bluuebunny/crossref_metadata_2025 huggingface dataset:
Artificial Neural Network-Based Planting Arrangement of Smart City in Green Ecological Environment
Authors: Meng Qingsong , Wang Shufeng
Published: April 2022

The traditional urban planting arrangement is largely limited by the designer’s idea and has a high repetition rate and a low reference reuse rate. Therefore, a scientific and reasonable planting arrangement of the urban environment is necessary. In this work, research on planting arrangements in a smart city is carried out under green ecology and environment. Firstly, the planting arrangement is analyzed based on the structure, characteristics, and basic principles of the artificial neural network (ANN) model. ANN is frequently applied in pattern recognition, signal processing, system identification, and optimization. In the field of control, neural networks are used to deal with the nonlinearity and uncertainty of the control system and to approximate the identification function of the system. Secondly, the output value of the planting arrangement in the smart city is calculated according to the error backpropagation algorithm. During this period, the weight is adjusted according to the Hebb criterion, and the relevant statistical model of planting arrangement in the smart city is analyzed by ANN. Finally, suggestions on planting arrangements are given. The research shows that steamed bun-shaped plants have the largest total number in smart cities, followed by spherical and bush-like plants. Planting arrangement for spherical and palm or coconut-form plants is more frequent while planting arrangements for wind-shaped plants have a lower frequency. In terms of the importance of the planting arrangement, these 18 types of plants are very important for the green ecological environment in the smart city. Finally, suggestions on planting arrangements are given according to the research.
Construction of a Mental Health Education Model for College Students Based on Fine-Grained Parallel Computing Programming
Authors: Zhu Jianjian , Xue Yanlong
Published: April 2022

Mental health and mental health problems of college students are becoming more and more obvious, and there is more and more negative news caused by psychological problems, and society from all walks of life has given high attention to this problem. Given the new situations and new problems, how to keep up with the times and reform and innovate in the content, method, and path of psychological education in colleges and universities is an important work of ideological and political education in colleges and universities. Because fine-grained category information can provide rich semantic clues, fine-grained parallel computing techniques are widely used in tasks such as sensitive feature filtering, medical image classification, and dangerous goods detection. In this study, we adopt a fine-grained parallel computing programming approach and propose a multiobjective matrix regular optimization algorithm that can simultaneously perform the joint square root, low-rank, and sparse regular optimization for bilinear visual features, which is used to stabilize the higher-order semantic information in bilinear features, improve the generalization ability of features, and apply it to the construction of mental health education models for college students to promote the construction of mental health education bases, improve mental health education network platform, and strengthen the construction of mental health education data platform. A new practical aspect has been added to the abstract. The saliency-guided data augmentation method in this study is an improvement on random data augmentation but reduces the randomness in the data augmentation process and significantly improves the results. The best result belongs to SCutMix data augmentation, which improves by 1.9% compared to the baseline network.
Application of Transcendental Bernstein Polynomials for Solving Two‐Dimensional Fractional Optimal Control Problems
Authors: Ghomanjani Fateme , Noeiaghdam Samad , Micula Sanda
Published: April 2022

The aim of this study is to introduce a novel method to solve a class of two‐dimensional fractional optimal control problems. Since there are some difficulties solving these problems using analytical methods, thus finding numerical methods to approximate their solution is a challenging topic. In this study, we use transcendental Bernstein series. In fact, for solving the problem, we generalize the Bernstein polynomials to a larger class of functions which can provide more accurate approximate solutions. The convergence theorem is proved. Some examples are solved to demonstrate the validity and applicability of this technique. Comparing the results with other methods, we can find the efficiency and applicability of the scheme.
Nutritional Components of Beverage Granules by Near-Infrared Spectroscopy Based on PLS Model
Authors: Bi Yuqi , Xu Dawei , Yang Lihui , Chu Lin , Zhang Bo , Li Yanting
Published: April 2022

The nutritional content of instant beverages sold in the market is uneven. The existing standards and regulatory system are not sound enough, and the phenomenon of the unknown nutritional content of ingredients frequently occurs, which makes the beverage lose its nutritional characteristics, and long-term drinking may cause harm to consumers’ health. Therefore, it is necessary to strengthen the quality and safety supervision of beverage granules and carry out quality and authenticity inspections and evaluations. The purpose of this study is to collect samples of milk tea powder and to detect its protein, total sugar, ash, fat, and moisture content. By analyzing its near-infrared spectral characteristics, the paper establishes the NIR (near-infrared spectrum) rapid detection model and the PLS (partial least square) model of protein and fat in milk powder. The study uses external inspection and cross-validation to judge the reliability of the model. The results showed that the relative error of protein was less than 1.9%, and the relative error of fat was less than 1.5%, which indicated that the model had good predictability. It provides a new strategy for the quality evaluation and formula process identification of milk tea powder. It also provides a strong reference for the improvement of local standards and the formulation of national standards for milk tea powder. The study provides strong data support for illegal businesses to take effective actions to abuse consumer sentiment and take advantage of the regulatory vacuum period.
Genistein Protects against Spinal Cord Injury in Mice by Inhibiting Neuroinflammation via TLR4-Mediated Microglial Polarization
Authors: Li Xin-Wu , Wu Peng , Yao Jian , Zhang Kai , Jin Gen-Yang
Published: April 2022

Objective. The present study was designed to study the effect of genistein on spinal cord injury (SCI) in mice and to explore its underlying mechanisms. Methods. We established SCI mouse model, and genistein was administered for treatment. We used the Basso, Beattie, and Bresnahan (BBB) exercise rating scale to evaluate exercise recovery, and the detection of spinal cord edema was done using the wet/dry weight method. Apoptosis was determined by TUNEL staining, and inflammation was evaluated by measuring inflammatory factors by an ELISA kit. The expression of M1 and M2 macrophage markers was determined using flow cytometry, and the expression of proteins was detected using immunoblotting. Results. Genistein treatment not only improved the BBB score but also reduced spinal cord edema in SCI mice. Genistein treatment reduced apoptosis by increasing Bcl2 protein expression and decreasing Bax and caspase 3 protein expression. It also reduced the expression of inflammatory cytokines (TNF-α, IL-1β, IL-6, and IL-8) in the SCI area of SCI mice. Flow cytometry analysis indicated that genistein treatment significantly decreased the ratio of M1 macrophages (CD45+/Gr-1-/CD11b+/iNOS+) and increased the ratio of M2 macrophages (CD45+/Gr-1-/CD11b+/Arginase 1+) in the SCI area of SCI mice on the 28th day after being treated with genistein. We also found that genistein treatment significantly decreased the expression of TLR4, MyD88, and TRAF6 protein in the SCI area of SCI mice on 28th day after being treated with genistein. Conclusion. Our findings suggested that genistein exerted neuroprotective action by inhibiting neuroinflammation by promoting the activation of M2 macrophages, and its underlying mechanisms might be related to the inhibition of the TLR4-mediated MyD88-dependent signaling pathway.
Using Convolutional Neural Networks for Segmentation of Multiple Sclerosis Lesions in 3D Magnetic Resonance Imaging
Authors: Abdullah Hamad Abdulsattar , Musa Jaber Mustafa , Altaf Ahmed Mohammed , Muttashar Abdulsahib Ghaida , Ibrahim Khalaf Osamah , Meraf Zelalem
Published: April 2022

Magnetic Resonance Imaging to detect its lesions is used to diagnose multiple sclerosis. Experts usually perform this detection process manually, but there is interest in automating it to speed up the diagnosis and monitoring of this disease. A variety of automatic image segmentation methods have been proposed to quickly detect these lesions. A Gaussian Mixture Model is first constructed to identify outliers in each image. Then, using a set of rules based on expert knowledge of multiple sclerosis lesions, those outliers of the model that do not match the lesions' characteristics are discarded. Furthermore, segmented lesions usually correspond to gray matter-rich brain regions. In some cases, false positives can be detected, but the rules used cannot eliminate all errors without jeopardizing the segmentation’s quality. The second method involves training a convolutional neural network (CNN) that can segment lesions based on a set of training images. This technique can learn a set of filters that, when applied to small sections of an image called “patches,” produce a set of characteristics that can be used to classify each voxel of the image as a lesion or healthy tissue. On the other hand, the results show that the networks are capable of producing results in the worked database comparable to those produced by the algorithms in the literature.
The Imaging Diagnosis of Patients with Shoulder Pain Caused by Sports Injury
Authors: Dong Xiaoqi , Wang Lu
Published: April 2022

The shoulder joint is the most complex and movable joint of the human body. A variety of diseases can affect the shoulder joint and cause shoulder pain. Sports injuries are an important and common cause of shoulder pain. In the clinical diagnosis of shoulder joint injury, the most commonly used diagnostic methods are X-ray photography and CT imaging, but X-ray photography has poor ability to distinguish shoulder joints and other tiny tissue structures and has a sense of inspiration for shoulder joint injuries. In addition, CT arthrography has a certain risk to the lesion and is easy to form trauma, and it cannot clearly show the shoulder joint structures such as the rotator cuff and the labrum. Therefore, this article conducts MR imaging diagnostic research on patients with shoulder pain caused by sports injuries and plays an important role in imaging. This article deeply studied the clinical manifestations of shoulder joint pain and image processing technology, designed a research experiment on imaging diagnosis results of patients with shoulder joint pain caused by sports injuries, selected 87 patients with shoulder joint pain in a hospital, and analyzed X-ray photography, CT imaging, and MR imaging diagnosis, three methods to compare the diagnostic accuracy and inspection results and conduct an in-depth analysis of the causes of shoulder joint injury. The experimental results showed that there were 87 patients with shoulder joint pain, 65 patients with rotator cuff tear were diagnosed using arthroscopy, and 63 patients with rotator cuff tear were diagnosed by MR imaging. The accuracy rate was as high as 95.6%. Among them, the proportion caused by sports injuries is the highest, reaching 56%.
Thakur’s Iterative Scheme for Approximating Common Fixed Points to a Pair of Relatively Nonexpansive Mappings
Authors: Gopi R. , Pragadeeswarar V. , De La Sen M.
Published: April 2022

In this work, we propose the three‐step Thakur iterative process associated with two mappings in the setting of Banach space. Using this Thakur iteration, we approximate a common fixed point for a pair of noncyclic, relatively nonexpansive mappings. And we support our main result with a numerical example. Also, we give a stronger version of our main result by using von Neumann sequences. Finally, we provide some corollaries on the convergence of common best proximity points in uniformly convex Banach space.
Hydrocortisone for Preventing Adverse Drug Reactions to Snake Antivenom: A Meta-Analysis
Authors: Feng Jihua , Wu Zimeng , Yu Qiao , Li Hongyuan , Ji Pan , Yang Yanli , Zeng Xiaoliang , Zheng Xiaowen , Zhao Chunling , Zhang Jianfeng
Published: April 2022

Objective. Pretreatment with hydrocortisone (prehydrocortisone) has been used to protect against adverse drug reactions (ADRs) following antivenom administration after snakebite. However, controversial results have been reported in studies evaluating its efficacy. Herein, we conducted a meta-analysis to evaluate the effect of prehydrocortisone on the risk of ADRs. Methods. We conducted a systematic search of PubMed, Embase, and Cochrane for relevant studies on the literature published up to December 6, 2020, with no language restrictions. Premedications, including hydrocortisone with or without other drugs, were compared with placebo or no premedication. Our primary end point was the risk of ADRs, which was reported as the number of patients who developed ADRs divided by the total number of snakebite patients administered with antivenom separately for the prehydrocortisone and control groups for each study. We evaluated pooled data using of a random-effects model. Results. Among 831 identified studies, 4 were eligible and included in our analysis (N = 1348 participants). Upon combining all eight comparisons from the four selected studies, the overall pooled odds ratio (OR) for ADRs was 0.47 (95% CI 0.19, 1.17;

p
=
0.11

; I2 = 68%). When the analysis was restricted to only articles using hydrocortisone with other drugs, the pooled OR was 0.19 (95% CI 0.05, 0.75;

p
=
0.02

; I2 = 55%). The result was not statistically significant when the analysis was restricted to studies using prehydrocortisone alone, or randomized controlled designs, or cohorts. Our study was limited by heterogeneity, quality, and a paucity of data. Conclusions. The findings in this study revealed that prehydrocortisone alone was ineffective. However, the substantial beneficial effect of prehydrocortisone combinations with premedications (injectable antihistamines or adrenaline) used against ADRs cannot be excluded. Therefore, the use of prehydrocortisone combinations with premedications (injectable antihistamines or adrenaline) as a prophylaxis may reduce the ADRs to antivenom.
Research on Vehicle Detection and Recognition Based on Infrared Image and Feature Extraction
Authors: Li Ke , Deng Rongchun , Cheng Yongkang , Hu Rongqun , Shen Keyong
Published: April 2022

Vehicle detection and identification and safe distance keeping technology have become the main content of current intelligent transportation system research. Among them, vehicle detection and recognition is one of the most important research contents, and it is also crucial to the safe driving of vehicles. Real-time detection and recognition of current vehicles can effectively prevent the occurrence of malignant traffic accidents such as rear-end collision. Because the infrared image has some shortcomings such as poor contrast, loud noise, and blurred edge, this paper mainly studies the color space preprocessing of the image and uses threshold segmentation method and infrared image enhancement to segment the front vehicle and background. That is to say, by analyzing the infrared image captured by infrared CCD, we use median filter to remove noise from the collected infrared image and then use the improved histogram equalization to enhance the contrast of the image. Vertical Sobel operator is selected to enhance the vertical edge of the image, and the image is segmented by binary segmentation method. Finally, vehicle detection and recognition are realized by vertical edge symmetry, aspect ratio, and gray-scale symmetry. The experimental image and experimental data analysis results show that the image processing technology studied in this paper has achieved the intended research purpose.
Nutritional and Therapeutic Potential of Sesame Seeds
Authors: Abbas Sabiha , Sharif Mian Kamran , Sibt-e-Abbas Muhammad , Fikre Teferra Tadesse , Sultan Muhammad Tauseef , Anwar Muhammad Junaid
Published: April 2022

The major issue of the current era is an unbalanced and poor diet like unhealthy fast foods, the main cause of various diseases. Most nutraceutical and pharma industries formulating the medicines from artificial sources are expensive and have several side effects. However, scientists are making efforts to find out the natural sources of medicines for the betterment of human health and treatment of diseases. Simultaneously, the worldwide preferences have shifted from artificial to natural resources and unconventional crops (i.e., oilseeds as protein source) and foods are becoming part of regular diet in most of the community, nutraceutical, and pharma industries. Sesame (SesamumindicumL.) is one of the unconventional crops providing multiple benefits due to its special bioactive components, such as sesamin, sesaminol, and gamma-tocopherol, and fatty acids composition like unsaturated fatty acids (i.e., oleic acid, linoleic acid, stearidonic acid, palmitoleic acid, and traces of linolenic acid). Sesame seed oil supplementation not only improves the quality of snack frying oils but also plays a key role in the formation of good quality healthy snack foods. Moreover, its seeds and oil play imperative role in the formulation of medicines utilized for different ailments. The current review highlights the importance and utilization of sesame seed and oil in pharmaceuticals, nutraceuticals, and food (especially snacks) industries.
Identification Method of Influencing Factors of Hospital Catering Service Satisfaction Based on Decision Tree Algorithm
Authors: Li Siyao , Xu Di , Liu Yang , Wang Rui , Zhang Jian
Published: April 2022

Entering the 21st century, material abundance has been greatly enriched, and living standards have been continuously improved. Now society is gradually moving towards the era of experience economy. From the perspective of experience economy, patients’ demands for hospitals are not only the satisfaction of medical technology, but their catering consumption also has begun to change to the pursuit of higher requirements. Decision tree algorithm is a kind of data mining algorithm. Data mining technology is a young technology for data analysis. It can simulate mathematical models or algorithms through data analysis, which greatly improves the prediction accuracy. This paper aims to study how to identify the influencing factors of hospital catering service satisfaction, and proposes the application of decision tree algorithm to the hospital catering service satisfaction research, and proposes decision tree-related algorithms, such as ID3, C4.5, and C5.0. Based on the analysis of patients’ satisfaction with the hospital catering service in a certain hospital, the results of the model study based on the decision tree algorithm show that the risk estimation value of the training set is 0.064, and the total correct percentage is 93.6%. The risk estimate for the test set was 0.065, for a total correct percentage of 93.5%. It can be seen that the effect of the model is good and can be effectively predicted.
Comparative Analysis of Different Efficient Machine Learning Methods for Fetal Health Classification
Authors: Alam Md Takbir , Khan Md Ashibul Islam , Dola Nahian Nakiba , Tazin Tahia , Khan Mohammad Monirujjaman , Albraikan Amani Abdulrahman , Almalki Faris A.
Published: April 2022

Obstetricians often utilize cardiotocography (CTG) to assess a child’s physical health throughout pregnancy because it gives data on the fetal heartbeat and uterine contractions, which helps identify whether the fetus is pathologic or not. Obstetricians have traditionally analyzed CTG data artificially, which takes time and is unreliable. As a result, creating a fetal health classification model is essential, as it may save not only time but also medical resources in the diagnosis process. Machine learning (ML) is currently extensively used in fields such as biology and medicine to address a variety of issues, due to its fast advancement. This research covers the findings and analyses of multiple machine learning models for fetal health classification. The method was developed using the open-access cardiotocography dataset. Although the dataset is modest, it contains some noteworthy values. The data was examined and used in a variety of ML models. For classification, random forest (RF), logistic regression, decision tree (DT), support vector classifier, voting classifier, and K-nearest neighbor were utilized. When the results are compared, it is discovered that the random forest model produces the best results. It achieves 97.51% accuracy, which is better than the previous method reported.
An Ex Vivo Study on Release, Uptake, and miRNA Profile of Exosomes in Rat Lens
Authors: Zhang Junfang , Luo Jiawei , Zhang Guowei , Qin Bai , Ren Xiumei , Guan Huaijin
Published: April 2022

Purpose. To identify the ability of releasing and uptaking exosomes in rat lens and characterize the exosomal microRNA profile of lens-derived exosomes. Methods. The rat lenses were cultured ex vivo and the medium was collected. The exosomes were isolated from medium and measured in size and concentration by nanoflow cytometry (nFCM) and transmission electron microscopy (TEM) and verified with CD63 and TSG101 by Western blot. The miRNAs in exosomes released from lens epithelial cells (LECs) were sequenced. The plasma exosomes labeled by PKH26 were used to verify the exosomes uptake LECs, and their colocalized fluorescence was imaged by confocal microscopy. Results. LECs released numerous exosomes into the medium through the capsule, which contained abundant miRNAs. The most abundant miRNAs included miR-184, let-7c-5p, let-7a-5p, let-7b-5p, let-7f-5p, miR-125a-5p, miR-204-5p, miR-125b-5p, miR-1b, and miR-23a-3p. The LECs but not the lens fibre cells showed exosome uptake. The LECs uptake more PKH26-labeled exosomes at day 7 than day 3 and day 14. Conclusions. Our results suggested that LECs can release and uptake exosomes through the capsule. Exosomes may be an important way for the lens to communicate among LECs, aqueous humour, vitreous body, and other ocular tissues.
Responding to the COVID-19 Pandemic with the R2D2 Teaching Model: An Organising Aid for Online Higher Education Learners
Authors: Bin Mubayrik Haifa F. A. , Al-Mutairi Nadia M. H.
Published: April 2022

During the COVID-19 pandemic, many papers have focused on remote learning and how to promote effective learning. The current research emphasises the applications of the R2D2 teaching model for online learning in higher education. R2D2 represents read, reflect, display, and do. This paper investigates the implementation of the R2D2 teaching model in higher education. The model’s characteristics, application in higher education, and use in diversity education are discussed. The detailed literature review of studies of the R2D2 teaching model shows that the approach has a positive impact on online teaching. Specifically, the R2D2 teaching model allows learners to interact with each other. The paper’s limitations and recommendations are presented in the conclusion.
A Glucagonoma Presenting as Cerebral Vein Thrombosis and Diabetes
Authors: Delli Colli Marina , Alamri Bader N. , Palma Laura , Rivera Juan
Published: April 2022

Glucagonomas are rare pancreatic neuroendocrine tumors (pNETs), malignant in 80% of cases, thus highlighting the importance of early diagnosis and treatment. Primary manifestations are diabetes, dermatosis, depression, weight loss, and deep vein thrombosis. Unlike other pNETs, glucagonomas are associated with a higher incidence of thromboembolic events, often resulting in death. We present the case of a glucagonoma patient whose primary manifestation was cerebral sinus venous thrombosis (CS-VT). Early diagnosis enabled curative resection. The purpose of this paper is to review the underlying mechanisms associated with increased coagulopathy in glucagonomas.
Algorithm for Energy Resource Allocation and Sensor-Based Clustering in M2M Communication Systems
Authors: Ajay P. , Nagaraj B. , Jaya J.
Published: April 2022

Recent years have seen a surge in curiosity in machine-to-machine (M2M) collaborations between academics and industry. Machine-to-machine communication devices (MTCDs) are able to communicate automatically and with minimum human intervention in an M2M communications infrastructure. While MTCDs are anticipated to deliver a range of services, resource allocation and clustering approaches in M2M transceivers face issues and limits due to the diverse quality of service (QoS) needs in various network conditions. A major issue in M2M communication systems is how to distribute and cluster resources. This article presents a clustering technique and collaborative resource allocation for MTCD resource management. The clustering and integrated resource allocation challenge is characteristic as a maximization of energy efficiency problem. As a consequence of the original optimization model’s inability to tackle nonlinear fractional utilizations, we separate the issue into two subproblems: power redistribution and cluster. We begin by obtaining the optimal power distribution plan through an iterative energy efficiency maximization algorithm and then offer a modified

K

-means technique for clustering. The effectiveness of the proposed approach is shown by the numerical solution.
Optimizing the Cultivation Path of College Students’ Innovation and Entrepreneurship Ability from the Perspective of the Internet
Authors: Wang Ying
Published: April 2022

At present, the path of college students’ innovation and entrepreneurship training is still not perfect, especially the understanding and mathematics of college students’ innovation and entrepreneurship concepts. Therefore, it is urgent to guide college students to adhere to the ideal of innovation and entrepreneurship, cultivate the spirit of innovation and entrepreneurship, cultivate the morality of innovation and entrepreneurship, and improve the comprehensive ability of innovation and entrepreneurship of college students. The in-depth development of the Internet has made communication around the world convenient and fast, all kinds of information can be obtained on the network, and various industries have achieved rapid development, and the education industry naturally needs to keep up with the pace of the times to adapt to the new normal of development. The deep integration of college students’ innovation and entrepreneurship teaching experience with “Internet +” technology has continuously improved the training and teaching quality of college students’ innovation and entrepreneurship and allowed contemporary college students to develop excellent entrepreneurial spirit and innovative talents. Therefore, the training path of college students’ innovation and entrepreneurship ability in this paper will be optimized according to Internet technology. It has been proved by practice that the innovative and entrepreneurial talent training path optimized by this paper can improve students’ educational ability well and play a guiding role for students’ innovation and entrepreneurship, thereby realizing the development of ideological and political education in colleges and universities.
An Automatic Isotropic Triangular Grid Generation Technique Based on an Artificial Neural Network and an Advancing Front Method
Authors: Lu Peng , Wang Nianhua , Chang Xinghua , Zhang Laiping , Wu Yadong , Zhang Hongying
Published: April 2022

To improve the efficiency and automation of the traditional advancing front method (AFM) of unstructured grid generation, a novel isotropic triangular generation technique is developed based on an artificial neural network (ANN). First, some existing high-quality triangular grids are used as data sources, and then an automatic extraction method of training dataset is proposed. Second, the dataset is input into the ANN to train the network by the back-propagation (BP) algorithm, and then some typical patterns are identified through iterative learning. Third, after inputting the initial discretized fronts, the grid generator starts from the shortest front, and the adjacent front information is collected as the input of the neural network to choose the most proper pattern and predict the coordinates of the new point until the grid covers the whole computational domain. Finally, the initial grid is smoothed to further improve the grid quality. Some typical two-dimensional (2D) geometries are tested to validate the capability of the ANN-based advancing front triangle generator. The experimental results demonstrate that the efficiency of the proposed ANN-based triangular grid generator is about 30 percent higher than that of the traditional AFM, and grid quality has also been improved significantly.
High Expression of CISD2 in Relation to Adverse Outcome and Abnormal Immune Cell Infiltration in Glioma
Authors: Zhang Fang , Cai Hua-Bao , Liu Han-Ze , Gao Shen , Wang Bin , Hu Yang-Chun , Cheng Hong-Wei , Liu Jin-Xiu , Gao Yang , Hong Wen-Ming
Published: April 2022

Glioma is a serious disease burden globally, with high mortality and recurrence rates. CDGSH iron sulfur domain 2 (CISD2) is an evolutionarily conserved protein that is involved in several cancers. However, its role in the prognosis and immune infiltration in glioma remains unclear. In our research, RNA-seq matrix and clinicopathological relevant data for CISD2 were downloaded from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. Human Protein Atlas was used to verify the CISD2 protein level in glioma, and STRING was used to establish relative coexpression gene network. The Kaplan-Meier plotter was adopted to analyze the effect of CISD2 on prognosis. The connection between CISD2 expression and immune infiltration was analyzed using single-sample GSEA (ssGSEA), TIMER, and GEPIA. In contrast to normal tissues, CISD2 expression was significantly higher in glioma tissues, and CISD2 presented a certain diagnostic value in distinguishing glioma tissues from normal tissues. Furthermore, the CISD2 level was correlated with age, histologic grade, histological type, isocitrate dehydrogenase (IDH) status, 1p/19q codeletion status, and primary therapy outcome of glioma, while high CISD2 mRNA expression was correlated with grave overall survival. Multivariate analysis demonstrated that CISD2 was an independent risk factor for patients with glioma. Functional enrichment analysis indicated that CISD2 could regulate proliferation, immune reaction, and mitochondrial function. The results from the ssGSEA and TIMER databases confirmed that CISD2 acts a prominent role in immune cell infiltration in the tumor microenvironment, especially in low-grade glioma (LGG). Furthermore, CISD2 expression was observably correlated to M2 polarization in macrophages with glioma progression. This is the first research to investigate the immune role of CISD2 in glioma. CISD2 may be an innovative prognostic biomarker and can act as a potential target for future therapy for glioma.
Awareness, Treatment, and Control of Diabetes in Bangladesh: Evidence from the Bangladesh Demographic and Health Survey 2017/18
Authors: Khan Nuruzzaman , Oldroyd John C. , Hossain Mohammad Bellal , Islam Rakibul M.
Published: April 2022

Background. The prevalence of diabetes is increasing in Bangladesh from ∼5% in 2001 to ∼13% in 2017/18 (∼8.4 million cases). The prevalence of undiagnosed diabetes was also found to be higher at 6% in 2017/18. However, very little is known about the management of diabetes assessed by diabetes awareness, treatment, and control. We aimed to estimate the age-standardised prevalence of awareness, treatment, and control of diabetes and its associated factors. Methods. Cross-sectional data from 1,174 Bangladeshi adults aged 18 years and older available from the most recent nationally representative Bangladesh Demographic and Health Survey (BDHS) 2017–18 were analysed. Outcomes were age-standardised prevalence of awareness, treatment, and control of diabetes, estimated using the direct standardisation. Multilevel mixed-effects Poisson regression models were used to identify factors associated with awareness, treatment, and control of diabetes. Results. Of the respondents we analysed, 30.9% (95% CI, 28.2–33.6) were aware that they had the condition, and 28.2% (95% CI, 25.6–30.7) were receiving treatment. Among those treated for diabetes, 26.5% (95% CI, 19.5–33.5) had controlled diabetes. The prevalence of diabetes awareness, treatment, and control was lower in men than women. Factors positively associated with awareness and treatment were increasing age and hypertension, while factors negatively associated with awareness and treatment were being men and lower education. Factors associated with poor control were secondary education and residing in Rajshahi and Rangpur divisions. Conclusions. This study provides evidence of poor management of diabetes in Bangladesh, especially in men. Less than one-third of the people with diabetes were aware of their condition. Just over one-fourth of the people with diabetes were on treatment, and among those who were treated only one-fourth had controlled diabetes. Interventions targeting younger people, in particular men and those with lower education, are urgently needed. Government policies that address structural factors including the cost of diabetes care and that strengthen diabetes management programmes within primary healthcare in Bangladesh are urgently needed.
Elucidation of Nature of Gene Action and Estimation of Combining Ability Effects for Fruit Yield Improvement and Yield Attributing Traits in Brinjal Landraces
Authors: Rajan Neha , Debnath Sandip , Dutta Avijit kr. , Pandey Brijesh , Singh Ajeet Kr. , Singh Ravindra Kumar , Singh Anjani Kumar , Dugbakie Betty Nokobi
Published: April 2022

Genetic progress in quantitative traits can be improved by understanding how genes interact and estimating the consequences of combining abilities. As a result, a randomized block design with three replications was used to conduct forty crossings using a line X tester mating design with ten lines and four testers. All of the qualities were shown to be highly variable based on the ANOVA (analysis of variance) results among lines, testers, and hybrids. An estimated predictability ratio showed a high prevalence of nonadditive gene action, which was further confirmed by the lower narrow-sense heritability values for all traits. Most of the characters had high general combining ability and specific combining ability estimates, showing the relevance of both additive and nonadditive gene effects, respectively. For all of these features, however, the specific combining ability variations were greater than the general combining ability variances. Since heterosis breeding can lead to better hybrids, it may be a good idea to do so. For most yield-related parameters, such as fruit diameter, fruit per plant, marketable fruit per plant, yield per plant, marketable yield per plant, and total yield, RKML-26 and RKML-34 were the best general combiners among all lines. So, these lines might be employed as parents in hybridization programme in future to get suitable recombinants for higher fruit yield. However, the best cross combinations for commercial hybrid exploitation were RKML-26 X Pusa purple cluster (PPC) and RKML-2 X Swarna Shyamli. These crosses exhibiting higher per se performance and desirable specific combining ability effects together with either both or at least one parent as a competent combiner would be rewarding for heterosis breeding. Combining traditional breeding methods with biotechnological approaches, according to a new study, is critical for the transfer of favorable genes (traits) into farmed plants.
Evaluation Method of Financial Accounting Quality in Colleges and Universities Based on Dynamic Neuron Model
Authors: Liu Lu
Published: April 2022

With the deepening of reform and opening up, great changes have taken place in the university financial management system. The role of financial analysis in university activities is becoming more and more obvious. In the new environment, especially in university financial reporting, we must establish effective reasonable and scientific financial analysis index system and quality evaluation team. In order to reflect the financial situation of colleges and universities, the university financial analysis indicators in this field have important theoretical and practical significance, such as finance, budget implementation, effective utilization of funds, risk prevention, and the formulation and application of such indicators. The financial management level of colleges and universities is improved, and the scientific development of colleges and universities is promoted. In this paper, we introduce the dynamic model of neurons, design a learning algorithm, and apply it to the quality evaluation of financial reports in colleges and universities. Through this research, a single-layer feedback network capable of fast learning and learning is established. This is not only helpful for universities to evaluate the quality of financial accounting business. However, enriching the significance of financial management in higher education has theoretical value.
Data Transmission Control of Vehicle Ad Hoc Network in Intelligent Transportation Systems for Smart Cities
Authors: Li Zhenhua , Yu Guicai
Published: April 2022

The superposition of various application data streams in smart cities can intensify the load of vehicular networks in intelligent transportation systems, which can have an impact on the popularity of smart cities. To improve the performance of large amount of data transmission in telematics, this study proposes a scheme to determine the network state using congestion parameters and routing parameters and matches different data transmission amounts according to different states of the network. The scheme first considers the possibility of the network congestion state. Once congestion is judged to occur, the amount of data sent is reduced, and reducing the data backlog can further mitigate the possibility of congestion formation on the network. Secondly, after rejecting the possibility of congestion, the routing situation of the network needs to be judged, in such cases, whether a change in vehicles in the multi-hop path leads to a path change or an interruption of the data transmission path. Congestion parameters and routing parameters evaluate the state of the network, and the size of the congestion window is appropriately limited by the routing parameters to prevent excessive data volume from causing backlogs in the vehicular network. Experimental simulations show that the proposed scheme exhibits good performance in both linear and crossover vehicular networking scenarios. The research results provide a useful reference for the data transmission of telematics in smart cities.
A Method for the Inversion of Reservoir Effective Permeability Based on Time-Lapse Resistivity Logging Data and Its Application
Authors: Zhang Hairong , Zhao Bin , Dong Shiqi , Wang Xixin , Jing Pengfei
Published: April 2022

Effective permeability is a key parameter for evaluating reservoirs and their productivity. With the wide application of resistivity logging tools in drilling, the advantages of resistivity logging in response to the dynamic invasion process of drilling fluids have become increasingly prominent. We analyzed the variation law of the measured resistivity data for different permeability formations at different times. In this study, we proposed an effective permeability modeling method based on the time-lapse resistivity logging data. First, based on the resistivity measurement data at different times, the dynamic resistivity profile of the reservoir was obtained through joint inversion; we then obtained the invasion depth and invasion zone resistivity of the drilling fluid at different times, along with the original formation resistivity. Subsequently, combined with parameters such as the soaking time, fluid viscosity, and saturation change of the drilling fluid, we obtained the phase permeability curve of the reservoir and dynamic effective permeability of the fluid near the wellbore. This study provides basic parameters for subsequent formation analyses and productivity prediction and substantially improves the reservoir evaluation technology from static to dynamic.
Solitary Wave Solutions of Conformable Time Fractional Equations Using Modified Simplest Equation Method
Authors: Razzaq Waseem , Habib Mustafa , Nadeem Muhammad , Zafar Asim , Khan Ilyas , Mwanakatwea Patrick Kandege
Published: April 2022

This study presents a modified simplest equation method (MSEM) to investigate some real and exact solutions of conformable time fractional Benjamin‐Bona‐Mahony (BBM) equation and Chan‐Hilliard (CH) equation. We use traveling wave transformation to obtain the results in the form of series solution. Some calculations are performed through Mathematica software to analyze the accuracy of this approach. Graphical representations are reported for more significant results at different fractional‐order which demonstrates that this approach is very simple, adequate, and legitimate.
Research on Fault Feature Extraction and Pattern Recognition of Rolling Bearing
Authors: Miao Xiaobin
Published: April 2022

Rolling bearings play a very important role. If the state of the rolling bearing is wrong, the whole equipment will fail, so we need to check the state of the running bearing. Fault Feature Extraction of Rolling Bearings. CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) analyzes the collected oscillation signals to obtain various natural state functions. Kurtosis and Correlation Factor Evaluation Index are used to screen IMF components with a large number of error data, and independent component analysis is carried out on the selected components. The corresponding processing and reconstruction are carried out to extract the feature frequency. The built-in signal processing method can deal with the problem of bearing fault signal and identify the specific fault frequency. Fault Pattern Recognition of Rolling Bearings. The decomposition acceleration sensor is used to analyze the data of bearing in different states, and then, several components are obtained. The components related to the error signal are found, and the corresponding feature vectors are calculated. Then, the dimension vector is reduced, and a particle flow optimization algorithm is used to identify specific error conditions after dimension reduction. Experiments show that this method has higher detection rate in identifying specific fault states.
A Nonsingular Fractional Derivative Approach for Heat and Mass Transfer Flow with Hybrid Nanoparticles
Authors: Asjad Muhammad Imran , Naz Rabia , Ikram Muhammad Danish , Iqbal Azhar , Jarad Fahd
Published: April 2022

This paper deals with the study of MHD Brinkman type fluid flow containing hybrid titanium (TiO2) and silver (Ag) nanoparticles with nonlocal noninteger type Atangana‐Baleanu (ABC) fractional differential operator. The problem is designed for the convective flow restrained in a microchannel. With the Mittag–Leffler kernel, the conventional governing equations are converted into dimensionless form and then generalised with noninteger order fractional operators. The solutions for temperature and velocity fields obtained via Laplace transform method and expressed in the series form. The effect of related parameters is dignified graphically with the help of Mathcad and presented in the graphical section. Finally, the results show that the AB fractional operator exhibited improved memory effect as compared to CF fractional operator. Furthermore, due to increasing the values volume fractional temperature can be enhanced and velocity decreases. In comparison between nanoparticles for different types of based fluid, velocity and temperature of water based (TiO2) and silver (Ag) is higher than other base fluids.
An Adaptive Information Borrowing Platform Design for Testing Drug Candidates of COVID-19
Authors: Su Liwen , Zhang Jingyi , Yan Fangrong
Published: April 2022

Background. There have been thousands of clinical trials for COVID-19 to target effective treatments. However, quite a few of them are traditional randomized controlled trials with low efficiency. Considering the three particularities of pandemic disease: timeliness, repurposing, and case spike, new trial designs need to be developed to accelerate drug discovery. Methods. We propose an adaptive information borrowing platform design that can sequentially test drug candidates under a unified framework with early efficacy/futility stopping. Power prior is used to borrow information from previous stages and the time trend calibration method deals with the baseline effectiveness drift. Two drug development strategies are applied: the comprehensive screening strategy and the optimal screening strategy. At the same time, we adopt adaptive randomization to set a higher allocation ratio to the experimental arms for ethical considerations, which can help more patients to receive the latest treatments and shorten the trial duration. Results. Simulation shows that in general, our method has great operating characteristics with type I error controlled and power increased, which can select effective/optimal drugs with a high probability. The early stopping rules can be successfully triggered to stop the trial when drugs are either truly effective or not optimal, and the time trend calibration performs consistently well with regard to different baseline drifts. Compared with the nonborrowing method, borrowing information in the design substantially improves the probability of screening promising drugs and saves the sample size. Sensitivity analysis shows that our design is robust to different design parameters. Conclusions. Our proposed design achieves the goal of gaining efficiency, saving sample size, meeting ethical requirements, and speeding up the trial process and is suitable and well performed for COVID-19 clinical trials to screen promising treatments or target optimal therapies.
Study on the Development Model of Rural Smart Tourism Based on the Background of Internet of Things
Authors: Li Hong
Published: April 2022

In recent years, the domestic self-help tourism market has developed rapidly, so it is necessary to develop a tourism guidance system based on the Internet of Things. The brilliant smart tourism has emerged under this background. This is a low-carbon smart operation model that integrates technology and management, that is, using big data technology as the basis to make traditional rural tourism and digitization more intelligent. Smart tourism can play a very good role in improving the management of rural tourism, developing rural economy, and improving tourism management and services, thereby promoting the development of the tourism industry. The research is aimed at the construction and development of the rural smart tourism strategic model based on big data technology. Through the actual investigation of rural smart tourism combined with related literature, the innovative application of big data is described and analyzed, and the real-time analysis of tourist attractions is carried out through big data. The density of people flow helps tourists choose routes according to the density, and manages the dense flow in time, thus effectively preventing the series of risks caused by the excessive flow of people and strengthening the management of tourists. It also puts forward the countermeasures that the development of rural smart tourism needs to improve the rural smart tourism information platform, formulate smart service standards, create a smart talent service platform, and create a new model of rural smart tourism marketing.