The Zika virus is a severe public health threat all across the world, owing to its spreading mechanism through Aedes mosquitoes and its ability to result in extreme neurological diseases, which include the congenital Zika syndrome and the Guillain-Barré syndrome, amongst others. Conventional monitoring techniques often fail because many asymptomatic cases render early diagnosis challenging. Machine learning (ML) techniques can be seen as a constructive development in addressing this challenge, which entails predicting and tracking the spread of diseases such as Zika through extensive and complex datasets. Data analytic ML systems also enhance early warning systems and situational uplift by using data from social media, climate history, and genetics. This helps reasonably to predict the mosquito population biologically and the environmental factors that favor the spread of the virus for a more practical approach from the public health sector. Over and above, some issues are still pending, especially regarding the quality of data, understanding the models and how to apply such models within the current health systems. These factors must be solved to implement ML successfully in surveillance practice. This review provides an overview of the issue, stating the potential of machine learning applications in the development of public health, whose actions focus on Zika and other diseases transmitted by vectors.

Air pollution is a critical environmental issue that threatens almost the world, and public health, ecosystems, and the sustainability of cities are affected by the severe impacts of air pollution. Urbanization and industrialization have been on the run, with escalating pollution levels. Hence, air monitoring and air quality prediction are necessary for such challenges. This review discusses advanced machine learning (ML), deep learning (DL) techniques, and IoT-based study hybrid frameworks for air-quality prediction in urban settings. Integration of different data sets such as meteorological parameters, concentrations of pollutants, and data from satellite imagery, these technologies provide strong and scalable solutions for real-time monitoring and forecasting. Some of the advancements include the use of IoT-enabled sensors, the use of convolutional and recurrent neural networks, and the development of location-specific predictive models. Despite significant evolution, several challenges of data sparsity, computational requirements, and model adaptability remain. This paper casts the technologies as transforming cities into smart and green cities and advancing the cause for continuous innovation and interdisciplinary collaboration to strengthen their effectiveness. These findings add to the advancement of knowledge on air quality prediction methodologies and their crucial role in sustainable urban development.

FRT is acknowledged as one of the successful advancements of biometric applications in security, surveillance, health care and innovative solutions. More so, the past decade has seen improvements in deep learning, pre-trained Neural Network Convolutional Neural Networks (CNNs), and combining methods such as ensembles, which have highly improved the FRT's Accuracy and efficiency. Nonetheless, several issues remain – facial expression, illumination, demographic biases or adversarial and backdoor threats. Such limitations require new approaches and tools to enhance FRT's reliability and ethical use. The current review also presents ethical concerns and the social consequences of using FRT.

Research on customer citizenship behaviour in the airline industry has received limited attention, with few studies exploring its significance. This study examines the influence of relationship quality on customer citizenship behaviour and the impact of customer perceived benefits on relationship quality in the Pakistani airline industry. Additionally, it investigates the mediating role of relationship quality and the moderating effect of conspicuous consumption in these relationships. Data were collected from 360 passengers of Pakistani airlines using cluster sampling, systematic intercept survey and analysed through partial least squares structural equation modelling (PLS-SEM) via Smart PLS. The findings indicate that customer citizenship behaviour is significantly influenced by relationship quality and customer perceived benefits, including functional and relational benefits. Relationship quality partially mediates the link between functional benefits and customer citizenship behaviour, while it fully mediates the relationship between relational benefits and customer citizenship behaviour. Furthermore, conspicuous consumption moderates the association between relationship quality and customer citizenship behaviour. This study contributes to the literature by employing social exchange theory as the primary framework and integrating the theory of conspicuous consumption within the airline industry context. The insights gained can help managers develop strategies to strengthen customer relationships, ultimately fostering customer citizenship behaviour and enhancing business success.

As optimization tasks become increasingly complex, particularly in feature selection, there is a growing need for algorithms capable of robustly balancing exploration and exploitation. In this work, we propose the Binary Swordfish Movement Optimization Algorithm (BSMOA), inspired by the synchronized and agile movements of swordfish. BSMOA employs adaptive parameters to navigate high-dimensional search spaces through dynamic exploration, exploitation, and elimination stages. Extensive experiments on benchmark datasets demonstrate that BSMOA outperforms state-of-the-art algorithms, including bHHO, bGWO, and bPSO, regarding average error, feature reduction, and computational efficiency. Key contributions of BSMOA include its improved balance between global and local search and its ability to achieve stable and accurate feature selection. This work has broad implications for applications in machine learning, engineering design, and other optimization domains, providing a reliable tool for tackling challenging binary optimization problems.

This study intends to examine how personality factors affect people's risk-aversion behavior and investment intentions, as well as how risk aversion mediates the link between personality traits and investment intentions, and how gender acts as a moderator. Individuals have five different personality qualities in this study, which are openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism, while short-term and long-term investment intents are employed as investment intentions. The data is gathered from 750 participants (students) with backgrounds in business and finance. The PLS-SEM method is used in the study to conduct the analysis of the data. The results support that investment intentions both short-term and long-term are positively associated with openness to experience, agreeableness, and conscientiousness. Long-term investment, however, is inversely correlated with neuroticism and risk aversion and positively correlated with extraversion. The relationship between extraversion and short-term investment intentions is fully mediated by risk aversion behavior, whereas the relationship between agreeableness and long-term investment intentions has no mediating effect. The study also discovers the partial mediation effect of risk aversion on the relationship between personality traits and investment intentions. The findings also showed that gender significantly moderates two relationships: the association between conscientiousness and long-term intentions and the relationship between openness to experience.