Groundwater is undeniably crucial to people's lives, particularly in coastal regions. Therefore, it is imperative to address this vital water source strategically and implement a management plan to maintain its optimal state. The salinization of groundwater poses a significant challenge for coastal communities, stemming from factors like excessive groundwater extraction from coastal aquifers, reduced recharge, rising sea levels, climate change, and other causes. Saltwater intrusion (SWI) is a prevalent issue that needs attention, as it significantly threatens groundwater quantity and quality. SWI happens when saline water infiltrates coastal aquifers, contaminating freshwater supplies. This review article aims to define SWI, explore its causes and influencing factors, and discuss various monitoring techniques. Additionally, it examines different modeling methods and management tools, including remote sensing, field surveys, modeling approaches, and optimization techniques. To mitigate the adverse effects of SWI, several control measures are outlined, along with their pros and cons. The final section reviews previous SWI studies and case studies from the Nile Delta, Sinai Peninsula, and North-West coast in Egypt. These studies offer suggestions, adaptations, and mitigation measures for future research.

Digital forensics in cloud computing environments presents significant challenges due to the distributed nature of data storage, diverse security practices employed by service providers, and jurisdictional complexities. This study aims to develop a comprehensive framework and improved methodologies tailored for conducting digital forensic investigations in cloud settings. A pragmatic research philosophy integrating positivist and interpretivist paradigms guides an exploratory sequential mixed methods design. Qualitative methods, including case studies, expert interviews, and document analysis were used to explore key variables and themes. Findings inform hypotheses and survey instrument development for the subsequent quantitative phase involving structured surveys with digital forensics professionals, cloud providers, and law enforcement agencies, across the globe. The multi-method approach employs purposive and stratified random sampling techniques, targeting a sample of 100-150 participants, across the globe, for qualitative components and 300-500 for quantitative surveys. Qualitative data went through thematic and content analysis, while quantitative data were analysed using descriptive and inferential statistical methods facilitated by software such as SPSS and R. An integrated mixed methods analysis synthesizes and triangulates findings, enhancing validity, reliability, and comprehensiveness. Strict ethical protocols safeguard participant confidentiality and data privacy throughout the research process. This robust methodology contributed to the development of improved frameworks, guidelines, and best practices for digital forensics investigations in cloud computing, addressing legal and jurisdictional complexities in this rapidly evolving domain.

Given the limited access to medical facilities, impeding lockdown, and social isolation during the COVID-19 pandemic, an upsurge in postpartum depression among pregnant mothers in their puerperal period has become more apparent alongside an eventual increase in suicidal behavior. This article aimed to discuss the crucial aspects of different clinical case studies treated during recent periods throughout the COVID-19 pandemic via teleconsultations. We hoped to demonstrate tremendous opportunities for the application of healthcare via therapeutic tools online in telemedicine to manage such conditions in a developing country like Bangladesh with a severe scarcity of healthcare infrastructure and resources.

The unrelenting proliferation of data, entwined with the prevalence of mobile devices, has given birth to an unprecedented growth of information obscured by noise. With the Internet of Things and myriad endpoint devices generating vast volumes of sensitive and critical data, organizations are tasked with extracting actionable intelligence from this deluge. Governments and enterprises alike, even under pressure from regulatory boards, have strived to harness the power of data and leverage it to enhance safety and security, maximize performance, and mitigate risks. However, the adversaries themselves have capitalized on the unequal battle of big data and artificial intelligence to inflict widespread chaos. Therefore, the demand for big data analytics and AI/ML for high-fidelity intelligence, surveillance, and reconnaissance is at its highest. Today, in the cybersecurity realm, the detection of adverse incidents poses substantial challenges due to the sheer variety, volume, and velocity of deep packet inspection data. State-of-the-art detection techniques have fallen short of detecting the latest attacks after a big data breach incident. On the other hand, computational intelligence techniques such as machine learning have reignited the search for solutions for diverse monitoring problems. Recent advancements in AI/ML frameworks have the potential to analyze IoT/edge-generated big data in near real-time and assist risk assessment and mitigation through automated threat detection and modeling in the big data and AI/ML domain. Industry best practices and case studies are examined that endeavor to showcase how big data coupled with AI/ML unlocks new dimensions and capabilities in improved vigilance and monitoring, prediction of adverse incidents, intelligent modeling, and future uncertainty quantification by data resampling correction. All of these avenues lead to enhanced robustness, security, safety, and performance of industrial processes, computing, and infrastructures. A view of the future and how the potential threats due to the misuse of new technologies from bandwidth to IoT/edge, blockchain, AI, quantum, and autonomous fields is discussed. Cybersecurity is again playing out at a pace set by adversaries with low entry barriers and debilitating tools. The need for innovative solutions for defense from the emerging threat landscape, harnessing the power of new technologies and collaboration, is emphasized.