Rivers State University (RVSU or RSU), formerly Rivers State University of Science and Technology (UST or RSUST), is a University located in the Diobu (Mile III) area of Port Harcourt, Rivers State, Nigeria. As of 2021, the Vice Chancellor of the University is Professor Nlerum Sunday Okogbule.
MXenes, a new class of two-dimensional (2D) transition metal carbides and nitrides, have shown remarkable potential in tribology for mitigating wear and friction. As a result of their unique structure, low shear resistance, and simplicity of modification, they are therefore excellent choices for lubricant additives and shielding layers in mechanical mechanisms due to their remarkable thermal stability, strength, and tendency for superficial chemistry. This study focuses on MXene functionalization, its role as a lubricant additive, and categorizes its mechanism of operation into phases. The compatibility of MXenes with other additives, alongside their synergistic and antagonistic effects, was also covered in the study. Furthermore, it highlights several untapped research areas, summarizes the challenges of lubrication, and presents potential solutions and future research opportunities. The potential and advancements of MXenes in lubrication have been thoroughly confirmed. In terms of friction reduction, the new MXene lubricants are expected to have outstanding potential for use in advanced manufacturing sectors, notably the aerospace and automotive industries, as well as in machining applications.
Abstract Background Sexual function is an important component of a patient’s life and subjective well-being and is considered as one of the important indices of quality of life. Objective This study sought to assess the prevalence of sexual dysfunction (SD) and its impact on the health related quality of life (HRQoL) of male patients with type 2 diabetes mellitus (T2DM). Method It was a cross sectional, descriptive, hospital-based study conducted among adult male diabetes mellitus patients aged between 30 and 75 years, who attended the diabetic outpatient clinics of two selected hospitals in Uyo, southern Nigeria over a twelve month period. Data on the socio-demographic and clinical details of the participants was collected using a semi-structured questionnaire. Data on sexual function and HRQoL of the patients were obtained using the International Index of Erectile Function Questionnaire (IIEF); and the World Health Organization quality of life instrument short version (WHOQOL-BREF) respectively. Results Two hundred and thirteen male patients with T2DM were recruited into the study with majority (88; 41.3%) of them aged between 50 and 59 years. About 71.8% (153) of the participants were identified as having SD. A further evaluation revealed that dysfunction was high in all the domains of sexual function with erectile dysfunction accounting for the most frequent form of SD: 97.4% (95% CI 93.4–99.3%). Our results showed that 85.6% of the patients who reported a poor overall quality of life were those with SD, while only 14.4% of those without SD reported a poor overall quality of life. This difference was statistically significant (p < 0.0001). Conclusion A high prevalence of SD among the patient population studied was observed. Erectile dysfunction was the most common type of SD among the patients. SD significantly impairs the HRQoL of male patients with T2DM. Evaluation of male T2DM patients for SD should form part of routine clinical care with the view of implementing suitable interventions to improve the HRQoL of the patient.
This study assessed the concentration, distribution pattern, and potential health risks of gaseous and particulate pollutants in selected teaching hospitals across three northwestern states. A cross-sectional, instrument-based environmental assessment was conducted across indoor, outdoor, and junction environments of the hospitals. Air pollutant concentrations were measured using the RASI700 BIO Portable Gas Analyzer through spot measurements (30–120 s) at breathing height (1.2–1.5 m), with triplicate readings obtained for each microenvironment. Data were statistically analyzed using Minitab 22. Health risks were estimated using the HQ and HI, while the AQI provided an overall air quality classification. Results showed clear spatial variations in pollutant concentration distribution. Indoor, outdoor, and junction measurements showed variable pollutant levels: CO (1.0–4.4 ppm), NO (3.3–5.2 ppm), NO2 (0.4–1.2 ppm), SO2 (0.2–1.0 ppm), CH4 (0.7–9.5 ppm), H2S (6–9 ppm), VOC (9.4–12.1 ppm), and PM2.5 (1.6–2.5 µg/m³). However, indoor air showed higher VOC (11.0–12.1 ppm) accumulation, while outdoor and junction areas recorded elevated NO2 (0.7–1.2 ppm) and SO2 (0.7–1.0 ppm) levels. PCA identified components dominated by NO₂, H₂S, and VOCs, indicating these pollutants contributed most to variance in air quality indicators. Cluster analysis grouped hospital sites distinct clusters, revealing site-specific emission patterns with junction sites forming the most polluted clusters, followed by outdoor and indoor areas. AQI values ranged from unhealthy to hazardous, with the highest levels recorded at AKTH indoor and outdoor, ABUTH outdoor and junction microenvironments, and KDSTH junction microenvironment for the duration of measurement. The calculated HQ and HI values indicated potential non-carcinogenic risks in several environments. HQ values for NO₂, SO₂, and H₂S exceeded unity (HQ > 1) across all the microenvironments, whereas CO, CH₄, and PM₂.₅ remained below unity (HQ < 1). Consequently, the cumulative HI was > 1 in all assessed microenvironments. The findings revealed instantaneous air quality challenges within hospital environments with pollutant concentrations varying across the different microenvironments. Routine air quality monitoring, improved ventilation, waste management control, and regulatory enforcement are essential to mitigate health risks and enhance environmental safety in hospital settings.
The intersection of social media and wildlife conservation has created new challenges and opportunities for shaping public perception, guiding responsible engagement and influencing conservation outcomes for primates, whose human-like traits make them highly engaging subjects. While digital platforms have the potential to broaden awareness, build conservation education, and increase funding, they also propagate content that distorts public perception and promotes harmful behaviors, such as primate pet ownership and exploitation. This paper critically examines the dual impact of social media on primate conservation, exploring how engagement-driven algorithms amplify both harmful and helpful content. Drawing on recent case studies and peer-reviewed literature, it highlights how digital media shapes public understanding, enables species exploitation, and simultaneously supports ethical campaigns and funding efforts. The paper contributes new strategies for ethical digital conservation, including platform regulation, influencer engagement, and digital literacy—areas seldom integrated within primate conservation discourse. As digital media continues to shape human-wildlife relationships, confronting its paradoxes is essential for ensuring the future of primates in a hyperconnected world.
Water quality degradation in data-scarce and pollution-prone regions such as the Niger Delta poses serious health and ecological risks. Traditional monitoring methods are limited by cost, temporal gaps, and lack of interpretability. This study develops a hybrid artificial intelligence (AI) framework integrating Long Short-Term Memory (LSTM), Extreme Gradient Boosting (XGBoost), and K-Means clustering for interpretable water-quality prediction and pattern discovery in under-monitored environments. The framework addresses the scarcity of temporal datasets by adapting LSTM to static physicochemical data through pseudo-sequential encoding, while XGBoost enhances regression and classification accuracy in small, heterogeneous samples. K-Means provides unsupervised insight into latent contamination clusters, complemented by Principal Component Analysis (PCA) for gradient-based visualization. Using 50 georeferenced samples from Yenagoa, Nigeria, ten key parameters were analyzed to compute the Water Quality Index (WQI). Results show that XGBoost achieved the highest predictive performance (R² = 0.95, AUC = 0.96), identifying iron, nitrate, and electrical conductivity as dominant drivers of poor water quality. Three chemically distinct clusters revealed spatial coherence with industrial and residential pollution zones, underscoring the region’s environmental vulnerability. The study demonstrates that hybridizing ensemble learning, deep networks, and clustering enhances both accuracy and interpretability in low-data contexts. By coupling supervised and unsupervised AI components, the proposed framework supports scalable, data-driven decision-making for water-resource management. Its transferability offers practical value for other developing regions facing similar data and infrastructure limitations, contributing to global Sustainable Development Goal 6 on clean water and sanitation. Key findings from this study include: • XGBoost was the most accurate model for both regression (R² = 0.95) and classification (AUC = 0.96). • Over 70
