Comparison of a deterministic and a data driven model to describe MBR fouling

Membrane bioreactors (MBRs) are a combination of activated sludge bioreactors and membrane filtration, enabling high quality effluent with a small footprint. However, they can be beset by fouling, which causes an increase in transmembrane pressure (TMP). Modelling and simulation of changes in TMP could be useful to describe fouling through the identification of the most relevant operating conditions. Using experimental data from a MBR pilot plant operated for 462days, two different models were developed: a deterministic model using activated sludge model n°2d (ASM2d) for the biological component and a resistance in-series model for the filtration component as well as a data-driven model based on multivariable regressions. Once validated, these models were used to describe membrane fouling (as changes in TMP over time) under different operating conditions. The deterministic model performed better at higher temperatures (>20°C), constant operating conditions (DO set-point, membrane air-flow, pH and ORP), and high mixed liquor suspended solids (>6.9gL-1) and flux changes. At low pH (<7) or periods with higher pH changes, the data-driven model was more accurate. Changes in the DO set-point of the aerobic reactor that affected the TMP were also better described by the data-driven model. By combining the use of both models, a better description of fouling can be achieved under different operating conditions

Transient Earth system responses to cumulative carbon dioxide emissions: linearities, uncertainties, and probabilities in an observation-constrained model ensemble

Information on the relationship between cumulative fossil CO2 emissions and multiple climate targets is essential to design emission mitigation and climate adaptation strategies. In this study, the transient response of a climate or environmental variable per trillion tonnes of CO2 emissions, termed TRE, is quantified for a set of impact-relevant climate variables and from a large set of multi-forcing scenarios extended to year 2300 towards stabilization. An  ∼ 1000-member ensemble of the Bern3D-LPJ carbon–climate model is applied and model outcomes are constrained by 26 physical and biogeochemical observational data sets in a Bayesian, Monte Carlo-type framework. Uncertainties in TRE estimates include both scenario uncertainty and model response uncertainty. Cumulative fossil emissions of 1000 Gt C result in a global mean surface air temperature change of 1.9 °C (68 % confidence interval (c.i.): 1.3 to 2.7 °C), a decrease in surface ocean pH of 0.19 (0.18 to 0.22), and a steric sea level rise of 20 cm (13 to 27 cm until 2300). Linearity between cumulative emissions and transient response is high for pH and reasonably high for surface air and sea surface temperatures, but less pronounced for changes in Atlantic meridional overturning, Southern Ocean and tropical surface water saturation with respect to biogenic structures of calcium carbonate, and carbon stocks in soils. The constrained model ensemble is also applied to determine the response to a pulse-like emission and in idealized CO2-only simulations. The transient climate response is constrained, primarily by long-term ocean heat observations, to 1.7 °C (68 % c.i.: 1.3 to 2.2 °C) and the equilibrium climate sensitivity to 2.9 °C (2.0 to 4.2 °C). This is consistent with results by CMIP5 models but inconsistent with recent studies that relied on short-term air temperature data affected by natural climate variability.

Operator's, organizational, and direct support maintenance manual : tank and pump unit, liquid dispensing, for truck mounting, mil design tank and pump units : gasoline engine driven model (97403) 13217E7100, NSN 4930-00-426-9960 : electric motor driven model (97403) 13217E7130, NSN 4930-01-130-7281.

"17 July 1984."

Organizational, direct support, and general support maintenance repair parts and special tools lists ... for refrigeration unit, mechanical, panel type, 10,000 Btu/hr, electric motor driven, model-REMD-K/II-10LS, NSN 4110-01-163-2140.

"3 March 1987."

Operator's, organizational, and direct support maintenance manual : tank and pump unit, liquid dispensing, for truck mounting, mil design tank and pump units : gasoline engine driven model (977403) 13217E7100, NSN 4930-00-426-9960 : electric motor driven model (97403) 13217E7130, NSN 4930-01-130-7281.

Includes index.

Hand receipt covering contents of Components of End Item (COEI), Basic Issue Items (BII), and Additional Authorization List (AAL) for tank and pump unit, liquid dispensing; for truck mounting mil design tank and pump units, gasoline engine driven, model (97403) 13217E7100, NSN 4930-00-426-9960, electric motor driven, model (97403) 13217E7130, NSN 4930-01-130-7281.

"7 October 1988."

First-principle data-driven models for assessment of motor disorders in Parkinson’s disease

Parkinson’s disease (PD) is an increasing neurological disorder in an aging society. The motor and non-motor symptoms of PD advance with the disease progression and occur in varying frequency and duration. In order to affirm the full extent of a patient’s condition, repeated assessments are necessary to adjust medical prescription. In clinical studies, symptoms are assessed using the unified Parkinson’s disease rating scale (UPDRS). On one hand, the subjective rating using UPDRS relies on clinical expertise. On the other hand, it requires the physical presence of patients in clinics which implies high logistical costs. Another limitation of clinical assessment is that the observation in hospital may not accurately represent a patient’s situation at home. For such reasons, the practical frequency of tracking PD symptoms may under-represent the true time scale of PD fluctuations and may result in an overall inaccurate assessment. Current technologies for at-home PD treatment are based on data-driven approaches for which the interpretation and reproduction of results are problematic.  The overall objective of this thesis is to develop and evaluate unobtrusive computer methods for enabling remote monitoring of patients with PD. It investigates first-principle data-driven model based novel signal and image processing techniques for extraction of clinically useful information from audio recordings of speech (in texts read aloud) and video recordings of gait and finger-tapping motor examinations. The aim is to map between PD symptoms severities estimated using novel computer methods and the clinical ratings based on UPDRS part-III (motor examination). A web-based test battery system consisting of self-assessment of symptoms and motor function tests was previously constructed for a touch screen mobile device. A comprehensive speech framework has been developed for this device to analyze text-dependent running speech by: (1) extracting novel signal features that are able to represent PD deficits in each individual component of the speech system, (2) mapping between clinical ratings and feature estimates of speech symptom severity, and (3) classifying between UPDRS part-III severity levels using speech features and statistical machine learning tools. A novel speech processing method called cepstral separation difference showed stronger ability to classify between speech symptom severities as compared to existing features of PD speech. In the case of finger tapping, the recorded videos of rapid finger tapping examination were processed using a novel computer-vision (CV) algorithm that extracts symptom information from video-based tapping signals using motion analysis of the index-finger which incorporates a face detection module for signal calibration. This algorithm was able to discriminate between UPDRS part III severity levels of finger tapping with high classification rates. Further analysis was performed on novel CV based gait features constructed using a standard human model to discriminate between a healthy gait and a Parkinsonian gait. The findings of this study suggest that the symptom severity levels in PD can be discriminated with high accuracies by involving a combination of first-principle (features) and data-driven (classification) approaches. The processing of audio and video recordings on one hand allows remote monitoring of speech, gait and finger-tapping examinations by the clinical staff. On the other hand, the first-principles approach eases the understanding of symptom estimates for clinicians. We have demonstrated that the selected features of speech, gait and finger tapping were able to discriminate between symptom severity levels, as well as, between healthy controls and PD patients with high classification rates. The findings support suitability of these methods to be used as decision support tools in the context of PD assessment.

Data-driven system identification via evolutionary retrieval of Takagi-Sugeno fuzzy models

Systemidentification, evolutionary automatic, data-driven model, fuzzy Takagi-Sugeno grammar, genotype interpretability, toxicity-prediction

The anti-inflammatory properties of intravenous immunoglobulin in a murine model of allergic airway disease ; effects on the development of regulatory T-cells

Les immunoglobulines intraveineuses (IVIg) constituent une préparation polyclonale d’IgG isolée et regroupée à partir du plasma sanguin de multiples donneurs. Initialement utilisé comme traitement de remplacement chez les patients souffrant d’immunodéficience primaire ou secondaire, les IVIg sont maintenant largement utilisées dans le traitement de plusieurs conditions auto-immunes, allergiques ou inflammatoires à une dose élevée, dite immunomodulatrice. Différents mécanismes d’action ont été postulés au fil des années pour expliquer l’effet thérapeutique des IVIg dans les maladies auto-immunes et inflammatoires. Entre autre, un nombre grandissant de données issues de modèles expérimentaux chez l’animal et l’humain suggère que les IVIg induisent l’expansion et augmentent l’action suppressive des cellules T régulatrices (Tregs), par un mécanisme qui demeure encore inconnu. Également, les patients atteints de maladies auto-immunes ou inflammatoires présentent souvent un nombre abaissé de Tregs par rapport aux individus sains. Ainsi, une meilleure compréhension des mécanismes par lesquels les IVIg modulent les cellules T régulatrices est requise afin de permettre un usage plus rationnel de ce produit sanguin en tant qu’alternative thérapeutique dans le traitement des maladies auto-immunes et inflammatoires. Par le biais d’un modèle expérimental d’allergie respiratoire induite par un allergène, nous avons démontré que les IVIg diminuaient significativement l’inflammation au niveau des voies aériennes ce, en association avec une différenciation des Tregs à partir des cellules T non régulatrices du tissu pulmonaire. Nous avons également démontré qu’au sein de notre modèle expérimental, l’effet anti-inflammatoire des IVIg était dépendant des cellules dendritiques CD11c+ (CDs) pulmonaires, puisque cet effet pouvait être complètement reproduit par le transfert adoptif de CDs provenant de souris préalablement traitées par les IVIg. À cet effet, il est déjà établi que les IVIg peuvent moduler l’activation et les propriétés des CDs pour favoriser la tolérance immunitaire et que ces cellules seraient cruciales pour l’induction périphérique des Tregs. C’est pourquoi, nous avons cherché à mieux comprendre comment les IVIg exercent leur effet sur ces cellules. Pour la première fois, nous avons démontré que la fraction d’IgG riche en acide sialique (SA-IVIg) (constituant 2-5% de l’ensemble des IgG des donneurs) interagit avec un récepteur dendritique inhibiteur de type lectine C (DCIR) et active une cascade de signalement intracellulaire initiée par la phosphorylation du motif ITIM qui est responsable des changements observés en faveur de la tolérance immunitaire auprès des cellules dendritiques et des Tregs. L’activité anti-inflammatoire de la composante SA-IVIg a déjà été décrite dans des études antérieures, mais encore une fois le mécanisme par lequel ce traitement modifie la fonction des CDs n’a pas été établi. Nous avons finalement démontré que le récepteur DCIR facilite l’internalisation des molécules d’IgG liées au récepteur et que cette étape est cruciale pour permettre l’induction périphérique des Tregs. En tant que produit sanguin, les IVIg constitue un traitement précieux qui existe en quantité limitée. La caractérisation des mécanismes d’action des IVIg permettra une meilleure utilisation de ce traitement dans un vaste éventail de pathologies auto-immunes et inflammatoires.

A benchmark-driven modelling approach for evaluating deployment choices on a multi-core architecture

The complexity of current and emerging architectures provides users with options about how best to use the available resources, but makes predicting performance challenging. In this work a benchmark-driven model is developed for a simple shallow water code on a Cray XE6 system, to explore how deployment choices such as domain decomposition and core affinity affect performance. The resource sharing present in modern multi-core architectures adds various levels of heterogeneity to the system. Shared resources often includes cache, memory, network controllers and in some cases floating point units (as in the AMD Bulldozer), which mean that the access time depends on the mapping of application tasks, and the core's location within the system. Heterogeneity further increases with the use of hardware-accelerators such as GPUs and the Intel Xeon Phi, where many specialist cores are attached to general-purpose cores. This trend for shared resources and non-uniform cores is expected to continue into the exascale era. The complexity of these systems means that various runtime scenarios are possible, and it has been found that under-populating nodes, altering the domain decomposition and non-standard task to core mappings can dramatically alter performance. To find this out, however, is often a process of trial and error. To better inform this process, a performance model was developed for a simple regular grid-based kernel code, shallow. The code comprises two distinct types of work, loop-based array updates and nearest-neighbour halo-exchanges. Separate performance models were developed for each part, both based on a similar methodology. Application specific benchmarks were run to measure performance for different problem sizes under different execution scenarios. These results were then fed into a performance model that derives resource usage for a given deployment scenario, with interpolation between results as necessary.

Quantifying differences in land use emission estimates implied by definition discrepancies

The quantification of CO2 emissions from anthropogenic land use and land use change (eLUC) is essential to understand the drivers of the atmospheric CO2 increase and to inform climate change mitigation policy. Reported values in synthesis reports are commonly derived from different approaches (observation-driven bookkeeping and process-modelling) but recent work has emphasized that inconsistencies between methods may imply substantial differences in eLUC estimates. However, a consistent quantification is lacking and no concise modelling protocol for the separation of primary and secondary components of eLUC has been established. Here, we review differences of eLUC quantification methods and apply an Earth System Model (ESM) of Intermediate Complexity to quantify them. We find that the magnitude of effects due to merely conceptual differences between ESM and offline vegetation model-based quantifications is ~ 20 % for today. Under a future business-as-usual scenario, differences tend to increase further due to slowing land conversion rates and an increasing impact of altered environmental conditions on land-atmosphere fluxes. We establish how coupled Earth System Models may be applied to separate secondary component fluxes of eLUC arising from the replacement of potential C sinks/sources and the land use feedback and show that secondary fluxes derived from offline vegetation models are conceptually and quantitatively not identical to either, nor their sum. Therefore, we argue that synthesis studies should resort to the "least common denominator" of different methods, following the bookkeeping approach where only primary land use emissions are quantified under the assumption of constant environmental boundary conditions.