Articles

<b>Background:</b> Sleep deprivation is increasingly prevalent among young adults due to academic, occupational, and social demands, making them susceptible to circadian disruption. Executive functioning—encompassing working memory, inhibitory control, and cognitive flexibility is essential for academic and professional success. This meta-analysis quantifies the effect of sleep deprivation on executive functioning in healthy young adults. Practical Implications: These findings highlight the need for evidence-based interventions such as university-level sleep education programs, flexible academic scheduling, and workplace policies promoting adequate sleep to optimize cognitive performance and productivity among young adults. <b>Methods: </b>Following PRISMA 2020 guidelines, PubMed, Scopus, PsycINFO, and Web of Science were searched (January 2000–March 2024) for studies assessing acute (<6 hours sleep or ≥24 hours total deprivation) or chronic (<6 hours/night over multiple days) sleep deprivation in young adults. Outcomes included validated executive function tests (e.g., Stroop, N-Back). Random-effects meta-analysis (Hedges’ g) was conducted using R (version 4.3.2) with metafor/meta packages. Heterogeneity (I², Q-test), sensitivity (leave-one-out), and publication bias (funnel plot, Egger’s test) were evaluated. <b>Results: </b>Out of 2,478 screened studies, 39 met inclusion criteria (n=4,578). Sleep deprivation had a moderate-to-large detrimental effect on executive functioning (Hedges’ g = -0.62, 95% CI [-0.78, -0.45], p<0.001). Subdomain analysis revealed greatest impairment in working memory (g = -0.71), followed by inhibitory control (g = -0.59) and cognitive flexibility (g = -0.49) (all p<0.001). Moderate heterogeneity was present (I² = 58%), with results robust to sensitivity analysis. Egger’s test indicated no significant publication bias (p=0.22). <b>Interpretation</b><b>: </b>Sleep deprivation significantly impairs executive functioning in young adults, especially working memory. Interventions improve sleep may enhance cognitive performance and should be integrated into public health strategies and educational policies. Future research should assess chronic restriction and individual vulnerability factors.

Deep learning approaches are very useful to enhance cybersecurity protocols for industry-integrated big data enterprise resource planning systems. This research study develops deep learning architectures of variational autoencoder, sparse autoencoder, and deep belief network for detecting anomalies, fraud, and preventing cybersecurity attacks. These cybersecurity issues occur in finance, human resources, supply chain, and marketing in the big data integrated ERP systems or cloud-based ERP systems. The main objectives of this creative research work are to identify the vulnerabilities in various ERP systems, databases, and the interconnected domains; to introduce a conceptual cybersecurity network model that incorporates variational autoencoders, sparse autoencoders, and deep belief networks; to evaluate the performance of the proposed cybersecurity model by employing the appropriate parameters with real-time and synthetic databases and simulated scenarios; and to validate the model performance by comparing it with traditional algorithms. A big data platform with an integrated business management system is known as an integrated ERP system, which plays an instrumental role in conducting business for various organizations in society. In recent times, as uncertainty and disparity increase, the cyber ecosystem becomes more complex, volatile, dynamic, and unpredictable. In particular, the number of cyber-attacks is increasing at an alarming rate; the resultant security breaches have a disruptive and disturbing effect on businesses around the world, with a loss of billions of dollars. To combat these threats, it is essential to develop a conceptual cybersecurity network model to secure systems by functioning as a mutually supporting and strengthening network model rather than working in isolation. In this dynamic and fluid environment, introducing a deep learning approach helps to support and prevent fraud and other illicit activities related to human resources and the supply chain, among others. Some cybersecurity vulnerabilities include, for example, database vulnerabilities, service level vulnerabilities, and system vulnerabilities, among others. The proposed methodology focuses only on database vulnerabilities, with the main aim of detecting and mitigating new potential vulnerabilities in other dependent domains as a future initiative.