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.

In this review are developed insights from the current research work to develop the concept of functional materials. This is understood as real modified substrates for varied applications. So, functional and modified substrates focused on nanoarchitectures, microcapsules, and devices for new nanotechnologies highlighting life sciences applications were revised. In this context, different types of concepts to proofs of concepts of new materials are shown to develop desired functions. Thus, it was shown that varied chemicals, emitters, pharmacophores, and controlled nano-chemistry were used for the design of nanoplatforms to further increase the sizes of materials. In this regard, the prototyping of materials was discussed, affording how to afford the challenge in the design and fabrication of new materials. Thus, the concept of optical active materials and the generation of a targeted signal through the substrate were developed. Moreover, advanced concepts were introduced, such as the multimodal energy approach by tuning optical coupling from molecules to the nanoscale within complex matter composites. These approaches were based on the confinement of specific optical matter, considering molecular spectroscopics and nano-optics, from where the new concept nominated as metamaterials was generated. In this manner, fundamental and applied research by the design of hierarchical bottom-up materials, controlling molecules towards nanoplatforms and modified substrates, was proposed. Therefore, varied accurate length scales and dimensions were controlled. Finally, it showed proofs of concepts and applications of implantable, portable, and wearable devices from cutting-edge knowledge to the next generation of devices and miniaturized instrumentation.

Land use changes have been demonstrated to exert a significant influence on urban planning and sustainable development, particularly in regions undergoing rapid urbanization. Tehran Province, as the political and economic capital of Iran, has undergone substantial growth in recent decades. The present study employs sophisticated Geographic Information System (GIS) instruments and the Google Earth Engine (GEE) platform to comprehensively track and analyze land use change over the past two decades. A comprehensive analysis of Landsat images of the Tehran metropolitan area from 2003 to 2023 has yielded significant insights into the patterns of land use change. The methodology encompasses the utilization of GIS, GEE, and TerrSet techniques for image classification, accuracy assessment, and change detection. The Kappa coefficients for the maps obtained for 2016 and 2023 were 0.82 and 0.87 for four classes: built-up, vegetation cover, barren land, and water bodies. The findings suggest that, over the past two decades, Tehran Province has undergone a substantial decline in ecological and vegetative areas, amounting to 2.4% (458.3 km²). Concurrently, the urban area and the barren lands have expanded by 287.5 and 125.5 km², respectively. The increase in water bodies during this period is likely attributable to the reduction of vegetation cover and dam construction in the region. The present study demonstrates that remote sensing and GIS are excellent tools for monitoring environmental and sustainable urban development in areas experiencing rapid urbanization and land use changes.

Cobalt-based sulfides have emerged as promising candidates for next-generation high-performance anode materials for lithium-ion batteries (LIBs) due to their high theoretical specific capacity and reversible conversion reaction mechanisms. However, their practical application is hindered by volume expansion effects and relatively low rate performance. Guided by theoretical principles, this study synthesizes nanoscale Bi/CoS-C and Bi/Co₄S₃-C (denoted as Bi/CS-C) composite materials using Co and Bi₂S₃ as precursors via a solid-state ball milling method. The electrochemical properties of these materials were systematically investigated. When employed as anodes for LIBs, Bi/CoS-C and Bi/CS-C exhibit excellent rate capabilities. At current densities of 0.1, 0.5, 1, 4, and 10 A/g, the reversible capacities of Bi/CoS-C were 939.2, 730.7, 655.6, 508.1, and 319 mAh/g, respectively. In contrast, Bi/CS-C exhibited reversible capacities of 760.4, 637.6, 591.9, 484.3, and 295.4 mAh/g, respectively. Moreover, Co₄S₃, as an active component, enables superior long-cycle performance compared to CoS. After 300 cycles at 0.2 A/g, the Bi/CoS-C and Bi/CS-C electrodes retained capacities of 193.1 and 788.8 mAh/g, respectively. This study demonstrates that nanostructure design and carbon-based composite materials can effectively mitigate the volume expansion issue of cobalt-based sulfides, thereby enhancing their rate performance and cycling stability. This strategy provides new insights for the development of high-performance anode materials for lithium-ion batteries and is expected to accelerate their practical application in next-generation energy storage devices.

The role of trace gases in the storage of heat in the atmosphere of the Earth and in the exchange of energy between the atmosphere and outer space is discussed. The molar heat capacities of the trace gases water vapor, carbon dioxide and methane are only slightly higher than those of nitrogen and oxygen. The contribution of trace gases carbon dioxide and methane to heat storage is negligible. Water vapor, with its higher concentration and conversion energies, contributes significantly to the heat storage in the atmosphere. Most of the heat in the Earth’s atmosphere is stored in nitrogen and oxygen, the main components of the atmosphere. The trace gases act as converters of infrared radiation into heat and vice versa. They are receivers and transmitters in the exchange of energy with outer space. The radiation towards space is favored compared to the reflection towards the surface of the Earth with increasing altitude by decreasing the density of the atmosphere and condensation of water vapor. Predictions of the development of the climate over a century by extrapolation are critically assessed.

Global energy agencies and commissions report a sharp increase in energy demand based on commercial, industrial, and residential activities. At this point, we need energy-efficient and high-performance systems to maintain a sustainable environment. More than 30% of the generated electricity has been consumed by HVAC-R units, and heat exchangers are the main components affecting the overall performance. This study combines experimental measurements, numerical investigations, and ANN-aided optimization studies to determine the optimal operating conditions of an industrial shell and tube heat exchanger system. The cold/hot stream temperature level is varied between 10 ℃ and 50 ℃ during the experiments and numerical investigations. Furthermore, the flow rates are altered in a range of 50–500 L/h to investigate the thermal and hydraulic performance under laminar and turbulent regime conditions. The experimental and numerical results indicate that U-tube bundles dominantly affect the total pumping power; therefore, the energy consumption experienced at the cold side is about ten times greater the one at the hot side. Once the required data sets are gathered via the experiments and numerical investigations, ANN-aided stochastic optimization algorithms detected the C10H50 scenario as the optimal operating case when the cold and hot stream flow rates are at 100 L/h and 500 L/h, respectively.