bluuebunny/crossref_metadata_2025 huggingface dataset:
Application of BIM Technology in Data Collection of Large-Scale Engineering Intelligent Construction
Authors: Zhao Heng
Published: August 2022

In order to solve the problem of data collection of intelligent construction of large-scale projects, the author proposes the application of BIM technology in the collection of intelligent construction data of large-scale projects; this method proposes to take advantage of the technical advantages of BIM; starting from the needs of the commercial complex operation and management stage, a BIM-based commercial complex operation management model is established, and the application content of this model to improve the efficiency of commercial complex operation and management is studied. The experimental results show that according to the design conditions and the depth of BIM application, the standard elements and variable factors can be divided into 70% and 30%, respectively. Conclusion. BIM can realize the refinement, intelligence, and quality of commercial complex operation.
Construction of Student Innovation and Entrepreneurship Experience System Integrating K-Means Clustering Algorithm
Authors: Zou Fanna , Li Rui
Published: August 2022

The traditional talent development model is unlikely to satisfy the demands of the times and socioeconomic growth. Innovation-driven development policies require a great deal of creativity and entrepreneurship. Student creativity and education in the enterprise have emerged as an essential source of concern for all segments of society. The traditional talent cultivation mode has been unable to meet the requirements of the economic growth of the society. College graduates are national talents of high quality and high level, which are important talent resources for colleges and universities to grow sustainably. Innovation and entrepreneurship education for college students is a common concern of the current society. The research and implementation of innovation and entrepreneurship education for Chinese college students are progressing rapidly, but the innovation and entrepreneurship education of Chinese college students began later and is not at a high level of development at present. Promoting student innovation and entrepreneurship requires further improvements in research and teaching methods. This article presents the scientific classification and the K-means algorithm. It defines the concepts related to the education system of change and entrepreneurship, the concepts related to the search for change and the theories of entrepreneurship, and the theoretical foundations of the development of the education system. Of the researchers, 39.47% were male; 60.53% were female; 13.68% were students; 70.53% were undergraduate students; and 15.79% were postgraduate students.
Design of Game Data Acquisition System Based on Artificial Intelligence Algorithm
Authors: Li Xiangrui , Wang Huayi
Published: August 2022

Game data collection system is a tool used to collect the behavior data of users about the game. It can be used for data analysis of user behavior so that game manufacturers can keep abreast of market dynamics and popular trends, and they also can have a deeper understanding of the behavioral habits and psychology of player user groups. The defects of the current data acquisition system include that the data are not encrypted. The network transmission efficiency is relatively low. The acquisition speed is slow, and the settings cannot be dynamically changed. This paper proposes to study how to enhance the acquisition ability and improve the analysis efficiency in the design of data acquisition system for solving these problems. Therefore, on the basis of artificial intelligence algorithm, this paper designs a game data collection system by using artificial neural network algorithm, support vector algorithm, and cluster analysis algorithm, which solves the basic problem of slow data collection in current data collection and plays a role in improving the efficiency of network transmission. The experimental results in this paper show that when the number of data is more than 300, the time-consuming time reaches more than 68 ms. When the number of written data is more than 300, it takes more than 181 ms. When the number of deleted data is more than 300, it takes more than 236 ms. From the above data, it shows that the designed game data collection system is rapid and efficient.
On the Freeze-Thaw Instability of an Open Pit Slope Using Three-Dimensional Laser Scanning and Numerical Simulation
Authors: Li Juzhou , Li Changhong , Li Guoqing , Hu Yongyue , Yi Xuefeng , Wang Yu
Published: August 2022

In order to reveal the instability law of open pit mine slope in high-cold and high-altitude area. Firstly, the slope structural plane is scanned by three-dimensional (3D) laser scanning technology, and the point cloud data is obtained to realize the intelligent identification of rock mass structural plane. The geometric parameters of rock mass are counted, and the physical structure model is established. Then, we carry out freeze-thaw cyclic tests on granite to obtain the corresponding mechanical parameters. Finally, according to the obtained mechanical parameters, we use RS2 finite element software to calculate the shear strength of structural plane and joint by generalized Hoek-Brown criterion and Barton-Bandis criterion, respectively, establish the geomechanical model, and use the finite element strength reduction method to calculate the safety factor of slope and judge the instability of slope. The results show that the physical and mechanical properties of granite deteriorate with the increase of the freeze-thaw cycle. Under the action of the freeze-thaw cycle, the pore water in the rock mass freezes and forms frost heaving force. The expansion of volume leads to the further development of joint fissures. The strength of rock slope decreases gradually with the increase of freeze-thaw cycle times, and the safety factor of slope decreases continuously. It shows that repeated freeze and thaw alternation makes the stability of mine slope worse and worse. The research results are helpful to prevent the occurrence of slope disasters in advance and are of great significance to effectively and safely manage the stability of slope, the treatment of open pit, and environmental treatment.
Wastewater Treatment Using a Photoelectrochemical Oxidation Process for the Coffee Processing Industry Optimization of Chemical Oxygen Demand (COD) Removal Using Response Surface Methodology
Authors: Abdi Firomsa Bidira , Samuel Zerihun Asmelash , Debela Seifu Kebede , Amibo Temesgen Abeto
Published: August 2022

The elimination of organic compounds in coffee processing effluent utilizing electrochemical oxidation (ECO) as well as a combination of electrochemical oxidation (ECO) and ultraviolet and hydrogen peroxide (UV/H2O2) was explored. Then, the percentage reduction of chemical oxygen demand (COD) was investigated. The effect of different experimental factors such as solution pH, sodium chloride (NaCl) concentration, calcium chloride (CaCl2) concentration, electric current, electrolysis duration, and hydrogen peroxide dosage on the percent removal efficiency of the hybrid electrochemical oxidation (ECO) with the ultraviolet and hydrogen peroxide (UV/H2O2) process has been investigated. The response surface methodology (RSM) based on central composite design (CCD) was used to organize the trial runs and optimize the results. The hybrid electrochemical oxidation (ECO) with the ultraviolet and hydrogen peroxide (UV/H2O2) process removed 99.61% of the chemical oxygen demand (COD) with a low power usage of 1.12 kWh/m3 compared to the other procedures, according to the experimental data analysis. These findings were obtained with a pH of 7, a current of 0.40 A, 1.5 g of CaCl2, and a total electrolysis period of 40 minutes. When it came to eliminating organic compounds from coffee manufacturing effluent, CaCl2 outperformed NaCl. Analysis of variance (ANOVA) with 95% confidence limits was used to examine the significance of independent variables and their interactions.
Overburden Deformation Rule in Super High Seam Fully Mechanized Caving Mining of Thick Unconsolidated Stratum
Authors: Liu Fei , Ma Zhanguo , Han Yongsheng
Published: August 2022

It is urgent for westward coal mine development to control surrounding rock of thick unconsolidated stratum, thin basement rock, and super high seam under geological conditions. Studying surrounding rock deformation control mechanism under special geological conditions in western regions is of practical guiding significance for the implementation of China’s energy strategy in western regions, with special mining geological conditions of the experimental mine as the background, deformation rule, failure characteristic, and stress distribution characteristic of surrounding rock in fully mechanized caving mining of thick unconsolidated stratum. By establishing the structural mechanics model of “sidestepped rock beam,” the stability criterion is determined. The horizontal displacement of surrounding rock in the middle of the working face is small, and the displacement at both ends is large. After the mining of the working face, an “O” ring appears on the main roof. The ring extends outward with the excavation, reaches the maximum at the initial weighting, and its displacement decreases from inside to outside. Finally, through theoretical analysis, experimental research, numerical calculation, and field observation, the thin bedrock and ultra-high coal seam are systematically analyzed, and its deformation and failure mechanism is revealed. Relevant research findings are successfully implemented on the scene and effectively ensured safety, high yield, and high efficiency of the mine.
A Nonexplosive Method for Simulating Stress Waves of Large-Scale Underground Explosions
Authors: Ji Yuguo , Pan Yuefeng , Deng Shuxin , Gao Fei , Zhao Zhangyong , Xu Tianhan , Yue Songlin , Li Zhihao
Published: August 2022

In the large-scale underground explosion, the dynamic mechanical behavior of deep rock mass under the coupled loading of the high in situ stress and the explosive stress wave is difficult to study. And the coupled loading of the in situ stress and the stress waves of large-scale underground explosions are hard to simulate. Based on this problem, an experimental device was developed, and a nonexplosive method for simulating stress waves of large-scale underground explosions was presented by us. In the experimental device, the impact energy is provided by the high-pressure gas in the air chamber, the stress wave is generated by the impact of the piston, and the waveform of the stress wave is adjusted by the composite pulse shaper. The adjusted stress wave can be transmitted to the container, where the coupled loading of the stress wave and the confining stress can be realized. The stress wave that corresponded to the real explosion is obtained by the developed device, and the function of the composite pulse shaper for adjusting the waveform is verified in the experiments by using a variety of mediums for wave adjustment. The experimental and calculation results showed that the stress wave corresponding to underground explosion at kilotons of equivalent on rock mass at great depth can be simulated by the experimental device, and the simulated explosion equivalent and buried depth can be adjusted by controlling the experimental conditions.
Assessing the Prognostic Capability of Immune-Related Gene Scoring Systems in Lung Adenocarcinoma
Authors: Liu Wenhao , Dong Ruihong , Gao Shuai , Shan Xiaodi , Li Mian , Yu Zhaoyan , Sun Liang
Published: August 2022

Background. Lung adenocarcinoma (LUAD) is the commonest of the subtypes of lung cancer histologically. For this study, we intended to analyze the expression profiling of the immune-related genes (IRGs) from an independently available public database and developed a potent signature predictive of patients’ prognosis. Methods. Gene expression profiles and the clinical data of lung adenocarcinoma were gathered from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA), and the obtained data were split into a training set (n = 226), test set (n = 83), and validation set (n = 400). IRGs were then gathered from the ImmPort database. A prognostic model was constructed by analyzing the training set. Then the GO and KEGG analysis was performed, and a gene correlation prognostic nomogram was constructed. Finally, external validation, such as immune infiltration and immunohistochemistry, was performed. Results. The 110 genes were significant by univariate Cox regression analysis and randomized survival forest algorithm for the training set and showed a good distinction between the low-risk-score and high-risk-score groups in the training set (

P
<
0.0001

) by screening for four prognosis-related genes (HMOX1, ARRB1, ADM, PDIA3) and validated by the test set GSE30219 (

P
=
0.0025

) and TCGA dataset (

P
=
0.00059

). Multivariate Cox showed that the four gene signatures were an individual risk factor for LUAD. In addition, the genes in the signatures were externally verified using an online database. In particular, PDIA3 and HMOX1 are essential genes in the prognostic nomogram and play an important role in the model of immune-related genes. Conclusion. Four immune-related genetic signatures are reliable prognostic indicators for patients with LUAD, providing a relevant theoretical basis and therapeutic rationale for immunotherapy.
Prediction Model of Rice Seedling Growth and Rhizosphere Fertility Based on the Improved Elman Neural Network
Authors: Jiao Feng , Chen Yang , Zhang Xinyue , Zhou Yuyue , Wang Linlin , Wu Jinhua
Published: August 2022

Rice developing prognostication is a key part of precise agricultural management, and its advancement is an intricate course of events involving the interplay of breed and environmental element. The traditional research method is based on data analysis of rice growth prediction modeling, mining the concealed rapport between rice productivity and circumstance element, for instance, weather, sunlight, and water, and then predicting its yield and analyzing the complex rapport between the circumstance element and growth in every developing phase. In this dissertation, the improved ElmanNN is accustomed to establish a prediction model, and the ElmanNN is accustomed to determine the rapport between the circumstance element and growth in every developing phase simultaneously so as to avoid the arithmetic falling into local optimum easily. In this dissertation, the improved genetic arithmetic is accustomed to optimize the initial weight and threshold of Elman neural network, and the range of weight value multitudinous layers in the mould are obtained by training the network with samples that have been tested in the last few years. Finally, the rapport between growth and yield in six different periods is independently modeled, and the training samples are build up separately one by one based on physiological parameters and environmental indicators of rice at every level. The experiments show that the accuracy for the prediction model in the light of the improved ElmanNN has been beneficial.
Effectiveness of Super-selective Embolization for Parasagittal Meningiomas and Its Effect on the Level of Inflammatory Factors
Authors: Zheng Zhaoke , Jia Linwei , Zhang Peihua , Tian Yaohui , Chen Xi
Published: August 2022

Objective. To evaluate the effectiveness of super-selective embolization for parasagittal meningiomas and its effect on the level of inflammatory factors. Methods. A total of 48 patients with parasagittal meningiomas diagnosed and treated in our hospital from September 2018 to March 2020 were randomly included and assigned to receive meningioma resection (control group) or meningioma resection plus super-selective embolization (study group), with 24 patients in each group. Outcome measures included clinical indices, tumor resection outcome, inflammatory factor levels, and follow-up results. Results. Patients in the study group had shorter operative time and less intraoperative bleeding volume than those in the control group (

P
<
0.05

). The study group had more patients with Simpson I resection (87.50%) than the control group (62.50%) (

P
<
0.05

). There was no statistically significant difference in the levels of inflammatory factors between the two groups of patients before treatment (

P
>
0.05

). After treatment, the levels of interleukin (IL)-6, tumor necrosis factor (TNF)-α, and hypersensitive C-reactive protein (hs-CRP) in patients in the study group were significantly lower than those of the control group. The results of the three-month follow-up showed that one patient in the study group was reoperated for tumor recurrence and there was no death record, while three patients in the control group were reoperated for tumor recurrence and one patient died. The difference in the recurrence and mortality between the two groups did not come up to the statistical standard (

P
>
0.05

). Conclusion. Super-selective embolization for parasagittal meningiomas contributes to reducing intraoperative bleeding, effectively improving tumor resection and surgical safety, and lowering inflammatory factor levels. Further trials are, however, required before clinical promotion.
Ageing Design of Urban Park Landscape Based on Computer Virtual Simulation Technology
Authors: Fan Jing
Published: August 2022

Greenery are the parks in the urban areas and it becomes progressively most significant as cities grow more crowded. In the end, urban parks contribute to the population’s healthiness and welfare by providing opportunities intended for physical and social activity, leisure, and relaxation. In order to construct “Digital Land,” computer and system software and hardware were used. Computer simulations are used to examine the value of a city garden’s landscape design. Digital models and multimedia performances are being built using computer-aided design (CAD), this underlines the need for digitizing data for landscape design. A region’s landscapes can only be accurately assessed if sufficient information is available on the elements that influence the people’s awareness of landscape quality, as well as the kind, method, and effective rate of each of them. We use an LVQANN (artificial neural network) to forecast the landscape aesthetic assessment of urban parks and priorities the model’s significant factors in this research. User viewpoint and artificial neural network modelling were utilized in conjunction to assess the aesthetic quality of the urban park’s environment. This was done for two reasons. The design of urban parks decision support system is known as MATLAB software’s multilayer perceptions model, which gives the ability to anticipate landscape visual significance in innovative parks. In this study, the ANN LVQ model is used to execute an ageing design of an urban park landscape based on a computer virtual simulation application. An example land is selected as input and area linked to sunny spot, top view, and so on is fixed. The ANN tool box is used to develop this application in MATLAB 2018b software. The following approach is used to create the ideal urban park landscape model. An accuracy of 89.23 percent, a sensitivity of 87.34 percent, and a recall of 78.93 percent were achieved, outperforming the approach and competing with the current model.
Evaluation of Ideological and Political Education Using Deep Learning Neural Networks
Authors: Sheng Shiwei , Gao Yong-ping
Published: August 2022

The demand for talents extends beyond theoretical knowledge to include students’ IPE (Ideological and Political Education). We can only continue to promote the rapid development of university teaching in China by putting in place a solid management system. It is the foundation of college students’ IPE in this era of rapid information technology development to accurately adapt to and understand the actual problems and requirements of IPE proposed by the development of the times and innovate and develop accordingly. Deep learning (DL) is a sophisticated machine learning algorithm that outperforms previous technologies in speech and image recognition. In this paper, DLNN (deep learning neural network) technology is applied to IPE evaluation and a set of university-specific IPE evaluation index systems is developed. The BPNN (BP Neural Network) evaluation model is used to train and study a specific amount of teaching quality data in the MATLAB simulation tool. On the prediction of IPE evaluation, the BPNN algorithm has a prediction error of about 0.050.4 according to the findings. The validity and accuracy of using the BPNN algorithm to model IPE quality are demonstrated.
Application of Nanocomposites with Thermal Barrier Coating in Heat Pump Heat Transfer Systems
Authors: Zhao Qi , Ma Shuang , Liu Lixin , Jin Hongwen , Chang Huinan , Liu Xiaoyue , Wu Wende
Published: August 2022

Air-source heat pumps are highly valued in many fields because of their energy-saving and efficient characteristics and they have a good market prospect. However, with the continuous development of heat pump technology, the thermal barrier coating materials used in air-source heat pumps seriously limit their growth. Nanoparticles are the smallest microparticles found at present. They have many excellent characteristics, and the nanomaterials made of nanoparticles have surface activity that ordinary materials do not have. Based on this, this paper combines nanostructures with thermal barrier coatings to study the application of thermal barrier coatings based on nanocomposites in heat pump systems.
Correlation, Regression Analysis, and Spatial Distribution Mapping of WQI for an Urban Lake in Noyyal River Basin in the Textile Capital of India
Authors: Ashwin K. R. N , Arulmozhi S. , Gopalan A. , Mageshkumar P. , Rangaraj A. , Panneerselvam Mukesh , Nirmala Devi B. , Aravindhan C. , Prasath E. , David Ladu Nyagong Santino
Published: August 2022

Nowadays, the major threat to humans occurs due to water quality deterioration. The quality of water creates a new sign for the public to prevent them from waterborne diseases. This study uses sensitive water quality parameters obtained from the northeast monsoon season, October 2021, at different locations in Mooli Kulam lake (11°07′17.6″ N, 77°22′59.9″ E) of Tiruppur District, Tamil Nadu, India. The parameters considered for the analysis of lake water quality are closely included with drinking and irrigation parameters. The northeast monsoon samples collected from the lake were analysed and the Water Quality Indexing was applied to the dataset using three methods, namely, the Weight Arithmetic method, the Canadian Council of Ministers of the Environment, and Horton’s method. The parameters are divided into drinking water variables and irrigation water variables. This study includes water quality index mapping using Inverse Distance Weighting interpolation of the spatial distribution method using ArcMap 10.8. The dataset was subjected to correlation and regression analysis in order to determine the most significant pollutant. A total of 10 sampling stations and 23 water quality parameters have been analysed. The results obtained show that the lake has high eutrophication with compounds of potassium, iron, and nitrates.
Performance Optimization of Surface Plasmon Resonance Imaging Sensor Network Based on the Multi-Objective Optimization Algorithm
Authors: Wang Zhiyou , Wang Maojin , Chen Ying , Hu Fangrong
Published: August 2022

In this work, we report performance optimization of a wireless sensor network (WSN) based on the plain silver surface plasmon resonance imaging (SPRi) sensor. At the sensor node level, we established the refractive index-thickness models for both gold and silver in the sensor and calculated the depth-width ratio (DWR) and penetration depth (PD) values of the sensor of different gold and silver thicknesses by the Jones transfer matrix and Kriging interpolation. We optimized the DWR and PD simultaneously by using the multi-objective optimization genetic algorithm (MOGA). In the following performance optimization of WSN, we simultaneously optimized the transmission success rate and information dimension with the number of nodes and transmission failure rate of the sensor node as variables by the same algorithm. By calculating the information dimension and the transmission success rate of each Pareto optimal solution, we obtained the number of nodes and transmission failure probability of the node available for practical deployment of WSN. The above results indicate that the Pareto optimal solution set obtained from MOGA can help to provide the best solution for the optimization of some certain performance parameters and also assist us in making the trade-off decision in the structure design and network deployment if optimal values of all the performance parameters can be obtained simultaneously.
Effects of the Combination of Continuous Nursing Care and Breathing Exercises on Respiratory Function, Self‐Efficacy, and Sleep Disorders in Patients with Lung Cancer Discharged from Hospital
Authors: Du Juan
Published: August 2022

The purpose of this study was to explore the effect of respiratory function and self‐efficacy of patients with lung cancer after surgery by the combination of continuing nursing care and breathing exercises and to assess its clinical value in improving the quality of patients’ sleep. 120 cases of lung cancer patients were divided into the control group (n = 60 cases) and the experimental group (n = 60 cases) randomly. The control group used conventional care methods for postoperative lung cancer patients, while the experimental group used continuous care combined with a respiratory exercise care model on the basis of conventional care, comparing the differences in the recovery of lung function, self‐efficacy, sleep quality, incidence of sleep disorders, and other indicators between the two groups. The average indicator of recovery condition in patients of experimental groups FEV1 (L), FEV1% pred (%), and FEV1/FVC (%) was higher than patients in control groups with lower symptom scores and complication rates (P < 0.05). The self‐efficacy score of patients in the experimental group was higher than that in the control group (P < 0.05) during sixty days of follow‐up. The sleeping time of the experimental group was significantly shorter than that of the control group (P < 0.05) and the sleep time was longer than that of the control group (P < 0.05), but the incidence of sleep disorders and drug use rate were lower than that of the control group (P < 0.05) before discharge. Continuous nursing care combined with respiratory exercises can significantly accelerate the recovery of lung function and respiratory function reconstruction in patients with lung cancer after surgery, improve the self‐efficacy score of patients, improve the sleep quality of patients, and be conducive to postoperative rehabilitation of patients.
Elevated Cardiac Troponin I following Asymptomatic Intradialytic Hypotension: A Pilot Study with a 2-Year Follow-Up
Authors: Alirezaei Toktam , Jebreil Moosavi Mir Jafar , Irilouzadian Rana , Taziki Elahe
Published: August 2022

Background. Intradialytic hypotension (IDH) has been recognized as a serious and frequent complication during hemodialysis (HD) of end-stage renal disease (ESRD) patients, but the effect of asymptomatic IDH on cardiac troponin I (cTnI) levels is not definitively elucidated. Methods. 70 asymptomatic HD patients with negative predialysis cTnI were included. They were on maintenance HD thrice weekly. All patients were monitored during the HD session for hemodynamic changes and symptoms related to IDH. Patients were followed for two years, and their outcomes are noted as an acute coronary syndrome (ACS), cardiac death, no ACS, noncardiac death, and kidney transplant. Results. Compared with the baseline blood pressure values, there was a drop in systolic blood pressure for all subjects, but according to the 2007 European Best Practice Guidelines on hemodynamic instability, asymptomatic IDH was defined in 27 (38.6%) patients. The results demonstrated a significant correlation (r = 0.492) (

p
<
0.05

) between asymptomatic IDH and elevated postdialysis levels of cTnI. In 2-year follow-up of patients, ACS and cardiac death happened more in patients with elevated cTnI. Conclusion. The results of our study suggest that asymptomatic IDH affects cTnI levels. Given that cTnI is a marker of myocardial damage and a predictor of cardiovascular mortality in ESRD patients, these findings recommend that considering the asymptomatic decrease in blood pressure levels during HD is very important and critical.
[Retracted] Health Information Prediction System of Infant Sports Based on Deep Learning Network
Authors: Qi Jie , Zhang Jun
Published: August 2022

The sensed data from infant sports and training programs are useful in analyzing their health conditions and forecasting any disorders or abnormalities. The sensed information is processed for providing errorless predictions for infant diseases/disorders, coupled with artificial intelligence and sophisticated healthcare technologies. The problem of noncongruent sensed data impacting the forecast occurs due to errors between consecutive training iterations. This problem is addressed using the deep learning (PEST‐DL) proposed perceptible error segregation technique. The training process is halted between two consecutive iterations generating errors until a similarity verification based on infant history is performed. The similarity output determines the errors due to mismatching data observations, and therefore, the data augmentation is performed. The first perceptible error is mitigated by training the learning paradigm with all possible infant history data in the learning process. This prevents prediction lag and data omissions due to discrete availability. The learning is trained from the identified error with the precise detected disorder/abnormality data previously detected. Therefore, the first and consecutive training data segregate error instances from the actual training iterations. This improves the prediction accuracy and precision with controlled error and time complexity.
Interactive Mode of Visual Communication Based on Information Visualization Theory
Authors: He Xiaoke
Published: August 2022

In the modern environment, visual communication design has become a comprehensive subject that combines technology and art. The development of various technologies has promoted the emergence of new design art forms, and at the same time, it has also promoted the change of design concepts and thinking modes. Visual representation, as a representation practice to express the meaning of information in the form of visual symbols, is a method and means to realize information visualization. Based on visual interface design, this article takes interactivity and user experience as innovation points and digs into the design process of interactive information visualization to achieve the purpose of information transmission. By analyzing the cognitive tasks of users in each stage of visual thinking activities in visual terminals, this article proposes to optimize the interactive design of visual communication from the key points of attention, consciousness, and memory. In order to verify the feasibility of the interactive mode of visual communication based on information visualization theory, this article has carried out several comparative experiments with different models. Experimental results show that this algorithm has a faster convergence speed and a greater generalization ability, the accuracy of the algorithm reaches 96.03%, and the highest evaluation of user satisfaction is 95.93%. The interactive mode of visual communication in this article can provide means and reference value for the development direction of visual communication design.
The Optimization of Distribution Path of Fresh Cold Chain Logistics Based on Genetic Algorithm
Authors: Zhang Bochao
Published: August 2022

The products of the enterprise are the logistics objects of the enterprise. Therefore, the company’s products are the primary factor affecting logistics costs. The products of different enterprises may be different in terms of the type, nature, volume, quality, and physical and chemical properties of the products, which will have different impacts on the cost of logistics activities such as warehousing, transportation, and material handling of enterprises. More and more enterprises have taken the cost of logistics distribution as one of the important indicators affecting the development of enterprises; especially, cold chain logistics has been rapidly developed and valued in recent years, because the requirements of such products for delivery time, distribution efficiency, and distribution environment are very strict, whether the goods can be distributed in a standard and reasonable environment and whether the delivery vehicle can deliver the goods within the time specified by the customer have greatly affected the safety of frozen and refrigerated food. Therefore, this paper reduces the cost of the distributor through the optimization of the distribution path of cold chain logistics and makes the goods distributed can be delivered to the customer faster and more reasonably by establishing an integrated optimization platform, which is of great significance for how to reduce the cost of enterprises. Therefore, this paper starts from the function with the lowest distribution cost as the goal, comprehensively considers the specific characteristics of cold chain logistics, given the relevant constraints, uses the improved genetic algorithm to iterate on the given scheme, sends the improved new scheme to the simulation software for simulation operation, then sends the results obtained by the operation to the genetic algorithm for the next iteration, and repeats it in turn until the prespecified conditions can be terminated. Therefore, this paper summarizes some problems and development status in cold chain logistics and distribution routes by consulting relevant literature, optimizes the scheme by using VRP model combined with constraints, establishes a distribution system model, and finally verifies and analyzes to obtain a more reasonable and satisfactory solution. The innovation of this paper is that the research on the VRP problem is optimized through an improved genetic algorithm, certain improvements have been made in the coding method and the operation of selection, crossover, variation, etc., and the improved genetic algorithm can greatly reduce the number of program iterations. Then, we use the integrated optimization platform to import the solution into FlexSim for simulation, each simulation of the new solution will be transmitted to MATLAB through the Excel table for the next optimization iteration, and we repeat the above steps until the preset conditions are met after the termination. This would lead to a more realistic and satisfactory solution.
Application of Internet of Things Technology in the Construction of Bored Pile Foundation in a High Altitude Environment
Authors: Liu Jiayin , Wang Yang , Guo Yingjun , Chen Zhipeng , Jiao Daowei
Published: August 2022

In order to solve the problem that the current geophysical method is difficult to locate in the mud of bored piles, the problem of effective exploration of karst caves at the bottom of piles, the author puts forward the application of Internet of Things technology and sonar technology in the construction of bored pile foundation in a high altitude environment. Combined with the propagation characteristics of sonar stress waves, a sonar detection method for karst caves at the bottom of bored piles is proposed, and a sonar detector for karst caves at the bottom of piles (JL-SONAR) and signal analysis software (PBCA) are developed. The JL-SONAR detector realizes the emission and acquisition of onsite sonar signals, and the PBCA software completes the analysis and arrangement of the detection data. This technology makes full use of the mud conditions of bored piles, the development of karst caves within 10 m of the pile bottom can be tracked and detected in the process of hole formation, which has the advantages of low cost, high speed, and high precision. Experimental results show that Project A has been drilled in multiple directions at the bottom of the pile, the rock cores on the west side of the pile bottom are mostly fragmented, the rock quality index RQD is 15–30, and the quality of the foundation rock at the pile bottom is extremely poor, which verify the results of sonar detection. No slurry leakage occurred in Project B after drilling, and the cores were mostly short columns. The rock quality index RQD was 75 to 90, and the quality of the foundation rock at the bottom of the pile was good, which verifies the results of sonar detection. Conclusion. The research results provide a new solution for karst cave exploration in karst areas, especially in liquid environments.
Identification of Pneumonia in Chest X-Ray Image Based on Transformer
Authors: Ma Yongjun , Lv Wei
Published: August 2022

The research of application models based on traditional convolutional neural networks has gradually entered the bottleneck period of performance improvement, and the improvement of chest X-ray image models has gradually become a difficult problem in the study. In this paper, the Swin Transformer is introduced into the application model of pneumonia recognition in chest X-ray images, and it is optimized according to the characteristics of chest X-ray images. The experimental results based on the model in this paper are compared with those of the model built with the traditional convolutional neural network as the backbone network, and the accuracy of the model is proved to be greatly improved. After the comparison experiments on two different datasets, the experimental results show that the accuracy of the model in this paper improves from 76.3% to 87.3% and from 92.8% to 97.2%, respectively. The experiments show that the accuracy of image enhancement based on the features of chest X-ray images in this model will be higher than the accuracy without image enhancement. In the experiments of this paper, the identification decision factors in the chest X-ray images were extracted by grad-cam combined with a transformer to find the corresponding approximate lesion regions.
Traffic Flow Prediction and Application of Smart City Based on Industry 4.0 and Big Data Analysis
Authors: Gong Yuqian
Published: August 2022

For smart city traffic flow prediction in the period of big data and industry 4.0, the prediction accuracy is low, the prediction is difficult, and the prediction effect is different in different geographical locations. This paper proposes a smart city traffic communication forecast based on Industry 4.0 and big data analysis application. Firstly, this paper theoretically explains the application scenario of urban traffic fault text big data and analyzes the characteristics of related problems, especially the fault problems. Secondly, the AC traffic prediction algorithm is studied, and the application analysis of PVHH, IDT, and Ford–Fulkerson algorithms is applied, respectively. Finally, the above three algorithms are used to predict and analyze traffic flow.
Study of HCP (Hexagonal Close-Packed) Crystal Structure Lattice through Topological Descriptors
Authors: Zhang Guoping , Saeed Saadia , Aslam Adnan , Kanwal Salma , Idrees Nazeran , Sumbal Shaikh Tahira
Published: August 2022

Chemical graph theory is a multidisciplinary field where the structure of the molecule is analyzed as a graphical structure. Chemical descriptors are one of the most important ideas employed in chemical graph theory; this is to associate a numerical value with a graph structure that often has correlation with corresponding chemical properties. In this paper, we investigate another very important closed-packed usual crystal structure defined as

HCP

(Hexagonal Close-Packed) crystal structure and its lattice formed by arranging its unit cells in a dimension for topological descriptors based on a neighborhood degree, reverse degree, and degree. Furthermore, we classify which descriptor is more dominating.
[Retracted] Analysis of the Relevance Environment between Marxist Philosophy and System Theory Based on Deep Learning
Authors: Jiang Xiaoming
Published: August 2022

In social science and natural science, MP (Marxist Philosophy) has played an active role in promoting its development, and MP also guides people’s practice and understanding. There is an inevitable connection with system theory MP. In a sense, both system theory and PMbelong to methodology and both contain the viewpoints of movement and development. In this paper, various text features in natural scenes are discussed in detail, and the original vector is studied by using CNN (Convective Neural Network) of DL (Deep Learning), so as to construct a one‐dimensional text vector and realize the mutual influence and continuous optimization of feature extraction and text clustering. The experimental results show that under the condition of calculating the current cosine similarity measure, the accuracy rate is the highest, reaching 93.67%. This algorithm can effectively improve its performance in text classification tasks on large data sets.
Layer-by-Layer Fabrication of PAH/PAMAM/Nano-CaCO3 Composite Films and Characterization for Enhanced Biocompatibility
Authors: Shifeta Naemi Tonateni , Hamukwaya Shindume Lomboleni , An Qi , Hao Huiying , Mashingaidze Melvin Mununuri
Published: August 2022

Nanoparticle production and functionalization for various biomedical uses are still challenging. Polymer composites constituted of poly(amidoamine) (PAMAM), polyallylamine hydrochloride (PAH), and calcium carbonate (CaCO3) nanoparticles have good biocompatibility with physiological tissue and fluids, making them excellent candidates for biomedical applications. This study investigated the characteristics of polymeric/nano-CaCO3 composite films based on a PAH/PAMAM matrix, which were fabricated through layer-by-layer synthesis on quartz glass substrates. It was found that the as-prepared elastic moduli of the resultant (PAH/PAMAM)n-CaCO3 (where n represents the number of bilayers) composite films varied from 1.40 to 23.70 GPa for different degrees of cross-linking when 0.1 M nano-CaCO3 particles were incorporated into the polymer matrix. The highly cross-linked (PAH/PAMAM)15-CaCO3 composite film had the highest recorded elastic modulus of 23.70 GPa, while it was observed that for all the composite films fabricated for the present study, the addition of the nano-CaCO3 particles approximately doubled the elastic modulus regardless of the degree of polymerization. Live/Dead assays were used to determine whether the produced composite films were compatible with human lung fibroblast cells. The findings indicate that the (PAH/PAMAM)7.5-CaCO3 composite film had the most positive effect on cell growth and proliferation, with the (PAH/PAMAM)15-CaCO3 composite film demonstrating significant ion transport behavior with low impedance, which was considered good for in vivo rapid cell-to-cell communication. Therefore, the (PAH/PAMAM)7.5-CaCO3 and (PAH/PAMAM)15-CaCO3 composite films are potential tissue engineering biomaterials, but further studies are essential to generate more data to evaluate the suitability of these composites for this and other biomedical functions.
Blockchain-Based Intelligent Interconnection System Optimization Decision
Authors: Wang Caifeng , Hao Shenghua , Ma Yufang
Published: August 2022

This article conducts a comprehensive analysis of the relevant content of blockchain; blockchain is a new field that has only emerged in recent decades, and it has a high level of new energy that cannot be compared with other fields. This article proposes the advantages and disadvantages of blockchain and the current factors that trigger network security defense. A multimicrogrid customer transaction system under the blockchain technology is also proposed. We will illustrate the compatibility between multimicrogrid and blockchain, which is self-adaptive and can be adjusted in time with market changes. Third, in order to ensure the normal operation of the distribution network, a smart contract dynamic constraint scheme is designed for power flow constraints and voltage offset problems. Multimicrogrid system as energy interconnection research on multiagent competitive game on multimicrogrid system is crucial to the development of energy Internet technology. Blockchain-based decision-making systems exist based on a comprehensive analysis of the needs and interests of microgrid operators, large users, and distributed aggregators. For the complex problem of multimicrogrid system competition game model, a suitable solution method (IACO) is proposed. The model and solution algorithm established by the experiment are accurately analyzed, and the simulation result is that the optimal electricity price strategy in the subdivision field can effectively balance the interests of different market players and achieve win-win and coordinated development. Experiments show that IACO can deal with the multiple problems of blockchain.
ECG Classification for Detecting ECG Arrhythmia Empowered with Deep Learning Approaches
Authors: Rahman Atta-ur , Asif Rizwana Naz , Sultan Kiran , Alsaif Suleiman Ali , Abbas Sagheer , Khan Muhammad Adnan , Mosavi Amir
Published: August 2022

According to the World Health Organization (WHO) report, heart disease is spreading throughout the world very rapidly and the situation is becoming alarming in people aged 40 or above (Xu, 2020). Different methods and procedures are adopted to detect and diagnose heart abnormalities. Data scientists are working on finding the different methods with the required accuracy (Strodthoff et al., 2021). Electrocardiogram (ECG) is the procedure to find the heart condition in the waveform. For ages, the machine learning techniques, which are feature based, played a vital role in the medical sciences and centralized the data in cloud computing and having access throughout the world. Furthermore, deep learning or transfer learning widens the vision and introduces different transfer learning methods to ensure accuracy and time management to detect the ECG in a better way in comparison to the previous and machine learning methods. Hence, it is said that transfer learning has turned world research into more appropriate and innovative research. Here, the proposed comparison and accuracy analysis of different transfer learning methods by using ECG classification for detecting ECG Arrhythmia (CAA-TL). The CAA-TL model has the multiclassification of the ECG dataset, which has been taken from Kaggle. Some of the healthy and unhealthy datasets have been taken in real-time, augmented, and fused with the Kaggle dataset, i.e., Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH dataset). The CAA-TL worked on the accuracy of heart problem detection by using different methods like ResNet50, AlexNet, and SqueezeNet. All three deep learning methods showed remarkable accuracy, which is improved from the previous research. The comparison of different deep learning approaches with respect to layers widens the research and gives the more clarity and accuracy and at the same time finds it time-consuming while working with multiclassification with massive dataset of ECG. The implementation of the proposed method showed an accuracy of 98.8%, 90.08%, and 91% for AlexNet, SqueezeNet, and ResNet50, respectively.