followed a pseudo–first-order kinetics for the bare catalysts, but the supported catalysts were best fitted to the Langmuir-Hinshelwood model. Supported systems were more efficient in terms of turnover and apparent rate constants. Diffuse reflectance spectroscopy with the use of Kubelka-Munk function allowed to measure bandgap energies. It was found that the absorption edge was reduced about 30% for the supported systems.
Research on Enterprise Financial Accounting Information Security Model Based on Big Data
Authors: Zhong Ran , Zhang Qianying , Zhao Yachao
Published: May 2022

Powered by the rapid development of the information age, big data technology has had a great impact on China’s socialist economic development. Big data technology is integrated into all fields of nowadays society, especially in the accounting industry of enterprises. Accounting computerization quickly replaces the traditional manual bookkeeping, realizes the timeliness and accuracy of accounting information data processing, and improves the work efficiency and quality of accounting staff to a great extent, but at the same time, the security of financial accounting information is also very prominent. To tackle this problem, this paper proposes a hybrid encryption algorithm based on double chaotic system and improved AES encryption algorithm. The improved AES algorithm uses affine transformation pairs (A7 and 6F) to generate new S-boxes. The double four-dimensional hyperchaotic system is transformed from two three-dimensional chaotic systems, and then, the transformed hyperchaotic system is used to generate chaotic sequences, and a block encryption scheme is designed. On Hadoop big data platform, double hyperchaotic encryption scheme and improved AES algorithm are combined. Simulation test results show that this method can safely transmit enterprise financial accounting information data packets. With the increase of computing cluster nodes, its encryption transmission efficiency continues to improve. This scheme not only solves the problem of enterprise financial accounting data security encryption but also realizes the parallel transmission of encrypted data in the information age, forming a double guarantee of data transmission security and efficiency.
Uzbek for My Heart: Language Choice and Identity Negotiation in Multilingual Uzbekistan
Authors: Zhao Liang
Published: May 2022

Central Asia is distinguished by its high level of multilingualism. Incorporating both language portrait and ethnographic methods, this paper attempts to uncover how young Uzbeks negotiate their use of language in multilingual Uzbekistan and its connections to education, opportunity, identity, and group membership; furthermore, it examines how they construct and negotiate their identities during this process. Under the lens of a micro and bottom-up approach, this research discovers that the youth of Uzbekistan regard multilingualism as a semiotic source of mobility that allows them to function adequately in a globalized world and that Uzbek, as a mother tongue, plays an important role in their ethnic and cultural identification. Being an Uzbek always occupies the first position among their many identities at the intersection of tradition and modernity, as well as localization and globalization, highlights the relationship between the mother tongue, as a heritage and individual development, which can be an important life anchor left for younger generations as a part of their own history and tradition, especially now, in the historical period we are living in, where relationships are characterized by high mobility and virtualization.
Construction of Evaluation Index System of Physical Education Teaching Environment in Colleges and Universities Based on 5G Technology
Authors: Tian Wen , Tie Yu , Wang Ruiting , Zhou Lu
Published: May 2022

A top notch actual training climate has an indispensable main impetus for the physical and mental improvement of youths and kids and the nature of actual instruction. This paper proposes an assessment file arrangement of actual instruction showing climate in schools and colleges in light of 5G innovation, planning to fabricate an assessment list arrangement of actual training educating in the climate of 5G data innovation in the coordination stage. The technique for this paper is to concentrate on the organization energy effectiveness model, layout a framework model and 5G list demonstrating, and afterward propose a showing climate assessment review and examination strategy. The job of these strategies is to plan sensible assessment norms based on hypothetical examination. It utilizes 5G data innovation and organization effectiveness and lastly constructs a school sports ecological assessment framework. Through the poll overview, this paper comprehends what is going on of actual schooling showing climate in Chinese universities and colleges and tracks down the purposes behind its disparities and issues. This paper develops actual training climate assessment file framework, researches the game climate assessment and actual exercise direction, and afterward proposes actual instruction and assessment framework under 5G innovation. The outcomes show that in the assessment list arrangement of actual instruction showing climate in schools and colleges, the heaviness of material climate is 0.6796, demonstrating that the most dire errand for schools and colleges to further develop the actual training climate is to work on the actual climate.
Machine Learning and Artificial Intelligence in the Food Industry: A Sustainable Approach
Authors: Kler Rajnish , Elkady Ghada , Rane Kantilal , Singh Abha , Hossain Md Shamim , Malhotra Dheeraj , Ray Samrat , Bhatia Komal Kumar
Published: May 2022

The goal of this research was to look into how artificial intelligence (AI) and machine learning (ML) techniques are being used in food industry and to come up with future research directions based on that. This study investigates the articles available on several scientific platforms that link both AI and supply chain from one side and ML and food industry from the other side, using a systematic literature review methodology. The findings of this research stated that although AI and machine learning technologies are yet in their beginning, the prospective for them to enhance the performance of the food industry (FI) is quite promising. Various investigators created AI and ML-related models that were verified and found to be effective in optimising FI, and so the use of AI and ML in FI networks provides competitive advantages for improvement. Other academics suggest that AI and machine learning are both now adding value, while others believe that they are still underutilised and that their tools and methodologies can harness the overall value of the food business. According to the findings, AI and machine learning have the potential to reduce economic losses, thereby supporting the food industry's efficiency and responsiveness.
The Iterative Method for Generalized Equilibrium Problems and a Finite Family of Lipschitzian Mappings in Hilbert Spaces
Authors: Kangtunyakarn Atid , Suwannaut Sarawut
Published: May 2022

In this research, we introduced the S‐mapping generated by a finite family of contractive mappings, Lipschitzian mappings and finite real numbers using the results of Kangtunyakarn (2013). Then, we prove the strong convergence theorem for fixed point sets of finite family of contraction and Lipschitzian mapping and solution sets of the modified generalized equilibrium problem introduced by Suwannaut and Kangtunyakarn (2014). Finally, numerical examples are provided to illustrate our main theorem.
Interactive English Online Teaching System Based on B/S Model
Authors: Gu Wanhong , Yuan Yaxiong
Published: May 2022

In order to improve the effect of online interactive teaching of English, this paper analyzes the online interactive teaching data of English in combination with the generation of confrontation network and simulates the human interaction process to perform simulation. Moreover, this paper introduces the strategy gradient of reinforcement learning to the generator of the generation confrontation network to solve the problem that the generation confrontation network is difficult to be used for dialogue generation. In addition, this paper combines intelligent algorithms to construct an online interactive teaching system for English. The experimental research results show that the interactive English online teaching system based on the B/S model proposed in this paper has a greater advantage than the traditional online English teaching method.
Deep Reinforcement Learning-Based UAV Data Collection and Offloading in NOMA-Enabled Marine IoT Systems
Authors: Dai Yanpeng , Liang Ziyi , Lyu Ling , Lin Bin
Published: May 2022

The rapid growth of maritime wireless communication demand and the complex offshore wireless communication environment have brought challenges to ensure the real-time and reliability of data transmission in the marine Internet of Things (MIoT). Unmanned aerial vehicles (UAVs) have great advantages in enhancing coverage and channel quality. Hence, we investigate a UAV-assisted data collection and data offloading system based on nonorthogonal multiple access (NOMA) technology in this paper. We jointly optimize the buoy-UAV association relationship, transmit powers, and the UAV trajectory to minimize the total mission completion time while ensuring data transmission requirements. We first propose a UAV trajectory optimization algorithm based on deep reinforcement learning (DRL). Then, we design a heuristic algorithm to effectively solve the subproblem of power control and the association relationship. Finally, we propose a joint optimization scheme to solve the minimization problem. Simulation results show the effectiveness of the proposed scheme.
Antiobesity Activity of Two Polyherbal Formulations in High‐Fat Diet‐Induced Obese C57BL/6J Mice
Authors: Pandeya Prakash Raj , Lamichhane Gopal , Lamichhane Ramakanta , Luo Jiao , Li Xiao-Jun , Rhee Su-jin , Xiangqian Liu , Jung Hyun-Ju
Published: May 2022

Obesity and overweight have posed a severe threat to humanity, needing urgent efforts for the development of safe and effective therapeutic interventions. In this research work, we have developed two polyherbal formulations A and B basically consisting of Helianthus tuberosus root powder (also called inulin of synanthrin) along with other herbs for the treatment of obesity. Evaluation of the antioxidant activity of both formulations using 1,1‐diphenyl‐2‐picrylhydrazyl (DPPH) and 2,2 ′‐azino‐bis (3‐ethylbenzothiazoline‐6‐sulfonic acid) (ABTS) free radical scavenging assays showed good antioxidant potentials. Both formulations A and B showed good antiobesity activity on a diet‐induced obesity (DIO) model of mice by effectively lowering the body weight of mice compared to the high‐fat diet (HFD) control mice, mainly by reducing the food efficiency ratio (FER). Furthermore, both formulations ameliorated lipoprotein misbalances induced by obesity and thus decreased the atherogenic index. Treatment with both formulations significantly decreased the liver and epididymal white adipose tissue (WAT) weight. This was supported by the improvement in steatosis of the liver and reduced hypertrophy in WAT on histological examination. In addition, formulations A and B have been seen as effective in controlling fasting blood glucose levels probably by alleviating HFD‐induced insulin resistance. All of these results collectively suggest that formulations A and B serve as potentially safe and effective herbal interventions to control obesity and its comorbidities.
Load Balancing Optimization of In-Memory Database for Massive Information Processing of Internet of Things (IoTs)
Authors: Wei Ran
Published: May 2022

Based on the analysis of the key technologies of the Internet of Things service platform architecture, a load balancing optimization scheme of in-memory database based on the massive information processing of the Internet of Things service platform is proposed. This scheme firstly proposes a system model that can satisfy the mass sensor information processing under the open platform environment and designs several functional unit modules of the system. By combining these functional units, the service can be configured for thousands of services and tenants. This paper presents an adaptive strategy selection method, which can automatically select the optimization strategy by dividing the position and querying the selection rate to improve the efficiency of the adaptive index algorithm. The index structure is initialized by parallel sorting algorithm, and the query statement is executed and the index structure is optimized by thread level parallel and radix sort methods. An elastic pipeline technique is proposed, which includes an elastic iterator model and a dynamic scheduler. The elastic iterator model is an upgrade of the traditional iterator model, adding the characteristics of dynamic multicore execution. In the process of query processing, dynamic scheduler monitors the load of each node in real time and dynamically adjusts the parallelism, so as to realize the load balance of in-memory database and maximize the utilization of hardware resources. The elastic pipeline realizes the isolation of parallelism from query compilation to avoid inappropriate parallelism allocation caused by missing and insufficient information during query compilation.