A Proposal of Standardised Data Model for Cloud Manufacturing Collaborative Networks

[EN] The growing amount of data to be handled by collaborative networks raises the need of introducing innovative solutions to fulfil the lack of affordable tools, especially for Small and Medium-Sized Enterprises, to manage and exchange data. The European H2020 Project Cloud Collaborative Manufacturing Networks develops and offers a structured data model, called Standardised Tables, as an organised framework to jointly work with existing databases to manage big data collected from different industries belonging to the CNs. The information of the Standardised Tables will be mainly used with optimisation and collaboration purposes. The paper describes an application of the Standardised Tables in one of the pilots of the aforementioned project, the automotive industry pilot, for solving the collaborative problem of a Materials Requirement Plan.The research leading to these results is in the frame of the “Cloud Collaborative Manufacturing Networks” (C2NET) project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 636909.Andres, B.; Sanchis, R.; Poler, R.; Saari, L. (2017). A Proposal of Standardised Data Model for Cloud Manufacturing Collaborative Networks. IFIP Advances in Information and Communication Technology. 560:77-85. https://doi.org/10.1007/978-3-319-65151-4_7S7785560Andres, B., Poler, R.: Models, guidelines and tools for the integration of collaborative processes in non-hierarchical manufacturing networks: a review. Int. J. Comput. Integr. Manuf. 2(29), 166–201 (2016)Zikopoulos, P., Eaton, C.: Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data. McGraw-Hill Osborne Media, New York (2011)Zhou, B., Wang, S., Xi, L.: Data model design for manufacturing execution system. J. Manuf. Technol. Manag. 16(8), 909–935 (2005)Steven, W.: Getting the MES model – methods for system analysis. ISA Trans. 35(2), 95–103 (1996)Reda, A.: Extracting the extended entity-relationship model from a legacy relational database. Inf. Syst. 28(6), 597–618 (2003)Teorey, T.J., Yang, D., Fry, J.P.: A logical design methodology for relational database using the extended entity-relationship model. ACM Comput. Surv. 18(2), 197–222 (1986)Victor, M., Arie, S.: Representing extended entity-relationship structures in relational databases: a modular approach. ACM Trans. Database Syst. 17(3), 423–464 (1992)CORDIS Europa, Factories of the Future, H2020-EU.2.1.5.1. - Technologies for Factories of the Future (2014)H2020 Project C2NET (2015). http://cordis.europa.eu/project/rcn/193440_en.htmlAndres, B., Sanchis, R., Poler, R.: A cloud platform to support collaboration in supply networks. Int. J. Prod. Manag. Eng. 4(1), 5–13 (2016)APICS, “SCOR Framework,” Supply Chain Operations Reference model (SCOR) (2017)Orbegozo, A., Andres, B., Mula, J., Lauras, M., Monteiro, C., Malheiro, M.: An overview of optimization models for integrated replenishment and producction planning decisions. In: Building Bridges Between Researchers and Practitioners. Book of Abstracts of the International Joint Conference CIO-ICIEOM-IISE-AIM (IJC2016), p. 68 (2016)Andres, B., Poler, R., Saari, L., Arana, J., Benaches, J.V., Salazar, J.: Optimization models to support decision-making in collaborative networks: a review. In: Building Bridges Between Researchers and Practitioners. Book of Abstracts of the International Joint Conference CIO-ICIEOM-IISE-AIM (IJC2016), p. 70 (2016)Andres, B., Sanchis, R., Lamothe, J., Saari, L., Hauser, F.: Combined models for production and distribution planning in a supply chain. In: Building Bridges Between Researchers and Practitioners. Book of Abstracts of the International Joint Conference CIO-ICIEOM-IISE-AIM (IJC2016), p. 71 (2016

Does \u2018bigger\u2019mean \u2018better\u2019? Pitfalls and shortcuts associated with big data for social research

\u2018Big data is here to stay.\u2019 This key statement has a double value: is an assumption as well as the reason why a theoretical reflection is needed. Furthermore, Big data is something that is gaining visibility and success in social sciences even, overcoming the division between humanities and computer sciences. In this contribution some considerations on the presence and the certain persistence of Big data as a socio-technical assemblage will be outlined. Therefore, the intriguing opportunities for social research linked to such interaction between practices and technological development will be developed. However, despite a promissory rhetoric, fostered by several scholars since the birth of Big data as a labelled concept, some risks are just around the corner. The claims for the methodological power of bigger and bigger datasets, as well as increasing speed in analysis and data collection, are creating a real hype in social research. Peculiar attention is needed in order to avoid some pitfalls. These risks will be analysed for what concerns the validity of the research results \u2018obtained through Big data. After a pars distruens, this contribution will conclude with a pars construens; assuming the previous critiques, a mixed methods research design approach will be described as a general proposal with the objective of stimulating a debate on the integration of Big data in complex research projecting

State-Dependent Architecture of Thalamic Reticular Subnetworks

Behavioral state is known to influence interactions between thalamus and cortex, which are important for sensation, action, and cognition. The thalamic reticular nucleus (TRN) is hypothesized to regulate thalamo-cortical interactions, but the underlying functional architecture of this process and its state dependence are unknown. By combining the first TRN ensemble recording with psychophysics and connectivity-based optogenetic tagging, we found reticular circuits to be composed of distinct subnetworks. While activity of limbic-projecting TRN neurons positively correlates with arousal, sensory-projecting neurons participate in spindles and show elevated synchrony by slow waves during sleep. Sensory-projecting neurons are suppressed by attentional states, demonstrating that their gating of thalamo-cortical interactions is matched to behavioral state. Bidirectional manipulation of attentional performance was achieved through subnetwork-specific optogenetic stimulation. Together, our findings provide evidence for differential inhibition of thalamic nuclei across brain states, where the TRN separately controls external sensory and internal limbic processing facilitating normal cognitive function.National Institute of Neurological Disorders and Stroke (U.S.) (NIH Pathway to Independence Career Award K99 NS 078115)Brain & Behavior Research Foundation (Young Investigator Award)National Institutes of Health (U.S.) ( Transformative R01 Award TR01-GM10498)National Institutes of Health (U.S.) (Grant R01-MH061976

A process pattern model for tackling and improving big data quality

Data seldom create value by themselves. They need to be linked and combined from multiple sources, which can often come with variable data quality. The task of improving data quality is a recurring challenge. In this paper, we use a case study of a large telecom company to develop a generic process pattern model for improving data quality. The process pattern model is defined as a proven series of activities, aimed at improving the data quality given a certain context, a particular objective, and a specific set of initial conditions. Four different patterns are derived to deal with the variations in data quality of datasets. Instead of having to find the way to improve the quality of big data for each situation, the process model provides data users with generic patterns, which can be used as a reference model to improve big data quality

Supply chain hybrid simulation: From Big Data to distributions and approaches comparison

The uncertainty and variability of Supply Chains paves the way for simulation to be employed to mitigate such risks. Due to the amounts of data generated by the systems used to manage relevant Supply Chain processes, it is widely recognized that Big Data technologies may bring benefits to Supply Chain simulation models. Nevertheless, a simulation model should also consider statistical distributions, which allow it to be used for purposes such as testing risk scenarios or for prediction. However, when Supply Chains are complex and of huge-scale, performing distribution fitting may not be feasible, which often results in users focusing on subsets of problems or selecting samples of elements, such as suppliers or materials. This paper proposed a hybrid simulation model that runs using data stored in a Big Data Warehouse, statistical distributions or a combination of both approaches. The results show that the former approach brings benefits to the simulations and is essential when setting the model to run based on statistical distributions. Furthermore, this paper also compared these approaches, emphasizing the pros and cons of each, as well as their differences in computational requirements, hence establishing a milestone for future researches in this domain.This work has been supported by national funds through FCT -Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2019 and by the Doctoral scholarship PDE/BDE/114566/2016 funded by FCT, the Portuguese Ministry of Science, Technology and Higher Education, through national funds, and co-financed by the European Social Fund (ESF) through the Operational Programme for Human Capital (POCH)

Parallel Driving and Modulatory Pathways Link the Prefrontal Cortex and Thalamus

Pathways linking the thalamus and cortex mediate our daily shifts from states of attention to quiet rest, or sleep, yet little is known about their architecture in high-order neural systems associated with cognition, emotion and action. We provide novel evidence for neurochemical and synaptic specificity of two complementary circuits linking one such system, the prefrontal cortex with the ventral anterior thalamic nucleus in primates. One circuit originated from the neurochemical group of parvalbumin-positive thalamic neurons and projected focally through large terminals to the middle cortical layers, resembling ‘drivers’ in sensory pathways. Parvalbumin thalamic neurons, in turn, were innervated by small ‘modulatory’ type cortical terminals, forming asymmetric (presumed excitatory) synapses at thalamic sites enriched with the specialized metabotropic glutamate receptors. A second circuit had a complementary organization: it originated from the neurochemical group of calbindin-positive thalamic neurons and terminated through small ‘modulatory’ terminals over long distances in the superficial prefrontal layers. Calbindin thalamic neurons, in turn, were innervated by prefrontal axons through small and large terminals that formed asymmetric synapses preferentially at sites with ionotropic glutamate receptors, consistent with a driving pathway. The largely parallel thalamo-cortical pathways terminated among distinct and laminar-specific neurochemical classes of inhibitory neurons that differ markedly in inhibitory control. The balance of activation of these parallel circuits that link a high-order association cortex with the thalamus may allow shifts to different states of consciousness, in processes that are disrupted in psychiatric diseases

On the use of simulation as a Big Data semantic validator for supply chain management

Simulation stands out as an appropriate method for the Supply Chain Management (SCM) field. Nevertheless, to produce accurate simulations of Supply Chains (SCs), several business processes must be considered. Thus, when using real data in these simulation models, Big Data concepts and technologies become necessary, as the involved data sources generate data at increasing volume, velocity and variety, in what is known as a Big Data context. While developing such solution, several data issues were found, with simulation proving to be more efficient than traditional data profiling techniques in identifying them. Thus, this paper proposes the use of simulation as a semantic validator of the data, proposed a classification for such issues and quantified their impact in the volume of data used in the final achieved solution. This paper concluded that, while SC simulations using Big Data concepts and technologies are within the grasp of organizations, their data models still require considerable improvements, in order to produce perfect mimics of their SCs. In fact, it was also found that simulation can help in identifying and bypassing some of these issues.This work has been supported by FCT (Fundacao para a Ciencia e Tecnologia) within the Project Scope: UID/CEC/00319/2019 and by the Doctoral scholarship PDE/BDE/114566/2016 funded by FCT, the Portuguese Ministry of Science, Technology and Higher Education, through national funds, and co-financed by the European Social Fund (ESF) through the Operational Programme for Human Capital (POCH)

Is It Time to Rethink Cord Management When Resuscitation Is Needed?

A newborn who receives a placental transfusion at birth, either from cord milking or delayed cord clamping, obtains about 30% more blood volume than the newborn whose cord is cut immediately. Receiving an adequate blood volume from placental transfusion at birth may be protective for the distressed neonate as it prevents hypovolemia and can support optimal perfusion to all organs. New research shows that ventilating before clamping the umbilical cord can reduce large swings in cardiovascular function and help to stabilize the newborn. Hypovolemia, often associated with nuchal cord or shoulder dystocia, may lead to an inflammatory cascade and subsequent ischemic injury. A sudden unexpected neonatal asystole at birth may occur from severe hypovolemia. The restoration of blood volume is an important action to protect the hearts and brains of these neonates. Current protocols for resuscitation imply immediate cord clamping and the care of the newborn away from the mother\u27s bedside. We suggest that an intrapartum care provider can achieve placental transfusion for the distressed neonate by milking the cord several times or resuscitating the neonate at the perineum with an intact cord. Milking the cord can be done quickly within the current Neonatal Resuscitation Program guidelines. Cord blood gases can be collected with delayed cord clamping. Bringing the resuscitation to the mother\u27s bedside is a novel concept and supports an intact cord. Adopting a policy for resuscitation with an intact cord in a hospital setting will take concentrated effort and team work by obstetrics, pediatrics, midwifery, and nursing

Survey of advances and challenges in intelligent autonomy for distributed cyber-physical systems

With the evolution of the Internet of things and smart cities, a new trend of the Internet of simulation has emerged to utilise the technologies of cloud, edge, fog computing, and high-performance computing for design and analysis of complex cyber-physical systems using simulation. These technologies although being applied to the domains of big data and deep learning are not adequate to cope with the scale and complexity of emerging connected, smart, and autonomous systems. This study explores the existing state-of-the-art in automating, augmenting, and integrating systems across the domains of smart cities, autonomous vehicles, energy efficiency, smart manufacturing in Industry 4.0, and healthcare. This is expanded to look at existing computational infrastructure and how it can be used to support these applications. A detailed review is presented of advances in approaches providing and supporting intelligence as a service. Finally, some of the remaining challenges due to the explosion of data streams; issues of safety and security; and others related to big data, a model of reality, augmentation of systems, and computation are examined