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.

Soil erosion is characterized by the wearing away or loss of the uppermost layer of soil, driven by water, wind, and human activities. This process constitutes a significant environmental issue, with adverse effects on water quality, soil health, and the overall stability of ecosystems across the globe. This study focuses on the Anuppur district of Madhya Pradesh, India, employing the Revised Universal Soil Loss Equation (RUSLE) integrated with Geographic Information System (GIS) tools to estimate and spatially analyze soil erosion and fertility risk. The various factors of the model, like rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), conservation practices (P), and cover management factor (C), have been computed to measure annual soil loss in the district. Each factor was derived using geospatial datasets, including rainfall records, soil characteristics, a Digital Elevation Model (DEM), land use/land cover (LULC) data, and information on conservation practices. GIS methods are used to map the geographical variation of soil erosion, providing important information on the area's most susceptible to erosion. The outcome of the study reveals that 3371.23 km2, which constitutes 91% of the district's total area, is identified as having mild soil erosion; in contrast, 154 km2, or 4%, is classified as moderate soil erosion, while 92 km2, representing 2.5%, falls under the high soil erosion category. Additionally, 50 km2, or 1.35%, is categorized as very high soil erosion and around 30 km2 of the study area is classified as experiencing severe soil erosion. The analysis further discovers that the annual soil loss in the district varies between 0 and 151 tons per hectare per year. This study indicates that most of the district is classified under low soil erosion; only a tiny fraction of the area is categorized as experiencing high and very high soil erosion. The study provides significant insights into soil erosion for policymakers and human society to bring their attention to the need for sustainable soil conservation practices in the undulating terrain/topography and agriculturally dominated district of Anuppur.

Instant and accurate evaluation of drug resistance in tumors before and during chemotherapy is important for patients with advanced colon cancer and is beneficial for prolonging their progression-free survival time. Here, the possible biomarkers that reflect the drug resistance of colon cancer were investigated using proton magnetic resonance spectroscopy (1H-MRS) in vivo. SW480[5-fluorouracil(5-FU)-responsive] and SW480/5-FU (5-FU-resistant) xenograft models were generated and subjected to in vivo 1H-MRS examinations when the maximum tumor diameter reached 1–1.5 cm. The areas under the peaks for metabolites, including choline (Cho), lactate (Lac), glutamine/glutamate (Glx), and myoinositol (Ins)/creatine (Cr) in the tumors, were analyzed between two groups. The resistancerelated protein expression, cell morphology, necrosis, apoptosis, and cell survival of these tumor specimens were assessed. The content for tCho, Lac, Glx, and Ins/Cr in the tumors of the SW480 group was significantly lower than that of the SW480/5-FU group (p < 0.05). While there was no significant difference in the degree of necrosis and apoptosis rate of tumor cells between the two groups (p > 0.05), the tumor cells of the SW480/5-FU showed a higher cell density and larger nuclei. The expression levels of resistance-related proteins (P-gp, MPR1, PKC) in the SW480 group were lower than those in the SW480/5-FU group (p < 0.01). The survival rate of 5-FU-resistant colon cancer cells was significantly higher than that of 5-FUresponsive ones at 5-FU concentrations greater than 2.5 μg/mL (p < 0.05). These results suggest that alterations in tCho, Lac, Glx1, Glx2, and Ins/Cr detected by 1H-MRS may be used for monitoring tumor resistance to 5-FU in vivo.

The tunable conduction of coumarin-based composites has attracted considerable attention in a wide range of applications due to their unique chemical structures and fascinating properties. The incorporation of graphene oxide (GO) further enhances coumarin properties, including strong fluorescence, reversible photodimerization, and good thermal stability, expanding their potential use in advanced technological applications. This review describes the developmental evolution from GO, GO-polymer, and coumarin-based polymer to the coumarin-GO composite, concerning their synthesis, characterization, unique properties, and wide applications. We especially highlight the outstanding progress in the synthesis and structural characteristics along with their physical and chemical properties. Therefore, understanding their structure-property relations is very important to acquire scientific and technological information for developing the advanced materials with interesting performance in optoelectronic and energy applications as well as in the biomedical field. Given the expertise of influenced factors (e.g., dispersion quality, functionalization, and loading level) on the overall extent of enhancement, future research directions include optimizing coumarin-GO composites by varying the nanofiller types and coumarin compositions, which could significantly promote the development of next-generation polymer composites for specific applications.

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.

This study examines the spatial distribution of consumption competitiveness and carrying capacity across regions, exploring their interrelationship and implications for sustainable regional development. An evaluation index system is constructed for both consumption competitiveness and carrying capacity using a range of economic, social, and environmental indicators. We apply this framework to regional data in China and analyze the resultant spatial patterns. The findings reveal significant regional disparities: areas with strong consumption competitiveness are often concentrated in economically developed regions, while high carrying capacity is notable in less populated or resource-rich areas. Notably, a mismatch emerges in some regions—high consumer demand is not always supported by adequate carrying capacity, and vice versa. These disparities highlight potential sustainability challenges and opportunities. In the discussion, we address reasons behind the spatial mismatch and propose policy implications to better align consumer market growth with regional resource and environmental capacity. The paper concludes that integrating consumption-driven growth strategies with carrying capacity considerations is essential for balanced and sustainable regional development.