bluuebunny/crossref_metadata_2025 huggingface dataset:
An Intelligent Trajectory Prediction Algorithm for Hypersonic Glide Targets Based on Maneuver Mode Identification
Authors: Li Mingjie , Zhou Chijun , Shao Lei , Lei Humin
Published: August 2022

Trajectory prediction for hypersonic glide targets is a difficult task that needs to be solved. To improve the prediction precision for hypersonic glide targets, based on the analysis of the target’s maneuver characteristic, an intelligent trajectory prediction algorithm based on the maneuver mode identification is proposed in this paper. Firstly, according to the typical maneuver modes of the target, a group of parameter suitable for maneuver mode identification and parameter estimation is proposed. The proposed maneuver parameters can reflect the maneuvering characteristics of the target than the other control parameters. Then, the rationality of parameters is analyzed. Secondly, using the long-short-term memory network (LSTM), the structure of intelligent trajectory prediction based on maneuver mode identification is proposed. The proposed prediction method is designed to improve the prediction accuracy by combining the target dynamic model with the flight data. Finally, the maneuver trajectory data set is established to train and test the method. For the test data set, when the observation time for the target is 200 s and the prediction time is 150 s, with a fast prediction speed, our method’s average error of spatial distance (AESD) is less than 2.9 km, and the maximum error of spatial distance (MESD) is less than 6.9 km. The result is better than other compared mainstream methods. And it is also proved valid with some observational error.
Mathematical Modeling of Real-Time Systems Using Heun and Piecewise Methods
Authors: Malik Gul Urfa , Paul Anand , Chee K.-W.-A.
Published: August 2022

It is often said that mathematical modeling is an implementation of mathematics in real-world problems with the aim of better understanding them, so we can say that mathematical modeling is linked to the solution of problems. Some of the essential principles and procedures of mathematical modeling are discussed using formulas and equations. We investigate the stability and convergence characteristics and demonstrate the suitability of different mathematical methods in a set of numerical examples. The described methods in our paper are the best choices for the simulation of linear phenomena and are more efficient for use with high-order spatial discretization. We emphasized the importance of mathematical modeling technologies used in computational tools. Our study shows that these new methods are more stable with lower errors.
N-Acetyl-L-Cysteine Protects Airway Epithelial Cells during Respiratory Syncytial Virus Infection against Mucin Synthesis, Oxidative Stress, and Inflammatory Response and Inhibits HSPA6 Expression
Authors: Chi Lei , Shan Yuxia , Cui Zhenze
Published: August 2022

Objective. Respiratory syncytial virus (RSV) infection is an important cause of hospitalization of children worldwide, leading to significant morbidity and mortality. RSV infection leads to increasing inflammatory and apoptosis events in the airway epithelium through mechanisms involving ROS generation. The antioxidant N-acetyl-L-cysteine (NAC) has been shown to inhibit influenza virus replication and to reduce the secretion of inflammatory and apoptotic mediators during virus infection. The study aims to investigate the effects of NAC on human bronchial epithelial cells BEAS-2B and HSPA6 expression during RSV infection. Methods. CCK-8 assays were performed to evaluate cell survival. The production of proinflammatory factors, TNF-α, IL-6, IL-1β, IL-18, and MUC5AC was examined by quantitative real-time PCR and ELISA. Oxidative stress was determined by reactive oxygen species (ROS), superoxide dismutase (SOD), malondialdehyde (MDA), and glutathione (GSH)/glutathione disulfide (GSSG) ratio. Immunoblotting analysis of epidermal growth factor receptor (EGFR) and its phosphorylation was performed. The antiviral effect of NAC was assessed by determining viral titers using plaque assay. Results. RSV infection reduced cell survival, promoted the release of proinflammatory factors, increased the ROS production and MDA concentration, and diminished the SOD activity and GSH/GSSG ratio, all which were attenuated by NAC treatment. Accordingly, NAC treatment inhibited the activation of EGFR and MUC5AC in BEAS-2B cells with RSV infection. Furthermore, NAC administration resulted in a marked decrease in RSV-induced HSPA6 expression in BEAS-2B cells. Concomitantly, EPB treatment led to an evident inhibition of RSV fusion gene and viral replication in RSV-infected BEAS-2B cells. Conclusion. This work supports the use of NAC to exert antimucin synthesis, anti-inflammatory, antioxidant, and antiviral effects on airway epithelium during RSV infection.
Design and Service Effect Evaluation of Agricultural Social Service Platform Based on 5G and Cloud Computing
Authors: Zhang Mengling , Zhang Yuhan , Weng Zhenling , Chen Zhaojiu
Published: August 2022

With the continuous development of information technology, various industries continue to use information technology to promote industrial upgrading and optimize the industrial structure. Agricultural informatization, as the only way for future agricultural development, will become an inevitable direction for the rejuvenation of traditional industries. The proposal of the development concept of “Mass Entrepreneurship, Mass Innovation” marks that my country has gradually entered an innovative society, and the development of public services and service products will become a powerful guarantee for promoting the construction of the agricultural informatization industry chain. This paper starts from the application of cloud computing and 5G in agriculture, considers the impact of big data on agriculture, and combines the business requirements and functional requirements of agricultural social services. The design of agricultural socialization service platform is realized, and finally, the service effect of the platform is evaluated by neural network. The following is a summary of the work done in this paper: (1) The research background and significance of the development concept of agricultural informatization services are expounded. Finally, the research content and method of this paper are discussed. (2) The technical principle and optimization algorithm of BPNN are introduced, and the evaluation index of the service effect of agricultural information platform is designed. (3) Select the appropriate parameters to build the experimental model and then train it, then compare the experimental effects of the traditional BP algorithm and the optimized three algorithms, and comprehensively evaluate the LM-BP model with the best performance.
A Novel Forecasting Approach by the GA-SVR-GRNN Hybrid Deep Learning Algorithm for Oil Future Prices
Authors: Wang Liang , Xia Yang , Lu Yichun
Published: August 2022

It is hard to forecasting oil future prices accurately, which is affected by some nonlinear, nonstationary, and other chaotic characteristics. Then, a novel GA-SVR-GRNN hybrid deep learning algorithm is put forward for forecasting oil future price. First, a genetic algorithm (GA) is employed for optimizing parameters regarding the support vector regression machine (SVR), and the GA-SVR model is used to forecast oil future price. Further, a generalized regression neural network (GRNN) model is built for the residual series for forecasting. Finally, we obtain the predicted values of the oil future price series forecasted by the GA-SVR-GRNN hybrid deep learning algorithm. According to the simulation, the GA-SVR-GRNN hybrid deep learning algorithm achieves lower MSE, RMSE, MAE, and MAPE relative to the GRNN, GA-SVR, and PSO-SVR models, indicating that the proposed GA-SVR-GRNN hybrid deep learning algorithm can fully reveal the prediction advantages of the GA-SVR and GRNN models in the nonlinear space and is a more accurate and effective method for oil future price forecasting.
Analysis of Efficacy, Complications, and Inflammatory Reactions of Bridge Combined Internal Fixation System for Periarticular Fractures of the Shoulder
Authors: Xu Haiyun , Fan Youliang , Lu Jiajun , Wu Yixiong , Feng Xiaoguang
Published: August 2022

Objective. The aim of the present research is to analyze the impact of the bridge combined internal fixation system (BCFS) on efficacy, complications, and inflammatory reactions of periarticular fractures of the shoulder. Methods. A retrospective analysis was performed on 100 patients with periarticular fractures of the shoulder admitted between January 2016 and January 2020. Patients were assigned to the observation group (OG) and control group (CG) according to different treatment schemes, with 50 cases in each group. Patients in OG were intervened by BCFS, while those in CG were routinely given plate fixation. The treatment outcome, complications, and inflammatory reaction of the two groups were compared. Results. The results showed better treatment outcome, shoulder joint function recovery, and inflammatory reaction alleviation of OG compared with CG. Besides, statistically shorter fracture healing and hospitalization time as well as fewer complications were determined in OG. Conclusion. These results demonstrate that compared with the plate fixation system, BCFS can significantly improve the surgical efficacy and healing efficiency and alleviate the inflammatory response of patients, with a low complication rate, all of which contribute to faster recovery of periarticular fractures of the shoulder. Hence, BCFS is an ideal choice for periarticular fractures of the shoulder that deserves clinical promotion.
Analysis of BIM in the Whole-Process Cost Management of Construction Projects
Authors: Liu Lirong , Li Shaoli , Li Cheng , Zhang Jing
Published: August 2022

In order to improve the quality of project cost management, this paper uses BIM technology to analyze the whole-process project cost to improve its management effect, uses the method of digital processing nuclear pulse signal to conduct simulation analysis, and conducts preliminary simulation. Moreover, this paper discusses the characteristics of cost management data, analyzes the process of BIM in the whole-process cost management of construction projects, and analyzes the possibility of using pulse digital method to improve stability. In addition, this paper constructs the whole-process cost management of engineering projects based on BIM. Through the experimental research, it is verified that the whole-process cost management system based on BIM constructed in this paper has a good effect and can effectively improve the quality of cost management of construction projects.
3D Automatic Segmentation of Brain Tumor Based on Deep Neural Network and Multimodal MRI Images
Authors: Qian Zhuliang , Xie Lifeng , Xu Yisheng
Published: August 2022

Brain tumor segmentation is an important content in medical image processing, and it is also a very common research in medicine. Due to the development of modern technology, it is very valuable to use deep learning (DL) and multimodal MRI images to study brain tumor segmentation. In order to solve the problems of low efficiency and low accuracy of brain tumor segmentation, this paper proposes DL to conduct research on multimodal MRI image segmentation, aiming to make accurate diagnosis and treatment for doctors. In addition, this paper constructs an automatic diagnosis system for brain tumors, uses GLCM and discrete wavelet transform (DWT) to extract features from MRI images, and then uses a convolutional neural network (CNN) for final diagnosis; finally, through four. The comparison of the results between the two algorithms proves that the CNN algorithm has the better processing power and higher efficiency.
On Some Structural Properties of Integer‐Based Graphs and Their Topological Indices
Authors: Bhatti Faqir M. , Malooq Muhammad , Ahmad Jihad , Zaman Iqra , Usman Muhammad
Published: August 2022

Integer partition plays an important role in different fields like graph theory and number theory. This paper comprises different properties of the integer partition‐based graphs. These properties give an idea about the structural properties like degree of the vertices. Furthermore, topological index of the graphs is considered as a transformation of a chemical structure into an integer number. We discussed two very important indices, namely, harmonic index and Randic index, for these particular graphs.
Study on Deformation Characteristics of Extended Highway in Operation Period
Authors: Zhou Pengfei , Cui Lizhuang , Zhang Xuesen , Ma Chuanyi , Liu Jian , Shao Yanni , Lv Gaohang , Xie Quanyi
Published: August 2022

Highway widening has become an effective way to improve the carrying capacity of existing expressways. However, assume that the differential settlement between the new and the old subgrades is not controlled. In that case, it will lead to longitudinal cracks on the pavement, which will seriously affect the safety of vehicles. In this study, the variations of elastic modulus and dynamic elastic modulus of new and old subgrades under traffic load were studied by tests. A hyperbolic model of dynamic elastic modulus increasing with strain is proposed. The numerical calculation model of new and old subgrades under traffic load is established. The deformation characteristics of new and old subgrades without treatment measures and setting up geogrid and geocell are studied. The main conclusions are as follows: (1) with the increase of traffic load and subgrade depth, the elastic modulus changes significantly in the early stage and small in the later stage. (2) Under the condition of no treatment measures, the vertical and horizontal deformation of the subgrade surface show a trend of increasing and then decreasing with the distance from the center line of the subgrade. (3) Setting geogrid and geocell can effectively reduce the differential settlement of the old and new subgrades. (4) The effect of setting geogrid to reduce the differential settlement of old and new subgrades is better than that of setting geogrid.
Application of Capital Asset Pricing Model Based on BP Neural Network in E-commerce Financing
Authors: Geng Guojing , Guan Zhigui
Published: August 2022

The study explores the risks and benefits of investors in e-commerce financing under the background of “double carbon” to maximize investors’ interests and reduce investment losses. The Back Propagation Neural Network (BPNN) algorithm model of e-commerce enterprise financing based on the Capital Asset Pricing Model (CAPM) is mainly studied. First, according to the worldwide literature, the theoretical concept and principle of the CAPM are deeply studied and analyzed. Then, from the perspective of “double carbon,” with the financing risk characteristics of listed companies responding to the “double carbon” policy as samples, the CAPM model of e-commerce financing under the BPNN algorithm is established. Next, the BPNN is used to input the financing samples of e-commerce enterprises and train the model. The verification experiment of the capital asset financing model of e-commerce enterprises is further conducted. The experimental results show that the model error is the smallest when the number of neurons in the hidden layer reaches about 20. Therefore, the number of neurons in the hidden layer of the model is set to 20. When the number of iterations in training reaches 3000, the financing risk model begins to show a convergence trend. Finally, it can be determined that the number of adaptive iterations of the model is 3000. When the learning rate is 0.03, the oscillation of the model is smaller and stabler, so the model learning rate is 0.03, and the final model error is only

9.96
×

10

−
8

. Based on this, e-commerce enterprises can achieve the purpose using this model to adjust the coefficient in financing in the future. The results have certain reference significance for e-commerce financing risk assessment under a “double carbon” background.
Application of Cognitive System Model and Gestalt Psychology in Residential Healthy Environment Design
Authors: Yang Jicheng , Yuan Chao
Published: August 2022

The influence of the environment on people is very large. According to the general rules of people’s daily routines, people spend at least half of their time in residential areas. Therefore, the environment of residential areas has a great impact on people’s emotions, behaviors, and even the development of living habits. Residential area refers to a residential area where residents live in clusters and form a certain scale, including buildings where people live, public buildings and facilities for rest, education, fitness, work, and even communication between people, green space, and traffic roads. Usually, the environmental design of urban residential areas usually meets the requirements of diverse functions, strong compatibility, and convenient travel, so as to facilitate residents’ living. In an ideal state, the environmental design of a residential area should not only meet the basic living conditions but also improve the living comfort of the residents. Because there is a close relationship between people’s psychology and behavior and the living environment of the community, environmental design that is in line with people’s positive and optimistic psychology helps residents maintain a happy mood. Therefore, from the perspective of environment to residents’ living comfort, this paper introduces a cognitive system model and Gestalt psychology to optimize the design of residential healthy environment. A more harmonious and comfortable living environment is established by using the principle of bottom, grouping, and simplification in Gestalt.
Experimental Investigation of the Seepage-Induced Failure Process in Granular Soils
Authors: Wang Yu , Duan Xiangbao , Gu Yanchang , Wang Shijun
Published: August 2022

Seepage-induced failure may disable the bearing capacity of foundations in dams and embankments. However, the evolution mechanism of the seepage failure process in granular soils is not well understood. In this paper, a series of laboratory hydraulic tests were performed to investigate the seepage failure process in sandy gravels and fine-grained sands. Seepage behaviors of the hydraulic gradient, seepage flow velocity, and permeability coefficient were observed, and then, the Reynolds number was obtained to describe the seepage regime. By linking the hydraulic gradients with the Reynolds number, the seepage failure process was quantitatively divided into four phases: (i) incubation (

Re
<
0.85

), (ii) formation (

0.85
≤
Re
≤
5

), (iii) evolution (

5
<
Re
≤
50

), and (iv) destruction (

50
<
Re

). The findings of the study identified an approximately linear relationship between the hydraulic gradient and the seepage velocity in the phases of incubation and formation in which the viscous drag effects are not negligible, corroborating Darcy’s view. However, in the phases of evolution and destruction, the hydraulic gradient and the seepage velocity are nonlinearly related, indicating that the inertial force plays a leading role, and the quadratic equation is relevant for the regime transition from laminar flow to turbulent flow. Finally, the mechanism of each phase in the seepage failure process was clarified. Fine content and uniformity coefficient are internal factors that affect the potential of seepage failure, while the seepage force that drives the transport of fine particles is an underlying cause that promotes the development of seepage failure. This study will be quite useful in identifying the limits of applicability of the well-known “Darcy’s law,” in further improving the physical modelling associated with fluid flow through granular soils.
The Effect of Temperature on Intestinal Flora Imbalance Based on Time Series Prediction Model
Authors: Zhu Jing , Xue Yafei , Yang Shuyuan
Published: August 2022

A series of effects caused by temperature change are the biggest problems faced by biological systems. These irregular environmental characteristics have brought new challenges to people and even animal groups. The immune function of human intestinal flora has a great protective effect on other organs and the body environment. It can help the human body carry out intestinal digestion, food absorption, nutritional metabolism, and so on. Based on the above situation, this paper uses the time series prediction model to study the factors affecting the imbalance of intestinal flora in the process of temperature change. Firstly, biological experiments are carried out with animals to simulate the human environment. Based on the sequence information of historical temperature change parameters, a temperature prediction device based on time series model is proposed. The effects of air factors and carbon dioxide content on the prediction results are evaluated by statistical analysis. Secondly, in order to ensure the accuracy of the experimental data, the neural network algorithm is used to optimize the model, and the white blood cell count is used to analyze the influence of temperature change on the intestinal flora structure of the two organisms. Finally, the experimental results are applied to the human environment to analyze the research results. The results showed that with the irregular change of temperature, the number of intestinal flora and internal colony structure also changed. The richness index in the normal temperature environment is relatively large, which can effectively explain the high richness of intestinal microflora in the experimental population. Further analyze the subjects and distinguish the animals according to sex. The number of Bacteroides in the intestinal flora of male animals was higher than that of female animals, but this phenomenon disappeared immediately after physical ligation. In addition, the intestinal flora abundance of female animals is higher than that of male animals, and the metabolic level is faster.
A Low-Cost Online Data Acquisition and Processing System for Industrial Pollutants’ Monitoring
Authors: Wu XiaoLong , Li HuiMing , Dou YueJin , Huang Di , Wu SiYuan
Published: August 2022

Recently, the prevention and control of industrial pollution has attracted extensive attention. Many studies based on emission data have achieved great success, but there are relatively few studies on the emission data acquisition. To this end, we propose a data acquisition and processing system (DAPS) for online monitoring of industrial pollutants. The system receives real-time emission data through the RS485, automatically generates periodic data, and saves it in the SQL Server database. Real-time Ethernet (RTE) is used to transmit control signals between the system and programmable logic controllers (PLCs). Emission data is transmitted to local industrial control systems and remote monitoring centers (RMCs) via a wireless network. We tested the acquisition function and communication function of the system through experiments and evaluated the overall performance of the system by observing the memory and CPU usage. The system is characterized by simple implementation and versatility and can be applied to continuous monitoring of various industrial pollutants.
Construction and Evaluation of Neural Network Correlation Model between Syndrome Elements and Physical and Chemical Indexes of Unstable Angina Pectoris Complicated with Anxiety
Authors: Chen Xiaoyang , Wang Yifei , Zhang Li , Xu Xuegong
Published: August 2022

Objective. Syndrome elements are regarded as the smallest unit of syndrome differentiation, which is characterized by indivisibility and random combination. Therefore, it can well fit the goal of syndrome differentiation and unity. Methods. Clinical physicochemical indicators are important references for disease diagnosis, but they are often not used too much in the process of TCM syndrome differentiation. In the era of intelligence, communicating TCM syndrome differentiation at the macro level with physiological and pathological processes at the micro level (i.e., these clinical physicochemical indicators) is an effective tool to realize intelligent medicine. Taking the collected relevant clinical physical and chemical indexes as the research object, on the basis of routine t-test and nonparametric test, logistic regression model is used to mine the main syndrome elements, and neural network multilayer perceptron is used to predict the feature model. Results. Compared with non-blood stasis patients, there were significant differences in HGB, PLT, Pt, PTA, Na+, TG, LDL, BNP, LVEDd, and EF in blood stasis patients. Taking blood stasis as the dependent variable and the above physical and chemical indexes with statistical significance (

P
<
0.05

) as independent variables. Compared with non-qi depression patients, there were significant differences in atpp, TG, TC, LDL, LVESD, and FS in qi depression patients (

P
<
0.05

). Taking Yin deficiency as dependent variable and the above physical and chemical indexes (Hgb, APTT, CKMB, LVEDd, and LVPW) with statistical significance (

P
<
0.05

) as independent variables, binary logistic regression analysis was carried out. Conclusion. The combination pattern of physical and chemical indexes obtained from the neural network model provides a clinical reference basis for identifying the syndrome elements of unstable angina pectoris complicated with anxiety, such as blood stasis, qi depression, Qi deficiency, yin deficiency, phlegm turbidity, and qi stagnation.
IoT Security Review: A Case Study of IIoT, IoV, and Smart Home
Authors: Ma Jinnan , Shangguan Xuekui , Zhang Ying
Published: August 2022

The Internet of Things (IoT) acts as a tremendous network that is constructed by fusing diverse sensors. IoT can achieve the interconnection of individuals, things, and machines at any place and time and improve the function performance of network applications. However, the security of IoT has always been a major problem that may limit the application perspective of IoT technologies. Nowadays, industrial IOT (IIoT), Internet of vehicles (IoV), and smart home have become the three primary emerging perspectives of the current IoT studies, and it is necessary to systematically highlight the security analysis of these three types of scenarios. Hence, in this paper, guided by the three major IoT application scenarios, i.e., IIoT, IoV, and smart home, we sum up the development status of IoT security technologies, analyzed corresponding technical difficulties, and discussed several future outlook of challenges and development trends for the IoT technology.
Evaluation and Analysis of English Teaching Ability Based on Nonlinear Random Matrix Model
Authors: Zhou Jianliang , Zhou Huixin
Published: August 2022

With the sustained, rapid, and steady development of China’s economy, the development of higher education is also faster and faster. With economic development and social progress, the demand for talents is also growing. Colleges and universities are the cradle of cultivating excellent talents and undertake the important task of cultivating reserve forces for the country. As an engineer of human soul, English teachers shoulder the responsibility of cultivating excellent talents and the hope of countless parents. For a long time, there have been many problems in the evaluation of College English teachers in China. For example, the assessment objective is too simple, only related to salary promotion and professional title evaluation. In terms of the content of evaluation, they often only pay attention to the quantity and level of scientific research results, ignoring the evaluation of potential, attitude, and other aspects, especially the social responsibility of teachers. Based on the theory of nonlinear random matrix and the theory of competence, this study establishes the quality evaluation model of university teachers in China by combining qualitative and quantitative methods. On this basis, it plays an important role in the selection, training, promotion, and further development of university teachers in China. First of all, according to a large number of domestic and foreign literature on competency, this study summarizes the competency theory and competency model. Using the hypothetical model, a questionnaire was designed and distributed to build a competency model of university teachers from four aspects: professional knowledge, related skills, work attitude, and personality motivation. Finally, the weight of each index in the model is calculated. This study not only constructs the competency evaluation model but also uses the principal component analysis method to calculate it in detail, which provides a good quantitative basis for the selection and performance evaluation of college teachers, so as to prevent the situation beyond its control. It paves an endless bright road for further stable and healthy development. Therefore, the overall research results of this study have greater practical application value and rich connotation.
Autophagy-Mediated Inflammatory Cytokine Secretion in Sporadic ALS Patient iPSC-Derived Astrocytes
Authors: BaofengFeng  , Amponsah Asiamah Ernest , Guo Ruiyun , Liu Xin , Zhang Jinyu , Du Xiaofeng , Zhou Zijing , He Jingjing , Ma Jun , Cui Huixian
Published: August 2022

Background. Astrocytes can be involved in motor neuron toxicity in amyotrophic lateral sclerosis (ALS) induced by noncell autonomous effects, and inflammatory cytokines may play the main role in mediating this process. However, the etiology of aberrant cytokine secretion is unclear. The present study assessed possible involvement of the mTOR-autophagy pathway in aberrant cytokine secretion by ALS patient iPSC-derived astrocytes. Method and Results. PBMCs from sporadic ALS patients and control subjects were reprogrammed into iPSCs, which were then differentiated into astrocytes and/or motor neurons. Comparison with control astrocytes indicated that conditioned medium of ALS astrocytes reduced the viability of the control motor neurons (

p
<
0.05

) assessed using the MTT assay. The results of ELISA showed that the concentrations of TNFα, IL1β, and IL6 in cell culture medium of ALS astrocytes were increased (

p
<
0.05

). ALS astrocytes had higher p62 and mTOR levels and lower LC3BII/LC3BI ratio and ULK1 and p-Beclin-1 (Ser15) levels (

p
<
0.05

), indicating defective autophagy. Exogenous inhibition of the mTOR-autophagy pathway, but not the activation of the pathway in control subject astrocytes, increased the levels of p62 and mTOR and concentration of IL-1β, TNF-α, and IL-6 in cell culture medium and decreased the LC3BII/LC3BI ratio and levels of ULK1 and p-Beclin-1 (Ser15), and these changes were comparable to those in ALS astrocytes. After 48 h of rapamycin (autophagy activator) and 3-methyladenine (autophagy inhibitor) treatments, the exogenous activation of the mTOR-autophagy pathway, but not inhibition of the pathway, in ALS astrocytes significantly reduced the concentrations of TNFα, IL1β, and IL6 in cell culture medium and reduced the levels of p62, while increasing the levels of LC3B-II/LC3B-I, ULK1, and p-Beclin-1 (Ser15), and these changes were comparable to those in control subject astrocytes. Conclusion. Alteration in the mTOR/ULK1/Beclin-1 pathway regulated cytokine secretion in ALS astrocytes, which was able to lead to noncell autonomous toxicity. Autophagy activation mitigated cytokine secretion by ALS astrocytes.
Analysis of Influencing Factors of Roadway Stability and Adaptability Criterion for Gob-Side Pre-Backfill Driving
Authors: Wu Rui , Wu Jianbiao , Chen Qijian , Li Linfeng
Published: August 2022

To reduce the loss of coal pillars between two adjacent longwall working faces, gob-side pre-backfill driving (GPD) procedure is suggested. This paper introduces in detail the principles and characteristics of the GPD procedure. Based on the arc triangle block (ATB) formed by the fractures of the main roof, the Winkel Elastic Foundation Beam model of the relevant rock strata structure is established and analyzed. The influence of the different breaking positions of the ATB of the main roof, the different geological conditions of the surrounding rock, and the parameters of the backfill body on the stability of the backfill body are analyzed. Also, the adaptability of the GPD procedure under any geological conditions is analyzed. The results show that the sensitivity of the aforementioned factors on the stability of the backfill from high to low is as follows: the thickness of the immediate roof, the elastic modulus of the main roof, the elastic modulus of the immediate roof, the thickness of the main roof, the thickness of the coal seam, the elastic modulus of the coal seam, the width of the backfill body, the buried depth, and the elastic modulus of the backfill body. According to the geological conditions of the Changcun Mine, it can be determined that if the fracture location of ATB of the main roof from the backfill is less than 5 m, the GPD procedure cannot be adopted. For this situation, the required strength of the backfill is high; the backfill is difficult to maintain, and it is prone to fracture failure. The GPD procedure cannot be used for all geological conditions. Before the GPD procedure is implemented, a pre-judgment must be made not to cause unnecessary losses.
A Node Placement Optimization Algorithm in Heterogeneous WSN Based on Neighbor Nodes
Authors: Zhang Yijie , Guo Zhan
Published: August 2022

Wireless sensor network (WSN) is an important part of the Internet of Things. With the application and development of the Internet of Things, the heterogeneity of WSN is put forward a higher demand. Node placement is a critical step in the heterogeneous wireless sensor network (HEWSN). It is very important to optimize the node placement to fundamentally prolong the lifetime, improve the performance, and decrease the cost of the HEWSN. In this paper, the node placement optimization problem of HEWSN with different sensing types’ nodes is studied. An improved algorithm named neighbor nodes directed evolved nondominated sorting genetic algorithm (NN-DENSGA) is proposed to solve the multiobjective optimization problem. Homogeneous neighbor nodes are considered to promote directional evolution, and heterogeneous neighbor nodes are considered to repair the coverage holes. In addition, a forced update strategy is designed to increase diversity of population. Experiments have shown that the proposed algorithm not only has improvements in optimization performance but also in convergence speed and stability.
Optimization Design of Street Public Space Layout on Account of Internet of Things and Deep Learning
Authors: Yu Shanshan , Wang Hao , Wang Yajun
Published: August 2022

With the gradual improvement of material living standards, people have higher and higher requirements for the livability of modern cities. As an important component of urban construction, the optimal layout of street public space has gradually received more and more attention. In the development stage of the new era, it is very important to improve the image of the city by transforming the street construction, optimizing the urban public space, and building a place full of vitality. Implementing the people-oriented connotation and improving the green travel components in the city, such as encouraging walking and increasing bicycles, are of great significance for optimizing the street public space. This article studies the relevant content of the optimization design of street public space layout based on the Internet of Things and deep learning and expounds the solutions for the optimization design of street public space layout based on the Internet of Things and deep learning. Design research provides cutting-edge scientific theories and evidence. This paper uses data to prove that based on the Internet of Things and deep learning technology, the optimized design of street public space layout has increased the latter’s recognition among residents by an average of 21.7%. The designed model has both space utilization and environmental protection. Very good results have been obtained.
Adverse Reaction of Acupuncture and Antihypertensive Drugs for Treatment of Essential hypertension: A Protocol for Bayesian Network Meta-Analysis
Authors: Han Jiawei , Yang Jiwei , Du Yuanhao
Published: August 2022

Background. Hypertension, as a high risk factor of cardiovascular disease, has led to a significant upward trend in the population and incidence of the disease. Hypertension patients need to take antihypertensive drugs for life, and therefore people gradually pay more attention to the adverse reactions of antihypertensive drugs. This study protocol outlines a plan to assess the adverse reaction of the different antihypertensive drugs and acupuncture in order for clinical application. Objective. To compare the side effects of different antihypertensive drugs and acupuncture in the treatment of hypertension. Methods and analysis. We will search the databases containing CNKI, Wan-Fang database, Chinese Scientific Journal Database(VIP), PubMed, Cochrane, and Embase, and randomized controlled trials (RCTs) of commonly used antihypertensive drugs or acupuncture for primary hypertension will be obtained. Then, Stata14.0 and Gemtc will be used to assess the statistics. Ethics and dissemination. Since no personal patient consent will be required in the study, there is no ethical approval. The results of this reporting will be submitted to a peer-reviewed publication. PROSPERO registration number: CRD42020152703.
Interval-Valued q-Rung Orthopair Fuzzy Choquet Integral Operators and Their Application in Group Decision-Making
Authors: Wan Benting , Huang Juelin , Chen Xi , Cheng Youyu , Wang Jing
Published: August 2022

In this paper, we develop a multiattribute group decision-making (MAGDM) method to solve problems with interactive attributes under interval-valued q-rung orthopair fuzzy set (IVq-ROFS) environment. Firstly, the interval-valued q-rung orthopair fuzzy Choquet integral average (IVq-ROFCA) operator is proposed to aggregate interval-valued q-rung orthopair fuzzy information. Then, we investigate the interval-valued q-rung orthopair fuzzy Choquet integral geometric (IVq-ROFCG) operator and offer several related properties. More importantly, for handling problems with interdependence between attributes for IVq-ROFS, a MAGDM method is developed based on the IVq-ROFCA operator. Finally, an example of the warning management system for hypertension is given to illustrate the proposed method, and parameter analysis and comparison analysis further verify the feasibility and validity of the proposed method.
Microscopic Response of Limestone Physical Deterioration under Water-Rock Alternation in the Acidic Environment
Authors: Liang Wei , Li Ke , Luo Jiashun , Xu Mengtang , Feng Fushou
Published: August 2022

In order to investigate the microscopic response mechanism of limestone deterioration under alternating water-rock action in the acidic environment, the porosity, water absorption, mass loss characteristic, and microcrack propagation characteristic were analyzed by laboratory wetting-drying cyclic tests. The results show that, with increasing the number of cycles, the porosity, water absorption, and mass deterioration of the limestone specimens showed an overall increasing trend; moreover, at the beginning of the cycles, the physical deterioration of the specimen was significantly affected by the wetting-drying cycles, and at the end of the cycles, the physical deterioration of the specimen tended to be stable. The porosity deterioration degree reached 30.324% at the beginning of the cycles; there is a slight fluctuation in 20 cycles and then decreases as the number of cycles increases. The growth rate of water absorption increases slowly in 5~15 cycles and reaches the peak value in 20 cycles, and the growth rate decreases rapidly in the latter stages of the cycles. The increase rate of mass deterioration degree decreases with the increase of cycle number, the maximum average value can reach 61.887% at the beginning of cycles and is relatively stable at 20~25 cycles, and the average value at the end of cycle is obviously reduced by 3.167%. The nuclear magnetic resonance (NMR) test shows that the number and size of pores in the rock gradually increase with the increase of the number of wetting-drying cycles, and the wetting-drying cycles aggravate the internal damage of the rock. The number of shear cracks and fragmentation of the specimens increase as the increase of the number of cycles, and the failure of the specimens is mainly in the form of shear damage in the uniaxial compression test.
The Impact of Public Service Motivation on Job Satisfaction in Public Sector Employees: The Mediating Roles of Work Engagement and Organizational Commitment
Authors: Lu Dan , Chen Chih Hung
Published: August 2022

In the public service sector, staff’s motivation is of great significance for the developments of both the service team and the governmental service departments. This research develops a model to study the effects of public service motivation on job satisfaction and further assesses the mediation effects of work engagement and organizational commitment. Five-hundred and fifty employees from Chinese public service sectors were engaged in the questionnaire-based study. The results show that public service motivation plays important roles in work engagement, organizational commitment, and job satisfaction. Furthermore, work engagement and organizational commitment are the factors to mediate public service motivation and job satisfaction. Therefore, our findings suggest that staff in the public sector with enhanced public service motivation generally appear more involved in their work, are more loyal to their organizations, and demonstrate more positive work attitudes to improve productivity.
RBFNN-Enabled Adaptive Parameters Identification for Robot Servo System Based on Improved Sliding Mode Observer
Authors: Li Ye , Wang Dazhi , Du Mingtian , Zhou Shuai , Cao Shuo , Li Yanming
Published: August 2022

Effective and accurate parameter identification, especially the identification of load torque, is one of the key factors to improve the control performance of the robot servo system. Sliding mode observer (SMO) has always been a common method for identifying load torque due to its advantages of simple implementation, strong robustness, and fast response. However, due to the discontinuity of the SMO switching function, the system will generate high-frequency chattering, which will reduce the accuracy of load torque identification and affect system performance. In this paper, an adaptive parameter identification method based on an improved sliding mode observer is proposed. A continuous deformation mode of saturation function based on boundary variation is proposed as the switching function to alleviate the chattering phenomenon. Meanwhile, the relationship between the sliding mode gain and the feedback gain of proposed SMO is defined so that it can be selected properly to improve the accuracy of identification, and the radial basis function neural network (RBFNN) is used to adaptively tune the boundary layer gain according to the speed change. Moreover, considering that the identification result of the load torque is related to the moment of inertia and the mismatch of the inertia will cause identification errors, the variable period integration method is proposed to identify the inertia and redefine the calculation period of the load torque and inertia. The effectiveness and superiority of the proposed method are verified by simulation experiments. Experimental results demonstrate that the improved SMO combines observer gain coefficient tuning and inertia matching can smoothly and accurately estimate the value of load torque, which is an adaptive identification method worthy of reference for robot servo system.
Nonlinear Matrix Ferroelectric Composites on Construction Quality and Safety Measures
Authors: Hu Danping , Yan Gongxing , Sun Hongquan
Published: August 2022

With the continuous emergence of concrete construction projects such as long-span bridges, high-rise buildings, nuclear power plants, and offshore platforms, researchers at home and abroad have made great progress in construction quality and safety measures in recent years. Construction quality is the main factor affecting the appearance and function of the building. Compared with traditional building construction materials, it has indeed made great improvements in organization and performance. For example, for nonlinear matrix ferroelectric composites, this functional material can better solve the problem of poor compatibility between general composites and concrete structural materials. It is not sensitive to humidity and can work in a concrete environment for a long time. These materials are indeed popular in building construction, various materials complement each other in performance and produce a synergistic effect so that the comprehensive performance of composite materials is better than the original composition materials and meets various high-quality requirements. But some properties are still not very clear. Therefore, this study mainly studies the influence laws of viscoelasticity, elastic viscoplasticity, and macro mechanical behavior of nonlinear matrix ferroelectric composites. Through theoretical analysis and experiments, it is concluded that the change of effective stress relaxation stiffness of nonlinear matrix ferroelectric composites tends to be stable with the increase of time, and the effective stress relaxation stiffness of composites increases with the increase of age volume fraction at t = 0 s.
An Improved Kalman Filter Based on Long Short-Memory Recurrent Neural Network for Nonlinear Radar Target Tracking
Authors: Song Fei , Li Yong , Cheng Wei , Dong Limeng , Li Minqi , Li Junfang
Published: August 2022

The target tracking of nonlinear maneuvering radar in dense clutter environments is still an important but difficult problem to be solved effectively. Traditional solutions often rely on motion models and prior distributions. This paper presents a novel improved architecture of Kalman filter based on a recursive neural network, which combines the sequence learning of recurrent neural networks with the precise prediction of Kalman filter in an end-to-end manner. We employ three LSTM networks to model nonlinear motion equation, motion noise, and measurement noise, respectively, and learn their long-term dependence from a large amount of training data. They are then applied to the prediction and update process of Kalman filter to calculate the estimated target state. Our approach is able to address the tracking problem of nonlinear maneuvering radar target online end-to-end and does not require the motion models and prior distributions. Experimental results show that our method is more effective and faster than the traditional methods and more accurate than the method with LSTM network alone.
Design and Performance Analysis of W-Band Low Noise Amplifier for RFIC-5G Mobile Systems
Authors: Hamdi Abdelaziz
Published: August 2022

This paper presents a new approach of the design of a W-band low noise amplifier (LNA) in a 0.13 µm CMOS technology for narrowband and wideband applications. The proposed LNA utilizes input matching bandwidth extensions based on the source degeneration topology, and it is designed and optimized at a center frequency of 94 GHz and a supply voltage of 1.2 V. The obtained results exhibit a noise figure of 3 dB, a power gain of 32 dB, and a VSWR of 1.1. The design technique of this LNA is based on an agreeable tradeoff between the available gain of 30 dB and the noise figure of 3 dB, which leads to good bilateral stability and high linearity described by an input third-order intercept point of -17 dBm. A detailed performance analysis is presented and discussed along this paper. With the aim of a complete and robust integration, all lumped elements and transmission lines are integrated on a silicon PCB having ɛr = 11.7 and a dielectric loss TanΔ = 0.001 for low manufacturing costs. The prominent results of this LNA indicate that it is suitable for 94 GHz-image-radar and RFIC-5G mobile systems.