Investigate the Structural Response of Ultra High Performance Concrete Column under the High Explosion

Most of the structures that are damaged by an explosion are not initially designed to resist this kind of load. In the overall structure of any building, columns play an important role to prevent the collapse of frame structure under blast impact. Hence, the main concept in the blast resistance design of the structure is to improve the blast load capacity of the column. In this study, dynamic analysis and numerical model of Ultra High Performance Concrete (UHPC) column under high explosive load, is presented. Based on the Johnson Holmquist 2 damage model and the subroutine in the ABAQUS platform, a total of twenty UHPC model of the column were calculated. The objective of the article is to investigate the structural response of the UHPC column and locate the most vulnerable scenarios to propose necessary recommendations for the UHPC column in the blast loading resistance design. The input parameters, including the effect of various shapes of cross-section, scaled distance, steel reinforcement ratio, and cross-section area, are analyzed to clarify the dynamic behavior of the UHPC column subjected to blast loading. Details of the numerical data, and the discussion on the important obtained results, are also provided in this paper

Benchmarking Jetson Edge Devices with an End-to-end Video-based Anomaly Detection System

Innovative enhancement in embedded system platforms, specifically hardware accelerations, significantly influence the application of deep learning in real-world scenarios. These innovations translate human labor efforts into automated intelligent systems employed in various areas such as autonomous driving, robotics, Internet-of-Things (IoT), and numerous other impactful applications. NVIDIA's Jetson platform is one of the pioneers in offering optimal performance regarding energy efficiency and throughput in the execution of deep learning algorithms. Previously, most benchmarking analysis was based on 2D images with a single deep learning model for each comparison result. In this paper, we implement an end-to-end video-based crime-scene anomaly detection system inputting from surveillance videos and the system is deployed and completely operates on multiple Jetson edge devices (Nano, AGX Xavier, Orin Nano). The comparison analysis includes the integration of Torch-TensorRT as a software developer kit from NVIDIA for the model performance optimisation. The system is built based on the PySlowfast open-source project from Facebook as the coding template. The end-to-end system process comprises the videos from camera, data preprocessing pipeline, feature extractor and the anomaly detection. We provide the experience of an AI-based system deployment on various Jetson Edge devices with Docker technology. Regarding anomaly detectors, a weakly supervised video-based deep learning model called Robust Temporal Feature Magnitude Learning (RTFM) is applied in the system. The approach system reaches 47.56 frames per second (FPS) inference speed on a Jetson edge device with only 3.11 GB RAM usage total. We also discover the promising Jetson device that the AI system achieves 15% better performance than the previous version of Jetson devices while consuming 50% less energy power.Comment: 18 pages, 7 figures, 5 table

Neural Sinkhorn Topic Model

In this paper, we present a new topic modelling approach via the theory of optimal transport (OT). Specifically, we present a document with two distributions: a distribution over the words (doc-word distribution) and a distribution over the topics (doc-topic distribution). For one document, the doc-word distribution is the observed, sparse, low-level representation of the content, while the doc-topic distribution is the latent, dense, high-level one of the same content. Learning a topic model can then be viewed as a process of minimising the transportation of the semantic information from one distribution to the other. This new viewpoint leads to a novel OT-based topic modelling framework, which enjoys appealing simplicity, effectiveness, and efficiency. Extensive experiments show that our framework significantly outperforms several state-of-the-art models in terms of both topic quality and document representations

MINIMIZING HEAVY METAL IN CRAFT-SETTLEMENT WASTEWATER BY SULFATE-REDUCING BACTERIA-DESULFOVIBRIO DESSULFURICANS

Joint Research on Environmental Science and Technology for the Eart

Reduced Need of Infiltration Anesthesia Accompanied With Other Positive Outcomes in Diode Laser Application for Frenectomy in Children

Introduction: The abnormal maxillary labial frenum is common in children during the primary or mixed dentition stage. A conventional surgery for this abnormality usually requires infiltration anesthesia which leads to fear in children and consequent noncooperation during the surgery. The aim of present study was to evaluate the reduction in the need of infiltration anesthesia, intraoperative bleeding control and postoperative pain and wound healing in children when using the diode laser for abnormal labial frenum in the maxilla.Methods: The present study was carried out among 30 children attending the Hanoi Medical University, Vietnam. A Diode Laser with 810 nm wavelength and power of 0.8 W was used for frenectomy.Results: The proportion of procedures without any need of infiltration anesthesia was 70%, while 93.34% of children demonstrated positive and very positive behavior. Proportion of indolence on the first day after surgery was 83.3%. While 83.3% of children did not take any analgesics, not a single child complained of any pain 3 days after surgery.Conclusion: Our results indicated that the use of diode laser showed several benefits in maxillary labial frenectomy in children. These included reducing the need of infiltration anesthesia, increasing the children’s cooperation as well as decreasing the postoperative pain

Programmation DC et DCA pour l'optimisation non convexe/optimisation globale en variables mixtes entières (Codes et Applications)

Basés sur les outils théoriques et algorithmiques de la programmation DC et DCA, les travaux de recherche dans cette thèse portent sur les approches locales et globales pour l'optimisation non convexe et l'optimisation globale en variables mixtes entières. La thèse comporte 5 chapitres. Le premier chapitre présente les fondements de la programmation DC et DCA, et techniques de Séparation et Evaluation (B&B) (utilisant la technique de relaxation DC pour le calcul des bornes inférieures de la valeur optimale) pour l'optimisation globale. Y figure aussi des résultats concernant la pénalisation exacte pour la programmation en variables mixtes entières. Le deuxième chapitre est consacré au développement d'une méthode DCA pour la résolution d'une classe NP-difficile des programmes non convexes non linéaires en variables mixtes entières. Ces problèmes d'optimisation non convexe sont tout d'abord reformulées comme des programmes DC via les techniques de pénalisation en programmation DC de manière que les programmes DC résultants soient efficacement résolus par DCA et B&B bien adaptés. Comme première application en optimisation financière, nous avons modélisé le problème de gestion de portefeuille sous le coût de transaction concave et appliqué DCA et B&B à sa résolution. Dans le chapitre suivant nous étudions la modélisation du problème de minimisation du coût de transaction non convexe discontinu en gestion de portefeuille sous deux formes : la première est un programme DC obtenu en approximant la fonction objectif du problème original par une fonction DC polyèdrale et la deuxième est un programme DC mixte 0-1 équivalent. Et nous présentons DCA, B&B, et l'algorithme combiné DCA-B&B pour leur résolution. Le chapitre 4 étudie la résolution exacte du problème multi-objectif en variables mixtes binaires et présente deux applications concrètes de la méthode proposée. Nous nous intéressons dans le dernier chapitre à ces deux problématiques challenging : le problème de moindres carrés linéaires en variables entières bornées et celui de factorisation en matrices non négatives (Nonnegative Matrix Factorization (NMF)). La méthode NMF est particulièrement importante de par ses nombreuses et diverses applications tandis que les applications importantes du premier se trouvent en télécommunication. Les simulations numériques montrent la robustesse, rapidité (donc scalabilité), performance et la globalité de DCA par rapport aux méthodes existantes.Based on theoretical and algorithmic tools of DC programming and DCA, the research in this thesis focus on the local and global approaches for non convex optimization and global mixed integer optimization. The thesis consists of 5 chapters. The first chapter presents fundamentals of DC programming and DCA, and techniques of Branch and Bound method (B&B) for global optimization (using the DC relaxation technique for calculating lower bounds of the optimal value). It shall include results concerning the exact penalty technique in mixed integer programming. The second chapter is devoted of a DCA method for solving a class of NP-hard nonconvex nonlinear mixed integer programs. These nonconvex problems are firstly reformulated as DC programs via penalty techniques in DC programming so that the resulting DC programs are effectively solved by DCA and B&B well adapted. As a first application in financial optimization, we modeled the problem pf portfolio selection under concave transaction costs and applied DCA and B&B to its solutions. In the next chapter we study the modeling of the problem of minimization of nonconvex discontinuous transaction costs in portfolio selection in two forms: the first is a DC program obtained by approximating the objective function of the original problem by a DC polyhedral function and the second is an equivalent mixed 0-1 DC program. And we present DCA, B&B algorithm, and a combined DCA-B&B algorithm for their solutions. Chapter 4 studied the exact solution for the multi-objective mixed zero-one linear programming problem and presents two practical applications of proposed method. We are interested int the last chapter two challenging problems: the linear integer least squares problem and the Nonnegative Mattrix Factorization problem (NMF). The NMF method is particularly important because of its many various applications of the first are in telecommunications. The numerical simulations show the robustness, speed (thus scalability), performance, and the globality of DCA in comparison to existent methods.ROUEN-INSA Madrillet (765752301) / SudocSudocFranceF