Miracle-dz/UniFAR-FTUs huggingface dataset:
In Vitro Methodologies to Assess the Effect of Processing of Rice on Protein Quality

Two control rice grains (brown rice and milled rice), cooked milled rice, cooked milled rice with sugar, cooked brown rice, one infant cereal and two rice cereals (puffed rice and crispy brown rice) were analyzed to evaluate the effect of processing (milling, cooking, industrial processing) on protein quality. The . in vitro analysis used were amino acid analysis of proteins by acid hydrolysis, the TNBS method for available lysine, enzymatic digestibility (ten-minute and twentyminute), the computed protein efficiency ratio (C-PER), and the modified computed protein efficiency ratio (MC-PER). The content of lysine was found to be statistically different for brown rice compared to milled rice. Grinding and cooking did not have an effect on the lysine content but industrial processing decreased the lysine content in infant rice cereal, crispy brown rice, and puffed rice. Available lysine was found to be statistically different for brown rice and milled rice. Grinding did not have an effect on available lysine; cooking reduced the available lysine in milled rice, and the addition of sugar did not change the available lysine in milled rice. Industrial processing decreased available lysine. Available lysine of infant rice cereal and puffed rice was statistically lower than the available lysine of long grain milled rice and available lysine of crispy brown rice was significantly different from long grain brown rice. Milling did not decrease the percent of available lysine, while grinding, cooking, and Industrial processing of rice decreased the percent of available lysine. According to the results, industrial CLEMSON UNIVERSITY LIBRARY
OPTIMAL VOLTAGE PROFILE FOR HEMISPHERICAL DEFLECTOR ENERGY ANALYZERS USING GENETIC ALGORITHM

Bu calismada, yuklu parcaciklarin enerjisine gore analizini gerceklestirebilen, yari kuresel saptirici analizorunu optimize etmek icin genetik algoritma (GA) yontemi kullanilmistir. Evrim kodlarindan esinlenen GA, bireyler populasyonundan olusmaktadir. Her adimda, GA, populasyon bireylerini ebeveyn olarak secip bu bireyleri gelecek nesil icin cocuklari uretmek icin kullanir. Boylece kusaklar boyunca, nufus en iyi bir cozume dogru gelismektedir. Bu calismanin amaci, enerji analizorune ait voltaj denklemleri araciligiyla, 180˚ yari kuresel enerji analizorleri icin en iyi voltaj profilini elde etmektir. Deneysel calismalarda en cok kullanilan analizor olan 180˚ yari kuresel saptirici analizoru icin en iyi voltaj degerleri yuksek dogrulukla bu calismada bulunmustur. GA ile analizor sistemi gibi karmasik bir elektron optiksel sistemin optimizasyonu, voltaj denklemleri kullanilarak elde edilmistir ve problemin zorlugu goz onune alindiginda oldukca iyi calistigi bulunmustur. Literaturde yer alan diger tekniklerin aksine, onerilen GA yontemi araciligiyla, en iyi voltaj profili kisa surede elde edilmektedir.
F ORECASTING M AXIMA IN C LIMATE T IME S ERIES

—Climate change is already altering the probabilities of weather hazards. Accurate prediction of climate extremes can inform effective preparation against weather-induced stresses. Accurately forecasting extreme weather events is a task that has attracted interest for many years. Classical and to a lesser extent, machine learning-based approaches have handled this issue; however, such systems are hard to tune or scale. While the prediction of extremes has been the subject of investigation across several communities, including meteorology, machine learning, and statistics, it has been subject to far less scrutiny than the prediction of conditional means. In this work, we offer a systematic comparison of existing approaches on prediction of maximum temperature. Further, motivated by this comparison, we propose a method to forecast maxima temperature in weather time series that uniﬁes deep learning with extreme value theory.
Avaliação de lectina solúvel em água de sementes de Moringa oleifera (WSMoL) no controle da biocorrosão

Bacteria are prokaryotic organisms able to adapting to the most varied types of habitat; can grow and adhere to many surfaces, forming complex communities known as biofilms. Biofilm development, in most cases, results in health risks in clinical environments and causes economic losses in different industries sectors, being the main cause of biocorrosion. Control strategies of biofilms involves the use of antibiotics and biocides; however, bacteria whitin biofilms are more resistant to various treatments. Various mechanisms of bacterial resistance to antimicrobial synthetic compounds are described. Researches have been developed in order to identify natural compounds able to act against planktonic microorganisms and inside biofilms for medical and industrial applications. Lectins are proteins that bind specifically to carbohydrates and may have important biological activities, acting as antibacterial and antibiofilm agents. This work evaluated the antibacterial activity of a water-soluble Moringa oleifera seed lectin (WSMoL) against bacteria that could be involved in biocorrosion and human infections (Bacillus sp., Bacillus cereus, Bacillus pumillus, Bacillus megaterium, Micrococcus sp., Pseudomonas sp., Pseudomonas fluorescens, Pseudomonas stutzeri and Serratia marcescens) by evaluating its effect on growth, survival and cell permeability. In addition, we also studied the formation of biofilm by S. marcescens and Bacillus sp. (both bacteria sensitive to WSMoL) and evaluated the effect of WSMoL on biofilm formation and under established biofilms for both strains. A prototype of WSMoL-coated surfaces by dipping coating and the viability of cells within biofilm developed under the coated-surface were also determinated. WSMoL inhibited the bacterial growth, induced agglutination and promoted the leakage of proteins from cells of all strains. Bactericidal effect was detected against Bacillus sp. , B. pumillus, B. megaterium, P. fluorescens and S. marcescens. Fluorescence microscopy of S. marcescens cells showed that WSMoL caused damage to its structure. The lectin was able to impair biofilms formation of S. marcescens and Bacillus sp. , mainly for Bacillus sp. at 41.6 and 20.8 μg/mL; the antibiofilm mechanism can be associated with the lost of cell viability. WSMoL did not destroy preformed S. marcescens biofilms; despite the fact that attached cells presented trapped and damaged in hollow structures made by lectin. On Bacillus sp. biofilm, the lectin at 208 μg/mL drastically reduced the number of attached cells on surface. WSMoL spontaneously adhere on glass surface producing a lectin-coated area at 116 μg/cm that was able to alter the viability of S. marcescens cells, which adhered to the surface and preventing Bacillus sp. attachment, impairing biofilm development, without interfering in cell viability. This work propose the use of WSMoL as a potential tool to control biofilms formed by corrosive and pathogenic bacteria, which pose a challenge in clinical and industrial settings.
A Convolution-Free LBP-HOG Descriptor For Mammogram Classification

In image based feature descriptor design, an iterative scanning process utilizing the convolution operation is often adopted to extract local information of the image pixels. In this paper, we propose a convolution-free Local Binary Pattern (CF-LBP) and a convolution-free Histogram of Oriented Gradients (CF-HOG) descriptors in matrix form for mammogram classification. An integrated form of CF-LBP and CF-HOG, CF-LBP-HOG, is subsequently constructed in a single matrix formulation. The proposed descriptors are evaluated using a publicly available mammogram database. The results show promising performance in terms of classification accuracy and computational efficiency.
Unsupervised Concatenation Hashing with Sparse Constraint for Cross-Modal Retrieval

With the advantage of low storage cost and high efficiency, hashing learning has received much attention in retrieval field. As multiple modal data representing a common object semantically are complementary, many works focus on learning unified binary codes. However, these works ignore the importance of manifold structre among data. In fact, it is still an interesting problem to directly preserve the local manifold structure among samples in hamming space. Since different modalities are isomerous, we adopt the concatenated feature of multiple modality feature to represent original object. In our framework, Locally Linear Embedding and Locality Preserving Projection are introduced to reconstruct the manifold structure of original space in the Hamming space. Besides, The L21-norm regularization are imposed on the projection matrices to further exploit the discriminative features for different modalities simultaneously. Extensive experiments are performed to evaluate the proposed method, dubbed Unsupervised Concatenation Hashing (UCH), on the three publicly available datasets and the experimental results show the superior performance of UCH outperforming most of state-of-the-art unsupervised hashing models.
Impedance control of a cable-driven SEA with mixed H2/H∞ synthesis

Purpose This paper aims to present an impedance control method with mixed H2/H∞ synthesis and relaxed passivity for a cable-driven series elastic actuator to be applied for physical human–robot interaction. Design/methodology/approach To shape the system’s impedance to match a desired dynamic model, the impedance control problem was reformulated into an impedance matching structure. The desired competing performance requirements as well as constraints from the physical system can be characterized with weighting functions for respective signals. Considering the frequency properties of human movements, the passivity constraint for stable human–robot interaction, which is required on the entire frequency spectrum and may bring conservative solutions, has been relaxed in such a way that it only restrains the low frequency band. Thus, impedance control became a mixed H2/H∞ synthesis problem, and a dynamic output feedback controller can be obtained. Findings The proposed impedance control strategy has been tested for various desired impedance with both simulation and experiments on the cable-driven series elastic actuator platform. The actual interaction torque tracked well the desired torque within the desired norm bounds, and the control input was regulated below the motor velocity limit. The closed loop system can guarantee relaxed passivity at low frequency. Both simulation and experimental results have validated the feasibility and efficacy of the proposed method. Originality/value This impedance control strategy with mixed H2/H∞ synthesis and relaxed passivity provides a novel, effective and less conservative method for physical human–robot interaction control.
Study of quantum symmetries for vertex-transitive graphs using intertwiner spaces.

We study quantum automorphism group of vertex-transitive graphs using intertwinner spaces of the magic unitary matrix associated to this quantum subgroups of $S_n^+$. We also give some applications to quantum symmetries of circulant graphs as $C_{13}(3,4)$ and $C_{17}(2,4,8)$.
An Alternative Set Model of Cognitive Jump

When we enumerate numbers up to some specific value, or, even if we do not specify the number, we know at the same time that there are much greater numbers which should be reachable by the same enumeration, but indeed we also congnize them without practical enumeration. Namely, if we deem enumeration to be a way of reaching a number without any "jump", there is a "jump'' in our way of cognition of such greater numbers. In this article, making use of a set theoretical framework by Vop\v{e}nka (1979) (alternative set theory) which describes such structure, we attempt to shed light on an analogous sturucture in human and social phenomenon. As an example, we examine a problem of common knowledge in electronic mail game presented by Rubinstein (1989). We show an event comes to common knowledge by a "cognitive jump".
Genetic susceptibility to childhood bronchiolitis

Bronchiolitis is an infection of the small airways of the lung and is a common reason for infant hospitalizations. The most common causative pathogen is the respiratory syncytial virus (RSV). Genetic factors are thought to influence the risk of bronchiolitis, and better knowledge of bronchiolitis genetics will likely help to elucidate the disease process. Severe bronchiolitis in childhood may predispose to asthma. Therefore, an effective treatment of bronchiolitis may affect the present-day as well as lifelong respiratory health. In this project, we aimed to identify genetic loci of bronchiolitis susceptibility by a genomewide association study (GWAS) and suitable follow-up studies, and to study a previously asthmaassociated CDHR3 variant for association across five bronchiolitis populations by meta-analysis. We performed the GWAS on a Finnish-Swedish case-control population and identified several loci below the suggestive genome-wide significance level. Of these, three variants showed nominal associations in a replication population from the Netherlands. One of the loci affected KCND3 expression, and two others were intergenic variants with putative regulatory potential. In a follow-up study conducted on a GWAS sub population, we identified the NKG2D locus as a candidate of susceptibility to bronchiolitis. The genomic region encompassing NKG2D variants was reportedly associated with NKG2D mRNA and protein abundance. We validated the association between NKG2D genotypes and protein expression with flow cytometry. The association between NKG2D and bronchiolitis was supported by a Finnish replication study. The meta-analysis was performed on populations from Denmark, Finland, Sweden, Germany, and the Netherlands. A potential virus-specific role for the CDHR3 variant was detected in a population that comprised mostly RSV-negative cases. In conclusion, we identified new candidates of bronchiolitis susceptibility in GWAS and subsequent studies. We found the CDHR3 variant was a potential susceptibility factor in severe non-RSV bronchiolitis and asthma. Our preliminary results provide interesting starting points for further studies. In the future, better understanding of the disease mechanisms and the relationship of bronchiolitis and asthma could provide means to design new therapeutic options.
A Systematic Review on Intrusion Detection Systems for Mobile Devices

In the recent years the use of mobile devices has increased drastically, so does the security concerns associated with it. To deal with the security threats to the mobile devices, many applications came into existence like firewall, Anti-virus. But in many cases these also fail to provide security in case of severe attack being done by an intruder. So more secure systems were introduced, which are known as Intrusion Detection Systems (IDS). In the beginning traditional IDSs were introduced which were in providing security to the devices which are static inside a particular network and do not change their networks but these IDSs failed to provide security to the mobile devices. So in order to provide security to the mobile devices, mobile Intrusion Detection Systems were introduced. In this paper we have surveyed the different existing mobile IDS and their advantages and limitations.
Perturbed Markov Chains and Information Networks

The paper is devoted to studies of perturbed Markov chains commonly used for description of information networks. In such models, the matrix of transition probabilities for the corresponding Markov chain is usually regularised by adding a special damping matrix multiplied by a small damping (perturbation) parameter $\varepsilon$. We give effective upper bounds for the rate of approximation for stationary distributions of unperturbed Markov chains by stationary distributions of perturbed Markov chains with regularised matrices of transition probabilities, asymptotic expansions for approximating stationary distributions with respect to damping parameter, as well as explicit upper bounds for the rate of convergence in ergodic theorems for $n$-step transition probabilities in triangular array mode, where perturbation parameter $\varepsilon \to 0$ and $n \to \infty$, simultaneously. The results of numerical experiments are also presented
Genetic analysis of drought stress response in Arabidopsis thaliana and Brassica rapa

Drought is the major abiotic stress affecting plant growth and limiting crop productivity worldwide. Plants have evolved three adaptive strategies, drought escape, drought avoidance and drought tolerance, to cope with drought. Knowledge on how Quantitative Trait Loci (QTL), or genes underlying these strategies interact with their environments will significantly increase our understanding and the success of breeding for drought tolerance. This thesis focused on phenotyping shoot and root traits ofA. thaliana and B. rapa grown on sand and in greenhouses,to further understand how plants can adapt to natural drought stress. In chapter 2, an already existing ArabidopsisRIL population was selected based on the differential root drought response of the two parental lines, Sha and Col, to be evaluated under different water regimes. Chapter 3 illustrated the use of GWAS in identifying candidate genes that are associated with pant response to drought. In order to apply the same methodology in crop breeding, chapter 4 introduces a contribution to the genetic mapping of a new B. rapa RIL population, consisting of 160 lines and genotyped with 270 different markers was achieved. The morphological and physiological responses of this population to drought was evaluated in chapter 5. The results presented in the present thesis demonstrate that QxE is an important component of the genetic variance and can play a great role in improving drought tolerance in future breeding programs. In general, several QTL and SNPs were mapped either with main effect or with interaction with environments QxE. Many of the mapped QTL showed conditional neutrality and antagonestic pleiotropy.
Deep Clustering With Intraclass Distance Constraint for Hyperspectral Images

The high dimensionality of hyperspectral images often results in the degradation of clustering performance. Due to the powerful ability of potential feature extraction and nonlinear representation, deep clustering algorithms have become a hot topic in hyperspectral remote sensing. Different tasks often need different features. However, the current deep clustering algorithms generally separate feature extraction from clustering, which results in the extracted features that are not constrained by clustering tasks. Therefore, the features extracted by these algorithms may not be suitable for clustering. To address this issue, we adopt intraclass distance as a constraint condition and proposed an intraclass distance constrained deep clustering algorithm for hyperspectral images. The proposed algorithm propagates the clustering error back to the feature mapping process of the autoencoder network, so as to realize the constraint of clustering objective on feature extraction and make the extracted features more suitable for clustering tasks. In addition, the proposed algorithm simultaneously completes network optimization and clustering, which is more efficient. Experimental results demonstrate the intense competitiveness of the proposed algorithm in comparison with state-of-the-art clustering methods for hyperspectral images.
Semantic Nearest Neighbor Fields Monocular Edge Visual-Odometry

Recent advances in deep learning for edge detection and segmentation opens up a new path for semantic-edge-based ego-motion estimation. In this work, we propose a robust monocular visual odometry (VO) framework using category-aware semantic edges. It can reconstruct large-scale semantic maps in challenging outdoor environments. The core of our approach is a semantic nearest neighbor field that facilitates a robust data association of edges across frames using semantics. This significantly enlarges the convergence radius during tracking phases. The proposed edge registration method can be easily integrated into direct VO frameworks to estimate photometrically, geometrically, and semantically consistent camera motions. Different types of edges are evaluated and extensive experiments demonstrate that our proposed system outperforms state-of-art indirect, direct, and semantic monocular VO systems.
On the Diversity of Uncoded OTFS Modulation in Doubly-Dispersive Channels

Orthogonal time-frequency space (OTFS) is a two-dimensional (2D) modulation technique designed in the delay-Doppler domain. A key premise behind OTFS is the transformation of a time-varying multipath channel into an almost non-fading 2D channel in the delay-Doppler domain such that all symbols in a transmission frame experience the same channel gain. It has been suggested in the recent literature that the OTFS can extract full diversity in the delay-Doppler domain, where full diversity refers to the number of multipath components separable in either the delay or Doppler dimension, but without formal analysis. In this paper, we present a formal analysis of the diversity achieved by the OTFS modulation along with supporting simulations. Specifically, we prove that the asymptotic diversity order of the OTFS (as SNR $\rightarrow \infty $ ) is one. However, in the finite SNR regime, the potential for a higher order diversity is witnessed before the diversity one regime takes over. Also, the diversity one regime is found to start at lower BER values for increased frame sizes. We also propose a phase rotation scheme for the OTFS using transcendental numbers and show that the OTFS, with this proposed scheme, extracts full diversity in the delay-Doppler domain.
Linguistic generalization and compositionality in modern artificial neural networks

In the last decade, deep artificial neural networks have achieved astounding performance in many natural language-processing tasks. Given the high productivity of language, these models must possess effective generalization abilities. It is widely assumed that humans handle linguistic productivity by means of algebraic compositional rules: are deep networks similarly compositional? After reviewing the main innovations characterizing current deep language-processing networks, I discuss a set of studies suggesting that deep networks are capable of subtle grammar-dependent generalizations, but also that they do not rely on systematic compositional rules. I argue that the intriguing behaviour of these devices (still awaiting a full understanding) should be of interest to linguists and cognitive scientists, as it offers a new perspective on possible computational strategies to deal with linguistic productivity beyond rule-based compositionality, and it might lead to new insights into the less systematic generalization patterns that also appear in natural language. This article is part of the theme issue ‘Towards mechanistic models of meaning composition’.
Second record of Diplosoma listerianum (Milne Edwards, 1841) five years after and 280 kilometres from the site of the first record in Nova Scotia

Five samples of Diplosoma listerianum, a non-indigenous colonial ascidian species complex, were collected via SCUBA between August 27 and September 28, 2017, from four sites in the coastal waters (depth ranging between 3.1 and 18.0 metres) around Brier Island, Nova Scotia, Canada. These samples constitute the second record of D. listerianum in Nova Scotia—five years after the first discovery and 280 kilometres (by sea) from the site of the first record. Semi-quantitative abundance of D. listerianum from Brier Island was determined and was found to be frequent or common in three of these four sites that were surveyed via SCUBA. The occurrence at several sites, coupled by the presence of eggs, ovaries, and larvae in some tissue samples suggest that there is presently an established population of D. listerianum off of Brier Island.