Miracle-dz/UniFAR-FTUs huggingface dataset:
Inverse modelling of in situ soil water dynamics: accounting for heteroscedastic, autocorrelated, and non-Gaussian distributed residuals

Abstract. Inverse modelling of in situ soil water dynamics is a powerful tool to test process understanding and determine soil hydraulic properties at the scale of interest. The observations of soil water state variables are typically evaluated using the ordinary least squares approach. However, the underlying assumptions of this classical statistical approach of independent, homoscedastic, and Gaussian distributed residuals are rarely tested in practice. In this case study, we estimated the soil hydraulic properties of a homogeneous, bare soil profile from field observations of soil water contents. We used a formal Bayesian approach to estimate the posterior distribution of the parameters in the van Genuchten–Mualem (VGM) model of the soil hydraulic properties. Three likelihood models that differ with respect to assumptions about the statistical features of the time series of residuals were used. Our results show that the assumptions of the ordinary least squares did not hold, because the residuals were strongly autocorrelated, heteroscedastic and non-Gaussian distributed. From a statistical point of view, the parameter estimates obtained with this classical statistical approach are therefore invalid. Since a test of the classic first-order autoregressive (AR(1)) model led to strongly biased model predictions, we introduced an modified AR(1) model which eliminates this critical deficit of the classic AR(1) scheme. The resulting improved likelihood model, which additionally accounts for heteroscedasticity and nonnormality, lead to a correct statistical characterization of the residuals and thus outperformed the other two likelihood models. We consider the corresponding parameter estimates as statistically correct and showed that they differ systematically from those obtained under ordinary least squares assumptions. Moreover, the uncertainty in the parameter estimates was increased by accounting for autocorrelation in the observations. Our results suggest that formal Bayesian inference using a likelihood model that correctly formalizes the statistical properties of the residuals may also prove useful in other inverse modelling applications in soil hydrology.
An unsplit step 3-D PML for use with the FDTD method

An important advance in the use of the finite-difference time-domain (FDTD) method has been the introduction of the perfectly matched layer (PML) to act as the absorbing boundary condition. The initial implementation required the E and H fields to be split. Recent advances have suggested a new unsplit step PML. This paper describes an FDTD implementation of this new unsplit PML in three dimensions, but implements them in the D and H fields. This has the advantage of isolating the PML from the rest of the FDTD computation, but, unlike the split step formulation, requires almost no additional computational resources.
Robust & stochastic model predictive control

In the thesis, two different model predictive control (MPC) strategies are investigated for linear systems with uncertainty in the presence of constraints: namely robust MPC and stochastic MPC. Firstly, a Youla Parameter is integrated into an efficient robust MPC algorithm. It is demonstrated that even in the constrained cases, the use of the Youla Parameter can desensitize the costs to the effect of uncertainty while not affecting the nominal performance, and hence it strengthens the robustness of the MPC strategy. Since the controller u = K x + c can offer many advantages and is used across the thesis, the work provides two solutions to the problem when the unconstrained nominal LQ-optimal feedback K cannot stabilise the whole class of system models. The work develops two stochastic tube approaches to account for probabilistic constraints. By using a semi closed-loop paradigm, the nominal and the error dynamics are analyzed separately, and this makes it possible to compute the tube scalings offline. First, ellipsoidal tubes are considered. The evolution for the tube scalings is simplified to be affine and using Markov Chain model, the probabilistic tube scalings can be calculated to tighten the constraints on the nominal. The online algorithm can be formulated into a quadratic programming (QP) problem and the MPC strategy is closed-loop stable. Following that, a direct way to compute the tube scalings is studied. It makes use of the information on the distribution of the uncertainty explicitly. The tubes do not take a particular shape but are defined implicitly by tightened constraints. This stochastic MPC strategy leads to a non-conservative performance in the sense that the probability of constraint violation can be as large as is allowed. It also ensures the recursive feasibility and closed-loop stability, and is extended to the output feedback case.
Improved algorithm for iris location based on active contour

Aim at the problem that traditional snake model is sensitive to the initial position and needs to rely on other mechanisms to locate snake points of the active contour in the vicinity of the target image resulting in effect of a weak boundary image is very pool,snake model improved energy function is applied.This energy function can equably search snake points.In the searching process,adjustment of internal energy function is increased gradually,every snake point contour approaching to the target contour by iteration until contour arrive to the boundary of iris.The experimental results show that this method is effective for iris localization.
The Bernstein—von Mises theorem for regression based on Gaussian Processes

Regression based on Gaussian Processes (GPR) is widely used in applications [1]. Theoretical results about its properties have been obtained only for some very specific cases [2], [3]. A theoretical analysis of GPR is needed for the case of high-dimensional data, limited sample size, and incorrect model specification. In GPR it is assumed that an unknown function is generated by a stochastic process. Therefore, it is very natural to carry out a theoretical analysis of GPR using the Bayesian approach. The central result of this approach is the Bernstein–von Mises (BvM) theorem, which states that the posterior distribution of the GPR parameters is close to the corresponding normal distribution. Classical asymptotic methods [4] are not suitable for analysis of the posterior distribution properties for the case of growing parameter dimension and finite data sample size. Therefore, new statistical approaches, based on the advanced theory of empirical processes, are necessary to perform the analysis (see [5]). In this paper we obtain a non-asymptotic bound on the accuracy of the approximation in the BvM theorem. The GPR can be constructed as follows. We are given a sample of unknown function values: D = (X,y) = {xi, yi = y(xi)}i=1, x ∈ X ⊆ R. We assume that the function y(x) is a realization of a zero-mean GP, and that the covariance between GP values is defined by some covariance function cov(y(x), y(x′)) = k(x,x′). In such a case the distribution of the vector of values y is normal: y ∝ N (0, K), where K = {k(xi,xj)}i,j=1 is the covariance matrix. The covariance function k(x,x′) is modelled by some parametric family kθ(x,x), θ ∈ Θ ⊂ R, where Θ is a compact set. The regression function is defined by the posterior mean Eθ{y(x) | D} = kθ (x)K−1 θ y, where kθ(x) = (kθ(x,x1), . , kθ(x,xn)) ⊤, and Eθ{ · } is the mathematical expectation with respect to the distribution N (0, Kθ), Kθ = {kθ(xi,xj)}i,j=1. Usually the parameter vector θ is estimated by the maximal (quasi-) likelihood estimator θ̃ = argmaxθ∈Θ L(θ), where the logarithm of the (quasi-) likelihood is equal to L(θ) = −[n log(2π) + log det(Kθ) + y⊤K−1 θ y]/2. Suppose that we are also given a certain prior distribution Π(dθ) of the parameter vector θ. Then the posterior distribution Law(θ | D) ∝ exp{L(θ)}Π(dθ) for the given sample describes the conditional distribution of a random vector θ. The purpose of this note is to study theoretical properties of the posterior distribution Law(θ | D). We consider the case of a non-informative prior distribution Π(dθ) and a possibly misspecified parametric model, that is, in general k(x,x′) ̸∈ {kθ(x,x),θ ∈ Θ}. Denote by ∥ · ∥p the matrix p-norm, by In ∈ Rn×n the identity matrix, and by E{ · } and Var{ · } the mathematical expectation and the covariance operator with respect to the distribution N (0, K). Let the following assumptions hold true: (A1) kθ(x,x) is three times continuously differentiable with respect to θ ∈ Θ for x,x′ ∈ X;
Viral Vectors in Gene Therapy: Where Do We Stand in 2023?

Viral vectors have been used for a broad spectrum of gene therapy for both acute and chronic diseases. In the context of cancer gene therapy, viral vectors expressing anti-tumor, toxic, suicide and immunostimulatory genes, such as cytokines and chemokines, have been applied. Oncolytic viruses, which specifically replicate in and kill tumor cells, have provided tumor eradication, and even cure of cancers in animal models. In a broader meaning, vaccine development against infectious diseases and various cancers has been considered as a type of gene therapy. Especially in the case of COVID-19 vaccines, adenovirus-based vaccines such as ChAdOx1 nCoV-19 and Ad26.COV2. S have demonstrated excellent safety and vaccine efficacy in clinical trials, leading to Emergency Use Authorization in many countries. Viral vectors have shown great promise in the treatment of chronic diseases such as severe combined immunodeficiency (SCID), muscular dystrophy, hemophilia, β-thalassemia, and sickle cell disease (SCD). Proof-of-concept has been established in preclinical studies in various animal models. Clinical gene therapy trials have confirmed good safety, tolerability, and therapeutic efficacy. Viral-based drugs have been approved for cancer, hematological, metabolic, neurological, and ophthalmological diseases as well as for vaccines. For example, the adenovirus-based drug Gendicine® for non-small-cell lung cancer, the reovirus-based drug Reolysin® for ovarian cancer, the oncolytic HSV T-VEC for melanoma, lentivirus-based treatment of ADA-SCID disease, and the rhabdovirus-based vaccine Ervebo against Ebola virus disease have been approved for human use.
Status języka polskiego wśród uczniów uczących się w klasach z językiem 
polskim jako językiem nauczania (na przykładzie Iwano-Frankiwska, Ukraina)

This article presents the results of a sociolinguistic survey of 2019 conducted among the youth of Polish and non-Polish descent studying together in classes dedicated to the Polish national minority in the 3rd Upper-Secondary School in Ivano-Frankivsk (formerly: Stanyslaviv). The survey was composed of 170 questionnaires concerning the status of the Polish language among the youth aged 12–17 studying in classes with Polish as the language of instruction. Under the statute of the School, pupils of non-Polish descent may also study in such classes. For the group of pupils of Polish descent, i.e. 42 pupils (25% of the total number of respondents), Polish has the status of an inherited language, the language of ancestors, and the economic status, Polish is associated with studying and living in Poland in the future. For the group of pupils of non-Polish descent, i.e. 128 pupils (75% of the total number of respondents), Polish has the economic status and the status of Polish as a second foreign language of instruction; around 40% of the pupils intend to emigrate to Poland in the future.
Optimal control problem for an elliptic equation which has exactly two solutions

An optimal control problem governed by a class of semilinear non‐well‐posed elliptic equations is discussed in this paper. It is pointed out that the state equation we considered has exactly two solutions and the control domain and cost functional maybe non‐convex. Using the relaxed controls, Pontryagin's maximum principle for the optimal pairs is obtained. Copyright © 2011 John Wiley & Sons, Ltd.
The determinants of financial distress cost: A case of emerging market

Abstract This study analyses the cost of financial distress of non-financial firms listed on the Pakistan stock exchange. Furthermore, it considers the moderating role of concentrated ownership in the relationship between debt and expected financial distress costs. We used the panel data of 214 firms from 2010 to 2018 to analyse the results. We apply fixed effect model to test the hypotheses. We find that ex-ante financial distress costs are based not only on the probability of financial distress but also affect the amount of time and money spent during the distress period. The use of tangible fixed assets and long-term leverage lowers the cost of financial distress, whereas the use of short-term debt has no significant impact on the cost of financial distress. Furthermore, the company’s ownership structure dampens the impact of these factors. Corporate management may reduce the cost of financial distress through better management of fixed assets and financial leverage.
An elementary proof of Rayleigh's principle

Rayleigh's principle states that the smallest eigenvalue of a regular Sturm-Liouville problem with regular boundary conditions is the minimum value of a certain functional, the so-called Rayleigh quotient, and that this value is attained at the corresponding eigenfunctions only. This can be proved by advanced methods. However, there is an elementary proof, which is presented in this note.
Linkage bias in estimating the association between childhood exposures and propensity to become a mother: an example of simple sensitivity analyses

Summary. Record linkage is a powerful tool to obtain individual follow‐up information that is held in routinely collected databases. However, this method is potentially limited not only by the quality of the original data but also by the temporal and geographic coverage of the routine data. Migration in particular is a factor that might introduce systematic bias even in analyses of data covering relatively large geographical areas. We describe a linkage application where emigration bias might be an issue and use the sensitivity analysis approach that has been described by Molenberghs and co‐workers and Kenward and co‐workers to assess the extent of this bias.
Simulation Methods for Analyzing Continuous-Time Event-History Models

This research was supported in part by National Institute of Child Health and Human Development contract NO1-HD-02849. Any opinions expressed in this chapter are those of the author and do not necessarily represent the views of the Urban Institute or any of its sponsors. I have benefited from conversations with Thomas Espenshade, Charles Calhoun, and Ralph Ginsberg and from comments on earlier drafts by Christopher Winship, Nancy Tuma, and two anonymous referees. Barbara Wilson of the National Center for Health Statistics provided tabulations of unpublished marriage statistics used in the empirical analysis. The assistance of Vicki Aaronson, Jon Johnson, and Terri Murray is greatly appreciated.
Spectral similarity-preserving hyperspectral band selection

Due to the high spectral resolution, hyperspectral images (HSI) have been widely used in land cover classification and material identification. Band selection is one of the necessary preprocessings to reduce the data volume and the redundancy therein for the subsequent analysis. Aiming at speeding up the search-based band selection process, this letter proposes a new technique from the perspective of spectral curve shape similarity. Through a newly defined measure for band subset discriminativeness (BSD), class-specific important bands (IBs) are retained which can preserve the spectral similarity of the samples from the same class and narrow down candidate band subset for the following search procedure. Then optimal search is performed in the aggregated band subset from all classes. Experiments on the Indian Pine benchmark data set have proved the efficiency and effectiveness of the proposed method.
Auxiliary signal design for failure detection in differential-algebraic equations

Fault detection and identification (FDI) are important tasks in most modern industrial and mechanical systems and processes. Many of these systems are most naturally modeled by differential-algebraic equations (DAE). This paper addresses active fault detection in DAE. A technique is presented to calculate an auxiliary test signal guaranteeing detection, assuming bounded additive noise. An efficient real time detection algorithm is also provided as are example simulations. The extension to model uncertainty is discussed.
Exploration of the Design of Spiderweb-Inspired Structures for Vibration-Driven Sensing

In the quest to develop large-area soft sensors, we can look to nature for many examples. Spiderwebs show many fascinating properties that we can seek to understand and replicate in order to develop large-area, soft, and deformable sensing structures. Spiders’ webs are used not only to capture prey, but also to localize their prey through the vibrations that they feel through their legs. Inspired by spiderwebs, we developed a large-area tactile sensor for localizing contact points through vibration sensing. We hypothesize that the structure of a web can be leveraged to amplify, filter, or otherwise morphologically tune vibrations to improve sensing capabilities. To explore this design space, we created a means of computationally designing and 3D printing web structures. By using vibration sensors mounted on the edges of webs to simulate a spider monitoring vibrations, we show how varying the structural properties affects the localization performance when using vibration sensors and long short-term memory (LSTM)-based neural network classifiers. We seek to explain the classification performance seen in different webs by considering various metrics of information content for different webs and, hence, provide insight into how bio-inspired spiderwebs can be used to assist large-area sensing structures.
Reliability and validity of the Chinese version of the Sakata Eating Behavior Scale short form and preliminary analysis of the factors related to the score of the scale

Background The obesity rate in the Chinese population is increasing and there is a lack of short and reliable scales for measuring obesity-related eating behavior in China. The EBS-SF (Sakata Eating Behavior Scale short form) has only 7 entries and has shown good reliability in studies such as those in Japan. Objective To translate the EBS-SF into Chinese, check its reliability, validity and explore the related factors. Method The EBS-SF was translated into Chinese. 3,440 residents were investigated and 34 respondents were retested. Item analysis and reliability and validity tests were carried out. Personality characteristics, family health status and depression were investigated using the BFI-10, FHS-SF and PHQ-9 to investigate the factors associated with EBS-SF. The t-test, ANOVA and Pearson correlation was used to explore the related factors of its scores. Result Among 3,440 residents, 1,748 (50.81%) were male and 1,692 (49.19%) were female; 1,373 (39.91%) were aged 36–50 years. All 7 items were qualified in the item analysis. As for reliability, the Cronbach's α was 0.870, the split-half reliability was 0.830, the test-retest correlation coefficient was 0.868. As for the structural validity, the standardized factor loadings were above 0.50, χ2 / df = 2.081,GFI = 0.999; NFI = 0.999; RFI = 0.996; RMSEA = 0.018, all qualified. The characteristics, personality, family health and depression were correlated with the score of the Chinese version of EBS short form. Conclusion The structural validity and reliability of the Chinese version of the EBS-SF are good and it can be used as a measurement tool to evaluate the eating behavior of Chinese. The scores of the EBS-SF may be related to the sociological characteristics, personality, family health, and depression status.
Study on the effect of ash layer on flow and heat transfer in DPF based on three-dimensional thermal lattice Boltzmann method

After the diesel engine works for a long time, dense ash layer will be formed on the diesel particulate filter (DPF) surface, which will increase the back pressure on the DPF surface and seriously affect the normal operation of the DPF. The DPF micro-structure is generated by 3D reconstruction technology. Moreover, the particle layer and ash layer are reconstructed by Quartet Structure Generation Set. The exhaust gas with a velocity of 0.05 m/s~0.25 m/s is introduced into the DPF model. Under the condition of 873 K temperature field, the 3D lattice Boltzmann method is used to explore the influence of the particle layer and ash layer accumulated in its micro-structure on its flow and heat transfer. The results show that with the accumulation of ash, the flow velocity in the DPF increases with the decrease of porosity. The existence of ash layer increases the pressure difference between the inlet and outlet of the structure, and the pressure difference between the inlet and outlet of the structure increases with the increase of the air inlet speed. Under the same flow conditions, the heat transfer process of structures containing ash stratification is faster, and the highest temperature point moves forward accordingly. This has certain guiding significance for exploring the thermal load of ash containing DPF structure.