Malaria, as one of the most serious infectious diseases causing public health problems in the world, affects about two-thirds of the world population, with estimated resultant deaths close to a million annually. The effects of this disease are much more profound in third world countries, which have very limited medical resources. When an intense outbreak occurs, most of these countries cannot cope with the high number of patients due to the lack of medicine, equipment and hospital facilities. The prevention or reduction of the risk factor of this disease is very challenging, especially in third world countries, due to poverty and economic insatiability. Technology can offer alternative solutions by providing early detection mechanisms that help to control the spread of the disease and allow the management of treatment facilities in advance to ensure a more timely health service, which can save thousands of lives. In this study, we have deployed an intelligent malaria outbreak early warning system, which is a mobile application that predicts malaria outbreak based on climatic factors using machine learning algorithms. The system will help hospitals, healthcare providers, and health organizations take precautions in time and utilize their resources in case of emergency. To our best knowledge, the system developed in this paper is the first publicly available application. Since confounding effects of climatic factors have a greater influence on the incidence of malaria, we have also conducted extensive research on exploring a new ecosystem model for the assessment of hidden ecological factors and identified three confounding factors that significantly influence the malaria incidence. Additionally, we deploy a smart healthcare application; this paper also makes a significant contribution by identifying hidden ecological factors of malaria.

Purpose: The authors empirically investigate the association between acquisition, ownership structure and accrual earnings management (AEM) on real earnings management (REM) using Gulf Cooperation Council (GCC)-listed firms' context.Design/methodology/approach: The authors' sample consists of 1,892 firm-year observations for the period from 2007–2017, and the authors adopt a panel data approach in investigating the interrelationships in this study. The authors employ different econometrics approach to test the authors' hypotheses.Findings: The findings reveal that acquiring companies engage more in AEM if compared to REM. In terms of ownership structure, institutional ownership and state ownership mitigate the engagement in REM, whereas foreign ownership is found to be an ineffective mechanism in reducing engagement in REM. The authors report similar findings on ownership structure for AEM. The authors also find that the GCC firms engage more in REM when the firms engage in AEM, suggesting a complementary relation between these two earnings management techniques. These findings are robust after controlling for different aspects including any endogeneity issue in the authors' models.Originality/value: The authors' research highlights the importance of understanding REM and AEM dynamics in GCC context. Also, the authors' findings on ownership structure suggest that GCC-listed firms can gain from institutional and state ownership which restricts earnings management, improving firm transparency and subsequently impacting firm performance.

Engineering is suffering a significant change driven by the integration of artificial intelligence (AI) into engineering optimization in design, analysis, and operational efficiency across numerous disciplines. This review synthesizes the current landscape of AI-driven optimization methodologies and their impacts on engineering applications. In the literature, several frameworks for AI-based engineering optimization have been identified: (1) machine learning models are trained as objective and constraint functions for optimization problems; (2) machine learning techniques are used to improve the efficiency of optimization algorithms; (3) neural networks approximate complex simulation models such as finite element analysis (FEA) and computational fluid dynamics (CFD) and this makes it possible to optimize complex engineering systems; and (4) machine learning predicts design parameters/initial solutions that are subsequently optimized. Fundamental AI technologies, such as artificial neural networks and deep learning, are examined in this paper, along with commonly used AI-assisted optimization strategies. Representative applications of AI-driven engineering optimization have been surveyed in this paper across multiple fields, including mechanical and aerospace engineering, civil engineering, electrical and computer engineering, chemical and materials engineering, energy and management. These studies demonstrate how AI enables significant improvements in computational modelling, predictive analytics, and generative design while effectively handling complex multi-objective constraints. Despite these advancements, challenges remain in areas such as data quality, model interpretability, and computational cost, particularly in real-time environments. Through a systematic analysis of recent case studies and emerging trends, this paper provides a critical assessment of the state of the art and identifies promising research directions, including physics-informed neural networks, digital twins, and human–AI collaborative optimization frameworks. The findings highlight AI’s potential to redefine engineering optimization paradigms, while emphasizing the need for robust, scalable, and ethically aligned implementations.

We are mainly interested in the impact of acquisition, ownership structure, and national governance quality on accrual earnings management (AEM) in the GCC listed companies’ context. Our sample is composed of 3210 firm-year observations for the period from 2007 to 2017. We employ panel data models in investigating the determinants of AEM for acquiring and non-acquiring firms. The findings reveal that acquiring firms involve more in earnings management than non-acquiring firms and that acquiring firms involve in AEM through income increasing rather than income decreasing. Institutional and state ownership are found to be an efficient tool in restraining companies’ engagement in earnings management whereas foreign ownership is shown to have no impact. National governance quality is found to be an efficient mechanism to reduce the companies’ engagement in earnings management. The study has both organizational and policy implications. In the organizational context, the GCC listed companies could benefit from attracting institutional and state owners to mitigate earnings management and therefore enhance firm performance. In the legislative context, policy makers are encouraged to concentrate on developing national governance systems to mitigate AEM.

This paper provides insights on how foreign bank entry modes (acquisition vs. greenfield investment) in an emerging market (Turkey) influenced bank strategies during the 2007–2009 global financial crisis. Using a comprehensive dataset comprising twenty-nine accounting variables from Turkish banks' financial statements during 2005–2010, we find important differences between foreign-acquired banks and foreign bank branches in lending and sourcing funds. We find that foreign bank branches continued to support international trade by issuing import loans during 2007–2009 global financial crisis, whereas foreign-acquired banks focused on issuing consumer and credit card loans. In terms of bank sourcing funds, we find that foreign-acquired banks were able to continue to use foreign currency deposits of Turkish residents and local interbank funding including participation (Islamic) banks. Foreign bank branches, on the other hand, relied on sourcing funds from international interbank funding and foreign currency deposits of residents abroad, which led to the necessity for them to change their strategies because of funding shortage in international markets. Our results show that the presence of foreign banks in Turkish banking sector enabled the continuity of bank lending activities in host market during the turmoil of 2007–2009 global financial crisis. Our findings on foreign bank entry mode provide new evidence and have important implications for both policy makers and practitioners in emerging markets.

In this paper, we consider the intertwined challenges that arise for process optimisation within smart manufacturing, reviewing fundamental aspects including concept drift in relation to the dynamic nature of manufacturing, the prevalence of heterogeneous data streams, integration of prior domain knowledge, and trustworthiness and transparency of machine learning models. We investigate the real-world nature of these challenges and the proposed solutions in the literature for each of the aforementioned challenges. Whilst other surveys exist for optimisation in the field of smart manufacturing, there is a lack of a comprehensive review addressing the underlying conceptual issues associated with the deployment and adoption of machine learning in smart manufacturing.