P. R. Thakur Government College

We propose a projected quasi-subgradient method for constrained, nondifferentiable, quasi-convex multiobjective minimization problems. Unlike existing approaches that rely on Lipschitz continuity, our method only requires Hölder continuity of the objective components, thereby covering a broader class of quasi-convex functions. Under these assumptions, we establish convergence of the generated sequence to a Pareto optimal solution and derive a sublinear rate of convergence that explicitly depends on the Hölder parameters, recovering the Lipschitz case as a special instance. The method is simple to implement, robust to nondifferentiability, and theoretically well-defined. Numerical experiments including application to portfolio optimization, electric vehicle charging network optimization and smart grid energy management are provided. Dolan-Moré performance profiles indicate that the proposed method outperforms.

Federated Learning (FL) is increasingly applied in sectors like healthcare, finance, and IoT, enabling collaborative model training while safeguarding user privacy. However, FL systems are susceptible to Byzantine adversaries that inject malicious updates, which can severely compromise global model performance. Existing defenses tend to focus on specific attack types and fail against untargeted strategies, such as multi-label flipping or combinations of noise and backdoor patterns. To overcome these limitations, we propose FedAOT-a novel defense mechanism that counters multi-label flipping and untargeted poisoning attacks using a metalearning-inspired adaptive aggregation framework. FedAOT dynamically weights client updates based on their reliability, suppressing adversarial influence without relying on predefined thresholds or restrictive attack assumptions. Notably, FedAOT generalizes effectively across diverse datasets and a wide range of attack types, maintaining robust performance even in previously unseen scenarios. Experimental results demonstrate that FedAOT substantially improves model accuracy and resilience while maintaining computational efficiency, offering a scalable and practical solution for secure federated learning.

Spoilage microorganisms and food borne pathogens are major public health concerns causing enormous economic losses globally. The organoleptic qualities of food products are frequently altered in an undesired way by conventional food preservation techniques. Lactic acid bacteria (LAB), on the other hand, naturally synthesise antimicrobial substances that successfully prevent the growth of harmful and spoilage microorganisms without lowering the quality of food. Owing to these traits, LAB-derived compounds like bacteriocins are commonly utilised in food and dairy processing industries. In the present study, Lacticaseibacillus rhamnosus and Limosilactobacillus fermentum were isolated from curd from commercially available brand and evaluated for their antimicrobial efficacy under a range of environmental conditions. Integrated spectroscopic profiling of antimicrobial metabolites present in crude supernatant of LAB reveal that glycosylated molecules featuring ester or peptide could play a key role in the inhibitory effects seen against both Gram-positive and Gram-negative bacteria.

In the current study, the flow characteristics of a mononano-fluid () and a hybrid nanofluid () driven by a rotating disk revolving at a constant angular velocity are analyzed. The distribution of flow consists of nonlinear thermal radiation, heat absorption or generation, binary chemical reactions, and thermal stratification. The leading structure of governing PDEs of flow is changed into ordinary boundary value problem (BVP) by applying suitable similarity transformations. The fifth-order Runge-Kutta-Felberg (RKF) method with shooting methodology is then employed to solve numerically the subsequent structure of equations. The charts and graphs are employed to show the comprehensive analysis of findings. One of the intriguing findings shows that as variable porosity and variable permeability parameters increase, the slope of the fluid's velocity along with the radial axis, as well as the rotational velocity for both liquids are simultaneously decreasing and increasing, respectively. However, for temperature profiles, opposite effects are viewed. Based on the statistical analysis presented in this article, we can infer that the correlation coefficients for four major physical quantities, , , , and , are quite significant. Consequently, there is a strong correlation between the parameters and the physical characteristics. Our current study is significant as it applies the classical rotating disk flow to contemporary hybrid nanofluids that have a variety of chemical and thermal impacts.

In this study, a ZnS/chitosan composite was synthesized through hydrothermal and evaluated for its photocatalytic performance in degrading Eosin blue (EB) dye under visible light. Analytical instruments were employed in this study to examine the optical, electronic, structural, thermal, chemical properties of prepared bare ZnS and ZnS/chitosan composite. The bandgap energies of hydrothermally prepared ZnS and ZnS/chitosan composite were 3.02 eV and 2.71 eV, respectively, with crystalline size found to be 29.1 nm for composite which is slightly larger than bare ZnS with 26.8 nm due to addition of biopolymer to metal sulfide. Further, the photocatalytic performance of prepared samples was executed over the degradation of EB dye under visible light illumination. Optimized conditions are pH 9, 0.1 g catalyst loading amount, 10 mg/L EB dye initial concentration and 90 min. A possible degradation mechanism was proposed based on the generation of reactive oxygen species under light irradiation. Reusability tests demonstrated consistent degradation performance, with a decrease from 100