Miracle-dz/UniFAR-FTUs huggingface dataset:
Laboratory time-resolved X-ray diffractometry for in situ studies of crystalline materials under uniaxial compression and vibration

A novel laboratory diffractometer for time-resolved high-resolution X-ray diffraction studies of reversible and irreversible processes in crystalline materials under uniaxial compression and vibration is described. Temporal resolution up to milliseconds for double-crystal and up to tens of seconds for triple-crystal diffraction experiments was achieved with a single adaptive bending X-ray optics element. Design solutions and techniques for applying and controlling uniaxial compression and vibration for in situ time-resolved studies are described. Results are presented for various static and dynamic load experiments, controlled by a system based on the TANGO Controls framework. Rocking curves of paratellurite (TeO2) under quasi-static compression and lithium fluoride (LiF) under ultrasonic vibration were measured with temporal resolution. Reciprocal-space maps of LiF under static compression and quartz (SiO2) under ultrasonic vibration were collected.
Connection between Root Test and Ratio Test for Series in Calculus Course

The Root Test and the Ratio Test for series $\sum_{n=0}^{\infty}a_n$ are usually discussed and proved independently in a Calculus course. This creates in students an impression that if one of these two tests is inconclusive because the radius $\rho$ is 1, then there is a chance that the second test can be useful. We suggest a new approach to the presentation of these two tests, which eliminates this confusion. Suppose in the Ratio Test $\lim_{n\to\infty}\frac{|a_n|}{|a_{n+1}|}=\rho$. We show that this implies that the Root Test provides the same value of $\rho$ in the limit of the root ( $\lim_{n\to\infty}\sqrt[n]{|a_n|}=\rho$.), and the limit of the root can be used for verification of the convergence of the series. This proof of the Root Test is easier than the standard proof of the Root Test and explains the relation between the two Tests.
The Universal Unit of Time, the Age of the Universe and the Exact Calculation of the Gravitational Constant G

It is hypothesised that the Universe is not only expanding in space, but also in time (i.e. time slows down as the Universe ages). A new universal unit of time, acting as a scaling factor for the Universe, is proposed. An equation to calculate the age of the Universe and an equation to calculate the exact value of the gravitational constant G from the other fundamental constants are introduced. This hypothesis opens the way to the development of a new cosmological model and the unification of quantum mechanics with general relativity.
Machine Learning Applied to Estimate the Battery SoH by using Various Algorithms in Python

: Accurate state of health (SOH) prediction is significant to guarantee operation safety and avoid latent failures of lithium-ion batteries. With the development of communication and artificial intelligence technologies, a body of researches have been performed toward precise and reliable SOH prediction method based on machine learning (ML) techniques. In this the conception of SOH is defined, and the state-of the-art prediction methods are classified based on their primary implementation procedure. As an essential step in ML-based SOH algorithms, the health feature extraction methods reported in the literature are comprehensively surveyed. Next, an exhausted comparison is conducted to elaborate the development of ML-based SOH prediction techniques. Not only their advantages and disadvantages of the application in SOH prediction are reviewed but also their accuracy and execution process are fully discussed. Finally, pivotal challenges and corresponding research directions are provided for more reliable and high-fidelity SOH prediction. The growing interest and recent breakthroughs in artificial intelligence and machine learning (ML) have actively contributed to an increase in research and development of new methods to estimate the state of health of battery by using various algorithms. This paper provides a survey of battery state estimation methods based on ML algorithms accuracy such as, Logistic Regression (LR), Linear Discriminant Analysis (LDA), Neighbors Classifier (KNN), Decision Tree Classifier (CART), and Gaussians (NB) with help of Python. Programming in Python with the help of SKlearn & Pandas.
Full prediction of unsaturated hydraulic conductivity - comparison of four different capillary bundle models

To model the water, solute and energy transport in porous media, it is essential to have accurate information about the soil hydraulic properties (SHP), i.e. the water retention curve (WRC) and the soil hydraulic conductivity curve (HCC). 10 Having reliable data information to parameterize these models is important, but equally critical is the selection of appropriate SHP models. While various expressions for the WRC are commonly compared, the capillary conductivity model proposed by Mualem (1976a) is widely used but seldom compared to alternatives. The objective of this study was to compare four different capillary bundle models in terms of their ability to accurately predict the HCC without scaling the conductivity function by a measured conductivity value. These expressions include two simpler models proposed by Burdine (1953) and 15 Alexander and Skaggs (1986), which assume a bundle of parallel capillaries with tortuous flow paths, and two more sophisticated models based on statistical cut-and-random-rejoin approaches, namely those proposed by Childs and Collis-George (1950) and the aforementioned model of Mualem (1976a). In order to check whether different parametrizations of the WRC interfere with the suitability of the conductivity models, we utilized four different capillary saturation models in combination with each of the conductivity prediction models, resulting in a total of 16 SHP model schemes. All schemes 20 were calibrated using 12 carefully selected datasets that provided water retention and hydraulic conductivity data over a wide saturation range. Subsequently, the calibrated models were tested and rated by their ability to predict the hydraulic conductivity of 23 independent datasets of soils with varying textures. The statistical cut-and-random-rejoin models, particularly the Mualem (1976a) model, outperformed the simpler capillary bundle models in terms of predictive accuracy. This was
Development and statistically validated UV spectrophotometric determination of testosterone in gel formulation

Objective : A simple, precise and accurate UV-spectrophotometric method is developed and statistically validated for estimation of Testosterone in gel formulation. The proposed method includes using regression equation, area under curve (AUC), first order derivative and second order derivative spectroscopic method. Methods : based on measurement of absorbance at a selected wavelength using UV-visible spectrophotometer with 1cm matched quartz cell and acetonitrile as a solvent. All developed methods obeyed Beer’s-lambert’s law in the concentration range of 5-25μg/mL, with correlation coefficient value less than 1. Results : The percent amount of drug estimated was nearly 100%, found to be a good agreement with label claim of marketed gel formulation. The recovery study was carried out at three different levels, the validation study data was found to be statistically significant as all the statistical parameters are within the acceptance range (% RSD <2.0 and S.D. <±2.0). Conclusions : The results of estimation and validation parameters like accuracy, precision, ruggedness, linearity and range were studied for all the developed methods and were found to be within limits. The results obtained were statistically compared using paired t-test and one way ANOVA analysis. The proposed method can be adopted for routine quality control for estimation of drug in formulation.
The climate in Poland (central Europe) in the first half of the last millennium, revisited

Abstract. The article presents updated knowledge on climate change in Poland (central Europe) in the first half of the last millennium (1001–1500). This knowledge is required to delimit the existence, duration, and scale of the Medieval Warm Period (MWP, also called the Medieval Climate Anomaly, MCA, here). To this end, it employs all available quantitative climate reconstructions created for Poland in the last 2 decades and four new reconstructions using three dendrochronological series and an extensive database of historical source data on weather conditions. The growth of conifers in lowland and upland Poland depends on the temperature in the cold season, especially in February and March. All available reconstructions based on dendrochronology data represent this time of the year. Summer temperatures were reconstructed using biological proxies and documentary evidence. The latter, however, is limited to the 15th century only. Winter temperature was used as the proxy for annual temperature proxies instead of the more usual use of summer temperature. The MWP occurred in Poland probably from the late 12th century to the first halves of the 14th or 15th centuries. All the analysed quantitative reconstructions suggest that the MWP in Poland was comparable to or warmer than the mean temperature in the period 1951–2000. The coldest conditions in the entire study period were noted in the first half of the 11th century (both winter and summer) and the second half of the 15th century (only winter). The greatest climate continentality occurred in the 15th century. Good agreement was found between the reconstructions of Poland's climate and many reconstructions available for Europe.
Constraining Long-Term NO x Emissions over the United States and Europe using Nitrate Wet Deposition Monitoring Networks

Nitrogen oxides (NO x = NO + NO 2 ) play a critical role in regulating tropospheric chemistry, yet NO x emission estimates are subject to large uncertainties, casting doubt on our ability to accurately model secondary pollutants such as ozone. Bottom-up emissions inventories are subject to a number of uncertainties related to estimates of emission activities, scaling 10 factors, and fuel sources. Here, we provide an additional constraint on NO x emissions and trends using nitrate wet deposition (NWD) fluxes from the United States National Atmospheric Deposition Program (NADP) and the European Monitoring and Evaluation Programme (EMEP). We use these NWD measurements to evaluate anthropogenic and total NO x trends and magnitudes in the global Community Emissions Data System (CEDS) emissions inventory and the GEOS-Chem chemical transport model from 1980-2020. Over both the United States and Europe, observed NWD trends track well with anthropogenic 15 NO x emissions from the CEDS inventory until 2010, after which NWD trends level out in contrast to continued decreases in CEDS. After 2010, NWD trends are able to reproduce total NO x emissions trends when the influences of both anthropogenic and background sources are considered. Observed NWD fluxes are also able to capture NO x emissions decreases over the 2020 COVID-19 lockdown period and are consistent with satellite and surface measurements of NO 2 . These results suggest that NWD fluxes constrain total NO x emissions well. We further compare modelled and observed NWD to provide an additional 20 line of evidence for potential overestimates of anthropogenic NO x in emissions inventories. Over the United States, we find consistent overestimates of NO x emissions in CEDS in summer from 1980-2017 averaging by 15-20%, with overestimates most prominent in the eastern US after 2000. Over Europe, we find that NO x is overestimated in all seasons, with the strongest average overestimates occurring in summer (175%, with a range of 50 to >500% depending on the site) and fall (170%, range of 39 to >500%). These overestimates may be reduced by cutting anthropogenic NO x emissions by 50% in CEDS over Europe 25 (i
Simulation of profile evolution from ramp-up to ramp-down and optimization of tokamak plasma termination with the RAPTOR code

The present work demonstrates the capabilities of the transport code RAPTOR as a fast and reliable simulator of plasma profiles for the entire plasma discharge, i.e. from ramp-up to ramp-down. This code focuses, at this stage, on the simulation of electron temperature and poloidal flux profiles using prescribed equilibrium and some kinetic profiles. In this work we extend the RAPTOR transport model to include a time-varying plasma equilibrium geometry and verify the changes via comparison with ATSRA code simulations. In addition a new ad hoc transport model based on constant gradients and suitable for simulations of L–H and H–L mode transitions has been incorporated into the RAPTOR code and validated with rapid simulations of the time evolution of the safety factor and the electron temperature over the entire AUG and TCV discharges. An optimization procedure for the plasma termination phase has also been developed during this work. We define the goal of the optimization as ramping down the plasma current as fast as possible while avoiding any disruptions caused by reaching physical or technical limits. Our numerical study of this problem shows that a fast decrease of plasma elongation during current ramp-down can help in reducing plasma internal inductance. An early transition from H- to L-mode allows us to reduce the drop in poloidal beta, which is also important for plasma MHD stability and control. This work shows how these complex nonlinear interactions can be optimized automatically using relevant cost functions and constraints. Preliminary experimental results for TCV are demonstrated.
Practical Structured Riemannian Optimization with Momentum by using Generalized Normal Coordinates

Adding momentum into Riemannian optimization is computationally challenging due to the intractable ODEs needed to define the exponential and parallel transport maps. We address these issues for Gaussian Fisher-Rao manifolds by proposing new local coordinates to exploit sparse structures and efficiently approximate the ODEs, which results in a numerically stable update scheme. Our approach extends the structured natural-gradient descent method of Lin et al. (2021a) by incorporating momentum into it and scaling the method for large-scale applications arising in numerical optimization and deep learning.
The semantic extension of pɛ ( TO CUT ) in Nzema communication

Verbs of separation and material disintegration are a category of verbs commonly referred to as ‘CUT and BREAK’ (C&B) verbs (Guerssel et al., 1985). This class of verbs has sparked a lot of arguments and discussions in the literature (cf. Guerssel et al., 1985; Levin, 1993; Essegbey, 2007; Lüpke, 2007; Majid et al., 2007; 2008; Hsiao, 2015; Agyepong, 2017). In Nzema, pɛ ‘to cut’ is the prototypical CUT-verb which describes actions that result in object separation/disintegration; usually carried out by using bladed instruments. This paper discusses the semantic extensions of pɛ ‘to cut’ in Nzema discourse. The paper employs the Cognitive Approach of Semantic Change (Sweetser, 1990) in exploring the semantic properties of pɛ , based on the cognitive-conceptual, environmental, and socio-cultural experiences of the people of Nzema. Data were obtained from primary and secondary sources. The paper demonstrates that the various extended usages have a close relationship with the original meaning of the verb. The paper also finds that the primary sense of pɛ ‘to cut’ evokes several contextual interpretations, such as pɛ awolɛ ‘cut childbearing’ (to stop procreation), pɛ azule ‘cut river’ (to travel overseas), pɛ ɔ nli ‘cut his/her mother’ (to insult one’s mother) among others. We note that the various meaning extensions of the verb are dependent on the context of use and/or communication.
COMPARISON OF IN VIVO AND IN VITRO

This paper is concerned with the comparison of in vivo and in vitro rumen bacterial dissimilations of some carbohydrates, anfino acids, and organic acids of metabolic importance. It was observed that sueeinate, I)L-aspartate, glucose, and maltose were readily fermented by rumen microorganisms, and that volatile fatty acids (VFA) were produced. The mnino acids DL-lysine, I)L-alanine, and glycine were not degraded to an appreciable extent to YFA by rmnen bacteria. In general, qualitative agreement between in vivo and in vitro washed cell suspension (WCS) experiments was obtained with DL-lysine, DL-alanine, and glycine, while the in vivo studies using D~-aspartate and suceinate agreed with the WCS in vitro studies of Sirotnak et al. (17j 18). With the substrates glucose and maltose, in vitro WCS experiments were both variable and in disagreement with the in vivo studies. Thus, the WCS technique appears most useful for studying short oneor two-step reactions presumed to occur in the rumen. This technique loses significance when studying nmltistep metabolic reactions and, in such cases, the results should be interpreted with caution until they are verified by in vivo experiments, or until further studies concerning the limitations of the WCS tec]mique are available.
Changes in gene expression and Estrogen Receptor 1 cistrome in mouse liver upon acute E 2 treatment 2 3

28 Transcriptional regulation by the Estrogen Receptor α (ER) has been investigated mainly in breast 29 cancer cell lines but estrogens such as 17β-Estradiol (E2) exert numerous extra-reproductive effects, 30 particularly in the liver where E2 exhibits both protective metabolic and deleterious thrombotic 31 actions. To analyze the direct and early transcriptional effects of estrogens in the liver, we determined 32 the E2-sensitive transcriptome and ER cistrome in mice following acute administration of E2 or 33 placebo. These analyses revealed the early induction of genes involved in lipid metabolism, which fits 34 with the crucial role of ER in the prevention of liver steatosis. Characterization of the chromatin state 35 of ER binding sites (BSs) in mice expressing or not ER demonstrated that ER is not required per se 36 for the establishment and/or maintenance of chromatin modifications at the majority of its BSs. This 37 is presumably a consequence of a strong overlap between ER and Hepatocyte nuclear factor 4 α 38 (Hnf4α) BSs. In contrast, 40% of the BSs of the pioneer factor Foxa2 were dependent upon ER 39 expression, and ER expression also affected the distribution of nucleosomes harboring dimethylated 40 H3K4 around Foxa2 BSs. We finally show that, in addition to a network of liver-specific transcription 41 factors including Cebpα/β and Hnf4α, ER might be required for proper Foxa2 function in this tissue. 42
Tracking Mechanisms and the Effect of User Consent on the Web

During the past few years, mostly as a result of legislation such as the GDPR and the CCPA, websites have started presenting users with consent banners. These banners are web forms where users can state their preference regarding data processing purposes and declare which cookies they would like to accept. Although requesting consent before storing any identifiable information is a good start towards respecting the user privacy, previous research has shown that websites do not always respect user choices. Furthermore, considering not only the ever decreasing reliance of trackers on cookies, but also the actions browser vendors take by blocking or restricting third-party cookies, we anticipate a world where stateless tracking emerges, either because trackers or websites do not use cookies, or because users simply refuse to accept any. In this thesis, we explore whether websites use more persistent and sophisticated forms of tracking in order to track users who explicitly denied consent. Such forms of tracking include first-party ID leaking, third-party ID synchronization and browser fingerprinting. Using a novel web crawler, we examined the consent banners of over 27,000 websites and our results suggest that websites do use such modern forms of tracking even before users had the opportunity to register their consent choice. To add insult to injury, when users choose to raise their voice, deny consent and reject all cookies, user tracking only intensifies. We measured that aggressive tracking takes place before users had the opportunity to make a selection in the consent banner, with more than 75% of tracking activities happening when users chose to deny consent. Consequently, we conclude that user choices play very little role with respect to sophisticated tracking mechanisms. Μηχανισμοί Ιχνηλάτησης και η Επίδραση της Συναίνεσης του Χρήστη στο Διαδίκτυο
Evaluating Deep Learning-based Vulnerability Detection Models on Realistic Datasets

The impact of software vulnerabilities on daily-used software systems is alarming. Despite numerous proposed deep learning-based models to automate vulnerability detection, the detection of software vulnerabilities remains a significant issue. While some techniques report high precision/recall scores of up to 99%, our experience leads us to believe that these models may underperform in realistic settings, specifically when evaluating vulnerability detection models on the entire source code repository of a project. Therefore, in this thesis, we create a more comprehensive vulnerability detection dataset (i.e., Comp-Vul), which aims to accurately represent the realistic settings where vulnerability detection models are deployed. Then, we evaluate the performance of two state-ofthe-art deep learning-based models, LineVul and DeepWukong, on the Comp-Vul dataset. Our results show that the performance of both models drops drastically, with precision dropping by 86% 95% and F1 score dropping by 88% 91%. Our further investigation shows that the ratio of vulnerable to non-vulnerable samples in the evaluation dataset significantly impacts the performance metrics of these models. When we visualize the embeddings produced by the models, we find that there is a substantial overlap between vulnerable and non-vulnerable samples. This shows that these models have difficulty distinguishing between vulnerable and non-vulnerable samples in the Comp-Vul dataset, resulting in a high number of false positives. We introduce a new program slice-level vulnerability detection technique named SliceVul , which leverages the powerful capabilities of Transformers and incorporates the semantic properties of source code programs such as data and control flow information. Our approach outperforms the existing state-of-the-art program slice-level vulnerability detection model, DeepWukong, when evaluated on the Comp-Vul dataset. Our study argues that accurately identifying vulnerabilities using deep learning remains a challenging task that requires improved approaches to model evaluation and design. Further research and development, complemented by realistic evaluation datasets, is required to enhance the performance of these methods.