bluuebunny/crossref_metadata_2025 huggingface dataset:
College English Reading Teaching Integrating Production Oriented Approach from the Perspective of Artificial Intelligence
Authors: Wang Xi , Yan Ke
Published: June 2022

The objectives are to solve many problems in traditional English reading teaching, such as the passive acceptance of students’ learning situation, the rigid teaching mode of teachers and the difficulty in taking into account the individual needs of each student, and the forced averaging of students’ English learning ability and level. Firstly, a detection method of English reading level based on the Backpropagation neural network (BPNN) is designed. Secondly, the teaching process and specific teaching plan are designed with the Production Oriented Approach (POA). Finally, the detection method of English reading level is verified by simulation experiments, and the effect of the teaching method integrating POA is analyzed. The results show that (1) when the learning rate is 0.26 and the training time is 1000 times, the mean square error (MSE) of the model is the smallest. (2) The results of the practical experiment indicate that the number of people who sign up for the college English proficiency test, the number of actual exams, and the pass rate of the test in the experimental group using the teaching method are higher than those in the control group. Moreover, the number of people in the experimental group increased more than that in the control group after the experiment. There is no significant difference in the test results of the two groups before the experiment, and the average test scores of the experimental group are obviously higher than those of the control group after the experiment. The

p

value of the two groups of results is less than 0.05 in the t-test, indicating that there is a significant difference in the English reading level between the two groups after the experiment. Simultaneously, the difference between the results of class A before and after the experiment is larger than that of class B, showing that the teaching method integrating POA is more effective for students with higher English levels. Therefore, compared with the traditional teaching method, the teaching mode integrating POA is more effective in improving the English reading level of college students. It aims to transform POA theory into practice and provide a great reference for college English reading teaching.
Research on Shipboard Material Scheduling Optimization Based on Improved Genetic Algorithm
Authors: Yuan Feihui , Li Jinghua , Zhou Qinghua , He Ming
Published: June 2022

The development of the cruise industry is an important part of the country’s “Marine power” and “One Belt, One Road” strategy. Improving the independent construction of cruise ships is of great significance to my country’s economic and social development. Aiming at the problem of the assembly order of cruise ship outfitting parts are many, the quantity is large, and the interchangeability of materials is higher than that of conventional ships, a cruise ship outfitting on-board assembly scheduling problem model is constructed, and a cruise ship outfitting on-board assembly material scheduling method oriented to generally outfitting is proposed. The improved genetic algorithm is used to dispatch the materials on the outfitting ships to realize the optimal utilization of the materials and materials, and the effectiveness of the method is proved by the example verification.
Secure Analysis for IIOT Systems Using Hyperchaotic Image Encryption
Authors: Zeng Haini , Zou Qiping
Published: June 2022

Like edge computing, intelligent cameras and image sensors are widely used in the Industrial Internet of Things (IIOT), including design and finished product quality inspection. However, the images generated by these sensors are constantly at risk of information leakage and privacy violations in the IIOT. Due to the involvement of third parties, traditional encryption algorithms are no longer adapted to image encryption for IIOT. In the context of the IIOT, an image encryption technology based on hyperchaotic systems and dynamic DNA coding is proposed. First, the image pixel position is scrambled by the hyperchaotic mapping index sequence, so that the image pixel matrix is dynamically DNA coded, and the base operation is performed on the given DNA sequence. Then, Keccak is used to calculate the hash value of the given DNA sequence as the initial value of the chaotic system and a certain number of base substitutions are performed on the DNA encoded pixel value according to the quaternary hyperchaotic sequence generated by the hyperchaotic system. Finally, ciphertext feedback and chaotic system iteration are used to further enhance the confusion and diffusion characteristics of the algorithm. The test results show that the algorithm not only has a large key space, strong sensitivity to keys, but also has strong resistance to exhaustive analysis attacks.
Design of Underground Space Intelligent Disaster Prevention System Based on Multisource Data Deep Learning
Authors: Yu Hairuo , Guo Zhongqiang
Published: June 2022

With the rapid development of national economy, the population to big cities gathered themselves together, and especially the first-line cities, lead to city continuously extend outward, city scale is more and more big, the surface space is completely unable to meet the needs of urban development and transportation, the demand such as life, development, and use of underground space has become the important way of solving the urban development diameter. With the vigorous development of underground space, many disaster problems, such as fire and flood, have also appeared in many places, which have brought huge human and financial losses to the society. In order to solve the problem of disaster in underground space, this paper summarizes the main disasters, and urban underground space analysis of the different degrees of the risk of disasters; the emergency toughness of disaster prevention concept, combined with intelligent technology application in urban underground space disaster warning and decision-making, according to the requirement of the underground space of disaster prevention wisdom, put forward to underground space disaster, disaster prevention expert database, such as multisource data fusion. Deep learning is used to realize the linkage of disaster rescue and recovery, and an intelligent disaster prevention system based on deep learning of multisource data is established. The results show that the urban underground space disasters mainly include fire, explosion, earthquake, flood, toxic, and combustible gas. Combining with the overlapping characteristics of different disasters and the inability to define the boundaries, the theory of emergency resilience disaster prevention provides effective suggestions and measures for the decision-making and treatment of underground space fires. The intelligent comprehensive disaster prevention system of urban underground space is established from the three aspects of predisaster prevention, rescue in disaster, and reconstruction after disaster, so as to realize the full coverage of intelligent disaster prevention in the whole life cycle of underground space and provide data support for integrated decision-making of disaster prevention and reduction. The research results have important guiding significance for digitization, informationization, and intelligent construction of sudden disaster decision-making in underground space.
The AHP (1-9 Value Scale) Level Method to Analyze the Development of Logistics under the Perspective of Low-Carbon Environmental Protection: Taking Shandong Province, China, as an Example
Authors: Xie Man , Peng Yao , Yang Jiao , Kang Wanjie
Published: June 2022

Reducing the total carbon emissions of the logistics industry is one of the basic requirements for the development of a low-carbon economy. Based on the AHP analytic hierarchy process and the 1-9 value scaling method, an evaluation index system for the low-carbon development of the logistics industry is constructed. At present, the development of low-carbon logistics requires the combination of low-carbon technology and low-carbon management to ensure the integrity and dynamics of low-carbon logistics control. The results of this study show that in the AHP index analysis of the development of logistics industry in Shandong Province, compared with 13 evaluation indexes of low-carbon logistics and traditional logistics, low-carbon logistics is significantly better than traditional logistics. Among them, the implementation of low-carbon logistics policy system has increased the energy utilization rate to 88.72%, the recovery rate of warehousing and logistics waste to 92.11%, and other relevant indicators have also been improved and optimized to varying degrees. Based on the EKC model curve, the total carbon emissions from 2015 to 2019 showed a downward trend month by month. The future development of low-carbon logistics mainly starts with reducing the total carbon emission of logistics transportation and improving energy utilization. Aiming at the main influencing factors of the development of low-carbon logistics, this paper puts forward specific measures for the development of low-carbon logistics, in order to promote the development of low-carbon logistics.
Noninvasive Evaluation of EGFR Expression of Digestive Tumors Using 99mTc-MAG3-Cet-F(ab′)2-Based SPECT/CT Imaging
Authors: Shi Dai , Zhang Yiqiu , Xu Zhan , Si Zhan , Cheng Yuan , Cheng Dengfeng , Liu Guobing
Published: June 2022

Purpose. This study is aimed at investigating the feasibility of cetuximab (Cet) F(ab[Formula: see text])2 fragment- (Cet-F(ab[Formula: see text])2-) based single photon emission tomography/computed tomography (SPECT/CT) for assessing the epidermal growth factor receptor (EGFR) expression in digestive tumor mouse models. Methods. Cet-F(ab[Formula: see text])2 was synthesized using immunoglobulin G-degrading enzyme of Streptococcus pyogenes (IdeS) protease and purified with protein A beads. The product and its in vitro stability in normal saline and 1% bovine serum albumin were analyzed with sodium dodecyl sulfate–polyacrylamide gel electrophoresis. The EGFR expression in the human colon tumor cell line HT29 and the human stomach tumor cell line MGC803 were verified using western blotting and immunocytochemistry. Cet-F(ab[Formula: see text])2 was conjugated with 5(6)-carboxytetramethylrhodamine succinimidyl ester to demonstrate its binding ability to the MGC803 and HT29 cells. Cet-F(ab[Formula: see text])2 was conjugated with NHS-MAG3 for 99mTc radiolabeling. The best imaging time was determined using a biodistribution assay at 1, 4, 16, and 24 h after injection of the 99mTc-MAG3-Cet-F(ab[Formula: see text])2 tracer. Furthermore, 99mTc-MAG3-Cet-F(ab[Formula: see text])2 SPECT/CT was performed on MGC803 and HT29 tumor-bearing nude mice. Results. HT29 cells had low EGFR expression while MGC803 cell exhibited the high EGFR expression. Cet-F(ab[Formula: see text])2 and intact cetuximab showed similar high binding ability to MGC803 cells but not to HT29 cells. Cet-F(ab[Formula: see text])2 and 99mTc-MAG3-Cet-F(ab[Formula: see text])2 showed excellent in vitro stability. The biodistribution assay showed that the target to nontarget ratio was the highest at 16 h ([Formula: see text], [Formula: see text]) after tracer injection. The 99mTc-MAG3-Cet-F(ab[Formula: see text])2-based SPECT/CT imaging revealed rapid and sustained tracer uptake in MGC803 tumors rather than in HT29 tumors with high image contrast, which was consistent with the results in vitro. Conclusion. SPECT/CT imaging using 99mTc-MAG3-Cet-F(ab[Formula: see text])2 enables the evaluation of the EGFR expression in murine EGFR-positive tumors, indicating the potential utility for noninvasive evaluation of the EGFR expression in tumors.
Mathematical Modelling and Torque Ripple Waning in BLDC Motor Using Outgoing-Phase Current Discharge Hysteresis Controlled ANFIS Controller
Authors: Senthilnathan A. , Palanivel P. , Kumar K. Ramash
Published: June 2022

This study focused on approaches for reducing torque ripples in Permanent Magnet Brushless Direct current motors (PMBLDC) to provide sophisticated performance and reliable machine drives for both industrial and consumer applications. Torque ripples are caused by current ripple, nonsinusoidal Back Electromotive Force (EMF), and cogging torque at the Brushless Direct Current Motor (BLDCM) output. Acoustic emissions are produced when the torque ripple creates vibrations in the mechanical system, which interacts with the motor housing and reduces the life span of the motor. The BLDC’s uses are limited due to these acoustic emissions. Proportional-Integral (PI), Fuzzy Logic Control (FLC), and Adaptive Neuro-Fuzzy Inference System (ANFIS) speed controller approaches were used to construct and analyze the mathematical model of the BLDC motor in the MATLAB environment. The Adaptive Neurofuzzy Inference System (ANFIS) speed control system has solved the shortcomings of the PI and Fuzzy Logic Control (FLC) techniques. Because of FLC interpolation and flexibility, ANFIS is one of the finest trade-offs between neural and fuzzy systems, allowing for smooth control. Model compactness, a smaller training set, and faster convergence are all benefits of the ANFIS approach over traditional feedforward NN. In this research proposal, a simple control approach based on outgoing phase current discharge hysteresis control (OGCDHC) with minimal torque ripple is presented.
Construction of Higher Education Teaching Quality Evaluation Model Based on Scientific Computing
Authors: Zhang Chao , Chen Yanyan , Qin Juanjuan
Published: June 2022

The recipients of higher education are the backbone of future social construction, so their education is particularly important. As the research group of scientific computing is immature, the use of it to perform related calculations can achieve a multiplier effect with half the effort. Especially now, the quality evaluation of higher education has not been determined. Therefore, this paper aims to use scientific computing to construct a scientific evaluation model for the teaching quality of higher education. For scientific computing, this paper used its data processing ability to design and improve the computational science model evaluation algorithm. For the evaluation model, it not only constructed it with the fuzzy evaluation model and analyzed the weights of the relevant indicators proposed but also designed a teaching evaluation system according to the established model in order to evaluate the teaching quality efficiently and accurately. Experiments showed that the evaluation result score for a teacher was 81.45, and 81.45 was between 80 and 90. The teacher’s classroom evaluation result was good, which showed the effectiveness of the evaluation model. The test results of the system showed that the system could load up to 80 people and run smoothly online at the same time.
Identification of Key Biomarkers and Immune Infiltration in Liver Tissue after Bariatric Surgery
Authors: Zhang Xiaoyan , Li Jingxin , Liu Tiancai , Zhao Min , Liang Baozhu , Chen Hong , Zhang Zhen
Published: June 2022

Background. Few drugs are clearly available for nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH); nevertheless, mounting studies have provided sufficient evidence that bariatric surgery is efficient for multiple metabolic diseases, including NAFLD and NASH, while the molecular mechanisms are still poorly understood. Methods. The mRNA expression profiling of GSE48452 and GSE83452 were retrieved and obtained from the Gene Expression Omnibus (GEO) database. The limma package was employed for identifying differentially expressed genes (DEGs), followed by clusterProfiler for performing Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, and GSEA software for performing GSEA analyses. The PPI network analyses were constructed using Metascape online analyses. WGCNA was also utilized to identify and verify the hub genes. CIBERSORT tools contributed to the analysis of immune cell infiltration of liver diseases. Results. We identify coexpressed differential genes including 10 upregulated and 55 downregulated genes in liver tissue after bariatric surgery. GO and KEGG enrichment analyses indicated that DEGs were remarkably involved in the immune response. GSEA demonstrated that DEGs were markedly enriched in the immune response before surgery, while most were enriched in metabolism after surgery. Seven genes were screened through the MCC algorithm and KME values, including SRGN, CD53, EVI2B, MPEG1, NCKAP1L, LCP1, and TYROBP. The mRNA levels of these genes were verified in the Attie Lab Diabetes Database, and only LCP1 was found to have significant differences and correlation with certain immune cells. Conclusion. Our knowledge of the mechanisms by which bariatric surgery benefits the liver and the discovery of LCP1 is expected to serve as potential biomarkers or therapeutic targets for NAFLD and NASH.
Building towards Automated Cyberbullying Detection: A Comparative Analysis
Authors: Al-Harigy Lulwah M. , Al-Nuaim Hana A. , Moradpoor Naghmeh , Tan Zhiyuan
Published: June 2022

The increased use of social media among digitally anonymous users, sharing their thoughts and opinions, can facilitate participation and collaboration. However, this anonymity feature which gives users freedom of speech and allows them to conduct activities without being judged by others can also encourage cyberbullying and hate speech. Predators can hide their identity and reach a wide range of audience anytime and anywhere. According to the detrimental effect of cyberbullying, there is a growing need for cyberbullying detection approaches. In this survey paper, a comparative analysis of the automated cyberbullying techniques from different perspectives is discussed including data annotation, data preprocessing, and feature engineering. In addition, the importance of emojis in expressing emotions as well as their influence on sentiment classification and text comprehension leads us to discuss the role of incorporating emojis in the process of cyberbullying detection and their influence on the detection performance. Furthermore, the different domains for using self-supervised learning (SSL) as an annotation technique for cyberbullying detection are explored.
Global Practical Conformable Stabilization by Output Feedback for a Class of Nonlinear Fractional‐Order Systems
Authors: Gassara Hamdi , Naifar Omar , Ben Makhlouf Abdellatif , Mchiri Lassaad
Published: June 2022

Researchers have explored the concept of “practical stability” in the literature, pointing out that stability investigations always guarantee “practical stability” and the inverse is not true. The concept “practical stability” means that the origin is not an equilibrium point and the convergence of the system state is towards a ball centered at the origin. The primary purpose of this work is to investigate the notation of practical stability for a new class of fractional-order systems using the general conformable derivative. As a second objective, the nonlinear condition chosen is novel in that it is not Lipschitz as is customary, which is original in and of itself. In addition, some new analysis related to the LMI techniques was used to prove the main results. To begin, a method of stabilization is provided. Following that, the proposed system’s observer design is presented. Also, the principle of separation is described. Finally, a numerical example is offered to demonstrate the proposed methodology’s validity.
Research on Virtual Interactive Animation Design System Based on Deep Learning
Authors: Liu Bing
Published: June 2022

With the rapid development of computer network technology, the advantages of virtual reality technology in the field of instant messaging are becoming more and more significant. Virtual reality technology plays an important role in communication networks, including enhanced resource utilization, device redundancy, immersion, interactivity, conceptualization, and holography. In this paper, we use the basic theory of Restricted Boltzmann Machine to establish a semisupervised spatio-temporal feature model through the animation capture data style recognition problem. The bottom layer can be pretrained with a large amount of unlabeled data to enhance the model’s feature perception capability of animation data, and then train the high-level supervised model with the labeled data to finally obtain the model parameters that can be used for the recognition task. The layer-by-layer training method makes the model have good parallelism, that is, when the layer-by-layer training method makes the model well parallelized, that is, when the bottom features cannot effectively represent the animation features, such as overfitting or underfitting, only the bottom model needs to be retrained, while the top model parameters can be kept unchanged. Simulation experiments show that the design assistance time of this paper’s scheme for animation is reduced by 10 minutes compared to baseline.
Construction of a Prognostic Immune-Related LncRNA Risk Model for Gastric Cancer
Authors: Ding Wei , Sun Pengcheng , Tan Yulin , Jiang Huaji , Xi Cheng , Zhuang Ling , Xu Yixin , Xu Xuezhong
Published: June 2022

Gastric cancer (GC) is one of the most common malignancies, and novel prognostic biomarkers for it are urgently required. This study is aimed at screening a group of immune-related lncRNAs (IRLs) in predicting the prognosis of GC patients. Genetic and clinical information from the 360 GC patients was included in this study. Eight IRLs in lncRNA-miRNA-mRNA network were screened out according to differential expression analysis. A novel risk score model with three IRLs (MIR4435-1HG, UCA1, and RP11-617F23.1) were identified, and patients were assigned to a high-risk group and a low-risk group. Patients in the low-risk group had a better prognosis. In addition, two nomograms were developed to predict the prognosis of GC. We evaluated the correlation between IRLs and the immune infiltration level of GC using TIMER. Furthermore, we verified that RP11-617F23.1 was significantly upregulated in human GC tissues compared with their adjacent tissues. And, patients with high RP11-617F23.1 expression in tumor tissues had poorer survival. In conclusion, we established a novel risk model based on IRLs for predicting the prognosis of GC. Meanwhile, a novel IRL, RP11-617F23.1, could serve as a predictor of prognosis for patients with GC.
Adaptive Weighted Strategy Based Integrated Surrogate Models for Multiobjective Evolutionary Algorithm
Authors: Bao Ke , Fang Wei , Ding Yourong
Published: June 2022

Although the integrated model has good convergence ability, it is difficult to solve the multimodal problem and noisy problem due to the lack of uncertainty evaluation. Radial basis function model performs best for different degrees of nonlinear problems with small-scale and noisy training datasets but is insensitive to the increase of decision-space dimension, while Gaussian process regression model can provide prediction fitness and uncertainty evaluation. Therefore, an adaptive weighted strategy based integrated surrogate models is proposed to solve noisy multiobjective evolutionary problems. Based on the indicator-based multiobjective evolutionary framework, our proposed algorithm introduces the weighted combination of radial basis function and Gaussian process regression, and U-learning sampling scheme is adopted to improve the performance of population in convergence and diversity and judge the improvement of convergence and diversity. Finally, the effectiveness of the proposed algorithm is verified by 12 benchmark test problems, which are applied to the hybrid optimization problem on the construction of samples and the determination of parameters. The experimental results show that our proposed method is feasible and effective.
Loosening of Access Threshold and Downturns in Listed Firms Performance: A Quasi‐Natural Experiment from the “IPO Green Channel” for Poverty Alleviation
Authors: Sun Yi , Cui Chunying , Gao Ya , Zhao Chunyan
Published: June 2022

The China Securities Regulatory Commission (CSRC) proposed the green channel policy for poverty alleviation Initial Public Offerings (IPOs), aiming to help poverty‐stricken areas reduce poverty and increase income, but it has an impact on the business performance of the companies involved. This paper uses data from public annual reports of listed companies to construct a policy intervention group and a control group and comparatively analyzes the degree of annual performance downturn of newly listed companies in A‐share from 2016 to 2017 through a counterfactual method, and the results show that the “IPO green channel” policy for poverty alleviation has an obvious negative effect on listed enterprises in poverty‐stricken counties. The results show that enterprises listed via the green channel have more significant performance downturn than those listed normally during the same period. This paper suggests that the distortion of the market issuance system causes the declining of the quality of IPO firms. Finally, this paper suggests that regulators should cooperate with local governments actively to ensure the smooth implementation of the government’s poverty alleviation efforts while balancing market equity and efficiency. The SFC should take more consideration of institutional compatibility for policy output in financial poverty alleviation to avoid institutional alienation and distortion of corporate behavior.
Interactions of Foundation Pit on the Underlying Adjacent Existing Underground Structures
Authors: Xu Wanzhong , Liu Baohua , Liu Jingshuo , Guo Chun
Published: June 2022

The influence of foundation pit excavation on deformation and stability of the supporting structure of underlying adjacent existing tunnel is a comprehensive geotechnical engineering problem, including unloading effect of foundation pit excavation and the coupling effect of deformation of the tunnel surrounding rock and supporting structure. Using numerical calculation methods, this paper changes the different excavation depths and widths of foundation pit and discusses the evolution law about force and deformation of tunnel supporting structure, and the changing relationship of the axial force of tunnel anchor with the distance between foundation pit and tunnel is obtained and compares the influence of different excavation way of foundation pit on the axial force of anchor rod near the existing tunnel. It is found that with the increase of distance between foundation pit and tunnel, the axial force of anchor rod at the top of tunnel under the foundation pit entirely symbolizes a trend of increases first and then decreases, the axial force of the leftmost anchor rod changes from pressure to tension, then decreases gradually. When the foundation pit depth increases, the axial force of the anchor rod at the top of the tunnel increases, the axial force of the leftmost anchor rod decreases, and the width of foundation pit absolutely has little effect on the axial force of the leftmost and the top tunnel.
A Computational Intelligence Model for Legal Prediction and Decision Support
Authors: Shang Xuerui
Published: June 2022

Legal judgment prediction (LJP) and decision support aim to enable machines to predict the verdict of legal cases after reading the description of facts, which is an application of artificial intelligence in the legal field. This paper proposes a legal judgment prediction model based on process supervision for the sequential dependence of each subtask in the legal judgment prediction task. Experimental results verify the effectiveness of the model framework and process monitoring mechanism adopted in this model. First, the convolutional neural network (CNN) algorithm was used to extract text features, and the principal component analysis (PCA) algorithm was used to reduce the dimension of data features. Next, the prediction model based on process supervision is proposed for the first time. When modeling the dependency relationship between sequential sub-data sets, process supervision is introduced to ensure the accuracy of the obtained dependency information, and genetic algorithm (GA) is introduced to optimize the parameters so as to improve the final prediction performance. Compared to our benchmark method, our algorithm achieved the best results on four different legal open data sets (CAIL2018_Small, CAIL2018_Large, CAIL2019_Small, and CAIL2019_Large). The realization of automatic prediction of legal judgment can not only assist judges, lawyers, and other professionals to make more efficient legal judgment but also provide legal aid for people who lack legal expertise.
Empirical Analysis of News Dissemination Effect Based on Energy Data in the Internet + Era
Authors: Han Ruiyu , Liu Jingmin
Published: June 2022

Matter, energy, and information are the three major elements that constitute modern society. In the system of three major elements, energy is the driving force to support human development. In today’s world, energy is more related to the overall security of the country. With the development of science and technology, technologies such as social media, cloud computing, and the Internet of Things have emerged and developed day by day, relying on the network. Big data and massive information flood people’s lives and have a huge impact on all walks of life. The convergence of information technology and the economy and society has led to the rapid growth of data. “Internet +” has risen as the national strategy, and data has become a national basic strategic resource. Based on researching the achievements of predecessors, combined with the first-hand data obtained from practice, this paper mainly uses the literature analysis method and the case analysis method to conduct research. In the case analysis, the content analysis method and sample analysis method are used to study the development status of energy news in the mobile Internet era in China and explore the development method suitable for energy news communication in the mobile Internet era.
A Cell Component-Related Prognostic Signature for Head and Neck Squamous Cell Carcinoma Based on the Tumor Microenvironment
Authors: Li Siyu , Gu Yajun , Wang Junguo , Ma Dengbin , Qian Xiaoyun , Gao Xia
Published: June 2022

Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous disease with a high mortality rate. The tumor microenvironment (TME) is composed of numerous noncancerous cells that contribute to tumorigenesis and prediction of therapeutic effects. In this study, we aimed to develop a cell component-related prognostic model based on TME. We screened cell component enrichments from samples in The Cancer Genome Atlas (TCGA) HNSCC cohort using the xCell algorithm. Univariate Cox and multivariate Cox regression analyses were performed to establish an optimal independent risk model. The prognostic value of the model was further validated using Gene Expression Omnibus datasets. We found that patients in the low-risk group had a better outcome and activated immunity and may benefit more from the immune checkpoint inhibitor therapy. We also explored microRNAs (miRNAs) that may regulate these identified cell components, and 11 miRNA expression levels influenced the overall survival time. Moreover, their target mRNAs were differentially expressed in TCGA cohort and enriched in pathways of cell cycle pathways, extracellular matrix receptor interaction, human papillomavirus infection, and cancer. In summary, our cell component-related signature was a promising prognostic biomarker that provides new insights into the predictive value of nontumor components in the TME.
New Practical Nonlinear Energy-Harvesting Models for Wireless-Powered Communications
Authors: Babaei Mohammadreza , Durak-Ata Lütfiye , Aygölü Ümit
Published: June 2022

As a massive number of nodes are expected to be connected to each other in next-generation wireless networks, the power supply of such multiple-antenna nodes is a challenge and requires new and sustainable solutions. Radio frequency energy harvesting (EH) is among the promising approaches to solve this issue. In this paper, two new mathematically tractable nonlinear EH (NL-EH) models are proposed, for which the system performance is evaluated in terms of average harvested power, throughput, and bit error probability. The system consists of one multiantenna power-constraint source harvesting its power from a dedicated power beacon and transmitting its signal to a destination equipped with multiple antennas. For a comprehensive analysis of the system, closed-form expressions are derived for Nakagami-

m

fading channels and the special case of Rayleigh channels. For comparison purposes, the performance derivations in Nakagami-

m

fading channels are provided for linear and piece-wise linear EH models given in the literature. Besides, the simulation results are obtained by applying the Monte-Carlo method for NL-EH models existing in the literature. The results provide a broadening view of EH systems and thoroughly compare the proposed NL-EH models with linear, piece-wise linear, and NL-EH models available in the literature. Hence, these provide better insights towards the analyses and design of EH systems.
Interaction Design of Wellness Building Space by Deep Learning and VR Technology in the Context of Internet of Things
Authors: Yang Mei , Wang Xinyu
Published: June 2022

This study explores the application of deep learning theory and virtual reality (VR) technology to the interactive behavior design of building space according to the interaction behavior of wellness building space in the context of the Internet of Things (IoT). Firstly, VR theory is made into an image-based 3-dimensional (3D) modeling process. Secondly, the interactive behavior information data is analyzed according to the theory of deep learning and edge computing. Finally, the particle swarm optimization (PSO) is used to analyze the predicted temperature with the wellness building space model, as well as to make a study based on the changes in the user’s psychological indicators. The results show that the model predictions of deep learning-edge computing are most like the actual environmental settings. Both PSO and deep learning algorithms have varying degrees of influence on the final prediction results. The average temperature of the wellness building space established by deep learning and edge computing is 24.58 degrees, the average measured value of the actual environment is 24.49 degrees, and the predicted values of the two are similar. While users experienced the interactive design of the health building, their heart rate dropped from 73.17 to 68.79 and gradually became stable. There is no obvious change in the user’s heart rate, which reflects the comfort of the wellness building space designed based on deep learning and VR interaction.
Quality Risk Management Algorithm for Cold Storage Construction Based on Bayesian Networks
Authors: Song Yaping , Wei Zhanguo
Published: June 2022

In the cold storage construction project, only by controlling the quality risk of the project can ensure that the cold storage can meet the expected use function and achieve the expected economic benefits after the completion of the cold storage. In order to effectively ensure the key pivot role of cold storage in cold chain logistics, a cold storage construction quality risk management system is constructed to identify and analyze quality risk factors from three dimensions: construction procedures, participating units, and work processes, construct a cold storage construction quality risk evaluation model based on Bayesian network, and through reverse reasoning analysis and sensitivity analysis, key quality risk factors are derived: inadequate quality assurance system, technical delivery is not in place, mismatch of building materials and equipment, inadequate training of skilled workers, completion acceptance is not careful or acceptance standards are unreasonable, and duration does not meet the requirements. Finally, in view of the above quality risks, suggestions and measures are put forward from five aspects: man, material, machine, method, and environment.
Research on the Forecast of the Combined GRU Model Based on the Optimized Bat Algorithm in the Bank Transaction Volume
Authors: Rong Jingfeng , Wang Di , Zhang Boya , Wang Yonggui
Published: June 2022

The accurate prediction of the transaction volume of the core accounting system is of great significance to the stable operation of commercial banks. After fully investigating the transaction volume history of the core accounting system and discovering the unique time-series attributes of the data, this study proposes a transaction volume prediction model of the core accounting system of commercial banks based on the improved bat algorithm and the optimized gating loop unit neural network. The chaos algorithm, reverse learning, evolution, and search mechanisms are implemented to improve the search efficiency of solving the global optimization problem, to overcome the shortcomings of the original bat algorithm, such as early maturation and easily falling into the trap of a local optimal solution, and to enhance the algorithm’s optimization ability and precision. Moreover, the bat algorithm’s optimization ability is fully utilized, and the optimal parameters of the GRU model, such as network layers and neural units, are determined. Finally, the effectiveness of the combination model is evaluated using historical transaction data from the core accounting system stored in a bank data warehouse. The experimental results show that the improved combined GRU model performs well in mean squared error, root mean squared error, and mean absolute error, which is superior to the original GRU model and the traditional time-series forecasting ARMA model. The proposed combined model can be effectively applied to the transaction volume prediction of the core accounting system of commercial banks.
Spontaneous Pneumomediastinum, Pneumoperitoneum, and Subcutaneous Emphysema beyond the Inguinal Ligament Secondary to Inversion Maneuvers
Authors: Plowman Keegan , Lindner David , Valle-Giler Jose Ruben
Published: June 2022

Pneumomediastinum is free air within the mediastinal cavity which can spread along tissue planes leading to the accumulation of large amounts of subcutaneous emphysema. Patient is a 21-year-old male with a history of autism spectrum disorder and rhabdomyolysis who presented with diffuse “popping under the skin” and was found to have crepitus extending from his neck to his bilateral ankles. He exercises frequently and performs chin-up pullovers and will often hold his breath during this movements. He uses an inversion table but denies any valsalva maneuvers or straining while inverted. Radiological imaging demonstrated pneumomediastinum, pneumoperitoneum, and diffuse subcutaneous emphysema extending into the pelvis. Diagnosis requires a combination of history, physical exam findings, and imaging findings. Patients with spontaneous pneumomediastinum typically experience self-limited disease, and efforts should be made to minimize low yield invasive testing. Most patients can be treated on an outpatient basis after monitoring and education about potential complications.
All-Round Control of High-Rise Buildings by Construction Organization Design Based on Distributed Control System
Authors: Pan Mingming , Wang Xu
Published: June 2022

The construction of high-rise buildings usually involves many problems. With the increasing population and demand for tall buildings, researchers have also proposed different design approaches for various aspects of tall buildings. The design of traditional construction organization is difficult to cover all systems of high-rise buildings comprehensively and efficiently. Among the high-rise buildings involved, the first important is safety, followed by energy conservation. The high floor and complex structure of high-rise buildings bring hidden dangers to safety, and the large volume brings a burden to energy conservation. In this paper, in order to solve the problems that the system is difficult to fully cover in high-rise buildings and the design of consumables and energy materials, the method of distributed system is adopted. The main aspects of high-rise buildings are studied in an all-round way, and a distributed system model is first constructed in this paper. Then, the lighting, air conditioning, and fire protection systems of high-rise buildings are correspondingly formed under this system. Through the simulation experiment of the B4 building in the business district, it is concluded that the average luminous flux of lighting under the distributed system of this paper is 11385 lm, saving more than 60% of energy. The error of the air-conditioning system is less than 0.2°C, and the energy consumption is saved by 10%. The fire detection window length of the fire protection system exceeds 0.15, which can detect the fire sensitively. In addition, the evacuation pressure of personnel is reduced by more than 20% in emergency situations. The results show that the system establishment of high-rise buildings under the distributed method in this paper can effectively improve the efficiency, save energy consumption, and reduce the pressure of resource utilization. The design of this paper unifies the overall and local control objectives, which can fully meet the needs of comfort and convenience.
Construction and Application of Groundwater Impact Analysis Model Based on Random Matrix Model
Authors: Xing Fang , Tan Ya
Published: June 2022

Traditional groundwater models do not consider the influence of random factors, so their results of simulation and prediction cannot reflect the actual dynamics of ongoing groundwater changes. By comparing the statistical properties of random multidimensional time series, the random matrix theory can reflect the degree of deviation from randomness in actual data and reveal the behavioral characteristics of the overall correlation in actual data. On the basis of summarizing and analyzing the previous literature, this study expounded the research status and significance of groundwater impact analysis models, elaborated on the development background, current status, and future challenges of the random matrix model, introduced the methods and principles of the finite isometric properties and observation feature dimension, conducted the spectral analysis of the random matrix of groundwater impact analysis data, analyzed the eigenvalue and the eigenvector distribution of the random matrix of the groundwater impact analysis data, constructed a groundwater impact analysis model based on the random matrix model, designed the hydrodynamic model for the groundwater impact analysis, discussed the parameter variation mechanism of the groundwater impact analysis, and finally carried out a case application and its result analysis. The study results show that in the membership calculation formula of the random matrix model, it is found that there is an inverse relationship between the expected value of the target index and its membership value. According to the initial fuzzy membership function value, the random matrix model uses the normalization formula and evaluation criteria of the catastrophe model to carry out a comprehensive quantitative recursive operation and finally determines the concept, characteristics, influencing factors, evaluation content, purpose, and scope of the groundwater impact analysis model. The random matrix model can not only reflect the spatial and temporal differences of groundwater-influencing factors but also reflect the dynamic mechanism of aquifer or groundwater system or the inherent law of groundwater movement, so it is more applicable and convenient to evaluate groundwater resources.
Singlet Oxygen, Photodynamic Therapy, and Mechanisms of Cancer Cell Death
Authors: Maharjan Prabal Singh , Bhattarai Hitesh Kumar
Published: June 2022

Photodynamic therapy (PDT) can be developed into an important arsenal against cancer; it is a minimally invasive therapy, which is used in the treatment or/and palliation of a variety of cancers and benign diseases. The removal of cancerous tissue is achieved with the use of photosensitizer and a light source, which excites the photosensitizer. This excitation causes the photosensitizer to generate singlet oxygen and other reactive oxygen species. PDT has been used in several types of cancers including nonmelanoma skin cancer, bladder cancer, esophageal cancer, head and neck cancer, and non-small cell lung cancer (NSCLC). Although it is routinely used in nonmelanoma skin cancer, it has not been widely adopted in other solid cancers due to a lack of clinical data showing the superiority of PDT over other forms of treatment. Singlet oxygen used in PDT can alter the activity of the catalase, which induces immunomodulation through HOCl signaling. The singlet oxygen can induce apoptosis through both the extrinsic and intrinsic pathways. The extrinsic pathway of apoptosis starts with the activation of the Fas receptor by singlet oxygen that leads to activation of the caspase-7 and caspase-3. In the case of the intrinsic pathway, disruption caused by singlet oxygen in the mitochondria membrane leads to the release of cytochrome c, which binds with APAF-1 and procaspase-9, forming a complex, which activates caspase-3. Mechanisms of PDT action can vary according to organelles affected. In the plasma membrane, membrane disruption is caused by the oxidative stress leading to the intake of calcium ions, which causes swelling and rupture of cells due to excess intake of water, whereas disruption of lysosome causes the release of the cathepsins B and D, which cleave Bid into tBid, which changes the mitochondrial outer membrane permeability (MOMP). Oxidative stress causes misfolding of protein in the endoplasmic reticulum. When misfolding exceeds the threshold, it triggers unfolding protein response (UPR), which leads to activation of caspase-9 and caspase-3. Finally, the activation of p38 MAPK works as an alternative pathway for the induction of MOMP.
Immune Infiltration of Ulcerative Colitis and Detection of the m6A Subtype
Authors: Gu Chenzheng , Wu Junlu , Zhang Wei , Yao Yiwen , Yan Wenhui , Yuan Yi , Wang Weiwei , Shang Anquan
Published: June 2022

Ulcerative colitis (UC) is an inflammatory bowel disease characterized by persistent colon inflammation. N6-methyladenosine (m6A) methylation is one of the most prevalent RNA modifications with key roles in both normal and illness, but m6A methylation in ulcerative colitis is unknown. This research investigated m6A methylation in UC. We examined the expression of known m6A RNA methylation regulators in UC using the Gene Expression Omnibus database (GEO database). First, we used m6A regulators to examine m6A change in UC samples. These two patient groups were created by clustering three m6A gene expression datasets. These genes were then utilized to build an m6A gene network using WGCNA and PPI. These networks were built using differentially expressed genes. The 12 m6A regulators were found to be dispersed throughout the chromosome. The study’s data were then connected, revealing positive or negative relationships between genes or signaling pathways. Then, PCA of the 12 m6A-regulated genes indicated that the two patient groups could be discriminated in both PC1 and PC2 dimensions. The ssGSEA algorithm found that immune invading cells could be easily distinguished across diverse patient groups. Both groups had varied levels of popular cytokines. The differential gene analysis of the two samples yielded 517 genes like FTO and RFX7. It found 9 hub genes among 121 genes in the blue module, compared their expression in two groups of samples, and found that the differences in expression of these 9 genes were highly significant. The identification of 9 possible m6A methylation-dependent gene regulatory networks suggests that m6A methylation is involved in UC pathogenesis. Nine candidate genes have been identified as possible markers for assessing UC severity and developing innovative UC targeted therapeutic approaches.