With the purpose of strengthening the knowledge and prevention of landslide disasters, this work develops a methodology that integrates geomorphological mapping with the elaboration of landslide susceptibility maps using geographic information systems (GIS) and the multiple logistic regression method (MLR). In Mexico, some isolated works have been carried out with GIS to evaluate slope stability. However, to date, no practical and standardized method has been developed to integrate geomorphological maps with landslide inventories using GIS. This paper shows the analysis carried out to develop a multitemporal landslide inventory together with the morphometric analysis and mapping technique for the El Estado River basin where, selected as the study area, is located on the southwestern slope of the Citlaltepetl or Pico de Orizaba volcano. The geological and geomorphological factors in combination with the high seasonal precipitation, the high degree of weathering and the steep slopes predispose its surfaces to landslides. To assess landslide susceptibility, a landslide inventory map was prepared using aerial photographs, followed by geomorphometric mapping (altimetry, slopes and geomorphology) and field work. With this information, landslide susceptibility was modeled using multiple logistic regression (MLR) within a GIS platform and the landslide susceptibility map was obtained.

First principles simulation studies using the density functional theory have been performed on (9, 0) Zigzag Singlewalled Carbon Nanotube (SWCNT) to investigate its electronic, optical and thermodynamic properties using CASTEP (Cambridge Sequential Total Energy Package) and DFTB (Density Functional based Tight Binding) modules of the Material Studio Software version 7.0. Various functionals and sub-functionals available in the CASTEP Module (using Pulay Density Mixing treatment of electrons) and various eigen-solvers and smearing schemes available in the DFTB module (using smart algorithm) have been tried out to chalk out the electronic structure. The analytically deduced values of the band gap obtained were compared with the experimentally determined value reported in the literature. By comparison, combination of Anderson smearing scheme and standard diaogonalizer produced best results in DFTB module while in the CASTEP module, GGA (General Gradient approximation) functional with RPBE (Revised-perdew-Burke-Ernzerh) as Sub-functional was found to be the most consistent. These optimized parameters were then used to determine various electronic, optical and thermodynamic properties of (9, 0) Singlewalled Nanotube. (9, 0) Singlewalled Nanotube, which is extensively being used for sensing NH₃, CH₄ & NO₂, has been picked up in particular as it is reported to exhibit a finite energy band gap in contrast to its expected metallic nature. The study is of utmost significance as it not only probes and validates the simulation route for predicting suitable properties of nanomaterials but also throws light on the comparative efficacy of the different approximation and rationalization quantum mechanical techniques used in simulation studies.

In the Fourth Industrial Revolution (4IR) era, the rapid digitalisation of services poses both opportunities and challenges for the banking sector. This study addresses how adopting artificial intelligence (AI) and online and mobile banking advancements can influence customer satisfaction, particularly in Kaduna State, Nigeria. Despite significant investments in AI and digital banking technologies, banks often struggle to align these innovations with customer expectations and satisfaction. Using Structural Equation Modeling (SEM), this research investigates the impact of customer satisfaction with online banking (C_O) on AI integration (I_A) and mobile banking convenience (C_M). The SEM model reveals that customer satisfaction with online banking significantly influences AI integration (path coefficient of 0.40) and mobile banking convenience (path coefficient of 0.68). These results highlight a crucial problem: while technological advancements in banking are growing, their effectiveness is highly dependent on customer satisfaction with existing digital services. The study underscores the need for banks to prioritise enhancing online banking experiences as a strategic lever to improve AI integration and mobile banking convenience. Consequently, the research recommends that Nigerian banks develop comprehensive frameworks to evaluate and optimise their technology integration strategies, ensuring that technological innovations align with customer needs and expectations in the rapidly evolving digital landscape.

The Oued Kert watershed in Morocco is essential for local biodiversity and agriculture, yet it faces significant challenges due to meteorological drought. This research addresses an urgent issue by aiming to understand the impacts of drought on vegetation, which is crucial for food security and water resource management. Despite previous studies on drought, there are significant gaps, including a lack of specific analyses on the seasonal effects of drought on vegetation in this under-researched region, as well as insufficient use of appropriate analytical tools to evaluate these relationships. We utilized the Standardized Precipitation Index (SPI) and the Normalized Difference Vegetation Index (NDVI) to analyze the relationship between precipitation and vegetation health. Our results reveal a very strong correlation between SPI and NDVI in spring (98%) and summer (97%), while correlations in winter and autumn are weaker (66% and 55%). These findings can guide policymakers in developing appropriate strategies and contribute to crop planning and land management. Furthermore, this study could serve as a foundation for awareness and education initiatives on the sustainable management of water and land resources, thereby enhancing the resilience of local ecosystems in the face of environmental challenges.

The major goal of decisions made by a business organization is to enhance business performance. These days, owners, managers and other stakeholders are seeking for opportunities of modelling and automating decisions by analysing the most recent data with the help of artificial intelligence (AI). This study outlines a simple theoretical model framework using internal and external information on current and potential clients and performing calculations followed by immediate updating of contracting probabilities after each sales attempt. This can help increase sales efficiency, revenues, and profits in an easily programmable way and serve as a basis for focusing on the most promising deals customising personal offers of best-selling products for each potential client. The search for new customers is supported by the continuous and systematic collection and analysis of external and internal statistical data, organising them into a unified database, and using a decision support model based on it. As an illustration, the paper presents a fictitious model setup and simulations for an insurance company considering different regions, age groups and genders of clients when analysing probabilities of contracting, average sales and profits per contract. The elements of the model, however, can be generalised or adjusted to any sector. Results show that dynamic targeting strategies based on model calculations and most current information outperform static or non-targeted actions. The process from data to decision-making to improve business performance and the decision itself can be easily algorithmised. The feedback of the results into the model carries the potential for automated self-learning and self-correction. The proposed framework can serve as a basis for a self-sustaining artificial business intelligence system.

The coconut industry has deep historical and economic importance in Sri Lanka, but coconut palms are vulnerable to water stress exacerbated by environmental challenges. This study explored using Sunn hemp (Crotalaria juncea L.) in major coconut-growing soils in Sri Lanka to improve resilience to water stress. The study was conducted at the Coconut Research Institute of Sri Lanka to evaluate the growth of Sunn hemp in prominent coconut soils—gravel, loamy, and sandy—to determine its cover crop potential. Sunn hemp was planted in pots with the three soil types, arranged in a randomized, complete design with 48 replicates. Growth parameters like plant height, shoot/root dry weight, root length, and leaf area were measured at 2, 4, 6, and 8 weeks after planting. Soil type significantly impacted all growth parameters. After 8 weeks, sandy soil showed the highest plant height and root length, while loamy soil showed the highest shoot/root dry weight and leaf area, followed by sandy and gravel soils. Nitrogen content at 6 and 8 weeks was highest in loamy soil plants. In summary, Sunn hemp produces more biomass in sandy soils, while loamy soils promote greater nutrient accumulation and growth. This suggests the suitability of Sunn hemp as a cover crop across major coconut-growing soils in Sri Lanka, improving resilience.