37 research outputs found
Influent generator : towards realistic modelling of wastewater flowrate and water quality using machine-learning methods
Depuis que l'assainissement des eaux usĂ©es est reconnu comme un des objectifs de dĂ©veloppement durable des Nations Unies, le traitement et la gestion des eaux usĂ©es sont devenus plus importants que jamais. La modĂ©lisation et la digitalisation des stations de rĂ©cupĂ©ration des ressources de l'eau (StaRRE) jouent un rĂŽle important depuis des dĂ©cennies, cependant, le manque de donnĂ©es disponibles sur les affluents entrave le dĂ©veloppement de la modĂ©lisation de StaRRE. Cette thĂšse vis e Ă faire progresser la modĂ©lisation des systĂšmes d'assainissement en gĂ©nĂ©ral, et en particulier en ce qui concerne la gĂ©nĂ©ration dynamique des affluents. Dans cette Ă©tude, diffĂ©rents gĂ©nĂ©rateurs d'affluent (GA), qui peuvent fournir un profil d'affluent dynamique, ont Ă©tĂ© proposĂ©s, optimisĂ©s et discutĂ©s. Les GA dĂ©veloppĂ©s ne se concentrent pas seulement sur le dĂ©bit, les solides en suspension et la matiĂšre organique, mais Ă©galement sur les substances nutritives telles que l'azote et le phosphore. En outre, cette Ă©tude vise Ă adapter les GA Ă diffĂ©rentes applications en fonction des diffĂ©rentes exigences de modĂ©lisation. Afin d'Ă©valuer les performances des GA d'un point de vue gĂ©nĂ©ral, une sĂ©rie de critĂšres d'Ă©valuation de la qualitĂ© du modĂšle est dĂ©crite. PremiĂšrement, pour comprendre la dynamique des affluents, une procĂ©dure de caractĂ©risation des affluents a Ă©tĂ© dĂ©veloppĂ©e et testĂ©e pour une Ă©tude de cas Ă l'Ă©chelle pilote. Ensuite, pour gĂ©nĂ©rer diffĂ©rentes sĂ©ries temporelles d'affluent, un premier GA a Ă©tĂ© dĂ©veloppĂ©. La mĂ©thodologie de modĂ©lisation est basĂ©e sur l'apprentissage automatique en raison de ses calculs rapides, de sa prĂ©cision et de sa capacitĂ© Ă traiter les mĂ©gadonnĂ©es. De plus, diverses versions de ce GA ont Ă©tĂ© appliquĂ©es pour diffĂ©rents cas optimisĂ©es en fonction des disponibilitĂ©s d'Ă©tudes et ont Ă©tĂ© des donnĂ©es (la frĂ©quence et l'horizon temporel), des objectifs et des exigences de prĂ©cision. Les rĂ©sultats dĂ©montrent que : i) le modĂšle GA proposĂ© peut ĂȘtre utilisĂ© pour gĂ©nĂ©rer d'affluents dynamiques rĂ©alistes pour diffĂ©rents objectifs, et les sĂ©ries temporelles rĂ©sultantes incluent Ă la fois le dĂ©bit et la concentration de polluants avec une bonne prĂ©cision et distribution statistique; ii) les GA sont flexibles, ce qui permet de les amĂ©liorer selon diffĂ©rents objectifs d'optimisation; iii) les GA ont Ă©tĂ© dĂ©veloppĂ©s en considĂ©rant l'Ă©quilibre entre les efforts de modĂ©lisation, la collecte de donnĂ©es requise et les performances du modĂšle. BasĂ© sur les perspectives de modĂ©lisation des StaRRE, l'analyse des procĂ©dĂ©s et la modĂ©lisation prĂ©visionnelle, les modĂšles de GA dynamiques peuvent fournir aux concepteurs et aux modĂ©lisateurs un profil d'affluent complet et rĂ©aliste, ce qui permet de surmonter les obstacles liĂ©s au manque de donnĂ©es d'affluent. Par consĂ©quent, cette Ă©tude a dĂ©montrĂ© l'utilitĂ© des GA et a fait avancer la modĂ©lisation des StaRRE en focalisant sur l'application de mĂ©thodologies d'exploration de donnĂ©es et d'apprentissage automatique. Les GA peuvent donc ĂȘtre utilisĂ©s comme outil puissant pour la modĂ©lisation des StaRRE, avec des applications pour l'amĂ©lioration de la configuration de traitement, la conception de procĂ©dĂ©s, ainsi que la gestion et la prise de dĂ©cision stratĂ©gique. Les GA peuvent ainsi contribuer au dĂ©veloppement de jumeaux numĂ©riques pour les StaRRE, soit des systĂšme intelligent et automatisĂ© de dĂ©cision et de contrĂŽle.Since wastewater sanitation is acknowledged as one of the sustainable development goals of the United Nations, wastewater treatment and management have been more important then ever. Water Resource Recovery Facility (WRRF) modelling and digitalization have been playing an important role since decades, however, the lack of available influent data still hampers WRRF model development. This dissertation aims at advancing the field of wastewater systems modelling in general, and in particular with respect to the dynamic influent generation. In this study, different WRRF influent generators (IG), that can provide a dynamic influent flow and pollutant concentration profile, have been proposed, optimized and discussed. The developed IGs are not only focusing on flowrate, suspended solids, and organic matter, but also on nutrients such as nitrogen and phosphorus. The study further aimed at adapting the IGs to different case studies, so that future users feel comfortable to apply different IG versions according to different modelling requirements. In order to evaluate the IG performance from a general perspective, a series of criteria for evaluating the model quality were evaluated. Firstly, to understand the influent dynamics, a procedure of influent characterization has been developed and experimented at pilot scale. Then, to generate different realizations of the influent time series, the first IG was developed and a data-driven modelling approach chosen, because of its fast calculations, its precision and its capacity of handling big data. Furthermore, different realizations of IGs were applied to different case studies and were optimized for different data availabilities (frequency and time horizon), objectives, and modelling precision requirements. The overall results indicate that: i) the proposed IG model can be used to generate realistic dynamic influent time series for different case studies, including both flowrate and pollutant concentrations with good precision and statistical distribution; ii) the proposed IG is flexible and can be improved for different optimization objectives; iii) the IG model has been developed by considering the balance between modelling efforts, data collection requirements and model performance. Based on future perspectives of WRRF process modelling, process analysis, and forecasting, the dynamic IG model can provide designers and modellers with a complete and realistic influent profile and this overcomes the often-occurring barrier of shortage of influent data for modelling. Therefore, this study demonstrated the IGs' usefulness for advanced WRRF modelling focusing on the application of data mining and machine learning methodologies. It is expected to be widely used as a powerful tool for WRRF modelling, improving treatment configurations and process designs, management and strategic decision-making, such as when transforming a conventional WRRF to a digital twin that can be used as an intelligent and automated system
A jet tagging algorithm of graph network with HaarPooling message passing
Recently methods of graph neural networks (GNNs) have been applied to solving
the problems in high energy physics (HEP) and have shown its great potential
for quark-gluon tagging with graph representation of jet events. In this paper,
we introduce an approach of GNNs combined with a HaarPooling operation to
analyze the events, called HaarPooling Message Passing neural network (HMPNet).
In HMPNet, HaarPooling not only extracts the features of graph, but embeds
additional information obtained by clustering of k-means of different particle
features. We construct Haarpooling from five different features: absolute
energy , transverse momentum , relative coordinates
, the mixed ones and . The results show that an appropriate
selection of information for HaarPooling enhances the accuracy of quark-gluon
tagging, as adding extra information of to the HMPNet outperforms
all the others, whereas adding relative coordinates information
is not very effective. This implies that by adding
effective particle features from HaarPooling can achieve much better results
than solely pure message passing neutral network (MPNN) can do, which
demonstrates significant improvement of feature extraction via the pooling
process. Finally we compare the HMPNet study, ordering by , with other
studies and prove that the HMPNet is also a good choice of GNN algorithms for
jet tagging
Increased transgene expression mediated by recombinant adeno-associated virus in human neuroglia cells under microgravity conditions
The space environment has the special characteristics of radiation, noise particularity and weightlessness, all of which have adverse effects on astronautsâ muscles, bones, neurons and immune system. Some reports have shown that chemotherapy and radiotherapy can increase the activity of the recombinant adeno-associated virus (AAV) which is widely used in gene therapy. In this paper, recombinant AAV2 (rAAV2) was first packaged with the enhanced green fluorescence protein (eGFP) gene and used to infect neuroglia cells including the U87 and U251 cell lines, under microgravity conditions; it was then detected by fluorescence microscopy and flow cytometry. The results show that microgravity affects the adhesion ability of cells, promotes transgene expression induced by rAAV2 and causes changes of viral infection receptors at different time points. These findings broaden the current understanding of the microgravity effects on rAAV, with significant implications in gene therapy and the mechanisms of increased virus pathogenicity under space microgravity.
Study of phase transition of Potts model with DANN
A transfer learning method, domain adversarial neural network (DANN), is
introduced to study the phase transition of two-dimensional q-state Potts
model. With the DANN, we only need to choose a few labeled configurations
automatically as input data, then the critical points can be obtained after
training the algorithm. By an additional iterative process, the critical points
can be captured to comparable accuracy to Monte Carlo simulations as we
demonstrate it for q = 3, 5, 7 and 10. The type of phase transition (first or
second-order) is also determined at the same time. Meanwhile, for the
second-order phase transition at q = 3, we can calculate the critical exponent
by data collapse. Furthermore, compared with the traditional supervised
learning, the DANN is of higher accuracy with lower cost.Comment: 25 pages, 23 figure
Machine Learning of Pair-Contact Process with Diffusion
The pair-contact process with diffusion (PCPD), a generalized model of the
ordinary pair-contact process (PCP) without diffusion, exhibits a continuous
absorbing phase transition. Unlike the PCP, whose nature of phase transition is
clearly classified into the directed percolation (DP) universality class, the
model of PCPD has been controversially discussed since its infancy. To our best
knowledge, there is so far no consensus on whether the phase transition of the
PCPD falls into the unknown university classes or else conveys a new kind of
non-equilibrium phase transition. In this paper, both unsupervised and
supervised learning are employed to study the PCPD with scrutiny. Firstly, two
unsupervised learning methods, principal component analysis (PCA) and
autoencoder, are taken. Our results show that both methods can cluster the
original configurations of the model and provide reasonable estimates of
thresholds. Therefore, no matter whether the non-equilibrium lattice model is a
random process of unitary (for instance the DP) or binary (for instance the
PCP), or whether it contains the diffusion motion of particles, unsupervised
leaning can capture the essential, hidden information. Beyond that, supervised
learning is also applied to learning the PCPD at different diffusion rates. We
proposed a more accurate numerical method to determine the spatial correlation
exponent , which, to a large degree, avoids the uncertainty of
data collapses through naked eyes. Our extensive calculations reveal that
of PCPD depends continuously on the diffusion rate , which
supports the viewpoint that the PCPD may lead to a new type of absorbing phase
transition.Comment: 15 pages, 11 figure
A deep-learning-based approach for seismic surface-wave dispersion inversion (SfNet) with application to the Chinese mainlandKey points
Surface-wave tomography is an important and widely used method for imaging the crust and upper mantle velocity structure of the Earth. In this study, we proposed a deep learning (DL) method based on convolutional neural network (CNN), named SfNet, to derive the vS model from the Rayleigh wave phase and group velocity dispersion curves. Training a network model usually requires large amount of training datasets, which is labor-intensive and expensive to acquire. Here we relied on synthetics generated automatically from various spline-based vS models instead of directly using the existing vS models of an area to build the training dataset, which enhances the generalization of the DL method. In addition, we used a random sampling strategy of the dispersion periods in the training dataset, which alleviates the problem that the real data used must be sampled strictly according to the periods of training dataset. Tests using synthetic data demonstrate that the proposed method is much faster, and the results for the vS model are more accurate and robust than those of conventional methods. We applied our method to a dataset for the Chinese mainland and obtained a new reference velocity model of the Chinese continent (ChinaVs-DL1.0), which has smaller dispersion misfits than those from the traditional method. The high accuracy and efficiency of our DL approach makes it an important method for vS model inversions from large amounts of surface-wave dispersion data
Complete chloroplast genome sequence of Salix sinopurpurea (Salicaceae)
Salix sinopurpurea is a morphologically special shrubby willow characterizing opposite leaves. Here, we reported the complete chloroplast (cp) genome sequence of Salix sinopurpurea. The cp genome is 155,546âbp in length, including a large single-copy (LSC) region of 84,412âbp, a small single-copy (SSC) region of 16,216âbp, and a pair of inverted repeated regions of 27,459âbp. The cp genome of Salix sinopurpurea encodes 130 genes, including 85 protein-coding genes, 37 tRNA genes, and eight rRNA genes. Phylogenetic tree showed that Salix sinopurpurea is closely related to Salix psammophila and Salix suchowensis
Feasibility Research on Fish Pose Estimation Based on Rotating Box Object Detection
A video-based method to quantify animal posture movement is a powerful way to analyze animal behavior. Both humans and fish can judge the physiological state through the skeleton framework. However, it is challenging for farmers to judge the breeding state in the complex underwater environment. Therefore, images can be transmitted by the underwater camera and monitored by a computer vision model. However, it lacks datasets in artificial intelligence and is unable to train deep neural networks. The main contributions of this paper include: (1) the world’s first fish posture database is established. 10 key points of each fish are manually marked. The fish flock images were taken in the experimental tank and 1000 single fish images were separated from the fish flock. (2) A two-stage attitude estimation model is used to detect fish key points. The evaluation of the algorithm performance indicates the precision of detection reaches 90.61%, F1-score reaches 90%, and Fps also reaches 23.26. We made a preliminary exploration on the pose estimation of fish and provided a feasible idea for fish pose estimation
A Brainnetome Atlas Based Mild Cognitive Impairment Identification Using Hurst Exponent
Mild cognitive impairment (MCI), which generally represents the transition state between normal aging and the early changes related to Alzheimerâs disease (AD), has drawn increasing attention from neuroscientists due that efficient AD treatments need early initiation ahead of irreversible brain tissue damage. Thus effective MCI identification methods are desperately needed, which may be of great importance for the clinical intervention of AD. In this article, the range scaled analysis, which could effectively detect the temporal complexity of a time series, was utilized to calculate the Hurst exponent (HE) of functional magnetic resonance imaging (fMRI) data at a voxel level from 64 MCI patients and 60 healthy controls (HCs). Then the average HE values of each region of interest (ROI) in brainnetome atlas were extracted and compared between MCI and HC. At last, the abnormal average HE values were adopted as the classification features for a proposed support vector machine (SVM) based identification algorithm, and the classification performance was estimated with leave-one-out cross-validation (LOOCV). Our results indicated 83.1% accuracy, 82.8% sensitivity and 83.3% specificity, and an area under curve of 0.88, suggesting that the HE index could serve as an effective feature for the MCI identification. Furthermore, the abnormal HE brain regions in MCI were predominately involved in left middle frontal gyrus, right hippocampus, bilateral parahippocampal gyrus, bilateral amygdala, left cingulate gyrus, left insular gyrus, left fusiform gyrus, left superior parietal gyrus, left orbital gyrus and left basal ganglia