bluuebunny/crossref_metadata_2025 huggingface dataset:
The Safety and Performance of Prominent Programming Languages
Authors: Bugden William , Alahmar Ayman
Published: April 2022

Background: The current primary focus of programming language benchmarking studies in the literature is performance with less attention on safety. However, this context has a research gap because the software industry has focused more on software safety than performance to safeguard clients. This paper attempts to address this research gap by benchmarking languages in both safety and performance. Furthermore, this study includes Rust, a relatively new language with promising safety and performance features. Methods: This paper compares six prominent programming languages (in alphabetical order: C, C[Formula: see text], Go, Java, Python and Rust) to determine which is the best in terms of safety and performance using quantitative and qualitative methods through actual testing of code and analysis of existing information. Results: The comparisons show that Rust was the safest language, outperforming all the other languages. Regarding performance, Rust, C and C[Formula: see text] performed comparably to each other and generally outperformed Go, Java and Python. Conclusion: It is possible to achieve a superior balance of software safety and performance with, at worst, a minimal performance drop; as Rust clearly demonstrates.
Knowledge Graph Construction for SOFL Formal Specifications
Authors: Li Jiandong , Liu Shaoying , Liu Ai , Huang Runhe
Published: April 2022

Formal specifications can provide a solid foundation for software development and support for techniques of software quality assurance, such as specification-based inspection and testing. To ensure that these techniques can be applied effectively in practice, efficiently and accurately understanding specifications becomes extremely important. While this may be relatively easy for well-trained developers in formal methods, it can be rather difficult for computer since computer does not easily understand specifications. This difficulty poses a challenge for realizing automatic specification-based verification techniques that are in high demand for reducing development cost and improving software reliability. In this paper, we address this problem by discussing how the formal specification can be transformed into a knowledge graph to provide comprehensible, well-organized details of the specification for developers and computers. The transformation is done by extracting and storing information about attributes of each component and by establishing relationships between components in a formal specification. We elaborate on a top-down approach of constructing a knowledge graph from a specification, including creating an ontology, designing the Entity–Relationship (ER) diagram of the relational database based on the created ontology, extracting and storing attribute and relationship information in the relational database, mapping ontology to its instances and relational data to RDF triples, and displaying knowledge graph. Further, we present a case study to show how our approach works on the formal specification of an ATM system. Finally, we describe three experiments to evaluate its performance in improving specification readability, effectively guiding inspectors to establish traceability links between specifications and programs, and detecting defects through program inspection, respectively.
Predicting Corona Virus Affected Patients Using Supervised Machine Learning
Authors: David H. Benjamin Fredrick , Suruliandi A. , Raja S. P.
Published: April 2022

The world is infected from the deadliest pandemic disease humankind has ever seen. Several medical practitioners have been encountered with the corona virus and are constantly losing their lives in the fight. Hence, the main objective of this research work is to characterize the clinical features of the patients and construct a novel dataset for machine learning to classify them accurately prior to treatment. The positive patients can be identified on many characteristics and the principle data for this research is considered on the basis of the exploratory analysis done on the various risk factors that is also associated with the mortality in the hospitals. As an outcome, this article presents a supervised machine learning model incorporating the insights, symptoms and classification of the corona virus infected person. The proposed model and the dataset are tested against six well known classifiers on various levels of cross folding and percentage splits. The proposed dataset is also tested against the actual patient records and was found that the model accurately categorizes them prior to their treatment. The experimental results for proposed techniques showed higher performance and better accuracy further creating an impact on then identification of corona virus patients.
STUDY ON THE HEAT TRANSFER CHARACTERISTICS OF MICROCHANNEL HEAT SINK WITH CANTOR FRACTAL STRUCTURE
Authors: WANG HELIN , WU CHENGBAI , MA YONGBIAO , CHEN XUEYE
Published: April 2022

This paper does a three-dimensional steady-state simulation analysis of a microchannel heat sink with a cantor fractal structure, and the layout, size, spacing, and fractal frequency of the baffle are optimized. Following further testing, it was determined that putting rectangular baffles on the same sidewall provides a superior effect, with a [Formula: see text]C lower effect than normal microchannels. The temperature of the two-time fractal structure is [Formula: see text]C lower than that of the zero-time fractal structure at the Reynolds number (Re) of 200. The control variable method is used to explore the size and spacing of the rectangular baffles, and the temperature and pressure drop are thoroughly analyzed to determine the ideal structure, [Formula: see text][Formula: see text]mm, [Formula: see text][Formula: see text]mm, [Formula: see text][Formula: see text]mm, and [Formula: see text][Formula: see text]mm. Following that, the influence of Re on heat transmission is addressed. Finally, to incorporate the fluid’s heat transmission and pressure drop, the enhanced heat transfer factor PEC is used to evaluate the overall heat transfer performance of the microchannel. In the Re range of 100–300, PEC is greater than 1, which is 10–42% higher than that of conventional microchannels.
A Note on Ideals of Gamma Nearness Rings
Authors: Öztürk Mehmet Ali , Tekin Özlem
Published: April 2022

The aim of this paper is to study the notion of ideals in gamma rings on weak nearness approximation spaces and explain some of the concepts and definitions. Also, an example is given related to the subject. The features described in this study will contribute greatly to the theoretical development of the gamma nearness rings theory. We defined that upper nearness ideal generated by a subset of the ring on weak nearness approximation spaces and introduce some properties of these ideals. In addition to this, prime nearness ideals and maximal nearness ideals of the gamma nearness rings are defined and some properties of these ideals are introduced.
Magnetic Properties of Co-Doped Bismuth Ferrite Nanoparticles Prepared by the Solution Method
Authors: Xia Puyue , Mo Jiajun , Liu Weiyi , Xia Yanfang , Liu Min
Published: April 2022

In this work, BiFeO3 and BiCo[Formula: see text]Fe[Formula: see text]O3 nanoparticles prepared by the solution method were further investigated the magnetic properties. At room temperature, BiFeO3 reveals the antiferromagnetic order. However, Co doping achieves ferromagnetic enhancement. The room-temperature ferromagnetism and the causes are ulteriorly studied with the help of ABK plots. The stronger coercivity is due to the oxygen vacancies and stronger magnetic anisotropy caused by Co substitution. Under variable temperatures, the bifurcate curves of zero-field-cooled and field-cooled indicate the existence of spin-glass state for both samples. The weak ferromagnetism at low temperature and the spin-state transition of cobalt ions at 270[Formula: see text]K are also found.
Collaborative Graph Learning for Session-based Recommendation
Authors: Pan Zhiqiang , Cai Fei , Chen Wanyu , Chen Chonghao , Chen Honghui
Published: April 2022

Session-based recommendation (SBR)
, which mainly relies on a user’s limited interactions with items to generate recommendations, is a widely investigated task. Existing methods often apply RNNs or GNNs to model user’s sequential behavior or transition relationship between items to capture her current preference. For training such models, the supervision signals are merely generated from the sequential interactions inside a session, neglecting the correlations of different sessions, which we argue can provide additional supervisions for learning the item representations. Moreover, previous methods mainly adopt the cross-entropy loss for training, where the user’s ground truth preference distribution towards items is regarded as a one-hot vector of the target item, easily making the network over-confident and leading to a serious overfitting problem. Thus, in this article, we propose a
Collaborative Graph Learning (CGL)
approach for session-based recommendation. CGL first applies the Gated Graph Neural Networks (GGNNs) to learn item embeddings and then is trained by considering both the main supervision as well as the self-supervision signals simultaneously. The main supervisions are produced by the sequential order while the self-supervisions are derived from the global graph constructed by all sessions. In addition, to prevent overfitting, we propose a
Target-aware Label Confusion (TLC)
learning method in the main supervised component. Extensive experiments are conducted on three publicly available datasets, i.e., Retailrocket, Diginetica, and Gowalla. The experimental results show that CGL can outperform the state-of-the-art baselines in terms of Recall and MRR.
Characterization of skeletal muscle wasting pathways in diaphragm and limb muscles of cystic fibrosis mice
Authors: Gusev Ekaterina , Liang Feng , Bhattarai Salyan , Broering Felipe E. , Leduc-Gaudet Jean-Philippe , Hussain Sabah N. , Radzioch Danuta , Petrof Basil J.
Published: April 2022

Patients with cystic fibrosis (CF) often suffer from skeletal muscle atrophy, most often attributed to physical inactivity and nutritional factors. CF is also characterized by abnormally elevated systemic inflammation. However, it is unknown whether the lack of a functional CF transmembrane conductance regulator (CFTR) gene predisposes to exaggerated inflammation-induced muscle proteolysis. CF mice ( CFTR−/−) and their wild-type (WT = CFTR+/+) littermate controls were systemically injected with Pseudomonas-derived lipopolysaccharide (LPS). After 24 h, the diaphragm and limb muscles (fast-twitch tibialis anterior, and slow-twitch soleus) were assessed for induction of inflammatory cytokines (TNFα, IL1β, and IL6), oxidative stress, canonical muscle proteolysis pathways (Calpain, Ubiquitin-Proteasome, Autophagy), muscle fiber histology, and diaphragm contractile function. At baseline, CF and WT muscles did not differ with respect to indices of inflammation, proteolysis, or contractile function. After LPS exposure, there was significantly greater induction of all proteolysis pathways (calpain activity; ubiquitin-proteasome: MuRF1 and Atrogin1; autophagy: LC3B, Gabarapl-1, and BNIP3) in CF mice for the diaphragm and tibialis anterior, but not the soleus. Proteolysis pathway upregulation and correlations with inflammatory cytokine induction were most prominent in the tibialis anterior. Diaphragm force normalized to muscle cross-sectional area was reduced by LPS to an equivalent degree in CF and WT mice. CF skeletal muscles containing a high proportion of fast-twitch fibers (diaphragm, tibialis anterior) exhibit abnormally exaggerated upregulation of multiple muscle wasting pathways after exposure to an acute inflammatory stimulus, but not under basal conditions.
Multispin Cross-Correlated Transverse Dipolar NMR Relaxation in Solution
Authors: Zuiderweg Erik R. P.
Published: April 2022

In this paper, we want to consider what would be involved in calculating the R2 relaxation of amide protons in a protein caused by dipolar interactions with nearby protons, for which there are many. NMR textbooks give analytical equations and sometimes derivations for solution NMR relaxation due to dipolar interactions between two spins. There are also closed equations for dipolar interactions between three spins, which include relaxation interference, also known as cross-correlated cross-relaxation. We here derive an expression for interference between four spins. For larger systems, such as amide protons in a protein, we develop a local-field methodology, from which solution relaxation interference can be computed for a basically limitless number of interacting spins.
Lute Acoustic Quality Evaluation and Note Recognition Based on the Softmax Regression BP Neural Network
Authors: Liu Lili
Published: April 2022

Note recognition technology has very important applications in instrument tuning, automatic computer music recognition, music database retrieval, and electronic music synthesis. This paper addresses the above issues by conducting a study on acoustic quality evaluation and its note recognition based on artificial neural networks, taking the lute as an example. For the acoustic quality evaluation of musical instruments, this paper uses the subjective evaluation criteria of musical instruments as the basis for obtaining the results of the subjective evaluation of the acoustic quality of the lute, similar to the acoustic quality evaluation, extracts the CQT and MFCC note signal features, and uses the single and combined features as the input to the Softmax regression BP neural network multiclassification recogniser; the classification coding of standard tones is used as the target for supervised network learning. The algorithm can identify 25 notes from bass to treble with high accuracy, with an average recognition rate of 95.6%; compared to other recognition algorithms, the algorithm has the advantage of fewer constraints, a wider range of notes, and a higher recognition rate.
Finite Difference Computation of Au‐Cu/Magneto‐Bio‐Hybrid Nanofluid Flow in an Inclined Uneven Stenosis Artery
Authors: Basha H. Thameem , Rajagopal Karthikeyan , Ahammad N. Ameer , Sathish S. , Gunakala Sreedhara Rao
Published: April 2022

The present study addresses the fluid transport behaviour of the flow of gold (Au)‐copper (Cu)/biomagnetic blood hybrid nanofluid in an inclined irregular stenosis artery as a consequence of varying viscosity and Lorentz force. The nonlinear flow equations are transformed into dimensionless form by using nonsimilar variables. The finite‐difference technique (FTCS) is involved in computing the nonlinear transport dimensionless equations. The significant parameters like angle parameter, the Hartmann number, changing viscosity, constant heat source, the Reynolds number, and nanoparticle volume fraction on the flow field are exhibited through figures. Present results disclose that the Lorentz force strongly lessens the hybrid nanofluid velocity. Elevating the Grashof number values enhances the rate of blood flow. Growing values of the angle parameter cause to reduce the resistance impedance on the wall. Hybrid nanoparticles have a superior wall shear stress than copper nanoparticles. The heat transfer rate is amplifying at the axial direction with the growing values of nanoparticles concentration. The applied Lorentz force significantly reduces the hybrid and unitary nanofluid flow rate in the axial direction. The hybrid nanoparticles expose a supreme rate of heat transfer than the copper nanoparticles in a blood base fluid. Compared to hybrid and copper nanofluid, the blood base fluid has a lower temperature.
Gastrodin and Vascular Dementia: Advances and Current Perspectives
Authors: Deng Chujun , Chen Huize , Meng Zeyu , Meng Shengxi
Published: April 2022

Gastrodia elata, a traditional Chinese medicine, has been widely used since ancient times to treat diseases such as dizziness, epilepsy, stroke, and memory loss. Gastrodin, one of the active components of Gastrodia elata, has been used in the treatment of migraine, epilepsy, Parkinson’s disease, dementia, and depression in recent years. It can improve cognitive function and related neuropsychiatric symptoms through various effects and is considered as a promising treatment for dementia. Vascular dementia is a kind of severe cognitive impairment syndrome caused by vascular factors, and it is the dementia syndrome with the largest number of patients besides Alzheimer’s disease. Although there is still a lack of evidence-based explorations, the paper reviewed the mechanism and methods of gastrodin in the treatment of vascular dementia, providing a reference for clinical therapy.
Prediction of Banks Efficiency Using Feature Selection Method: Comparison between Selected Machine Learning Models
Authors: Assous Hamzeh F.
Published: April 2022

This study aims to examine the main determinants of efficiency of both conventional and Islamic Saudi banks and then choose the best fit model among machine learning prediction models (i.e., support vector machine (SVM), Chi‐squared automatic interaction detector (Chaid), linear regression, and neural network (NN)). The data were collected from the annual financial reports of Saudi banks from 2014 to 2018. The Saudi banking sector consists of 11 banks, 4 of which are Islamic. In this study, the major financial ratios are subgrouped into the profitability ratios, managerial practices, asset and loans, capital adequacy ratios, and liquidity. First, regression analysis is implemented with efficiency ratio as a dependent variable and the proxies of banks’ profitability, liquidity, asset quality, management ratios, and capital adequacy ratios as independent variables. Next, the feature selection is applied for different prediction models. Subsequently, 4 prediction models (i.e., SVM, CHAID, linear regression, and a neural network) were developed to choose the best fit. The performance metrics have also been evaluated. Regression results exhibit that the efficiency of both conventional and Islamic banks is highly affected by profitability, liquidity, and managerial practices. Finally, we choose the best prediction model with the highest R2 in the training and the testing phases with/out feature selection that is the CHAID model. The best predictors of cost efficiency for Saudi banks are the capital ratios, namely, CAR total and CAR tier 1. Findings are theoretically and practically important to academics, investors, and policymakers. Policymakers can benefit from the novelty of this study in building an early warning system using the CHAID model to predict different financial distress scenarios.
Synthesis of Modified Phase-Changing Material with Latent Heat and Thermal Conductivity to Store Solar Energy Using a Carbon Nanotube
Authors: Vedanarayanan V. , Srinivasan J. Dilli , Arulvendhan K. , Kumaran P. Thirusenthil , Selvakumar R. , Asif H. S. , Siddique M. H. , Chimdi Jifara
Published: April 2022

MicroPCMs’ excellent thermal capacity and photothermal translation features benefit solar energy storage applications significantly. A successful in situ polymerization procedure was employed to build microencapsulated phase-change materials using n-hexadecanol as the core and melamine-formaldehyde resin as the outer shell, and the thermal characteristics of the microPCMs were evaluated. In terms of micromorphology, the incorporation of hydroxylated carbon nanotubes into microPCMs with a compact shell has little effect on their spherical structure. MicroPCMs’ melting heat and latent heat are both 51.5°C with a 0.2 weight percent dose of hydroxylated carbon nanotubes, and n-energy hexadecanol’s storage efficiency is determined to be 75.25 percent. Thermal conductivity and photothermal conversion efficiency of microencapsulated phase-change materials engendered with increased hydroxylated carbon nanotube dosage have improved significantly, laying the foundation for improved photothermal storage efficiency. When 0.6 weight % hydroxylated carbon nanotubes are added to the mixture, microencapsulated phase-change materials have a thermal conduction of 0.3597 Wm−1·K−1 and 181.5 J·g−1. Additionally, all of the improved microPCMs show exceptional thermal stability across 500 heat cycles. Because of their large thermal capability and efficient photothermal conversion, the new microPCMs appear to be an appealing option for solar energy storage in direct-absorption solar collector systems.
Research on the Method of Acquiring Customer Individual Demand Based on the Quantitative Kano Model
Authors: Du Laihong , Chen Hua , Fang Yadong , Liang Xiaowei , Zhang Yujie , Qiao Yidan , Guo Zhen
Published: April 2022

In order to realize accurate marketing by analyzing customer individual demand, a new quantitative Kano model method is put forward, and it is helpful to provide customized products for heterogeneous customer classification groups. By improving the traditional Kano model, the customer satisfaction and the importance degree of products are defined, and the quantitative Kano demand model is established. Customers are classified as the price preference group, the brand preference group, and the service priority group, and decision-making of product attribute quality improvement for customer classification is realized. Lastly, electric vehicles (EVs) are selected as a study case, and their various demands for different classifications of customers are discussed by questionnaire survey and calculation of satisfaction and the importance degree. Furthermore, different customer group demands are classified as attractive demands, expected demands, nondifferential demands, or essential demands, and the important product attribute acquisition process for various customers is discussed to improve enterprise market competitiveness.
Assessment of Exhaustive Exercise under Internet of Things Exercise Monitoring System and Mechanism Analysis of Cardiovascular Endothelial Cell Apoptosis Induced by Oxidative Stress
Authors: Wang Guandong
Published: April 2022

To investigate the effect of high-intensity exhaustive exercise on body and the molecular mechanism of oxidative stress regulating apoptosis of cardiovascular endothelial cells (CVECs), 70 college students who took an aerobics course were recruited as the research subjects, all of which were divided into the high-intensity group (35 students) and the moderate-intensity group (35 students). The Internet of Things- (IoT-) based human exercise information monitoring system was employed to collect the electrocardiogram, serum indexes, and oxidative stress indexes of the two groups of subjects after exercise. Moreover, 30 clean male SD rats were divided into exhaustive exercise group (

n
=
10

), routine exercise group (

n
=
10

), and blank control group (

n
=
10

) using treadmill exercise as stress source, and various physiological indexes of the three groups were compared. In human experiments, the time-domain indexes of heart rate variability (HRV), 50 ms in the total number of sinus heartbeats (PNN50), the standard deviation of all NN intervals (SDNN), standard deviation of mean NN interval (SDANN), standard deviation of difference between adjacent NN intervals (SDSD), root mean square (RMSSD) in the high intensity group were lower than those in the moderate intensity group (

P
<
0.05

). The levels of T-sensitive troponin I (Tnl-ultra), cortisol (COR), high-sensitive troponin T (TnT-hs), high-sensitive C-reactive protein (hs-CRP), and human heart-type fatty acid binding protein (HUMAN H-FABP) in high intensity group were higher than those in moderate intensity group (

P
<
0.05

). Experiments on animals found that SOD content and mRNA expression of fork-head box 03A (FOX03A) and silencing-information regulator 1 (Sirtl) in serum of rats in exhaustion exercise group were lower than those in the routine exercise group and blank control group, while lactate dehydrogenase (LDH) and malondialdehyde (MDA) contents were opposite (

P
<
0.05

). In short, strenuous exercise made the body in the state of acute stress, leading to the disturbance of cardiac autonomic nervous function and sympathetic nervous function, and increasing the risk of malignant arrhythmia. Oxidative stress induced by exhaustive exercise may promote the apoptosis of CVECs by reducing the expression of Sirtl and FOX03A and locating FOX03A to the nucleus.
A Power Transformer Fault Prediction Method through Temporal Convolutional Network on Dissolved Gas Chromatography Data
Authors: Xing Mengda , Ding Weilong , Li Han , Zhang Tianpu
Published: April 2022

The power transformer is an example of the key equipment of power grid, and its potential faults limit the system availability and the enterprise security. However, fault prediction for power transformers has its limitations in low data quality, binary classification effect, and small sample learning. We propose a method for fault prediction for power transformers based on dissolved gas chromatography data: after data preprocessing of defective raw data, fault classification is performed based on the predictive regression results. Here, Mish-SN Temporal Convolutional Network (MSTCN) is introduced to improve the accuracy during the regression step. Several experiments are conducted using data set from China State Grid. The discussion of the results of experiments is provided.
Measurement of Total Flow Rates in Horizontal Well Oil-Water Two-Phase Flows by the Application of BP Neural Network Algorithm to Production Array Logs
Authors: Zhang Xin , Song Hongwei , Guo Haimin , Shi Xinlei
Published: April 2022

In the modern petroleum industry system, oil-water two-phase flows exist widely. Among them, the total flow rate of mixture fluid in a horizontal well is difficult to obtain due to the phase segregation caused by gravity. Therefore, it is a difficult and hot issue. To obtain the total flow rate of oil-water two-phase flows in horizontal wells, in this paper, Multiple Array Production Suite (MAPS), which is also called Production Array Logs (PAL), is used to conduct simulation experiments, uses BP neural network (BPNN) algorithm to train the data samples, and establishes the prediction models of the total flow rates of oil-water two-phase flows in horizontal wells. The results showed that the average relative error was less than 10%, which justify that the BPNN has good practicability in using data of MAPS in oil-water two-phase flows horizontal wells to predict the total flow rates, and it provides a new method and theoretical support for obtaining flow rates in horizontal wells.
IL-6 Signaling Link between Inflammatory Tumor Microenvironment and Prostatic Tumorigenesis
Authors: Ene Cosmin-Victor , Nicolae Ilinca , Geavlete Bogdan , Geavlete Petrisor , Ene Corina Daniela
Published: April 2022

Benign prostatic hyperplasia and prostate cancer are tumoral pathologies characterized by the overexpression of inflammatory processes. The exploration of tumor microenvironment and understanding the sequential events that take place in the stromal area of the prostate could help for an early management of these pathologies. This way, it is feasible the hypothesis that normalizing the stromal environment would help to suppress or even to reverse tumor fenotype. A number of immunological and genetic factors, endocrine dysfunctions, metabolic disorders, infectious foci, nutritional deficiencies, and chemical irritants could be involved in prostate tumor development by maintaining inflammation, affecting local microcirculation, and promoting oxidative stress. Inflammatory processes activate hyperproliferative programs that ensure fibromuscular growth of the prostate and a number of extracellular changes. Acute and chronic inflammations cause accumulation of immunocompetent cells in affected prostate tissue (T cells, macrophages, mastocytes, dendritic cells, neutrophils, eosinophils, monocytes). Prostate epithelial and stromal cells, peri-prostatic fat cells, prostatic microvascular endothelial cells, and inflammatory cells produce cytokines, generating a local inflammatory environment. Interleukin-6 (IL-6) proved to be involved in the prostate tumor pathogenesis. IL-6 ability to induce pro- and anti-inflammatory responses by three mechanisms of signal transduction (classical signaling, transsignaling, cluster signaling), to interact with a diversity of target cells, to induce endocrine effects in an autocrine/paracrine manner, and the identification of an IL-6 endogenous antagonist that blocks the transmission of IL-6 mediated intracellular signals could justify current theories on the protective effects of this cytokine or by alleviating inflammatory reactions or by exacerbating tissue damage. This analysis presents recent data about the role of the inflammatory process as a determining factor in the development of benign and malign prostate tumors. The presented findings could bring improvements in the field of physiopathology, diagnosis, and treatment in patients with prostate tumors. Modulation of the expression and activity of interleukin-6 could be a mean of preventing or improving these pathologies.
Resource Collection Algorithm for Entrepreneurship and Employment Education in Universities Based on Data Mining
Authors: Yang Fen
Published: April 2022

Graduate unemployment is one of the serious challenges in China, including the graduates of a large number of public and private higher education institutions. The collection of entrepreneurial employment education resources in colleges and universities is a basic project and a key link to promote the rapid development of education informatization. Data mining has various applications in different fields such as health care, smart agriculture, smart cities, smart businesses, and education, but is playing a vital role in the field of education and businesses. The applications of data mining provide new technical tools and development directions to realize the common construction, sharing, and collection of entrepreneurial employment education resources in colleges and universities. The closed nature of teaching resources within colleges and universities leads to the inability of external search engines to search them, which hinders the search and access of teachers and students and seriously affects the smooth implementation of current innovation and entrepreneurship employment work. Aiming at the real demand of entrepreneurial employment education resource collection in colleges and universities and the characteristics of on-campus resources, this study proposes a data mining-based algorithm for entrepreneurial employment education resource collection in colleges and universities. The algorithm obtains entrepreneurial employment demands from the academic affairs system, collects on-campus online teaching resources through internal crawlers, and provides services for teachers, students, and employees through online teaching resource collection drive subalgorithm and quick recommendation subalgorithm. We also compared the proposed model with the CLR model. The case analysis and performance experiments show that the proposed algorithm has a good resource mining effect, high user satisfaction, and high recommendation efficiency, occupies fewer system resources, and shows high performance as compared to the CLR model.
Administration of Melatonin in Diabetic Retinopathy Is Effective and Improves the Efficacy of Mesenchymal Stem Cell Treatment
Authors: Abdel-Kawi Samraa H. , Hashem Khalid S.
Published: April 2022

Stem cell transplantation is a promising therapeutic technique for the treatment of a variety of diseases; nevertheless, stem cell therapy may not always work as well as it could. The goal of this study was to test the hypothesis that employing a powerful antioxidant like melatonin improves stem cell transplantation success and potentiates stem cell function in the therapy of diabetic retinopathy. For this purpose, 50 adult male rats were divided into the following: control group: this group received 0.5 ml of 0.1 M of sodium citrate buffer (

pH
=
4.5

) (intraperitoneal (I.P.)). The confirmed diabetic rats were divided into 4 groups: diabetic group: confirmed diabetic rats received no treatments with a regular follow of the blood glucose profile for 8 weeks; melatonin group: confirmed diabetic rats received melatonin (5 mg/kg/day); stem cell group: the confirmed diabetic rats were given intravitreal injection of stem cells (2 μl cell suspension of stem cells (

3
×

10

4

 
cells
/
μ
l

)); and melatonin+stem cell group: confirmed diabetic rats received melatonin (5 mg/kg/day), orally once daily for 8 weeks, and 2 μl cell suspension of stem cells (

3
×

10

4

 
cells
/
μ
l

) was carefully injected into the vitreous cavity. Our results showed that administration of melatonin and/or stem cell restored the retinal oxidative/antioxidant redox and reduced retinal inflammatory mediators. Coadministration of melatonin and stem cells enhanced the number of transplanted stem cells in the retinal tissue and significantly reduced retinal BDEF, VEGF, APOA1, and RBP4 levels as compared to melatonin and/or stem alone. We may conclude that rats treated with melatonin and stem cells had their retinal oxidative/antioxidant redox values restored to normal and their histological abnormalities reduced. These findings support the hypothesis that interactions with the BDEF, VEGF, APOA1, and RBP4 signaling pathways are responsible for these effects.
Hybrid Deep Feature Fusion of 2D CNN and 3D CNN for Vestibule Segmentation from CT Images
Authors: Zhang Ruicong , Zhuo Li , Chen Meijuan , Yin Hongxia , Li Xiaoguang , Wang Zhenchang
Published: April 2022

The accurate vestibule segmentation from CT images is essential to the quantitative analysis of the anatomical structure of the ear. However, it is a challenging task due to the tiny size, blur boundary, and drastic variations in shape and size. In this paper, according to the specific characteristics and segmentation requirements of the vestibule, a vestibule segmentation network with a hybrid deep feature fusion of 2D CNN and 3D CNN is proposed. First, a 2D CNN is designed to extract the intraslice features through multiple deep feature fusion strategies, including a convolutional feature fusion strategy for different receptive fields, a feature channel fusion strategy based on channel attention mechanism, and an encoder-decoder feature fusion strategy. Next, a 3D DenseUNet is designed to extract the interslice features. Finally, a hybrid feature fusion module is proposed to fuse the intraslice and interslice features to effectively exploit the context information, thus achieving the accurate segmentation of the vestibule structure. At present, there is no publicly available dataset for vestibule segmentation. Therefore, the proposed segmentation method is validated on two self-established datasets, namely, VestibuleDataSet and IEBL-DataSet. It has been compared with several state-of-the-art methods on the datasets, including the general DeeplabV3+ method and specific 3D DSD vestibule segmentation method. The experimental results show that our proposed method can achieve superior segmentation accuracy.
Connections between Children’s Eating Habits, Mental Health, and Parental Stress
Authors: Tommasi Marco , Toro Francesca , Salvia Alessandra , Saggino Aristide
Published: April 2022

Background. Obesity and eating disorders are increasing in occidental countries and can undermine physical and psychological health. Therefore, preventing the insurgency of unhealthy eating habits in childhood is fundamental. Parents can play an important role in assisting pediatricians, psychiatrists, and clinical psychologists in the diagnosis of eating disorders because they have an active role in observing and assessing the quality of their children’s eating habits. Methods. In our study, we collected data from a sample of children (n = 125) and their parents (n = 161) without symptoms related to eating disorders. Parents assessed the eating habits, behavior problems, and mental health of their children and parental stress. In addition, we measured body mass index, anxiety, and lifestyle in children. Data were analyzed with bivariate correlation and MIMIC models. Results. Both mothers’ and fathers’ assessments of children’s eating habits are reliable. Unhealthy eating habits are connected with children’s behavioral problems and parental stress. We did not find significant differences in feeding styles and ways of assessing the quality of eating habits between mothers and fathers. Our study showed greater sensitivity of mothers toward the physical fitness of their children rather than fathers. Conclusions. Mothers and fathers both proved to be good observers of their children’s eating behavior, and they could cooperate with medical and psychological operators in preventing the risk of obesity.
Side Wear Prediction of a Subway Outer Rail on Small Radius Curves Based on System Dynamics of Discrete Supported Track
Authors: Yin Xianxian , Kou Linlin , Wei Xiukun , Zheng Haichao
Published: April 2022

When a subway train runs on a small radius curve, it causes serious side wear of the outer rail, and a large number of the outer rails of the curve are replaced frequently due to excessive side wear. This paper proposes three prediction models based on the response surface methodology (RSM), support vector machine (SVM), and the relative vector machine (RVM) to estimate the remaining useful life (RUL) of a metro outer rail at a small radius curve determined by rail side wear. In these proposed models, the directly measurable parameters, including various track geometries, axle loads, and primary suspension stiffnesses related to vehicle types, are taken into account. These parameters are proposed from the side wear mechanism and subjected to the method of global sensitivity analysis to rank them in order of importance. The rail side wear for various scenarios is calculated through a numerical simulation model of the vehicle‐unballasted track and Specht friction wear. Furthermore, with the help of simulation experiments and field cases, the prediction performances of three different prediction models are analyzed in detail. The comparison indicates that the predictive model based on RVM is superior in the prediction of RUL based on rail wear. The findings presented in this paper can provide certain reference values for infrastructure managers (IMs).
Infrared and Visible Image Fusion Based on Iterative Control of Anisotropic Diffusion and Regional Gradient Structure
Authors: Ji Jingyu , Zhang Yuhua , Lin Zhilong , Li Yongke , Wang Changlong , Hu Yongjiang , Yao Jiangyi
Published: April 2022

To improve the fusion performance of infrared and visible images and effectively retain the edge structure information of the image, a fusion algorithm based on iterative control of anisotropic diffusion and regional gradient structure is proposed. First, the iterative control operator is introduced into the anisotropic diffusion model to effectively control the number of iterations. Then, the image is decomposed into a structure layer containing detail information and a base layer containing residual energy information. According to the characteristics of different layers, different fusion schemes are utilized. The structure layer is fused by combining the regional structure operator and the structure tensor matrix, and the base layer is fused through the Visual Saliency Map. Finally, the fusion image is obtained by reconstructing the structure layer and the energy layer. Experimental results show that the proposed algorithm can not only effectively deal with the fusion of infrared and visible images but also has high efficiency in calculation.
Response of Two Maize (Zea mays L.) Varieties to Times of NPK (15-15-15) Fertilizer Application
Authors: Afreh Daniel Ntiamoah , Afari Michelle Akua Boatemaa , Adjei Remember Roger , Sarfo Boateng Amanda , Santo Kwadwo Gyasi , Abdulai Muntala
Published: April 2022

Nitrogen rate and time of application are among the major abiotic factors limiting the productivity of maize. The research was, therefore, carried out to examine the response of two maize varieties (Omankwa and Pioneer hybrid) to four different times of N : P : K 15 : 15 : 15 fertilizer application during the major growing season in Ghana. The experiment was arranged in a randomized complete block design with eight treatment combinations with three replications. Our findings showed that the two maize varieties responded to the time of fertilizer application differently. The interactive effect indicated that the application of fertilizer during planting produced the highest grain yield (4.7 tons ha−1) of the Omankwa (open-pollinated variety) while the application of fertilizers two/four weeks after planting increased the grain yield (6.5 tons ha−1) of the hybrid. The growth and yield parameters were not significantly affected by the treatment combinations. In order for farmers to obtain good yield, it is recommended that farmers should apply N : P : K 15 : 15 : 15 during planting for the Omankwa variety and 14/28 days after planting for the Pioneer hybrid variety in Ghana.
Research on Protection Performance of Surge Protective Devices against Electromagnetic Pulse
Authors: Zhao Zhongyi , Wang Shenyu , Cui Yongzhi
Published: April 2022

To evaluate the protection performance of SPD (surge protective device) against electromagnetic pulse, the response ability of several typical surge protective devices to wide and narrow electromagnetic pulses was tested by using a SPD response ability test system. The results showed that SPD commonly used in lightning surge protection had certain ability to suppress electromagnetic pulse conduction disturbance. Gas discharge tubes presented typical clamping characteristics for wide pulses. MOV and TVS had obvious clamping effect on wide pulses, while had no clamping effect on narrow pulses, but could obviously reduce its peak value. Zener diodes had obvious clamping effect on narrow pulses, and the clamping voltage control accuracy was high.
Compliant Mechanism Soft Robot Design and Peristaltic Movement Optimization Using Random Search
Authors: Páramo-Carranza L. A. , Lopez-González A. , Tejada Juan C.
Published: April 2022

In this article, we use the concept of auxetic structures as inspiration for the design of a compliant mechanism that allows the integration of a soft robot whose movement is based on the peristaltic movements of invertebrates. The TPU mechanism allows for smooth movement of the robot using only two servo motors. To guarantee maximum displacement, a time and angle optimization procedure using photogrammetry and random search was carried out, allowing the advance distance of the soft robot to be maximized.
Bibliometric Study of Trends in the Diabetic Nephropathy Research Space from 2016 to 2020
Authors: Shao Ying
Published: April 2022

Background. Diabetic nephropathy (DN) is one of the most common microvascular complications of diabetes mellitus (DM), but no bibliometric studies pertaining to DN have been published within the last 5 years. Objectives. Most prior studies have focused on specific problems in the DN field. This study attempts to sort out and visualize the knowledge framework in this research space from a holistic and highly generalized perspective. Readers can quickly understand and master the knowledge regarding DN research conducted from 2016 to 2020, in addition to predicting future research hotspots and possible directions for development in this field in a comprehensive and scientifically valid manner. Methods. Literature information, discourse matrices, and co-occurrence matrices were generated using BICOMB. gCLUTO was used for biclustering analyses and visualization. Strategic diagrams were generated using GraphPad Prism 5. The social network analysis (SNA) was analyzed and plotted using Ucinet 6.0 and Netdraw. Results. In total, 55 high-frequency MeSH terms/MeSH subheadings were selected and grouped into 5 clusters in a biclustering analysis. These analyses revealed that extensive studies of the etiology, diagnosis, and treatment of DN have been conducted over the last 5 years, while further research regarding DN-related single nucleotide polymorphisms, miRNAs, and signal transduction are warranted as these research areas remain relatively immature. Conclusion. Together, these results outline a robust knowledge structure pertaining to the field of DN-related research over the last 5 years, providing a valuable resource for readers by enabling the easy comprehension of relevant information. In addition, this analysis highlights predicted DN-related research directions and hotspots.
Analysis of the Correlation between the Distribution of MTHFR Gene and the Severity and Renal Function of Elderly Patients with H-Type Hypertension
Authors: Zhang Chi , Dou Zhijie , Zhao Cui , Li Jun , Xin Qiuping , Feng Yumei , Xie Yunbo , Cao Junjie
Published: April 2022

In order to investigate the correlation between the distribution of methylenetetrahydrofolate reductase (MTHFR, MR) C677T gene and the severity of disease and renal function in elderly patients with H-type hypertension, a total of 170 patients with H-type hypertension admitted to our hospital from September 2020 to February 2021 and 173 elderly patients with non-H-type hypertension during the same period are selected as the research objects and are respectively included in the observation group and control group. Baseline data and distribution of MR C677T genotype are compared between the two groups, influencing factors of H-type hypertension are analyzed by logistic regression analysis, and differences and correlation of homocysteine (Hcy) and estimated glomerular filtration rate (eGFR) levels in different MR C677T genotypes are compared. The influence of MR C677T genotype on the severity and renal function of patients with H-type hypertension is further analyzed. There are many influencing factors for H-type hypertension, including advanced age, high-sodium diet, and MR TT genotype. Patients with hypertension with these risk factors should be monitored. H-type hypertension has a high frequency of carrying TT genotype and T allele, and the TT genotype in H-type hypertension will affect the Hcy and eGFR levels. Therefore, Hcy level of H-type hypertension patients with TT genotype should be actively controlled to help control the disease and reduce kidney damage. MR TT genotype can be used as a reference index in the early screening of H-type hypertension.