This study examines the influence of internal and external locus of control as mediators of financial literacy, financial attitudes, financial beliefs, and financial behavior of students in Timor-Leste. This study uses a quantitative approach with a survey method to collect sample data from students throughout Timor-Leste. Structural equation modeling (SEM) analyzes the relationship between financial literacy, financial attitudes, financial beliefs, internal and external locus of control, and financial behavior. The study’s results highlight the mediating role of internal and external locus of control in the relationship between financial literacy, financial attitudes, financial beliefs, and financial behavior of students in Timor-Leste. These findings can provide insight into the complex relationship between these factors in financial decision-making. Practical implications for educational institutions and policymakers in Timor-Leste, namely emphasizing the importance of considering internal and external locus control in financial literacy programs to improve students’ financial behavior. This study aims to fill the knowledge gap about student financial literacy by expanding the understanding of the relationship between these factors.

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. Ad

Modified chitosan hybrids were obtained via chemical reaction of chitosan with two pyrazole aldehyde derivatives to produce two chitosan Schiff bases, Cs-SB1, and Cs-SB2, respectively. FTIR spectroscopy and scanning electron microscopy confirmed both chemical structures and morphology of these Schiff bases. Thermal gravimetric analysis showed an improvement of thermal properties of these Schiff bases. Both chitosan Schiff bases were evaluated in a batch adsorption approach for their ability to remove Cu(II) ions from aqueous solutions. Energy dispersive X-ray for the Schiff bases adsorbed metal ions in various aqueous solutions was performed to confirm the existence of adsorbed metal ions on the surface substrate and their adsorptive efficiency for Cu(II) ions. Results of the batch adsorption method showed that prepared Schiff bases have good ability to remove Cu(II) ions from aqueous solutions. The Langmuir isotherm equation showed a better fit for both adsorbents with regression coefficients (R² = 0.97 and 0.99, respectively) with maximum adsorption capacity for Cu(II) of 10.33 and 39.84 mg/g for Cs-SB1 and Cs-SB2, respectively. All prepared compounds, pyrazoles and two chitosan Schiff bases, showed good antimicrobial activity against three Gram +ve bacteria, three Gram –ve bacteria and Candida albicans, with varying degrees when compared to the standard antimicrobial agents.

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.

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.