A Deep Fusion Matching Network Semantic Reasoning Model

The Guanxi mediating role linking organizational justice to contextual performance with age as a moderator

Guanxi, a distinctive Chinese concept, reflects a shared vision of relationships and connections that include mutual understanding, trust, and a deep bond between individuals. Recognized for its potency in shaping the relationships that facilitate business undertakings and access to key resources, Guanxi is postulated as a potential mediator in the nexus between organizational justice and contextual work performance. The depth of Guanxi, intertwined with Chinese culture and values, may be perceived differently across age groups. Specifically, as Chinese millennials usually interact with global paradigms, generational disparities might emerge in valuing these traditional constructs. This study delves into how the dimensions of Guanxi-Ganqing (emotional connection), Renqing (reciprocity), and Xinren (loyalty)-mediate the relationship between organizational justice and contextual work performance, with chronological age as a moderator. The present study includes a convenience sample of 630 Chinese employees, aged 22-67 years, who participated in a quantitative online survey. The findings endorse the mediation role of Guanxi. The total influence of justice was found to be significant, as well as the indirect impacts, that were statistically salient. Although the age-moderated mediation was not wholly substantiated, the age-specific indirect effects of Renqing and Xinren did present significant variances between millennials and those above 42 years. The relevance of this study extends beyond the academic field, shedding light on the cultural dynamics at play within Chinese organizational settings. By unveiling the relationships between Guanxi, organizational justice, and performance, and by elucidating the age-specific variations therein, this research provides insights for organizational leaders and human resource professionals. Based on these findings, businesses can craft targeted interventions that capitalize on the strengths of Guanxi, ensuring fair practices and enhancing performance across diverse age groups. Further, recognizing the unique attributes and values of different generational cohorts can aid in fostering a harmonious, culturally attuned, and efficient workplace environment.

Research on consumers' purchase intention of cultural and creative products-Metaphor design based on traditional cultural symbols

Chinese traditional cultural symbols possess great aesthetic and cultural value, and are widely utilized in product design. In this study, we explore the relationship between metaphor design based on traditional cultural symbols, customer experience and cultural identity, and further estimate how these three variables stimulate consumers' perceived value to generate consumers' purchase intention. Based on existing traditional cultural literature and Stimulus-organism-response theory (SOR), we proposed a theoretical research model to characterize the relationship among metaphor design based on traditional cultural symbols, customer experience, cultural identity, perceived value and consumers' purchase intention. A research survey was conducted and 262 questionnaires were collected in total with 241 valid. We used Smart PLS graph version 3.0 for data analysis. Results indicate that the cognition of metaphor design based on traditional cultural symbols and customer experience has a direct and significant impact on the emotional value thereby, eliciting consumers' purchase intention, metaphor design based on traditional cultural symbols is directly and indirectly (i.e., through customer experience or perceived value) positively associated with consumers' purchase intention, also customer experience is directly and indirectly (i.e., through perceived value) associated with consumer purchase intention, cultural identity mediates the indirect effect of customer experience and perceived value on purchase intention, the moderating role of cultural identity between customer experience and perceived value is not significant. Our findings help to expand the existing literature on consumer purchase intentions by rationally using traditional cultural symbols in the product metaphor design.

Evaluation of student failure in higher education by an innovative strategy of fuzzy system combined optimization algorithms and AI

This research suggests two novel metaheuristic algorithms to enhance student performance: Harris Hawk's Optimizer (HHO) and the Earthworm Optimization Algorithm (EWA). In this sense, a series of adaptive neuro-fuzzy inference system (ANFIS) proposed models were trained using these methods. The selection of the best-fit model depends on finding an excellent connection between inputs and output(s) layers in training and testing datasets (e.g., a combination of expert knowledge, experimentation, and validation techniques). The study's primary result is a division of the participants into two performance-based groups (failed and non-failed). The experimental data used to build the models measured fourteen process variables: relocation, gender, age at enrollment, debtor, nationality, educational special needs, current tuition fees, scholarship holder, unemployment, inflation, GDP, application order, day/evening attendance, and admission grade. During the model evaluation, a scoring system was created in addition to using mean absolute error (MAE), mean squared error (MSE), and area under the curve (AUC) to assess the efficacy of the utilized approaches. Further research revealed that the HHO-ANFIS is superior to the EWA-ANFIS. With AUC = 0.8004 and 0.7886, MSE of 0.62689 and 0.65598, and MAE of 0.64105 and 0.65746, the failure of the pupils was assessed with the most significant degree of accuracy. The MSE, MAE, and AUC precision indicators showed that the EWA-ANFIS is less accurate, having MSE amounts of 0.71543 and 0.71776, MAE amounts of 0.70819 and 0.71518, and AUC amounts of 0.7565 and 0.758. It was found that the optimization algorithms have a high ability to increase the accuracy and performance of the conventional ANFIS model in predicting students' performance, which can cause changes in the management of the educational system and improve the quality of academic programs.

Early energy performance analysis of smart buildings by consolidated artificial neural network paradigms

The assessment of energy performance in smart buildings has emerged as a prominent area of research driven by the increasing energy consumption trends worldwide. Analyzing the attributes of buildings using optimized machine learning models has been a highly effective approach for estimating the cooling load (CL) and heating load (HL) of the buildings. In this study, an artificial neural network (ANN) is used as the basic predictor that undergoes optimization using five metaheuristic algorithms, namely coati optimization algorithm (COA), gazelle optimization algorithm (GOA), incomprehensible but intelligible-in-time logics (IbIL), osprey optimization algorithm (OOA), and sooty tern optimization algorithm (STOA) to predict the CL and HL of a residential building. The models are trained and tested via an Energy Efficiency dataset (downloaded from UCI Repository). A score-based ranking system is built upon three accuracy evaluators including mean absolute percentage error (MAPE), root mean square error (RMSE), and percentage-Pearson correlation coefficient (PPCC) to compare the prediction accuracy of the models. Referring to the results, all models demonstrated high accuracy (e.g., PPCCs >89%) for predicting both CL and HL. However, the calculated final scores of the models (43, 20, 39, 38, and 10 in HL prediction and 36, 20, 42, 42, and 10 in CL prediction for the STOA, OOA, IbIL, GOA, and COA, respectively) indicated that the GOA, IbIL, and STOA perform better than COA and OOA. Moreover, a comparison with various algorithms used in earlier literature showed that the GOA, IbIL, and STOA provide a more accurate solution. Therefore, the use of ANN optimized by these three algorithms is recommended for practical early forecast of energy performance in buildings and optimizing the design of energy systems.

Role of green financial assets, financial technology and the green energy on the development of a green economy

A major issue for governments in the past few decades has been environmental deterioration caused by economic activity. Researchers are increasingly interested in the factors that contribute to environmental deterioration. The study aims to test the role of green bond financing on energy efficiency investment and economic growth. In this investigation, we use the ARDL estimator to investigate the relationships between the financial technology, green bonds, green stock, green supply chain and the development of green energy. The importance of green supply chain, green energy, green bonds and financial technology has been identified as major variables. According to the study's findings, green supply chain, green finance and sustainable economic growth are all essential and positive indicators of a composite assessment of sustainable practices. Green bonds, reducing greenhouse gas emissions and green economic development all play a necessary part in green finance development.

A novel ensemble approach for estimating the competency of bank telemarketing

Having a reliable understanding of bank telemarketing performance is of great importance in the modern world of economy. Recently, machine learning models have obtained high attention for this purpose. In order to introduce and evaluate cutting-edge models, this study develops sophisticated hybrid models for estimating the success rate of bank telemarketing. A large free dataset is used which lists the clients' information of a Portuguese bank. The data are analyzed by four artificial neural networks (ANNs) trained by metaheuristic algorithms, namely electromagnetic field optimization (EFO), future search algorithm (FSA), harmony search algorithm (HSA), and social ski-driver (SSD). The models predict the subscription of clients for a long-term deposit by evaluating nineteen conditioning parameters. The results first indicated the high potential of all four models in analyzing and predicting the subscription pattern, thereby, revealing the competency of neuro-metaheuristic hybrids. However, comparatively speaking, the EFO yielded the most reliable approximation with an area under the curve (AUC) around 0.80. FSA-ANN emerged as the second-accurate model followed by the SSD and HSA with respective AUCs of 0.7714, 0.7663, and 0.7160. Moreover, the superiority of the EFO-ANN is confirmed against several conventional models from the previous literature, and finally, it is introduced as an effective model to be practically used by banking institutions for predicting the likelihood of deposit subscriptions.

A fuzzy decision-making system for video tracking with multiple objects in non-stationary conditions

Computer vision remains challenged by tracking multiple objects in motion frames, despite efforts to improve surveillance, healthcare, and human-machine interaction. This paper presents a method for monitoring several moving objects in non-stationary settings for autonomous navigation. Additionally, at each phase, movement information between successive frames, including the new frame and the previous frame, is employed to determine the location of moving objects inside the camera's field of view, and the background in the new frame is determined. With the help of a matching algorithm, the Kanade-Lucas-Tomasi (KLT) feature tracker for each frame is determined. To get the new frame, we access the matching feature points between two subsequent frames, calculate the movement size of the feature points and the camera movement, and subtract the previous frame of moving objects from the current frame. Every moving object within the camera's field of view is captured at every moment and location. The moving items are categorized and segregated using fuzzy logic based on their mass center and length-to-width ratio. Our algorithm was implemented to investigate autonomous navigation surveillance of three types of moving objects, such as a vehicle, a pedestrian, a bicycle, or a motorcycle. The results indicate high accuracy and an acceptable time requirement for monitoring moving objects. It has a tracking and classification accuracy of around 75 % and processes 43 frames per second, making it superior to existing approaches in terms of speed and accuracy.

Digital technology and its application in supply chain management: new evidence from China's economy

The purpose of this article is to investigate the influence that practices using information technology (IT) have on the development of a competitive advantage across the supply chain. An organization has a competitive advantage when it has qualities that give the required foundations for it to separate itself from other organizations that are also in its industry. Pressure is applied to the corporate environment as a result of competition and ongoing changes, such as the introduction of new products and technical advancements, the decline of product lifestyles, and the proliferation of products. In order to maintain a competitive edge and achieve financial success in business, organizations are necessary for responding to changes in the market. Through the use of supply chain markets, companies are able to react quickly to unforeseen shifts in the market, and these shifts may be turned into lucrative business possibilities. One of the most significant things that firms can do to assist themselves is make use of information technology to improve their supply chain management agility. From March 2021 through January 2022, the area of China will have a total sample size of 247 persons fill out a questionnaire as part of the data collection process. In each and every questionnaire, the measurements were taken using a Likert scale with five points. The partial least square-structural equation modeling (PLS-SEM) approach is used to the causal model in order to assess the model's reliability and validity. This technique is used to evaluate the causal model. The findings indicate that information technology has a favorable impact on the adaptability of supply chain management systems. In addition, the findings that were collected have shown that there are four factors that influence the SCM systems. These factors are the IT skills and knowledge, the integration of IT-based systems, the IT infrastructure, and the design of global position system and geographic information systems. In addition, this research offers practitioners recommendations for implementing digital technology for supply chain management and building suitable business strategies at various stages of digitalization.

Barriers to green human resources management (GHRM) implementation in developing countries: evidence from China

Because of the current climate adaptation and long-term viability advancements, campaigners both locally and globally are pressuring businesses to embrace green practices. But there are challenges to putting green policies into action. The goal of this research was to analyze the most significant challenges encountered by Chinese businesses when attempting to implement environmentally responsible HR practices (GHRM). There were seventeen setbacks found, and these were sorted into five main groups. In order to pilot test the survey questions, we spoke with twenty experts in the fields of human resources and environmental management. One hundred and ninety-nine questionnaires were subsequently distributed to a random sample of company CEOs (19), HR managers (30), CFOs (30), and HR directors (40). The PSI approach was used to establish a hierarchy of the most significant obstacles and their subobstacles. Twenty-three percent of GHRM barriers in the research area were attributable to economic factors. The absence of financial resources emerged as the most crucial obstacle overall (with a score of 0.99) and among the subbarriers. The second most common barrier was found to be political and regulatory (20.1%), while the least common was found to be cultural and educational (18.2%). Government and financial institutions can help businesses overcome the most significant obstacles by offering low-interest loans for the development and implementation of sustainable business strategies and initiatives. As such, this study complements the current body of literature on green HR. Examining the challenges faced when trying to put GHRM into practice in a poor country context, this helps policymakers and practitioners in China and other similar economies understand environmental innovation barriers and develop policies to overcome them.

Assessing the supply chain management of waste-to-energy on green circular economy in China: an empirical study

One of the industries that makes a significant contribution to the overall amount of greenhouse gas emissions around the globe is agriculture. In this regard, the use of bioenergy in the agricultural and food processing industries might benefit from the implementation of circular economy techniques. Despite the fact that just roughly 9% of the global economy is circular, there have been worldwide efforts to improve that reality. The linear economy, commonly known as the "take-make-use-dispose" model, is in sharp contrast to the circular economy, also known as "grow-make-use-restore," which seeks to influence the flow of materials and energy in order to maximize the benefits to the environment and minimize any associated costs. Garbage-to-energy, also known as WTE, is the focus of both academics and businesses as a direct result of the increasingly diminishing number of energy supplies and the ever-increasing amount of garbage. This project intends to turn trash into profit, lessen the impact waste has on the environment, and generate energy from biowaste by conceptualizing a focus on the supply chain characteristics of waste-to-energy processing. The adoption of a waste-to-energy (WTE) supply chain as a district energy system should be a viable solution toward a circular industrial economy that can solve energy consumption, waste management, and greenhouse gas emission concerns all at once. In the framework of a "circular economy," this study investigates how the management of waste-to-energy supply chains impacts the performance of businesses. The present investigation makes use of life cycle assessments, technical innovation, waste-to-energy conversion, and capacities related to circular economies. The study makes use of data obtained from an online survey that was administered between March 2021 and November 2021 to employees of 285 representative samples drawn from 457 European enterprises and firms that have accepted the concepts of the circular economy. The data is examined using a technique known as partial least squares structural equation modeling (PLS-SEM for short). The findings indicate that waste-to-energy serves as a mediator between the life cycle assessment and the capabilities of the circular economy and that sustainable supply chain management, sustainable supply chain design, technological progress, and waste-to-energy all have positive effects on these metrics.

Assessing the impact of green human resource management practices on environmental performance in China: role of higher education

The goal is for governments and executives to make environmental protection and the preservation of natural resources a priority. The current research examines how green human resource management practices have altered environmental performance in China's manufacturing sector. The survey used a trustworthy and valid questionnaire adapted from the literature to obtain the data. Random sampling method has been applied to collect data from manufacturers in China's Guangdong Province. Workers in China's industrial sector are the focus of this study, and each individual is treated as a separate unit of analysis. Three hundred of the 500 questionnaires were returned with sufficient data for statistical analysis. The predicted serial mediation model was analyzed using structural equation modeling (SEM) and the PROCESS model 4. The findings revealed that green HRM practices have a major impact on environmental performance and pro-environmental actions partially mediate the relationship between GHRM and environmental performance. In addition, higher education helped moderate the effect of green HRM on environmental outcomes. In terms of environmental performance, green recruitment, green selection, and green performance, green rewards via higher education has the greatest impact (p 0.01 significance level). Through an extension of the ability-motivation-opportunity theory, this study offers useful tips for policymakers, new and current organizations, and, in particular, manufacturing enterprises, on how to implement an incentive plan to promote environmentally friendly activities and product development, which in turn will increase customer loyalty.

Integrating multi-criteria decision-making with hybrid deep learning for sentiment analysis in recommender systems

Expert assessments with pre-defined numerical or language terms can limit the scope of decision-making models. We propose that decision-making models can incorporate expert judgments expressed in natural language through sentiment analysis. To help make more informed choices, we present the Sentiment Analysis in Recommender Systems with Multi-person, Multi-criteria Decision Making (SAR-MCMD) method. This method compiles the opinions of several experts by analyzing their written reviews and, if applicable, their star ratings. The growth of online applications and the sheer amount of available information have made it difficult for users to decide which information or products to select from the Internet. Intelligent decision-support technologies, known as recommender systems, leverage users' preferences to suggest what they might find interesting. Recommender systems are one of the many approaches to dealing with information overload issues. These systems have traditionally relied on single-grading algorithms to predict and communicate users' opinions for observed items. To boost their predictive and recommendation abilities, multi-criteria recommender systems assign numerous ratings to various qualities of products. We created, manually annotated, and released the technique in a case study of restaurant selection using 'TripAdvisor reviews', 'TMDB 5000 movies', and an 'Amazon dataset'. In various areas, cutting-edge deep learning approaches have led to breakthrough progress. Recently, researchers have begun to focus on applying these methods to recommendation systems, and different deep learning-based recommendation models have been suggested. Due to its proficiency with sparse data in large data systems and its ability to construct complex models that characterize user performance for the recommended procedure, deep learning is a formidable tool. In this article, we introduce a model for a multi-criteria recommender system that combines the best of both deep learning and multi-criteria decision-making. According to our findings, the suggested system may give customers very accurate suggestions with a sentiment analysis accuracy of 98%. Additionally, the metrics, accuracy, precision, recall, and F1 score are where the system truly shines, much above what has been achieved in the past.

The multi-modal fusion in visual question answering: a review of attention mechanisms

Visual Question Answering (VQA) is a significant cross-disciplinary issue in the fields of computer vision and natural language processing that requires a computer to output a natural language answer based on pictures and questions posed based on the pictures. This requires simultaneous processing of multimodal fusion of text features and visual features, and the key task that can ensure its success is the attention mechanism. Bringing in attention mechanisms makes it better to integrate text features and image features into a compact multi-modal representation. Therefore, it is necessary to clarify the development status of attention mechanism, understand the most advanced attention mechanism methods, and look forward to its future development direction. In this article, we first conduct a bibliometric analysis of the correlation through CiteSpace, then we find and reasonably speculate that the attention mechanism has great development potential in cross-modal retrieval. Secondly, we discuss the classification and application of existing attention mechanisms in VQA tasks, analysis their shortcomings, and summarize current improvement methods. Finally, through the continuous exploration of attention mechanisms, we believe that VQA will evolve in a smarter and more human direction.

Exploring the Impact of Time Spent Reading Product Information on E-Commerce Websites: A Machine Learning Approach to Analyze Consumer Behavior

In this study, we aim to investigate the influence of the time spent reading product information on consumer behavior in e-commerce. Given the rapid growth of e-commerce and the increasing importance of understanding online consumer behavior, our research focuses on gaining a deeper understanding of customer navigation on e-commerce websites and its effects on purchasing decisions. Recognizing the multidimensional and dynamic nature of consumer behavior, we utilize machine learning techniques, which offer the capacity to handle complex data structures and reveal hidden patterns within the data, thereby augmenting our comprehension of underlying consumer behavior mechanisms. By analyzing clickstream data using Machine Learning (ML) algorithms, we provide new insights into the internal structure of customer clusters and propose a methodology for analyzing non-linear relationships in datasets. Our results reveal that the time spent reading product-related information, combined with other factors such as bounce rates, exit rates, and customer type, significantly influences a customer's purchasing decision. This study contributes to the existing literature on e-commerce research and offers practical implications for e-commerce website design and marketing strategies.

Machine learning based IoT system for secure traffic management and accident detection in smart cities

In smart cities, the fast increase in automobiles has caused congestion, pollution, and disruptions in the transportation of commodities. Each year, there are more fatalities and cases of permanent impairment due to everyday road accidents. To control traffic congestion, provide secure data transmission also detecting accidents the IoT-based Traffic Management System is used. To identify, gather, and send data, autonomous cars, and intelligent gadgets are equipped with an IoT-based ITM system with a group of sensors. The transport system is being improved via machine learning. In this work, an Adaptive Traffic Management system (ATM) with an accident alert sound system (AALS) is used for managing traffic congestion and detecting the accident. For secure traffic data transmission Secure Early Traffic-Related EveNt Detection (SEE-TREND) is used. The design makes use of several scenarios to address every potential problem with the transportation system. The suggested ATM model continuously modifies the timing of traffic signals based on the volume of traffic and anticipated movements from neighboring junctions. By progressively allowing cars to pass green lights, it considerably reduces traveling time. It also relieves traffic congestion by creating a seamless transition. The results of the trial show that the suggested ATM system fared noticeably better than the traditional traffic-management method and will be a leader in transportation planning for smart-city-based transportation systems. The suggested ATM-ALTREND solution provides secure traffic data transmission that decreases traffic jams and vehicle wait times, lowers accident rates, and enhances the entire travel experience.

Evaluation Model of Physical Education Teaching Effect Based on AHP Algorithm

The multifaceted sources of physical education teaching factors and the uncertainty of evaluation have an impact on the qualitative and quantitative evaluation results of teaching effects. In order to improve the evaluation accuracy of the physical education teaching effect, the evaluation model of the physical education teaching effect was designed based on the AHP algorithm. The evaluation model is based on monitoring the whole process of teaching. Based on the multifaceted sources of physical education teaching factors and the uncertainty of evaluation, the overall objectives and selects three-level evaluation indicators were analyzed. The AHP algorithm was used to establish the hierarchical structure and obtained the total ranking and comprehensive score of the hierarchy. The test results show that the teaching evaluation model designed in this paper has an RMSE mean value of 1.923, which has higher evaluation accuracy and is conducive to the improvement of teaching quality.

Study of Membranes with Nanotubes to Enhance Osmosis Desalination Efficiency by Using Machine Learning towards Sustainable Water Management

Water resources management is one of the most important issues nowadays. The necessity of sustainable management of water resources, as well as finding a solution to the water shortage crisis, is a question of our survival on our planet. One of the most important ways to solve this problem is to use water purification systems for wastewater resources, and one of the most necessary reasons for the research of water desalination systems and their development is the problem related to water scarcity and the crisis in the world that has arisen because of it. The present study employs a carbon nanotube-containing nanocomposite to enhance membrane performance. Additionally, the rise in flow brought on by a reduction in the membrane's clogging surface was investigated. The filtration of brackish water using synthetic polyamide reverse osmosis nanocomposite membrane, which has an electroconductivity of 4000 Ds/cm, helped the study achieve its goal. In order to improve porosity and hydrophilicity, the modified raw, multi-walled carbon nanotube membrane was implanted using the polymerization process. Every 30 min, the rates of water flow and rejection were evaluated. The study's findings demonstrated that the membranes have soft hydrophilic surfaces, and by varying concentrations of nanocomposite materials in a prescribed way, the water flux increased up to 30.8 L/m²h, which was notable when compared to the water flux of the straightforward polyamide membranes. Our findings revealed that nanocomposite membranes significantly decreased fouling and clogging, and that the rejection rate was greater than 97 percent for all pyrrole-based membranes. Finally, an artificial neural network is utilized to propose a predictive model for predicting flux through membranes. The model benefits hyperparameter tuning, so it has the best performance among all the studied models. The model has a mean absolute error of 1.36% and an R² of 0.98.

The impact of TV series consumption on cultural knowledge: An empirical study based on gratification-cultivation theory

This study aims to clarify the media-induced trends of cross-cultural transmission and examine the implicit promotional potential for cultural branding. The gratification and cultivation theories are used to explore the promotional media prospect in forming perceptions of foreign cultures' traditions, habits, norms, and values to contribute to international communication. We analyzed the theoretical applicability in the case of China-Thailand contemporary media culture. A total of 856 Chinese series watchers were surveyed. Structural equation modeling was used to analyze the path effect of consumption of Chinese TV series on other endogenous variables. Results showed that cross-cultural media product consumption strengthens bilateral relations. Moreover, the acceptance and appropriation during engagement with media characters and producers lead to favorable attitudes toward the target culture. Results confirm the positive mutual association between the gratification and cultivation theories and their applicability in the current context. This study offers an important contribution through its finding that the need for gratification significantly and positively impacts consumption and cross-cultural learning and raises cross-cultural awareness, thereby leading to sustainable practices.

Influencing factors of farmers’ participation in domestic waste classification: An empirical analysis based on the semi-nonparametric estimation extended model

Farmers are the main participants of domestic waste classification, and their willingness and behavior to participate are directly related to the success or failure of domestic waste classification and the construction of “beautiful countryside.” Based on the analysis of the influence mechanism of exemplary behavior and social supervision on farmers’ participation willingness and behavior, an empirical analysis of 988 survey data of farmers in Henan Province is carried out using a semi-non-parametric estimation extended model. The results show that: (1) 85.63% of farmers are willing to participate in the classification of domestic waste, but their willingness and behavior are not consistent. (2) The exemplary behavior of relatives can only increase the willingness of farmers. The exemplary behavior of neighbors and village cadres not only has a positive impact on the behavior, but also facilitates the transformation of willingness to behavior. (3) The supervision of village cadres can increase the willingness of farmers. Although the supervision of villagers and cleaners will reduce the willingness of farmers, it has a significant positive impact on the behavior of farmers. Based on the research conclusions, suggestions are made to play the leading role of village cadres, attach importance to the supervision of villagers and cleaners, broaden publicity channels and strengthen publicity to special groups, improve supporting policies and classification equipment, in order to promote the classification of rural domestic waste.

Classification of Marine Mammals Using the Trained Multilayer Perceptron Neural Network with the Whale Algorithm Developed with the Fuzzy System

The existence of various sounds from different natural and unnatural sources in the deep sea has caused the classification and identification of marine mammals intending to identify different endangered species to become one of the topics of interest for researchers and activist fields. In this paper, first, an experimental data set was created using a designed scenario. The whale optimization algorithm (WOA) is then used to train the multilayer perceptron neural network (MLP-NN). However, due to the large size of the data, the algorithm has not determined a clear boundary between the exploration and extraction phases. Next, to support this shortcoming, the fuzzy inference is used as a new approach to developing and upgrading WOA called FWOA. Fuzzy inference by setting FWOA control parameters can well define the boundary between the two phases of exploration and extraction. To measure the performance of the designed categorizer, in addition to using it to categorize benchmark datasets, five benchmarking algorithms CVOA, WOA, ChOA, BWO, and PGO were also used for MLPNN training. The measured criteria are concurrency speed, ability to avoid local optimization, and the classification rate. The simulation results on the obtained data set showed that, respectively, the classification rate in MLPFWOA, MLP-CVOA, MLP-WOA, MLP-ChOA, MLP-BWO, and MLP-PGO classifiers is equal to 94.98, 92.80, 91.34, 90.24, 89.04, and 88.10. As a result, MLP-FWOA performed better than other algorithms.

Prediction and Control of Input and Output for Industry–University–Research Collaboration Network in Construction Industry

An unreasonable allocation of resources has led to a low rate of output in the industry–university–research collaboration network. A solution to this problem is to control and predict the input and output. However, the network has the characteristics of strong nonlinearity and insufficient samples. It is difficult for the existing control methods to migrate to collaboration networks because the traditional control methods, including Proportional–Integral–Derivative (PID) control and Model Predictive Control (MPC), are usually not applied to the system with strong nonlinearity and the controlled system needs to have specific parameters, while the modern control methods, including feedforward control and feedback control, have their limitations in both parameters and other aspects. In addition, there is a lack of research on the control and output prediction of collaboration networks, and there is no effective and applicable scheme for the control and prediction. Considering the nonlinearity and insufficient samples of the collaboration network, a Feedforward Control–Feedback Control Model based on the Multi-Layer Perceptron (FCFCM-MLP) is proposed in this paper. Adopting the controller structure of the Grid Search-Multilayer Perceptron (GS-MLP), a control block diagram, a feedforward controller, a feedback controller, and prediction methods such as Harris Hawk Optimization-Support Vector Regression (HHO-SVR) are designed for the FCFCM-MLP, which effectively realizes the feedforward control, feedback control, and prediction of inputs and outputs. In this paper, simulation tests on output-feedback tracking control are conducted with real statistics of papers jointly produced by the industry–university–research collaboration network in the construction industry. The results show that the proposed model has obvious effectiveness. Specifically, compared with the model composed of other controller structures and prediction methods, the optimal model Particle Dynamic Multiple Perturbation_Butterfly Optimization Algorithm-Support Vector Regression_Grid Search-Multi-Layer Perceptron (PDM_BOA-SVR_GS-MLP) obtained in this paper can minimize the predictive control error and effectively improve the control accuracy.

From developing to developed: Mechanisms of health inequalities among seniors in China and Japan under macro-field control

The behavioral characteristics, health statuses, and survival times of seniors in China and Japan using the fixed cohort method and constructed relationship models among capital, habitus, and health based on Pierre Bourdieu's social theory of practice. It was first found that capital, habitus, and health have a capital-based triangle generative structural relationship. Second, basic sources of health inequalities include the direct effect of capital and the indirect effect of capital through habitus, i.e., class habitus controlled by capital has class attributes and is also one of the sources of health inequalities. Third, time-space conversion of the field is not just the change in the total amount or composition of an individual's capital but also includes the development and improvement of the macro-social environment, causing altered intensities of the impacts of capital and habitus on health. Fourth, the macro-social structures of developing countries significantly differ. The direct effect of capital on health is far greater than the indirect effect of capital on health through habitus, and health inequalities are mainly derived from the direct role of capital. Fifth, with socioeconomic development and improvements in social welfare systems, health inequalities have been generally reduced but have not been eliminated, and the mechanism of health inequalities in developed countries has gradually shifted from the direct effect of capital to class habitus.

A comprehensive and bibliometric review on the blockchain-enabled IoT technology for designing a secure supply chain management system

Blockchain is a well-known prominent technology that has gotten a lot of interest beyond the financial industry, attracting researchers and practitioners from numerous businesses and fields. Specific uses of blockchain in supply chain management (SCM) are addressed in business practice. By combining two perspectives on blockchain in SCM, this study provides comprehensive knowledge in this field using a bibliometric approach. We will explore the worldwide research trend in related topic areas. By collecting data from the Web of Science, we collected 400 articles related to our research topic from 2016 until early 2021. We eliminated research in the form of technical reports, editorials, comments, and consultancy articles to maintain the quality of the data gathering. VOSviewer is used to create visualization maps based on text and bibliographic information. The examination uncovered helpful information, such as annual publishing and citation patterns, the top research topic, the top authors, and the most supporting funding organizations in this field.

University Archives Autonomous Management Control System under the Internet of Things and Deep Learning Professional Certification

The current work aims to meet the needs of the development of archives work in colleges and universities and the modernization of management to realize the standards and standardization of all aspects of archives business construction in colleges and universities, so as to improve the political and professional quality of archives cadres. First, the radio frequency identification (RFID) technology based on the Internet of things (IoT) digitizes the university archive labels. Meanwhile, the filing cabinet's intelligent security system preserves confidential files. Second, the convolutional neural network (CNN) algorithm under deep learning is introduced and college profile information is identified. Finally, the concept of professional certification is used to clarify the purpose of the university archives automation management system. Different activation functions are used to analyze the recognition accuracy loss and recognition accuracy of university archives. The identification error of You Only Look Once (YOLO) of the ReLU-convolutional neural network (R–CNN) of college archives is analyzed. The results show that the selection of rectified linear units (ReLU) activation function for CNN can effectively reduce the loss of identification accuracy of college archives and can improve the accuracy of identification of college archives. The algorithm based on the ReLU activation function has a smaller recognition error accuracy in college archives than that of the YOLO algorithm. The recognition error of the YOLO algorithm is slightly higher than that of the R–CNN. The font recognition error of archival information based on the R–CNN is relatively large. However, the conclusion is reasonable due to the recognition difficulties of handwritten archival fonts. The file positioning recognition error rate is 19.00%, the file printing font recognition error rate is 4.75%, and the image recognition error rate is 1.90%. These results have a certain reference value for the process of identifying information in the automatic management of university archives by CNN under different activation functions.

Implementation of Trusted Traceability Query Using Blockchain and Deep Reinforcement Learning in Resource Management

To better track the source of goods and maintain the quality of goods, the present work uses blockchain technology to establish a system for trusted traceability queries and information management. Primarily, the analysis is made on the shortcomings of the traceability system in the field of agricultural products at the present stage; the study is conducted on the application of the traceability system to blockchain technology, and a new model of agricultural product traceability system is established based on the blockchain technology. Then, a study is carried out on the task scheduling problem of resource clusters in cloud computing resource management. The present work expands the task model and uses the deep Q network algorithm in deep reinforcement learning to solve various optimization objectives preset in the task scheduling problem. Next, a resource management algorithm based on a deep Q network is proposed. Finally, the performance of the algorithm is analyzed from the aspects of parameters, structure, and task load. Experiments show that the algorithm is better than Shortest Job First (SJF), Tetris^∗, Packer, and other classic task scheduling algorithms in different optimization objectives. In the traceability system test, the traceability accuracy is 99% for the constructed system in the first group of samples. In the second group, the traceability accuracy reaches 98% for the constructed system. In general, the traceability accuracy of the system proposed here is above 98% in 8 groups of experimental samples, and the traceability accuracy is close for each experimental group. The resource management approach of the traceability system constructed here provides some ideas for the application of reinforcement learning technology in the construction of traceability systems.

Evaluation of College English Teaching Quality Based on Improved BT-SVM Algorithm

With the development of teaching evaluation program, colleges and universities have reformed according to the actual situation of the school. With the development of evaluation activities, many universities are eager to establish their own teaching quality evaluation system, so as to pre-evaluate the teaching quality of schools. SVM is one of the most widely used machine learning algorithms that enables efficient statistical learning with a very limited number of samples. Considering the excellent learning performance of SVM, it is very suitable for the teaching quality evaluation system. In this paper, we optimize the existing multiple classification algorithm for binary trees and propose a new method. Learning the popular teaching quality evaluation system in colleges and universities, the binary tree support vector machine classification algorithm, and design comparison experiment, the experimental results show that the evaluation model proposed in this paper has strong generalization ability and higher classification accuracy and better classification efficiency.

An Empirical Analysis on the Impact of Innovation Network Structure on Crossover Innovation Performance of Emerging Technologies

The crossover innovation springing up in emerging technologies has drawn wide attention from scholars. Innovation network, as an effective way for major innovation-driven entities towards less relevant risks and higher efficiency, can significantly affect the crossover innovation performance. This paper analyzes the evolution law of the innovation network of autonomous driving technology based on the Social Network Analysis (SNA) and by using the data on joint applications for invention patents of such technology during 2006–2020. Furthermore, the structural eigenvalues of the network evolution are calculated for the regression analysis of the relationship between network structure and crossover innovation performance. The empirical results show that network centrality, structural hole, and relationship intensity have a positive effect on crossover innovation performance of emerging technologies, while network clustering has a negative effect. Emerging technology enterprises should constantly improve their technological innovation ability, improve their status and influence in the innovation network, establish cooperation with appropriate innovation partners, further expand their own technical knowledge fields, and obtain innovation resources by optimizing the network structure so as to enhance the crossover innovation performance.

A comprehensive and systematic literature review on the employee attendance management systems based on cloud computing

Attendance is critical to the success of any business or industry. As a result, most businesses and institutions require a system to track staff attendance. On the other hand, cloud computing technology is being utilized in the human resource management sector. It may be an excellent option for processing and storing large amounts of data and improving management effectiveness to a desirable level. Hence, this paper examines cloud infrastructures for employee attendance management in which the articles are categorized into three groups. The results show that cloud infrastructure has a significant and positive impact on the management of employee attendance systems. Also, the results reveal that the radio frequency identification authentication protocol protects the privacy of tags and readers against database memory. When references operate properly, they help the people concerned and society by making workplaces more efficient and safer.

Construction of Emergency Procurement System and System Improvement Based on Convolutional Neural Network

At this stage, countries around the world have their own operating management model for the procurement system of emergency equipment. This article analyzes the influencing factors affecting the operation of the emergency procurement system through a convolutional neural network analysis method, and the contract management of the emergency procurement system is realized. Management and monitoring and balance of interests on supply and demand also meet the requirements of the construction and improvement of emergency procurement systems at this stage. During the construction and improvement of the emergency procurement system, through the monitoring and management of the procurement system, standardize the management of emergency procurement contracts, and implement the management of the memorandum of emergency procurement contracts to maximize the benefits of supply and demand of emergency equipment, and meet the requirements of different emergency levels in the future equipment procurement requirements.

A BERT-Based Aspect-Level Sentiment Analysis Algorithm for Cross-Domain Text

Cross-domain text sentiment analysis is a text sentiment classification task that uses the existing source domain annotation data to assist the target domain, which can not only reduce the workload of new domain data annotation, but also significantly improve the utilization of source domain annotation resources. In order to effectively achieve the performance of cross-domain text sentiment classification, this paper proposes a BERT-based aspect-level sentiment analysis algorithm for cross-domain text to achieve fine-grained sentiment analysis of cross-domain text. First, the algorithm uses the BERT structure to extract sentence-level and aspect-level representation vectors, extracts local features through an improved convolutional neural network, and combines aspect-level corpus and sentence-level corpus to form a sequence sentence pair. Then, the algorithm uses domain adversarial neural network to make the feature representation extracted from different domains as indistinguishable as possible, that is, the features extracted from the source domain and the target domain have more similarity. Finally, by training the sentiment classifier on the source domain dataset with sentiment labels, it is expected that the classifier can achieve a good sentiment classification effect in both source and target domain, and achieve sentence-level and aspect-level sentiment classification. At the same time, the error pooled values of the sentiment classifier and the domain adversary are passed backwards to realize the update and optimization of the model parameters, thereby training a model with cross-domain analysis capability. Experiments are carried out on the Amazon product review dataset, and accuracy and F1 value are used as evaluation indicators. Compared with other classical algorithms, the experimental results show that the proposed algorithm has better performance.

Research on Tiny Target Detection Technology of Fabric Defects Based on Improved YOLO

Fabric quality plays a crucial role in modern textile industry processes. How to detect fabric defects quickly and effectively has become the main research goal of researchers. The You Only Look Once (YOLO) series of networks have maintained a dominant position in the field of target detection. However, detecting small-scale objects, such as tiny targets in fabric defects, is still a very challenging task for the YOLOv4 network. To address this challenge, this paper proposed an improved YOLOv4 target detection algorithm: using a combined data augmentation method to expand the dataset and improve the robustness of the algorithm, obtaining the anchors suitable for fabric defect detection by using the k-means algorithm to cluster the ground truth box of the dataset, adding a new prediction layer in yolo_head in order to have a better effect on tiny target detection, integrating a convolutional block attention module into the backbone feature extraction network, and innovatively replacing the CIOU loss function with the CEIOU loss function to achieve accurate classification and localization of defects. Experimental results show that compared with the original YOLOv4 algorithm, the detection accuracy of the improved YOLOv4 algorithm for tiny targets has been greatly increased, the AP value of tiny target detection has increased by 12%, and the overall mean average precision (mAP) has increased by 3%. The prediction results of the proposed algorithm can provide enterprises with more accurate defect positioning, reduce the defect rate of fabric products, and improve their economic effect.

Evaluating the Performance of Inclusive Growth Based on the BP Neural Network and Machine Learning Approach

In this paper, we use the panel data of 281 cities in China from 2005 to 2020 for capturing the factors driving urban inclusive growth (IG). In doing this, we employ the BP neural network algorithm combined with the DEA model to measure the urban inclusive growth efficiency (IGE). Furthermore, a nest of machine learning (ML) algorithms are introduced to explore the drivers of urban IGE, which overcomes the defects of endogeneity and multicollinearity of traditional econometric methods. We find for the overall sample that entrepreneurship and innovation contribute the most to IGE, accounting for about 35%, respectively, and they are the most critical drivers, while the heterogeneity test results reveal that the contribution of influencing factors has changed for different regions such as the eastern region, the central region, and the western region. Based on the experimental results of the ML model, we provide some policy suggestions for China and similar developing countries and emerging economies to promote IG.

Research on Long Text Classification Model Based on Multi-Feature Weighted Fusion

Text classification in the long-text domain has become a development challenge due to the significant increase in text data, complexity enhancement, and feature extraction of long texts in various domains of the Internet. A long text classification model based on multi-feature weighted fusion is proposed for the problems of contextual semantic relations, long-distance global relations, and multi-sense words in long text classification tasks. The BERT model is used to obtain feature representations containing global semantic and contextual feature information of text, convolutional neural networks to obtain features at different levels and combine attention mechanisms to obtain weighted local features, fuse global contextual features with weighted local features, and obtain classification results by equal-length convolutional pooling. The experimental results show that the proposed model outperforms other models in terms of accuracy, precision, recall, F1 value, etc., under the same data set conditions compared with traditional deep learning classification models, and it can be seen that the model has more obvious advantages in long text classification.

Repertoire Construction for Critical Cross-Cultural Literacy of English Majors: Based on the Research Paradigm of Systemic Functional Linguistics

The ambiguous development trend of cultural globalization brings both opportunities and challenges to China's cultural development. English major in colleges and universities, a discipline of cross-cultural education, should look at the cultural communication of the target country dialectically based on the national consciousness of the home country. Since the end of the 20th century, administrators and scholars have paid attention to critical thinking, critical cultural awareness, and critical skills in cross-cultural communication, which are important components of the cross-cultural meaning system. Therefore, all these are collectively referred to as critical cross-cultural literacy (CCCL). On the basis of the research paradigm of systemic functional linguistics (SFL), a language is a semiotic system that creates meaning. Thus, to help students construct and improve their individual CCCL repertoire, teachers need to guide them to critically study and analyze the discourse purpose of the textbook author as well as their language methods and strategies to enrich their meaning potential.

Effect of cloud-based information systems on the agile development of industrial business process management

Business process management (BPM) has been the main driver behind company optimization and operational efficiency. However, the digitization era we live in necessitates that organizations be agile and adaptable. Delivering unprecedented rates of automation-fueled agility is necessary to be a part of this digital revolution. On the other hand, BPM automation cannot be done only by concentrating on procedure space and traditional planning methodologies. With the introduction of BPM, where the deployment of BPM with cloud computing has undergone enormous development lately, cloud computing has been considered a particularly active topic of study. Cloud computing points to the provision of dependable computing environments based on improved infrastructure availability and service quality without imposing a significant cost load. This research aims to discover the relationship between technical factors, financial factors, environmental factors, security of the cloud-based information systems, and the agile development of industrial BPM (IBPM). The present study aims to fill this gap and show how partial least squares structural equation modeling (SEM) can be employed in this field. Importance–performance map analysis (IPMA) evaluated the importance and performance of factors in the SEM. IPMA enables the identification of factors with relatively low performance but relatively high importance in shaping dependent variables. The empirical findings showed that four key factors (technical, financial, environmental, and security) positively influence the agile development of IBPM.

The Impact of SIPOC on Process Reengineering and Sustainability of Enterprise Procurement Management in E-Commerce Environments Using Deep Learning

In order to better promote the healthy and long-term development of enterprise procurement management process, under the background of e-commerce environment, Suppliers-Inputs-Process-Outputs-Customers (SIPOC) model, deep learning and related theories of enterprise procurement management are expounded and proposed. Then, D electric power enterprise is studied as samples. After understanding the current situation of procurement management of the enterprise, there are a series of problems in the enterprise, such as complex process, and no correlation between procurement management process and overall strategic planning. Finally, through the analysis of the early warning indicators of the enterprise by the deep learning algorithm, the procurement management process has caused certain risks to the financial management level of the enterprise, and the procurement management process of the enterprise needs to be adjusted. The material record and consumption scheme of the enterprise is optimized by using the SIPOC organizational system model.