Miracle-dz/UniFAR-FTUs huggingface dataset:
A rockburst grade evaluation method based on principal component analysis and the catastrophe progression method

Rockburst disasters always have a great influence on engineering practice. In order to accurately predict the occurrence of rockburst hazards, this paper proposes a rockburst rating evaluation method based on principal component analysis (PCA) and the catastrophe progression method, taking into account several influencing factors. In this paper, 15 indicators, such as strength brittleness factor (R), stress factor (P), and rock quality index (RQD) (reflecting the strength and fragmentation degree of rock mass), were selected from seven samples and were analyzed and downscaled by principal component analysis. Combined with the catastrophe progression method, each layer index was dimensionless and normalized to determine the mutation level value of each layer. Based on the principle of complementarity or non-complementarity, to determine the total mutation level value, the layer index was used to divide the rockblast-level interval and predict the rockblast level. The results show that the method proposed in this paper can be used to not only distinguish the importance of each of the same level of indicators but also avoid the impact of superimposed factor correlations between the same level of indicators, making the results more objective. This paper presents accurate rock explosion assessment results and an actual engineering situation. The number of factors affecting the assessment of the rock explosion level provides new insights.
Optimal stopping problems with applications to mathematical finance

The main contribution of the present thesis is a solution to finite horizon optimalstopping problems associated with pricing several exotic options, namely the Americanlookback option with fixed strike, the British lookback option with fixed strike,American swing put option and shout put option. We assume the geometric Brownianmotion model and under the Markovian setting we reduce the optimal stoppingproblems to free-boundary problems. The latter we solve by probabilistic argumentswith help of local time-space calculus on curves ([52]) and we characterise optimalexercise boundaries as the unique solution to certain integral equations. Then usingthese optimal stopping boundaries the option price can be obtained.The significance of Chapters 2 and 3 is a development of a method of scaling strikewhich helps to reduce three-dimensional optimal stopping problems, for lookbackoptions with fixed strike, including a maximum process to two-dimensional one withvarying parameter. In Chapter 3 we show a remarkable example where, for somevalues of the set parameters, the optimal exercise surface is discontinuous whichmeans that the three-dimensional problem could not be tackled straightforwardlyusing local time-space calculus on surfaces ([55]). This emphasises another advantageoffered by the reduction method.In Chapter 4 we study the multiple optimal stopping problems with a put payoffassociated to American swing option using local time-space calculus. To our knowledgethis is the first work where a) a sequence of integral equations has been obtainedfor consecutive optimal exercise boundaries and b) the early exercise premium representationhas been derived for swing option price. Chapter 5 deals with the shoutput option which allows the holder to lock the profit at some time ? and then attime T take the maximum between two payoffs at ? and T. The novelty of the workis that it provides a rigorous analysis of the free-boundary problem by probabilisticarguments and derives an integral equation for the optimal shouting boundary alongwith the shouting premium representation for the option price in some cases. Thisapproach can also be applied to other shout and reset options.In Chapter 6 we discuss a problem of the smooth-fit property for the American putoption in an exponential Levy model. In [2] the necessary and sufficient conditionwas obtained for the perpetual case. Recently Lamberton and Mikou [40] coveredalmost all cases for an exponential Levy model with dividends on finite horizon andwe study remaining cases. Firstly, we take the logarithm of the stock price as a Levyprocess of finite variation with zero drift and finitely many jumps, and prove thatone has the smooth-fit property without regularity unlike in the infinite horizon case.Secondly, we provide some analysis and calculations for another case uncovered in[40] where the drift is positive but for all maturities and removing the additionalcondition they used.The result of Chapter 1 is contained in the publication [33] and results of Chapters2-5 are exposed in preprints [34], [17] and [35] that are submitted for publication.
First Case Reports of Nontuberculous Mycobacterial (NTM) Lung Disease in Ecuador: Important Lessons to Learn

Nontuberculous mycobacteria (NTM) lung infections are often misdiagnosed as tuberculosis, which can lead to ineffective antibiotic treatments. In this report, we present three cases of NTM lung infections in Ecuador that were initially diagnosed and treated as tuberculosis based on the results of sputum smear microscopy. The patients, all male, included two immunocompetent individuals and one HIV-positive subject. Unfortunately, sputum culture was not initiated until late in the course of the disease and the cause of the lung infection, Mycobacterium avium complex (MAC), was only identified after the patients had either passed away or were lost to follow-up. These cases are the first documented cases of NTM lung infections in the English medical literature from Ecuador. We emphasize the importance of accurate diagnosis of NTM infections by culture and identification to species level. Sputum smear staining alone cannot differentiate between mycobacterial species, which can lead to misidentification and ineffective treatments. Additionally, reporting NTM pulmonary disease as a notifiable disease to national TB control programs is recommended to obtain accurate prevalence data. These data are critical in determining the importance of this public health problem and the necessary actions needed to address it.
An approximate analytical solution of nonlinear fractional diffusion equation by homotopy analysis method

In the present paper, the approximate analytical solution of a nonlinear diffusion equation with fractional time derivative () and with the diffusion term as () are obtained with the help of analytical method of nonlinear problem called the Homotopy Analysis Method (HAM). By using initial value, the explicit solution of the equation for different particular cases have been derived which demonstrate the effectiveness, validity, potentiality and reliability of the method in reality. Numerical results of the fast and slow diffusion for different particular cases are presented graphically. The numerical solutions show that only a few iterations are needed to obtain accurate approximate solutions. Key words: Partial differential equation, nonlinear fractional diffusion equation, Brownian motion, homotopy analysis method.
Ensemble learning with speaker embeddings in multiple speech task stimuli for depression detection

Introduction As a biomarker of depression, speech signal has attracted the interest of many researchers due to its characteristics of easy collection and non-invasive. However, subjects’ speech variation under different scenes and emotional stimuli, the insufficient amount of depression speech data for deep learning, and the variable length of speech frame-level features have an impact on the recognition performance. Methods The above problems, this study proposes a multi-task ensemble learning method based on speaker embeddings for depression classification. First, we extract the Mel Frequency Cepstral Coefficients (MFCC), the Perceptual Linear Predictive Coefficients (PLP), and the Filter Bank (FBANK) from the out-domain dataset (CN-Celeb) and train the Resnet x-vector extractor, Time delay neural network (TDNN) x-vector extractor, and i-vector extractor. Then, we extract the corresponding speaker embeddings of fixed length from the depression speech database of the Gansu Provincial Key Laboratory of Wearable Computing. Support Vector Machine (SVM) and Random Forest (RF) are used to obtain the classification results of speaker embeddings in nine speech tasks. To make full use of the information of speech tasks with different scenes and emotions, we aggregate the classification results of nine tasks into new features and then obtain the final classification results by using Multilayer Perceptron (MLP). In order to take advantage of the complementary effects of different features, Resnet x-vectors based on different acoustic features are fused in the ensemble learning method. Results Experimental results demonstrate that (1) MFCC-based Resnet x-vectors perform best among the nine speaker embeddings for depression detection; (2) interview speech is better than picture descriptions speech, and neutral stimulus is the best among the three emotional valences in the depression recognition task; (3) our multi-task ensemble learning method with MFCC-based Resnet x-vectors can effectively identify depressed patients; (4) in all cases, the combination of MFCC-based Resnet x-vectors and PLP-based Resnet x-vectors in our ensemble learning method achieves the best results, outperforming other literature studies using the depression speech database. Discussion Our multi-task ensemble learning method with MFCC-based Resnet x-vectors can fuse the depression related information of different stimuli effectively, which provides a new approach for depression detection. The limitation of this method is that speaker embeddings extractors were pre-trained on the out-domain dataset. We will consider using the augmented in-domain dataset for pre-training to improve the depression recognition performance further.
Searching for Large Raindrops: A Global Summary of Two-Dimensional Video Disdrometer Observations

AbstractA dataset containing 9637 h of two-dimensional video disdrometer observations consisting of more than 240 million raindrops measured at diverse climatological locations was compiled to help characterize underlying drop size distribution (DSD) assumptions that are essential to make precise retrievals of rainfall using remote sensing platforms. This study concentrates on the tail of the DSD, which largely impacts rainfall retrieval algorithms that utilize radar reflectivity. The maximum raindrop diameter was a median factor of 1.8 larger than the mass-weighted mean diameter and increased with rainfall rate. Only 0.4% of the 1-min DSD spectra were found to contain large raindrops exceeding 5 mm in diameter. Large raindrops were most abundant at the tropical locations, especially in Puerto Rico, and were largely concentrated during the spring, especially at subtropical locations. Giant raindrops exceeding 8 mm in diameter occurred at tropical, subtropical, and high-latitude continental locations. The .
Gender diversity on corporate boards and earnings management: Evidence for European Union listed firms

Abstract Using a sample of 3.808 non-financial European Union listed companies from 2011 to 2020, this study extends previous research by empirically examining how board gender diversity affects the magnitude of earnings management. The results support the predicted (negative) relationship between female directors and earnings management. We also find that when a critical mass of three or more female directors is reached, they can have a voice, which can have a positive impact on earnings quality (less earnings management). The results based on this study offer useful information for regulators in European Union countries. The results also provide useful information to investors in evaluating the impact of board gender diversity on earnings quality. The major contribution of the current study is that in contrast to similar studies, we also present our evidence by country. In addition, we test the critical mass hypothesis to evaluate the ability of female directors to impact earnings management based on their numerical representation on the board of directors, an issue that has drawn reduced attention from empirical studies.
Sobolev Estimation of Approximate Regressions

This paper focuses on the estimation of an approximated function and its derivatives. Let us assume that the data-generating process can be described by a family of regression models , where a is a multi-index of differentiation such that Dαϕ(xi) is the αth derivative of ϕ(xi) with respect to xi. The estimated model is characterized by a family Dαf(Xi|θ), where Dαf(Xi|θ) is the αth derivative of f(xi,|θ) and θ is an unknown parameter. The model is in general misspecified; that is, there is no θ such that Dαf(Xi|6) is equal to Dαϕ(Xi). Three different problems are discussed. First, the asymptotic behavior of the seemingly unrelated regression estimator of θ is shown to achieve the best approximation, in the Sobolev norm sense, of ϕ by an element of (f(Xi|θ)|θ ε Θ). Second, in the case of polynomial approximations, the expected derivatives of the limit of the estimated regression and of the true regression are proved to be equal if and only if the set of explanatory variables has a normal distribution. Third, different sets of α are introduced, and the different limits of estimated regressions characterized by these sets are proved to be equal if and only if the explanatory variables have a normal distribution. This result leads to a specification test.
Investigation of verbal and visual working memory by multi-channel time-resolved functional near-infrared spectroscopy

Working memory (WM) is fundamental for a number of cognitive processes, such as comprehension, reasoning and learning. WM allows the short-term maintenance and manipulation of the information selected by attentional processes. The goal of this study was to examine by time-resolved fNIRS neural correlates of the verbal and visual WM during forward and backward digit span (DF and DB, respectively) tasks, and symbol span (SS) task. A neural dissociation was hypothesised between the maintenance and manipulation processes. In particular, a dorsolateral/ventrolateral prefrontal cortex (DLPFC/VLPFC) recruitment was expected during the DB task, whilst a lateralised involvement of Brodmann Area (BA) 10 was expected during the execution of the DF task. Thirteen subjects were monitored by a multi-channel, dual-wavelength (690 and 829 nm) time-resolved fNIRS system during 3 minutes long DF and DB tasks and 4 minutes long SS task. The participants’ mean memory span was calculated for each task: DF: 6.46±1.05 digits; DB: 5.62±1.26 digits; SS: 4.69±1.32 symbols. No correlation was found between the span level and the heart rate data (measured by pulse oximeter). As expected, DB elicited a broad activated area, in the bilateral VLPFC and the right DLPFC, whereas a more localised activation was observed over the right hemisphere during either DF (BA 10) or SS (BA 10 and 44). The robust involvement of the DLPFC during DB, compared to DF, is compatible with previous findings and with the key role of the central executive subserving in manipulating processes.
Characterization of Non-Cholesterol Sterols in Microglia Cell Membranes Using Targeted Mass Spectrometry

Background: Non-cholesterol sterols, as well as plant sterols, cross the blood–brain barrier and, thus, can be incorporated into cell membranes, affecting the cell’s inflammatory response. The aim of our work was to develop an analytical protocol for a quantitative assessment of the sterol composition within the membrane microdomains of microglia. Methods: A protocol for cell membrane isolation using OptiPrepTM gradient ultracentrifugation, in combination with a targeted mass spectrometry (LC-MS/MS)-based assay, was developed and validated for the quantitative analysis of free sterols in microglia cell membranes. Results: Utilizing an established LC-MS/MS assay, cholesterol and seven non-cholesterol sterols were analyzed with a limit of detection from 0.001 to 0.05 mg/L. Applying the detergent-free isolation of SIM-A9 microglia cell membranes, cholesterol (CH), desmosterol (DE), lanosterol (LA) stigmasterol (ST), beta-sitosterol (SI) and campesterol (CA) were quantified with coefficients of variations between 6 and 29% (fractions 4–6, n = 5). The highest concentrations of non-CH sterols within the microglia plasma membranes were found in the microdomain region (DE>LA>SI>ST>CA), with ratios to CH ranging from 2.3 to 435 lower abundancies. Conclusion: By applying our newly developed and validated analytical protocol, we show that the non-CH sterol concentration is about 38% of the total sterol content in microglia membrane microdomains. Further investigations must clarify how changes in the non-sterol composition influence membrane fluidity and cell signaling.
Algebraic Algorithms in Combinatorial Optimization

In this thesis we extend the recent algebraic approach to design fast algorithms for two problems in combinatorial optimization. First we study the linear matroid parity problem, a common generalization of graph matching and linear matroid intersection, that has applications in various areas. We show that Harvey’s algorithm for linear matroid intersection can be easily generalized to linear matroid parity. This gives an algorithm that is faster and simpler than previous known algorithms. For some graph problems that can be reduced to linear matroid parity, we again show that Harvey’s algorithm for graph matching can be generalized to these problems to give faster algorithms. While linear matroid parity and some of its applications are challenging generalizations, our results show that the algebraic algorithmic framework can be adapted nicely to give faster and simpler algorithms in more general settings. Then we study the all pairs edge connectivity problem for directed graphs, where we would like to compute minimum s-t cut value between all pairs of vertices. Using a combinatorial approach it is not known how to solve this problem faster than computing the minimum s-t cut value for each pair of vertices separately. While a recent matrix formulation inspired by network coding directly implies a faster algorithm, we show that the matrix formulation can be computed more efficiently for graphs with good separators. As a consequence we obtain a faster algorithm for simple directed planar graphs, bounded genus graphs and fixed minor free graphs. Our algorithm takes amortized sublinear time to compute the edge connectivity between one pair of vertices.
Evaluation of Short-Season Soybean Genotypes for Resistance and Partial Resistance to Phytophthora sojae

Phytophthora root and stem rot caused by Phytophthora sojae Kaufmann and Gerdemann is a soil-borne disease severely affecting soybean production worldwide. Losses caused by P. sojae can be controlled by both major genes and quantitative trait locus. Here, we tested 112 short-season soybean cultivars from Northeast China for resistance to P. sojae. A total of 58 germplasms were resistant to 7–11 P. sojae strains. Among these, Mengdou 28 and Kejiao 10-262 may harbor either Rps3a or multiple Rps genes conferring resistance to P. sojae. The remaining 110 germplasms produced 91 reaction types and may contain new resistance genes or gene combinations. Partial resistance evaluation using the inoculum layer method revealed that 34 soybean germplasms had high partial resistance, with a mean disease index lower than 30. Combining the results of resistance and partial resistance analyses, we identified 35 excellent germplasm resources as potential elite materials for resistance and tolerance in future breeding programs. In addition, we compared the radicle inoculation method with the inoculum layer method to screen for partial resistance to P. sojae. Our results demonstrate that the radicle inoculation method could potentially replace the inoculum layer method to identify partial resistance against P. sojae, and further verification with larger samples is required in the future.
Leveraging Hofstede’s Cultural Dimensions for Devising COVID-19 Control Strategies

The COVID-19 Pandemic has caused unprecedented global challenges, including significant socioeconomic disruptions and the closure of schools and universities in almost all countries. Understanding the role of culture in shaping individual and societal responses to the Pandemic is crucial. This review article examines the applicability of Hofstede's cultural dimensions framework in devising effective COVID-19 control strategies. The article provides an overview of each dimension and its impact on the implementation of COVID-19 control strategies. It also includes a case analysis of four countries - India, the United Kingdom, the United States of America, and Poland - to illustrate the interplay between culture and COVID-19 control strategies. The insights provided by Hofstede's framework and the case analysis demonstrate that cultural differences can significantly impact the success of COVID-19 control strategies. By taking these differences into account, governments and public health authorities can improve the effectiveness of their COVID-19 control measures.
Approximate one-sided tolerance bounds on the number of failures using Poisson regression

Safety studies of a nuclear reactor often center their interest on the probable number, of component failures in a time span of duration To . Using the asymptotic normality of the estimator from Poisson regression, we develop approximate upper tolerance bounds for the distribution of the number of failures. Tables are given for easy computation of such bounds when the bound itself is less than 50. An example consisting of 90 failure records for nuclearreactor valve types provides illustration of the tolerance bound computations and dataanalytic techniques for validating a Poisson regression model.
Implementation of Complex Fuzzy Logic Modules with VLSI Approach

Summary The hybrid intelligent systems are gaining popularity due to extensive success of these systems in many real world complex problems. In this paper, the implementation of the complex fuzzy logic modules have been undertaken with VLSI design approach. Complex fuzzy logic is a generalization of traditional fuzzy logic based on complex fuzzy sets. The general scheme of Complex Fuzzy Logic Systems(CFLS) is also discussed here. The various stages of computation of fuzzy inferences is discussed alongwith their implementation. The design of various modules of a complex fuzzy processor like Intersection detector module, Execution module, Rule detection module, Defuzzification module have been carried out and the results are evaluated with logic analyser. The inference rules are constructed and "fired" like traditional fuzzy logic. The complex fuzzy sets are characterized by complex-valued membership functions which is the novelty. The range of membership functions is extended from the traditional fuzzy range of (0,1) to the unit circle in the complex plane. It provides a method for describing membership in a set in terms of a complex number.