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Smart labs and AI-powered discovery, next-generation microbiology is moving away from conventional culture-based techniques and toward intelligent, data-driven paradigms. Smart labs use automation robotics, real-time analytics, and Internet of Things (IoT) sensors to monitor environmental factors, metabolic fluxes, and microbiological development with previously unheard-of precision. These methods provide quick pathogen detection, high-throughput screening of microbial consortia, and predictive modelling of antibiotic resistance—all of which are essential for tackling global issues like pandemics and Antimicrobial Resistance (AMR). By analysing large omics databases (genomics, metagenomics, proteomics), artificial intelligence (AI) algorithms—in particular, Machine Learning (ML) and deep neural networks—speed up discovery. Generative Adversarial Networks (GANs) mimic new antimicrobial peptides, whereas Convolutional Neural Networks (CNNs) categorise microbial morphologies using imaging data.
The vision of data-driven microbiology has evolved over the past few years to take advantage of the benefits of big data, bioinformatics and Artificial Intelligence (AI) for learning about microbiological systems. Recent developments of high throughput sequencing, metagenomics, transcriptomics and proteomics studies generated lots of complex data that demand efficient data analysis and data interpretation by powerful computational tools. The data handling, processing and annotation of these datasets provide a foundation for the discovery of microbial diversity, functional potential and evolution, with the help of bioinformatics. At the same time, AI and machine learning algorithms enable pattern recognition, predictive modelling, and hypothesis generation, leading to more insight into the microbial behaviour, interactions, and adaptation to different environmental conditions. The technologies have revolutionized the discovery of new fields like microbial ecology, infectious disease diagnosis, monitoring of antimicrobial resistance and industrial biotechnology.
Lead-free ferroelectric ceramics based on sodium bismuth titanate (Na0.5Bi0.5TiO3, NBT) are promising candidates for environmentally benign multifunctional devices; however, their practical application is limited by high coercive fields and restricted electromechanical response. In this study, 0.94Na0.5Bi0.5-x-yErxYbyTiO3–0.06BaTiO3 (NBEY-BT) ceramics with (x = y = 0.0 0.1) were synthesized via a conventional solid-state route to investigate the effect of Er3+/Yb3+ co-doping on the structural, microstructural, ferroelectric, and optical properties. X-ray diffraction combined with Rietveld refinement confirms a single-phase perovskite structure with coexisting rhombohedral (R3c) and tetragonal (P4mm) phases, characteristic of morphotropic phase boundary behavior. Er3+/Yb3+ co-substitution increases the tetragonal phase fraction and induces local lattice strain without forming secondary phases. Microstructural analysis reveals dense ceramics with uniform grain distribution and a reduced average grain size upon rare-earth doping. Ferroelectric measurements show well-saturated polarization–electric field hysteresis loops, with the co-doped composition exhibiting a reduced coercive field and enhanced domain switchability while maintaining high polarization. A pronounced improvement in piezoelectric performance is achieved in the doped composition, which exhibits higher d₃₃ values at lower electric fields due to facilitated non-180° domain switching and polarization rotation near the morphotropic phase boundary. Furthermore, efficient Yb3+-sensitized Er3+ upconversion photoluminescence under 980 nm excitation is observed, introducing multifunctional optical functionality absent in the undoped ceramic. Notably, only the Er3+/Yb3+-substituted ceramics display strong upconversion photoluminescence under 980 nm excitation, characterized by green (4S3/2 → 4I15/2) and red (4F9/2 → 4I15/2) emissions arising from efficient Yb3+ → Er3+ energy transfer. The coexistence of ferroelectric polarization and upconversion luminescence in NBEY-BT ceramics highlights their potential as multifunctional dipolar luminescent materials for advanced optoelectronic, sensing, and energy-harvesting applications.
Purpose This paper introduces a novel four-dimensional chaotic electronic circuit modeled using the Caputo-Fabrizio (CF) Fractional derivative (FD), which features a non-singular, exponentially decaying kernel. The existence, uniqueness, and stability of the system's solutions are rigorously established through Picard approximation, Banach's fixed point theorem, and an iterative Laplace transform (LT) technique. A customized fractional Euler method is developed to perform numerical simulations, revealing rich dynamical behavior that depends sensitively on the fractional order a ? (0, 1). The results demonstrate that the proposed fractional-order model effectively captures memory effects, offering a more realistic framework for analyzing complex nonlinear electronic systems compared to classical integer-order approaches. Design/methodology/approach The study introduces a novel four-dimensional chaotic electronic circuit model using the CF FD, which features a non-singular, exponentially decaying kernel. The authors employ Picard approximation and Banach's fixed point theorem to rigorously establish the existence, uniqueness, and stability of the system's solutions. An iterative scheme based on the LT is applied to analyze stability, while a customized fractional Euler method is developed for numerical simulations. This approach enables the exploration of the system's dynamic behavior under varying fractional orders (0 < a = 1), demonstrating how memory effects influence chaos in electronic circuits and offering a more realistic modeling framework compared to classical integer-order systems. Findings The most significant findings of this study revolve around the successful development and analysis of a novel four-dimensional chaotic electronic circuit modeled using the CF FD, which features a non-singular, exponentially decaying kernel. The authors rigorously proved the existence, uniqueness, and stability of the system's solutions using Picard approximation, Banach's fixed point theorem, and an iterative LT technique. A customized fractional Euler method was introduced for numerical simulations, revealing that the system exhibits rich chaotic dynamics highly sensitive to the fractional order a (0 < a = 1). These results demonstrate that fractional-order modeling with the CF operator captures memory effects more realistically than classical integer-order models, offering enhanced fidelity in representing real-world electronic systems with inherent memory dependence. Originality/value This work is original in proposing the first four-dimensional chaotic electronic circuit modeled with the CF FD-characterized by a non-singular, exponentially decaying kernel-offering a more physically realistic representation of memory effects than classical fractional models. Its value lies in the rigorous theoretical analysis (existence, uniqueness, and stability via Picard approximation and Banach's fixed point theorem), the development of a tailored fractional Euler method for simulation, and the demonstration that system dynamics are highly sensitive to the fractional order. These contributions advance both fractional calculus theory and its application in secure communications, circuit design, and chaos-based engineering.
Federated learning is a decentralized model training paradigm with significant potential. However, the quality of Federated Network's client updates can vary due to non-IID data distributions, leading to suboptimal global models. To address this issue, we propose a novel client selection strategy called FedPA (Performance-Based Federated Averaging). This proposed model selectively aggregates client updates based on a predefined performance threshold. Only clients whose local models achieve an F1 score of 70% or higher after training are included in the aggregation process. Clients below this threshold receive the updated global model but do not contribute their parameters. In this way, the low-performance clients are still in the process of learning and, after some rounds, will be able to contribute. If no client meets the performance threshold in a given round, the system falls back to standard FedAvg aggregation. This ensures the global model continues to improve even when most clients perform poorly. We evaluate FedPA on a subset of the MURA dataset for abnormality detection in radiographs of four bone types. Compared to baseline federated learning algorithms such as Federated Averaging (FedAvg), Federated Proximal (FedProx), Federated Stochastic Gradient Descent (FedSGD), and Federated Batch Normalization (FedBN), FedPA consistently ranks first or second across key performance metrics, particularly in accuracy, F1 score, and recall. Moreover, FedPA demonstrates notable efficiency, achieving the lowest average round time (832270 s) and minimal memory usage (83645.58 MB), all without relying on GPU resources. These results highlight FedPA's effectiveness in improving global model quality while reducing computational overhead, positioning it as a promising approach for real-world federated learning applications in resource-constrained environments.
