Miracle-dz/UniFAR-FTUs huggingface dataset:
A generalized Spatio-Temporal Threshold Clustering method for identification of extreme event patterns

Extreme weather and climate events such as floods, droughts, and heat waves can cause extensive societal damages. While various statistical and climate models have been developed for the purpose of simulating extremes, a consistent definition of extreme events is still lacking. Furthermore, to better assess the performance of the climate models, a variety of spatial forecast verification measures have been developed. However, in most cases, the spatial verification measures that are widely used to compare mean states do not have sufficient theoretical justification to benchmark extreme events. In order to alleviate inconsistencies when defining extreme events within different scientific communities, we propose a new generalized Spatio-Temporal Threshold Clustering method for the identification of extreme event episodes, which uses machine learning techniques to couple existing pattern recognition indices with high or low threshold choices. The method consists of five main steps: (1) construction of essential field quantities; (2) dimension reduction; (3) spatial domain mapping; (4) time series clustering; and (5) threshold selection. We develop and apply this method using a gridded daily precipitation dataset derived from rain gauge stations over the contiguous United States. We observe changes in the distribution of conditional frequency of extreme precipitation from large-scale well-connected spatial patterns to smaller-scale more isolated rainfall clusters, possibly leading to more localized droughts and heat waves, especially during the summer months. The proposed method automates the threshold selection process through a clustering algorithm and can be directly applicable in conjunction with modeling and spatial forecast verification of extremes. Additionally, it allows for the identification of synoptic-scale spatial patterns that can be directly traced to the individual extreme episodes, and it offers users the flexibility to select an extreme threshold that is linked to the desired geometrical properties. The approach can be applied to broad scientific disciplines.
The Development of Regional Working Unit Structuring Model in the East Nusa Tenggara Provincial Government

The restructuring f the regional working unit (OPD) as a form of institutional restructuring carried out in the framework of bureaucratic reform needs to pay attention to the structuring model that is by the needs and conditions of the Regional Government, so that the composition and formation of regional apparatus organizations can be following the needs of the Regional Government. In the Government Regulation (PP) No. 18 of 2016 concerning Regional Working Unit, there was a change in the provisions on the implementation of organizational working unit restructuring with the previous Government Regulation namely Government Regulation (PP) No. 41 of 2007 concerning the Regional Working Unit, wherein the Government Regulation regulates the arrangement of regional apparatus organizations based on the area, number of inhabitants and local original income or regional financial capacity whereas Government Regulation (PP) No. 18 of 2016 concerning the Regional Working Unit regulates the organizational arrangement of regional working unit based on compulsory governmental affairs and selected governmental affairs which are the authority of regional governments with the principle of Rigthzising. So that with the change in the organizational structuring model of the regional working unit, it is expected to be able to realize the form and composition of an effective and efficient regional working unit in accordance with the regional government institutional requirements.
Comparison of the Effects of Topical Ebselen, Propolis and Steroid Applications on Acute Radiodermatitis: Preliminary Results

Objective: Radiodermatitis is one of the early complications of radiotherapy (RT). In an acute setting, skin changes may vary from mild hyperemia to necrosis. This study aimed to compare the effects of vaseline, mometasone furoate (MF), propolis and ebselen on acute radiodermatitis and demonstrate an alternative topical treatment method that could be suggested to reduce adverse effects. Methods: A total of 40 male Wistar-Hannover rats were separated into 4 groups with 10 members to be applied with vaseline, steroid (0.01% MF), propolis (30%) and ebselen (1%), respectively. All groups received radiation at a dose of 3,000 cGy. Following the application of RT, topical applications were applied to all groups and repeated once daily until the experiment was terminated. Skin biopsies were taken from all subjects and examined in terms of erosion, epidermis atrophy, basal layer degeneration, neovascularisation, polymorphonuclear leukocyte (PMNL) infiltration, loss of hair follicles and collagenisation. Results: The level of erosion was found to be significantly lower in the MF group. Epidermal atrophy was statistically significant lower in propolis and vaseline groups. Propolis significantly decreased basal layer degeneration compared to other treatments. Neovascularisation rates were significantly higher in vaseline and ebselen groups. No significant difference was found between groups in terms of PMNL, loss of hair follicles and collagenisation. Conclusion: Different agents with different mechanisms of action may be used according to the physiopathological progress of radiodermatitis. Data obtained in this study suggest that combined use of MF, propolis and ebselen according to erythema phases may mitigate the clinical progression of acute radiodermatitis.
Expression of Hypoxia-Inducible Factor-1 (cid:1) and -2 (cid:1) in Hypoxic and Ischemic Rat Kidneys

Oxygen tensions in the kidney are heterogeneous, and their changes presumably play an important role in renal physiologic and pathophysiologic processes. A family of hy-poxia-inducible transcription factors (HIF) have been identified as mediators of transcriptional responses to hypoxia, which include the regulation of erythropoietin, metabolic adaptation, vascular tone, and neoangiogenesis. In vitro , the oxygen-regulated subunits HIF-1 (cid:1) and -2 (cid:1) are expressed in inverse relationship to oxygen tensions in every cell line in-vestigated to date. The characteristics and functional significance of the HIF response in vivo are largely unknown. High-amplification immunohistochemical analyses were used to study the expression of HIF-1 (cid:1) and -2 (cid:1) in kidneys of rats exposed to systemic hypoxia bleeding anemia, functional anemia (0.1% carbon monoxide), renal ischemia, or cobaltous chloride (which is known to mimic hypoxia). These treatments led to marked nuclear accumulation of HIF-1 (cid:1) and -2 (cid:1) in different renal cell populations. HIF-1 (cid:1) was mainly induced in tubular cells, including proximal segments with exposure to anemia/carbon monoxide, in distal segments with cobaltous chloride treatment, and in connecting tubules and collecting
DESIGN CONTROL OF SURFACE MARINE VEHICLE USING DISTURBANCE COMPENSATING MODEL PREDICTIVE CONTROL (DC-MPC)

This research studied ship motion control by considering four degrees of freedom (DoF): yaw, roll, sway, and surge in which comprehensive mathematical modeling forming a nonlinear differential equation. Furthermore, this research also investigated solutions for fundamental yet challenging steering problems of ship maneuvering using advanced control method: Disturbance Compensating Model Predictive Control (DC-MPC) method, which based on Model Predictive Control (MPC). The DC-MPC allows optimizing a compensated control then consider sea waves as the environmental disturbances. Those sea waves influence the control and also becomes one of the constraints for the system. The simulation compared the varying condition of Horizon Prediction (Np) and another method showing that the DC-MPC can manage well the given disturbances while maneuvering in certain Horizon Prediction. The results revealed that the ship is stable and follows the desired trajectory.
Evaluation of Phytochemicals Found in the Leaves of Jasminum sambac L. for Antimicrobial Activity

Jasminum sambac Linn. (Family—Oleaceae) was tested for antibacterial activity against three important pathogenic bacteria, i.e., Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa. Powdered leaf material was extracted with different solvents from non-polar to polar viz., n-hexane, chloroform, ethyl acetate, methanol, and water using Soxhlet apparatus. All the solvent extracts were evaporated to dryness using a hot dry air oven. The dry residue was dissolved in respective mother solvents (1:10 w/v) and tested for the presence or absence of various phytochemicals. An antimicrobial test was also followed to check the antimicrobial activity. Among five solvents tested, methanol, n-hexane, and ethyl acetate extracts showed significant antibacterial activity and chloroform extract showed less activity when compared with control (respective mother solvent). Water extract did not show any activity against all three microorganisms.
An evidence-based data science perspective on the prediction of heart failure readmissions

The prevention of unplanned 30-day readmissions of patients discharged with a diagnosis of heart failure (HF) remains a profound challenge among hospital enterprises. Despite the many models and indices developed to predict which HF patients will readmit for any unplanned cause within 30 days, predictive success has been meager. Using simulations of HF readmission models and the diagnostics most often used to evaluate them (C-statistics, ROC curves), we demonstrate common factors that have contributed to the lack of predictive success among studies. We reveal a greater need for precision and alternative metrics such as partial C-statistics and precision-recall curves and demonstrate via simulations how those tools can be used to better gauge predictive success. We suggest how studies can improve their applicability to hospitals and call for a greater understanding of the uncertainty underlying 30-day all-cause HF readmission. Finally, using insights from sampling theory, we suggest a novel uncertainty-based perspective for predicting readmissions and non-readmissions.
‘Mini PS’: A new mini melon breeding line exploiting the “Dudaim” variability – Short Communication

‘Piel de Sapo’ is one of the most consumed market class of melons in the Mediterranean area and it represents an important economic crop in Spain. The ‘Mini PS’ melon breeding line, which bears two main introgressions from the dudaim ‘Queen’s pocket’ melon in the Piel de Sapo genetic background, was evaluated for its fruit quality traits in three environments. Some interesting commercial characteristics were detected, such as a notable decrease in the fruit weight and a rounder shape, compared with Piel de Sapo, while the other quality traits were not altered. Thus, this mini melon line, ideal as a personal melon, may be useful in the development of new melon cultivars.
Geometric Morphometric Data Augmentation Using Generative Computational Learning Algorithms

The fossil record is notorious for being incomplete and distorted, frequently conditioning the type of knowledge that can be extracted from it. In many cases, this often leads to issues when performing complex statistical analyses, such as classification tasks, predictive modelling, and variance analyses, such as those used in Geometric Morphometrics. Here different Generative Adversarial Network architectures are experimented with, testing the effects of sample size and domain dimensionality on model performance. For model evaluation, robust statistical methods were used. Each of the algorithms were observed to produce realistic data. Generative Adversarial Networks using different loss functions produced multidimensional synthetic data significantly equivalent to the original training data. Conditional Generative Adversarial Networks were not as successful. The methods proposed are likely to reduce the impact of sample size and bias on a number of statistical learning applications. While Generative Adversarial Networks are not the solution to all sample-size related issues, combined with other pre-processing steps these limitations may be overcome. This presents a valuable means of augmenting geometric morphometric datasets for greater predictive visualization.
Phylogenomic analysis clarifies the evolutionary origin of Coffea arabica

Interspecific hybridization events have played a major role in plant speciation, yet the evolutionary origin of hybrid species often remains enigmatic. Here, we inferred the evolutionary origin of the allotetraploid species Coffea arabica, which is widely cultivated for Arabica coffee production. We estimated genetic distances between C. arabica and all species that are known to be closely related to C. arabica using genotyping‐by‐sequencing (GBS) data. In addition, we reconstructed a time‐calibrated multilabeled phylogenetic tree of 24 species to estimate the age of the C. arabica hybridization event. Ancestral states of self‐compatibility were also inferred to shed new light on the evolution of self‐compatibility in Coffea. Coffea canephora and C. eugenioides were confirmed as the putative progenitor species of C. arabica. These species most likely hybridized between 1.08 million and 543 000 years ago, coinciding with periods of environmental upheaval, which may have induced range shifts of the progenitor species that facilitated the emergence of C. arabica.
Computational identification of fertility functions of bovine Reprimo gene

Improvement in fertility is possible through gene assisted selection. Cattle fertility and genes underlying it should be thoroughly studied and exploited to find solution to declining cattle fertility. Reprimo (RPRM) gene is a pleiotropic gene involved in suppression of cancer, regulation of mitotic cell cycle, cell cycle arrest and regulation of survival. Comparison of protein tertiary structures is important in inferring functional characteristics of new proteins. This study used computational approach to identify some fertility functions of bovine RPRM gene using motif prediction and protein structure comparison. Amino acid sequences of bovine RPRM gene and some other cattle fertility genes were retrieved from GenBank. Motifs in the amino acid sequence of bovine RPRM gene were predicted using PROSITE software. The domain structure of bovine RPRM protein was predicted using simple modular architecture research tool (SMART). Protein tertiary structures (3D structures) of bovine RPRM gene and other cattle fertility genes were predicted with Phyre 2 software. To have structural and functional similarity, it has been found that protein structure after superimposition should have Root Mean Square Deviation (RMSD) value less than or equal to 2A. The protein 3D structures of other cattle fertility genes were superposed against the protein 3D structure of bovine RPRM gene using SuperPose web server and the proteins with RMSD value of 2A or less were predicted as proteins with similar functions and structures as bovine RPRM gene. The predicted motifs (N-glycosylation site, N-myristoylation site, and cAMP and cGMP-dependent protein kinase phosphorylation site) and protein structure comparison revealed that, bovine RPRM gene and bovine growth hormone gene have the same fertility function with alpha carbon and backbone root mean square deviations of 1.94 A and 1.81 A, respectively. It follows therefore that other fertility functions of bovine RPRM gene included sexual maturation, steroidogenesis, gametogenesis, gonadal differentiation and gonadotrophin secretion which are the functions of growth hormone gene. Keywords : Reprimo gene, fertility, growth hormone gene, motifs, root mean square deviation
A Closed-Form Model-Free Implied Volatility Formula through Delta Families

In this article, we derive a closed-form explicit model-free formula for the (Black-Scholes) implied volatility. The method is based on the novel use of the Dirac Delta function, corresponding delta families, and the change of variable technique. The formula is expressed through either a limit or as an infinite series of elementary functions, and we establish that the proposed formula converges to the true implied volatility value. In numerical experiments, we verify the convergence of the formula, and consider several benchmark cases, for which the data-generating processes are respectively the stochastic volatility inspired model, and the stochastic alpha beta rho model. We also establish an explicit formula for the implied volatility expressed directly in terms of respective model parameters, and use the Heston model to illustrate this idea. The delta family and change of variable technique that we develop are of independent interest and can be used to solve inverse problems arising in other applications. TOPIC: Derivatives Key Findings ▪ A novel closed-form representation of the Black-Scholes implied volatility is developed by utilizing a delta-family technique. ▪ Convergence and error analyses of approximate forms of this representations are presented. ▪ This technique is applied to the parametric SVI and SABR models as well as the stochastic volatility Heston model.
Effects of the prewhitening method, the time granularity, and the time segmentation on the Mann–Kendall trend detection and the associated Sen's slope

Abstract. The most widely used non-parametric method for trend analysis is the Mann-Kendall test associated with the Sen's slope. The Mann-Kendall test requires serially uncorrelated time series, whereas most of the atmospheric processes exhibit positive autocorrelation. Several prewhitening methods have been designed to overcome the presence of lag-1 autocorrelation. These include a prewhitening, a detrending and/or a correction for the detrended slope and the original variance of the time series. The choice of which prewhitening method and temporal segmentation to apply has consequences for the statistical significance, the value of the slope and of the confidence limits. Here, the effects of various prewhitening methods are analyzed for seven time series comprising in-situ aerosol measurements (scattering coefficient, absorption coefficient, number concentration and aerosol optical depth), Raman Lidar water vapor mixing ratio and the tropopause and zero degree levels measured by radio-sounding. These time series are characterized by a broad variety of distributions, ranges and lag-1 autocorrelation values and vary in length between 10 and 60 years. A common way to work around the autocorrelation problem is to decrease it by averaging the data over longer time intervals than in the original time series. Thus, the second focus of this study is evaluation of the effect of time granularity on long-term trend analysis. Finally, a new algorithm involving three prewhitening methods is proposed in order to maximize the power of the test, to minimize the amount of erroneous detected trends in the absence of a real trend and to ensure the best slope estimate for the considered length of the time series.
Identification of Leverage Points in Principal Component Regression and r −k Class Estimators with AR(1) Error Structure

Lineer regresyon modellerinde kaldirac gozlemlerin belirlenmesi siklikla siradan en kucuk kareler ve bazi yanli tahmin ediciler uzerinden arastirilmistir. Son zamanlarda kaldirac ve etkin gozlemlerin belirlenmesi genel lineer regresyon modellerinde de populer olmustur. Bu calismada birinci dereceden otoregresif hata yapisina sahip genel lineer regresyon modelinde temel bilesenler regresyon ve r-k sinif tahmin edicileri icin sirasiyla yeni bir projeksiyon matrisi ve bir yari projeksiyon matrisi onerilmektedir. Bu matrislerin bazi yararli ozellikleri sunulmustur. Literaturde bulunan genellestirilmis en kucuk kareler ve ridge regresyon tahmin edicilerinden elde edilen kaldirac gozlemleri, bir simulasyon calismasi ve sayisal bir ornek uzerinden onerilen temel bilesen regresyonu ve r-k sinifi tahmin edicileriyle karsilastirilmistir. Literaturde birinci dereceden otoregresif hata yapisi nedeniyle birinci kaldirac ayri olarak ele alinmaktadir. Bu nedenle, temel bilesen regresyonu ve r-k sinifi tahmin edicileriyle elde edilen ilk kaldiraclarin davranislari, uygulamalar araciligiyla otokorelasyon katsayisina ve onyargi parametresine gore de incelenmistir. Sonuclar, temel bilesen regresyonu ve r-k tahmin edicileriyle elde edilen ilk gozlemin kaldiracliginin, genellestirilmis en kucuk kareler ve sirt regresyon tahmin edicileriyle elde edilenden daha kucuk oldugunu gostermistir.
Enhanced Stability of Spatially Confined Copper Nanoparticles in an Ordered Mesoporous Alumina for Dimethyl Ether Synthesis from Syngas

A spatial confinement effect of copper nanoparticles in an ordered mesoporous γ-Al2O3, which is synthesized by an evaporation induced self-assembly (EISA) method, was investigated to verify the enhanced catalytic activity and stability with less aggregation of copper crystallites during direct synthesis of dimethyl ether (DME) from syngas. The surface acidity of the mesoporous Al2O3 and the metallic copper surface area significantly altered catalytic activity and stability. The ordered mesopore structures of Al2O3 were effective to suppress the aggregation of copper nanoparticles even under reductive CO hydrogenation conditions through the spatial confinement effect of the ordered mesopores of Al2O3 as well as the formation of strongly interacted copper nanoparticles with the mesoporous Al2O3 surfaces by partial formation of the interfacial CuAl2O4 species. The aggregation of copper nanoparticles on the bifunctional Cu/meso-Al2O3 having an ordered mesoporous structure was effectively suppressed due to the.
Implementation of a counter current packed column for fractionation of key six-carbon apple aromas using supercritical CO2

Supercritical fluid extraction of liquid mixtures with supercritical CO2 is an endof-line process that is typically used to fractionate materials extracted by conventional methods. Main fractionation applications include lipid mixtures, essential oils, and alcoholic beverages. CounterCurrent Supercritical Fluid Fractionation (CC-SFF) in packed columns is an unexplored area of research in Chile. Therefore, this thesis offers a comprehensive review on the use of CC-SFF in packed columns and less common SFF technologies for liquid mixtures such as membrane contactors, mixer-settler arrangements, and spray processes. CC-SFF can be used as an alternative technology to concentrate and fractionate natural fruit aroma essences with large amounts of water. A super-concentrated aroma extract obtained by CC-SFF would be a product of higher added value than the actual products obtained by conventional methods, and therefore, the use of CC-SFF could be attractive to producers of concentrated fruit juices. The objective of this work was to implement a newly acquired CC-SFF packed column for the fractionation of characteristic six-carbon (C-6) apple aromas. For this
Reversible Adversarial Attack based on Reversible Image Transformation

In order to prevent illegal or unauthorized access of image data such as human faces and ensure legitimate users can use authorization-protected data, reversible adversarial attack technique is rise. Reversible adversarial examples (RAE) get both attack capability and reversibility at the same time. However, the existing technique can not meet application requirements because of serious distortion and failure of image recovery when adversarial perturbations get strong. In this paper, we take advantage of Reversible Image Transformation technique to generate RAE and achieve reversible adversarial attack.