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Go to Editorial ManagerNetworked Control Systems (NCSs) are increasingly deployed in critical infrastructure, yet their performance is often compromised by communication impairments such as time-varying delays and packet loss. These disturbances can undermine stability, reliability, and overall efficiency. To address these challenges, this study proposes an AI-augmented supervisory control framework that integrates communication metrics directly into the control strategy rather than relying solely on conventional error signals. The architecture employs machine learning classifiers—specifically K-Nearest Neighbors (KNN) and Random Forest (RF)—to assess real-time network conditions (normal, degraded, and emergency) and dynamically adjust the parameters of a core PID controller. Under degraded conditions, PID gains are attenuated to enhance robustness against latency, while in emergency scenarios, a conservative safe-mode configuration is activated to preserve plant stability under severe packet loss. Comparative analysis demonstrates that Random Forest, due to its ensemble-based design, achieves superior situational awareness, smoother mode transitions, and more reliable global stability than KNN. Moreover, RF reduces volatile parameter updates, thereby protecting physical actuators from excessive stress and extending component lifespans. The findings highlight the potential of ensemble learning approaches to enhance resilience and operational safety in delay-prone NCS environments.
In this study, we deposited the Al2O3 nanostructure on 304 stainless steel using PLD technique. The stainless-steel specimens were successfully coated with Al2O3 nanostructure, and surface morphology was examined using an optical microscope. The findings confirmed the nanostructured nature of the films. Tafel curve analysis was used to determine the polarization of the sample before being subjected to laser shock penning treatment. Additional tests were then carried out on the samples following pulse laser deposition.
Federated learning (FL) offers a robust and privacy-preserving approach for developing collaborative intrusion detection systems (IDS). However, statistical variance severely hinders its practical application. Although privacy-preserving federated learning models have been used to develop intrusion detection systems for cyberattacks, problems arise when statistical variance is present. In practice, the performance of the FedAvg algorithm is significantly affected by the heterogeneous distribution of customer data in a real-world network. This distribution causes skewness among customer data, resulting in poor detection accuracy, delayed convergence, and model instability. In this paper, presents conduct a comprehensive comparison of the Scaffold algorithm with the FedAvg baseline using the CICIDS2017 datasets. Because the Scaffold algorithm addresses the client skew problem using control variables, it is considered a state-of-the-art federated optimization technique under the heterogeneous partitioning approach. This paper documents the importance of using the Scaffold algorithm as a reliable and essential tool for building high-performance detection systems in a variety of scientific settings. Therefore, our results demonstrate that Scaffold achieved more stable convergence and outperformed FedAvg, with a 15.1% increase in F1-score and a 13.6% higher overall accuracy under highly skewed data distributions. The present evaluation process operates through simulation testing, but physical testbed implementation remains essential for future work to evaluate real-world deployment challenges.
Autism spectrum disorder (ASD) is a neurological condition marked by impaired communication abilities, social detachment, and repetitive behaviors in individuals. Global health organization facing difficulties in establishing an effective ASD diagnostic system that facilitates precise analysis and early autism prediction. It is a scientific issue that necessitates resolution. This research presents an approach for the early prediction of children with ASD utilizing significant variables through machine learning (ML) methods. Three stages comprise the suggested technique. First, a 1250-case ASD dataset was identified and preprocessed. Five extremely effective traits with high Pearson correlation coefficient (PCC) are chosen from 10 chosen ASD feature dataset through its paces using five ML techniques: Naive Bayes (NB), K-Nearest Neighbor (kNN), Decision Tree(DT), Support Vector Machine (SVM), and AdaBoostM1 (ABM1). The proposed framework is assessed in the third phase utilizing five measurements such as accuracy, precision, predicting time ,recall, and F1-score, . The findings revealed that: NB and K-NN approaches exhibit superior accuracy rates of 99.2% and 97.2%, with minimal prediction times of approximately 0.3 and 0.45 seconds, correspondingly. Conversely, the DT and AdBM1 methods demonstrate a minor decline in accuracy, achieving 94.8% and 87.6%, respectively, along with increased prediction times. Nonetheless, the SVM approach exhibits the least performance, achieving an accuracy of 80.4% with prediction time of 0.84 seconds. This research aligns with several Sustainable Development Goals (SDGs), including ٍSDG3, which focuses on good health, SCG10, which addresses reducing inequalities, including those related to disability. and SCG9 by strengthening technological capabilities, particularly in scientific research