bluuebunny/crossref_metadata_2025 huggingface dataset:
Application of the Slope Coefficient of the Equivalent M–C Criterion in Layered Rock Mass Engineering
Authors: Liu Qian , Hu Qizhi , Zhang Fan , Ding Zhigang , Bao Wencheng
Published: August 2022

The slope coefficient ω is defined based on the insufficiency of the area equivalent method, the slope of the equivalent M–C criterion obtained from the instantaneous equivalent, and the optimal first‐order approximation to reduce the error between the simulated value and the measured value of the surrounding rock and ensure the safety of the project. Different ω conditions are set to obtain multiple equivalent M–C strength parameter combinations. The above combinations are input to the ubiquitous joint model of FLAC3D, and the surrounding rock strength of layered tunnels with different inclination angles (0°, 30°, 45°, 60°, and 90°) is corrected. The results show that (1) after the tunnel excavation is completed, the displacement of key points (e.g., the vault and waist) increases when the slope coefficient is increased and the deviator stress decreases when the slope coefficient is increased. (2) After the area equivalent method is revised, the displacement and the deviator stress are more significantly affected by the inclination of the rock strata than the uncorrected ones, suggesting that the equivalent area can more effectively highlight the anisotropy characteristics of the layered surrounding rock. (3) After the simulation results of the displacement and the deviator stress at the respective key point are comprehensively modified, the optimal slope coefficient corresponding to each rock layer inclination is obtained, and the area is optimized by ensuring reasonableness to reduce the error between the simulated value and the measured value. (4) The layered surrounding rock at a dip angle of 30° is studied. The development of the plastic zone is promoted when the slope coefficient is increased, and the rock shear failure and the joint shear failure occur simultaneously on both sides of its axis.
A Genomic Instability-Related Long Noncoding RNA Signature for Predicting Hepatocellular Carcinoma Prognosis
Authors: Lu Jing , Cao Wanyue , He Zeping , Wang Haoyu , Hao Jialing , Xu Junming
Published: August 2022

Background. Long noncoding RNAs (lncRNAs) are found to be novel biomarkers for hepatocellular carcinoma (HCC) and play an important role in tumor progression. We established a genomic instability-related long noncoding RNA signature (GIlncSig) as an independent prognosis factor and also investigated its impact on prognosis significance. Method. Somatic mutation profiles, clinical characteristics, and RNA sequencing data were obtained from The Cancer Genome Atlas (TCGA) database. Lasso regression was used to construct GIlncSig. KEGG was used to identify the possible biological pathways. ESTIMATE and CIBERSORT algorithms were used to calculate the immune microenvironment scores and proportion of immune cells in HCC patients. The expression of LINC00501 was conducted by qRT-PCR. Cell proliferation was measured by EdU, CCK-8, and colony formation assay, and cell migration and invasion ability were measured by wound healing and transwell assay. Results. 135 genomic instability-related lncRNAs were identified, and GIlncSig was constructed using 13 independent lncRNAs with significant prognosis values. Based on the GIlncSig, high-risk group had worse clinical outcomes than low-risk group, while high-risk group also had higher UBQLN4, KRAS, ARID1A, and PIK3CA expression. Moreover, the efficiency of GIlncSig combining single-gene mutation was higher than single-gene mutation alone such as TP53. The results of CIBERSORT and ESTIMATE showed that GS group and GU group had significantly different immune infiltration. In addition, LINC00501 was identified as a potential biomarker in HCC with strong relationship with clinical characteristics. In vitro assays validated that LINC00501 promoted proliferation and migration of HCC cell lines. Conclusion. Our results showed that GIlncSig serves as a potential independent prognosis factor to predict HCC patients’ prognosis for exploring potential mechanism and therapy strategy. Besides, LINC00501 plays an important role in the progression of HCC, which may be a potential therapy target.
Design and Purchase Intention Analysis of Cultural and Creative Goods Based on Deep Learning Neural Networks
Authors: Sun YuanHong
Published: August 2022

With the rise of cultural and creative industries, cultural creativity has gradually become an important factor to promote the value of design in the future, and it is also a trend to integrate “cultural elements” into the design of products. At present, the cultural and creative industries in Western countries and Taiwan are the mainstay of their economic development. We should actively absorb their successful experiences and, with the support of national policies, carry out effective and lasting development of them, as well as continuously improve the quality of cultural and creative products. In today’s steady economic development, the emotionalization of cultural and creative consumption has gradually formed a new consumption trend. When cultural and creative consumers buy stationery, they will inevitably have three situations: purchasing instinct, purchasing behavior, and reflection. Therefore, this paper adopts the method based on machine learning to conduct in-depth research on the users of cultural and creative products of “Forbidden Day and Night Set Gift Box.” Through the research on the cultural and creative consumption intention of “Forbidden Day and Night Set Gift Box,” it can effectively promote the development of domestic cultural and creative enterprises, and then promote the customer satisfaction of cultural and creative enterprises. This paper makes a detailed analysis of it from the perspectives of data processing, feature engineering, classification prediction models, and future development directions. By studying the cultural and creative behavior of users, a deep learning model based on neural network is established. The feature extraction, feature preprocessing, feature selection, and asymmetric data collection in the process of data processing are discussed in depth. In order to further improve the prediction accuracy and conduct more in-depth research, this paper establishes a deep learning prediction model based on depth. This model is experimentally validated and it can be observed that the model is 10% more efficient than the traditional model, so the model can learn data better from the user’s behavior in several aspects, and the proposed and practice of this model has good practical significance.
High Speed Grinding of EN31 Steel with Alumina Nanofluid Coolant
Authors: Sathish T. , Saravanan R. , Vijayan V. , Karthick R. , Karthikeyan N. , Rajkumar S.
Published: August 2022

Grinding is a choice for obtaining high surface quality and closer dimensional tolerance. For meeting this objective economically, the material removal rate (MRR) must be sacrificed to certain extent. The MRR could be increased by either increase in wheel speed, depth of cut, and feed rate. An increase in MRR increases the surface temperature leading to thermal defects on surfaces. Improving the MRR without compromising the surface quality is a challenging objective. Many investigations are progressing for machining various materials under different cooling environments to meet such challenges. This experimental novel aim is to investigate the performance of Al2O3 nanofluid in high-speed grinding of EN31 steel under flood cooling method for reduction of surface roughness and cutting zone temperature. Taguchi’s full factorial design was employed for experimental investigation. The factors such as feed, depth of cut and cutting fluid environment were considered for analysing the responses of cutting zone temperature and surface roughness. The thermal analysis on the work piece was carried out with experimental values by the finite element analysis method. The nanofluids outperform in terms of reduction of surface roughness and downsizing cutting zone temperature. The proposed nanofluid-based grinding significantly reduced the surface roughness and cutting zone temperature.
Data-Driven Green Development Efficiency of Regional Sci-Tech Finance: A Case Study of the Yangtze River Delta
Authors: Wang Yuan , Liu Hongjun , Zhou Shuling , Liu Fan , Yang Yaliu , Zhu Juan , Xu Yi
Published: August 2022

Green development is an important connotation of high-quality development and is one of the goals of scientific and technological innovation. This study constructs a data-driven measurement model of the green development efficiency of regional sci-tech finance, measures the green development efficiency of sci-tech finance by using the super-slack-based measure model, and deeply analyses and evaluates the changes in green development efficiency of regional sci-tech finance by calculating Malmquist index. This study calculates the green development efficiency of sci-tech finance in the Yangtze River Delta. Results show that the green development efficiency of sci-tech finance in the Yangtze River Delta is on the rise as a whole and maintains an efficient state, but differences are observed between provinces and cities. This study provides theoretical and methodological support for the evaluation of the green development efficiency of regional sci-tech finance and serves as reference for policy makers and researchers of sci-tech finance.
Resurveying Corrective Feedback Meta-Analysis
Authors: Li Junfei , Tan Dingliang
Published: August 2022

Corrective feedback (CF) is provided to learners to rectify errors. In recent years, the study of CF in oral and written forms has attracted considerable attention in language acquisition studies. Some studies have comprehensively summarized the effects of CF through meta-analysis, but there are still some differences and limitations in CF meta-analysis, which need to be resurveyed. Based on the findings of a meta-analysis of five different types of CF, this study evaluates the procedures, ideas, and conclusions of the CF meta-analysis. The findings show that different types of CF effects have inconsistent and conflicting findings because of different data sources, variable collinearities, learner differences, and effect generation differences. Consequently, the research systematically discusses the publication bias, the influence of learner variables, the method of measurement for each type of effect, and the connotations of the meta-analysis results.
Empirical Analysis of Early Childhood Enlightenment Education Using Neural Network
Authors: Cheng Jingyi , Cheng Jianjun
Published: August 2022

This exploration aims to study the value orientation and essence of early childhood enlightenment education based on the deep neural network (DNN). Based on the acquisition and feature learning of cross-media education big data, the DNN correlation learning of cross-media education big data, and the intelligent search of cross-media education big data based on the DNN, the intelligent search system of cross-media children’s enlightenment education big data based on DNN is designed and implemented. The system includes three functional modules: the feature learning module of cross-media infant enlightenment education big data, the deep semantic correlation learning module of cross-media childhood enlightenment education big data, and the intelligent search module of cross-media childhood enlightenment education big data based on DNN. This exploration realizes the acquisition and feature learning of big data of cross-media early childhood enlightenment education, DNN learning of cross-media education big data of early childhood enlightenment, and intelligent computing of cross-media education big data based on DNN. The experimental results show that the proposed system’s mean average precision (MAP) performance is improved by 15.6% on the public dataset of early childhood enlightenment education published by the Ministry of Education. Moreover, the system has also significantly improved the MAP performance of the constructed dataset in the field of early childhood enlightenment education; that is, the MAP performance has been improved by 20.6% on the dataset of Taylor’s University in Malaysia (NUS-WIDE). This exploration has certain theoretical significance and empirical value for early childhood enlightenment education research.
A Vehicle Path Planning Algorithm Based on Mixed Policy Gradient Actor‐Critic Model with Random Escape Term and Filter Optimization
Authors: Nai Wei , Yang Zan , Lin Daxuan , Li Dan , Xing Yidan
Published: August 2022

The transportation system of those countries has a huge traffic flow is bearing great pressure on transportation planning and management. Vehicle path planning is one of the effective ways to alleviate such pressure. Deep reinforcement learning (DRL), as a state‐of‐the‐art solution method in vehicle path planning, can better balance the ability and complexity of the algorithm to reflect the real situation. However, DRL has its own disadvantages of higher search cost and earlier convergence to the local optimum, as vehicle path planning issues are usually in a complex environment, and their action set can be diverse. In this paper, a mixed policy gradient actor‐critic (AC) model with random escape term and filter operation is proposed, in which the policy weight is both data driven and model driven. The empirical data‐driven method is used to improve the poor asymptotic performance, and the model‐driven method is used to ensure the convergence speed of the whole model. At the same time, in order to avoid the model converging local optimum, a random escape term has been added in the policy weight update process to overcome the problem that it is difficult to optimize the non‐convex loss function, and the random escape term can help to explore the policy in more directions. In addition, filter optimization has been innovatively introduced in this paper, and the step size of each iteration of the model is selected through the filter optimization algorithm to achieve the better iterative effect. Numerical experiment results have shown that the model proposed in this paper can not only improve the accuracy of the solution without losing the accuracy but also speed up the convergence speed and improve the utilization of data.
Increasing the Penetration of Electric Vehicles in Distribution Networks Using Optimal Charging/Discharging Control and Reactive Power Support in the Presence of Nonlinear Loads
Authors: Partovi Mojtaba , Esmaeili Saeid , Aein Morteza
Published: August 2022

Electrical vehicles (EVs) are among the fastest-growing electrical loads that change both temporally and spatially on distribution networks. The large-scale integration of EVs equipped with power electronic-based chargers into distribution networks, to meet new electrical load demands, can cause instability and power quality issues. Moreover, the absence of control strategies for the smart charging and discharging of EVs at their plug-in intervals poses serious challenges to them. Accordingly, the implementation of controlled charging/discharging scheduling of EV batteries along with the use of charger capabilities, such as reactive power support, is a must. Against this background, this paper introduces an integrated model to solve the problem of simultaneous active and reactive power management in distribution networks subject to network operation constraints imposed by EV batteries and chargers. To this end, this problem is modeled as an optimization problem. In this respect, minimization of the costs associated with power generation and losses and improvement of the total harmonic distortion of voltage (THDv) on network buses are two terms of the objective function. The problem is solved by a hybrid technique named the “ PSO-GA algorithm” that takes advantage of both the genetic algorithm (GA) and the particle swarm optimization (PSO) method. Accordingly, the effectiveness of the proposed model is examined in a standard IEEE 33-bus distribution network populated with EVs and nonlinear devices (NLDs). The results obtained show that the maximum possible penetration rate of EVs into the network is facilitated, while technical and financial goals of the network and parking lots are ensured.
Influence of Wheatgrass Juice on Techno-Functional Properties and Bioactive Characteristics of Pasta
Authors: Bawa Keshavdeep , Singh Arashdeep , Brar Jaswinder Kaur , Surasani Vijay Kumar Reddy , Pandiselvam Ravi
Published: August 2022

Pasta is an excellent source for fortification of ingredients and wheatgrass juice (WGJ) as a natural source of vital nutrients and antioxidants; the study was taken to develop WGJ-rich functional pasta. Wheatgrass juice (WGJ) was added at the rate of 33, 66, and 100% by replacing water during the mixing process of pasta. The samples were assessed by cooking quality, proximate composition, antioxidant properties, color, texture attributes, and sensory evaluation. Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM) were performed. Incorporation of WGJ significantly (

p
<
0.05

) decreased the optimum cooking time of pasta, whereas water absorption capacity was increased. Cooking loss in pasta increased from 2.89 to 3.21% with increasing levels of WGJ from 33 to 100%. The addition of wheatgrass juice to pasta improved the nutritional and antioxidant profile significantly (

p
<
0.05

), as evidenced by increases in protein, phenolics, flavonoids, chlorophyll, and antioxidant activities (FRAP, DPPH, and ABTS). The incorporation of wheatgrass juice reduced the L value, whereas

a

∗

of the pasta enhanced gradually. With the addition of WGJ, the stiffness and hardness of the pasta changed dramatically. FTIR spectra validated the existence of bioactive compounds and chlorophyll pigments in pasta. Sensory data revealed that pasta containing 100% of WGJ was acceptable with the highest overall acceptability score of 7.72.
[Retracted] Meta‐Analysis of the Relationship between the Prognosis of Acute Cerebral Infarction Intravenous Lysis and Cerebral Microbleeds Based on Intelligent Medical Care
Authors: Duan Kui , Wu Yufang
Published: August 2022

The accelerated pace of life leads to people’s unhealthy living habits such as irregular diet, irregular work and rest, and fatigued work. The incidence of acute cerebral infarction (ACI) is increasing year by year. Intravenous thrombolysis is the best solution for clinical treatment of ACI, but intravenous thrombolysis increases the risk of cerebral microbleeds and even seriously damages the brain of patients. It is crucial to analyze the relationship between intravenous thrombolysis of ACI and cerebral microbleeds. Using intelligent medical methods such as BP neural network (BPNN), meta‐analysis was carried out on the prognosis of ACI intravenous thrombolysis and cerebral microbleeds, and the basic data indicators, living habits indicators, and ACI intravenous thrombolysis indicators of ACI patients were analyzed. The experimental results showed that the odds ratios (OR) of systolic blood pressure before ACI intravenous thrombolysis, blood glucose concentration after ACI intravenous thrombolysis, diastolic blood pressure before ACI intravenous thrombolysis, and diastolic blood pressure after ACI intravenous thrombolysis on cerebral microbleeds after ACI intravenous thrombolysis were 0.97, 0.44, 0.13, and 0.07, respectively. Long‐term intravenous thrombolysis with ACI, high systolic blood pressure after NIHSS thrombolysis with high scores, and blood glucose concentration before thrombolysis had OR >1, which were risk factors for cerebral microbleeds after intravenous thrombolysis with ACI. Therefore, paying attention to the risk factors of cerebral microbleeds during ACI intravenous thrombolysis can effectively reduce cerebral microbleeds after ACI intravenous thrombolysis and improve the treatment efficiency of ACI patients.
Research on the Design of Accompanying Products for the Mentally Retarded Elderly Based on AHP
Authors: Zhou Zhengjun , Zhou Tao , Ma Heyang , Liu Chunli , Yue Han , Wang Ling
Published: August 2022

As the number of elderly patients with dementia is increasing year by year, accompanying products for the elderly with dementia are becoming more and more important. Senile dementia has become a major disease that affects countries around the world and, at the same time, it has brought enormous pressure on social care. Therefore, this research aims to obtain a new set of design methods for dementia elderly care products by introducing the combination of AHP and fuzzy evaluation methods and applying them to design examples. And then break through the existing design path of accompanying products for the elderly with dementia, and relieve the social burden. This study takes the intelligent care robot for the elderly with dementia as an example. Due to the particularity of user groups, this article proposed a design method for elderly care products that integrates mathematical theoretical models. This design method is convenient for designers to have a more accurate data reference when designing and positioning, and summarizing the supporting product design scheme that can better meet the actual needs of the elderly with dementia. Through SET factors to analyze the driving force of demand for dementia elderly care products, and combined with a questionnaire survey to establish the design requirements of dementia elderly care products for analysis of hierarchy process, it effectively defines the design demand attributes of dementia elderly intelligent care robots. The research results are applied to the design of the intelligent accompanying care robot for the demented elderly, the comprehensive fuzzy evaluation of the design scheme is carried out, and the direction of further optimization of the scheme is obtained. This article proposed a design method for accompanying care products for the elderly with dementia by combining F-AHP (Fuzzy Comprehensive Evaluation Method and Analytic Hierarchy Process). This design method is based on a rigorous mathematical theoretical model. Compared with the traditional design method, this research focused on quantitative analysis and evaluation of the actual design requirements of the dementia elderly care products and had more accurate design priority data reference in the scheme design. The research results show that based on the combination of F-AHP, this article redefines the design path of accompanying products for the elderly with dementia, and concludes that the functions that need to be improved and optimized in the future of intelligent accompanying robots for the elderly with dementia are mainly: executive ability training, computing ability training, and observation ability training. At the same time, the design process of the intelligent escort robot for the elderly with dementia can also be applied to the design of intelligent elderly products, which can improve the service experience of intelligent elderly products in a targeted manner.
Predicting Irrigation Water Quality Indices Based on Data-Driven Algorithms: Case Study in Semiarid Environment
Authors: Dimple Dimple , Rajput Jitendra , Al-Ansari Nadhir , Elbeltagi Ahmed
Published: August 2022

Ascertaining water quality for irrigational use by employing conventional methods is often time taking and expensive due to the determination of multiple parameters needed, especially in developing countries. Therefore, constructing precise and adequate models may be beneficial in resolving this problem in agricultural water management to determine the suitable water quality classes for optimal crop yield production. To achieve this objective, five machine learning (ML) models, namely linear regression (LR), random subspace (RSS), additive regression (AR), reduced error pruning tree (REPTree), and support vector machine (SVM), have been developed and tested for predicting of six irrigation water quality (IWQ) indices such as sodium adsorption ratio (SAR), percent sodium (%Na), permeability index (PI), Kelly ratio (KR), soluble sodium percentage (SSP), and magnesium hazards (MH) in groundwater of the Nand Samand catchment of Rajasthan. The accuracy of these models was determined serially using the mean squared error (MSE), correlation coefficients (r), mean absolute error (MAE), and root mean square error (RMSE). The SVM model showed the best-fit model for all irrigation indices during testing, that is, RMSE: 0.0662, 4.0568, 3.0168, 0.1113, 3.7046, and 5.1066; r: 0.9364, 0.9618, 0.9588, 0.9819, 0.9547, and 0.8903; MSE: 0.004381, 16.45781, 9.101218, 0.012383, 13.72447, and 26.078; MAE: 0.042, 3.1999, 2.3584, 0.0726, 2.9603, and 4.0582 for KR, MH, SSP, SAR, %Na, and PI, respectively. The KR and SAR values were predicted accurately by the SVM model in comparison to the observed values. As a result, machine learning algorithms can improve irrigation water quality characteristics, which is critical for farmers and crop management in various irrigation procedures. Additionally, the findings of this research suggest that ML models are effective tools for reliably predicting groundwater quality using general water quality parameters that may be acquired directly on periodical basis. Assessment of water quality indices may also help in deriving optimal strategies to utilise inferior quality water conjunctively with fresh water resources in the water-limited areas.
Impaired Modulation of Motor and Functional Performance in Patients after Total Knee Arthroplasty: A Prospective Observational Study
Authors: Temporiti Federico , De Leo Davide , Adamo Paola , Papa Gabriele , Traverso Francesco , Maffiuletti Nicola , Gatti Roberto
Published: August 2022

Submaximal levels of effort are required for the performance of the most common daily tasks. Inaccuracy in modulating motor outputs during submaximal tasks has been reported as indicator of safety during daily activities in subjects with lower limb musculoskeletal disorders. The study is aimed at investigating performance modulation ability during motor and functional tasks in patients after total knee arthroplasty (TKA). Sixteen patients with end‐stage osteoarthritis undergoing TKA and twenty age‐matched healthy participants performed isokinetic knee extension, sit‐to‐stand, and walking tasks at three levels of self‐estimated effort (100%, 50%, and 25%) the day before (T0) and 4 days after surgery (T1). Maximum performance in terms of peak torque (PT—knee extension), overshoot (OS—sit‐to‐stand), and walking speed was evaluated. Subsequently, relative error (RE) between target and observed performance was computed for the submaximal tasks (RE50% and RE25%). Our results showed a decline of maximum performance after surgery, which resulted lower in patients compared to healthy subjects. RE50% and RE25% for knee extension (involved limb) (p < 0.001) and RE25% for sit‐to‐stand (p < 0.001) increased from pre‐ to postsurgery. At T0, knee extension RE25% and walking RE50% and RE25% were higher in patients. At T1, RE50% and RE25% were higher in patients for knee extension (involved limb), sit‐to‐stand, and walking. In conclusion, the ability to modulate motor and functional performance decreased after TKA and resulted impaired when compared to healthy age‐matched subjects. Based on relationship between ability to modulate motor outputs and risk of falling, the role of modulation ability as indicator of readiness for discharge and safe return to daily activities deserves further investigations in patients in early phase after TKA.
Research on Architecture of Digital Bond Trading Contract System Based on Blockchain Technology
Authors: Xu Zheng , Liu Ji
Published: August 2022

The traditional identification method of the digital bond trading contract system is single chain identification. Due to a large number of digital bond trading users, single chain identification will affect the performance of the system in the trading process. Therefore, we aim to propose a digital bond trading contract system based on blockchain technology and study the system architecture. In this paper, the architecture of the bond trading system is optimized by using the technology of blockchain, and the software architecture is developed by using the technology of blockchain. Through the performance test of the system, the advantages of the system architecture in practical application are verified. Firstly, the overall hardware architecture of the system is designed, and the internal structure of the controller in the control layer is redeployed. Combining the Mork tree and Patricia tree, the data structure based on blockchain and the block header data structure of the transaction contract system are optimized. By analyzing the operation process of the smart contract, the process of the smart contract consensus algorithm is optimized. The system performance test results show that the performance of the designed system in system transaction throughput, transaction delay, and system security is better than the traditional system, which verifies the effectiveness and reliability of the designed system.
Quantification of Phenolic and Flavonoid Content, Antioxidant Activity, and Proximate Composition of Some Legume Seeds Grown in Nepal
Authors: Sharma Khaga Raj , Giri Govinda
Published: August 2022

This study was carried out to evaluate some legume seeds growing in Nepal for their proximate composition, quantification of total phenolic (TPC) and flavonoid (TFC) contents, and in vitro, antioxidant and antidiabetic activities. These included legume grains such as chickpeas (Cicer arietinum), pea (Pisum sativum), mung bean (Vigna mungo), lima bean (Phaseolus lunatus), broad bean (Vicia faba), lentil (Lens culinaris), soybean (Glycine max), and common bean (Phaseolus vulgaris). The legume seeds were ground to make the flour which was extracted with methanol. The phenolic and flavonoid content was estimated by Folin-Ciocalteu’s phenol and aluminum chloride colorimetric methods. The in vitro antioxidant and antidiabetic activity was evaluated by using DPPH (1,1-diphenyl-2-picrylhydrazyl) free radical scavenging and α-amylase enzyme inhibition assay. The different legumes showed considerable variations in their phenolic contents (

30.64
±
1.50

 mg·GAE/g to

46.65
±
1.25

 mg·GAE/g legume seeds). Similarly, the total flavonoid contents showed

135.5
±
10.88

 mg·QE/g to

191.7
±
8.73

 mg·QE/g legume seeds. The in vitro antioxidant activity was evaluated in IC50 which ranged from

31.60
±
0.06

 μg/mL to

69.74
±
0.89

 μg/mL. The α-amylase inhibition was evaluated in IC50 which ranged from 217.38 μg/mL to 425.75 μg/mL as compared to the standard acarbose of 52.76 μg/mL. This study suggested that legumes are good sources of proteins, carbohydrates, and fats mainly for vegetarian people. The selection of the right legume species could be a good source of natural antioxidants and antidiabetics for nutraceutical uses and the beneficial effects of legumes from human health perspectives. Legume seeds growing in Nepal could be used as a sustainable and cheap meat alternative and are considered the most important food source.
Tseng‐Type Algorithms for the Split Variational Inclusion
Authors: Zhu Li-Jun , Yin Tzu-Chien
Published: August 2022

In this paper, we study iterative methods for solving the split variational inclusion in Hilbert spaces. We consider a Tseng‐type algorithm with self‐adaptive step sizes for finding a solution of the split variational inclusion. We show the weak convergence of the proposed sequence under some mild assumptions.
Disturbance Observer-Based Sliding Mode Control for MEMS with Prescribed Performance
Authors: Wang Yuru , Song Zhankui , Dong Jie , Pan Zhenxiang
Published: August 2022

In this study, we study the performance guaranteed tracking problem for the uncertain MEMS gyroscope dynamics. A fixed-time nonlinear function and the transformed error are introduced to establish the preset time control property for the closed-loop system. Then, a funnel sliding mode controller is presented to stabilize the system. To compensate for the uncertainty and external disturbance, a novel lumped disturbance observer is proposed. Different from most existing compensation control results for the MEMS gyroscope with antidisturbance that can only achieve uniformly ultimately bounded, the proposed control scheme is able to ensure that the system errors converge to the preassigned compact sets within a given time rather than infinite time and the system outputs track the desired command asymptotically. Simulation verification also confirms the effectiveness of the proposed approach.
A Geo-Social Characterization of Health Impact from Air Pollution in Mexico Valley
Authors: Zagal Flores Roberto , Claramunt Christophe , Mata Rivera Miguel Felix , Garay Jiménez Laura Ivoone , Jiménez Hernández Hugo , Herrera Navarro Ana Marcela , Argüelles Cruz Amadeo José
Published: August 2022

The impact of the air pollution phenomenon has been long studied, but most often with a fragmented approach, without closely looking at the relationship between different components that characterize it, such as sensor-based data, health data from institutional databases, and data on how it is perceived by human beings in social media. The research developed in this study introduces an integrated methodological framework that analyses sensor data on air pollution distributed in space and time, combined with health data and social data narratives that reflect how different communities perceive this phenomenon in space and time; exploring how these different heterogeneous sources can be combined to better understand the impact of air pollution phenomena at the large-city level in the Valley of Mexico. We introduce a Spatio-temporal data integration and mining framework that aims to discover trends and insights regarding the distribution of the impact of an air pollution phenomenon in terms of human health and perception. The main peculiarity of our methodological framework is the integration of different large data sources by combining a series of methods: NLP (topic modeling), data mining (data cubes, unsupervised learning, and clustering), and GIS capabilities (spatial interpolation, choropleth maps) that together provide a better understanding of the quantitative and qualitative patterns emerging at a different spatial scale and temporal granularity. Overall, this shows how social data, when combined with quantitative data, can provide a better understanding of the impact of a given phenomenon, such as air pollution.
Consensus of Time‐Varying Interval Uncertain Multiagent Systems via Reduced‐Order Neighborhood Interval Observer
Authors: Luo Hui , Zhao Jin , Yin Quan
Published: August 2022

This work focuses on a multiagent system (MAS) with time‐varying interval uncertainty in the system matrix, where multiple agents interact through an undirected topology graph and only the bounding matrices on the uncertainty in the system matrix are known. A reduced‐order interval observer (IO), which is named the reduced‐order neighborhood interval observer (NIO), is designed to estimate the relative state of each agent and those of its neighbors. It is shown that the reduced‐order IO can guarantee the consensus of the uncertain multiagent system. Finally, simulation examples are proposed to verify the theoretical findings.
A Study on the Impact of Wushu Sports Health on College Students’ Mental Health
Authors: Sun Yijia , Zhang Bo , Ji Aijun , Sun Wei
Published: August 2022

At the current university stage, university students are subjected to a variety of pressures such as role change adaptation pressure, study and life pressure, emotional pressure, economic pressure, employment pressure, and interpersonal pressure, which seriously affect their physical and mental health. Students with poor psychological tolerance may develop psychological disorders and, in serious cases, may even lead to mental illness. The results of the study indicated that 17%–20.23% of university students have psychological disorder problems. Excessive and long‐lasting stress can cause psychological, depression and anxiety, physical fatigue, and discomfort, which can lead to various diseases and even death. It is, therefore, of great importance to study the state of mental health and stress among university students as well as to develop countermeasures to improve their mental health. This study focuses on the impact of martial arts sports on the mental health of university students. As a type of sport, martial arts sports are particularly special in which they can intertwine the nurturing spirit of traditional culture with the nurturing spirit of sports. The development of martial arts sports in colleges and universities can not only enhance the physical quality of college students but also promote the development of their physical and mental health. Therefore, this article takes Taijiquan as an example to explore the study of the impact of martial arts sports health on college students’ mental health.
Vocational Education Platform Based on Block Chain and IoT Technology
Authors: Li Deming , Hu Rui , Lin Zhuliang
Published: August 2022

Due to the poor effect of the traditional vocational education platform, it is difficult to meet the requirements of the current Internet education platform. To solve this problem, this paper designs a vocational education platform system based on blockchain and Internet of Things (IoT) technology. First, the system access control strategy based on blockchain technology is introduced into the teaching system so as to improve the security of the teaching system. Then, in order to solve the single point of failure and efficient transmission problems of access control in the IoT environment, this paper proposes an access control strategy based on main side chain cooperation. This strategy introduces a side chain to expand the blockchain and improve the data carrying capacity of the blockchain. To meet the requirements of high-speed data transmission, this paper designs an access control model suitable for the main side chain blockchain structure. Experimental results show that compared with other algorithms, the data request speed of the system in this paper is faster, and the storage space is smaller. Therefore, the proposed technology based on blockchain and IoT can meet the use requirements of vocational education platforms.
[Retracted] A Digital Way for Fabricating a Resin‐Bonded Fixed Partial Denture Combined with Periodontal Splint in the Mandibular Anterior Region
Authors: Shi Xiaorui , Zhan Yalin , Liu Feng
Published: August 2022

Teeth loosening are caused by insufficient supporting tissue, inflammation of periodontal tissue, or occlusal trauma, which is one of the main clinical symptoms of periodontitis. A digital technique for fabricating resin‐bonded splinting fixed partial dentures (S‐FPDs) is presented. It restores the missing mandible anterior teeth and splints the adjacent periodontally mobile teeth of the patient at the same time. The S‐FPDs is designed and fabricated by computer‐aided design and computer‐aided manufacturing (CAD/CAM) technology with flexible PMMA material. The workflow is straightforward, convenient, and accurate. Meanwhile, the restoration can meet the esthetic requirements and help with the stability of adjacent loosening natural teeth spontaneously. It could be considered a medium‐term provisional restorative treatment option, but further controlled investigations still be needed.
Chaotic Oscillations in a Fractional-Order Circuit with a Josephson Junction Resonator and Its Synchronization Using Fuzzy Sliding Mode Control
Authors: Ramakrishnan Balamurali , Cimen Murat Erhan , Akgul Akif , Li Chunbiao , Rajagopal Karthikeyan , Kor Hakan
Published: August 2022

A linear resistive capacitive inductive shunted model of a Josephson junction with a topologically nontrivial behaviour is considered in this paper. We have considered a fractional-order flux-controlled memristor to effectively model the feedback flux effects across the Josephson junction (JJ). The mathematical model of the proposed JJ oscillator is derived, and the dimensionless model is used to study the various dynamical properties of the oscillator. The stability plot shows that the proposed oscillator has both stable and unstable regions of oscillations for different choices of equilibrium points and fractional order. The bifurcation plots are presented to understand the route to crisis, and we have also shown that the oscillator has regions of coexisting attractors. We have also achieved the synchronization of the proposed oscillator using fuzzy sliding mode control with the master and slave systems considered with different parameter sets. The chattering amplitude is estimated by using the fuzzy logic, and it is used in the synchronization algorithm to minimize the error.
A Gaussian High-Dimensional Random Matrix-Based Method for Detecting Abnormal Student Behaviour in Chinese Language Classrooms
Authors: Zhang Xuyun
Published: August 2022

In this paper, a Gaussian high-dimensional random matrix approach is used to conduct an in-depth study and analysis of the detection of abnormal student behaviour in Chinese language classrooms, and a corresponding model is designed for practical application. The Gaussian high-dimensional random matrix technique and recurrent neural network technique are applied to build a basic technical framework for intelligent proctoring. An innovative method of human posture estimation is used to complete the proctoring task, and the structure of the Gaussian high-dimensional random matrix method is simplified and the model is compressed to achieve real-time and parallelism of the method. A PolSAR sparse representation classification (OGRM_SRC) algorithm based on orthogonal Gaussian random matrices (OGRM) is proposed to construct an observation dictionary based on polarisation features and OGRM to select typical ground target samples in polarized feature images. The system not only functions as a psychometric assessment of students but also is capable of detecting and analysing abnormal student behaviour based on the improved forest algorithm. The residuals of the pixel to be classified are calculated relative to each atom in the observation dictionary, and the minimum reconstruction residual is used as the classification criterion. The OGRM_SRC algorithm is applied to classify the pixel, and the final classification results are obtained and evaluated for accuracy. We propose a fine-grained action recognition optimisation method for recurrent neural networks, fusing temporal attention information and spatial attention information to improve the action recognition method and improve the model’s ability to recognise fine-grained abnormal behaviours such as looking left, looking right, and copying with head down. Based on this, we conducted some comparative experiments to validate the effectiveness of our work and verified that the system achieved a recognition efficiency of 76.8 FPS and a recognition accuracy of 98.5% in a graphical computing processor.
Knowledge and Attitude of Sciatica Pain and Treatment Methods among Adults in Saudi Arabia
Authors: Hashem Majdi , AlMohaini Reem Abdulrahman , AlMedemgh Norah Ibrahim , AlHarbi Sara Abdulmajed , Alsaleem Lena Saleh
Published: August 2022

Background. Sciatica is a relatively common condition, with a lifetime incidence varying from 13% to 40%. The corresponding annual incidence of an episode of sciatica ranges from 1% to 5%. The exact cause of sciatica is unknown to this day; treatment methods and practices differ between individuals based on their cultural background, socioeconomic status, and religious beliefs. This study aimed to assess the knowledge and attitude toward sciatica pain among adults in Saudi Arabia. Methods. A cross-sectional study was conducted among the adult population in Saudi Arabia. A self-administered questionnaire was distributed among the study population using an online survey. Questions were divided into four groups, each containing multiple questions covering the following aspects: demographic data, past medical history, and the assessment of knowledge and attitudes regarding sciatica. Results. A total of 3,764 respondents were involved in this study, with an age range of 18–65 years old (females 59.8%). The mean knowledge score was 3.8 (SD 2.1), with the majority having poor knowledge (60.1%). The most common source of sciatica information was an orthopedic or a neurosurgeon, while the most common self-treatment used was painkiller medications (30.8%). The mean attitude score was 35.8 (SD 5.3), with most respondents having a neutral attitude (80.3%). The factors correlated with an increase in knowledge and attitude were having a bachelor’s or higher degree and living in an urban area. Conclusion. While the attitude of the adult population toward sciatica pain seems adequate, their knowledge was shown to be deficient. Furthermore, when comparing diagnosed individuals living in cities with those in rural areas, both knowledge and attitudes were shown to be better in individuals living in cities. Awareness programs by health institutions and healthcare professionals are needed to enhance patients knowledge. Various media can be utilized to enhance patients knowledge including social media platforms.
Efficacy and Safety of Pembrolizumab Monotherapy for Recurrent/Unresectable/Metastatic Oral Squamous Cell Carcinoma: A Single-Center Study in China
Authors: Li Jieying , He Zongxuan , Tao Yueqin , Yang Xiaochen , Ge Shengyou , Xu Haoyue , Shang Wei , Song Kai
Published: August 2022

Background. Although pembrolizumab is recommended as a first-line treatment for advanced recurrent/unresectable/metastatic (R/U/M) head and neck squamous carcinoma, the differences in its efficacy among different populations need to be investigated. Methods. We reviewed 15 consecutive patients with R/U/M oral squamous cell carcinoma (OSCC) treated with pembrolizumab monotherapy at the Affiliated Hospital of Qingdao University between February 2021 and May 2022. All the 15 patients had known programmed death-ligand 1 expression and received multiple cycles of pembrolizumab monotherapy as first-line treatment. We evaluated and analyzed patients’ basic characteristics, time to first remission, the clinical efficacy of pembrolizumab monotherapy, and treatment-related adverse reactions. Results. The objective response rate of the 15 patients was 60%. Six patients (40.0%) achieved partial response, while three patients (20.0%) achieved complete response. In our study, the objective response status of the patients was observed in two to five cycles (mean, 3.6 cycles). For patients who responded well to immunotherapy, the mean Karnofsky Performance Status (KPS) score after treatment was significantly higher than that before treatment (

P
<
0.001

). The progression-free survival rates were 66.9% and 50.1% at 6 months and 1 year, respectively. Eight adverse events were observed, comprising four cases of rash and one case each of hypothyroidism, interstitial pneumonia, cheilitis, and cerebral thrombosis. Conclusion. Our study suggests that pembrolizumab is beneficial to the most responsive patients with R/U/M OSCC in our single-center study and may shed light on the management of OSCC.