Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Dapagliflozin on Indicators of Myocardial Fibrosis and Levels of Inflammatory Factors in Heart Failure Patients

Objective To explore the effect of dapagliflozin on the myocardial fibrosis and the levels of inflammatory factors in heart failure patients. Methods 60 patients with T2DM who were diagnosed as acute left heart failure or acute exacerbation of chronic left heart failure in the Department of Cardiology of our hospital from November 1, 2020, to December 31, 2021, during hospitalization were the study subjects. According to the treatment regimen, they were divided into the experimental group (EG) which received dapagliflozin and conventional drugs and the control group (CG) which received conventional drugs, with 30 cases in each group to compare and analyze the clinical indicators such as myocardial fibrosis and inflammatory factors. Results The levels of TNF-α, IL-1β, IL-6, and hs-CRP in the two groups were decreased gradually after treatment, and the levels of TNF-α, IL-1β, IL-6, and hs-CRP in the EG were visibly lower compared with those in the CG at week 4 of treatment (P < 0.05). The cardiac function evaluation of patients showed that the levels of LVEF and LVEDD in both groups were gradually improved after treatment, with a significant difference from the fourth week. In other words, compared with the CG, the LVEF level in the EG was obviously higher (P < 0.05), the LVEDD level was distinctly lower (P < 0.05), and the levels of ST2, BNP, and MCP-1 in the EG were clearly lower at week 4 of treatment (P < 0.05) with a statistical significance in difference. Conclusion Dapagliflozin has a definite curative effect in heart failure patients with type 2 diabetes mellitus, which can effectively reduce the inflammatory response of patients and inhibit the myocardial fibrosis, and has a potential value in improving cardiac function and promoting prognosis.
Anti-inflammatory and memory-enhancing properties of Chinese herbal extracts: The possible application in Alzheimer’s disease

Alzheimer's disease (AD) is a progressive brain disease that causes cognitive impairment in seniors. The beta-amyloid (Aβ) deposition and intracellular neurofibrillary tangles are two pathological hallmarks of AD. The increase of AD hallmarks causes inflammatory response enhancement, reduction of synaptic plasticity, and impaired cognition. The percentage of the aging population is growing along with the number of AD patients; however, effective treatment of AD is still limited. Therefore, developing preventive and therapeutic drugs for AD with fewer adverse side effects is urgently needed. The crude extracts from herbs such as Centella asiatica, Dendrobium catenatum, Litsea cubeba, Nardostachys jatamansi, Convolvulus pluricaulis, Melissa officinalis, Magnolia officinalis, Withania somnifera, and Nigella sativa improved memory performance and reduced inflammation response in various diseases. In addition, herbal blends usually have minimum aversive effects and can be mixed into diet and served as nutritional supplements. Hence, it is promising to develop Chinese herbal extracts to prevent or treat early AD. This review article highlights the currently available treatments of AD and the therapeutic effects of a group of crude extracts from Chinese herbs that can prevent cognitive decline and reduce the excessive inflammatory response. The possible clinical use of these Chinese herbal extracts in AD is also discussed.
Approximation on the complex sphere

We develop new elements of harmonic analysis on the complex sphere on the basis of which Bernstein’s, Jackson’s and Kolmogorov’s inequalities are established. We apply these results to get order sharp estimates of m-term approximations. The results obtained is a synthesis of new results on classical orthogonal polynomials, harmonic analysis on manifolds and geometric properties of Euclidean spaces.
Data-Driven Control: Overview and Perspectives *

Process systems are characterized by nonlinearity, uncertainty, large scales, and also the need of pursuing both safety and economic optimality in operations. As a result they are difficult to control effectively. Data-driven techniques such as machine learning algorithms can provide complementary tools and insights to classical model-based control by enhancing the capability of modeling the dynamics of complex systems and the maintenance of control performance. Moreover, by learning the behavior of plants and controllers as black boxes, data-driven techniques can enable a completely model-free control paradigm. Hence, data-driven process control has the potential to mitigate the challenges of state-of-the-art control technology and yield generic, adaptive, and scalable strategies. This paper aims at providing an overview and conceptual classification of the main approaches in this emerging and promising field, and identifying current limitations and future directions.
PEMBERDAYAAN PEREMPUAN MELALUI PELATIHAN PENGOLAHAN MAKANAN UNTUK MENGATASI STUNTING BALITA DI DAERAH PERDESAAN

Stunting is global malnutrition problem. The adequate consumption is essential determinants of children malnourish. Women's empowerment is an important strategy to improve nutritional status of underfive children. This research was to determine the effectiveness of empowering mothers to increase knowledge, attitude, and skill of providing healthy food. This was an experimental study with pre-post test design. The located was in rural locus stunting areas in Kulon Progo on 2019. Women empowerment was provided by three days training about stunting and providing healthy food for stunting children. The dependent variable were knowledge, attitude and practise were observed by a structure questionnaire pre and post-intervention and check list. Subjects were 30 mothers who consenting to be participant. Data pre-post test of knowledge and attitude were analyzed by Wilcoxon test. Result showed that knowledge and attitudes increased by 9.7 and 14.3 point respectively and statistically significant. While after intervention as many as 80% mothers has excellent skills on providing healthy food for stunting children. In general this research increases knowledge and attitudes of mother’s, also edvancing skill on providing healthy food for improving nutritional children.
The Effect of Instantaneous Noise Injection on the Behavior of Dissipative Kerr Solitons in a Fiber Kerr Resonator

We numerically investigated the behavior of dissipative Kerr solitons (DKSs) in fiber Kerr resonators when random noise fields are suddenly applied to the system. Simulation results based on an iterative computation of an Ikeda-map-like equation system show the first observation of the annihilation and excitation of DKS under the stimulation of the noise with different intensities. Switching among different states of DKSs triggered by high amplitude noise is observed. These results have confirmed the robustness of DKSs against noise perturbations and provide new insights into the DKS dynamics in Kerr resonators, thus opening a new way to achieve different DKS states in fiber Kerr resonators.
Recognition of Odia Character in an Image by Dividing the Image into Four Quadrants

This paper deals with optical character recognition of Odia characters written in a particular font family ‘AkrutiOriAshok-99’ with different font sizes 18, 20, 22, 24, 26, 28, 36, 48 and 72 in Bold style. The font ‘AkrutiOriAshok-99’ is a font from the typing software ‘Akruti’. The basic idea behind the approach followed in this paper is the character decomposition into four quadrants and then extracting features from each quadrant. The image processing techniques like converting the image to gray, resizing of image and converting gray image to binary are used in this approach. The system explained in this paper has two major parts: DictionaryBuilding and FindingMatch. For DictionaryBuilding, dictionary of images which are created either by scanning a document or a document converted to image, both written in same font family in different sizes. The features are extracted from each image in any font size in the ‘Dictionary’ using Preprocessing, FindPath, GettingFeaturesLeft or GettingFeaturesRight, VisitSubQuad, RemainingSubQuad, WriteToExcel and CommonFeature modules. The part FindingMatch is responsible for finding a correct match in the dictionary for the input image. For this, FeatureExtraction and Recognition modules have been used. Longest Common Subsequence (LCS) has been used for finding the common feature in DictionaryBuilding as well as finding the correct match. A total of 1800 characters, 200 characters of each font size have been tested and 98.1% of correctness has been
Sur Le Groupe D’automorphismes D’un Espace Fibré Principal Analytique Complexe

Dans ce mémoir nous démontrons que le groupe d’automorphismes d’un espace fibre principal analytique complexe à base compacte est un groupe de Lie complexe par la topologie de la convergence compacte et ensuite nous étudierons quelques relations entre le groupe d’automorphismes d’un espace fibre principal analytique complexe et le groupe d’homéomorphisme analytiques de sa base.
Closed Loop System Identification With Known Feedback: A Non Asymptotic Viewpoint

In this paper, we develop a method to estimate the parameters of an unknown closed loop discrete time LTI system with feedback. This is accomplished using regularized least squares and a subspace algorithm for system identification. Under several assumptions on the underlying system dynamics we obtain non-asymptotic bounds on estimation error and demonstrate that our algorithm converges to the true parameters of the system over time. We show that even when the dimension of the underlying system is unknown, accurate reduced order approximations can be recovered. We also present extensions of these results to unstable open loop plants and more general multiple input multiple output systems. Simulations demonstrate the efficacy of this method for single input single output systems and its potential for use in adaptive control.
Implementation of Low-Level Laser Therapy in Dentistry: A Review

A type of light therapy known as low-level laser therapy (LLLT) uses only one wavelength of light. Low-level lasers (LLL) do not have a warming effect on the tissues; instead, they have an effect called photobiostimulation. LLL do not evaporate the tissue. The use of LLL to manage a range of illnesses is known as LLLT. Helium-neon lasers are an illustration of an LLLT product. Gallium arsenide, the infrared semiconductor made of gallium aluminum arsenide, is also an example. The performance powers range from 50 to 500 mW with electromagnetic spectrum wavelengths in the red and near-infrared region spanning from 630 to 980 nm and pulsed or continuous-wave emission. In periodontics, LLLT has gained prominence for several applications, including wound healing and pain relief after non-surgical and surgical procedures.
Asynchronous Approximate Byzantine Consensus via Multi-hop Communication

In this paper, we consider a multi-agent resilient consensus problem, where some of the agents try to prevent the normal ones from reaching consensus. Specifically, we study an approximate consensus problem for the class of Byzantine adversaries when agents update asynchronously and there are time delays in the agents’ communication. Our approach is based on that of the socalled weighted mean subsequence reduced (W-MSR) algorithm with a special emphasis on its use in agents capable to communicate with multi-hop neighbors. We find a tight graph condition for the proposed multi-hop weighted MSR algorithm to solve the asynchronous approximate Byzantine consensus problem. The proposed algorithm is more light weighted compared to the conventional flooding-based algorithm. At the end, we also provide numerical examples to show the efficiency of the proposed algorithm.
Problems associated with uncertain parameters and missing physics for long‐term ionosphere‐thermosphere forecasting

Data assimilation models like the Utah State University (USU) Global Assimilation of Ionospheric Measurements (GAIM) models use physics‐based models of the ionosphere, ionosphere‐plasmasphere, or thermosphere and a Kalman filter as a basis for assimilating a diverse set of measurements. With a sufficient amount of data and with multiple data types, the data assimilation models can provide reliable specifications and near‐term forecasts. However, for long‐term forecasts (5 days or longer) stand‐alone or coupled physics‐based models are needed. Unfortunately, the various physics‐based models contain several uncertain parameters and processes as well as missing physics. Further complications arise for coupled physics‐based models because of coupling issues and error propagation from model to model. Some of the problems are associated with the magnetosphere and lower atmosphere drivers, the adopted set of physics‐based equations, the parameterization of physical processes, the values adopted for the transport coefficients, the numerical techniques used, the spatial and temporal resolutions adopted, and the uncertainties in the initial and boundary conditions. Examples of the type of problems the space weather community faces in its attempt at long‐term ionosphere‐thermosphere forecasting are given.
Factors Affecting the Uptake of Combination Therapy for the Treatment of COVID-19

Aims: To analyze factors affecting the use of a combination of herbs and medication or supplements for COVID-19 treatment among self-isolation patients. Methodology: A cross-sectional study was conducted to 268 COVID-19 patients with home self-isolation in Indonesia. Data were collected by using a google form questionnaire, consisting of closed-formatted and open-ended questions, from October 9th to December 9th, 2021. A multivariate analysis was performed using a logistic regression test. Results: The proportion of COVID-19 patients who used a combination of herbs and medication or supplements was 78.4%. Vitamin C, vitamin D, and paracetamol were the medication and supplements most frequently taken by patients. The most commonly consumed herbs were honey (70.7%), ginger (50.18%), and saffron (26.74%). The multivariate analysis results showed that the duration of infection, sunbathing activities, and marital status were factors significantly associated with the use of a combination of herbs and medication or supplements (P<0.05). Conclusion: This study highlighted that most COVID-19 patients used herbs in combination with medication or supplements for their treatment. Therefore, there is a necessity for education, especially for patients and health professionals about the potential herb-drug interactions.
Identification of Chlorogenic Acid from Morus alba Leaves by UV-Vis Spectroscopy, FTIR, UPLC-QTOF-MS and Quantification by HPTLC

ABSTRACT The present study aims to characterize chlorogenic acid extracted from Morus alba leaves by using suitable methods such as UV-Vis spectroscopy, Fourier transform infrared spectroscopy (FTIR), ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) and further quantification by high-performance thin layer chromatography (HPTLC). Sonication method and methanol solvent (50:50%, v/v) is found more effective for the extraction of chlorogenic acid from leaf extract. UV-Vis spectra has shown maximum absorbance at λmax 330 nm, and found overlapping with standard chlorogenic acid. The functional groups of chlorogenic acid were identified by FTIR which indicated the presence of hydroxyl, carboxyl and carbonyl groups. In UPLC-QTOF-MS study, the mass spectra and total ion chromatogram analysis revealed the presence of structural isomers for chlorogenic acid at same m/z of parent ion 353. Mass fragments of daughter ions indicated base peak at m/z 191, m/z 173 and 179 were assigned as 5-O-caffeoylquinic acid, 4-O-caffeoylquinic acid and 3-O-caffeoylquinic acid. Quantitative analysis of chlorogenic acid was performed by HPLTC, overlapping absorption spectra of plant extract revealed great consistency with standard, confirms the specificity of HPTLC method. Similar retention factor (Rf) at 0.14 confirms the presence of chlorogenic acid in leaf extract. The standard plot shows good linear relationship from 200 to 1000 ng/spot (R2 = 0.9986). The detection limit was 15.45 ng/spot while limit of quantification was 46.84 ng/spot, with good average recovery rate between 97.96–99.03%. The amount of chlorogenic acid was quantified 10.8 mg/g dried weight extract.
Approximate Error Feedback Controller for Tracking and Disturbance Rejection for Linear Distributed Parameter Systems

In this paper we present an error feedback controller for approximate tracking and disturbance rejection for linear distributed parameter systems. The controller is approximate because it is only guaranteed to produce a small tracking error rather than an asymptotic zero tracking error. However, the asymptotic tracking error can be reduced by solving a sequence of controllers, similar in spirit to cascade controllers, where the error at one level becomes the target to track at the next level. At each step the error is reduced geometrically so that achieving a desired tracking level seldom requires more than one or two iterations. An example is given in which the plant is governed by a convection-diffusion equation with non-constant coefficients. In order to compare the effectiveness of our controller with the classical internal model controller the reference signal and disturbance are generated by a six dimensional exosystem. We note that while our approximate controller achieves a small error almost immediately the internal model controller requires much longer transient to achieve a similar level. Furthermore, our controller can handle much more general reference and disturbance signals as demonstrated in a second numerical simulation.
Efficient identification for modeling high-dimensional brain dynamics

System identification poses a significant bottleneck to characterizing and controlling complex systems. This challenge is greatest when both the system states and parameters are not directly accessible, leading to a dual-estimation problem. Current approaches to such problems are limited in their ability to scale with many-parameter systems, as often occurs in networks. In the current work, we present a new, computationally efficient approach to treat large dual-estimation problems. In this work, we derive analytic back-propagated gradients for the Prediction Error Method which enables efficient and accurate identification of large systems. The PEM approach consists of directly integrating state estimation into a dual-optimization objective, leaving a differentiable cost/error function only in terms of the unknown system parameters, which we solve using numerical gradient/Hessian methods. Intuitively, this approach consists of solving for the parameters that generate the most accurate state estimator (Extended/Cubature Kalman Filter). We demonstrate that this approach is at least as accurate in state and parameter estimation as joint Kalman Filters (Extended/Unscented/Cubature) and Expectation-Maximization, despite lower complexity. We demonstrate the utility of our approach by inverting anatomically-detailed individualized brain models from human magnetoencephalography (MEG) data.
d-like symmetry of the order parameter and intrinsic Josephson effects in Bi 2 Sr 2 CaCu 2 O 8+δ cross-whisker junctions

An intrinsic tunnel junction was made using two Bi 2 Sr 2 CaCu 2 O 8 + Φ Φ single-crystal whiskers. The two whiskers with a cross angle were overlaid at their c planes and connected by annealing. The angular dependence of the critical current density along the c axis is of the d-wave symmetry. However, the angular dependence is much stronger than that of the conventional d wave. Furthermore, the current vs voltage characteristics of the cross-whisker junctions show a multiple-branch structure at any cross angle, indicating the formation of the intrinsic Josephson-junction array.
The Laurent Phenomenon and Discrete Integrable Systems (The breadth and depth of nonlinear discrete integrable systems)

The Laurent phenomenon is the property that the solution to an initial value problem of a discrete equation is expressed as a Laurent polynomial of the initial values. This concept has arisen from the study of cluster algebras, for which it is known that any cluster variable is a Laurent polynomial of the initial cluster variables. In this paper, we leave the connection with cluster algebras aside and study the Laurent phenomenon for its own sake. We will explain that most of the discrete bilinear equations that appear in the field of integrable systems exhibit this phenomenon and we shall discuss its relation to integrability. Finally, we shall introduce a technique for calculating the algebraic entropies relying on this phenomenon. For reasons of brevity we shall omit most proofs of the theorems we present.
Decreased Serum Dickkopf-1 Levels After Hypoglycemic Therapy in Patients with Type 2 Diabetes Mellitus

Aim Dickkopf-1 (DKK-1) is a major inhibitor of Wingless protein signaling pathway, which is involved in glucose metabolism and cardiovascular diseases. The aim of the study was to investigate the changes of serum DKK-1 levels after hypoglycemic treatments and the relationship between DKK-1 and clinical parameters. Materials and Methods The study was a sub-study of a previously published clinical trial (the effect of Acarbose on glycemic variability in patients with type 2 diabetes mellitus using premixed insulin compared to metformin). All subjects underwent anthropometric and biochemical assessments at baseline and endpoint. Serum DKK-1 levels of 92 subjects were measured at baseline and after 12-week hypoglycemic treatment. Results DKK-1 levels were significantly decreased after hypoglycemic treatment for 12 weeks (P < 0.001). ΔDKK-1 levels were not correlated with improvement of metabolic parameters (all P > 0.05) but were negatively correlated with baseline DKK-1 levels (r = −0.263, P = 0.011). Spearman correlation showed that baseline DKK-1 levels were positively related to baseline total cholesterol (r = 0.226, P = 0.030) and low-density lipoprotein cholesterol (LDL-C) (r = 0.277, P = 0.007). Compared with the higher baseline DKK-1 group (≥3700 pg/mL), subjects in the lower baseline DKK-1 group (<3700 pg/mL) had significantly lower baseline glycated hemoglobin A1c levels (P = 0.008) and LDL-C levels (P = 0.048). Systolic and diastolic pressure were decreased more significantly in the lower baseline DKK-1 group than that in the higher baseline DKK-1 group (both P < 0.05). Conclusion Serum DKK-1 levels were decreased after hypoglycemic treatments. Patients with lower baseline DKK-1 levels were featured by more favorable cardiometabolic factors.
Model Predictive Control of Spray Coating of Perovskite Quantum Dots for Application in Perovskite Solar Cells

Recently, spray coating of perovskite quantum dots (QDs) have received significant attention as it provides a fast-scalable manufacturing alternative to problem-strike spin coating of QDs for production of high efficiency perovskite solar cells (PSCs). However, optimal design of a spray coating process for QDs requires an an accurate model that can consider the various macroscopic and surface-level phenomena. To this end, a multiscale model was developed to describe the spray coating dynamics. Specifically, a discrete element method (DEM)-based multiscale model was developed to accurately describe the interplay between the macroscopic (i.e., heat and mass transfer) phenomenon, and the microscopic molecular interactions. Also, the proposed model was integrated with a model predictive controller (MPC) framework to ensure adequate set-point (i.e., film thickness and roughness) tracking performance. Specifically, the deposition rate and the angle of the spray cone were manipulated to achieve the desired film characteristics. Overall, the simulation results (a) describe evaporation of spray droplets on the substrate surface; (b) the effect of microscopic interactions on particle aggregation; and (c) demonstrate minimization of film roughness while achieving a desired film thickness using a MPC framework.