Sri Sairam College of Engineering, formerly known as Shirdi Sai Engineering College, is an engineering institution located in Bangalore, Karnataka, India. This Institution is affiliated to Visvesvaraya Technological University, Belagavi ..
Predicting clinically significant drug–drug interactions (DDIs) continues to be an unresolved challenge in contemporary pharmacovigilance, primarily due to the inadequacy of current computational frameworks in addressing the nonlinear, multi-scale characteristics of simultaneous drug metabolism. This paper presents the Quantum Graph-Differential (QGD) model an exact mathematical framework that combines quantum-inspired graph theory with a set of interconnected fractional differential equations to describe and forecast pairwise drug–drug interactions (DDIs). The principal component of our construction is the quantum interaction graph 𝒢_Q = (V, E, W_Q) , wherein the vertex set represents distinct drug molecules as quantum states within a finite-dimensional Hilbert space, and the complex-valued edge weights are obtained from the overlap of shared metabolic pathways and transporter affinity profiles.A Schrödinger-type equation on 𝒢_Q governs drug–drug coupling, and the graph Hamiltonian H is constructed from a novel fractional quantum graph Laplacian ℒ_Q^α , α∈ (0,1] . A hybrid quantum-classical dynamical model is created by coupling the time evolution of the interaction wavefunction Ψ _s(t) to a compartmental pharmacokinetic/pharmacodynamic (PK/PD) ordinary differential equation system. Using Banach fixed-point and semigroup theory, we prove existence, uniqueness, and long-time asymptotic stability of solutions. Using the QGD framework on a selected dataset of 7,428 clinically confirmed DDI pairs from DrugBank v5.1, TWOSIDES, and FAERS, our model outperforms five established baselines by 1.5–13.9 percentage points in AUC, with an average precision of 0.948 and an AUC of 0.962. Quantum edge weighting alone explains a 3.7
The creation of effective healthcare artificial intelligence (AI) systems demands access to large and diverse datasets dispersed in various healthcare institutions. Nevertheless, strict privacy policies in the United States such as HIPAA, in Europe such as GDPR, and other laws in other countries pose a tremendous hindrance to centralized aggregation of data. Although Federated Learning (FL) allows all involved parties to jointly train models without centrally storing sensitive patient information, gradient updates exchanged in the training process may reveal much valuable personal information via more advanced reconstruction attacks. The purpose of the study is to create and fully test a privacy-guaranteed federated learning model that facilitates and federates diagnostic AI training among multiple healthcare facilities and formally guarantees some differential privacy and retains clinic-quality model performance. According to the research, we introduce PP-FL-DP (Privacy-Preserving Federated Learning with Differential Privacy), a unified system introducing three innovations: (1) Hierarchical Privacy-Preserving Architecture (HPPA) with defense-in-depth, i.e., local differential privacy, secure aggregation, and Byzantine-robust features; (2) Sensitivity-Aware Gradient Perturbation (SAGP) with layer-wise adaptive clipping, based on empirical gradient sensitivity analysis; and (3) Three different healthcare datasets, including breast cancer mammography (N = 12,500 images, 5 federated clients), diabetic retinopathy fundus imaging (N = 35,000 images, 8 clients), and ECG arrhythmia detection (N = 45,000 recordings, 6 clients) were thoroughly evaluated. Hyperparameters (batch size, 16,32, 64, 128, learning rate, 0.001-0.1 and aggregation threshold, τ = 0.1–0.9) were fully analyzed. PP-FL-DP had a diagnostic accuracy of 96.2 ± 0.4
A double slope solar still (DSS) gives more productivity than a single slope solar still (SSS). However, its efficiency remains low and it is strongly dependent on climatic conditions. This study aims to enhance the performance of DSS by using natural fibers (Banana, Sisal, and Palm) as wick materials in combination with an Al2O3 and Glauber Salt (GS) based nano phase change material (nano-PCM). The addition of Nano-PCM composite improves the storage capacity of heat energy, and natural fibers improve the capillary action and spreading of water. Experimental study reveals that the sisal fiber with Nano-PCM composites provides the best productivity of 4.58 l/day, which is 115.02 % higher than the conventional DSS. This improved water productivity of the sisal and Nano-PCM combination is due to its highest evaporative heat transfer coefficient value of 164.24 W/m2K. The average energy efficiency and exergy efficiency are also high for the sisal fiber and NanoPCM combination, which provides 35.34 % and 3.33 %, respectively. The average energy efficiency and exergy efficiency of the sisal fiber and nano PCM combination are 76.2 % and 114.4 % higher than the conventional DSS. Economic study reveals that the water production cost of sisal and nano PCM is just $0.012 per liter, with a payback (PB) period of around 143 days (4.7 months), whereas the production cost of CSS is $0.021, with a PB period of 231 days (7.7 months), respectively.
The main objective of the present analysis is to investigate the effect of incorporating silane-treated Helianthus annuus waste derived cellulose into sugarcane leaf fiber-reinforced vinyl ester composites. Both the fiber and filler materials were effectively surface treated using 3-aminopropyltrimethoxysilant. The composites were prepared and their interlaminar shear strength (ILSS), wear resistance, water absorption, and flammability properties were evaluated in accordance with ASTM standards. The test results revealed that silane treatment significantly influenced the overall performance of the composites. Among the tested specimens, the BSC5 composite, which was treated with silane and reinforced with 1 vol
Artificial intelligence (AI) is being applied by the bakery industry to substitute existing practices with data-informed strategies to enhance the accuracy of quality predictions, process optimization, and shelf-life prediction. This review discusses how AI, specifically machine learning (ML) algorithms such as artificial neural networks (ANNs), support vector machines (SVMs), random forests (RF) and deep neural networks (DNNs), can be applied in bread-making and product innovation. With AI models, there is the possibility of analyzing the interactions between formulation, processing and quality features in a complex manner to predict the loaf volume, crumb structure, staling and risk of spoilage. The optimization of formulations with the help of AI is also used to produce cost-effective, nutritionally fortified products without losing the sensory quality. Interest of AI in combination with improved methods like gas chromatography-olfactometry and texture profiling, E-nose/E-tongue systems improves the senses prediction prior to execution. Besides, the combination of AI and digital twins, kinetic models, and IoT systems enhance real-time analysis and operational performance, which can lead to sustainability, less food waste, and individualized and health-focused bakery items.
