bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of Loss Mechanisms in CZGSe Thin-Film Kesterite Solar Cells: A Statistical Distribution for Defects and Traps
Authors: Chris A. Benisha , Routray Soumyaranjan , Massoud Yehia
Published: October 2022

Kesterite materials is popular for low cost photovoltaic and opto-electronic applications. Currently the achievable efficiency of kesterite based CZTSe material is only 12% due to poor quality of material. As an alternative, substituting Sn with Ge as Cu-Zn-Ge-Se (CZGSe) not only enhances the optical properties of materials but also improves the performance of solar cell. The carrier dynamics of the device is examined with variation of different parameters like doping. The worst-case behavior of the device is also examined by adding different loss mechanisms such as deep defects and traps. A remarkable efficiency of 19% and worst case efficiency across a range of 11 to 12% could achieved from the proposed device at a defect concentration of 1×1016 cm−3 eV−1.
[Retracted] Research on the Purification of Environmental Pollution by Biomaterials and Its Treatment Methods in the Development of Rural Cultural and Creative Industries
Authors: Fan Ting , Fu Tianyou , Xu Qun , Gjoni Gazmir
Published: October 2022

Since our country put forward the rural revitalization strategy, we have attached great importance to the construction and development of urban and rural areas. In recent years, we have continuously enhanced the driving force of development and further promoted rural reform. However, in the construction of rural cultural industry, how to purify environmental pollution is a key and difficult point. Through the comprehensive analysis of the role and effect of biomaterials in environmental pollution purification, this study compares the comprehensive role of different materials in environmental purification, compares the trend of environmental pollution in the development of rural industries in recent years, and analyzes the coupling degree of environmental pollution purification in different technological environments. Through the analysis, we can draw a conclusion that under the action of nanomaterials and biomaterials, some water pollution, air pollution, construction waste pollution, and noise pollution in industrial development have achieved good results and impact. Through continuous practice and research, it can be seen that biotechnology and materials can play a relatively good role in the environmental pollution control. And this treatment technology has the advantages of low cost, high efficiency, and no secondary pollution. It also plays an important role in the treatment of pollutants and the development of new energy. It conforms to the concept of sustainable development and environmental construction in the modern society and provides more reference data and research value for the future research and development prospects of nanomaterials and biomaterials in industrial development and environmental governance.
Analysis and Evaluation of Ecological Environment Monitoring Based on PIE Remote Sensing Image Processing Software
Authors: Wang Xiaofeng , Wang Zhengran
Published: October 2022

With the continuous improvement of people’s demand for ecological environment quality, the research on ecological environment monitoring, analysis and evaluation had been paid more and more attention by relevant departments and personnel. Because the images collected by remote sensing technology were many and multi-source, the features extracted from remote sensing images using traditional methods had been difficult to meet the needs of related industry applications. Therefore, this paper made use of the advantages of PIE remote sensing image processing software in data analysis and processing, and put forward the research on ecological environment monitoring, analysis and evaluation methods. Firstly, on the basis of summarizing the concepts and related problems of ecological environment, this paper analyzed the processing methods of remote sensing data sources of ecological environment, and explained the evaluation standards and common methods of ecological environment. Secondly, the composition of PIE remote sensing image processing technology system and its application advantages were described, the common indicators and analysis methods of ecological environment monitoring were given, and the index system and model of ecological environment comprehensive evaluation were established. Finally, through the analysis of experimental cases, the results showed that the ecological environment monitoring analysis and evaluation method proposed in this paper was feasible. Compared with the traditional methods, the method proposed in this paper could objectively evaluate the ecological environment. This paper can not only provide support for the analysis and processing of remote sensing image data, but also provide an important reference for the application of remote sensing technology in the field of ecological environment.
Breast MRI Segmentation and Ki-67 High- and Low-Expression Prediction Algorithm Based on Deep Learning
Authors: Li Yuan-Zhe , Huang Yin-Hui , Su Xian-yan , Gu Zhen-qi , Lai Qing-Quan , Huang Jing , Li Shu-Ting , Wang Yi
Published: October 2022

Background and Objective. Breast cancer is a common malignant tumor that seriously threatens the health of women in my country and even around the world. The proliferation marker Ki-67 has been utilized to distinguish luminal B from luminal A tumors and is a reliable indicator of more aggressive breast cancer growth. If a reliable prediction method for breast cancer patients to avoid invasive damage can be found to predict Ki-67 before pathological examination, it will be very beneficial for doctors to formulate later treatment plans and provide more useful treatment options. Methodology. This paper proposes a tumor segmentation and prediction framework based on the combination of improved attention U-Net and SVM. The framework first improves on attention U-Net by introducing coefficients for learning multidimensional attention. Make the attention mechanism more aware of the main situation in the segmentation process. At the same time, the segmented breast MRI results and corresponding labels were input into the SVM classifier to accurately predict the expression of Ki-67. Results. The DSC, PPV, and sensitivity of our combined model are 0.94, 0.93, and 0.94, respectively, with better segmentation performance. And we compare with the segmentation frameworks of other papers and find that our combined model can make accurate segmentation of breast tumors. Conclusion. Our method can adapt to the variability of breast tumors and segment breast tumors accurately and efficiently. In the future, it can be widely used in clinical practice, so as to help the clinic better formulate a reasonable diagnosis and treatment plan for breast cancer patients.
Smart Fluids and Their Applications in Drilling Fluids to Meet Drilling Technical Challenges
Authors: Jingen Deng , Egwu Saviour Bassey , Xionghu Zhao
Published: October 2022

This article presents extensive analysis and review on recent developments in smart fluids as well as future opportunities of smart drilling fluids utilization in oil and gas well drilling while focusing on the following smart fluids: smart nanoparticles, electrorheological, magnetorheological, and viscoelastic surfactant (VES) fluids. The distinctive properties of nanoparticles such as tiny particle sizes, high specific surface area, mechanical strength, and thermal stability make them suitable for utilization in drilling fluids. In bentonite water-based drilling fluid systems, this review suggests that charged nanoparticles are capable of displacing exchangeable ions in between bentonite clay platelets, thereby forming intercalates which can interact with clay surfaces through electrostatic attraction or repulsion. In improving wellbore stability, it is presented in this review that nanoparticles are able to invade and plug ultratiny pore spaces in shale formations, thereby further enhancing shale formations’ mechanical strength and wellbore stability. According to this review, the magnitude of changes in properties of smart electrorheological and magnetorheological fluids largely depends on the intensity of applied electric and magnetic fields. The intensity of smart fluids properties alteration due to applied field would equally depend on wt.% concentration and chemical compositions of particles susceptible to electric and magnetic fields. Based on review carried out on VES smart fluids, attractive and repulsive forces in the smart VES fluids solution result in the formation of micelles which can cause changes in viscoelastic property of the formulated smart viscoelastic fluids. The more the concentration of charged ions in the base fluid which VES fluids come in contact with, the higher the viscoelasticity of the smart VES fluids. According to this review, utilization of smart materials in drilling fluids can result in meeting oil and gas well drilling technical challenges including enhancing wellbore stability, improving hole cleaning performance, lost circulation control, fluid loss control, enhancing rate of penetration, pressure drop control, and easing cutting carrying efficiency of drilling fluids. This review equally suggests that the utilization of smart fluids such as smart magnetorheological and electrorheological fluids would facilitate drilling automation and real-time data acquisition processes, which is the future technology in oil and gas drilling.
Analysis of the Influence of Multimedia Information Fusion on the Psychological Emotion of Financial Investment Customers under the Background of e-Commerce
Authors: Zhu Tehong
Published: October 2022

With the gradual deepening of the research on financial investment, some data show that investors’ limited rationality and self-psychological emotions do not always seek to maximize their own interests when making investment decisions, but lead to investment failure due to the influence of their bad psychological emotions. With the rise of e-commerce and the development of multimedia, multimedia information fusion and presentation are realized, and multiple terminals interact with each other in real time and effectively. It is in this context that this paper analyzes the impact of multimedia information fusion on the psychological emotions of financial investment customers in the context of e-commerce and formulates a correct and effective investment plan in line with financial investment customers. The results of the experiment are as follows: (1) the background of the current e-commerce development, the principle of multimedia information fusion, and the problems existing in the emotional impact of financial investment customers are analyzed, the research direction of the experiment is determined, and the target problem is analyzed according to the information fusion technology, which provides technical support for the research of this paper. (2) Among the existing multimedia information fusion design algorithms, Mgr fusion algorithm, advanced MCR fusion algorithm, multimodal fusion reordering algorithm, and trust function algorithm are introduced to compress and analyze the complex multimedia data information in different fields, which not only shortens the time required for a large amount of data information fusion. And The emotional data information of financial investment customers is efficiently counted and classified.
Prevalence and Early Prediction of Diabetes Using Machine Learning in North Kashmir: A Case Study of District Bandipora
Authors: Bhat Salliah Shafi , Selvam Venkatesan , Ansari Gufran Ahmad , Ansari Mohd Dilshad , Rahman Md Habibur
Published: October 2022

Diabetes is one of the biggest health problems that affect millions of people across the world. Uncontrolled diabetes can increase the risk of heart attack, cancer, kidney damage, blindness, and other illnesses. Researchers are motivated to create a Machine Learning methodology that can predict diabetes in the future. Exploiting Machine Learning Algorithms (MLA) is essential if healthcare professionals are able to identify diseases more effectively. In order to improve the medical diagnosis of diabetes this research explored and contrasts various MLA that can identify diabetes risk early. The research includes the analysis on real datasets such as a clinical dataset gathered from a doctor in the Indian district of Bandipora in the years April 2021–Feb2022. MLA are currently important in the healthcare sector due to their prediction abilities. Researchers are using MLA to improve disease prediction and reduce cost. In this Paper author developed a methodology using Machine Learning Algorithms for Diabetes Disease Risk Prediction in North Kashmir. Six MLA have been successfully used in the experimental study such as Random Forest (RF), Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Gradient Boost (GB), Decision Tree (DT), and Logistic Regression (LR). RF is the most accurate classifier with the uppermost accuracy rate of 98 percent followed by MLP (90.99%), SVM (92%), GBC (97%), DT (96%), and LR (69%), respectively, with the balanced data set. Lastly, this study enables us to effectively identify the prevalence and prediction of diabetes.
Organoleptic Quality Attributes and Their Association with Morphological Traits in Arabica Coffee (Coffea arabica L.) Genotypes
Authors: Merga Sakata Wakuma , Gebreselassie Abtew Wosene , Garedew Weyessa
Published: October 2022

Arabica coffee is an essential crop in the national economy of Ethiopia. There is little information available about the cup quality profile of Teppi coffee, despite several sites across the country having conducted research on the organoleptic properties of various coffee types. Therefore, an experiment was conducted to determine the quality attributes of the genotypes and correlate their inherited quality with morphological traits. The experiment was superimposed on five-year-old plants of 17 coffee genotypes during the 2018 cropping season at Teppi and Gemadro sites. Using the wet processing method, six kilograms of red coffee cherries were carefully harvested, processed, and sun dried to a moisture content of 10.5% to 11.0%. To obtain the necessary number of replications, each sample was divided into three, and three cups for each sample were prepared for the tasting sessions. Organoleptic quality attributes were scored using the standard established. The analysis of variance for aromatic quality, acidity, flavor, and overall cup quality showed a highly significant difference (

P

 = 0.01) among the tested coffee genotypes. All of the organoleptic quality attributes evaluated showed a no significant correlation with morphological traits like body (r = −0.11) and hundred-bean weight and, flavor (r = −0.1) and bean thickness. However, there was a strong correlation between organoleptic quality attributes like flavor (r = 0.95

∗
∗

) and acidity (r = 0.94

∗
∗

), demonstrating that any organoleptic characteristic is an important element in beverage quality. It is challenging to simultaneously choose for cup quality and bean physical features because there were no significant correlations between organoleptic quality and these traits. In this study, genotype 3/8 showed outstanding performance across locations for all organoleptic quality attributes, which can be used in the Arabica coffee-breeding program.
A Storage Optimization Model for Cloud Servers in Integrated Communication, Sensing, and Computation System
Authors: Wang Zhoukai , Wang Huaijun , He Liu , Lv Yang , Wei Zhaoying , Li Xuan
Published: October 2022

The massive amount of sensing and communication data that needs to be processed during the production process of complex heavy equipment generates heavy storage pressure on the cloud server-side, thus limiting the convergence of sensing, communication, and computing in intelligent factories. To solve the problem, based on machine learning techniques, a storage optimization model is proposed in this paper for reducing the storage pressure on the cloud server and enhancing the coupling between communication and sensing data. At first, based on the operation rules of the distributed file system on the cloud server, the proposed model screens and organizes the system logs. With the filtered logs, the model sets feature labels, constructs feature vectors, and builds sample sets. Then, based on the ID3 decision tree, a file elimination model is trained to analyze the files stored in the cloud server and predict their reusability. In practice, the proposed model is applied in the Hadoop Distributed File System and helps the system delete underutilized and low-value files and save storage space. Experiments show that the proposed model can effectively reduce the storage load on the cloud server and improve the integration efficiency of multisource heterogeneous data during complex heavy equipment production.
[Retracted] Design of Service Robot Based on User Emotion Recognition and Environmental Monitoring
Authors: Yang Dongxu
Published: October 2022

Robots may be able to comprehend human emotions better by adding speech emotion recognition and environment monitoring functions to human‐computer interaction systems. Robots can offer more humanized services by adapting to human emotions, resulting in a comfortable and cordial interaction between humans and robots. Improve the environment for communication and computer‐human interaction and also interactive computer experience. In order for service robots to perform fluid human‐computer interaction, this paper designs a sentiment analysis model based on CNN (convective neural network) to detect the feeling of interacting objects. It also builds a sentiment analysis model and an open domain dialogue system suitable for service robots. Examine the emotions experienced by the objects while they conversed. According to test results, the sentiment classification method used in this article performs more accurately on the dataset than the conventional model, and the final sentiment analysis model’s F1 value can reach 0.931, which is better able to identify an emotional state. Using all voice samples as the input content of the network would eliminate the confusion between neutral emotions and other nonneutral emotions, boosting the accuracy of sentiment analysis in comparison to the fixed‐length processing method of dividing or filling samples.
Development and Validation of a Novel High-Performance Liquid Chromatography (HPLC)/Refractive Index Detector (RID) Method to Determine Residual Polyvinyl Alcohol (PVA) in Microspheres
Authors: Yin Xia , Bin Chen , Wang Yanqing , Xu Peng
Published: October 2022

Polyvinyl alcohol (PVA) is a commonly used emulsifier for the preparation of DL-lactide-co-glycolide (PLGA) and polylactic acid (PLA) microspheres with sustained-release profile. Although water-soluble PVA is normally considered to have low toxicity, monitoring residual level of PVAs is still necessary, especially for the chronic disease treatment medications containing PVA. In this study, a robust and validated method was developed for the determination of PVA using high-performance liquid chromatography (HPLC) and refractive index detector (RID). PVA in sustained-release microspheres containing PLGA, aripiprazole, mannitol, and PVA as active substances was analyzed by size exclusion chromatography (SEC) using acetonitrile/water/trifluoroacetic acid (TFA) (300 : 700 : 1; v/v/v) as mobile phase at a flow rate of 0.5 mL/min. The retention time was 11.2 min and PVA was well separated with other components with good linearity (r2 = 0.9997) in the range of 2.5∼75 μg/mL. The limit of detection (LOD) was 0.51 μg/mL and the limit of quantification (LOQ) was 2.53 μg/mL. The relative standard deviations of intra- and interday precision under different concentrations were not more than 2.1%, and the recoveries were all in the ranges of 95∼105%. The established method has been demonstrated to be accurate, precise, repeatable, specific, and robust and, therefore, suitable for routine analysis of PVA in not only pharmaceutical field but also food and textile industries.
[Retracted] On the Feasibility of Integrating the Concept of Contemporary Green Environment Development in Creating Screen Prints by Digital Means
Authors: Xue Lei
Published: October 2022

In the 21st century with the popularization of digital technology, as a printmaking born out of industrial printing, it has undergone a revolutionary evolution. Silkscreen printmaking is a type of print with high technology content, strong dependence on media, and no traditional load. Thirty years ago, as soon as silkscreen prints settled in China, they began artistic exploration and experimentation with a brand‐new attitude. Several generations of screen print workers have achieved it, expanded it, and made it independent in the fusion of modern language methods and oriental cultural spirit, and in the exploration of screen vocabulary, characteristics and character, it has an independent status and ranks among the international elite qualifications. This paper analyzes the root problems of high‐tech printmaking tools, materials, technical principles, and causes of aesthetics. Starting from the background of the concept of green development, it expounds its related concepts, analyzes the theoretical origin of green development, and clearly recognizes the high‐tech. The limitation of printmaking in the development space—that is, the expression language of digital plate making, is relatively correct in theory, so as to carry out step‐by‐step exploration and practice.
Experimental Analysis and Optimization of Tribological Properties of Self-Lubricating Aluminum Hybrid Nanocomposites Using the Taguchi Approach
Authors: Selvaraj Vinoth Kumar , Jeyanthi S. , Thiyagarajan Raja , Senthil Kumar M. , Yuvaraj L. , Ravindran P. , Niveditha D. M. , Gebremichael Yigezu Bantirga
Published: October 2022

In recent times, tribological properties are gaining and grabbing great attention in metal matrix composites. They can provide significant benefits such as a lower coefficient of friction, wear resistance, high strength, and stiffness. Considering all these parameters, this research article mainly focuses on developing an aluminum hybrid nanocomposite material fabricated by powder metallurgy. Then, the results were examined using a pin-on-disk apparatus. Further optimization techniques such as the Taguchi approach under Design of Experiments have been adopted to obtain a minimal outcome of various assumed parameters such as A. percentage weight fraction of graphite content (Gr), B. the sliding distance, C. the sliding speed, and D. the stress applied. In addition, we have chosen parameters such as friction and wear loss for optimizing the outcome, including the main effect plots for the S-N ratio and the Analysis of Variance (ANOVA) approach. Based on the experimental results, we have noticed that friction and wear loss coefficient increase with increased applied load and sliding distance. Also, it was noted that there was a slight decrease in the coefficient of friction and wear loss when an increment was made in the graphite content, respectively. It was perceived that the sample containing 10% of graphite (Gr) could create a self-lubricating effect that significantly reduced wear loss and the coefficient of friction. Finally, by considering all these achieved results, aluminum nanocomposites can be employed in automobile, defense, and aerospace applications as they can reduce the weight of the components with improved wear behavior and more thermal stability.
The Relationship between Physical Activity and Health‐Related Quality of Life (HINT‐Eight) in Middle‐Aged Korean Women
Authors: Lee Myung-Nam , Kim Sang-Dol , Choi Young-Soon
Published: October 2022

The purpose of this study was to provide basic data on health by examining the effect of HINT‐eight on the physical activity of middle‐aged Korean women using secondary data from the 2019 KNHANES. The subjects were 1,428 middle‐aged women aged 45‐64. Multiple regression was performed to verify the relationship between physical activity and HINT‐eight. Following confirmation of the association between HINT‐eight and physical activity, it was found that the subdomains of QOL, such as stair climbing, vitality, work, sleeping, and happiness, were connected to physical activity. Stair climbing positively affected physical activity, education level, and grip strength and negatively affected age. Pain positively affected education level and grip strength and negatively affected age. Vitality positively affected physical activity, monthly household income, and grip strength. Working positively affected education level, monthly household income, and grip strength and negatively affected age. Depression positively affected monthly household income. Memory and sleep positively affected education level and negative effect on age. Happiness positively affected physical activity and monthly household income. In this study, physical activity was found to have an effect on various factors of QOL. Physical activity in middle age is an important influencing factor on the QOL in old age; to improve the QOL in older adult, it is necessary to make efforts to improve physical activity in middle age.
Difficulties and Countermeasures of Game Localization Translation Based on the IoT and Big Data
Authors: Sun Qiaoke
Published: October 2022

With the development of international social facilitation brought about by the technological progress of various social media, games are spread internationally as a key application for entertainment and social interaction. At present, my country has introduced more than tens of thousands of games from abroad. There are cultural differences and grammatical differences between different countries. The introduced game dubbing and NPC subtitles are not completely the same as the meaning of the country of origin of the game. The result of the incomprehensible words usually makes the game player unable to understand part of the game in the game, while affecting the game experience; it also hinders international cultural exchanges and curbs the strategic expansion of game companies. Big data technology is an inevitable product of the rapid development of data science. It uses various data collection, data storage, and data analysis tools to provide data-based decision support for users in need. The data plane provides tool support for translation. IoT technology relies on various sensors and microdevices to collect status information of different things. IoT devices all have a central server. Currently, game companies translate overseas games through teamwork. Using IoT devices, teams can translate status and progress information, stored in the server to support the progress control of game localization translation. This paper firstly conducts a survey on the questionnaires issued by the translation team leaders of multinational game companies, conducts statistical processing on the collected data, analyzes and summarizes the difficulties existing in the current game localization translation, and finally designs a corresponding game based on big data and Internet of Things technology. In localization translation strategy, through follow-up investigations on companies that have adopted the strategy, it is found that the dilemma of game localization translation has been solved.
Incidence, Associated Factors, and Outcome of Delirium among Patients Admitted to ICUs in Oman
Authors: Al-Hoodar Rasha Khamis , Lazarus Eilean Rathinasamy , Al Omari Omar , Al Zaabi Omar
Published: October 2022

Background. The incidence of delirium is high up to 46.3% among patients admitted to ICU. Delirium is linked to negative patient outcomes like increased duration of mechanical ventilation use, prolonged ICU stay, increased mortality rate, and healthcare costs. Despite the importance of delirium and its consequences that are significant, there is a scarcity of studies which explored delirium in Oman. Objectives. This study was conducted to assess the incidence of delirium, the association between the selected predisposing factors and precipitating factors with delirium, determine the predicators of delirium, and evaluate its impacts on ICU mortality and ICU length of stay among ICU patients in Oman. Methods. A multicenter prospective observational design was used. A total of 153 patients were assessed two-times a day by bedside ICU nurses through the Intensive Care Delirium Screening Checklist (ICDSC). Results. The results revealed that the delirium incidence was 26.1%. Regression analysis showed that sepsis, metabolic acidosis, nasogastric tube use, and APACHE II score were independent predictors for delirium among ICU patients in Oman and delirium had significant impacts on ICU length of stay and mortality rate. Conclusion. Delirium is common among ICU patients and it is associated with negative consequences. Multidisciplinary prevention strategies should be implemented to identify and treat the modifiable risk factors.
The Impact of Emotional Intelligence on Internet Addiction: A Case Study of Vietnamese Students
Authors: Vu Ngan Hoang , Vu Mai Thi , Mai Bao Quoc
Published: October 2022

Internet addiction has attracted significant attention due to its adverse effects on humans, especially young people. This study is aimed at investigating the impact of emotional intelligence on Internet addiction. Data was collected from 744 Vietnamese students in Vietnam. SPSS 20.0 software was used for descriptive statistics, reliability testing, factor analysis, and regression. The empirical results showed that emotional intelligence had a negative influence on Internet addiction. Specifically, the components self-emotion appraisal (SEA), others’ emotion appraisal (OEA), and regulation of emotion (ROE) significantly affected Internet addiction. However, the effect of the component use of emotion (UOE) on Internet addiction was not found to be statistically significant. Overall, the results of the study indicate that improving emotional intelligence may reduce the extent of Internet addiction among Vietnamese students.
Minimum Friction Losses in Planetary Stages of Wind Turbine Gearboxes
Authors: Pedrero Jose I. , Sanchez Miryam B. , Guerra David , Calvo-Irisarri Jose , Pleguezuelos Miguel , Fernandez-Sison Alfredo
Published: October 2022

Mechanical efficiency is not a critical design factor of wind turbine gearboxes since wind energy is abundant and costless. The design and sizing of the gears will be determined by the requirements of power density, gear ratio, fatigue safety, and allowable noise and vibration levels. However, reducing power losses results in lower lubricant heating, and therefore, a smaller cooling system is required with lower induced thermal stresses. Reducing friction losses will therefore be beneficial for the mechanical behavior of the gearbox. This paper provides a new method for the calculation of the optimal values of the outside radii and rack shift coefficients of planetary gear stages to minimize the friction power losses, maintaining unalterable geometrical and operating parameters that ensure the preestablished values of the aforementioned critical design factors.
The Chain Mediating Effect of Network Behavior and Decision Self-Efficacy between Work Skills and Perceived Employability Based on Social Cognitive Theory
Authors: Yang Liping , Zhang Hong
Published: October 2022

The purpose of this study is to investigate the chain mediating effects of networking behaviors and decision self-efficacy between work skills development and perceived employability. Structural equations modeling is used to analyze data collected from 813 Chinese students. The results show the following: first, the work skills development is positively correlated with perceived employability. Second, network behavior and decision self-efficacy each have a mediating effect between work skills development and perceived employability. Finally, this study found a chain mediating effect of network behavior and decision self-efficacy between work skills development and perceived employability. Therefore, this research shows that Work-Integrated Learning (WIL) needs to focus not only on skills development and employability outcomes but also on developing a strong network-based platform for stakeholders. In addition, higher education institutions and workplaces should also provide career guidance and counseling centers to help students build confidence in career decision-making and ensure students’ mental health care and healthy career development.
Optimization Design of Drilling Fluid Chemical Formula Based on Artificial Intelligence
Authors: Chen Li
Published: October 2022

Through the research and development of the regression prediction function of support vector machine, this paper applies it to the prediction of drilling fluid performance parameters and the formulation design of drilling fluid. The research in this paper can reduce the experimental workload and improve the efficiency of drilling fluid formulation design. The apparent viscosity (AV), plastic viscosity (PV), API filter loss (FLAPI), and roll recovery (R) of the drilling fluid were selected as the inspection objects of the drilling fluid performance parameters, and the support vector machine was used to establish a model for predicting the drilling fluid performance parameters. This predictive model was used as part of the overall drilling fluid formulation optimization design model. For a given drilling fluid performance parameter requirement, this model can be applied to reverse the addition of various treatment agents, and finally, the prediction accuracy of the model is verified by experiments.
Spatial-Temporal Differentiation and Dynamic Evolution of Digital Finance Inclusive Development in the Yangtze River Delta Economic Cluster of China
Authors: Dong Hai , Du Meng , Zhou Xiangjun
Published: October 2022

Clarifying the sources of the digital finance inclusive development gap helps profoundly understand the regional characteristics of inclusive digital finance and benefits and formulate and implement specific policies scientifically and reasonably. Based on the 2011–2020 “Peking University Digital Finance Inclusive Index,” this study explores the regional disparity in digital finance inclusive development and its sources in the Yangtze River Delta economic cluster using the methods of the center of gravity shift, standard deviation ellipse, nested Theil index difference decomposition, and Kernel density. We found that inclusive digital finance shows a rising trend year by year, with evident heterogeneity and spatial agglomeration characteristics; interprovincial differences are the primary sources of the overall differences in inclusive digital finance in the Yangtze River Delta. The spatial effect of digital inclusion finance between the Yangtze River Delta regions has continuity. Because of the significant positive spatial correlation of digital inclusion finance between regions, digital inclusion finance in this region is vulnerable to potential shocks from neighboring regions. Moreover, with or without the spatial lag term, the level of inclusive digital finance in all regions of the Yangtze River Delta shows a leap forward. This paper looks at the regional gap of inclusive digital finance and its structural decomposition in the Yangtze River Delta city cluster from three perspectives: time trend, spatial structure, and dynamic evolution. This gives an empirical basis for the different kinds of digital finance inclusive development policies and a guide for making decisions to speed up the formation of a regional digital finance inclusive synergistic development path.
Application of Informational Big Data in Case Study and Collection of Basic Educational Psychology
Authors: Nie Juan , Mo Jianghong
Published: October 2022

In the new situation of modernization, there is an influx of diverse social thinking. At the same time, coupled with the influence of COVID-19, which has swept the world, the ideological and psychological space of college students has been greatly impacted. In this context, the ideological and psychological health of college students is an important value for the education of college students. To be specific, as an important place for cultivating college students, colleges and universities should pay attention to students’ thoughts and ideas from their hearts. In addition, colleges and universities should give full play to the role of educational psychology in colleges and universities and actively promote the synergistic development of all educational sectors in schools, so as to promote the realization of the goal of education in the new era. In recent years, a series of mental health problems such as anxiety, depression, low self-esteem, and interpersonal sensitivity have become frequent among college students, and some have even developed suicidal ideation. This has a very serious negative impact on individuals, families, and society. Therefore, if the mental health problems of college students can be detected early, the relevant school departments and counselors can provide timely and targeted help to such students. At the same time, these at-risk students can receive early treatment, thus reducing the harm. As a result, it is quite valuable to find an effective method to identify students with mental health problems. Traditionally, researchers have used questionnaires to survey students about their mental health. However, this approach has the disadvantage of being easily concealed and inefficient. In recent years, researchers have begun to use weblogs to identify students with mental health problems, but this approach still has shortcomings. First of all, they still use questionnaires to obtain labels. In addition, students’ psychological activities may not only be reflected in their online behavior but also in their other daily behaviors. Big data in higher education plays a crucial role in analyzing and identifying students with psychological abnormalities. As a result, this research mainly extracts the behavioral characteristics of students by cleaning and transforming a large amount of disorganized student school data based on the educational data collected from school cards, academic affairs systems, access control systems, and related business systems. What is more, this study further analyzes the differences in behavioral characteristics between normal and abnormal students through hypothesis testing and finally establishes a model to identify abnormal students and evaluate the results.
Research on the Evaluation Method of University Bi-Entrepreneurship Curriculum Based on IoT Integrated with AHP Algorithm
Authors: Jing Peng
Published: October 2022

The Internet of Things (IoT) is essential for the success and adoption of digital entrepreneurship in universities. The Internet of Things (IoT) is the integration of electronic objects, peripherals, cities, and other items and equipment with embedded software, electronics, actuators, network connections, and sensors that allow these things and equipment to share and gather data. The usage of IoT for university bi-entrepreneurship can equalize the playing field in many areas of the economy, creating options like as remote work at any time and from anywhere. Nowadays, universities are playing an essential part in the careers of students. In this regard, government departments provide considerable assistance in recruiting individuals and companies in a variety of ways, including funding and tax policies. However, many factors affect university students’ innovation and entrepreneurship education. Therefore, it is necessary to establish a quality evaluation system of innovation and entrepreneurship education, which can guide university students’ targeted innovation and entrepreneurship by improving the comprehensive ability and employability of these students. Therefore, this paper constructs an evaluation index system of university students’ innovation and entrepreneurship education quality, including several dimensions and several sub-indicators. On this basis, the analytic hierarchy process (AHP) algorithm is used to construct the judgment matrix, and the ranking weight of each comparison element is calculated according to the judgment matrix. The suggested system first constructs a hierarchical model to conceptualize the relationship between different elements and then builds a judgment matrix. After establishing the judgment matrix, the calculation methods of the characteristic root, square root, and idempotent relative weight are systematically checked. The experimental results reveal that the proposed evaluation system has a stronger impact on promoting university students’ creativity, entrepreneurship, and employment.
Study of Progress on Nanocrystalline Cellulose and Natural Fiber Reinforcement Biocomposites
Authors: Aravind T. , Ashraf Mohd. Shaikhul , A. S. Rajesh , Ahalya N. , Rawat Mahavir Singh , Uma B. , Sharma Rajneesh , Subbiah Ram , Sida SisayKetema
Published: October 2022

Cellulosic biomass hydrolysis yields a nanoscale substance known as nanocrystalline cellulose (NCC). Gel, liquid, or powder is adaptable to a variety of uses. Nanocrystalline cellulose has unique renewability, biodegradability, and mechanical and physicochemical qualities, and abundance boosts the material’s mechanical strength by many orders of magnitude when introduced into the material matrix (polymer, ceramic, or metal). Nanocrystalline cellulose is not related with any serious environmental issues because it is a natural substance. The progress of this biomaterial as a green and renewable biomaterial for the fabrication of lightweight and biodegradable composite materials gives further impetus. The current aim of nanocrystalline cellulose research is to optimise nanocrystalline cellulose characteristics for dispersion in hydrophilic and hydrophilic media. To assess the nanocrystalline cellulose reinforcing, antibacterial, stability, hydrophilicity, and biodegradability, imaging methods and protocols in complicated matrices will need to be developed. This review includes a discussion on nanocrystalline cellulose biocomposites.
Investigation of Annealing and Crystallite Size on the Photoluminescence Properties of Ca2Al2SiO7: Eu Phosphors Prepared by Spray Pyrolysis
Authors: Bakare Fetene Fufa , Aga Fekadu Gochole , Hirpho Bona Woliyi , Shih Shao-Ju , Yeh Wen-Ling
Published: October 2022

Crystalline gehlenite (Ca2Al2SiO7) phosphor, doped with Eu ions, was successfully synthesized by the spray pyrolysis method followed by annealing temperature. The crystallite size and photoluminescence characterization were investigated as function of the annealing temperature. The X-ray diffraction pattern indicates that all peaks could be ascribed to the tetragonal Ca2Al2SiO7 phase in which the dopant could not affect the crystal structures. All annealed powders emitted red luminescence at 613 nm under excitation of 394 nm. However, at 1300°C annealing temperature for Ca2Al2SiO7 doped with Eu ions, the maximum photo luminescent intensity has been obtained.
Research on Intelligent Power Marketing Inspection Model Based on Knowledge Graph
Authors: Wang Jinghua , Chen Bingxu , Wang Yue , Xu Zihan , Zhao Wanru
Published: October 2022

The traditional topic-based auditing model lacks the ability of multi-object and multi-abnormal correlation auditing, which makes it impossible to solve the multi-scenario and multi-factor correlation-based auditing problem. This paper designs an intelligent power marketing audit model based on a knowledge graph. First, an entity identification and relationship extraction method for power marketing business based on NLP (natural language processing) and sequence annotation technology is proposed, and the description content is imported into the knowledge graph database; then, semantic disambiguation and knowledge are carried out by using bidirectional encoder representation from transformers (BERT). Link to build a knowledge map of business audit rules: Finally, an experimental analysis is carried out by taking the copying and receiving business with a large business volume in the marketing audit work as an example, and it is verified that the proposed model can effectively improve the information analysis ability and the audit accuracy of the audit work.
Temporal Convolutional Network with Wavelet Transform for Fall Detection
Authors: Lu Xilin , Ling Yuanxiang , Liu Shuzhi
Published: October 2022

Fall detection is a challenging task for human activity recognition but is meaningful in health monitoring. However, for sensor-based fall prediction problems, using recurrent architectures such as recurrent neural network models to extract temporal features sometimes could not accurately capture global information. Therefore, an improved WTCN model is proposed in this research, in which the temporal convolutional network is combined with the wavelet transform. Firstly, we use the wavelet transform to process the one-dimensional time-domain signal into a two-dimensional time-frequency domain signal. This method helps us to process the raw signal data efficiently. Secondly, we design a temporal convolutional network model with ultralong memory referring to relevant convolutional architectures. It avoids the gradient disappearance and explosion problem usefully. In addition, this paper also conducts experiments comparing our WTCN model with typical recurrent architectures such as the long short-term memory network in conjunction with three datasets, UniMiB SHAR, SisFall, and UMAFall. The results show that WTCN outperforms other traditional methods, the accuracy of the proposed algorithm is up to 99.53%, and human fall behavior can be effectively recognized in real time.
Intelligent Construction Optimization Control of Construction Project Schedule Based on the Fuzzy Logic Neural Network Algorithm
Authors: Yu Xiaobing , Zuo Hengzhong
Published: October 2022

At present, the field of construction engineering is limited by various situations, such as complex construction environments and many uncertain factors. Therefore, on the basis of the engineering network diagram, this paper proposes a construction project schedule management method based on the fuzzy logic neural network algorithm. By building a neural network, a large amount of historical data is input, and computers are allowed to calculate the key routes, thus predicting the construction period, and a construction project in a city is taken as an example for simulation experiments. The traditional construction period management scheme expects a construction period of 55 days. The planned construction period optimized by the project management technology integrating fuzzy logic neural network algorithm is 55 days, which is 2 days less than the traditional construction project schedule management technology and will not cause construction period delay. The simulation results show that this algorithm is more accurate and more efficient in calculating the key lines when dealing with large-scale projects, which can help the construction unit to quickly find the optimal strategy and effectively reduce the construction delay and capital loss caused by uncertainty factors.