bluuebunny/crossref_metadata_2025 huggingface dataset:
A Novel Niche Quantum Ant Colony-Based Clustering Optimization Approach for Wireless Auto Meter Reading Network
Authors: Li Jingyun , Qin Hu , Liu Yang , Zhou Jie , Zhang Yao
Published: May 2022

Since the low cost and high flexibility, wireless automatic meter reading network (WAMRN) is widely used by utility companies to realize automatic collection and transmission of remote energy consumption information. Considering that WAMRN is composed of several wireless communication nodes, the lifetime of the network will be affected by factors such as the changeable deployment environment and the limited energy of nodes. Thus, a novel niche quantum ant colony-based WAMRN clustering optimization method is proposed in this paper to address the problem of how to make full use of the limited energy to extend network lifetime and improve data transmission efficiency. In the proposed approach, a clustering model of WAMRN is defined; moreover, an improved Niche Quantum Ant Colony Optimization (NQACO) is proposed to optimize the model therefore to obtain an optimal clustering scheme, which can help WAMRN reduce unnecessary energy loss to achieve the purpose of extending the lifetime of the entire network. To verify the performance of the proposed method, NQACO is compared with some popular clustering methods, i.e., GA and SA, under different scenarios. The results show that under the premise of ensuring network communication, NQACO is superior to the other two methods in reducing the total energy consumption and prolonging the network lifetime.
The Application of Artificial Intelligence Technology in the Asset Management of Start-Ups in the Context of Deep Learning
Authors: Fu Qi , Li Xiaotong
Published: May 2022

With the coninuous improvement and development of artificial intelligence (AI) technology, this technology has been used in the asset management of companies. To improve the asset management level of Chinese start-ups, firstly, back-propagation neural network (BPNN) has been studied in depth, and an evaluation system of the company’s asset quality has been established. Secondly, the BPNN is integrated with the evaluation indicators of asset quality, and an evaluation model of asset quality based on BPNN is constructed. Next, start-up A is taken as the experimental object; the evaluation score of the asset quality of A company is input into the model, which proves that there is still a certain gap between the asset management level of start-ups and mature companies. Finally, to find out the problems of the company’s asset quality, the traditional financial analysis method is used to carry out a specific microanalysis of the evaluation indicators of its asset quality. In view of the existing problems, suggestions are put forward for prudent investment, improve inventory operation efficiency, increase investment in R&D and innovation, improve the quality of sales outlets, and increase the proportion of high-quality intangible assets. The asset quality evaluation system for start-ups established here includes 19 evaluation indicators. The BPNN-based asset quality evaluation model selects 5 mature companies in the same industry as sample companies. The scores of the evaluation indicators of asset quality of the 5 sample companies in the past three years are normalized and input into the model. The model contains 19 nodes of the input layer, 39 nodes of the hidden layer, and 1 node of the output layer. The target error rate is 0.001, the learning rate is 0.1, the number of training times is 1000, and the training function is the trainlm function. This research has a certain reference for the application of AI technology in the asset management of start-ups.
Prognostic Accuracy of qSOFA and SIRS for Mortality in the Emergency Department: A Meta-Analysis and Systematic Review of Prospective Studies
Authors: Ruan Hailin , Ke Dianshan , Liao Dalin
Published: May 2022

Objective. This meta-analysis aimed to determine the prognostic performance of quick sequential organ failure assessment (qSOFA) score in comparison to systemic inflammatory response syndrome (SIRS) in predicting in-hospital mortality in the emergency department (ED) patients. Methods. Eligible studies comparing the performance of qSOFA and SIRS in predicting in-hospital death of ED patients were identified from searching PubMed, Embase, and Cochrane. Raw data were collected, and the pooled sensitivity and specificity were calculated for qSOFA and SIRS. The summary receiver operating curve was also plotted to calculate the area under the curve. Results. A total of 16 prospective studies with 35,756 patients and 2,285 deaths were included. The pooled sensitivity was 0.43 (95% CI: 0.32–0.54) and 0.8 (95% CI: 0.73–0.86) for qSOFA and SIRS, respectively. The pooled specificity was 0.89 (95% CI: 0.84–0.93) and 0.39 (95% CI: 0.3–0.5) for qSOFA and SIRS, respectively. The area under the summary receiver operating curve was 0.76 (95% CI: 0.72–0.8) and 0.67 (95% CI: 0.62–0.72) for qSOFA and SIRS, respectively. A significant heterogeneity was observed for both qSOFA and SIRS studies. Conclusion. The present meta-analysis suggested that qSOFA had a higher specificity but a lower sensitivity as compared with SIRS in predicting in-hospital mortality in the ED patients. qSOFA appeared to be a more concise and simple way to recognize patients at high risk for death. However, the use of SIRS in the ED cannot be completely replaced since the sensitivity of qSOFA was relatively lower.
An Android Malware Detection Leveraging Machine Learning
Authors: Shatnawi Ahmed S. , Jaradat Aya , Yaseen Tuqa Bani , Taqieddin Eyad , Al-Ayyoub Mahmoud , Mustafa Dheya
Published: May 2022

Android applications have recently witnessed a pronounced progress, making them among the fastest growing technological fields to thrive and advance. However, such level of growth does not evolve without some cost. This particularly involves increased security threats that the underlying applications and their users usually fall prey to. As malware becomes increasingly more capable of penetrating these applications and exploiting them in suspicious actions, the need for active research endeavors to counter these malicious programs becomes imminent. Some of the studies are based on dynamic analysis, and others are based on static analysis, while some are completely dependent on both. In this paper, we studied static, dynamic, and hybrid analyses to identify malicious applications. We leverage machine learning classifiers to detect malware activities as we explain the effectiveness of these classifiers in the classification process. Our results prove the efficiency of permissions and the action repetition feature set and their influential roles in detecting malware in Android applications. Our results show empirically very close accuracy results when using static, dynamic, and hybrid analyses. Thus, we use static analyses due to their lower cost compared to dynamic and hybrid analyses. In other words, we found the best results in terms of accuracy and cost (the trade-off) make us select static analysis over other techniques.
Analysis of the Queueing‐Inventory System with Impatient Customers and Mixed Sales
Authors: Zhang Yuying , Yue Dequan , Sun Li , Zuo Jinpan
Published: May 2022

A queueing‐inventory system with server’s vacations and impatient customers is investigated. Continuous review (s, S) policy is considered for the queueing‐inventory system. The server leaves for a vacation once the inventory is zero. The customer arrived waits for service with a probability p or is lost forever with a probability 1 − p when the server is off due to a vacation. Customers are impatient during period when the customer is waiting for service in the queue or when the server is off due to a vacation. The matrix geometric solution of the steady‐state probability is obtained using the method of truncated approximation. Furthermore, some performance measures are obtained using the steady‐state probability. Finally, the effect of system parameters on the performance measures, the optimal policy and the optimal cost are presented by numerical examples.
The Role of Mitochondrial Dynamin in Stroke
Authors: Li Chenchen , Chen Chunli , Qin Haiyun , Ao Chuncao , Chen Jinlun , Tan Jieqiong , Zeng Liuwang
Published: May 2022

Stroke is one of the leading causes of death and disability in the world. However, the pathophysiological process of stroke is still not fully clarified. Mitochondria play an important role in promoting nerve survival and are an important drug target for the treatment of stroke. Mitochondrial dysfunction is one of the hallmarks of stroke. Mitochondria are in a state of continuous fission and fusion, which are termed as mitochondrial dynamics. Mitochondrial dynamics are very important for maintaining various functions of mitochondria. In this review, we will introduce the structure and functions of mitochondrial fission and fusion related proteins and discuss their role in the pathophysiologic process of stroke. A better understanding of mitochondrial dynamin in stroke will pave way for the development of new therapeutic options.
Cross-Sectional Analysis of Impulse Indicator Saturation Method for Outlier Detection Estimated via Regularization Techniques with Application of COVID-19 Data
Authors: Muhammadullah Sara , Urooj Amena , Mengal Muhammad Hashim , Khan Shahzad Ali , Khalaj Fereshteh
Published: May 2022

Impulse indicator saturation is a popular method for outlier detection in time series modeling, which outperforms the least trimmed squares (LTS), M-estimator, and MM-estimator. However, using the IIS method for outlier detection in cross-sectional analysis has remained unexplored. In this paper, we probe the feasibility of the IIS method for cross-sectional data. Meanwhile, we are interested in forecasting performance and covariate selection in the presence of outliers. IIS method uses Autometrics techniques to estimate the covariates and outlier as the number of covariates

P
>
n

observations. Besides Autometrics, regularization techniques are a well-known method for covariate selection and forecasting in high-dimensional analysis. However, the efficiency of regularization techniques for the IIS method has remained unexplored. For this purpose, we explore the efficiency of regularization techniques for out-of-sample forecast in the presence of outliers with 6 and 4 standard deviations (SD) and orthogonal covariates. The simulation results indicate that SCAD and MCP outperform in forecasting and covariate selection with 4 SD (20% and 5% outliers) compared to Autometrics. However, LASSO and AdaLASSO select more covariates than SCAD and MCP and possess higher RMSE. Overall, regularization techniques possess the least RMSE than Autometrics, as Autometrics possesses the least average gauge at the cost of the least average potency. We use COVID-19 cross-sectional data collected from 1 July 2021 to 30 September 2021 for real data analysis. The SCAD and MCP select CRP level, gender, and other comorbidities as an important predictor of hospital stay with the least out-of-sample RMSE of 7.45 and 7.50, respectively.
Research on the RFID Anticollision Strategy Based on Decision Tree
Authors: Zhang Hui , Gao Ling , Luo Han-guang , Zhai Yannan
Published: May 2022

RFID can automatically read the information stored on the tag. When multiple tags send data to the reader and writer at the same time, multiple tag collision will occur, resulting in the reader and writer being unable to successfully identify the tag. To solve this problem, an RFID multitag identification method based on decision tree is proposed. The Manchester code is used to represent different level states of voltage. The reader extracts the collision bits in the tag ID by using the Manchester code, and these collision bits form the new tag ID. In the process of decision tree search, the “XOR” command operation is added, and the query prefix is selectively sent to the tag to mark all collision bits. The process will continue until one tag is identified; then, reset the remaining unrecognized tags and continue to participate in a new round of identification. The simulation results show that the RFID multitag identification method based on decision tree can reduce the time complexity and communication overhead and improve the throughput, so it has high search efficiency.
Prediction of Early Alzheimer Disease by Hippocampal Volume Changes under Machine Learning Algorithm
Authors: Shang Qun , Zhang Qi , Liu Xiao , Zhu Lingchen
Published: May 2022

This research was aimed at discussing the application value of different machine learning algorithms in the prediction of early Alzheimer’s disease (AD), which was based on hippocampal volume changes in magnetic resonance imaging (MRI). In the research, the 84 cases in American Alzheimer’s disease neuroimaging initiative (ADNI) database were selected as the research data. Based on the scoring results of cognitive function, all cases were divided into three groups, including cognitive function normal (normal group), early mild cognitive impairment (e-MCI group), and later mild cognitive impairment (l-MCI group) groups. Each group included 28 cases. The features of hippocampal volume changes in MRI images of the patients in different groups were extracted. The samples of training set and test set were established. Besides, the established support vector machine (SVM), decision tree (DT), and random forest (RF) prediction models were used to predict e-MCI. Metalinear regression was utilized to analyze MRI feature data, and the predictive accuracy, sensitivity, and specificity of different models were calculated. The result showed that the volumes of hippocampal left CA1, left CA2-3, left CA4-DG, left presubiculum, left tail, right CA2-3, right CA4-DG, right presubiculum, and right tail in e-MCI group were all smaller than those in normal group (

P
<
0.01

). The corresponding volume of hippocampal subregions in l-MCI group was remarkably reduced compared with that in normal group (

P
<
0.001

). The volumes of regions left CA1, left CA2-3, left CA4-DG, right CA2-3, right CA4-DG, and right presubiculum were all positively correlated with logical memory test-delay recall (LMT-DR) score (

R

2

=
0.1702

, 0.3779, 0.1607, 0.1620, 0.0426, and 0.1309;

P
<
0.001

). The predictive accuracy of training set sample by DT, SVM, and RF was 86.67%, 93.33%, and 98.33%, respectively. Based on the changes in the volumes of left CA4-DG, right CA2-3, and right CA4-DG, the predictive accuracy of e-MCI and l-MCI by RF model was both higher than those by DT model (

P
<
0.01

). Besides, the predictive accuracy, sensitivity, and specificity of e-MCI by RF model was all notably higher than those by DT model (

P
<
0.01

). The above results demonstrated that the effective early AD prediction models were established by the volume changes in hippocampal subregions, which was based on RF in the research. The establishment of early AD prediction models offered certain reference basis to the diagnosis and treatment of AD patients.
Echinacoside Induces Mitochondria-Mediated Pyroptosis through Raf/MEK/ERK Signaling in Non-Small Cell Lung Cancer Cells
Authors: Shi Ye , Cao Hui , Liu Zhengcheng , Xi Lei , Dong Changqing
Published: May 2022

Background. Various natural compounds are effective in cancer prevention and treatment with fewer side effects than conventional radiotherapy and chemotherapy. Considering the uncertainty of the antitumor mechanism of Echinacoside (Ech) and the fact that no study on Ech against non-small cell lung cancer (NSCLC) has been explored previously, this study inquired into the anti-NSCLC effect of Ech and explored its potential mechanisms. Methods. The IC50 to Ech of the NSCLC cells was calculated based on a series of cell viability assays. Different concentrations of Ech were used to treat the cells; the proliferation activity of the cells was evaluated using EdU staining. Mitochondrial membrane potential was detected by JC-1 staining. Levels of cytokines IL-1β and IL-18 were measured by ELISA. GSH and MDA levels were measured by microplate reader. Expression of cytochrome c, NLRP3, caspase-1, IL-1β, c-Myc, c-Fos, and Raf/MEK/ERK pathway proteins was evaluated by western blot. Meanwhile, we used xenograft, immunohistochemical staining, and H&E staining to evaluate the pharmacological effects of Ech in mice in vivo. Results. ECH inhibited the proliferation of NSCLC cells. Ech increased the expression of pyroptosis-related proteins. Besides, Ech perturbed the mitochondrial membrane potential with the release of mitochondrial cytochrome c, accompanied by increased oxidative stress. Ech inhibited the phosphorylation levels of Raf/MEK/ERK signaling pathway and subsequently reduced c-myc and c-fos protein expression. In addition, Ech effectively restrained the growth of tumors in vivo. Conclusions. Ech inhibited the Raf/MEK/ERK signaling. Impaired mitochondria activated inflammasome, which in turn led to the pyroptosis of NSCLC cells. These findings can provide some ideas on how to use pyroptosis to treat NSCLC.
Provable Secure and Lightweight Vehicle Message Broadcasting Authentication Protocol with Privacy Protection for VANETs
Authors: Xie Qi , Zheng Panpan , Ding Zixuan , Tan Xiao , Hu Bin
Published: May 2022

With the emergence of intelligent transportation and the widespread use of vehicle network equipment, the vehicle ad hoc network (VANET) is widely used in the communication among participating entities to obtain traffic information, such as speed, traffic congestion, road conditions, and accident. In VANET, the secure and efficient message broadcasting protocol can effectively reduce the message transmission delay, to meet the requirements of openness, real-time, and high-speed mobility in the VANET environment. However, most related researches mainly rely on third parties, such as TA and RSU, to ensure the identity authentication of vehicles and the security of message transmission, which are not suitable for infrastructure-less scenario. Therefore, designing a protocol for decentralized message broadcasting, identity authentication without a third party, and the safe message transmission is meaningful. In this study, we propose a lightweight vehicle message broadcasting authentication protocol, which realizes the identity authentication of the message broadcasting vehicle and vehicle-to-vehicle (V2V) key agreement without the need of a trusted third party. In our protocol, the vehicle can verify the identity of the sending vehicle to identify the reliability of the message source and prevent malicious messages. Meanwhile, the vehicle can update its pseudonym identity to resist tracking attacks, but the trusted authority can always track the real identity of the malicious message sending vehicle according to the sent messages. The proposed protocol is proved secure by formal security proof. In addition, our scheme provides better security and computational efficiency over others by comparing with related schemes.
Thoracoscopic Lobectomy versus Segmentectomy in the Treatment of Patients with Early-Stage Lung Cancer
Authors: Li Chuankui , Ma Xiaoxiao , Yang Yifan , Li Qicai , Sang Haiwei , Wang Guowen , Tao Tao , Wang Zuyi
Published: May 2022

Objective. To assess the clinical efficacy of thoracoscopic lobectomy and segmentectomy in the treatment of patients with early-stage lung cancer. Methods. A total of 70 patients with early-stage non-small cell lung cancer who were treated in our hospital from April 2018 to May 2020 were recruited and assigned at a ratio of 1 : 1 to receive either segmentectomy (observation group) or lobectomy (control group). Outcome measures included clinical efficacy, surgery-related indicators, pulmonary function indicators (forced vital capacity (FVC) and forced expiratory volume in one second (FEV1)), postoperative complications, and recurrence and metastasis. Results. There was no significant difference in the clinical efficacy between the two groups (

P
>
0.05

). Segmentectomy was associated with a longer operation time and shorter hospital stay compared to lobectomy (

P
<
0.05

). There was no statistical significance in the amount of intraoperative blood loss and the number of lymph nodes dissected (

P
>
0.05

). Segmentectomy resulted in significantly higher FVC and FEV1 levels in patients compared to lobectomy (

P
<
0.05

). There was no significant difference in the incidence of postoperative complications between the two groups (

P
>
0.05

). The two groups of patients were followed up for 12 months after the operation, and there was no recurrence or metastasis in either group. Conclusion. The two surgical methods have similar efficacy and safety profiles, but for the treatment of patients with early-stage lung cancer, thoracoscopic segmentectomy is associated with a shorter hospital stay and better protection of the lung function of patients compared to lobectomy.
Vocal Music Teaching Pharyngeal Training Method Based on Audio Extraction by Big Data Analysis
Authors: Huang Chunyuan
Published: May 2022

In the process of vocal music learning, incorrect vocalization methods and excessive use of voice have brought many problems to the voice and accumulated a lot of inflammation, so that the level of vocal music learning stagnated or even declined. How to find a way to improve yourself without damaging your voice has become a problem that we have been pursuing. Therefore, it is of great practical significance for vocal music teaching in normal universities to conduct in-depth research and discussion on “pharyngeal singing.” Based on audio extraction, this paper studies the vocal music teaching pharyngeal training method. Different methods of vocal music teaching pharyngeal training have different times. When the recognition amount is 3, the average recognition time of vocal music teaching pharyngeal training based on data mining is 0.010 seconds, the average recognition time of vocal music teaching pharyngeal training based on Internet of Things is 0.011 seconds, and the average recognition time of vocal music teaching pharyngeal training based on audio extraction is 0.006 seconds. The recognition time of the audio extraction method is much shorter than that of the other two traditional methods, because the audio extraction method can perform segmented training according to the changing trend of physical characteristics of notes, effectively extract the characteristics of vocal music teaching pharyngeal training, and shorten the recognition time. The learning of “pharyngeal singing” in vocal music teaching based on audio extraction is different from general vocal music training. It has its unique theory, concept, law, and sound image. In order to “liberate your voice,” it adopts large-capacity and large-scale training methods.
DNMT3a-Mediated Enterocyte Barrier Dysfunction Contributes to Ulcerative Colitis via Facilitating the Interaction of Enterocytes and B Cells
Authors: Cheng Bo , Rong Ai-mei , Li Wenlu , Bi Xiuqian , Qiu Xinguang
Published: May 2022

Objective. Dysfunction of the enterocyte barrier is associated with the development of ulcerative colitis (UC). This study was aimed at exploring the effect of DNMT3a on enterocyte barrier function in the progression of UC and the underlying mechanism. Method. Mice were given 3.5% dextran sodium sulphate (DSS) in drinking water to induce colitis. The primary intestinal epithelial cells (IECs) were isolated and treated with lipopolysaccharide (LPS) to establish an in vitro inflammatory model. We detected mouse clinical symptoms, histopathological damage, enterocyte barrier function, B cell differentiation, DNA methylation level, and cytokine production. Subsequently, the effect of DNMT3a from IECs on B cell differentiation was explored by a cocultural experiment. Result. DSS treatment significantly reduced the body weight and colonic length, increased disease activity index (DAI), and aggravated histopathological damage. In addition, DSS treatment induced downregulation of tight junction (TJ) protein, anti-inflammatory cytokines (IL-10 and TGF-β), and the number of anti-inflammatory B cells (CD1d+) in intestinal epithelial tissues, while upregulated proinflammatory cytokines (IL-6 and TNF-α), proinflammatory B cells (CD138+), and DNA methylation level. Further in vitro results revealed that DNMT3a silencing or TNFSF13 overexpression in IECs partly abolished the result of LPS-induced epithelial barrier dysfunction, as well as abrogated the effect of IEC-regulated B cell differentiation, while si-TACI transfection reversed these effects. Moreover, DNMT3a silencing decreased TNFSF13 methylation level and induced CD1d+ B cell differentiation, and the si-TNFSF13 transfection reversed the trend of B cell differentiation but did not affect TNFSF13 methylation level. Conclusion. Our study suggests that DNMT3a induces enterocyte barrier dysfunction to aggravate UC progression via TNFSF13-mediated interaction of enterocyte and B cells.
The Preparation and Preservation of Soybean Protein Isolate Were Studied Based on the Internet of Things
Authors: Niu Chunyan , Jiang Xiaokun
Published: May 2022

With the gradual deepening of people’s understanding of the concept of environmental protection, this situation has brought new challenges to the preservation and packaging of food. Pure green, natural, and harmless edible film has become a hot topic. Edible protein membranes include animal protein membrane, plant protein membrane, and composite protein membrane. They are mainly used in the field of food production and food safety. The traditional food film has a poor water barrier in a humid environment, which cannot effectively avoid the anaerobic respiration of fruits. At the same time, it will also cause environmental pollution. In order to solve these problems, this paper studies the preparation and preservation of soybean protein isolate based on the Internet of things. Firstly, polyphenols were fused with soybean protein isolate by ultrasonic technology to form an edible fresh-keeping film. The edible membrane of the soybean protein isolate was tested, and the optimum conditions for the formation of the edible membrane were determined by the orthogonal test. The effects of soybean protein isolate, glycerol, reducing agent, and other additives on the film-forming properties were analyzed. Finally, the changes in properties and preservation indexes after film formation were analyzed. The results showed that the film-forming temperature of polyphenol soybean protein isolate edible film was positively correlated with the film properties. The prepared food preservative film can be used for the preservation of vegetables and other foods and has a good retention effect on food nutrients.
State Aware-Based Prioritized Experience Replay for Handover Decision in 5G Ultradense Networks
Authors: Wu Dong-Fang , Huang Chuanhe , Yin Yabo , Huang Shidong , Guo Qianqian , Zhang Lin
Published: May 2022

The traditional handover decision methods depend on the handover threshold and measurement reports, which cannot efficiently resolve the frequent handover issue and ping-pong effect in 5G (5 generation) ultradense networks. To reduce the unnecessary handover and improve the QoS (quality of service), combine with the analysis of dwell time, we propose a state aware-based prioritized experience replay (SA-PER) handover decision method. First, the cell dwell time is computed by the geometrical analysis of real-time locations of mobile users in cellular networks. The constructed state aware sequence including SINR, load coefficient, and dwell time is normalized by max-min normalization method. Then, the handover decision problem in 5G ultradense networks is formalized as a discrete Markov decision process (MDP). The random sampling and small batch sampling affect the performance of deep reinforcement learning methods. We adopt the prioritized experience replay (PER) method to resolve the learning efficiency problems. The state space, action space, and reward functions are designed. The normalized state aware decision matrix inputs the DDQN (double deep Q-network) method. The competitive and collaborative relationships between vertical handover and horizontal handover in 5G ultradense networks are mainly discussed. And the high average network throughput and long average cell dwell time make sure of the communication quality for mobile users.
Quantitative Analysis of Internet of Things Technology on the National Economic Accounting: A Prediction Model Based on the FCM-BP Neural Network Algorithm
Authors: Song Jian-Tao
Published: May 2022

The development of Internet of Things (IoT) technology is of great significance for modern financial settlements based on information technology. The IoT technology, which can provide a convenient approach for accounting, has the advantages of intelligent processing, reliable transmission, and comprehensive perception. For national economic accounting, a new integrated system for monitoring the environment is developed and designed by using embedded development technology and sensor technology. The system uses a wireless sensor network environment monitoring system IoT platform with embedded internal processors. Analyze and design the system as a whole, including the construction of the basic platform of the system, the design of the internal plates and circuits of the system, the monitoring design of the input node, and the monthly design of the output interface calculation. Finally, a physical model is built, and data measurement and analysis are carried out under different conditions, and the evaluation and advantage analysis of the system’s operating status are given. The system can carry out all-round, multilevel and three-dimensional real-time monitoring of the construction site environment, including dust, PM2.5, temperature, humidity, wind speed, carbon dioxide, and other indicators in the construction site environment. In addition, the system can upload various monitoring data to the detection system through the internal network. The system has the functions of monitoring, alarming, recording, querying, and counting of the target monitoring station and can also be linked with the environmental control device. The construction site staff can conduct real-time supervision through the mobile terminal and computer terminal management platform. In addition, it can also meet the role of real-time remote monitoring and online guidance and regulation. It has reference value for the safety and management of the actual operation process of the project.
Design of English Translation Model of Intelligent Recognition Based on Deep Learning
Authors: Zhang Qian , Zhou Haiping , Tsai Sang-Bing
Published: May 2022

In the context of globalization, the international common language is English and it is more and more widely used. However, there is a shortage of talents with high English proficiency in all industries. According to the demand of the Chinese market, we should pay attention to students’ English language ability. Deep learning is a machine learning method based on feature learning and feature hierarchical structure, which simulates the analysis and learning of the human brain, carries out feature transformation layer by layer, trains a single-layer network each time, and transforms the features of samples in the original space to a new feature space, thus making classification or prediction easier. There are still some defects in the current machine translation (MT) results of online MT, especially the disadvantages of low efficiency and low accuracy of MT when the full-text range uses the server to compare the data of different languages to obtain the grammar and text-related rules among different languages. Therefore, other modern intelligent recognition technologies should be adopted to achieve accurate English MT. Generally speaking, the closer the training data is to the domain of the target text, the higher the quality of sentence alignment and the more sentence pairs, the more helpful it is to learn more accurate translation rules, so as to obtain a more robust translation.
Exploring Molecular Mechanisms of Aloe barbadmsis Miller on Diphenoxylate-Induced Constipation in Mice
Authors: Tang Ruying , Zhang Jianjun , Nan Haipeng , Lv Ruilin , Chen Xiuhong , Liu Yao , Wang Xiangshan , Wang Linyuan
Published: May 2022

Aloe barbadensis Miller (Aloe) known as a common succulent perennial herb had been traditionally used in constipation for more than 1,000 years. Aloe contained anthraquinones and other active compounds which had laxative effect and could modulate constipation. However, the therapeutic effects and mechanisms of aloe in constipation were still unclear. To explore the therapeutic effects and mechanisms of aloe in treating constipation, we employed network pharmacology, molecular docking, and mice experiments in this study. Our network pharmacology indicated that beta-carotene, sitosterol, campest-5-en-3beta-ol, CLR, arachidonic acid, aloe-emodin, quercetin, and barbaloin were the main active ingredients of aloe in treating constipation. Besides, the MAPK signaling pathway was the principal pathway utilized by aloe in treating constipation. Molecular docking results revealed that beta-carotene and sitosterol were acting as interference factors in attenuating inflammation by binding to an accessory protein of ERK, JNK, AKT, and NF-κB p65. Otherwise, in vivo experiments, we used diphenoxylate-induced constipation mice model to explore the therapeutic effects and mechanisms of aloe. Results showed that aloe modulated the constipation mice by reducing the discharge time of first melena, improving the fecal conditions, increasing the gastric intestinal charcoal transit ratio, and improving the intestinal secretion in small intestine. Besides, aloe played an important regulation in promoting intestinal motility sufficiency and the levels of neurotransmitters balance with 5-HT, SP, and VIP on constipation mice. Moreover, aloe significantly inhibited the mRNA and proteins expressions of ERK, JNK, AKT and NF-κB p65 in colon. Our study proved that aloe could reverse diphenoxylate-induced changes relating to the intestinal motility, intestinal moisture, and inhibition of the MAPK (ERK, JNK)/AKT/NF-κB p65 inflammatory pathway. Our study provided experimental evidences of the laxative effect of aloe, which was beneficial to the further research and development of aloe.
A Novel Prognostic Stratified System Based on Tumor Budding and the Cell Nest Size in Ureter Urothelial Carcinoma
Authors: Li Jialin , Li Xinyue , Yang Jing
Published: May 2022

At present, malignant tumor stratification based on the TNM stage is very important for predicting patient prognosis and selecting appropriate treatment. The prognostic factor of ureter urothelial carcinoma is mainly based on the stage according to AJCC (8th) TNM classification. None of the histomorphologic features is recommended to assess patient’s prognosis. Recently, a novel three-tiered grading system based on tumor budding and the cell nest size (referred as TBNS system) has been applied to be highly prognostic for some squamous cell carcinomas, including esophageal, pulmonary, uterine cervix cancer, and endocervical endocarcinoma. In this study, we explored the application of this TBNS grading system in ureter urothelial carcinoma consisting 87 surgically resected cases and no neoadjuvant therapy. Tumor budding and the cell nest size were assessed and correlated with clinicopathological data and survival. The results showed that higher tumor budding, cell nest size, and TBNS grading system were strongly related to shorter overall survival (OS), disease-specific survival (DSS), and disease-free survival (DFS). Multivariate survival analysis showed the TBNS grading system to be closely related to the independent prognosis of DFS and DSS. In conclusion, the TBNS grading system based on tumor budding and cell nest size, if further validated, could satisfactorily predict the prognosis of uterine urothelial carcinoma and be applicable in routine pathologic description of this cancer type.
An Elliptic Curve Signcryption Scheme and Its Application
Authors: Zhang Ping , Li Yamin , Chi Huanhuan
Published: May 2022

Two basic security requirements in communication are confidentiality and authentication. Signcryption is an ideal technique to transmit encrypted and authenticated data. In view of the shortcomings of existing signcryption schemes and the high security of elliptic curve cryptography (ECC), we design a ECC-based signcryption scheme and evaluate it in terms of security, computational overhead, and communication overhead. Finally, we consider the application of our secure and efficient signcryption scheme in the smart lock key management system and analyze the bit-oriented performance of the designed key management scheme.
FDCNet: Presentation of the Fuzzy CNN and Fractal Feature Extraction for Detection and Classification of Tumors
Authors: Molaei Sepideh , Ghorbani Niloofar , Dashtiahangar Fatemeh , Peivandi Mohammad , Pourasad Yaghoub , Esmaeili Mona
Published: May 2022

The detection of brain tumors using magnetic resonance imaging is currently one of the biggest challenges in artificial intelligence and medical engineering. It is important to identify these brain tumors as early as possible, as they can grow to death. Brain tumors can be classified as benign or malignant. Creating an intelligent medical diagnosis system for the diagnosis of brain tumors from MRI imaging is an integral part of medical engineering as it helps doctors detect brain tumors early and oversee treatment throughout recovery. In this study, a comprehensive approach to diagnosing benign and malignant brain tumors is proposed. The proposed method consists of four parts: image enhancement to reduce noise and unify image size, contrast, and brightness, image segmentation based on morphological operators, feature extraction operations including size reduction and selection of features based on the fractal model, and eventually, feature improvement according to segmentation and selection of optimal class with a fuzzy deep convolutional neural network. The BraTS data set is used as magnetic resonance imaging data in experimental results. A series of evaluation criteria is also compared with previous methods, where the accuracy of the proposed method is 98.68%, which has significant results.
Application of Computer 3D Modeling Technology in Modern Garden Ecological Landscape Simulation Design
Authors: Gong Ping , Li Jiangxiao
Published: May 2022

The use of digital technology (DT) to systematically plan and design UL can further progress the urban environment. Digital landscape as a new vector of landscape planning and design is oriented to DT. The promotion of DT improves the science and accuracy of UL planning and designs and promotes sustainable development. A city in the south was used as an example to reconstruct and restore the historical appearance of the city from four aspects: virtual process, reconstruction method, digital construction, and virtual interactive roaming technology, and a new way was found for the application of digital landscape in urban construction. The scientific and creative nature of UL is becoming increasingly prominent as the digital era progresses, and UL is no longer just an exterior manifestation of artistic configuration but also a humanized spatial combination guided by data analysis.
RLAS-BIABC: A Reinforcement Learning-Based Answer Selection Using the BERT Model Boosted by an Improved ABC Algorithm
Authors: Gharagozlou Hamid , Mohammadzadeh Javad , Bastanfard Azam , Ghidary Saeed Shiry
Published: May 2022

Answer selection (AS) is a critical subtask of the open-domain question answering (QA) problem. The present paper proposes a method called RLAS-BIABC for AS, which is established on attention mechanism-based long short-term memory (LSTM) and the bidirectional encoder representations from transformers (BERT) word embedding, enriched by an improved artificial bee colony (ABC) algorithm for pretraining and a reinforcement learning-based algorithm for training backpropagation (BP) algorithm. BERT can be comprised in downstream work and fine-tuned as a united task-specific architecture, and the pretrained BERT model can grab different linguistic effects. Existing algorithms typically train the AS model with positive-negative pairs for a two-class classifier. A positive pair contains a question and a genuine answer, while a negative one includes a question and a fake answer. The output should be one for positive and zero for negative pairs. Typically, negative pairs are more than positive, leading to an imbalanced classification that drastically reduces system performance. To deal with it, we define classification as a sequential decision-making process in which the agent takes a sample at each step and classifies it. For each classification operation, the agent receives a reward, in which the prize of the majority class is less than the reward of the minority class. Ultimately, the agent finds the optimal value for the policy weights. We initialize the policy weights with the improved ABC algorithm. The initial value technique can prevent problems such as getting stuck in the local optimum. Although ABC serves well in most tasks, there is still a weakness in the ABC algorithm that disregards the fitness of related pairs of individuals in discovering a neighboring food source position. Therefore, this paper also proposes a mutual learning technique that modifies the produced candidate food source with the higher fitness between two individuals selected by a mutual learning factor. We tested our model on three datasets, LegalQA, TrecQA, and WikiQA, and the results show that RLAS-BIABC can be recognized as a state-of-the-art method.
Study on the Interpretation Method of Layered Flow Imaging Logging for Oil–water, Two-Phase Flow in Horizontal Wells
Authors: Qiongqin Jiang , Wenguang Song , Gaoming Yu , Zizhao Gao , Yu Qian , Haimin Guo
Published: May 2022

In the production of oil and gas fields, horizontal wells can obtain larger reservoir drainage area. Single well production is large and the production cycle is long. Especially for the development of reservoirs with thin production layer, small porosity, and low permeability, it shows the incomparable effect of vertical wells. Due to gravity separation in horizontal wells, the distribution of fluids in horizontal cross sections is more complicated. There are many influencing factors, such as gas lock, water lock, and flow instability. In horizontal well oil–water two-phase flow, the flow separation in different flow zones complicates the reading of the turbine flowmeter due to the change the cross-sectional area of the fluid. A small deviation of the horizontal well inclination causes significant changes in holdup and flow velocity. Well deviation causes backflow and circulation. In this paper, the capacitance water holdup and turbine flowmeter data processing method of FILT in oil–water two-phase flow are studied by the oil–water, two-phase flow experiment. The stratified flow interpretation model of oil–water two-phase flow and the chart fitting calculation method are proposed and realized. This is the innovation of this paper. Through the verification of experimental data, the relative errors under various conditions are less than 10%. Only when the moisture content is 20%, the error is greater than 10%. The interpretation accuracy of oil–water two-phase flow can fully meet the actual needs of production. It provides a strong basis for accurately finding the producing water point and scientifically plugging water in horizontal wells. The findings of this study can help the better understanding of the oil–water two-phase flow stratification flow interpretation model and the chart fitting calculation method.
An Innovative Methodology for Network Latency Detection Based on IoT Centered Blockchain Transactions
Authors: Rao D. Nageswara , Vidhya G. , Rajesh M. V. , Jain Vipin , Alharbi Adel R. , Kumar Harish , Halifa Awal
Published: May 2022

The new IoT apps will not be able to inspire people to utilize them and may ultimately lose all their potential if an interoperable and trustworthy ecosystem is not provided. IoT has its extra security difficulties such as information storage, administration, privacy concerns, and authentication. The presently deployed IoT apps have encountered various security and privacy assaults globally. Due to being less secure and low powered, the IoT devices present a simple entryway to the adversaries to obtain access to the corporate networks, leading to giving easy control over all of the data of the users. The objective of this doctorate proposed work will be to solve the security associated difficulties in multiple IoT domains like the e-commerce, vehicular ad hoc networks (VANET), mobile ad hoc networks (MANET), and Internet of Drones (IoD). The proposed study focuses on the development of a distributed framework for IoT based on blockchain. The framework includes the usage of Ethereum-based smart contracts and auction models to increase the income and QoS for both the seller and the buyer and the development of a DAG chain-based distributed framework for parking lot allocation in a network of automobiles. The suggested model includes the requirement of obtaining agreement among the nodes with probability one in such a circumstance. The suggested model demonstrates to be predictable as typical voting-based consensus protocols like Practical Byzantine Fault Tolerance (PBFT) and at the same time can accommodate a high number of nodes even in an asynchronous setting. Research on Byzantine fault-tolerant systems has been ongoing for more than four decades, and although the solutions were shown to be feasible early on, they remained unworkable for a long time. With PBFT, the first feasible solution was provided in 1999, and this sparked fresh research that has resulted in unique applications that are still being developed today employing this technology. Despite the fact that the safety and liveness properties of PBFT-type protocols have been thoroughly investigated, when it comes to practical performance, only empirical results—often obtained in artificial environments—are known, and imperfections in the communication channels are not explicitly considered. It is our goal in this paper to propose the first performance model for PBFT that takes into account the effect of unreliable channels as well as the usage of alternative transport protocols across those channels. We also performed a large number of simulations to test the model and acquire a better understanding of the influence of different deployment factors on the total transaction timeframe.
Data Analysis and Knowledge Mining of Machine Learning in Soil Corrosion Factors of the Pipeline Safety
Authors: Zhao Zhifeng , Chen Mingyuan , Fan Heng , Zhang Nailu
Published: May 2022

The purpose of this research is to enhance the ability of data analysis and knowledge mining in soil corrosion factors of the pipeline. According to its multifactor characteristics, the rough set algorithm is directly used to analyze and process the observation data without considering any prior information. We apply rough set algorithm to delete the duplicate same information and redundant items and simplify the condition attributes and decision indicators from the decision table. Combined with the simplified index, the decision tree method is used to analyze the root node and branch node of it, and the knowledge decision model is constructed. With the Python machine learning language and PyCharm Community Edition software, the algorithm functions of rough set and decision tree are realized, so as to carry out artificial intelligence analysis and judgment of the soil corrosion factor data in pipeline. Taking the area of loam soil corrosion as an example, the data analysis and knowledge mining of its multifactors original data are carried out through the model. The example verifies that the evaluation and classification rules of the model meet the requirements, and there are no problems such as inconsistency and heterogeneity. It provides decision-making service and theoretical basis for the soil corrosion management of pipeline.