In Kenya, gender-based violence (GBV) against women is still a major social issue that has to be addressed immediately and effectively. In response, prognosticating and preventing cases of gender-based violence through the incorporation of machine learning (ML) modeling has become a viable strategy. This study provides critical insights for policymakers, practitioners, and researchers by synthesizing the body of literature on the use of machine learning approaches in predicting and reducing gender-based violence against Kenyan women. When it comes to determining trends, patterns, and risk variables related to gender-based violence, machine learning modeling has a lot of promise......

KEYWORDS;- Machine Learning, Modeling, Gender-Based Violence.

Computer aided facilities management (CAFM) system are essential for efficient management of built environment. However their implementation of often hundred by technical financial operations strategic and infrastructure challenges. this study artificially device the challenges and remedial measures associated with CAFM system in Lagos State identify effective mitigation strategies. A thematic analysis review that the three most critical challenge facing CAFM system in Lagos State include...................... And the most successful media measure employed by stakeholders in the state include training, technical support and maintenance, stakeholders engagement amongst other. The best migration strategy engaged by real estate firm's in Lagos State in proper phase implementation followed by continuous monitoring results from this study will provide valuable insight for facilities managers building owners and stakeholders in Lagos State seeking to reap more benefits from CAFM system.

KEYWORDS;- Computer: Aided facilities management (CAFM), mitigation strategies, challenges remedial measures, facility management, integration, real estate

This study investigates the performance of a solar-powered Combined Cooling, Heating, and Power (CCHP) system designed specifically for the climate of Tripoli, Libya. With the country's high solar potential and the pressing need for sustainable energy solutions, this system combines a regenerative Brayton cycle with a lithium bromide–water absorption cooling unit to maximize energy efficiency. Solar energy is captured using a heliostat field and concentrated on a central receiver to heat air, which then drives a turbine to generate electricity. The remaining thermal energy is used for both heating and cooling applications, allowing the system to meet multiple energy demands simultaneously. A thermodynamic model was developed to evaluate the system under steadystate conditions, using realistic local climate data. The results for the month of June indicate a net electrical output of 5761 kW, along with 2424 kW of heating and 7603 kW of cooling. The system achieved an energy utilization factor of 73.41% and an exergy utilization factor of 36.83%. Electrical energy and exergy efficiencies were recorded at 26.8% and 29.6%, respectively, while the coefficient of performance (COP) of the absorption cooling unit was 0.82. What sets this work apart is its focus on Libya's specific environmental conditions, which are often overlooked in existing research. The findings highlight the potential of solar-powered CCHP systems to improve energy sustainability in sun-rich, high-temperature regions and offer valuable insights for future renewable energy planning in similar contexts. .

KEYWORDS;- Combined Cooling Heating and Power (CCHP); Regenerative Brayton cycle; Absorption refrigeration; Libya

This research investigates the role of big data analytics and machine learning in optimizing drilling operations, with a specific focus on predicting optimal drilling parameters to mitigate unplanned downtime (UDT). Conducted over two years at various oil drilling sites in Canada, the study highlights the integration of Logging While Drilling (LWD) and Measurement While Drilling (MWD) data into predictive models. The findings demonstrate a significant reduction in UDT through the development of machine learning algorithms that analyze historical drilling data to forecast and optimize the Rate of Penetration (ROP). Despite the advancements, challenges such as real-time data integration and anomaly detection were identified, emphasizing the need for enhanced data quality and management frameworks. The implications of this research underscore the necessity for drilling companies to adopt data-driven strategies and invest in workforce training to fully realize the potential of predictive analytics. By providing actionable insights, this study contributes to the ongoing evolution of drilling practices, paving the way for more efficient and resilient operations in the oil and gas industry. .

KEYWORDS;- Drilling Optimization, Big Data Analytics, Machine Learning, Unplanned Downtime, Predictive Maintenance.

This meta-analysis investigates the effects of automation and digitalisation on drilling operations, emphasising the contributions of artificial intelligence (AI), the Internet of Things (IoT), and robotics in improving decision-making and minimising non-productive time (NPT). A thorough analysis of 10 recent research reveals that the incorporation of these technologies results in substantial enhancements in operational efficiency, safety, and economic viability in the oil and gas industry. AI-driven analytics provide enhanced real-time decision-making, potentially reducing drilling time by as much as 30%. Robotics and automation diminish human exposure to perilous tasks, hence improving safety and alleviating dangers. Nonetheless, obstacles like as the implementation of autonomous drilling platforms, data transmission difficulties in remote regions, and the necessity for labour skill enhancement persist as substantial impediments. This examination delineates essential chances for enhancement, encompassing the creation of predictive maintenance algorithms and digital twins for risk simulation. The results highlight the imperative for industry stakeholders to engage in technology innovations and training initiatives to optimise the advantages of automation and digitalisation. This study provides significant insights for improving drilling operations and assuring increased efficiency, safety, and sustainability in the changing oil and gas sector landscape. .

KEYWORDS;- Automation, Digitalisation, Drilling Operations, Artificial Intelligence, Non-Productive Time (NPT)

Geostatistical analysis through computed mean vector azimuth (MVA) for the studied cross-bedding structures in oolitic limestones of the Wadi Al Qattarah Formation in the Wadi Al Aqar-NE Libya was done by using the recommended standard formula [MVA = tan-1(Σsinθ/ Σcosθ)]. Graphic presentation of the studied cross-bedding directions has revealed a strong unidirectional orientation of cross-beds with minor reversals at some localities. In each unit, the primary mode is to the southeast, whereas the secondary mode is essentially asymmetrical about the mode. The strike of the cross-bedding sets in these deposits is normal to this trend. These data indicate that marine paleocurrent systems during the deposition of the oolitic limestones of the Wadi Al Qattarah Formation moved back and forth perpendicular to the shore and were probably the result of the strong ebb and flow of tides. The framework of these marine paleocurrents has suggested the inferred paleogeography of the Wadi Al Qattarah Formation in Wadi Al Aqar Quarry, in which the dispersal of the oolitic and skeletal limestone ridges in the southeast direction (ebb tide direction), changing to lagoonal settings in the northwestern and northeastern directions (flood tide direction). .

KEYWORDS;- Geostatistics, Graphic presentation, Directional data, Outcrop charactrirzation, Cross bedding, Paleocurrent, Wadi Al Qattarah Formation, Wadi Al Aqar, Ebb tide, Flood tide, Paleogeography.