Deriving a systematic approach to changeable manufacturing system design

It has long been argued that Factories are long life and complex products. The complexity of designing factories, and their underlying manufacturing systems, is further amplified when dealing with continuously changing customer demands. At the same time, due to research fragmentation, little if any scientific explanations are available supporting and exploiting the paradigm that "factories are products". In order to address this weakness, this paper presents research results arising from a comparative analysis of systematic "product design" and "manufacturing system design" approaches. The contribution emerging from this research is an integrated systematic design approach to changeable manufacturing systems, based on scientific concepts founded upon product design theories, and is explained through a case study in the paper. This research is part of collaboration between the CERU University of Malta and IAO Fraunhofer aimed at developing a digital decision support tool for planning changeable manufacturing systems.peer-reviewe

A case for assisting ‘product family’ manufacturing system designers

Manufacturing system design is a complex and demanding activity and the system designer has to take many factors into consideration during the development process including the demand and technological requirements of the products or product families. Central to this activity is the synthesis decision making process, during which the designer defines the elements that will make up the manufacturing system. This research identifies in the decision making process a critical activity and contributes a phenomena that can be used by a framework to support designers to address complex issues such as changeability and the evolution of products over the manufacturing system life-cycle.This research work was partially funded through an ERDF Project (Project No. ERDF083). The first and second authors would therefore like to thank the Malta Council for Science and Technology (MCST) who is administering this project.peer-reviewe

Information support and interactive planning in the digital factory : approach and industry-driven evaluation

In the modern world we are continuously surrounded by information. The human brain has to analyse and interpret this information to transform into useable knowledge that is then used in decision making activities. The advent and implementation of Industry 4.0 will make it a requirement for systems within factories to interact and share large quantities of information with each other. This large volume of information will make it even more difficult for the human resources within the factory to sift through the large amount of information required since there is a limit to the information that our brains can cope with. Just in time information retrieval (JITIR) within the digital factory environment aims to provide support to the human stakeholders in the system by proactively yet non-intrusively providing the required information at the right time based on the users context. This paper will therefore provide an insight into the cognitive difficulties experienced by humans in the digital factory and how JITIR can tackle these challenges. By validating the JITIR concept, several industry scenarios have been evaluated: an exemplary model, concerning the machine tool industry, is presented in the paper. The results of this research are a set of guidelines for the development of a digital factory support tool.peer-reviewe

Modeling of system knowledge for efficient agile manufacturing : tool evaluation, selection and implementation scenario in SMEs

In the manufacturing world, knowledge is fundamental in order to achieve effective and efficient real time decision making. In order to make manufacturing system knowledge available to the decision maker it has to be first captured and then modelled. Therefore tools that provide a suitable means for capturing and representation of manufacturing system knowledge are required in several types of industrial sectors and types of company’s (large, SME). A literature review about best practice for capturing requirements for simulation development and system knowledge modeling has been conducted. The aim of this study was to select the best tool for manufacturing system knowledge modelling in an open-source environment. In order to select this tool, different criteria were selected, based on which several tools were analyzed and rated. An exemplary use case was then developed using the selected tool, Systems Modeling Language (SysML). Therefore, the best practice has been studied, evaluated, selected and then applied to two industrial use cases by the use of a selected opens source tool.peer-reviewe

Towards knowledge capturing and innovative human-system interface in an open-source factory modelling and simulation environment

The capital value of knowledge is fundamental in modern cyber-physical systems. The user is provided with information from multiple and sometimes conflicting sources. This large amount of information must then be analyzed and interpreted by the human brain. Information must be used in a profitable way, and transformed into useable knowledge, which can then be effectively used in decision making activities. It is therefore critical to provide the required information and knowledge in order to support digital factory activities such as modelling and simulation of manufacturing systems. The advent and implementation of Industry 4.0 will make it a requirement for virtual planning and real systems within smart factories, to interact and share large quantities of information with each other. In this respect, one of the major challenges is making sure that the right people have the right information, at the right time to make the right decisions. The aim of this research is to provide a multi-level just-in-time simulation tool to support decision making in digital factory planning. The tool makes use of a suitable means for the capturing and representation of manufacturing system knowledge in several types of industrial sectors and types of companies (large, SME). Furthermore, to support the modelling activity, the human-system interface of the simulation tool makes use of just-in-time information retrieval (JITIR). The aim of JITIR is to proactively yet non-intrusively provide the required information at the right time based on the users’ context during the modeling and simulation activitypeer-reviewe

N-glycosylation of mouse TRAIL-R and human TRAIL-R1 enhances TRAIL-induced death.

APO2L/TRAIL (TNF-related apoptosis-inducing ligand) induces death of tumor cells through two agonist receptors, TRAIL-R1 and TRAIL-R2. We demonstrate here that N-linked glycosylation (N-glyc) plays also an important regulatory role for TRAIL-R1-mediated and mouse TRAIL receptor (mTRAIL-R)-mediated apoptosis, but not for TRAIL-R2, which is devoid of N-glycans. Cells expressing N-glyc-defective mutants of TRAIL-R1 and mouse TRAIL-R were less sensitive to TRAIL than their wild-type counterparts. Defective apoptotic signaling by N-glyc-deficient TRAIL receptors was associated with lower TRAIL receptor aggregation and reduced DISC formation, but not with reduced TRAIL-binding affinity. Our results also indicate that TRAIL receptor N-glyc impacts immune evasion strategies. The cytomegalovirus (CMV) UL141 protein, which restricts cell-surface expression of human TRAIL death receptors, binds with significant higher affinity TRAIL-R1 lacking N-glyc, suggesting that this sugar modification may have evolved as a counterstrategy to prevent receptor inhibition by UL141. Altogether our findings demonstrate that N-glyc of TRAIL-R1 promotes TRAIL signaling and restricts virus-mediated inhibition

Higher COVID-19 pneumonia risk associated with anti-IFN-α than with anti-IFN-ω auto-Abs in children

We found that 19 (10.4%) of 183 unvaccinated children hospitalized for COVID-19 pneumonia had autoantibodies (auto-Abs) neutralizing type I IFNs (IFN-alpha 2 in 10 patients: IFN-alpha 2 only in three, IFN-alpha 2 plus IFN-omega in five, and IFN-alpha 2, IFN-omega plus IFN-beta in two; IFN-omega only in nine patients). Seven children (3.8%) had Abs neutralizing at least 10 ng/ml of one IFN, whereas the other 12 (6.6%) had Abs neutralizing only 100 pg/ml. The auto-Abs neutralized both unglycosylated and glycosylated IFNs. We also detected auto-Abs neutralizing 100 pg/ml IFN-alpha 2 in 4 of 2,267 uninfected children (0.2%) and auto-Abs neutralizing IFN-omega in 45 children (2%). The odds ratios (ORs) for life-threatening COVID-19 pneumonia were, therefore, higher for auto-Abs neutralizing IFN-alpha 2 only (OR [95% CI] = 67.6 [5.7-9,196.6]) than for auto-Abs neutralizing IFN-. only (OR [95% CI] = 2.6 [1.2-5.3]). ORs were also higher for auto-Abs neutralizing high concentrations (OR [95% CI] = 12.9 [4.6-35.9]) than for those neutralizing low concentrations (OR [95% CI] = 5.5 [3.1-9.6]) of IFN-omega and/or IFN-alpha 2

Identification of genetic variants associated with Huntington's disease progression: a genome-wide association study

Background Huntington's disease is caused by a CAG repeat expansion in the huntingtin gene, HTT. Age at onset has been used as a quantitative phenotype in genetic analysis looking for Huntington's disease modifiers, but is hard to define and not always available. Therefore, we aimed to generate a novel measure of disease progression and to identify genetic markers associated with this progression measure. Methods We generated a progression score on the basis of principal component analysis of prospectively acquired longitudinal changes in motor, cognitive, and imaging measures in the 218 indivduals in the TRACK-HD cohort of Huntington's disease gene mutation carriers (data collected 2008–11). We generated a parallel progression score using data from 1773 previously genotyped participants from the European Huntington's Disease Network REGISTRY study of Huntington's disease mutation carriers (data collected 2003–13). We did a genome-wide association analyses in terms of progression for 216 TRACK-HD participants and 1773 REGISTRY participants, then a meta-analysis of these results was undertaken. Findings Longitudinal motor, cognitive, and imaging scores were correlated with each other in TRACK-HD participants, justifying use of a single, cross-domain measure of disease progression in both studies. The TRACK-HD and REGISTRY progression measures were correlated with each other (r=0·674), and with age at onset (TRACK-HD, r=0·315; REGISTRY, r=0·234). The meta-analysis of progression in TRACK-HD and REGISTRY gave a genome-wide significant signal (p=1·12 × 10−10) on chromosome 5 spanning three genes: MSH3, DHFR, and MTRNR2L2. The genes in this locus were associated with progression in TRACK-HD (MSH3 p=2·94 × 10−8 DHFR p=8·37 × 10−7 MTRNR2L2 p=2·15 × 10−9) and to a lesser extent in REGISTRY (MSH3 p=9·36 × 10−4 DHFR p=8·45 × 10−4 MTRNR2L2 p=1·20 × 10−3). The lead single nucleotide polymorphism (SNP) in TRACK-HD (rs557874766) was genome-wide significant in the meta-analysis (p=1·58 × 10−8), and encodes an aminoacid change (Pro67Ala) in MSH3. In TRACK-HD, each copy of the minor allele at this SNP was associated with a 0·4 units per year (95% CI 0·16–0·66) reduction in the rate of change of the Unified Huntington's Disease Rating Scale (UHDRS) Total Motor Score, and a reduction of 0·12 units per year (95% CI 0·06–0·18) in the rate of change of UHDRS Total Functional Capacity score. These associations remained significant after adjusting for age of onset. Interpretation The multidomain progression measure in TRACK-HD was associated with a functional variant that was genome-wide significant in our meta-analysis. The association in only 216 participants implies that the progression measure is a sensitive reflection of disease burden, that the effect size at this locus is large, or both. Knockout of Msh3 reduces somatic expansion in Huntington's disease mouse models, suggesting this mechanism as an area for future therapeutic investigation