The understanding of learning materials by students is the primary goal of education. However, this is not easily achieved by students. Teachers must make various innovations so that students can easily grasp the learning materials. Free-falling objects are one of the main topics in physics. Learning materials can be delivered through project-based activities in the classroom. Through class projects, empirical research can be conducted by both students and teachers. This article presents the tools and testing results related to the motion of granular materials as project-based learning. The flow rate of granular material in this project will illustrate the relationship between distance and time in free-fall motion. Therefore, this research designs and constructs a granular flow rate measurement system based on multiple sensors and a microcontroller to demonstrate the concept of free-falling objects through project-based learning. The method used is the design and construction of a device consisting of electronic and mechanical systems. The granular motion will be detected by the sensors. The main part of the electronic system consists of a microcontroller and five infrared sensors, which include five transmitters and five receivers. The mechanical system consists of a granular holding platform. Several types of granular materials are used for testing the flow rate measurement system. The lowest flow rate among the tested granular materials is around 70 grams/s for basil seeds, and the highest flow rate is for colorstone, with a flow rate of around 200 grams/s. The results also align with the basic physics concept of freefalling objects, which states that velocity increases as they approach the earth's surface due to the influence of gravity and distance. With the results obtained, this project-based learning device can be used to validate existing theoretical concepts.

Water waste, particularly in home shower systems, remains a significant global concern, with showers accounting for substantial water consumption. This research proposes an IoT-based solution to mitigate water wastage using smart technology. According to surveys, the average shower duration is approximately 8 minutes, with a flow rate averaging 8 Liters per minute, resulting in significant water use per shower. Our approach integrates IoT technology, utilizing Arduino as a gateway device for data management. Water usage data is collected and stored in a cloud-based platform, Thing Speak, enabling users to monitor consumption patterns daily, monthly, and annually. The system employs hardware components including a solenoid valve, Arduino microcontroller, ESP 8266 WiFi module, LCD display, and sound player, complemented by software components like the Arduino IDE and ThinkSpeak database. Operationally, upon activation, the system controls water flow for the initial four minutes before alerting users to conserve water through visual and auditory cues. This study’s methodology involves the design and implementation of an IoT-enabled smart shower system, demonstrating its efficacy in reducing water consumption through real-time monitoring and user feedback. Results indicate a significant reduction in water usage compared to conventional shower systems, thereby highlighting the potential of IoT technology in promoting sustainable water management practices at the household level. 

The healthcare landscape is rapidly evolving with the integration of advanced technologies to enhance patient care, monitoring, and overall medical practices. In this era of innovation, Light-Fidelity (Li-Fi) has emerged as a promising solution with the potential to revolutionize patient monitoring systems. This research aims to address current limitations in Li-Fi-based patient monitoring systems, such as data security concerns and the inability to provide continuous monitoring without on-site medical personnel. It is driven by the urgent need to tackle critical healthcare challenges arising from a significant shortage of medical personnel, particularly in certain regions and countries. The objective is to develop a Li-Fi-based patient monitoring system that can remotely and continuously monitor patient vital signs and medical data. The methodology involves a comprehensive approach that integrates advanced technology, data collection, data processing, and web application development. Results indicate that the developed system prioritizes performance and security, with evaluations based on latency, security vulnerabilities, and data throughput. This research advances Li-Fi's potential in healthcare, paving the way for innovative applications that can enhance patient care, improve healthcare outcomes, and potentially transform the entire healthcare industry.

In this study, we investigate the effectiveness of deep learning models with thermal images for gender categorization. In order to explore the possibilities of thermal imaging as a tool for gender identification, the study focuses on two sophisticated convolutional neural network (CNN) architectures: InceptionV3 and AlexNet. Thermal imaging is a powerful substitute for traditional visual data because it provides distinct physiological insights.A collection of thermal imaging datasets was assembled, methodically preprocessed, and divided into training and testing sets. For this comparison analysis, two well-known CNNs AlexNet, a fundamental model recognised for its straightforward yet efficient design, and InceptionV3, a complex model acclaimed for its inception modules were chosen. The training subset was used to carefully refine both models so they could accurately capture the subtleties of thermal-based gender traits.Accuracy was the main criterion used to assess the performance of the revised models on the testing subset. According to our results, InceptionV3 performs noticeably better than AlexNet, with an accuracy of 92.3% as opposed to 82.6% for AlexNet. This disparity in performance demonstrates how much better InceptionV3 is at identifying and deciphering minute thermal patterns and physiological indicators that are essential for precise gender categorization. This study highlights how sophisticated CNN architectures may improve gender categorization using thermal images, both in terms of accuracy and dependability. We provide a path for future research to investigate more intricate and integrated strategies, like multi-modal fusion and sophisticated feature extraction techniques, to further enhance the resilience of thermal-based gender classification systems by proving the efficacy of InceptionV3 over a more conventional model like AlexNet.

This study's subject is the effectiveness of substituting Thar Coal Fly Ash (TCFA) for ordinary Portland cement, also known as OPC. Tharparkar, Pakistan, possesses the world’s third largest coal reserves, with deposited coal fuel of 175 billion tons and capable of providing energy for over 200 years. Thar Coal is a lignite type that produces 7-10% of by-products in ashes; among them, Fly Ash is a significant waste. Reusing this waste as a partial cement replacement offers an environmentally friendly solution. This study prepared concrete specimens with varying proportions of TCFA (0%, 10%, 20%, and 30% by mass) as cement substitutes. Compressive strength tests were conducted on 36 cubes (100mm x 100mm x 100mm) with different fly ash percentages at a proportion to water to cement of 0.52. Ages 7, 14, and 28 days for curing were considered. The findings demonstrate that a higher TCFA component enhances the workability of the concrete. At all curing ages, the strength in compression at a 20% TCFA replacement level was greater than that of standard concrete. However, as the cement replacement was increased to 30%, there was a slight decrease in the comparative compressive strength compared to regular concrete. The tensile strength of the splitting test, performed after twenty-eight days of curing age, reveals that it surpassed conventional concrete for all replacement levels. Considering the favorable outcomes in workability, constrictive strength, durability strength, and substantial economic and environmental benefits, there is much potential for using TCFA as a cement substitute in the construction sector.