Miracle-dz/UniFAR-FTUs huggingface dataset:
Electro-thermal analytical model and simulation of the self-heating effects in multi-finger 4H-SiC power MESFETs

A three-dimensional (3D) electro-thermal analytical model to accurately predict the temperature distribution in multi-finger silicon carbide (SiC) based high-power field-effect transistors has been proposed. The results of the analytical and numerical investigation of self-heating effects have also been presented. The analytical results are well supported by the two-dimensional electro-thermal simulation results obtained by Atlas. The models give an approximate but explicit influence on temperature distribution in terms of the structure parameter and operation condition, such as the gate-to-gate pitch, the thickness of the substrate and the source–drain bias. The obtained results can be used for optimization of the thermal design of the multi-finger 4H-SiC power MESFETs.
Smurf: System for Modelling with Uncertainty Reduction, and Forecasting

Smurf is an open source modular system developed in Python for running and cycling data assimilation (DA) systems. It is organised around three super classes for numerical model management, assimilation schemes and observation instruments. Any new item can be easily plugged in by defining a child class that will override as many methods as necessary. Non intrusive, Smurf can be used in any applicative domain for numerical models written in any language. CORRESPONDING AUTHOR: Isabelle Mirouze
Deriving colour palettes from images of natural landscapes

The colours of natural landscapes represent important information about the character of the region in which the landscape is based. The colours extracted from the natural landscapes are considered as the harmonious colour arrangements that could provide a pleasing visual experience to human. In this study, methods to develop colour palettes based on analyses of digital images of natural landscapes are explored. A psychophysical experiment in which participants select five colours that are representative of digital images of landscapes is first described; this generates data that can be used as ground-truth data against which other (more automatic) methods could be evaluated. Automatic methods for generating colour palettes from images using cluster analysis in RGB and CIELAB colour space. A colour-difference metric was used to compare the palettes generated from the designers to automatically generated palettes. There was no statistically significant effect of colour space (RGG v. CIELAB) on the colour differences between visual palettes and those derived using cluster analysis.
HIGH PRESSURE COMPRESSION RTM-A NEW PROCESS FOR MANUFACTURING HIGH VOLUME CONTINUOUS FIBER REINFORCED COMPOSITES

The current paper addresses High Pressure Compression Resin Transfer Molding (HP-CRTM) for the manufacturing of continuous fiber reinforced composites with high fiber volume content. The HP-CRTM process is a combination of resin transfer molding (RTM) and compression molding. In this process, the preform is placed into the mold cavity and then the mold is closed partially to obtain a small gap between the mold surface and the fiber preform. The resin is introduced through a suitable injection point into the gap and flows easily over the preform and may partially impregnate the preform as well. Once the required amount of resin is injected into the gap and the injection point is closed, the mold closes further and applies high compression pressure to squeeze the resin into the preform. In this step, the preform is compacted to achieve the desired part thickness and fiber volume fraction. The objective of the proposed study is to investigate the effects of parameters such as mold opening distance and fiber orientation on the quality of the HP-CRTM components. The influence of these process variables on the component quality and the mechanical properties is analyzed. Finally the applicability of the HP-CRTM process for high volume manufacturing is discussed. Introduction Classical RTM Process The resin transfer molding (RTM) process is well established for low volume manufacturing applications and has recently gained great interest in the manufacturing of higher volumes. This interest is strongly motivated by the need of lightweighting for the automotive and aerospace industries. The classical RTM process in its current status, however, is limited by the low volume production capacity of the preforming processes, long impregnation time and lack of robust injection equipments. These issues hinder the use of RTM process for high volume manufacturing of the components. Figure 1 shows the injection sequence in the classical RTM process. The three dimensional preform is placed into the cavity and the mold is completely closed. The mold closure leads to compaction of the preform to the final part thickness thereby leading to reduction in the preform permeability. The reactive resin (premixed theromset resin and hardener) is then injected into the cavity under pressure which is typically 1 to 20 bar [1]. The resin flows along the length of the preform and impregnates the fibers. Ideally, once the impregnation process is completed the resin undergoes a curing reaction and after a defined time interval, the part can be demolded.
Leaf Blade versus Petiole Analysis for Nutritional Diagnosis of Vitis vinifera L. cv. Tempranillo

The temporal stability and the analysis reproducibility of nutrient concentration of leaf blades and petioles of Vitis vinifera L. cv. Tempranillo were evaluated at a vineyard representing the soil and climate conditions within the AOC Rioja region of Spain. Leaf blade and petiole were sampled throughout the growing season and analyzed for N, P, K, Ca, Mg, Fe, Mn, Zn, Cu, and B concentrations. For reproducibility of the analyses, leaf blade had a lower percent coeffcient of variation (%CV) of total N, and K than petiole, while petiole had a lower %CV than leaf blade for B. Phosphorus and Mg had similar %CV in blade and petiole at flowering, while blade had lower %CV at veraison for both nutrients. Calcium, Mn, and Fe had similar %CV in both tissues throughout the growing season. Seasonal changes of nutrient concentration in leaves confirmed the need for specific reference values for both leaf blades and petioles in each phenological stage. In general, nutrient concentration trends throughout the growing season showed short periods without differences between consecutive samplings near the 100% capfall phenological stage, while no statistical differences were found between samples obtained at any time throughout veraison. Results suggest that blade could be a more appropriate tissue for total N and K evaluation, whereas petiole is more appropriate for B evaluation. Calcium, Mn, and Fe will have a similar accuracy in both tissues at any phenological stage. While P and Mg had similar reproducibility in blade and petiole at flowering, blade seems be a better tissue for P and Mg nutritional diagnosis at veraison.
Predicting Index Returns from the Market Structure Disagreement: Evidence from China

—The factors with information are key to predict stock returns. Previous studies on disagreement mainly focus on the report of the analysts and the sentiment or belief of investors, as well as the trading volume or turnover, ignore the structure among stocks. In this paper, we come up with a new concept of the market structure disagreement and measure it based on the K-means clustering algorithm and Gini impurity. The experiments for the CSI100 stock market show that the market structure disagreement could improve the predicting direction accuracy of machine learning algorithms nearly 1.5%. Speciﬁcally, trading volumes and net capital inﬂows affect the market structure disagreement, which increases with the log difference of trading volumes and decreases with the growth rate of net capital inﬂows. This paper proposes a new information factor, structure disagreement, which is signiﬁcantly helpful for investors with market timing, especially for the investors using machine learning.
Evaluation of high-resolution atmospheric and oceanic simulations of the California Current System

This paper is the first of two that present a 16-year hindcast solution from a coupled physical and biogeochemical model of the California Current System (CCS) along the U. S. West Coast and validate the physical solution with respect to mean, seasonal, interannual, and sub-seasonal fields and, to a lesser degree, eddy variability. Its companion paper is Deutsch et al. (2021a). The intent is to construct and demonstrate a modeling tool that will be used for mechanistic explanations, attributive causal assessments, and forecasts of future evolution for circulation and biogeochemistry, with particular attention to the increasing oceanic stratification, deoxygenation, and acidification. A well-resolved mesoscale (dx = 4 km) simulation of the CCS circulation is made with the Regional Oceanic Modeling System over a hindcast period of 16 years from 1995 to 2010. The oceanic solution is forced by a high-resolution (dx = 6 km) regional configuration of the Weather and Research Forecast (WRF) atmospheric model. Both of these high-resolution regional oceanic and atmospheric simulations are forced by lateral open boundary conditions taken from larger-domain, coarser-resolution parent simulations that themselves have boundary conditions from the Mercator and Climate Forecast System reanalyses, respectively. We show good agreement between the simulated atmospheric forcing of the oceanic and satellite measurements for the spatial patterns and temporal variability for the surface fluxes of momentum, heat, and freshwater. The simulated oceanic physical fields are then evaluated with satellite and in situ measurements. The simulation reproduces the main structure of the climatological upwelling front and cross-shore isopycnal slopes, the mean current patterns (including the California Undercurrent), and the seasonal, interannual, and subseasonal variability. It also shows agreement between the mesoscale eddy activity and the windwork energy exchange between the ocean and atmosphere modulated by influences of surface current on surface stress. Finally, the impact of using a high frequency wind forcing is assessed for the importance of synoptic wind variability to realistically represent oceanic mesoscale activity and ageostrophic inertial currents.
Fate of major degradation products of Atrazine in Iowa soils

Abstract Five Iowa soils, sampled at two depths each were treated with 14C‐deethylatrazine (DEA) and 14C‐hydroxyatrazine (HYA) and incubated for nine and 17 weeks. 14CO2 evolution was monitored over this period. The degradation of DEA was influenced by both soil type and soil depth. For each soil studied, the degradation of DEA was much higher in the surface layer compared to the respective subsurface layer. The major degradation products from DEA included CO2, soil bound residues, and polar metabolites. Deethylhydroxyatrazine (DEHYA) and didealkylatrazine (DAA) were detected in minor quantities. Increased mineralization of DEA in the extended incubations confirmed the susceptibility of DEA to microbial degradation. In the complete metabolism studies with HYA, the major components identified included HYA, soil bound residues, and CO2. Deethylhydroxyatrazine deisopropylhydroxyatrazine and ammeline were found in smaller quantities. The relatively long persistence (45 to 69% remaining) of HYA was observed in.
Mesoporous carbon with uniquely combined electrochemical and mass transport characteristics for polymer electrolyte membrane fuel cells

The design of electrodes for polymer electrolyte membrane fuel cells (PEMFCs) is a delicate balance of electrochemical and mass transport issues. High performance fuel cell electrode materials require nanoarchitectures with established nanoscopic reaction zones and efficient molecular transport of gas- or liquid-phase reactants and products to and from the electrochemical reaction zones. Mesoporous carbon (MC), with uniquely combined electrochemical and mass transport characteristics is an ideal electrode material for polymer electrolyte membrane fuel cells as its mesoscopic structures not only enables electrocatlysts to be highly dispersed, but also offers ideal pore morphologies that facilitate mass transport. Recently, a wide variety of applications of MCs in PEMFCs have been exploited. This article provides a review of these past efforts with an attempt to gain a better understanding of the role of MCs in PEMFCs. The contribution of MCs in the gas diffusion layer is addressed first and their roles in the catalyst layer are then discussed. The advantages and disadvantages, the acting mechanism to promote electrochemical and mass transport characteristics, and the strategies to improve present electrode materials are discussed.
LaCuO25+x and YCuO2.5+x Delafossites: Materials with Triangular Cu2+δ Planes

The YCuO2 and LaCuO2 delafossites consist of alternating triangular planes of Ln3+ octahedra and O -Cu1+-O sticks. Here it is shown that under a limited set of conditions they may be oxidized to form new delafossite-derived phases YCuO2.5+x, 0 ≤ x ≤ 0.05, and LaCuO25+x, 0.07 ≤ x ≤ 0.18. Electron microscopy and powder X-ray diffraction are employed to characterize the resulting structures. Temperature-dependent resistivities and magnetic susceptibilities are reported. Characterization by soft X-ray photoabsorbtion spectroscopy, which shows them to be electronically unlike cuprates such as La2-xSrxCuO4, is also reported.
mycoflora and natural co-occurrence of toxins in red chillies from Jammu and Kashmir

Quality deterioration of dried red chillies during storage poses a major threat as they are hygroscopic. Moreover, due to unscientific methods of collection, drying and post-harvest storage, red chillies are prone to fungal contamination and their toxins. In view of this, samples of dried red chillies were collected from different regions of Jammu and Kashmir and analysed for fungal species and associated toxins. Investigations revealed the presence of several fungal species, which are known producers of aflatoxins ( ), cyclopiazonic acid ( and and sterigmatocystin and spp.). Qualitative analysis by thin layer chromatography (TLC) and then quantification by high performance liquid chromatography (HPLC) showed that 36 % of the samples were positive for aflatoxin B1 (0.24 2.14 Aspergillus flavus Aspergillus flavus, A. fumigatus,A. tamarii,A. oryzae,A.versicolor, Penicillium griseofulvum P.chrysogenum) (Aspergillus nidulans, A.versicolor,A. sydowii, Chaetomium Emericella μg/g), 5%samples were positive for aflatoxin B2 (2.08 2.14 μg/g), 31%of samples were positive for cyclopiazonic acid (1.65 144.78 μg/g) and 23 % samples were positive for sterigmatocystin (5.10 24.03 μg/g). In addition, co-occurrence of mycotoxins like aflatoxin and cyclopiazonic acid; aflatoxin and sterigmatocystin; aflatoxin, cyclopiazonic acid and sterigmatocystin were detected in 15, 5 and 5% samples, respectively. Detection of more than one mycotoxin from chilli pericarp samples is a matter of great concern as the co-occurrence of mycotoxins may generate additive or synergistic effect in consumers.
Enzimatik polimerizasyon yöntemi ile polilaktik asit sentezi ve biyobozundurulması

Bu calismada, enzimatik halka acilimi polimerizasyon yontemi kullanilarak polilaktik asit sentezi gerceklestirilmis ve elde edilen polimerin proteaz enzimi ile biyobozundurulmasi incelenmistir. Candida cylindracea lipaz katalizorlugunde laktitten halka acilimi reaksiyonunun optimize edilmesi amaciyla, reaksiyon sartlari degistirilerek farkli sicakliklarda gerceklestirilen polimerizasyon islemlerinde, reaksiyona giren Candida cylindracea lipaz konsantrasyonuna bagli olarak olusan polilaktik asitin molekul agirligi, polidispersitesi ve donusum verimi degerleri tespit edilmistir. Elde edilen polilaktik asit, proteaz DSM enzimi ile biyobozundurma islemine tabii tutulmus ve polimerin yapisindaki ester gruplari ile biyobozundurma mekanizmasi takip edilmistir. Biyobozunma suresince molekul agirligi kaybi izlenmis ve orjinal polimerin morfolojik ve termal gecis ozelliklerinin degisimi incelenmistir. Candida cylindracea lipaz kullanilarak laktitin halka acilimi polimerizasyonunda, yuksek molekul agirlikli polimer sentezinde, en uygun reaksiyon sarti, sicakligin 80 oC ve lipaz konsantrasyonunun agirlikca %4 oldugu durum olarak belirlenmistir. Bu sicaklik degerinde, sabit enzim konsantrasyonunda, surenin artmasiyla birlikte molekul agirligi ve monomer donusumu belirgin bir sekilde artis gostermis, polidispersite degerleri yaklasik 1.5 civarinda olan polimerler sentezlenmistir. Proteaz DSM ile yapilan biyobozundurma islemi sonucunda ise, ilk 15 gunluk sure icersinde, polimerin bozunmasina iliskin molekul agirligi kaybi son derece dusuk seviyede oldugu, bu sureden sonra molekul agirligi kaybinda belirgin bir artis gozlenerek 90 gunluk sure sonunda %23’luk bir kayip meydana geldigi saptanmistir. Biyobozundurma islemine tabii tutulan polimerin cesitli ozellikleri incelenerek orijinal polimer ile arasindaki farklar tespit edilmistir. Anahtar Kelimeler: Lipaz, halka acilimi polimerizasyonu, biyobozunma.
Pattern Sampling for Shapelet-based Time Series Classification

Subsequence-based time series classification algorithms provide accurate and interpretable models, but training these models is extremely computation intensive. The asymptotic time complexity of subsequence-based algorithms remains a higher-order polynomial, because these algorithms are based on exhaustive search for highly discriminative subsequences. Pattern sampling has been proposed as an effective alternative to mitigate the pattern explosion phenomenon. Therefore, we employ pattern sampling to extract discriminative features from discretized time series data. A weighted trie is created based on the discretized time series data to sample highly discriminative patterns. These sampled patterns are used to identify the shapelets which are used to transform the time series classification problem into a feature-based classification problem. Finally, a classification model can be trained using any off-the-shelf algorithm. Creating a pattern sampler requires a small number of patterns to be evaluated compared to an exhaustive search as employed by previous approaches. Compared to previously proposed algorithms, our approach requires considerably less computational and memory resources. Experiments demonstrate how the proposed approach fares in terms of classification accuracy and runtime performance.
Towards generalisable hate speech detection: a review on obstacles and solutions

Hate speech is one type of harmful online content which directly attacks or promotes hate towards a group or an individual member based on their actual or perceived aspects of identity, such as ethnicity, religion, and sexual orientation. With online hate speech on the rise, its automatic detection as a natural language processing task is gaining increasing interest. However, it is only recently that it has been shown that existing models generalise poorly to unseen data. This survey paper attempts to summarise how generalisable existing hate speech detection models are and the reasons why hate speech models struggle to generalise, sums up existing attempts at addressing the main obstacles, and then proposes directions of future research to improve generalisation in hate speech detection.
Fast Graph Learning with Unique Optimal Solutions

Graph Representation Learning (GRL) has been advancing at an unprecedented rate. However, many results rely on careful design and tuning of architectures, objectives, and training schemes. We propose efficient GRL methods that optimize convexified objectives with known closed form solutions. Guaranteed convergence to a global optimum releases practitioners from hyper-parameter and architecture tuning. Nevertheless, our proposed method achieves competitive or state-of-the-art performance on popular GRL tasks while providing orders of magnitude speedup. Although the design matrix ($\mathbf{M}$) of our objective is expensive to compute, we exploit results from random matrix theory to approximate solutions in linear time while avoiding an explicit calculation of $\mathbf{M}$. Our code is online: http://github.com/samihaija/tf-fsvd
Automatic Generation of Interpolants for Lattice Samplings: Part I - Theory and Analysis

Interpolation is a fundamental technique in scientific computing and is at the heart of many scientific visualization techniques. There is usually a trade-off between the approximation capabilities of an interpolation scheme and its evaluation efficiency. For many applications, it is important for a user to be able to navigate their data in real time. In practice, the evaluation efficiency (or speed) outweighs any incremental improvements in reconstruction fidelity. In this two-part work, we first analyze from a general standpoint the use of compact piece-wise polynomial basis functions to efficiently interpolate data that is sampled on a lattice. In the sequel, we detail how we generate efficient implementations via automatic code generation on both CPU and GPU architectures. Specifically, in this paper, we propose a general framework that can produce a fast evaluation scheme by analyzing the algebro-geometric structure of the convolution sum for a given lattice and basis function combination. We demonstrate the utility and generality of our framework by providing fast implementations of various box splines on the Body Centered and Face Centered Cubic lattices, as well as some non-separable box splines on the Cartesian lattice. We also provide fast implementations for certain Voronoi splines that have not yet appeared in the literature. Finally, we demonstrate that this framework may also be used for non-Cartesian lattices in 4D.
Open-Retrieval Conversational Machine Reading

In conversational machine reading, systems need to interpret natural language rules, answer high-level questions such as"May I qualify for VA health care benefits? ", and ask follow-up clarification questions whose answer is necessary to answer the original question. However, existing works assume the rule text is provided for each user question, which neglects the essential retrieval step in real scenarios. In this work, we propose and investigate an open-retrieval setting of conversational machine reading. In the open-retrieval setting, the relevant rule texts are unknown so that a system needs to retrieve question-relevant evidence from a collection of rule texts, and answer users' high-level questions according to multiple retrieved rule texts in a conversational manner. We propose MUDERN, a Multi-passage Discourse-aware Entailment Reasoning Network which extracts conditions in the rule texts through discourse segmentation, conducts multi-passage entailment reasoning to answer user questions directly, or asks clarification follow-up questions to inquiry more information. On our created OR-ShARC dataset, MUDERN achieves the state-of-the-art performance, outperforming existing single-passage conversational machine reading models as well as a new multi-passage conversational machine reading baseline by a large margin. In addition, we conduct in-depth analyses to provide new insights into this new setting and our model.