Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimization of the current potential for stellarator coils

Stellarator plasma confinement devices have no continuous symmetries, which makes the design of appropriate coils far more subtle than for axisymmetric devices such as tokamaks. The modern method for designing coils for stellarators was developed by Peter Merkel [P. Merkel, Nucl. Fusion 27, 867 (1987)]. Although his method has yielded a number of successful stellarator designs, Merkel’s method has a systematic tendency to give coils with a larger current than that required to produce a stellarator plasma with certain properties. In addition, Merkel’s method does not naturally lead to a coil set with the flexibility to produce a number of interesting plasma configurations. The issues of coil efficiency and flexibility are addressed in this paper by a new method of optimizing the current potential, the first step in Merkel’s method. The new method also allows the coil design to be based on a freer choice for the plasma–coil separation and to be constrained so space is preserved for plasma access.
Mechanism of Detecting the Construction Quality of a Diaphragm Wall by an Infrared Thermal Field and Engineering Application

During underground space exploitation in the urbanization process, numerous foundation pits were constructed where a diaphragm wall was often used as a retaining structure and waterproof curtain. Due to complicated engineering geological conditions or improper construction, diaphragm walls and wall joints often exhibit quality defects. Groundwater leaked from these quality defects to foundation pits during excavation, endangering the safety of the pit and surrounding facilities. The current leakage identification of the underground retaining structure was performed by artificial visual detection, which cannot satisfy the engineering requirement. The temperature field in the leakage area of the diaphragm wall was different from other areas. The leakage wall imaging system using a thermal imager was efficient in visualizing leaking, which was not visible to the naked eye. In this study, infrared thermal imaging technology was introduced in potential leakage detection for the diaphragm wall of a foundation pit. The infrared radiation characteristics of the diaphragm wall leakage and the potential leakage parts were studied through laboratory simulation tests and on-site detection methods. The maximum temperature appeared at the water outlet and the surface of the defect with hidden defect, and the temperature field was symmetrically distributed along the cross-section direction. In the potential leakage area, the temperature difference at the penetration point was 23.4 °C when the initial water pressure was 10 kPa. The temperature difference at the penetration point was 21.8 °C when the initial water pressure was 30 kPa. In the field test, the maximum temperature difference between the leakage area and the surrounding wall was 4.5 °C. The study can provide a reference for similar engineering.
A polar low named Vera: the use of potential vorticity diagnostics to assess its development

20 November 2008 the Norwegian Meteorological Institute issued an extreme weather warning for Trøndelag County. A storm was expected in the afternoon. The storm, a polar low (PL), was named ‘Vera’. Vera was the second of two polar lows that developed along a wedge of warm air at the rear of a synoptic‐scale low that moved northeastwards into northern Norway. The dynamical development may be explained by classic dynamical theory: low‐level warm air seclusion and shallow secondary circulation in a frontal zone that couples to transient upper level disturbances. The study focuses on the potential vorticity (PV) perspective of the PL development and we have modified the initial PV field to study the effect of each individual PV perturbation. Two PV and two low‐level temperature anomalies have been chosen. The PV anomalies retained their structure during the development while the temperature anomalies became less coherent and difficult to define by the end of the cyclogenesis period. By modifying the initial conditions in the upper troposphere over Greenland, it is shown that in this case the upper PV anomaly had the strongest effect on the development, and that this effect remained strong throughout the cyclogenesis until the polar low made landfall. The effect from the low level PV anomaly became large after onset of cyclogenesis, reflecting the positive feedback, although its impact on the development remained smaller than that of the upper PV anomaly. It is also shown that the topography of Greenland was important in determining the correct position of this polar low development. Copyright © 2011 Royal Meteorological Society
Regression Yield Analysis for Semiconductor Manufacturing Based on Fabrication Conditions

In semiconductor manufacturing, there are hundreds of processes for building devices on wafers. All wafers have their paths, and the yield values of wafers vary depending on the fabrication condition. Yield regression modeling based on production data is useful for understanding the impact of processes and predicting yield in advance to plan manufacturing. In this study, we utilize the variations of processes and the chambers of instruments to build a yield model. Following the proposed framework, the optimized extreme gradient boosting model predicts yield with 0.847 mean absolute error value. In this paper, we also apply Shapley additive explanation value method to explain the prediction model to extract relative instances for further causality analysis and process modification. Further research is required to establish a sophisticated yield prediction and this approach would be useful in the categorical production data application.
Graph Representation Learning based Vulnerable Target Identification in Ransomware Attacks

The increased digitization of commercial and consumer workflows, accelerated cloud adoption, and the growing sophistication of cyber criminals have resulted in ransomware emerging as a major threat of cyber attacks in cloud and data services. While research in malware detection can be partially adapted for ransomware, specific ransomware infection patterns can be leveraged to improve the detection efficiency. In this paper, we focus on identifying vulnerable targets in ransomware attacks, aiming to accelerate the ransomware detection process as well as enable better data backup policies design. Specifically, we make three contributions. First, we characterize lexical features and hierarchical file structure features on those ransomware infected files and folders. Second, we model the data backup as an attributed tree graph, learn a new feature representation of the nodes with graph neural networks, and train a classifier based on the new features. Third, with real-world snapshot backup instances, we demonstrate the superior performance of the graph representation learning based approach over several baselines. Compared to the traditional full-scan approaches, our finding suggests that the vulnerable ransomware attack target identification can result in a more efficient ransomware detection process via focused inspection of most vulnerable data in the backups. Our method can also be easily integrated into existing ransomware detection systems for accelerated cyber resiliency.
On a class of steady confined Stokes flows with chaotic streamlines

The general incompressible flow uQ(x), quadratic in the space coordinates, and satisfying the condition uQ = · n = 0 on a sphere r = 1, is considered. It is shown that this flow may be decomposed into the sum of three ingredients – a poloidal flow of Hill's vortex structure, a quasi-rigid rotation, and a twist ingredient involving two parameters, the complete flow uQ(x) then involving essentially seven independent parameters. The flow, being quadratic, is a Stokes flow in the sphere. The streamline structure of the general flow is investigated, and the results illustrated with reference to a particular sub-family of ‘stretch–twist–fold’ (STF) flows that arise naturally in dynamo theory. When the flow is a small perturbation of a flow u1(x) with closed streamlines, the particle paths are constrained near surfaces defined by an ‘adiabatic invariant’ associated with the perturbation field. When the flow u1 is dominated by its twist ingredient, the particles can migrate from one such surface to another, a phenomenon that is clearly evident in the computation of Poincaré sections for the STF flow, and that we describe as ‘trans-adiabatic drift’. The migration occurs when the particles pass a neighbourhood of saddle points of the flow on r = 1, and leads to chaos in the streamline pattern in much the same way as the chaos that occurs near heteroclinic orbits of low-order dynamical systems. The flow is believed to be the first example ofa steady Stokes flow in a bounded region exhibiting chaotic streamlines.
Association of Shunt Size and Long-Term Clinical Outcomes in Patients with Cryptogenic Ischemic Stroke and Patent Foramen Ovale on Medical Management

Introduction: Patent foramen ovale (PFO) is a potential source of cardiac embolism in cryptogenic ischemic stroke, but it may also be incidental. Right-to-left shunt (RLS) size may predict PFO-related stroke, but results have been controversial. In this cohort study of medically-managed PFO patients with cryptogenic stroke, we aimed to investigate the association of shunt size with recurrent stroke, mortality, newly detected atrial fibrillation (AF), and to identify predictors of recurrent stroke. Methods: Patients with cryptogenic stroke who screened positive for a RLS using a transcranial Doppler bubble study were included. Patients who underwent PFO closure were excluded. Subjects were divided into two groups: small (Spencer Grade 1, 2, or 3; n = 135) and large (Spencer Grade 4 or 5; n = 99) shunts. The primary outcome was risk of recurrent stroke, and the secondary outcomes were all-cause mortality and newly detected AF. Results: The study cohort included 234 cryptogenic stroke patients with medically-managed PFO. The mean age was 50.5 years, and 31.2% were female. The median period of follow-up was 348 (IQR 147-1096) days. The rate of recurrent ischemic stroke was higher in patients with large shunts than in those with small shunts (8.1% vs. 2.2%, p = 0.036). Multivariate analyses revealed that a large shunt was significantly associated with an increased risk of recurrent ischemic stroke [aOR 4.09 (95% CI 1.04–16.0), p = 0.043]. Conclusions: In our cohort of cryptogenic stroke patients with medically managed PFOs, those with large shunts were at a higher risk of recurrent stroke events, independently of RoPE score and left atrium diameter.
The Role of Diet in Tinnitus Onset: A Hospital-Based Case-Control Study from Italy

Knowledge on the role of diet in tinnitus onset is mostly based on few cross-sectional studies. In 2016–2019 we conducted a hospital-based case-control study in northern Italy on 185 incident idiopathic tinnitus cases and 198 controls, providing data on dietary habits through a 37-item food-frequency questionnaire. Odds ratios (OR) for tinnitus risk were derived through unconditional multiple logistic regression models. Moderate-to-high vs. low intake of caffeine (OR, 0.49; 95% confidence interval (CI), 0.24–0.99) and butter (OR, 0.46; 95% CI, 0.23–0.93), and high vs. low intake of poultry (OR, 0.43; 95% CI, 0.23–0.81), prosciutto (OR, 0.44; 95% CI, 0.23–0.85), and legumes (OR, 0.50; 95% CI, 0.28–0.92) were inversely associated with tinnitus onset. Other food items, including cereals, red meat, fish, vegetables, and fruit did not show any statistically significant relationship. The variety of food consumed decreased the risk of tinnitus (OR for at least 20 vs. less than 16 different food items, 0.47; 95% CI, 0.24–0.90). Our findings highlight the importance of diet in tinnitus onset and confirm a potential inverse association of protein-rich food and caffeine on the incidence of tinnitus. Confirmation of our findings in longitudinal studies is necessary before proving any diet recommendations for tinnitus prevention.
Learning Latent Road Correlations from Trajectories

A core component of the Intelligent Transportation System (ITS) is road network, which forms the most basic transport infrastructure, and becomes widely applied in many traffic applications. In most traffic models, the spatial representation of road network is learned only through static graph connection while dynamic driver preference and traffic conditions in the real world are ignored. Therefore, in this paper, a novel trajectory-based road network representation is proposed. By mining vehicle trajectories, our proposed method can learn dynamic route choice through embeddings of each road in a next-hop prediction model. Then road correlations are calculated by the embeddings to build a latent correlation graph that can be applied in various traffic-related applications. Extensive experiment results prove the effectiveness and rationality of our proposed approach.
Solubility-driven thin film structures of regioregular poly(3-hexyl thiophene) using volatile solvents

Using atomic force microscopy and grazing-incidence x-ray diffraction, the authors found that the use of volatile CH2Cl2 not only offers an advantage in minimizing regioregular poly(3-hexyl thiophene) (RR P3HT) film deposition time, but also directs the desirable parallel orientation of π-π stacking planes of RR P3HT with respect to solid substrates, for both spin and drop castings. The substrate temperature effects have been investigated to support claims that the substrate-induced crystallization of RR P3HT preseeds the parallel oriented crystals prior to spin casting, when the warm CH2Cl2 solution is loaded on a “cold” substrate held at room temperature.
Status of Swamp Deer Rucervus duvaucelii duvaucelii (G. Cuvier, 1823) in grassland-wetland habitats in Dudhwa Tiger Reserve, India

Swamp Deer is an endemic species of the Indian subcontinent, mainly inhabiting tall grasslands in river floodplains. Populations of the northern subspecies (Rucervus duvaucelii duvaucelii), restricted to the Terai regions of northern India and western Nepal, have undergone dramatic declines over the past two─three decades. In this communication, we present results from systematic population counts conducted in Dudhwa Tiger Reserve between 2021─2022. These counts were conducted at 11 sites, each with two─three independent observers. The exercises covered summer, monsoon, post-monsoon, and winter seasons. We found the largest congregations of Swamp Deer in June during the summer and February in winter. In the monsoon months of July─September the Swamp Deer tend to use Sal woodlands along with farmlands adjoining the reserve edge. This is part of an ongoing research project on long-term monitoring and conservation of the threatened alluvial grasslands of the central Terai in Uttar Pradesh, India. Institutionalizing these monitoring practices for frontline forest staff is essential for understanding long-term population dynamics of the threatened Swamp Deer to develop conservation protocols for grasslands and their fauna.
Effects of Grain Size and Doping on Photovoltaic Current in (Pb1-xLax)TiO3 Ferroelectric Ceramics

The effects of grain size and doping on the photovoltaic current in (Pb1-xLax)TiO3 ceramics were investigated under uniform illumination. The effects are discussed on the basis of an electro-potential barrier associated with the grain boundary. Photo-excited electrons were trapped at the grain boundary and then built an electro-potential barrier, which impeded the movement of electrons. The maximum photovoltaic current was obtained at the critical grain size; the electro-potential barrier played a minimum role. By increasing the concentration of lanthanum, the critical grain size decreased.
Semantic Segmentation based network for 6D pose estimation

Recognizing the position and pose of targets is important for robots to perceive the environment and is an important prerequisite for robots to complete intelligent grasping. In order to reduce the search scope of 6D space and increase the accuracy in the case of shade or truncation, we also use the pixel-level prediction, predicting confidence scores. Besides that, a parallel branch network is proposed which includes a task flow processing semantic object segmentation label for each point in the forecast. which is very important for dealing with objects in complex scenes. Experimental results show that the proposed method is effective in predicting 6D pose of occluded objects.
A New Method of Expressing Point Model Based on Kd-Tree for Plane Area

According to the space equal features of midpoint segmentation KD-tree, anisotropic quantitative method is proposed. After having used this method, the coordinate figures of sampling points in point model were quantified again; and the quantitative results have been represented as encoding of space partition methods and results in the process of constructing KD-tree. Meantime, the effectiveness that the method was used to express point model have been simulated and studied. It turned out that the quantitative method is very suitable for large flat area, because it is not only directly reduce the amount of point model geometry data, but also greatly reduce sampling points in case of keeping the numerical precision of the model geometry data unchanged, so as to further reduce the amount of point model data.
The formation mechanism and generation conditions of urban residents' public safety behavior

The formation mechanism and generation condition configuration of urban residents' public safety behavior crucial for reducing urban public safety accidents were identified using the structural equation model (SEM) and the fsQCA method to analyze 465 survey samples. The findings showed that organizational safety communication, community safety support, family safety climate, safety consciousness, and safety motivation are driving factors influencing individual safety behavior and that residents' safety behavior is positively influenced by emotional safety culture. Furthermore, nine generational configurations of these antecedent variables are sufficient to explain the emergence of high levels of safety behavior. Because most previous research has focused on the linear effects of single or multiple factors on outcome variables while ignoring “combined effects,” this study employed fsQCA to identify “combined effects” among antecedent factors of safety behavior from multiple perspectives. The results revealed that no single factor, but rather a combination of factors, led to high levels of safety behavior, shedding light on the importance of “combined effects” among variables and providing theoretical and practical insights into urban residents' public safety behavior.
AnyStreamKM: Anytime k-medoids Clustering for Streaming Data

Stream Clustering algorithms have gained a lot of importance in the recent past due to rapid rising utilities of IoT systems and applications. Anytime algorithms and frameworks play a key role in handling streams that have data arriving/generating at variable rates. They are capable of handling both slow and fast stream speeds, at the same time generate the result with highest possible accuracy. In this paper, we present AnyStreamKM, which is a framework for anytime k-medoids clustering of data streams. It uses a proposed hierarchical data indexing structure known as AnyKMTree that stores the incoming data from the stream in the form of hierarchy of micro-clusters. AnyKMTree is an adaptation of R-tree with its splitting strategy inspired from the design principles of k-medoids clustering. AnyKMTree not only supports anytime features but is also capable of filtering out noise and outliers. Our experimental analysis establishes that AnyKMTree produces micro-clusters that are more compact and purer than the state-of-the-art methods. Also, when offline k-medoids clustering such as PAM (Partitioning Around Medoids) is applied on the micro-clusters produced by AnyKMTree, the resultant clustering has been found to be of higher quality than the state-of-the-art methods.
Astrophysical gasdynamics confronts reality - The shaping of planetary nebulae

We present two-dimensional numerical simulations, which use techniques of radiation gasdynamics to simulate the structures of planetary nebulae (PNs). Our model incorporates realistic volume emissivities in order to fully account for the conversion of mechanical and thermal energy into radiation. The model also produces detailed predictions of observables such as projected structure (e.g., Hα and [N II] images) and kinematic patterns. Virtually the full range of PN morphologies are easily reproduced, as are the basic kinematics, ionization structures, and temperatures
Measuring the pitch control of professional football players using spatiotemporal tracking data

We study pitch control in football, using data from six complete seasons of the English Premier League. Our objective is to investigate features of pitch control in the data. We process the data to ensure consistency of the tracking and event datasets. This represents the largest coherent dataset analysed in the literature and allows the observation of consistent patterns across several seasons’ data. We demonstrate that teams playing in front of a crowd at home control on average 2.9±0.2% more of the pitch than teams playing away, which reduces to 1.5±0.6% in matches played behind closed doors. We observe that match by match the difference in pitch control between the teams has a weak, positive correlation with the difference in expected goals (Pearson correlation R = 0.38). As a further manifestation of home advantage we find that in games which the two teams have equal pitch control, on average the home team accumulates greater expected goals ( 0.16±0.03 ). The concept of weighted pitch control is introduced, by assigning a weight to regions of the pitch. We demonstrate that pitch control of the penalty box of the out-of-possession team is negatively correlated with expected goals in each of the six seasons, and interpret this apparently counter-intuitive result.
Images of Astrophysical Masers and their Variability in a Turbulent Medium: The 25 GHz Methanol Masers

We examine the influence of chance coherences in turbulent velocities for the creation of the images of astrophysical masers. Images are computed that represent the numerous masing features that are commonly observed in an extended masing region. Changes as a function of Doppler velocity can be seen in the appearance of these images. Variability in the images also is evident as a result of the evolution of the turbulent velocities with time. Representative turbulent velocity fields are obtained by standard methods involving the statistical sampling of Fourier components, and the medium is treated as incompressible for computing the time evolution of the turbulence. Comparisons with observational data focus on the 25 GHz methanol masers, for which there is evidence that their structure may be caused mainly by coherences in the velocities.
BULANIK ORTAMLARDA GRUP KARARI VERMEYE YARDIMCI BİR YÖNTEM: FUZZY TOPSIS VE BİR UYGULAMA

Calismanin amaci, belirsizlik altinda kararlarin verildigi bulanik ortamlarda, grup karari vermede yararlanilan Fuzzy TOPSIS yontemini ortaya koymaktir. Bu amacla departmanli bir magazada bir mulakat gerceklestirilmistir. Dort karar verici once satis elemani aliminda dikkate alinan karar kriterlerini, sonra da bu karar kriterlerine gore satis elemani adaylarini dilsel degiskenlerle degerlendirmislerdir. Degerlendirmeler ucgen fuzzy sayilara donusturulmus ve yontemin algoritmasi kullanilarak adaylar hesaplanan yakinlik katsayilarina gore siralanmistir. Calisma, Fuzzy TOPSIS yonteminin insan kaynagini secme surecinde bir grup karari verme yontemi olarak kullanilabilecegini gostermistir.