The study considers security issues in water transport within the coastal corridors of Rivers State, Nigeria. This study adopts a mixed method approach in collecting data from respondents comprising passengers, water transport operators, and locals living along the water routes inselected communities. Data obtained from respondents were subjected to descriptive and inferential statistical analysis, including the Kruskal-Wallis ANOVA test (at p<0.05). Results show that the common security issues faced include lack of adequate lighting and safety of the jetties (WM=3.75, 61.71%), inadequate security personnel (WM=3.70, 60.45%), piracy (WM=3.68, 61.71%), and poor surveillance systems (WM=3.62, 57.93%). No significant spatial variation was observed on all security issues considered (χ²=3.76-4.30, p>0.05) based on the Kruskal-Wallis test. The causes of insecurity include high levels of youth unemployment (WM=3.81, 65.49%), lack of adequate surveillance (WM=3.80), poor infrastructure development (WM=3.77), and poor law enforcement (WM=3.75).The study also found that insecurity greatly influences passenger safety and logistics activities in terms of disrupted business (WM = 3.84, 66.75%), higher threat to loss of lives (WM = 3.83), and high transport costs (WM = 3.81). Also, findings revealed no statistically significant differences in terms of location (χ² = 3.79-4.25, p > 0.05) while measures currently employed to address insecurity such as patrols (WM = 3.45), inter-agency cooperation (WM = 3.45) were observed to be moderately effective, but surveillance technology (WM = 3.36) and emergency plans (WM = 3.35) were seen as ineffective. The study concludes that insecurity along the state’s waterways is characterized by uniform structural characteristics as a result of socio-economic, institutional, and infrastructure deficiencies. Therefore, coordinated surveillance systems, adequate funding, community engagement and participation, youth empowerment and improved enforcement mechanism were strongly recommended for urgent action.

Hyperuricemia, associated with gout and uric acid nephrolithiasis (FitzGerald et al., 2020; Li et al., 2014), is recognized as a contributor to metabolic dysfunction (Borghi et al., 2021; Ali, 2026). Evidence indicates a bidirectional relationship between serum uric acid (SUA) and insulin resistance (IR) (Sun et al., 2021; Xu et al., 2016; Oh and Cho, 2025; Sakallı et al., 2023). Intracellular uric acid may promote metabolic dysfunction through reactive oxygen species (ROS), endothelial nitric oxide synthase (eNOS) uncoupling, endoplasmic reticulum (ER) stress, NLRP3 inflammasome activation, and de novo lipogenesis (Ali, 2026; Park et al., 2023; Wu et al., 2020; Choi et al., 2014; Lanaspa et al., 2012). Conversely, hyperinsulinemia may reduce renal urate clearance by increasing GLUT9a/b and URAT1 activity (Li et al., 2014; Sun et al., 2021; Mandal et al., 2021). Elevated SUA is associated with increased risk of type 2 diabetes mellitus (T2DM) and metabolic syndrome, non-alcoholic fatty liver disease (NAFLD), and diabetic kidney disease (DKD) (Borghi et al., 2021; Xu et al., 2016; Ford et al., 2007; Alanazi, 2025; Bhole et al., 2010; Kramer et al., 2009). Randomized controlled trial evidence regarding urate-lowering therapies (ULT) and insulin resistance remains inconsistent (Zhao and Zhao, 2026; Perez-Pozo et al., 2010; Takir et al., 2015; Beddhu et al., 2016). SGLT2 inhibitors and GLP-1 receptor agonists provide metabolic benefits and enhance renal urate excretion (Mandal et al., 2021; de Boer et al., 2022; Chino et al., 2014). This review summarizes uric acid physiology, insulin signaling, mechanisms, evidence, and therapeutic implications.

Counterfeit currency is a serious financial problem that can cause monetary losses to individuals, businesses, and financial institutions. Traditional currency verification mainly depends on manual inspection of security features, which can be time-consuming and may lead to incorrect decisions, especially when counterfeit notes are designed to closely resemble genuine currency. To address this issue, this project proposes a Fake Indian Currency Detection and Classification system using Deep Learning. The proposed system uses a Convolutional Neural Network (CNN) to analyse currency-note images and automatically classify them as Real or Fake. The input image is first collected and preprocessed to improve its quality and make it suitable for model analysis. The CNN then learns important visual patterns and distinguishing characteristics from the currency images during training. The trained model is integrated into a Django-based web application, where users can upload a currency image and obtain the classification result. This approach reduces manual effort and provides a faster and more convenient method for preliminary currency verification. The experimental results show that the proposed CNN model achieves an accuracy of 97%, demonstrating its effectiveness in distinguishing genuine Indian currency from counterfeit currency.

Stock market volatility forecasting remains a fundamental challenge in financial econometrics, particularly in emerging markets where structural inefficiencies, information asymmetry and regulatory uncertainty consistently amplify market risk. This study investigates the forecasting performance of traditional econometric models for predicting stock price volatility in the pharmaceutical sectors of the Dhaka Stock Exchange (DSE). Daily closing stock price data for four major pharmaceutical companies like Square Pharmaceuticals, Beximco Pharmaceuticals, IBN SINA Pharmaceutical Industry and Renata Limited were collected over the period from January 2015 to January 2025. The methodology proceeded through sequential stages: stationarity testing using the Augmented Dickey–Fuller test, ARIMA mean filtering, and GARCH-family volatility modeling under Normal, Student-t and Generalized Error distributional assumptions. Results show that asymmetric GARCH specifications with Student-t and GED distributions consistently outperformed symmetric normal-distribution models across all four companies. GARCH models remain essential for volatility and risk characterization in the pharmaceutical sector of the DSE.

Smart AgroVision is a deep-learning-based system designed to identify rice varieties quickly and accurately. Users can upload a rice grain image through a simple web application. The system processes the image and analyzes important features such as grain shape, texture, color, and size. Advanced deep learning models are used to learn these characteristics and recognize the rice variety efficiently. The predicted variety and its confidence score are then displayed to the user and stored for future reference. This approach reduces the need for manual identification and expert involvement. The system can support farmers, researchers, and agricultural industries by providing a fast, reliable, and easy-to-use rice variety identification solution. The combination of DenseNet121 for feature extraction and MobileNetV2 for classification yielded excellent results, with high accuracy and low loss. The achieved 98.48% accuracy confirms that the system can deliver fast, consistent, and reliable predictions, making it a practical solution for farmers, researchers, and agricultural industries.