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The purpose of this paper is to establish a more reasonable evaluation system and model for the development level of rural tourism, and provides a method for quantifying the development level of regional rural tourism.

This paper provides a method for evaluating the development level of rural tourism, constructs an evaluation index system according to the connotation of rural tourism, then calculates the index weight by entropy method, and makes a comprehensive evaluation by grey relational analysis. Taking the development of rural tourism in 11 cities in Jiangxi Province as the research object, the ranking results of 11 cities were obtained by using grey relational analysis.

The overall development level of rural tourism in Jiangxi Province is positive, but the spatial distribution is uneven, showing the characteristics of “low-level aggregation and high-level dispersion”. The barrier model diagnoses that the degree of financial input is the biggest constraint to the development level of rural tourism in Jiangxi Province.

This study puts forward an evaluation model based on entropy weight and grey relational analysis, which is an important supplement to the grey relational analysis method system and has a positive role in promoting the quantitative evaluation of regional rural tourism level.

This study aims to clarify the psychological mechanism of international contractors' fraud by linking performance pressure to fraudulent intention through the displacement of responsibility and addressing the moderating role of moral intensity.

Based on moral disengagement theory, performance pressure was hypothesized to be positively associated with fraudulent intention by mediating the displacement of responsibility. Drawing on the issue-contingent theory, moral intensity was hypothesized to inhibit the relationship between performance pressure and displacement of responsibility in three aspects: magnitude of consequences (MC), probability of effect (PE) and social consensus (SC). The scenario-based questionnaire was conducted to collect information from contractors spread across 50 countries. The partial least squares structural equation modeling was employed to assess the proposed model.

The results demonstrated that performance pressure was positively associated with the fraudulent intention, and displacement of responsibility exerted a positive partial mediating impact between performance pressure and fraudulent intention. Regarding moral intensity in the moderating analysis, the negative moderating role of MC and PE was significant, while that of SC was insignificant.

This study provides international construction practitioners with a deep understanding of the formation mechanism of fraud at the psychological level.

It clarifies the psychological mechanism from performance pressure to fraudulent intention by integrating a mediation impact from the displacement of responsibility and a moderation effect from MC and PE. It contributes to the sparse research on how situational factors shape individuals' fraudulent intentions in the international context. It provides a fresh perspective on fraud by constructing a formation model from moral psychological theories.

The aim of this paper is to review the literature on the prediction of cutting tool life. Tool life is typically estimated by predicting the time to reach the threshold flank wear width. The cutting tool is a crucial component in any machining process, and its failure affects the manufacturing process adversely. The prediction of cutting tool life by considering several factors that affect tool life is crucial to managing quality, cost, availability and waste in machining processes.

This study has undertaken the critical analysis and summarisation of various techniques used in the literature for predicting the life or remaining useful life (RUL) of the cutting tool through monitoring the tool wear, primarily flank wear. The experimental setups that comprise diversified machining processes, including turning, milling, drilling, boring and slotting, are covered in this review.

Cutting tool life is a stochastic variable. Tool failure depends on various factors, including the type and material of the cutting tool, work material, cutting conditions and machine tool. Thus, the life of the cutting tool for a particular experimental setup must be modelled by considering the cutting parameters.

This submission discusses tool life prediction comprehensively, from monitoring tool wear, primarily flank wear, to modelling tool life, and this type of comprehensive review on cutting tool life prediction has not been reported in the literature till now. The future suggestions provided in this review are expected to provide avenues to solve the unexplored challenges in this field.

Introduction: The insurance sector provides security to society by pooling resources to manage risks. Insurers’ improved ability to analyse risks by examining vast amounts of granular data has considerably refined this technique. Compiling and analysing the fine data sets is now transformed into the ‘Big Data’ technique. The introduction of big data analytics (BDA) is transforming the insurance industry and the role data plays in insurance.

Purpose: This chapter will attempt to examine the applications and role of big data in the insurance sector and how big data affects the different insurance segments like health insurance, property and casualty, and travel insurance. This chapter will also describe the disruptive impact of big data on the insurance market.

Methodology: Systematic research is carried out by analysing case studies and literature studies, emphasising how BDA is revolutionary for the insurance market. For this purpose, various articles and studies on BDA in the insurance market are selected and studied.

Findings: The execution of big data is continuously increasing in the insurance sector. The performance of big data in the insurance market results in cost reduction, better access to insurance services, and more fraud detection that benefits the customers and stakeholders. Therefore, big data has revolutionised the insurance market and assisted insurers in targeting customers more precisely.

This study examines how Confucianism, as an informal system, alleviates manager–shareholder conflicts and thus decreases managerial behavior of keeping higher levels of cash reserves. This study also investigates whether formal governance mechanisms (state ownership and institutional investors) moderate the relationship between Confucianism and cash holdings.

This study opts a sample of Chinese listed firms over the period of 2004–2015. The geographical-proximity-based method was followed to measure Confucianism, which is the distance between a firm's registered address and the national Confucianism centers.

The results indicate that Confucianism adversely influences cash holdings. The authors’ findings illustrate that Confucian culture promotes ethical behavior, and therefore, firms in a strong Confucianism environment keep a lower level of cash reserves. The authors further document that the effect of Confucianism on cash holding is weaker for state-owned firms but stronger for firms with low institutional ownership.

The findings provide implications for policymakers, academicians, and corporations. The results suggest that culture can reduce cash holdings. Especially, in emerging markets, such as China, where formal mechanisms are relatively less effective, informal institutions can serve an alternative system for alleviating adverse effects of agency conflicts.

This study contributes to the literature in two ways. First, this study contributes to cash holdings literature by showing that culture (Confucianism) is negatively associated with cash holdings. Second, this study extends the incumbent literature that seeks to explore how Confucian culture influences corporate behavior. To the best of the authors knowledge, this is the first study that identifies that Confucianism is associated with cash holdings.

The emergence of metaverse banking services (MBS) enables customers to interact and socialise in a virtual environment. However, there is a lack of research on MBS adoption. This study aims to examine the key factors influencing customer behaviour in adopting MBS, with a specific focus on Pakistan as a developing country.

Semi-structured interviews were conducted with 22 Pakistani banking customers, and the resulting data were transcribed and subjected to thematic analysis using NVivo software.

This qualitative investigation into the determinants of MBS adoption encompasses a wide range of facilitators, inhibitors and customer resources. These findings ultimately contribute fresh perspectives to the field, challenging prevailing beliefs and offering new insights into the complex dynamics driving customer behaviour in the MBS context.

Since this study only focused on Pakistan with a limited scope, future studies on MBS adoption would benefit from a comparative analysis across several countries, especially in Asian nations.

This study advances our understanding of MBS adoption by revealing key determinants of customer intentions. Moreover, it offers actionable guidance for banking professionals, marketers and policymakers to navigate the implementation of MBS and unlock promising avenues for growth and innovation.

The first scholarly inquiry into MBS adoption seeks to expand extant knowledge by elucidating customers' viewpoints, thereby revealing novel insights into the key factors that influence customer behaviour within the MBS landscape.

By tradition, sensors are used to measure one desired parameter; all other parameters influencing the sensor are considered as interfering inputs, to be eliminated if possible. Hence most of existing sensors are specifically intended for measuring one parameter, e.g. temperature, and the ideal temperature sensor should be as immune to all other parameters as possible. True, we sometimes use primitive sensor fusion, e.g. when calculating heat flow by combining separate measurements of temperature difference and of fluid flow.

Construction organizations must maintain a productive workforce without sacrificing their health and safety. The global construction sector loses billions of dollars yearly to poor health and safety practices. This study aims to investigate benefits derivable from using wearable technologies to improve construction health and safety. The study also reports the challenges associated with adopting wearable technologies.

The study adopted a quantitative design, administering close-ended questions to professionals in the Nigerian construction industry. The research data were analysed using descriptive and inferential statistics.

The study found that the critical areas construction organizations can benefit from using WSDs include slips and trips, sensing environmental concerns, collision avoidance, falling from a high level and electrocution. However, key barriers preventing the organizations from adopting wearable technologies are related to cost, technology and human factors.

The time and cost lost to H&S incidents in the Nigerian construction sector can be reduced by implementing the report of this study.

Studies on WSDs have continued to increase in developed countries, but Nigeria is yet to experience a leap in the research area. This study provides insights into the Nigerian reality to provide directions for practice and theory.

The aim of continuous learning is to obtain and fine-tune information gradually without removing the already existing information. Many conventional approaches in streaming data classification assume that all arrived new data is completely labeled. To regularize Neural Networks (NNs) by merging side information like user-provided labels or pair-wise constraints, incremental semi-supervised learning models need to be introduced. However, they are hard to implement, specifically in non-stationary environments because of the efficiency and sensitivity of such algorithms to parameters. The periodic update and maintenance of the decision method is the significant challenge in incremental algorithms whenever the new data arrives.

Hence, this paper plans to develop the meta-learning model for handling continuous or streaming data. Initially, the data pertain to continuous behavior is gathered from diverse benchmark source. Further, the classification of the data is performed by the Recurrent Neural Network (RNN), in which testing weight is adjusted or optimized by the new meta-heuristic algorithm. Here, the weight is updated for reducing the error difference between the target and the measured data when new data is given for testing. The optimized weight updated testing is performed by evaluating the concept-drift and classification accuracy. The new continuous learning by RNN is accomplished by the improved Opposition-based Novel Updating Spotted Hyena Optimization (ONU-SHO). Finally, the experiments with different datasets show that the proposed learning is improved over the conventional models.

From the analysis, the accuracy of the ONU-SHO based RNN (ONU-SHO-RNN) was 10.1% advanced than Decision Tree (DT), 7.6% advanced than Naive Bayes (NB), 7.4% advanced than k-nearest neighbors (KNN), 2.5% advanced than Support Vector Machine (SVM) 9.3% advanced than NN, and 10.6% advanced than RNN. Hence, it is confirmed that the ONU-SHO algorithm is performing well for acquiring the best data stream classification.

This paper introduces a novel meta-learning model using Opposition-based Novel Updating Spotted Hyena Optimization (ONU-SHO)-based Recurrent Neural Network (RNN) for handling continuous or streaming data. This is the first work utilizes a novel meta-learning model using Opposition-based Novel Updating Spotted Hyena Optimization (ONU-SHO)-based Recurrent Neural Network (RNN) for handling continuous or streaming data.

The purpose of this paper is to construct a grey incidence model for panel data that can reflect the incidence direction and degree between indicators.

Firstly, this paper introduces the concept of a negative matrix and preprocesses the data of each indicator matrix to eliminate differences in dimensions and magnitudes between indicators. Then a model is constructed to measure the incidence direction and degree between indicators, and the properties of the model are studied. Finally, the model is applied to a practical problem.

The grey-directed incidence degree is 1 if and only if corresponding elements between the feature indicator matrix and the factor indicator matrix have a positive linear relationship. This degree is −1 if and only if corresponding elements between the feature indicator matrix and the factor indicator matrix have a negative linear relationship.

The example shows the number of days with good air quality is negatively correlated with the annual average concentration of each pollutant index. PM_2.5, PM₁₀ and O₃ are the main pollutants affecting air quality in northern Henan.

This paper introduces the negative matrix and constructs a model from the holistic perspective to measure the incidence direction and level between indicators. This model can effectively measure the incidence between the feature indicator and factor indicator by integrating information from the point, row, column and matrix.