Koneru Lakshmaiah Education Foundation

Evaluation of Method “Whiteness” in the Stability Analysis of Dexamethasone and PVP‐I Ophthalmic Suspensions: Synergizing LC‐MS/MS, In Silico Toxicology, and AGREE/GAPI Metrics

ABSTRACT This study presents a holistic “White Analytical Chemistry” approach for the stability‐indicating evaluation of dexamethasone in complex povidone‐iodine (PVP‐I) ophthalmic suspensions. Moving beyond traditional validation, the method's “whiteness” was quantitatively asse...

BODIPY-Integrated Porous Materials: Design Principles, Functional Architectures, and Emerging Applications

Abstract The incorporation of functional organic chromophores into porous architectures has emerged as a powerful strategy to bridge molecular photophysics with materials-level performance. Among these, boron-dipyrromethene (BODIPY) derivatives offer a unique combination of stron...

BioTwin-PE: An Uncertainty-Aware Multimodal State-Space Framework and Validation Protocol for Longitudinal Placental Monitoring

Placental assessment is necessarily intermittent, while ma- ternal and fetal physiology evolve continuously. This mis- match limits the ability of conventional visit-level prediction models to represent changes occurring between examinations. We present BioTwin-PE, a methods fram...

Structure-based discovery of phospholipase A1 (PlaF) inhibitors in Pseudomonas aeruginosa using integrated in silico and experimental approaches

Phospholipase A1 (PlaF) is a membrane-bound virulence factor in Pseudomonas aeruginosa, involved in glycerophospholipid remodeling and host-pathogen signaling. Inhibiting PlaF represents a promising strategy to reduce the virulence and pathogenesis of multidrug-resistant P. aerug...

BioTwin-PE: An Uncertainty-Aware Multimodal State-Space Framework and Validation Protocol for Longitudinal Placental Monitoring

Placental assessment is necessarily intermittent, while ma- ternal and fetal physiology evolve continuously. This mis- match limits the ability of conventional visit-level prediction models to represent changes occurring between examinations. We present BioTwin-PE, a methods fram...

A hybrid deep reinforcement learning framework for proactive cloud network intrusion detection using spatiotemporal feature learning

Traditional signature-based and static machine-learning-based intrusion detection systems (IDSs) have limited ability to adapt to changing traffic characteristics, and dynamic cloud-based infrastructures are increasingly vulnerable to new and zero-day attacks. Given this drawback...

Behavioural and cognitive determinants of financial loss and mental health deterioration following cybercrime victimisation

Abstract The rapid growth in the use of digital technologies has led to increased exposure to cybercrime, with significant financial and psychological consequences. This study examines the behavioural and cognitive factors that affect cybercrime victimisation and its outcomes. Dr...

Behavioural biometric authentication and secure key agreement for smart networks using machine learning and lightweight elliptic curve cryptography

The fast integration of Internet of Things (IoT) into smart environments, introduces significant security challenges, especially in user authentication and secure data exchanges. These security challenges increase unauthorized access, information leakage, and disruption of servic...

A Scalable Cloud-Native Lakehouse Architecture for Epidemiological Forecasting and Constrained Resource Allocation

Disease surveillance systems in resource-constrained public health settings face two distinct challenges: detecting outbreak anomalies in real time, and translating those detections into operationally defensible resource-allocation decisions. Existing approaches typically address...

Hydrogen-assisted combustion of GQD–DEE-enhanced WCO biodiesel in a dual-fuel CI engine

Abstract The relatively low performance and elevated nitrogen oxide (NOx) emissions associated with biodiesel-fueled compression ignition (CI) engines continue to limit their widespread application. To address these challenges, the present study investigates the combined influenc...

A fuzzy graph-based prediction model for crop yield forecasting under climatic variability

Accurate crop yield prediction under climatic variability is fundamental to food security planning and agricultural policy. Classical statistical and machine learning approaches often struggle to handle imprecise, incomplete, or linguistically described agronomic data arising fro...