bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of Volatiles of Rose Pepper Fruits by GC/MS: Drying Kinetics, Essential Oil Yield, and External Color Analysis
Authors: Borges de Oliveira Kênia , Carocho Marcio , Finimundy Tiane , Resende Osvaldo , Célia Juliana Aparecida , Gomes Francileni Pompeu , Quequeto Wellytton Darci , José de Campos Bastos Fabiano , Barros Lillian , Ferreira Junior Weder Nunes
Published: May 2022

Condiments and culinary supplements are subjected to long-term storage and may undergo physical, chemical, and biological changes that can influence their quality. Thus, the objective of the present study was to analyze the drying kinetics of rose pepper (Schinus terebinthifolius Raddi) fruits in an oven with forced air circulation at different temperatures, namely, 45, 55, 65, and 75°C, and determine the effective diffusion coefficient and activation energy using different mathematical models. Furthermore, the effects of the different drying temperatures were analyzed for external color parameters and yield of essential oil contents by gas chromatography coupled to a mass spectrometer. Of the ten models used for fitting, Thompson’s model was one with the best fitting to represent the drying of rose pepper fruits. The diffusion coefficient increases with the elevation of drying air temperature, described by the Arrhenius equation, with activation energy of 53.579 kJ·mol−1. The color of the fruits decreased in lightness (L

∗

) with the increase in temperature. Of the thirty-eight terpenes identified, α-pinene and cis-ocimene were the most abundant, with the overall highest yield being found at a drying temperature of 45°C.
Application of Artificial Intelligence Technology in Music Education Supported by Wireless Network
Authors: Jiang Qin
Published: May 2022

With the increasing application and use of information technology in college music classrooms getting deeper and deeper, the role of music informatization teaching classrooms becomes more and more prominent. In this context, it will be an important research topic to strengthen information technology education, promote the innovation and development of music education in colleges and universities, and build a rich information technology music classroom. Artificial intelligence technology’s implementation in the area of music education is developing and becoming more common, and to some extent, it has become a stimulant of music teaching, although there are certain negatives. The arrival of wireless networks, especially 5G, has drawn strong attention from all walks of life, and its characteristics of “large bandwidth, ultralow latency, and connectivity of everything” have been applied and explored in the field of music education, making the teaching mode, teaching methods, and interactive experience develop in a better direction. At the same time, VR, AR, AI, big data, and other technologies based on a wireless network have also been optimized and applied to the field of music education, which will further promote the development of intelligent music education. This study examines the impact of informatization on music education creativity in academic institutions and then goes on to address informatization tactics in music education innovation practice in order to develop a new model of music education digitization that is current. Focusing on music education, the applications of artificial intelligence in music education are listed, and a music education framework based on artificial intelligence algorithms and wireless networks is proposed. Finally, the proposed framework’s usefulness is demonstrated in relevant tests, and the prospects for artificial intelligence technology in the realm of music are examined.
The Synchronization Analysis of Cohen–Grossberg Stochastic Neural Networks with Inertial Terms
Authors: Li Zhi-Ying , Jiang Wang-Dong , Zhang Yue-Hong
Published: May 2022

The exponential synchronization (ES) of Cohen–Grossberg stochastic neural networks with inertial terms (CGSNNIs) is studied in this paper. It is investigated in two ways. The first way is using variable substitution to transform the system to another one and then based on the properties of

i

t
^

o

integral, differential operator, and the second Lyapunov method to get a sufficient condition of ES. The second way is based on the second-order differential equation, the properties of calculus are used to get a sufficient condition of ES. At last, results of the theoretical derivation are verified by virtue of two numerical simulation examples.
Construction of Enterprise Financial Early Warning Model Based on Logistic Regression and BP Neural Network
Authors: Lyu Jincheng
Published: May 2022

At present, the number of enterprises in financial crisis in China is rising sharply, and the ability of enterprises to resist risks is generally weak. Therefore, it is necessary to establish a corporate financial crisis early warning system, to detect the signs of corporate financial crisis before it arrives and to inform managers in advance, so that effective measures can be taken as soon as possible to eliminate hidden dangers. This paper selects the two-year data of 40 companies from 2017 to 2019 as training samples and the data of 20 companies as prediction samples. After testing, 12 index variables that can reflect the financial problems of energy companies are finally selected as the basis for modeling. Then, we use Logistic and BP neural network modeling, respectively, to study and compare the data from 2017 to 2019 to predict the financial risk in the following year. The results show that the BP neural network model in the two models is better than the Logistic model in terms of fitting degree or prediction accuracy for enterprise financial early warning. Therefore, the BP neural network model has a better effect and is more suitable for the practical application of enterprises in China.
Application of a Random Forest Algorithm in Natural Landscape Animation Design
Authors: Zhao Licheng , Zhang Kaixin
Published: May 2022

Natural landscape simulation is one of the most popular research contents in computer graphics in the field of research simulation system. The natural landscape animation scene can immerse viewers in the scene, and it is widely used in visual simulation systems. Simulating natural scenery on a computer is a powerful method for studying the rules of the scenery's growth process as well as the mystery of life. The simulation of natural scenery is of particular importance and has far-reaching implications. The most important aspect of optimizing natural landscape design is creating a natural landscape animation that users enjoy. This article proposes a natural landscape animation design method with a self-learning function based on this concept. The random forest model (RF) is introduced in this method and applied to the entire animation design process. RF can generate a learning model with user evaluation as the classification result to guide the automatic design of natural landscape animation, resulting in user-satisfying animations. Simultaneously, the RF-based natural landscape animation design can continuously update the learning model based on user needs and is self-learning. The experimental part of this article verifies the effectiveness of the natural landscape animation design proposed in this article by comparing the selection rate of user satisfaction and dissatisfaction scenes, and further demonstrates that the method in this article can improve the natural landscape. The market application value of user satisfaction generated by animation is high.
Improved Data Security in Cloud Environment for Test Automation Framework and Access Control for Industry 4.0
Authors: Tiwari Pradeep Kumar , Pandey Sunil Kr , Thamba Meshach W. , Parashar Jyoti , Kumar Ankit , Altuwairiqi Majid , Krah Daniel
Published: May 2022

In analyzing project regressions, automation has emerged as a major agenda in managing changes in software which requires minimum manual intervention. For rapid testing environment, software development processes such as Agile, Scrum, and XP processes depend on continuous integration tools. There is no single tool to handle the project automation, and the main challenge is dependency on multiple tools. The proposed automation tool should support configuration, execution, and debugging facility. Integrating the project automation works such as software configuration management tools Mercurial and Git, job scheduling tools like Jenkins and Apache Continuum, test management tools like TestNG and Selenium need tight integration which is a challenge. The existing PKI infrastructure for access control does not share data among the software tools and processes increasing the complexity when an organization needs to leverage the existing cloud services. The proposed approach optimizes the execution time by taking single CSV with input test case and metadata information and efficiently group and executes the tests automatically. The proposed method includes implementation of security access control mechanism for the jobs execution platform in cloud environment.
Oscillatory Behavior of Even-Order Half-Linear Neutral Differential Equations
Authors: Sangeetha S. , Thamilvanan S. K. , Thandapani Ethiraju
Published: May 2022

This paper discusses some sufficient conditions for oscillatory behavior of even-order half-linear neutral differential equation. An example is given to illustrate the main result.
The Numerical Simulation of Heat and Mass Transfer on Geothermal System-A Case Study in Laoling Area, Shandong, China
Authors: Gao Cheng , Zhang Le , Sun Chuanxiang , He Jiayuan
Published: May 2022

Geothermal resources have become increasingly attractive and promising because of their abundant resource base and environmental protection, especially for hydrothermal resources, which are widely developed for heating in winter. It is indicated that numerical simulation is an important tool for high efficiency geothermal system development. Compared with other methods, numerical simulation is a more comprehensive, scientific, and effective method in the evaluation of recoverable resources and the formulation of development plans. However, there are some problems in the existing software, such as deviation in physical property calculation, incomplete multifields coupling, and poor applicability for low-permeability reservoir and large-scale models. Therefore, based on the optimized multiphysics coupling mathematical model and MPI architecture, a simulator for a multiphysics-coupling geothermal system (SMG) was developed by Sinopec. A case study in the Laoling area, Shandong, China was primarily conducted by using geothermal software SMG, where the effect of well spacing and injection fluid temperature on the production was illustrated. Moreover, a geothermal recoverable resources evaluation method based on numerical simulation is proposed and used for the evaluation of regional geothermal dynamic recoverable resources in Laoling.
Surface Adjustment Method Based on Fuzzy Theory for Cable Net Structures under Multi-Uncertainties
Authors: Li Tuanjie , Yan Li , Shi Zhiyang , Zeng Zijie , Tang Yaqiong
Published: May 2022

Because of manufacturing errors, measuring errors, and unpredictable service environment, the cable net structure to be further adjusted is in an uncertainty state. In this paper, the uncertain factors including elastic deformation, thermal deformation, and measurement uncertainties are considered as fuzzy variables, which are equivalent into fuzzy tensions to simplify calculation. A fuzzy force density method is developed for accuracy analysis of the cable net structures under multi-uncertainties, and an optimization model is developed for surface adjustment. The above method is applied to numerical model adjustment of circular truss cable net structure. The results show that the adjusted surface accuracy is significantly enhanced and its fuzziness is concentrated compared with the initial surface accuracy, which verify the validity of the proposed method.
Application of Combined Detection of Echocardiography and Serum NT-ProBNP in the Diagnosis of Diastolic Heart Failure and Its Effect on Left Ventricular Morphology and Diastolic Function
Authors: Yang Wei , Zhang Zhonghua , Liang Defeng
Published: May 2022

Objective. This study is to assess the application of combined detection of echocardiography and serum N-terminal pro B-type natriuretic peptide (NT-ProBNP) in the diagnosis of diastolic heart failure (DHF) and its effect on left ventricular morphology and diastolic function. Methods. Thirty patients with DHF with enrolled in our hospital between January 2019 and January 2021 were included in the experimental group, and thirty healthy individuals during the same period were included in the control group. The blood pressure, heart rate (HR), left ventricular morphology, diastolic function, and serum NT-ProBNP levels were compared between the two groups. Results. DHF was associated with higher levels of diastolic blood pressure (DBP), systolic blood pressure (SBP), HR, left ventricular diameter (LVD), interventricular septum thickness (IVST), left ventricular posterior wall thickness (LVPWT), left atrial volume index (LAVI), left ventricular end-diastolic volume (LVEDV), serum NT-ProBNP, maximum early ventricular filling velocity/early diastolic velocity of the mitral annulus (E/Ea) ratio, and aortic regurgitation (AR) and lower levels of left ventricular ejection fraction (LVEF), flow propagation velocity (VP), and systolic/diastolic (S/D) ratio versus healthy subjects (all at

P
<
0.05

). Conclusion. The combined detection of echocardiography and serum NT-ProBNP yields a high clinical value in the diagnosis of DHF deficiency, as it can accurately evaluate the patient's left heart morphology and diastolic function, so it is worthy of clinical promotion and application.
Capability of Intermittent Bus Lane Utilization for Regular Vehicles
Authors: Lin Yingying , Zhang Nan , Dong Hongzhao
Published: May 2022

Intermittent bus lanes (IBLs) can improve road capacity by allowing other regular vehicles to drive in the idle space of a dedicated bus lane. However, excessive vehicles in the IBL will cause additional bus delays. To avoid such problems, this study proposes a method to determine the capability of IBL permitted for regular vehicles first, and then use it as the total amount restriction of lane-borrowing vehicles to implement a bus lane control strategy that will improve road capacity and avoid additional bus delays. A model for calculating the capability of IBL is also provided. Vehicles between two buses are designated as potentially lane-borrowing vehicles that could follow the buses to leave the road section. The evolution process of these vehicles in the unit is analyzed using kinematic wave theory to obtain the formed traffic queue length. Using the rear bus trajectory to set the length limit on the traffic queue, the estimated total amount of lane-borrowing vehicles is corrected to establish the final capability of the IBL. The applicability of the method was evaluated from three perspectives: bus departure interval, road traffic saturation, and near-side bus stop. The simulation results showed that the proposed method can guarantee no additional bus delay compared to the situation of a dedicated bus lane. It can also improve road capacity more than traditional IBL under any degree of saturation and bus departure interval. Compared with traditional IBL, the average travel time of regular vehicles is shorter, except when the degree of saturation is high and the bus departure interval is large.
Metaheuristic Methods for Efficiently Predicting and Classifying Real Life Heart Disease Data Using Machine Learning
Authors: Ramirez-Asis Elia , Guzman-Avalos Magna , Mazumdar Bireshwar Dass , Padmaja D Lakshmi , Mishra Manmohan , Hirolikar Deepali S , Kaliyaperumal Karthikeyan
Published: May 2022

The heart attack happens if the flow of blood leads to blocks in any of the blood veins and vessels liable for delivering blood into internal parts of the heart. In the modern life activities and habits, the males and females hold the same responsibility and burden of risk. The absence of understanding frequently leads to a postponement in dealing with the heart attack issues, which could worsen the injury and in most of the situations shown to be dead. Several researchers have applied data mining techniques to diagnose illnesses, and the results have been encouraging. Some methods forecast a specific illness, whereas others predict a wide spectrum of illnesses. In addition, the accuracy of sickness predictions can be improved. This post went into great length on the many approaches of data classification that are currently available. Algorithms primarily represent themselves through representations. Data classification is a typical but computationally intensive task in the area of information technology. A huge amount of data must be analysed in order to come up with an effective plan for fighting disease. Metaheuristics are frequently employed to tackle optimization issues. The accuracy of computing models can be improved by using metaheuristic techniques. Early disease diagnosis, severity evaluation, and prediction are all popular uses for artificial intelligence. For the sake of patients, health care costs, and slowed course of disease, this is a good idea. Machine learning approaches have been used to achieve this. Using machine learning and metaheuristics, this study attempts to classify and forecast human heart disease.
Discovery of Traditional Chinese Medicine Prescription Patterns Containing Herbal Dosage Based on Multilevel Top-K Weighted Association Rules
Authors: Zhu Xiaolin
Published: May 2022

In traditional Chinese medicine (TCM), drug dosage is an important part of the prescription. Different doses of the same drug can have varying curative effects. Doctors must determine the drug combination and dosage in clinical practice based on the patient’s symptoms and treatment efficacy. Existing studies on the prescription pattern of TCM on the treatment of osteoporosis only analyze the frequency that a certain drug combination is used, without considering the treatment efficacy or drug dosage. As a result, we searched for and recorded existing literature on randomized controlled trials of TCM treatment of osteoporosis, calculated weights based on the treatment efficacy of the prescriptions used in the randomized controlled trials, and created the TCM weighted transaction database. Then, a new multilevel Top-K weighted association rule algorithm is proposed to obtain effective prescription patterns that include drug dosages, which can assist doctors in clinical practice in choosing a combination of drugs to form a prescription with good curative effects.
The Pain Intensity/Quality and Pain Site Association with Muscle Activity and Muscle Activity Distribution in Patients with Chronic Low Back Pain: Using a Generalized Linear Mixed Model Analysis
Authors: Shigetoh Hayato , Nishi Yuki , Osumi Michihiro , Morioka Shu
Published: May 2022

Background. Pain can alter muscle activity, although it is not clear how pain intensity and site location affect muscle activity. This study aimed to reveal the complex associations among the pain site, pain intensity/quality, muscle activity, and muscle activity distribution. Methods. Electromyographic signals were recorded from above a bilateral lumbar erector spinae muscle with a four-channel electrode in 23 patients with chronic low back pain while they performed a lumbar bending and returning task. We calculated the average value of muscle activity during the extension phase (agonist activity) and the centroid of muscle activity, as well as the distance between the centroid of muscle activity and pain site. We also assessed the pain site and pain intensity/quality by the interview and questionnaire method. A generalized linear mixed model analysis was performed to determine the relationships among pain intensity/quality, pain site, and muscle activity. Results. The results showed that muscle activity during the extension phase was significantly negatively associated with neuropathic pain and “pain caused by light touch.” In addition, the distance between the centroid of muscle activity and pain site during the extension phase was significantly positively associated with intermittent pain, “throbbing pain,” “splitting pain,” “punishing-cruel,” and “pain caused by light touch.” Conclusions. Our findings suggest the existence of a motor adaptation that suppresses muscle activity near the painful area as the pain intensity increases. Furthermore, the present study indicates that the presence or absence of this motor adaptation depended on the pain quality.
Molecular Evidence of Emerged Pulmonary Lophomoniasis due to Lophomonas blattarum among Hospitalized Patients in Southwestern Iran: A National Registry-Based Study
Authors: Mokhtarian Kobra , Taghipour Simin , Nakhaei Maryam , Taheri Amirmasoud , Sharifpour Ali , Fakhar Mahdi , Ziaei Hezarjaribi Hajar
Published: May 2022

Objectives. Lophomonas protozoan is an emerging pathogen transmitted through arthropods such as cockroaches. Lophomoniasis is still a mysterious disease with many unknown epidemiological aspects. The current study aimed to determine the prevalence of lophomoniasis among patients who were hospitalized in Hajar Hospital, Shahrekord, southwestern Iran, using a conventional PCR technique. Methods. In this retrospective study, 132 frozen bronchoalveolar lavage fluid (BALF) specimens from patients with respiratory disorders hospitalized in Hajar Hospital, Shahrekord district, southwestern Iran, were analyzed during 2020-2021. Samples are referred to the Iranian National Registry Center for Lophomoniasis (INRCL), Mazandaran Province, Northern Iran, for detecting Lophomonas spp. infection by a conventionally small subunit ribosomal RNA (SSU rRNA) PCR test. Results. A total of 132 frozen BALF specimens were examined, 36 (27.3%) tested Lophomonas spp. positive using the conventional PCR technique. Also, based on sequencing data and blast analysis, the presence of L. blattarum species was confirmed. The average age of Lophomonas spp.‐ positive patients was 67.02 ± 15.14 years. Out of the 36 positive subjects, 63.9% were male and 36.1% female. Male and Lophomonas infection had a significant correlation (

p
=
0.001

). Our findings revealed that L. blattarum infected nonsmokers more than smokers (

p
=
0.001

). The most common underlying disease was also bronchitis Conclusion. Our results showed, for the first time, that pulmonary lophomoniasis caused by L. blattarum is a common and emerging disease in the study area, southwestern Iran. Furthermore, our findings support the use of the PCR test to detect Lophomonas infection in archived frozen clinical samples.
A Narrative Review of the Antitumor Activity of Monoterpenes from Essential Oils: An Update
Authors: Machado Thaíssa Q. , da Fonseca Anna C. C. , Duarte Allana B. S. , Robbs Bruno K. , de Sousa Damião P.
Published: May 2022

Monoterpenes are a group of natural products that have been widely studied due to their therapeutic potential against various pathologies. These compounds are abundant in the chemical composition of essential oils. Cancer is a term that covers more than 100 different types of malignant diseases and is among the leading causes of death in the world. Therefore, the search for new pharmacotherapeutic options applicable to cancer is urgent. In this review, studies on the antitumor activity of monoterpenes found in essential oils were selected, and botanical, chemical, and pharmacological aspects were discussed. The most investigated monoterpenes were carvacrol and linalool with highly significant in vitro and in vivo tumor inhibition in several types of cancers. The action mechanisms of these natural products are also presented and are wildly varied being apoptosis the most prevalent followed by cell cycle impairment, ROS production, autophagy, necroptosis, and others. The studies reported here confirm the antitumor properties of monoterpenes and their anticancer potential against various types of tumors, as demonstrated in in vitro and in vivo studies using various types of cancer cells and tumors in animal models. The data described serve as a reference for the advancement in the mechanistic studies of these compounds and in the preparation of synthetic derivatives or analogues with a better antitumor profile.
Energy Management Prediction in Hybrid PV-Battery Systems Using Deep Learning Architecture
Authors: Refaai Mohamad Reda A. , Vonteddu Shanmukha Naga Raju , Nunna Prasanthi Kumari , Kumar P. Suresh , Anbu C. , Markos Mebratu
Published: May 2022

On-grid predictive energy management using machine learning is presented in this paper. A photovoltaic array considered in this study is one of the kinds of a renewable sources of energy, where the battery bank acts as a technology for energy storage, in order to optimise energy exchange with the utility grid using logistic regression. The model of prediction can accurately estimate photovoltaic energy output and load one step ahead using a training technique. The optimization problem is constrained by the maximum amount of CO2 produced and the maximum amount of charge stored in a battery bank. The proposed model is tested on dynamic electricity costs. Compared with existing energy systems, the proposed strategy and prediction model can handle more than half of the annual load need.
Manta Ray Foraging Optimization with Vector Quantization Based Microarray Image Compression Technique
Authors: A. Alkhaldi Nora , Abdulaziz Abdullah Alsedais Rawabi , Halawani Hanan T. , Abdelkhalek Aboutaleb Sayed M.
Published: May 2022

DNA microarray technologies enable the analysis of the expression of numerous genes in an individual experiment and become an important approach in the field of medicine and biology for investing genetic function, regulation, and interaction. Microarray images can be investigated well for obtaining the contained genetic data. But is it undesirable to retain the genetic data and avoid the microarray images? Due to considerable attention to DNA microarray and several experiments being performed under distinct conditions, a massive quantity of data gets produced over the globe. In order to store and share the microarray images, effective storage and communication models are needed in a natural way. Vector quantization (VQ) is a commonly utilized tool for compressing images, which mainly aims to produce effective codebooks comprising a collection of codewords. Therefore, this paper presents a manta ray foraging optimization (MRFO) with Linde–Buzo–Gray (LBG) based microarray image compression (MRFOLBG-MIC) technique. The LBG model is commonly utilized to design local optimal codebooks to compress images. The construction of codebooks can be defined as a nondeterministic polynomial time (NP) hard problem and can be resolved by the MRFO algorithm. The codebooks produced from LBG-VQ are optimized using the MRFO algorithm to attain optimum optimal codebooks. When the codebooks are produced by the MRFOLBG-MIC algorithm, Deflate model can be applied to compress the index tables. The design of the MRFO algorithm with LBG and Deflate based index table compression demonstrate the novelty of the work. For demonstrating the enhanced compression efficacy of the MRFOLBG-MIC model, a wide-ranging experimental validation process is performed using a benchmark dataset. The experimental outcomes inferred that the MRFOLBG-MIC model accomplished superior outcomes over the other existing models.
Bifurcation Analysis of Periodic Oscillation in a Hematopoietic Stem Cells Model with Time Delay Control
Authors: Ma Suqi
Published: May 2022

Underlying the state feedback control, the complex dynamical disease of the hematopoietic stem cells model based on Mackey’s mathematical description is analyzed. The bifurcating periodical oscillation solutions of the system are continued by applying numerical simulation method. The limit point cycle bifurcation and period doubling bifurcation are observed frequently in the continuation process. The attraction basins of the positive equilibrium solution shrink as the differentiate rate is ascending and the observed Mobiüs strain is simulated with boundary as the period-2 solution. The period doubling bifurcation leads to period-2, period-4, and period-8 solutions which are simulated. Starting from period doubling bifurcation point, the continuation of the bifurcating solution routes to homoclinic solution is finished. The simulation results improve the comprehension related to the spontaneous dynamical character manifested in the hematopoietic stem cells model.
Research Progress on Humidity-Sensing Properties of Cu-Based Humidity Sensors: A Review
Authors: Wang Jingyu , Zeng Wen
Published: May 2022

Novel humidity sensors based on semiconducting metal oxides with good humidity-sensing properties have attracted extensive attention, which due to their high sensitivity at room temperature, high safety, low hysteresis, and long-term stability. As a typical p-type semiconductor metal oxide, CuO is considered to be a high-performance moisture-sensitive material; however, with the development of production, the complex working environment has put forward higher requirements for its humidity sensitivity, especially sensitivity and stability. In this regard, workers around the world are working to improve the moisture-sensing properties of sensing elements. In this review, the humidity-sensing properties of CuO-based moisture-sensitive materials are comprehensively summarized, focusing on effective measures to improve the moisture-sensing properties of CuO-based moisture-sensitive materials, including surface modification and nanocomposites. The future research of semiconducting metal oxide humidity-sensitive materials is also prospected.
Construction of Virtual Simulation College Students Innovation and Entrepreneurship Platform Using Internet of Things Technology
Authors: Wang Xiaorui , Li Jiayu
Published: May 2022

IoT technology originated from the third scientific and technological revolution, which refers to the connection of objects and networks through information sensor devices according to an agreed protocol. It communicates and circulates information through the information dissemination medium during the connection process to achieve smart recognition, supervision, and other functions. The Internet of Things is to interconnect all items with the Internet through radio frequency identification, infrared sensors, global positioning systems, laser scanners, and other sensing equipment according to the agreed communication protocol. It is a kind of network that realizes intelligent identification, positioning, analysis, monitoring, and management. The development of virtualization technology promotes the development of educational computers, and the reform of basic education curriculum promotes the profound reform of science education. It is also an inevitable requirement for cultivating talents with scientific culture and innovation ability. Virtual simulation technology is the product of combining simulation technology and virtual reality technology on the basis of the rapid development of information technology such as multimedia technology, virtual reality technology, and network communication technology. This paper is aimed at studying the construction of a virtual simulation college student innovation and entrepreneurship platform based on the IoT technology. This paper takes college students as the research object, and according to the application characteristics of local higher education institutions, establishes a systematic training platform for college students’ innovation ability to improve students’ innovation and entrepreneurship ability. This paper shows that the proportion of students’ innovative ability is as high as 56%, and the proportion of innovative activities organized by schools is as high as 70%. Students are generally not very motivated to innovate.
Effects and Satisfaction of Comfort Nursing plus Psychological Nursing in the Clinical Nursing of Neurology Patients: A Comparative Study
Authors: Zhang Lihua , Zhang Wei , Jiang Yuping , Yao Kaifeng
Published: May 2022

Objective. To explore the effects and satisfaction of comfort nursing plus psychological nursing in the clinical nursing of neurology patients. Methods. In this prospective study, 90 neurology patients admitted to our hospital from January 2019 to January 2020 were recruited and randomized into a control group and an experimental group with 45 cases in each group. The control group received routine care, and the experimental group received comfort care plus psychological care. The comfort scores and nursing satisfaction of the two groups were compared. The Hospital Anxiety and Depression Scale (HAD) was used to assess the emotional state of patients before and after the intervention. The Exercise of Self-care Agency Scale (ESCA) was used to assess the patient’s self-care ability after the intervention. Results. The comfort scores of the experimental group were higher than those of the control group (

P
<
0.05

). The experimental group showed significantly higher satisfaction than the control group (

P
<
0.05

). Patients in the experimental group had lower HAD scores after intervention than those in the control group (

P
<
0.05

). After the intervention, the experimental group had higher ESCA scores and a higher Barthel index than the control group (

P
<
0.05

). Conclusion. Comfort nursing plus psychological care improves nursing satisfaction and self-care ability of neurology patients and relieves their negative emotions.
Relatively Bounded Perturbations of a Closed Linear Relation and Its Adjoint
Authors: Xu Guixin
Published: May 2022

This paper is concerned with the relatively bounded perturbations of a closed linear relation and its adjoint in Hilbert spaces. A stability result about orthogonal projections onto the ranges of linear relations is obtained. By using this result, two perturbation theorems for a closed relation and its adjoint are given. These results generalize the corresponding results for single‐valued linear operators to linear relations and some of which weaken certain assumptions of the related existing results.
The Incident Field Optimization Method for the Electromagnetic Inverse Problems
Authors: Li Hao , Chen Lijia , Qiu Jinghui
Published: May 2022

In this letter, an incident field optimization (IO) method integrated with contrast source inversion (CSI) method and multiplicative regularized CSI (MR-CSI) method is proposed to deal with the electromagnetic inverse problems. For the conventional iterative inversion methods, the incident field is a prior knowledge and is usually obtained by the calibration process. Through the synthetic data experiments, it can be found that a small distortion of the incident field has little impact on the inversion results, but a high distortion will lead to fault inversion results or no convergence. In this letter, the incident field optimal factor, being updated during the iterative process, is introduced into the CSI and MR-CSI method, named as IO-CSI and MR–IO–CSI. The new methods have good inversion performance even with high incident field distortion. The proposed methods are applied to the Fresnel experiment data directly without the calibration procedure. Both the single-frequency and multifrequency inversion experiments demonstrate that the IO-CSI and MR–IO–CSI method can achieve good inversion results, indicating the method’s practical applicability.
Cloud-Based IoE Enabled an Urban Flooding Surveillance System
Authors: Dhaya R. , Ahanger Tariq Ahamed , Asha G. R. , Ahmed Emad A. , Tripathi Vikas , Kanthavel R. , Atiglah Henry Kwame
Published: May 2022

A flood is defined as a surplus of water or sludge on parched soil, and a flood has originated through the runoff of water inside the water route from the various water sources like canals, etc. Intense rainfall, deforestation, urbanization, deprived water and sewerage administration, and lack of concentration toward the environment of the hydrological scheme have been the causes of urban flooding. In addition, there is a deficiency in flood assessment due to the impediment in getting data on floods to the control room from the flood-affected area. To diminish the possessions due to flooding, there ought to be an immediate move of captured statistics as of the hectic region en route to the observation room with no further wait for a completely fledged technique in the wireless settings data from the Internet of Things (IoT). The Internet of Everything (IoE) is a concept that extends the Internet of Things. In view of the fact that the wireless nodes are changeable in their environment, those effects lead to unsteadiness and uncertainty in information distribution. Therefore, there is a requirement for flood-predictable region data that may be exaggerated between the source and the control room. In the past, there were a lot of techniques set up and put into practice intended for keeping an eye on the flood spots. However, one of the biggest challenges is to have data sharing without delay and loss of data among source and destination nodes. In addition to that, the video quality also needs to be taken into consideration at the same time in receipt, as it is a tough task to determine and preplan the flood happenings completely from the normal disaster that makes scientific complicatedness more than the information being received in a wireless ad-hoc environment using IoT-based sensors. Considering all the abovementioned reasons, the proposed work comprises of three folded goals, namely, the design of a mobile ad-hoc flooding environment, the development of an urban flood high definition video surveillance system using IoT-based sensors, and experimental work on simulation.
DCAF12 and HSPA1A May Serve as Potential Diagnostic Biomarkers for Myasthenia Gravis
Authors: Nong Weidong , Huang Fang , Mao Fengping , Lao Dayuan , Gong Zhuowei , Huang Wen
Published: May 2022

Background. Myasthenia gravis (MG) is an autoimmune disease that severely affects the life quality of patients. This study explores the differences in immune cell types between MG and healthy control and the role of immune‐related genes in the diagnosis of MG. Methods. The GSE85452 dataset was downloaded from the Gene Expression Omnibus (GEO) database and analyzed using the limma package to determine differentially expressed genes (DEGs) between patients with MG and the control group. Differentially expressed immune cells were analyzed using single‐sample gene set enrichment analysis (GSEA), while immune cell‐associated modules were identified by weighted gene coexpression network analysis (WGCNA). Then, the expression of the identified hub genes was confirmed by RT‐PCR in peripheral blood mononuclear cells (PBMCs) of MG patients. The R package pROC was used to plot the receiver operating characteristics (ROC) curves. Results. The modules related to CD56bright natural killer cells were identified by GSEA and WGCNA. The proportion of CD56bright natural killer cells in the peripheral blood of MG patients is low. The results of RT‐PCR showed that the levels of DDB1‐ and CUL4‐associated factor 12 (DCAF12) and heat shock protein family A member 1A (HSPA1A) were significantly decreased in peripheral blood mononuclear cells of MG patients compared with healthy controls. The ROC curve results of DCAF12 and HSPA1A mRNA in MG diagnosis were 0.780 and 0.830, respectively. Conclusions. CD56bright NK cell is lower in MG patients and may affect MG occurrence. DCAF12 and HSPA1A are lowly expressed in PBMCs of MG patients and may serve as the diagnostic biomarkers of MG.
A Decomposition‐Based Harmony Search Algorithm for Multimodal Multiobjective Optimization
Authors: Xu Wei , Gao Weifeng , Dang Qianlong
Published: May 2022

Multimodal multiobjective optimization problem (MMOP) is a special kind of multiobjective optimization problem (MOP) with multimodal characteristics, where multiple different Pareto optimal sets (PSs) map to the same Pareto optimal front (PF). To handle MMOPs, a decomposition‐based harmony search algorithm (called MOEA/D‐HSA) is devised. In MOEA/D‐HSA, multiple individuals who are assigned to the same weight vector form a subpopulation for finding multiple different PSs. Then, an environmental selection method based on greedy selection is designed to dynamically adjust the subpopulation scale for keeping the population diversity. Finally, the modified harmony search algorithm and elite learning strategy are utilized to balance the diversity and convergence of the population. Experimental results on the CEC 2019 test suite reveal that MOEA/D‐HSA has superior performance than a few state‐of‐the‐art algorithms.
Study on Cost Accounting and Control of Environmental Pollution Loss in Guangdong Province
Authors: Chen Shaolan , Zheng Wenhui
Published: May 2022

Environmental problems have become a major factor affecting the world's economic development. In order to reduce environmental pollution and improve environmental quality. This paper takes Guangdong Province as the research object, adopts the basic principles of human capital method and medical cost usage environmental pollution loss cost, incorporates environmental factors into the national economic accounting system, establishes the green GDP cost accounting method, and develops the environmental pollution economic loss accounting index; it adopts the environmental pollution economic loss accounting method, counts the number of affected objects within the scope of environmental pollution, so as to establish a comprehensive environmental economic accounting system, incorporating environmental factors into the economic system; the economic losses calculated in accordance with the established environmental pollution accounting procedures, converting tangible losses into monetary losses and counting the environmental pollution costs caused by the economy of Guangdong Province, analyzing the root causes of the deficiencies and problems in environmental damage cost control in Guangdong Province, and proposing methods to effectively control the costs of air, water, solid waste, and polluted environment. The results of the study provide a new direction and basis for future industrial pollution control in Guangdong Province and take into account both social and economic benefits.
Association of Interleukin‐1 Beta and Interleukin‐1 Receptor Antagonist Gene Polymorphisms and Plasma Levels with Diabetic Nephropathy
Authors: Liao Xueling , Xiao Yanan , Elbelt Ulf , Weylandt Karsten H. , Li Kanghui , Deng Jie , Zeng Ning , Xue Chao
Published: May 2022

Objective. We investigated the relationships between interleukin‐ (IL‐) 1β and IL‐1 receptor antagonist (IL‐1Ra) gene polymorphism and plasma levels in patients with diabetic nephropathy (DN). Methods. The genotype and allele frequency distribution of IL‐1β and IL‐1Ra in 61 patients with DN and 48 healthy controls (HCs) were determined by kompetitive allele‐specific PCR (KASP), and the plasma concentrations of IL‐1β and IL‐1Ra in DN patients and HCs were measured by enzyme‐linked immunosorbent assays (ELISA). Results. Significant differences were detected in the distribution of IL‐1β (−511C/T) genotype and allele frequencies between the DN and HC groups (P < 0.05), with the T genotype being more frequent in DN patients than HCs (OR = 2.84, 95% CI: 1.489–5.416). The IL‐1β (+3953C/T) and IL‐1Ra (+8006C/T) genotypes and allele frequencies were not significantly different between the two groups (P > 0.05). The plasma IL‐1β level was significantly higher (P < 0.01), while the plasma IL‐1Ra concentration was significantly lower in the DN group than the HC group (P < 0.05). Furthermore, the plasma IL‐1β level was significantly different between IL‐1β (−511C/T) locus variants (P < 0.05). Conclusion. The IL‐1β (−511C/T) gene polymorphism was significantly associated with DN risk in the population of northern Guangxi, China, and the T allele maybe responsible for genetic susceptibility to DN.
Analysis of Resting Energy Consumption and Its Influencing Factors in Stroke Patients with Severe Neurological Diseases: A Retrospective Clinical Study
Authors: Feng Hui , Pan Huaping , Yao Wei , Mei Chengyao
Published: May 2022

Background: This study aimed to observe the changes of resting energy metabolism in patients with severe neurological diseases, and to explore the effects of tracheostomy status, stroke severity, and complications on resting energy expenditure (REE) and respiratory quotient (RQ). Methods: A retrospective study was conducted in 105 patients with neurological rehabilitation who were hospitalized in the Rehabilitation Department of the Affiliated Jiangning Hospital of Nanjing Medical University from August 2018 to October 2021. REE was measured by Italian Cosmed k4b2 indirectly, and white blood cell count and C-reactive protein (CRP) were collected. Results: Among the 105 patients, there were 18 cases of mild stroke, 45 cases of moderate stroke, and 42 cases of severe stroke. The difference between predicted REE and actual REE among different degrees of stroke patients was statistically significant (p < 0.05); there was no significant difference in RQ values among different degrees of stroke patients (p > 0.05). Hemoglobin, albumin, and body mass index were significantly and positively correlated with predicted REE and actual REE, while CRP was significantly negatively correlated with predicted REE and actual REE. There was no significant difference in predicted REE, actual REE, and RQ between renal insufficiency, type 2 diabetes mellitus, and chronic obstructive pulmonary disease (p > 0.05). The CRP level could affect the REE of stroke patients. Conclusion: Metabolic vehicle assay has a certain clinical value in accurately evaluating the metabolic needs and feeding level of patients.
Change in Urinary Myoinositol/Citrate Ratio Associates with Progressive Loss of Renal Function in ADPKD Patients
Authors: Dekker Shosha E.I. , Verhoeven Aswin , Frey Daria , Soonawala Darius , Peters Dorien J.M. , Mayboroda Oleg A. , de Fijter Johan W. ,   on behalf of the DIPAK Consortium
Published: May 2022

Introduction: In autosomal dominant polycystic kidney disease (ADPKD) patients, predicting renal disease progression is important to make a prognosis and to support the clinical decision whether to initiate renoprotective therapy. Conventional markers all have their limitations. Metabolic profiling is a promising strategy for risk stratification. We determined the prognostic performance to identify patients with a fast progressive disease course and evaluated time-dependent changes in urinary metabolites. Methods: Targeted, quantitative metabolomics analysis (1H NMR-spectroscopy) was performed on spot urinary samples at two time points, baseline (n = 324, 61% female; mean age 45 years, SD 11; median eGFR 61 mL/min/1.73 m2, IQR 42–88; mean years of creatinine follow-up 3.7, SD 1.3) and a sample obtained after 3 years of follow-up (n = 112). Patients were stratified by their eGFR slope into fast and slow progressors based on an annualized change of > −3.0 or ≤ −3.0 mL/min/1.73 m2/year, respectively. Fifty-five urinary metabolites and ratios were quantified, and the significant ones were selected. Logistic regression was used to determine prognostic performance in identifying those with a fast progressive course using baseline urine samples. Repeated-measures ANOVA was used to analyze whether changes in urinary metabolites over a 3-year follow-up period differed between fast and slow progressors. Results: In a single urinary sample, the prognostic performance of urinary metabolites was comparable to that of a model including height-adjusted total kidney volume (htTKV, AUC = 0.67). Combined with htTKV, the predictive value of the metabolite model increased (AUC = 0.75). Longitudinal analyses showed an increase in the myoinositol/citrate ratio (p < 0.001) in fast progressors, while no significant change was found in those with slow progression, which is in-line with an overall increase in the myoinositol/citrate ratio as GFR declines. Conclusion: A metabolic profile, measured at a single time point, showed at least equivalent prognostic performance to an imaging-based risk marker in ADPKD. Changes in urinary metabolites over a 3-year follow-up period were associated with a fast progressive disease course.