Dr. Shakuntala Misra National Rehabilitation University

The process of decision making involves uncertainty due to lack of agreement among experts, inaccuracy in measurements and incomplete information. Current frameworks are inadequate in dealing with cases in which hesitation, indiscernibility, and parameterization may all take place simultaneously. The article proposes a new Hesitant Fuzzy Soft Rough Set (HFSRS) model that combines hesitant fuzzy soft sets and rough sets with dynamic $$\:\varvec{\beta\:}$$ covers that changes approximation boundaries in relation to hesitant membership levels. The suggested framework deals with severe constraints such as the impossibility to model parameter-dependent hesitation, duality violation of the classical fuzzy rough sets, and fixed thresholding processes that cannot be used in a noisy environment. The three fundamental properties provided by mathematical formalization: (a) duality preservation to provide logical consistency important for safety-critical applications, (b) monotonicity to provide predictable behavior important to explainable AI systems, and (c) topological consistency to provide hierarchical uncertainty modeling. HFSRS is empirically validated using synthetically generated datasets (500 photovoltaic modules with three fault indicators adjusted to IEC 61215-2:2021 standards) to achieve 92 per cent accuracy versus 85 per cent on classical rough sets, 86 per cent on fuzzy rough sets, 88 per cent on intuitionistic fuzzy rough sets, with 35 per cent reduction in boundary region and AUC of 0.97 versus 0.92 on competing methods running 30 times The best 0.65 -threshold of the beta value balances accuracy and coverage. The HFSRS-TOPSIS algorithm provides practitioners with strong decision support, computational tractability of $$\:\left(O\right(n\:\times\:\:m\:\times\:\:k\left)\right)$$ on a dataset of up to $$\:{10}^{4}$$ objects.

Biological synthesis of nanoparticles has been widely explored as a sustainable alternative to physical and chemical routes. Nevertheless, the majority of current methods are based on intact microbial cells or crude plant extracts, which leads to lack of reproducibility, biomolecular interference, and difficulties in mechanistic interpretation and scale-up. We present microbial secretome-mediated nanotechnology as a new and under researched paradigm in this review, which separates nanoparticle synthesis from cellular complexity and preserves biological precision. We quantitatively analyze the composition of microbial secretomes, redox enzymes, extracellular proteins, peptides, polysaccharides, and membrane-derived vesicles and map their respective functions in nanoparticle nucleation, growth, stabilization, and functionalization. Emerging biochemical and mechanistic evidence is creating a direct understanding of how the composition of the secretome relates to the nanoparticle morphology, surface chemistry, and bioactivity, moving beyond organism-specific reports. Notably, we critically assess secretome-mediated synthesis by comparing and contrasting it with whole-cell and plant-extract techniques, highlighting specific benefits including; enhanced batch-to-batch reproducibility, minimized polysaccharide and biomass contamination, better control over particle size and shape, reduced toxicity hazards, and increased feasibility of downstream processing and scale-up. Emerging approaches, such as secretome fractionation, omics-inspired secretome engineering, and vesicle-mediated nano-delivery, are mentioned as directions for developing tunable and application-specific nanomaterials. Altogether, the given review offers a mechanistic and translational roadmap of the microbial secretome-mediated nanotechnology, which cannot be regarded as the extension of green synthesis, but rather as a controllable bio-nanoengineering platform with significant potential in medical, agrarian, and environmental cleanup fields.

This paper presents a neuro-symbolic artificial intelligence (NSAI) algorithm that integrates neural networks with symbolic reasoning to optimize microgrid energy management and power quality in distribution systems. The proposed hybrid architecture combines deep reinforcement learning with knowledge-based symbolic rules to address the complex challenges of modern power systems with high renewable penetration. The algorithm was evaluated using MATLAB/Simulink simulations on a modified IEEE 33-bus distribution system under five operational scenarios: normal operation, high renewable penetration (up to 70

The rapid growth of Industrial Internet of Things (IIoT) networks has created new cybersecurity risks that traditional centralized intrusion detection systems can’t handle because they can’t scale up, protect privacy, or work in environments with limited resources. This study introduces a lightweight, blockchain-enabled federated intrusion prevention platform. Our research integrates three fundamental concepts: (i) a Proof-of-Trust (PoT) agreement mechanism specifically designed for IIoT devices with limited resources, replacing traditional consensus algorithms that use a lot of processing power; (ii) Byzantine fault-tolerant trust management that can keep the system’s integrity even with up to 33

Density Functional Theory (DFT) calculations have been performed to study the spectral, nonlinear optical (NLO), chemical reactivity and thermodynamical parameters of Flavone-5,7,4′-trihydroxy-8-C-β-glucopyranoside, a bioactive natural compound reported to be extracted from the seeds of Cucumis Sativus Linn. (cucumber), to explore its viable applications as a multifunctional compound. In the present study, the computational IR, NMR and UV-Visible spectra have been analyzed along with the HOMO-LUMO energy gap of 0.16338 a.u., indicating significant chemical reactivity, while the molecular electrostatic potential (MESP) surface highlights multiple nucleophilic regions over the hydrogen atoms and an electrophilic region over the oxygen atom. NLO analysis reveals that the compound possesses a high total dipole moment of 4.0947 Debye and a first-order hyperpolarizability of 1261.641 a.u. A molecular docking approach has further been employed to examine the bioactivity of the title molecule against receptor proteins related to challenging diseases such as Alzheimer’s disease, Hepatitis B, Japanese encephalitis, Parkinson’s disease and diabetes mellitus, which demonstrated high binding energy and supported its multifunctional drug behaviour. Out of these protein receptors, the strongest binding affinities of -11.1 kcal/mol and − 10.0 kcal/mol are observed against the galantamine inhibitor of acetylcholinesterase enzyme and Alzheimer’s amyloid precursor protein copper binding domain, respectively. Furthermore, its absorption, distribution, metabolism, excretion, and toxicity analyses indicate favorable pharmacokinetic and safety profiles, supporting its viability as a promising drug candidate. Overall, the study reveals the title molecule as a biologically active compound having NLO application potential and multifunctional therapeutic prospects. A relatively small HOMO–LUMO gap of 0.16338 a.u. for Flavone-5,7,4′-trihydroxy-8-C-β-glucopyranoside signals high chemical reactivity. Large total dipole moment of 4.0947 Debye and a first-order hyperpolarizability of 1261.641 a.u. reveal the compound to be nonlinearly optically active. MESP surface reveals an electrophilic region located around the oxygen atom and multiple nucleophilic regions located around hydrogen atoms, making it suitably reactive. Molecular docking results exhibit its potential to be a multifunctional drug candidate. ADMET analysis reveals the drug likeness and low toxicity of the titled compound.