ABSTRACT
This study undertakes a comprehensive investigation into the phytochemical profile and potential dye properties of Cola acuminata extracts obtained using a range of solvents, including methanol (MeOH), ethyl acetate (EtOAc), and hexane (n-hexane). Phytochemical screening revealed a diverse array of bioactive compounds, with the MeOH extract exhibiting high levels of phenolic compounds, flavonoids, and tannins, underscoring its efficacy in extracting polar phytochemicals. The MeOH extract demonstrated superior antioxidant activity across multiple in vitro assays, including the hydroxyl radical scavenging assay (HRSA), 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) assay, and 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay, with inhibition percentages of 57.43%, 65.59%, and 62.88%, respectively. The estimated IC50 values for the HRSA and DPPH assays were approximately 300 µg/mL, indicating that higher concentrations are required to achieve significant antioxidant activity relative to the positive control, butylated hydroxytoluene (BHT), which exhibited a maximum inhibition of 75.25%. The EtOAc extract was found to be rich in terpenoids and quinones, suggesting its potential for extracting semi-polar compounds with putative antimicrobial properties. The n-hexane extract contained notable amounts of phenolic compounds and tannins, albeit with a lower overall phytochemical diversity. The presence of alkaloids in all extracts highlights their biological significance, including potential anti-inflammatory and antimicrobial activities. The findings of this study underscore the potential of Cola acuminata as a valuable source of natural dye and bioactive compounds with applications in the pharmaceutical and leather tanning industries. Future research directions should focus on the application and characterization of specific phytochemicals responsible for coloring activities, as well as elucidating their action on leather during tanning process.

ABSTRACT
This study investigated the adoption, perceived usefulness, and impact of Canva on innovative learning outcomes among undergraduate students in Nigerian universities, aiming to understand how digital visual design tools can enhance creativity, critical thinking, collaboration, and visual literacy in higher education. Employing a quantitative research approach with a cross-sectional survey design, data were collected from 200 undergraduate students across selected public and private universities using a structured questionnaire comprising sixteen items measuring Canva adoption, perceived usefulness, and innovative learning outcomes on a five-point Likert scale. The validity of the instrument was ensured through expert review for face and content validity, while reliability was confirmed via Cronbach’s alpha, exceeding the acceptable threshold of 0.70. Descriptive analyses, including mean and standard deviation, revealed high levels of Canva adoption and perceived usefulness, alongside moderately high innovative learning outcomes among respondents. Inferential analyses using Pearson correlation demonstrated a strong positive relationship between Canva adoption and innovative learning outcomes (r = 0.624, p < 0.05), and a significant positive influence of perceived usefulness on innovative learning outcomes (r = 0.672, p < 0.05), leading to the rejection of both null hypotheses. The findings indicate that students’ engagement with Canva, coupled with their perception of its academic value, significantly contributes to enhanced creative and innovative competencies in learning. The study recommends that Nigerian universities integrate Canva and similar digital tools into curricula, provide training to enhance digital literacy, and ensure adequate institutional support, to foster innovative learning environments that prepare students for the demands of the digital age.

ABSTRACT
Water pollution is a worldwide issue caused by human interference with nature. The physicochemical variables of water samples of the New Calabar River, Choba section were studied to develop guideline data on the pollution status. Water samples were obtained from three stations based on effluent characteristics. The physicochemical characteristics were analysed using standard conventional techniques. The findings showed the difference concentrations of selected physicochemical parameters with the use of one- way ANOVA to analyze the results in three locations. During the study, the physicochemical parameters of surface water in wet and dry seasons were measured using standard methods for temperature, total dissolved solids, turbidity, electrical conductivity, pH, salinity, dissolved oxygen, chemical oxygen demand, and nitrate. The results showed that temperature varied between 27.90a ± 0.97 and 29.02 ± 1.71a ℃ across the stations and in both seasons with a mean TDS of 172.52 ±2.43 which is significantly different across stations and between the seasons. Turbidity recorded the highest value of 22.98a ± 12.66 NTU in station 1. All the values recorded in the three station in both dry and wet season were above the permissible limit. Higher and lower conductivity value of 474.03±5.37a and 279.17±5.92c µS/CM was recorded in station 1 and 3 during the dry season. pH of surface water varied from 7.08± 0.27b to 8.53±0.22a Mg/l in the two seasons while a higher salinity was recorded during the dry season across the stations. DO ranged from 5.53c ± 0.88 to 6.65±0.24a mg/l across the stations and in both seasons. Others were COD that ranged from 120.02±2.36c to 223.02±5.67a mg/kg in both seasons. Turbidity and nitrate were above the WHO and NESREA permissible limit. There was a seasonal variation between the dry and wet season in most of the parameters studied. This study has shown that despite the visibility anthropogenic activities in the study area, the surface water is still

ABSTRACT
Breast cancer remains one of the leading causes of morbidity and mortality among women worldwide, and early detection through breast self-examination (BSE) has proven to be an effective preventive measure. This study examined the attitudes and practices of breast self-examination among female undergraduates in Emuoha Local Government Area, Rivers State, Nigeria. The objectives were to assess the level of awareness, attitude, and frequency of BSE practice, and to identify factors influencing students’ participation in the exercise. A descriptive cross-sectional survey design was adopted. A structured questionnaire was administered to a sample of female undergraduate students selected through simple random sampling. Data were analyzed using descriptive statistics such as frequency counts, percentages, and means, and the results were presented in tables. The findings revealed that although most respondents had adequate awareness and positive attitudes toward BSE, only a small proportion practiced it regularly and correctly. Major barriers identified included forgetfulness, lack of knowledge of proper technique, fear of detecting a lump, and negligence. The study further found that students in higher levels of study demonstrated better knowledge and practice compared to those in lower levels, suggesting the influence of educational exposure. Despite their awareness of breast cancer risks and the importance of early detection, many respondents had not integrated BSE into their regular health routines. The study concluded that while awareness and attitudes toward BSE among female undergraduates are encouraging, actual practice remains low. It recommends continuous health education, practical demonstrations, inclusion of breast health in university orientation programs, and the use of peer and media-based interventions to promote consistent practice. Strengthening these strategies will enhance early detection of breast abnormalities and contribute to reducing breast cancer

ABSTRACT
Caregiver burden is a significant concern in the management of individuals with cognitive impairment and Alzheimer’s disease, particularly as functional abilities decline. This study aimed to examine the relationship between Instrumental Activities of Daily Living (IADL) performance and caregiver burden among caregivers of older adults. A total of 60 caregivers participated in the study. Descriptive statistics revealed a mean IADL score of 3.10 (SD = 1.311), indicating moderate functional dependence among care recipients, and a mean Caregiver Burden Assessment (CBA) score of 52.27 (SD = 6.822), reflecting moderate to relatively high perceived burden. Pearson correlation analysis demonstrated a weak positive correlation between IADL and CBA scores (r = .143); however, the relationship was not statistically significant (p = .276). These findings suggest that functional dependence in instrumental daily activities alone may not significantly predict caregiver burden in this sample. The results highlight the multifactorial nature of caregiver burden and underscore the need to consider additional psychological, social, and contextual factors when addressing caregiver well-being. Further research with larger samples is recommended to better understand the determinants of caregiver burden.

ABSTRACT
Indigenous communities across the Indian subcontinent have, for millennia, practiced principles that align closely with what is now recognized as the circular economy. Their ways of life, deeply inter-twined with nature, establishing sustainable resource management and regenerative ecological systems. This research adopts a qualitative approach, relying on the analysis of existing case studies to explore how Indian tribal communities have historically embodied the essence of the circular economy. The study synthesizes insights from multiple documented cases, drawing upon ethnographic records, environmental studies, and community development reports to interpret the cyclical and sustainable nature of indigenous resource practices. The findings from these analyzed cases reveal that traditional Indian tribal societies have long implemented efficient and regenerative systems through locally developed knowledge frameworks. Practices such as zero-waste agriculture, where every by-product of cultivation is reused or reintegrated into the ecosystem, demonstrate remarkable ecological foresight. Community-based systems of sharing and collective ownership further minimize waste and ensure equitable distribution of resources. This study concludes that the traditional practices of Indian tribal communities represent well-established models of circular living long before the term “circular economy” was conceptualized in modern discourse. By revisiting and integrating these indigenous models into present-day sustainability frameworks, policymakers and environmental planners can develop holistic, culturally grounded strategies that not only promote resource efficiency but also strengthen community resilience and ecological harmony.

ABSTRACT
Summary Regulatory compliance in transportation infrastructure asset management (TIAM) is hindered by aging assets, disjointed oversight, and changing policies. Traditional ways of inspecting and reporting by hand don't work well when you need to do a lot of them. This paper examines recent developments in Artificial Intelligence (AI) aimed at enhancing compliance via automation, real-time analytics, and decision support. We highlight techniques like natural language processing (NLP) for parsing regulatory texts and graph neural networks (GNNs) for modeling asset interdependencies. These are based on peer-reviewed studies on AI applications in transportation that were published between 2015 and 2025. For example, Graph SAGE and other GNNs have shown that they can accurately predict road accidents with less than 22% mean absolute error on traffic datasets [3]. Case studies of U.S. bridge inspections using AI-enabled digital twins show that labor costs can be cut by 20–30% and that each project could save up to $15 million [4, 5]. To deal with interpretability, explainable AI (XAI) methods find a balance between accuracy and openness. However, more complicated models often give up performance for less clarity [8]. Federated learning enables privacy-preserving model training utilizing distributed infrastructure data [9]. Climate simulations based on SSP2-4.5 scenarios show that AI can make the road network more resistant to floods, affecting as much as 13.1% of segments [11]. This work encourages reliable AI for long-term TIAM governance.