Professional Burnout Syndrome (PBS) among health-care professionals has been considered a threat to both staff well-being and patient safety, especially in a high-stress medical environment. While robotics and AI have been increasingly integrated into healthcare, their impact on PBS has not been explored in detail yet. Therefore, this paper proposes a real-time PBS detection framework for healthcare professionals using emotion recognition. This framework includes a deep learning-based emotion detection system for humanoid companion robots, which then correlates emotional trends with the Circumplex model to identify burnout risk. Our experimental evaluation results across five deep learning architectures, MobileNet, RegNetY, Swin Transformer, ConvNeXt V2, and EVA-02, show a highest accuracy of 74.05% on a public emotion dataset. These results demonstrate the feasibility of integrating such systems into healthcare workflows for early PBS warnings. Also, this work suggests a human-in-the-loop diagnostic model, where robotic emotion detection complements clinical expertise by providing proactive support, strengthening workforce resilience, and maintaining the quality of patient care.

Blockchain, as a trustless network, has provided diverse benefits for a wide range of application domains, such as enhancing data management in terms of data security, traceability, accountability, transparency, and decentralization. However, the detection of cybersecurity vulnerabilities in blockchain has initiated a debate on whether this inherently trustless technology needs further trust support or not. In this work, we explore mutual trust and trustless cooperation. First, we examine blockchain-assisted trust management to highlight the specific trustless trait of blockchain. Then, as consensus is an important component of blockchain technology, we examine the role of trust in evaluating the trustworthiness of peer participants in the blockchain consensus process and enhancing the growth of a consistent chain. Finally, we derive research findings in the promising cooperation between trustless and trust.

Recently, microservices have been examined as a solution for reshaping and improving the flexibility, scalability, and maintainability of critical infrastructure systems. However, microservice systems are also suffering from the presence of a substantial number of potentially vulnerable components that may threaten the protection of critical infrastructures. To address the problem, this paper proposes to leverage the concept of antifragility built in a framework for building self-learning microservice systems that could be strengthened by faults and threats instead of being deteriorated by them. To illustrate the approach, we instantiate the proposed approach of autonomous machine learning through an experimental evaluation on a benchmarking dataset of microservice faults.

Big Data processing has become crucial in many domains because the amount of the produced data has enormously increased almost everywhere. The effective selection of the right Big Data processing tool is hard due to the high number and large variety of the available state-of-the-art tools. Many research results agree that there is no one best Big Data solution for all needs and requirements. It is therefore essential to be able to navigate more efficiently in the world of Big Data processing tools. In this paper, we present a map of current Big Data processing tools, recommended according to their capabilities and advantageous properties identified in previously published academic benchmarks. This map—as a navigation diagram—is aimed at helping researchers and practitioners to filter a large amount of available Big Data processing tools according to the requirements and properties of their tasks. Additionally, we provide recommendations for future experiments comparing Big Data processing tools, to improve the navigation diagram.

With the development of Big Data applications in Smart Cities, various Big Data applications are proposed within the domain. These are however hard to test and prototype, since such prototyping requires big computing resources. In order to save the effort in building Big Data prototypes for Smart Cities, this paper proposes an enhanced sampling technique to obtain a coreset from Big Data while keeping the features of the Big Data, such as clustering structure and distribution density. In the proposed sampling method, for a given dataset and an e > 0, the method computes an e-coreset of the dataset. The e-coreset is then modified to obtain a sample set while ensuring the separation and balance in the set. Furthermore, by considering the representativeness of each sample point, our method can helps to remove noises and outliers. We believe that the coreset-based technique can be used to efficiently prototype and evaluate Big Data applications in the Smart City.

3D data have been widely used to represent complex data objects in different domains such as virtual reality, 3D printing or biological data analytics. Due to complexity of 3D data, it is usually featured as big 3D data. One of the typical big 3D data is the protein data, which can be used to visualize the protein structure in a 3D style. However, the 3D data also bring various data quality problems, which may cause the delay, inaccurate analysis results, even fatal errors for the critical decision making. Therefore, this paper proposes a novel big 3D data process model with specific consideration of 3D data quality. In order to validate this model, we conduct a case study for cleaning and analyzing the protein data. Our case study includes a comprehensive taxonomy of data quality problems for the 3D protein data and demonstrates the utility of our proposed model. Furthermore, this work can guide the researchers and domain experts such as biologists to manage the quality of their 3D protein data.

With the rapid development of the Big Data and Internet of Things (IoT), Big Data technologies have emerged as a key data analytics tool in IoT, in which, data clustering algorithms are considered as an essential component for data analysis. However, there has been limited research that addresses the challenges across Big Data and IoT and thus proposing a research agenda is important to clarify the research challenges for clustering Big Data in the context of IoT. By tackling this specific aspect - clustering algorithm in Big Data, this paper examines on Big Data technologies, related data clustering algorithms and possible usages in IoT. Based on our review, this paper identifies a set of research challenges that can be used as a research agenda for the Big Data clustering research. This research agenda aims at identifying and bridging the research gaps between Big Data clustering algorithms and IoT.