Network automation has been bred by the deployment of 5G based Industrial Internet-of-Things (IIoT) in Industry 4.0, and further approaching pervasive AI and human-robot-interaction/-collaboration toward 6G based Industry 5.0. Hitherto, to the best of the authors knowledge, research efforts are still required to provide a comprehensive review of the state-of-the-art network automation technologies for IIoT in 5G based Industry 4.0 and summary of challenges for next-generation network automation regarding the stricter network requirements of 6G based Industry 5.0. Therefore, in this article, we conduct a comprehensive overview of the state-of-the-art network automation technologies, standardizations, and corresponding impact on IIoT of Industry 4.0. We also forecast the next-generation network automation development toward 6G based Industry 5.0. This article provides blueprint of the next-generation network automation, meanwhile conducting comprehensive overview of the SoA network automation technologies in Industry 4.0, which gains high referable value for the researchers in the relative domain.

The development in the Internet-of-Things (IoT) has advanced healthcare. The direction of healthcare has been transformed from treatment to health management. Thus, more data are being generated and aggregated than ever before while healthcare moves toward patient-oriented and analytic application. This paper discusses the different aspects of smart healthcare along with the health data and patient-oriented health management.

Nowadays, our society has been experiencing the development of Healthcare 4.0, focusing on digitalization by utilizing cloud/fog computing-based consumer Internet-of-Things (CIoT). Meanwhile, Industry 5.0 has been defined recently, targeting improving the current Industry 4.0. Inspired by Industry 5.0, we are among the first wave of researchers to propose Healthcare 5.0, specifically in the perspective of fog/cloud computing. Concretely, we perform a comprehensive vertical and horizontal summary of Healthcare 1.0 -4.0 and Industry 1.0 -4.0, which enables us to be among the first research efforts to provide a unique prediction of the corresponding upcoming Healthcare 5.0, based on Industry 5.0. In particular, we predict the potential development in Healthcare 5.0, specifically for fog/cloud computing, on top of a comprehensive overview of the role and state-of-the-art development in Healthcare 4.0. We also give our unique insight of the potential technologies of CIoT and fog/cloud computing in Healthcare 5.0, as well as the corresponding opportunities and challenges. Therefore, for the first time, this article provides deep and well-thought insight to open a new era towards the future fog/cloud computing for healthcare system development towards Healthcare 5.0.

Many traffic injuries and deaths are caused by the drowsiness of drivers during driving. Existing drowsiness detection schemes are not accurate due to various reasons. To resolve this problem, an accurate driver drowsiness classifier (DDC) has been developed using an electrocardiogram genetic algorithm-based support vector machine (ECG GA-SVM). In existing studies, a cross correlation kernel and a convolution kernel have both been applied for performing the classification. The DDC is designed by a Mercer kernel KDDC formed by commuting the cross correlation kernel K_xcorr,ij and the convolution kernel K_conv,ij. K_xcorr,ij, and captures the symmetric information among ECG signals from different classes, while Kconv,ij captures the antisymmetric information among ECG signals from the same class. The final KDDC (a precomputed kernel) is obtained by a genetic mutation using a multiobjective genetic algorithm. This renders an optimal KDDC that confidently serves as the full descriptor of the drowsiness. The performance of KDDC is compared with the most prevailing kernels. The obtained DDC yields an overall accuracy of 97.01%, sensitivity of 97.16%, and specificity of 96.86%. The analysis reveals that the accuracy of KDDC is better than those of both K_xcorr,ij and K_conv,ij by more than 11%, and typical kernels including linear, quadratic, third order polynomial, and Gaussian radial basis function by 17-63%, respectively. Comparing with related works using the image-based method and the biometric signal-based method, KDDC improves the accuracy by 48.4-87.2%. Testing results showed that KDDC has a less than 1% deviation from simulated results. Also, the average delay of DDC was bounded by 0.55 ms. This renders the real time implementation. Thus, the developed ECG GA-SVM provides an accurate and instantaneous warning to the drivers before they fall into sleep. As a result this ensures the public transport safety.

Introduction: Despite the many benefits immunotherapy has brought to patients with different cancers, its clinical applications and improvements are still hindered by drug resistance. Fostering a reliable approach to identifying sufferers who are sensitive to certain immunotherapeutic agents is of great clinical relevance.Methods: We propose an ELISE (Ensemble Learning for Immunotherapeutic Response Evaluation) pipeline to generate a robust and highly accurate approach to predicting individual responses to immunotherapies. ELISE employed iterative univariable logistic regression to select genetic features of patients, using Monte Carlo Tree Search (MCTS) to tune hyperparameters. In each trial, ELISE selected multiple models for integration based on add or concatenate stacking strategies, including deep neural network, automatic feature interaction learning via self-attentive neural networks, deep factorization machine, compressed interaction network, and linear neural network, then adopted the best trial to generate a final approach. SHapley Additive exPlanations (SHAP) algorithm was applied to interpret ELISE, which was then validated in an independent test set.Result: Regarding prediction of responses to atezolizumab within esophageal adenocarcinoma (EAC) patients, ELISE demonstrated a superior accuracy (Area Under Curve [AUC] = 100.00%). AC005786.3 (Mean [|SHAP value|] = 0.0097) was distinguished as the most valuable contributor to ELISE output, followed by SNORD3D (0.0092), RN7SKP72 (0.0081), EREG (0.0069), IGHV4-80 (0.0063), and MIR4526 (0.0063). Mechanistically, immunoglobulin complex, immunoglobulin production, adaptive immune response, antigen binding and others, were downregulated in ELISE-neg EAC subtypes and resulted in unfavorable responses. More encouragingly, ELISE could be extended to accurately estimate the responsiveness of various immunotherapeutic agents against other cancers, including PD1/PD-L1 suppressor against metastatic urothelial cancer (AUC = 88.86%), and MAGE−A3 immunotherapy against metastatic melanoma (AUC = 100.00%).Discussion: This study presented deep insights into integrating ensemble deep learning with self-attention as a mechanism for predicting immunotherapy responses to human cancers, highlighting ELISE as a potential tool to generate reliable approaches to individualized treatment.

A multi-interface ZigBee building area network (MIZBAN) for a high-traffic advanced metering infrastructure (AMI) for high-rise buildings was developed. This supports meter management functions such as Demand Response for smart grid applications. To cater for the high-traffic communication in these building area networks (BANs), a multi-interface management framework was defined and designed to coordinate the operation between multiple interfaces based on a newly defined tree-based mesh (T-Mesh) ZigBee topology, which supports both mesh and tree routing in a single network. To evaluate MIZBAN, an experiment was set up in a five-floor building. Based on the measured data, simulations were performed to extend the analysis to a 23-floor building. These revealed that MIZBAN yields an improvement in application-layer latency of the backbone and the floor network by 75% and 67%, respectively. This paper provides the design engineer with seven recommendations for a generic MIZBAN design, which will fulfill the requirement for demand response by the U.S. government, i.e. a latency of less than 0.25 s.