Academic literature on the topic 'Proton Learning Model'
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Journal articles on the topic "Proton Learning Model"
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Dhuri, Dattaraj B., Dimitra Atri, and Ahmed AlHantoobi. "An Explainable Deep-learning Model of Proton Auroras on Mars." Planetary Science Journal 5, no. 6 (2024): 136. http://dx.doi.org/10.3847/psj/ad45ff.
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Abstract Proton auroras are widely observed on the dayside of Mars, identified as a significant intensity enhancement in the hydrogen Lyα (121.6 nm) emission at altitudes of ∼110 and 150 km. Solar wind protons penetrating as energetic neutral atoms into Mars’ thermosphere are thought to be primarily responsible for these auroras. Recent observations of spatially localized “patchy” proton auroras suggest a possible direct deposition of protons into Mars’ atmosphere during unstable solar wind conditions. Improving our understanding of proton auroras is therefore important for characterizing the interaction of the solar wind with Mars’ atmosphere. Here, we develop a first purely data-driven model of proton auroras using Mars Atmosphere and Volatile Evolution (MAVEN) in situ observations and limb scans of Lyα emissions between 2014 and 2022. We train an artificial neural network that reproduces individual Lyα intensities and relative Lyα peak intensity enhancements with Pearson correlations of ∼94% and ∼60% respectively for the test data, along with a faithful reconstruction of the shape of the observed altitude profiles of Lyα emission. By performing a Shapley Additive Explanations (SHAP) analysis, we find that solar zenith angle, solar longitude, CO2 atmosphere variability, solar wind speed, and temperature are the most important features for the modeled Lyα peak intensity enhancements. Additionally, we find that the modeled peak intensity enhancements are high for early local-time hours, particularly near polar latitudes, and the induced magnetic fields are weaker. Through SHAP analysis, we also identify the influence of biases in the training data and interdependences between the measurements used for the modeling, and an improvement of those aspects can significantly improve the performance and applicability of the ANN model.
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Asuroglu, Tunc. "Enhancing precision in proton therapy: Utilizing machine learning for predicting Bragg curve peak location in cancer treatment." Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 66, no. 2 (2024): 140–61. http://dx.doi.org/10.33769/aupse.1417403.
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In proton beam therapy, the Bragg peak is the point where protons lose energy the fastest. This point is crucial for dose control, preserving healthy tissues, minimizing lateral scattering, and the success of treatment planning. However, accurately predicting the location of the Bragg peak is challenging due to the complex interactions of protons with tissues. This study proposes a machine learning (ML) approach to predict the exact location of the Bragg peak from phantom tissue proton beam therapy experiments. A dataset comprising the eight most commonly used biomaterials, which mimic human tissue in proton therapy procedures, has been curated for this study. Various ML models are benchmarked to find the most successful approach. ML model parameters are further optimized using a metaheuristic approach to achieve the highest prediction capability. In addition, feature contributions of each feature in the dataset are analyzed using an explainable artificial intelligence (XAI) technique. According to experimental results, Random Forest (RF) model that is optimized with Genetic Algorithm (GA) achieved 0.742 Correlation Coefficient (CC) value, 0.069 Mean Absolute Error (MAE) and 0.145 Root Mean Square Error (RMSE) outperforming other ML models. The proposed approach can track and predict the movement of the proton beam in real-time during treatment, enhancing treatment safety and contributing to the more effective management of the treatment process. This study is the first to predict exact Bragg curve peak locations from proton beam therapy experiments using ML approaches. The optimized ML model can provide higher precision in identifying the needed beam dosage for targeted tumor and improving treatment outcomes.
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Fathul, Jannah, Fahlevi Reja, Sari Raihanah, Radiansyah, Yuda, and Azizah Ni'mah. "Improving Learning Activities and Writing Skills in Indonesian Language Content the Environmental Theme of Our Friends Using the Proton Model at Sdn Hatungun 1 Tapin." International Journal of Social Science And Human Research 05, no. 11 (2022): 5091–96. https://doi.org/10.5281/zenodo.7333040.
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Learning activities have a big influence on the success of a lesson, this also has a big influence on students' writing skills. But in fact, the method used when learning is still one-way, learning is less meaningful so that students are unable to find ideas/ideas into written form, students' writing interest is low because they use multiple choice questions, many students are still not correct in determining the choice. words (diction) in a sentence, the placement of punctuation marks is not right and they don't understand what a nonfiction story is. This study aims to determine the increase in learning activities and writing skills of elementary school students using the PROTON model. This study uses a qualitative research approach with the type of research in the form of Classroom Action Research which consists of four meetings, data analysis uses two methods, namely qualitative and quantitative. The results showed that by using the PROTON model on the Indonesian content of the Friends of Our Environment Theme there was an increase in student learning activities at the fourth meeting, namely 87.5% in the very active category and in students' writing skills at the fourth meeting as much as 93.75% in the very category. good. The results of this study are expected to be used as an alternative in improving the learning activities and writing skills of elementary school students, especially in the Indonesian content of the Environmental Theme of Our Friends in grade 5.
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Li, Meng, and Dong Ding. "Accelerated Discovery of Proton-Conducting Perovskites through Density Functional Theory and Machine Learning." ECS Meeting Abstracts MA2022-02, no. 49 (2022): 1913. http://dx.doi.org/10.1149/ma2022-02491913mtgabs.
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Hydrogen is an important energy carrier resource in response to limiting greenhouse gas emissions. Proton-conducting perovskite oxide is one of the key materials for highly efficient carbon-neutral hydrogen technologies, such as hydrogen production, CO2 hydrogenation, and ammonia synthesis. Many attempts have been made based on doped perovskites made of well-tested materials, such as BaZrO3, BaCeO3, BaHfO3, BaTiO3, and SrZrO3. However, the resulting perovskites have often suffered stability and conductivity problems. Furthermore, complex phenomena occurring during hydration present challenges for expanding the materials library. Herein, we demonstrate accelerated discovery of proton-conducting perovskites with high conductivity using machine learning (ML) predictions. We constructed consistent training data using density functional theory (DFT) which enable high accuracy of ML model. DFT computations were performed on > 1000 doped perovskite compositions to get their properties of lattice parameters, point defects (e.g., O vacancies, H interstitials), density of states, hydration energy, and proton migration energy. Several ML algorithms including Linear Regression, Bayesian Ridge Regression, Random Forest Regression, Neural networks, and k-Nearest Neighbor were tested for minimum errors and coefficient of determination. The multidimensional relationships between a set of >50 features and conductivity were mapped out using the optimized ML model. We screened a large material space of A-site and B-site doped perovskites to predict potential proton-conducting materials for various energy applications. The outcomes are promising for accelerating the design and applications of proton-conducting perovskite oxides in hydrogen technologies.
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Pastor-Serrano, Oscar, and Zoltán Perkó. "Learning the Physics of Particle Transport via Transformers." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (2022): 12071–79. http://dx.doi.org/10.1609/aaai.v36i11.21466.
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Particle physics simulations are the cornerstone of nuclear engineering applications. Among them radiotherapy (RT) is crucial for society, with 50% of cancer patients receiving radiation treatments. For the most precise targeting of tumors, next generation RT treatments aim for real-time correction during radiation delivery, necessitating particle transport algorithms that yield precise dose distributions in sub-second times even in highly heterogeneous patient geometries. This is infeasible with currently available, purely physics based simulations. In this study, we present a data-driven dose calculation algorithm predicting the dose deposited by mono-energetic proton beams for arbitrary energies and patient geometries. Our approach frames particle transport as sequence modeling, where convolutional layers extract important spatial features into tokens and the transformer self-attention mechanism routes information between such tokens in the sequence and a beam energy token. We train our network and evaluate prediction accuracy using computationally expensive but accurate Monte Carlo (MC) simulations, considered the gold standard in particle physics. Our proposed model is 33 times faster than current clinical analytic pencil beam algorithms, improving upon their accuracy in the most heterogeneous and challenging geometries. With a relative error of 0.34±0.2% and very high gamma pass rate of 99.59±0.7% (1%, 3 mm), it also greatly outperforms the only published similar data-driven proton dose algorithm, even at a finer grid resolution. Offering MC precision 4000 times faster, our model could overcome a major obstacle that has so far prohibited real-time adaptive proton treatments and significantly increase cancer treatment efficacy. Its potential to model physics interactions of other particles could also boost heavy ion treatment planning procedures limited by the speed of traditional methods.
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Ball, Richard D., Alessandro Candido, Juan Cruz-Martinez, et al. "Evidence for intrinsic charm quarks in the proton." Nature 608, no. 7923 (2022): 483–87. http://dx.doi.org/10.1038/s41586-022-04998-2.
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AbstractThe theory of the strong force, quantum chromodynamics, describes the proton in terms of quarks and gluons. The proton is a state of two up quarks and one down quark bound by gluons, but quantum theory predicts that in addition there is an infinite number of quark–antiquark pairs. Both light and heavy quarks, whose mass is respectively smaller or bigger than the mass of the proton, are revealed inside the proton in high-energy collisions. However, it is unclear whether heavy quarks also exist as a part of the proton wavefunction, which is determined by non-perturbative dynamics and accordingly unknown: so-called intrinsic heavy quarks1. It has been argued for a long time that the proton could have a sizable intrinsic component of the lightest heavy quark, the charm quark. Innumerable efforts to establish intrinsic charm in the proton2 have remained inconclusive. Here we provide evidence for intrinsic charm by exploiting a high-precision determination of the quark–gluon content of the nucleon3 based on machine learning and a large experimental dataset. We disentangle the intrinsic charm component from charm–anticharm pairs arising from high-energy radiation4. We establish the existence of intrinsic charm at the 3-standard-deviation level, with a momentum distribution in remarkable agreement with model predictions1,5.We confirm these findings by comparing them to very recent data on Z-boson production with charm jets from the Large Hadron Collider beauty (LHCb) experiment6.
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Kim, Jiwoong, Chang-Seong Moon, Hokyeong Nam, et al. "Multi-Jet Event classification with Convolutional neural network at Large Scale." Journal of Physics: Conference Series 2438, no. 1 (2023): 012103. http://dx.doi.org/10.1088/1742-6596/2438/1/012103.
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Abstract We present an application of Scalable Deep Learning to analyze simulation data of the LHC proton-proton collisions at 13 TeV. We built a Deep Learning model based on the Convolutional Neural Network (CNN) which utilizes detector responses as two-dimensional images reflecting the geometry of the Compact Muon Solenoid (CMS) detector. The model discriminates signal events of the R-parity violating Supersymmetry (RPV SUSY) from the background events with multiple jets due to the inelastic QCD scattering (QCD multi-jets). With the CNN model, we obtained x1.85 efficiency and x1.2 expected significance with respect to the traditional cut-based method. We demonstrated the scalability of the model at a Large Scale with the High-Performance Computing (HPC) resources at the Korea Institute of Science and Technology Information (KISTI) up to 1024 nodes.
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Indraniyati, Indraniyati, Abdul Hadjranul Fatah, and Nopriawan Berkat Asi. "Pemahaman Konsep Struktur Atom Setelah Pembelajaran Menggunakan Model Discovery Learning Berbantuan LKS pada Siswa Kelas X MIA-1 SMA Negeri 1 Paku." Jurnal Ilmiah Kanderang Tingang 11, no. 1 (2020): 180–92. http://dx.doi.org/10.37304/jikt.v11i1.85.
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Model Discovery Learning merupakan nama lain dari pembelajaran penemuan. Sesuai dengan namanya, model ini mengarahkan siswa untuk dapat menemukan sesuatu melalui proses pembelajaran yang dijalaninya. Siswa diarahkan untuk terbiasa menjadi saintis. Tujuan penelitian ini adalah untuk mendeskripsikan pemahaman konsep struktur atom: partikel penyusun inti atom, nomor atom, nomor massa, isotop, isoton, dan isobar. Setelah pembelajaran menggunakan model Discovery Learning berbantuan LKS pada Siswa Kelas X MIA-1 SMA Negeri 1 Paku, Barito Timur TahunAjaran 2017/2018. Penelitian ini melibatkan 25 siswa kelas X MIA-1 SMA Negeri 1 Paku, Barito Timur. Data hasil pemahaman konsep siswa diperoleh melalui pemberian tes tertulis berbentuk essay (uraian) terhadap siswa sebelum dan sesudah menggunakan model Discovery Learning, lembar pengamatan pengelolaan pembelajaran, lembar aktivitas belajar siswa menggunakan rubrik penilaian aktivitas kelompok. Data dianalisis dengan teknik deskriptif. Hasil penelitian menunjukkan bahwa sebagian besar pemahaman konsep siswa sudah benar menuliskan dan menjelaskan simbol atom sebagai lambang unsur yang dilengkapi dengan nomor atom dan nomor massa berjumlah 80%. Mendeskripsikan pengertian nomor atom (jumlah proton) sebagai identitas suatu unsur berjumlah 77,33%. Mendeskripsikan pengertian nomor massa sebagai jumlah proton dan neutron dalam suatu inti atom berjumlah 68%. Menuliskan nomor massa, jumlah proton, jumlah neutron, jumlah elektron pada unsur yang diketahui notasinya berjumlah 69,33%, menjelaskan pengertian isotop, isobar, isoton berjumlah 74,66%. Rata-rata pemahaman konsep siswa pada materi struktur atom berjumlah 73,86%.
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Mohamed Zabidi, Zubainun, Nurul Batrisyia Muhamad Suhaimy, Ahmad Nazib Alias, Nur Diyana Nazihah Fuadi, and Nur Hanisah Hamzi. "Prediction Of Carboxylic Acid Toxicity Using Machine Learning Model." Malaysian Journal of Applied Sciences 8, no. 2 (2023): 28–36. http://dx.doi.org/10.37231/myjas.2023.8.2.357.
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Carboxylic acids are organic compounds characterized by the presence of a carboxyl functional group capable of donating a proton and forming carboxylate ions in aqueous solutions. The carboxylic acid has widely been used in in manufacturing and medical applications. The rapid growth in carboxylic acid has established a need to predict its toxicity. The purpose of this paper to build predictive toxicity of carboxylic acid models by using five molecular descriptors (refractive index, The octanol/water partition coefficient (log P), acid dissociation constant (pKa), density, and dipole moment) through Machine Learning algorithms. The accuracy of the Machine Learning algorithm was determined by using three different types of models which are Decision Tree, Random Forest and k-Nearest Neighbour (k-NN). Among the machine learning algorithms used, we have determined that the decision tree is the best model for predicting the toxicity of carboxylic acid. This finding demonstrates that the decision tree model exhibits an acceptable level of performance in predicting toxicity within the field of toxicology.
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JUNG, Emrae JUNG, and Erhan ATAY. "Internationalization of the Automotive Industry by Extending IOL3 model: A Case Study of Geely Automobile." Eurasian Journal of Business and Economics 15, no. 29 (2022): 1–17. http://dx.doi.org/10.17015/ejbe.2022.029.01.
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Chinese companies have been heavily expanding their businesses globally during recent decades. In the literature, studies examine the dynamics behind their expansion strategies and build multinational Chinese companies called 'dragon multinationals.’ Pointing out the shortcomings of the ownership-locationinternalization (OLI) paradigm to explain the internationalization of these dragon multinationals, the linkage-leverage-learning (LLL) model was introduced by Mathews (2006). It was extended to the inward linkages-outward linkages-leveragelearning (IOL3) model by Lu et al. (2017). This paper aims to investigate forwarded linkages to understand how these linkages are utilized during further expansions of Chinese multinational companies (MNCs) in developing countries. Inward linkages that Geely gained through earlier acquisitions were studied through secondary sources. Then, Geely's latest acquisition of Proton was examined to identify forwarded linkages. Interviews were conducted with the management of Proton and its suppliers to define sources of know-how transferred to Proton and classify them as direct and indirect forwarded linkages.
Dissertations / Theses on the topic "Proton Learning Model"
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Pontes, Miranda James William. "Federation of heterogeneous models with machine learning-assisted model views." Electronic Thesis or Diss., Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2025. http://www.theses.fr/2025IMTA0454.
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L’Ingénierie Dirigée par les Modèles (IDM) promeut les modèles comme un élément clé pour répondre à la complexité croissante du cycle de vie des systèmes logiciel. L’ingénierie de systèmes avec l’IDM implique divers modèles représentant différentes aspects du système. Cette hétérogénéité nécessite des capacités de fédération de modèles pour intégrer des points de vue spécifiques à de multiples domaines. Les solutions de Vues sur les Modèles (Model Views) répondent à ce défi mais manquent encore de support à l’automatisation. Cette thèse explore l’intégration de l’Apprentissage Automatique (AA), notamment les Réseaux de Neurones en Graphes (GNN) et Grands Modèles de Langage (LLM), pour améliorer la définition et construction de telles vues. La solution proposée introduit une approche en deux volets dans la solution technique EMF Views. Cela a permis d’automatiser partiellement la définition des vues sur modèles à la conception, et de calculer dynamiquement les liens inter-modèles à l’exécution. Nos résultats indiquent que l’application de techniques d’apprentissage profond (DL), dans ce contexte spécifique de l’IDM, permet déjà d’atteindre un premier niveau d’automatisation intéressant. Plus globalement, cet effort de recherche contribue au développement actuel de solutions plus intelligentes pour l’IDM<br>Model-driven engineering (MDE) promotes models as a key element in addressing the increasing complexity of the software systems’ lifecycle. Engineering systems with MDE involves various models representing different system aspects. This heterogeneity requires model federation capabilities to integrate viewpoints specific to multiple domains. Model View solutions address this challenge but still lack more automation support. This thesis explores the integration of Machine Learning (ML), notably Graph Neural Networks (GNNs) and Large Language Models (LLMs), in order to improve the definition and building of such views. The proposed solution introduces a twofold approach within the EMF Views technical solution. This allowed to partially automate the definition of model views at design time, and to dynamically compute inter-model links at runtime. Our results indicate that the application of Deep Learning (DL) techniques, in this particular MDE context, already allows to achieve a first relevant level of automation. More globally, this research effort contributes to the ongoing development of more intelligent MDE solutions
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Arige, Abhaya Dhathri. "Simplification of 3D CAD models with deep learning for augmented reality." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAS018.
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Dans l'Industrie 4.0, l'utilisation d'appareils de Réalité Augmentée (RA) tels que HoloLens a acquis une acceptation significative pour la formation des opérateurs de ligne d'assemblage dans diverses industries. La simplification des modèles CAO 3D pour la formation en RA est essentielle pour une meilleure performance des applications. Notre recherche se concentre sur le développement de méthodes et de techniques visant à rationaliser des modèles CAO 3D complexes, les rendant adaptés aux applications de RA.Notre recherche met en avant le rôle des modèles 3D en RA, améliorant l'expérience virtuelle en superposant des modèles CAO sur le monde réel. Nous détaillons les applications de la RA dans la formation des opérateurs et comment l'intégration de modèles CAO 3D améliore la compréhension des instructions et des procédures.Nous avons réalisé une revue approfondie de la littérature sur la simplification des modèles CAO pour leur intégration dans des scénarios de réalité augmentée (RA). Nos conclusions indiquent que les techniques de simplification basées sur le maillage excellent dans la préservation des éléments essentiels des modèles CAO, offrant un contrôle précis sur les niveaux de détail.De plus, nous avons effectué quatre types distincts d'évaluations dans notre recherche. Ces évaluations comprenaient des évaluations objectives utilisant des techniques basées sur le maillage issu de la littérature existante, des avis d'experts impliquant un examen approfondi de chaque modèle simplifié pour déterminer le niveau de simplification en fonction des plages de sommets, des tests en conditions réelles assistés par HoloLens2, qui ont révélé des améliorations du taux de rafraîchissement lors de l'utilisation de modèles CAO au lieu de leurs versions originales.Pour conclure nos évaluations, nous avons également réalisé des évaluations par les utilisateurs, en donnant la priorité à l'expérience utilisateur dans notre étude. Ces évaluations ont confirmé que les modèles simplifiés sont hautement capables de remplacer les versions originales. Cependant, il a été observé qu'une simplification supplémentaire est nécessaire, en particulier pour les modèles CAO complexes.La méthodologie principale propose une approche innovante axée sur la segmentation du maillage et la simplification adaptative grâce à l'utilisation de méthodes d'apprentissage profond. Pour réduire la complexité associée à la segmentation et à la simplification 3D, nous avons projeté les données dans le domaine 2D pour effectuer la segmentation et avons ensuite cartographié les résultats dans le domaine 3D. Nous avons illustré ce cadre à l'aide d'une fonction spécifique appelée "chaînes continues" pour expliquer le processus de simplification. Par la suite, nous avons réalisé une analyse comparative par rapport à des techniques de pointe établies, démontrant la performance supérieure de notre méthodologie. Dans nos futures recherches, nous visons à élargir la portée de notre cadre pour englober plusieurs caractéristiques et les régions fonctionnelles à l'intérieur des modèles CAO<br>As a part of Industry 4.0 the use of Augmented Reality (AR) devices like HoloLens has gained significant acceptance for training assembly line operators in various industries. When employing Computer-Aided Design (CAD) models to create assembly line instructions for training purposes, preserving all redundant information becomes unnecessary. Utilizing simplified CAD models leads to improved run-time performance of the applications in which they are employed. This specific research project is tasked with developing methods and techniques to streamline complex 3D CAD models, making them suitable for AR applications.In this research, we explain how 3D models play a significant role in augmented reality (AR) by enriching the virtual experience through the superimposition of computer-aided design (CAD models) onto the real world. The study goes on to offer detailed descriptions of numerous applications of AR in operator training. Furthermore, it elucidates how the integration of 3D CAD models contributes to a deeper understanding of instructions and procedures within these training scenarios.We conducted an in-depth literature review in the field of CAD model simplification to determine which simplification techniques are most suitable for integration into augmented reality (AR) scenarios. Our research revealed that mesh-based simplification techniques are particularly effective in preserving the essential features of CAD models while offering the advantages of precise control over the level of detail.Additionally, we have carried out four distinct types of assessments as part of our research. These assessments encompassed objective evaluations that applied mesh-based techniques from existing literature, subjective assessment involving a thorough examination of each simplified model to determine the level of simplification based on vertex ranges, real-world testing conducted with the assistance of the HoloLens2 that demonstrated framerate enhancements when employing simplified CAD models in place of their original versions. To conclude our evaluations, we conducted user assessments, as user experience holds utmost importance in our study. They demonstrated that the simplified models possess a high degree of capability in substituting the original counterparts. However, it was noted that more simplification is required, particularly for intricate CAD models.An innovative approach centered around segmentation and adaptive simplification through the utilization of deep learning methods is proposed as the main methodology. To illustrate this framework, we employed a specific feature called "continuous chains". We subsequently conducted a comparative analysis against established state-of-the-art techniques, demonstrating that our methodology outperforms existing approaches. In our future research, we intend to expand the scope of our framework to encompass multiple features in CAD model
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Tahoun, Mohamed. "Object Shape Perception for Autonomous Dexterous Manipulation Based on Multi-Modal Learning Models." Electronic Thesis or Diss., Bourges, INSA Centre Val de Loire, 2021. http://www.theses.fr/2021ISAB0003.
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Cette thèse propose des méthodes de reconstruction 3D d’objets basées sur des stratégies multimodales d'apprentissage profond. Les applications visées concernent la manipulation robotique. Dans un premier temps, la thèse propose une méthode de reconstruction visuelle 3D à partir d’une seule vue de l’objet obtenue par un capteur RGB-D. Puis, afin d’améliorer la qualité de reconstruction 3D des objets à partir d’une seule vue, une nouvelle méthode combinant informations visuelles et tactiles a été proposée en se basant sur un modèle de reconstruction par apprentissage. La méthode proposée a été validée sur un ensemble de données visuo-tactiles respectant les contraintes cinématique d’une main robotique. L’ensemble de données visuo-tactiles respectant les propriétés cinématiques de la main robotique à plusieurs doigts a été créé dans le cadre de ce travail doctoral. Cette base de données est unique dans la littérature et constitue également une contribution de la thèse. Les résultats de validation montrent que les informations tactiles peuvent avoir un apport important pour la prédiction de la forme complète d’un objet, en particulier de la partie invisible pour le capteur RGD-D. Ils montrent également que le modèle proposé permet d’obtenir de meilleurs résultats en comparaison à ceux obtenus avec les méthodes les plus performantes de l’état de l’art<br>This thesis proposes 3D object reconstruction methods based on multimodal deep learning strategies. The targeted applications concern robotic manipulation. First, the thesis proposes a 3D visual reconstruction method from a single view of the object obtained by an RGB-D sensor. Then, in order to improve the quality of 3D reconstruction of objects from a single view, a new method combining visual and tactile information has been proposed based on a learning reconstruction model. The proposed method has been validated on a visual-tactile dataset respecting the kinematic constraints of a robotic hand. The visual-tactile dataset respecting the kinematic properties of the multi-fingered robotic hand has been created in the framework of this PhD work. This dataset is unique in the literature and is also a contribution of the thesis. The validation results show that the tactile information can have an important contribution for the prediction of the complete shape of an object, especially the part that is not visible to the RGD-D sensor. They also show that the proposed model allows to obtain better results compared to those obtained with the best performing methods of the state of the art
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Soumm, Michaël. "Refining machine learning evaluation : statistical insights into model performance and fairness." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG094.
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Cette thèse aborde les limitations des méthodologies d’évaluation en apprentissage automatique en introduisant des approches statistiques rigoureuses adaptées de l’économétrie. À travers des applications dans trois domaines distincts de l’apprentissage automatique, nous démontrons comment les outils statistiques peuvent améliorer la robustesse, l’interprétabilité, et l’équité de l’évaluation des modèles. Dans l’apprentissage incrémental de classes, nous examinons l’importance des méthodes de pré-entraînement par rapport au choix de l’algorithme incrémental et montrons que celles-ci sont décisives dans les performance finales ; dans les systèmes de reconnaissance faciale, nous quantifions les biais démographiques et démontrons que des données synthétiques équilibrées démographiquement peuvent réduire significativement les disparités de performance entre les groupes ethniques ; dans les systèmes de recommandation, nous développons de nouvelles mesures basées sur la théorie de l’information pour analyser les variations de performance entre les profils d’utilisateurs, révélant que les méthodes d’apprentissage profond ne surpassent pas systématiquement les approches traditionnelles et soulignant l’importance des schémas comportementaux des utilisateurs. Ces résultats démontrent l’importance de la rigueur statistique dans l’évaluation de l’apprentissage automatique et fournissent des lignes directrices pratiques pour améliorer l’évaluation des modèles dans diverses applications<br>This thesis addresses limitations in machine learning evaluation methodologies by introducing rigorous statistical approaches adapted from econometrics. Through applications in three distinct machine learning do-mains, we demonstrate how statistical tools can enhance model evaluation robustness, interpretability, and fairness. In class incremental learning, we examine the importance of pretraining methods compared to the choice of the incremental algorithm and show that these methods are crucial in determining final performance ; in face recognition systems, we quantify demographic biases and show that demographically-balanced synthetic data can significantly reduce performance disparities across ethnic groups ; in recommender systems, we develop novel information theory-based measures to analyze performance variations across user profiles, revealing that deep learning methods don’t consistently out-perform traditional approaches and highlighting the importance of user behavior patterns. These findings demonstrate the value of statistical rigor in machine learning evaluation and provide practical guidelines for improving model assessment across diverse applications
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Stock, Pierre. "Efficiency and Redundancy in Deep Learning Models : Theoretical Considerations and Practical Applications." Thesis, Lyon, 2021. http://www.theses.fr/2021LYSEN008.
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Les réseaux de neurones profonds sont à l'origine de percées majeures en intelligence artificielle. Ce succès s'explique en partie par un passage à l'échelle en termes de puissance de calcul, d'ensembles de données d'entrainement et de taille des modèles considérés -- le dernier point ayant été rendu possible en construisant des réseaux de plus en plus profonds. Dans cette thèse, partant du constat que de tels modèles sont difficiles à appréhender et à entrainer, nous étudions l'ensemble des réseaux de neurones à travers leurs classes d'équivalence fonctionnelles, ce qui permet de les grouper par orbites et de ne manipuler qu'un représentant bien choisi. Ces considérations théoriques nous ont permis de proposer une variante de l'algorithme de descente de gradient stochastique qui consiste à insérer, au cours des itérations, des étapes permettant de choisir le représentant de la classe d'équivalence courante minimisant une certaine énergie. La redondance des paramètres de réseaux profonds de neurones mise en lumière dans ce premier volet amène naturellement à la question de l'efficience de tels réseaux, et donc de leur compression. Nous développons une nouvelle méthode de compression, appelée iPQ et reposant sur de la quantification vectorielle, prouvant qu'il est possible de réduire considérablement la taille d'un réseau tout en préservant sa capacité de prédiction. En combinant iPQ avec une procédure de pré-conditionnement appelée Quant-Noise qui consiste à injecter du bruit de quantification dans le réseau avant sa compression, nous obtenons des résultats état de l’art en termes de compromis taille/capacité de prédiction. Voulant confronter nos recherches à des contraintes de type produit, nous proposons enfin une application de ces algorithmes permettant un appel vidéo à très faible bande passante, déployée sur un téléphone portable et fonctionnant en temps réel<br>Deep Neural Networks led to major breakthroughs in artificial intelligence. This unreasonable effectiveness is explained in part by a scaling-up in terms of computing power, available datasets and model size -- the latter was achieved by building deeper and deeper networks. In this thesis, recognizing that such models are hard to comprehend and to train, we study the set of neural networks under the prism of their functional equivalence classes in order to group networks by orbits and to only manipulate one carefully selected representant. Based on these theoretical considerations, we propose a variant of the stochastic gradient descent (SGD) algorithm which amounts to inserting, between the SGD iterations, additional steps allowing us to select the representant of the current equivalence class that minimizes a certain energy. The redundancy of the network's parameters highlighted in the first part naturally leads to the question of the efficiency of such networks, hence to the question of their compression. We develop a novel method, iPQ, relying on vector quantization that drastically reduces the size of a network while preserving its accuracy. When combining iPQ with a new pre-conditioning technique called Quant-Noise that injects quantization noise in the network before its compression, we obtain state-of-the-art tradeoffs in terms of size/accuracy. Finally, willing to confront such algorithms to product constraints, we propose an application allowing anyone to make an ultra-low bandwidth video call that is deployed on-device and runs in real time
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Cappuzzo, Riccardo. "Deep learning models for tabular data curation." Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS047.
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La conservation des données est un sujet omniprésent et de grande envergure, qui touche tous les domaines, du monde universitaire à l'industrie. Les solutions actuelles reposent sur le travail manuel des utilisateurs du domaine, mais elles ne sont pas adaptées. Nous étudions comment appliquer l'apprentissage profond à la conservation des données tabulaires. Nous concentrons notre travail sur le développement de systèmes de curation de données non supervisés et sur la conception de systèmes de curation qui modélisent intrinsèquement les valeurs catégorielles dans leur forme brute. Nous implémentons d'abord EmbDI pour générer des embeddings pour les données tabulaires, et nous traitons les tâches de résolution d'entités et de correspondance de schémas. Nous passons ensuite au problème de l'imputation des données en utilisant des réseaux neuronaux graphiques dans un cadre d'apprentissage multi-tâches appelé GRIMP<br>Data retention is a pervasive and far-reaching topic, affecting everything from academia to industry. Current solutions rely on manual work by domain users, but they are not adequate. We are investigating how to apply deep learning to tabular data curation. We focus our work on developing unsupervised data curation systems and designing curation systems that intrinsically model categorical values in their raw form. We first implement EmbDI to generate embeddings for tabular data, and address the tasks of entity resolution and schema matching. We then turn to the data imputation problem using graphical neural networks in a multi-task learning framework called GRIMP
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Ben-Younes, Hedi. "Multi-modal representation learning towards visual reasoning." Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS173.
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La quantité d'images présentes sur internet augmente considérablement, et il est nécessaire de développer des techniques permettant le traitement automatique de ces contenus. Alors que les méthodes de reconnaissance visuelle sont de plus en plus évoluées, la communauté scientifique s'intéresse désormais à des systèmes aux capacités de raisonnement plus poussées. Dans cette thèse, nous nous intéressons au Visual Question Answering (VQA), qui consiste en la conception de systèmes capables de répondre à une question portant sur une image. Classiquement, ces architectures sont conçues comme des systèmes d'apprentissage automatique auxquels on fournit des images, des questions et leur réponse. Ce problème difficile est habituellement abordé par des techniques d'apprentissage profond. Dans la première partie de cette thèse, nous développons des stratégies de fusion multimodales permettant de modéliser des interactions entre les représentations d'image et de question. Nous explorons des techniques de fusion bilinéaire, et assurons l'expressivité et la simplicité des modèles en utilisant des techniques de factorisation tensorielle. Dans la seconde partie, on s'intéresse au raisonnement visuel qui encapsule ces fusions. Après avoir présenté les schémas classiques d'attention visuelle, nous proposons une architecture plus avancée qui considère les objets ainsi que leurs relations mutuelles. Tous les modèles sont expérimentalement évalués sur des jeux de données standards et obtiennent des résultats compétitifs avec ceux de la littérature<br>The quantity of images that populate the Internet is dramatically increasing. It becomes of critical importance to develop the technology for a precise and automatic understanding of visual contents. As image recognition systems are becoming more and more relevant, researchers in artificial intelligence now seek for the next generation vision systems that can perform high-level scene understanding. In this thesis, we are interested in Visual Question Answering (VQA), which consists in building models that answer any natural language question about any image. Because of its nature and complexity, VQA is often considered as a proxy for visual reasoning. Classically, VQA architectures are designed as trainable systems that are provided with images, questions about them and their answers. To tackle this problem, typical approaches involve modern Deep Learning (DL) techniques. In the first part, we focus on developping multi-modal fusion strategies to model the interactions between image and question representations. More specifically, we explore bilinear fusion models and exploit concepts from tensor analysis to provide tractable and expressive factorizations of parameters. These fusion mechanisms are studied under the widely used visual attention framework: the answer to the question is provided by focusing only on the relevant image regions. In the last part, we move away from the attention mechanism and build a more advanced scene understanding architecture where we consider objects and their spatial and semantic relations. All models are thoroughly experimentally evaluated on standard datasets and the results are competitive with the literature
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Ayed, Ibrahim. "Neural Models for Learning Real World Dynamics and the Neural Dynamics of Learning." Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS434.
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Cette thèse se situe à l'intersection de deux domaines : d'une part celui des systèmes dynamiques, et notamment ceux qui peuvent être représentés par des équations différentielles d'évolution, et, d'autre part, celui des l'apprentissage profond. Son objectif est alors double : Il s'agit d'abord de chercher à modéliser, grâce aux techniques modernes de l'apprentissage profond, des phénomènes physiques complexes, dans divers cadres d'intérêt pour les praticiens. Ensuite, nous avons également tenté d'employer des outils issus des théories mathématiques permettant l'étude des équations différentielles afin de mieux comprendre certains aspects des dynamiques induites par l'apprentissage de réseaux de neurones profonds et leur fonctionnement<br>The work presented in this thesis was initially motivated by the discrepancy between the impressive performances of modern neural networks and the lack of applications to scientific problems for which data abounds. Focusing on evolution problems which are classically modelled through ordinary or partial differential equations~(O/PDEs) naturally brought us to consider the more general problem of representing and learning such equations from raw data with neural networks. This was the inception of the first part of our work. The point of view considered in this first part has a natural counterpart: what about the dynamics induced by the trajectories of the NN's weights during training or by the trajectories of data points within them during inference? Can they be usefully modelled? This question was the core of the second part of our work and, while theoretical tools other than O/PDEs happened to be useful in our analysis, our reasoning and intuition were fundamentally driven by considerations stemming from a dynamical viewpoint
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Belilovsky, Eugene. "Apprentissage de graphes structuré et parcimonieux dans des données de haute dimension avec applications à l’imagerie cérébrale." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLC027.
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Cette thèse présente de nouvelles méthodes d’apprentissage structuré et parcimonieux sur les graphes, ce qui permet de résoudre une large variété de problèmes d’imagerie cérébrale, ainsi que d’autres problèmes en haute dimension avec peu d’échantillon. La première partie de cette thèse propose des relaxation convexe de pénalité discrète et combinatoriale impliquant de la parcimonie et bounded total variation d’un graphe, ainsi que la bounded `2. Ceux-ci sont dévelopé dansle but d’apprendre un modèle linéaire interprétable et on démontre son efficacacité sur des données d’imageries cérébrales ainsi que sur les problèmes de reconstructions parcimonieux.Les sections successives de cette thèse traite de la découverte de structure sur des modèles graphiques “undirected” construit à partir de peu de données. En particulier, on se concentre sur des hypothèses de parcimonie et autres hypothèses de structures dans les modèles graphiques gaussiens. Deux contributions s’en dégagent. On construit une approche pour identifier les différentes entre des modèles graphiques gaussiens (GGMs) qui partagent la même structure sous-jacente. On dérive la distribution de différences de paramètres sous une pénalité jointe quand la différence des paramètres est parcimonieuse. On montre ensuite comment cette approche peut être utilisée pour obtenir des intervalles de confiances sur les différences prises par le GGM sur les arêtes. De là, on introduit un nouvel algorithme d’apprentissage lié au problème de découverte de structure sur les modèles graphiques non dirigées des échantillons observés. On démontre que les réseaux de neurones peuvent être utilisés pour apprendre des estimateurs efficacaces de ce problèmes. On montre empiriquement que ces méthodes sont une alternatives flexible et performantes par rapport aux techniques existantes<br>This dissertation presents novel structured sparse learning methods on graphs that address commonly found problems in the analysis of neuroimaging data as well as other high dimensional data with few samples. The first part of the thesis proposes convex relaxations of discrete and combinatorial penalties involving sparsity and bounded total variation on a graph as well as bounded `2 norm. These are developed with the aim of learning an interpretable predictive linear model and we demonstrate their effectiveness on neuroimaging data as well as a sparse image recovery problem.The subsequent parts of the thesis considers structure discovery of undirected graphical models from few observational data. In particular we focus on invoking sparsity and other structured assumptions in Gaussian Graphical Models (GGMs). To this end we make two contributions. We show an approach to identify differences in Gaussian Graphical Models (GGMs) known to have similar structure. We derive the distribution of parameter differences under a joint penalty when parameters are known to be sparse in the difference. We then show how this approach can be used to obtain confidence intervals on edge differences in GGMs. We then introduce a novel learning based approach to the problem structure discovery of undirected graphical models from observational data. We demonstrate how neural networks can be used to learn effective estimators for this problem. This is empirically shown to be flexible and efficient alternatives to existing techniques
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Darwaish, Asim. "Adversary-aware machine learning models for malware detection systems." Electronic Thesis or Diss., Université Paris Cité, 2022. http://www.theses.fr/2022UNIP7283.
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La popularisation des smartphones et leur caractère indispensable les rendent aujourd'hui indéniables. Leur croissance exponentielle est également à l'origine de l'apparition de nombreux logiciels malveillants et fait trembler le prospère écosystème mobile. Parmi tous les systèmes d'exploitation des smartphones, Android est le plus ciblé par les auteurs de logiciels malveillants en raison de sa popularité, de sa disponibilité en tant que logiciel libre, et de sa capacité intrinsèque à accéder aux ressources internes. Les approches basées sur l'apprentissage automatique ont été déployées avec succès pour combattre les logiciels malveillants polymorphes et évolutifs. Au fur et à mesure que le classificateur devient populaire et largement adopté, l'intérêt d'échapper au classificateur augmente également. Les chercheurs et les adversaires se livrent à une course sans fin pour renforcer le système de détection des logiciels malveillants androïd et y échapper. Afin de lutter contre ces logiciels malveillants et de contrer les attaques adverses, nous proposons dans cette thèse un système de détection de logiciels malveillants android basé sur le codage d'images, un système qui a prouvé sa robustesse contre diverses attaques adverses. La plateforme proposée construit d'abord le système de détection des logiciels malveillants android en transformant intelligemment le fichier Android Application Packaging (APK) en une image RGB légère et en entraînant un réseau neuronal convolutif (CNN) pour la détection des logiciels malveillants et la classification des familles. Notre nouvelle méthode de transformation génère des modèles pour les APK bénins et malveillants plus faciles à classifier en images de couleur. Le système de détection ainsi conçu donne une excellente précision de 99,37% avec un Taux de Faux Négatifs (FNR) de 0,8% et un Taux de Faux Positifs (FPR) de 0,39% pour les anciennes et les nouvelles variantes de logiciels malveillants. Dans la deuxième phase, nous avons évalué la robustesse de notre système de détection de logiciels malveillants android basé sur l'image. Pour valider son efficacité contre les attaques adverses, nous avons créé trois nouveaux modèles d'attaques. Notre évaluation révèle que les systèmes de détection de logiciels malveillants basés sur l'apprentissage les plus récents sont faciles à contourner, avec un taux d'évasion de plus de 50 %. Cependant, le système que nous avons proposé construit un mécanisme robuste contre les perturbations adverses en utilisant son espace continu intrinsèque obtenu après la transformation intelligente des fichiers Dex et Manifest, ce qui rend le système de détection difficile à contourner<br>The exhilarating proliferation of smartphones and their indispensability to human life is inevitable. The exponential growth is also triggering widespread malware and stumbling the prosperous mobile ecosystem. Among all handheld devices, Android is the most targeted hive for malware authors due to its popularity, open-source availability, and intrinsic infirmity to access internal resources. Machine learning-based approaches have been successfully deployed to combat evolving and polymorphic malware campaigns. As the classifier becomes popular and widely adopted, the incentive to evade the classifier also increases. Researchers and adversaries are in a never-ending race to strengthen and evade the android malware detection system. To combat malware campaigns and counter adversarial attacks, we propose a robust image-based android malware detection system that has proven its robustness against various adversarial attacks. The proposed platform first constructs the android malware detection system by intelligently transforming the Android Application Packaging (APK) file into a lightweight RGB image and training a convolutional neural network (CNN) for malware detection and family classification. Our novel transformation method generates evident patterns for benign and malware APKs in color images, making the classification easier. The detection system yielded an excellent accuracy of 99.37% with a False Negative Rate (FNR) of 0.8% and a False Positive Rate (FPR) of 0.39% for legacy and new malware variants. In the second phase, we evaluate the robustness of our secured image-based android malware detection system. To validate its hardness and effectiveness against evasion, we have crafted three novel adversarial attack models. Our thorough evaluation reveals that state-of-the-art learning-based malware detection systems are easy to evade, with more than a 50% evasion rate. However, our proposed system builds a secure mechanism against adversarial perturbations using its intrinsic continuous space obtained after the intelligent transformation of Dex and Manifest files which makes the detection system strenuous to bypass
Books on the topic "Proton Learning Model"
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Byrne, John H., ed. The Oxford Handbook of Invertebrate Neurobiology. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190456757.001.0001.
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Invertebrates have proven to be extremely useful models for gaining insights into the neural and molecular mechanisms of sensory processing, motor control, and higher functions, such as feeding behavior, learning and memory, navigation, and social behavior. Their enormous contribution to neuroscience is due, in part, to the relative simplicity of invertebrate nervous systems and, in part, to the large cells found in some invertebrates, like mollusks. Because of the organizms’ cell size, individual neurons can be surgically removed and assayed for expression of membrane channels, levels of second messengers, protein phosphorylation, and RNA and protein synthesis. Moreover, peptides and nucleotides can be injected into individual neurons. Other invertebrate systems such as Drosophila and Caenorhabditis elegans are ideal models for genetic approaches to the exploration of neuronal function and the neuronal bases of behavior. The Oxford Handbook of Invertebrate Neurobiology reviews neurobiological phenomena, including motor pattern generation, mechanisms of synaptic transmission, and learning and memory, as well as circadian rhythms, development, regeneration, and reproduction. Species-specific behaviors are covered in chapters on the control of swimming in annelids, crustacea, and mollusks; locomotion in hexapods; and camouflage in cephalopods. A unique feature of the handbook is the coverage of social behavior and intentionality in invertebrates. These developments are contextualized in a chapter summarizing past contributions of invertebrate research as well as areas for future studies that will continue to advance the field.
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Iori, Giulia, and James Porter. Agent-based Modeling for Financial Markets. Edited by Shu-Heng Chen, Mak Kaboudan, and Ye-Rong Du. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199844371.013.43.
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This chapter discusses a step in the evolution of agent-based model (ABM) research in finance. Agent-based modeling has concentrated on the development of stylized market models, which have been extremely useful for understanding how complex macro-scale phenomena emerge from micro-rules. In order to further develop ABMs from proof of concept into robust tools for policy makers, to control and forecast complex real-world financial markets, it is essential to permit agents to behave as active data-gathering decision makers with sophisticated learning capabilities. The main focus of this chapter is to show how agent based models (ABMs) in financial markets have evolved from simple zero- intelligence agents that follow arbitrary rules of thumb into sophisticated agents described by microfounded rules of behavior. The chapter then briefly looks at the challenges posed by and approaches to model calibration and provides examples of how ABMs have been successful at offering useful insights for policy making.
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Yates, Jan M. Interactive Distance Learning in PreK-12 Settings. Greenwood Publishing Group, Inc., 2003. http://dx.doi.org/10.5040/9798400671012.
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The wide array of possibilities for interactive distance learning in today's schools can be daunting. This book will help educators make the transition from technology-based learning modalities and integrate elements of distance learning into the curriculum. With emphasis on Internet-based delivery formats, author Jan M. Yates presents the latest research and proven techniques for creating effective distance-learning opportunities that enhance student achievement. This guide is indispensable for anyone serious about distance learning. Included are an introduction, models and examples of distance learning, distance learning settings, discussions of support technologies and their uses, evaluation of interactive distance learning activities, a wealth of information about Web sites, vendors, and useful materials.
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Graves, Colleen, Aaron Graves, and Diana L. Rendina. Challenge-Based Learning in the School Library Makerspace. ABC-CLIO, LLC, 2017. http://dx.doi.org/10.5040/9798400624421.
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An invaluable how-to text that details the workshop model, addresses the design challenges, and explains the best avenues for curriculum-based learning in the school library makerspace. A successful school makerspace needs an enthusiastic maker community, school-wide participation, and staff support. How do you build this type of learning at your school? The innovative team behind Challenge-Based Learning in the School Library Makerspace addresses common questions and concerns and describes step-by-step how to introduce challenge-based learning into the school library makerspace. Intended for librarians and school staff who have already started thinking in terms of makerspaces but need further help sustaining programming and want to know more about Makerspace 2.0, this helpful guide details the workshop model, various real-world design challenges, and the process for implementing curriculum-based learning in the school library makerspace. Readers will be empowered to go beyond the initial implementation of a makerspace and to draw from an arsenal of proven methodologies for designing challenges for student learning. Additionally, the book enables the addition of curriculum connections to library programming, shows how to connect your students to local experts and the global maker community, and eases you into more productive collaboration with other librarians.
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Busi, Kimberly, and Kristin Berman. Integration and Dynamic Adaptation in the Formation of a Novel 2e School Model. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190645472.003.0020.
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Education for twice exceptional (2e) children has proven to be a dilemma for many institutions as these children bring many complexities requiring a diverse and integrated group of professionals working together. As 2e children grow in a setting that can address their need for self-regulation, executive functioning, support of learning differences, and advanced level academics, professionals must continually assess and adapt their practices. The Quad Preparatory School has developed a model that integrates best practices from the fields of psychology, speech pathology, occupational therapy, special education, and gifted pedagogy employing instruction in a one-on-one setting adding group work when children are ready. The model uses a curriculum framework providing a context for studies in all disciplines leading to project work initiated by the strengths and interests of the students. The model has been successful in its use of dynamic adaptation to personalize the educational experience of 2e children.
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Lanning, Scott, and Caitlin Gerrity. Concise Guide to Information Literacy. 3rd ed. Libraries Unlimited, 2022. http://dx.doi.org/10.5040/9798400630101.
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This flexible text can serve as the basis of a course in information literacy or as a supplemental text or basic research guide in any course. Both a students' textbook and an instructional reference for educators, this brief but information-rich text teaches students what information literacy is and why it's such an important skill to develop. Authors Scott Lanning and Caitlin Gerrity concentrate on developing skills and behaviors that positively impact the information literacy process. They teach such skills as evaluating and using information and behaviors like exploring, analyzing, and creating. Updated to incorporate the new AASL standards, this third edition ofConcise Guide to Information Literacyincludes new information on the value of curiosity and choice in the research process, offers a new model of the research process (the Reflective Inquiry Model), and updates the Decision Points Information Seeking Model that describes how student researchers choose to use the information they've found. This book has proven to be invaluable for high school and college students learning about information literacy and librarians and teachers in upper high school and community college settings.
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Hedman, Shawn. A First Course in Logic. Oxford University Press, 2004. http://dx.doi.org/10.1093/oso/9780198529804.001.0001.
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The ability to reason and think in a logical manner forms the basis of learning for most mathematics, computer science, philosophy and logic students. Based on the author's teaching notes at the University of Maryland and aimed at a broad audience, this text covers the fundamental topics in classical logic in an extremely clear, thorough and accurate style that is accessible to all the above. Covering propositional logic, first-order logic, and second-order logic, as well as proof theory, computability theory, and model theory, the text also contains numerous carefully graded exercises and is ideal for a first or refresher course.
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Hussain, Ibrahim, and David H. Gutmann. Familial CNS Tumor Syndromes. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199937837.003.0134.
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Neurofibromatosis type 1 (NF1) is an inherited cancer predisposition syndrome affecting 1 in 2,500 to 3,000 individuals worldwide. Key clinical features of NF1 include pigmentary abnormalities, learning disabilities, and orthopedic problems. Individuals with NF1 are prone to the development of benign peripheral nerve sheath tumors, and 15% to 20% of affected children harbor low-grade gliomas of the optic pathway. Since the discovery of the NF1 gene and its protein neurofibromin, advances in understanding the molecular mechanisms of NF1 have resulted in the discovery of new treatments. In addition, genetically-engineered animal models of NF1-associated tumorigenesis have served as platforms for validating molecular targets for future medical therapies.
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Russo, Christina T., and Cathy Swan. Your Library Is the Answer. ABC-CLIO, LLC, 2015. http://dx.doi.org/10.5040/9798216040071.
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Today's tech-savvy and digitally connected students present a new challenge for today's school librarians. This book offers the 21st-century tools and know-how necessary for educators to appeal to and challenge students to learn—and to want to learn. What are the best ways to motivate students to become engaged and develop a passion for learning? Can appealing to their desire for socialization and constant communication—attributes of their lives outside of education—via the integration of cutting-edge technologies and "new media" in the library or classroom serve to ignite creativity, curiosity, and critical thinking? This book shows how you can make use of non-traditional tools such as popular social networks, collaborative technologies, and cloud computing to teach information and communications technologies integrated with the school curriculum to improve student learning—and demonstrates how these same technologies can help you measure skills and mastery learning. The book provides an easy-to-follow blueprint for using collaborative techniques, innovation, and teaching for creativity to achieve the new learning paradigm of self-directed learning, such as flipping the classroom or library. Readers of this book will find concrete, step-by-step examples of proven lesson plans, collaborative models, and time-saving strategies for the successful integration of American Association of School Librarians (AASL) standards. The authors—both award-winning teachers—explain the quantitatively and qualitatively measurable educational value of using these technologies for core curricular and information and communications technologies instruction, showing that they both enhance student learning outcomes and provide data for measuring their impact on learning.
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Gouthier, Matthias, ed. Erfolgreiche Wege zur Service Excellence. Nomos Verlagsgesellschaft mbH & Co. KG, 2022. http://dx.doi.org/10.5771/9783748928430.
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Excellent service does not just happen by itself. All over the world, best-in-class companies use concepts which have become established under the label of ‘service excellence’. The edited volume ‘Successful Paths to Service Excellence—Learning the 1 x 1 of Excellent Services from Service Champions’ is devoted to the question of how service excellence is implemented as well as experienced by and in successful companies. The structure of the book is based on the model of service excellence as anchored in the new ISO standard 23592. To this end, proven experts from a wide range of industries present best practices that demonstrate successful ways of implementing service excellence. With contributions by Dr. Ferri Abolhassan, Dr. Björn Becker, Sabine Börnsen, Philippe Clarinval, Svenja Daniel, Prof. Dr. Matthias Gouthier, Enrico Jensch, Juliane Köninger, Michael Moritz, Christian Polenz, Christopher Rastin, Carsten K. Rath, Matthias Raquet and Dr. Kristina Rodig.
Book chapters on the topic "Proton Learning Model"
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Zhang, Yuanyuan, Xiao Wang, Zicong Zhang, Yunhan Huang, and Daisuke Kihara. "Assessment of Protein–Protein Docking Models Using Deep Learning." In Protein-Protein Docking. Springer US, 2024. http://dx.doi.org/10.1007/978-1-0716-3985-6_10.
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Gadekar, Aumkar, Shreya Oak, Abhishek Revadekar, and Anant V. Nimkar. "MMAP: A Multi-Modal Automated Online Proctor." In Machine Learning and Big Data Analytics (Proceedings of International Conference on Machine Learning and Big Data Analytics (ICMLBDA) 2021). Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82469-3_28.
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Masso, Majid, and Iosif I. Vaisman. "Structure-Based Machine Learning Models for Computational Mutagenesis." In Introduction to Protein Structure Prediction. John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470882207.ch18.
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Eti Proto, Meltem, and Ceren Koç Sağlam. "Furniture Design Education with 3D Printing Technology." In Makers at School, Educational Robotics and Innovative Learning Environments. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77040-2_13.
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AbstractThree-dimensional printing technology has an important place in furniture and interior design, a strong global sector that responds rapidly to the changing needs and expectations of the individual and society. The main objective of design education should be to equip us to imagine new models of life. Among the most attractive benefits of 3D printing technology that make it a boon to designers working in the building and furniture sector are that it enables them to seek original forms that cannot be produced in molds, it generates less waste, and is accessible to all. Today, innovation in the profession, innovative materials, and knowledge of innovative production technologies that feed creative thinking have become ever important features of design education. This knowledge will allow us to imagine, discuss and pioneer design production ideas for new life models. This paper discusses 3D printing technology, the furniture design studio method and its contribution to design education in the Production Techniques courses of the Interior Architecture Department of Marmara University’s Faculty of Fine Arts led by Professor Meltem Eti Proto, Instructor Can Onart, Lecturer T. Emre Eke, and Research Assistant Ceren Koç Sağlam.
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Veena, M. B., and Gagan Bagewadi. "Identification of Plant Leaf Disease Using Synthetic Data Augmentation ProGAN to Improve the Performance of Deep Learning Models." In Evolutionary Artificial Intelligence. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-8438-1_14.
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"A Learning Model for Today." In Robot-Proof, 2nd ed. The MIT Press, 2024. http://dx.doi.org/10.7551/mitpress/15620.003.0007.
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"A Learning Model for the Future." In Robot-Proof. The MIT Press, 2017. http://dx.doi.org/10.7551/mitpress/11456.003.0006.
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Muggleton, Stephen. "Inverting Entailment and Progol." In Machine Intelligence 14. Oxford University PressOxford, 1996. http://dx.doi.org/10.1093/oso/9780198538608.003.0006.
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Abstract This paper firstly provides a re-appraisal of the development of techniques for inverting deduction, secondly introduces Mode-Directed Inverse Entailment (MDIE) as a generalisation and enhancement of previous approaches and thirdly describes an implementation of MDIE in the Progol system. Progol is implemented in C and available by anonymous ftp. The re-assessment of previous techniques in terms of inverse entailment leads to new results for learning from positive data and inverting implication between pairs of clauses.
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Mozetičt, I., J. Stefan Institute, I. Bratko, et al. "Varying Levels of Abstraction in Qualitative Modelling." In Machine Intelligence 12. Oxford University PressOxford, 1991. http://dx.doi.org/10.1093/oso/9780198538233.003.0017.
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Abstract We describe a formalism for hierarchically representing qualitative models at various levels of abstraction. The formalism is based on logic, namely on typed Horn clauses also known as database clauses. The notion of abstraction is realized through a hierarchy of types for the domains of predicates. The abstraction hierarchy can be used in gener ating explanations with an adjustable degree of detail; also in improving search efficiency in solving tasks of diagnosis and control, as well as the learning of qualitative models. Results obtained at a simpler, more abstract level, can be used to guide the search at a more detailed and combinatorially more complex level. The corresponding algorithms are presented as PROLOG programs and their behaviour studied on example problems including a qualitative model of the heart.
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Sokouti, Babak, and Massoud Sokouti. "Security of Internet-, Intranet-, and Computer-Based Examinations in Terms of Technical, Authentication, and Environmental, Where Are We?" In Advanced Methodologies and Technologies in System Security, Information Privacy, and Forensics. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7492-7.ch008.
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Worldwide, increasing trends on distance learning provided by different educational and academic organizations require robust secure environments for carrying out the distance examinations. The security of online examinations is prone to many threats including the local cheaters and outside attackers. Several studies have been carried out in terms of technical, authentication algorithms, and environmental monitoring (supervised or unsupervised). None of these categories can satisfy the required security services to stop candidate cheating during the examination. A robust secure model will be needed to include all three categories in order to provide secure environments for examinees while no manual supervision is required by proctor or professors.
Conference papers on the topic "Proton Learning Model"
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Franić, Nikola, Ivan Pivac, Frano Barbir, and Ivan Peko. "Voltage Prediction of Proton Exchange Membrane Fuel Cells in Various Air Stoichiometries Using a Deep Learning Model Approach." In 2024 9th International Conference on Smart and Sustainable Technologies (SpliTech). IEEE, 2024. http://dx.doi.org/10.23919/splitech61897.2024.10612556.
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Cabrales, P., V. V. Onecha, J. M. Udías, D. Izquierdo-García, and J. L. Herraiz. "PROTOTWIN-PET: Patient-Specific Deep Learning Models for 3D Dose Verification in Proton Therapy with PET." In 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD). IEEE, 2024. http://dx.doi.org/10.1109/nss/mic/rtsd57108.2024.10655000.
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Dhaked, Dheeraj Kumar, Purushottam Kumar, and Sanjib Ganguly. "Development of Data Driven Model for Proton Exchange Membrane Fuel Cell Using Machine Learning Approaches." In 2024 IEEE 3rd International Conference on Control, Instrumentation, Energy & Communication (CIEC). IEEE, 2024. http://dx.doi.org/10.1109/ciec59440.2024.10468283.
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Zhu, Shaopeng, Yifeng Wang, Qinghui Xiong, Jun Geng, and Huipeng Chen. "Fault Diagnosis of Proton Exchange Membrane Fuel Cells Based on Deep Learning and Transfer Learning." In SAE 2024 Vehicle Powertrain Diversification Technology Forum. SAE International, 2025. https://doi.org/10.4271/2025-01-7076.
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<div class="section abstract"><div class="htmlview paragraph">To accurately identify the fault types of proton exchange membrane fuel cell (PEMFC) systems under continuously varying operating currents, this study develops a comprehensive PEMFC system model and proposes a robust fault diagnosis method based on the ResNet50 convolutional neural network (CNN) and transfer learning (TL). Initially, using Matlab/Simulink, a PEMFC model is constructed based on the electrochemical reaction mechanisms and empirical formulas that characterize the operation of the fuel cell. This model primarily includes the fuel cell stack and various auxiliary systems, such as the thermal management system, air supply system, and hydrogen supply system, each crucial for optimal performance. By varying the model parameters, sensor data is generated for five distinct operating conditions. After preprocessing the data, the Gramian Angular Field (GAF) technique is utilized to convert the time series data from each sensor into fault data images, which then serve as input for the ResNet50 CNN. Ultimately, the implementation of transfer learning involves utilizing the pre-trained weights of the ResNet50 model in the training process of this model. This approach aims to improve both the convergence rate and the generalization capacity of the classification model. A comprehensive dataset for fault diagnosis has been established, comprising a total of 4,000 samples, with 800 image samples generated for each distinct operating state. The diagnostic results demonstrate that the integrated PEMFC system attains an exceptional diagnostic accuracy of 100.0% across five distinct operational scenarios: standard operating conditions, reduced air pressure at the compressor inlet, increased air temperature at the compressor inlet, heightened stack temperature, and an obstructed anode gas supply line. These results demonstrate that the proposed method not only exhibits high classification accuracy but also displays remarkable robustness in fault diagnosis applications.</div></div>
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Lv, Hang, Fengxiang Chen, and Yaowang Pei. "Thermal Management of Air-Cooled PEMFC: Machine Learning-Based Warm Starting of Active Set Methods in Model Predictive Control." In SAE 2024 Vehicle Powertrain Diversification Technology Forum. SAE International, 2025. https://doi.org/10.4271/2025-01-7071.
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<div class="section abstract"><div class="htmlview paragraph">This paper proposes a method that speeds up the Model Predictive Control (MPC) algorithm in the thermal management system of air-cooled Proton Exchange Membrane Fuel Cell (PEMFC), with an integration of machine learning and Active Set Method (ASM) of quadratic programming. Firstly, the parameters of the electrochemical model and mass transfer model of PEMFC are identified by swarm intelligence algorithms such as particle swarm algorithm and bat algorithm, and a semi-empirical model that can simulate actual dynamics is established. Based on this, a model predictive controller based on Active Set Method (ASM) is designed, and the optimization solution algorithm is optimized to solve the problem of slow and poor real-time performance. Combined with machine learning methods such as K-nearest neighbor algorithm and support vector machine, the warm start of the optimization solution algorithm is realized to improve the solution efficiency. The results show that using the warm-start MPC algorithm, the average number of iterations required for each optimization step can be reduced to 1/2~1/3 of the number of iterations required for cold start, indicating that the warm-start MPC algorithm combined with Machine Learning can effectively improve the solution efficiency and control performance of the air-cooled PEMFC thermal management system.</div></div>
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Lin, Lianshan, Hoang Tran, Majdi I. Radaideh, et al. "Material Model Parameters Optimization in Liquid Mercury Target Dynamics Simulation With Machine Learning Surrogates." In ASME 2023 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/imece2023-113604.
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Abstract A pulsed spallation target is subjected to very short (∼0.7μs) but intense loads (23.3 kJ) from repeated proton pulses, which knock away neutrons from the mercury atoms’ nuclei for a wide range application in physics, engineering, medicine, petroleum exploration, biology, chemistry, etc. The effect of this pulsed loading on the stainless-steel target module which contains the flowing mercury target material is difficult to predict not only due to its short but intense explosive-like physical reaction, but also the nonlinear material behavior of the liquid mercury in the structure. Injecting small helium bubbles in the mercury has been an efficient method of mitigating the pressure wave at high power level stage. However, prediction of the resultant loading on the target is more difficult when helium gas is intentionally injected into the mercury. A 2-phase material model that incorporates the Rayleigh-Plesset (R-P) model is expected to address this complex multi-physics dynamics problem by including the bubble dynamics in the liquid mercury. A parameter sensitivity study was firstly employed to understand their impact on the simulation strains. The investigated parameters included E, μ, γ, σ, n, VFgas, and gas cumulative volume curve control parameters a and b. Verification and validation results from sparse polynomial expansions (SPE) method and directional Gaussian smoothing (DGS) optimization show that the surrogate model had training error of ∼7% and validation error of ∼15%, indicating that machine learning methods and surrogate models can help optimize the uncertain parameters in the complex 2-phase material model. This approach is expected to fill the knowledge gap between unknown liquid-gas mixture material model and measured vessel strain responses.
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Sag, Canda Deniz, and Onur Sahin. "Predicting Jet Count in Proton-Proton Collisions using Machine Learning and Deep Learning Models." In 2023 8th International Conference on Computer Science and Engineering (UBMK). IEEE, 2023. http://dx.doi.org/10.1109/ubmk59864.2023.10286647.
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Lin, Tong, Leiming Hu, Shawn Litster, and Levent Burak Kara. "Prediction of Nitrogen Concentration in Fuel Cells Using Data-Driven Modeling." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-98477.
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Abstract This paper presents a set of data-driven methods for predicting nitrogen concentration in proton exchange membrane fuel cells (PEMFCs). The nitrogen that accumulates in the anode channel is a critical factor giving rise to significant inefficiency in fuel cells. While periodically purging the gases in the anode channel is a common strategy to combat nitrogen accumulation, such open-loop strategies also create sub-optimal purging decisions. Instead, an accurate prediction of nitrogen concentration can help devise optimal purging strategies. However, model based approaches such as CFD simulations for nitrogen prediction are often unavailable for long-stack fuel cells due to the complexity of the chemical environment, or are inherently slow preventing them from being used for real-time nitrogen prediction on deployed fuel cells. As one step toward addressing this challenge, we explore a set of data-driven techniques for learning a regression model from the input parameters to the nitrogen build-up using a model-based fuel cell simulator as an offline data generator. This allows the trained machine learning system to make fast decisions about nitrogen concentration during deployment based on other parameters that can be obtained through sensors. We describe the various methods we explore, compare the outcomes, and provide future directions in utilizing machine learning for fuel cell physics modeling in general.
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Cruz Martinez, Juan Manuel, Stefano Carrazza, and Roy Stegeman. "Studying the parton content of the proton with deep learning models." In Artificial Intelligence for Science, Industry and Society. Sissa Medialab, 2020. http://dx.doi.org/10.22323/1.372.0008.
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Orehova, Ekaterina, Sergey Govyazin, and Iurii Stroganov. "LEARNING DATABASE QUERIES WITH PROLOG." In eLSE 2019. Carol I National Defence University Publishing House, 2019. http://dx.doi.org/10.12753/2066-026x-19-107.
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A database is a collection of some knowledge. Knowledge can be presented as some semantic network. Entity-relationship model is one of representations of the semantic network. When using the entity-relationship model, it is possible to distinguish entities and relations between these entities. The entity-relationship model can be then converted into a database schema. The user interacts with the database by writing requests and receiving answers containing the requested information. There are several ways to write queries to databases with different convenience of creating and speed of execution. The article reviews three different approaches to writing queries: SQL query, Prolog query and Object-Relational Mapping (ORM) query. Each of the approaches has its own advantages and disadvantages. You need to know the basics of relational algebra to write queries with the SQL language, while ORM libraries and the Prolog don't require any additional knowledge. Writing queries with the Prolog language is similar to writing text in natural language, which makes these queries understandable for people who have never worked with databases. There was made the comparison of the plainness of the approaches when explaining them to listeners who are studying databases. The listeners participated in the compilation were divided into groups according to their specialties. The following groups took part in the study: first-year students of an economic and managerial specialty, engineering students and students with a specialty software engineering. The purpose of this comparison is to determine the method of compiling database queries, which is most suitable for teaching students of various specialties.
Reports on the topic "Proton Learning Model"
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Arnold, Zachary, Joanne Boisson, Lorenzo Bongiovanni, Daniel Chou, Carrie Peelman, and Ilya Rahkovsky. Using Machine Learning to Fill Gaps in Chinese AI Market Data. Center for Security and Emerging Technology, 2021. http://dx.doi.org/10.51593/20200064.
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In this proof-of-concept project, CSET and Amplyfi Ltd. used machine learning models and Chinese-language web data to identify Chinese companies active in artificial intelligence. Most of these companies were not labeled or described as AI-related in two high-quality commercial datasets. The authors' findings show that using structured data alone—even from the best providers—will yield an incomplete picture of the Chinese AI landscape.
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Gupta, Sweta, and Mohamed Abouaziza. Closing England's Maths Attainment Gap through One-to-One Tutoring – Global Solutions. Institute of Development Studies (IDS), 2021. http://dx.doi.org/10.19088/ids.2021.050.
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In the aftermath of COVID-related school closures in the UK, students lost two months of learning, but the loss among the 1.7 million disadvantaged students has been much larger at seven months. This disadvantaged gap is almost entirely driven by maths attainment. One-to-one tutoring is proven to be effective at helping students catch up, but private tutoring is most likely to be taken up by children from affluent households, further widening the disadvantaged gap in learning. This report discusses the feasibility of an innovative tutoring delivery model that uses the global graduate market to deliver tutoring at a scale that can solve this problem and a price that schools can afford. While the report discusses the overall opportunity that the emerging market economies of South- and South-East Asia provide, it also presents the Third Space Learning model in Sri Lanka as a case study to investigate the practicalities of the global online tutoring model.
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Douglas, Thomas, and Caiyun Zhang. Machine learning analyses of remote sensing measurements establish strong relationships between vegetation and snow depth in the boreal forest of Interior Alaska. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41222.
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The seasonal snowpack plays a critical role in Arctic and boreal hydrologic and ecologic processes. Though snow depth can be different from one season to another there are repeated relationships between ecotype and snowpack depth. Alterations to the seasonal snowpack, which plays a critical role in regulating wintertime soil thermal conditions, have major ramifications for near-surface permafrost. Therefore, relationships between vegetation and snowpack depth are critical for identifying how present and projected future changes in winter season processes or land cover will affect permafrost. Vegetation and snow cover areal extent can be assessed rapidly over large spatial scales with remote sensing methods, however, measuring snow depth remotely has proven difficult. This makes snow depth–vegetation relationships a potential means of assessing snowpack characteristics. In this study, we combined airborne hyperspectral and LiDAR data with machine learning methods to characterize relationships between ecotype and the end of winter snowpack depth. Our results show hyperspectral measurements account for two thirds or more of the variance in the relationship between ecotype and snow depth. An ensemble analysis of model outputs using hyperspectral and LiDAR measurements yields the strongest relationships between ecotype and snow depth. Our results can be applied across the boreal biome to model the coupling effects between vegetation and snowpack depth.
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Wandeler, Christian, and Steve Hart. The Central Valley Transportation Challenge. Mineta Transportation Institute, 2022. http://dx.doi.org/10.31979/mti.2022.2029.
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The Central Valley Transportation Challenge provides underserved minority students, who are primarily from rural areas, with high quality transportation-related educational experiences so that they learn about transportation-related topics and opportunities in transportation careers. The CVTC is a project-based learning program that brings university faculty and students to K–12 classrooms in rural areas. The project operated with three main objectives: (1) support K–12 teachers’ understanding and implementation of the CVTC programs; (2) connect K–12 students with university faculty and students, and transportation professionals through the CVTC program; and (3) develop an online hub with transportation-related lesson plans and sequences. The results of this study are reported as five case studies and a description of the online hub. The case studies illustrate how different pedagogical approaches and uses of technology were implemented and how the project connections between the schools, community members and professionals from transportation-related fields were developed. In addition, to support the sustainability of transportation-related learning across subsequent years, the research team created an online transportation resource repository. This hub was populated with lessons and units developed by pedagogical and content experts. The lessons cover the grades K–12 and range from brief lessons to very engaging and holistic two-week-long lesson sequences. The CVTC has proven to be a highly flexible and adaptive model due to the use of technology and the teachers’ experience and pedagogical expertise. The timing of the program during the COVID-19 pandemic also provided the students that were learning from home with an engaging learning experience and some relief for teachers who were already dealing with a lot of adjustments. In that sense, the program reached traditionally underserved students, but did so in a critical time where these students faced even more obstacles.
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Perdigão, Rui A. P. Information physics and quantum space technologies for natural hazard sensing, modelling and prediction. Meteoceanics, 2021. http://dx.doi.org/10.46337/210930.
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Disruptive socio-natural transformations and climatic change, where system invariants and symmetries break down, defy the traditional complexity paradigms such as machine learning and artificial intelligence. In order to overcome this, we introduced non-ergodic Information Physics, bringing physical meaning to inferential metrics, and a coevolving flexibility to the metrics of information transfer, resulting in new methods for causal discovery and attribution. With this in hand, we develop novel dynamic models and analysis algorithms natively built for quantum information technological platforms, expediting complex system computations and rigour. Moreover, we introduce novel quantum sensing technologies in our Meteoceanics satellite constellation, providing unprecedented spatiotemporal coverage, resolution and lead, whilst using exclusively sustainable materials and processes across the value chain. Our technologies bring out novel information physical fingerprints of extreme events, with recently proven records in capturing early warning signs for extreme hydro-meteorologic events and seismic events, and do so with unprecedented quantum-grade resolution, robustness, security, speed and fidelity in sensing, processing and communication. Our advances, from Earth to Space, further provide crucial predictive edge and added value to early warning systems of natural hazards and long-term predictions supporting climatic security and action.
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Harris, L. B., P. Adiban, and E. Gloaguen. The role of enigmatic deep crustal and upper mantle structures on Au and magmatic Ni-Cu-PGE-Cr mineralization in the Superior Province. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/328984.
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Aeromagnetic and ground gravity data for the Canadian Superior Province, filtered to extract long wavelength components and converted to pseudo-gravity, highlight deep, N-S trending regional-scale, rectilinear faults and margins to discrete, competent mafic or felsic granulite blocks (i.e. at high angles to most regional mapped structures and sub-province boundaries) with little to no surface expression that are spatially associated with lode ('orogenic') Au and Ni-Cu-PGE-Cr occurrences. Statistical and machine learning analysis of the Red Lake-Stormy Lake region in the W Superior Province confirms visual inspection for a greater correlation between Au deposits and these deep N-S structures than with mapped surface to upper crustal, generally E-W trending, faults and shear zones. Porphyry Au, Ni, Mo and U-Th showings are also located above these deep transverse faults. Several well defined concentric circular to elliptical structures identified in the Oxford Stull and Island Lake domains along the S boundary of the N Superior proto-craton, intersected by N- to NNW striking extensional fractures and/or faults that transect the W Superior Province, again with little to no direct surface or upper crustal expression, are spatially associated with magmatic Ni-Cu-PGE-Cr and related mineralization and Au occurrences. The McFaulds Lake greenstone belt, aka. 'Ring of Fire', constitutes only a small, crescent-shaped belt within one of these concentric features above which 2736-2733 Ma mafic-ultramafic intrusions bodies were intruded. The Big Trout Lake igneous complex that hosts Cr-Pt-Pd-Rh mineralization west of the Ring of Fire lies within a smaller concentrically ringed feature at depth and, near the Ontario-Manitoba border, the Lingman Lake Au deposit, numerous Au occurrences and minor Ni showings, are similarly located on concentric structures. Preliminary magnetotelluric (MT) interpretations suggest that these concentric structures appear to also have an expression in the subcontinental lithospheric mantle (SCLM) and that lithospheric mantle resistivity features trend N-S as well as E-W. With diameters between ca. 90 km to 185 km, elliptical structures are similar in size and internal geometry to coronae on Venus which geomorphological, radar, and gravity interpretations suggest formed above mantle upwellings. Emplacement of mafic-ultramafic bodies hosting Ni-Cr-PGE mineralization along these ringlike structures at their intersection with coeval deep transverse, ca. N-S faults (viz. phi structures), along with their location along the margin to the N Superior proto-craton, are consistent with secondary mantle upwellings portrayed in numerical models of a mantle plume beneath a craton with a deep lithospheric keel within a regional N-S compressional regime. Early, regional ca. N-S faults in the W Superior were reactivated as dilatational antithetic (secondary Riedel/R') sinistral shears during dextral transpression and as extensional fractures and/or normal faults during N-S shortening. The Kapuskasing structural zone or uplift likely represents Proterozoic reactivation of a similar deep transverse structure. Preservation of discrete faults in the deep crust beneath zones of distributed Neoarchean dextral transcurrent to transpressional shear zones in the present-day upper crust suggests a 'millefeuille' lithospheric strength profile, with competent SCLM, mid- to deep, and upper crustal layers. Mechanically strong deep crustal felsic and mafic granulite layers are attributed to dehydration and melt extraction. Intra-crustal decoupling along a ductile décollement in the W Superior led to the preservation of early-formed deep structures that acted as conduits for magma transport into the overlying crust and focussed hydrothermal fluid flow during regional deformation. Increase in the thickness of semi-brittle layers in the lower crust during regional metamorphism would result in an increase in fracturing and faulting in the lower crust, facilitating hydrothermal and carbonic fluid flow in pathways linking SCLM to the upper crust, a factor explaining the late timing for most orogenic Au. Results provide an important new dataset for regional prospectively mapping, especially with machine learning, and exploration targeting for Au and Ni-Cr-Cu-PGE mineralization. Results also furnish evidence for parautochthonous development of the S Superior Province during plume-related rifting and cannot be explained by conventional subduction and arc-accretion models.
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Beshouri, Greg. PR-309-14212-WEB Field Demonstration of Fully Integrated NSCR System. Pipeline Research Council International, Inc. (PRCI), 2019. http://dx.doi.org/10.55274/r0011623.
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Wednesday, October 9, 2019 3:30 pm. ET PRESENTER: Bob Goffin, Advanced Engine Technologies HOST: Chris Nowak, Kinder Morgan MODERATOR: Gary Choquette, PRCI CLICK THE DOWNLOAD/BUY BUTTON TO ACCESS THE WEBINAR REGISTRATION LINK While superficially a "simple and proven" technology, non-selective catalytic reduction (NSCR) control is in fact extremely complex, far more complex than the control of lean burn engines. Using a systems approach, PRCI research partners defined the most common failure modes for each of the components of the NSCR system. Both regulators and operators often make simplistic assumptions regarding the reliability and robustness of NSCR control. Real world experience has shown those assumptions to be unfounded. Legacy NSCR systems can go "out of compliance" resulting in gross emissions deviations while remaining "in control." This webinar will review the reasons for those deviations and then postulates a system design capable of remaining both "in control" and "in compliance." This system was then designed, developed, installed and tested. The results confirmed the theoretical analysis resulting in satisfactory system performance. The result offers regulators and operators guidelines on procuring and/or developing NSCR systems that will satisfy regulatory expectations. Learning outcomes/Benefits of attending include: - Explains for legacy rich burn engines can be upgraded with NSCR and advanced controls - Explores the instrumentation required - Looks at control algorithms involved Who should attend: - Pipeline operators - Reliability engineers and technicians - Emissions compliance specialists Recommended pre-reading: PR-309-14212-R01 Field Demonstration of Fully Integrated NSCR System Not able to attend? Register anyway to automatically receive a link to the webinar recording to view on-demand at your convenience. Attendance is limited to the first 500 registrants to join the webinar. All remaining registrants will receive a link to view the webinar recording. After registering, you will receive a confirmation email containing information about joining the webinar.
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Elmann, Anat, Orly Lazarov, Joel Kashman, and Rivka Ofir. therapeutic potential of a desert plant and its active compounds for Alzheimer's Disease. United States Department of Agriculture, 2015. http://dx.doi.org/10.32747/2015.7597913.bard.
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We chose to focus our investigations on the effect of the active forms, TTF and AcA, rather than the whole (crude) extract. 1. To establish cultivation program designed to develop lead cultivar/s (which will be selected from the different Af accessions) with the highest yield of the active compounds TTF and/or achillolide A (AcA). These cultivar/s will be the source for the purification of large amounts of the active compounds when needed in the future for functional foods/drug development. This task was completed. 2. To determine the effect of the Af extract, TTF and AcA on neuronal vulnerability to oxidative stress in cultured neurons expressing FAD-linked mutants.Compounds were tested in N2a neuroblastoma cell line. In addition, we have tested the effects of TTF and AcA on signaling events promoted by H₂O₂ in astrocytes and by β-amyloid in neuronal N2a cells. 3. To determine the effect of the Af extract, TTF and AcA on neuropathology (amyloidosis and tau phosphorylation) in cultured neurons expressing FAD-linked mutants. 4. To determine the effect of A¦ extract, AcA and TTF on FAD-linked neuropathology (amyloidosis, tau phosphorylation and inflammation) in transgenic mice. 5. To examine whether A¦ extract, TTF and AcA can reverse behavioral deficits in APPswe/PS1DE9 mice, and affect learning and memory and cognitive performance in these FAD-linked transgenic mice. Background to the topic.Neuroinflammation, oxidative stress, glutamate toxicity and amyloid beta (Ab) toxicity are involved in the pathogenesis of Alzheimer's diseases. We have previously purified from Achilleafragrantissimatwo active compounds: a protective flavonoid named 3,5,4’-trihydroxy-6,7,3’-trimethoxyflavone (TTF, Fl-72/2) and an anti-inflammatory sesquiterpenelactone named achillolide A (AcA). Major conclusions, solutions, achievements. In this study we could show that TTF and AcA protected cultured astrocytes from H₂O₂ –induced cell death via interference with cell signaling events. TTF inhibited SAPK/JNK, ERK1/2, MEK1 and CREBphosphorylation, while AcA inhibited only ERK1/2 and MEK1 phosphorylation. In addition to its protective activities, TTF had also anti-inflammatory activities, and inhibited the LPS-elicited secretion of the proinflammatorycytokinesInterleukin 6 (IL-6) and IL-1b from cultured microglial cells. Moreover, TTF and AcA protected neuronal cells from glutamate and Abcytotoxicity by reducing the glutamate and amyloid beta induced levels of intracellular reactive oxygen species (ROS) and via interference with cell signaling events induced by Ab. These compounds also reduced amyloid precursor protein net processing in vitro and in vivo in a mouse model for Alzheimer’s disease and improvedperformance in the novel object recognition learning and memory task. Conclusion: TTF and AcA are potential candidates to be developed as drugs or food additives to prevent, postpone or ameliorate Alzheimer’s disease. Implications, both scientific and agricultural.The synthesis ofAcA and TTF is very complicated. Thus, the plant itself will be the source for the isolation of these compounds or their precursors for synthesis. Therefore, Achilleafragrantissima could be developed into a new crop with industrial potential for the Arava-Negev area in Israel, and will generate more working places in this region.
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Landau, Sergei Yan, John W. Walker, Avi Perevolotsky, Eugene D. Ungar, Butch Taylor, and Daniel Waldron. Goats for maximal efficacy of brush control. United States Department of Agriculture, 2008. http://dx.doi.org/10.32747/2008.7587731.bard.
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Background. Brush encroachment constitutes a serious problem in both Texas and Israel. We addressed the issue of efficacy of livestock herbivory - in the form of goat browsing - to change the ecological balance to the detriment of the shrub vegetation. Shrub consumption by goats is kept low by plant chemical defenses such as tannins and terpenes. Scientists at TAES and ARO have developed an innovative, cost-effective methodology using fecal Near Infrared Spectrometry to elucidate the dietary percentage of targeted, browse species (terpene-richredberry and blueberry juniper in the US, and tannin-rich Pistacialentiscus in Israel) for a large number of animals. The original research objectives of this project were: 1. to clarify the relative preference of goat breeds and the individual variation of goats within breeds, when consuming targeted brush species; 2. to assess the heritability of browse intake and validate the concept of breeding goat lines that exhibit high preference for chemically defended brush, using juniper as a model; 3. to clarify the relative contributions of genetics and learning on the preference for target species; 4. to identify mechanisms that are associated with greater intake of brush from the two target species; 5. to establish when the target species are the most vulnerable to grazing. (Issue no.5 was addressed only partly.) Major conclusions, solutions, achievements: Both the Israel and US scientists put significant efforts into improving and validating the technique of Fecal NIRS for predicting the botanical composition of goat diets. Israeli scientists validated the use of observational data for calibrating fecal NIRS, while US scientists established that calibrations could be used across animals differing in breed and age but that caution should be used in making comparisons between different sexes. These findings are important because the ability to select goat breeds or individuals within a breed for maximal efficiency of brush control is dependent upon accurate measurement of the botanical composition of the diet. In Israel it was found that Damascus goats consume diets more than twice richer in P. lentiscus than Mamber or Boer goats. In the US no differences were found between Angora and Boer cross goats but significant differences were found between individuals within breeds in juniper dietary percentage. In both countries, intervention strategies were found that further increased the consumption of the chemically defended plant. In Israel feeding polyethylene glycol (PEG, MW 4,000) that forms high-affinity complexes with tannins increased P. lentiscus dietary percentage an average of 7 percentage units. In the US feeding a protein supplement, which enhances rates of P450-catalyzed oxidations and therefore the rate of oxidation of monoterpenes, increased juniper consumption 5 percentage units. However, the effects of these interventions were not as large as breed or individual animal effects. Also, in a wide array of competitive tannin-binding assays in Israel with trypsin, salivary proteins did not bind more tannic acid or quebracho tannin than non-specific bovine serum albumin, parotid saliva did not bind more tannins than mixed saliva, no response of tannin-binding was found to levels of dietary tannins, and the breed effect was of minor importance, if any. These fundings strongly suggest that salivary proteins are not the first line of defense from tannin astringency in goats. In the US relatively low values for heritability and repeatability for juniper consumption were found (13% and 30%, respectively), possibly resulting from sampling error or non-genetic transfer of foraging behavior, i.e., social learning. Both alternatives seem to be true as significant variation between sequential observations were noted on the same animal and cross fostering studies conducted in Israel demonstrated that kids raised by Mamber goats showed lower propensity to consume P. lentiscus than counterparts raised by Damascus goats.
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Evidence-informed planning and action in Central Asia: Learnings from the Tajikistan Adolescent Wellbeing and Health Pilot Project. Population Council, 2021. http://dx.doi.org/10.31899/sbsr2021.1046.
Full textAbstract:
To address adolescent health and wellbeing in Tajikistan, the Aga Khan Foundation (AKF) and Population Council used a hybrid human-centered (HCD) and evidence-based program design to engage adolescent girls, boys, and caregivers in a guided process of defining key issues and program areas. The design informed the development of a first-of-its-kind program model for AKF and in Tajikistan: coordinated community-based groups for adolescent girls and boys, caregivers' groups, and an institutional stakeholder community of practice in Tajikistan. Design and implementation experiences established "proof of concept" as a basis to expand the approach across the country and region. The pilot generated valuable lessons and resources to inform and support both expansion and new programming.