SIMAD University

The rapid integration of ChatGPT into educational and research contexts has generated significant debate regarding its pedagogical value, methodological reliability, and ethical implications. This study employs a SWOT (Strengths, Weaknesses, Opportunities, and Threats) analytical framework to critically examine the multifaceted impact of ChatGPT on higher education and scientific research, drawing on a systematic review of secondary literature. The analysis identifies key strengths, including enhanced research efficiency, academic writing support, multilingual accessibility, and potential for personalized learning. However, these advantages are counterbalanced by notable weaknesses, particularly concerns related to fabricated citations, response inaccuracy, algorithmic bias, and limited higher-order reasoning capabilities. The findings further reveal important opportunities for innovation, such as AI-assisted curriculum development, adaptive learning pathways, and professional development support, provided that robust institutional oversight mechanisms are in place. At the same time, significant threats emerge, including increased risks of academic misconduct, erosion of scholarly integrity, discriminatory outputs, and potential overreliance that may weaken critical thinking skills. In response, the study advocates structured governance frameworks, AI literacy initiatives, and assessment reforms designed to preserve academic rigor while enabling responsible innovation. By offering a theoretically grounded and critically balanced synthesis, this review contributes to the evolving discourse on generative AI in academia and provides a foundation for future empirical investigation and policy development. • ChatGPT improves research productivity, accuracy, writing, and supports personalized, inclusive learning. • Weaknesses include difficulty judging quality, bias risks, misleading outputs, and limited higher-order reasoning. • Opportunities include creating learning materials, enabling complex learning, and supporting professional development. • Threats include plagiarism, academic dishonesty, bias, and reduced critical thinking from AI overreliance. • The study recommends guidelines, AI literacy, and assessment reform for responsible ChatGPT use in education and research.

The quest for sustainable energy systems that function in severe settings has generated interest in microbial fuel cells (MFCs) utilizing radiation-resistant bacteria. These extremophiles, such as Deinococcus radiodurans, Thermococcus gammatolerans, and Rubrobacter radiotolerans, exhibit remarkable DNA repair systems, antioxidative defenses, and protein-protection mechanisms that facilitate prolonged metabolic activity in the presence of strong ionizing radiation. This review aims to critically assess recent advances in electrode nanomaterials, biofilm engineering, and system optimization approaches to improve microbial fuel cell (MFC) performance. In contrast to traditional electrogens like Shewanella oneidensis and Geobacter sulfurreducens, which quickly deteriorate under radiation stress, these organisms preserve their extracellular electron transfer (EET) capability and biofilm integrity in adverse conditions. Their incorporation into MFCs broadens prospective uses in space exploration, radioactive waste treatment, deep subterranean bioenergy extraction, and autonomous environmental surveillance. In interplanetary environments, radiation-resistant microbial fuel cells could supply continuous electricity for life-support systems and instruments in scenarios where solar energy is inconsistent. In nuclear reactors and polluted locations, they could concurrently immobilize radionuclides and produce power for distant sensors. Deep systems may utilize native extremophiles to facilitate seismic and hydrogeological monitoring, whilst autonomous biosensors could function in disaster areas for prolonged environmental surveillance. By integrating extremophile resilience with MFC adaptability, these systems provide a self-sustaining, low-maintenance power source that operates effectively in environments where traditional bio-electrochemical technologies are ineffective. The review examines microbial adaptations to radiation, material degradation in radioactive conditions, electron transport pathways, and the contribution of extremophiles to the enhancement of MFC performance. Utilizing these creatures for energy production signifies a new frontier in bioenergy research, with considerable ramifications for planetary science, nuclear safety, and sustainable infrastructure in harsh conditions.

Somalia is experiencing rapid urban expansion alongside rising electricity demand, posing significant challenges for sustainable development and infrastructure planning in a context characterized by severe data limitations. Empirical evidence on the urban–energy nexus in Somalia remains scarce, particularly studies employing formal time-series forecasting techniques. This study addresses this gap by applying Autoregressive Integrated Moving Average (ARIMA) models to forecast urbanization and electricity consumption using quarterly data obtained from international statistical sources. Following the Box–Jenkins methodology and information criteria, ARIMA (4,1,2) and ARIMA (5,1,2) are identified as the optimal models for urbanization and electricity consumption, respectively. Diagnostic tests confirm model adequacy and stability. The forecasts indicate a sustained increase in both urbanization and electricity consumption through 2030, reflecting rising population density and escalating energy needs. The study establishes a robust univariate forecasting benchmark for Somalia and provides empirical insights to inform integrated urban–energy policies and sustainable infrastructure investment. Future research may extend this analysis by incorporating multivariate or hybrid forecasting approaches to enhance policy relevance.

Somalia's recurring hunger crises are often viewed as emergencies that can be addressed by emergency food aid. In reality, chronic food insecurity now stems from structural fault lines-poverty, conflict, displacement, climate volatility, weak land tenure, under-resourced ministries, and fractured markets-that short-term food aid cannot repair. Today, 4.3 million Somalis face acute food insecurity, and more than 700,000 children are acutely malnourished, even though the country boasts a 3333-km coastline, fertile river valleys and a rich tradition of pastoralism and farming. Cycles of drought have decimated herds, forced pastoralists into urban slums, and eroded coping mechanisms, while conflict blocks access to productive land and drives up dependence on imports for over 80% of staple foods. Climate change is tightening this vise through erratic rains, scorching heat, and flash floods. To achieve sustainable food security, Somalia must shift from emergency relief to long-term investments in resilient and inclusive food systems. That means channelling at least five percent of public expenditure into irrigation, water harvesting, and extension services; securing land rights to spur on-farm investment; scaling early-warning and climate-smart technologies; and rebuilding rural infrastructure, cold chains, and digital marketplaces so smallholders can reach consumers. Nutrition gains hinge on diversifying production of fruits, vegetables, legumes, and animal proteins, linked to community health and education programs. A national Food Systems Coordination Council should align humanitarian and development actors with regional frameworks, while public-private partnerships unlock finance for Somali agribusiness innovations. Ending hunger is not only a humanitarian obligation; it is a prerequisite for stability, growth, and social justice. With political will, integrated governance, and sustained investment, Somalia can move beyond food aid, harness its land and heritage, and lay the foundation for a resilient, self-reliant future.

Institutional delivery dropout (IDD), defined as delivery outside a health facility despite attending antenatal care (ANC), remains a significant barrier to reducing maternal mortality in Nigeria. Traditional statistical models often fall short of capturing the complex, non-linear interactions among the socio-demographic factors that drive this critical health behavior. Using a comprehensive dataset of 16,100 women from the 2018 Nigeria Demographic and Health Survey (NDHS), we applied and compared seven diverse machine learning (ML) algorithms, including models such as Support Vector Machine (SVM), Gradient Boosting (GB), and Extreme Gradient Boosting (XGBoost). The model performance was systematically evaluated using metrics such as accuracy, Area Under the Receiver Operating Characteristic curve (AUROC), F1-score, and detailed confusion matrices. Furthermore, SHapley Additive explanations (SHAP) were used to provide transparent interpretations of feature importance and predictive contributions. Gradient Boosting was the best-performing model, achieving the highest F1-score (0.755) and AUROC (0.82). SVM achieved the highest accuracy (0.740) and recall (0.780). SHAP identified education level, household wealth, and religion as strong predictors of IDD. The performance metrics reported with confidence intervals showed modest variability across the models. Machine learning approaches were effective in identifying women at an increased risk of institutional delivery dropout. SHAP analysis provides insights into the key sociodemographic predictors of IDD, highlighting the value of interpretable ML methods in maternal health research.

Abstract Vector-borne and zoonotic diseases (VBZDs) remain a critical global health challenge, disproportionately affecting low- and middle-income countries and driving significant morbidity, mortality, and economic loss. This review synthesizes emerging evidence on nano-enabled smart drug delivery system (SDDS) within the One Health framework, emphasizing their role in advancing targeted therapy, controlled release, and theranostics for VBZD management. Nano-enabled platforms such as liposomes, polymeric nanoparticles, nanogels, metal–organic frameworks, and biomimetic carriers are designed for stimuli-responsive and site-specific delivery, enhancing therapeutic precision, lowering dosing frequency, and reducing the risk of drug resistance. Controlled-release formulations of agents such as artemisinin derivatives, amphotericin B, and ivermectin have demonstrated superior efficacy in the treatment of malaria and leishmaniasis, and in vector control applications. Targeted delivery strategies extend beyond human therapies to address viral reservoirs, infected macrophages, and arthropod vectors, highlighting the cross-species applications central to One Health. Theranostic innovations combine imaging and therapy to enable early disease detection, real-time monitoring, and the design of adaptive treatment strategies. Applications span human, animal, and environmental domains, from nanovaccines and veterinary antimicrobials to nanoparticle-based larvicides and biosensors for vector surveillance. Future directions underscore the integration of AI-driven nanodesign, scalable and eco-friendly synthesis, and harmonized regulatory frameworks to increase accessibility in resource-limited settings. Although clinical trials are limited, ongoing translational efforts indicate the transformative potential of nanomedicine in VBZD control. Ultimately, integrating nano-enabled SDDS into a One Health framework provides a sustainable and collaborative approach to reduce the global burden of zoonotic and vector-borne diseases.

The measles vaccine represents one of the most efficacious means of safeguarding pediatric populations against the measles virus. Empirical evidence consistently demonstrates that over 99% of recipients who complete the two-dose regimen achieve immunity to the disease. Despite this high efficacy, suboptimal vaccine coverage persists as a significant impediment to the containment and eventual eradication of measles. To augment immunization coverage, routine vaccination programs are often supplemented by second-dose opportunities administered through Supplemental Immunization Activities (SIAs), particularly in countries with a high measles burden. This study aims to evaluate the measles vaccination coverage attained after the nationwide SIAs in May 2025 as well as vaccination coverage prior to SIAs. This study was conducted from May 26th to June 5th, 2025. A two-stage cluster sampling design was utilized, where 938 clusters were randomly selected from a national sampling frame of 6,936. This sampling frame covered all accessible districts, villages, and nomadic areas throughout the country. The sample size was calculated using the WHO-2018 manual, resulting in the selection of 134 clusters and 1,780 households from each state, culminating in 12,832 interviews overall. A total of 355 interviewers, equipped with the KoboCollect app for digital data collection, conducted surveys. We calculated vaccine coverage during supplemental immunization activities (SIAs) and prior to SIAs (which means routine immunization). To calculate the national coverage, a survey weighting methodology was applied. Specific procedures were used to determine cluster and household weights, following the formula outlined in the WHO Vaccination Coverage Cluster Survey 2018. Both descriptive and inferential statistics were applied. Descriptive statistics, including frequency counts and proportions, enabled us to summarize the general attributes of the sample. Conversely, inferential statistics were used to estimate national vaccine coverage, featuring point estimates and confidence intervals (Wilson 95% Confidence Intervals), with the application of survey weights. Ethical approval was obtained from the Somali National University Ethical Committee. Of 17,700 children across seven states, 46.5% were girls and 53.5% were boys. Most children (56.2%) lived in urban areas, followed by 25.8% in rural areas and 14.2% in nomadic areas. Additionally, 3.76% of children lived in Internally Displaced Persons (IDP) camps. Weighted coverage estimates indicate that 73% (CI: 68.14-77.3) of children in Somalia received the measles vaccine during SIAs. Regarding the regional variation of vaccine coverage, the highest coverage was observed in Somaliland (87.4%, CI: 81,7-91.3) and Puntland (87.1%, CI: 82.7-90.5), while the lowest were found in Hirshabelle (48%, CI: 32.6-63.8) and Southwest (50%, CI: 40.4-59.7) states. In terms of geographic settlements, the nomadic population had the lowest measles vaccine coverage during SIAs (70.9%, CI: 57.8-81.3), and had the highest zero-dose children of 15.4% (CI: 8.9-25.2) compared to urban and rural populations. The national measles coverage prior to SIA was found to be 78,6% (CI: 74.6-82.09). The SIAs achieved a national coverage rate of 39.8% among children who had previously received no doses (zero-dose children) and 82% among those who had received at least one dose prior. The predominant reason for children not being vaccinated during the SIAs was a lack of awareness about the vaccination campaign. The lowest national vaccine coverage and the highest proportion of zero-dose children were predominantly found in the Hirshabelle and Southwest states and among nomadic communities. This difference in vaccine coverage among states and communities is concerning and suggests the need for targeted intervention to address these gaps.

Long COVID, often called the “pandemic after the pandemic”, is silently reshaping the lives of millions across the world. While the world’s attention shifts to recovery and rebuilding, a growing number of people continue to suffer long after their initial COVID-19 infection. With symptoms ranging from relentless fatigue and brain fog to breathlessness and emotional distress, Long COVID is more than a lingering illness, it is a complex, disabling condition that threatens health, livelihoods, and dignity. This review explores the profound human and societal cost of Long COVID. For many, especially in low- and middle-income countries, the road to recognition and care remains filled with uncertainty. Diagnostic tools are lacking, healthcare systems are overwhelmed, and too many patients feel invisible, disbelieved, undiagnosed, and unsupported. The burden is not shared equally. Women, frontline workers, and those living in poverty are among the most affected, often facing stigma, financial loss, and long-term disability with little help. Despite these challenges, hopeful solutions are emerging. Some health systems are beginning to integrate Long COVID care into primary health services, while community-based surveillance and virtual rehabilitation teams offer promising models. Yet access remains uneven, and ethical concerns around global research equity, funding, and inclusion persist. This review calls for a shift in how we understand and respond to Long COVID, not just as a clinical condition, but as a human rights issue. An inclusive, coordinated, and compassionate public health response is urgently needed. If we fail to act, we risk abandoning those who have already borne the brunt of the pandemic’s long shadow.