bluuebunny/crossref_metadata_2025 huggingface dataset:
Facilitation and inhibition effects of anodal and cathodal tDCS over areas MT+ on the flash-lag effect
Authors: Wang Wu , Lei Xiao , Gong Wenxiao , Liang Kun , Chen Lihan
Published: June 2022

Perceived positions of moving objects are related to neural activities in areas MT+. We demonstrate that tDCS over areas MT+ can modulate the FLE, and further anodal and cathodal tDCS facilitated and inhibited the FLE with a moving object heading to but not deviating from the side of the brain stimulated, respectively. These findings suggest a causal role of area MT+ in motion-induced location prediction and contribute to understanding the neural mechanism of the FLE.
Model-based characterization of the selectivity of neurons in primary visual cortex
Authors: Bartsch Felix , Cumming Bruce G. , Butts Daniel A.
Published: June 2022

Visual neurons are increasingly being studied with more complex, natural visual stimuli, and increasingly complex models are necessary to characterize their response properties. Here, we describe a battery of analyses that relate these more complex models to classical characterizations. Using such model-based characterizations of V1 neurons furthermore yields several new insights into V1 processing not possible to capture in more classical means to measure their visual selectivity.
Differential motor system entrainment to auditory and visual rhythms
Authors: Comstock Daniel C. , Balasubramaniam Ramesh
Published: June 2022

We investigated neural entrainment in the motor system for both auditory and visual isochronous rhythms using electroencephalogram. Counter to expectations, our findings suggest stronger entrainment for visual rhythms than for auditory rhythms. Motor system activity was isolated with a novel procedure using independent component analysis as a means of blind source separation, along with known markers of mu activity from the motor system to identify motor components.
[Retracted] Exploring Evaluation of Enterprise Economic Benefits Using Big Data
Authors: Liu Xiaoqian , Li Yujia
Published: June 2022

The purpose is to improve Chinese enterprises’ economic benefit evaluation system based on big data and promote sustainable enterprise production. This paper studies the power supply enterprises‐oriented Evaluation Index System (EIS) under the big data environment. Firstly, it expounds on the construction theory of the enterprise economic benefit model. Secondly, the comprehensive Grey Model (GM) based on improved weight and the power consumption prediction model based on Least Mean Square (LMS) neural network (NN) algorithm are introduced. Finally, the comprehensive GM model based on improved weight is used to evaluate the economic benefits of power supply enterprises. The power consumption prediction model based on the LMS‐NN algorithm is used to predict the sustainable development of power supply enterprises. The results show that the profitability and solvency of joint‐stock power companies are about 90 and 100, respectively, and the social contribution of state‐owned power supply enterprises is the strongest. Lastly, it is predicted that the region will have 134.8 billion kWh of electricity and about 137.2 billion kWh of power consumption in 2020. The growth model and trend are consistent, but there are some errors in the specific power consumption data. Therefore, the audit method based on big data has a good evaluation effect on the economic benefits of enterprises. For example, the profits of private and joint‐stock power supply enterprises are relatively high. In contrast, state‐owned power supply enterprises have outstanding social contribution ability. The big data method is used to predict the power consumption in some areas, and the predicted value is consistent with the actual value. This study provides a reference for the follow‐up economic benefit evaluation and sustainable development of enterprises.
Construction of Risk Control Decision-Making Model Platform for Transportation Hub Projects under the Background of Message Technique
Authors: Zhong Ping , Yin Hang , Li Yuanfu
Published: June 2022

With the acceleration of urbanization, the construction and operation of comprehensive transportation hub have promoted the development of regional economy. However, due to its huge scale, high building density, and crowded public space, the safety status of the comprehensive transportation hub is worrying. As the node of various transportation modes, the efficient operation of the transportation system is closely related to it, but many potential risks threaten the operation safety of integrated transportation hub all the time. Therefore, risk management is an important work to ensure the safe operation of integrated transportation hub. With the deepening of technology application, the application scope of information technology has also been expanded to complete the analysis and simulation of technical scheme and construction organization process, find problems in time, and take preventive measures, so as to optimize the design scheme, reduce unnecessary losses, and speed up the construction progress. In this study, information technology is used to construct the risk control decision model of transportation hub project. According to the research, the change of the target value of the algorithm in this study is 29% better than that of the traditional algorithm, and it can get better results and get the optimal solution quickly when applied to the intelligent traffic dispatching management system.
Application of Endoscopic Ultrasound Combined with Multislice Spiral CT in Diagnosis and Treatment of Patients with Gastrointestinal Eminence Lesions
Authors: Xiong Jie , Jiang Jie , Chen Ying , Chen Ye , Xie Chenyi , Xu Shuchang
Published: June 2022

Objective. To evaluate the application of endoscopic ultrasound (EUS) combined with multislice spiral CT (MSCT) in the diagnosis and treatment of patients with gastric eminence lesions. Methods. A total of 160 patients with gastric eminence lesions enrolled in our hospital from June 2018 to June 2021 were included and received EUS and MSCT. The results of the two examinations and the postoperative pathological results were compared. Results. The common pathological types of gastric eminence lesions include polyps and stromal tumors, with the most common sites of lesions in the gastric antrum, followed by the fundus of the stomach and the gastric body. Gastric eminence lesions mostly originate from the mucosal layer and muscularis mucosa, accounting for 83.13% of the total. With pathological results as the gold standard, the detection rate of MSCT was 90.63%, and that of EUS was 78.13%. With the joint diagnosis as a reference, the receiver operating curve (ROC) revealed a higher diagnostic efficiency of MSCT and EUS. Conclusion. The accuracy of MSCT in the diagnosis of gastric eminence lesions is significantly higher than that of EUS, both of which can offer useful guidance for the choice of endoscopic treatment methods. The combination of MSCT and EUS examination before endoscopic gastroscopy may provide a better treatment efficacy on gastric protruding lesions with high safety.
The Promoting Effect of Mental Health Education on Students’ Social Adaptability: Implications for Environmental
Authors: Jin Ying
Published: June 2022

Students’ mental health has always been a hot topic in colleges and universities. How to make students integrate into the collective and society faster and better is also one of the most difficult problems for educators. Students want to improve their competitiveness; not only must they have superb professional skills, but at the same time, they must have good social adaptability. Only in this way can they quickly complete their identity change and quickly adapt to workplace life. Students’ social adaptability is not innate, but gradually formed through long‐term positive mental health education. Considering the relationship between psychoeducation and social adaptability, in general, a good psychoeducation system is conducive to the development of students’ social adaptability, and they promote each other. Good mental health education is multifaceted and sustainable for the development of social adaptability. In this context, it is necessary to formulate a targeted positive mental health education plan according to the actual situation of students, so as to comprehensively improve the comprehensive quality of students in this way.
Exploration in the Mechanism of Zhisou San for the Treatment of Cough Variant Asthma Based on Network Pharmacology
Authors: Guo De-hai , Hao Jin-ping , Li Xing-jie , Miao Qing , Zhang Qiong
Published: June 2022

Background. Cough variant asthma (CVA) has no definitive diagnosis or pathogenic causes, and there is currently no effective and safe treatment. Methods. The network pharmacology was employed to investigate possible targets of Zhisou San (ZSS) in CVA treatment. The main chemical constituents of seven herbs in ZSS were collected based on the TCMSP. To explain the main mechanism, we sequentially screened the targets of each active ingredient and constructed the network of “herb-ingredient-target-disease.” The core targets of ZSS were further confirmed by the molecular docking analysis. Furthermore, pulmonary function, histopathology, and biochemical assays in mice were used to investigate the effect of ZSS on the treatment of CVA. Results. A total of 137 active ingredients and 86 potential targets for the ZSS in the treatment of CVA were screened, which were connected with the regulation of inflammatory response and immune balance, such as IL-17 signaling pathway, Th17 cell differentiation, TNF signaling pathway, Toll-like receptor signaling pathway, MAPK signaling pathway, T-cell receptor signaling pathway, Th1 and Th2 cell differentiation, and other signaling pathways closely related to the pathogenesis of CVA. Thereinto, 29 core targets contained 8 of the highest scores and could evidently bind to components such as stigmasterol, quercetin, stemoninine B, luteolin, and β-sitosterol predicted by molecular docking. Furthermore, experiments in vivo were conducted for further validation that ZSS had essential effects on lung function and histopathology as well as the inflammatory state in CVA mice, which was significantly related to regulating the Th17/Treg immune balance to reduce inflammation as the important pharmacological mechanism. Conclusion. This study revealed that ZSS has multicomponent and multipathway characteristics of ZSS in the treatment of CVA, which was primarily associated with inflammation and Th17/Treg immune balance. This study provides a scientific foundation for systematically elaborating the pharmacological activities and mechanism of ZSS, as well as explaining the reliability of the TCM compatibility theory.
Revenue Stacking for BESS: Fast Frequency Regulation and Balancing Market Participation in Italy
Authors: Rancilio Giuliano , Bovera Filippo , Merlo Marco
Published: June 2022

Battery energy storage systems (BESS) are considered a relevant flexible resource for supporting the balancing of a RES-penetrated power grid. Since their cost structure is characterized by very high capital costs, it is of utmost importance to ensure efficient and effective operations from a techno-economic perspective. The possibility of services (and revenues) stacking is one of the most discussed optimization solutions. The present work provides a novel approach for BESS modeling, including the stacking of two diverse ancillary services, a dedicated balancing market bidding model, and a state-of-charge management strategy. Fast frequency regulation is proposed as a power-based service, requiring large ramping capability, but asking BESS activation just for a limited amount of time. For the remainder, BESS power can be traded on balancing market (BM): energy-based services, such as tertiary regulation, could be effectively coupled with power-based, fast regulations, increasing the economic attractiveness of investments in BESS. The case of fast reserve (FR), a new high-speed frequency response service proposed by the Italian TSO in Italy, is assessed in this study. FR provision foresees a capacity-based remuneration (k€/MW/year) and requires to ensure 1000 hours per year of availability. After assessing its cost-effectiveness as a stand-alone service, a sequential multiservice strategy is proposed, where BESS provides FR for 1000 hours, while for the rest of the time it is dedicated to the provision of replacement reserve (RR). Performances of BESS are evaluated considering the reliability of the provision, its operational efficiency, and investment’s economics. Performed tests demonstrate how, within the current Italian regulatory framework, the investment’s rate of return improves thanks to the multiservice approach. In particular, while maintaining a proper reliability, the minimum acceptable remuneration from FR yearly auctions decreases by 13%; at the same time, self-dispatching of energy through BM calls reduces the need to purchase energy on day-ahead market and keeps BESS state-of-charge far from saturation regions, thus also increasing its lifetime.
Saam Acupuncture for Treating Functional Dyspepsia: A Feasibility Randomized Controlled Trial
Authors: Lee Boram , Kwon O.-Jin , Kim Joo-Hee , Kang Jung Won , Kim Tae-Hun , Lee Seunghoon , Kim Jungeun , Kim Ae-Ran , Jung So Young , Park Hyo-Ju , Choi Sun-Mi
Published: June 2022

Objective. Functional dyspepsia (FD) is a common gastrointestinal disorder that significantly affects sufferers’ quality of life and increases the economic burden on society. Saam acupuncture, a form of traditional Korean acupuncture, is frequently used to treat FD in Korean medicine clinical settings. This study aimed to evaluate the feasibility and preliminary effectiveness and safety of Saam acupuncture for treating FD. Methods. We conducted a pilot, pragmatic, assessor-blinded randomized controlled trial. Patients with FD according to the ROME III criteria were randomly allocated to an acupuncture plus usual care group or a usual care group. Saam acupuncture based on individualized FD and systemic symptoms was conducted in the acupuncture group three times per week for 4 weeks. Study feasibility outcomes, including recruitment, completion, and acupuncture adherence rates, were calculated. In addition, preliminary evaluation of participant responses to the intervention was tested using the gastrointestinal symptom (GIS), FD-related quality of life (FD-QoL), visual analog scale (VAS), patient global assessment (PGA), and EuroQol-5 Dimensions (EQ-5D) scores. Results. Twenty-four participants who met the eligibility criteria were included. The recruitment and completion rates of the clinical trials were 60% and 79.2%, and the acupuncture adherence rate was 83.3%. Although there was no significant difference between the two groups in the dyspepsia symptoms as measured by GIS, VAS, and PGA at Week 4, significant differences were found between the two groups at the follow-up assessments (Weeks 8 and 12). In particular, the early satiety subscore of GIS was significantly improved in the Saam acupuncture group compared with the usual care group at Week 4. The quality of life measured by FD-QoL and EQ-5D improved only in the Saam acupuncture group, although there were no significant differences between the two groups. No adverse events related to Saam acupuncture were reported. Conclusions. Saam acupuncture can be a feasible, preliminarily effective, and safe treatment for FD. Further confirmatory trials with a larger sample size are needed to confirm its effectiveness and safety. The trail is registered with CRIS-KCT0000164, URL: https://cris.nih.go.kr/cris/search/detailSearch.do/2098.
The Construction of Innovation and Entrepreneurship Incentive Mechanism for College Students Based on Hierarchical Analysis
Authors: Guan Zhongmin , Xu Xiaoli
Published: June 2022

Innovation and entrepreneurship (I&E) are of great significance to China’s modern economic development, and college students are the most innovative and entrepreneurial potential group. I&E requires college students through the effective combination of various incentive methods on the basis of the individual differences and diversity of college students, so that the I&E enthusiasm of college students can be continuously and optimally exercised. Based on the theory of hierarchical analysis, this paper establishes the evaluation system of the implementation effect of college students’ I&E incentive mechanism by setting the evaluation indexes of the effect of college students’ I&E incentive mechanism, calculates and obtains the weights of each evaluation index, and provides a reference basis for the design of college students’ I&E incentive mechanism. Our experiment demonstrate that the CR values of the judgment matrices calculated are less than 0.1, so the consistency test of the judgment matrix is passed.
Gorilla Troops Optimizer Combined with ANFIS for Wire Cut EDM of Aluminum Alloy
Authors: Natarajan Elango , Kaviarasan V. , Lim Wei Hong , Ramesh S. , Palanikumar K. , Sekar T. , Mok V. H.
Published: June 2022

Wire cut EDM is a quite regularly used machining process in mechanical and electronic industries. This research has attempted to machine aluminum alloy for which experimental design was prepared using Box-Behnken design. Different combinational options of pulse-on time (P1), pulse-off time (P2), servo wire feed (WF), and current (I) were investigated and surface roughness after machining was observed. Collected 27 datasets were further used in Adaptive Neuro Fuzzy Inference System (ANFIS) to produce about 500 datasets. These 500 datasets are approximated data derived from experimental datasets, known as synthetic data. Data model was further developed and used in Gorilla Troops Optimizer (GTO) to locate the optimum machining parameters. With the excellent three search operators: move towards other gorillas, migrate towards unknown places, and migrate towards known places, GTO has produced the lowest surface roughness value of 0.500953 μm when the machining parameters of pulse-on time, pulse-off time, wire feed, and current values were set as 121 μs, 52 μs, 3 m/min, and 166A, respectively. To ensure the accuracy of the synthetic data-based model and optimality, verification and validation were conducted. Wilcoxon signed rank test was conducted for the pairwise comparison of GTO with each of its competing algorithms at the significance level of σ = 0.05. Friedman test was conducted to calculate the average ranking of each algorithm and to detect the global differences between all compared algorithms. Outperforming performance by GTO algorithm in machining of the selected material is found.
[Retracted] Logic and Three Wheel Drive Analysis from New Urbanization to URI and Rural Revitalization under the Background of Ecological Environment Sustainability
Authors: Guo Hailiang
Published: June 2022

In order to explore the new urban‐rural relationship (URR), develop urban‐rural integration (URI), and realize rural revitalization (RRT), we should break down the barriers between developed cities and backward villages, and gradually realize the flow of production and optimize the combination, promote the coordinated development of rural and urban; this study analyzes the logic and three wheel drive from new urbanization to URI and RRT under the background of ecological environment sustainability, constructs a three wheel transmission model of rural economic development under the mode of URI, collects data through social survey, and analyzes the correlation of data by using the Spearman formula, using the curve estimation formula to analyze the data consistency, and explores the relationship between ecological environment investment and URI and RRT. Through continuous development, the gap between urban and rural areas has been significantly reduced, and major progress has been made in urban planning, construction, and management, and the rate of urban‐rural integration has steadily increased, accelerating the pace of urban‐rural integration.
Decomposition and Recognition of English Speech Features Based on Neutrosophic Set Fuzzy Control and Random Matrix Theory
Authors: Su Na , Othman Rohani
Published: June 2022

In order to improve the effect of special decomposition and recognition of English speech, based on the idea of neutrosophic set fuzzy control, this paper uses Bayesian method as the basic algorithm of speech recognition to improve the algorithm in combination with English waveform characteristics. Moreover, this paper uses a semi-supervised learning method to process English speech waveform data, collects relevant data through the English speech input system, and then labels the data and obtains a new English speech data set through training and learning. In addition, this paper uses multiple iterations of labeling to obtain the ideal output data, uses neutrosophic set fuzzy control algorithms and machine learning algorithms to perform English speech feature decomposition and recognition, and uses feature parameter extraction methods to perform signal feature extraction. Finally, this article combines the needs of English speech recognition to build a system model and uses simulation tests to perform performance analysis of English speech feature decomposition and recognition model. The results of the research show that the improved algorithm and system model proposed in this paper have relatively good effects.
Evaluation of Necessity of Cholesterol Supplementation in Diets of Two Marine Teleosts, Turbot (Scophthalmus maximus) and Tiger Puffer (Takifugu rubripes): Effects on Growth and Lipid Metabolism
Authors: Meng Xiaoxue , Bi Qingzhu , Cao Lin , Ma Qiang , Wei Yuliang , Duan Mei , Liang Mengqing , Xu Houguo
Published: June 2022

Two 70-day feeding trials were conducted to reevaluate the necessity of cholesterol supplementation in diets of two important marine fish species, turbot and tiger puffer. Graded levels (0, 0.5, 1.0, 2.0, and 4.0% of dry matter) of cholesterol were added into the basal diet (with 30% fishmeal but no fish oil) to make five experimental diets, which were designated as control, CHO-0.5, CHO-1.0, CHO-2.0, and CHO-4.0, respectively. The dietary cholesterol concentration was measured to be 0.11%, 0.65%, 1.10%, 2.32%, and 4.59%, respectively. Each group had triplicate tanks, and each tank was stocked with 30 fish. The results showed that compared to the control, dietary cholesterol supplementation had no significant effect on the growth of both turbot and tiger puffer. Nevertheless, excess dietary cholesterol (CHO-2.0 and CHO-4.0) led to significant lower weight gain of tiger puffer compared to CHO-1.0. The feed intake decreased with increasing levels of dietary cholesterol. In general, the cholesterol supplementation decreased the crude lipid content in the liver, whereas 0.5% dietary cholesterol supplementation tended to increase the crude lipid content in the muscle of both species. Dietary cholesterol supplementation significantly regulated the lipid-related biochemical parameters in serum, liver, and muscle, and compared to turbot, tiger puffer lipid compositions had a higher buffering capacity in response to changes in dietary cholesterol level. Dietary cholesterol supplementation increased the 20:4n-6 content in the liver. Dietary cholesterol supplementation significantly downregulated the gene expression of HMG-COAr, upregulated the CYP7A1 expression, and downregulated the expression of lipid absorption and biosynthesis genes, but had no significant effect on gene expression of CPT1 and lipid transport-related genes (ApoA1, ApoA4, ApoB100, and ApoEα) in the liver. In conclusion, under the present experimental condition, dietary cholesterol supplementation had no significant effect on the growth of turbot and tiger puffer. In both species, dietary cholesterol supplementation significantly regulated the lipid accumulation and metabolism.
Three‐Dimensional Teaching Method for Art Design Major from the Perspective of “Internet Economy”
Authors: Zhang Xike
Published: June 2022

In view of the relevant teaching problems in the field of art and design, in order to study the teaching methods and contents of art design major from the perspective of the Internet economy, the evaluation indexes corresponding to the Internet are introduced into the partial least squares regression (PLS) path analysis model based on the relevant theoretical basis of the Internet. Through the method of model testing and relevant data analysis, the three‐dimensional teaching method of art design is discussed, and the art design major is divided into five influencing factors, which are studied, respectively, according to the different influencing degrees. Finally, the model is verified by the experimental method, and the model is used to predict the three‐dimensional teaching of art design from the perspective of Internet economy. The research shows that the overall proportion of economic strength remains between 70% and 90%, while the corresponding level of network design is relatively low. This study shows that the impact of economic strength research and related policies on the Internet economy is higher than the corresponding level of network design. Three‐dimensional teaching occupies a dominant position in art design major. The proportion of network design and design frontier is relatively small, indicating that three‐dimensional and information‐based teaching can have a great impact on the art and design major from the perspective of the Internet. Different factors have different influences on art and design majors in Internet economy. The order of influence degree is innovation ability > design expression > design thinking. Based on the realization of Internet theory, this paper studies the three‐dimensional teaching method of art design specialty, so as to provide a theoretical basis for the application and research of Internet in other fields.
Antimicrobial Potential of Extract from a Pseudomonas aeruginosa Isolate
Authors: Amankwah Francis Kwaku Dzideh , Gbedema Stephen Yao , Boakye Yaw Duah , Bayor Marcel Tunkumgmen , Boamah Vivian Etsiapah
Published: June 2022

Microorganisms are one of the main sources of antimicrobial agents and over 50% of antibiotics currently used in hospitals are metabolites from microbes. This study aimed to isolate microorganisms from the Dompoase landfill site, Kwame Nkrumah University Physics Garden, Kosiko River, and Ada Foah seashore of Ghana and screen their metabolites for antimicrobial activity. Forty-eight (48) microorganisms were isolated and their metabolites were screened against Staphylococcus aureus, Enterococcus faecalis, Escherichia coli, Klebsiella pneumoniae, Vibrio cholerae, Salmonella typhi, Pseudomonas aeruginosa, Streptococcus pyogenes, Proteus mirabilis, and Candida albicans using the agar well diffusion method. Ten (10) of the isolates exhibited antimicrobial activity. Isolate DO5, identified as P. aeruginosa isolate, from the Dompoase landfill site was selected for fermentation because it exhibited the highest activity against all the test organisms. DO5 produced optimum antimicrobial activity when fermented for 11 days at 30°C. In the agar diffusion method, the extract of isolate DO5 recorded zones of inhibition ranging between 11.67 ± 0.23 and 21.50 ± 0.71 mm. The MIC and MBC recorded for the DO5 extract ranged from 3.13–25.0 mg/mL and from 6.25–50.0 mg/mL, respectively. Column chromatography analysis yielded eight (8) subfractions from the DO5 extract. IR analysis revealed the presence of functional groups such as alcohols, esters, and hydrocarbons in the fractions. GC-MS analysis identified nine compounds that have been reported to have antimicrobial agents. The DO5 metabolites stand the chance to be developed into potent antibiotics for infection treatment.
Active Learning for Imbalanced Data: The Difficulty and Proportions of Class Matter
Authors: Cai Xinmeng
Published: June 2022

Deep learning methods have achieved great success in many fields, but it is severely limited by the quantity and quality of training data. When the number of labeled samples is small and the datasets is class-imbalanced, the model is difficult to perform well. In this paper, an active learning method for the small and class-imbalanced labeled datasets is proposed. In addition to the uncertainty of instances which traditional active learning methods take into account, the difficulty and the proportion of samples of different classes are also considered in our method too, so that our active learning method is better oriented to the dataset with unbalanced classes. This paper first explains the motivation of the proposed method and then introduces the frameworks of the method in detail. Finally, experiments on three datasets prove that our method can obtain better results than traditional active learning methods based on uncertainty.
Efficient Detection and Recovery of Malicious PowerShell Scripts Embedded into Digital Images
Authors: Schaffhauser Andreas , Mazurczyk Wojciech , Caviglione Luca , Zuppelli Marco , Hernandez-Castro Julio
Published: June 2022

Due to steady improvements in defensive systems, malware developers are turning their attention to mechanisms for cloaking attacks as long as possible. A recent trend exploits techniques like Invoke-PSImage, which allows embedding a malicious script within an innocent-looking image, for example, to smuggle data into compromised devices. To address such a class of emerging threats, new mechanisms are needed, since standard tools fail in their detection or offer poor performance. To this aim, this work introduces Mavis, an efficient and highly accurate method for detecting hidden payloads, retrieving the embedded information, and estimating its size. Experimental results collected by considering real-world malicious PowerShell scripts showcase that Mavis can detect attacks with a high accuracy (100%) while keeping the rate of false positives and false negatives very low (0.01% and 0%, respectively). The proposed approach outperforms other solutions available in the literature or commercially through “as a service” model.
[Retracted] Measuring the Awareness of Chronic Kidney Disease (CKD) with Environmental Evaluation among Adult Diabetic Patients in Hail Region, Saudi Arabia
Authors: Nahlah Fahad Alreshidi , Ahmed Alkhateeb Abdulrahman , Rabah Alsharari Abdulrahman , Naif Alharbi Abdulrhman , Abdullaziz Hamed Alazmmy
Published: June 2022

Introduction. Chronic kidney disease (CKD) is one of the main chronic complications of T2DM that happens among T2DM patients who have uncontrolled glucose. Because CKD is considered a silent disease, the diagnosis is usually made at late stages when there will be few chances to prevent the adverse outcome. Aim. The goal of this study was to assess adult diabetic patients’ awareness of developing chronic kidney disease at the community level in Hail region, Saudi Arabia, in 2022. Patients and Methods. This is a cross‐sectional study conducted among diabetic patients in the Hail region, Saudi Arabia. A self‐administered questionnaire translated into Arabic was distributed among patients with DM. The questionnaire covers social and demographic variables (such as age, gender, relationship status, and so on) as well as a 7‐item questionnaire to assess the DM population’s knowledge of CKD. Results. 400 DM patients responded to our survey (51% females vs 49% males). Patients who were diagnosed with type 2 diabetes were 23.8% and 40.5% had a diabetes duration of 5–15 years. Nearly half (46.8%) were considered as a poor level of awareness, 29.3% had a moderate, and 24% had a good awareness level. Factors associated with an increased level of awareness were being a bachelor’s degree, being unmarried, being a student, and having a doctor as a source of CKD information. Conclusion. There was a deficiency in the level of awareness among the diabetic patients in our region. Patients who were single with better education and who were well informed by the doctors about CKD information tend to be more aware of CKD as compared to other DM patients. Further research is warranted in order to establish the awareness level of DM patients regarding CKD and its complications.
Geometric Brownian Motion-Based Time Series Modeling Methodology for Statistical Autocorrelated Process Control: Logarithmic Return Model
Authors: Lee Siaw Li , Liew Chin Ying , Chen Chee Khium , Voon Li Li
Published: June 2022

Fitting a time series model to the process data before applying a control chart to the residuals is essential to fulfill the basic assumptions of statistical process control (SPC). Autoregressive integrated moving average (ARIMA) model has been one of the well-established time series modeling approaches that is extensively used for this purpose and is widely recognized for its accuracy and efficiency. Nevertheless, the research community commented that its iterative stages are laborious and time-consuming. In addressing this gap, a novel time series modeling technique with its conceptual assumptions of attributes that was derived from the geometric Brownian motion (GBM) law was developed in this study. It was termed as the logarithmic return (LR) model. Then, the model was employed and tested on a real-world autocorrelated data, whereby the results were assessed and benchmarked with the ARIMA model. The findings for LR model reported a mean average percentage error that ranged between 1.5851% and 3.3793% (less than 10%), which were as accurate as the ARIMA model. The running time (in second of CPU time) taken by the LR model was at least 96.2% faster than the ARIMA model. Interestingly, the corresponding multivariate control chart constructed from the LR model also portrayed a similar general conclusion as that of its counterpart. The LR model was obviously parsimonious and easier to compute and took a shorter running time than the ARIMA model. Therefore, it possessed the potential as an alternative time series modeling methodology for the ARIMA model in the procedures of SPC.
Intelligent Research Based on Deep Learning Recognition Method in Vehicle-Road Cooperative Information Interaction System
Authors: Jiao Hongbin
Published: June 2022

The vehicle-road collaborative information interaction system is an emerging technology system that realizes the sharing of information between vehicles, vehicles and roads between traffic road information, and driving vehicle information. It is of positive significance for improving the urban transportation construction system and promoting urban economic development. This paper conducts intelligent research on the deep learning recognition method based on the vehicle-road collaborative information interaction system. First, this article comprehensively expounds the concept of the vehicle-road collaborative information interaction system and then introduces the specific components, functions, and applications of the system structure. Then, this article researches on deep learning recognition methods and introduces three deep learning recognition methods. They are background extraction method, YOLOv2 method, and DeepSORT method. Finally, this paper conducts simulation comparison experiments between deep learning algorithms and traditional algorithms. It evaluates the feasibility of the algorithm in the vehicle-road collaborative information interaction system in three aspects: vehicle target detection, vehicle flow identification, and emergency decision-making. The experimental results show that the value of the intersection ratio of vehicle target detection in the deep learning recognition method is 8.66% higher than that of the traditional algorithm, the recall rate is 7% higher than that of the traditional algorithm, and the vehicle flow recognition accuracy is 1.8% higher than that of the traditional algorithm. The early warning time in emergency decision-making is also shorter than that of traditional algorithms, which shows the unique superiority and feasibility of deep learning algorithms in the vehicle-road collaborative information interaction system.
Dietary Incorporation of Ractopamine Hydrochloride Improves Body Composition and Metabolic Enzyme Activity in Calbasu, Labeo calbasu Fingerlings, despite High- and Low-Protein-Supplemented Diet
Authors: Singh Ranjan , Das Rakesh , Singh Aprajita , Jain K. K. , Srivastava Prem Prakash , Deo Ashutosh. D.
Published: June 2022

A 60-day feeding trial was conducted to examine the effect and interaction of dietary ractopamine hydrochloride (Rac) supplementation in diets with varied levels of protein and lipid for L. calbasu fingerlings. To attain this, the fish body compositions, digestive and metabolic enzyme activity were assessed. In total, nine diets were fed in triplicates to four hundred and five healthy Calbasu, Labeo calbasu fingerlings, based on ractopamine supplementation, viz., control (0 mg/kg), T1 (10 mg/kg), and T2 (20 mg/kg). The diets were further assigned in different levels of protein and lipid, viz., 35% protein and 6% lipid, 30% protein and 9% lipid, and 25% protein and 12% lipid. At the end of the trial, it has been observed that the inclusion of ractopamine hydrochloride at 20 mg/kg-1 of basal diet significantly (

p
<
0.05

) improved the protein content in fed fish compared to the control. In addition, metabolic enzyme activity for hexokinase, glucose-6-phosphate, and glucose-6-phosphate dehydrogenase along with aspartate amino transferase and alanine amino transferase activity were significantly (

p
<
0.05

) improved with the inclusion of 20 mg/kg ractopamine hydrochloride. However, the supplementation of ractopamine hydrochloride does not improve (

p
>
0.05

) digestive enzyme activity, viz., amylase, lipase, and protease activity in the fed fish. Conclusively, it implies that adding ractopamine hydrochloride at 20 mg/kg of basal diet (high and low protein content) enhances the whole-body protein content and improves the metabolic enzyme activity in L. calbasu fingerlings. Hence, ractopamine hydrochloride has a beneficial effect in L. calbasu fingerlings and would be a desirable feed supplement for this new species culture system.
On Recent Developments in Biosynthesis and Application of Au and Ag Nanoparticles from Biological Systems
Authors: Verma Ashutosh Kumar , Kumar P.
Published: June 2022

Gold nanoparticles (AuNPs) and silver nanoparticles (AgNPs) are extensively studied nanoparticles (NPs) and are known to have profound applications in medicine. The researcher made continuous efforts for the environmental-friendly and economical methods, such as biogenic methods known as green synthesis. There are many strategies for separating and applying gold (Au) and silver (Ag) nanoparticles, of which biological routes have emerged as efficient, low-cost, and environmentally friendly techniques. This review focuses on recent developments of green synthesized AuNPs and AgNPs using biogenic sources such as algae, animals, plants, microbes, bacteria, fungi, and so on. Hence, it discusses their numerous biomedical applications and separating Au and Ag nanoparticles from plants, bacteria, fungi, and algae.
A Semianalytical Approach for the Solution of Nonlinear Modified Camassa–Holm Equation with Fractional Order
Authors: Fang Jiahua , Nadeem Muhammad , Wahash Hanan A.
Published: June 2022

This paper presents the approximate solution of the nonlinear acoustic wave propagation model is known as the modified Camassa–Holm (mCH) equation with the Caputo fractional derivative. We examine this study utilizing the Laplace transform (ℒ T) coupled with the homotopy perturbation method (HPM) to construct the strategy of the Laplace transform homotopy perturbation method (ℒ T‐HPM). Since the Laplace transform is suitable only for a linear differential equation, therefore ℒ T‐HPM is the suitable approach to decompose the nonlinear problems. This scheme produces an iterative formula for finding the approximate solution of illustrated problems that leads to a convergent series without any small perturbation and restriction. Graphical results demonstrate that ℒ T‐HPM is simple, straightforward, and suitable for other nonlinear problems of fractional order in science and engineering.
The Impact of Emotional Intelligence on Burnout Aspects in Medical Students: Iranian Research
Authors: Shariatpanahi Ghazal , Asadabadi Mahsa , Rahmani Ashkan , Effatpanah Mohammad , Ghazizadeh Esslami Golnaz
Published: June 2022

Background. Burnout is a psychological syndrome identified by different degrees of emotional exhaustion depersonalization and a low sense of personal accomplishment, which is an increasing problem among medical students. Emotional intelligence is a set of noncognitive skills that propagates an individual’s capability to encounter environmental complications, particularly burnout, successfully. This research was conducted to investigate the relationship between emotional intelligence and academic burnout among medical students. Methods. This cross-sectional study was carried out on 136 medical students at Tehran University of Medical Sciences in 2018. Data were collected by a demographic checklist, Siberia Schering’s Emotional Intelligence questionnaire, and Bresso Academic Burnout questionnaire and analyzed by SPSS22 software using descriptive methods and linear regression tests. The significance level was set at a

P

value <0.05. Results. The study enrolled 136 medical students, including 70 (51.5%) men and 66 (48.5%) women, with an average age of 24.94 (SD = 1.03). Besides, 112 students were single (82.4%), and 24 were married (17.6%). Most students’ emotional intelligence and academic burnout were moderate (89% and 69.1%, respectively). According to the results, none of the burnout dimensions significantly correlated with age and gender. Among emotional intelligence dimensions, students possessing higher self-motivation experienced more significant academic fatigue. Students with higher self-awareness and self-regulation experienced lower academic fatigue, academic apathy, academic inefficiency, and academic burnout. In general, medical students with higher scores on emotional intelligence experienced significantly lower academic fatigue, academic apathy, academic inefficiency, and academic burnout. There was a significant negative association between emotional intelligence and burnout (β = −0.496,

P

 < 0.001). Conclusion. Emotional intelligence appeared as a significant factor in protecting medical students against academic burnout, so enhancing emotional intelligence through training may be an essential intervention for reducing burnout.
Defining the Environmental Public Interest Litigation in the Era of Artificial Intelligence
Authors: Wei Liting , Li Ming
Published: June 2022

Environmental public interest litigation is a category of modern litigation that aims to protect the rights and welfares of the public environment. In China, environmental public interest litigation is a common mean to protect the environment and has a vital role in protecting the environment. This paper aims to study how to use the advantages of the artificial intelligence era to study the definition of environmental public interest litigation. This paper proposes how to define environmental public interest litigation and proposes a data mining classification algorithm based on the era of artificial intelligence to analyze the definition of public interest litigation. The experimental results of this paper show that with the promulgation and implementation of the new environmental protection law in 2016, the number of environmental public interest litigation cases nationwide has increased significantly compared with previous years. There are 43,589 environmental public interest litigation cases nationwide, and the difficulty of work is 38%. It can be seen that public interest litigation has become one of the judicial channels generally accepted by the public. It has become a powerful weapon for safeguarding the interests of the public environment. The use of the data mining classification algorithm based on the era of artificial intelligence can make the case definition of environmental public interest litigation easier, thus improving work efficiency.
Study on Movement Law of High-Position Thick and Hard Roof and Mine Earthquake Control by Ground Fracturing Technology
Authors: Liang Yanbo , Cheng Yuanfang , Han Zhongying , Yan Chuanliang , Zhang Xiufeng , Wang Chao , Han Songcai
Published: June 2022

The field monitoring data showed that the fracture of high-position thick and hard roof (THR) was the main reason for the frequent occurrence of high-energy mine earthquake events (

magnitude
>
2.0

) in the no. 6 mining district of Dongtan coal mine. In order to study the fracture characteristics of high-level THR and seek a reasonable and effective control method, taking the 63up06 working face as the engineering background, the FLAC model was built to simulate the strata movement law before and after fracturing. The simulation results showed that the THR first breaking span was 340 m, the tensile failure runs through the whole THR, and the subsidence increased rapidly when THR reached the first breaking span. The THR breakage is related to the continuous upward transfer of horizontal compressive stress, which results in the concentration of horizontal compressive stress in THR. After ground fracturing, the first breaking span of THR decreased, and different fracturing positions have a great impact on the THR fracture form. When the fracturing position is close to the center of the first breaking span, there is no large cantilever breaking form in the THR, the THR breaks on both sides of the top and sinks along the hydraulic fracture in the middle, and the collapse degree is relatively uniform. Then, the fracturing test was carried out in the 63up06 working face, the results showed that during the first 400 m of the 63up06 working face mining after fracturing, 702 microseismic events occurred in total, including 12 high-energy vibration events which include 4 mine earthquakes of magnitude 2.0 and 8 mine earthquakes of magnitude 1.0-2.0. Compared with the adjacent working face, the quantity of high-energy mine earthquake events in the 63up06 working face has been significantly controlled.
Road Sign Recognition Method Based on Segmentation and Attention Mechanism
Authors: Chen Tianao , Chen Aotian
Published: June 2022

With the development of autonomous driving, low-cost visual perception solutions have become a current research hotspot. However, the performance of the pure visual scheme in unfriendly environments such as low light, rain and fog, and complex traffic scenes has a large room for improvement. Moreover, with the development and application of deep learning, the balance between the accuracy and real-time performance of deep learning models is a difficult problem for current research. Aiming at the problems of large differences in the target scale of pavement signs and the difficulty of balancing model accuracy and real-time performance, a ground semantic cognition method based on segmentation and attention mechanism is proposed. The lightweight semantic segmentation model ERFNet is used to realize the semantic segmentation of pavement signs and the instantiation of lane lines. When only lane line detection is required, the prediction branch of lane line existence is introduced based on the lightweight semantic segmentation model ERFNet to realize lane line instantiation cognition, solve the imbalance of positive and negative lane line detection samples, and obtain the final lane line detection result via postprocessing. Deep features were used to guide shallow layers to extract semantic features at high resolution, and the model performance was further optimized without increasing the inference cost.