Miracle-dz/UniFAR-FTUs huggingface dataset:
Camera response function assessment in multispectral HDR imaging

Multispectral imaging techniques improve the accuracy of spectral reconstruction as well as color measurement in image-based data capture systems compared to the conventional RGB cameras. On the other hand, high dynamic range imaging provides recording a higher range of scene radiance values when the dynamic range of the camera does not cover values between the minimum and maximum radiances of the scene. In high dynamic range imaging, camera response function plays a crucial role in the irradiance map reconstruction. It could be either absolutely measured by laboratory equipment or relatively estimated through a set of images with different exposure times. In this work, camera response function assessment has been addressed for a filter wheel multispectral camera. Both measurement and estimation methods are investigated through experiments and results are compared to the ground truth data. The results showed high dynamic range imaging with measured absolute camera response function has two benefits: it measures radiance in the same scale and physical units, and outperformed in terms of the precision of reflectance measurement.
SsNEP2 Contributes to the Virulence of Sclerotinia sclerotiorum

Sclerotinia sclerotiorum is a notorious soilborne fungal pathogen that causes serious economic losses globally. The necrosis and ethylene-inducible peptide 1 (NEP1)-like proteins (NLPs) were previously shown to play an important role in pathogenicity in fungal and oomycete pathogens. Here, we generated S. sclerotiorum necrosis and ethylene-inducible peptide 2 (SsNEP2) deletion mutant through homologous recombination and found that SsNEP2 contributes to the virulence of S. sclerotiorum without affecting the development of mycelia, the formation of appressoria, or the secretion of oxalic acid. Although knocking out SsNEP2 did not affect fungal sensitivity to oxidative stress, it did lead to decreased accumulation of reactive oxygen species (ROS) in S. sclerotiorum. Furthermore, Ssnlp24SsNEP2 peptide derived from SsNEP2 triggered host mitogen-activated protein kinase (MAPK) activation, increased defense marker gene expression, and enhanced resistance to Hyaloperonospora arabidopsidis Noco2. Taken together, our data suggest that SsNEP2 is involved in fungal virulence by affecting ROS levels in S. sclerotiorum. It can serve as a pathogen-associated molecular pattern (PAMP) and trigger host pattern triggered immunity to promote the necrotrophic lifestyle of S. sclerotiorum.
Application of generic algorithms in aerodynamic optimisation design procedures

Abstract Direct optimisation techniques using different methods are presented and compared for the solution of two common flows: a two dimensional diffuser and a drag minimisation problem of a fixed area body. The methods studied are a truncated Newton algorithm (gradient method), a simplex approach (direct search method) and a genetic algorithm (stochastic method). The diffuser problem has a known solution supported by experimental data, it has one design performance measure (the pressure coefficient) and two design variables. The fixed area body also has one performance measure (the drag coefficient), but this time there are four design variables; no experimental data is available, this computation is performed to assess the speed/progression of solution. In all cases the direct search approach (simplex method) required significantly smaller number of evaluations than the generic algorithm method. The simplest approach, the gradient method (Newton) performed equally to the simplex approach for the diffuser problem but it was unable to provide a solution to the four-variable problem of a fixed area body drag minimisation. The level of robustness obtained by the use of generic algorithm is in principle superior to the other methods, but a large price in terms of evaluations has to be paid.
Extraction of the edge/areal components and path of the reverse gate leakage in a GaN HEMT from measurements

Reverse gate leakage, I G, limits the reliability of gallium nitride high electron mobility transistors. We extract the components of I G flowing into the edge and area of the gate from the measured I G versus gate to source voltage, V GS, data of both low and high I G devices. The components are separated by analyzing the change in I G with gate length, L G. We estimate the short and long channel limits of L G for which the I G flows predominantly into the edge and area, respectively. Prior one-dimensional I G models based on the areal field are valid for simulating long channel devices. However, the I G of short channel devices should be modeled using the edge field, and can be reduced by techniques like field plate or high-k passivation which reduce the edge field. Also, we find the measured I G to be independent of the un-gated length, implying that I G flows via the channel rather than surface. Our work dispels any prior misperception of I G being area dominated irrespective of L G and gives the right direction for modeling and control of I G.
Valorization of ‘Rossa di Tropea’ Onion Waste through Green Recovery Techniques of Antioxidant Compounds

The aim of this work was to maximize the recovery of the bioactive components from an important solid waste derivate from Tropea onion processing. To achieve this, three different extractive procedures (conventional for maceration, ultrasound, and microwave-assisted) were employed, using only food-grade extraction solvents such as water and ethanol:water (50:50). Solvent, temperature, microwave power, time, and their interaction were studied as the principal factors that might affect the extractability rates. The obtained data suggest that the hydroalcoholic mixture proved to be the best for each of the techniques developed and at each time and temperature considered. In particular, the best results were achieved by conventional extraction for 60 min at 40 °C (total flavonoids content: 25.64 ± 1.40 mg QE g−1 d.w.; total anthocyanins content: 0.78 ± 0.01 mg C-3-GLUC g−1 d.w.). The UHPLC analysis of the optimally obtained extract revealed that the principal phytochemicals recovered were quercetin (5322.61 ± 0.32 mg kg−1) and quercetin 3-4′-diglucoside (1023.80 ± 0.34 mg kg−1) after conventional and ultrasound-assisted extraction, respectively. In this perspective, the implementation of sustainable, food-grade extraction processes to recover value-added substances from solid onion waste could play a crucial role both in reducing the waste load and in formulating natural food additives with functional properties, with a potential direct industrial impact.
Exploring the Reasons of Willingness to Engage in Intercultural Communication Among College Students in China

With the development of international understanding education, the cultivation of intercultural willingness has also been paid more attention to colleges and universities. Therefore, the paper conducted a questionnaire analysis of students’ willingness to engage in intercultural communication in a university in Sichuan Province against the background of international understanding education, and explored the reasons that affect students' willingness to engage in intercultural communication. Combining the results of statistical analysis and international understanding education, the paper puts forward corresponding suggestions to improve students' willingness to communicate across cultures.
Soy Phospholipids Exert a Renoprotective Effect by Inhibiting the Nuclear Factor Kappa B Pathway in Macrophages

Complications associated with chronic kidney disease (CKD), which involves kidney inflammation, are a major health problem. Soy protein isolate (SPI) reportedly inhibits CKD exacerbation; however, its detailed action mechanism remains obscure. Therefore, the role of the polar lipid component of SPI in suppressing inflammation was investigated. Zucker fatty rats were divided into three groups and fed a diet containing casein, SPI, or casein + SPI ethanol extract (SPIEE) for 16 weeks. The isoflavones and phospholipids of SPIEE were evaluated for their anti-inflammatory effects. Rats in the SPI and casein + SPIEE groups showed reduced levels of the urinary N-acetyl-β-d-glucosaminidase and renal IL-1β mRNA (an inflammatory marker) compared with those in the casein group. In proximal tubular cells, genistein significantly inhibited monocyte chemoattractant protein-1 (MCP-1) expression induced by an IL-1β stimulus. In macrophages, soybean phospholipids suppressed lipopolysaccharide-induced IL-1β gene expression by inhibiting the phosphorylation of inhibitor κB and p65. Phosphatidylinositol (PI) was found to be essential for inhibition of IL-1β expression. SPIEE inhibited the exacerbation of kidney disease. Genistein and soybean phospholipids, especially soybean-specific phospholipids containing PI, effectively inhibited the inflammatory spiral in vitro. Hence, daily soybean intake may be effective for inhibiting chronic inflammation and slowing kidney disease progression.
The effect of COVID‐19 on long‐distance transport services in France

Abstract This paper explores the effect of COVID‐19 on long‐distance transport services in France. These services have been deeply impacted by the different lockdowns imposed. However, until now, few papers have considered this topic. The research examines the long‐distance transportation market before COVID‐19 to understand how it adapted to the COVID‐19 countermeasures. The empirical research is based on a large pool of data pertaining to four transport routes in France from September 2019 to November 2021 for four modes (coach, carpooling, train, and air). Prices and frequencies are the main variables. The study's results must be considered carefully because the pandemic is ongoing. The first finding is the severe crisis in terms of supply during the first lockdown in March 2020. Throughout the rest of 2020 and 2021, services increased slowly, with rail and carpooling recovering to almost their 2019 levels, though this is not yet the case for coach and air services. The second finding is that the dominant mode, rail, enjoys the greatest market concentration, especially in comparison to air, which is still experiencing difficulties because of the reduction in the business market. The last finding highlights the persistence of conventional services during the different lockdowns and the high variability of low‐cost services, which disappeared during the crisis. However, mainly thanks to state financial support, no operator went bankrupt or left the market. Finally, low‐cost services were recovering faster in autumn 2021 than conventional services, thereby increasing their market share. The crisis in the business market may explain the difficulty conventional services face whereas low‐cost services are better adapted to the leisure market.
Detection of QTLs for Plant Height Architecture Traits in Rice (Oryza sativa L.) by Association Mapping and the RSTEP-LRT Method

Plant height (PH) and its component traits are critical determinants of lodging resistance and strongly influence yield in rice. The genetic architecture of PH and its component traits were mined in two mapping populations. In the natural population composed of 504 accessions, a total of forty simple sequence repeat (SSR) markers associated with PH and its component traits were detected across two environments via association mapping. Allele RM305-210 bp on chromosome 5 for PH had the largest phenotypic effect value (PEV) (−51.42 cm) with a reducing effect. Allele RM3533-220 bp on chromosome 9 for panicle length and allele RM264-120 bp on chromosome 8 for the length of upper first elongated internode (1IN) showed the highest positive PEV. Among the elongated internodes with negative effects being desirable, the allele RM348-130 bp showed the largest PEV (−7.48 cm) for the length of upper second elongated internode. In the chromosome segment substitution line population consisting of 53 lines, a total of nine QTLs were detected across two environments, with the phenotypic variance explained (PVE) ranging 10.07–28.42%. Among the detected QTLs, q1IN-7 explained the largest PVE (28.42%) for the 1IN, with an additive of 5.31 cm. The favorable allele RM257-125 bp on chromosome 9 for the 1IN increasing was detected in both populations. The favorable alleles provided here could be used to shape PH architecture against lodging.
Fluids in Porous Media: Transport and Phase Changes

This book introduces the reader into the field of the physics of processes occurring in porous media. It targets Master and PhD students who need to gain fundamental understanding the impact of confinement on transport and phase change processes. The book gives brief overviews of topics like thermodynamics, capillarity and fluid mechanics in order to launch the reader smoothly into the realm of porous media. In-depth discussions are given of phase change phenomena in porous media, single phase flow, unsaturated flow and multiphase flow. In order to make the topics concrete the book contains numerous example calculations. Further, as much experimental data as possible is plugged in to give the reader the ability to quantify phenomena.
LNIFT: Locally Normalized Image for Rotation Invariant Multimodal Feature Matching

Severe nonlinear radiation distortion (NRD) is the bottleneck problem of multimodal image matching. Although many efforts have been made in the past few years, such as the radiation-variation insensitive feature transform (RIFT) and the histogram of orientated phase congruency (HOPC), almost all these methods are based on frequency-domain information that suffers from high computational overhead and memory footprint. In this article, we propose a simple but very effective multimodal feature matching algorithm in the spatial domain, called locally normalized image feature transform (LNIFT). We first propose a local normalization filter to convert original images into normalized images for feature detection and description, which largely reduces the NRD between multimodal images. We demonstrate that normalized matching pairs have a much larger correlation coefficient than the original ones. We then detect oriented FAST and rotated brief (ORB) keypoints on the normalized images and use an adaptive nonmaximal suppression (ANMS) strategy to improve the distribution of keypoints. We also describe keypoints on the normalized images based on a histogram of oriented gradient (HOG), such as a descriptor. Our LNIFT achieves rotation invariance the same as ORB without any additional computational overhead. Thus, LNIFT can be performed in near real-time on images with 1024 $\times 1024$ pixels (only costs 0.32 s with 2500 keypoints). Four multimodal image datasets with a total of 4000 matching pairs are used for comprehensive evaluations, including synthetic aperture radar (SAR)–optical, infrared–optical, and depth–optical datasets. Experimental results show that LNIFT is far superior to RIFT in terms of efficiency (0.49 s versus 47.8 s on a $1024 \times 1024$ image), success rate (99.9% versus 79.85%), and number of correct matches (309 versus 119). The source code and datasets will be publicly available at https://ljy-rs.github. io/web.
Agent-Based Recommendation in E-Learning Environment Using Knowledge Discovery and Machine Learning Approaches

E-learning is a popular area in terms of learning from social media websites in various terms and contents for every group of people in this world with different knowledge backgrounds and jobs. E-learning sites help users such as students, business workers, instructors, and those searching for different educational institutions. Excluding the benefits of this system, there are various challenges that the users face in online platforms. One of the important challenges is the true information and right content based on these resources, search results and quality. This research proposes virtual and intelligent agent-based recommendation, which requires users’ profile information and preferences to recommend the proper content and search results based on their search history. We applied Natural Language Processing (NLP) techniques and semantic analysis approaches for the recommendation of course selection to e-learners and tutors. Moreover, machine learning performance analysis applied to improve the user rating results in the e-learning environment. The system automatically learns and analyzes the learner characteristics and processes the learning style through the clustering strategy. Compared with the recent state-of-the-art in this field, the proposed system and the simulation results show the minimizing number of metric errors compared to other works. The achievements of the presented approach are providing a comfortable platform to the user for course selection and recommendations. Similarly, we avoid recommending the same contents and courses. We analyze the user preferences and improving the recommendation system performance to provide highly related content based on the user profile situation. The prediction accuracy of the proposed system is 98% compared to hybrid filtering, self organization systems and ensemble modeling.
Enhancing indoor positioning based on filter partitioning cascade machine learning models

This paper proposes a method, called the filter partitioning machine learning classification (FPMLC). It can enhance the accuracy of indoor positioning based on fingerprinting using machine learning algorithms and prominent access points (APs). FPMLC selects limited information from groups of signal strengths and combines a clustering task and a classification task. There are three processes in FPMLC, i.e., feature selection to choose prominent APs, clustering to determine approximated positions, and classification to determine fine positions. This work demonstrates the procedure for FPMLC creation. The results of FPMLC are compared with those of a primitive method using measured data. FPMLC is compared with well-known machine learning classifiers, i.e., Decision Tree, Naive Bayes, and Artificial Neural Networks. The performance comparison is done in terms of accuracy and error distance between classified positions and actual positions. The appropriate number of selected prominent APs and the number of clusters are assigned in the clustering process. The result of this study shows that FPMLC can increase performance for indoor positioning of all classifiers. Additionally, FPMLC is the most optimized model using a Decision Tree as its classifier.
COMMISSIONING AND FIRST RESULTS FROM CHANNELING RADIATION AT FAST

X-rays have widespread applications in science and industry, but developing a simple, compact, and high-quality X-ray source remains a challenge. Our collaboration has explored the possible use of channeling radiation driven by a 50 MeV low-emittance electron beam to produce narrowband hard X-rays with photon energy of 40 to 140 keV [8,9,11]. Here we present the simulated X-ray spectra including the background bremsstrahlung contribution, and a description of the required optimization of the relevant electron-beam parameters necessary to maximize brilliance of the resulting X-ray beam. Results are presented from our test of this, carried out at the Fermilab Accelerator Science & Technology (FAST) facility’s 50-MeV low-energy electron injector. As a result of the beam parameters, made possible by the photo-injector based SRF linac, the average brilliance at FAST was expected to be about one order of magnitude higher than that in previous experiments.
Identifikasi Bahan Kimia Obat (BKO) Glibenklamid Pada Jamu Antidiabetes Dengan Menggunakan Metode Kromatografi Lapis Tipis (KLT) Dan Spektrofotodensitometri

Untuk mengetahui adanya kandungan glibenklamid dalam jamu diabetes, dilakukan pengujian kualitatif yaitu menggunakan lempeng KLT. Metode KLT digunakan karena KLT merupakan metode yang sederhana dan cepat. KLT digunakan secara luas untuk analisis obat Penelitian ini untuk mengidentifikasi bahan kimia obat glibenklamid pada jamu antidiabetes dengan menggunakan metode kromatografi lapis tipis. Sampel yang digunakan adalah 3 macam jamu antidiabetes yaitu Delites,Jakeni,Wei Yi Wang Berdasarkan hasil penelitian dapat disimpulkan bahwa jamu Wei Yi Wang (0003.K) positif mengandung bahan kimia obat (BKO) glibenklamid. Jamu Delites (0001.K) dan jamu Jakeni (0002.K) negatif mengandung bahan kimia obat (BKO). Hal ini dapat dilihat dari nilai Rf masing-masing sampel dan nilai Rf dari baku pembanding glibenklamid dan Spike serta dipertegas dengan hasil uji spektrofotodensitometri yang menunjukan peak (puncak gelombang) yang sama.
Review of gravitational wave solutions in quadratic metric-affine gauge gravity

In this paper, we consider the quadratic Metric-Affine Gauge gravity Lagrangian, which contains all the algebraic invariants up to quadratic order in torsion, nonmetricity and curvature. The goal is to collect the known exact solutions for this theory that describe the propagation of a gravitational wave and some useful properties. We concentrate on solutions whose connections are different from the Levi-Civita one and provide also the extension of already known axial torsion solutions to more general Lagrangians. In the end, we will briefly discuss colliding waves and Riemannian solutions.
Artificial intelligence reshapes current understanding and management of osteoarthritis: A narrative review

Current practice of osteoarthritis has its insufficiencies which researchers are tackling with artificial intelligence (AI). This article discusses three kinds of AI models, namely diagnostic models, prediction models and morphological models. Diagnostic models enhance efficiency in diagnosis by providing an automated algorithm in knee images processing. Prediction models utilize behavioral and radiological data to assess the risk of osteoarthritis before symptom onset and needs to perform surgery. Morphological models detect biomechanical changes to facilitate understanding of pathophysiology and provide personalized intervention. Through reviewing present evidence, we demonstrate that AI could assist doctors in diagnosis, predict osteoarthritis and guide future research.
Study of transformation of complex business associations to ecosystems

The new reality, determined, on the one hand, by the complexity and non-linearity of socio-economic relations and, on the other hand, by the development of digitalization and the expanding possibilities of integration of various processes, requires new approaches to the study of entrepreneurial forms and associations. Network and cluster scientific methodologies were quite revolutionary at the time but remain quite productive. The authors of the article investigate the evolution of views and discuss the feasibility of applying an ecosystem approach to the study of large business structures such as "group of companies". The logic is as follows: the larger the business association, the more it is embedded in the environment, formally (legally) located behind the contour of this structure. Therewith, it is important to take into account the current trends in the development of the ecosystem approach, the specifics and structure of the group of companies. The article attempts, based on a combination of inductive and deductive methods, to determine the prospects for possible research directions of Russian groups of companies based on an ecosystem approach.