Applying natural fibre-reinforced polymer composites for automobile body parts has not gained much attention in Nigeria. Natural fibres appear to be a good alternative to non-biodegradable synthetic fibres. This work aimed to develop natural fibres obtained from plantain pseudo-stem for reinforcement with polyester composites for automobile bumper fascia application. The plantain fibres were manually extracted and treated using the mercerization process. Compressive and impact tests were carried out on the laminates which were prepared according to the ASTM D695 and ASTM D256 standards, respectively. The plantain fibre-reinforced composite automobile bumper was then constructed using the hand lay-up technique. Laminates with volume fractions 0.3 and 0.4 are produced and analysed for impact and compression tests. From the experimental results, it was observed that the Impact strength for a volume fraction 0.3 and 0.4 was 12.22 kJ/m2 and 13.83 kJ/m2, respectively and compressive strength for a volume fraction 0.3 and 0.4 was 65.3x103 kN/mm2 and 67.4x103 kN/mm2, respectively. The study shows that plantain fibre-reinforced polyester composites could be an alternate candidate for automotive bumper fascia.

The adoption of artificial intelligence (AI) in various sectors, including healthcare, has gained significant popularity due to its potential to improve services. In the medical field, misdiagnosis has been a major problem, leading to increased mortality rates. Accurate diagnosis is crucial for effective treatment and management of diseases. This research aims to develop a machine-learning model for segmenting small blood vessels in magnetic resonance angiography (MRA) and magnetic resonance imaging (MRI) datasets using bilateral filtering. The research identifies the limitations of existing machine learning models in blood vessel segmentation, particularly the loss of important edge information due to convolutions that blur images. To address this issue, a non-linear bilateral filter is introduced to enhance the segmentation of blood vessels in MRI images. The proposed framework aims to improve the accuracy of the segmentation algorithm by reducing image blurring and noise through bilateral filtering. The objectives of this research include training and testing a machine-learning prototype using bilateral filtering, exploring the weaknesses of existing models in blood vessel segmentation, and developing a machine-learning model specifically designed for segmenting small blood vessels using bilateral filtering. Various studies have proposed machine learning algorithms, such as convolutional neural networks, for blood vessel segmentation. The review emphasizes the importance of bilateral filtering in improving classification accuracy by reducing image blurring. In conclusion, this research aims to contribute to the field of medical image analysis by developing a framework that utilizes bilateral filtering to enhance the segmentation of small blood vessels in MRA and MRI datasets. The proposed machine learning model has the potential to improve the accuracy of blood vessel segmentation, enabling more accurate diagnoses and reducing misdiagnosis-related mortality rates.

Dubai Electricity and Water Authority (DEWA) is considered as a benchmark for other utilities due to its sustainable initiatives, where it addresses Dubai's increasing energy and water demands through innovative and environmentally-oriented practices. This paper discusses DEWA’s advancements in renewable energy integration, smart grid technologies, and sustainable infrastructure, highlighting the Mohammed bin Rashid Al Maktoum Solar Park and the Hatta Hydroelectric Power Plant as its major initiatives. Furthermore, DEWA's efforts in water management, green building design, and community engagement also serve as examples to its commitment to sustainability. With notable achievements in energy production efficiency, significant reductions in carbon emissions, and enhanced energy management systems, DEWA greatly contributes to Dubai's Clean Energy Strategy 2050.

This study proposes an artificial intelligence-driven model that can enhance productivity and reduce manufacturing costs in the brewery industry of Nigeria. The research initiated with a critical literature review on the factors of productivity in the knowledge-intensive industries, choosing thereupon the brewery sector based on expert advice. In total, three predictive models were developed, namely Artificial Neural Network, Machine Learning, and a hybrid Artificial Neural Network-Machine Learning model, for predicting productivity. The Mean Squared Error was 0.001399 for the Artificial Neural Network model, Root Mean Squared Error was 0.037407, and Mean Absolute Error was 0.037283, while the Machine Learning had Mean Squared Error of 0.040378, Root Mean Squared Error of 0.200943, and Mean Absolute Error of 0.183000, the hybrid having Mean Squared Error of 0.013982, Root Mean Squared Error of 0.118247, and Mean Absolute Error of 0.110141. It also proved the fact that the Machine Learning model is able to predict productivity based on maintenance, Mean Time Before Failure, and Mean Time to Repair indicators since the obtained values for this type of model had lower errors than all the others: Mean Absolute Error = 0.08508, Mean Squared Error = 0.19275, Root Mean Squared Error = 0.43903.