Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Mixture of Normal Distributions to Detect Treatment Effects when the Frequentist Method Fails

Background: Results from a clinical trial can either support the efficacy and safety of a new compound or fail to provide such evidence. One reason for ‘non[1]positive’ result is due to the underlying assumption of normality and homogeneity of variances, which are quite often violated when analyzing data from clinical trials, despite randomization. A question of interest is can we obtain more informative results when using mixture of normal distributions or linear models (MLMs) in such cases. Introduction: MLM can be used when traditional methods fail. MLMs “search” within the variability in data to identify components or subgroups of individuals (also known as latent classes) who have common intercepts and common slopes of change in a variable/endpoint of interest but whose intercepts and slopes are different from other subsets of patients. Thus, MLMs can be used to identify subgroups of patients exhibiting differential response to treatment within each treatment arm. The purpose of our study was to examine the usefulness of using MLM in such circumstances. Methods: Data of 155 subjects taken from a Multicenter, randomized, double blind, placebo controlled trial that evaluated the efficacy of Cpn10, administered twice weekly subcutaneously to treat Rheumatoid Arthritis was taken to evaluate the usefulness of MLM. The primary efficacy measure ACR20 was analyzed using a 3-step process: first, MLM was used to estimate RA duration using a 3-component model. The second step took the results of the first step to inform the logistic model and its analyses. Model was fitted with an intercept, MLM components, treatment arm, RA duration (linear and quadratic), dose response (modeled as an interaction effect), age and baseline weight. LOCF was used to impute for missing data. Data was analyzed using MLM and SAS v 9.0. Results: The model was a good fit to the data with a likelihood ratio significant at p=0.026, and a significant increase in the -2log L. We also observed low p-values for those variables that were non normal. Overall and for the 75 mg dose, Cpn 10 was efficacious relative to placebo, p<0.050. We also observed that dose response was significant at p><0.15 Conclusion: The use of MLM adds value because it can be used to understand the disease experience or the value of treatment when traditional statistical methods cannot. Key words: Mixture of linear models, normality, entropy.
A nonlinear version of Halanay's inequality for the uniform convergence to the origin

A nonlinear version of Halanay's inequality is studied in this paper as a sufficient condition for the convergence of functions to the origin, uniformly with respect to bounded sets of initial values. The same result is provided in the case of forcing terms, for the uniform convergence to suitable neighborhoods of the origin. Related Lyapunov methods for the global uniform asymptotic stability and the input-to-state stability of systems described by retarded functional differential equations, with possibly nonconstant time delays, are provided. The relationship with the Razumikhin methodology is shown.
Evaluation of the Anti-Diabetic Activity of Sophora Interrupta: Pharmacological Screening Against Streptozotocin-Induced Diabetic Rats

Introduction: Diabetes Mellitus (DM) is a highly prevalent metabolic disorder characterized by chronic hyperglycaemia. Though multiple conventional therapies are available these therapies are reported to have side effects. Aim: To evaluation of the anti-diabetic activity of Sophora interrupta by using the streptozotocin-induced diabetic rat model. Plants are potential sources of phytoconstituents with varied pharmacological activities. Methodology: The leaves and the stem bark of Sophora interrupta (SI) were collected and initial results of the phytochemical screening revealed that all the bark and leaf extract of the plant showed the presence of flavonoids, Saponins, steroids, alkaloids, tannins, phenolic compounds, triterpenoids and carbohydrates. Results: The results of acute toxicity studies demonstrated that animals did not display any drug-related behavioural, physi-ological and psychological changes. The streptozotocin model was used for the induction of diabetes. In diabetic rats, decreased bodyweight, High-density (HDL), glutathione (GSH), Superoxide dismutase Catalase (CAT) and increased level of Blood glucose, total cholesterol (TC), triglycerides (TG), low-density lipoprotein (LDL), very-low-density lipoprotein (VLDL) and TBARs was observed which was normalized by 200 mg/kg and 500 mg/kg extract of the plant. Conclusion: It shows that the ethanolic extract of SI showed significant antioxidant and antidiabetic activity directing it as a bet-ter therapeutic regimen in the treatment of diabetes and associated complications.
Comparison finite element analysis on duralium strength against multistage artificial aging process

Purpose: The complex mechanisms of tool wear in metal cutting have not been possible to investigate in detail by the experimental methods traditionally employed. However, as a result of both the continuous development of numerical methods, such as the Finite Element Method (FEM) and the development of ever more powerful computers, the evaluation of the chip formation process and the evolution of tool wear is now possible. Design/methodology/approach: : In the work presented in this paper, numerical methods are employed to study the effect of a single embedded hard carbide particle on tool wear and tool tip deformation. An important advantage of this approach is that particle size and position can easily be changed, thus making it possible to investigate the influence of these parameters on tool wear. Findings: The results reveal that the position, and in particular the size of carbide particles, have a dramatic impact on tool wear. In particular, particles larger than a certain size (about 5μm) cause significant plastic deformation of the tool tip, when passing in sufficient proximity. Research limitations/implications: An effort has been made to obtain the corrected version of the stability polynomial, the corresponding stability region and the range of Re(z) for the RK-Butcher algorithm. Originality/value: The present article sheds some light on different numerical integration algorithms involved in robot arm model problem.
Design Optimization of High Temperature Superconducting Magnets and Null-Flux Coils for Electrodynamic Suspension Train

The introduction of high temperature superconducting (HTS) magnet, which is capable of working at liquid nitrogen temperature, and the use of null-flux coil, make the liquid-helium-free electrodynamic suspension (EDS) train available. In essence, the electromagnetic force of a superconducting EDS train is generated by electromagnetic interaction between the onboard superconducting magnets and the ground null-flux coils. In order to obtain an efficient HTS EDS train, the design optimization of the HTS magnets and null-flux coils is indispensable. This study is devoted to this aspect. We first establish an improved load-line method for efficiently estimating the critical current of HTS magnets and an improved semianalytical model to accurately calculate the induced currents and electromagnetic forces in the EDS train. Based on above two improvements, the onboard HTS magnets and corresponding null-flux coils are then designed and optimized to enhance the overall performances of the HTS EDS train. At final, according to the optimized results, the HTS magnets and null-flux coils were manufactured and measured, followed by the performance analyses of the obtained HTS EDS train. The results have shown that the electromagnetic performances of the designed HTS EDS train are excellent, demonstrating the effectiveness of the optimization methodology.
Designing Ruby: Protocol for a 2-Arm, Brief, Digital Randomized Controlled Trial for Internalized Weight Bias

Background Weight bias internalization, also known as weight self-stigma, is a serious health concern for individuals with higher body weight. Weight bias internalization is associated with the greater avoidance of health care and health-promoting activities, disordered eating, social isolation, and weight gain. Elevated weight bias internalization has been associated with low self-compassion, yet few investigations have explored self-compassion as a potential mechanism for reducing internalized weight bias. Objective Ruby is a 2-arm randomized controlled trial that was designed to test the efficacy of a 4-week digital self-compassion intervention to reduce internalized weight bias compared with a wait-list control. Methods Adults with elevated internalized weight bias and a BMI of >30 kg/m2 (N=80) were recruited. Ruby is a standalone digital trial that will be delivered entirely via a smartphone and will involve web-based data collection and text messages. The intervention content will include psychoeducation and daily mindfulness practices with a focus on self-compassion and body concerns. We will use intent-to-treat analyses to examine changes in weight bias internalization throughout time by treatment arm. The analyses will be conducted by using one-way analysis of covariance models and linear mixed models. Results The protocol was designed in May 2020 and approved in December 2020. Data collection is currently underway. Conclusions Ruby will be the first digital standalone, self-compassion–based intervention designed to reduce internalized weight bias. Owing to its standalone digital delivery, Ruby may be a highly scalable treatment for internalized weight bias that can be delivered on its own or combined with other treatments. We expect Ruby to be accessible to many, as participants can access the digital intervention at times of the day that are the most convenient in their schedule and are not burdened by in-person time commitments, which can be a barrier for participants with competing demands on their time and resources. If efficacious, Ruby will be poised to expand a burgeoning body of literature related to psychological intervention in this area. Trial Registration ClinicalTrials.gov NCT04678973; https://clinicaltrials.gov/ct2/show/NCT04678973 International Registered Report Identifier (IRRID) DERR1-10.2196/31307
A Generalized Interleaved Approach Feasible to Static Synchronous Compensator Applications

This work investigates a generalized cascaded full-bridge (GCF) concept, which uses a full-bridge converter as a basic submodule, combined with the interleaving and cascading techniques. When compared with the traditional cascaded full-bridge (CFB) converter, the GCF topologies present the advantages of reduced current and voltage stresses on the semiconductors, and flexible design. Thereby, the resulting structures become an alternative solution for high-power applications involving high current and high voltage levels. The generalized analysis involving design guidelines for the synthesis of each topology, the system model, and possible modulation strategies are described in detail. A comparative study with the CFB-based topologies shows that the introduced structures are capable of achieving improved performance in terms of reduced losses in the power semiconductors and lower device count. A small-scale laboratory prototype of a static synchronous compensator rated at 2.2 kVA/311 V is implemented to validate the design assumptions considering the steady-state and transient performances.
Creating Customer Engangement and Customer Value within 15 second : How Tiktok Works for Content Marketing

Introduction/Main Objectives: This study iaims to analyze the effect of tiktok marketing-based content on customer engagement and customer value. Background Problems: Sosial media become major platform for marketing activities, and during pandemic covid on 2020 the social media used for marketing activities have shifted from Instagram to Tiktok. Therefore, this research explained how 15 second video can create customer engangement and customer value on product or services. Novelty: This research focused on customer engangement and customer value based on marketing content, but empirical studies of the context still limited or based on fan engangement (personal branding). Research Methods: This study was conducted using a quantitative analysis technique with path analysis method. The data obtained by distributing questionnaires online to 276 respondent which all of them are active users. Finding/Results: The result shows that video on tiktok with 15 second until 60 second duration has an impact for user. Different from existing media sosial platform, the effectiveness of using Tiktok requires accuracy and creativity of content. Interesting content does not necessarily attract consumers, but the content will provide product value or consumer attitudes towards the product. Conclusion: 90% of respondent agree that using Tiktok as marketing tools considered as an effective way. Especially when Tiktok become new-rising social media platform during the Pandemic 2020. Almost teens download the apps to join the trend, and until today, all the former infuencer on Instagram moves to Tiktok for creating the product ads content.
A Critical Aspect of Dynamic Stability in Autonomous Microgrids: Interaction of Droop Controllers Through the Power Network

It is explored in this article that the interaction of droop controllers through the power network (IDCPN) is the dominant factor affecting the dynamic stability of an autonomous networked microgrid (ANMG) and new models are developed to support the IDCPN. The models are developed to analyze the impacts of three parts on the IDCPN: 1) the X/R ratio of the power network impedance; 2) the droop controllers including low-pass filters, and 3) the impedance characteristics of the grid-forming voltage source inverters (VSIs). The low-frequency oscillations (LFO), excited by IDCPN, is identified and quantified by modeling the first and second parts. The critical impact of the low X/R ratio on amplifying the LFO is clarified. Then, the output impedance of the grid-forming VSI, including the virtual inductance loop (VIL), is developed and rigorously observed that reveals inefficient performance. It is shown that the VIL improves dynamic performance by providing sufficient damping to suppress LFOs and not by effectively boosting the X/R of the VSI output impedance. This, however, is problematic in the current limiting that puts the ANMG at instability risk because of the resistive–capacitive impedance characteristics of the VSIs. A ${{\boldsymbol{H}}_\infty }$ robust controller is proposed to replace VIL for suppressing LFO and stabilizing ANMG. Numerical/simulation results are provided to prove the accuracy of the models.
Mutation of TRIM28-Lys304 to Gln Attenuates its Interaction with KRAB-ZNFs and Promotes Differentiation of Erythroleukemic K562 Cells

BackgroundTRIM28/KAP1/TIF1β is a key epigenetic modifier. Genetic ablation of trim28 is embryonic lethal although RNAi-mediated knockdown in somatic cells yields viable cells. Reduction in TRIM28 abundance at the cellular or organismal level results in polyphenism. Posttranslational modifications such as phosphorylation and sumoylation have been shown to regulate TRIM28 activity. Moreover, the methylation of DNA, RNA and histones and acetylation of histones are key epigenetic modifications that regulate gene expression. A number of lysine residues of TRIM28 are subject to acetylation, but how acetylation of TRIM28 affects its functions remains poorly understood. ResultsHere we report that, compared with wild-type TRIM28, the acetylation-mimic mutant TRIM28-K304Q has an altered interaction with Krüppel-associated box zinc-finger proteins (KRAB-ZNFs), with consequent effects on the phenotype of the erythroleukemic cell line K562. TRIM28-K304Q was comparable with its wild-type counterpart with respect to intracellular level, homodimerization, phosphorylation at S473 and S824, and interactions with heterochromatin-binding protein HP1. The expression of embryonic-related and fetal globin genes was activated in TRIM28-K304Q mutant cells. Transcriptome analysis revealed that TRIM28-K304Q and TRIM28 knockout K562 cells had similar global gene expression profiles, yet the profiles differed considerably from that of wild-type K562 cells. The gene expression ensemble of mutant K562 cells indicated a general induction of differentiation-promoting genes and attenuation of proliferation-promoting genes. ConclusionsThese results suggest that acetylation/deacetylation of K304 in TRIM28 or TRIM28-K304Q constitutes a switch for regulating its interaction with KRAB-ZNFs and alters the gene regulation of this key epigenetic modifier as demonstrated by the acetylation mimic TRIM28-K304Q.
Robust Low Rank and Sparse Representation for Multiple Kernel Dimensionality Reduction

In the fields of pattern recognition and data mining, two problems need to be addressed. First, the curse of dimensionality degrades the performance of many practical data processing techniques. Second, due to the existence of noise and outliers, feature extraction on corrupted data cannot be effectively achieved. Recently, some representation based methods have produced promising results. However, these methods cannot handle the case in which nonlinear similarity exists and have failed to provide the quantized interpretability for the importance of features. In this paper, we propose a novel low rank and sparse representation method to realize dimensionality reduction and robustly extract latent low dimensional discriminative features. Specifically, we first adopt multiple kernel learning to map the original data into an embedded reproducing kernel Hilbert space (RKHS) and then kernel based similarity discriminative projection is learned to explore the within-class and between-class variability. Notably, this low dimensional feature learning strategy is definitely integrated into the low rank matrix recovery of the kernel matrix. Next, we introduce the regularization of $l_{2,1}$ norm on error matrix to eliminate noise and on projection matrix to lead the selected features to be more compact and interpretable. The non-convex optimization problem is effectively solved by the alternating direction method of multipliers (ADMM) methods. Extensive experiments on seven benchmark datasets are conducted to demonstrate the effectiveness of our method.
Iris Segmentation Based on Black Hole Algorithm for Biometric System

Current iris recognition schemes such as IntegroDifferential method, Hough Transform, Watershed Transform Circle Fitting, and Circular Hough Transformation (CHT) are used to find circular parameters between pupil and iris. Segmentation process of an eye image using the circular parameters toextracts the iris region still can be further improved. In this paper, we introduced an optimization method of circular parameters detection for iris segmentation based on Black Hole Algorithm (BHA). The proposed segmentation algorithm utilizes a computational model of the pixels’ value to detect the iris boundary. The BHA searches for center radius of both pupil and iris. The system tests the CASIA Iris Interval V3 database by on MATLAB. The segmented images show an accuracy of 98.3%. In short, the segmentation-based on BHA is efficient to identify the iris for any future access control applications.
Micro-modular air driven combustion nozzle: Experimental and numerical modelling studies towards optimal geometric design

Experimental and numerical modelling studies on optimized design of micro-modular air driven combustion nozzle have been developed. Moreover, optimal angle of outlet swirler (vortex generator) connected in series with the nozzle has been analyzed in the context of minimization of NOx in the flue gases. Numerical modelling by ANSYS verifying the experimental data has been carried out. It has been established that an angle of about 20 degree of the outlet vortex generator has been able to assure optimal flame length and combustion efficiency.
Related Variety of Regional Smart Specialization Strategies

Purpose: The paper seeks to find the related variety of smart specialization strategies in European regions. Design/Methodology/Approach: We apply the product space methodology of Hidalgo et al. (2007) to identify the most frequent occurrences of specialization in industry, scientific or policy domains in regional innovation strategies and construct a matrix of their co-occurrences. Findings: Although to some extent regions utilize unrelated variety, i.e., homogeneous industry sections, scientific or policy domains, most regions benefit from cross-linkages between sections and domains (related variety). This latter variety may be grouped into two interconnected domains: (1) quality of life and well-being, including high-tech products that facilitate various spheres of social and economic life; (2) sustainable urban development with smart cities, green transportation, and energy conservation and effectiveness. Practical Implications: Tracing the related variety of smart specializations may facilitate an understanding of the localization and urbanization economies that benefit regions and the mechanisms behind entrepreneurial discovery processes. Moreover, the results can show the extent to which regions can differentiate themselves from others. Originality/Value: The product space methodology has not been applied to smart specializations to date. Based on this methodology and network analysis, the study provides the most common related varieties present in regional strategy documents, which can be analyzed in more detail in future research.
Measurements of 540–1740 MHz Brightness Temperatures of Sea Ice During the Winter of the MOSAiC Campaign

A ground-based ultra-wideband radiometer operating at 540, 900, 1380, and 1740 MHz was used to measure microwave thermal emissions from an Arctic sea ice floe as part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Expedition. The instrument was deployed on a drifting ice floe near 86°N, 120°E in leg 1 of the expedition (December 2019) and observed second-year ice (potentially with refrozen melt ponds) that experienced new ice growth at its base over a ten-day period. Measured circularly polarized brightness temperatures were compared with the predictions of a radiative transfer (RT) model for a layered medium consisting of ocean, growing new ice, desalinated remnant second-year ice/refrozen melt pond, and snow layers. Characteristics of the sea ice composition used in the model were determined from in-situ measurements. Comparisons of the measured and modeled wideband brightness temperatures showed good agreement consistently over the observation period and for various off-nadir observation angles. The results demonstrate the capabilities of 0.5–2 GHz microwave radiometry for observing sea ice properties and also show the impact of a saline ice layer at the ice bottom on the measured brightness temperature.
ANALYZING CHEST X-RAY TO DIFFERENTIATE SYMPTOMS OF COVID-19 THROUGH ML APPROACH

— The motive behind the project is to build a machine learning model for detection of Covid-19. Using this model, it is possible to classify images of chest x-rays into normal patients, pneumatic patients, and covid-19 positive patients. This CNN based model will help drastically to save time constraints among the patients. Instead of relying on limited RT-PCR kits, just a simple chest x-ray can help us determine health of the patient. Not only we get immediate results, but we can also practice social distancing norms more effectively.