In this paper, steady two-dimensional laminar incompressible magnetohydrodynamic flow over an exponentially shrinking sheet with the effects of slip conditions and viscous dissipation is examined. An extended Darcy Forchheimer model was considered to observe the porous medium embedded in a non-Newtonian-Casson-type nanofluid. The governing equations were converted into nonlinear ordinary differential equations using an exponential similarity transformation. The resultant equations for the boundary values problem (BVPs) were reduced to initial values problems (IVPs) and then shooting and Fourth Order Runge-Kutta method (RK-4th method) were applied to obtain numerical solutions. The results reveal that multiple solutions occur only for the high suction case. The results of the stability analysis showed that the first (second) solution is physically reliable (unreliable) and stable (unstable).

An experimental study was undertaken to assess the heat-transfer coefficient (HTC) of graphene nanoplatelets-pentane nanofluid inside a gravity-assisted heat pipe (HP). Influence of various parameters comprising heat flux, mass fraction of the nanoparticles, installation angle and filling ratio (FR) of the working fluid on the HTC of the HP was investigated. Results showed that the HTC of the HP was strongly improved due to the presence of the graphene nanoplatelets. Also, by enhancing the heat flux, the HTC of the HP was improved. Two trade-off behaviors were identified. The first trade-off belonged to the available space in the evaporator and the heat-transfer coefficient of the system. Another trade-off was identified between the installation angle and the residence time of the working fluid inside the condenser unit. The installation angle and the FR of the HP were identified in which the HTC of the HP was the highest. The value of installation angle and filling ratio were 65° and 0.55, respectively. Likewise, the highest HTC was obtained at the largest mass fraction of the graphene nanoplatelets which was at wt. % = 0.3. The improvement in the HTC of the HP was ascribed to the Brownian motion and thermophoresis effects of the graphene nanoplatelets.

This study introduces an innovative and compact wearable Long-Range (LoRa) patch antenna developed for monitoring vital signs, with a focus on heart rate and body temperature, in medical applications of Wireless Body Area Networks (WBAN). The antenna functions within the 868 MHz and 915 MHz LoRa bands, filling a notable gap in current literature regarding compact, wearable antennas operating below 1 GHz. Fabricated on a Rogers Duroid RO3003™ substrate, the antenna incorporates a U-slot on a conventional rectangular patch, a matching stub, and a partial ground plane to enhance impedance matching and performance efficiency. Furthermore, the antenna displays a bidirectional radiation pattern in the E -plane and an omnidirectional pattern in the H -plane at both frequencies, achieving a peak gain of 2.12 dBi and a radiation efficiency of 99.8% at 868 MHz. The antenna, measuring 80×60mm ² (0.23 λ ₀ × 0.17 λ ₀ ), was designed, simulated, and optimized using CST Microwave Studio (MWS) software. The performance under bending conditions was also assessed, revealing a bending an excellent efficiency with minimal impact on bandwidth and gain. Specific Absorption Rate (SAR) analysis indicated that all values were within the safety limits set by FCC and ICNIRP standards. Supervised regression machine learning models, specifically the ensemble regression model, were employed to predict resonance frequencies based on various antenna parameters, resulting in an R-squared score of 87.68%. This approach significantly reduced the computational time required for full-wave simulations, streamlining the design process. Real-world experimental validation involved open-field testing of the fabricated prototype for WBAN LoRa applications. The performance, evaluated on a LoRa transceiver system utilizing the LoRa SX1276, demonstrated the superior capabilities of the proposed antenna in heart rate and temperature monitoring, with an average RSSI improvement of -5 dBm at various points within a range of up to 1 km. This confirmed its improved signal transmission and reception capabilities in vital sign monitoring. The proposed antenna shows strong performance metrics and significant potential for WBAN in long-range applications, as evidenced by thorough experimental validations.

Many wheelchair people depend on others to control the movement of their wheelchairs, which significantly influences their independence and quality of life. Smart wheelchairs offer a degree of self-dependence and freedom to drive their own vehicles. In this work, we designed and implemented a low-cost software and hardware method to steer a robotic wheelchair. Moreover, from our method, we developed our own Android mobile app based on Flutter software. A convolutional neural network (CNN)-based network-in-network (NIN) structure approach integrated with a voice recognition model was also developed and configured to build the mobile app. The technique was also implemented and configured using an offline Wi-Fi network hotspot between software and hardware components. Five voice commands (yes, no, left, right, and stop) guided and controlled the wheelchair through the Raspberry Pi and DC motor drives. The overall system was evaluated based on a trained and validated English speech corpus by Arabic native speakers for isolated words to assess the performance of the Android OS application. The maneuverability performance of indoor and outdoor navigation was also evaluated in terms of accuracy. The results indicated a degree of accuracy of approximately 87.2% of the accurate prediction of some of the five voice commands. Additionally, in the real-time performance test, the root-mean-square deviation (RMSD) values between the planned and actual nodes for indoor/outdoor maneuvering were 1.721 × 10⁻⁵ and 1.743 × 10⁻⁵, respectively.