Polymer-dispersed liquid crystal (PDLC) film is an active smart film penetrating the market due to its unique functionalities. These functional characteristics include switchable tint capabilities, which shield building residents from the sun’s harmful ultraviolet (UV) rays, improve energy-saving features, and produce higher cost-efficiency. Although PDLC films are promising in several applications, there is still ambiguity on the performance of PDLC films. Particularly, the sizing effects’ (such as film thickness and area) correlation with visible light transmission (VLT), ultraviolet rejection (UVR), infrared rejection (IRR), light intensity, current consumption, and apparent power consumption is not well understood. Therefore, this study investigated the sizing effects of PDLC films, including the thickness effect on VLT, UVR, IRR, light intensity, and area influence on current and apparent power consumptions. The varying applied voltage effect on the light transmittance of the PDLC film was also effectively demonstrated. A 0.1 mm PDLC film was successfully presented as a cost-efficient film with optimal parameters. Consequently, this study paves the way for a clearer understanding of PDLC films (behavior and sizing effects) in implementing economic PDLC films for large-scale adoption in commercial and residential premises.

Electrochromic devices (ECDs) are devices that change their optical properties in response to a low applied voltage. These devices typically consist of an electrochromic layer, a transparent conducting substrate, and an electrolyte. The advancement in solid-state ECDs has been driven by the need for improved durability, optical performance, and energy efficiency. In this study, we investigate varying the temperature to the casting solution for polymethylmethacrylate (PMMA)-based electrolytes for solid-state ECDs with a structure of glass/ITO/WO₃/PMMA electrolyte/ITO/glass. The electrochromic layer, composed of WO₃, was deposited using the sol-gel method, while the electrolyte, comprising lithium perchlorate (LiClO₄) in propylene carbonate (PC) with PMMA, was prepared via solution casting. Various electrolyte samples were heated at different temperatures of 25, 40, 60, 80, and 100 °C to analyze the impact on the devices’ performance. Our findings indicate that the devices with electrolytes at 25 °C exhibited superior anodic and cathodic diffusion. An increase in heating temperature corresponded with an increase in switching time. Notably, the sample heated at higher temperatures (60, 80, and 100 °C) demonstrated exceptional cycle stability. Nevertheless, samples with higher temperatures displayed a decrease in optical modulation. Additionally, the 100 °C sample exhibited the highest coloration efficiency compared to other samples at lower temperatures. This research highlights the potential of varying the temperature of solution casting on PMMA-based electrolytes in optimizing the performance of solid-state ECDs, particularly regarding coloration efficiency and durability.

Access control systems protect homes and businesses in the continually evolving security industry. This paper designs and implements a Raspberry Pi-based facial recognition door lock system using artificial intelligence and computer vision for reliability, efficiency, and usability. With the Raspberry Pi as its CPU, the system uses facial recognition for authentication. A camera module for real-time image capturing, a relay module for solenoid lock control, and OpenCV for image processing are essential. The system uses the DeepFace library to detect user emotions and adaptive learning to improve recognition accuracy for approved users. The device also adapts to poor lighting and distances, and it sends real-time remote monitoring messages. Some of the most important things that have been achieved include adaptive facial recognition, ensuring that the system changes as it is used, and integrating real-time notifications and emotion detection without any problems. Face recognition worked well in many settings. Modular architecture facilitated hardware–software integration and scalability for various applications. In conclusion, this study created an intelligent facial recognition door lock system using Raspberry Pi hardware and open-source software libraries. The system addresses traditional access control limits and is practical, scalable, and inexpensive, demonstrating biometric technology’s potential in modern security systems.

With the recent advancement in Internet-of-Thing (IoT) technology, smart farming has rapidly evolved as an alternative solution to improve the effectiveness of plant growth and provide a better farming management. The implementation of smart farming involves the use of technology such as big data, the internet of things (IoT), and cloud servers. It is widely known as precision farming because the process of plant growth is managed by software and a sensor-monitoring system. The applications of smart farming are widely implemented in indoor farming especially hydroponic farming. Hydroponic indoor farming is a fast-growth sector due to the lack of land for agriculture caused by urbanization. For indoor farming, grow-light is a crucial factor for plant growth. The most effective and commonly used grow light is the Light Emitting Diodes (LED) grow light. The utilization of LED grows light offers many advantages over traditional grow lighting including less power consumption, generating lower heat and recyclable. Multiple research and investigations have been done on LED’s wavelength and intensities effect on the plants. However, the duration of exposure to LED grow light on the plants has the most significant influences on the growth rate of plants. In this project, an investigation into the photoperiods of the LED grow-light has been deployed for lettuce. The photoperiods are 14 hours of LED, 8 hours LED, combination of 8 hours sunlight and 3 hours LED, and compare with 8 hours sunlight. The presented results concluded that 14 hours of LED grow light duration on hydroponic vegetables has the most promising growth rate in indoor hydroponics lettuce farming.

Electrochromic (EC) smart windows are a type of glass window that can change from transparent to darker colour shades when a small voltage is applied. Titanium dioxide (TiO2) can be utilised as an EC material for EC smart windows. Although the TiO2 sol-gel spin-coating method is commonly used, the effect of the number of TiO2 layers was not reported. Thus, this paper investigates the effect of the number of TiO2 layers. The increasing number of TiO2 film layers demonstrated a noticeable increase in the thin films ‘anodic and cathodic diffusion coefficient, particularly with 11 TiO2 layers. Additionally, the colouration and bleaching time was revealed to have a low correlation as the number of layers increased. Nonetheless, the lower number of TiO2 layers resulted in lower colouring transmittance. Comparatively, the colouration efficiency for all films did not exhibit any significant change. Hence this study on the effect of the TiO2 layering technique can open a new pathway in understanding the EC properties of TiO2-based EC devices.

Access control systems protect homes and businesses in the continually evolving security industry. This paper designs and implements a Raspberry Pi-based facial recognition door lock system using artificial intelligence and computer vision for reliability, efficiency, and usability. With the Raspberry Pi as its CPU, the system uses facial recognition for authentication. A camera module for real-time image capturing, a relay module for solenoid lock control, and OpenCV for image processing are essential. The system uses the Deep Face library to detect user emotions and adaptive learning to improve recognition accuracy for approved users. The device also adapts to poor lighting and distances and sends real-time remote monitoring messages. The system's average face identification time is 564.43 ms, and emotion detection time is 327.63ms, ensuring real-time performance. Major achievements include introducing adaptive facial recognition, ensuring the system evolves with usage, and smoothly integrating real-time notifications and emotion detection. Face recognition accuracy was high in various environments. Modular architecture facilitated hardware-software integration and scalability for varied applications. In conclusion, this study created an intelligent facial recognition door lock system using Raspberry Pi hardware and open-source software libraries. The system addresses traditional access control limits and is practical, scalable, and inexpensive, demonstrating biometric technology's potential in modern security systems.