Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Modeling of Oil Transport in Vapor Compression Systems

In this paper, we propose a model describing the dynamics of refrigerant-oil mixture in vapor compression systems. This model uses the approach presented by Thome (1995) to calculate the thermodynamic properties of refrigerant- oil mixture. The governing equations for one-dimensional ﬂow are modiﬁed with augmentation of oil mass balance. Partial derivatives of mixture density are carefully computed to ensure the consistence of the choice of state variables for all ﬂow conditions. The existing models for heat exchanger and other components are reﬁned accordingly. Numerical simulation for the start-up operation of an air-conditioning cycle is conducted to evaluate the eﬃcacy of the proposed model. The results for the eﬀect of oil on system performance are provided. ABSTRACT In this paper, we propose a model describing the dynamics of refrigerant-oil mixture in vapor compression systems. This model uses the approach presented by Thome (1995) to calculate the thermodynamic properties of refrigerant-oil mixture. The governing equations for one-dimensional flow are modified with augmentation of oil mass balance. Partial derivatives of mixture density are carefully computed to ensure the consistence of the choice of state variables for all flow conditions. The existing models for heat exchanger and other components are refined accordingly. Numerical simulation for the start-up operation of an air-conditioning cycle is conducted to evaluate the efficacy of the proposed model. The results for the effect of oil on system performance are provided. This work presented a new model to describe the dynamics of refrigerant-oil mixture in vapor compression systems. Numerical analysis was conducted to explore the effect of oil on system performance of an air-conditioning cycle. It was found that the presence of oil led to incomplete evaporation even with large superheat leaving the evaporator. Meanwhile, simulation results indicated that oil tended to accumulate more in the condenser than the evaporator. Future work includes the modeling of oil retention in the suction line and accumulator, and development of a more rigorous compressor model to capture the underlying physics of flooded start so that a comprehensive investigation on refrigerant-oil behavior under various modes of operation can be performed.
A Framework for Optimizing Spark Configurations

The rising importance of data in modern life, industry, and society introduces a huge interest in processing them. Data-driven approaches nowadays are ubiquitous. Due to the increasing amount of data, the need to process large amounts of data has led to the development of complex, distributed, and scalable processing frameworks. Such a framework is the Apache Spark framework. It offers a rich set of functionalities like classic SQL analytics, machine learning functionalities, graph processing functionalities, and many more. However, the broad range of functionalities can potentially lead to problems. One of them is that due to the different requirement characteristics of the various Spark applications, the standard configuration of the Spark cluster may not be optimally adapted. A suboptimal configuration can lead to higher execution times or lower cluster throughput. Higher execution times can lead to higher costs in environments where the execution time is directly coupled to the billed costs, like in a cloud environment. Besides the financial aspect, a better-configured Spark application may also better use the provided resources and reduce the execution time, thus increasing the throughput. This work addresses this problem by designing and implementing an optimization framework for optimizing Spark configurations of a given Spark application. The optimization framework is then applied in a case study on two exemplary use cases using a Spark cluster in a Databricks environment of a private cloud to demonstrate its practicability. The results show the framework can optimize Spark configurations in general while causing only minimal effort to the applicant. However, outperforming the standard Spark configuration in the exemplary use cases proves to be challenging, especially due to observed runtime variances in the cloud environment. The distinction between statistical variance and real improvement is complex.
Deep Reinforcement Learning for Profiled Side Channel Attacks

Under the worst-case attack assumption, profiling side-channel analysis is recognized as one of the most potent attack methods for cryptographic implementations. The success of such attacks relies on the effectiveness of the profiling model in retrieving the leakage information. Previous research indicates that a proper selection of the input features could significantly increase the attack performance. However, due to the countermeasures and technology nodes’ advances, such features become more difficult to extract with the conventional approaches. In extreme cases, manual inspection has to be performed, where extensive device knowledge is needed. To this day, an optimal feature selection method has not yet been found. This thesis proposes an automatic framework based on Proximal Policy Optimization to find, select and scale down features. The input features are first grouped in small regions. The best candidates selected by the framework are further scaled down with an online-optimized Dimensionality Reduction Neural Network. Finally, the framework rewards the performance of these features with the results of a Template Attack. Based on the experimental results, the proposed framework is able to extract features that lead to near state-of-the-art performance on several commonly used datasets.
Hubble Space Telescope Resolved Image and Spectra of the z ≃ 2 QSO 1345+584

QSO 1345+584 has been spatially resolved by direct images and in spectral images, and has asymmetrically extended flux to the west, where its inner radio structure is seen. The brightest knots in the resolved flux correspond closely with knots in the curved radio jet, and the brightest knot has a velocity of approach of some 3000 km s-1 with respect to the nucleus. Other parts of the line-emitting material appear to follow a systematic velocity field with values up to 1000 km s-1 with respect to the nucleus. The signal from the resolved continuum is not detected spectroscopically, but accounts for two-thirds of the (rest-UV) flux, so that it is likely to originate in hot stars. The QSO lies in or behind a compact group of galaxies of comparable brightness and irregular and knotty morphology, which probably form a dense physical group with very young stellar populations.
THE PROBLEM OF TRANSLATING DIFFERENT SHADES OF 'AL-KHAWF' IN THE HOLY QURAN

The Holy Quran is a vivid aesthetic masterpiece in its language. Translating qur'anic texts is a problematic area for translators. The current study examines the problem of translating near-synonyms of the word 'Alkhawf' in the Holy Quran. The study is based on the translation of Taqi al-Din al-Hilali and Muhammad Muhsin Khan (1996). The study adopts Mona Baker's equivalence theory (1992) because it is closely related to analyzing the problems of Arabic-English Quranic translation. The Holy Quran contains many lexical items that express the emotional sense of 'fear' as a core meaning. It has been found that four Bakarian non-equivalence translation strategies are adopted: translation by a more general word (superordinate) which constitutes (61.25%), translation by paraphrase using a related word (35.00%), translation by paraphrasing using unrelated words (2.50%) and translation by omission (1.25%). It could be concluded that Arabic is richer in synonymy than English. The Arabic language uses lexical items that exhibits the features of similarity but they cannot be used interchangeably. In this study, the frequency of the word 'fear' in the target language is statistically calculated to reveal the semantic void. Results have shown that the word 'Al-khawf' and its near-synonyms are mostly rendered to the word 'fear' in the target language. The study recommends that the translators of the Holy Qur‟an should accurately pinpoint the nuances among near-synonyms to produce better translation.
Contours actifs pour signaux sur graphes *

– Cet article propose une adaptation pour les signaux sur graphes d’un mod`ele de segmentation qui combine les mod`eles de contours actifs g´eod´esiques et de contours actifs sans arˆetes. De plus, des termes sp´eciﬁques d´ependant des graphes sont introduits dans la formulation. Cette adaptation est r´esolue en utilisant une formulation par ensemble de niveaux avec une descente de gradient qui peut ˆetre exprim´ee comme un processus d’´evolution morphologique. Les r´esultats exp´erimentaux sur diff´erents types de signaux de graphes montrent les avantages de l’approche. Abstract – This paper proposes an adaptation for signal on graphs of a segmentation model that combines the Geodesic Active Contour and the Active Contour Without Edges models. In addition, speciﬁc terms depending on graphs are introduced in the formulation. This adaptation is solved using a level set formulation with a gradient descent that can be expressed as a morphological front evolution process. Experimental results on different kinds of graphs signals show the beneﬁt of the approach.
Dirac and Majorana Feynman Rules with four-fermions

A compact method for amplitude calculations in theories with Dirac and Majorana effective operators is discussed. Using the renormalizable formalism of Denner et al., [1,2] for propagators, vertices and fermion (number) flow and introducing new "reading-rules", it is shown that fermions can be treated as scalars in the diagrams. The effect of Fermi-statistics appears only in overall signs and is determined once for whole classes of diagrams. Each vertex in this method corresponds to two or more vertices in the standard treatment of effective theories. As such, the advantages of this approach grow together with the number of four-fermion vertices in a given diagram. The discussion develops around effective field theories based on the Standard Model, up to four-fermions and to any order in perturbation theory. Even so, the framework is more general and can be applied elsewhere.
Top-quark condensate at finite temperature and electroweak symmetry restoration

The gap equation at ﬁnite temperature in the top-quark condensate scheme of electroweak symmetry breaking is proven to have the identical form in both the imaginary and the real time formalism of thermal ﬁeld theory. By means of the gap equation, combined with the basic relation to deﬁne the vacuum expectation value v of the eﬀective Higgs ﬁeld, we analyse the dependence on temperature T and chemical potential µ of the dynamical top-quark mass as the order parameter characteristic of symmetry breaking, and obtain the µ − T criticality curve for symmetry restoration. We ﬁnd out that the critical temperature T c = 2 v for µ = 0 and the critical chemical potential µ c = 2 πv/ √ 3 for T = 0. When µ = 0, the top-quark mass near T c has the leading ( T 2 c − T 2 ) 1 / 2 behavior with an extra factor dependent on temperature T and the momentum cut-oﬀ Λ. However, it is generally argued that the symmetry restoration at T ≥ T c is still a second-order phase transition.
Optical substructure and BCG offsets of Sunyaev-Zel'dovich and X-ray-selected galaxy clusters

We used optical imaging and spectroscopic data to derive substructure estimates for local Universe ($z < 0.11$) galaxy clusters from two different samples. The first was selected through the Sunyaev-Zel'dovich (SZ) effect by the Planck satellite and the second is an X-ray selected sample. In agreement to X-ray substructure estimates we found that the SZ systems have a larger fraction of substructure than the X-ray clusters. We have also found evidence that the higher mass regime of the SZ clusters, compared to the X-ray sample, explains the larger fraction of disturbed objects in the Planck data. Although we detect a redshift evolution in the substructure fraction, it is not sufficient to explain the different results between the higher-z SZ sample and the X-ray one. We have also verified a good agreement ($\sim$60$\%$) between the optical and X-ray substructure estimates. However, the best level of agreement is given by the substructure classification given by measures based on the brightest cluster galaxy (BCG), either the BCG$-$X-ray centroid offset, or the magnitude gap between the first and second BCGs. We advocate the use of those two parameters as the most reliable and cheap way to assess cluster dynamical state. We recommend an offset cut of $\sim$0.01$\times$R$_{500}$ to separate relaxed and disturbed clusters. Regarding the magnitude gap the separation can be done at $\Delta m_{12} = 1.0$. The central galaxy paradigm (CGP) may not be valid for $\sim$20$\%$ of relaxed massive clusters. This fraction increases to $\sim$60$\%$ for disturbed systems.
Resolutions of unbounded complexes

Various types of resolutions of unbounded complexes of sheaves are constructed, with properties analogous to injectivity, flatness, flabbiness, etc. They are used to remove some boundedness conditions for the existence of the derived functors of functors such as Hom, tensor product, sections over an open subset, inverse and direct images, and for the validity of various formulae involving these derived functors.
Selective RNA Labeling by RNA-Compatible Type II Restriction Endonuclease and RNA-Extending DNA Polymerase

RNAs not only offer valuable information regarding our bodies but also regulate cellular functions, allowing for their specific manipulations to be extensively explored for many different biological and clinical applications. In particular, rather than temporary hybridization, permanent labeling is often required to introduce functional tags to target RNAs; however, direct RNA labeling has been revealed to be challenging, as native RNAs possess unmodifiable chemical moieties or indefinable dummy sequences at the ends of their strands. In this work, we demonstrate the combinatorial use of RNA-compatible restriction endonucleases (REs) and RNA-extending polymerases for sequence-specific RNA cleavage and subsequent RNA functionalization. Upon the introduction of complementary DNAs to target RNAs, Type II REs, such as AvrII and AvaII, could precisely cut the recognition site in the RNA-DNA heteroduplexes with exceptionally high efficiency. Subsequently, the 3′ ends of the cleaved RNAs were selectively and effectively modified when Therminator DNA polymerase template-dependently extended the RNA primers with a variety of modified nucleotides. Based on this two-step RNA labeling, only the target RNA could be chemically labeled with the desired moieties, such as bioconjugation tags or fluorophores, even in a mixture of various RNAs, demonstrating the potential for efficient and direct RNA modifications.
Blood Cell Responses Following Heavy Alcohol Consumption Coincide with Changes in Acute Phase Reactants of Inflammation, Indices of Hemolysis and Immune Responses to Ethanol Metabolites

Aberrations in blood cells are common among heavy alcohol drinkers. In order to shed further light on such responses, we compared blood cell status with markers of hemolysis, mediators of inflammation and immune responses to ethanol metabolites in alcohol-dependent patients at the time of admission for detoxification and after abstinence. Blood cell counts, indices of hemolysis (LDH, haptoglobin, bilirubin), calprotectin (a marker of neutrophil activation), suPAR, CD163, pro- and anti-inflammatory cytokines and autoantibodies against protein adducts with acetaldehyde, the first metabolite of ethanol, were measured from alcohol-dependent patients (73 men, 26 women, mean age 43.8 ± 10.4 years) at baseline and after 8 ± 1 days of abstinence. The assessments also included information on the quantities of alcohol drinking and assays for biomarkers of alcohol consumption (CDT), liver function (AST, ALT, ALP, GGT) and acute phase reactants of inflammation. At baseline, the patients showed elevated values of CDT and biomarkers of liver status, which decreased significantly during abstinence. A significant decrease also occurred in LDH, bilirubin, CD163 and IgA and IgM antibodies against acetaldehyde adducts, whereas a significant increase was noted in blood leukocytes, platelets, MCV and suPAR levels. The changes in blood leukocytes correlated with those in serum calprotectin (p < 0.001), haptoglobin (p < 0.001), IL-6 (p < 0.02) and suPAR (p < 0.02). The changes in MCV correlated with those in LDH (p < 0.02), MCH (p < 0.01), bilirubin (p < 0.001) and anti-adduct IgG (p < 0.01). The data indicates that ethanol-induced changes in blood leukocytes are related with acute phase reactants of inflammation and release of neutrophil calprotectin. The studies also highlight the role of hemolysis and immune responses to ethanol metabolites underlying erythrocyte abnormalities in alcohol abusers.
Biological Properties of Latex, Aqueous Extracts and Bee Products of Euphorbia officinarum L.: A Short Review

Euphorbia officinarum L. is a Moroccan endemic plant known as “Tikiout” and “Daghmus” that can also be found in Mauritania, Western Sahara, and Algeria. In the present review, “Euphorbia officinarum”, “metabolites” “hemisynthesis” were the keywords used for the research in the Web search engine Google Scholar and in the database Web of Science. Triterpenes, phytosterols and ingol diterpenes were isolated and identified in the latex of Moroccan E. officinarum. More than sixty triterpenes were obtained by hemisynthesis from natural triterpenes. Some of these derivatives had insecticidal and antimicrobial activity (phytopathogenic bacteria). The total phenol content and the antioxidant and anti-α-glucosidase activities were dependent on the time and temperature of extractions and also on the plant solvent ratio. The antioxidant activity of monofloral honey of E. officinarum origin was attributed to the phenol fraction (this fraction, previously isolated from honey samples, had better activity than the entire honey).
Noninvasive Diagnostics of Renal Amyloidosis: Current State and Perspectives

Amyloidoses is a group of diseases characterized by the accumulation of abnormal proteins (called amyloids) in different organs and tissues. For systemic amyloidoses, the disease is related to increased levels and/or abnormal synthesis of certain proteins in the organism due to pathological processes, e.g., monoclonal gammopathy and chronic inflammation in rheumatic arthritis. Treatment of amyloidoses is focused on reducing amyloidogenic protein production and inhibition of its aggregation. Therapeutic approaches critically depend on the type of amyloidosis, which underlines the importance of early differential diagnostics. In fact, the most accurate diagnostics of amyloidosis and its type requires analysis of a biopsy specimen from the disease-affected organ. However, absence of specific symptoms of amyloidosis and the invasive nature of biomaterial sampling causes the late diagnostics of these diseases, which leads to a delayed treatment, and significantly reduces its efficacy and patient survival. The establishment of noninvasive diagnostic methods and discovery of specific amyloidosis markers are essential for disease detection and identification of its type at earlier stages, which enables timely and targeted treatment. This review focuses on current approaches to the diagnostics of amyloidoses, primarily with renal involvement, and research perspectives in order to design new specific tests for early diagnosis.
Build-A-FLAIR: Synthetic T2-FLAIR Contrast Generation through Physics Informed Deep Learning

Purpose: Magnetic resonance imaging (MRI) exams include multiple series with varying contrast and redundant information. For instance, T2-FLAIR contrast is based upon tissue T2 decay and the presence of water, also present in T2- and diffusion-weighted contrasts. T2-FLAIR contrast can be hypothetically modeled through deep learning models trained with diffusion- and T2-weighted acquisitions. Methods: Diffusion-, T2-, T2-FLAIR-, and T1-weighted brain images were acquired in 15 individuals. A convolutional neural network was developed to generate a T2-FLAIR image from other contrasts. Two datasets were withheld from training for validation. Results: Inputs with physical relationships to T2-FLAIR contrast most significantly impacted performance. The best model yielded results similar to acquired T2-FLAIR images, with a structural similarity index of 0.909, and reproduced pathology excluded from training. Synthetic images qualitatively exhibited lower noise and increased smoothness compared to acquired images. Conclusion: This suggests that with optimal inputs, deep learning based contrast generation performs well with creating synthetic T2-FLAIR images. Feature engineering on neural network inputs, based upon the physical basis of contrast, impacts the generation of synthetic contrast images. A larger, prospective clinical study is needed.
ASYMMETRIC MAGNETIC RECONNECTION IN SOLAR FLARE AND CORONAL MASS EJECTION CURRENT SHEETS

We present two-dimensional resistive magnetohydrodynamic simulations of line-tied asymmetric magnetic reconnection in the context of solar flare and coronal mass ejection current sheets. The reconnection process is made asymmetric along the inflow direction by allowing the initial upstream magnetic field strengths and densities to differ, and along the outflow direction by placing the initial perturbation near a conducting wall boundary that represents the photosphere. When the upstream magnetic fields are asymmetric, the post-flare loop structure is distorted into a characteristic skewed candle flame shape. The simulations can thus be used to provide constraints on the reconnection asymmetry in post-flare loops. More hard X-ray emission is expected to occur at the footpoint on the weak magnetic field side because energetic particles are more likely to escape the magnetic mirror there than at the strong magnetic field footpoint. The footpoint on the weak magnetic field side is predicted to move more quickly because of the requirement in two dimensions that equal amounts of flux must be reconnected from each upstream region. The X-line drifts away from the conducting wall in all simulations with asymmetric outflow and into the strong magnetic field region during most of the simulations with asymmetric inflow. There is net plasma flow across the X-line for both the inflow and outflow directions. The reconnection exhaust directed away from the obstructing wall is significantly faster than the exhaust directed toward it. The asymmetric inflow condition allows net vorticity in the rising outflow plasmoid which would appear as rolling motions about the flux rope axis.