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The fourth industrial revolution (4IR) means the ubiquitous digitization of economic processes using more economical and efficient production technologies. Orientation to productivity, flexibility and low production costs results in a slow process of dehumanization of industry and concentration only on implementing Industry 4.0 (I4.0) digital technologies. A natural consequence of this trend is the concern of governments, employees and communities about new challenges and the importance of man in the economic ecosystem. The hope is the emergence of a new industry concept suggested by the European Commission (EU), which expands the components of the existing I4.0 concept to include human-centric, environmental and resilience aspects. Industry 5.0 (I5.0) is an excellent alternative to the development of today's digital and dehumanized world. The article aims to identify the key research areas related to the formation of the role of the human being and the safe work environment in implementing the I5.0 concept. The article analyzes the research areas related to implementing the I5.0 concept based on a systematic review of the literature indexed in the Web of Science and Scopus databases. Identifies key issues related to the role of humans in the I5.0 environment. In addition, the priority directions for developing the identified research areas and their impact on forming a safe work environment are determined based on the knowledge of experts with experience in implementing digital technologies of the 4IR.

Hexagonal close-packed (hcp) metals such as Mg, Ti, and Zr are lightweight and/or durable metals with critical structural applications in the automotive (Mg), aerospace (Ti), and nuclear (Zr) industries. The hcp structure, however, brings significant complications in the mechanisms of plastic deformation, strengthening, and ductility, and these complications pose significant challenges in advancing the science and engineering of these metals. In hcp metals, generalized plasticity requires the activation of slip on pyramidal planes, but the structure, motion, and cross-slip of the associated 〈c + a〉 dislocations are not well established even though they determine ductility and influence strengthening. Here, atomistic simulations in Mg reveal the unusual mechanism of 〈c + a〉 dislocation cross-slip between pyramidal I and II planes, which occurs by cross-slip of the individual partial dislocations. The energy barrier is controlled by a fundamental step/jog energy and the near-core energy difference between pyramidal 〈c + a〉 dislocations. The near-core energy difference can be changed by nonglide stresses, leading to tension–compression asymmetry and even a switch in absolute stability from one glide plane to the other, both features observed experimentally in Mg, Ti, and their alloys. The unique cross-slip mechanism is governed by common features of the generalized stacking fault energy surfaces of hcp pyramidal planes and is thus expected to be generic to all hcp metals. An analytical model is developed to predict the cross-slip barrier as a function of the near-core energy difference and applied stresses and quantifies the controlling features of cross-slip and pyramidal I/II stability across the family of hcp metals.

BackgroundThere is not a specific focus on Good Clinical Practice (GCP) in Huntington Disease (HD) in Italy. LIRH Foundation has been collecting, especially during the last ten years, much feedback about the patients’ journeys along the National Health System.AimsTo provide GCP Recommendations for Health Care Professionals (HCPs) and HD Centres, in line with patients‘ needs and expectations, as they are based on feedback from HD affected people and their families.MethodsAfter reviewing the existing literature and organizing two focus groups with patients and caregivers, we launched an online survey, addressed to individuals with undisclosed HD risk, patients, positive gene carriers, HD tested negative individuals and caregivers.Results313 respondents from 18 out of 20 Regions (52 with HD diagnosis, 57 with undisclosed HD risk, 53 positive gene carriers, 42 negative gene carriers, 107 unaffected partners, 1 preferred not to specify) accepted to participate. The top three reported best practices refer to humanity (36,2%) listening skills (29,2%) and psychological support (16,2%). The top three ‘must have’ of an HD Centre for the respondents are: Expertise (34%), Research (18%) and Multidisciplinarity (10%).ConclusionsThe survey proved that there was the need to identify the key areas to be improved in the clinics for the sake of patients and families. Based on the survey’s responses, five GCP recommendations were issued. We can summarize them in five words 1) counselling, 2) follow-up, 3) family plan, 4) time, 5) network.

Hydrogen (H2) is one of the best candidates to replace current petroleum energy resources due to its rich abundance and clean combustion. However, the storage of H2 presents a major challenge. There are two methods for storing H2 fuel, chemical and physical, both of which have some advantages and disadvantages. In physical storage, highly porous organic polymers are of particular interest, since they are low cost, easy to scale up, metal-free, and environmentally friendly. In this review, highly porous polymers for H2 fuel storage are examined from five perspectives: (a) brief comparison of H2 storage in highly porous polymers and other storage media; (b) theoretical considerations of the physical storage of H2 molecules in porous polymers; (c) H2 storage in different classes of highly porous organic polymers; (d) characterization of microporosity in these polymers; and (e) future developments for highly porous organic polymers for H2 fuel storage. These topics will provide an introductory overview of highly porous organic polymers in H2 fuel storage.

We present an automated algorithm for unified rejection and repair of bad trials in magnetoencephalography (MEG) and electroencephalography (EEG) signals. Our method capitalizes on cross-validation in conjunction with a robust evaluation metric to estimate the optimal peak-to-peak threshold – a quantity commonly used for identifying bad trials in M/EEG. This approach is then extended to a more sophisticated algorithm which estimates this threshold for each sensor yielding trial-wise bad sensors. Depending on the number of bad sensors, the trial is then repaired by interpolation or by excluding it from subsequent analysis. All steps of the algorithm are fully automated thus lending itself to the name Autoreject. In order to assess the practical significance of the algorithm, we conducted extensive validation and comparisons with state-of-the-art methods on four public datasets containing MEG and EEG recordings from more than 200 subjects. The comparisons include purely qualitative efforts as well as quantitatively benchmarking against human supervised and semi-automated preprocessing pipelines. The algorithm allowed us to automate the preprocessing of MEG data from the Human Connectome Project (HCP) going up to the computation of the evoked responses. The automated nature of our method minimizes the burden of human inspection, hence supporting scalability and reliability demanded by data analysis in modern neuroscience. [Display omitted] •A strategy for artifact rejection in M/EEG using peak-to-peak thresholds is proposed•The thresholds are estimated using cross-validation with a robust error metric•The method detects and repairs outlier data segments for each sensor•Comparison with competing methods on 200 subjects with ground truth responses

A modified and extended version, HCPex, is provided of the surface-based Human Connectome Project-MultiModal Parcellation atlas of human cortical areas (HCP-MMP v1.0, Glasser et al. 2016). The original atlas with 360 cortical areas has been modified in HCPex for ease of use with volumetric neuroimaging software, such as SPM, FSL, and MRIcroGL. HCPex is also an extended version of the original atlas in which 66 subcortical areas (33 in each hemisphere) have been added, including the amygdala, thalamus, putamen, caudate nucleus, nucleus accumbens, globus pallidus, mammillary bodies, septal nuclei and nucleus basalis. HCPex makes available the excellent parcellation of cortical areas in HCP-MMP v1.0 to users of volumetric software, such as SPM and FSL, as well as adding some subcortical regions, and providing labelled coronal views of the human brain.

ChAdOx1 nCov-19 and Ad26.COV2.S are approved vaccines inducing protective immunity against SARS-CoV-2 infection in humans by expressing the Spike protein of SARS-CoV-2. We analyzed protein content and protein composition of ChAdOx1 nCov-19 and Ad26.COV2.S by biochemical methods and by mass spectrometry. Four out of four tested lots of ChAdOx1 nCoV-19 contained significantly higher than expected levels of host cell proteins (HCPs) and of free viral proteins. The most abundant contaminating HCPs belonged to the heat-shock protein and cytoskeletal protein families. The HCP content exceeded the 400 ng specification limit per vaccine dose, as set by the European Medicines Agency (EMA) for this vaccine, by at least 25-fold and the manufacturer’s batch-release data in some of the lots by several hundred-fold. In contrast, three tested lots of the Ad26.COV2.S vaccine contained only very low amounts of HCPs. As shown for Ad26.COV2.S production of clinical grade adenovirus vaccines of high purity is feasible at an industrial scale. Correspondingly, purification procedures of the ChAdOx1 nCov-19 vaccine should be modified to remove protein impurities as good as possible. Our data also indicate that standard quality assays, as they are used in the manufacturing of proteins, have to be adapted for vectored vaccines.

In the last decade, researchers have focused on digital technologies within Industry 4.0. However, it seems the Industry 4.0 hype did not fulfil industry expectations due to many implementation challenges. Today, Industry 5.0 proposes a human-centric approach to implement digital sustainable technologies for smart quality improvement. One important aspect of digital sustainability is reducing the energy consumption of digital technologies. This can be achieved through a variety of means, such as optimizing energy efficiency, and data centres power consumption. Complementing and extending features of Industry 4.0, this research develops a conceptual model to promote Industry 5.0. The aim of the model is to optimize data without losing significant information contained in big data. The model is empowered by edge computing, as the Industry 5.0 enabler, which provides timely, meaningful insights into the system, and the achievement of real-time decision-making. In this way, we aim to optimize data storage and create conditions for further power and processing resource rationalization. Additionally, the proposed model contributes to Industry 5.0 from a social aspect by considering the knowledge, not only of experienced engineers, but also of workers who work on machines. Finally, the industrial application was done through a proof-of-concept using manufacturing data from the process industry, where the amount of data was reduced by 99.73% without losing significant information contained in big data.

As the research of HCPS in EI is ongoing, an increasing number of problems should be addressed, such as the leading role or aid function definition of humans, the coordination of humans with cyber and physical equipment, the state awareness of information handling, etc. Therefore, an increasing number of advanced techniques should be considered and adopted, such as IOT, AI and big data, etc., the main target of which is to improve the timely, digitalized, informationized and intelligent characters of the related tasks. This paper first introduces the applications of CPS in every industry, especially in the power grid and EI (Energy Internet), the introduction of which can be classified as a framework design, support technologies and algorithms, system function, power ancillary service, Energy Internet realization, etc. Then, this paper summarizes the related technologies of Human-Cyber-Physical Systems, which mainly include the areas of system design, theoretical analysis, technique development and application forecasting, which emphasizes its applications in EI. For the development of HCPS, it uses the human-in-the-loop concept and digital transformation as the future research and development directions, the performance of which can be largely promoted through these means. With the aid of HCPS, the optimal system performance of EI can be achieved more robustly, which finally evolves into an integrated and comprehensive modern power grid.

In order to meet the increasingly complex expectations of customers, many companies must increase efficiency and agility. In this sense, Industry 4.0 technologies offer significant opportunities for improving both operational and decision-making processes. These developments make it possible to consider an increase in the level of operational systems and teams’ autonomy. However, the potential for strengthening the decision-making process by means of these new technologies remains unclear in the current literature. To fill this gap, a Delphi study using the Régnier Abacus technique was conducted with a representative panel of 24 experts. The novelty of this study was to identify and characterize the potential for enhancing the overall decision-making process with the main Industry 4.0 groups of technologies. Our results show that cloud computing appears as a backbone to enhance the entire decision-making process. However, certain technologies, such as IoT and simulation, have a strong potential for only specific steps within the decision-making process. This research also provides a first vision of the manager’s perspectives, expectations, and risks associated with implementing new modes of decision-making and cyber-autonomy supported by Industry 4.0 technologies.