Miracle-dz/UniFAR-FTUs huggingface dataset:
Operation of locomotives of diesel traction on hilly section of railway

The results of substantiation of the kinematic parameters of the logistics of freight trains and locomotives of diesel traction are presented, taking into account the stopping process at the intermediate and terminal stations of a virtual hilly section of the railway. Tabular and graphic data of the logistic kinematic parameters of the movement of the studied freight trains and locomotives of diesel traction are obtained, taking into account the organization of stops on a virtual hilly section of a high-speed railway, as well as regression equations for determining their of numerical values, which are recommended for implementation in the practice of the locomotive complex of the Uzbek railways.
Accurate Prediction of Myocardial Infarction By Comparing Logistic Regression Algorithm with CatBoost Classifier

Aim: The forecast of Myocardial Infarction for humans employing a Machine learning model by corresponding a Logistic Regression Algorithm with a CatBoost Classifier. The accuracy is enhanced by utilizing the novel LR Classifier. Materials and Methods: The study utilized a total of 20 sample iterations, with 10 samples per group. Group 1 was analyzed using a logistic regression algorithm, while Group 2 was analyzed using a decision tree classifier. The statistical power was set at 80%, and the confidence level was set at 95%. Results: The accuracy of the outcome with logistic regression is 94.61% and CatBoost Classifier is 79.516%, both the groups are statistically significant as p = 0.015 (<0.05) is the significant value in the independent sample T-test between LR and CB Classifier. Conclusion: This research concludes that the logistic regression algorithm gives the most accurate mortality with the difference of 15.1%, compared to the CatBoost Classifier.
Experimental study of turbulence at the plasma edge of ASDEX Upgrade tokamak with an ultra-fast swept reflectometer

Plasma confinement is limited by energy and particle transport, in which turbulence plays an important role. In this work the measurements of the turbulence characteristics carried out on the ASDEX Upgrade tokamak are presented during the transition from the Low (L) to the High (H) confinement mode which goes through an Intermediate (I) phase where turbulence and shear flows strongly interact. One of the most widely accepted theories concerning the L-H transition describes how the turbulence in the plasma edge is stabilized by radial electric field gradients: the E×B flow shear stabilizes turbulence and decreases the radial size of turbulent structures. As a consequence, a transport barrier forms in the edge where the plasma density, the temperature, and their gradients increase. The detailed physical mechanism of the formation of the transport barrier as well as the reason for the residual transport across this barrier are not yet well understood. The density dynamics is measured by an ultra-fast swept reflectometer with a time resolution as high as 1 μs. Studies of the electron density profile dynamics, the density turbulence level, radial wavenumber and frequency spectra during L-H transitions have been performed. The reflectometer measurements show that the density large scale fluctuations decrease after an L-H transition, which confirms the theoretical predictions of the turbulence reduction by sheared flows and supports previous experimental evidences. I-phases for various plasma conditions are documented and the density evolution is compared with the turbulence level. Moreover the results on high frequency coherent modes appearing at the plasma edge are presented.
An MCMC method for diffusion bridges

We present and study a Langevin MCMC approach for sampling nonlinear diffusion bridges. The method is based on recent theory concerning stochastic partial differential equations (SPDEs) reversible with respect to the target bridge, derived by applying the Langevin idea on the bridge pathspace. In the process, a Random-Walk Metropolis algorithm and an Independence Sampler are also obtained. The novel algorithmic idea of the paper is that proposed moves for the MCMC algorithm are determined by discretising the SPDEs in the time direction using an implicit scheme, parametrised by θ ∈ [0,1]. We show that the resulting infinite-dimensional MCMC sampler is well-defined only if θ = 1/2, when the MCMC proposals have the correct quadratic variation. Previous Langevin-based MCMC methods used explicit schemes, corresponding to θ = 0. The significance of the choice θ = 1/2 is inherited by the finite-dimensional approximation of the algorithm used in practice. We present numerical results illustrating the phenomenon and the theory that explains it. Diffusion bridges (with additive noise) are representative of the family of laws defined as a change of measure from Gaussian distributions on arbitrary separable Hilbert spaces; the analysis in this paper can be readily extended to target laws from this family and an example from signal processing illustrates this fact.
Listening to CLIL Practitioners: An Overview of Bilingual Teachers’ Perceptions in Bogota 1 Escuchando a los docentes de AICLE: una visión general de las percepciones de los docentes bilingües en Bogotá

Content and Language Integrated Learning (CLIL) is an educational approach that combines linguistic and subject-based learning to develop the necessary competencies for success in a globalized world. A mixed-method study researched how CLIL is implemented in (10) K-11 schools in Bogota, Colombia, collecting data from 121 in-service teachers in private schools using an array of instruments, such as surveys, structured interviews, and semi-structured interviews. Using the grounded theory approach, data analysis showed that the participants had limited awareness of the CLIL approach and principles. Accordingly, the findings revealed that there is a need for more focus on fundamental CLIL concepts and more formalized teacher training programs. Furthermore, context-orientated resources also emerged as a priority amongst participants, considering that schools were merely trying to replicate European models of delivering CLIL without clear adaptations or consideration for the voices of local teachers. As a result, bilingual schools in Bogota, which are more inclined to use CLIL-oriented approaches, should establish a network to facilitate CLIL training programs to equip in-service teachers who are immersed in content and language environments.
Aryl hydrocarbon receptor gene expression in ankylosing spondylitis and its correlation with interleukin-17, RAR-related orphan receptor gamma t expression, and disease activity indices

Objectives: Considering the role of T helper (Th)17 cells in the pathogenesis of ankylosing spondylitis (AS), the aim of this study was to determine the correlation between aryl hydrocarbon receptor (AHR) gene expression and the expression of Th17-related genes including interleukin (IL)-17 and RAR-related orphan receptor gamma t (RORγt) transcription factor. Patients and methods: Thirty patients with AS (26 males, 4 females; mean age: 36.1±8.1 years) and 30 age- and sex-matched healthy individuals (26 males, 4 females; mean age: 36.2±14.6 years) were recruited for the case-control study between June 2021 and January 2022. Ribonucleic acid (RNA) was extracted from peripheral blood cells and expression levels of AHR, IL-17, RORγt, and AHR repressor (AHRR) genes were evaluated using real-time polymerase chain reaction technique. The serum level of IL-17 was evaluated with enzyme-linked immunosorbent assay. Results: The results showed a nonsignificant elevation of AHR, IL-17, and RORγt gene expression in the patient group compared to the control. There was a direct correlation between AHR gene expression and IL-17 and RORγt genes and a negative correlation between AHR and AHRR expression. Moreover, AHR gene expression showed a weak correlation with disease activity indices, including Bath Ankylosing Spondylitis Disease Activity Index, Bath Ankylosing Spondylitis Functional Index, Bath Ankylosing Spondylitis Metrology Index, Bath Ankylosing Spondylitis Global Score, and Ankylosing Spondylitis Quality of Life. Moreover, the serum level of IL-17 was higher in AS patients compared to the healthy group (p=0.02). Conclusion: Upregulated expression of the AHR gene in ankylosing spondylitis and its correlation with IL-17 and ROR-γ t gene expression suggests that it could be a potential diagnostic and therapeutic target for AS.
Sensory Interactions between Sweetness and Fat in a Chocolate Milk Beverage

Fat and sugar-reduced foods and beverages have become increasingly popular for a variety of reasons, mainly relating to health and wellbeing. Depending on the food or beverage, it may be difficult to reduce the fat and/or sugar content and still maintain optimal sensory properties for the specific product. One way of approaching the problem is to gain a better understanding of how a product is affected by a reduction in fat and/or sugar. This paper aims to investigate the sensory interactions between fat and sweetness perception in a chocolate-flavored milk beverage by using a descriptive analysis with a trained sensory panel. The reduction of fat significantly reduced the sweetness intensity of the chocolate milk, while the reduction of sucrose significantly decreased the cream flavor and the fruity and lactic flavor. The perception of acesulfame-K was affected by fat concentration, similarly to sucrose. These results highlight the importance of considering the effects of reducing either sugar and fat on product attributes that are not directly related to the sugar or fat.
A STUDY ON COMPLICATIONS AND THEIR MANAGEMENT DURING PSEUDOEXFOLIATION CATARACT SURGERY

Background: To analyze the clinical profile of pseudoexfoliation cataract, intraoperative complications encountered and measures to manage them. Materials and Methods: A prospective cross sectional time-bound study was done with patients of pseudoexfoliation associated with cataract and willing for surgery were included. All the patients were planned for manual small incision cataract surgery with rigid posterior chamber intraocular lens implantation under peribulbar anaesthesia and all the surgeries were performed by 2 experienced surgeons. Any intraoperative manipulations were noted and recorded. Results: A total of 710 patients were selected for cataract surgery, out of which 52 patients had pseudo exfoliation cataract. Insufficient mydriasis < 6mm was seen in 39 patients. Pupil dilatation in patients with pseudoexfoliation material distribution on pupil margin and lens was 5 +_0. 5 mm. We implanted rigid PCIOL in the bag in 48 patients, 2 patients had posterior capsular rent where intraocular lens was placed in the sulcus in one and in another patient iris claw lens was placed. Secondary glaucoma following cataract surgery was seen in 15 patients and visual acuity > 6/12 was recorded in 30 patients and visual acuity > 6/18 was seen in 46 patients. Conclusion: Although cataract surgery in pseudoexfoliation is challenging, intraoperative complications can be managed better with thorough preoperative evaluation and modification in surgical technique.
Projection subspace clustering

Gene expression data is a kind of high dimension and small sample size data. The clustering accuracy of conventional clustering techniques is lower on gene expression data due to its high dimension. Because some subspace segmentation approaches can be better applied in the high-dimensional space, three new subspace clustering models for gene expression data sets are proposed in this work. The proposed projection subspace clustering models have projection sparse subspace clustering, projection low-rank representation subspace clustering and projection least-squares regression subspace clustering which combine projection technique with sparse subspace clustering, low-rank representation and least-square regression, respectively. In order to compute the inner product in the high-dimensional space, the kernel function is used to the projection subspace clustering models. The experimental results on six gene expression data sets show these models are effective.
Concentric Eyewall Formation in Typhoon Sinlaku (2008). Part III: Horizontal Momentum Budget Analyses

This is a follow-up work to two prior studies examining secondary eyewall formation (SEF) in Typhoon Sinlaku (2008). This study shows that, in the SEF region, the majority of the elevated winds are supergradient. About two-thirds of the rapid increase in tangential wind tendencies immediately prior to SEF are attributed to agradient wind tendencies. This suggests the importance of nonlinear, unbalanced dynamical processes in SEF in addition to the classical axisymmetric balanced response to forcings of heating and momentum. In the SEF region, analyses show two distinct responsible processes for the increasing azimuthal tangential wind in two vertical intervals. Within the boundary inflow layer, the competing effect between the mean radial influx of absolute vorticity and deceleration caused by surface friction and subgrid diffusion yields a secondary maximum of positive tendency. Analyses further demonstrate the major impact of the mean radial influx of absolute vorticity on SEF. Above the boundary inflow layer, the vertical advection acts to vertically extend the tangential wind jet via the lofting of the enhanced tangential momentum farther upward. The roles of the nonlinear unbalanced dynamics in these two processes are discussed in this paper. From a Lagrangian perspective, the persistently increasing agradient force outweighs the frictional loss, effectively decelerating boundary layer inflowing air across the SEF region. This explains the sharpening of the radial gradient of boundary layer inflow, which is shown to be responsible for the buildup of a zone with concentrated boundary layer convergence. The previously proposed unbalanced dynamical pathway to SEF is elaborated upon and supported by the current results and discussion.
On-orbit inspection of a rotating object using a moving observer

Many objects in space such as orbital debris, defunct satellites, and natural objects are uncooperative, meaning that they are not equipped with working sensors or actuators. There are several reasons to want to visit these uncooperative space objects with an inspector satellite: to deorbit or deflect dangerous debris; to inspect and/or repair defunct satellites; or to observe, sample, or extract resources from natural objects such as asteroids and comets. After performing orbital rendezvous with the target object, the first phase of proximity operations involves observing the object from a safe distance and planning the subsequent phases. This thesis focuses on observing the target object during the first phase of proximity operations. Its objective is to develop a framework and efficient algorithms to obtain a sufficiently detailed and accurate estimate of the target’s structure and dynamics to serve as a platform to plan and execute the subsequent mission objectives. Specifically, subsequent phases will require an estimate of the target’s rotational motion model to facilitate path planning, a 3D map for relative pose determination, and the inertial properties of the target to aid in stabilization of its spin. Since the dynamics of the target and inspector may have similar time constants, the trajectory of the inspector must be estimated to disambiguate the motion of the target from that of the inspector. The algorithms presented in this thesis require that the inspector satellite be equipped with a star tracker, an inertial measurement unit, and a dense 3D visual sensor such as stereo cameras or LiDAR. The inspector trajectory is estimated in a manner which is independent of target motion, using a factor graph approach. Here, star tracker orientation measurements, gyroscope and acceleration measurements, and range and bearing measurements to the target’s centroid are input as factors into a trajectory factor graph. Taken together, these measurements make the inspector’s trajectory observable. Mapping is performed by estimating the inspector’s trajectory in an arbitrary target-fixed frame. Frame-to-frame matching and visual odometry are used to find pose constraints and build a pose graph. In addition to adding pose constraint factors between successive frames, loop closure is performed between temporally non-adjacent
Deep learning subgrid-scale parametrisations for short-term forecasting of sea-ice dynamics with a Maxwell elasto-brittle rheology

Abstract. We introduce a proof of concept to parametrise the unresolved subgrid scale of sea-ice dynamics with deep learning techniques. Instead of parametrising single processes, a single neural network is trained to correct all model variables at the same time. This data-driven approach is applied to a regional sea-ice model that accounts exclusively for dynamical processes with a Maxwell elasto-brittle rheology. Driven by an external wind forcing in a 40 km×200 km domain, the model generates examples of sharp transitions between unfractured and fully fractured sea ice. To correct such examples, we propose a convolutional U-Net architecture which extracts features at multiple scales. We test this approach in twin experiments: the neural network learns to correct forecasts from low-resolution simulations towards high-resolution simulations for a lead time of about 10 min. At this lead time, our approach reduces the forecast errors by more than 75 %, averaged over all model variables. As the most important predictors, we identify the dynamics of the model variables. Furthermore, the neural network extracts localised and directional-dependent features, which point towards the shortcomings of the low-resolution simulations. Applied to correct the forecasts every 10 min, the neural network is run together with the sea-ice model. This improves the short-term forecasts up to an hour. These results consequently show that neural networks can correct model errors from the subgrid scale for sea-ice dynamics. We therefore see this study as an important first step towards hybrid modelling to forecast sea-ice dynamics on an hourly to daily timescale.
Wide Band Microstrip Patch antenna for fast switching and 5G Application

- The proposed microstrip patch antenna for Si substrate in CMOS technology is proposed, the top of a proposed antenna is loaded with fractal shape and the reflector is a ground. The antenna is fed at a center of the excited patch the antenna is compact in size and easily fabricated through photolithography. The substrate used in a antenna is SI whose permittivity is 12.5. The micrpostrip patch antenna is operated in a THz frequency ranging from 22 to 50 GHz. The antenna is operated in a hybrid mode because of this the gain of an antenna is to be improved such mode show the -10 db bandwidth as well as -3db gain and directivity of a proposed antenna. The antenna is operated in a Tera hertz which is suitable for wide gain and high bandwidth for a Terahertz frequency application. The dimension of the via hole is 1.6mm (1600 micro meter). The excited patch and reflector is separated by a feeding probe or SMA connector of 50 ohms. The impedance matching between source to load is done through loading the open wire of dimension 1.6mm,
Terrestrial laser scanning for the monitoring of bridge load tests – two case studies

Terrestrial laser scanning (TLS) technology has various applications due to its capability of acquiring detailed 3D information about objects within a limited time-period. Yet, it is not widely used for the deformation monitoring of structures. To investigate the suitability of TLS for such tasks, a time-of-flight type Leica ScanStation C10 was used to determine the vertical deformations for two bridge load tests in Estonia, namely for the Loobu highway bridge and the Tartu railway overpass. The TLS results were verified with precise levelling, reflectorless tacheometry and dial gauges. Generally, deformation estimates obtained from TLS and other measurement techniques show sub-millimetre agreements (in terms of standard deviations). The maximum differences between the TLS and precise levelling results were 3.4 and 0.8 mm for the Loobu and the Tartu study, respectively. Since TLS has not yet reached the same accuracy as conventional geodetic high-precision techniques, it cannot fully replace them in high accuracy applications. However, TLS can be considered a complementary survey method for load tests as it provides valuable entire surface covering 3D information.
Effect of chitosan complexes on the bacterial community of cecum and productivity of broiler chickens

The search and development of natural biological additives that have a comprehensive effect as immunostimulants and improve digestion in poultry is relevant. This study was carried out at the Selection and Genetics Center “Zagorskoe EPH”. The control and experimental groups were formed of the 1-day-old Ross-308 cross broilers (35 heads in each). Six groups were formed. The broilers of the group No.1 (control) received basic feed (BF) with the addition of feed antibiotic Maxus. The group No.2 (control) received BF without feed antibiotic. The broilers of the group No.3 (experimental) received BF and the KH-1 chitosan complex. The group No.4 (experimental) received BF and the KHM chitosan complex with the addition of copper nanoparticles. The group No.5 (experimental) received BF and drinking preparation based on the KH-Aqua chitosan complex. The group No.6 (experimental) received BF and drinking preparation based on the KH-Aqua chitosan complex enriched with copper nanoparticles. The bacterial community of the gut cecum was analyzed using the molecular genetics method of next-generation sequencing (NGS).
Learning Compact Representations for LiDAR Completion and Generation

LiDAR provides accurate geometric measurements of the 3D world. Unfortunately, dense LiDARs are very expensive and the point clouds captured by low-beam LiDAR are often sparse. To address these issues, we present UltraLiDAR, a data-driven framework for scene-level LiDAR completion, LiDAR generation, and LiDAR manipulation. The crux of UltraLiDAR is a compact, discrete representation that encodes the point cloud's geometric structure, is robust to noise, and is easy to manipulate. We show that by aligning the representation of a sparse point cloud to that of a dense point cloud, we can densify the sparse point clouds as if they were captured by a real high-density LiDAR, drastically reducing the cost. Furthermore, by learning a prior over the discrete codebook, we can generate diverse, realistic LiDAR point clouds for self-driving. We evaluate the effectiveness of UltraLiDAR on sparse-to-dense LiDAR completion and LiDAR generation. Experiments show that densifying real-world point clouds with our approach can significantly improve the performance of downstream perception systems. Compared to prior art on LiDAR generation, our approach generates much more realistic point clouds. According to A/B test, over 98.5% of the time human participants prefer our results over those of previous methods. Please refer to project page https://waabi.ai/research/ultralidar/ for more information.