Educational Challenges For Cyber-Physical Systems Modelling

“Cyber Physical Systems” (CPS), continuously connected to the rest of the world, communicating autonomously with other systems and with a certain degree of autonomy for taking decisions, pose certain challenges to industries, but to the training of the next generation of engineers as well. The inclusion of different aspects of CPS in the educational programs is important to prepare the students for the development of such systems. The paper aims at looking at the development of CPS, identifying where and how it is important to train the students in particular. The core of the paper is dedicated to multi-paradigm modelling as a key enabler for mastering the complexity of CPS and focuses on educational challenges and relies on the authors’ experience with the SysML tool TTool and the WoPANets tool that implements network calculus theory

Selection of native Tunisian microalgae for simultaneous wastewater treatment and biofuel production

This paper focuses on the selection of native microalgae strains suitable for wastewater treatment and biofuel production. Four Chlorophyceae strains were isolated from North-eastern Tunisia. Their performances were compared in continuous mode at a 0.3 1/day dilution rate. The biomass productivity and nutrient removal capacity of each microalgae strain were studied. The most efficient strain was identified as Scenedesmus sp. and experiments at different dilution rates from 0.2 to 0.8 1/day were carried out. Maximal biomass productivity of 0.92 g/L·day was obtained at 0.6 1/day. The removal of chemical oxygen demand (COD), ammonium and phosphorus was in the range of 92-94%, 61-99% and 93-99%, respectively. Carbohydrates were the major biomass fraction followed by lipids and then proteins. The saponifiable fatty acid content was in the 4.9-13.2% dry biomass range, with more than 50% of total fatty acids being composed of saturated and monosaturated fatty acids

Molecular Targets for 17α-Ethynyl-5-Androstene-3β,7β,17β-Triol, an Anti-Inflammatory Agent Derived from the Human Metabolome

HE3286, 17α-ethynyl-5-androstene-3β, 7β, 17β-triol, is a novel synthetic compound related to the endogenous sterol 5-androstene-3β, 7β, 17β-triol (β-AET), a metabolite of the abundant adrenal steroid dehydroepiandrosterone (DHEA). HE3286 has shown efficacy in clinical studies in impaired glucose tolerance and type 2 diabetes, and in vivo models of types 1 and 2 diabetes, autoimmunity, and inflammation. Proteomic analysis of solid-phase HE3286-bound bead affinity experiments, using extracts from RAW 264.7 mouse macrophage cells, identified 26 binding partners. Network analysis revealed associations of these HE3286 target proteins with nodes in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways for type 2 diabetes, insulin, adipokine, and adipocyte signaling. Binding partners included low density lipoprotein receptor-related protein (Lrp1), an endocytic receptor; mitogen activated protein kinases 1 and 3 (Mapk1, Mapk3), protein kinases involved in inflammation signaling pathways; ribosomal protein S6 kinase alpha-3 (Rsp6ka3), an intracellular regulatory protein; sirtuin-2 (Sirt2); and 17β-hydroxysteroid dehydrogenase 1 (Hsd17β4), a sterol metabolizing enzyme

Modelling gas-liquid mass transfer in wastewater treatment : when current knowledge needs to encounter engineering practice and vice versa

Abstract Gas–liquid mass transfer in wastewater treatment processes has received considerable attention over the last decades from both academia and industry. Indeed, improvements in modelling gas–liquid mass transfer can bring huge benefits in terms of reaction rates, plant energy expenditure, acid–base equilibria and greenhouse gas emissions. Despite these efforts, there is still no universally valid correlation between the design and operating parameters of a wastewater treatment plant and the gas–liquid mass transfer coefficients. That is why the current practice for oxygen mass transfer modelling is to apply overly simplified models, which come with multiple assumptions that are not valid for most applications. To deal with these complexities, correction factors were introduced over time. The most uncertain of them is the α-factor. To build fundamental gas–liquid mass transfer knowledge more advanced modelling paradigms have been applied more recently. Yet these come with a high level of complexity making them impractical for rapid process design and optimisation in an industrial setting. However, the knowledge gained from these more advanced models can help in improving the way the α-factor and thus gas–liquid mass transfer coefficient should be applied. That is why the presented work aims at clarifying the current state-of-the-art in gas–liquid mass transfer modelling of oxygen and other gases, but also to direct academic research efforts towards the needs of the industrial practitioners

Pre-dialysis clinic attendance improves quality of life among hemodialysis patients

BACKGROUND: Although previous research has demonstrated that referral to pre-dialysis clinics is associated with favourable objective outcomes, the benefit of a pre-dialysis clinic from the perspective of patient-perceived subjective outcomes, such as quality of life (QOL), is less well defined. METHODS: A retrospective incident cohort study was conducted to determine if pre-dialysis clinic attendance was a predictor of better QOL scores measured within the first six months of hemodialysis (HD) initiation. Inclusion criteria were HD initiation from January 1 1998 to January 1 2000, diagnosis of chronic renal failure, and completion of the QOL questionnaire within six months of HD initiation. Patients receiving HD for less than four weeks were excluded. An incident cohort of 120 dialysis patients was identified, including 74 patients who attended at least one pre-dialysis clinic and 46 patients who did not. QOL was measured using the SF 36-Item Health Survey. Independent variables included age, sex, diabetes, pre-dialysis clinic attendance and length of attendance, history of ischemic heart disease, stroke, peripheral vascular disease, heart failure, malignancy, and chronic lung disease, residual creatinine clearance at dialysis initiation, and kt/v, albumin and hemoglobin at the time of QOL assessment. Bivariate and multivariate linear regression analyses were used to identify predictors of QOL scores. RESULTS: Multivariate analysis suggested that pre-dialysis clinic attendance was an independent predictor of higher QOL scores in four of eight health domains (physical function, p < 0.01; emotional role limitation, p = 0.01; social function, p = 0.01; and general health, p = 0.03), even after statistical adjustment for age, sex, residual renal function, kt/v, albumin, and co-morbid disease. Pre-dialysis clinic attendance was also an independent predictor of the physical component summary score (p = 0.03). CONCLUSIONS: We conclude that pre-dialysis clinic attendance favourably influences patient-perceived quality of life within six months of dialysis initiation