Emerging Minds Journal for Student Research

Limitations of Digital Forensic Investigation in Nigeria’s Rural Comunities

2025-10-23T02:04:43+03:00

Digital forensic investigation has become an indispensable tool for contemporary criminal justice systems. In Nigeria, however, rural communities remain significantly disadvantaged in the application of digital forensics due to deep-seated infrastructural, institutional, and socio-cultural limitations. This study employs a literature-based methodology, drawing on peer-reviewed scholarships, government reports, and comparative studies from other developing countries, to critically examine these challenges. The findings indicate that inadequate infrastructure such as unreliable electricity, poor telecommunication networks, limited internet penetration, and weak transportation systems constitutes the primary barrier to effective forensic practice in rural areas. Equally significant are the shortages of skilled personnel and inadequate training opportunities among law enforcement, which frequently lead to the mishandling of digital evidence and diminished
prosecutorial outcomes. Institutional fragmentation, lack of rural-specific frameworks, and funding disparities further constrain forensic capacity. Socio-cultural factors, including low digital literacy, mistrust in techno-logical processes, and judicial underappreciation of digital evidence, exacerbate the problem. Comparative lessons from Kenya, India, Brazil and South Africa demonstrate that policy interventions, rural-based training programs, and innovative mobile forensic laboratories can mitigate these obstacles. For Nigeria, these insights highlight the need to decentralize forensic institutions, strengthen infrastructure, and adopt policies tailored to rural realities. The study concludes that unless targeted reforms are enacted, digital forensics in Nigeria will remain urban-centric, leaving rural communities vulnerable to cybercrime and digital exploitation. By situating Nigeria’s challenges within global debates on digital justice, the paper underscores the urgency of
inclusive reforms to bridge the rural-urban divide in forensic investigation.

The Evaluation of the Efficacy and Safety of the Use of Psilocybin in the Treatment of Adults with Treatment-Resistant Depression

2026-01-08T23:55:44+03:00

Treatment-resistant depression (TRD) has been well-researched within scientific literature, although the therapeutic value of psilocybin is not fully understood. The aim of this systematic review is to determine a stable and effective dosage unit to inform health professionals of the benefits of psilocybin, using peer-reviewed literature and meta-analysis. The review will also compare selective serotonin reuptake inhibitors (SSRIs) with psychotherapy to draw conclusions and recommendations of psilocybin therapy to improve day-to-day living for affected patients. PubMed and the University of Portsmouth Discovery online database (EBSCOhost) were individually utilised from December 2024 to March 2025. Five open-label studies and 2 randomised controlled trials (RCTs) were selected to assess psilocybin efficacy and safety. Appraisal checklists along with search criteria were used to determine eligibility and reliability of these data. The random-effects meta-analyses demonstrated that psilocybin at 25 mg within specific integrated sessions was effective at treating TRD compared to 10 mg and 1 mg by comparing clinical trials between two doses and single doses. Psilocybin at 25 mg was found to significantly reduce patients’ depressive severity compared to the baseline, which was prevalent in the two-dose studies (n = 5) compared to the single-dose studies (n = 2), due to the number of studies produced. The overall evidence suggests that psilocybin is an effective therapeutic for treatment-resistant depression, with a dosage unit of 25 mg administered as a single capsule per dosing session, with one dose per clinical session. Limitations to the evidence and this review have affected the overall results; therefore, more relevant studies are needed.

A Machine Learning Approach to Measuring Time Delays in Microlensed Type la Supernovae

2026-01-12T01:49:11+03:00

The discrepancy between early and late-Universe measurements of the Hubble constant, commonly referred to as the Hubble tension, remains one of the most significant open problems in modern cosmology. Strongly-lensed Type Ia supernovae provide a promising and independent probe of the cosmic expansion rate through time-delay cosmography, but their practical application is hindered by microlensing distortions and limited observational cadence. In this work, we present a machine-learning–based method for estimating time delays in microlensed Type Ia supernova light curves. Using realistically simulated lensed supernova datasets, we train and evaluate a Random Forest regression model to recover time delays between unresolved image pairs. We show that data-driven approaches can mitigate microlensing-induced biases relative to traditional cross-correlation methods and recover delays with improved robustness under challenging observational conditions. While this study does not perform a full cosmological inference, the results demonstrate the potential role of machine-learning techniques in future time-delay cosmography pipelines aimed at addressing the Hubble constant tension.