bluuebunny/crossref_metadata_2025 huggingface dataset:
Prediction of Enterprise Economy by Network Communication Business Based on BP Neural Network in Big Data Security Environment
Authors: Kong Sibo
Published: August 2022

From the environmental and security status quo faced by big data, although big data is unstructured or more difficult to filter and analyze, it does not mean that big data is necessarily more secure. In order to be invincible in the fierce market competition, an enterprise must conduct an in-depth understanding and investigation of the rapid changes in the market and economic development. The current era is already the Internet era. The arrival of the era of network communications has greatly facilitated people’s lives. However, how to seize the opportunities in the era of network communication and make economic predictions based on actual situations are particularly important for enterprises. Therefore, we are required to make reasonable economic predictions of network communication services in order to seize opportunities and meet challenges. By analyzing the development and application of artificial neural network (NN), this article briefly introduces its development and principles. Based on the reality of a network communication industrial enterprise, it uses data modeling and comparative analysis to establish a logistic regression model, a decision tree model, and a BP NN. The model compares the customer types and data predictions of the enterprise’s data traffic under the three models. From the results of the model analysis, it can be seen that in the analysis of the three models, the ROC curve analysis, the BP NN predicts that the cumulative hit rate in the ROC curve is wider. More users who can handle traffic services will be covered. In the cumulative customer lift analysis, under the first set of data (that is, when the depth is 20), the cumulative depths of the decision tree model, logic analysis model, and BP neural system prediction model are 2.5, 3.4, and 3.6, respectively. The BP neural system prediction model has the highest value of cumulative depth. In the cumulative capture response percentage analysis, the cumulative capture response percentage from low to low is the decision tree model and logic analysis. From BP NN prediction model, we can draw the conclusions. The network system can play a good role in forecasting and help enterprise managers make economic decisions.
Evaluation of Short‐Term Efficacy of PD‐1 Monoclonal Antibody Immunotherapy for Lymphoma by Positron Emission Tomography/Computed Tomography Imaging with Convolutional Neural Network Image Registration Algorithm
Authors: Fang Jie , Chen Zhendong
Published: August 2022

The objective of this study was to investigate the value of PET/CT imaging based on CNN image registration algorithm in evaluating the short‐term efficacy of PD‐1 monoclonal antibody immunotherapy for lymphoma. 36 patients with advanced lymphoma confirmed by histology or cytochemistry and treated with PD‐1 monoclonal antibody admitted to hospital were included. In addition, 38 normal controls were from healthy volunteers. All patients were treated with PD‐1 monoclonal antibody intravenous infusion, and the image data were processed by CT with intelligent segmentation algorithm. Medication method: nivolumab 3 mg/kg for 2 weeks; pembrolizumab 2 mg/kg for 3 weeks; and continuous medication until tumor progression or unacceptable adverse reactions. Efficacy was evaluated every 3 cycles. Imaging examinations were performed after 3 weeks of medication to evaluate the therapeutic effect. The concentrations of IL‐2, IL‐7, basic fibroblast growth factor (FGF), granulocyte‐macrophage colony‐stimulating factor (GM‐CSF), platelet‐derived growth factor (PDGF‐bb), and other factors in the normal control group were significantly higher than those in the advanced lymphoma patients, and the differences were statistically significant (P < 0.05). The PET/CT imaging automatic segmentation accuracy of the CNN image registration algorithm was greater than 81%, and 27 patients were treated for more than 3 cycles, including 1 case of partial remission (PR) (3.7%), 16 cases of stable disease (SD) (59.3%) after 3 cycles of treatment, and 10 cases of progressive disease (PD) (37%). After 6 cycles of treatment, 1 case was PR (8.3%), 7 cases were SD (58.4%), and 4 cases were PD (33.3%). Adverse reactions included fever, fatigue, gastrointestinal reactions, hypothyroidism, and interstitial pneumonia. PD‐1 monoclonal antibody immunotherapy had a certain short‐term effect on lymphoma.
Optimal Allocation Method of 5G Communication System Resources Assisted by Artificial Intelligence Technology
Authors: Liu Hua
Published: August 2022

5G technology has been rapidly developed under the trend of utilization in various countries. It is an information dissemination technology with faster and more stable transmission speed. Compared with 4G technology, 5G technology has a low delay rate and higher transmission speed, and it can be applied in tasks such as the Internet of Vehicles and the transmission of 3D images. For 5G technology, the communication system is a huge challenge. It not only needs to deal with the transmission speed between users and users but also needs to deal with the efficiency between users and channels. 5G researchers will spend a lot of material and financial resources to solve these problems. This study uses artificial intelligence technology to analyze its application in resource allocation of 5G communication systems. It also uses the collaborative filtering algorithm to realize the automatic recommendation and matching problem of 5G communication system resources. The research results show that artificial intelligence technology has good feasibility in the resource allocation problem of 5G communication system. It can achieve the resource allocation task of 5G technology communication system with high efficiency and accuracy, and all prediction errors are within 3% for the characteristics of the three 5G communication systems in this study.
Design and Implementation of a Personalized Tourism Recommendation System Based on the Data Mining and Collaborative Filtering Algorithm
Authors: Nan Xiang , Kayo kanato  , Wang Xiaolan
Published: August 2022

A personalized tourism recommendation system provides convenient and economically affordable travel information for individuals/groups. This recommendation system banks on accumulated and analyzed data for providing context-aware travel solutions. For improving the recommendation efficiency and data analysis of such systems, this article introduces a mining and filtering harmonized collaborative process, named as the collaborative mining and filtering process (CMFP), for reducing the data processing overheads and improving the recommendation ratio. In this process, the accumulated data from the global and personal travel, expenditure, and other information are collaboratively analyzed. This analysis is powered by knowledge-based transfer learning for reducing the retardation in the large data processing. Based on the context-based data analysis, the filtering and mining are jointly performed for providing recommendations. In the filtering process, the maximum processed contextual data are extracted for updating the current knowledge base. From this base, the recommendation for adaptable travel is recommended for the user. This process’s performance is analyzed using the metrics accuracy, data handling rate, mining time, and overhead.
Task Offloading and Resource Allocation Strategy Based on Deep Learning for Mobile Edge Computing
Authors: Yu Zijia , Xu Xu , Zhou Wei
Published: August 2022

For the problems of unreasonable computation offloading and uneven resource allocation in Mobile Edge Computing (MEC), this paper proposes a task offloading and resource allocation strategy based on deep learning for MEC. Firstly, in the multiuser multiserver MEC environment, a new objective function is designed by combining calculation model and communication model in the system, which can shorten the completion time of all computing tasks and minimize the energy consumption of all terminal devices under delay constraints. Then, based on the multiagent reinforcement learning system, system benefits and resource consumption are designed as rewards and losses in deep reinforcement learning. Dueling-DQN algorithm is used to solve the system problem model for obtaining resource allocation method with the highest reward. Finally, the experimental results show that when the learning rate is 0.001 and discount factor is 0.90, the performance of proposed strategy is the best. Furthermore, the proportions of reducing energy consumption and shortening completion time are 52.18% and 34.72%, respectively, which are better than other comparison strategies in terms of calculation amount and energy saving.
Pan-Cancer Analysis Reveals the Relation between TRMT112 and Tumor Microenvironment
Authors: Xu Haitao , Jiang Caihong , Yao Fusheng , Liang Hong , Yan Hong , Chen Dangui , Wu Youzhi , Zhong Long
Published: August 2022

Dysregulated epigenetic modifications play a critical role in cancer development where TRMT112 is a member of the transfer RNA (tRNA) methyltransferase family. Till now, no studies have revealed the linkage between TRMT112 expression and diverse types of tumors. Based on TCGA data, we first probed into the relation between TRMT112 and prognosis and the potential role of TRMT112 in tumor microenvironment across 33 types of tumor. TRMT112 presented with increased expression in most cancers, which was significantly prognostic. Furthermore, TRMT112 was associated with tumor-associated fibroblasts in a variety of cancers. Additionally, a positive relationship was identified between TRMT112 expression and multiple tumor-related immune infiltrations, such as dendritic cells, CD8+ T cells, macrophages, CD4+ T cells, neutrophils, and B cells in lung adenocarcinoma and breast invasive carcinoma. In summary, our results suggest that TRMT112 might be a potential prognostic predictor of cancers and involved in regulating multiple cancer-related immune responses to some extent.
Pyroptosis-Related Gene Model Predicts Prognosis and Immune Microenvironment for Non-Small-Cell Lung Cancer
Authors: Luo Lianxiang , Yao Xinyue , Xiang Jing
Published: August 2022

Non-small-cell lung cancer (NSCLC) has a high incidence and mortality worldwide. Moreover, it needs more accurate means for predicting prognosis and treatments. Pyroptosis is a novel form of cell death about inflammation which was highly related to the occurrence and development of tumors. Despite having some studies about pyroptosis-related genes (PRGs) and cancer, the correlation has not been explored enough between PRGs and immune in NSCLC. In this study, we constructed a PRG model by WGCNA to access the prognosis value PRGs have. The testing cohort (

n
=
464

) with four datasets from the GEO database conducted a survival analysis to confirm the stability of the prognostic model. The risk score and age are examined as independent prognostic factors. Based on the PRGs, we found multiple pathways enriched in immune in NSCLC. Separating samples into three subtypes by consensus cluster analysis, Cluster 3 was identified as immune-inflamed phenotype with an optimistic prognostic outcome. A three-gene PRG signature (BNIP3, CASP9, and CAPN1) was identified, and BNIP3 was identified as the core gene. Knockdown of BNIP3 significantly inhibited the growth of H358 cells and induced pyroptosis. In conclusion, the model construction based on PRGs provides novel insights into the prediction of NSCLC prognosis, and BNIP3 can serve as a diagnostic biomarker for NSCLC.
Numerical Study of Unsteady Pressure Fluctuation at Impeller Outlet of a Centrifugal Pump
Authors: Ma Xiaojie , Zheng Lulu , Qu Jinglei , Wang Mengmeng
Published: August 2022

Intense fluid-dynamic interaction at the impeller outlet strongly affects the unsteady flow and pressure stability within the centrifugal pump. In order to have a better understanding of the pressure fluctuation of centrifugal pumps, a numerical calculation is carried out by using the RNG k-epsilon turbulence model under various flow rates. The numerical calculation results are compared with the experimental results in order to verify the reliability of the calculation model. The amplitude and frequency distribution of pressure fluctuation at the impeller outlet is obtained and analyzed in the time and frequency domain. The research results show that the blade passing frequency is the dominant frequency of the pressure fluctuation. And the pressure fluctuation is a periodic fluctuation. As the flow rate decreases, the periodicity of the pressure fluctuation decreases. Besides, the amplitude and intensity of pressure fluctuation are closely related to flow rate and spatial location. At the low flow rate, the amplitude of pressure fluctuation in the time domain and frequency domain is enlarged greatly, especially near the tongue region. The pressure difference distribution on both sides of the blade surface is extremely uneven, and the pressure changes significantly.
Research Design of Fashion Elements Identification of Clothing Based on Decision Tree Algorithm and IoT
Authors: Wang Li
Published: August 2022

Attributable to challenges of social event enormous volumes of material area information in a setting of less mining research, foreseeing the qualities of articles of clothing turns into a significant open issue which gets increasingly more consideration from the materials research local area. In this exploration work, the field of data mining endeavors to anticipate clothing insurance factors completely plan on understanding the computational individual of learning. Qualities of dress learning are being explored as a methodology for settling on the decision and utilization of getting ready data and their outcomes. It is seen from the results got by experimentation that the linear regression hushes up engaging as a result of adequacy as far as high forecast rate and linear regression can find the dress protection execution in a most proficient way in contrast with any remaining inclining calculations tested.
TAFM: A Recommendation Algorithm Based on Text-Attention Factorization Mechanism
Authors: Zhang Xianrong , Li Ran , Wang Simin , Li Xintong , Sun Zhe
Published: August 2022

The click-through rate (CTR) prediction task is used to estimate the probabilities of users clicking on recommended items, which are extremely important in recommender systems. Recently, the deep factorization machine (DeepFM) algorithm was proposed. The DeepFM algorithm incorporates a factorization machine (FM) to learn not only low-order features but also the interactions of higher-order features. However, DeepFM lacks user diversity representations and does not consider the text. In view of this, we propose a text-attention FM (TAFM) based on the DeepFM algorithm. First, the attention mechanism in the TAFM algorithm is used to address the diverse representations of users and goods and to mine the features that are most interesting to users. Second, the TAFM model can fully learn text features through its text component, text attention component, and N-gram text feature extraction component, which can fully explore potential user preferences and the diversity among user interests. In addition, the convolutional autoencoder in the TAFM can learn some higher-level features, and the higher-order feature mining process is more comprehensive. On the public dataset, the better performing models in the existing models are deep cross network (DCN), DeepFM, and product-based neural network (PNN), respectively, and the AUC score metrics of these models hover between 0.698 and 0.699. The AUC score of our design model is 0.730, which is at least 3% higher than that of the existing models. The accuracy metric of our model is at least 0.1 percentage points higher than that of existing models.
Analysis of Realistic Problems and Practical Paths of “Three-Wide Education” in Higher Education Based on Text Analysis and Mining
Authors: Yin Yuan , Guo Ling
Published: August 2022

As the basic concept of higher education, the three comprehensive education is a reliable guarantee for strengthening and improving moral work in higher education, which is a complex of talents meeting the requirements of the times, and “universal education” is a comprehensive training for students to improve their cultural and spiritual moral level to meet the requirements of future social consumption. In order to study the comprehensive reform and development of moral education in universities, this paper reviews and standardizes the formation and development of comprehensive reform in universities, which is closely related to the development of comprehensive reform: strengthening the interconnection of systems in the process of training all employees, strengthening the effective interaction between cultures in the whole process, and ensuring the complete organic integration of cultures. The authors propose a solution for the comprehensive reform of “three-completion education” in universities. The authors offer some reasonable suggestions for the development of moral education in colleges and universities.
Stability Analysis and Construction Parameter Optimization of Tunnels in the Fractured Zone of Faults
Authors: Huang Banma , Chen Haibo , Duan Chenglong , Li Wenhu
Published: August 2022

In order to improve the construction method of highway tunnel fault, improve the excavation level, improve the construction efficiency, reduce the project cost, and shorten the construction period, so as to find a specific road, this paper puts forward the research method of tunnel stability analysis and construction parameter optimization in the fault fracture zone. First, this paper analyzes the specific geographical and geological environment of the construction site. Second, this paper compares and analyzes the effects of the drilling and blasting method and full-face tunnel boring machine (TBM) in construction and further analyzes the surrounding rock deformation, over excavation, and under excavation, as well as the range of loose circles when the surrounding rock is stable. Then, this paper discusses the minimum smooth blasting parameters under these conditions. Finally, the actual blasting effect of tunnel construction is tested and the optimization algorithm model of tunnel fault drilling and blasting parameters is established. The results show that the proposed optimization model of drilling and blasting construction parameters for highway tunnel faults based on the Support Vector Regression (SVR) algorithm combined with a genetic algorithm (GA) has a short calculation time and high parameter optimization accuracy. It is very feasible to optimize the construction parameters of the fault drilling and blasting method, which can greatly improve the construction efficiency, carry out the detailed simulation, reduce the cost, and increase safety. In summary, it has a certain reference significance for the optimization of highway tunnel construction and future research by drilling and blasting method under complex geological conditions in my country.
Antimicrobial Step-Down Therapy versus Conventional Antimicrobial Therapy in the Treatment of Patients with Sepsis
Authors: Peng Zhuo , Niu Zequn , Zhang Rui , Pan Longfei , Feng Hui , Zhou Yang , Pei Honghong
Published: August 2022

Objective. This study was to evaluate the efficacy of antimicrobial step-down therapy versus conventional antimicrobial therapy in the treatment of patients with sepsis. Methods. Between September 2020 and September 2021, 65 patients with sepsis treated in the intensive care unit (ICU) of our hospital were recruited and assigned at a ratio of 1 : 1 to receive either conventional antimicrobial therapy (sulbactam plus cefoperazone) (control group) or antimicrobial step-down therapy (imipenem/cilastatin) (observation group). The results of drug sensitivity tests and clinical effects were evaluated comprehensively after 3-5 d of treatment, downgraded, and upgraded, or maintenance treatment was administered for 10 d. Outcome measures included clinical and laboratory indices and treatment efficacy. Results. Antimicrobial step-down therapy resulted in a significantly higher efficacy and lower levels of white blood cell (WBC) count and C-reactive protein (CRP) versus conventional antimicrobial therapy (

P
<
0.05

). The patients given antimicrobial step-down therapy showed a significantly shorter duration of antimicrobial drug administration, temperature recovery, time of respiratory support, and ICU stays versus conventional antimicrobial therapy (

P
<
0.05

). Conclusion. Antimicrobial step-down therapy contributes to the mitigation of inflammatory responses in patients with sepsis and shortens the duration of antimicrobial drug use and ICU stay versus conventional antimicrobial therapy. The reliability of the conclusions can be further increased if multicenter and large sample clinical observations can be conducted, which is the direction of endeavor for future clinical studies.
MRI Extraprostatic Extension Grade: Accuracy and Clinical Incremental Value in the Assessment of Extraprostatic Cancer
Authors: Xiang Jun-Yi , Huang Xiao-Shan , Xu Jian-Xia , Huang Ren-Hua , Zheng Xiao-Zhong , Xue Li-Ming , Liu Yu-Long
Published: August 2022

Objective. The purpose was to compare the accuracy of extraprostatic extension (EPE) grade on MRI predicting EPE with Partin tables, Memorial Sloan Kettering Cancer Center nomogram (MSKCCn), and combined models and to analyze the clinical incremental value of EPE grade. Materials and Methods. 105 prostate cancer patients confirmed by pathology after radical prostatectomy in our hospital from 2017 to 2021 were selected. The clinical stage, PSA, Gleason score, number of positive biopsy cores, and percentage of positive biopsy cores were recorded. Evaluate EPE grade according to EPE grade criteria, and calculate the probability of predicting EPE with Partin tables and MSKCCn. EPE grade is combined with Partin tables and MSKCCn to construct EPE grade+Partin tables and EPE grade+MSKCCn models. Calculate the area under the curve (AUC), sensitivity, and specificity of EPE grade, Partin tables, MSKCCn, EPE grade+Partin tables, and EPE grade+MSKCCn and compare their diagnostic efficacy. The clinical decision curve was used to analyze the clinical net income of each prediction scheme. Results. The AUC of EPE grade was 0.79, Partin tables was 0.50, MSKCCn was 0.78, the EPE grade+Partin table model was 0.79, and the EPE grade+MSKCCn model was 0.83. After EPE grade was combined with Partin tables and MSKCCn, the diagnostic efficiency of clinical model was significantly improved (P < 0.05). There was no significant difference in the diagnostic efficacy of the combined model compared with the single EPE grade (P > 0.05). The calibration curve of the combined model shows that it has a good calibration degree for EPE. In the analysis of the decision curve, the net income of the EPE grade is higher than that of Partin tables and MSKCCn and is equal to the EPE grade+Partin tables and is slightly lower than that of EPE grade+MSKCCn. The clinical net income of the combined model is obviously higher than that of individual clinical models. Conclusion. The accuracy of EPE classification in predicting prostate cancer EPE is high, and combined with the clinical model, it can significantly improve the diagnostic efficiency of the clinical model and increase the clinical benefit.
Determination of Five Coumarins in Angelicae Pubescentis Radix from Different Origins by HPTLC-Scanning
Authors: Ji Dangtong , Li Qian , Yang Hanting , Fan Yue , Wang Ting , Chen Yuying
Published: August 2022

The HPTLC method is widely used in the field of quality evaluation and component analysis of traditional Chinese medicine (TCM). This work developed an HPTLC method to determine the five effective components of osthole, columbianadin, isoimperatorin, oxypeucedanin, and imperatorin in Angelicae Pubescentis Radix (APR) from twelve different origins, and the quality difference was analyzed by comprehensive factor analysis and cluster analysis. The results showed that the calibration curves of five components exhibited good linearity within the linear ranges (0.8–4.0 μg). The RSD of precision was 1.06%–1.21%, and the repeatability and stability tests were good. The results of cluster analysis showed that the APR from 12 different areas was divided into two categories, and at the same time, it was found that the quality of Dazhou in Sichuan and Huating in Gansu was better than in other areas. In this study, a simple, rapid, and efficient method for quality evaluation of TCM was established by the HPTLC method.
Direction-Finding Using Co-prime Array under Impulsive Noise
Authors: Sha Xiang , Cui Guolong
Published: August 2022

In order to achieve more accurate estimates of the existing direction-finding approaches under impulsive noise, a robust direction-finding algorithm using a coprime array is proposed in this work. In order to suppress the strong impulsive noise, we introduce an infinite norm normalization approach, and on this basis, a weighted signal subspace fitting equation using a coprime array is derived. Furthermore, we propose a quantum-inspired moth-flame algorithm to minimize the derived weighted signal subspace fitting function. Simulation results represent that our direction-finding method has the most excellent performance compared to other conventional methods. Besides, our method can address coherent sources without any additional approach.
A Rare Case of Concrescence of Mandibular Third Molar and Supernumerary Fourth Molar
Authors: Wang Jiao , Wang Errui , Yang Xin , Yuan Lu , Li Zhige , Zhang Jie , Zhang Baoping
Published: August 2022

Concrescence reveals a rare developmental anomaly in which two fully formed teeth are joined along the root surfaces by cementum, and generally occurs in maxillary molars, especially in a third molar and a supernumerary tooth. Very few cases have been reported about the concrescence of a third molar and a supernumerary fourth molar. Based on our available knowledge, this case report described a rare presentation in which concrescence is observed between a third molar and a supernumerary fourth molar in the mandible by diagnosing with cone-beam CT and histological examination.
Discussion on Innovative Methods of Higher Teacher Education and Training Based on New Artificial Intelligence
Authors: Deng Hao , Jia Wanwan , Chai Deyi
Published: August 2022

In Chinese colleges, teachers’ teaching level and proficiency in business knowledge are an important part of maintaining the knowledge system of colleges and universities. Therefore, in order to improve the teaching level of Chinese colleges and improve the knowledge reserve of college students, we should spare no effort to develop and improve the professional level of each teacher. The research on teacher training mode based on the development of teachers’ teaching ability is of great significance to the development of colleges and the improvement of teaching quality. The new generation of artificial intelligence plays a pivotal role in the training of college teachers. This paper randomly selects 897 undergraduate teachers from 10 private colleges in a certain region of China who participate in higher teacher training for research. It mainly studies the application effect of several typical artificial intelligence technologies in evaluating higher teacher education and training. Specifically, the paper uses artificial intelligence as a carrier to predict the performance indicators of the three main modules involved in higher teacher education and training. The prediction results of several artificial intelligence techniques show that the support vector machine method based on immune algorithm optimization corresponds to the largest coefficient of determination and the smallest root mean square error. Therefore, it predicts the best. Furthermore, the comparison results between the predicted data and the measured data show that the artificial intelligence technology has a better prediction effect on the three module indicators. At the same time, there is a good exponential function relationship between the prediction data corresponding to the two main modules of organization and personnel.
ATAXIC: An Algorithm to Quantify Transcriptomic Perturbation Heterogeneity in Single Cancer Cells
Authors: Liu Qian , Lu Qiqi , Wang Xiaosheng
Published: August 2022

The single-cell RNA sequencing (scRNA-seq) has recently been widely utilized to quantify transcriptomic profiles in single cells of bulk tumors. The transcriptomic profiles in single cells facilitate the investigation of intratumor heterogeneity that is unlikely confounded by the nontumor components. We proposed an algorithm (ATAXIC) to quantify the heterogeneity of transcriptomic perturbations (TPs) in single cancer cells. ATAXIC calculated the TP heterogeneity level of a single cell based on the standard deviations of the absolute z-scored gene expression values for tens of thousands of genes, reflecting the asynchronous degree of transcriptomic alterations relative to the central (mean) tendency. By analyzing scRNA-seq datasets for eight cancer types, we revealed that ATAXIC scores were likely to correlate positively with the enrichment scores of various proliferation and oncogenic signatures, DNA damage repair, treatment resistance, and unfavorable phenotypes and outcomes in cancer. The ATAXIC scores varied among different cancer types, with lung cancer and melanoma having the lowest average scores and clear cell renal cell carcinoma having the highest average scores. The low TP heterogeneity in lung cancer and melanoma could bestow relatively higher response rates to immune checkpoint inhibitors on both cancer types. In conclusion, ATAXIC is a useful algorithm to quantify the TP heterogeneity in single cancer cells, as well as providing new insights into tumor biology.
Nanogels Containing Foeniculum vulgare Mill. and Mentha piperita L. Essential Oils: Mosquitoes’ Repellent Activity and Antibacterial Effect
Authors: Sanei-Dehkordi Alireza , Abdollahi Abbas , Safari Mojdeh , Karami Farnaz , Ghaznavi Ghazal , Osanloo Mahmoud
Published: August 2022

Foeniculum vulgare Mill. and Mentha piperita L. are two common medicinally important plants with a wide range of biological activities such as insecticide and antibacterial effects. In this study, the chemical composition of their essential oils was investigated using GC-MS analysis. After that, their nanoemulsions were prepared; optimum samples with droplet sizes of 74 ± 7 and 136 ± 5 nm were gelified. The viscosity of the prepared nanogels and the successful loading of the essential oil in them were investigated. The efficacy of the nanogel containing M. piperita essential oil as a repellent and antibacterial agent was more potent than the nanogel containing F. vulgare essential oil. Its completely protected time against Anopheles stephensi, the main malaria mosquito vector, was 120 ± 8 min. Moreover, the growth of Escherichia coli and Staphylococcus aureus after treatment with 5000 µg/mL of nanogel containing M. piperita essential oil was reduced by 100 and 65%, respectively. Considering natural constituents, a straightforward preparation method, and high efficacy, the nanogel containing M. piperita essential oil could be introduced for further investigation against other mosquitoes and bacterial species.
Hydrothermal Corrosion of SiC Coupons Suppressed by Magnetron Sputtered Cr Coatings
Authors: Dai Shuxin , Zou Zhiming , Wang Renda , Li Yuhang , Li Bingsheng , Ge Fangfang , Li Peng
Published: August 2022

SiC-based components are sometimes susceptible to aqueous dissolution in LWR coolant environments. To address this issue, ~10 μm thick Cr coatings was deposited on reaction-bonded silicon carbide (RBSC) plates by magnetron sputtering. Corrosion behavior of Cr-coated SiC and -uncoated SiC coupons was studied by immersing in autoclave (345°C and 16.5 MPa). The weight loss of the Cr coated SiC coupons (3.02% after the 93-days) in the autoclave tests was effectively reduced due to their Cr-coated surfaces, in contrast to the uncoated ones (20.4% after the 78-days). Moreover, microstructural and compositional evolutions were examined by using scanning electron microscopy (SEM), X-ray diffraction (XRD), and Raman spectroscopy. It was revealed that a continuous and dense Cr2O3 layer formed on the surface after the hydrothermal corrosion, which can suppress the in-diffusion of corrosive medium.
Distributed Blockchain-Based Platform for Unmanned Aerial Vehicles
Authors: Ahamed Ahanger Tariq , Aldaej Abdulaziz , Atiquzzaman Mohammed , Ullah Imdad , Yousufudin Muhammad
Published: August 2022

Internet of Things (IoT)-inspired drone environment is having a greater influence on daily lives in the form of drone-based smart electricity monitoring, traffic routing, and personal healthcare. However, communication between drones and ground control systems must be protected to avoid potential vulnerabilities and improve coordination among scattered UAVs in the IoT context. In the current paper, a distributed UAV scheme is proposed that uses blockchain technology and a network topology similar to the IoT and cloud server to secure communications during data collection and transmission and reduce the likelihood of attack by maliciously manipulated UAVs. As an alternative to relying on a traditional blockchain approach, a unique, safe, and lightweight blockchain architecture is proposed that reduces computing and storage requirements while keeping privacy and security advantages. In addition, a unique reputation-based consensus protocol is built to assure the dependability of the decentralized network. Numerous types of transactions are established to characterize diverse data access. To validate the presented blockchain-based distributed system, performance evaluations are conducted to estimate the statistical effectiveness in the form of temporal delay, packet flow efficacy, precision, specificity, sensitivity, and security efficiency.
Construction and Simulation of the Enterprise Financial Risk Diagnosis Model by Using Dropout and BN to Improve LSTM
Authors: Yang Weiwei , Jia Chaoxian , Liu Ruifeng
Published: August 2022

In view of the financial risks faced by listed enterprises, how to accurately predict the risks is an important work. However, the traditional LSTM financial diagnosis model has the disadvantage of low accuracy; the specific reason is that the LSTM model has the problems of overfitting and gradient disappearance in risk diagnosis. Therefore, Dropout is adopted to solve the overfitting problem in the process of premodel prediction, and the BN algorithm is used to solve the gradient disappearance problem in the process of iteration. In order to verify the feasibility of above improvements, the financial data of China’s A-share listed enterprises from 2017 to 2020 are taken as samples to analyze the financial data of listed enterprises through single-step dimension and multistep dimension. The experimental results show that under the analysis of two dimensions, the financial prediction accuracy of the improved LSTM for T-2∼T-3 years can reach 83.96% and 91.19%, respectively, which indicates that through the above improvements, the model can be improved and has certain reference value.
SpotFuzzer: Static Instrument and Fuzzing Windows COTs
Authors: Gu Yeming , Shu Hui , Ma Rongkuan , Yan Lin , Zhu Lei
Published: August 2022

The security research on Windows has received little attention in the academic circle. Most of the new methods are usually designed for the Linux system and are difficult to transplant to Windows. Fuzzing for Windows programs always suffers from its closed source. Therefore, we need to find an appropriate way to achieve feedback from Windows programs. To our knowledge, there are no stable and scalable static instrumentation tools for Windows yet, and dynamic tools, such as DynamoRIO, have been criticized for their performance. To make matters worse, dynamic instrumentation tools have very limited usage scenarios and are impotent for many system services or large commercial software. In this paper, we proposed SpotInstr, a novel static tool for instrumenting Windows binaries. It is lightweight and can instrument most Windows PE programs in a very short time. At the same time, SpotInstr provides a set of filters, which can be used to select instrumentation points or restrict the target regions. Based on these filters, we propose a novel selective instrumentation method which can speed up both instrumentation and fuzzing. After that, we design a system called SpotFuzzer, which leverages the ability of SpotInstr and can fuzz most Windows binaries. We tested SpotInstr and SpotFuzzer in multiple dimensions to show their superior performance and stability.
A General Criterion for Unidirectionally Coupled Generalized Chaotic Synchronization with a Desired Manifold
Authors: Yu Xiang , Zuo Zhaolun , Zhu Shijian , Zhang Xuxin
Published: August 2022

In this paper, a general criterion for unidirectionally coupled generalized chaotic synchronization between the response system and the original chaotic system in the form of a desired manifold is presented. The expression of the response system constructed with linear error feedback is given, and the validity of the criterion is proved based on the Lyapunov stability theory. Two numerical examples are used to construct unidirectional coupled chaotic systems in the form of different types of manifolds. Numerical simulation is carried out to verify the feasibility of the construction method and the criterion.
[Retracted] Ecological Adaptability and Application of Traditional Historical Buildings under the Background of Environmental Protection
Authors: Yi Min , Wu Wei , Su Heng , Chen Huiyuan
Published: August 2022

Traditional historical buildings carry the culture and spirit of China for thousands of years and have a unique charm that modern buildings do not have. However, traditional historical buildings have gradually declined over time, and their restoration not only takes time and materials but also causes certain harm to the environment. Therefore, this paper has aimed to explore how traditional historical buildings can develop in a sustainable and ecologically adaptive image in modern times. For traditional historical buildings, this paper has taken traditional cave dwellings in northern Shaanxi as an example to analyze their ecological adaptability, and based on the analysis results, traditional cave dwellings have been transformed. Specifically, this paper has taken the comfort of residential houses as the evaluation standard and has selected the thermal stability of the building and the energy consumption of heat supply as indicators to compare and analyze the traditional cave dwellings and the renovated cave dwellings. The experimental results of this paper have found that the traditional historical buildings are the crystallization of the wisdom of the ancients for thousands of years. However, due to the limitation of technology, the lighting and thermal stability of traditional cave dwellings are not high. After scientific design, the lighting and thermal stability of the transformed cave are 100% and 30% higher than those of the traditional cave, respectively, and the heating energy consumption is reduced by 50%.
Feature Extraction of Color Symbol Elements in Interior Design Based on Extension Data Mining
Authors: Zhang Chunpeng
Published: August 2022

In order to improve the indoor color symbol elements in the design of visual communication ability, the interior design color symbol element feature extraction process is needed. We need to put forward a kind of extension data mining based on the interior design color symbol element feature extraction methods, build the visualization visual expression and big data characteristics of the distribution of the interior design of texture color symbol elements in intelligent information collection model, indoor color symbol elements in the design of the texture image and character segmentation, and extraction of texture and color symbol element in interior design edge feature point, based on the texture and edge profile characteristics of the distribution in the design of indoor color symbol elements of texture segmentation and matching, and extract the edge of the color symbol elements in interior design outline feature points. The extracted edge contour feature points of color image in interior design are used as the extended data feature components for texture matching, and the color symbol element feature extraction is realized according to the prior data distribution and extension data mining results. Simulation results show that this method has good adaptive performance in texture matching and feature extraction of color symbol elements in interior design and has a strong texture discrimination ability, which improves the visual communication ability of color symbol elements in interior design.
Quantitative Evaluation Model of Stock Market Liquidity by Macroeconomic Factors
Authors: Wang Kaifei
Published: August 2022

In order to further understand the effects of macroeconomic factors on the stock market volatility and liquidity and solve the problem that the traditional volatility measurement model loses high-frequency data information in the modeling of the influence of macroeconomic factors on stock market volatility, monthly consumer price index, daily exchange rate, and monthly money supply are taken as the main indicators to investigate the stock market liquidity in the research. Meanwhile, CARCH-MIDAS model is used to investigate the factors affecting stock market liquidity. Through the model test, it is found that the H value of the volatility effect model of the three factors is 0.0307, and the H value of the horizontal effect model is 0.0220, and the result of the horizontal effect model is closest to 1%. The results show that CARCH-MIDAS model is relatively accurate in quantitative evaluation and prediction of the stock market liquidity and volatility.
An Aggressive Cuckoo Search Algorithm for Optimum Power Allocation in a CDMA-Based Cellular Network
Authors: Mwitia Shawn Muthomi , Segera Davies Rene
Published: August 2022

This paper proposes an aggressive cuckoo search algorithm for optimum power allocation in a CDMA-based cellular network. To make the cuckoo search algorithm aggressive, adaptive parameters are used to vary the step size and probability of discovery. Furthermore, the Lévy flight is replaced with the Beta distribution to further improve the performance of the algorithm. To prove that the proposed algorithm is superior, the algorithm is tested on 23 benchmark test functions and its results are compared with those of 10 other standard optimization algorithms and 4 other advanced optimization algorithms. The performance of the proposed algorithm is proved via the statistical analysis of the results using the Wilcoxon rank-sum test. The proposed algorithm is then utilized in determining the optimal uplink power for multiple users in a CDMA-based cellular network in three different scenarios through Rician fading channels. The resultant allocated power should ensure that each mobile station meets its predetermined signal-to-interference-and-noise ratio while utilizing the least amount of power.
Trametenolic Acid Ameliorates the Progression of Diabetic Nephropathy in db/db Mice via Nrf2/HO-1 and NF-κB-Mediated Pathways
Authors: Duan Qi , Tian Lingling , Feng Jiabing , Ping Xinbo , Li Lihong , Yaigoub Hasnaa , Li Rongshan , Li Yafeng
Published: August 2022

Diabetic nephropathy (DN) is a fatal complication of diabetes and the main cause of end-stage renal disease. Due to the suboptimal effects of current treatments, there is an urgent need to develop new therapeutic strategies for DN. Trametenolic acid (TA), a lanostane-type tetracyclic triterpenoid, is one of the main active ingredients extracted from the natural product Inonotus obliquus. Our study was aimed at clarifying the potential protective effects of TA on DN and its underlying mechanism. In this research, C57BLKS/db (db/db) mice were used as the spontaneous DN model, and TA (10 mg/kg/d) was intraperitoneally injected for 4 consecutive weeks. Ratio of right kidney weight/body weight was calculated, and the contents of serum creatinine (Scr), blood urea nitrogen (BUN), and urine albumin were detected. The activities of superoxide dismutase (SOD) and catalase (CAT) and the contents of reductive glutathione (GSH) and malondialdehyde (MDA) were measured. The histopathological changes of renal tissues were observed by hematoxylin and eosin (HE), periodic acid-Schiff (PAS), and Masson staining. The protein expressions of nuclear factor erythroid 2-related factor 2 (Nrf2), heme oxygenase-1 (HO-1), NAD(P)H:quinone oxidoreductase-1 (NQO-1), nuclear factor kappa B (NF-κB), proinflammation cytokine tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), interleukin-1β (IL-1β), Nephrin, and Podocin were detected by western blot assay. Immunohistochemistry was utilized to detect expressions of collagen III (COL-III) and fibronectin (FN). Our results showed that TA administration significantly reduced the ratio of right kidney weight/body weight, BUN, Scr, and urine albumin levels and alleviated the histopathological changes of DN mice. Moreover, TA administration remarkably increased GSH content and SOD and CAT activities and decreased MDA content. Western blot assay demonstrated that TA activated Nrf2 signaling and increased the expression of downstream antioxidant enzymes HO-1 and NQO-1. Further studies illustrated that NF-κB signaling was inhibited, and downstream proinflammation cytokine expressions of TNF-α, IL-6, and IL-1β were also downregulated. In addition, we also found that TA administration significantly increased the expression of nephrin and podocin proteins and reduced the protein expression of COL-III and FN. These findings suggested that TA exhibited a renoprotective effect by ameliorating oxidative stress and inflammation via Nrf2/HO-1 and NF-κB signaling pathways.
Accumulative Plastic Deformation of the Improved Completely Weathered Granite Subgrade in High-Speed Railway
Authors: Qu Changzi , Zhao Jing , Wang Zheng , Liu Ze , Hou Ganghui , Zhou Xiangxiang
Published: August 2022

The improvement and reuse of completely weathered granite (CWG) with poor engineering properties for backfilling embankments can help protect the environment and bring great economic benefits. The embankment of high-speed railways demands an extremely low additional subgrade settlement under long-term dynamic loads. To analyze the applicability of the improved CWG used for subgrade filling, a series of laboratory dynamic triaxial and field cyclic loading tests were carried out. On the basis of the test results, a calculation model of accumulative plastic strain considering the dynamic stress ratio and the cement content was established. Using the proposed model and the numerically calculated dynamic stress, the accumulative plastic deformation of an embankment filled with improved CWG under various driving conditions was calculated and discussed. The variation of accumulative plastic deformation of the improved CWG with loading cycles could be described by a power function. The dynamic stress level had a significant influence on the accumulative plastic strain, and the strain in the bottom layer of the subgrade bed was greatly attenuated. Accordingly, most of the accumulative plastic deformation attenuation occurred within the depth range of the subgrade bed. The final total accumulative plastic deformations of the embankment were less than 2 mm (5 mm for the limit value) after 4 million times of high-speed train loadings, proving that the improved CWG can be used for subgrade filling. The influence of the axle load on accumulative plastic deformation was more significant than that of the train speed. The proposed calculation model of the accumulative plastic strain could provide valuable reference for similar railway engineering.