Computer based surgical training is believed to be capable of providing a controlled virtual environment for medical professionals to conduct standardized training or new experimental procedures on virtual human body parts, which are generated and visualised three-dimensionally on a digital display unit. The main objective of this study was to conduct virtual phacoemulsification cataract surgery to compare performance by users with different proficiency on a virtual reality platform equipped with a visual guidance system and a set of performance parameters. Ten experienced ophthalmologists and six medical residents were invited to perform the virtual surgery of the four main phacoemulsification cataract surgery procedures – 1) corneal incision (CI), 2) capsulorhexis (C), 3) phacoemulsification (P), and 4) intraocular lens implantation (IOL). Each participant was required to perform the complete phacoemulsification cataract surgery using the simulator for three consecutive trials (a standardized 30-min session). The performance of the participants during the three trials was supported using a visual guidance system and evaluated by referring to a set of parameters that was implemented in the performance evaluation system of the simulator. Subjects with greater experience obtained significantly higher scores in all four main procedures – CI1 (ρ = 0.038), CI2 (ρ = 0.041), C1 (ρ = 0.032), P2 (ρ = 0.035) and IOL1 (ρ = 0.011). It was also found that experience improved the completion times in all modules – CI4 (ρ = 0.026), C4 (ρ = 0.018), P6 (ρ = 0.028) and IOL4 (ρ = 0.029). Positive correlation was observed between experience and anti-tremor – C2 (ρ = 0.026), P3 (ρ = 0.015), P4 (ρ = 0.042) and IOL2 (ρ = 0.048) and similarly with anti-rupture – CI3 (ρ = 0.013), C3 (ρ = 0.027), P5 (ρ = 0.021) and IOL3 (ρ = 0.041). No significant difference was observed between the groups with regards to P1 (ρ = 0.077). Statistical analysis of the results obtained from repetitive trials between two groups of users reveal that augmented virtual reality (VR) simulators have the potential and capability to be used as a feasible proficiency assessment tool for the complete four main procedures of phacoemulsification cataract surgery (ρ < 0.05), indicating the construct validity of the modules simulated with augmented visual guidance and assessed through performance parameters.

This study analyzed the crosstalk in mechanomyographic (MMG) signals from elbow flexors during isometric muscle actions from 20% to 100% maximum voluntary isometric contraction (MVIC). Twenty-five young, healthy, male participants performed the isometric elbow flexion, forearm pronation, and supination tasks at an elbow joint angle of 90 deg. The MMG signals from the biceps brachii (BB), brachialis (BRA), and brachioradialis (BRD) muscles were recorded using accelerometers. The cross-correlation coefficient was used to quantify the crosstalk in MMG signals, recorded in a direction transverse to muscle fiber axis, among the muscle pairs (P1: BB and BRA, P2: BRA and BRD, and P3: BB and BRD). In addition, the MMG RMS and MPF were quantified. The mean normalized RMS and mean MPF exhibited increasing (r > 0.900) and decreasing (r < −0.900) trends, respectively, with increases in the effort levels in all three tasks. The magnitude of crosstalk ranged from 0.915% to 21.565% in all three muscle pairs. The crosstalk was found to exhibit high positive correlations with submaximal to maximal flexion [P1 (r = 0.970), P2 (r = 0.951), and P3 (r = 0.824)], pronation [P1 (r = 0.811), P2 (r = 0.763), and P3 (r = 0.901)] and supination [P1 (r = 0.898), P2 (r = 0.838), and P3 (r = 0.852)] torque levels (eight out of nine p-values were < 0.05). Regardless of the high positive correlation between crosstalk and level of effort, the crosstalk remained at a low range (0.915–21.565%) with increases in the torque levels.

Waterjet Technology has been used vastly in our world nowadays due to its advantages and it can be implemented in many industrial sectors or even in the medical sector and food industry sector. Nozzle is a component that has been utilized in waterjet which is employed in a wide range of engineering applications to control the rate of flow, velocity, and the jet pressure of the water. This paper discusses the CFD analysis on a pure waterjet nozzle to obtain the output of the water that jets out from three different diameters of nozzle and select the effective nozzle diameter to be used for the fruit peeling and cutting process. The pressure used for the analysis are 200MPa, 300MPa and 400MPa, which was analysed for three different nozzle diameter 0.76mm, 1.02mm and 1.27mm. From CFD analysis, it is established that as the pressure loss of the water jet increases, the outlet velocity of the jet increases. Furthermore, for fruit peeling and cutting process the impact angle of the nozzle should be prioritised as the peeling of the fruit should be smooth and even before cutting the fruit. Thus, the most suitable parameters were found to be 400MPa and 1.02mm of pressure and nozzle diameter respectively. This will allow for the intended fruit cutting process with a stand-off distance that can be ranged from 1mm to 9mm.

In the dynamic landscape of financial transactions, the escalating threat of fraudulent activities necessitates cutting-edge solutions for real-time detection. This research introduces an innovative approach utilizing the Kaggle credit card dataset, focusing on comparing the effectiveness of hybrid models versus purely supervised learning models. While traditional models rely solely on supervised learning, this study explores the potential performance gains of integrating unsupervised learning (USL) into supervised learning (SL) frameworks. The core investigation centers on whether unsupervised clustering can enhance pattern recognition in unlabeled data and subsequently improve the performance of supervised models. This research not only evaluates the practical benefits of hybrid methodologies in fraud detection, but also advances real-time analytics through Power BI, aiming to provide a more comprehensive and adaptive solution to emerging financial threats. The algorithms yield an accuracy of 99.75% and a remarkably low underkill rate of 0.20%, demonstrating the effectiveness of integrating human oversight with advanced machine learning techniques.