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Mobile Service of Big Data can be supported by the fused technologies of computing, communication and digital multimedia. The purpose of this paper is to propose new agent-based proactive migration method and system for Big Data Environment (BDE).

First, the authors have designed new relative fusion method for making decision based on fuzzy-neural network. The method can make the fusion belief degree to be improved. Then the authors have proposed agent-based proactive migrating method with service discovery and key frames selection strategy. Finally, the authors have designed the application system, which can support proactive seamless migration function for big data. The method has innovation in which mobile service task of big data can dynamically follow its mobile user from one device to another device.

The authors have proposed agent-based proactive migrating method with service discovery and key frames selection strategy. The method has innovation in which mobile service task of big data can dynamically follow its mobile user from one device to another device. The designed system is convenient to work and use during mobility, and which is useful or helpful for mobile user in the BDE.

The authors have clarified and realizes how to transfer service tasks among different distances in Big Data Environment (BDE). The authors have given a formal description and classification of the mobile service task, which is independent of the realization mechanism. In the designed and developed application system, the new idea adopts fuzzy-neural control theory to make decision for task-oriented proactive seamless migration application.

The paper's purpose is twofold: to provide a predictive model for estimating in‐hospital mortality rates after coronary artery bypass grafting (CABG) in Spanish autonomous regions (AR) after adjusting relevant factors; and to determine whether there is a difference between expected and observed mortality rates.

All patients registered in a minimum basic data set (MSBD) undergoing CABG between 2000 and 2004 were selected. After bivariate analysis to explore associations between in‐hospital death and other variables, a multivariate analysis using logistic regression was conducted. The predictive model was evaluated using calibration and discrimination techniques. Standardized mortality ratios by AR were calculated.

The expected Spanish in‐hospital mortality rate after CABG was 7.68 and the observed rate was 7.69 deaths per 100 operations. Discrimination obtained with the model resulted in an area under the curve of 0.70 (95 per cent CI, 0.69‐0.71). When each AR's mortality rate is calculated and compared with the observed rate, some ARs present an observed mortality rate higher or lower than the expected rate according to adjusted variables in the model.

The MSBD registry does not contain patients' critical data, such as arterial damage severity, or in which hospital procedures were performed.

There are factors related to individual patient variation, financial resources or healthcare quality in different ARs, which should be investigated in follow‐up studies.

The paper shows that, although the global expected mortality rate is almost the same as the observed Spanish mortality rate, this similarity disappears when AR rates are compared.

This paper is motivated by a concern about the ability of the average Malaysian income to catch up with the rapidly increasing house prices in Peninsular Malaysia. Financial innovation in financial system now regards houses as a financial asset and speculation vehicle. Therefore, a house purchase is made to acquire not merely a necessity but also a financial asset which can generate future returns. Given the problems in the housing market, this paper aims to examine the determinants of house prices in Malaysia, including those such as income, population, foreign inflow and speculation.

This study adopts panel data analyses, namely, the fixed effect model (FEM) and the pooled mean group (PMG), and uses data at state level in quarterly frequency, spanning from 2005Q1 to 2013Q4.

Based on the results of FEM, these determinants influence house prices significantly. Moreover, the PMG results suggest that there is convergence in the model, which are indicated by the significant and negative sign of the error correction term. In conclusion, the rapidly increasing house price is not caused by speculation activities in the housing market. More precisely, Malaysian income is capable of catching up with the increasing house prices.

As income remains to be one of the major drivers in influencing Malaysian house price, Malaysian Government shall continue the policies of supply low cost houses to the low-income groups and My First Home Scheme (SRP) by offering less stringent rules in applying house loan for the first-time house buyers.

This study used the actual data of foreign housing purchase obtained from Malaysia Valuation and Property Services Department to represent foreign inflow; therefore, the results will reflect the impact of foreign inflow in a better manner.

In the modern digital realm, while artificial intelligence (AI) technologies pave the way for unprecedented opportunities, they also give rise to intricate cybersecurity issues, including threats like deepfakes and unanticipated AI-induced risks. This study aims to address the insufficient exploration of AI cybersecurity awareness in the current literature.

Using in-depth surveys across varied sectors (N = 150), the authors analyzed the correlation between the absence of AI risk content in organizational cybersecurity awareness programs and its impact on employee awareness.

A significant AI-risk knowledge void was observed among users: despite frequent interaction with AI tools, a majority remain unaware of specialized AI threats. A pronounced knowledge difference existed between those that are trained in AI risks and those who are not, more apparent among non-technical personnel and sectors managing sensitive information.

This study paves the way for thorough research, allowing for refinement of awareness initiatives tailored to distinct industries.

It is imperative for organizations to emphasize AI risk training, especially among non-technical staff. Industries handling sensitive data should be at the forefront.

Ensuring employees are aware of AI-related threats can lead to a safer digital environment for both organizations and society at large, given the pervasive nature of AI in everyday life.

Unlike most of the papers about AI risks, the authors do not trust subjective data from second hand papers, but use objective authentic data from the authors’ own up-to-date anonymous survey.

Though widely recognized as essential for improving work performance across various domains, self-efficacy’s specific role in managing construction workforces remains understudied. This knowledge gap restricts our ability to uncover new factors that enhance workforce management effectiveness and ultimately boost construction labor productivity (CLP). To address this, our study proposes and tests a novel model. This model explores the impact mechanism of self-efficacy on CLP by investigating the mediating role of work motivation. By delving into this crucial yet underexplored area, we aim to provide valuable insights for construction project managers and researchers alike, paving the way for more effective workforce management strategies and consequently, improved CLP.

This study utilizes a mixed-method approach, incorporating both qualitative and quantitative methodologies. Data from 112 rebar workers at five construction sites in Vietnam underwent analysis using Cronbach’s alpha, exploratory factor analysis (EFA), confirmatory factor analysis (CFA) and structural equation modeling (SEM) to examine the novel research model.

The results indicate a positive and significant association between self-efficacy and CLP. Additionally, work motivation emerged as a full mediator in the relationship between self-efficacy and CLP. Specifically, individuals with higher self-efficacy set ambitious goals and invest more effort in their pursuit, leading to increased work motivation and, ultimately, heightened productivity levels.

The significant implications of the current study extend to construction managers and policymakers alike. Construction managers can leverage the findings to devise targeted interventions aimed at enhancing the self-efficacy and work motivation of their workforce, potentially resulting in noteworthy enhancements in CLP. Policymakers, too, can benefit from these findings by formulating policies that actively support the cultivation of self-efficacy and work motivation among construction workers. Such policies have the potential to foster a more productive and efficient construction industry, aligning with the broader goals of workforce development and industry enhancement.

This study expands existing knowledge by identifying the important role of self-efficacy in work performance enhancement and the mediating role of work motivation in terms of these relationships.

Sustainable supply chain management (SSCM) practices and green entrepreneurial preference (GEP) have gained increasing attention from academicians; however, their impacts on business' competitive performance (BCP) post-coronavirus disease of 2019 (COVID-19) remain unclear. Although SSCM is vital for supporting BCP, the previous publications indicate the absence of significant relationships among GEP, SSCM and BCP. This study tries to fill this literature gap by investigating if GEP and SSCM can shape BCP. This study also suggests the moderation effect of digital innovations such as artificial intelligence and big data analytics (AIBD) on those relationships from a COVID-19 viewpoint.

Data were collected from 245 Halal food firms in Yemen, and the research framework was assessed using structural equation modeling (SEM).

The empirical findings show that there are significant impacts of GEP on SSCM and subsequently on BCP. The findings also reveal that SSCM practice mediates GEP-BCP link. Besides, digital innovations such as AIBD positively moderate the link of GEP-SSCM.

This study is the first attempt that advises Halal food firms to formally adopt GEP, SSCM and digital innovations to boost BCP, especially in uncertain times like post-COVID-19. Unlike earlier studies that observe SSCM usage as a direct predictor of firm performance, this study delivers an innovative insight that digital innovations can assist in GEP and SSCM incorporation in the in-house operations of the firms post-COVID-19.

Electricity plays an essential role in nations' economic development. However, coal and renewables currently play an important part in electricity production in major world economies. The current study aims to forecast the electricity production from coal and renewables in the USA, China and Japan.

Two intelligent grey forecasting models – optimized discrete grey forecasting model DGM (1,1,α), and optimized even grey forecasting model EGM (1,1,α,θ) – are used to forecast electricity production. Also, the accuracy of the forecasts is measured through the mean absolute percentage error (MAPE).

Coal-powered electricity production is decreasing, while renewable energy production is increasing in the major economies (MEs). China's coal-fired electricity production continues to grow. The forecasts generated by the two grey models are more accurate than that by the classical models EGM (1,1) and DGM (1,1) and the exponential triple smoothing (ETS).

The study confirms the reliability and validity of grey forecasting models to predict electricity production in the MEs.

The mass exodus of the professional healthcare workforce has become a cankerworm for a developing nation like Nigeria, and this worsens the already depleted healthcare systems in underdeveloped nation. This study investigated the rationale behind medical workers' brain-drain syndrome and the quality healthcare delivery in the Nigerian public healthcare sector.

To stimulate an understanding of the effect of the phenomenon called brain drain, the study adopted a diagnostic research design to survey the public healthcare personnel in government hospitals. The study administered a battery of adapted research scales of different measures to confirm the variables of interest of this study on a probability sampling strategy. The study surveyed 450 public healthcare sector employees from four government hospitals to gather pertinent data. The study used a structural equation model (SEM) and artificial neural networks (ANNs) to analyse the collected data from the medical personnel of government hospitals.

The findings of this study are significant as postulated. The study discovered that poor quality worklife experienced by Nigerian medical personnel was attributed to the brain-drain effect and poor healthcare delivery. The study further demonstrated that job dissatisfaction suffered among the public healthcare workforce forced the workforce to migrate to the international labour market, and this same factor is a reason for poor healthcare delivery. Lastly, the study discovered that inadequate remuneration and pay discouraged Nigerian professionals and allied healthcare workers from being productive and ultimately pushed them to the global market.

Practically, this study has shown three major elements that caused the mass movement of Nigerian healthcare personnel to other countries of the world and that seems novel given the peculiarity of the Nigerian labour market. The study is original and novel as much study has not been put forward in the public healthcare sector in Nigeria concerning this phenomenon.

The Sertão, located in the Northeastern region of Brazil, is the most populous semi-arid region in the world. The region also faces the highest rates of poverty, food insecurity and climate risks in this country. Basic economic activities, such as extensive livestock and dairy farming, tend to be mainly affected by the increasing temperatures and recurrent droughts taking place in the past decades. This paper aims to analyze farmers’ responses to climatic variability in the Sertão.

Analyses are based on farm-level data of the Agricultural Census 2006 and on historical climate data gathered by meteorological stations. The climate impacts and the effectiveness of adaptive strategies are compared between three groups of farms, which discriminate different levels of social and environmental vulnerability. Four production functions are modeled (milk, cattle, goat and sheep) accounting for sample selectivity bias.

In response to increasing temperatures, farmers tend to shift their activities mainly to cattle and dairy farming. But the overall productivity tends to reduce with the recurrence of droughts. Decreasing precipitation affects mainly the production of milk of smallholder family farmers and the cattle herd of non-family farmers.

Analyses do not account for short- and medium-run productive impacts of extreme droughts, which usually have devastating socioeconomic effects in the region.

Smallholder family farmers are the most vulnerable group who deserve more social and technical intervention, as they lack basic social and technological resources that can greatly improve their productivities and overcome the impacts of decreasing precipitation.