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Item type:Item, Seismic Performance Evaluation of a Fire-Damaged Reinforced Concrete High Rise Building by Non-linear Time History Analysis(Research and Development Wing, MIST, 2026-06) Md Abdul Momin; Khondaker Sakil AhmedConcrete is widely used worldwide for the construction of buildings and infrastructure that remain stable under fire. It shows some damage and deformations that require maintenance. Although rehabilitation and restoration procedures for fire-damaged buildings are usually time-consuming and costly, the post-fire resilience of the structure is very important for understanding its capacity and condition. In the seismic zone, the buildings must satisfy the seismic demand after the potential repair and retrofitting of the fire-damaged building. A simple, verified simulation procedure for post-fire seismic analysis is necessary to advance understanding of the seismic performance of RC structures after fire. However, individual computer programs that can perform well in both thermal and seismic analysis may help structural engineers make decisions. This study presents an in-depth seismic performance evaluation of a fire-damaged high-rise reinforced concrete (RC) building in Dhaka, Bangladesh. The building, which suffered severe fire damage on the 7th to 9th floors, underwent rigorous structural investigation and testing, including core extraction and rebar tensile testing. According to ACI 562, the residual strengths of concrete and reinforcement were calculated. A comprehensive finite element model was developed in ETABS to simulate the structure’s static and dynamic behavior. Nonlinear Time History Analysis (NLTHA) was performed using nine ground motion records scaled to Bangladesh National Building Code (BNBC 2020) spectra. The comparative results are presented in terms of storey drift and displacement, stiffness, and demand /capacity ratios of column and shear wall. In most cases, it meets the allowable limits for different performance criteria. The proposed assessment procedure can capture the actual conditions of a fire-damaged building at different performance levels, helping stakeholders make further strategic decisions about the damaged structure.Item type:Item, Impact of Biomass Particle Morphology on Pyrolysis and Gasification Processes: Insights from TGA and Reaction Kinetics(2026-06-30) Shaon Md Tariqur Rahman; Md Mahabubur Rahman Sharon; Altab HossainThis study investigates the influence of biomass particle size and shape on pyrolysis and gasification of pine wood in a laboratory-scale fixed-bed reactor. Biomass particles have been categorized into fine, medium, coarse, and large size ranges and shaped into spherical, cylindrical, flaky, and irregular geometries. Pyrolysis has been conducted at temperatures up to 800°C under nitrogen, while gasification has been performed at 800–1200°C in a CO₂-steam mixture. Thermo-Gravimetric Analysis (TGA) has been employed to monitor weight loss and heat flow. The findings have revealed that smaller, spherical particles significantly enhance decomposition rates, achieving higher gasification efficiencies at lower temperatures. These particles have produced hydrogen-rich syngas with an H₂/CO ratio of 1.1, while larger, irregular particles have favored carbon monoxide generation. Emission analysis has demonstrated that fine particles have reduced NOx, SO₂, and CO₂ emissions by up to 85%, 80%, and 70%, respectively. Kinetic analysis has shown that smaller particles require lower activation energy (31.23 kJ mol−¹) compared to larger particles (39.52 kJ mol−¹). This study emphasizes the critical influence of biomass particle size and geometry in optimizing reactor design, improving conversion efficiency, and minimizing environmental impacts. Unlike prior studies that vary particle size alone, this work simultaneously resolves the combined effects of particle size (four classes) and shape (four geometries) using TGA quantification, thereby filling a critical gap in the literature on size- and shape-resolved biomass conversion kinetics. These findings directly inform feedstock preparation protocols for optimizing fixed-bed reactor performance in bioenergy applications.Item type:Item, Interpretable Spatio-Temporal Attention for Shared Charging Load Prediction(Research and Development Wing, MIST, 2026-06) Haitao Li; Yingai Jin; Zhipeng Jiang; Hoque Md. Emdadul; Dala Laurent NorbertUrban bus electrification and the proliferation of shared charging infrastructure have intensified the demand for accurate, interpretable load forecasting to support reliable grid dispatch and energy management. Existing spatio-temporal graph neural networks, however, rely on static geographic-distance adjacency matrices and fixed spatial-temporal fusion weighting, limiting their ability to capture the dynamic, dual-entity nature of scheduled bus fleets and private electric vehicle charging demand—while offering little operational transparency. To address these limitations, we propose the Spatio-Temporal Attention Network (ST-Attention), an interpretable forecasting framework built on three core components: a behavior-driven multi-layer adjacency matrix that encodes bus origin-destination flows, gravity-model private vehicle flows, and spatial proximity to reflect true traffic-induced correlations; a parallel spatial-temporal encoding framework combining graph attention and Transformer modules; and a gated fusion mechanism that dynamically weights spatial and temporal features at each node and time step. This design enables the model to adapt to shifting dominant factors throughout the day. Evaluated on a physics-grounded simulation dataset of shared charging stations, ST-Attention achieves a Mean Absolute Percentage Error of 53.45% and a Mean Absolute Error of 85.07 kW, with only a marginal accuracy gap relative to black-box baselines, while delivering full interpretability. The learned gating weights further reveal actionable operational patterns—explicitly indicating when spatial spillover versus local historical context drives each prediction—providing grid operators with transparent, reliable insights for infrastructure planning and safety-critical dispatch.Item type:Item, MIST Newsletter 2025(Research and Development Wing, MIST, 2025-12)Item type:Item, MIST Newsletter 2025(Research and Development Wing, MIST, 2025-06)