Miracle-dz/UniFAR-FTUs huggingface dataset:
Shock structure and temperature overshoot in macroscopic multi-temperature model of mixtures

The paper discusses the shock structure in macroscopic multi-temperature model of gaseous mixtures, recently established within the framework of extended thermodynamics. The study is restricted to weak and moderate shocks in a binary mixture of ideal gases with negligible viscosity and heat conductivity. The model predicts the existence of temperature overshoot of heavier constituent, like more sophisticated approaches, but also puts in evidence its non-monotonic behavior not documented in other studies. This phenomenon is explained as a consequence of weak energy exchange between the constituents, either due to large mass difference, or large rarefaction of the mixture. In the range of small Mach number it is also shown that shock thickness (or equivalently, the inverse of Knudsen number) decreases with the increase of Mach number, as well as when the mixture tends to behave like a single-component gas (small mass difference and/or presence of one constituent in traces).
Prediction of a Flash Flood in Complex Terrain. Part II: A Comparison of Flood Discharge Simulations Using Rainfall Input from Radar, a Dynamic Model, and an Automated Algorithmic System

Abstract Three techniques were employed for the estimation and prediction of precipitation from a thunderstorm that produced a flash flood in the Buffalo Creek watershed located in the mountainous Front Range near Denver, Colorado, on 12 July 1996. The techniques included 1) quantitative precipitation estimation using the National Weather Service’s Weather Surveillance Radar-1988 Doppler and the National Center for Atmospheric Research’s S-band, dual-polarization radars, 2) quantitative precipitation forecasting utilizing a dynamic model, and 3) quantitative precipitation forecasting using an automated algorithmic system for tracking thunderstorms. Rainfall data provided by these various techniques at short timescales (6 min) and at fine spatial resolutions (150 m to 2 km) served as input to a distributed-parameter hydrologic model for analysis of the flash flood. The quantitative precipitation estimates from the weather radar demonstrated their ability to aid in simulating a watershed’s response to preci.
Nanodiamonds around HD 97048 and Elias 1

We present an analysis of ISO-SWS observations of the Herbig Ae/Be stars HD 97048 and Elias 1. Besides the well-known family of IR emission bands at 3.3, 6.2, "7.7", 8.6 and 11.2 mum these objects show strong, peculiar emission features at 3.43 and 3.53 mum. The latter two features show pronounced substructure which is very similar in the two sources. Comparison of the spectra of HD 97048 and Elias 1 with laboratory spectra of H-terminated diamond surfaces show excellent and very convincing agreement in peak position and spectral detail (Guillois et al. 1999). The position of the 3.53 mum band indicates a temperature of similar to1000 K. An analysis of the radiative energy budget makes us conclude that the diamond carrier of the 3.53 mum feature has typical sizes of 1-10 nm for HD 97048. A fit of the 3.53 mum feature with a theoretical, calculated profile indicates that the emitting diamonds in HD 97048 see a FUV flux of 5.8 x 10(-3) [W/cm(2)]. The derived diamond mass, 1.5 x 10(-10) M-circle dot, is only a tiny fraction of the total circumstellar dust mass and corresponds to only about 1 parts per billion relative to hydrogen. We discuss the origin of the diamond around these Herbig Ae/Be stars and conclude that most likely they are formed in situ. The implications for the nanodiamonds discovered in meteorites are also discussed.
STRONG CONSISTENCY OF ESTIMATORS FOR MULTIVARIATE ARCH MODELS

This paper deals with the asymptotic properties of quasi-maximum likelihood estimators for multivariate heteroskedastic models. For a general model, we give conditions under which strong consistency can be obtained; unlike in the current literature, the assumptions on the existence of moments of the error term are weak, and no study of the various derivatives of the likelihood is required. Then, for a particular model, the multivariate GARCH model with constant correlation, we describe the set of parameters where these conditions hold.
Dealing with Overfitting in the Context of Liveness Detection Using FeatherNets with RGB Images

: With the increased use of machine learning for liveness detection solutions comes some shortcomings like overﬁtting, where the model adapts perfectly to the training set, becoming unusable when used with the testing set, defeating the purpose of machine learning. This paper proposes how to approach overﬁtting without altering the model used by focusing on the input and output information of the model. The input approach focuses on the information obtained from the different modalities present in the datasets used, as well as how varied the information of these datasets is, not only in number of spoof types but as the ambient conditions when the videos were captured. The output approaches were focused on both the loss function, which has an effect on the actual ”learning”, used on the model which is calculated from the model’s output and is then propagated backwards, and the interpretation of said output to deﬁne what predictions are considered as bonaﬁde or spoof. Throughout this work, we were able to reduce the overﬁtting effect with a difference between the best epoch and the average of the last ﬁfty epochs from 36.57% to 3.63%.
Evaluating Architectures and Hyperparameters of Self-supervised Network Projections

: Self-Supervised Network Projections (SSNP) are dimensionality reduction algorithms that produce low-dimensional layouts from high-dimensional data. By combining an autoencoder architecture with neighborhood information from a clustering algorithm, SSNP intend to learn an embedding that generates visually separated clusters. In this work, we extend an approach that uses cluster information as pseudo-labels for SSNP by taking outlier information into account. Furthermore, we investigate the influence of different autoencoders on the quality of the generated two-dimensional layouts. We report on two experiments on the autoencoder’s architecture and hyperparameters, respectively, measuring nine metrics on eight labeled datasets from different domains, e.g., Natural Language Processing. The results indicate that the model’s architecture and the choice of hyperparameter values can influence the layout with statistical significance, but none achieves the best result over all metrics. In addition, we found out that using outlier information for the pseudo-labeling approach can maintain global properties of the two-dimensional layout while trading-off local properties.
Model Transparency: Why Do We Care?

: Artiﬁcial intelligence (AI) and especially machine learning (ML) has been increasingly incorporated into a wide range of critical applications, such as healthcare, justice, credit risk assessment, and loan approval. In this paper, we survey the motivations for caring about model transparency, especially as AI systems are becoming increasingly complex leviathans with many moving parts. We then brieﬂy outline the challenges in providing computational solutions to transparency.
A Two-Temperature Model for Turbulent Flow and Heat Transfer in a Porous Layer

A new model of turbulent flow and of two-temperature heat transfer in a highly porous medium is evaluated numerically for a layer of regular packed particles. The layer can have heat exchange from the defining surfaces. The commonly used models of variable morphology functions for porosity and specific surface were used to obtain comparisons with other works in a relatively high Reynolds number range. A few outstanding features of the closure models for additional integral terms in equations of flow and heat transfer are advanced. Closures were developed for capillary and globular medium morphology models. It is shown that the approach taken to close the integral resistance terms in the momentum equation for a regular structure can be obtained in a way that allows the second order terms for laminar and turbulent regimes to naturally occur. These terms are taken to be close to the Darcy term or Forchheimer terms for different flow velocities. The two-temperature model was compared with a one-temperature model using thermal diffusivity coefficients and effective coefficients from various authors. Calculated pressure drop along a layer showed very good agreement with experiment for a porous structure of spherical beads. A simplified model with constant coefficients was comparedmore » with analytical solutions. « less
IN SILICO AND GENETIC ANALYSIS RELATED TO TILLERING ABILITY IN MAIZE

Maize developed from its ancestor, teosinte, about 10,000 years ago. The evolution has gone from teosinte with multiple tillers to single-tiller maize plants. An investigation took place to identify and sequence genes related to tillering ability in maize and perform In silico analysis. Mating proceeded by manual pollination between the commercial hybrid SC2031 of maize (Zea mays L.) and the teosinte genotype Domiata (Durra rayyana). The parents, F1 hybrids, and their F2 progenies gained evaluation for tillering ability. The SC2031 (low or no-tillers) exhibited fragments ranging from 75 to 420 bp. The fragment observed with 262 bp size was in the F1 and nine out of 10 low-tillering F2 progenies. The Domiata parent, a high-tillering variety, exhibited three fragments ranging from 82 to 534 bp. The fragment with 445 in size occurred in nine out of 10 high-tillering F2 progenies. The fragment of 262 bp, which may be responsible for the low tillering ability, and the fragment of 445 bp, for the high tillering ability, were eluted from the gel, sequenced, and submitted to GenBank. The predicted protein, T1-L, of the DNA sequence from the low-tillering parent has a molecular weight of 6.69 kD and pI of 6.53, belonging to the GRAS family, which plays an important role as transcriptional factors required for the adequate pattern of radial rooting and shooting. The predicted protein, T1-H, of the DNA sequence from the high-tillering parent has a molecular weight of 14.08 kD, and pI of 7.97, belonging to the TEOSINTE BRANCHED 1, CYCLOIDEA, PCF1 (TCP) family that plays an essential role as transcriptional factors required for apical dominance.
Clinicopathological Spectrum of Benign Skin Adnexal Tumors in the Pakistani Population: A Single-Center Study

Background Skin adnexal tumors (SATs) are categorized per the site of origin, for example, hair follicles, sebaceous glands, and sweat glands. In our population, there is limited information related to the clinicopathological characteristics of these tumors. Management and prognosis depend largely upon the morphological type of the tumor. In this study, we assessed the disease spectrum and most prevalent subtypes of benign SATs. Methodology An analysis of 565 cases was conducted in this cross-sectional study between January 2018 and December 2022, using a non-probability consecutive sampling approach. Patient age, gender, site of involvement, and diagnosis were documented according to the fourth edition of the WHO Classification of Skin Tumors published in 2018. Data were entered and analyzed using SPSS Version 26.0 (IBM Corp., Armonk, NY). Results Our study had 565 patients, out of which 271 (47.9%) were males and 294 (52.1%) were females. The mean age was 40.97±19.3 years (range, 2-100 years). Anatomical site variations were as follows, head and neck (n=336, 59.4%), extremities (n=124, 22%), trunk (n=84, 14.9%), and genital areas (n=21, 3.7%). The most common histological subtypes of benign SATs were sweat gland origin (n=350, 62.0%), followed by hair follicle origin (n=161, 28.5 %), and sebaceous gland origin (n=54, 9.5%). Conclusion Sweat gland tumors were the most prevalent class of benign SATs in our study, in which hidradenoma and poroma were the most frequent subtypes. Hair follicle origin was the second most prevalent class of tumors with pilomatricoma being the most frequent. Sebaceous tumors were overall uncommon; nevus sebaceous of Jadasshon was the most common tumor in this class.
Association of Serum Bilirubin Levels with Macro- and Microvascular Complications in Chinese People with Type 2 Diabetes Mellitus: New Insight on Gender Differences

Background Previous studies suggested protective effects of bilirubin against cardiovascular disease, with a possible gender difference. However, the relationship between serum total bilirubin (TBIL) with diabetic macro- and microvascular complications remains unknown. We aimed to examine the association of macro- and microvascular complications with serum TBIL levels. Methods Serum TBIL was measured in 648 patients with T2DM. Demographic and clinical data were obtained from the inpatient medical record system. Serum TBIL was measured with an automatic biochemistry analyzer according to routine protocols. Parameters of vascular complications, including ankle-brachial index, carotid intima-media thickness, estimated glomerular filtration rate and the urinary albumin to creatinine ratio, were measured and calculated. The association between TBIL and diabetic macro- and microvascular complications was analyzed. Results In multivariable logistic regression, after adjustment for age, sex, body mass index and diabetic duration, higher serum TBIL levels were significantly associated with decreased odds of microalbuminuria (OR = 0.31, [95% CI] 0.16–0.61, P = 0.003) and chronic kidney disease (OR = 0.19, [95% CI] 0.09–0.41, P < 0.001). These associations were only found in male but not in female patients. However, no significant relationship was found between TBIL and peripheral arterial disease or carotid hypertrophy. Conclusion Our findings suggest that physiological higher TBIL level might be a protective factor for diabetic microvascular complications.
AN ANALOGUE OF CIRCULAR UNITS FOR PRODUCTS OF ELLIPTIC CURVES

Abstract We construct certain elements in the motivic cohomology group $H^3_{\mathcal{M}}(E\times E',\mathbb{Q}(2))$, where $E$ and $E'$ are elliptic curves over $\mathbb{Q}$. When $E$ is not isogenous to $E'$ these elements are analogous to circular units in real quadratic fields, as they come from modular parametrizations of the elliptic curves. We then find an analogue of the class-number formula for real quadratic fields, which specializes to the usual quadratic class-number formula when $E$ and $E'$ are quadratic twists. AMS 2000 Mathematics subject classification: Primary 11F67; 14G35. Secondary 11F11; 11E45; 14G10
DeNos22: A Pipeline to Learn Object Tracking Using Simulated Depth

: We propose a novel pipeline to construct a learning based 6D object pose tracker, which is solely trained on synthetic depth images. The only required input is a (geometric) CAD model of the target object. Training data is synthesized by rendering stereo images of the CAD model, in front of a large variety of backgrounds generated by point-based re-renderings of prerecorded background scenes. Finally, depth from stereo is applied in order to mimic the behavior of depth sensors. The synthesized training input generalizes well to real-world scenes, but we further show how to improve real-world inference using robust estimators to counteract the errors introduced by the sim-to-real transfer. As a result, we show that our 6D pose trackers achieve state-of-the-art results without any annotated real-world data, solely based on a CAD-model of the target object.
Performance study for Francis-99 by using different turbulence models

The three-dimensional numerical investigation for turbine-99 at the best efficiency operation point, part load operation point and full load operation point was conducted by using the different turbulence models. By comparing the results of numerical simulation and experimental results, it was found that: there is a certain deviation between the numerical simulation results obtained by different turbulence models and experimental values, and the deviation increase with the reduction of output. Compared to other turbulence model, the result obtained by the standard k-e turbulent model has a relatively small difference with the experimental results. The main causes for the big difference between the numerical simulation and model test include two aspects: (1) the mesh generation and boundary conditions setting lead to differences between the research object and the actual model, (2) it is difficult to accurately simulate the unstable flow such as impact, flow separation and vortex in the turbine. Therefore, in the future actual flow pattern simulation, besides the reasonable choice of turbulence model, based on the actual flow characteristics, the boundary conditions and the simulation results will be amended to reduce the deviation between the numerical simulation and experimental results as much as possible.
Police legitimacy and culture revisited through the lens of self-legitimacy

ABSTRACT This conceptual article contributes to the scientific debate on police culture and police legitimacy by exploring and refining the concept of self-legitimacy. It argues that endogenously constructed self-legitimacy co-produces and reinforces certain core characteristics of police culture. ‘Self-legitimacy’ in this context is the degree to which those in power believe in the moral justice of their power. Endogenous self-legitimation processes occur when officers identify with the professional police identity and the police organisation, and self-legitimacy is brought about by those in power attributing unique characteristics to themselves and seeking validation from an inner circle of similar power-holders. Drawing on the analysis, suggestions are made on how police culture and police legitimacy can be influenced by facilitating a shift in officers’ perception of their ‘professional identity’.
On the Design of GDPR Compliant Workflows for Responsible Neuroimage Data Sharing

: Sharing medical data may facilitate advancing research as this may allow understanding the mechanisms of certain diseases, develop new drugs and medication schemes and find cures. However, as these data originate from humans, the issue of individual privacy rises since certain data modalities, as Neuroimages, if not properly curated, may reveal the identity of the individual described by these data As legislation around the globe attempts to set rules for protecting privacy, techniques and methodologies have been proposed to allow for data publishing, also complying with the law. In this paper, we aspire to provide practitioners with workflows for ethical neuroimage data publishing under the GDPR, EU’s latest data protection regulation.
Low-frequency linear-mode regimes in the tokamak scrape-off layer

Motivated by the wide range of physical parameters characterizing the scrape-off layer (SOL) of existing tokamaks, the regimes of low-frequency linear instabilities in the SOL are identified by numerical and analytical calculations based on the linear, drift-reduced Braginskii equations, with cold ions. The focus is put on ballooning modes and drift wave instabilities, i.e., their resistive, inertial, and ideal branches. A systematic study of each instability is performed, and the parameter space region where they dominate is identified. It is found that the drift waves dominate at high R/Ln, while the ballooning modes at low R/Ln; the relative influence of resistive and inertial effects is discussed. Electromagnetic effects suppress the drift waves and, when the threshold for ideal stability is overcome, the ideal ballooning mode develops. Our analysis is a first stage tool for the understanding of turbulence in the tokamak SOL, necessary to interpret the results of non-linear simulations.
Testicular Strain Elastography in Fertile and Infertile Men - A Comparative Cross Sectional Study

Background: Infertility affects one fifth to one sixth of couples in the reproductive age group. A condition involving the male partner contributes to approximately 50% of cases of infertility. Conventional ultrasonography has the limitation of only functional analysis of testicular tissue, whereas elastography is a promising technique in this field. Materials and Methods: A total of 82 patients between 20 – 45 years of age were evaluated between February 2018 to January 2019. The patients were divided into two groups based on semen analysis as Infertile (Group 1) and Fertile (Group 2). Semen analyses, B-mode, colourdoppler and sonoelastography examinations were performed. Measurements of testicular volumes, resistive indices (RI) in intraparenchymal arteries, strain value, strain ratio (SR) and presence of varicocele were recorded. Results: Mean age of the patients were (Group1, 31.68 ± 3.78 years and Group 2, 29.68 ± 3.18 years). Mean Testicular volumes between Group 1 and 2 (13.87 ± 3.50 vs. 14.99 ± 1.98) were not significantly different. Mean Strain ratios between Group 1 and 2 (0.37 ± 0.08 vs. 0.22 ± 0.09, P <0.001) were significantly different. The SRs between men with various sperm abnormalities did not show any significant difference. Conclusion: Strain elastography results were found to be significantly different in patients with abnormal sperm counts. This technique therefore proves to be a useful tool for the evaluation of male infertility. But further large scale studies may be needed to clarify the value of this imaging modality in the assessment of male infertility.
Automatic Fracture Detection and Characterization in Borehole Images Using Deep Learning-Based Semantic Segmentation

: Fracture analysis represents one of the key investigations that needs to be carried in borehole logs. Identifying fractures, as well as other similar features (like breakouts or foliations) is essential for characterizing the reservoir where the drilling took place. However, identifying and characterizing the fractures from borehole images is a very time and resource consuming task, that require extensive knowledge from geological experts. For this reason, developing semi-automated or automated tools would facilitate and increase the productivity of fracture analysis, since even for one reservoir, experts need to analyze and interpret hundreds of meters of borehole images. This paper presents a deep learning based approach for application of automatic fracture detection and characterization in borehole images, relying on state-of-the-art convolutional neural network for accurate semantic segmentation of fractures. Target images consists of color borehole images, as opposed to acoustic or drill-core images, and uses real world data, both for training the deep learning model and testing the whole system. The system is evaluated by using multiple metrics and the final outputs of the system are the parameters of the sinusoids that define the predicted fractures.
The bowed string and the player

Relations between bowing parameters, i.e., force applied, bow position, and velocity, are derived in terms of load impedance presented by bridge to string, characteristic impedance, and frictional coefficients. The range between least applied force needed to couple bow to load during sticking and maximum permitting uncoupling following sticking provides the generous tolerance, variable but typically in the ratio of 1 to 10, that makes the bowed string so flexible in performance. Domains of string behavior recognized by players are related graphically to bowing parameters. An electromagnetic method for observing particle velocity in the string reveals small but significant ripples caused by forces at the bow that the idealized explanation ignores. In one very flexible string, force exerted on the bridge varied approximately inversely with frequency out to the 15th harmonic, whereas for a string equivalent to a gut G for violin force became zero near the 7th. Elastic effects are considered, and it is sugges.
Corps minimaux contenant l’algebre du groupe libre a deux generateurs

The aim of this paper is to prove that, except in caracteristic 2, the division rings considered in H. Lorenz's paper [1] do not only contain free associative algebras, but also free group algebras. The main ideas are due to L. Makar-Limanov who proved that result for D1 [2]