Rheumatoid arthritis (RA) is an autoimmune disorder whose treatment is mostly restricted to pain and symptom management and to the delay of joint destruction. Mesenchymal stem/stromal cells from the umbilical cord tissue (UC-MSCs) have previously been proven to be immunomodulatory and more efficient than bone marrow-derived MSCs in causing remission of local and systemic arthritic manifestations in vivo. Given the paracrine nature of UC-MSC activity, their application as active substances can be replaced by their secretome, thus avoiding allogeneic rejection and safety issues related to unwanted grafting. In this work, we aimed at demonstrating the viability of applying the 3D-primed UC-MSC secretome for the amelioration of arthritic signs. A proteomic analysis was performed to both, media conditioned by UC-MSC monolayer (CM2D) and 3D cultures (CM3D). The analysis of relevant trophic factors confirmed secretome profiles with very significant differences in terms of therapeutic potential. Whereas, CM3D was characterised by a prevailing expression of anti-inflammatory cytokines such as IL-10 and LIF, along with trophic factors involved in different mechanisms leading to tissue regeneration, such as PDGF-BB, FGF-2, I-309, SCF, and GM-CSF; CM2D presented relatively higher levels of IL-6, MCP-1, and IL-21, with recognised pro-inflammatory roles in joint disease and pleiotropic effects in the progression of rheumatoid arthritis (RA). Accordingly, different motogenic effects over mouse chondrocytes and distinct capacities of inducing glycosaminoglycan synthesis in vitro were observed between CM3D and CM2D. Finally, the evaluation of arthritic manifestations in vivo, using an adjuvant-induced model for arthritis (AIA), suggested a significantly higher therapeutic potential of CM3D over CM2D and even UC-MSCs. Histological analysis confirmed a faster remission of local and systemic arthritic manifestations of CM3D-treated animals. Overall, the results show that the use of UC-MSC CM3D is a viable and better strategy than direct UC-MSC administration for counteracting AIA-related signs. This strategy represents a novel MSC-based but nonetheless cell-free treatment for arthritic conditions such as those characterising RA.

For decades, industrial companies have been collecting and storing high amounts of data with the aim of better controlling and managing their processes. However, this vast amount of information and hidden knowledge implicit in all of this data could be utilized more efficiently. With the help of data mining techniques unknown relationships can be systematically discovered. The production of semiconductors is a highly complex process, which entails several subprocesses that employ a diverse array of equipment. The size of the semiconductors signifies a high number of units can be produced, which require huge amounts of data in order to be able to control and improve the semiconductor manufacturing process. Therefore, in this paper a structured review is made through a sample of 137 papers of the published articles in the scientific community regarding data mining applications in semiconductor manufacturing. A detailed bibliometric analysis is also made. All data mining applications are classified in function of the application area. The results are then analyzed and conclusions are drawn.

The growing competitiveness in the automotive industry and the strict standards to which it is subject, require high quality standards. For this, quality tools such as the failure mode and effects analysis (FMEA) are applied to quantify the risk of potential failure modes. However, for qualitative defects with subjectivity and associated uncertainty, and the lack of specialized technicians, it revealed the inefficiency of the visual inspection process, as well as the limitations of the FMEA that is applied to it. The fuzzy set theory allows dealing with the uncertainty and subjectivity of linguistic terms and, together with the expert systems, allows modeling of the knowledge involved in tasks that require human expertise. In response to the limitations of FMEA, a fuzzy FMEA system was proposed. Integrated in the design, measure, analyze, improve and control (DMAIC) cycle, the proposed system allows the representation of expert knowledge and improves the analysis of subjective failures, hardly detected by visual inspection, compared to FMEA. The fuzzy FMEA system was tested in a real case study at an industrial manufacturing unit. The identified potential failure modes were analyzed and a fuzzy risk priority number (RPN) resulted, which was compared with the classic RPN. The main results revealed several differences between both. The main differences between fuzzy FMEA and classical FMEA come from the non-linear relationship between the variables and in the attribution of an RPN classification that assigns linguistic terms to the results, thus allowing a strengthening of the decision-making regarding the mitigation actions of the most “important” failure modes.

In the last few decades, the fast technological development has caused high competitiveness among companies, encouraging a pursuit for strategies that allow them to gain competitive advantage, such as the monitoring of performance by using key performance indicators (KPIs). However, its selection process is complex since there are several KPIs available to evaluate performance and different relationships between them. To overcome this challenge, the use of a multiple criteria decision-making model (MCDM) was proposed, namely the analytic network process (ANP) through which a reduced number of them are prioritized. To identify which KPIs are suitable for the press cast and die manufacturing industry, a literature review was made, and 58 unique KPIs were identified. Thus, to validate the proposed methodology, a case study was carried out in an automotive press molding industry. With the implementation of the proposed ANP model it was possible to identify 9 KPIs that ensure the correct molding process monitoring, while being aligned with the Balanced Scorecard criteria. The results show that the proposed model is suitable for selecting KPIs for the molding industry.

Additive manufacturing (AM) is an emerging area with the potential to modify present business models in the near future. In contrast with conventional manufacturing (CM), AM allows the development of a product from a smaller amount of raw material, while allowing an improvement in properties in terms of weight and functionality. Its production flexibility and creativity in terms of materials have enabled not only the industry to use this technology, but also has been used in healthcare (e.g., in the production of human tissue) and by the final consumer. Despite the invaluable opportunities that this technology could provide, the uncertainties concerning its future developments and impacts on business models remain. New business models in AM will convey the need to: specialize the workforce in the design of new parts produced locally or remotely; regulation in the use and sharing of intellectual property rights by partner companies or between users; regulate the possibility of reverse engineering of highly customized products; etc. The present research proposes a conceptual maturity model to support the phases of evolution of AM in the industry, in supply chains, and in terms of open business models.