bluuebunny/crossref_metadata_2025 huggingface dataset:
Beyond the Transformer: A Novel Polynomial Inherent Attention (PIA) Model and Its Great Impact on Neural Machine Translation
Authors: ELAffendi Mohammed , Alrajhi Khawlah
Published: September 2022

This paper describes a novel polynomial inherent attention (PIA) model that outperforms all state-of-the-art transformer models on neural machine translation (NMT) by a wide margin. PIA is based on the simple idea that natural language sentences can be transformed into a special type of binary attention context vectors that accurately capture the semantic context and the relative dependencies between words in a sentence. The transformation is performed using a simple power-of-two polynomial transformation that maintains strict consistent positioning of words in the resulting vectors. It is shown how this transformation reduces the neural machine translation process to a simple neural polynomial regression model that provides excellent solutions to the alignment and positioning problems haunting transformer models. The test BELU scores obtained on the WMT-2014 data set are 75.07 BELU for the EN-FR data set and 66.35 BELU for the EN-DE data set—well above accuracies achieved by state-of-the-art transformer models for the same data sets. The improvements are, respectively, 65.7% and 87.42%.
Basketball Big Data and Visual Management System under Metaheuristic Clustering
Authors: Xia Hailong , Liu Long
Published: September 2022

This study aims to discuss the application value of KMC algorithm optimized by heuristic method in basketball big data analysis and visual management. Because the data in basketball big data is too complicated and incomplete, the extraction of information is not direct and effective enough. Based on the metaheuristic K-Means clustering (KMC) algorithm, the weights and genetic algorithm are introduced to optimize it, and the University of California at Irvine (UCI) data set is applied to analyze the big data clustering performance of the optimized KMC algorithm. The 2018-2019 season National Basketball Association (NBA) shooting guards are selected as the research objects, and the optimized KMC algorithm is used to process the data and analyze the NBA scoring functional factors. It is found that the number of clusters increased from 2 to 16. After optimization, the Between-Within Proportion (BWP) value of the KMC algorithm only drops by 0.35, and the improved BWP (IBWP) value only drops by 0.288, which shows the smallest drop among all the algorithms. When the number of nodes is 4, the running time of the optimized KMC algorithm for processing the COVTYPE data set is 1922 s after optimization, and the running time for processing the IRIS data set is the shortest (113 s). When the number of parallel nodes is 10, the speedup ratio of the optimized KMC algorithm for processing COVTYPE data set is 4.16, and the maximal expansion rate is 0.81. The clustering accuracy of traditional KMC algorithm is 89.33%. After optimization, the clustering accuracy of KMC algorithm is 98.67%. The leader factor, offensive contribution factor, shooting stability factor, and passing ability factor in the core grouping are all at the maximum, which are 0.59, 0.51, 0.47, and 0.43, respectively. The optimized KMC algorithm has been shown to reduce the number of iterations, reduce convergence time, and improve clustering accuracy. The optimized KMC algorithm has been shown to reduce the number of iterations, reduce convergence time, and improve clustering accuracy. The conclusion of this study can provide reference basis for big data clustering and visual management.
UPLC/Q-TOF MS-Based Urine Metabonomics Study to Identify Diffuse Axonal Injury Biomarkers in Rat
Authors: Zhang Peng , Wang Sheng , Liu Meiqing , Li Wenhui , Li Leilei , Zhu Shisheng , Nie Qianyun , Li Qifu
Published: September 2022

Diffuse axonal injury (DAI) represents a frequent traumatic brain injury (TBI) type, significantly contributing to the dismal neurological prognosis and high mortality in TBI patients. The increase in mortality can be associated with delayed and nonspecific initial symptoms in DAI patients. Additionally, the existing approaches for diagnosis and monitoring are either low sensitivity or high cost. Therefore, novel, reliable, and objective diagnostic markers should be developed to diagnose and monitor DAI prognosis. Urine is an optimal sample to detect biomarkers for DAI noninvasively. Therefore, the DAI rat model was established in this work. Meanwhile, the ultraperformance liquid chromatography quadrupole-time-of-flight hybrid mass spectrometry- (UPLC/Q-TOF MS-) untargeted metabolomics approach was utilized to identify the features of urine metabolomics to diagnose DAI. This work included 57 metabolites with significant alterations and 21 abnormal metabolic pathways from the injury groups. Three metabolites, viz., urea, butyric acid, and taurine, were identified as possible biomarkers to diagnose DAI based on the great fold changes (FCs) and biological functions during DAI. The present study detected several novel biomarkers for noninvasively diagnosing and monitoring DAI and helped understand the DAI-associated metabolic events.
Polymorphisms in the ASAP1 and SP110 Genes and Its Association with the Susceptibility to Pulmonary Tuberculosis in a Mongolian Population
Authors: Cui Xiaogang , Yuan Tianqi , Ning Pengyuan , Han Jie , Liu Yue , Feng Jiao , Lian Fan , Hao Mingyuan , Dong Li , Hao Jinqi , Xing Li , Wu Changxin
Published: September 2022

Tuberculosis (TB) remains one of the deadliest infectious diseases in the world. Previous genome-wide association studies suggested that single-nucleotide polymorphisms (SNPs) in some genes could indicate the susceptibility to TB in some populations. Herein, we studied the association of SNPs in the immunity-related genes, i.e., ASAP1 and SP110 genes with the susceptibility to TB in a Mongolian population in China. A case–control study was performed with 197 TB patients and 217 healthy controls. Six SNPs in ASAP1 and six SNPs in SP110 were selected for genotyping test by second-generation sequencing technique. A SNP in SP110 gene (rs722555) was identified to be associated with susceptibility to TB in the Mongolian population (

p
<
0.05

). The T allele of rs722555 in SP110 gene was associated with a 36% increase of risk at TB (OR 1.36, 95% CI 1.03–1.81), and the CT+TT genotype of rs722555 was associated with a 74% increase of risk at TB (OR 1.74, 95% CI 1.16–2.60) in the dominant genetic model. None of SNPs in ASAP1 gene tested in this study were significantly associated with TB susceptibility, while some individuals with SNPs (rs10956514, rs4733781, rs2033059, rs12680942, rs1017281, rs1469288, and rs17285138) in the ASAP1 gene tended to have a reduced risk at TB. In conclusion, this study suggested that the rs722555 SNP in SP110 gene might be a risk factor for TB in a Mongolian population.
Use of Drugs Associated with QT Interval Prolongation at the Hospital Level during the COVID-19 Pandemic in Colombia
Authors: Gaviria-Mendoza Andrés , Machado-Duque Manuel Enrique , Valladales-Restrepo Luis Fernando , Tovar-Yepes Carlos Fernando , Machado-Alba Jorge Enrique
Published: September 2022

Background. Many of the therapeutic proposals for COVID-19 have been associated with adverse effects, including the risk of QT interval prolongation and torsades de pointes (TdP). The objective was to determine the use of drugs with a risk of QT interval prolongation in 21 clinics/hospitals in Colombia from January to December 2020. Methods. This cross-sectional study identified drug use according to pharmacological groups with potential risk of QT interval prolongation according to a risk classification: conditional, possible, and known risk of TdP. Descriptive analyses were performed. Results. A total of 355,574 patients who received QT-prolonging drugs were identified (equivalent to 51.4% of all inpatients treated during the study period). Of the group of patients on QT drugs, 54.4% used at least one drug with conditional risk, 52.6% with possible risk, and 40.3% with known risk. The most commonly used belonged to the group of drugs for the nervous system (63.0%), alimentary tract and metabolism (56.8%), anti-infectives for systemic use (13.0%), and the cardiovascular system (11.7%). On average, patients received

2.0
±
1.5

risk drugs. Regarding drugs initially considered against COVID-19, 2,120 patients (0.6%) received azithromycin, 802 (0.2%) received chloroquine, 517 received hydroxychloroquine (0.1%), and 265 received lopinavir/ritonavir (0.1%). Conclusion. The high proportion of patients treated at the hospital level who receive drugs with risk of prolonging the QT interval should alert those responsible for their care to avoid fatal outcomes, especially during the COVID-19 epidemic, when some QT drugs are being used more frequently.
Design and Application of Interactive Teaching System for Electronic Engineering Specialty Based on ZigBee
Authors: He Qian
Published: September 2022

Today, ZigBee technology has become one of the most widely used wireless communication technologies. Electronic engineering majors need a more efficient management system, which is supported by a database system to achieve overall learning management. The new system can improve students’ dominance in moral education and student leadership. Therefore, this system will also bring educational leadership. At the same time, this system is also the first of its kind. It is an interactive system for communication between schools, students, and parents. It provides a timely and comprehensive platform for information retrieval and management and improves educational arrangements, educational management and students. Nowadays, various colleges and universities are constantly reforming and innovating their teaching management systems, and their teaching methods are also more diversified. This leads to the increased complexity of teaching management, and the existing teaching system software functions cannot meet the increasing demand. Therefore, this paper first introduces the concept and characteristics of ZigBee technology; then, the routing algorithm of the ZigBee network layer is used to analyze the role of the ZigBee technology in interactive systems. In the undergraduate class, applying the interactive teaching system proposed in this paper to student practice, experiments were also performed on this system. The results show that it can be found that in the daily teaching management and operation, the system meets a variety of operational and performance requirements, which is actually the test steps passed before the platform is deployed. In addition, based on the characteristics of ZigBee, the system platform has good practicability and is easy to maintain; after making corrections to the exposed problems, good experimental results have been obtained, and the purpose of the system has been achieved.
miR-100-5p Is a Novel Biomarker That Suppresses the Proliferation, Migration, and Invasion in Skin Cutaneous Melanoma
Authors: Zhang Xiao , Deng Yuqi , Liang Xiao , Rao Yamin , Zheng Haiyan , Liu Fei , Luo Xusong , Yang Jun , Chen Jun , Sun Di
Published: September 2022

Description. Cutaneous melanoma (SKCM) is one of the most common skin malignancies. miR-100-5 has been found to highly express microRNAs in a variety of cancers. This is a novel biomarker to inhibit the proliferation, migration, and invasion of cutaneous melanoma. However, its function and potential mechanisms in SKCM remain unknown. Objective. To better understand the role and underlying mechanisms of SKCM, we conducted bioinformatics analysis and in vivo experiments. Results. We found its role as a tumor suppressor gene in SKCM and its effect on prognosis. In addition, this study found that miR-100-5p had a bidirectional effect on SKCM microenvironment. After exploring the relationship between the two, it was found that tumors with intermediate miR-100-5p expression had the highest level of immune cell infiltration. In addition, the value of miR-100-5p was assessed by survival analysis, univariate Cox regression analysis, and nomogram prognostic prediction. Finally, we constructed a regulatory network to illustrate the regulatory relationship of miR-100-5p. Conclusions. In conclusion, the antitumor effect of miR-100-5p is revealed, and the present study is followed by a discussion of its molecular regulatory network, followed by novel insights into SKCM therapy.
Application of Polypyrrole Cellulose Nanocrystalline Composite Conductive Material in Garment Design
Authors: Su Si , Hu Shaoying , Liu Qi
Published: September 2022

The Chinese nation has a long cultural history and has deep attainments in food, clothing, art, and other cultural fields. With the development of science, technology, economy, and culture, new materials continue to appear, providing new ideas for clothing design. Polypyrrole is a common conductive polymer. The pure pyrrole monomer presents a colorless oily liquid at room temperature, slightly soluble in water and nontoxic. Nanocrystals, also called nanoscale crystals, use high-energy polymer spheres to pack calcium, magnesium ions, and bicarbonate in water to produce a water-insoluble crystal structure. Conductive composite materials mainly refer to composite conductive polymer materials, which are composed of polymers and various conductive substances through a certain composite method. This article aims to study the application of polypyrrole cellulose nanocrystalline composite conductive material in clothing design. Starting from the structural characteristics of the polypyrrole cellulose nanocrystalline composite conductive material, this article uses case analysis to study deeply the suitable polypyrrole cellulose nanocrystalline composite conductive material. This article can effectively use the innovative application method of its appearance style, so as to realize its application in clothing design. Starting from the functional properties of the polypyrrole cellulose nanocrystalline composite conductive material, the specific application of the polypyrrole cellulose nanocrystalline composite conductive material in different clothing designs is analyzed. Combining the postmodernist clothing style characteristics, aesthetic habits, and the characteristics of polypyrrole cellulose nanocrystalline composite conductive materials, this paper studies the innovative style design of polypyrrole cellulose nanocrystalline composite conductive materials. The experimental results in this paper show that when the reaction time is 2 min, the reaction rate at this time is zero, indicating that this time is in the initial stage of the reaction. After 4 minutes, as the reaction time increases, the reaction rate shows an increasing trend; when the reaction time is longer than 10 minutes, the reaction rate increases slowly and has a downward trend, which indicates the end of the reaction. The highest average reaction rate is about 7.5 mg/min.
Clinical Observation of Fuzheng Xiaoji Granule in the Treatment of Stage IIIC Colorectal Cancer
Authors: Lv Lingling , Shen Xiaoheng , Zhang Jun , Li Qiong , Wu Junwei , Wu Yuan , Chen Jingxian , Zhu Wenhua , Zheng Lan
Published: September 2022

Objective. This study aimed to evaluate the effectiveness and safety of Fuzheng Xiaoji granule in patients with stage IIIC colorectal cancer. Methods. A total of 150 patients with stage IIIC colorectal cancer treated in Shanghai Ruijin Hospital from January 2019 to January 2022 were selected. They were divided into treatment and control groups according to a 2 : 1 random number table. There were 100 cases in the treatment group and 50 cases in the control group. The treatment group was administered Fuzheng Xiaoji (FZXJ) granule, and the control group was administered the placebo orally. The primary endpoint was disease-free survival (DFS). In addition, after 6 months, the changes in Traditional Chinese Medicine (TCM) symptom score (fatigue, emotional depression, chest tightness, insomnia, anorexia, abdominal distension, abdominal pain, soreness and weakness in the waist and legs, chills, and dysphoria in the chest, palm, and soles) were compared. Results. The DFS was 34.37 ± 2.91 months in the control group and 37.0 ± 1.08 months in the treatment group (

p
<
0.05

). Compared with the control group, the treatment group showed less fatigue, abdominal distension, and soreness and weakness in the waist and legs (

p
<
0.05

), significantly. The scores of emotional depression and anorexia decreased obviously, with a significant difference between the control and treatment groups (

p
<
0.01

). There were no significant differences between the control and treatment groups in the incidence of chest tightness, insomnia, abdominal pain, chills, and dysphoria in the chest, palm, and soles (

p
>
0.05

). Conclusion. Fuzheng Xiaoji granule can improve patients’ symptoms and prolong the DFS.
An Empirical Client Cloud Environment to Secure Data Communication with Alert Protocol
Authors: Soni Dheresh , Srivastava Deepak , Bhatt Ashutosh , Aggarwal Ambika , Kumar Sunil , Shah Mohd Asif
Published: September 2022

Cloud computing is a technology that provides resources and utility services based on user demand. Due to this demand, efficient cloud security protocols are highly required, especially at the time of data communication for user authentication and data aggregation. The data communication scenarios are majorly affected by the security threats in the cloud computing environment. This article provides a practical approach to developing an efficient and empirical cloud framework in terms of cloud protocol. The framework uses fuzzy c-means (FCM) algorithm to group data, and calculation is done individually or associatively to rank the text data. Uploaded data are passed to a simple additive weighting (SAW) algorithm for ranking and making decision selection. The framework executes in three phases, namely data preprocessing, clustering, and automatic data security with an alert mechanism. The process is completely automated so there is no need of considering the individual files for the processing and the data held will be appropriately correlated with the sharing inter-cloud environment. To inspect security issues, the proposed framework is secured by three different security algorithms. The encryption process is completed by Rivest Cipher 6 (RC6); the substitution process is done by Advanced Encryption Standard (AES); and key generation is done by RC6, AES, and Rivest-Shamir-Adleman (RSA) approaches collectively. Based on the given situations, these standard approaches were automatically applied separately or collectively. Unauthorized access trapping and data deletion mechanism are also provided in the proposed framework. The experimental results with a comparative study depicted the effectiveness of the proposed work.
Transformation of Superposed Quantum States Using Measurement Operators
Authors: Ito Hideaki , Iida Saburou
Published: September 2022

Quantum computation based on a gate model is described. This model initially creates a superposition

|

ψ

0

〉

consisting of

N
=

2

n

states, and these states are labeled by an

n

qubit index value

j

. Two working qubits

|
0

〉

w
k
0

and

|
0

〉

w
k
1

are added for a measurement. Moreover, one marking qubit

|
0

〉

m
k

is added to discriminate between states in a superposition. Thus,

|

ψ

1

〉
=

1
/

N

∑

j
=
0

N
−
1

|

j
〉

⊗
|
0

〉

w
k
0

⊗
|
0

〉

w
k
1

⊗
|

0

〉

m
k

. The Hadamard transformation is applied to

|
0

〉

w
k
0

and

|
0

〉

w
k
1

.

|

ψ

2

〉
=

1
/

N

1
/

2

2

∑

j
=
0

N
−
1

|

j
〉

⊗

|

0

〉

w
k
0

+

|

1

〉

w
k
0

⊗

|

0

〉

w
k
1

+

|

1

〉

w
k
1

⊗
|
0

〉

m
k

. After a computation, a set of states is divided into two subsets; one is a subset bad (B) and the other is a subset good (G).

|

ψ

3

〉
=

1
/

N

1
/

2

2

∑

j
∈
B

|

j
〉

⊗

|

0

〉

w
k
0

+

|

1

〉

w
k
0

⊗

|

0

〉

w
k
1

+

|

1

〉

w
k
1

⊗

|

0

〉

m
k

+

∑

j
∈
G

|

j
〉

⊗

|

0

〉

w
k
0

+

|

1

〉

w
k
0

⊗

|

0

〉

w
k
1

+

|

1

〉

w
k
1

⊗

|

1

〉

m
k

. After a marking, a superposition is measured by POVM. The measurement is described by a collection of four measurement operators. The measurement transforms

|

ψ

3

〉

into

|

ψ

4

〉
=

1
/

N

∑

j
∈
B

|

j
〉

⊗

|

0

〉

w
k
0

⊗

|

0

〉

w
k
1

⊗

p

0

sin
 
 
θ

|

0

〉

m
k

+

∑

j
∈
G

|

j
〉

⊗

|

0

〉

w
k
0

⊗

|

0

〉

w
k
1

⊗

p

1

cos
 
 
θ

|

1

〉

m
k

/
D

; here,

D
=

card

B

p
0
∗

p

0

sin

2

θ
+
card

G

p
1
∗

p

1

cos

2

θ

, and

p
0
∗

p

0

sin

2

θ
+

p
1
∗

p

1

cos

2

θ
=
2

which is derived from the completeness equation. The state

|
0

〉

m
k

and the state

|
1

〉

m
k

before the measurement are transformed into

p

0

sin
θ
|
0

〉

m
k

and

p

1

cos
θ
|
1

〉

m
k

, respectively. This paper describes these measurement operators.
University Archives Autonomous Management Control System under the Internet of Things and Deep Learning Professional Certification
Authors: Ma Yue , Dai Bing , Ding Baorong
Published: September 2022

The current work aims to meet the needs of the development of archives work in colleges and universities and the modernization of management to realize the standards and standardization of all aspects of archives business construction in colleges and universities, so as to improve the political and professional quality of archives cadres. First, the radio frequency identification (RFID) technology based on the Internet of things (IoT) digitizes the university archive labels. Meanwhile, the filing cabinet's intelligent security system preserves confidential files. Second, the convolutional neural network (CNN) algorithm under deep learning is introduced and college profile information is identified. Finally, the concept of professional certification is used to clarify the purpose of the university archives automation management system. Different activation functions are used to analyze the recognition accuracy loss and recognition accuracy of university archives. The identification error of You Only Look Once (YOLO) of the ReLU-convolutional neural network (R–CNN) of college archives is analyzed. The results show that the selection of rectified linear units (ReLU) activation function for CNN can effectively reduce the loss of identification accuracy of college archives and can improve the accuracy of identification of college archives. The algorithm based on the ReLU activation function has a smaller recognition error accuracy in college archives than that of the YOLO algorithm. The recognition error of the YOLO algorithm is slightly higher than that of the R–CNN. The font recognition error of archival information based on the R–CNN is relatively large. However, the conclusion is reasonable due to the recognition difficulties of handwritten archival fonts. The file positioning recognition error rate is 19.00%, the file printing font recognition error rate is 4.75%, and the image recognition error rate is 1.90%. These results have a certain reference value for the process of identifying information in the automatic management of university archives by CNN under different activation functions.
CIMA: A Novel Classification‐Integrated Moving Average Model for Smart Lighting Intelligent Control Based on Human Presence
Authors: Putrada Aji Gautama , Abdurohman Maman , Perdana Doan , Nuha Hilal Hudan
Published: September 2022

Smart lighting systems utilize advanced data, control, and communication technologies and allow users to control lights in new ways. However, achieving user comfort, which should be the focus of smart lighting research, is challenging. One cause is the passive infrared (PIR) sensor that inaccurately detects human presence to control artificial lighting. We propose a novel classification‐integrated moving average (CIMA) model method to solve the problem. The moving average (MA) increases the Pearson correlation (PC) coefficient of motion sensor features to human presence. The classification model is for a smart lighting intelligent control based on these features. Several classification models are proposed and compared, namely, k‐nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), näive Bayes (NB), and ensemble voting (EV). We build an Internet of things (IoT) system to collect movement data. It consists of a PIR sensor, a NodeMCU microcontroller, a Raspberry Pi‐based platform, a relay, and LED lighting. With a sampling rate of 10 seconds and a collection period of 7 days, the system achieved 56852 data records. In the PC test, movement data from the PIR sensor has a correlation coefficient of 0.36 to attendance, while the MA correlation to attendance can reach 0.56. In an exhaustive search of an optimum classification model, KNN has the best and the most robust performance, with an accuracy of 99.8%. It is more accurate than direct light control decisions based on motion sensors, which are 67.6%. Our proposed method can increase the correlation value of movement features on attendance. At the same time, an accurate and robust KNN classification model is applicable for human presence‐based smart lighting control.
Design and Implementation of Office Automation System Based on Internet of Things Technology
Authors: Xiao Ying , Chen Ya , Tang Yujian , Jung Hoekyung
Published: September 2022

In order to solve the problem of office automation, this paper proposes the design and implementation of office automation system based on Internet of things technology. This method is to carry out mobile office function through smart phones to realize business message exchange and real-time processing of process approval business. The system adopts b/s structure and adopts the form of front-end machine to realize information sharing between the mobile system and the original system. When users choose to send mail or reply to mail, the client can read the current user name as the sender by itself. The number of uploaded attachments is limited to three at most, and the total size of attachments is not greater than 100 m. Conclusion. This method effectively improves the work efficiency of employees.
Application of 3D Digital Image Processing Technology in Modern Packaging Design
Authors: Meng Kun , Liu Fulong
Published: September 2022

In this paper, a modern packaging model is constructed by combining three-dimensional digital image processing technology, which has achieved the effect of improving the quality of modern packaging design. In addition, this paper proposes a new target recognition and tracking scheme, which uses the method of servo motor to control the synchronous conveyor belt to monitor the moving objects in real time and proposes a decision analysis algorithm to avoid repeated identification of multiple moving targets. At the same time, the software structure and execution flow of the visual control system are designed, the level of each functional module of the software is divided, and the key technology of the software is studied. The experimental research shows that the 3D digital image processing technology is effective in improving modern packaging design methods and can improve the interactivity and user experience of modern packaging design.
A Novel Cooperative Relaying-Based Vertical Handover Technique for Unmanned Aerial Vehicles
Authors: Haider Zeeshan , Muztazhar Gilani Syed Mushhad , Jamal Tauseef , Javaid Shumaila , Fahim Hamza , Cheikhrouhou Omar , Hamdi Monia
Published: September 2022

The real-time monitoring and autonomous decision making through unmanned aerial vehicles (UAVs) are the potential applications of future networks. Vertical handover in future networks is a mechanism to switch communication between different network access technologies like Wireless Local Area Network (WLAN), Worldwide Interoperability for Wireless Microwave Access (WiMAX), Third-Generation (3G), Fourth-Generation (4G), and Fifth-Generation (5G) mobile technologies. These technologies have significant importance in providing fast, reliable, and timely communication. However, during a vertical handover, an inadequate delay and packet loss can cause considerable disruption in maintaining communication sessions and results in intolerable end-to-end delay, disconnectivity, and poor packet delivery ratio. The proposed work addresses the vertical handover method in UAVs communication by designing a relay-based vertical handover technique. The relay UAVs is an assistant node, requiring an organized and intelligent deployment that assists in vertical handover and communication by minimizing the average packet loss and average delay from source to destination. Moreover, a multicriteria handover parameter triggering is used for seamless and more extended network coverage. Extensive simulations using S-shaped and U-shaped trajectories are designed and simulated for relay-based vertical handover performance evaluation. The results obtained show that our proposed relay-based handover method offers seamless connectivity and high-performance experienced during the vertical handover process. The extensive comparison with state-of-the-art techniques proves that the proposed method is better in terms of 18% handover success rate, 21% end-to-end delay, and 29% packet loss.
Data Collection and Analysis of Physical Education Teaching Practice Based on Multisensor Perception
Authors: Tong JinFeng , Ge Liu
Published: September 2022

Because our country is constantly updating training standards, the first mechanism for assessing physical fitness is far from current needs. A good assessment process can help to stimulate student learning, identify strengths and weaknesses, and better improve sports knowledge. In this paper, based on the multisensor perception of physical education and teaching practice data collection and analysis, it is found that the original sensor data often have some defects, but the Kalman filter can be processed, which can make the data more accurate. After comparing the data, it can be found that each group of data basically has an error of 0.02–0.9. After processing, the data better reflect the changes in the measurement. With the reform of the professional evolution of boxers, higher requirements have been placed on athletes. The sensor can be continuously tested. According to the experiment, the basic probability of different sensors on the test paper can be found that the fused sensor data are 1/2, while the single sensor data are 1/6, and the data of a single sensor are much lower than the confidence of fused sensors, effectively improving the comprehensive ability of boxing.
Differences in Drivers’ Glance Behavior and Lateral Control Ability during Full-Touch Interaction Mode and Conventional Interaction Mode: A Case Study of Road Experiments
Authors: Zhao Xia , Li Zhao , Fu Rui , Wang Chang , Guo Yingshi
Published: September 2022

In recent years, the full-touch human–machine interaction (HMI) mode has gained popularity in the automotive market. However, little research has been conducted on how this interaction mode affects drivers’ glance behavior and lateral control ability. In this study, we evaluated the visual engagement and driving performance of 30 participants while driving two vehicles equipped with either the full-touch interaction mode (FTIM) or the conventional interaction mode (CIM) provided by the original equipment manufacturer (OEM). We found that both air conditioning–related tasks required more visual engagement, longer task completion time, and worse lateral vehicle control under FTIM. Furthermore, the gray correlation analysis demonstrated that FTIM exhibited slightly different disadvantages in the two secondary tasks. In the temperature adjustment task, the correlations of glance behavior and lateral control ability between the two interactive modes were 0.688 and 0.680, respectively. In the airflow adjustment task, the correlations of glance behavior and lateral control ability between the two interactive modes were 0.659 and 0.668, respectively. In addition, this study revealed that driving speed had significant effects on glance behavior and lateral driving performance in both interaction modes. As speed increased, self-adjusting glance behavior was evident in performing the secondary task; however, this behavior could not compensate for the deterioration in lateral driving performance caused by the increased speed. The findings will help improve drivers’ perception of FTIM and provide theoretical guidance for the design development of HMI mode.
A Novel AI-Based Approach for Better Segmentation of the Fungal and Bacterial Leaf Diseases of Rice Plant
Authors: Rathore Yogesh Kumar , Janghel Rekh Ram , Pandey Saroj Kumar , Kumar Ankit , Singh Kamred Udham , Shah Mohd Asif
Published: September 2022

Rice is the most consumed food for more than half the world. All over the world, approximately 15% of the rice get wasted because of leaf diseases. A computer-aided system needs a clear segmented lesion to detect such diseases, but blurriness, bad contrast, and dust particles on leaves are the challenge in proper segmentation and, further, for better feature extraction. In this work, first, SegNet deep learning model was trained to separate the weed from the images captured from the field; then, in the next step, a novel automated segmentation technique named RPK-means proposed combining random path (RP) and K-means clustering to separate lesion spots from the leaf images. The work of the model is multifold. First, the SegNet model is trained for weed separation; then, two clusters of the image are generated by

K

-means clustering to find out pixel coordinates lying on the lesion spot and healthy part of the leaves. Thereafter, to separate the lesion part from the background, automatic segmentation is performed by the novel random path

K

-means (RPK-means) method using coordinate positions obtained at the last stage. Fungal and bacterial diseases like brown spot, rice blast, sheath blight, leaf scaled, and bacterial blight have been collected from the field to perform the experiments. Experimental result shows that the performance of the deep learning classifier increased by approximately 2-6% while applying to RPK-means preprocessed images, rather than the traditional

K

-means segmentation technique.
Insight on Efficacy of Renal Artery Denervation for Refractory Hypertension with Chronic Kidney Diseases: A Long-Term Follow-Up of 24-Hour Ambulatory Blood Pressure
Authors: Cai Han , Fang Zhoufei , Lin Ruofan , Cai Wenqin , Han Ying , Su Jinzi
Published: September 2022

Aims. To explore the long-term efficacy and safety of renal denervation in patients with RHT and CKD, a post hoc analysis of eGFR subgroups was completed. Methods. Fifty-four patients with refractory hypertension with chronic kidney disease were treated with RDN and enrolled in the study. Patients were divided into three groups according to eGFR: eGFR 46–90 ml/min group, eGFR 15–45 ml/min group, and eGFR <15 ml/min group. The planned follow-up period was 48 months to assess 24 h ambulatory blood pressure, renal function, type of antihypertensive medication, and RDN complications. Results. The ablation sites of the GFR 46–90 ml/min group and GFR 15–45 ml/min group were 32.57 ± 2.99 and 29.53 ± 5.47, respectively. No complications occurred in the GFR 46–90 ml/min group. The GFR<15 ml/min group was treated with 27.07 ± 5.59  ablation. Renal artery dissection occurred in each group of GFR 15–45 ml/min and GFR <15 ml/min. And renal stent implantation artery was performed on these two patients. No severe renal artery stenosis occurred. There were no significant differences in Scr and eGFR between the three groups at each follow-up point. Compared with baseline, SBP was significantly of each group decreased to varying degrees at each follow-up time point. SBP decreased most in the GFR 46–90 ml/min group. Compared with baseline, the type of antihypertensive drugs used in the GFR46-90 ml/min group decreased significantly except for 36 and 48 months. At 48 months’ postadmission, there was a significant decrease in the type of antihypertensive medication used in the GFR15-45 ml/min group, and there was no significant decrease in the type of antihypertensive medication used in the GFR<15 ml/min group. Conclusions. RDN can safely reduce SBP in CKD patients combined with RHT for 48 months, with the most pronounced reduction in the GFR15-45 ml/min group. The variety of antihypertensive drugs was significantly reduced after RDN. This was particularly evident in patients with GFR 15–45 ml/min.
Effect of Temperature on the Fatigue Life Assessment of Suspension Bridge Steel Deck Welds under Dynamic Vehicle Loading
Authors: Xiao Xinhui , Zhang Haiping , Li Zhaochao , Chen Fanghuai
Published: September 2022

The present study proposes a novel fatigue life prediction considering the temperature load, which may be neglected in the traditional assessment of suspension bridge steel deck welds under dynamic vehicle load. Vehicle fatigue, pavement temperature, and temperature gradient models are developed based on the test data from the weight-in-motion system, U-rib welds, pavement temperature, and environment temperature. The U-rib-to-deck and U-rib-to-U-rib welds fatigue stresses are obtained considering both vehicle and temperature loads with transient analysis method in ANSYS package. Then, the temperature gradient fatigue stress spectra are calculated. After that, the fatigue life of two weld types is predicted considering the coupled vehicle-temperature loads. The results indicate that the fatigue stress varies linearly with the temperature of the asphalt concrete. The effect of the temperature on the weld’s fatigue life decreases as the distance increases between the welds and the pavement. The dynamic vehicle load results in a higher fatigue stress than the temperature gradient, indicating that the vehicle load contributes mainly to the bridge’s fatigue damage. Finally, it is calculated that the fatigue damage of two weld types is magnified 5.06 and 1.50 times when the temperature effect is considered after 100-year service of Nanxi Yangtze River Bridge.
A Study on the Application of Deep Learning Methods Based on Nonlinear Random Matrices in the Design of Intelligent Research Management System
Authors: Lu Qiang , Feng Chong , Yu Yang
Published: September 2022

The database of the intelligent science management system includes the exchange of data between the basic data of the school and the various business systems, with centralised storage and centralised management, constituting a unified data centre. The completed intelligent science management system will become a reliable source of data to integrate the system to be developed with existing systems, making the data of the whole system unified, thus providing reliable data for information enquiry and decision analysis within the university, and laying a good foundation for the construction of the digital campus. The NRM_DeepNet model is an application model of the deep learning method of the nonlinear random matrix in the intelligent research management system, mainly in data processing and analysis, using deep learning technology for data classification processing, and then for research data modal feature extraction, the process of modal extraction applies the nonlinear random matrix for feature weighting. In the process of modal extraction, a nonlinear random matrix is applied to weight the features, and finally, different research data features of different research projects can be classified as intelligent and regularised. In this paper, we combine the practice of scientific research management, introduce the nonlinear random matrix and deep learning technology into the intelligent scientific research management system, and update its data and system management so as to realize intelligent and humanised scientific research management, thus reducing the labour intensity and improving the efficiency of scientific research work.
Study on Large Deformation Prediction and Control Technology of Carbonaceous Slate Tunnel in Lixiang Railway
Authors: Yang Jinlong
Published: September 2022

The construction of railway tunnel in carbonaceous slate environment is easy to cause rock mass disturbance, which leads to large deformation of surrounding rock and then threatens the safety of tunnel construction and operation. Therefore, based on the Wanlamu tunnel and Huajiaopo tunnel projects of Lixiang railway, combined with the regional geological conditions of the tunnel, and using the field monitoring technology and statistical principle, this paper analyzes the characteristics of tunnel surrounding rock pressure, secondary lining cracking, and initial support deformation under the conditions of different bedding slate. It is analyzed that the large deformation of carbonaceous slate surrounding rock is mainly related to the bedding, characteristics of surrounding rock, bias pressure, and the existence of high ground stress. The tunnel deformation is mainly manifested in peripheral convergence and vault settlement, and the peripheral convergence value is greater than the vault settlement value. In this study, the maximum daily convergence deformation of the tunnel peripheral convergence exceeds 117.8 mm, and the maximum convergence deformation can reach 951.7 mm, which is seriously affected by the slate bedding. According to statistics, more than 90% of the cross-section surrounding rock deformation of the tunnel with the slate bedding angle of 30° exceeds 600 mm. Then, based on BP neural network, the large deformation prediction model of railway tunnel under the carbon slate environment is constructed, and the accurate prediction of large deformation of carbon slate tunnel is realized, with the minimum prediction error of only 0.81%. It is pointed out that the main factors leading to the large deformation of the carbon slate tunnel are the lithology of the slate, the difficulty in predicting the deformation of the surrounding rock, and the failure of the initial support to close in time. The tunnel deformation can be effectively controlled by strengthening the geological work, optimizing the design parameters (enhancing the support stiffness, multiple support, and increasing the reserved deformation), and mining active reinforcement.
UAV Formation Control under Communication Constraints Based on Distributed Model Predictive Control
Authors: Chen Qi-jie , Jin Yu-qiang , Yan Ting-long , Wang Tao-yu , Wang Yao
Published: September 2022

A distributed model predictive control method is used to transform the formation and maintenance problem of fixed-wing UAV formation during flight into an online rolling optimization problem to solve in this paper. Firstly, the state estimation model of the neighborhood UAV is established according to the relative information of the UAV. Secondly, the error state model in the three-dimensional inertial coordinate frame of the UAV is established without considering the time delay, sensor error, and external interference. Thirdly, a cost function is designed by introducing the error state of the UAV in the neighborhood. Finally, four UAVs are used to verify that under the action of the controller, the UAVs can quickly form and maintain the desired formation while tracking the reference line.
Highly Robust Synthetic Aperture Radar Target Recognition Method Based on Simulation Data Training
Authors: Hu Liping , Yao Canming , Huang Jian , Liu Jinfan , Wang Guanyong
Published: September 2022

Sufficient synthetic aperture radar (SAR) data is the key element in achieving excellent target recognition performance for most deep learning algorithms. It is unrealistic to obtain sufficient SAR data from the actual measurements, so SAR simulation based on electromagnetic scattering modeling has become an effective way to obtain sufficient samples. Simulated and measured SAR images are nonhomologous data. Due to the fact that the target geometric model of SAR simulation is not inevitably consistent with the real object, the SAR sensor model in SAR simulation may be different from the actual sensor, the background environment of the object is also inevitably different from that of SAR simulation, the error of electromagnetic modeling method itself, and so on. There are inevitable differences between the simulated and measured SAR images, which will affect the recognition performance. To address this problem, an SAR simulation method based on a high-frequency asymptotic technique and a discrete ray tracing technique is proposed in this paper to obtain SAR simulation images of ground vehicle targets. Next, various convolutional neural networks (CNNs) and AugMix data augmentation methods are proposed to train only on simulated data, and then target recognition on MSTAR measured data is performed. The experiments show that all the CNNs can achieve incredible recognition performance on the nonhomologous SAR data, and the RegNetX-3.2GF achieves state-of-the-art performance, up to 84.81%.
Behavicker: Eavesdropping Computer-Usage Activities through Acoustic Side Channel
Authors: Chen Mengqi , Lin Jiawei , Liu WeiFeng , Wu Kaishun
Published: September 2022

Computers are widely used for business and entertainment purposes throughout our modern lives. Computer kits provide a variety of services such as text processing, programming, shopping, and gaming. Computers have greatly enhanced the quality of our lives; however, we discover an often-overlooked fact that engaging in computer-related activities may be eavesdropped upon by an attacker by sniffing the emitted acoustic signals from keyboard and mouse. The activity of eavesdropping via acoustic side channel has lower requirements in terms of hardware instrumentation and is easier to implement in real-world applications than other side channel attacks that have been presented in previous work. In this paper, we design and implement a system, namely, Behavicker, to validate the feasibility of this kind of attack. Unlike conventional activity recognition, Behavicker infers high-level computer-usage activities with a semantics-preserving multiscale learning scheme, based on the recognition of basic keyboard and mouse events including left click, right click, middle click, scrolling up, and scrolling down. Real-world experiments show that Behavicker can recognize six interaction events with an accuracy of 88.3% and infer computer-usage activities with an accuracy of 82.7% in an indoor environment.
Simulation of the Uniaxial Mechanical Properties and Crack Evolution of Coal Pillar-Artificial Dam in Abandoned Mines
Authors: Lyu Xin , Yang Ke , Fang Juejing , Duan Minke , Tang Jinzhou
Published: September 2022

The key to the construction of underground reservoirs in abandoned mines is the construction of coal pillar-artificial dams, and the choice of bonding parameters between the coal pillars and artificial dams is the deciding factor that determines the engineering stability. Based on the analysis of the force state of coal pillar-artificial dams, the influence of the interface angle was analyzed. Seven sets of coal pillar-artificial dam specimens were prepared and a PFC3D numerical model was constructed to carry out the uniaxial compression test without lateral pressure. Based on the strength, deformation, and energy evolution characteristics of the coal pillar-artificial dam, the influence of the angle of the coal pillar-artificial dam interface on the performance of the specimen was analyzed. The research results showed that the force state of the coal pillar-artificial dam can be divided into three types. The composite simulation curve showed obvious postpeak viscosity. The compressive strength, peak strain, and average dissipated energy curves of the coal pillar-artificial dam showed a unimodal trend that first increased and then decreased. The total energy and elastic energy of the coal pillar-artificial dam showed an increasing trend during loading. The simulated initiation stress and damage stress of the coal pillar-artificial dam specimens were intermediate to that of the coal pillars and the artificial dams, which first increased and then decreased with the increase in inclination, reaching the peak at 70°.