bluuebunny/crossref_metadata_2025 huggingface dataset:
The Construction of Mountain Bike Athletes’ Performance Measurement Index System Based on Multi-Source Information Fusion
Authors: Yang Qingbin
Published: March 2022

Mountain biking is a recognized fitness sport. Therefore, studying the performance measurement system of mountain biking athletes is an important way to supervise and realize scientific training. At present, many views on the issue of function measurement are somewhat divergent. The only research is limited to the discussion of function components, which lacks systemicity, and there is little research on the construction of the function measurement index system. Combining the practice of mountain bike training, the article conducts a more detailed research on the construction of performance measurement index system, focusing on what to do and the principles to be followed. The article takes a total of 28 mountain bikers from a cycling team as the research object, and uses the literature review method, expert consultation method, and functional test mathematical statistics method to analyze the obtained data and improve the data sorting table. On this basis, statistics create descriptive statistics of the data. Experiments show that if the ankle joints and flexors and extensors of mountain bikers are tested at an angular velocity of 60°/s and 180°/s, the peak moment flexion-extension ratio is 20%–36%, hemoglobin rises within 12–24 h, creatine kinase recovers within 36–72 h, and blood urea nitrogen recovers within 24–36 h.
Novel Organic-Inorganic Hybrid Polystyrene Nanoparticles with Trichromatic Luminescence for the Detection of Latent Fingerprints
Authors: Wang Xiao , Liao Tao , Wang Haiyan , Hao Hongxia , Yang Qinglai , Zhou Hong , Ma Yu , Zhi Minjie , Wang Jiahao , Fan Ruihang
Published: March 2022

This article explored the application of novel organic-inorganic hybrid polystyrene nanoparticles (PSNPs) with trichromatic luminescence for the detection of latent fingerprints. The PSNPs were synthesized by encapsulated Eu(DBM)3phen, coumarin 6, and FDBT into the polystyrene nanoparticles through the swelling method and applied them to visualize latent fingerprints. The PSNPs had a spherical morphology with an average diameter of 310.7 nm, and they emitted trichromatic fluorescence (525 nm/570 nm/610 nm) under 365 nm excitation wavelength with green/yellow/red color under filters. They were less likely to aggregate, float or stain the background when treating fingerprints. The developed fingerprints with excellent clarity of ridges and contrast could be viewed, and the digital magnification of fluorescence-developed fingerprints provided more minutiae details about some regional patterns. The colorimetric and fluorescent trichromatic light could provide complementary signals without the background interference from fluorescent substrates and/or complex multicolor surfaces, which improved the applicability of fluorescent nanoparticles for fingerprints development. PSNPs are promising for the detection of latent fingerprints and practical criminal investigations with their ease of operation, eco-friendly properties, and excellent trichromatic optical performance.
On the Dynamics and Stability of the Crime and Punishment Game
Authors: Quinteros María J. , Villena Marcelo J.
Published: March 2022

We study the dynamics and stability of the economics of crime and punishment game from an evolutionary perspective. Specifically, we model the interaction between agents and controllers as an asymmetric game exploring the dynamics of the classic static model using a replicator dynamics equation, given exogenous levels of monitoring and criminal sanctions. The dynamics show five possible equilibria, from which three are stable. Our results show that a culture of honest agents is never stable; however when the penalty is high enough, the system will neutrally tend to an equilibrium of honest agents and a monitoring firm. By contrast, when the probability of detecting wrongdoing is small, the system, in some cases, will remain in a transient state, in which it is impossible to predict the proportion of honest agents.
Cotton Cellulose-Derived Hydrogel and Electrospun Fiber as Alternative Material for Wound Dressing Application
Authors: Jirawitchalert Supidcha , Mitaim Samon , Chen Ching-Yi , Patikarnmonthon Nisa
Published: March 2022

Cotton has been recognized as a useful biomaterial over decades, and it has been widely applied in the textile industry. However, a large amount of cotton waste is generated during the manufacturing processes, but it has been considered as a low-value product. With high content of cellulose remaining in cotton waste, our study focuses on transforming cotton cellulose into a valuable product. Cellulose was extracted from cotton waste and modified into two main materials for wound dressing application: hydrogel-based water absorbent materials and electrospun composite nanofibers. In order to enhance the water absorption, carboxymethyl cellulose (CMC), the modified cellulose with functional group prone to interact with water molecules, has been developed in this study. The hydrogel-based CMC was created by using the chemical cross-linking reaction of epichlorohydrin (ECH). The hydrogel demonstrated the swelling and reswelling ability by 1718 ± 137% and 97.95 ± 9.76%, respectively. Meanwhile, cellulose/PEG in trifluoroacetic acid (TFA) was successfully fabricated as nonwoven composite by a conventional electrospinning technique. The fabrics provided highly appropriated properties as wound dressing, including the following: water absorption was up to 1300 times and water vapor permeability controlled in the range of 2163–2285 g·m−2·day−1. This showed the preliminary information for recovering cotton waste into valuable products.
An In Vivo and In Vitro Assessment of the Probiotic Potentials of Indigenous Halotolerant Bacteria on Growth Performance and Digestive Enzymes of White Leg Shrimp (Litopenaeus vannamei) in High-Salinity Waters
Authors: Mirbakhsh Maryam , Ghaednia Babak , Tabatabaee Bafroee Akram Sadat
Published: March 2022

The present study was aimed at evaluating Bacillus subtilis subsp. inaquosorum strain IS02 (B.s.i. IS02; GenBank accession number: JN856456.1) to determine the growth kinetics of antimicrobial metabolite production in different ranges of environmental factors, such as salinity (15-55‰), pH (5-9), and temperature (30-40°C); to evaluate the lifespan of antimicrobial metabolites in different ranges of salinity (0-57‰), pH (2-12), and temperature (35-121°C); and to investigate the effect of B.s.i. IS02 as an additive to functional feed on the digestive activity of Litopenaeus vannamei under high-salinity conditions. Based on the present findings, the bacterium maintained its antimicrobial properties up to 45‰ salinity (

10729.93
±
23.93
 
A
U
 
m

l

−
1

). There was no significant difference in the antimicrobial activity of metabolites at 35°C and 40°C (

p
>
.05

). In the experimental trials, the shrimp postlarvae were fed different concentrations of B.s.i. IS02 (106, 107, and 108 CFU kg-1 diets) under high-salinity (>55‰) conditions. The activity of digestive enzymes, amylase, lipase, and protease in the probiotic-fed shrimp postlarvae was significantly higher than that of the control group (

p
<
.05

). The present findings revealed that B.s.i. IS02 maintained the antimicrobial metabolite production, retained the antibacterial lifespan in high-salinity waters, and improved the growth and digestive enzyme activities of shrimps.
Effect of F11R Gene Knockdown on Malignant Biological Behaviors of Pancreatic Cancer Cells
Authors: Zhang HaiDi , Zhang RenDan , Yao Jiaxin , Hu XianHua , Pu Yu , He Shuai , Yu Jinchuan , Zhu Huiling , Mu Bo , Zhao ChunYan
Published: March 2022

F11R receptor (F11R/junctional adhesion molecule-A/F11R-A) is preferentially concentrated at tight junctions and influences epithelial cell morphology and migration. Numerous studies have shown that the aberrant expression of F11R contributes to tumor progression including pancreatic cancer. However, the significance of F11R in various tumors is controversial, and the role of F11R in regulating the malignant behaviors of human pancreatic cancer is unknown. To investigate the role of F11R in the carcinogenesis of pancreatic cancer and the potential targets of F11R as a therapeutic target for pancreatic cancer, we knocked down F11R in the pancreatic cancer cell line PANC-1 using lentiviral approaches. We found that F11R silencing led to decreased cell proliferation, a loss of cell invasiveness, cell cycle arrest in the G1 phase, and enhanced cell apoptosis. The present results suggest that F11R may be a promising therapeutic target for pancreatic cancer.
Development and Optimization of Nanoemulsion from Ethanolic Extract of Centella asiatica (NanoSECA) Using D-Optimal Mixture Design to Improve Blood-Brain Barrier Permeability
Authors: Jusril Nor Atiqah , Abu Bakar Syahrul Imran , Khalil Khalilah Abdul , Md Saad Wan Mazlina , Wen Ng Kwok , Adenan Mohd Ilham
Published: March 2022

The evidence on the neuroprotective impact of Centella asiatica (C. asiatica) has been greatly documented in recent years. However, a major obstacle that remains to be overcome is the capacity of the active molecules in C. asiatica to cross the blood-brain barrier (BBB). In this study, we explored the possibilities of using a D-optimal mixture design to fabricate nanoemulsion of C. asiatica (NanoSECA) for better brain bioavailability. The parameters for optimization were the percentage of water (10–80% w/v) and virgin coconut oil (VCO) (10–80% w/v). Nanoemulsions were formulated using a high-pressure homogenization approach and were characterized for their physicochemical properties. The optimal VCO-based nanoemulsion (VBN: F2) conditions were found at 80% (w/v) of water and 10% (w/v) of VCO. Subsequently, viability tests were conducted on neuroblastoma (SH-SY5Y) and macrophage (RAW 264.7) cell lines. NanoSECA was distinguished for its antioxidant, acetylcholinesterase (AChE), anti-inflammatory, and parallel artificial membrane permeability assay (PAMPA) activities in vitro. The NanoSECA has a particle size of 127.833 ± 8.280 nm, zeta potential (ZP) of −24.9 ± 0.011 mV, polydispersity index (PDI) of 0.493 ± 4.681, percentage prediction error (PPE) of −12.02%, and pH of 6.0 ± 0.006 and is also stable under different storage conditions. Cell viability was improved in a dose-dependent manner on SH-SY5Y and RAW 264.7 cell lines. In addition, NanoSECA significantly reduced the AChE activity, suppressing the level of proinflammatory mediators and oxidative stress. Moreover, NanoSECA showed high BBB permeation with a high value of experimental permeability to cross the BBB. Thus, NanoSECA could efficiently potentiate the central nervous system (CNS) therapeutic activities through enhanced penetration of BBB. These nano-delivery systems are crucial to unlock the full potential of C. asiatica for treating numerous CNS disorders.
Study on the Mechanism and Effect of a Partition Wall Controlling Foundation Pit Deformation in the Soft‐Soil Area
Authors: Feng Chunlei , Zhang Dingli , Wang Hualao , Zhang Xuan
Published: March 2022

Based on the soft‐soil layer and several practical subway stations projects, the effect of partition wall main parameters on internal force and the deformation of retaining structure was thoroughly investigated via numerical simulation and orthogonal experiment. Results show that the influence of distance between partition wall and retaining structure, L, on controlling the internal forces of the retaining structure and maximum ground settlement, is more obvious; the maximum settlement can be reduced by more than 60%. Moreover, the influence of partition wall depth under the ground, H, within a certain range can substantially reduce the maximum lateral palisade structure; the lateral displacement can be reduced by about 50%; however, within the scope of the economic, the stiffness of the partition wall, E, with impact on retaining structure internal force and deformation is relatively weak. Finally, the applicability of the research results is verified.
Design of “Immersive” Teaching Situation Simulation System for Law Course Based on Artificial Intelligence
Authors: Tian Yanyan
Published: March 2022

The method of scene mapping provides a very effective technical support for classroom participants to roam freely in a virtual scene larger than the real space. When the virtual scene is larger than the area in the real space, the existing scene mapping method will cause distortion of the original virtual scene and affect the user’s roaming experience and visual experience. In order to obtain the scene map with low isometric distortion when the virtual scene scale is much larger than the real space, this paper proposes the block map to divide and group the roads in the virtual scene. For each group of roadblocks, the mapping is obtained by minimizing the isometric distortion energy of the mapping on the premise that the adjacent road blocks are smooth and continuous at the junction. The segmented roadblocks are mapped to the interior of the real intersections, and then the mapping of the whole scene is optimized globally to further reduce the isometric distortion of the whole mapping. Based on the above theories, the design scheme of immersive virtual legal reality classroom is proposed, including the overall framework of immersive virtual legal reality classroom, interactive scheme design, interactive behavior design, and required technical scheme, etc. The application technology, educational application, and application experience are analyzed in combination with the constructed immersive virtual legal reality scene for corresponding innovation and improvement. By applying immersive virtual reality technology in the field of education, the results show that the simulation system has good practicability and stronger stability and can provide a better immersive experience for law class participants.
E-Commerce Precision Marketing Model Based on Convolutional Neural Network
Authors: Liu Xia
Published: March 2022

With the rapid development of network and informatization of the consumer market in my country, the application and maturity of technologies such as the Internet, terminal equipment, logistics, and payment and the continuous improvement of people’s consumption concepts, online shopping has gradually become the mainstream purchase method for Chinese consumers, and e-commerce has gradually become one of the important driving forces to promote the sustained and vigorous development of China's economy. Under the traditional marketing model, companies do not fully understand the needs of users. The sales staff's thinking is only how to sell products to users. They do not know the specific consumer needs, so they can only focus on the product. Based on these foundations, this research uses convolutional neural networks and applies this model to precision marketing to obtain accurate portraits of consumers, thereby increasing the company's turnover. After comparing different models and conducting some experiments, it is concluded that (1) through the collection and analysis of W enterprise data, the training and testing conditions of the CNN model, LSTM model, LSTM attention model, and CNN + LSTM attention model are compared. It is concluded that the CNN + LSTM attention model and the LSTM attention model perform better, and the accuracy of testing and training is higher. (2) Through the fitting of the model, it is found that Sn(%) = 70.71, Sp(%) = 86.25, Acc(%) = 81.07, and MCC = 0.752 of the CNN + LSTM attention model are the best fitting models. The men and women stratification and gender stratification of users are predicted, and it is found that men in the W company are the main purchasing power, and in the age stratification, it is found that the population of 41–50 accounts for the highest proportion. (3) The average accuracy rate of the LSTM attention model is as high as 66.6%, the average recall rate is 82.3%, and the F1 score is 73.1%. This model has met expectations for precision marketing forecasts. (4) Using the CNN + LSTM attention model to predict the marketing input for the next year, it is found that the use of precision marketing will increase the profit of W company. The average annual data show that the monthly revenue of precision marketing has increased by 73.5%.
Optimization of Saffron Essential Oil Nanoparticles Using Chitosan-Arabic Gum Complex Nanocarrier with Ionic Gelation Method
Authors: Astutiningsih Fitri , Anggrahini Sri , Fitriani Aprilia , Supriyadi Supriyadi
Published: March 2022

This study is aimed at optimizing the Saffron essential oil (SEO) nanoparticles using the ionic gelation method. Response surface methodology (RSM) with Box-Behnken design (BBD) was applied to investigate the optimum conditions and the effects of three independent variables: LWCS concentration (0.1-0.3%), Arabic gum concentration (9.6-9.8%), and ratio (core: wall material) (1 : 5, 1 : 7.5, 1 : 10) on the responses ofz-average, polydispersity index (PDI), and zeta potential. The results showed that the quadratic model developed from the RSM was statistically significant (pvalue < 0.05). The quadratic model can be used to describe well the relationship between the variables on the response observed. The lack of fit was nonsignificant (pvalue > 0.05) relative to pure error for all response variables, indicating that the model fitted well. The model equation obtained for the process through RSM was adequate. The LWCS concentration and Arabic gum concentration had a significant effect onz-average and PDI. The ratio (oil: Arabic gum/LWCS) has a significant effect on zeta potential. The optimum condition was the LWCS concentration of 0.1% and Arabic gum concentration of 9.6%, and the ratio (oil: Arabic gum/LWCS) 1 : 5 produced the optimum SEO nanoparticles with az-average value of 16.24, PDI of 0.495, and zeta potential of 15.76. The verification values were close to the predictive value given by the Design Expert® 12 program withpvalue > 0.05 at the 95% confidence level. Therefore, the application of the RSM with Box-Behnken was suitable for optimizing the saffron oil nanoparticles with desirable responses.
The Properties of the Tide Current and Sediment at a Port of Southern China
Authors: Shen Hongyan , Xie Ruicai , Song Liqin
Published: March 2022

Environmental factors of projects in the sea area, such as tidal current characteristics and sediment transport patterns, play a decisive role in the safe operation of the harbor. Based on the observation data of tide level, tidal current, suspended sediment, and surface sediment in the Port of Shenquan, this paper focuses on the study of the characteristics of tidal current and sediment of projects in the sea area. According to the measured tide hydrograph, it can be concluded that the tide in the Port of Shenquan is irregular diurnal, and the flow of rising and falling tides is basically rectilinear. The velocity of the neap tide is higher than that of spring tide, which is mainly related to the strong winds and waves. The sand source is limited, and the sediment concentration in the water is very low. The hourly sediment concentration in each station fluctuates within a narrow range, independent of hourly velocity change. The sediment is coarse and fine, with a distribution pattern of coarse near shore, fine in deep water, coarse in East and West, and fine in the middle, and the sorting coefficient of sediment is medium to well.
Research on Ecoenvironmental Quality Evaluation System Based on Big Data Analysis
Authors: Li Pingheng
Published: March 2022

Comprehensive and objective evaluation of ecological environment quality is of great significance to regional sustainable development. In this study, Landsat remote sensing images of 1991, 2000, 2004, 2010, 2013, 2018, and 2019 are selected to evaluate the changes of ecological environment quality in the Headwaters of Dongjiangyuan River by using remote sensing ecological index RSEI. The influencing factors of ecological environment change in Dongjiangyuan River are also discussed. The results showed that, from 1991 to 2019, the ecoenvironmental quality of the Dongjiangyuan River showed a good trend of development. Humidity index, greenness index, and dryness index all fluctuated in a small range; the greenness and dryness showed an overall increase. The average temperature in the Headwaters of the Dongjiangyuan River presents a rising trend. This study establishes the evaluation system of ecological environment quality from two dimensions of time and space and gives the change rule of environmental quality quantitatively, which provides the theoretical basis for the ecological environment management of Dongjiangyuan River.
Research on Channel Modeling and Communication Coverage of Wireless Sensor Networks in Barrier Area of Nuclear Power Plants
Authors: Deng Zhi-Guang , Wu Qian , Lv Xin , Zhu Bi-Wei , Xiang Mei-Qiong , Wang Xue-Mei , Zhu Jia-Liang
Published: March 2022

In view of the multimetal barrier environment of nuclear power plant, by considering the factors such as transmission power, transmission position, and multipath interference, based on the simulation of metal pipes and equipment, this paper carries out the barrier area channel modeling in logarithmic fading mode and makes quantitative analysis on the channel transmission, path loss, channel power characteristics, and so on under the metal barrier environment. Based on the channel modeling, this paper optimizes the coverage of the network in the obstacle area by using the improved teaching and learning group intelligent algorithm. The simulation results show that the improved teaching and learning algorithm can optimize the network coverage of the obstacle area well, and under the four obstacle modules, 14 nodes can cover the whole area by more than 99%. This provides a solution to the problem of network coverage in the practical application of wireless sensor networks.
HeteroFL Blockchain Approach-Based Security for Cognitive Internet of Things
Authors: Wadhwa Shivani , Rani Shalli , Kaur Gagandeep , Koundal Deepika , Zaguia Atef , Enbeyle Wegayehu
Published: March 2022

Cognitive learning is progressively prospering in the field of Internet of Things (IoT). With the advancement in IoT, data generation rate has also increased, whereas issues like performance, attacks on the data, security of the data, and inadequate data resources are yet to be resolved. Recent studies are mostly focusing on the security of the data which can be handled by blockchain. Blockchain technology records the learned data into the block which is generated after completing proper consensus mechanism. In this paper, Hetero Federated Learning approach is used to apply cognitive learning on data produced by Internet of Thing devices. Security on cognitiveIoT data is provided by blockchain using Proof of Work consensus mechanism. By applying blockchain over heteroFL approach, we have conducted various simulations to check the performance of our proposed framework. Parameters taken into consideration during performance evaluation are effect of number of blocks on memory utilization and impact of data sample size on accuracy according to different learning rates.
Fractional Analysis of Coupled Burgers Equations within Yang Caputo-Fabrizio Operator
Authors: Shah Nehad Ali , El-Zahar Essam R. , Chung Jae Dong
Published: March 2022

This work applies a novel analytical technique to the fractional view analysis of coupled Burgers equations. The proposed problems have been fractionally analyzed in the Caputo-Fabrizio sense. The Yang transformation was initially applied to the specified problem in the current approach. The series form solution is then obtained using the Adomian decomposition technique. The desired analytical solution is obtained after performing the inverse transform. Specific examples of fractional Burgers couple systems are used to validate the proposed technique. The current strategy has been found to be a useful methodology with a close match to actual solutions. The proposed method offers a lower computing cost and a faster convergence rate. As a result, the suggested technique can be applied to a variety of fractional order problems.
Changes on Sugar and Starch Contents during Seed Development of Synergistic Sweet Corn and Implication on Seed Quality
Authors: Harakotr Bhornchai , Sutthiluk Warisa , Rithichai Panumart
Published: March 2022

Synergistic sweet corn equipped with multiple-recessive genes encoding sugar synthesis is proposed through hybrid breeding to improve the balance eating quality including flavor, texture, and aroma. However, the drawback on seed quality occurs such as low germination and poor seedling vigor. This study aimed to investigate the changes of carbohydrate contents on seed quality of five sweet corn genotypes differing in the number of equipped recessive genes during seed development. Seeds were sampled at 4-day intervals from 18 to 46 days after pollination (DAP) and analyzed for seed germination, sugar, water-soluble polysaccharide, and starch. Then, their relationships were analyzed by using time series regression analysis. Although there were significant differences among 5 corn genotypes in their seed germination and carbohydrate contents, some genotypes showed responses in similar patterns. The optimal time to harvest seeds was genotype-dependent, which were 38 DAP for triple-recessive gene (btbtsh2sh2wxwx) and single-recessive genes (BtBtsh2sh2WxWx and Sh2Sh2susuWxWx) and 42 DAP for double-recessive genes (BtBtsh2sh2wxwx). The regression analysis revealed that seed germinability could be predicted by total starch content in synergistic sweet corn lines during seed development stages; however, this prediction seemed to be negligible in sweet corn genotypes equipped with a single-recessive gene. Implications and further suggestions for establishing an effective seed production technique and seed quality of synergistic sweet corn are discussed.
Establishment of a Combined Diagnostic Model of Abdominal Aortic Aneurysm with Random Forest and Artificial Neural Network
Authors: Duan Yixuan , Xie Enrui , Liu Chang , Sun Jingjing , Deng Jie
Published: March 2022

Objectives. Abdominal aortic aneurysm (AAA), a disease with high mortality, is limited by the current diagnostic methods in the early screening. This study aimed to screen novel and significant biomarkers and construct a diagnostic model for AAA by using a novel machine learning method, i.e., an ensemble of the random forest (RF) algorithm and artificial neural network (ANN). Methods and Results. Through a search of the Gene Expression Omnibus (GEO) database, two large‐sample gene expression datasets (GSE57691 and GSE47472) were downloaded and preprocessed. Differentially expressed genes (DEGs) in GSE57691 were identified by R software, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Essential metabolic pathways related to positive regulation of cell death and NAD binding were found. Then, RF was used to identify key genes from the DEGs, and an AAA diagnostic model was established by ANN. A transcription factor (TF) regulatory network of key genes related to angiogenesis and endothelial migration was constructed. Finally, a validation dataset was used to validate the model and the area under the receiver operating characteristic curve (AUC) value was high. Conclusion. Potential AAA‐associated gene biomarkers were identified by RF, and a novel early diagnostic model of AAA was established by ANN. The AUC indicated that the diagnostic model had a highly satisfactory diagnostic performance. In conclusion, this study will provide a promising theoretical basis for further clinical and experimental studies.
Novel Distance Measures for Single-Valued Neutrosophic Fuzzy Sets and Their Applications to Multicriteria Group Decision-Making Problem
Authors: Jin Yun , Kamran Muhammad , Salamat Nadeem , Zeng Shouzhen , Khan Riaz Hussain
Published: March 2022

Single-valued neutrosophic sets are a hybrid of fuzzy sets that are used to represent uncertain, imprecise, partial, and inconsistent information in the actual world. The focus of this research is to develop two novel distance measures for single-valued neutrosophic fuzzy sets (SVNFSs). We introduced two new distance measures nameddηG,YanddζG,Yfor SVNFSs and apply these measures to different examples and also compare them with existing measures to show the validity of our proposed measures. Our results are reliable and useful for decision-making problems. We also proposed an algorithm for multicriteria group decision-making. Based on this algorithm, we find the ranking matrices using proposed distance measures. We also give an example to demonstrate the notion and concept of our algorithm.
Effect of Early Continuous Veno-Venous Haemofiltration in Severe Acute Pancreatitis for the Prevention of Local Pancreatic Complications
Authors: Yadav Satish Chandra , Zhang Bingqiang
Published: March 2022

Objective. To compare the conventional treatment and continuous veno-venous haemofiltration (CVVH) in severe acute pancreatitis (SAP) for the prevention of pseudocyst and walled-off necrosis. Patients and Methods. Forty-two patients were divided into two treatment groups: conventional treatment group contained 24 patients and CVVH had 18. Conventional treatment group patients were treated symptomatically and according to the causes. CVVH group patients were treated symptomatically, and CVVH was done within 2 hours of admission. Results. In both groups, there was a decrease in amylase, lipase, CRP, IL-6, IL-10, TNF-alpha, Ranson score, Balthazar score, and APACHE-II score after 72 hours, but the decrease was significantly greater in CVVH patients. There were no any local pancreatic complications in CVVH patients, but 1 patient had an acute peripancreatic fluid collection, 2 patients had pseudocyst, and 2 patients had walled-off necrosis (WON), and a mortality one was seen in the conventional treatment group. Conclusion. The present study shows that early CVVH may be able to prevent the formation of pseudocyst and win in SAP patients.
Research on Data Mining of College Students’ Physical Health for Physical Education Reform
Authors: Zhu Lei , Zhang Ling
Published: March 2022

The traditional data mining method of students’ physical health has some problems, such as low recall rate of data mining, long mining time, and poor mining accuracy. Therefore, this paper proposes a data mining method of college students’ physical health for physical education reform. Using association rules to construct the correspondence between the fitness test data, the fitness test data can be classified and the data training model can be built. The decision tree of data attribute was built, and the physical health data was segmented by the segmentation technology. The information entropy of health data was calculated by the decision tree, and the information gain of health data sample set was obtained. The C4.5 algorithm was used to improve the ID3 algorithm. The improved decision tree was used to obtain the physique data splitting attribute, and the information gain rate was obtained by the ID3 algorithm correction. The

k

-means algorithm is used to divide the data into clusters, according to which the physical health data mining of college students is realized. Experimental results show that the recall rate of the physical health data mining method proposed in this paper is as high as 96%, the data mining time is only 3 s, and the accuracy of data mining is as high as 98%, indicating that the method proposed in this paper can improve the physical health data mining effect.
Intrusion Detection System for Industrial Internet of Things Based on Deep Reinforcement Learning
Authors: Tharewal Sumegh , Ashfaque Mohammed Waseem , Banu Sayyada Sara , Uma Perumal , Hassen Samar Mansour , Shabaz Mohammad
Published: March 2022

The Industrial Internet of Things has grown significantly in recent years. While implementing industrial digitalization, automation, and intelligence introduced a slew of cyber risks, the complex and varied industrial Internet of Things environment provided a new attack surface for network attackers. As a result, conventional intrusion detection technology cannot satisfy the network threat discovery requirements in today’s Industrial Internet of Things environment. In this research, the authors have used reinforcement learning rather than supervised and unsupervised learning, because it could very well improve the decision-making ability of the learning process by integrating abstract thinking of complete understanding, using deep knowledge to perform simple and nonlinear transformations of large-scale original input data into higher-level abstract expressions, and using learning algorithm or learning based on feedback signals, in the lack of guiding knowledge, which is based on the trial-and-error learning model, from the interaction with the environment to find the best good solution. In this respect, this article presents a near-end strategy optimization method for the Industrial Internet of Things intrusion detection system based on a deep reinforcement learning algorithm. This method combines deep learning’s observation capability with reinforcement learning’s decision-making capability to enable efficient detection of different kinds of cyberassaults on the Industrial Internet of Things. In this manuscript, the DRL-IDS intrusion detection system is built on a feature selection method based on LightGBM, which efficiently selects the most attractive feature set from industrial Internet of Things data; when paired with deep learning algorithms, it effectively detects intrusions. To begin, the application is based on GBM’s feature selection algorithm, which extracts the most compelling feature set from Industrial Internet of Things data; then, in conjunction with the deep learning algorithm, the hidden layer of the multilayer perception network is used as the shared network structure for the value network and strategic network in the PPO2 algorithm; and finally, the intrusion detection model is constructed using the PPO2 algorithm and ReLU (R). Numerous tests conducted on a publicly available data set of the Industrial Internet of Things demonstrate that the suggested intrusion detection system detects 99 percent of different kinds of network assaults on the Industrial Internet of Things. Additionally, the accuracy rate is 0.9%. The accuracy, precision, recall rate, F1 score, and other performance indicators are superior to those of the existing intrusion detection system, which is based on deep learning models such as LSTM, CNN, and RNN, as well as deep reinforcement learning models such as DDQN and DQN.
Optimal Design of Ecological Landscape Spatial Structure Based on Edge Computing of Internet of Things
Authors: An Ru
Published: March 2022

Ecological landscape space refers to a whole composed of many different ecosystems in a larger area. The spatial structure of the ecological landscape is the spatial arrangement and combination of the components and elements of the landscape. With the sustainable development of today’s world, the procedure of urbanization is also accelerating. The urbanization procedure has significantly changed the types of urban landscapes and the spatial structure of urban landscapes, which will also produce corresponding ecological effects. No matter how good or bad the ecological effect is, it will have a certain impact on people’s lives. Therefore, continuously promoting the majorization of the spatial structure of urban ecological landscape is a significant method to promise the healthy improvement of the city. This article is aimed at studying the optimal design of urban ecological landscape spatial structure based on edge computing of the Internet of Things. This paper uses city A as the experimental object to design a space structure majorization experiment of urban ecological landscape based on the edge computing of the Internet of Things, and the experiment draws a conclusion. The urban ecological landscape structure majorization plan based on the edge computing of the Internet of Things increases the biodiversity index of city A by 1.2, indicating that the method of optimizing the structure of the ecological landscape based on the edge computing of the Internet of Things has a better effect of optimizing the structure of the ecological landscape.
Wear Behavior of Different Generations of Zirconia: Present Literature
Authors: Jitwirachot Kuljirarnat , Rungsiyakull Pimduen , Holloway Julie A. , Jia-mahasap Wissanee
Published: March 2022

Objective. The wear behavior of the novel zirconia generation is less well understood and may be affected by compositional modifications compared to the conventional zirconia. Materials and Methods. Combinations of keywords such as “zirconia,” “high translucent,” and “wear” were searched in PubMed and Google Scholar databases up to May 2021. The total of 23 relevant articles was selected according to inclusion criteria. Results. Reports show comparable wear resistance of translucent zirconia to the conventional zirconia despite an increased cubic phase content and lower mean flexural strength. A meticulously polished surface creates the lowest surface roughness, producing favorable zirconia wear resistance and antagonist wear compared to a glazed surface. In comparison to other ceramic materials, zirconia produces the least wear on an enamel antagonist and almost undetectable wear when opposed by zirconia. Wear when paired against resin materials yields a favorable outcome, whereas wear behavior against a metal antagonist varies with the surface hardness of the metal. Conclusions. All zirconia generations are considered wear-friendly to all types of antagonists. Nonetheless, comparative studies on antagonist wear opposing zirconia of different compositions are still limited and further investigation is required.
Smart Grid Information Management System Relying on MAS Technology and Complex Scientific Management Thinking
Authors: Tong Jing
Published: March 2022

With the continuous social and economic development, more and more attention has been paid to the safety and efficiency of the power grid. In this paper, the importance and technical support for the informatization and intelligent implementation of the smart grid are explored based on the complex scientific management thinking combined with MAS technology, with the smart grid as the breakthrough point. Subsequently, the framework structure of the smart grid information management system based on multiagent technology is put forward, focusing on expounding the design and functions of agents at the equipment level, subsystem level, and total system level in the system. The system functions are verified and analyzed through the test examples. The practice has proved that the smart grid information management system designed in this paper can provide technical support for smart grid informatization and intelligent implementation.
Is 9-G DBT-Guided Vacuum-Assisted Breast Biopsy Sufficient to Completely Remove T1 Breast Cancers (below 20 mm)? Analysis of 146 Patients with Histology as Reference Standard
Authors: Garlaschi Alessandro , Valente Irene , Brunetti Nicole , De Giorgis Sara , Massa Barbara , Calabrese Massimo , Tagliafico Alberto Stefano
Published: March 2022

Objectives: Vacuum-assisted breast biopsy (VABB) is a safe procedure comparable to surgical biopsy for the characterization of distortions, microcalcifications, and mass lesions. Vacuum-assisted excision of T1 breast tumors could be on potential management in alternative to surgery. The primary objective of this work was to assess the therapeutic success of the stereotaxic vacuum breast biopsy in small breast cancer (T1N0M0) lesions excision. Methods: From our electronic database, all the vacuum breast biopsies performed from January 1, 2015, to December 1, 2019, have been retrospectively reevaluated. N = 2,200 cases were identified and n = 145 ensured “mammographic complete removal” at the end of vacuum-assisted excision treatment and were considered for analysis. Surgical gold standard was used. Results: N = 143 procedures were successfully completed with complete removal of mammographic calcifications. The mean size of the lesions completely excised with VABB was 8.9 ± 3.6 mm (range, 3–23 mm). Lesions below 10 mm were n = 118 and lesion with diameter >10 mm were n = 28. N = 3/146 cases (4.4%), relapses were observed in follow-up (at 12–24 up to a maximum of 60 months): the mean size of relapsed lesions completely excised was 3.6 ± 2.1 mm (range, 2–6 mm). No relapse before 12 months were observed. The mean size of the lesions in these patients with relapse at the time of the first VABB procedures was 13 ± 6.5 mm (range, 7–12 mm). N = 117/118 (99%) lesions excised using VABB without relapse after 1 year of follow-up had a diameter below 10 mm. Conclusions: Vacuum breast biopsy could safely remove small breast cancers (T1N0M0) with few relapses.
A pot line rectiformer scheme with hybrid-shunt active power filter
Authors: Albasri Fadhel Abbas , Al-Mawsawi Sayed Ali , Al-Mahari Mahmood
Published: March 2022

Passive harmonic filters (PHF) are commonly used in conventional potline rectifier transformers (Rectiformer) schemes of aluminum smelters to improve the power quality and reduce harmonic distortions to an acceptable level. However, changes in operating conditions and system configuration affect the performance capability of PHF in attenuating harmonics. In this paper, a Rectiformer scheme with hybrid-shunt active power filter (SAPF) is used instead of PHF and compared with the other commonly applied schemes in aluminum smelter in terms of harmonic attenuation capability. The SAPF potline Rectiformer scheme and other schemes are modeled and simulated using MATLAB/Simulink.  A control strategy suitable for potline load behavior is furnished in this paper for SAPF. The simulation results reveal the performance superiority of the proposed SAPF as compared to the conventional and ALBA pot line (PL) 6 schemes.
Design of semi-Z source inverter topology with reduced number switches for four quadrant control of direct current motor
Authors: Anjum Waseah , Panda Sudeekshya , Ramasamy Palanisamy
Published: March 2022

This paper proposes a novel approach to 4-quadrant speed control of a direct current (DC) motor based on the use of a semi-Z source inverter which has 2 active switches and 2 switching states. The semi-Z source inverter topology is analyzed for design purposes and results and relationships are discussed. The objective of this simulation is to design and analyze the performance of closed loop speed control of a DC motor using a semi-Z source inverter under realistic operating conditions. The findings of this project can be useful for designing several household appliances as well as systems like lathe machines, hoists, conveyors, and fans. Closed loop system designed for automatic speed control of a DC motor is obtained by using MATLAB/Simulink. The simulation results show that the proposed system works as intended.
A comparative analysis of torque ripple reduction techniques for sensor BLDC drive
Authors: Mahalingam Karthika , Chellaiah Ramji Nisha Kandencheri
Published: March 2022

Brushless direct current motors (BLDC) are prominent due to their high efficiency and less maintenance. However, BLDC motor applications are limited due to the requirement of a complex controller, the existence of torque ripple, and high cost. This research work describes the comparative analysis of different control strategies of torque ripple reduction in BLDC motors and it discusses various topologies viz. cuk converter, simple voltage modulator scheme, charged capacitor, Z-source inverter, and Quasi- Z source inverter-based toque ripple reduction techniques. The effectiveness of different control strategies has been verified using MATLAB Simulink and it helps to understand different control strategies for torque ripple reduction during the entire speed range. The analysis of MATLAB results reveal that the voltage modulation scheme reduces the torque ripple considerably during the entire speed range along with the noise- less operation of the BLDC motor when compared with other topologies.
Optimization of PID parameters for controlling DC motor based on the aquila optimizer algorithm
Authors: Aribowo Widi , Supari Supari , Suprianto Bambang
Published: March 2022

This study presents the application of the aquila optimizer (AO) algorithm to determine the parameters of the proportional integral derivative (PID) controller to control the speed of a dc motor. The AO method is inspired by the most popular bird of prey in the northern hemisphere named Aquila. Initially, the proposed AO algorithm is applied to unimodal and multimodal benchmark optimization problems. To get the performance of the AO method, the controller is compared with other methods, namely Seagull optimization algorithm (SOA), marine predators algorithm, giza pyramids construction (GPC), and chimp optimization algorithm (ChOA). The results represent that the AO is promising and shows the effectiveness. Determination of PID parameters using the AO method for dc motor speed control system shows superior performance.
Lightning rod system: mathematical analysis using the rolling sphere method
Authors: Martínez Jairo Mora , Angarita Eliana Maria Noriega , Alvarez José Ricardo Núñez , Crespo Michelle Hernandez , Pertuz Paulo José Fruto
Published: March 2022

In electrical protection, there is a method of electrical protection of buildings against atmospheric discharges called the electro-geometric method or the rolling sphere method. So far, it is possible to achieve the implementation of this method graphically, that is, representing through plans and technical drawings, the protection conditions of the analyzed structure and obtaining from these graphic representations the protection parameters with the consequent errors caused by the scales and dimensions of the work plane. In the present work, a mathematical model is obtained that allows, using specific calculations, to analyze the dynamic behavior of a protection system against atmospheric discharges without worrying about the limitations given by the scales and planes. The set of equations obtained in the model allows us to determine the different parameters that define the protection system against atmospheric discharges (lightning) without depending on the graphical representation of the system's topology.
Z-source reversing voltage multilevel inverter for photovoltaic applications with inherent voltage balancing
Authors: Najafi Ehsan , Rashidi Amir Hosein , Dehghan Seyed Mohammad
Published: March 2022

Photovoltaic (PV) inverters have been widely used in solar energy to change the direct current (DC) voltage of PV panels to the grid alternating current (AC) voltage. They are comprised of DC-DC section for maximum power point tracking (MPPT) and DC-AC part to provide required output AC voltage. This paper provides a unique configuration that integrates both sections in one part. A Z-source multilevel inverter is proposed based on Z-source networks and reversing voltage (RV) multilevel inverter. The Z-source network can be used to increase the level of the input voltages and MPPT as well. In addition, the prop used topology uses multilevel topology to accommodate more DC inputs and benefit from features of multilevel level inverters in AC output. Moreover, using Z-source network can minimize equated monthly installment (EMI) due to the lower stress on the semiconductor switches. Finally, a seven level topology is designed and simulated to show the performance of proposed topology.