Open University of Mauritius

The integration of cyber-physical systems, the Internet of Things (IoT) and data-driven decision-making processes under Industry 4.0 has brought significant transformation to the manufacturing and product development landscape (Lasi et al., 2014; Xu et al., 2018). As this evolution advances towards Industry 5.0, there is an increasing demand for adaptable, intelligent, and efficient development processes, especially as products grow in complexity and consumer expectations for customisation continue to rise. Virtual reality (VR), one of the digital tools driving this shift, has emerged as a powerful tool that supports smart and safe prototyping (Figure 11.1). According to Steuer (1992), VR is the simulation of a three-dimensional, computer-generated environment where users can interact in ways that closely mimic physical or realistic engagement using specialised equipment. Similarly, Riva et al. (2015) define VR as a system that enables users to interact with virtual environments projected through computers, typically accessed via head-mounted displays, wearable devices, or independent screens equipped with user input sensors. Crucially, VR enables the visualisation, immersion, collaboration, and testing of ideas without the need for tangible artefacts. This chapter explores the role of VR in manufacturing prototyping, with particular emphasis on the use of both high-fidelity and low-fidelity VR environments. It further examines the strategic advantages, operational implications, and emerging challenges associated with VR-enabled prototyping.

Background: One Anastomosis Gastric Bypass (OAGB) has gained recognition as an effective bariatric procedure. However, bile reflux remains a debated complication, with varying reported incidence rates. This review aims to provide a comprehensive analysis of bile reflux after OAGB, exploring its incidence, diagnostic methods, influencing factors. Methods: A review of randomized controlled trials and cohort study was conducted, focusing on bile reflux after OAGB. The studies were identified through a thorough search of databases using keywords such as “bile reflux,” “OAGB,” and “bariatric surgery.” Diagnostic methods included hepatobiliary scintigraphy, endoscopy, and assessment of symptoms. Results: The incidence of bile reflux after OAGB ranged from 3.4% to 70%, influenced by diagnostic approaches and surgical techniques. Hepatobiliary scintigraphy and endoscopy were the primary diagnostic tools, with scintigraphy detecting bile reflux in up to 70% of patients with OAGB. Surgical modifications, such as adjusting the length of the biliopancreatic limb and incorporating anti-reflux stitches, significantly reduced the incidence of bile reflux. Comparative studies did not show significant differences in the incidence of bile reflux between OAGB and Roux-en-Y gastric bypass (RYGB). Conclusion: Bile reflux after OAGB is a measurable but often asymptomatic complication, manageable through surgical optimization and standardized diagnostic protocols. Long-term monitoring and further research are essential to refine surgical techniques and improve patient outcomes, ensuring that OAGB continues to be an option in metabolic and bariatric surgery.

Fingerprint recognition technology is integral to biometric security systems, providing secure and reliable identification through unique human fingerprint patterns. However, challenges such as low contrast, high intra-class variability, and partial fingerprints often compromise the efficiency and accuracy of traditional recognition systems. This research addresses these challenges by employing advanced deep learning techniques, specifically Convolutional Neural Networks (CNNs), to enhance fingerprint recognition performance. We propose a methodological approach that leverages state-of-the-art CNN architectures tailored to capture intricate fingerprint details. The study utilizes the Sokoto Coventry Fingerprint Dataset (SOCOFing), which includes diverse fingerprint types and synthetic alterations to evaluate model performance under realistic conditions. Through a comparative analysis of various CNN configurations, we assessed the models based on efficiency and accuracy, using metrics such as accuracy, precision, recall, and F1-score. Our experimental results demonstrate significant improvements in fingerprint recognition capabilities. The optimized CNN model achieved an accuracy of 98.61%, a precision of 97.12%, a recall of 97.46%, and an F1-score of 97.29%. These results validate the effectiveness of CNNs in handling complex biometric data and underscore their potential to enhance the reliability and security of fingerprint recognition systems. The study concludes that deep learning, through the use of CNNs, offers a powerful solution to the limitations of traditional fingerprint recognition techniques. This will pave the way for more sophisticated and accurate biometric security systems in practical applications. The research findings contribute to ongoing advancements in neural network architectures, enhancing their applicability in increasingly automated and data-driven security environments.

The current research has aimed to find out concept of mind as per Sāṅkhya philosophy and its yogic practices and how is it related to neuroscience. The history of yoga and yogic practices stretches back to the earliest periods of human civilization, and Indian culture is one of the main sources of information on the benefits of yogic activities for people. However, the practice of yoga is gradually disappearing these days because of a lack of research interests and humankind's links to worldly work and lifestyle choices. This study's main goal was to spread awareness of the idea of mind, which was emphasised by yogic philosophies, especially Sāṅkhya yoga, with regard to neuroscience and scientific objectivity, in order to usher in current faith and belief.