National Office for Technology Acquisition and Promotion

The evaluation of wind and solar energy potential in Maiduguri is essential for addressing energy challenges, supporting sustainable development, and promoting socio-economic growth while mitigating environmental issues. This study analyzes 39 years of meteorological data (1984 to 2022) to evaluate wind energy potential through the Weibull distribution function and estimates solar energy potential by calculating the electrical power generated from a single photovoltaic (PV) panel in Maiduguri. The results revealed that the monthly mean wind speed has its highest value to be 4.7869 ms-1, in February and the least to be 2.5495 ms-1, in September. Maiduguri has its highest value of temperature to be 33.21 ℃ and the lowest to be 23.1℃. The highest solar radiation in March was observed to be 272.105 Wm-2 and the lowest in August as 212.41 Wm-2. The wind power density (WPD) for Maiduguri has its highest value to be 63.77 Wm-2 and its lowest value as 9.63 Wm-2, which ranged between 0-99 Wm-2 and falls in the class 1 of the Battelle-Pacific Northwest National Laboratory (PNNL) wind classification, suitable for small-scale wind turbines. The most probable wind speed was determined to be 4.73 ms-1 and the highest monthly maximum energy carrying-wind speed was 4.93 ms-1. The Weibull statistics' two parameters were discovered to lie between 8.1946 ≤k ≤ 20.2485 and 2.9977 ms-1≤c ≤ 4.8547 ms-1. The estimated solar PV output power was highest with 18.65 W in March. The correlation between monthly WPD and solar radiation in Maiduguri is 38.12%, indicating its potential for simultaneous electric power generation.

This paper discusses the importance of economic freedom and free trade in promoting economic development. It explains how commerce encourages growth, specialization, and international cooperation. Using ideas such as Ricardo’s comparative advantage, the study highlights how trade allows countries and individuals to benefit through exchange, innovation, and institutional support, ultimately contributing to long-term economic progress

This study presents a novel three-dimensional global model of atmospheric temperature developed using Artificial Neural Networks (ANNs) trained on radio occultation (RO) data from the COSMIC I and COSMIC II satellite missions. Over 14.7 million quality-controlled profiles were used, providing approximately 9.5 billion data points that capture temperature variability across latitude, longitude, altitude (0-60 km), and time (2006-2025). The global domain was divided into 1,296 spatial grid cells (10° × 5°) to enable localized ANN training and ensure efficient handling of regional atmospheric dynamics. Model performance was evaluated through cross-validation and independent testing against radiosonde measurements from 684 stations worldwide. Results show mean absolute errors (MAE) of 1.5-4.5°C and root-mean-square errors (RMSE) of 2.5-6.5°C, with best performance in the tropical troposphere and increasing errors toward high latitudes. The model successfully reproduces key climatological structures (including the tropospheric lapse rate, stratospheric inversion, and seasonal hemispheric asymmetries), and accurately captures diurnal and annual thermal cycles. Long-term simulations (2006-2025) reveal a distinct tropospheric warming trend (∼ +0.07°C per year at 11 km) and a corresponding stratospheric cooling (∼ -0.03°C per year near 32 km), consistent with established satellite and reanalysis records. These results demonstrate that ANN-based frameworks can effectively model global atmospheric thermal structure and evolution, providing a scalable approach for future climate monitoring and forecasting applications.

This article considers the impact of ethnic dynamics and electoral systems in the December 2023 elections in the Iraqi province of Kirkuk, where long-standing tensions between Kurdish, Arab, and Turkmen populations were the main influences in the political landscape. The adoption of an ‘open list’ electoral system and the rise of independent candidates from various ethnic groups marked a key turning point. The effects of these changes both increased voter participation and resulted in a fragmented outcome that required coalition-building. The study suggests that reforms in institutions and civic education have facilitated inter-ethnic cooperation and created more effective governance in Kirkuk. The article concludes by suggesting that the historic power struggle between different groups in Kirkuk could gradually develop into a more effective political process that encourages collaboration.

Токенізація активів реального сектору стає дедалі більш популярною серед інвесторів, що посилює вплив цієї тенденції на глобальні ринки капіталу. Метою роботи був аналіз поточного стану ринку токенізованих активів та дослідження гіпотетичного впливу цього ринку на глобальні ринки капіталу. В роботі було використано методи статистичного аналізу, кореляційного аналізу, регресійного аналізу, метод ARIMA, тестування за допомогою теста Грейнджера. В результаті дослідження було відзначено прогнозне зростання ринку токенізованих активів у 27 разів у 2030 році порівняно з 2023 роком, що свідчить про посилення їх впливу на глобальні ринки капіталу. Аналіз засвідчив позитивну залежність ринкової капіталізації токена PAXG від ціни золота. Також було виявлено, що волатильність токена вища, ніж фізичного золота, що робить його більш нестабільним у короткостроковій перспективі. За тестом Грейнджера було підтверджено причинно-наслідковий вплив ціни золота на ринкову капіталізацію PAXG у короткостроковій перспективі, тоді як зворотний вплив капіталізації токена на ціну золота не виявлено. Регресійний аналіз продемонстрував, що ціна золота може пояснити близько 44,5% варіації ринкової капіталізації PAXG, однак існують і інші значущі фактори впливу. Прогнозні тенденції показали, що капіталізація токена випереджає зростання ціни золота, тож можна припустити, що подальше зростання ринку токенів може чинити вплив на ціну фізичного золота. Подальші дослідження можуть стосуватися оцінки впливу інших класів токенізованих активів на глобальні ринки капіталу.

Introduction Lassa fever remains a major public health challenge in Nigeria. Effective contact tracing is essential for early case identification, timely isolation, and outbreak containment. However, its implementation has faced notable challenges. This study analyzed five years (2020–2024) of national surveillance data to evaluate the performance of contact tracing and its role in detecting secondary Lassa fever cases, with the goal of informing future strategies. Methods A retrospective descriptive analysis was conducted using Microsoft Excel on data from Nigeria Centre for Disease Control (NCDC) Lassa fever situation reports (2020–2024). The population included all contacts listed from confirmed cases. Key indicators assessed were: number of confirmed cases, contacts listed, contacts completing follow-up, symptomatic contacts, confirmed secondary cases, and contacts lost to follow-up. Descriptive statistics were applied to observe performance trends and implementation gaps. Results Between 2020 and 2024, there were 5,297 confirmed Lassa fever cases and 25,929 contacts listed. Of these, 24,824 (95.7%) completed follow-up. A total of 492 (1.9%) developed symptoms, and 199 (0.77%) were confirmed positive. While follow-up completion improved reaching zero losses in 2023 contact positivity rates remained consistently low and declined over the years. Losses to follow-up re-emerged in 2024, reflecting ongoing operational challenges and limiting the overall effectiveness of detecting secondary transmission. Conclusion Despite high follow-up completion, Lassa fever contact tracing in Nigeria shows limited success in identifying symptomatic and positive secondary cases, a critical gap for public health. Urgent implementation of targeted interventions, enhanced surveillance training, are crucial to strengthen contact tracing and reduce Lassa fever transmission.

Abstract Flowing Bottom Hole Pressure (FBHP) serves as a critical parameter in reservoir engineering, providing insights into reservoir behaviour and aiding in production optimization. Leveraging production data from a sandstone reservoir. This study investigates the utilization of machine learning models to predict Flowing Bottom Hole Pressure (FBHP). Support Vector Regressor (SVR), Multilayer Perceptron (MLP), and Random Forest Regression (RFR) algorithms were independently trained with various hyper-parameters to ensure optimal model selection and parameter combinations. Six different combinations of the available input production data were utilized for training and testing purposes. SVR demonstrated superior performance in FBHP prediction for the Sandstone formation, exhibiting a coefficient of determination (R2) close to 0.9999 across all six scenarios for both training and testing datasets. Additionally, other regression analyses proved effective in predicting FBHP for the Sandstone formation, achieving R2 values exceeding 90% with the input production dataset scenarios. These high R2 values underscore the accuracy and reliability of the predictive models, indicating their proficiency in capturing the complex relationships between input features and FBHP. By accurately forecasting FBHP, these models offer valuable insights for reservoir management and production optimization strategies. They enable engineers and operators to make informed decisions, leading to enhanced hydrocarbon recovery and increased operational efficiency. The robustness of the machine learning models demonstrates their potential to revolutionize reservoir engineering practices, paving the way for more effective and sustainable oil and gas production operations.

Digital health technologies are pivotal for strengthening health systems and accelerating progress toward Universal Health Coverage (UHC). In Nigeria, while digital health interventions have been widely introduced, disparities in maturity and effectiveness are evident across different states. This study comprehensively assesses digital health maturity across ten Nigerian states, leveraging the Global Digital Health Index (GDHI) framework and the UN Foundation/ICT4SOML maturity model. Employing a mixed-methods approach that includes key informant interviews, document reviews, and quantitative scoring, the analysis evaluates seven core domains: leadership and governance, strategy and investment, legislation and compliance, standards and interoperability, infrastructure, workforce, and services and applications. The analysis also situates surveyed states along a maturity spectrum from experimentation to mainstreaming. Findings reveal substantial variations in digital health readiness among the ten states. Lagos emerged as the most advanced, demonstrating robust governance structures, interoperability initiatives, and strategic investments in ICT infrastructure. In contrast, states like Gombe, Niger, Bauchi, Sokoto, Borno, Nasarawa and Yobe were the least matured, hindered by weak workforce capacity, poor infrastructure, and a lack of standardized interoperability. The assessment further identifies heavy reliance on donor-driven projects, limited state ownership, and fragmented governance as key barriers to sustainable digital health implementation. To address these gaps, the study recommends establishing a National Digital Health Observatory for benchmarking and peer learning, alongside targeted state-level capacity building and infrastructure investments. The findings highlight the urgent need for stronger political commitment, structured governance, and the domestication of national digital health policies at the sub-national level. By mapping states on a digital health maturity spectrum, this study offers a clear framework for targeted interventions, optimized resource allocation, and accelerated progress toward a resilient, interoperable digital health ecosystem across Nigeria's states.

Abstract Metasurfaces are artificially engineered composite structures composed of arrays of sub-wavelength resonators that can exhibit negative values of permeability and permittivity at resonant frequencies. This unique approach to realizing new properties has been explored in the development of highly advanced THz bio-sensors, optical sensing, filters, and intelligent reflecting surfaces for 6G and beyond. Several techniques have been developed to study and characterize metasurfaces. Since analytical methods have not previously been used to characterize and study the resonant frequency of metasurfaces with arrays of unit cells, experimental techniques and 3D full-wave numerical solvers are commonly employed. However, these methods are computationally expensive, requiring significant time, cost, and resources. In contrast, analytical methods provide closed-form solutions with reduced computational complexities. This paper presents a novel analytical technique for analyzing metasurfaces consisting of four circular doubly split double-ring unit cells, which have not been previously studied analytically. A lumped-element equivalent circuit for the metasurface was developed, and the induced current equation was derived using mesh current analysis. The resonant frequency and peak induced current were analyzed by varying the mean radius and inter-ring distance. Results show that increasing the inter-ring distance from 1.1 mm to 1.3 mm increases the resonant frequency by 3%, while increasing the mean radius decreases the resonant frequency by 6%. The metasurface exhibits a 2.6 GHz bandwidth and a Q-factor of 2.17 at a resonant frequency of 5.63 GHz. Comparisons with 3D numerical solvers demonstrate the performance and validation of the analytical method, with an average error of 10%. The average time for the 3D numerical solver to compute the resonant frequency and other parameters was 68.55 s, while the analytical method calculates the resonant frequency in 2.2751 s, providing a strong impetus for developing efficient analytical models for metasurfaces.