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Developing cost-effective and highly active electrocatalysts for the oxygen evolution reaction (OER) is crucial for advancing sustainable energy applications. High-entropy alloys (HEAs) made from earth-abundant transition metals, thanks to their remarkable stability and electrocatalytic performance, provide a promising alternative to expensive electrocatalysts typically derived from noble metals. While pristine HEA surfaces have been theoretically investigated, and the effect of oxygen coverage on conventional metal electrocatalysts has been examined, the impact of surface oxygen coverage on the electrocatalytic performance of HEAs remains poorly understood. To bridge this gap, we employ density functional theory (DFT) calculations to reconstruct the free energy diagram of OER intermediates on CoFeNiCr HEA surfaces with varying oxygen coverages, evaluating their impact on the rate-limiting step and theoretical overpotential. Our findings reveal that increased oxygen coverage weakens the adsorption of HO* and O*, but not HOO*. As a result, the theoretical overpotential for the OER decreases with higher oxygen coverage, and the rate-limiting step shifts from the third oxidation step (HOO* formation) at low coverage to the first oxidation step (HO* formation) at higher coverage.

Deep-learning algorithms hold promise in processing physiological signal data, including electrocardiograms (ECGs) and electroencephalograms (EEGs). However, healthcare often requires long-term monitoring, posing a challenge to traditional deep-learning models. These models are generally trained once and then deployed, which limits their ability to adapt to the dynamic and evolving nature of healthcare scenarios. Continual learning—known for its adaptive learning capabilities over time—offers a promising solution to these challenges. However, there remains an absence of consolidated literature, which reviews the techniques, applications, and challenges of continual learning specific to physiological signal analysis, as well as its future directions. Bridging this gap, our review seeks to provide an overview of the prevailing techniques and their implications for smart healthcare. We delineate the evolution from traditional approaches to the paradigms of continual learning. We aim to offer insights into the challenges faced and outline potential paths forward. Our discussion emphasizes the need for benchmarks, adaptability, computational efficiency, and user-centric design in the development of future healthcare systems.

Towards the massive amount of data generated in our daily work and life, embedded systems, with economical but powerful storage and computing resources, are inevitably becoming the most suitable platform for the Edge Computing for the Internet of Things. However, embedded system servers may also threaten individuals by storing individuals’ private data for years. This paper proposes a Resilient Tag-based Privacy Protection (RTPP) scheme for embedded systems. Specifically, to protect the privacy against the hackers and other non-users, we employ a pseudo-random number encryption technique with the chaos-based principle so that the third party cannot easily steal the private data and reduce the risk of personal privacy leakage. To protect the individuals’ interests, we propose a new approach to controlling the life cycle table of data to enable individuals themselves the flexibility to control the life cycle of private data. Unlike existing data lifetime management methods, the RTPP can support the retrieval of tags in the data life cycle table to control the corresponding privacy while automatically adding or removing tags. Our system automatically adjusted the survival period of private data in the life cycle table through the change of leaf weights, controlled the charge movement on the surface of flash memory, and finally achieved the resilient adjustment process of the life cycle of private data in the embedded system. The security proof and performance evaluation show that the proposed RTPP scheme is provable secure in the automatic privacy lifecycle tuning model for embedded systems and efficient in practice.

This essay presents the main trends of changes in the labor market in connection with the digitalization of the world economy. The interrelated problems of increasing labor productivity, reducing workplaces, and ensuring effective demand are considered. The authors propose considering digitalization as a way of promoting creativity in workplaces, albeit having a delayed effect due to the lag in time for the establishment of a world new economic order, during which services are becoming the main product, and digital platforms and ecosystems are the main actors in the global economy. “Creativization” is a new term suggested by the authors, which means a workplace that is being transformed by digitalization, whereby monotonous work processes are replaced by intellectually intensive operations, art elements, and unique high-performance activities.

When deep neural network (DNN) is extensively utilized for edge AI (Artificial Intelligence), for example, the Internet of things (IoT) and autonomous vehicles, it makes CMOS (Complementary Metal Oxide Semiconductor)-based conventional computers suffer from overly large computing loads. Memristor-based devices are emerging as an option to conduct computing in memory for DNNs to make them faster, much more energy efficient, and accurate. Despite having excellent properties, the memristor-based DNNs are yet to be commercially available because of Stuck-At-Fault (SAF) defects. A Mapping Transformation (MT) method is proposed in this paper to mitigate Stuck-at-Fault (SAF) defects from memristor-based DNNs. First, the weight distribution for the VGG8 model with the CIFAR10 dataset is presented and analyzed. Then, the MT method is used for recovering inference accuracies at 0.1% to 50% SAFs with two typical cases, SA1 (Stuck-At-One): SA0 (Stuck-At-Zero) = 5:1 and 1:5, respectively. The experiment results show that the MT method can recover DNNs to their original inference accuracies (90%) when the ratio of SAFs is smaller than 2.5%. Moreover, even when the SAF is in the extreme condition of 50%, it is still highly efficient to recover the inference accuracy to 80% and 21%. What is more, the MT method acts as a regulator to avoid energy and latency overhead generated by SAFs. Finally, the immunity of the MT Method against non-linearity is investigated, and we conclude that the MT method can benefit accuracy, energy, and latency even with high non-linearity LTP = 4 and LTD = −4.