The global imperative to decarbonize energy systems and mitigate climate change has catalyzed intense research and development into alternative energy carriers. Hydrogen, the most abundant element in preconceived universe, is emerging as a pivotal vector in this transition, offering a versatile, high-energy-density fuel that can be produced from various sources. This paper explores the burgeoning "Hydrogen Horizon," focusing on the technical innovations driving its advancement, particularly within the renewable energy paradigm. It critically compares blue hydrogen (produced from fossil fuels with carbon capture) and green hydrogen (produced from renewable electricity via electrolysis), examining their respective production processes, economic viabilities, and environmental implications using relevant data. The pros and cons of different hydrogen production pathways are discussed, alongside a comparative analysis with the incumbent oil and gas industry. The paper concludes that while blue hydrogen may serve as a transitional technology, green hydrogen, propelled by continuous technical innovation, holds the ultimate promise for a truly sustainable energy future.

Fifteen coal samples were collected from Dangote, Zouma. and Omelewu coal sites, Kogi, State. Five samples from each site were subjected to laboratory tests which include proximate analysis, ultimate analysis, calorific value and total Sulphur content determination to determine their suitability for power generation. ASTM D3173, ASTM D5142, ASTM D3175, and ASTM D5865 standards were used respectively. Tests were carried out at FUTA laboratories and Sheda Science and Technology laboratory, Abuja, Nigeria. Dangote coal is a sub-bituminous B, low Sulphur and medium ash coal; Zouma sub-bituminous C, low Sulphur, medium ash coal, while Omelewu coal is a sub-bituminous C, low Sulphur, low ash coal. The coal samples analyzed are suitable for power generation (Heating value: 8,300 - 9,500 Btu/lb; Moisture content: 16.52% - 17.49%; Low Sulphur content <1.0); low to medium Ash contents 8.0-15.0%) and is in agreement with requirements published by coal-fired power plant operators. Gross calorific values, inherent moisture and contents of Zouma sub-bituminous coal make it more largely suitable for pulverized coal combustion when compared with the coal fuel used for the Genessee Phase 3 power station in Canada.

Wearable cardiovascular devices have emerged as essential tools for continuous heart rate monitoring. Especially for the early detection and management of heart related conditions. This article explores the advancements and functionalities of four categories of the wearable heart monitoring products like, smart patches, in – ear heart monitoring devices, smartwatches and smart rings. The smart patches offer clinical grade heart rhythm data with mobility, allowing real- time analysis without hospital equipment. In- ear monitors, utilizing photoplethysmography (PPG) sensors, provide accurate readings from the ear canal due to its rich blood supply. Smartwatches combine multi sensor capabilities, including heart rate, blood oxygen levels, and ECG, with smartphone integration for user friendly health tracking. Smart rings, the most discreet among them, prioritize minimalism while offering essential metrics like heart rate variability (HRV), sleep, and activity data. These technologies collectively push the boundaries of personalized healthcare, providing accessible, real time cardiovascular monitoring.

This study developed a machine learning-based failure predictive application software to improve the operational efficiency and reliability of turbo-compressors in gas injection plants. The Gas injection plant produced below maximum capacity due to failure problems of the Turbo-compressors, these affected the targeted oil production negatively. The unavailability and unreliable gas plant led to revenue losses. The failure analysis revealed that equipment and material issues, human factors, external factors, and management-related issues contributed to equipment failures. Machine learning techniques, specifically Logistic Regression, Support Vector Machines (SVM), Boosted Trees, and Artificial Neural Networks (ANN), were employed to develop the failure predictive application software. The results showed that the Efficient Linear SVM model achieved a true positive rate of 99.5% for detecting failures and 99.9% classification precision for non-failure events. The Boosted Trees model achieved a true positive rate (TPR) of 99.5% for detecting failures, although it demonstrated a 0.5% false negative rate, highlighting the need for further optimization and integration with ensemble techniques to minimize operational risks. The SVM model further showcased 99.9% classification precision for non-failure events and a minimal false negative occurrence. The nearly perfect R-values across training, validation, and test datasets, coupled with minimal MSE values at the optimal number of epochs displayed by the ANN model further confirmed that the model can generalize effectively to unseen data. The outcomes of this research yielded a highly effective, computationally efficient machine learning-based application software capable of reliably predicting turbo-compressor failures. The study concluded that the developed application software is a powerful tool for predicting failures in gas injection plants, supporting decision-making processes, and enhancing operational safety. Recommendations for future works included refining existing models, exploring additional feature engineering techniques, and evaluating the robustness of the models under varying operational conditions.