The textile industry has a great role in the improvement of any country’s economy. Moreover, the ready-made garments need different coloured high yarn quality, so yarn should be rewinded on plastic cones for dyeing. However, manufacturers are facing the problem of tension variation during soft winding process that mainly affects the yarn quality. Consequently, to overcome the tension variation drawbacks, the attainment of constant optimal tension values is required in order to: (1) Increase the winding speed while maintaining the yarn quality, (2) Improve the dyeing quality, and (3) Reduce the water consumption during the dyeing process. In this paper, a proposed yarn tension control technique is introduced to upgrade the soft winding machine, thus maintain the yarn quality and improve the manufacturing capacity. The proposed technique has been tested on Polyester yarn samples classified as; fine, medium and coarse yarn counts, to cover most yarn sizes used in the industry. Arduino Mega 2560 controller is utilized to implement the proposed tension control. The results are compared to the conventional system to advocate the effectiveness and capability of the proposed technique in overcoming the trade-off between tension control and machine speed that occurs in conventional system using variable tension levels.

Chronic wounds are commonly colonized with bacteria in a way that prevents full healing process and capacity for repair. Nano-chitosan, a biodegradable and nontoxic biopolymer, has shown bacteriostatic activity against a wide spectrum of bacteria. Effectively, pulsed electromagnetic fields are shown to have both wound healing enhancement and antibacterial activity. This work aimed to combine the use of nano-chitosan and exposure to a pulsed electric field to overcome two common types of infectious bacteria, namely P. aeruginosa and S. aureus. Here, bacteria growing rate, growth kinetics and cell cytotoxicity (levels of lactate dehydrogenase, protein leakage and nucleic acid leakage) were investigated. Our findings confirmed the maximum antibacterial synergistic combination of nano-chitosan and exposure against P. aeruginosa than using each one alone. It is presumed that the exposure has influenced bacteria membrane charge distribution in a manner that allowed more chitosan to anchor the surface and enter inside the cell. Significantly, cell cytotoxicity substantiates high enzymatic levels as a result of cell membrane disintegration. In conclusion, exposure to pulsed electromagnetic fields has a synergistic antibacterial effect against S. aureus and P. aeruginosa with maximum inhibitory effect for the last one. Extensive work should be done to evaluate the combination against different bacteria types to get general conclusive results. The ability of using pulsed electromagnetic fields as a wound healing accelerator and antibacterial cofactor has been proved, but in vivo experimental work in the future to verify the use of such a new combination against infectious wounds and to determine optimum treatment conditions is a must.

Egypt faces extreme traffic congestion in its cities, which results in long travel times, large lines of parked cars, and increased safety hazards. Our study suggests a multi-modal approach that combines critical infrastructure improvements with cutting-edge technologies to address the ubiquitous problem of traffic congestion. Assuring vehicles owners of their timely arrival, cutting down on fuel usage, and improving communication using deep learning approach and optimization algorithm within the potential of IoT enabled 5G framework are the main goals. The traffic management system incorporates detection cameras, Raspberry Pi 3 microcontroller, an Android application, cloud connectivity, and traditional traffic lights that are powered using PV modules and batteries to secure the traffic controllers operation in case of grid outage and assure service continuity. The model examines the difficulties associated with Internet of Things (IoT) communication, highlighting possible interference from device-to-device (D2D) devices and cellular user equipment. This all-encompassing strategy aims to reduce fuel consumption, increase road safety and improve traffic efficiency. The model predicts a significant increase in Egypt’s urban mobility by utilizing the possibilities of IoT and 5G technologies, which would improve Egypt’s towns’ livability and efficiency. The goal of this paper is to modernize Egypt’s traffic management system and bring it into compliance with global guidelines for intelligent transportation networks.

Computational analysis is utilized to examine the effects of introducing nanoparticles to a square lid-driven cavity to enhance the hydro-magnet mixed convection. Constant temperatures are imposed along the square container’s vertical edges. Both the top and bottom surfaces are covered with insulation. The lid is thought to move in two ways: increasing or decreasing free convection. In addition, a horizontal magnetic field is applied uniformly. For various Hartmann numbers (Ha) (0:100), Richardson numbers (Ri) (0.001:10), and solid volume fractions (0:0.1), the results are reported. This research is based on a constant Grashof number (Gr) of 104. The effects of parameters, including the Richardson number, Hartmann number, solid volume percentage on the stream, isothermal lines, and local Nusselt numbers (Nu), were investigated numerically. In addition, given various parametric settings, the anticipated results for the average Nusselt (Nu_avg) are shown and discussed. For all tested parameters, increasing the magnetic field makes the orientation of the lid more effective on heat and fluid movement. The magnetic field reduces heat and fluid flow. The heat transmission is aided by increasing the solid volume percentage. The effect of nanoparticles on flow and heat transmission is being studied. At Ri = 0.001, the effect of lid orientation is not significant. The highest reduction in heat transfer occurs when adding flow at Ri = 10, = 0, and Ha = 100.

With a fast growth in world population, the citizens expect a highly sophisticated lifestyle with the help of new technologies. The heavy traffic and congestion on the road in the crowded urban cities have been occurred due to the increase of the population in these cities accordingly, the number of vehicles. As a result, the importance of traffic lights which give safety to the users on roads and reduce the traffic jam that causes great loose in both time and energy as fuel. This paper focuses on the identification of the conditions for establishing the optimum appropriate distance between two successive traffic lights in order to decrease the traffic jamming with optimizing the dynamic traffic light time that needed to reduce the jam. The proposed approach is based on the development of analytical model for the suggested idea and the assessment of its performance.

As buildings account for nearly one-third of global energy consumption, improving their energy performance and renewable integration is essential for achieving sustainability targets. Traditional building energy management systems (BEMS), often rule-based and static, struggle to adapt to fluctuating demands, variable tariffs, and the intermittency of solar resources. This study introduces an integrated, artificial intelligence (AI)-driven BEMS framework that jointly optimizes rooftop photovoltaic (PV) sizing and adaptive demand-side management (DSM) using reinforcement learning (Q-Learning) and benchmarks its performance against two established deterministic tools: HOMER Pro for techno-economic PV sizing and particle swarm optimization (PSO) for DSM load scheduling. Using realistic hourly building loads, meteorological data, and time-of-use pricing, the Q-Learning model converged to a PV–inverter configuration closely aligned with HOMER Pro’s optimum, achieving a slightly lower net present cost (–1.85%) and a modest increase in renewable fraction (+3.1%). In DSM applications, Q-Learning consistently outperformed PSO by shifting a larger share of flexible loads and securing higher daily cost reductions. Under grid-only conditions, Q-Learning reduced energy costs by 7.58% in winter and 8.27% in summer, while PV-integrated scenarios achieved savings of 35.14% and 26.89%, respectively. These results demonstrate that reinforcement learning can effectively enhance the performance of conventional BEMS approaches by providing more adaptive scheduling aligned with tariff structures and solar availability. The proposed framework supports more efficient, flexible, and sustainable building operations, highlighting the practical potential of AI-driven energy management in modern grid-interactive environments.