Abstract Soil–structure interaction (SSI) is an essential concept in earthquake engineering that explains how the ground and a structure influence each other during seismic shaking. While SSI can occasionally reduce structural demands, its overall impact is case-dependent and has contributed to observed damage in earthquakes such as Mexico (1985), Kobe (1995), and Adana–Ceyhan (1998). This study provides an evaluative review of SSI research, focusing on peer-reviewed studies employing advanced numerical modeling under dynamic seismic loading. Among the various approaches, the Finite Element Method (FEM) and the coupled Finite Element–Boundary Element Method (FEM–BEM) are the most widely adopted, mainly because of their strong theoretical foundation in continuum mechanics and their effectiveness in modeling wave propagation. Rising techniques such as the Finite–Discrete Element Method (FDEM) and the Applied Element Method (AEM) have gained attention for their ability to capture highly nonlinear behavior, including progressive collapse and soil discontinuities under extreme conditions. The review also identifies several key factors that govern SSI behavior, including the structure-to-soil stiffness ratio, building aspect ratio, and the proximity of seismic excitation frequencies to the coupled system’s natural frequency. Critical gaps in current research are also highlighted, emphasizing the need for large-scale shake-table and centrifuge experiments, investigations of fully saturated liquefiable soils, and simplified machine-learning-assisted design tools that translate complex numerical findings into practical engineering applications. By bringing together developments in numerical methods, key findings, and future research directions, this review aims to serve as a useful reference for both researchers and practicing engineers. An extensive bibliography is provided at the end, making this review a valuable resource for beginners in SSI.
Iron (Fe) toxicity is one of the most important abiotic stresses limiting growth, development, and grain yield attributes of rice, especially in acid sulphate soils. Silicon (Si), a beneficial element, has been reported to regulate Fe-defense responses and adaptive strategies in several plant species. The objective of this study was to assess the effects of foliar application of Si on the performance of rice, including yield traits, grown under Fe stress. Seeds of two rice genotypes, Azucena (Fe-tolerant) and IR29 (Fe-sensitive), were germinated, 4-week-old seedlings were transferred to the soil substrate, and were grown up to booting stage. Individual rice plant was treated with 0 and 1
Understanding the non-stationary behavior and temporal dependence properties of rainfall characteristics, along with their associations with the El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD), is crucial for the planning and management of agriculture and water resources sectors in Thailand. The study employed the Mann–Kendall test and the Mann–Whitney–Pettitt test to identify monotonic trends and abrupt shifts in rainfall characteristics in Thailand during 1951–2023, respectively. Short-term and long-term persistence were quantified using the autocorrelation coefficient and the Hurst exponent (H), respectively, while correlation analysis examined the association between rainfall characteristics and ENSO/IOD. Results indicated that a small proportion of meteorological rainfall observations exhibit significant trends, shifts, and short-term persistence. However, significant long-term persistence is more prevalent, particularly for indices describing moderate-intensity rainfall and wet-day frequency. Further, ENSO exhibits significant associations with both seasonal and extreme rainfall, with the strength and spatial extent of these associations varying across timescales, regions, and rainfall indicators. The warm phase of ENSO is generally associated with reduced rainfall, fewer rainy days, shorter wet spells, and longer dry spells, while the cold phase exhibits the opposite pattern. The linkage between IOD and rainfall characteristics is generally weak, inconsistent, and confined to specific regions and indices, yet still crucial at sensitive locations across Thailand. Moreover, the scattered nature of significant trends, shifts, and persistence in a few stations for seasonal and extreme rainfall indicates the existence of substantial unresolved spatial heterogeneity in Thailand.
The Lower Mekong Region (LMR), which includes Vietnam, Thailand, Laos, Cambodia, and Myanmar, faces growing energy demand driven by urbanization and economic development. Wind energy offers a promising alternative, yet comprehensive wind suitability assessments for this region remain limited. This study bridges this gap by using machine learning (ML) models like Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), and XGBoost to assess wind power potential across the LMR. These models were trained on 11 spatial variables, including wind speed, elevation, and proximity to infrastructure, to classify land suitability. The ensemble model, combining outputs from all classifiers, demonstrated a 23.17 Wind suitability assessed via ML models; ensemble boosted mapping accuracy and coherence. Coastal Vietnam and eastern Laos show highest suitability for wind farm development. Variable importance shows wind speed and power are top predictors in all ML models. Ensemble results identify 23.17
In developing countries like Bangladesh, demand for energy services is expected to grow with the expansion of economic activity and population. Reliance on fossil fuels with a less diversified fuel mix poses challenges to sustainable energy sector growth. Studies indicate that there are feasible options for increasing the share of clean fuel in the energy mix if efforts are started without delay. Energy intensity is very slowly declining. Expediting access to efficient, modern technologies in all sectors can reduce wasteful energy use in service provision. There is a need for absolute growth in energy consumption to achieve a decent living standard (DLS) for all, and to support the aspiration level of the people of Bangladesh for a good life with higher wellbeing, urbanization, and access to modern appliances and infrastructure. This paper examines how alternative future scenarios, shaped by structural and demographic change, shifts in the fuel mix, technological innovation, and, above all, GDP growth aimed at enhancing human wellbeing, could influence energy demand pathways over time. We developed the SPINE (Service Provision Imagined with No Emissions) model, based on a decomposition approach for energy use, and applied it to generate alternative pathways to achieve Bangladesh’s nationally determined contributions (NDC) and energy demand consistent with the IPCC’s low energy demand (LED) scenario.
