Analysis of a Signorini problem with nonlocal friction in thermo-piezoelectricity

We consider a mathematical model which describes the frictional unilateral contact between a thermo-piezoelectric body and a rigid electrically conductive foundation. The thermo-piezoelectric constitutive law is assumed to be nonlinear and the contact is modeled with the Signorini condition, the nonlocal Coulomb friction law with slip dependent friction coefficient and the regularized electrical and thermal conductivity conditions. The variational form of this problem is a coupled system which consists of a nonlinear variational inequality for the displacement field and two nonlinear variational equations for the electric potential and the temperature. The existence of a unique weak solution to the problem is proved by using abstract results for elliptic variational inequalities and fixed point arguments

Data fusion of activity and CGM for predicting blood glucose levels

This work suggests two methods—both relying on stacked regression and data fusion of CGM and activity—to predict the blood glucose level of patients with type 1 diabetes. Method 1 uses histories of CGM data appended with the average of activity data in the same histories to train three base regressions: a multilayer perceptron, a long short- term memory, and a partial least squares regression. In Method 2, histories of CGM and activity data are used separately to train the same base regressions. In both methods, the predictions from the base regressions are used as features to create a combined model. This model is then used to make the final predictions. The results obtained show the effectiveness of both methods. Method 1 provides slightly better results

Multi-lag stacking for blood glucose level prediction

This work investigates blood glucose level prediction for type 1 diabetes in two horizons of 30 and 60 minutes. Initially, three conventional regression tools—partial least square regression (PLSR), multilayer perceptron, and long short-term memory—are deployed to create predictive models. They are trained once on 30 minutes and once on 60 minutes of historical data resulting in six basic models for each prediction horizon. A collection of these models are then set as base-learners to develop three stacking systems; two uni-lag and one multi-lag. One of the uni-lag systems uses the three basic models trained on 30 minutes of lag data; the other uses those trained on 60 minutes. The multi-lag system, on the other hand, leverages the basic models trained on both lags. All three stacking systems deploy a PLSR as meta-learner. The results obtained show: i) the stacking systems outperform the basic models, ii) among the stacking systems, the multi-lag shows the best predictive performance with a root mean square error of 19.01 mg/dl and 33.37 mg/dl for the prediction horizon of 30 and 60 minutes, respectively

Modeling, Sharing, and Recursion for Weak Reduction Strategies using Explicit Substitution

We present the lambda sigma^a_w calculus, a formal synthesis of the concepts ofsharing and explicit substitution for weak reduction. We show howlambda sigma^a_w can be used as a foundation of implementations of functionalprogramming languages by modelling the essential ingredients of suchimplementations, namely weak reduction strategies, recursion, spaceleaks, recursive data structures, and parallel evaluation, in a uniform way.First, we give a precise account of the major reduction strategiesused in functional programming and the consequences of choosing lambda-graph-reduction vs. environment-based evaluation. Second, we showhow to add constructors and explicit recursion to give a precise accountof recursive functions and data structures even with respect tospace complexity. Third, we formalize the notion of space leaks in lambda sigma^a_wand use this to define a space leak free calculus; this suggests optimisationsfor call-by-need reduction that prevent space leaking and enablesus to prove that the "trimming" performed by the STG machine doesnot leak space.In summary we give a formal account of several implementationtechniques used by state of the art implementations of functional programminglanguages.Keywords. Implementation of functional programming, lambdacalculus, weak reduction, explicit substitution, sharing, recursion, spaceleaks

Fish hosts of the freshwater mussel Unio foucauldianus Pallary, 1936

The life cycle of Unio foucauldianus Pallary, 1936, a critically endangered freshwater mussel species (Bivalvia: Unionida), includes a parasitic phase using fish as hosts. Therefore, to develop more efficient conservation strategies it is essential to know which are the suitable fish hosts of U. foucauldianus. In this study, two approaches were used to assess the fish hosts of U. foucauldianus: the determination of infestation rates of fishes under natural conditions through monthly sampling (from January to June) in the Laabid River (Oum Rbia basin) and the N'Fis River (Tensift basin), and artificial infestation in laboratory trials using fish species from both rivers. The natural infestation of fish was detected from February to June, with a peak in May. Fully metamorphosed juveniles were only detected in native fish species, i.e. Luciobarbus ksibi (Boulenger, 1905), Carasobarbus fritschii (Gunther, 1874), Luciobarbus zayanensis Doadrio, Casal-lopez & Yahyaoui, 2016, Labeobarbus maroccanus (Gunther, 1874), and Luciobarbus magniatlantis (Pellegrin, 1919). The two non-native fish species used do not function as effective hosts. Given the increasing human pressure on native fish species in the Mediterranean biodiversity hotspot, including the increased number of non-native fish introductions, urgent conservation measures are discussed for this and other freshwater mussel species.The authors would like to express their thanks to the High Commission for Water and Forests (HCEFLCD) for granting permission to use electrofishing in the Moroccan basins. This study was conducted within the scope of the project ‘Biodiversity and conservation of the critically endangered freshwater mussels in Morocco: ecogeographic, genetic and physiological information’, funded by the Mohamed Bin Zayed Species Conservation Fund (ref. 15256799), and the project ‘Breeding the most endangered bivalve on Earth: argaritifera marocana’, funded by the IUCN SOS (Save our Species) fund (ref. 2015B‐015)

Signal fragmentation based feature vector generation in a model agnostic framework with application to glucose quantification using absorption spectroscopy

This paper proposes feature vector generation based on signal fragmentation equipped with a model interpretation module to enhance glucose quantification from absorption spectroscopy signals. For this purpose, near-infrared (NIR) and mid-infrared (MIR) spectra collected from experimental samples of varying glucose concentrations are scrutinised. Initially, a given spectrum is optimally dissected into several fragments. A base-learner then studies the obtained fragments individually to estimate the reference glucose concentration from each fragment. Subsequently, the resultant estimates from all fragments are stacked, forming a feature vector for the original spectrum. Afterwards, a meta-learner studies the generated feature vector to yield a final estimation of the reference glucose concentration pertaining to the entire original spectrum. The reliability of the proposed approach is reviewed under a set of circumstances encompassing modelling upon NIR or MIR signals alone and combinations of NIR and MIR signals at different fusion levels. In addition, the compatibility of the proposed approach with an underlying preprocessing technique in spectroscopy is assessed. The results obtained substantiate the utility of incorporating the designed feature vector generator into standard benchmarked modelling procedures under all considered scenarios. Finally, to promote the transparency and adoption of the propositions, SHapley additive exPlanations (SHAP) is leveraged to interpret the quantification outcomes

Interpretable machine learning for inpatient COVID-19 mortality risk assessments: diabetes mellitus exclusive interplay

People with diabetes mellitus (DM) are at elevated risk of in-hospital mortality from coronavirus disease-2019 (COVID-19). This vulnerability has spurred efforts to pinpoint distinctive characteristics of COVID-19 patients with DM. In this context, the present article develops ML models equipped with interpretation modules for inpatient mortality risk assessments of COVID-19 patients with DM. To this end, a cohort of 156 hospitalised COVID-19 patients with pre-existing DM is studied. For creating risk assessment platforms, this work explores a pool of historical, on-admission, and during-admission data that are DM-related or, according to preliminary investigations, are exclusively attributed to the COVID-19 susceptibility of DM patients. First, a set of careful pre-modelling steps are executed on the clinical data, including cleaning, pre-processing, subdivision, and feature elimination. Subsequently, standard machine learning (ML) modelling analysis is performed on the cured data. Initially, a classifier is tasked with forecasting COVID-19 fatality from selected features. The model undergoes thorough evaluation analysis. The results achieved substantiate the efficacy of the undertaken data curation and modelling steps. Afterwards, SHapley Additive exPlanations (SHAP) technique is assigned to interpret the generated mortality risk prediction model by rating the predictors’ global and local influence on the model’s outputs. These interpretations advance the comprehensibility of the analysis by explaining the formation of outcomes and, in this way, foster the adoption of the proposed methodologies. Next, a clustering algorithm demarcates patients into four separate groups based on their SHAP values, providing a practical risk stratification method. Finally, a re-evaluation analysis is performed to verify the robustness of the proposed framework

Classification before regression for improving the accuracy of glucose quantification using absorption spectroscopy

This work contributes to the improvement of glucose quantification using near-infrared (NIR), mid-infrared (MIR), and combination of NIR and MIR absorbance spectroscopy by classifying the spectral data prior to the application of regression models. Both manual and automated classification are presented based on three homogeneous classes defined following the clinical definition of the glycaemic ranges (hypoglycaemia, euglycaemia, and hyperglycaemia). For the manual classification, partial least squares and principal component regressions are applied to each class separately and shown to lead to improved quantification results compared to when applying the same regression models for the whole dataset. For the automatic classification, linear discriminant analysis coupled with principal component analysis is deployed, and regressions are applied to each class separately. The results obtained are shown to outperform those of regressions for the entire dataset

Modeling Sharing and Recursion for Weak Reduction Strategies using Explicit Substitution

Projet EURECAWe \emph{present} the $\lambda\sigma_w^a$-calculus, a formal synthesis of the concepts of sharing and explicit substitution for weak reduction. We show how $\lambda\sigma_w^a$ can be used as a foundation of implementations of functional programming languages by modeling the essential ingredients of such implementations, namely \emph{weak reduction strategies}, \emph{recursion}, \emph{space leaks}, \emph{recursive data structures}, and \emph{parallel evaluation}, in a uniform way. First, we give a precise account of the major reduction strategies used in functional programming and the consequences of choosing $\lambda$-graph-reduction vs. environment-based evaluation. Second, we show how to add \emph{constructors and explicit recursion} to give a precise account of recursive functions and data structures even with respect to space complexity. Third, we formalize the notion of \emph{space leaks} in $\lambda\sigma_w^a$ and use this to define a space leak free calculus; this suggests optimisations for call-by-need reduction that prevent space leaking and enables us to prove that the «trimming» performed by the STG machine does not leak space. In summary we give a formal account of several implementation techniques used by state of the art implementations of functional programming languages

An overview of the welfare of animals used for scientific and educational purposes in Algeria

This study describes the welfare and animals used for scientific and educational purposes in the field of laboratory animal sciences in Algeria. The aim of this study is to provide an overview of the status of the care and use of animals and to improve implementing plans and animal welfare measures. A literature review was performed using online databases and reference lists of the US National Library of Medicine to assess the prevalence of animal use for research in Algeria between 2013 and 2017. Also a retrospective study was conducted using the Pasteur Institute of Algeria report for 2015 to assess the prevalence of animal use in both teaching and research. The first workshop on animal experimentation was organized in 2013 in collaboration with international animal laboratory organizations (ICLAS and OIE) and involving the participation of universities, research centers, veterinary schools and the Pasteur Institute of Algeria. In addition, after accreditation of the Algerian Association of Experimental Animal Sciences, a number of training workshops and courses relating to laboratory animal sciences were organized. In Algeria the use of laboratory animals in research and education is a subject of debate regarding the need to establish regulations and to propose an appropriate ethical framework for the use of animals. Finally, some actions have been already taken in Algeria to promote the ethical use of animals but many more sustainable actions are needed and require cooperation, harmonization of policies and establishment of regional and international networks for experience exchange