Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-Contact and Self-Calibrated Photopyroelectric Method for Complete Thermal Characterization of Porous Materials

A general theory of a photopyroelectric (PPE) configuration, based on an opaque sample and transparent pyroelectric sensor, backing and coupling fluids is developed. A combined back-front detection investigation, based on a frequency scan of the phase of the PPE signals, followed by a self-normalization of the phases’ behavior, leads to the possibility of simultaneously measuring both thermal effusivity and diffusivity of a solid sample. A particular case of this configuration, with no coupling fluid at the sample/backing interface and air instead of coupling fluid at the sample/sensor interface (non-contact method) is suitable for simultaneous measurement ofboth thermal diffusivity and effusivity (in fact complete thermal characterization) of porous solids. Compared with the already proposed configurations for investigations of porous materials, this novel configuration makes use of a fitting procedure with only one fitting parameter, in order to guarantee the uniqueness of the solution. The porous solids belong to a class of materials which are by far not easy to be investigated using PPE. To the best of our knowledge, porous materials represent the only type of compounds, belonging to condensed matter, which were not taken into consideration (until recently) as potential samples for PPE calorimetric investigations. Consequently, the method proposed in this paper complete the area of applications of the PPE method. Applications on some porous building materials and cellulose-based samples validate the theory.
Resilient Asynchronous $H_{\infty}$ Control for Discrete-Time Markov Jump Singularly Perturbed Systems Based on Hidden Markov Model

This paper studies the resilient asynchronous <inline-formula> <tex-math notation="LaTeX">${H} _{\infty }$ </tex-math></inline-formula> control problem for slow sampling discrete-time uncertain Markov jump singularly perturbed systems (SPSs). Compared with slow state variables feedback controller, a new controller is proposed for slow sampling discrete-time SPSs, which has less conservatism. A more realistic situation, i.e., the system modes cannot be directly acquired for controller design, is considered with the help of hidden Markov model (HMM). The goal is to design a resilient asynchronous controller based on HMM such that the closed-loop system is stochastically stable while meeting an expected <inline-formula> <tex-math notation="LaTeX">${H}_{\infty }$ </tex-math></inline-formula> performance in the presence of random controller gain fluctuation and the system modes hiding for controller. By utilizing matrix inequality techniques and Lyapunov function method, some criteria are established for the existence of the resilient asynchronous controller. The superiority and practicability of the obtained theoretical results are demonstrated by a numerical example and an inverted pendulum system.
A powered Gronwall-type inequality and applications to stochastic differential equations

In this paper we study a powered integral inequality involving a finite sum, which can be used to solve the inequalities with singular kernels. We present that the solution of the inequality is decided by a finite recursion, whose result is proved to be a continuous, bounded or asymptotic function. Meanwhile, in order to overcome an obstacle from powers of integrals, we modify the method of monotonization into the powered monotonization. Furthermore, relying on the result and our technique of concavification, we discuss a generalized stochastic integral inequality, and give an estimate of the mean square. In the end, as applications, we study uniform boundedness and continuous dependence of solutions for a class of stochastic differential equation in mean square.
The Effect of Company Size, Financial Leverage, Profitability, and Dividend Payout Ratio on Income Smoothing Practices

This study aims to test whether company size, financial leverage, profitability, and dividend payout ratio affect earnings smoothing practices in LQ-45 index companies on the Indonesia Stock Exchange in 2018-2020. This research uses quantitative methods. The population in this study is the LQ-45 company index with a sample recruitment technique using purposive sampling. The selected samples were 19 companies through predetermined sample criteria. Hypothesis testing uses logistic regression analysis, and data is processed using E-Views 10 software. The results of this study indicate that company size has a significant positive effect on the practice of the role of profit, financial leverage has a significant and negative effect on the practice of the role of profit, profitability has an effect and is significant positive effect on income smoothing practices, dividend payout ratio has no effect on income smoothing practices in LQ 45 index companies on the Indonesia Stock Exchange in 2018-2020.
Bayesian Model Selection And Estimation Without Mcmc

This dissertation explores Bayesian model selection and estimation in settings where the model space is too vast to rely on Markov Chain Monte Carlo for posterior calculation. First, we consider the problem of sparse multivariate linear regression, in which several correlated outcomes are simultaneously regressed onto a large set of covariates, where the goal is to estimate a sparse matrix of covariate effects and the sparse inverse covariance matrix of the residuals. We propose an Expectation-Conditional Maximization algorithm to target a single posterior mode. In simulation studies, we find that our algorithm outperforms other regularization competitors thanks to its adaptive Bayesian penalty mixing. In order to better quantify the posterior model uncertainty, we then describe a particle optimization procedure that targets several high-posterior probability models simultaneously. This procedure can be thought of as running several ``mutually aware'' mode-hunting trajectories that repel one another whenever they approach the same model. We demonstrate the utility of this method for fitting Gaussian mixture models and for identifying several promising partitions of spatiallyreferenced data. Using these identified partitions, we construct an approximation for posterior functionals that average out the uncertainty about the underlying partition. We find that our approximation has favorable estimation risk properties, which we study in greater detail in the context of partially exchangeable normal means. We conclude with several proposed refinements of our particle optimization strategy that encourage a wider exploration of the model space while still targeting high-posterior probability models. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Statistics First Advisor Edward I. George
Modelling Group Heterogeneity for Small Area Estimation Using M‐Quantiles

Small area estimation typically requires model‐based methods that depend on isolating the contribution to overall population heterogeneity associated with group (i.e. small area) membership. One way of doing this is via random effects models with latent group effects. Alternatively, one can use an M‐quantile ensemble model that assigns indices to sampled individuals characterising their contribution to overall sample heterogeneity. These indices are then aggregated to form group effects. The aim of this article is to contrast these two approaches to characterising group effects and to illustrate them in the context of small area estimation. In doing so, we consider a range of different data types, including continuous data, count data and binary response data.
VLBI Probes of Jet Physics in Neutrino-Candidate Blazars

In recent years, evidence has accumulated that some high-energy cosmic neutrinos can be associated with blazars. The strongest evidence for an individual association was found in the case of the blazar TXS 0506+056 in 2017. In July 2019, another track-like neutrino event (IC190730A) was found spatially coincident with the well-known bright flat-spectrum radio quasar PKS 1502+106. PKS 1502+106 was not found to be in a particularly elevated gamma-ray state, but exhibited a remarkably bright radio outburst at the time of the neutrino detection, similar to TXS 0506+056. We have performed a multi-frequency VLBI study from 15 GHz up to 86 GHz on TXS 0506+056, PKS 1502+106 and one additional neutrino-candidate blazar (PKS 0215+015) to study the radio structure of neutrino candidate blazars in response to their neutrino association. We have obtained target of opportunity observations with the VLBA for all three sources within $\sim$1 month from their associated neutrino events and are performing multi-epoch studies of the jet kinematics at 15 GHz as part of the MOJAVE program. Here, we present first results on TXS 0506+056 at 86 GHz and one additional 43 GHz image obtained 27 days after IC170922A, closer in time to the neutrino event than previously published images. We also give an overview about our recent work on PKS 1502+106 and PKS 0215+015.
Genomic Regions and Candidate Genes Associated with Milk Production Traits in Holstein and Its Crossbred Cattle: A Review

Genome-wide association studies (GWAS) are a powerful tool for identifying genomic regions and causative genes associated with economically important traits in dairy cattle, particularly complex traits, such as milk production. This is possible due to advances in next-generation sequencing technology. This review summarized information on identified candidate genes and genomic regions associated with milk production traits in Holstein and its crossbreds from various regions of the world. Milk production traits are important in dairy cattle breeding programs because of their direct economic impact on the industry and their close relationship with nutritional requirements. GWAS has been used in a large number of studies to identify genomic regions and candidate genes associated with milk production traits in dairy cattle. Many genomic regions and candidate genes have already been identified in Holstein and its crossbreds. Genes and single nucleotide polymorphisms (SNPs) that significantly affect milk yield (MY) were found in all autosomal chromosomes except chromosomes 27 and 29. Half of the reported SNPs associated with fat yield and fat percentage were found on chromosome 14. However, a large number of significant SNPs for protein yield (PY) and protein percentage were found on chromosomes 1, 5, and 20. Approximately 155 SNPs with significant influence on multiple milk production traits have been identified. Several promising candidate genes, including diacylglycerol O-acyltransferase 1, plectin, Rho GTPase activating protein 39, protein phosphatase 1 regulatory subunit 16A, and sphingomyelin phosphodiesterase 5 were found to have pleiotropic effects on all five milk production traits. Thus, to improve milk production traits it is of practical relevance to focus on significant SNPs and pleiotropic genes frequently found to affect multiple milk production traits.
Ensemble Methods for Anomaly Detection

Anomaly detection has many applications in numerous areas such as intrusion detection, fraud detection, and medical diagnosis. Most current techniques are specialized for detecting one type of anomaly, and work well on specific domains and when the data satisfies specific assumptions. We address this problem, proposing ensemble anomaly detection techniques that perform well in many applications, with four major contributions: using bootstrapping to better detect anomalies on multiple subsamples, sequential application of diverse detection algorithms, a novel adaptive sampling and learning algorithm in which the anomalies are iteratively examined, and improving the random forest algorithms for detecting anomalies in streaming data. We design and evaluate multiple ensemble strategies using score normalization, rank aggregation and majority voting, to combine the results from six well-known base algorithms. We propose a bootstrapping algorithm in which anomalies are evaluated from multiple subsets of the data. Results show that our independent ensemble performs better than the base algorithms, and using bootstrapping achieves competitive quality and faster runtime compared with existing works. We develop new sequential ensemble algorithms in which the second algorithm performs anomaly detection based on the first algorithm’s outputs; best results are obtained by combining algorithms that are substantially different. We propose a novel adaptive sampling algorithm which uses the score output of the base algorithm to determine the hard-todetect examples, and iteratively resamples more points from such examples in a complete unsupervised context. On streaming datasets, we analyze the impact of parameters used in random trees, and propose new algorithms that work well with high-dimensional data, improving performance without increasing the number of trees or their heights. We show that further improvements can be obtained with an Evolutionary Algorithm. ENSEMBLE METHODS FOR ANOMALY DETECTION
Aeroacoutic investigation of th effect of a detached flat plate on the noise from a square cylinder

This paper presents an experimental investigation of the aerodynamic noise generated by a square cylinder in the presence of a detached thin flat-plate located in its wake. Experiments were performed in an Anechoic Wind Tunnel (AWT) at Reynolds numbers between 1.55 x 10 4 < ReD < 2.3 x 10 4 (D being the width of the cylinder) and Mach numbers of 0.07 < M0 < 0.1. A flat-plate with a length L = D was placed along the centreline of the wake at a gap distance G from its trailing-edge (TE). The gap was varied between 0 < G < 4D whereby the aeroacoustic behaviour was studied using microphone measurements. It was found that the thin detached flat-plate reduced the Sound Pressure Level (SPL) by approximately 7 dB at the Aeolian tone frequency.
A New and Simple Prediction Equation For Health-Related Fitness: Use of Honest Assessment Predictive Modeling

Background: The five components of health-related fitness are cardiorespiratory endurance, muscular strength, muscular endurance, body composition, and flexibility. To assess an individual on all five components can be time consuming. Thus, it would be useful to fitness specialists if a simpler and valid fitness assessment was available to measure overall health-related fitness. The purpose of this study was to employ honest assessment predictive modeling to find a parsimonious set of variables that can predict overall health-related fitness. Methods: Data used for this study came from college students who completed a fitness test battery. An overall health-related fitness score (T-score) was constructed using maximal oxygen consumption (VO2, ml/kg/min), 1RM bench press (BP, lb), maximal push-up repetition (PU, #), and percent body fat (PBF, %). The set of possible predictor variables consisted of participant age (yr), sex (male/female), body mass index (BMI, kg/m2), waist circumference (WC, cm), 1RM leg press (LP, lb), countermovement vertical jump (VJ, in), flexed arm hang (FAH, sec), physical activity rating (PAR, 0 thru 10), and sit-and-reach (SNR, cm). The honest assessment predictive modeling procedure comprised three steps: 1) development of competing models using a TRAINING dataset, 2) selecting an optimal model using a separate VALIDATION dataset, and 3) assessing fitness score construct validity using a final SCORING dataset. Results: Stepwise model selection with Schwarz Bayesian criterion (SBC) on the TRAINING data resulted in five possible models including sex, VJ, PAR, and WC. Results on the VALIDATION data indicated a three-variable model had the lowest average squared error (ASE) and consisted of sex, VJ, and PAR (F=107.8, pR2=.82, SEE=3.09). Finally, predicted values from the SCORING data showed that athletes (Mean=54.9, SD=5.1) had a significantly (p.001) greater mean fitness score than non-athletes (Mean=39.8, SD=4.8). Conclusion: This study presents a valid equation that can simply predict overall health-related fitness in college students.
The Importance of the “Time Factor” for the Evaluation of Inhibition Mechanisms: The Case of Selected HDAC6 Inhibitors

Simple Summary Protein lysine acetylation is, with phosphorylation, the most common regulatory post-translational modification of proteins. Histone deacetylases (HDAC) catalyze the removal of acetyl groups from histone and non-histone proteins, participating in the modulation of several pathways. Histone deacetylase 6 is perhaps the most complex among histone deacetylases, comprising two catalytic domains, an N-terminal microtubule-binding domain and a C-terminal ubiquitin–binding domain. Interfering with its catalytic activity by using small synthetic molecules has been shown to be beneficial in the treatment of cancer, and neurological and immunological disorders. Thus, the development of potent and selective inhibitors of HDAC6 is an active field of medicinal chemistry. We shall here discuss the importance of monitoring the kinetics of onset and relief of inhibition to contribute important information on inhibition mechanisms during drug design/development campaigns using selected HDAC6 inhibitors as examples. Abstract Histone deacetylases (HDACs) participate with histone acetyltransferases in the modulation of the biological activity of a broad array of proteins, besides histones. Histone deacetylase 6 is unique among HDAC as it contains two catalytic domains, an N-terminal microtubule binding region and a C-terminal ubiquitin binding domain. Most of its known biological roles are related to its protein lysine deacetylase activity in the cytoplasm. The design of specific inhibitors is the focus of a large number of medicinal chemistry programs in the academy and industry because lowering HDAC6 activity has been demonstrated to be beneficial for the treatment of several diseases, including cancer, and neurological and immunological disorders. Here, we show how re-evaluation of the mechanism of action of selected HDAC6 inhibitors, by monitoring the time-dependence of the onset and relief of the inhibition, revealed instances of slow-binding/slow-release inhibition. The same approach, in conjunction with X-ray crystallography, in silico modeling and mass spectrometry, helped to propose a model of inhibition of HDAC6 by a novel difluoromethyloxadiazole-based compound that was found to be a slow-binding substrate analog of HDAC6, giving rise to a tightly bound, long-lived inhibitory derivative.
A Three-Stage Algorithm for Solving Incompressible Flow Problems

The present study focuses on a reformulation of the Fractional-Step, Artificial Compressibility with Pressure-Projection (FSAC-PP) method by which a diffusion equation for the pressure is obtained through a velocity projection concept. The proposed method is a Fractional-Step Velocity-Projection (FSVP) approach, which is validated for a laminar flow in a lid driven cavity. The results are compared to the classical hyperbolic Artificial Compressibility (AC) method and reference data taken from the literature to demonstrate the strength of the proposed FSVP method.
Design Strategies of an Hourly Update 3DVAR Data Assimilation System for Improved Convective Forecasting

AbstractThis study examines two strategies for improving the analysis of an hourly update three-dimensional variational data assimilation (3DVAR) system and the subsequent quantitative precipitation forecast (QPF). The first strategy is to assimilate synoptic and radar observations in different steps. This strategy aims to extract both large-scale and convective-scale information from observations typically representing different scales. The second strategy is to add a divergence constraint to the momentum variables in the 3DVAR system. This technique aims at improving the dynamic balance and suppressing noise introduced during the assimilation process. A detailed analysis on how the new techniques impact convective-scale QPF was conducted using a severe storm case over Colorado and Kansas during 8 and 9 August 2008. First, it is demonstrated that, without the new strategies, the QPF initialized with an hourly update analysis performs worse than its 3-hourly counterpart. The implementation of the two-step.
Effect of Human Capital Investment on Income Inequality in Kenya

Human capital investment has a crucial role in economic growth, and as such, it has been regarded as a significant aspect of government spending. The Gini index score reported an average of 41.6 percent in 2018, which is higher than the generally recognized perfect equality Gini index of 20%, suggesting that Kenya has been suffering from high income disparity. There has been a widespread belief that income inequality and human capital investment are mutually exclusive. The theoretical and empirical approaches in the literature provide mixed findings on the relationship. From 1990 to 2019, this study examined the effect of human capital investment on income inequality in Kenya while adjusting for interest rates and GDP per capita. The study adopted a causal research design to determine if a cause-and-effect association between the variables occurs. The time series data were subjected to diagnostic tests to ensure the presumptions of ordinary least squares held. Health expenditure was found to have a negative and statistically significant effect on income inequality after controlling for the interest rate and GDP per capita. After accounting for changes in interest rates and GDP per capita, the result shows that education investment has a negative and statistically insignificant effect on income inequality. The human development index was discovered to have a negative and statistically significant effect on disparity in income which was verified by the robust check. An inverted U was found using the Kuznets test, which was performed to broaden the scope of the research but yielded an insignificant result. The study recommends the formulation and implementation of policies that adhere to the Abuja Declaration on Health, which requires that 15% of government expenditure be allocated to health. The study recommends strict adherence to the 100% transition from primary to post-primary education. The study's conclusions are pertinent to the development and implementation of successful policies that encourage human capital investment, resulting in a decrease in Kenya's levels of income inequality.
CONFLICT OF IRISH CULTURAL IDENTITY IN BRIAN FRIEL’S TRANSLATION

Having been colonized by Britain, Ireland was in a very deprived condition for a very long period, especially for poor Catholic Irish. Britain had destroyed Irish civil rights by forcing them in a massive potato plantation merely to fulfill British people’s need for food, which caused the Irish in a great famine and poverty. Written by an Irish playwright in 1980, which is set in agricultural land, northern side of Ireland, Brian Friel’s Translations illustrates clearly the suffering of Irish villagers in 19th century. By applying Homi K. Bhabha’s post-colonialism theory, the dialogues from the play are analyzed to explain Irish cultural identity conflict. This play is analyzed by using a descriptive qualitative method. The result of analysis reflects the mimicry concept from Bhabha, in which it shows an ambition that Irish tradition must be vanished and replaced by British tradition. The dialogues in Brian Friel’s Translation describe that Irish language and tradition which tells a lot of Greek mythology must be replaced with the culture of Standard English language. The translations on Irish local place names into English language are forced for the sake of British imperialism importance in the Ordnance survey.