Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficacy and Safety of Oral Factor XIa Inhibitors in Stroke Prevention: A Systematic Review and Meta-Analysis

Introduction: Despite preventive measures, stroke rates remain high in the primary and secondary prevention settings. Factor XIa inhibition may offer a novel, safe and effective antithrombotic option for stroke prevention. Methods: We conducted a systematic review and meta-analysis including all available randomized controlled clinical trials (RCTs) that investigated the efficacy and safety of factor XIa inhibitors versus controls in primary or secondary stroke prevention. The primary efficacy and safety outcomes of interest were symptomatic ischemic stroke (IS) and the composite of major bleeding and clinically relevant non-major bleeding. Results: Four phase II dose-finding RCTs were included, comprising a total of 4732 patients treated with factor XIa inhibitors versus 1798 controls. Treatment with factor XIa inhibitors did not reduce the risk of IS compared to controls (RR: 0.89; 95% CI: 0.67–1.17). The composite of symptomatic IS and covert infarcts on brain MRI (RR: 1.01; 95% CI: 0.87–1.18), the composite of symptomatic IS and transient ischemic attack (TIA; RR: 0.78; 95% CI: 0.61–1.01), and the composite of major adverse cardiovascular events (RR: 1.07; 95% CI: 0.87–1.31) did not differ between the treatment groups. Treatment with factor XIa inhibitors did not increase the risk of the composite of major bleeding and clinically relevant non-major bleeding (RR: 1.19; 95% CI: 0.65–2.16), major bleeding alone (RR: 1.19; 95% CI: 0.64–2.22), intracranial bleeding (RR: 0.91; 95% CI: 0.26–3.19) or all-cause mortality (RR: 1.21; 95% CI: 0.77–1.90). Conclusion: This meta-analysis provides reassuring evidence regarding the safety of factor XIa inhibitors. These findings, coupled with potential signals of efficacy in reducing IS (and TIA), underscore the importance of ongoing phase III RCTs for providing definitive data regarding the effect of factor XIa inhibition on stroke prevention.
Generative Adversarial Nets

We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to ½ everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples.
Preconditioners for incompressible magnetohydrodynamics

The main goal of this thesis is to design efficient numerical solutions to incompressible magnetohydrodynamics (MHD) problems, with focus on the solution of the large and sparse linear systems that arise. The MHD model couples the Navier-Stokes equations that govern fluid dynamics and Maxwell’s equations which govern the electromagnetic effects. We consider a mixed finite element discretization of an MHD model problem. Upon discretization and linearization, a large block 4-by-4 nonsymmetric linear system needs to be (repeatedly) solved. One of the principal challenges is the presence of a skewsymmetric term that couples the fluid velocity with the magnetic field. We propose two distinct preconditioning techniques. The first approach relies on utilizing and combining effective solvers for the mixed Maxwell and the Navier-Stokes sub-problems. The second approach is based on algebraic approximations of the inverse of the matrix of the linear system. Both approaches exploit the block structure of the discretized MHD problem. We perform a spectral analysis for ideal versions of the proposed preconditioners, and develop and test practical versions. Large-scale numerical results for linear systems of dimensions up to 107 in two and three dimensions validate the effectiveness of our techniques. We also explore the use of the Conjugate Gradient (CG) method for saddle-point problems with an algebraic structure similar to the time-harmonic Maxwell problem. Specifically, we show that for a nonsingular saddle-point matrix with a maximally rankdeficient leading block, there are two sufficient conditions that allow for CG to be used. An important part of the contributions of this thesis is the development of numerical software that utilizes state-of-the-art software packages. This software is highly modular, robust, and flexible.
Investigate the role of Certain Explanatory Variables on Risk Aversion and Sorting among Employees in Iraqi Kurdistan

`Understanding the significance of the risk attitude on sorting between public and private workers helps policymakers in introducing reforms in the labor market regulations that prevent oversized public sector jobs and encourage selection into private sector jobs. The current study attempts to assess the role of certain factors such as gender, type of employment and work training on risk aversion among employees in the Kurdistan region of Iraq. Given that public sector jobs provide higher job security than private sector jobs, workers’ risk attitudes can be an important determinant of sector choice. The data of the study was collected from a sample of 300 fourth-stage evenings shift college students from the universities of Sulaimani, Charmo University and Sulaimani Polytechnic University who have already been employed in either the public or private sector. The results of the study indicated that the models containing Gender, Employment type and Work Training as predictors were significant for some of the risk aversion and sorting, particularly 20% and 10% of risk aversion. One-third of risk aversion was found to be related to gender, suggesting that males had reported the aforementioned risk more than females. However, no statistically significant associations were found between 50% of risk aversion and all the independent variables.
COVER SHEET FOR PROPOSAL TO THE NATIONAL SCIENCE FOUNDATION

REQUESTED AMOUNT $ PROPOSED DURATION (1-60 MONTHS) months REQUESTED STARTING DATE SHOW RELATED PRELIMINARY PROPOSAL NO. IF APPLICABLE CHECK APPROPRIATE BOX(ES) IF THIS PROPOSAL INCLUDES ANY OF THE ITEMS LISTED BELOW BEGINNING INVESTIGATOR (GPG I.A) DISCLOSURE OF LOBBYING ACTIVITIES (GPG II.C) PROPRIETARY & PRIVILEGED INFORMATION (GPG I.B, II.C.6) HISTORIC PLACES (GPG II.C.9) SMALL GRANT FOR EXPLOR. RESEARCH (SGER) (GPG II.C.11) VERTEBRATE ANIMALS (GPG II.C.11) IACUC App. Date HUMAN SUBJECTS (GPG II.C.11) Exemption Subsection or IRB App. Date INTERNATIONAL COOPERATIVE ACTIVITIES: COUNTRY/COUNTRIES INVOLVED (GPG II.C.9)
In vitro antioxidant activity of alginate nanoparticles encapsulating the aqueous extract of Coccinia grandis L.

Bioactive compounds in medicinal plants are more susceptible to preventing oxidative stress. Encapsulation of herbal extracts has empowered the properties and characteristics of bioactive compounds. Nanoencapsulation allows the enhancement of the stability of extracts and targeted drug delivery. The present study aims to determine the antioxidant activity of alginate nanoparticles encapsulating the aqueous extract of Coccinia grandis L. (Family: Cucurbitaceae). The aqueous extract of C. grandis (AqCG) was prepared by using ultrasonication (40 °C, 20 min, 40 kHz) followed by refluxing (2½ h). The prepared AqCG (1–5 mg/mL) encapsulated alginate nanoparticles were synthesized by ionic gelation with the addition of extracts and CaCl2. Characterization of nanoparticles was performed via encapsulation efficiency (EE%), loading capacity (LC%), particle size (PS), scanning electron microscopy (SEM), zeta potential and Fourier transform infrared (FTIR) spectroscopy analysis. The antioxidant activity of the nanoparticles was evaluated in vitro by the ferric reducing antioxidant (FRAP) assay, 2,2-di-phenyl-1-picrylhydrazyl (DPPH) radical scavenging assay and 2,2’-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radical scavenging assay. One-way analysis of variance (ANOVA) followed by Tukey’s posthoc test was used to analyze the data. Maximum LC% (3.07 ± 0.11) and average particle size (71 nm from SEM) were obtained for alginate nanoparticles encapsulated at 4 mg/mL extract concentration. The IC50 values for DPPH, ABTS, and FRAP were 6.49 ± 0.10 mg/mL, 0.24 ± 0.01 mg/mL, and 20.63 ± 0.28 mg Trolox equivalent/g of extract respectively for alginate nanoparticles encapsulating the AqCG. Nanoparticles have shown a significant difference in IC50 values compared to Trolox (p < 0.05). The successful encapsulation of the AqCG in the alginate matrix was evidenced by FTIR and SEM analysis. Encapsulation contributed to enhancing the antioxidant activity in terms of ABTS assay when compared to the AqCG. However, in vitro release and stability studies are warranted to facilitate the development of a commercially viable nanonutraceutical using alginate nanoparticles encapsulating the AqCG.
Modeling of memory-dependent derivatives in orthotropic medium with three-phase-lag model under the effect of magnetic field

Abstract The present article deals with a two-dimensional problem of generalized thermoelasticity with memory-dependent derivatives (MDDs). The problem is considered in homogeneous orthotropic medium in the context of three-phase-lag model in the presence of a magnetic field. The matrix differential equation is formed by using Laplace and Fourier transforms into the considered equations which are solved by eigenvalue approach. The effect of magnetic field on the considered parameters is presented graphically and compared with other thermoelastic models. The effect of different time delay and kernel function on the considered parameter is also presented graphically. Communicated by Seonho Cho.
An Improved Anonymous Authentication Scheme for Internet of Medical Things Based on Elliptic Curve Cryptography

With the continuous development of wearable devices and the Internet of Things, Internet of Medical Things (IoMT) consisting of wireless body area networks is booming. The IoMT system can play the role of continuous and remote monitoring of various medical parameters of patients, which provides a new direction for future medical diagnosis and can well increase the efficiency of medical services. However, the medical sensitive data collected during the communication process is vulnerable to various attacks when transmitted through wireless channels, which can easily induce to the leakage of patients’ privacy data and other security problems. In addition, owing to the limited computing resources of wearable devices, designing a secure authentication protocol is still a challenge that requires concerted efforts. In this paper, an improved lightweight anonymous authentication protocol is proposed, and the proposed scheme can guarantee the required security in IOMT system. It can be verified by the formal and informal security proofs presented in the paper. And the overall performance has been improved, including the improvement of security performance and the reduction of communication cost.
Representations of algebraic groups

Part I. General theory: Schemes Group schemes and representations Induction and injective modules Cohomology Quotients and associated sheaves Factor groups Algebras of distributions Representations of finite algebraic groups Representations of Frobenius kernels Reduction mod $p$ Part II. Representations of reductive groups: Reductive group Simple $G$-modules Irreducible representations of the Frobenius kernels Kempf's vanishing theorem The Borel-Bott-Weil theorem and Weyl's character formula The linkage principle The translation functors Filtrations of Weyl modules Representations of $G_rT$ and $G_rB$ Geometric reductivity and other applications of the Steinberg modules Injective $G_r$-modules Cohomology of the Frobenius kernels Schubert schemes Line bundles on Schubert schemes Truncated categories and Schur algebras Results over the integers Lusztig's conjecture and some consequences Radical filtrations and Kazhdan-Lusztig polynomials Tilting modules Frobenius splitting Frobenius splitting and good filtrations Representations of quantum groups References List of notations Index.
Classification and Application of Long-duration Flows Based on Flow Behavior

Long-duration flows are extended network flows in the Internet that result from various network activities such as file transfers, persistent connections, and control command transmissions. These flows are utilized by a broad range of applications in the Internet, both benign and malicious, and their management and security are crucial for the functioning of the Internet. In this study, we categorize long-duration flows into three types: control flows, mixed flows, and information flows, based on their purpose for existence. Subsequently, features are extracted based on three characteristics: flow, time series, and packet length. The selected features are used to construct a dataset for training a classification model. The empirical analysis of real-world traffic data from high-speed network boundaries demonstrates that the classification model is capable of accurately identifying control flows in long-duration flows and determining specific applications within them.
Impacts of Regional Growth on Farmland Development in the Northeast U.S

This study models the relationship between regional growth and agricultural land development in the Northeast United States. A system of simultaneous equations is estimated using three-stage-least squares on county-level data. Results indicate that regional growth puts upward pressure on agricultural land values and downward pressure on the stock of agricultural land. Farm performance and some farmland protection policies were not effective in limiting farmland development.
Machine Learning-Aided Real-Time Optimized Multibeam for 6G Integrated Satellite-Terrestrial Networks: Global Coverage for Mobile Services

Future wireless networks beyond the fifth generation (5G) have been envisioned to increase many folds, in terms of key performance metrics such as latency, data rate, reliability, mobility, and user's quality of experience. However, one of the biggest difference between the sixth generation (6G) and its predecessor is the ability to support high speed connectivity of mobile services for global coverage, especially focusing on under-connected areas (e.g., forests, oceans, deserts, and mountains). Integrated satellite-terrestrial networks (ISTNs) provide the best solution to unravel the unconnected issues of isolated areas not only on the ground but also in the air and on the sea. Despite this premise, the research in ISTNs is still in its infancy with many open problems, associated with long delay communications, large coverage for massive number of devices, and highly demanding real-time services. To alleviate these problems, this article introduces optimal multi-beam design empowered by machine learning techniques for 6G ISTNs, with respect to the real-time constraints of mission-critical services. First, we give an overview of the multibeam design for 6G ISTNs with user clustering approaches, energy-efficiency radio resource allocation, and distributed computing. Then, the frameworks for intelligent multibeam based on machine learning, real-time optimisation, and game theory, are proposed. As case studies, we investigate the performance of machine learning-aided real-time optimisation for 6G ISTNs in disaster relief and real-time optimal multibeam in satellite-terrestrial IoT networks. Potential research directions incorporating digital twin and quantum-inspired optimisation are presented to further support optimal multibeam design in 6G ISTNs.
Rising Inequality: Transitory or Permanent? New Evidence from a Panel of U.S. Tax Returns 1987-2006.1

Using a new, large, and con dential panel of tax returns from the Internal Revenue Service and a variety of methods, we analyze the role of permanent and transitory components in the evolution of inequality in male earnings and household income in the U.S. for the period 1987-2006. For male earnings, we nd that the permanent variance increased over the sample period, while the transitory variance did not increase, on net. As a result, the increase in male earnings inequality between 1987 and 2006 was entirely of a permanent nature. By contrast, for household income, we nd that the transitory component contributed about 30-40 percent of the increase in inequality. A number of sources of household income (including spousal earnings, transfers, investment income, and business income) contributed to the increase in the transitory variance of household income. Finally, we nd that the tax system played an important role in reducing all components of inequality, but it did not alter the broad trends in the evolution of the di¤erent variance components.
Comparison of Metamerism Estimates in Video Paths using CAM's Models

The paper deals with the issues of metamerism in multimedia and telecommunication paths. Attention is focused on the end devices of the end-to-end path. A comparison is made in the assessments using traditional coordinate systems for representing colors, and in the coordinates of modern color models CAM16, CAM20u, ZCAM. Numerical differences in the obtained data are shown under the same input conditions. Statistical data of metamer estimates using Mac Adam ellipses are presented. A study was conducted in the work, showing that the spectral compositions of colors can be perceived as one color (based on McAdam's data), but when processed, their number can be different. Based on research, we can say that the differences can reach to 15%. When processing ZCAM models, the number of colors taken into account during transmission will be greater, which makes it possible to reduce the degree of influence of metamerism on color reproduction.
Government Debt Management: The Long and the Short of It

Standard optimal Debt Management (DM) models prescribe a dominant role for long bonds and advocate against issuing short bonds. They require very large positions in order to complete markets and assume each period that governments repurchase all outstanding bonds and reissue (r/r) new ones. These features of DM are inconsistent with U.S. data. We introduce incomplete markets via small transaction costs which serves to make optimal DM more closely resemble the data : r/r are negligible, short bond issuance substantial and persistent and short and long bonds positively co-vary. Intuitively, long bonds help smooth taxes over states and short bonds over time. Solving incomplete market models with multiple assets is challenging so a further contribution of this article is introducing a novel computational method to find global solutions.
Frontal Face Tracking in the Thermal Infrared Imaging for Autism Spectrum Disorder Children

The research and application of therapeutic robotics for children have gained significant attention. This study introduces a system for emotion recognition in Autism Spectrum Disorder (ASD) children that utilizes infrared thermal imaging cameras to capture the frontal facial images. The paper focuses on the development to automate, recognize and track the subjects frontal face using the Shape-Adapted Mean Shift algorithm (SAMShift) applied to thermal images. This system targets the identification of affective states in ASD children and employs non-invasive thermal imaging instead of invasive sensor patches. By integrating tracking and affective state analysis within a single modality, the system addresses the limitations of traditional visual-based systems, which often suffer from invasiveness due to the use of sensor patches, difficulty in automating and tracking facial features, and challenges in accurately identifying affective states. The SAMShift algorithm, implemented in the LabVIEW® vision module environment, is utilized for tracking the frontal face image. Experimental results demonstrate the effectiveness of the proposed method, achieving an average accuracy of 89.20% and an average precision of 95.40%. This research contributes to advancing the field of therapeutic robotics for children with ASD by providing an automated, non-invasive, and accurate approach to emotion recognition.
Exploring the potential of long offset reflections in AVO inversion and AVO analysis

Amplitude-variation-with-offset (AVO) analysis is strongly dependent on interpretation of the estimated traveltime parameters. In practice, we can estimate two or three traveltime parameters that require interpretation within the families of two- or three-parameter velocity models, respectively. Increasing the number of model parameters improves the quality of overburden description and reduces errors in AVO analysis. We have analyzed the effect of two- and three-parameter velocity model interpretation for the overburden on AVO data and have developed error estimates in the reservoir parameters.
Collaborative Inference for MEC Services Based on Multimodal Deep Neural Network

Collaborative inference can effectively ease the computational burden imposed on mobile devices by deep neural networks (DNNs), such as employing multimodal DNNs to achieve a comprehensive understanding of the environment. Although reinforcement learning (RL)-based collaborative inference adapts to dynamic environments and enhances inference performance for DNNs, especially those with chain-structured architectures, the policy selection for multimodal DNNs must account for multiple computation-intensive feature encoders. This paper proposes a hierarchical reinforcement learning (RL)-based collaborative inference scheme for multimodal DNNs that concurrently determines the partition point and identifies the appropriate DNN-based feature encoder for each modality within the mobile edge computing system. The hierarchical structure designed for policy selection has alleviated the exponential growth of the modalities. Moreover, the mobile device maintains a confidence score based on probabilities of predefined classes to assess the risk of inference policies that results in low inference accuracy and thus avoids exploring the risk policy. Simulation results demonstrate that our proposed scheme surpasses the Neurosurgeon benchmark in reducing inference latency and energy consumption, all while maintaining a high level of confidence score without significant degradation.
Mathematical analysis of temperature results of the total solar eclipse in China on 22 July 2009

In the paper we present the analysis of the temperature data of the total solar eclipse in China, close to Shanghai, on the 22 nd of July, 2009. We examine the relation between the change of the visible arc of the Sun and the temperature decrease. We also examine the relation between the height of the measurement points above the ground and the value of the lowest decrease of temperature. We estimate the minimum height, where the decrease of the temperature is not remarkable. We present some new idea for further measurements, based on the results of the analysis. This work is the productum of the co-operation of the Slovak Central Observatory and the University of West Hungary, Department of Mathematics and