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Nowadays, the controversial issue of medical cannabis is getting more and more attention by numerous health professionals who decide to study the subject. Among the various components of Cannabis Sativa, two main phytocannabinoids stand out from a therapeutic point of view: on the hand Δ9-THC for its analgesic, myorelaxant, antiemetic and orexigenic action and on the other hand cannabidiol for its antiemetic, anxiolytic, neuroprotective, antiepileptic’s and anti-inflammatories properties. Those two compounds act on the endocannabinoid system mediated by cannabinoid receptors CB1 and CB2. However, the attractive properties of medical cannabis are counterbalanced by the significant appearance of psychoactive and cognitive side effects as well as by the development of an addition. Considering the current state of knowledge and therapeutic alternatives now on the market, does medical cannabis constitute a real therapeutic interest for the treatment of nausea and vomiting or for anorexia associated with weight loss among patients undergoing chemotherapy ? La question complexe du cannabis médical est un sujet majeur auquel de nombreux professionnels de la santé s’intéressent actuellement. Parmi les multiples composant du Cannabis Sativa, deux phytocannabinoïdes principaux retiennent l’attention d’un point de vue thérapeutique : premièrement le Δ9-THC pour son action analgésique, myorelaxante, antiémétique et orexigène et ensuite le cannabidiol pour ses propriétés antiémétiques, anxiolytiques, neuroprotectrices, antiépileptiques et anti-inflammatoires. Ceux-ci agissent au niveau du système endocannabinoïde médié par les récepteurs aux cannabinoïdes CB1 et CB2. Les propriétés intéressantes du cannabis médical sont cependant contrebalancées par l’apparition importante d’effets secondaires psychoactifs et cognitifs ainsi que par le développement d’une dépendance. Au vu de l’état des connaissances actuelles et des alternatives thérapeutiques dans le traitement des nausées et vomissement ainsi que dans l’anorexie associée à une perte de poids chez les patients sous chimiothérapie ?

Medical Marijuana --- Chemotherapy, Adjuvant --- Healthcare Failure Mode and Effect Analysis

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Floating offshore wind farms are one of the most important pillars to manage climate
change issues. Using large-scale series production methods, larger wind turbines with
extended full load hours for floating offshore installations could result in reduced levelized
costs for energy. These devices are installed in deeper water and away from shore. Due to
their distance from the shore, wind farms need offshore substations for the power storage
or conversion before sending it to the onshore grid. GICON has developed an innovative
design for High Voltage (HVAC) Offshore Substation on a TLP substructure. This thesis
mainly focuses on the development of the operational and pathway concept for the
substation topside. The operational concept defines the strategies regarding the maintenance
and operations based on the offshore standard regulations. It also involves the risk
assessment of the platform and the development of tools regarding Hazard Identification
Study (HAZID) and Failure Mode Effect and Criticality Analysis (FMECA). The pathway
concept includes the development of the detailed layout of the topside, safety plan,
emergency response plan, and lifting arrangement for the electrical equipment. Special
attention has been given to the DNV GL regulations regarding offshore substations.

Hazard Identification --- Failure Mode Effect and Criticality Analysis --- DNV GL --- HVAC Substation --- Ingénierie, informatique & technologie > Ingénierie mécanique

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Ontwerpmethodologie --- produktontwikkeling --- reverse engineering --- innovatiemanagement --- klantgerichtheid --- benchmarking --- milieugericht ontwerpen (ecodesign) --- rapid prototyping --- levenscyclusanalyse (lca) --- fmea (failure mode and effects analysis) --- ontwerptechniek --- (zie ook: fouten-analyse) --- Ontwerpmethodologie. --- Industrial design --- New products --- Product design --- Product management --- Brand management --- Management, Product --- Commercial products --- New product development --- NPD (Marketing) --- Product development --- Products, New --- Design, Industrial --- Design and construction --- Marketing --- Mechanical drawing --- Design --- Management --- Design, industrial --- Production management

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This book reports recent advances on Process Monitoring (PM) to cope with the many challenges raised by the new production systems, sensors and “extreme data” conditions that emerged with Industry 4.0. Concepts such as digital-twins and deep learning are brought to the PM arena, pushing forward the capabilities of existing methodologies to handle more complex scenarios. The evolution of classical paradigms such as Latent Variable modeling, Six Sigma and FMEA are also covered. Applications span a wide range of domains such as microelectronics, semiconductors, chemicals, materials, agriculture, as well as the monitoring of rotating equipment, combustion systems and membrane separation processes.

spatial-temporal data --- pasting process --- process image --- convolutional neural network --- Industry 4.0 --- auto machine learning --- failure mode effects analysis --- risk priority number --- rolling bearing --- condition monitoring --- classification --- OPTICS --- statistical process control --- control chart pattern --- disruptions --- disruption management --- fault diagnosis --- construction industry --- plaster production --- neural networks --- decision support systems --- expert systems --- failure mode and effects analysis (FMEA) --- discriminant analysis --- non-intrusive load monitoring --- load identification --- membrane --- data reconciliation --- real-time --- online --- monitoring --- Six Sigma --- multivariate data analysis --- latent variables models --- PCA --- PLS --- high-dimensional data --- statistical process monitoring --- artificial generation of variability --- data augmentation --- quality prediction --- continuous casting --- multiscale --- time series classification --- imbalanced data --- combustion --- optical sensors --- spectroscopy measurements --- signal detection --- digital processing --- principal component analysis --- curve resolution --- data mining --- semiconductor manufacturing --- quality control --- yield improvement --- fault detection --- process control --- multi-phase residual recursive model --- multi-mode model --- process monitoring --- n/a

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This book reports recent advances on Process Monitoring (PM) to cope with the many challenges raised by the new production systems, sensors and “extreme data” conditions that emerged with Industry 4.0. Concepts such as digital-twins and deep learning are brought to the PM arena, pushing forward the capabilities of existing methodologies to handle more complex scenarios. The evolution of classical paradigms such as Latent Variable modeling, Six Sigma and FMEA are also covered. Applications span a wide range of domains such as microelectronics, semiconductors, chemicals, materials, agriculture, as well as the monitoring of rotating equipment, combustion systems and membrane separation processes.

Technology: general issues --- spatial-temporal data --- pasting process --- process image --- convolutional neural network --- Industry 4.0 --- auto machine learning --- failure mode effects analysis --- risk priority number --- rolling bearing --- condition monitoring --- classification --- OPTICS --- statistical process control --- control chart pattern --- disruptions --- disruption management --- fault diagnosis --- construction industry --- plaster production --- neural networks --- decision support systems --- expert systems --- failure mode and effects analysis (FMEA) --- discriminant analysis --- non-intrusive load monitoring --- load identification --- membrane --- data reconciliation --- real-time --- online --- monitoring --- Six Sigma --- multivariate data analysis --- latent variables models --- PCA --- PLS --- high-dimensional data --- statistical process monitoring --- artificial generation of variability --- data augmentation --- quality prediction --- continuous casting --- multiscale --- time series classification --- imbalanced data --- combustion --- optical sensors --- spectroscopy measurements --- signal detection --- digital processing --- principal component analysis --- curve resolution --- data mining --- semiconductor manufacturing --- quality control --- yield improvement --- fault detection --- process control --- multi-phase residual recursive model --- multi-mode model --- process monitoring --- spatial-temporal data --- pasting process --- process image --- convolutional neural network --- Industry 4.0 --- auto machine learning --- failure mode effects analysis --- risk priority number --- rolling bearing --- condition monitoring --- classification --- OPTICS --- statistical process control --- control chart pattern --- disruptions --- disruption management --- fault diagnosis --- construction industry --- plaster production --- neural networks --- decision support systems --- expert systems --- failure mode and effects analysis (FMEA) --- discriminant analysis --- non-intrusive load monitoring --- load identification --- membrane --- data reconciliation --- real-time --- online --- monitoring --- Six Sigma --- multivariate data analysis --- latent variables models --- PCA --- PLS --- high-dimensional data --- statistical process monitoring --- artificial generation of variability --- data augmentation --- quality prediction --- continuous casting --- multiscale --- time series classification --- imbalanced data --- combustion --- optical sensors --- spectroscopy measurements --- signal detection --- digital processing --- principal component analysis --- curve resolution --- data mining --- semiconductor manufacturing --- quality control --- yield improvement --- fault detection --- process control --- multi-phase residual recursive model --- multi-mode model --- process monitoring