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.

This study comprehensively evaluates the system performance by considering the thermodynamic and exergy analysis of hydrogen production by the water electrolysis method. Energy inputs, hydrogen and oxygen production capacities, exergy balance, and losses of the electrolyzer system were examined in detail. In the study, most of the energy losses are due to heat losses and electrochemical conversion processes. It has also been observed that increased electrical input increases the production of hydrogen and oxygen, but after a certain point, the rate of efficiency increase slows down. According to the exergy analysis, it was determined that the largest energy input of the system was electricity, hydrogen stood out as the main product, and oxygen and exergy losses were important factors affecting the system performance. The results, in line with other studies in the literature, show that the integration of advanced materials, low-resistance electrodes, heat recovery systems, and renewable energy is critical to increasing the efficiency of electrolyzer systems and minimizing energy losses. The modeling results reveal that machine learning programs have significant potential to achieve high accuracy in electrolysis performance estimation and process view. This study aims to contribute to the production of growth generation technologies and will shed light on global and technological regional decision-making for sustainable energy policies as it expands.

The current manuscript overviews the potential of inimitable zero dimensional carbon nanoentities, i.e., nanodiamonds, in the form of hybrid nanostructures with allied nanocarbons such as graphene and carbon nanotube. Accordingly, two major categories of hybrid nanodiamond nanoadditives have been examined for nanocompositing, including nanodiamond-graphene or nanodiamond/graphene oxide and nanodiamond/carbon nanotubes. These exceptional nanodiamond derived bifunctional nanocarbon nanostructures depicted valuable structural and physical attributes (morphology, electrical, mechanical, thermal, etc.) owing to the combination of intrinsic features of nanodiamonds with other nanocarbons. Consequently, as per literature reported so far, noteworthy multifunctional hybrid nanodiamond-graphene, nanodiamond/graphene oxide, and nanodiamond/carbon nanotube nanoadditives have been argued for characteristics and potential advantages. Particularly, these nanodiamond derived hybrid nanoparticles based nanomaterials seem deployable in the fields of electromagnetic radiation shielding, electronic devices like field effect transistors, energy storing maneuvers namely supercapacitors, and biomedical utilizations for wound healing, tissue engineering, biosensing, etc. Nonetheless, restricted research traced up till now on hybrid nanodiamond-graphene and nanodiamond/carbon nanotube based nanocomposites, therefore, future research appears necessary for further precise design varieties, large scale processing, and advanced technological progresses.

This paper examines the detrimental impact of rapid inflation on the quality of private education in developing countries. By focusing on the financial challenges faced by private schools, the study highlights the tension between education policy and economic realities. While private schools often attract parents with smaller class sizes and specialized programs, the core motivation lies in investing in children’s future through quality education. However, this study demonstrates how inflation can cripple this sector. The case of Turkey exemplifies this challenge. Post-pandemic inflation created a financial stranglehold on private schools, as rising costs made it difficult to adjust teacher salaries. This, in turn, led to teacher demotivation and a mass exodus, ultimately compromising educational quality. Furthermore, government interventions aimed at protecting parents from high tuition fees, through limitations on fee increases, inadvertently sacrificed the very quality they sought to safeguard. The paper concludes by advocating for alternative policy approaches that prioritize direct support for education system during economic downturns. Such measures are crucial for ensuring a strong and resilient education system that benefits all stakeholders, including parents, students, and the nation as a whole.