Chaotic maps that can provide highly secure key sequences and ease of structure implementation are predominant requirements in image encryption systems. One Dimensional (1D) chaotic maps have the advantage of a simple structure and can be easily implemented by software and hardware. However, key sequences produced by 1D chaotic maps are not adequately secure. Therefore, to improve the 1D chaotic maps sequence security, we propose two chaotic maps: 1D Improved Logistic Map (1D-ILM) and 1D Improved Quadratic Map (1D-IQM). The proposed maps have shown higher efficiency than existing maps in terms of Lyapunov exponent, complexity, wider chaotic range, and higher sensitivity. Additionally, we present an efficient and fast encryption method based on 1D-ILM and 1D-IQM to enhance image encryption system performance. This paper also introduces a key expansion method to reduce the number of chaotic map iteration needs, thereby decreasing encryption time. The security analyses and experimental results are confirmed that 2D Correlation Coefficient (CC) Information Entropy (IE), Number of Pixels Change Rate (NPCR), Unified Average Changing Intensity (UACI), Mean Absolute Error (MAE), and decryption quality are able to meet the encryption security demands (CC = −0.00139, IE = 7.9990, NPCR = 99.6114%, UACI = 33.46952% and MAE = 85.3473). Furthermore, the proposed keyspace reaches 10²⁴⁰, and the encryption time is 0.025s for an image with a size of 256 × 256. The proposed system can yield efficacious security results compared to obtained results from other encryption systems.

Vegetation encroachment along electric power transmission lines is one of the major environmental challenges that can cause power interruption. Many technologies have been used to detect vegetation encroachment, such as light detection and ranging (LiDAR), synthetic aperture radar (SAR), and airborne photogrammetry. These methods are very effective in detecting vegetation encroachment. However, they are expensive with regard to the coverage area. Alternatively, satellite imagery can cover a wide area at a relatively lower cost. In this paper, we describe the statistical moments of the color spaces and the textural features of the satellite imagery to identify the most effective features that can increase the vegetation density classification accuracy of the support vector machine (SVM) algorithm. This method aims to distinguish between high- and low-density vegetation regions along the power line corridor right-of-way (ROW). The results of the study showed that the statistical moments of the color spaces contribute positively to the classification accuracy while some of the gray level co-occurrence matrix (GLCM) features contribute negatively to the classification accuracy. Therefore, a combination of the most effective features was used to achieve a recall accuracy of 98.272%.

Circularly polarized synthetic aperture radar (CP-SAR) is known to be insensitive to polarization mismatch losses caused by the Faraday rotation effect and antenna misalignment. Additionally, the dual-circular polarimetric (DCP) mode has proven to have more polarimetric information than that of the corresponding mode of linear polarization, i.e., the dual-linear polarimetric (DLP) mode. Owing to these benefits, this paper investigates the feasibility of CP-SAR for rice monitoring. A ground-based CP-radar system was exploited, and C-band anechoic chamber data of a self-cultivated Japanese rice paddy were acquired from germination to ripening stages. Temporal variations of polarimetric observables derived from full-circular polarimetric (FCP) and DCP as well as synthetically generated DLP data are analyzed and assessed with regard to their effectiveness in phenology retrieval. Among different observations, the $H$ / $\overline{\alpha }$ plane and triangle plots obtained by three scattering components (surface, double-bounce, and volume scattering) for both the FCP and DCP modes are confirmed to have reasonable capability in discriminating the relevant intervals of rice growth.

The Internet of Things (IoT) application has been seen across various sectors to improve management practices. In medical waste management post-pandemic, the integration of IoT and artificial intelligence (AI) has notably enhanced sorting methods. The increased medical waste has brought about significant environmental challenges, impacting community health, land, and ocean ecosystems. We aim to develop a medical waste sorting system integrated with the You Only Look Once model (YOLO) and IoT for monitoring purposes. We can observe how effective the YOLO model is in sorting applications. This work trained and compared YOLO models from YOLO v5 to YOLO v9. YOLO v8 outperformed, and we implemented it into the sorting system. The mechanical, software, and programming elements were combined to develop the sorting prototype. From the training results, YOLO v8 achieved 98% mean average precision (mAP), 0.958 and 0.963 precision and recall, respectively. Other than that, the sorting evaluation was done during the final testing, with 93.75% accuracy. Further results were explained in the paper. The developed sorting prototype detected, classified, and sorted the medical waste, including facemasks, gloves, syringes, and sharp waste, based on the YOLO model. Further development is essential to improve the system in many aspects. Therefore, this system can be implemented practically in actual medical waste management. This work is a move that aligns with the third Sustainable Development Goal, where we focused on an automated medical waste system, ensuring sustainable health and Good Health and Well-being (SDG3).

Sabah electricity (SESB) operates a vertically integrated electricity utility in East Malaysia. It is currently embarking on an initiative to improve the reliability of its 11 kV distribution network which is prone to trippings due to vegetation growth along their long spur lines, Apart from rentice management, another proposed solution is to install auto-reclosers (AR) at appropriate locations in their network. This work deals in developing a methodology to determine the most economic location of ARs and their numbers to be installed. In order to do that, data in the form of customer types, load levels and network topology were collected from site visits at SESB. The study then used an estimated value of lost load (VoLL) to quantify the financial losses that customers suffer given a loss of supply. These losses would then be summed with the cost of ARs for a particular AR placement scenario to form an objective function. The best placement and numbers of AR can be decided by determining the least cost option from the said objective function.