Miracle-dz/UniFAR-FTUs huggingface dataset:
4.0. Seroprevalence of viral Hepatitis B in the Marrakech

https://doi.org/10.30574/gscarr.2022. 11.1. 0098 Abstract Viral hepatitis B (HBV) is a major public health problem worldwide. Indeed, the World Health Organization (WHO) estimates that more than 2 billion the number of people who have been exposed to this virus, about 30% of the world population, 18% in sub-Saharan Africa and 41% in Asia, of which 5% with chronic infection or 300-350 million. So the HBV prevalence is 5.4% globally, against 1% for the HIV and 3% for hepatitis C. More than 1 million of them die each year complications essentially cirrhosis and hepatocellular carcinoma. The diagnostic of HBV (recent or chronic infection) relies on research HBs antigen in the serum of patients, it is the witness of a recent or past infection with HBV according to the presence or absence of other serological markers (HBe Ag, anti-HBs Ig and total anti-HBc and anti-HBe antibody IgM) but they do not provide information on the status of viral city of origin, occupation, sexual risk, homosexuality, intravenous drug use, history of STIs, dental care, transfusion, dialysis, medical staff, tattooing. The prevalence rate in this study was 0.55%. This rate is very low compared to national studies: the prevalence of hepatitis B among blood donors in Morocco is 1.34% with a minimum recorded in El Jadida (0.43%) and a maximum observed in Errachidia (2.86%) [9], and 1.66% in a Moroccan working population during a free hepatitis B screening campaign in the following 15 Moroccan cities: Rabat, Sale , Kenitra, Casablanca, Eljadida, Mohammedia, Khouribga, Benslimane, Berrechid, kalaat sraghna, Safi,Settat, Béni Mellal, Marrakech and Agadir [10]. High prevalence noted in at-risk populations: 4.25% in patients hospitalized at in
Hot accretion flow in black hole binaries: a link connecting X-rays to the infrared

Multiwavelength observations of Galactic black hole transients have opened a new path to understanding the physics of the innermost parts of the accr etion flows. While the processes giving rise to their X-ray continuum have been studied extensively, the emission in the optical and infrared (OIR) energy bands was less investigated and remains poorly understood. The standard accretion disc, which may contribute to the flux at t hese wavelengths, is not capable of explaining a number of observables: the infrared excesse s, fast OIR variability and a complicated correlation with the X-rays. It was suggested that these energy bands are dominated by the jet emission, however, this scenario does not work in a number of cases. We propose here an alternative, namely that most of the OIR emission is produced by the extended hot accretion flow. In this scenario, the OIR bands are dominated by the synchrotron radiation from the non-thermal electrons. An additional contributio n is expected from the outer irradiated part of the accretion disc heated by the X-rays. We discuss properties of the model and compare them to the data. We show that the hot-flow scenario is consistent with many of the observed spectral data, at the same time naturally explaini ng X-ray timing properties, fast OIR variability and its correlation with the X-rays.
Characterizing the Blood-Stage Antimalarial Activity of Tafenoquine in Healthy Volunteers Experimentally Infected With Plasmodium falciparum

Background The long acting 8-aminoquinoline tafenoquine may be a good candidate for mass drug administration if it exhibits sufficient blood stage antimalarial activity at doses low enough to be tolerated by glucose 6-phosphate dehydrogenase (G6PD) deficient individuals. Methods Healthy G6PD-normal adults were inoculated with Plasmodium falciparum 3D7-infected erythrocytes on day 0. Different single oral doses of tafenoquine were administered on day 8. Parasitemia, and concentrations of tafenoquine and the 5,6-orthoquinone metabolite in plasma/whole blood/urine were measured and standard safety assessments performed. Curative artemether-lumefantrine therapy was administered if parasite regrowth occurred, or on day 48+/-2. Outcomes were parasite clearance kinetics, pharmacokinetic and pharmacokinetic/pharmacodynamic (PK/PD) parameters from modelling, and dose simulations in a theoretical endemic population. Results Twelve participants were inoculated and administered 200 mg (n=3), 300 mg (n=4), 400 mg (n=2), or 600 mg (n=3) tafenoquine. The parasite clearance half-life with 400 mg or 600 mg (5.4 h and 4.2 h respectively) was faster than with 200 mg or 300 mg (11.8 h and 9.6 h respectively). Parasite regrowth occurred after dosing with 200 mg (3/3 participants) and 300 mg (3/4 participants), but not after 400 mg or 600 mg. Simulations using the PK/PD model predicted that 460 mg and 540 mg would clear parasitaemia by a factor of 10^6 and 10^9, respectively, in a 60 kg adult. Conclusions Although a single dose or tafenoquine exhibits potent P. falciparum blood stage antimalarial activity, the estimated doses to effectively clear asexual parasitemia will require prior screening to exclude G6PD deficiency.
Breakthrough-Induced Loop Formation in Evolving Transport Networks

Transport networks, such as vasculature or river networks, provide key functions in organisms and the environment. They usually contain loops whose significance for the stability and robustness of the network is well documented. However, the dynamics of their formation is usually not considered. The growth of such structures is often driven by the gradient of an external field. During network evolution, extending branches compete for the available flux, which leads to effective repulsion between them and screening of the shorter ones. Yet, in remarkably diverse processes, from unstable fluid flows to the canal system of jellyfish, loops suddenly form near the breakthrough when the longest branch reaches the boundary of the system. We provide a physical explanation for this universal behavior. Using a 1D model, we explain that the appearance of effective attractive forces results from the field drop inside the leading finger as it approaches the outlet. Furthermore, we numerically study the interactions between two fingers, including screening in the system and its disappearance near the breakthrough. Finally, we perform simulations of the temporal evolution of the fingers to show how revival and attraction to the longest finger leads to dynamic loop formation. We compare the simulations to the experiments and find that the dynamics of the shorter finger is well reproduced. Our results demonstrate that reconnection is a prevalent phenomenon in systems driven by diffusive fluxes, occurring both when the ratio of the mobility inside the growing structure to the mobility outside is low and near the breakthrough.
Control-quality driven design of cyber-physical systems with robustness guarantees

Many cyber-physical systems comprise several control applications sharing communication and computation resources. The design of such systems requires special attention due to the complex timing behavior that can lead to poor control quality or even instability. The two main requirements of control applications are: (1) robustness and, in particular, stability and (2) high control quality. Although it is essential to guarantee stability and provide a certain degree of robustness even in the worst-case scenario, a design procedure which merely takes the worst-case scenario into consideration can lead to a poor expected (average-case) control quality, since the design is solely tuned to a scenario that occurs very rarely. On the other hand, considering only the expected quality of control does not necessarily provide robustness and stability in the worst-case. Therefore, both the robustness and the expected control quality should be taken into account in the design process. This paper presents an efficient and integrated approach for designing high-quality cyber-physical systems with robustness guarantees.
State-Dependent Representation Independence ( Technical Appendix )

3 Logical Relation Proofs 15 3.1 Basic Properties . 15 3.1. 1 Properties of Step-Indexed Construction . 15 3.1. 2 Approximation Yields Valid Semantic Objects . 17 3.1. 3 World Extension and Store Satisfaction Properties . 18 3.1. 4 Validity of Type Interpretations . 19 3.1. 5 Substitution Property . 20 3.2 Fundamental Property . 21 3.3 Soundness w.r. t. Contextual Equivalence . 43
GPredict: Generic Predictive Concurrency Analysis

Predictive trace analysis (PTA) is an effective approach for detecting subtle bugs in concurrent programs. Existing PTA techniques, however, are typically based on adhoc algorithms tailored to low-level errors such as data races or atomicity violations, and are not applicable to high-level properties such as "a resource must be authenticated before use" and "a collection cannot be modified when being iterated over". In addition, most techniques assume as input a globally ordered trace of events, which is expensive to collect in practice as it requires synchronizing all threads. In this paper, we present GPredict: a new technique that realizes PTA for generic concurrency properties. Moreover, GPredict does not require a global trace but only the local traces of each thread, which incurs much less runtime overhead than existing techniques. Our key idea is to uniformly model violations of concurrency properties and the thread causality as constraints over events. With an existing SMT solver, GPredict is able to precisely predict property violations allowed by the causal model. Through our evaluation using both benchmarks and real world applications, we show that GPredict is effective in expressing and predicting generic property violations. Moreover, it reduces the runtime overhead of existing techniques by 54% on DaCapo benchmarks on average.
Stability guaranteed control: time domain passivity approach

A general framework for expanding the time-domain passivity control approach, to large classes of control systems is proposed. We show that large classes of control systems can be described from a network point of view. Based on the network presentation, the large classes of control systems are analyzed in a unified framework. In this unified network model, we define "virtual input energy", which is a virtual source of energy for control, and "real output energy" that is physically transferred to a plant to allow the concept of passivity to be used to study the stability of large classes of control systems. For guaranteeing the stability condition, the time-domain passivity controller for two-port is applied. Design procedure is demonstrated for a motion control system. The developed method is tested with numerical simulation in the regulation of a single link flexible manipulator. Totally stable control is achieved under wide variety of operating condition and uncertainties without any model information.
PIMS Mathematical Modeling in Industry Workshop XVIII Team 3 Final Report : Fast Calculation of Diffraction by Photomasks

4 Finite Difference Frequency Domain 7 4.1 Forcing Term Expressed as Deviation from a Background . 7 4.2 Setting up FDFD Equations . 8 4.3 Electric Field FDFD Equation . 8 4.4 Magnetic Field FDFD Equation . 9 4.5 First-Order FDFD Equation . 9 4.6 Performance of the Perfectly Matched Layer . 9 4.7 Condition Number of Equations . 10
Out-of-sample extrapolation using semi-supervised manifold learning (OSE-SSL): Content-based image retrieval for prostate histology grading

In this paper, we present an out-of-sample extrapolation (OSE) scheme in the context of semi-supervised manifold learning (OSESSL). Manifold learning (ML) takes samples with high dimensionality and learns a set of low dimensional embeddings. Embeddings generated by ML preserve nonlinear relationships between samples allowing dataset visualization, classification, or evaluation of object similarity. Semi-supervised ML (SSL), a recent ML extension, exploits known class labels to learn embeddings, which may result in greater separation between samples of different classes compared to unsupervised ML schemes. Most ML schemes utilize the eigenvalue decomposition (EVD) to learn embeddings. For instance, Graph Embedding (GE) learns embeddings by EVD on a similarity matrix that models high dimensional feature vector similarity between samples. In datasets where new samples are acquired, such as a content-based image retrieval (CBIR) system, recalculating EVD is infeasible. OSE schemes obtain new embeddings without recalculating EVD. The Nyström method (NM) is an OSE algorithm where new embeddings are estimated as a weighted sum of known embeddings. Known embeddings must describe the embedding space for NM to accurately estimate new embeddings. In this paper, NM and semi-supervised GE (SSGE) are combined to learn embeddings which cluster samples by class and rapidly calculate embeddings for new samples without recalculating EVD. OSE-SSL is compared to (i) NM paired with GE (NM-GE), and (ii) SSGE obtained for the full database, where SSGE results represent ground truth embeddings. OSE-SSL, NM-GE, and SSGE are evaluated in their ability to: (1) cluster samples by label, measured by Silhouette Index (SI); (2) CBIR accuracy, measured by area under the precision-recall curve (AUPRC). In a synthetic Swiss roll dataset of 2000 samples, OSE-SSL requires training on 50% of the dataset to achieve SI and AUPRC similar to SSGE while NM-GE requires 70% of dataset to achieve SI and AUPRC similar to GE. For a prostate histology dataset of 888 glands, a CBIR system was evaluated on its ability to retrieve images according to Gleason Grade. OSE-SSL had AUPRC of 0.6 while NM-GE had AUPRC of 0.3.
A hierarchical framework for image-based human age estimation by weighted and OHRanked Sparse Representation-based classification

Human age estimation based on face images can figure in a wide variety of real-world applications. In this paper, we propose a novel and efficient facial age estimation algorithm which decides human age in a hierarchical framework. Biologically, human lives can be roughly divided into two stages, the period from birth to adulthood and the period from adulthood to old age, which are quite different from each other in face growth and aging forms. Considering that craniofacial growth occurs mainly in the first stage while keeps basically stable in the second, based on the shape features, the coarse step of the framework determines which age stage the test sample belongs to using a quadratic function. On the other hand, since the face appearance of individuals in the same age group or even of the same age does have some similarities in common, accurate age estimation within the age stage is solved by Sparse Representation-based classification (SRC) in the fine step. However, SRC requires sufficient training samples in each class and in practice this assumption often does not hold, making the performance of age estimation limited. Accordingly, we take use of the idea of Ordinal Hyperplanes Ranker (OHRank) and weights of samples' numbers in each class to solve the aforementioned problem, improving the age estimation results. Results of experiments conducted on the FG-NET Database demonstrate the effectiveness of our method.
Occlusion Leak Compensation for Optical See-Through Displays Using a Single-Layer Transmissive Spatial Light Modulator

We propose an occlusion compensation method for optical see-through head-mounted displays (OST-HMDs) equipped with a singlelayer transmissive spatial light modulator (SLM), in particular, a liquid crystal display (LCD). Occlusion is an important depth cue for 3D perception, yet realizing it on OST-HMDs is particularly difficult due to the displays' semitransparent nature. A key component for the occlusion support is the SLM—a device that can selectively interfere with light rays passing through it. For example, an LCD is a transmissive SLM that can block or pass incoming light rays by turning pixels black or transparent. A straightforward solution places an LCD in front of an OST-HMD and drives the LCD to block light rays that could pass through rendered virtual objects at the viewpoint. This simple approach is, however, defective due to the depth mismatch between the LCD panel and the virtual objects, leading to blurred occlusion. This led existing OST-HMDs to employ dedicated hardware such as focus optics and multi-stacked SLMs. Contrary to these viable, yet complex and/or computationally expensive solutions, we return to the single-layer LCD approach for the hardware simplicity while maintaining fine occlusion—we compensate for a degraded occlusion area by overlaying a compensation image. We compute the image based on the HMD parameters and the background scene captured by a scene camera. The evaluation demonstrates that the proposed method reduced the occlusion leak error by 61.4% and the occlusion error by 85.7%.
Nontechnical Loss Detection for Metered Customers in Alexandria Electricity Distribution Company Using Support Vector Machine

Non-technical losses (NTL) during transmission and distribution (T&D) of electrical energy is a major problem faced by utility companies which is very difficult to fight and detect. For that, more of power utilities spend thousands dollar for research centres to find efficient methods for detecting and controlling abnormalities. Electricity theft and billing irregularities forms the main portion of NTL. With the introduction of smart meter, the frequency of reporting energy consumption data to the utility company has been increased. Incoming and outgoing energy could be monitored and analyzed. Electricity theft is a complex problem with many parameters to be evaluated before implementing any measures to detect and control that. These parameters include some issues like social, economic, regional, managerial, infrastructural, and corruption. In recent years, several data mining and research studies on fraud detection and prediction techniques have been carried out in the electricity distribution sector. Support vector machine (SVM) technique has dominated the research for classifying data and detecting fraudulent electricity customers. SVM technique has good ability in data mining and data classification. The paper objective is to analyze the metered energy consumption data recorded and predict the pattern or the form of daily user’s energy consumption, then using SVM to classify the data whether normal or theft. The suggested technique is then tested using real data from Alexandria Electricity Distribution Company (AEDC). The proposed technique was able to distinguish between healthy and theft cases.
Ni/Li Disordering in Layered Transition Metal Oxide: Electrochemical Impact, Origin, and Control.

Lithium ion batteries (LIBs) not only power most of today's hybrid electric vehicles (HEV) and electric vehicles (EV) but also are considered as a promising system for grid-level storage. Large-scale applications for LIBs require substantial improvement in energy density, cost, and lifetime. Layered lithium transition metal (TM) oxides, in particular, Li(Ni xMn yCo z)O2 (NMC, x + y + z = 1) are the most promising candidates as cathode materials with the potential to increase energy densities and lifetime, reduce costs, and improve safety. In order to further boost Li storage capacity, a great deal of attention has been directed toward developing Ni-rich layered TM oxides. However, structural disorder as a result of Ni/Li exchange in octahedral sites becomes a critical issue when Ni content increases to high values, as it leads to a detrimental effect on Li diffusivity, cycling stability, first-cycle efficiency, and overall electrode performance. Increasing effort has been dedicated to improving the electrochemical performance of layered TM oxides via reduction of cationic mixing. Therefore, it is important to summarize this research field and provide in-depth insight into the impact of Ni/Li disordering on electrochemical characteristics in layered TM oxides and its origin to accelerate the future development of layered TM oxides with high performance. In this Account, we start by introducing the Ni/Li disordering in LiNiO2, the experimental characterization of Ni/Li disordering, and analyzing the impact of Ni/Li disordering on electrochemical characteristics of layered TM oxides. The antisite Ni in the Li layer can limit the rate performance by impeding the Li ion transport. It will also degrade the cycling stability by inducing anisotropic stress in the bulk structure. Nevertheless, the antisite Ni ions do not always bring drawbacks to the electrochemical performance; some studies including our works found that it can improve the thermal stability and the cycling structure stability of Ni-rich NMC materials. We next discuss the driving forces and the kinetic advantages accounting for the Ni/Li exchange and conclude that the steric effect of cation size and the magnetic interactions between TM cations are the two main driving forces to promote the Ni/Li exchange during synthesis and the electrochemical cycling, and the low energy barrier of Ni2+ migration from the 3a site in the TM layer to the 3b site in the Li layer further provides a kinetic advantage. Based on this understanding, we then review the progress made to control the Ni/Li disordering through three main ways: (i) suppressing the driving force from the steric effect by ion exchange; (ii) tuning the magnetic interaction by cationic substitution; (iii) kinetically controlling Ni migration. Finally, our brief outlook on the future development of layered TM oxides with controlled Ni/Li disordering is provided. It is believed that this Account will provide significant understanding and inspirations toward developing high-performance layered TM oxide cathodes.
Market Making with Machine Learning Methods

3 Signal Modeling 5 3.1 Machine Learning Methods . 5 3.1. 1 Support Vector Machine . 5 3.1. 2 Random Forest . 7 3.1. 3 Stochastic Gradient Descent . 8 3.2 Alpha Model . 10 3.3 Risk Management Strategy . 10
Knowledge Graph Embedding with Hierarchical Relation Structure

The rapid development of knowledge graphs (KGs), such as Freebase and WordNet, has changed the paradigm for AI-related applications. However, even though these KGs are impressively large, most of them are suffering from incompleteness, which leads to performance degradation of AI applications. Most existing researches are focusing on knowledge graph embedding (KGE) models. Nevertheless, those models simply embed entities and relations into latent vectors without leveraging the rich information from the relation structure. Indeed, relations in KGs conform to a three-layer hierarchical relation structure (HRS), i.e., semantically similar relations can make up relation clusters and some relations can be further split into several fine-grained sub-relations. Relation clusters, relations and sub-relations can fit in the top, the middle and the bottom layer of three-layer HRS respectively. To this end, in this paper, we extend existing KGE models TransE, TransH and DistMult, to learn knowledge representations by leveraging the information from the HRS. Particularly, our approach is capable to extend other KGE models. Finally, the experiment results clearly validate the effectiveness of the proposed approach against baselines.