KR Mangalam University

This study presents an anisotropic strain-gradient elasticity framework in which direction-dependent microstructural interactions arise from a geometrically defined nonlocal domain. The resulting characteristic-length tensor comes from the second moments of a three-dimensional ellipsoidal averaging domain, giving it a direct geometric meaning similar to a fabric tensor. A positive-definite strain-gradient energy is discretized using smooth, continuously differentiable Hermite finite elements in one, two, and three dimensions, together with a corrected consistent mass formulation. For an axial characteristic length of 0.20 m and transverse lengths of 0.02 m, rotating the microstructural orientation produces a monotonic response. Static displacement increases by about 4%, while the fundamental frequency decreases from 186.3 Hz to 173.1 Hz. The normalized frequency ranges from 1.053 to 0.979 relative to a volume-equivalent isotropic reference of 176.8 Hz. The axial length dominates the transverse contribution, producing a 5.8% stiffness increase compared with only 0.5% in the transverse-dominant case. A Directional Stiffening Index, measured against a volume-equivalent isotropic reference, is predominantly positive, ranging from 0% to about 30% across the anisotropy ratios and orientations studied. Only a few high-angle configurations show small negative values. These trends allow the stiffness and vibration frequency of anisotropic microstructured materials to be tailored through controlled variation of characteristic length and orientation.

Digital Forensic Microbiology combines digital forensics and traditional microbiology, enabling contamination source attribution and outbreak reconstruction for foodborne pathogens and other microorganisms. Digital forensics track evidence from collection to analysis. The increasing digitization of microbiology, driven by metagenomics and genome sequencing, prompts consideration of pathogen-related evidence that spans computer-based, archival, and research microbiology. When digital microbiological evidence accumulates, rigorously tracing its chain of custody, documenting data provenance, and assuring integrity, authenticity, non-repudiation, and reproducibility within both regulatory and non-regulatory contexts become vital. Digital forensic microbiology, encompassing machine-learning source attribution, AI-aided outbreak reconstruction, and comprehensive evidence-integrity protocols, supports a diverse range of microorganisms, acts spanning foodborne contaminations to healthcare-associated infections, and broad traces across the contamination continuum.

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by cognitive failure, memory impairment, and behavioral disturbances. The disease is associated with complex pathological mechanisms including amyloid-β (Aβ) plaque deposition, tau hyperphosphorylation, oxidative stress, mitochondrial dysfunction, and chronic neuroinflammation. Despite extensive research, currently available therapeutic options provide only symptomatic relief and fail to halt disease progression. Consequently, increasing attention has been directed toward natural bioactive compounds with multi-target therapeutic potential. Marine ecosystems represent a vast reservoir of structurally unique biomolecules, among which marine-derived polysaccharides have emerged as promising candidates for neuroprotection. Polysaccharides such as fucoidan, alginate, carrageenan, chitosan, ulvan, chondroitin sulfate, and hyaluronic acid exhibit diverse biological activities, including antioxidant, anti-inflammatory, anti-amyloidogenic, and neuroprotective effects. These biomolecules can modulate several critical intracellular signaling pathways implicated in AD pathology, including the NF-κB, MAPK, PI3K/Akt/GSK-3β, Nrf2/ARE, STAT3, and NLRP3 inflammasome pathways. By regulating these pathways, marine polysaccharides can reduce oxidative stress, suppress neuroinflammatory responses, inhibit amyloid aggregation, attenuate tau pathology, and promote neuronal survival. Additionally, certain polysaccharides such as chitosan and alginate have demonstrated significant potential as nanocarriers for targeted drug delivery across the blood–brain barrier. This review summarizes recent advances in understanding the signaling pathways associated with AD and highlights the emerging therapeutic potential of marine-derived polysaccharides as multi-target neuroprotective agents. Overall, these marine biomolecules represent promising candidates for developing novel therapeutic strategies to mitigate neurodegeneration and improve cognitive function in Alzheimer’s disease.

Timely forecasting of influenza-like illness (ILI) and early identification of antigenic drift are critical for informing vaccine strain selection and reducing the impact of seasonal outbreaks. This study proposes a multi-modal deep learning framework that integrates nucleotide-level large language model (LLM) embeddings with time-series forecasting and explainable AI for enhanced influenza surveillance. More than one million curated HA and NA sequences (2000–2024) were used to fine-tune DNABERT-2, generating 768-dimensional embeddings capable of capturing high-resolution genetic variation. These embeddings supported the construction of a weekly mutation intensity index and accurate viral clade classification. A Temporal Fusion Transformer (TFT) combined these genetic features with CDC FluView ILI and laboratory-confirmed case data to predict ILI incidence up to eight weeks in advance. The model achieved perfect sequence classification (macro-F1 = 1.00, MCC = 1.00) and strong forecasting performance (MAE = 0.668). Explainability analyses, including integrated gradients and SHAP, highlighted biologically meaningful features consistent with known antigenic sites. To further interpret drift dynamics, we introduce the Antigenic Change Risk Index (ACRI), a composite score that identifies periods of elevated drift risk. Overall, this work demonstrates that transformer-based nucleotide embeddings, when integrated with epidemiological data in an interpretable framework, can deliver accurate real-time ILI forecasting and antigenic change monitoring, providing a scalable and reproducible tool to strengthen influenza preparedness and vaccine policy.

Accurate prediction of ultimate load-carrying capacity (failure load) of reinforced concrete (RC) beam is important for structural assessment, preliminary design, retrofit planning and decision support. Conventional empirical and code-based approaches are still useful, but they have a tendency to reduce the complexity of the nonlinear interaction of geometry, reinforcement, and material properties. This study proposed a Bayesian-optimized machine learning framework for predicting RC beam ultimate load-carrying capacity (failure load) using tabular engineering data and also converting the final predictor in the form of a readable graphical user interface. The dataset included 3234 records of the beam with nine input variables representing the beam geometry, concrete grade, ratio of longitudinal reinforcement, stirrup details, steel yield strength and concrete compressive strength with the target response being the ultimate load-carrying capacity (failure load) in kN. Seven regression models, that is, Linear Regression, Support Vector Regression, Decision Tree Regressor, Random Forest Regressor, Gradient Boosting Regressor, Extreme Gradient Boosting and Multilayer Perceptron were trained and compared using 80:20 train-test split and 5-fold cross validation with Hyperparameter optimization (Bayesian Optimization) Model performance was assessed in terms of R^2, RMSE, MAE, and MAPE and SHAP analysis was employed for the interpretation of the best-performing model. The results indicated that the Multilayer Perceptron had the highest predictive accuracy with the test R^2 of 0.9992, RMSE of 21.3059 kN, MAE of 17.0777 kN, and MAPE of 0.4570