bluuebunny/crossref_metadata_2025 huggingface dataset:
English Education Tutoring Teaching System Based on MOOC
Authors: Mi Wang
Published: May 2022

In the process of continuous reform and development of education and teaching, some traditional teaching modes are gradually eliminated, whereas new teaching modes are gradually recognized by teachers and students. These modes are widely used in education and teaching due to their overwhelming characteristics. Among these emerging teaching modes, massive open online course (MOOC) is a relatively advanced teaching mode with a better application effect. Quickly and accurately detecting the cheating behavior of MOOC learners is of great significance for maintaining the development of the MOOC platform and English education counseling. This paper studies a deep-learning-based hybrid model for MOOC cheating detection. The model greatly improves the detection performance of a single model by integrating CNN, a bidirectional gated recurrent unit, and an attention mechanism. The proposed model selects the English learning behavior data of a MOOC platform to verify the performance of the algorithm. Simulation results show that the proposed scheme can greatly help MOOC-based English education tutoring.
How Government Subsidy Impacts on the Supply Chain Decision for LNG-Fuelled Ships
Authors: Li Yan , Yang Jiao
Published: May 2022

LNG-fuelled ships help fight environmental pollution induced by fossil fuel. Chinese government has also considered encouraging the shipbuilder’s initiative of producing or introducing LNG-fuelled ships through subsidies. However, rare scholars have concerned the impact of government subsidy on the supply chain decision of LNG-fuelled ships in the existing studies. This study takes green degree and government subsidy into account in the LNG-fuelled ship supply chain of a manufacturer and an owner and studies the pricing, market demand, and supply chain benefits. Our main contributions are considering the correlation between the green degree of LNG ships and government subsidy factors and exploring the influence of government subsidy on supply chain decision-making under decentralized and centralized decision-making by ship manufacturers and owners. The results show that government subsidies are conducive to promoting the whole profit and the market demand in the ship supply chain. Centralized decision generates higher profits than decentralized decisions. In the centralized decision model, government subsidies and the green degree of LNG-fuelled ship are positively correlated with the market demand. In the decentralized decision model, the price of an LNG-fuelled ship charged by shipbuilder when providing subsidies for owners is greater than that when providing subsidies for shipbuilder. Manufacturers and owners should pay attention to the greenness of ships, and the government should adopt appropriate subsidy policies to encourage the production and application of LNG ships.
Performance of Steel Fiber‐Reinforced Concrete Pier under Impact Load
Authors: Dessalegn Melaknesh , Wondimu Temesgen
Published: May 2022

Vehicular impact on bridge piers is one of the leading causes of bridge failure. Due to the increase in vehicle‐pier collisions, many researchers have studied the performance of RC bridge piers under vehicle collisions. Adding steel fiber is an alternative way to improve the impact resistance of concrete structures and enhance their capacity for energy absorption. However, a study on the performance of piers with steel fiber‐reinforced concrete under impact load is limited. This study examines the performance of steel fiber‐reinforced concrete (SFRC) bridge piers subjected to impact loading. To study the impact behavior of SFRC piers, a numerical model of bridge piers with impact mass was developed by LS‐DYNA. The reliability of the numerical analysis was validated. To investigate the effects of impact mass, impact velocity, the volume of steel fiber, concrete grade, and percentage of longitudinal reinforcement on the impact behaviors of piers and the impact forces, parametric studies were carried out. The finite element analysis revealed that the addition of steel fiber resists lateral deformation and significantly affected the failure modes of bridge piers under impact loading. Moreover, the deformation of the impacted piers and the impact force is mainly affected by the addition of steel fiber, impact energy, and percentage of longitudinal reinforcement and is not sensitive to concrete strength.
[Retracted] Research on the Flow Space Planning Model of a Classical Garden Based on an Ant Colony Optimization Algorithm
Authors: Tang YuLu , Madina Zamira
Published: May 2022

The traditional spatial planning model has a large error and poor spatial planning effect, which cannot adapt to the construction of classical gardens. The flow spatial planning model of classical gardens based on an ant colony optimization algorithm is designed. The aim of the study is to perform the following: Analysis of classical garden flow space scale, quantifying the spatial flow scale of classical gardens, modifying classical garden spatial sequence based on the ant colony optimization algorithm, and constructing the flow space planning model of classical garden. By means of a comparison experiment, the error of the new model is small, and the effect of spatial planning is increased, which is of great popularization value.
Stability Evaluation of Proppant in Fractures of Gas Storage in Yulin Gas Field
Authors: Wang Ping , Gu TianLi , Gao Zhanwu , Fan Jiayong , Huang Hai , Qu Zhan , Han Qiang , Ren Zongxiao
Published: May 2022

In nearly a hundred years of construction, underground gas storage has become the main natural gas storage and peak regulation means in the world. For the gas storage, the large production and high flow rate of the gas well in the actual production process will cause the backflow of proppant filled in the supporting fracture, which will bring great harm to the gas field production. In this article, when the proppant fracture reaches a stable state in the process of gas injection and production, the stress of proppant particles is analyzed, the critical velocity of proppant reflux is calculated, and then the critical production model is established; calculate the permeability change during proppant migration, then calculate the fluid velocity and production, and determine the fluid velocity range of injection and production wells in gas storage. The parameter sensitivity of velocity and flow model is analyzed. The results show that with the increase of closure stress, the critical gas flow and critical gas velocity of proppant backflow gradually increase, and the proppant filling layer is more stable. The smaller the thickness and width of the filling layer, the greater the critical gas flow and critical gas velocity of proppant backflow, the more stable the proppant filling layer, and the lesser the chance of backflow. The higher the saturation, the lower the critical gas velocity, and the more prone the proppant to reflux. It has important guiding significance for realizing the optimization of gas well production and maintaining efficient production efficiency.
The Pathogenesis and Influencing Factors of Adult Hypertension Based on Structural Equation Scanning
Authors: Wu Yaqiong , Ma Guangyu , Feng Nana , Zhang Zhiqiang , Zhang Sijie , Li Xingtao
Published: May 2022

Explore the pathogenesis and influencing factors of adult hypertension based on structural equation scanning. Using a multistage random sampling method, randomly select 2 community health service centers in each administrative area of a certain city and conduct a sample survey of residents in the community. According to the predetermined sample size

n

, multiply by 1.3 (

1.3
n

) to draw a sample. Community doctors and medical students who have been uniformly trained form an investigation team draw up a questionnaire by consulting the literature, seek expert opinions, and then make changes based on the questions in the preinvestigation. Experiment result shows that the average systolic blood pressure of the experimental subjects was

126.13
+
15.36
 
mmHg

and the average diastolic blood pressure was

79.52
+
8.81
 
mmHg

; males are higher than females and increase with age. The prevalence rate of hyperemia is 26.3%, and the prevalence rate of prehypertension among the survey subjects is 55.4%; that of males (62.6%) is higher than that of females (49.2%). The prevalence of isolated systolic hypertension was 7.5%, and that of men (6.9%) was lower than that of women (7.9%). The awareness rate of hypertension was 66.5%, and the treatment rate of hypertension was 62.7%; the control rate of hypertension was 13.2%, and the control rate of hypertension treatment was 25.7%; all the abovementioned rates are higher for women than for men, and they all tend to increase with age which proved that being overweight is a risk factor for hypertension, dyslipidemia, and hypertension. Hypertension, dyslipidemia, and family history of hypertension are risk factors for hypertension. There is a positive correlation between hypertension and dyslipidemia.
Field and Laboratory Evaluation of Pesticidal Plants for Bioactivity against Rape and Tomato Pests in Malawi
Authors: Kayange C. D. , Nyirenda S. P. , Mwamlima L. H. , Kamanula J. F. , Munthali C. R. Y. , Njera D. , Maluwa P.
Published: May 2022

Insect pests cause serious damage through feeding in the process and may also transmit plant diseases. Although most resource-poor farmers rely on the use of synthetic insecticides for controlling insect pests, it is generally considered too expensive for them. Plant products may be a safer alternative approach and play a significant role in insect pest management and crop protection amongst resource-poor farmers. Laboratory and field evaluation of locally available pesticidal plants were evaluated. The study was conducted at Jenda and Nchenachena in Malawi with rape and tomato to assess the efficacy of some selected pesticidal plants for the control of vegetable pests. Largely, the choice of the selected pesticidal plants was based on resource-poor farmers’ knowledge in the area. The results of the tested plant extracts suggested that some plant extracts could reduce the infestation of red mites and aphids below economic threshold levels. Under laboratory experiments, all plant species caused a significant increase in mortality of Trypanosoma evansi after 24 h. However, Dolichos kilimandscarichus, Tephrosia vogelii, Azadirachta indica, and Bedotia madagascarensis had significantly at

P
<
0.05

greater mite mortality than the untreated. This suggests that these pesticidal plants could be an alternative to acaricidal and insecticidal pesticides against vegetable pests. These pesticidal plants caused an impressive reduction of red mites and aphids and protected tomatoes and rapes from serious damage and hence could be incorporated into integrated management for the control of vegetable pests.
Evaluation of Weighted Nuclear Norm Minimization Algorithm for Ultrasound Image Denoising
Authors: Mahaboob Basha Shaik , Neto Aloísio Vieira Lira , Menezes José Wally M. , Chelloug Samia Allaoua , Abd Elaziz Mohamed , de Albuquerque Victor Hugo C.
Published: May 2022

Anatomical structures manifested in ultrasound (US) images are crucial in efficient disease diagnosis. This modality has been used to analyze different tissue properties, such as blood flow, in-depth tissue motion, and elasticity. Analysis of these US images is posing a critical challenge, as these images are corrupted with noise primarily induced during acquisition. The biological structures intended to be investigated need to be detected, enhanced, and preserved during image processing-based diagnosis. US-based common carotid artery (CCA) images were considered in this study, and five denoising techniques were explored for noise removal after converting the images to grayscale to identify efficient preprocessing for effective diagnosis. Furthermore, filtered images were subjected to different entropy-inspired segmentation for qualitative validation and to segment the CCA. The objective of this paper is a deliberate attempt to investigate the possible use of edge- and structure-preserving filtering techniques to segment tissues of interest. The weighted nuclear norm minimization (WNNM) approach appears to be effective in removing noise and simultaneously preserving the sensitive structures. Quantitative validation with peak signal to noise ratio (PSNR), structural symmetry index measure (SSIM), and feature similarity index measure (FSIM) found to be

27.84
±
1.04

 dB;

0.76
±
0.01

and

0.87
±
0.01

were observed to be superiorly high with WNNM filtering. The input image and the filtered image histograms are also compared for qualitative validation. The key finding in this study can be attributed to the ability to remove noise from US images corrupted with noise while preserving the anatomical details. Furthermore, it can be hypothesized that the anatomical structures under the influence of noise can be efficiently preprocessed and can be fed as a viable image towards segmentation followed by recognition and morphological inference.
Salient Region Detection via Multiple Hierarchy and Competition Mechanism
Authors: Zhu Dongtao , Yang Xing , Hu Yihua , Liang Zhenyu
Published: May 2022

The detection of salient regions has attracted an increasing attention in machine vision. In this study, a novel and effective framework for saliency region detection is proposed to solve the problem of the low detection accuracy of traditional methods. Firstly, we divide the image into three levels. Second, each level uses three different feature methods to generate different feature saliency maps. Subsequently, a novel integration mechanism, termed competition mechanism, is introduced into the coarse saliency maps at the same level, and the two coarse saliency maps with the highest similarity are selected for fusion to ensure the effectiveness of the salient region map. Accordingly, after adjusting the scales of the saliency map after the fusion of different levels, among three coarse saliency maps of the different levels, the two feature maps with the most significant difference are selected to fuse to further obtain the final refined saliency map. Finally, using the proposed method, experiments on three benchmark datasets were conducted. As demonstrated by the experimental results, the proposed algorithm is superior to other state-of-the-art methods.
Fractal Visual Art Graphic Design Based on Computer-Aided Algorithms
Authors: Zhou Qihao , Liu Teng , Wu Guorong , Chen Xuhui
Published: May 2022

Embedded machine vision algorithm development platform is of great significance. Based on the elaboration of visual communication design, this paper further details the design of fractal visual art graphics based on computer-aided algorithms to design edge detection schemes, where edges represent sudden changes in the signal and are able to characterize the rich information of an image. In response to the problems of poor robustness and difficult parameter selection of the currently widely used edge detection algorithms, the platform encapsulates joint edge detection graphic components in a higher-order processing library to achieve the requirements of high-precision detection and to realise enhanced edge detection effects. Finally, the experimental analysis shows that the proposed algorithm has a more accurate detection effect and can meet the user's high-precision detection requirements.
Multistrategy Improved Whale Optimization Algorithm and Its Application
Authors: Liu Lisang , Zhang Rongsheng
Published: May 2022

To address the shortcomings of the whale optimization algorithm (WOA) in terms of insufficient global search ability and slow convergence speed, a differential evolution chaotic whale optimization algorithm (DECWOA) is proposed in this paper. Firstly, the initial population is generated by introducing the Sine chaos theory at the beginning of the algorithm to increase the population diversity. Secondly, new adaptive inertia weights are introduced into the individual whale position update formula to lay the foundation for the global search and improve the optimization performance of the algorithm. Finally, the differential variance algorithm is fused to improve the global search speed and accuracy of the whale optimization algorithm. The impact of various improvement strategies on the performance of the algorithm is analyzed using different kinds of test functions that are randomly selected. The particle swarm optimization algorithm (PSO), butterfly optimization algorithm (BOA), WOA, chaotic feedback adaptive whale optimization algorithm (CFAWOA), and DECWOA algorithm are compared for the optimal search performance. Experimental simulations are performed using MATLAB software, and the results show that the improved whale optimization algorithm has a better global optimization-seeking capability. The improved whale optimization algorithm is applied to the distribution network fault location of IEEE-33 nodes, and the effectiveness and accuracy of the distribution network fault zone location based on the multistrategy improved whale optimization algorithm is verified.
Application of Mathematical Methods Based on Improved Fuzzy Computing in Building and Urban Design in the Environment of Industry 4.0
Authors: Duan Chuan
Published: May 2022

In order to improve the performance of the urban building in urban design, this paper uses the mathematical method based on an improved fuzzy calculation to construct an intelligent building and urban design system. Moreover, this paper quantitatively studies the pedestrian wind environment of high-rise buildings and determines the optimal building aerodynamic shape and optimal building layout in the full wind direction. In addition, based on the results of the whole watershed analysis of CFD numerical simulation, this paper reveals the mechanism of building shape and layout in the pedestrian wind environment of high-rise buildings. Finally, this paper constructs an intelligent model to improve the effect of urban architectural design. Through the model research results, we can see that the urban design intelligent system proposed in this paper meets the needs of urban design in the environment of Industry 4.0.
Construction of Correlation Analysis Model of College Students’ Sports Performance Based on Convolutional Neural Network
Authors: Jiang Guangtao
Published: May 2022

This paper proposes a network model recurrent fully connected network (RFC-Net) based on recurrent full convolution and polarization change. RFC-Net enriches the network by reconstructing and fine-tuning the fully convolutional network and adding recurrent convolutions to it. By studying the data mining technology of multidimensional association rules, based on the existing algorithms, this paper improves the shortcomings of the algorithms and realizes an efficient and practical method for data mining based on interdimensional multidimensional association rules. On the basis of mastering the actual student information, the effectiveness of the method is tested, and an employment analysis system based on association rules is established. Aiming at the fact that traditional grade prediction methods ignore the different influences of different behavioral characteristics on grades, and considering that behavioral data in different periods have different influences on student grades, the grade prediction problem is abstracted into a time series classification problem. The mechanism is combined with long short-term memory neural network to construct a performance prediction model based on Attention-BiLSTM. Experiments show that the prediction model proposed in this paper improves the accuracy and effectively improves the prediction quality compared with the logistic regression model with a better prediction effect in the traditional benchmark model and the long short-term memory neural network model without the introduction of the attention mechanism. Research shows that physical performance and academic performance are not contradictory. We must face up to the status of physical exercise in schools; as long as physical exercise is properly arranged, it can inspire students to form a spirit of unity, interaction, positivity, and perseverance in cultural studies.
Developing an Intelligent System with Deep Learning Algorithms for Sentiment Analysis of E-Commerce Product Reviews
Authors: Alzahrani Mohammad Eid , Aldhyani Theyazn H. H. , Alsubari Saleh Nagi , Althobaiti Maha M. , Fahad Adil
Published: May 2022

Most consumers rely on online reviews when deciding to purchase e-commerce services or products. Unfortunately, the main problem of these reviews, which is not completely tackled, is the existence of deceptive reviews. The novelty of the proposed system is the application of opinion mining on consumers’ reviews to help businesses and organizations continually improve their market strategies and obtain an in-depth analysis of the consumers’ opinions regarding their products and brands. In this paper, the long short-term memory (LSTM) and deep learning convolutional neural network integrated with LSTM (CNN-LSTM) models were used for sentiment analysis of reviews in the e-commerce domain. The system was tested and evaluated by using real-time data that included reviews of cameras, laptops, mobile phones, tablets, televisions, and video surveillance products from the Amazon website. Data preprocessing steps, such as lowercase processing, stopword removal, punctuation removal, and tokenization, were used for data cleaning. The clean data were processed with the LSTM and CNN-LSTM models for the detection and classification of the consumers’ sentiment into positive or negative. The LSTM and CNN-LSTM algorithms achieved an accuracy of 94% and 91%, respectively. We conclude that the deep learning techniques applied here provide optimal results for the classification of the customers’ sentiment toward the products.
Multicultural Integration of Urban International Communities in the Innovation Ecosystem
Authors: Yu Zi , Yu Huajun
Published: May 2022

Purpose. This article aims to bring the issue of multicultural integration of urban international community into the perspective of innovation ecosystem. Combined with theory, it probes into the relationship between multicultural integration and innovation ecosystem. The study, based on in-depth questionnaire and document analysis, examines the status quo of foreign residents’ participation in local social and cultural life from the measurement of identity, cultural differences, values, ideologies etc., and the obstacles encountered with the focus of spirit, material, and management. Design/Methodology/Approach. This study adopts the data sampling methods of purview sampling and snowball sampling to investigate cultural integration from two dimensions of sociocultural (behavioral) integration and psychological (emotional/affective) integration. The scale adopts the 4-point score of Likert formula and uses the mean coefficient (the average score of each item) to judge the difficulty of psychological integration of foreign residents. Findings. First, the social and cultural integration perceived by foreign residents is at a medium level. The conflicts of values, the differences of social and cultural systems, identity, and language ability are the main obstacles and difficulties in the multicultural integration of foreign residents. Second, at the psychological level, many foreign residents conducting the survey did not have too much psychological pressure, most of them could regulate their emotions by themselves, but the problem of psychological integration still exists; therefore, relevant effective intervention is needed. Third, foreign residents’ views on the social and cultural environment of their communities affect multicultural integration and stability of their communities. Practical Implications. This study helps understand the factors affecting multicultural integration in international community, which will be helpful for the researcher to understand more about the current issue of multicultural integration in international community and for the relevant department to adopt suitable policy and measurements to facilitate multicultural integration in international community.
Topological Structure Analysis of Software Using Complex Network Theory
Authors: Xu Xinxin , Zhang Zengyou , Liang Yan , Wang Liya
Published: May 2022

Due to people’s increasing dependence on software, the emergence of software defects will lead to serious consequences. And the essential cause of software defects is the increasing complexity of software. The premise of reducing software defects is to understand the software topology to ensure software quality. The software topology refers to the connection between the internal elements of the software, and it has become an important factor affecting the quality of the software. In this paper, we use complex network theory as a tool to analyze the software topology. Firstly, we extract the software structural information from the source code of the software system and abstract the extracted software structural information with software network theory. Secondly, the metrics widely used in complex networks are introduced to analyze the built software network. When tracking the values of these metrics in the software system, we have a deeper understanding of the software topology. These results provide a different dimension to understanding the software topology, which has important guiding significance for the subsequent understanding of the software and is also very useful for reducing software defects and ensuring software quality.
Research on Performance Optimization Algorithm of Resource and Environment Audit Based on Computer Technology
Authors: Wang Yuhui
Published: May 2022

With the rapid development of the economy and society, the sustainable development of resources and environment has been paid more and more attention. As an important part of the national environmental supervision system, resources and environmental audits have attracted great attention from the society. In order to explore the application effect of intelligent networking technology in resource and environment audit, this study carries out monitoring sampling, information collection, and later data processing optimization in resource and environment audit projects through the reference of artificial intelligence technology and method and combined with Internet of things big data analysis, which brings new Turks to the development of natural resource and environment audit and helps to improve the efficiency of audit work. Through the application of new technology in the sampling process of environmental information, the staff use new technology to collect, analyze, and mine data and explore how the new technology can promote the efficiency of resource and environmental audit and better promote the construction of ecological civilization.
Investigation on the Effect of Butyl Acrylate (nBA) to Improve the Toughness Properties of Methacrylate-Based Waterproofing Adhesive Material (MMA) for the Steel Bridge Deck
Authors: Zhang Mengya
Published: May 2022

Waterproof adhesion materials (WAL) for steel bridge decks should have comprehensive strong bonding capabilities to steel substrates/pavement layers, water resistance, and persistence to harsh conditions. Methacrylate-based adhesive (MMA), as a typical WAL material for steel bridge decks, lacks sufficient flexibility and diverse compatibility. In this article, flexible, stabile at high temperature, and well-bonded poly (methyl methacrylate)/n-butyl acrylate copolymer [poly(MMA-r-nBA)] coatings were developed with toughening as a breakthrough point. The above properties are obtained by forming an interpenetrating network through interchain van der Waals forces. In addition to the flexibility provided by the long-chain structure of n-butyl acrylate, its unique molecular structure allows it to improve the high-temperature stability and lower the glass transition temperature of MMA. Due to the reduced glass transition temperature, it can also be matched to various pavement layers. Thus, the poly(MMA-r-nBA) composite polymer coatings can be applied to steel bridge decks that require long-term service in various conditions.
A Multimedia Learning for Chinese Character Image Recognition via Human-Computer Interaction Network
Authors: Dai Zhenhua , Yang Jie
Published: May 2022

As science and technology continue to develop, Chinese character image recognition technology is being used in a wide range of fields. This computer-based technology is a practical way of automatically recognizing images of text. Typically used in Chinese character education, it provides a new form of human–computer interaction for students. In addition, multimedia technology can provide a rich learning environment for students, which can present information about Chinese characters in the form of pictures, sounds, and videos, thus compensating for the disadvantages of learning Chinese characters by rote in the traditional educational process. The combination of Chinese character image recognition technology and multimedia technology can not only enrich the process of learning Chinese characters, but also promote students’ motivation to learn, thus providing a new and more modern approach to Chinese character education. Based on the study of Chinese character image recognition technology, this research combines it with multimedia information, to achieve the image recognition of Chinese character and multimedia information representation. The combined technology can provide significant references for course design and Chinese learners.
Reconfiguration Control Design of UAV against Actuator Faults Based on Control Allocation Method
Authors: Cui Yuwei , Li Aijun , Duan Biao , Wasif Shabbir
Published: May 2022

The paper concentrates on the problem of fault-tolerant control of UAV against actuator faults from the perspective of flight control system architecture. Using backstepping control method and inverse optimization theory, the design of backstepping optimal control law was constructed. Based on the fault monitoring mechanism of vehicle management computer in the distributed flight control and control allocation system, a fault-tolerant control design method was established in the case of multiple failure modes of the actuators, which compensates the influence caused by the failures. Finally, the effectiveness of the proposed strategy was verified by numerical simulation.
Synthesis of Nickel-Zinc Ferrite Nanoparticles by the Sol-Gel Auto-Combustion Method: Study of Crystal Structural, Cation Distribution, and Magnetic Properties
Authors: Hasan Sarwar , Azhdar Bruska
Published: May 2022

Spinel ferrite nanocomposites of Ni1–xZnxFe2O4 (x = 0.25 and 0.75) were synthesized by sol-gel auto-combustion and annealed between 250°C and 1000°C. A single-phase spinel structure was found through X-ray diffraction (XRD). The crystallite size is in the range of 17.55–66.98 nm, and lattice parameters are in the range of 8.351–8.434 Å. X-ray analysis revealed a slight shift of the peaks towards shorter angles when the zinc concentration increased from 0.25 to 0.75. XRD measurements revealed the metal ion distribution in the spinel ferrite system. For each sample, XRD data were used to compute structural characteristics such as lattice spacing, lattice constant, crystallite size, oxygen position parameter, tetrahedral and octahedral ionic radii, and bond lengths. Energy dispersive spectroscopy (EDS) spectra and field emission-electron scanning microscope (FESEM) were used to evaluate the elemental content and morphology. EDS analysis confirmed the presence of expected elements in the samples and confirmed the high doping rate of more than 180% of Zn ions in Ni ferrite. The evaluated particle sizes were determined to be 79.2 and 118.4 nm for zinc content of 0.25 and 0.75, respectively. The nearly spherical shape of the nanoparticles was shown in the transmission electron microscope (TEM). The magnetic moment, remanent, coercivity, and saturation magnetization were calculated by using vibrating sample magnetometer (VSM) results. The saturation magnetization magnitudes showed the influence of cation distribution.
Improving the Efficiency of Photovoltaic Panels Using Machine Learning Approach
Authors: Khilar Rashmita , Suba G. Merlin , Kumar T. Sathesh , Samson Isaac J. , Shinde Santaji Krishna , Ramya S. , Prabhu V. , Erko Kuma Gowwomsa
Published: May 2022

Photovoltaic (PV) solar panels account for a major portion of the smart grid capacity. On the other hand, the accumulation of solar panels dust is a significant challenge for PV-based systems. The accumulation of solar panels dust results in a significant reduction in the amount of energy produced. Because of the country’s low wind velocity and rainfall, frequent cleaning of solar panels is necessary either by manual or automated means. Cleaning activities should only be initiated when absolutely essential to reduce maintenance costs and increase the power output of solar panels that have been projected to be affected by dust accumulation. In this paper, we develop a deep belief network model to detect the dust particles in the solar panels installed as a large unit. The study takes into account various input metrics that includes solar irradiance, temperature level, and dust level on the panels. These metrics are used for the estimation of the level of dust present in the atmosphere and how often the panels can be cleaned at regular intervals. The simulation is conducted to test the efficacy of the model in cleaning the panels. The results are estimated in terms of accuracy, precision, recall, and F-measure. The results of the simulation show that the proposed model achieves higher accuracy rate of more than 99% than other methods.
COVID-19 in Coincidence with Transient Distal Renal Tubular Acidosis in an Infant
Authors: Razavi-Amoli Seyedeh-Kiana , Mohammadjafari Hamid , Zamanfar Daniel , Navaeifar Mohammad Reza , Sadati-Lamradi Zahra , Rezai Mohammad Sadegh
Published: May 2022

Background. Post-COVID-19 nephropathies have been reported profusely in the literature with diverse pathophysiological mechanisms. To the best of our knowledge, this is the first report of transient distal (type 1) renal tubular acidosis (dRTA) in an infant with confirmed COVID-19. Case Presentation. We describe a 32-day-old female with diarrhea and fever without respiratory complaints. Her weight, height, and head circumference were normal for age. The primary lab test showed leukocytosis, neutrophilia, elevated inflammatory markers, and non-anion-gap metabolic acidosis. Real-time polymerase chain reaction (RT-PCR) and elevated SARS-CoV-2 immunoglobulin M confirmed COVID-19, while echocardiography and spiral chest computed tomography scan were normal. Intravenous fluid therapy and supportive care were initiated. Blood culture was positive for Klebsiella pneumoniae. Amikacin and cefotaxime were ordered. Although diarrhea and dehydration gradually improved, venous blood gas still showed metabolic acidosis. Due to the alkaline urine and hypokalemic-hyperchloremic metabolic acidosis, dRTA was diagnosed. Notably, the patient dramatically responded to Shohl’s solution. Conclusions. Regarding the various manifestations of COVID-19, the possible association between dRTA and COVID-19 needs further investigation in children.
Multiobjective Optimization of Surface Roughness and Tool Wear in High-Speed Milling of AA6061 by Machine Learning and NSGA-II
Authors: Nguyen Anh-Tu , Nguyen Van-Hai , Le Tien-Thinh , Nguyen Nhu-Tung
Published: May 2022

This work addresses the prediction and optimization of average surface roughness (Ra) and maximum flank wear (Vbmax) of 6061 aluminum alloy during high-speed milling. The investigation was done using a DMU 50 CNC 5-axis machine with Ultracut FX 6090 fluid. Four factors were examined: the table feed rate, cutting speed, depth of cut, and cutting length. Three levels of each factor were examined to conduct 81 experiment runs. The response parameters in these experiments were measurements of Ra and Vbmax. We applied a two-pronged approach that combines machine learning (ML) and a Nondominated Sorting Genetic Algorithm (NSGA-II) to model and optimize Ra and Vbmax. Four ML models were used to predict Ra and Vbmax: linear regression (LIN), support vector machine regression (SVR), a gradient boosting tree (GBR), and an artificial neural network (ANN). The input variables were the significant factors that affect the surface quality and tool wear: the feed rate, depth of cut, cutting speed, and cutting time. Several quality metrics were employed to quantify the performance of the models, such as the root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). As a result, SVR and ANN were found to have the best predictive performance for Ra and Vbmax. These models and the NSGA-II-based approach were then employed for multiobjective optimization of cutting parameters during high-speed milling of aluminum 6061. Fifty Pareto solutions were found with Ra in the range of 0.257 to 0.308 µm and Vbmax in the range of 136.198 to 137.133 μm. Experimental validations were then conducted to confirm that the optimum solution was within an acceptable error range. More precisely, the absolute percentage errors for Ra and Vbmax were 2.5% and 1.5%, respectively. This work proposes an effective strategy for efficiently combining machine learning techniques and the NSGA-II multiobjective optimization algorithm. The experimental validations have reflected the potential for applying this strategy in various machining-optimization problems.
Potential Role of Yoga Intervention in the Management of Chronic Non-malignant Pain
Authors: Gupta Shivani , Gautam Surabhi , Kumar Uma , Arora Taruna , Dada Rima
Published: May 2022

Pain is an unpleasant and upsetting experience. Persistent pain has an impact on an individual’s quality of life which causes stress and mood disorders. There are currently no pain-relieving techniques available that can eliminate pain and offer relief without causing any adverse effects. These factors draw attention to traditional treatments like yoga and meditation, which can reduce biological stress and hence increase immunity, as well as alleviate the psychological and emotional suffering produced by pain. Yoga reduces the stress response and the pain cascade via the downregulation of the hypothalamus-pituitary-adrenal (HPA) axis and vagal stimulation. Yoga is a cost-effective growing health practice that, unlike pharmaceuticals, has no side effects and can help patients stay in remission for longer periods of time with fewer relapses. Yoga not only reduces stress and depression severity but also improves functional status and reduces pain perception. This article highlights the impact of yoga on pain management and on a malfunctioning immune system, which leads to improved health, pain reduction, disease management, and improvement in overall quality of life.
Stabilization for the Stochastic Heat Equation with Boundary Control
Authors: Ma Baolin , Sun Yiyue , Zheng Guojie
Published: May 2022

This paper investigates the stabilization of an unstable stochastic heat equation. By the backstepping technique, a boundary feedback control is designed to stabilize the unstable stochastic system, including the 2nd‐moment and almost‐sure exponential stabilization. We also prove that the system with a disturbance in the control channel possesses good robust stability under our control strategy and suitable conditions. Finally, we also provide numerical simulations which illustrate the effectiveness of the theoretical results.
Celecoxib Reverse Invasion and Metastasis of Gastric Cancer through Lnc_AC006548.28-miR-223-LAMC2 Pathway
Authors: Jin Guohua , Zhang Jianguang , Cao Tingting , Zhu He , Shi Yang
Published: May 2022

Celecoxib, a specific cyclooxygenase-2 (COX-2) inhibitor, is a traditional nonsteroidal antipyretic analgesic and anti-inflammatory drug commonly used in clinic, which has inhibitory effect on colorectal cancer, gastric cancer, and other malignant tumors. This study showed that Celecoxib could significantly reverse the invasion and metastasis of gastric cancer and improved the pathological changes due to GC. We collected the clinical specimens to analyze the correlation between the expression of Lnc_AC006548.28, miR-223, and LAMC2. In the mouse model, Celecoxib can slowdown the growth of GC tumor and the occurrence of this effect may depend on Lnc_AC006548.28-miR-223-LAMC2 pathway, in vitro transfection, RT-PCR, western blot, CCK8, small chamber assay, flow cytometry, and immunohistochemistry to retest the protective effect of celecoxib. Our results showed that Celecoxib could reverse invasion and metastasis of gastric cancer through Lnc_AC006548.28-miR-223-LAMC2 pathway.
[Retracted] Mitochondrial‐Targeted Antioxidant Peptide SS31 Prevents RPE Cell Death under Oxidative Stress
Authors: He Yuan , Chen Zejun , Zhang Ruixue , Quan Zhuoya , Xu Yun , He Beilei , Ren Yuan
Published: May 2022

This work aims at investigating the protective effects of the mitochondria‐targeted peptide SS31, on mitochondria function, preventing human retinal pigment epithelial cell‐19 (ARPE‐19) cell apoptosis. The ARPE‐19 cells were subjected to 24 h of intervention with H2O2 of various concentrations (0, 100, 150, 200, 250, 300, and 500 μmol/L). Various concentrations of SS31 (10 nM, 100 nM, and 1 μmol/L) pretreated the cells for 2 h. The MTT assay determined cell viability. ARPE‐19 cell apoptosis was observed by 4 ′,6‐diamidino‐2‐phenylindole (DAPI) staining under fluorescence microscope and detected by Annexin‐V/PI staining under flow cytometry. The measurement of reactive oxygen species (ROS) release level used MitoSOX Red (a mitochondrial superoxide indicator) and the probe 2 ′‐7 ′dichlorofluorescin diacetate (DCFH‐DA). And with the use of a JC‐1 probe, the mitochondrial membrane potential (MMP; ΔΨm) was analyzed. Reverse transcription polymerase chain reaction (RT‐PCR) and real‐time PCR were responsible for measuring the levels of apoptosis related genes (Bcl‐2, Bax, and Caspase‐3). The cell viability increased significantly with SS31 pretreated (P < 0.05). In the SS31 + H2O2 group, the fluorescence of the cell nuclei with DAPI staining was weaker than H2O2 along group accordance with the decreased ratio of apoptotic cells (P < 0.05). The ROS generation decreased in SS31 pretreated group, with the increased ΔΨm. The RT‐PCR result showed decreased Bax gene and Caspase‐3 gene expression with SS31 pretreatment, while increased antiapoptotic gene Bcl‐2 (P < 0.05). We provide evidence that SS31 promotes resilience of RPE cells to oxidative stress by stabilizing mitochondrial function.
An Accurate Method of Determining Attribute Weights in Distance-Based Classification Algorithms
Authors: Liu Fengtao , Wang Jialei
Published: May 2022

Weight determination aims to determine the importance of different attributes; determining accurate weights can significantly improve the accuracy of classification and clustering. This paper proposes an accurate method for attribute weight determination. The method uses the distance from the sample point of each class to the class center point. It can minimize the weights and determines the attribute weights of the constraints through the objective function. In this paper, the attribute weights obtained by the exact solution are applied to the K-means clustering algorithm; three classic machine learning data sets, the iris data set, the wine data set, and the wheat seed data set, are clustered. Using the normalized mutual information as the evaluation index, a confusion matrix was established. Finally, the clustering results are visualized and compared with other methods to verify the effectiveness of the proposed method. The results show that this method improves the normalized mutual information by 0.11 and 0.08, respectively, compared with the unweighted and entropy weighted methods for iris clustering results. Furthermore, the performance on the wine data set is improved by 0.1, and the performance on the wheat seed data set is improved by 0.15 and 0.05.
Research on Multiple Affective Responses Design of Product Based on Kansei Engineering and TOPSIS-AISM
Authors: Wang Mingru , Cheng Xufeng , He Zixuan
Published: May 2022

Affective response reflects the user’s emotional needs, and capturing affective response is very important for a product. But consumers’ emotional needs are complex, and a product design system based on a single affective response (SAR) of consumers cannot produce an appropriate design scheme when considering affective responses (MARs). This study proposes a MARs product appearance design method based on KE-TOPSIS-AISM to explore the transformation between MARs and appearance design. First, Kansei Engineering (KE) is used to get consumer perceptual adjectives and a product sample database. Second, the product design elements and market styles were compared in partial order using the TOPSIS of positive and negative ideal point distance and close degree, and the pros and cons ranking was obtained. Meanwhile, the AISM is used to create topological hierarchy diagrams. Finally, a project verifies the practicality of the method, providing a novel notion for MARs product appearance design study.
Technical Research on Moving Target Monitoring and Intelligent Tracking Algorithm Based on Machine Vision
Authors: Xu Pengfei , Zhou Zhiqing , Geng Zexun
Published: May 2022

Machine vision is an important branch of the rapid development of modern artificial intelligence, and it is a key technology to convert the image information of monitoring targets into digital signals. However, due to the wide range of machine vision applications, this research focuses on its application in video surveillance. In the era of artificial intelligence, the detection and tracking of moving objects have always been a key issue in video surveillance. The simulation of human vision is realized by combining the relevant functions of the computer and the image acquisition device, which enables the computer to have the ability to recognize the surrounding environment through images. The intelligent video analysis technology can automatically analyze and extract the key useful information from the video source with the powerful data processing ability of the computer, so as to realize the computer’s “understanding” of the video. It allows the computer to “understand” what is shown in the video or what kind of “event” happened and provides a new method and reliable basis for accident detection and accident analysis. Therefore, after a brief introduction to machine vision, moving target monitoring methods, and intelligent tracking algorithms, this paper will focus on moving target monitoring and intelligent tracking strategies for video surveillance. In addition, this paper will focus on introducing the principle of intelligent tracking algorithm through formulas and compare the accuracy and success rate of target monitoring and intelligent tracking between the machine vision-based algorithm and other algorithms during the experiment. Finally, experiments show that the monitoring and tracking effect of machine vision combined with “cloud” is the best, and the overall average can reach 85.7%. Based on this, this paper fully confirms the feasibility of the moving target monitoring and intelligent tracking algorithm based on machine vision.