The significance of Distributed Generators (DGs) in the technical and economic operations of electric power distribution systems cannot be overemphasized in recent times. This is essential as a result of the incessant increase in electrical energy demand, which is becoming considerably difficult to meet with the conventional means of energy supply. Thus, DGs offer better alternatives for providing a quality supply of energy near the site of consumption. This type of energy supply is cleaner and cheaper most of the time due to the lessened transmission losses, which consequently reduced the cost of operation at the transmission and distribution levels of the power system. In this work, an approach for placement and sizing of solar PV DGs into radial distribution networks (RDN) based on the solar PV capacity factor of the site was analyzed using particle swarm optimization. The aim of this study is to analyze the effect of the approach on the real and reactive power losses within the network as well as the bus voltage profile. Constraints on credible system operation parameters, which includes bus voltage limits, power balance, and power flow limits, are considered in the formulation of the optimization problem. In order to verify the viability of the deployed approach, steady-state performance analyses were executed on IEEE 33-bus RDN; and the results obtained were compared with the results from other approaches reported in the literature.

Healthcare facilities generate heterogeneous waste streams that must be accurately segregated at the point of disposal to mitigate occupational exposure risks, reduce downstream treatment costs, and ensure compliance with stringent biomedical waste regulations. However, most existing automated waste segregation systems have been developed for domestic or general-purpose scenarios and are poorly adapted to the operational complexity and safety requirements of hospital environments. In this study, a hospital-specific automated waste segregation system was designed, implemented, and experimentally evaluated for real-time classification of five clinically relevant waste categories: infectious waste, sharps, pharmaceutical waste, recyclable waste, and general waste. The proposed system integrates an ultrasonic sensor with a Raspberry Pi 4B platform executing a lightweight MobileNetV2 model, coupled with a motorised mechanical sorting mechanism. A curated dataset comprising 6,868 labelled hospital-waste images was constructed and used to fine-tune the model to ensure robustness under embedded deployment constraints. Experimental validation under simulated hospital disposal scenarios demonstrated an overall classification accuracy of 97%, with end-to-end segregation cycle times ranging from 8 to 12 seconds per item across repeated trials. These results indicate that high-accuracy, real-time waste classification can be achieved using low-cost embedded hardware and compact deep learning architectures. The proposed approach establishes a practical and scalable foundation for intelligent healthcare waste management at the point of disposal, offering a viable pathway toward safer clinical environments, improved operational efficiency, and the broader adoption of edge AI solutions in resource-constrained healthcare settings.

Auto Thermal Control device is an electronic based device which employs the application of temperature sensors to controlling household appliances without human interference directly. In this work, thermal source is used to regulate electrical fan and room heater depending on ambient temperature. The room heater, which is adjusted to a set temperature, switches ‘ON’ when the temperature of a room is low (cold). While the same is switches ‘OFF’ with increase in the room temperature. This triggers ‘ON’ an electric fan at different speeds, and thus cools the room. A temperature sensor, tthermistor, monitors change in room temperature. Two types of thermistor exists: Positive Temperature Coefficient, PTC. An increasee in the resistance of PTC results in increasee in temperature). In the Negative Temperature Coefficient, NTC; a decreasee in resistance yields to temperature increase. This article explored a NTC thermistor. The design could be a ready product in the market of the developing nation where environmental automation is yet fully deployed.

Maternal health forms a pivotal public health concern since poor pregnancy outcomes often arise from delayed detection of physiological abnormalities. Recent advances in Internet of Things (IoT)-based maternal health monitoring devices have enabled automated risk assessment of real-time clinical data. This study developed a Machine Learning (ML) framework to categorize maternal health risk into High, Medium, and Low Levels using a dataset of 1,014 records collected from maternal health centres. Six physiological indices, including body temperature, heart rate, systolic, and diastolic blood pressure, blood glucose level, and age were analyzed via five-fold cross validation. Logistic Regression (LR), Decision Tree (DT), and Random Forest (RF) models were trained and evaluated. RF model achieved the best performance of 0.88 accuracy and F1-score of 0.88. Taken together, recall of 0.91 for high-risk cases, area under the curve (AUC) of 0.973, log loss of 0.449, mean squared error (MSE) of 0.228, root mean squared error (RMSE) of 0.478, and mean absolute error (MAE) of 0.157 demonstrated reliable prenatal risk prediction despite limited resources in healthcare settings.