Miracle-dz/UniFAR-FTUs huggingface dataset:
Systèmes à commutation : recherche du mode actif, identification de la loi de commutation

Les systemes a commutation representent une classe particuliere de systemes hybrides. Ils sont decrits par plusieurs modeles de fonctionnement et chaque modele, definissant un mode du systeme, est actif sous certaines conditions operatoires particulieres. Lorsque la loi de commutation regissant le passage d'un modele de fonctionnement a l'autre est parfaitement connue, il est aise de manipuler de tels systemes car le mode actif peut etre connu a chaque instant. Par contre, dans la situation ou aucune information n'est disponible sur l'evolution de la loi de commutation, il est plus ardu de proceder au diagnostic ou encore de synthetiser une loi de commande sur ces systemes. Cet article traite de la reconnaissance du mode actif sur la base d'observations de l'entree et de la sortie du systeme. L'identification des parametres de la loi de commutation lorsque celle-ci est decrite par un modele de regression, est egalement abordee.
Targeting Viral Ion Channels: A Promising Strategy to Curb SARS-CoV-2

SARS-CoV-2 is the etiological agent COVID-19, one of the most impactful health crises afflicting humanity in recent decades. While research advances have yielded several treatment and prevention options, the pandemic is slow to abate, necessitating an expansion of our treatment arsenal. As a member of the coronaviridae, SARS-CoV-2 contains several ion channels, of which E and 3a are the best characterized. Since ion channels as a family are excellent drug targets, we sought to inhibit both viroporins as a means to curb infectivity. In a previous targeted study, we identified several blockers to each channel from an extensive drug repurposing library. Herein, we examined the ability of said compounds on the whole virus in cellulo. Gratifyingly, many of the blockers exhibited antiviral activity in a stringent assay examining protection from viral-driven death. In particular, darapladib and flumatinib, both 3a blockers, displayed potent antiviral activity. Furthermore, appreciable synergism between flumatinib and several E blockers was identified in a concentration regime in which the compounds are present in human plasma following oral administration. Taken together, targeting ion channels represents a promising approach to both augment and complement our antiviral arsenal against COVID-19.
Examining the Duplication of Flight Test Data Centers

Aircraft flight test data processing began with on site data analysis from the very first aircraft design. This method of analyzing flight data continued from the early 1900’s to the present day. Today each new aircraft program builds a separate data center for post flight processing (PFP) to include operations, system administration, and management. Flight Test Engineers (FTE) are relocated from geographical areas to ramp up the manpower needed to analyze the PFP data center products and when the first phase of aircraft design and development is completed the FTE headcount is reduced with the FTE either relocated to another program or the FTE finds other employment. This paper is a condensed form of the research conducted by the author on how the methodology of continuing to build PFP data centers cost the aircraft company millions of dollars in development and millions of dollars on relocation plus relocation stress effects on FTE which can hinder productivity. This method of PFP data center development can be avoided by the consolidation of PFP data centers using present technology.
Associations between Sociodemographic Factors, Lifestyle Behaviors, Pregnancy-Related Determinants, and Mediterranean Diet Adherence among Pregnant Women: The GESTAFIT Project

We examined sociodemographic factors, lifestyle behaviors, and pregnancy-related determinants associated with adherence to the Mediterranean diet (MD) during pregnancy. A total of 152 Caucasian pregnant women were included in this cross-sectional study. Dietary habits and MD adherence were assessed with a food frequency questionnaire. Physical activity (PA) levels and physical fitness (PF) components (cardiorespiratory fitness, relative muscle strength, and flexibility) were objectively measured. A clustered overall PF index was calculated. Participants with a high MD adherence were older, had a lower body mass index (BMI), spent more time in moderate–vigorous PA, had a greater overall PF, cardiorespiratory fitness, and relative muscle strength compared to participants with low MD adherence (all, p < 0.05). When we explored factors associated with improved MD adherence with logistic regression analysis, we found that the following factors: lower pre-pregnancy BMI (OR = 2.337; p = 0.026), meeting PA recommendations (OR = 2.377; p = 0.045), higher relative muscle strength (OR = 2.265; p = 0.016), and higher overall PF (OR = 5.202; p = 0.004) increased the chances to adhere to the MD. Older age, lower BMI, greater PF, and meeting PA recommendations were associated with higher MD adherence. These factors should be considered for a better design of educational programs and guidelines focused on improving materno–fetal health status during pregnancy.
Challenges Faced by Female Commercial Sex Workers in Puducherry, India: A Qualitative Inquiry

Sex work is considered one of the oldest professions in the world. In India, selling sex in private is legal, while all other activities related to sex work, such as pimping, brothel-keeping, are considered to be illegal. Despite their vulnerability to HIV and other sexually transmitted infections, the research has been less focused on the other challenges faced by sex workers. This article aims to understand the major challenges faced by female commercial sex workers (CSWs) through a qualitative study covering 15 CSWs who work in Puducherry Union Territory in South India. The data were collected by using an in-depth interview guide, and the case study method. Thematic analysis was employed to analyse the data. The article discusses in detail the challenges CSWs face in terms of economical, psychological and social spheres of their lives.
Self-Supervised Learning for Heterogeneous Audiovisual Scene Analysis

Due to the difficulty of annotating large amounts of training data, directly learning the association of sound and its makers in natural videos is a challenging task for machines. In this paper, we present a novel audiovisual model that introduces a soft-clustering module as the audio and visual content detector, and regards the pervasive property of audiovisual concurrency as the latent supervision for inferring the correlation among detected contents. Furthermore, we discover for the first time that the complexity of data has an impact on the training efficiency and subsequent performance of audiovisual model, i.e., more complex data brings more obstacles to the model training, and degrades the performance of downstream audiovisual tasks. To address the issue of audiovisual learning, we propose a novel heterogeneous audiovisual scene analysis module that trains the model from simple to complex scene. We show that such ordered learning procedure rewards the model the merits of easy training and fast convergence. Meanwhile, our audiovisual model can also provide effective unimodal representation and cross-modal alignment performance. We further deploy the well-trained model into practical audiovisual sound localization and separation tasks. We show that our localization model significantly outperforms existing methods, based on which we show comparable performance in sound separation task by comparison to several related SOTA audiovisual learning methods without referring external visualsupervision.
Nitrogen Speciation in Silicate Melts at Mantle Conditions From Ab Initio Simulations

Nitrogen (N) is a major ingredient of the atmosphere, but a trace component in the silicate Earth. Its initial inventory in these reservoirs during Earth's early differentiation requires knowledge of N speciation in magmas, for example, whether it outgasses as N2 or is sequestered in silicate melts as N3−, which remains largely unconstrained over the entire mantle regime. Here we examine N species in anhydrous and hydrous pyrolitic melts at varying P‐T‐redox conditions by ab‐initio calculations, and find N‐N bonding under oxidizing conditions from ambient to lower mantle pressures. Under reducing conditions, N interacts with the silicate network or forms N‐H bonds, depending on the availability of hydrogen. Redox control of N speciation is demonstrated valid over a P‐T space encompassing probable magma ocean depths. Finally, if the Earth accreted from increasingly oxidized materials toward the end of its accretion, an N‐enriched secondary atmosphere might be produced and persist until later impacts.
Nitrogen cycle impacts on CO2 fertilisation and climate forcing of land carbon stores

Anthropogenic fossil fuel burning increases atmospheric carbon dioxide (CO2) concentration, which is adjusting the climate system. The direct impact of rising CO2 levels and climate feedback alters the terrestrial carbon stores. Land stores are presently increasing, offsetting a substantial fraction of CO2 emissions. Less understood is how this human-induced carbon cycle perturbation interacts with other terrestrial biogeochemical cycles. These connections require quantification, as they may eventually suppress land fertilisation, and so fewer emissions are allowed to follow any prescribed future global warming pathway. Using the new Joint UK Land Environment Simulator-CN large-scale land model, which contributed to Coupled Model Intercomparison Project Phase 6 as the land component of the UK Earth System Model v1 climate model, we focus on how the introduction of the simulated terrestrial nitrogen (N) cycle modulates the expected evolution of vegetation and soil carbon pools. We find that the N-cycle suppresses, by approximately one-third, any future gains by the global soil pool when compared to calculations without that cycle. There is also a decrease in the vegetation carbon gain, although this is much smaller. Factorial simulations illustrate that N suppression tracks direct CO2 rise rather than climate change. The finding that this CO2-related effect predominantly influences soil carbon rather than vegetation carbon, we explain by different balances between changing carbon uptake levels and residence times. Finally, we discuss how this new generation of land models may gain further from emerging point knowledge held by the detailed ecological modelling community.
Design, Development and Characterization of Nanoemulsion developed by High Pressure Homogenization (HPH) method Containing Antifungal Drug

Nanoemulsion is one the most attractive drug delivery for researchers to treat fungal diseases and getting interested in increasing the solubility of low soluble drugs. The objective of this research was to develop a topical nanoemulsion formulation of Posaconazole drug with the intention to enhance its aqueous solubility and local action. By employing High Pressure Homogenization (HPH) method, oil in water nanoemulsion was formulated with 0.2% cinnamon oil as lipid phase, 0.4% tween-80 and poloxamer-188 as surfactants, 0.2% transcutol as a co-surfactant. The physical characteristics of formulations were found to be stable after thermodynamic stability testing. In-vitro diffusion study for optimized nanoemulsion was performed using a dialysis bag membrane and cumulative % drug release was determined. Viscosity and percent drug content was observed to be 0.0593 cps and 90.21 ± 0.23% respectively. The optimized nanoemulsion formulation (F8) was evaluated to be transparent and thermodynamically stable, with -9.46 zeta potential, 78.79 nm particle size, 0.315 polydispersity index. The optimized Nanoemulsion was stable for 3 month in three different temperature conditions. The result from the release study was indicative of improved solubility of Posaconazole, which may serve to boost up the action of the drug for the treatment of fungal diseases. Keywords: Posaconazole, Nanoemulsion, HPH method, Physical Characterization
Multi-frequency Class-D inverter for rectifier characterisation in high frequency inductive power transfer systems

This paper presents the development and utilisation of a voltage source multi-frequency Class-D inverter in a cost-effective test rig for rectifiers in 6.78 MHz to 13.56 MHz inductive power transfer (IPT) systems without the need of the magnetic link. Apart from the inverter, the rig compromises a tuned receiving (Rx) coil of the IPT system and a rectifier under test. With the inverter emulating a magnetically induced voltage in an Rx coil, rectifiers experience IPT conditions while they are isolated from the rest of the system. Therefore, they are accurately analysed with commercially available instruments, without affecting other parts of the IPT system. The Class-D inverter was implemented with Si MOSFETs and is capable of delivering power greater than 200 W with efficiencies higher than 80 %.
Interference Management in Heterogeneous Cellular Networks

II ACKNOWLEDGEMENTS . IV DEDICATION . V TABLE OF CONTENTS . VI LIST OF TABLES. X LIST OF FIGURES . XI LIST OF SYMBOLS . XIV LIST OF ABBREVIATIONS . XVI CHAPTER 1: INTRODUCTION. 1 1.1 BACKGROUND AND MOTIVATION . 1 1.2 PROBLEM STATEMENT . 4 1.3 LITERATURE REVIEW . 6 1.3. 1 Interference management strategies in HetNets . 7 1.3. 2 Performance analysis of HetNets under Rician fading . 10 1.3. 3 Performance analysis of HetNets under lognormal shadowed Rayleigh fading . 13 1.3. 4 Performance analysis of HetNets with hexagonal tessellated macro BSs . 15 1.3. 5 Fractional frequency reuse in HetNets . 19 1.3. 6 Performance analysis of HetNets with multiple antennas . 21 1.4 THESIS OBJECTIVES AND CONTRIBUTIONS. 23 1.5 THESIS ORGANIZATION . 33 CHAPTER 2: SPATIAL DEPLOYMENT OF BASE STATIONS AND INTERFERENCE MODELS IN CELLULAR NETWORKS. 35 2.
Autophagic pathway contributes to low-nitrogen tolerance by optimizing nitrogen uptake and utilization in tomato

Abstract Autophagy is a primary process involved in the degradation and reuse of redundant or damaged cytoplasmic components in eukaryotes. Autophagy has been demonstrated to facilitate nutrient recycling and remobilization by delivering intracellular materials to the vacuole for degradation in plants under nutrient starvation. However, the role of autophagy in nitrogen (N) uptake and utilization remains unknown. Here, we report that the ATG6-dependent autophagic pathway regulates N utilization in tomato (Solanum lycopersicum) under low-nitrogen (LN) conditions. Autophagy-disrupted mutants exhibited weakened biomass production and N accumulation compared with wild-type (WT), while ATG6 overexpression promoted autophagy and biomass production under LN stress. The N content in atg6 mutants decreased while that in ATG6-overexpressing lines increased due to the control of N transporter gene expression in roots under LN conditions. Furthermore, ATG6-dependent autophagy enhanced N assimilation efficiency and protein production in leaves. Nitrate reductase and nitrite reductase activities and expression were compromised in atg6 mutants but were enhanced in ATG6-overexpressing plants under LN stress. Moreover, ATG6-dependent autophagy increased plant carbon fixation and photosynthetic capacity. The quantum yield of photosystem II, photosynthetic N use efficiency and photosynthetic protein accumulation were compromised in atg6 mutants but were restored in ATG6-overexpressing plants. A WT scion grafted onto atg6 mutant rootstock and an atg6 scion grafted onto WT rootstock both exhibited inhibited LN-induced autophagy and N uptake and utilization. Thus, ATG6-dependent autophagy regulates not only N uptake and utilization as well as carbon assimilation but also nutrient recycling and remobilization in tomato plants experiencing LN stress.
Grasp synthesis of multifingered robotic hands

Research and development leading to the realization of a fully autonomous and robust multifingered robotic hand have been going on for three decades. Yet none can be found in an industrial application. The difficulties stem from many factors, one of which is that we lack general and efficient algorithms for the grasp synthesis. The grasp synthesis refers to the following problem: given the model of a rigid object, determine a set of finger positions on its boundary that supports the object and makes it resistant to external forces/torques. Such a grasp is also termed as a form-closure grasp and is an essential part of the robot hand control scheme. This thesis makes a thorough study on the grasp analysis, synthesis and related topics, with the particular attention paid to apply the grasp synthesis algorithms to the experimental regrasping task of a rectangular object. In addition, noting the similarity between robotic grasping and fixture layout design in manufacturing, we further apply the grasp synthesis algorithms to the fixture synthesis of generic workpieces. The grasp analysis associates with inquiring whether the given grasp characterized by contact locations and associated contact models is form-closure. By fully exploring the geometric meanings of form-closure conditions, we show that the grasp analysis can be transformed to a ray-shooting problem, which is dual to a Linear Programming (LP) problem based on the duality between convex hulls and convex polytopes. The algorithm developed examines both the frictionless and frictional grasps in two or three dimensions. For the grasp synthesis, we first consider synthesizing a grasp when some of the fingers are located in the known positions on the object, where the possible contact regions and the most favorable positions for the other fingers are calculated through inequality theory and nonlinear optimization techniques, respectively. Then we consider the grasp synthesis from a random initial grasp, where an efficient search strategy is developed and formulated as a quadratic programming problem that leads the initial non-form-closure grasp towards a form-closure configuration. The two cases cover most circumstances in practice and the approaches can be applied to synthesize grasps for both polyhedral and curved objects. The hand kinematics is further taken into account for practicality when the grasp synthesis of a curved object is dealt with. A large number of various numerical simulations verify the efficiency and usefulness of these approaches. Finally, an experiment setup is built on our Yaskawa five-fingered hand system, where the task of rotating a rectangular object through regrasping is completed. The grasp analysis and synthesis algorithms are applied to obtain proper contact locations during the process and grasping forces are optimized with the emphasis on the balance of the internal forces.
Automatic multi-stereo-vision reconstruction method of complicated tubes for industrial assembly

Purpose Complicated tubes extensively exist in the industrial equipment. The manufacturing precision of the tubes is difficult to be ensured with bending machine. Therefore, the tubes’ 3D geometric error should be fixed according to measurement results. However, there are no convenient methods to accomplish the measurement accurately and effectively. Thus, this paper aims to propose a new tube measurement method to achieve tube's automatic measurement. The accurate measurement results can be used to fix the geometric error of the tube to achieve stress-free assembly. Design/methodology/approach Tubes’ shape can be determined by control points. First, the point clouds of the centre line by multi-stereo-vision technology are reconstructed. Then, the point clouds to the spine of the tube are thinned by moving least-squares and segmented into lines and arcs. Finally, the control points are calculated and the model is reconstructed. The authors can get the tube’s geometric dimensions from the model. Findings The experiment results indicate that the multi-stereo-vision technology can solve the occlusion problem and measure the complicated tubes efficiently and accurately. Originality/value The paper proposed a tube measurement method. The repeatability measuring precision was 0.12 mm, and the absolute measuring precision was within 0.78 mm. The tube spectra assessed in this paper are in the range of angles between two adjacent line segments of 3-177° and the shortest length of the line segment is greater than 5 mm, confirming that the proposed algorithm can measure various complicated tubes effectively and accurately.
Industrial policy and comparative political economy: A literature review and research agenda

The Great Recession renewed calls for a return of state activism in support of the European economy. The widespread nationalizations of ailing companies and the growing activism of national development banks led many to celebrate the re-appearance of industrial policy. Despite this reappraisal, however, comparative political economy (CPE) contributions to the study of industrial policy are still scarce. This review article critically overviews the existing literature linking CPE and industrial policy meeting three goals. First, to trace the evolution of the goals, protagonists, and policy instruments of industrial policy in the European Union (EU) since the post-war period. Second, to provide a periodization of the evolution of industrial policy based on the distinction between the inward-looking forms of state intervention prevalent until the late 1970s and the open-market industrial policy of later decades. Third, to outline future research pathways based on the integration between CPE, heterodox economics, and economic geography.
Coronavirus Pneumonia Classification Using X-Ray and CT Scan Images With Deep Convolutional Neural Network Models

Pneumonia is a life-threatening infectious disease affecting one or both lungs in humans. There are mainly two types of pneumonia: bacterial and viral. Likewise, patients with coronavirus can develop symptoms that belong to the common flu, pneumonia, and other respiratory diseases. Chest X-rays are the common method used to diagnose coronavirus pneumonia and it needs a medical expert to evaluate the result of X-ray. Furthermore, DL has garnered great attention among researchers in recent years in a variety of application domains such as medical image processing, computer vision, bioinformatics, and many others. In this paper, we present a comparison of Deep Convolutional Neural Networks models for automatically binary classification query chest X-ray & CT images dataset with the goal of taking precision tools to health professionals based on fined recent versions of ResNet50, InceptionV3, and VGGNet. The experiments were conducted using a chest X-ray & CT open dataset of 5856 images and confusion matrices are used to evaluate model performances.