This study presents a comprehensive theoretical investigation of the conformational landscape, thermodynamic, electronic, photophysical behavior, and radiative lifetime of the aniline monohydrated dimer cluster (An2W1) using DFT and TD-DFT approaches at the B3LYP/aug-cc-pVTZ level. Conformational analysis identified eight distinct conformers, with conformer 01 being the most stable due to strong O–H···N, N···O–H, and N–H···N hydrogen bonding interactions. Thermodynamic parameters revealed minimal variations across conformers, though conformer 01 exhibited the lowest dipole moment and entropy, confirming its high stability. Vibrational infrared (IR) analysis confirmed the existence of hydrogen bonding in conformer 01, with the calculated frequencies correlating closely with the experimental results. UV/Vis absorption spectra showed significant intramolecular charge transfer (ICT) transitions, with notable redshifts and large Stokes shifts, particularly in conformer 08, indicating twisted ICT states. Fluorescence and phosphorescence lifetimes were computed using oscillator strengths and transition energies, revealing that conformer 04 had the longest fluorescence lifetime (32.44 ns), and conformer 07 exhibited the longest phosphorescence lifetime (9.924 µs). These findings confirm that the position of water and weak non-covalent interactions significantly influence photophysical responses and excited-state dynamics. Frontier molecular orbital (FMO) and chemical reactivity descriptor analysis revealed a correlation between energy gap and chemical stability. NBO, QTAIM and RDG-NCI analyses further confirmed stabilizing donor–acceptor, H-bonding and van der Waals interactions. These findings offer comprehensive insight into the structure–property relationships of An2W1 clusters and their potential in in molecular photonics and photophysical applications.
Achieving superior light harvesting in stacked-absorber heterojunction solar cells requires precise electronic and optical alignment between adjacent absorber layers. This necessity stems from the complex, non-linear interdependence among key material parameters including bandgap (E-g), electron affinity (E-a), and effective density of states (N-C, N-V) that collectively dictate carrier generation, transport, and absorptive efficiency. In this work, we integrate machine-learning (ML) models with conventional SCAPS-1D simulations to identify optimal absorber-layer properties and guide the rational design of stacked architecture. Five ML algorithms are trained for thousands of data combinations followed by parameter optimization for high-E-g top absorbers (L1) and low-E-g bottom absorbers (L2). Explainable AI (SHAP; SHapley additive explanations) analysis reveals critical design rules, highlighting the roles of density of states, free-carrier concentration, and favorable band offsets, specifically, lower N-C and higher N-a for L2, and the opposite for L1, with minimal VBM/CBM discontinuity (similar to +/- 0.02 eV). Using these ML-derived criteria, we propose high-absorption stacks incorporating ZnSnN2 (ZTN), CdTe and Cu3PSe4 (CPSe) as L1 and MoTe2 as L2, with CdS and WSe2 as window and hole-transport layers. The optimized device structures achieve power-conversion efficiencies, power conversion efficiency of 39.77%, 39.49%, and 39.65% with V-OC approximate to 1.15 V, J(SC) approximate to 39 mA cm(-2), and FF approximate to 86% under ideal interface conditions. We further discuss the physical origins of the enhanced photocurrent and voltage, along with possible key challenges and practical considerations for experimental realization. Thus, this study demonstrates a data-driven hybrid design framework that advances the development of next-generation high-efficiency stacked-absorber solar cells by identifying optimized material properties and device configurations that surpasses single-junction performance limits.
Non-coding RNAs (ncRNAs) influence gene expression and diverse physiological and pathological processes. Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) play roles in cancer and neurodegenerative diseases. LncRNAs, such as HOTAIR, MALAT1, and NEAT1, regulate chromatin remodeling through histone modification. Dysregulated expression leads to oncogenesis and neuronal dysfunction. Conversely, miRNAs such as miR-21 and miR-155, as post-transcriptional regulators, act in oncogenic signaling and neuroinflammation. MiR-21 targets PTEN and PDCD4, leading to aberrant activation of the PI3K/AKT and NF-κB pathways, thereby promoting cancer cell proliferation, while influencing neuronal apoptosis and neuroprotection. Similarly, miR-155 modulates SHIP1 and SOCS1 through AKT and JAK/STAT pathways in both cancer and neurodegenerative contexts. The convergence of these signaling cascades reveals a shared molecular framework that links tumor progression to neurodegeneration. Understanding the intricate lncRNA–miRNA–mRNA regulatory networks provide valuable insights into disease mechanisms and clinical applications regarding ncRNAs as biomarkers. Further research integrating transcriptomics, functional genomics, and therapeutic delivery systems is essential to harness the full diagnostic and therapeutic potential of ncRNAs.
This study investigates and compares the seismic performance of structures with different types of plan irregularities, emphasizing the effects of the simultaneous application of bidirectional ground motion components. C-, L-, and T-shaped buildings, as well as a regular square-shaped building, are analyzed under site-specific recorded PEER ground motions in accordance with seismic code requirements. Structural responses, including interstory drift, story displacement, base shear, floor acceleration, and torsional irregularity are evaluated using modal analysis and nonlinear time-history analysis, considering both simultaneous and non-simultaneous application of bidirectional ground motion components. Three site-specific ground motion records with varying peak PGAs are used for nonlinear dynamic analysis and seismic response evaluation of horizontally uneven buildings. The results indicate that the flexural periods of all analyzed buildings are comparable; however, buildings with plan irregularities exhibit significantly longer torsional periods. The simultaneous application of bidirectional ground motions substantially amplifies structural responses, particularly in irregular buildings. For the regular building, top-story displacement and interstory drift increase by approximately 5
Dhaka, one of the world’s fastest-growing megacities, faces severe urban flood risks driven by rapid urbanization, inadequate drainage infrastructure, and climate change–induced extreme weather. This study develops an integrated urban flood forecasting system for a densely built-up area of Dhaka using the MIKE + hydrodynamic model. The system links hydrological and hydraulic modules to simulate the complex interactions among surface runoff (pluvial), river overflow (fluvial), drainage networks, and overland flow. Real-time data from rainfall, river gauge, and pump stations are incorporated to improve forecasting precision. Sensitivity analysis identified ten influential parameters, optimized during model calibration. Historical flood events from 2019–2022 were used for calibration and validation, yielding high performance with Nash–Sutcliffe Efficiency (NSE) above 0.70, R2 above 0.80 and Mean Absolute Error (MAE) below 0.1 m. The model’s robustness was further verified using major flood events in 2020 2021, 2022, 2024 and 2025 validated against flood marks and community reports and observed data. This advanced forecasting framework offers reliable early warnings and scenario simulations, strengthening decision-making and emergency response for flood-prone urban areas in Dhaka.
