Salman Jalalifar

Drowning poses a significant threat, resulting in unexpected injuries and fatalities. To promote water sports activities, it is crucial to develop surveillance systems that enhance safety around pools and waterways. This paper presents an overview of recent advancements in drowning detection, with a specific focus on image processing and sensor-based methods. Furthermore, the potential of artificial intelligence (AI), machine learning algorithms (MLAs), and robotics technology in this field is explored. The review examines the technological challenges, benefits, and drawbacks associated with these approaches. The findings reveal that image processing and sensor-based technologies are the most effective approaches for drowning detection systems. However, the image-processing approach requires substantial resources and sophisticated MLAs, making it costly and complex to implement. Conversely, sensor-based approaches offer practical, cost-effective, and widely applicable solutions for drowning detection. These approaches involve data transmission from the swimmer’s condition to the processing unit through sensing technology, utilising both wired and wireless communication channels. This paper explores the recent developments in drowning detection systems while considering costs, complexity, and practicality in selecting and implementing such systems. The assessment of various technological approaches contributes to ongoing efforts aimed at improving water safety and reducing the risks associated with drowning incidents.

Further efforts are still needed to refine and optimise complex thermochemical pyrolysis processes crucial in waste management and clean energy production. In this work, a comparative artificial intelligence (AI) based modelling study is conducted using four supervised machine learning models, including artificial neural network (ANN), random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGB) to predict the three-phase product yields of pyrolysis. The models were trained using a database of previous experiments focused on continuous pyrolysis in fluidised bed reactors, with biomass feedstock characteristics and pyrolysis conditions as input features. A reactor dimension parameter through H/D (the ratio of the reactor height, H and the reactor diameter, D), for the first time, is also included as an input feature. The models are optimised through feature reduction and 5-fold cross …

Drowning is considered amongst the top 10 causes of unintentional death, according to the World Health Organization (WHO). Therefore, anti-drowning systems that can save lives by preventing and detecting drowning are much needed. This paper proposes a robust and waterproof sensor-based device to detect distress in swimmers at varying depths and different types of water environments. The proposed device comprises four main components, including heart rate, blood oxygen level, movement, and depth sensors. Although these sensors were designed to work together to boost the system’s capability as an anti-drowning device, each could operate independently. The sensors were able to determine the heart rate to an accuracy of 1 beat per minute (BPM), 1% SpO2, the acceleration with adjustable sensitivities of ±2 g, ±4 g, ±8 g, and ±16 g, and the depth up to 12.8 m. The data obtained from the sensors were sent to a microcontroller that compared the input data to adjustable threshold values to detect dangerous situations. Being in hazardous situations for more than a specific time activated the alarming system. Based on the comparison made in the program and measuring the time of submersion, a message indicating drowning or safe was sent to a lifeguard to continuously monitor the swimmer’ condition via Wi-Fi to an IP address reachable by a mobile phone or laptop. It is also possible to continuously monitor the sensor outputs on the device’s display or the connected mobile phone or laptop. The threshold values could be adjusted based on biometric parameters such as swimming conditions (swimming pool, beach, depth, etc.) and …

A new, facile, low-cost, and green sol–gel route for the synthesis of the MgFe2O4@CoCr2O4 magnetic nanocomposite is reported. The photocatalytic performances of the prepared magnetic nanocomposite was investigated for the degradation of organic dye under visible light irradiation. The synthesized magnetic photocatalyst depicted high degradation performance for Reactive Blue 222 dye under the optimized conditions. The nanocomposite dosage, initial dye concentration, dark and visible light, irradiation time, and reusability of photocatalysis had a notable influence on dye degradation performance. Fourier transforms infrared spectroscopy (FTIR), powder X-ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy (SEM), dispersive X-ray analysis (EDX), Brunauer-Emmett-Teller (BET), vibrating sample magnetometer (VSM), UV–Vis diffuse reflectance spectroscopy (DRS …

This study presents computational fluid dynamics (CFD) analysis of the effects of wettability, viscosity and interfacial tension (IFT) for enhanced oil recovery (EOR) with varying pore configuration. A more realistic pore-throat geometry is studied which was motivated by oil-containing rock configuration that indicates the importance of pore geometry in EOR. The results are compared with those obtained for a simple geometry. Both saturated and unsaturated conditions are considered while the IFT varies. For both geometries, the saturated condition presents 99% of oil recovery, for the water-wet and intermediate states while it is about 88% for the oil-wet state. However, there is a significant difference in the temporal evolution of the oil recovery factor, as the complex model is 1.4 to 3 times slower than the simplified one to achieve maximum oil recovery factor under the same conditions. For unsaturated conditions …

This study uses computational fluid dynamics to model powder fluidisation in a turbulent channel flow. A two-fluid model is adopted for simulations. Closures are provided through the kinetic theory of granular flow where particle energy fluctuations are captured through granular temperature. Simulation results are compared to the experimental evacuation time data for a lactose carrier powder across different inlet Reynolds numbers. Different turbulence closures were tested, with the k-ε RNG model most aligned to experimental data. The effects of packing limit, coefficient of restitution and turbulence dispersion were also studied. Results show that the inlet turbulence intensity and dispersion models have marginal effects on evacuation, whereas maximum packing limit significantly influences powder fluidisation. The two-fluid model generates excellent agreement with the experimental data for all tested Reynolds …

It is critical to comprehend the safety aspects of hydrogen fuel cell vehicles (FCVs) in semi-confined and confined environments. The hydrogen jet fire is a key hazard resulting from coincidental hydrogen release from onboard storage followed by ignition. The rise in temperature and depletion of oxygen inside the tunnel may cause calamitous debacles. In this study, comprehensive computational fluid dynamics (CFD) simulations were designed to understand the interactions of multiple hydrogen fires in a confined environment. CFD simulations for hydrogen and liquefied petroleum gas (LPG) jet fires were conducted inside a reduced scale model tunnel. The model is initially validated against the experimental data for a single LPG fire scenario. A parametric study was then made to understand the impact of the fire location in the tunnel and the ventilation velocity. The results show an increasing-decreasing …

The formation of a butt weld bead occurs at the conjoined pipes during the process of butt-welding for pipelines in water and wastewater networks. Pressure losses in pipes with weld beads are often underestimated in design practice that affects the safety and reliability of the system. This paper focuses on the effect of weld beads and pressure loss in polyethylene (PE) pipes using computational fluid dynamics (CFD) simulations. The CFD model is first validated against the theoretical results and previous weld bead pressure loss experimental data. Both k-ε and k-ω turbulent models are examined, confirming k-ω shear stress transport (SST) model agree better with the theoretical pressure loss, which was considered for all inlet Reynolds numbers. The CFD simulations are conducted for long PE weal bead pipes with varying pipe diameters. The results confirm that as the pipe diameter increases the local …