The destructive geohazard of landslides produces significant economic and environmental damages and social effects. State-of-the-art advances in landslide detection and monitoring are made possible through the integration of increased Earth Observation (EO) technologies and Deep Learning (DL) methods with traditional mapping methods. This assessment examines the EO and DL union for landslide detection by summarizing knowledge from more than 500 scholarly works. The research included examinations of studies that combined satellite remote sensing information, including Synthetic Aperture Radar (SAR) and multispectral imaging, with up-to-date Deep Learning models, particularly Convolutional Neural Networks (CNNs) and their U-Net versions. The research categorizes the examined studies into groups based on their methodological development, spatial extent, and validation techniques. Real-time EO data monitoring capabilities become more extensive through their use, but DL models perform automated feature recognition, which enhances accuracy in detection tasks. The research faces three critical problems: the deficiency of training data quantity for building stable models, the need to improve understanding of AI's predictions, and its capacity to function across diverse geographical landscapes. We introduce a combined approach that uses multi-source EO data alongside DL models incorporating physical laws to improve the evaluation and transferability between different platforms. Incorporating explainable AI (XAI) technology and active learning methods reduces the uninterpretable aspects of deep learning models, thereby improving the trustworthiness of automated landslide maps. The review highlights the need for a common agreement on datasets, benchmark standards, and interdisciplinary team efforts to advance the research topic. Research efforts in the future must combine semi-supervised learning approaches with synthetic data creation and real-time hazardous event predictions to optimise EO-DL framework deployments regarding landslide danger management. This study integrates EO and AI analysis methods to develop future landslide surveillance systems that aid in reducing disasters amid the current acceleration of climate change.

Exposure to high-frequency (HF) electromagnetic fields (EMF) has various effects on living tissues involved in biodiversity. Interactions between fields and exposed tissues are correlated with the characteristics of the exposure, tissue behavior, and field intensity and frequency. These interactions can produce mainly adverse thermal and possibly non-thermal effects. In fact, the most expected type of outcome is a thermal biological effect (BE), where tissues are materially heated by the dissipated electromagnetic energy due to HF-EMF exposure. In case of exposure at a disproportionate intensity and duration, HF-EMF can induce a potentially harmful non-thermal BE on living tissues contained within biodiversity. This paper aims to analyze the thermal BE on biodiversity living tissues and the associated EMF and bio-heat (BH) governing equations.

The rapid growth of e-commerce in South Africa has increased the demand for efficient last-mile delivery. Motorcycle delivery drivers play a crucial role in the last-mile delivery process to bridge the gap between retailers and consumers. However, these drivers face significant challenges that impact both logistical efficiency and their socio-economic well-being. This study critically analyzes media narratives on the safety and working conditions of motorcycle delivery drivers in the e-commerce sector in South Africa. The thematic analysis of newspaper articles identified recurring themes. This study reveals critical safety and labor vulnerabilities affecting motorcycle delivery drivers in South Africa’s e-commerce sector. Key findings include heightened risks of violence, hijackings, and road accidents, exacerbated by inadequate infrastructure and safety gear. Coupled with low wages, job insecurity, and limited benefits, these conditions expose drivers to significant precarity. Policy interventions are urgently needed for driver safety and sustainable logistics. By integrating insights from multiple disciplines, this study offers a comprehensive understanding of the complex challenges within this rapidly growing sector.

Industrial plastics have seen considerable progress recently, particularly in manufacturing non-lethal projectile holders for shock absorption. In this work, a variety of percentages of alumina (Al2O3) and carbon black (CB) were incorporated into high-density polyethylene (HDPE) to investigate the additive material effect on the consistency of HDPE projectile holders. The final product with the desired properties was controlled via physical, thermal, and mechanical analysis. Our research focuses on nanocomposites with a semicrystalline HDPE matrix strengthened among various nanocomposites. In the presence of compatibility, mixtures of variable compositions from 0 to 3% by weight were prepared. The reinforcement used was verified by X-ray diffraction (XRD) characterization, and thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) were used for thermal property investigation. Alumina particles increased the composites’ thermal system and glass transition temperature. Mechanical experiments indicate that incorporating alumina into the matrix diminishes impact resistance while augmenting static rupture stress. Scanning electron microscopy (SEM) revealed a consistent load distribution. Ultimately, we will conduct a statistical analysis to compare the experimental outcomes and translate them into mathematical answers that elucidate the impact of filler materials on the HDPE matrix.

This study explores approaches to optimizing inclusive education through international and local perspectives. It examines the role of educators in inclusive settings, highlights strategies for early detection of children’s developmental needs, and evaluates inclusive school management practices. Using qualitative case study methods, the research includes comprehensive observations and interviews at Fatma Kenanga Islamic Character School. Findings emphasize the importance of individualized learning plans, shadow teacher involvement, and collaborative stakeholder engagement. Integrating global insights, this study contributes to advancing inclusive education practices in Indonesia and beyond.