K. R. Mangalam University, is a private university located in Gurugram district, India. The university was established in 2013 by the K. R. Mangalam Group through the Haryana Private Universities (Amendment) Act, 2013. The university is approved by University Grants Commission (UGC) and is competent to award degrees as instructed by UGC under section 22 of the UGC Act, 1956.K.
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
This article focuses on the study of fixed point theorems for PRESICS type contractive mappings within the framework of "soft metric spaces", particularly when the underlying parameter set is finite. By extending classical contraction principles, we establish rigorous existence and uniqueness results that broaden the current theory of soft metric spaces. The necessity of the imposed conditions is illustrated through carefully designed examples, demonstrating that these assumptions cannot, in general, be relaxed. In addition, explanatory remarks are included to clarify the scope and relevance of the results in relation to existing fixed point theorems. Furthermore, we explore potential applications in nonlinear analysis, showing how the developed theoretical framework can guarantee the existence, uniqueness, and iterative convergence of solutions to nonlinear problems under uncertainty. These findings not only generalize known results to a wider setting but also provide a foundation for future research and practical applications in mathematics and applied sciences.
In this paper, we investigate warped products on pseudo-generalized quasi-Einstein manifolds under affine connections. We explore their fundamental properties, establish conditions for their existence, and prove that these manifolds can be nearly quasi-Einstein and pseudo quasi-Einstein. To illustrate, we provide examples in Riemannian and Lorentzian geometries, confirming their existence. Finally, we construct and analyze an explicit example of a warped product on a pseudo-generalized quasi-Einstein manifold with respect to affine connections.
