Miracle-dz/UniFAR-FTUs huggingface dataset:
Anisotropic Strange Star Model Beyond Standard Maximum Mass Limit by Gravitational Decoupling in f(Q)$f(Q)$ Gravity

The current theoretical development identified as the gravitational decoupling via Complete Geometric Deformation (CGD) method that has been introduced to explore the nonmetricity Q effects in relativistic astrophysics. In the present work, we have investigated the gravitationally decoupled anisotropic solutions for the strange stars in the framework of f(Q)$f(Q)$ gravity by utilizing the CGD technique. To do this, we started with Tolman metric ansatz along with the MIT Bag model equation of state related to the hadronic matter. The solutions of the governing equations of motions are obtained by using two approaches, namely the mimicking of the θ sector to the seed radial pressure and energy density of the fluid model. The obtained models describe the self‐gravitating static, compact objects whose exterior solution can be given by the vacuum Schwarzschild Anti‐de Sitter spacetime. In particular, we modeled five stellar candidates, viz., LMC X‐4, PSR J1614‐2230, PSR J0740+6620, GW190814, and GW 170817 by using observational data. The rigorous viability tests of the solutions have been performed through regularity and stability conditions. We observed that the nonmetricity parameter and decoupling constant show a significant effect on stabilizing to ensure the physically realizable stellar models. The innovative feature of this work is to present the stable compact objects with masses beyond the 2M⊙$2 M_{\odot }$ without engaging of exotic matter. Therefore, the present study shows a new perception and physical significance about the exploration of ultra‐compact astrophysical objects.
Mining Reaction and Diffusion Dynamics in Social Activities

Large quantifies of online user activity data, such as weekly web search volumes, which co-evolve with the mutual influence of several queries and locations, serve as an important social sensor. It is an important task to accurately forecast the future activity by discovering latent interactions from such data, i.e., the ecosystems between each query and the flow of influences between each area. However, this is a difficult problem in terms of data quantity and complex patterns covering the dynamics. To tackle the problem, we propose FluxCube, which is an effective mining method that forecasts large collections of co-evolving online user activity and provides good interpretability. Our model is the expansion of a combination of two mathematical models: a reaction-diffusion system provides a framework for modeling the flow of influences between local area groups and an ecological system models the latent interactions between each query. Also, by leveraging the concept of physics-informed neural networks, FluxCube achieves high interpretability obtained from the parameters and high forecasting performance, together. Extensive experiments on real datasets showed that FluxCube outperforms comparable models in terms of the forecasting accuracy, and each component in FluxCube contributes to the enhanced performance. We then show some case studies that FluxCube can extract useful latent interactions between queries and area groups.
Representation Learning for Maximization of MI, Nonlinear ICA and Nonlinear Subspaces with Robust Density Ratio Estimation

Contrastive learning is a recent promising approach in unsupervised representation learning where a feature representation of data is learned by solving a pseudo classification problem from unlabelled data. However, it is not straightforward to understand what representation contrastive learning yields. In addition, contrastive learning is often based on the maximum likelihood estimation, which tends to be vulnerable to the contamination by outliers. To promote the understanding to contrastive learning, this paper first theoretically shows a connection to maximization of mutual information (MI). Our result indicates that density ratio estimation is necessary and sufficient for maximization of MI under some conditions. Thus, contrastive learning related to density ratio estimation as done in popular objective functions can be interpreted as maximizing MI. Next, with the density ratio, we establish new recovery conditions for the latent source components in nonlinear independent component analysis (ICA). In contrast with existing work, the established conditions include a novel insight for the dimensionality of data, which is clearly supported by numerical experiments. Furthermore, inspired by nonlinear ICA, we propose a novel framework to estimate a nonlinear subspace for lower-dimensional latent source components, and some theoretical conditions for the subspace estimation are established with the density ratio. Then, we propose a practical method through outlier-robust density ratio estimation, which can be seen as performing maximization of MI, nonlinear ICA or nonlinear subspace estimation. Moreover, a sample-efficient nonlinear ICA method is also proposed. We theoretically investigate outlier-robustness of the proposed methods. Finally, the usefulness of the proposed methods is numerically demonstrated in nonlinear ICA and through application to linear classification.
Size-dependent mobility of skyrmions beyond pinning in ferrimagnetic GdCo thin films

Magnetic skyrmions are swirling magnetic textures that can be eﬃciently driven with spin-orbit torques with a deﬂected trajectory. However, pinning slows skyrmions down and alters their trajectory, which prevents a quantitative comparison to analytical models. Here, we study skyrmions driven by spin-orbit torques at room temperature in ferrimagnetic GdCo thin ﬁlms, an amorphous material with low pinning. Above a sharp current depinning threshold, we observe a clearly linear velocity increase with current that extrapolates to zero and a constant deﬂection angle, reaching high velocities up to 200 m/s. The mobility increases and the depinning threshold current decreases with the skyrmion diameter, which we vary using an external magnetic ﬁeld. An analytical model based on the Thiele equation quantitatively reproduces these ﬁndings with a single ﬁtting parameter. This validates the linear ﬂow regime description and shows, in particular, the important role of skyrmion size in its dynamics.
The advantages of random sampling versus cutting-of-the-tail: the application of a stratified sample design for the collection of data on special financial institutions in the Netherlands

The Netherlands is home to a large number of Special Financial Institutions (SFIs). SFIs are subsidiaries of foreign companies with almost exclusively financial relations with other foreign entities. The main motives behind the presence of these companies in the Netherlands are fiscal and regulatory. Interest in the sector has heightened since the 2008 financial crisis, especially from those working on macroprudential issues relating to shadow banking. As mentioned in the Global Shadow Banking Monitoring Report 2013 of the Financial Stability Board, the Netherlands has one of the largest non-bank financial intermediaries sectors in the world, relative to GDP2. SFIs make up the majority of this. It is therefore very important to have statistical data about SFIs that is accurate and reliable.
Automatic Target Detection in Satellite Images using Deep Learning

— Automatic target detection in satellite images is a challenging problem due to the varying size, orientation and background of the target object. The traditionally engineered features such as HOG, Gabor feature and Hough transform do not work well for huge data of high resolution. Robust and computationally efficient systems are required which can learn presentations from the massive satellite imagery. In this paper, a target detection system for satellite imagery is proposed which uses EdgeBoxes and Convolutional Neural Network (CNN) for classifying target and non-target objects in a scene. The edge information of targets in satellite imagery contains very prominent and concise attributes. EdgeBoxes uses the edge information to filter the set of target proposals. CNN is a deep learning classifier with a high learning capacity and a capability of automatically learning optimum features from training data. Moreover, CNN is invariant to minor rotations and shifts in the target object. Encouraging experimental results have been obtained on a large dataset which shows the optimum performance and robustness of our system in complex scenes.
Glycaemic control and factors affecting it in type 1 diabetes in children: experience from a tertiary care centre in India

Introduction Optimal glycaemic control is essential for the prevention of future micro- and macrovascular complications in type 1 diabetes (T1D). The type of insulin, the type of insulin delivery device, the caregiver’s knowledge, the patient’s age, duration of diabetes, and self-monitoring of blood glucose affect glycaemic control in type 1 diabetes. In the present study, we analysed glycaemic control and factors affecting it at a tertiary care centre in northern India. Material and methods A retrospective review of records of patients registered between 2015 and 2018 was done. The data on demographic and disease-related factors were collected from the records. The different groups were compared with the t-test, one-way ANOVA, or Kruskal-Wallis test. Results The mean age at the time of evaluation was 10.43 ±4.04 years (2–21 years), and the mean disease duration was 46.61 ±28.49 months (16–141 months). Most of the patients were prepubertal and using a basal-bolus regimen. The mean glycated haemoglobin (HbA1c ) was 7.96 ±1.46%, but only 24% had HbA1c below the International Society of Paediatric and Adolescent Diabetes (ISPAD) recommended desirable level of below 7%. Forty-six patients suffered one or more micro-macrovascular complications, and dyslipidaemia was the most common complication. Children with a longer duration of disease (8.39 ±1.42% vs. 7.59 ±1.65%), an episode of DKA (diabetes ketoacidosis) within a year of evaluation (9.19 ±2.54% vs. 7.93 ±1.39%), lower maternal (8.22 ±1.37% vs. 7.56 ±1.45%) and paternal education (8.26 ±1.67% vs. 7.78 ±1.30%), and hyperthyroid state (9.43 ±2.28% vs. 7.91 ±1.45%) had higher HbA1c. Conclusions Better diabetes education focusing on parents with lower education strata and children with longer disease duration and poor compliance can help improve glycaemic control in developing countries like India.
Tribological behavior of AA7075-TiC composites by powder metallurgy

Purpose Aluminium is the most preferred material in engineering structural components because of its excellent properties. Furthermore, the properties of aluminium may be enhanced through metal matrix composites and an in-depth investigation on the evolved properties is needed in view of metallurgical, mechanical and tribological aspects. The purpose of this study is to explore the effect of TiC addition on the tribological behavior of aluminium composites. Design/methodology/approach Aluminium metal matrix composites at different weight percentage of titanium carbide were produced through powder metallurgy. Produced composites were subjected to sliding wear test under dry condition through Taguchi’s L9 orthogonal design. Findings Optimal process condition to achieve the minimum wear rate was identified though the main effect plot. Sliding velocity was identified as the most dominating factor in the wear resistance. Practical implications The production of components with improved properties is promoted efficiently and economically by synthesizing the composite via powder metallurgy. Originality/value Though the investigations on the wear behavior of aluminium composites are analyzed, reinforcement types and the mode of fabrication have their significance in the metallurgical and mechanical properties. Thus, the produced component needs an in-detail study on the property evolution.
Profiles and Risk Factors for Teen Dating Violence in Spain

Teen dating violence (TDV) refers to a wide range of partner-directed harmful behaviors among adolescents. Since it was first documented in the 1980s, there is a growing interest in TDV due to its potentially devastating mid- and long-term consequences. Aiming at the early detection of TDV onset with prevention purposes, research has focused on the detection of typologies of perpetrators and/or victims as well as on identifying risk and protective factors for its occurrence. Research with Spanish adolescents, however, is very limited. To fill this gap, we recruited a total of 2,319 adolescents from different regions in Spain, out of which, 1,079 reported having had a romantic partner during the last year. These filled out measures of TDV (perpetration and victimization), school aggression, hostile and benevolent sexism, empathy, assertiveness, psychological inflexibility (general measures), and psychological inflexibility with prejudice thoughts. A cluster analysis revealed that adolescents could be divided into two clusters as a function of their TDV profile: Cluster 1, including close to 76% of the sample (boys and girls), presented low TDV perpetration and victimization; Cluster 2, including 24% of the sample (boys and girls), presented higher TDV perpetration and victimization. Regression analyses revealed that, as compared to those in Cluster 1, adolescents in Cluster 2 were more likely to be older boys who scored high in benevolent sexism, overt and relational school aggression, and personal distress, and low in behavior regulation skills, perspective taking, and practical personal ability. We discuss the implications of these findings for the design of evidence-based TDV prevention campaigns.
Team Functioning and Performance in Child Advocacy Center Multidisciplinary Teams

The quality of teamwork in Child Advocacy Center (CAC) multidisciplinary teams is likely to affect the extent to which the CAC model improves outcomes for children and families. This study examines associations between team functioning and performance in a statewide sample of CAC teams. Multidisciplinary team members (N = 433) from 21 CACs completed measures of affective, behavioral, and cognitive team functioning. Team performance was assessed with three measures: team member ratings of overall performance, ratings of mental health screening/referral frequency, and caregiver satisfaction surveys. Linear mixed models and regression analyses tested associations between team functioning and performance. Affective team functioning (i.e., liking, trust, and respect; psychological safety) and cognitive team functioning (i.e., clear direction) were significantly associated with team members’ ratings of overall performance. Behavioral team functioning (i.e., coordination) and cognitive team functioning were significantly associated with mental health screening/referral frequency. Team functioning was not associated with caregiver satisfaction with CAC services. Aspects of team functioning were associated with team members’ perceptions of overall performance and mental health screening/referral frequency, but not caregiver satisfaction. Understanding associations between team functioning and performance in multidisciplinary teams can inform efforts to improve service quality in CACs and other team-based service settings.
The Association Between a Second Course of Cardiac Rehabilitation and Cardiovascular Outcomes Following Repeat Percutaneous Coronary Intervention Events

Although evidence supports cardiac rehabilitation (CR) after an initial coronary artery disease (CAD) event, it is unclear whether a repeat course of CR (CR × 2) is beneficial after a recurrent CAD event. We found that among individuals who had previously participated in CR after an initial PCI, CR × 2 was associated with improved clinical outcomes. Purpose: Survivors of coronary artery disease (CAD) events are at risk for repeat events. Although evidence supports cardiac rehabilitation (CR) after an initial CAD event, it is unclear whether a repeat course of CR (CR × 2) is beneficial after a recurrent CAD event. The purpose of this study was to determine the association of CR × 2 with clinical outcomes in persons undergoing repeat percutaneous coronary intervention (PCI). Methods: We assessed the prevalence of CR × 2 and its impact on cardiovascular outcomes in individuals who experienced a repeat PCI at the Mayo Clinic hospitals between January 1, 1998, and December 31, 2013. Landmark analyses were used to calculate unadjusted and propensity score adjusted mortality rates and cardiovascular (CV) events rates for patients who underwent CR × 2 compared with those who did not. Results: Among 240 individuals who had a repeat PCI and who had participated in CR after their first PCI, 97 (40%) participated in CR × 2. Outcomes were assessed for a mean follow-up time of 7.8 yr (IQR 7.1-9.0 yr). Propensity score-based inverse probability weighting analysis revealed that CR × 2 was associated with significantly lower target lesion revascularization (HR = 0.47: 95% CI, 0.26-0.86; P = .014), lower combined end point of CV death, myocardial infarction, and target lesion revascularization (HR = 0.57: 95% CI, 0.36-0.89; P = .014), and lower CV hospitalization (HR = 0.60; 95% CI, 0.43-0.84; P = .003). Conclusion: A second course of CR following repeat PCI is associated with a lower risk of adverse clinical outcomes. These findings support current policies that allow for repeat courses of CR following recurrent CV events.
Asymmetric depth‐filtration: A versatile and scalable method for high‐yield isolation of extracellular vesicles with low contamination

Abstract We developed a novel asymmetric depth filtration (DF) approach to isolate extracellular vesicles (EVs) from biological fluids that outperforms ultracentrifugation and size‐exclusion chromatography in purity and yield of isolated EVs. By these metrics, a single‐step DF matches or exceeds the performance of multistep protocols with dedicated purification procedures in the isolation of plasma EVs. We demonstrate the selective transit and capture of biological nanoparticles in asymmetric pores by size and elasticity, low surface binding to the filtration medium, and the ability to cleanse EVs held by the filter before their recovery with the reversed flow all contribute to the achieved purity and yield of preparations. We further demonstrate the method's versatility by applying it to isolate EVs from different biofluids (plasma, urine, and cell culture growth medium). The DF workflow is simple, fast, and inexpensive. Only standard laboratory equipment is required for its implementation, making DF suitable for low‐resource and point‐of‐use locations. The method may be used for EV isolation from small biological samples in diagnostic and treatment guidance applications. It can also be scaled up to harvest therapeutic EVs from large volumes of cell culture medium.
Electron Microscopy of Cells Grown on Polyacrylamide Hydrogels

The composition of the cell culture environment profoundly affects cultured cells. Standard cell culture equipment such as plastic and glass provide extremely stiff surfaces compared to physiological cell environments (i.e., tissue). A growing body of evidence documents the artificial behavior and morphology of cells cultured on supraphysiologically stiff surfaces, such as glass (elastic modulus ca. 70,000 MPA) or plastic (e.g., polystyrol ca. 3300 MPA). Therefore, polymer‐based hydrogels are increasingly employed as more physiologically appropriate (<100 kPA) supports for 2D or 3D culture. Since multiple properties that influence the cultured cells may be easily adjusted, hydrogels have become versatile tools for studying cells in a more native in vitro environment. Polyacrylamide‐based hydrogels can be used as culture substrates for a broad variety of adherent cells and are easy to handle in most downstream biological assays, such as immunohistochemistry or molecular biology methods. We faced, however, serious difficulties with processing high stiffness polyacrylamide‐based hydrogels for electron microscopy. To overcome this problem, we developed a simple protocol for embedding and processing cells grown on high stiffness polyacrylamide hydrogels that do not require modifications of routine embedding protocols. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC.
DeepACSA: Automatic Segmentation of Cross-Sectional Area in Ultrasound Images of Lower Limb Muscles Using Deep Learning

ABSTRACT Purpose Muscle anatomical cross-sectional area (ACSA) can be assessed using ultrasound and images are usually evaluated manually. Here, we present DeepACSA, a deep learning approach to automatically segment ACSA in panoramic ultrasound images of the human rectus femoris (RF), vastus lateralis (VL), gastrocnemius medialis (GM) and lateralis (GL) muscles. Methods We trained three muscle-specific convolutional neural networks (CNN) using 1772 ultrasound images from 153 participants (age = 38.2 yr, range = 13–78). Images were acquired in 10% increments from 30% to 70% of femur length for RF and VL and at 30% and 50% of muscle length for GM and GL. During training, CNN performance was evaluated using intersection-over-union scores. We compared the performance of DeepACSA to manual analysis and a semiautomated algorithm using an unseen test set. Results Comparing DeepACSA analysis of the RF to manual analysis with erroneous predictions removed (3.3%) resulted in intraclass correlation (ICC) of 0.989 (95% confidence interval = 0.983–0.992), mean difference of 0.20 cm2 (0.10–0.30), and SEM of 0.33 cm2 (0.26–0.41). For the VL, ICC was 0.97 (0.96–0.968), mean difference was 0.85 cm2 (−0.4 to 1.31), and SEM was 0.92 cm2 (0.73–1.09) after removal of erroneous predictions (7.7%). After removal of erroneous predictions (12.3%), GM/GL muscles demonstrated an ICC of 0.98 (0.96–0.99), a mean difference of 0.43 cm2 (0.21–0.65), and an SEM of 0.41 cm2 (0.29–0.51). Analysis duration was 4.0 ± 0.43 s (mean ± SD) for analysis of one image in our test set using DeepACSA. Conclusions DeepACSA provides fast and objective segmentation of lower limb panoramic ultrasound images comparable with manual segmentation. Inaccurate model predictions occurred predominantly on low-quality images, highlighting the importance of high-quality image for accurate prediction.
Real‐life benefits of intrajejunal levodopa infusion therapy in four patients with the parkinsonian variant of progressive supranuclear palsy: A 1‐year follow‐up data report

Progressive supranuclear palsy (PSP) is a progressive neurodegenerative condition presenting with different clinical endophenotypes. The parkinsonian variant of PSP (PSP‐P) is characterised by early but fading responsiveness to high‐dose levodopa therapy; however, high‐dose oral therapy is often associated with intolerance due to dopaminergic side effects and so doses may have to be capped despite clinical benefits. Evidence from animal models and real‐life registries suggest far higher doses of levodopa can be tolerated if given in a continuous drug delivery (CDD) manner. We investigated tolerance and possible clinical benefits in patients with PSP‐P still responsive to levodopa after initiating CDD in the form of intrajejunal levodopa infusion (IJLI) therapy as part of a compassionate usage program (CU).