The widespread usage of smart devices with various city-centric services speeds up and improves civic life, in contrast to growing privacy and security concerns. Security issues are exacerbated when e-government service providers trade their services within a centralised framework. Due to security concerns, city-centric centralised services are being converted to blockchain-based systems, which is a very time-consuming and challenging process. The interoperability of these blockchain-based systems is also more challenging due to protocol variances, an excessive amount of local transactions that raise scalability and rapidly occupy memory. In this paper, we have proposed a framework for interoperability across various blockchain-based smart city services. It also summarises how independent service providers might continue self-service choices (i.e., local transactions) without overloading the blockchain network and other organisations. A simulated interoperability network is used to show the network’s effectiveness. The experimental outcomes show the scalability and memory optimization of the blockchain network.

The rapid growth of Internet of Things (IoT) devices has significantly increased the cyber-attack surface, rendering traditional centralized Intrusion Detection Systems (IDSs) impractical due to privacy concerns and communication bottlenecks. Federated Learning (FL) has become a privacy-preserving alternative, but most FL-based IDSs function as “black boxes," relying on correlation-based explainability methods that do not reveal the underlying causal mechanisms of attacks. These limitations impede root-cause analysis and the creation of reliable security solutions. This paper introduces a new framework, Causal Explainable Federated Learning for IoT Intrusion Detection (Causal-FL-ID), which integrates causal reasoning directly into the FL process. In this setup, distributed IoT clients perform local causal discovery and send lightweight, privacy-preserving causal summaries to a central server. The server combines these summaries to create a global causal graph, providing deep insights into the causes of an intrusion. This method enables counterfactual reasoning, allowing security analysts to simulate interventions and assess their potential impact on threat mitigation. Extensive experiments on the IDSIoT2024 dataset demonstrate that the proposed framework achieves high prediction accuracy around $$98.5\%$$ and scales effectively with 10, 25, and 50 clients. The results confirm that Causal-FL-ID not only delivers strong performance with manageable communication costs but also offers stable, transparent, and causally grounded explanations, marking a significant step toward more interpretable and resilient security systems for complex IoT environments.

Ensuring information security in the quantum era is a growing challenge due to advancements in cryptographic attacks and the emergence of quantum computing. To address these concerns, this paper presents the mathematical and ... | Find, read and cite all the research you need on Tech Science Press

Transcranial Direct Current Stimulation (tDCS) and Transcranial Magnetic Stimulation (tMS) have received widespread clinical use as techniques within a Non-Invasive Brain Stimulation (NIBS) domain, whose primary focus is modulation of neural activity to treat neurological and psychiatric disorders. Despite these advancements, precision targeting of deep brain structures remains a challenge faced with great need of another innovation that will improve precision and reduce the risks. A novel methodology integrating transcranial Focused Ultrasound (tFUS) with real-time functional imaging modalities, including functional Magnetic Resonance Imaging (fMRI) and Near-Infra-Red Spectroscopy (NIRS), is proposed in this study as the Integrated Focused Ultrasound and Real-Time Imaging Control System (IFURTICS). Closed loop algorithms employed by IFURTICS allow it to dynamically vary stimulation parameters in response to real-time feedback on neural activity, allowing for accurate targeting of sensitive networks while minimizing deleterious collateral effects. Clinical trials using standard datasets of fMRI and NIRS have proved that the approach improved targeting accuracy by ∼18 %, reduced off-target effects by ∼55 % and enhanced therapeutic outcomes by 50 % over current methods, suggesting its potential as a transformative approach to precision neuro-modulation.

The rapid expansion of Internet of Things (IoT) environments has introduced significant security challenges, particularly due to limited labeled data and increasing vulnerability to adversarial attacks. Traditional intrusion detection systems (IDS) struggle to generalize under such constraints, while existing few-shot learning (FSL) approaches remain sensitive to adversarial perturbations. This work proposes a hybrid intrusion detection framework that integrates prototypical few-shot learning with adversarial training to address both data scarcity and robustness. The proposed model employs a Prototypical Network to learn discriminative embeddings from limited samples and incorporates adversarial optimization into the episodic training process to enhance resilience against gradient-based attacks, including FGSM, C&W, DeepFool, and PGD. Experimental evaluation on the IDSIoT2024 dataset demonstrates that the proposed framework achieves high classification performance with only three samples per class, attaining over $$93\%$$ accuracy under benign conditions. In adversarial settings, performance initially degrades significantly but is restored to $$80-90\%$$ accuracy after adversarial training under white-box gradient-based attacks. The results confirm that integrating adversarial training within the few-shot learning paradigm improves both generalization and robustness, making the proposed approach suitable for secure and resource-constrained IoT environments.