Emotion recognition involves accurately interpreting human emotions from various sources and modalities, including questionnaires, verbal, and physiological signals. With its broad applications in affective computing, computational creativity, human-robot interactions, and market research, the field has seen a surge in interest in recent years. This paper presents a systematic review of multimodal emotion recognition (mer) techniques developed from 2014 to 2024, encompassing verbal, physiological signals, facial, body gesture, and speech as well as emerging methods like sketches emotion recognition. The review explores various emotion models, distinguishing between emotions, feelings, sentiments, and moods, along with human emotional expression, categorized in both artistic and non-verbal ways. It also discusses the background of automated emotion recognition systems and introduces seven criteria for evaluating modalities alongside a current state analysis of mer, drawn from the human-centric perspective of this field. By selecting the PRISMA guidelines and carefully analyzing 45 selected articles, this review provides comprehensive perspectives into existing studies, datasets, technical approaches, identified gaps, and future directions in MER. It also highlights existing challenges and potential applications of the MER.

The emerging of the fourth industrial revolution, also known as Industry 4.0 (I4.0), from the advancement in several technologies is viewed not only to promote economic growth, but also to enable a greener future. The 2030 Agenda of the United Nations for sustainable development sets out clear goals for the industry to foster the economy, while preserving social well-being and ecological validity. However, the influence of I4.0 technologies on the achievement of the Sustainable Development Goals (SDG) has not been conclusively or systematically investigated. By understanding the link between the I4.0 technologies and the SDGs, researchers can better support policymakers to consider the technological advancement in updating and harmonizing policies and strategies in different sectors (i.e., education, industry, and governmental) with the SDGs. To address this gap, academic experts in this paper have investigated the influence of I4.0 technologies on the sustainability targets identified by the UN. Key I4.0 element technologies have been classified to enable a quantitative mapping with the 17 SDGs. The results indicate that the majority of the I4.0 technologies can contribute positively to achieving the UN agenda. It was also found that the effects of the technologies on individual goals varies between direct and strong, and indirect and weak influences. The main insights and lessons learned from the mapping are provided to support future policy.

The advent of smart manufacturing and the exposure to a new generation of technological enablers have revolutionized the way manufacturing process is carried out. Cyber-Physical Production Systems (CPPS) are introduced as main actors of this manufacturing shift. They are characterized for having high levels of communication, integration and computational capabilities that led them to a certain level of autonomy. Despite the high expectations and vision of CPPS, it still remains an exploratory topic. Multi-Agent Systems (MAS) have been widely used by software engineers to solve traditional computing problems, e.g., banking transactions. Because of their high levels of distribution and autonomous capabilities, MAS have been considered by the research community as a good solution to design and implement CPPS. This work first introduces a collection of requirements and characteristics of smart manufacturing. A comprehensive review of various research applications is presented to understand the current state of the art and the application of agent technology in manufacturing. Considering the smart manufacturing requirements and current research application, a SWOT analysis was formulated which identifies pros and cons of the implementation of agents in industry. The SWOT analysis was further validated by an industrial expert evaluation and the main findings and discussion of the results are presented.

Data security has become paramount, especially with the exponential growth of data, the rise of cyber threats, and the increasing prevalence of remote work. Data confidentiality, integrity, and availability are crucial, particularly in sensitive domains like healthcare and cloud computing. While robust access control (AC) is essential, it must be balanced with maintaining operational efficiency. In access control models, there is a general lack of a comprehensive cross-domain overview, insufficient focus on evolving access control requirements, and inadequate analysis of challenges and issues. This paper comprehensively analyzes access control models (ACMs) through a two-pronged approach. First, we conduct a Narrative Literature Review(NLR) to classify traditional ACMs, evaluating their security strengths and weaknesses and compatible security protocols. Second, we perform a Systematic Literature Review (SLR) of emerging ACMs developed over the past decade across various domains. This systematic review has three primary objectives: (1) to introduce and analyze these emerging ACMs, (2) to present their current technological status, and (3) to identify key challenges and promising research directions. By combining these approaches, this paper offers a comprehensive overviewofACMapplications and techniques, identifies existing challenges, and explores future research directions to guide advancements in access control.

The concept of smart manufacturing has attracted a huge attention in last years as answer to the increasing complexity, heterogeneity, and dynamism of manufacturing ecosystems. This vision embraces the conception of autonomous and self-organized elements, capable of self-management and self-decision making under a context-aware and intelligent infrastructure. While dealing with dynamic and uncertain environments, these solutions are also contributing to generate social impact introducing sustainability into the industrial equation thanks to the development of task-specific resources that can be easily adapted, re-used and shared. A lot of research under the context of self-organization in smart manufacturing has been produced in last decade considering different methodologies and under different contexts. Most of these works are still in a conceptual or experimental stage and have been developed under different application scenarios. Thus, it is necessary to evaluate their design principles and potentiate their results. The objective of this paper is threefold. First, to introduce main ideas behind self-organization in smart manufacturing. Then, through a systematic literature review, describe the current status in terms of technological and implementation details, methodologies used, and some of the potential future research. Finally, the presentation of an outlook that summarizes main results of this work and their interrelation to facilitate the development of self-organized manufacturing solutions. By providing a holistic overview of the field, we expect that this work can be used by academics and practitioners as a guide to generate awareness of possible requirements, industrial challenges and opportunities that future self-organizing solutions can have towards a smart manufacturing transition.

Big data is defined as a large set of data that could be structured or unstructured. In manufacturing shop-floor, big data incorporates data collected at every stage of the production process. This includes data from machines, connecting devices, and even manufacturing operators. The large size of the data available on the manufacturing shop-floor presents a need for the establishment of tools and techniques along with associated best practices to leverage the advantage of data-driven performance improvement and optimization. There also exists a need for a better understanding of the approaches and techniques at various stages of the data life cycle. In the work carried out, the data life-cycle in shop-floor is studied with a focus on each of the components - Data sources, collection, transmission, storage, processing, and visualization. A narrative literature review driven by two research questions is provided to study trends and challenges in the field. The selection of papers is supported by an analysis of n-grams. Those are used to comprehensively characterize the main technological and methodological aspects and as starting point to discuss potential future research directions. A detailed review of the current trends in different data life cycle stages is provided. In the end, the discussion of the existing challenges is also presented.