Miracle-dz/UniFAR-FTUs huggingface dataset:
Numerical modelling of natural convection of binary mixtures: case of a helium buoyant jet in an air-filled enclosure

Ce travail porte sur l'etude des mecanismes de melange et dispersion d'un jet d'helium dans une cavite semi-confinee remplie d'air. Ce phenomene est pris comme un cas modele de l'injection d'un gaz leger dans un fluide lourd produisant un panache. Ce theme est en lien avec les questions de securite des systemes bases sur l'hydrogene. Un modele numerique a ete developpe combinant les conditions limites adequates avec les equations de conservation de la masse (melange et une espece), de la quantite de mouvement, ainsi que la loi d'etat et la variation des proprietes physiques en fonction du melange. En premier lieu, le panache d'un melange eau glycerol est considere comme cas de validation par comparaison avec des resultats experimentaux [Rogers & Morris 09]. Le developpement d'un panache axisymetrique est modelise pour de grands nombres de Grashof et petit nombres de Reynolds d'injection. Un bon accord est obtenu pour la vitesse d'ascension du panache ainsi que le type et la forme de la tete. Une loi d'echelle modifiee est proposee prenant en compte le ratio de viscosite des deux fluides. Dans le cas du melange helium-air, une cavite 2D est tout d'abord consideree. Les lois d'echelle auto-similaires pour des panaches plans stationnaires en milieu infini avant impact avec le plafond [Gebhart et al. 88] ont ete reproduites numeriquement pour les profils de vitesse verticale et de masse volumique sur l'axe. Puis une cavite cylindrique a ete consideree pour modeliser une experience menee au CEA [Cariteau & Tkatschenko 12]. Les resultats numeriques sont compares aux donnees des experiences et d'un benchmark numerique. L'effet de l'hypothese d'axisymetrie a ete mis en evidence.
Promoting Academic Achievement: The Role of Self-efficacy in Predicting Students’ Success in the Higher Education Settings

Self-efficacy forms a major factor that influences many life aspects, as a result, it has been researched by many psychologists and educators worldwide from different perspectives. Embracing the context of Bandura’s Self- efficacy Theory as a theoretical framework, this quantitative empirical research paper introduces an overview of the role that self–efficacy plays in shaping higher education students’ academic achievement among a diverse group of college students. The diverse group comprises 374 students who took part in this research study and responded to the General Self–efficacy Scale (GSE) to disclose the perception of their self-efficacy and the ability to perform the required academic tasks. Multiple analysis techniques including Descriptive analysis, Pearson Correlation Coefficient, t-test for independent sample, and Simple Linear Regression were conducted to achieve the paper’s objectives. Data analysis demonstrated a strong positive correlation between students’ general self–efficacy and academic achievement. Moreover, students’ self–efficacy was found as a significant predictor of students’ academic achievement. Findings indicated that no gender-based differences in students’ general self-efficacy among higher education students. The discoveries of this research study can be implemented in instructional designing, curriculum development, and college counseling services to promote students’ academic achievement.
Enhanced forward stimulated Brillouin scattering in silicon photonic slot waveguide Bragg grating

We study the forward stimulated Brillouin scattering process in a suspended silicon slot waveguide Bragg grating. Full-vectorial formalism is applied to analyze the interplay of electrostriction and radiation pressure. We show that radiation pressure is the dominant factor in the proposed waveguide. The Brillouin gain strongly depends on the structural parameters and the maximum value in the order of 106 W−1 m−1 is obtained in the slow light regime, which is more than two orders larger than that of the stand-alone strip and slot waveguides.
Modulation of optical force acting on small particles by using photochemical reaction and its application to control of optical transportation speed

The speed of optical transportation of microparticles was modified by changing dissipative force acting on the particles through the switching of the absorption spectrum of molecules contained in the particles. Linear and orbital motions of microparticles in water were achieved by using slightly focused laser beams. Polymer microparticles containing photo-chromic molecules under the photoirradiation configuration were transported by the optical force due to scattering. The transportation speed was increased by changing the color of the particles by the photoinduced isomerization from transparent (non-resonant) to resonant with the manipulation beams.
Validity and Applicability of the Global Leadership Initiative on Malnutrition (GLIM) Criteria in Patients Hospitalized for Acute Medical Conditions

(1) Background: The objectives of this study were to evaluate the concurrent and predictive validity and the applicability of the global leadership initiative on malnutrition (GLIM) criteria in patients hospitalized for acute medical conditions. (2) Methods: prospective cohort study with patients hospitalized for acute medical conditions. For validation, the methodology proposed by the GLIM group of experts was used. Sensitivity and specificity values greater than 80% with respect to those for the subjective global assessment (SGA) were necessary for concurrent validation. The time necessary to complete each nutritional assessment test was determined. (3) Results: A total of 119 patients were evaluated. The SGA was applied to the entire cohort, but the GLIM criteria could not be applied to 3.4% of the patients. The sensitivity and specificity of the GLIM criteria with respect to those for the SGA to detect malnutrition were 78.0 and 86.2%, respectively. The GLIM predictive validity criterion was fulfilled because patients with malnutrition more frequently had a hospital stay >10 days (odds ratio of 2.98 (1.21–7.60)). The GLIM criteria required significantly more time for completion than did the SGA (p = 0.006). (4) Conclusion: The results of this study do not support the use of the GLIM criteria over the SGA for the diagnosis of malnutrition in patients hospitalized for acute medical conditions.
Colorectal Cancer Archaeome: A Metagenomic Exploration, Tunisia

Colorectal cancer (CRC) is a serious public health problem known to have a multifactorial etiology. The association between gut microbiota and CRC has been widely studied; however, the link between archaea and CRC has not been sufficiently studied. To investigate the involvement of archaea in colorectal carcinogenesis, we performed a metagenomic analysis of 68 formalin-embedded paraffin fixed tissues from tumoral (n = 33) and healthy mucosa (n = 35) collected from 35 CRC Tunisian patients. We used two DNA extraction methods: Generead DNA FFPE kit (Qiagen, Germantown, MD, USA) and Chelex. We then sequenced the samples using Illumina Miseq. Interestingly, DNA extraction exclusively using Chelex generated enough DNA for sequencing of all samples. After data filtering and processing, we reported the presence of archaeal sequences, which represented 0.33% of all the reads generated. In terms of abundance, we highlighted a depletion in methanogens and an enrichment in Halobacteria in the tumor tissues, while the correlation analysis revealed a significant association between the Halobacteria and the tumor mucosa (p < 0.05). We reported a strong correlation between Natrialba magadii, Sulfolobus acidocaldarius, and tumor tissues, and a weak correlation between Methanococcus voltae and healthy adjacent mucosa. Here, we demonstrated the feasibility of archaeome analysis from formol fixed paraffin-embedded (FFPE) tissues using simple protocols ranging from sampling to data analysis, and reported a significant association between Halobacteria and tumor tissues in Tunisian patients with CRC. The importance of our study is that it represents the first metagenomic analysis of Tunisian CRC patients’ gut microbiome, which consists of sequencing DNA extracted from paired tumor-adjacent FFPE tissues collected from CRC patients. The detection of archaeal sequences in our samples confirms the feasibility of carrying out an archaeome analysis from FFPE tissues using a simple DNA extraction protocol. Our analysis revealed the enrichment of Halobacteria, especially Natrialba magadii, in tumor mucosa compared to the normal mucosa in CRC Tunisian patients. Other species were also associated with CRC, including Sulfolobus acidocaldarius and Methanococcus voltae, which is a methanogenic archaea; both species were found to be correlated with adjacent healthy tissues.
A Survey of Audio Classification Using Deep Learning

Deep learning can be used for audio signal classification in a variety of ways. It can be used to detect and classify various types of audio signals such as speech, music, and environmental sounds. Deep learning models are able to learn complex patterns of audio signals and can be trained on large datasets to achieve high accuracy. To employ deep learning for audio signal classification, the audio signal must first be represented in a suitable form. This can be done using signal representation techniques such as using spectrograms, Mel-frequency Cepstral coefficients, linear predictive coding, and wavelet decomposition. Once the audio signal is represented in a suitable form, it can then be fed into a deep learning model. Various deep learning models can be utilized for audio classification. We provide an extensive survey of current deep learning models used for a variety of audio classification tasks. In particular, we focus on works published under five different deep neural network architectures, namely Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Autoencoders, Transformers and Hybrid Models (hybrid deep learning models and hybrid deep learning models with traditional classifiers). CNNs can be used to classify audio signals into different categories such as speech, music, and environmental sounds. They can also be used for speech recognition, speaker identification, and emotion recognition. RNNs are widely used for audio classification and audio segmentation. RNN models can capture temporal patterns of audio signals and be used to classify audio segments into different categories. Another approach is to use autoencoders for learning the features of audio signals and then classifying the signals into different categories. Transformers are also well-suited for audio classification. In particular, temporal and frequency features can be extracted to identify the characteristics of the audio signals. Finally, hybrid models for audio classification either combine various deep learning architectures (i.e. CNN-RNN) or combine deep learning models with traditional machine learning techniques (i.e. CNN-Support Vector Machine). These hybrid models take advantage of the strengths of different architectures while avoiding their weaknesses. Existing literature under different categories of deep learning are summarized and compared in detail.
A Modeling and Data-Driven Control Framework for Rigid-Soft Hybrid Robot With Visual Servoing

In this letter, a rigid-soft hybrid robot with visual servoing is designed to improve robotic properties of accuracy and safe interaction, where the hybrid robot is connected by a soft robot and six degrees of freedom rigid robot in series. The series structure of rigid and soft parts brings the coupling and complexity to modeling. For synthesis modeling, we develop a state-space equation of the hybrid robot by the combination of the rigid and soft parts, where the rigid model is built by differential kinematics, and Piecewise Constant-Curvature and Lagrange equations are used to model the soft part with consideration of the elastic deformation caused by gravity loading. Based on the synthesis model, a model predictive controller is designed to eliminate the error between the hybrid robot terminal and the target position. Considering the imprecise modeling process disturbs the control performance, a data-driven strategy named input mapping is incorporated into the predictive controller to reduce the inaccuracy model effect and improve the control performance. In addition, the feedback error of the hybrid robot is transferred in the robot terminal coordinate system to avoid hand-eye calibration work. Finally, the experiments on a rigid-soft hybrid robot show the proposed method converges nearly twice as fast as the model predictive control method and two state-of-the-art motion planning methods for the visual servoing of the soft-rigid hybrid robot system.
An ISAR Image Component Recognition Method Based on Semantic Segmentation and Mask Matching

The inverse synthetic aperture radar (ISAR) image is a kind of target feature data acquired by radar for moving targets, which can reflect the shape, structure, and motion information of the target, and has attracted a great deal of attention from the radar automatic target recognition (RATR) community. The identification of ISAR image components in radar satellite identification missions has not been carried out in related research, and the relevant segmentation methods of optical images applied to the research of semantic segmentation of ISAR images do not achieve ideal segmentation results. To address this problem, this paper proposes an ISAR image part recognition method based on semantic segmentation and mask matching. Furthermore, a reliable automatic ISAR image component labeling method is designed, and the satellite target component labeling ISAR image samples are obtained accurately and efficiently, and the satellite target component labeling ISAR image data set is obtained. On this basis, an ISAR image component recognition method based on semantic segmentation and mask matching is proposed in this paper. U-Net and Siamese Network are designed to complete the ISAR image binary semantic segmentation and binary mask matching, respectively. The component label of the ISAR image is predicted by the mask matching results. Experiments based on satellite component labeling ISAR image datasets confirm that the proposed method is feasible and effective, and it has greater comparative advantages compared to other classical semantic segmentation networks.
Machine Learning-Enabled Quantitative Analysis of Optically Obscure Scratches on Nickel-Plated Additively Manufactured (AM) Samples

Additively manufactured metal components often have rough and uneven surfaces, necessitating post-processing and surface polishing. Hardness is a critical characteristic that affects overall component properties, including wear. This study employed K-means unsupervised machine learning to explore the relationship between the relative surface hardness and scratch width of electroless nickel plating on additively manufactured composite components. The Taguchi design of experiment (TDOE) L9 orthogonal array facilitated experimentation with various factors and levels. Initially, a digital light microscope was used for 3D surface mapping and scratch width quantification. However, the microscope struggled with the reflections from the shiny Ni-plating and scatter from small scratches. To overcome this, a scanning electron microscope (SEM) generated grayscale images and 3D height maps of the scratched Ni-plating, thus enabling the precise characterization of scratch widths. Optical identification of the scratch regions and quantification were accomplished using Python code with a K-means machine-learning clustering algorithm. The TDOE yielded distinct Ni-plating hardness levels for the nine samples, while an increased scratch force showed a non-linear impact on scratch widths. The enhanced surface quality resulting from Ni coatings will have significant implications in various industrial applications, and it will play a pivotal role in future metal and alloy surface engineering.
A quantitative systems pharmacology model for certolizumab pegol treatment in moderate-to-severe psoriasis

Background Psoriasis is a chronic immune-mediated inflammatory systemic disease with skin manifestations characterized by erythematous, scaly, itchy and/or painful plaques resulting from hyperproliferation of keratinocytes. Certolizumab pegol [CZP], a PEGylated antigen binding fragment of a humanized monoclonal antibody against TNF-alpha, is approved for the treatment of moderate-to-severe plaque psoriasis. Patients with psoriasis present clinical and molecular variability, affecting response to treatment. Herein, we utilized an in silico approach to model the effects of CZP in a virtual population (vPop) with moderate-to-severe psoriasis. Our proof-of-concept study aims to assess the performance of our model in generating a vPop and defining CZP response variability based on patient profiles. Methods We built a quantitative systems pharmacology (QSP) model of a clinical trial-like vPop with moderate-to-severe psoriasis treated with two dosing schemes of CZP (200 mg and 400 mg, both every two weeks for 16 weeks, starting with a loading dose of CZP 400 mg at weeks 0, 2, and 4). We applied different modelling approaches: (i) an algorithm to generate vPop according to reference population values and comorbidity frequencies in real-world populations; (ii) physiologically based pharmacokinetic (PBPK) models of CZP dosing schemes in each virtual patient; and (iii) systems biology-based models of the mechanism of action (MoA) of the drug. Results The combination of our different modelling approaches yielded a vPop distribution and a PBPK model that aligned with existing literature. Our systems biology and QSP models reproduced known biological and clinical activity, presenting outcomes correlating with clinical efficacy measures. We identified distinct clusters of virtual patients based on their psoriasis-related protein predicted activity when treated with CZP, which could help unravel differences in drug efficacy in diverse subpopulations. Moreover, our models revealed clusters of MoA solutions irrespective of the dosing regimen employed. Conclusion Our study provided patient specific QSP models that reproduced clinical and molecular efficacy features, supporting the use of computational methods as modelling strategy to explore drug response variability. This might shed light on the differences in drug efficacy in diverse subpopulations, especially useful in complex diseases such as psoriasis, through the generation of mechanistically based hypotheses.
Chlamydia trachomatis genotypes in sexually transmitted disease clinic attendees in Colombo, Sri Lanka

Introduction: Chlamydia trachomatis (CT) is the most common cause of non-gonococcal urethritis. Prevalence of CT infections in Sri Lanka was 17.1% in females in 2015. Real-time PCR for diagnostic testing of CT was started in 2015 but screening programmes have not been initiated due to the high cost. The aim of this study was to determine the genotypes of CT in patients from Colombo, since genotypic distribution in Sri Lanka is not known.Methods: Stored first void urine samples (n=208) from a previous study on patients attending two clinics in Colombo were used. Samples that were positive by Artus C. trachomatis Plus RG Real time PCR (Qiagen) were retested with a nested PCR described previously, which generated 15 ompA sequences suitable for standard sequencing.Results: Genovars E (n=6; 40%), F (n=5; 33.3%), G (n=3; 20%) and H (n=1; 6.7%) were identified.Conclusions: As seen in most parts of the world, genovar E and F were the most commonly detected CT genovars among patients in Colombo.
Cloud Metering and Monitoring

For Service Oriented Infrastructures (SOI) and Cloud services, monitoring and metering are crucial tasks. For the proper execution of cloud applications & the monitoring of SLA compliance, the data gathered through monitoring is required. The motivations for and challenges in monitoring and metering cloud systems are discussed. The methods for monitoring the network and execution environment on virtualized infrastructures will be discussed together with the monitoring tools that are now available on various commercial and research platforms. Every distributed computing system has a basic feature that includes monitoring chores. Every service should be watched over in order to evaluate its effectiveness and enable remedial measures in the event of failure. A functional snapshot of the system's behaviour along the time axis is what monitoring data reflects. Such information is essential for pinpointing the source of issues or for fine-tuning various system components. For example, failure detection and recovery procedures require a monitoring component to determine whether to restart a specific server or subsystem based on the data gathered by the monitoring system.
A Multi-Agent Intrusion Detection System Optimized by a Deep Reinforcement Learning Approach with a Dataset Enlarged Using a Generative Model to Reduce the Bias Effect

Intrusion detection systems can defectively perform when they are adjusted with datasets that are unbalanced in terms of attack data and non-attack data. Most datasets contain more non-attack data than attack data, and this circumstance can introduce biases in intrusion detection systems, making them vulnerable to cyberattacks. As an approach to remedy this issue, we considered the Conditional Tabular Generative Adversarial Network (CTGAN), with its hyperparameters optimized using the tree-structured Parzen estimator (TPE), to balance an insider threat tabular dataset called the CMU-CERT, which is formed by discrete-value and continuous-value columns. We showed through this method that the mean absolute errors between the probability mass functions (PMFs) of the actual data and the PMFs of the data generated using the CTGAN can be relatively small. Then, from the optimized CTGAN, we generated synthetic insider threat data and combined them with the actual ones to balance the original dataset. We used the resulting dataset for an intrusion detection system implemented with the Adversarial Environment Reinforcement Learning (AE-RL) algorithm in a multi-agent framework formed by an attacker and a defender. We showed that the performance of detecting intrusions using the framework of the CTGAN and the AE-RL is significantly improved with respect to the case where the dataset is not balanced, giving an F1-score of 0.7617.
Progress in Reducing the Uncertainty of Measurement of Planck’s Constant in Terms of the Information Approach

Aims: The purpose of this work is to prove that only by implementing a weighted and thorough theoretical information approach to the development of a physical and mathematical model for measuring Planck’s constant, it is possible to prepare a reasonable justification for calculating the required relative uncertainty. Place and Duration of Study: Mechanical & Refrigeration Consultation Expert, between June 2018 and February 2019. Methodology: Using the principles of information theory and similarity theory, a dimensionless parameter (comparative uncertainty) was formulated to compare the experimental results of measurements of Planck’s constant and the simulated data. Results: Examples of the application of the proposed original method to measure Planck’s constant using the Kibble balance and X-ray crystal density methods are given. Conclusion: The proposed information-oriented approach is theoretically justified and does not include such concepts as a statistically significant trend, cumulative values of consensus or statistical control, which are characteristic of the statistical expert tool adopted in CODATA. We tried to show how the mathematical and, apparently, rather arbitrary expert formalism can be replaced by a simple, theoretically grounded postulate on the use of information in measurements.