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.

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 myo-inositol (Ins)/creatine (Cr) in the tumors, were analyzed between two groups. The resistance-related 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-FU-responsive 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.

Recent technological advances in the fields of biomaterials and tissue engineering have spurred interest in biopolymers for various biomedical applications. The advantage of biopolymers is their favorable characteristics for these applications, among which proteins are of particular importance. Proteins are explored widely for 3D bioprinting and tissue engineering applications, wound healing, drug delivery systems, implants, etc., and the proteins mainly available include collagen, gelatin, albumin, zein, etc. Zein is a plant protein abundantly present in corn endosperm, and it is about 80% of total corn protein. It is a highly renewable source, and zein has been reported to be applicable in different industrial applications. Lately, it has gained attention in biomedical applications. This research interest in zein is on account of its biocompatibility, non-toxicity, and certain unique physico-chemical properties. Zein comes under the GRAS category and is considered safe for biomedical applications. The hydrophobic nature of this protein gives it an added advantage and has wider applications in drug delivery. This review focuses on details about zein protein, its properties, and potential applications in biomedical sectors.

The purpose of this study is to look at the negative environmental impacts and social problems, which require a government response to maintain the sustainability of the palm oil industry. This research uses Online Research Methods (ORMs) to collect data and information through the internet and other digital technologies. The collected data was then coded using Nvivo 12 Plus. The purpose of this study is to fill the research void left by previous researchers by analyzing investment strategies and services in supporting the sustainability of the palm oil industry in Riau Province. This study shows that to support the potential of the palm oil industry to remain optimal, the central and local governments coordinate to provide investment services and pay attention to the sustainability issues of the palm oil industry. Some important aspects to consider are strengthening regulations, an integrated plantation licensing system, improving access to markets, RSPO certification, realization of foreign investment, downstream industry, replanting programme, plantation revitalisation programme, and sustainable plantation partnerships. However, there are still some crucial challenges, particularly land conflicts, climate change, environmental issues, limited technology and innovation, and export market dependence. These challenges may hamper future investment opportunities.

This contribution aims to appraise, analyze and evaluate the literature relating to the interaction of electromagnetic fields (EMF) with matter and the resulting thermal effects. This relates to the wanted thermal effects via the application of fields as well as those uninvited resulting from exposure to the field. In the paper, the most popular EMF heating technologies are analyzed. This involves on the one hand high frequency induction heating (HFIH) and on the other hand microwave heating (MWH), including microwave ovens and hyperthermia medical treatment. Then, the problem of EMF exposure is examined and the resulting biological thermal effects are illuminated. Thus, the two most common cases of wireless EMF devices, namely digital communication tools and inductive power transfer appliances are analyzed and evaluated. The last part of the paper concerns the determination of the different thermal effects, which are studied and discussed, by considering the governing EMF and heat transfer (or bio heat) equations and their solution methodologies.