Miracle-dz/UniFAR-FTUs huggingface dataset:
A Review of Industrial Aeroelasticity Practices at Dassault Aviation for Military Aircraft and Business Jets

Aircraft structure design is a complex industrial process that requires multidisciplinary analyzes and considerations in fields as diverse as aerodynamics, structure, materials and systems, as well as the right compromise between the constraints imposed by these different fields, in order to meet the overall performances required for aircraft. In the field of business jets and military aircraft, given the research into ever more efficient aerodynamic formulas, the constant desire to design "as light as possible", and the increase in fuselage sizes, aircraft flexibility has increased considerably over the last few decades. This has required the consideration of increasingly complex aeroelastic coupling phenomena that are present in the flight envelope from the very first phases in aircraft development. The challenge goes far beyond the domain of aerostructural performance alone, since aeroelasticity can also have a significant impact on related domains, such as aircraft performances, handling qualities, or system design. It has merely reinforced the potentially major impacts of aeroelasticity on the risks, costs and deadlines for new aircraft programs: aeroelasticity is now seen as one of the main disciplines in design, and as one of the "critical" processes in the aircraft development logic. This highly-challenging context has been the source of major and constant modifications in the field of aeroelasticity since the 1990s at Dassault Aviation. Today, this trend continues, and aeroelasticity will have to tackle a series of entirely new challenges and needs, and continue to reinvent itself at the same pace if it is to avoid hampering innovation and future technological breakthroughs. In this perspective, this article gives an overview of the current best industrial ractices in terms of aeroelasticity in the military aircraft and business jet domains at Dassault Aviation. The main aspects of this challenging and exciting field are covered: the numerical methods and tools, the experimental validation process, the aircraft program expectations and aspects relating to human organization. It discusses the principles and guidelines rather than details about the basic equations and methods. The last part presents the future industrial challenges in the field of aeroelasticity for Dassault Aviation.
Road Crack Detection Using Quaternion Neural Networks

Automatic road crack detection can play an significant role in improving driving safety. However, a number of factors make this task challenging. Background complexity, as well as the fact that cracks are visually inhomogenous to one another also contribute to this difficulty. Furthermore, cracks in road surfaces can be easily confused with foreign objects, shadows and background textures leading to detection ambiguities. Timely detection of cracks is important for both drivers and road maintenance crews. Quaternion neural networks (QNNs) are a relatively new class of neural networks that employ quaternionvalued activations and parameters. They benefit from reduced costs in terms of hardware as they require fewer parameters. In this work, we explore the usability of QNNs for automatic road crack detection. To this end, we propose quaternionic versions of deep networks and evaluate their performance in datasets with images of road cracks. We show that the proposed models are light-weight in terms of parameter requirements while they are on par in terms of performance with real-valued networks for crack detection, with potential applications in resource constrained scenarios or in cases where few training data is available.
A Geography-Based P2P Overlay Network for Fast and Robust Blockchain Systems

Numerous blockchain systems with various consensus protocols have emerged to achieve high transaction rates (2<inline-formula><tex-math notation="LaTeX">$\sim$</tex-math><alternatives><mml:math><mml:mo>∼</mml:mo></mml:math><inline-graphic xlink:href="zhao-ieq1-3189667.gif"/></alternatives></inline-formula>10K tps). However, their underlying P2P network primitives constrain further improvements due to two problems (i) high message redundancy and (ii) long broadcast convergence time. The first problem is caused by the excessive robustness of the dominant broadcast approach Gossip. All state-of-the-art blockchain systems only tolerate 20-50% node failure while Gossip can withstand up to 90%. The reason for (ii) is that existing broadcast topologies ignore geographical distances among nodes and incur paths with unnecessarily high latency. We present <sc>FRing</sc>, a geography-based P2P overlay network for fast and robust broadcast in blockchain systems. <sc>FRing</sc> has three main features: sufficient robustness, low message redundancy, and fast convergence. To reduce convergence time, <sc>FRing</sc> forms the network topology by considering geographical proximity. A novel broadcast algorithm based on <sc>FRing</sc> topology is proposed to lower message redundancy while maintaining sufficient robustness. One major challenge is to eliminate the risk of topology inference by traffic pattern analysis. <sc>FRing</sc> leverages Intel SGX to guarantee nodes’ behavior integrity and incorporates pattern obfuscation to prevent traffic pattern analysis. The evaluation shows that <sc>FRing</sc> improved the throughput of EOS by 220% and Hyperledger Fabric by 210%.
Heatmaps and consensus clustering for ego network exploration

Background: Researchers need visualization methods (using statistical and interactive techniques) to efficiently perform quality assessments and glean insights from their data. Data on networks can particularly benefit from more advanced techniques since typical visualization methods, such as node-link diagrams, can be difficult to interpret. We use heatmaps and consensus clustering on network data and show they can be combined to easily and efficiently explore nonparametric relationships among the variables and networks that comprise an ego network data set. Methods: We used ego network data from the Québec Adipose and Lifestyle Investigation in Youth (QUALITY) cohort used to evaluate this method. The data consists of 35 networks centered on individuals (egos), each containing a maximum of 10 nodes (alters). These networks are described through 41 variables: 11 describing the ego (e.g. fat mass percentage), 18 describing the alters (e.g. frequency of physical activity) and 12 describing the network structure (e.g. degree). Results: Four stable clusters were detected. Cluster one consisted of variables relating to the interconnectivity of the ego networks and the locations of interaction, cluster two consisted of the ego’s age, cluster three contained lifestyle variables and obesity outcomes and cluster four was comprised of variables measuring alter importance and diet. Conclusions: This exploratory method using heatmaps and consensus clustering on network data identified several important associations among variables describing the alters’ lifestyle habits and the egos’ obesity outcomes. Their relevance has been identified by studies on the effect of social networks on childhood obesity.
Botanical Oils Isolated from Simmondsia chinensis and Rosmarinus officinalis Cultivated in Northern Egypt: Chemical Composition and Insecticidal Activity against Sitophilus oryzae (L.) and Tribolium castaneum (Herbst)

The rice weevil, Sitophilus oryzae (L.), and the red flour beetle, Tribolium castaneum (Herbst), are key stored-product pests in Egypt and worldwide. The extensive use of synthetic insecticides has led to adverse effects on the environment, human health, and pest resistance. As a result, environmentally friendly pest management alternatives are desperately required. The botanical oils of jojoba, Simmondsia chinensis (L.), and rosemary, Rosmarinus officinalis L. plants growing in Egypt were extracted, identified by gas chromatography/mass spectrometry (GC–MS), and evaluated for their insecticidal activity against S. oryzae and T. castaneum. The main constituents identified in BOs were carvyl acetate (20.73%) and retinol (16.75%) for S. chinensis and camphor (15.57%), coumarin (15.19%), verbenone (14.82%), and 1,8-cineole (6.76%) for R. officinalis. The S. chinensis and R. officinalis BOs caused significant contact toxicities against S. oryzae and T. castaneum adults, providing LC50 values of 24.37, 68.47, and 11.58, 141.8 ppm at 3 days after treatment (DAT), respectively. S. chinensis oil exhibited significant fumigation toxicity against both insects; however, it was more effective against S. oryzae (LC50 = 29.52 ppm/L air) than against T. castaneum (LC50 = 113.47 ppm/L air) at 3 DAT. Although the essential oil (EO) of R. officinalis significantly showed fumigation toxicity for S. oryzae (LC50 = 256.1 and 0.028 ppm/L air at 1 and 3 DAT, respectively), it was not effective against T. castaneum. These BOs could be beneficial for establishing IPM programs for suppressing S. oryzae and T. castaneum.
Downlink OTFS Non-Orthogonal Multiple Access Receiver Design based on Cross-Domain Detection

Orthogonal time frequency space (OTFS) modulation has recently been recognized as a promising modulation scheme for high-mobility wireless communications systems. This paper considers a new code-domain non-orthogonal multiple access (CD-NOMA) scheme for downlink OTFS systems, and the sparse code multiple access (SCMA), a kind of CD-NOMA, is utilized to support ultra-massive communication devices in high-mobility environments. Inspired by cross-domain detection, we propose a cross-domain-based receiver to jointly carry out the OTFS symbol detection and SCMA decoding, in the presence of fractional Doppler shifts. The proposed OTFS-SCMA detector performs equalization (linear estimation) in the time domain and performs denoising and SCMA decoding in the DD domain. Extrinsic information is passed between two domains via the corresponding unitary transformation to ensure the orthogonality of errors from each domain and is updated iteratively. Our numerical results demonstrate the effectiveness of the proposed algorithm, which enables an increased spectral efficiency compared to the conventional OTFS transmission and a good error performance.
Hollow-Core Fiber-Based Biosensor: A Platform for Lab-in-Fiber Optical Biosensors for DNA Detection

In this paper, a novel platform for lab-in-fiber-based biosensors is studied. Hollow-core tube lattice fibers (HC-TLFs) are proposed as a label-free biosensor for the detection of DNA molecules. The particular light-guiding mechanism makes them a highly sensitive tool. Their transmission spectrum is featured by alternations of high and low transmittance at wavelength regions whose values depend on the thickness of the microstructured web composing the cladding around the hollow core. In order to achieve DNA detection by using these fibers, an internal chemical functionalization process of the fiber has been performed in five steps in order to link specific peptide nucleic acid (PNA) probes, then the functionalized fiber was used for a three-step assay. When a solution containing a particular DNA sequence is made to flow through the HC of the TLF in an ‘optofluidic’ format, a bio-layer is formed on the cladding surfaces causing a red-shift of the fiber transmission spectrum. By comparing the fiber transmission spectra before and after the flowing it is possible to identify the eventual formation of the layer and, therefore, the presence or not of a particular DNA sequence in the solution.
Cubic Planar Graph and Its Application to Road Network

In this research article, we present the notion of a cubic planar graph and investigate its related properties. The cubic graphs are more effective than both interval-valued and fuzzy graphs as it represents the level of participation (membership degree) of vertices and edges both in interval form and as a fuzzy number. Moreover, it handles the uncertainty and vagueness more efficiently than both interval-valued fuzzy graph and fuzzy graph. The interval indicates a continuous process, whereas the point indicates a specific process. We introduce the terms cubic multigraph, cubic strong and weak edges, and degree of planarity for cubic planar graphs. Some fundamental theorems based on these concepts are also elaborated. We also propose the idea of a cubic strong and weak fuzzy faces and cubic dual graph. Some results related to these concepts are also established. Comparison with the existing method shows the worth of our proposed work.
Vitamin B12 Regulates the Transcriptional, Metabolic, and Epigenetic Programing in Human Ileal Epithelial Cells

Vitamin B12 (VB12) is a micronutrient that is essential for DNA synthesis and cellular energy production. We recently demonstrated that VB12 oral supplementation coordinates ileal epithelial cells (iECs) and gut microbiota functions to resist pathogen colonization in mice, but it remains unclear whether VB12 directly modulates the cellular homeostasis of iECs derived from humans. Here, we integrated transcriptomic, metabolomic, and epigenomic analyses to identify VB12-dependent molecular and metabolic pathways in human iEC microtissue cultures. RNA sequencing (RNA-seq) revealed that VB12 notably activated genes involved in fatty acid metabolism and epithelial cell proliferation while suppressing inflammatory responses in human iECs. Untargeted metabolite profiling demonstrated that VB12 facilitated the biosynthesis of amino acids and methyl groups, particularly S-adenosylmethionine (SAM), and supported the function of the mitochondrial carnitine shuttle and TCA cycle. Further, genome-wide DNA methylation analysis illuminated a critical role of VB12 in sustaining cellular methylation programs, leading to differential CpG methylation of genes associated with intestinal barrier function and cell proliferation. Together, these findings suggest an essential involvement of VB12 in directing the fatty acid and mitochondrial metabolisms and reconfiguring the epigenome of human iECs to potentially support cellular oxygen utilization and cell proliferation.
Procollagen C-Endopeptidase Enhancer Protein 2 (PCPE2) Reduces Atherosclerosis in Mice by Enhancing SR-BI Mediated HDL-Cholesteryl Ester Uptake

Results: PCPE2 protects against diet-induced reverse cholesterol transport and mediated ester. Significance: These findings establish a role for the mediated and ABSTRACT Studies in human populations have shown a significant correlation between procollagen c-endopeptidase enhancer protein 2 (PCPE2) single nucleotide polymorphisms and plasma HDL cholesterol concentrations. PCPE2 is a 52 kDa glycoprotein located in the extracellular matrix and enhances the cleavage of C-terminal procollagen by bone morphogenetic protein 1 (BMP1). Our studies focused on investigating the basis for the elevated concentration of enlarged plasma HDL in PCPE2 deficient mice and determining if they protected against diet-induced atherosclerosis. PCPE2 deficient mice were crossed with LDL receptor deficient mice to obtain LDLr -/- , PCPE2 -/- mice, which had elevated HDL levels compared to LDLr -/- mice with similar LDL concentrations. We found that LDLr -/- , PCPE2 -/- mice had significantly more neutral lipid and CD68+ infiltration in the aortic root than LDLr -/- mice. Surprisingly, in light of their elevated HDL levels, the extent of aortic lipid deposition in LDLr -/- , PCPE2 -/-mice was similar to that reported for LDLr -/- , apoA-I -/- mice, which lack any apoA-I/HDL. Furthermore, LDLr -/- , PCPE2 -/-mice had reduced HDL apoA-I fractional clearance and macrophage to fecal reverse cholesterol transport rates compared to LDLr -/- mice, despite in liver SR-BI expression. PCPE2 was shown to enhance SR-BI function by increasing the rate of
Underestimation of Radiation Doses by Compliance of Wearing Dosimeters among Fluoroscopically-Guided Interventional Medical Workers in Korea

This study aimed to estimate the level of underestimation of National Dose Registry (NDR) doses based on the workers’ dosimeter wearing compliance. In 2021, a nationwide survey of Korean medical radiation workers was conducted. A total of 989 medical workers who performed fluoroscopically-guided interventional procedures participated, and their NDR was compared with the adjusted doses by multiplying the correction factors based on the individual level of dosimeter compliance from the questionnaire. Ordinal logistic regression analysis was performed to identify the factors for low dosimeter wearing. Based on the data from the NDR, the average annual effective radiation dose was 0.95 mSv, while the compliance-adjusted dose was 1.79 mSv, yielding an 89% increase. The risks for low compliance with wearing a badge were significantly higher among doctors, professionals other than radiologists or cardiologists, workers not frequently involved in performing fluoroscopically-guided interventional procedures, and workers who did not frequently wear protective devices. This study provided quantitative information demonstrating that the NDR data may have underestimated the actual occupational radiation exposure. The underestimation of NDR doses may lead to biased risk estimates in epidemiological studies for radiation workers, and considerable attention on dosimetry wearing compliance is required to interpret and utilize NDR data.
INVESTIGATING IRISH ENGLISH WITH ICEIRELAND

The investigation of specific features of Irish English has a long tradition. Yet, with the arrival of large corpora and corpus tools, new avenues of research have opened up for the discipline. The present paper investigates features commonly ascribed to Irish English on the basis of the ICE Ireland corpus in comparison with ICE corpora representing other varieties of English. We use several corpus tools to access the ICE corpora. First, an offline concordance program, AntConc V 3.3 (Anthony 2004). Second, Corpus Navigator, an online corpus query tool allowing researchers to query regular expressions on the surface texts. Third, we are in the process of writing a version of Dependency Bank (Lehmann and Schneider 2012) which contains a selection of ICE corpora, and which will be called ICE online. This research methodology allows us to reassess how specific features found in Irish English are in comparison with other international varieties of English and illustrates that even simple corpus-based search patterns can produce powerful results.
Data-Free Solution of Electromagnetic PDEs Using Neural Networks and Extension to Transfer Learning

In this article, we present a data-free method to solve differential equation using artificial neural networks (ANN). This method exploits the universal function approximation nature of a neural network to mimic a specified partial differential equation (PDE) and provide its solution. Specifically, use of simple feed-forward artificial neural network (FF-ANN) is shown to demonstrate the solution of 1-D second-order differential equations without use of any prior data. The article also demonstrates that the similarity in two PDEs is akin to similarity in the optimized weight matrices attained after the solution using FF-ANN. This property is then utilized for a transferred learning to enable faster convergence of a new PDE, based on the prior solution of a similar PDE. The concept is shown while considering a general form of second-order PDE, and considering specific cases of scalar in-homogeneous wave equation form and Poisson Equation form. Error convergence below 10−6 is shown and the transferred learning process shows typical time acceleration by a factor of 1.5–3 for the considered equations.
Antimicrobial contaminant removal by Multistage Slow Sand Filtration

In rural areas, where passive filtration of surface water is a viable option, aquatic systems can become contaminated by antimicrobials from wastewater treatment plants or diffuse pollution. This study examined slow sand filter biomass (schmutzdecke) sorption behavior and removal efficiencies in a pilot roughing and slow sand filter that was fed 0.2 mg/L of five compounds from four classes of antimicrobials. High‐performance liquid chromatography/tandem mass spectrometry (HPLC MS/MS) was used to analyze aqueous antimicrobial concentrations. Schmutzdecke sorption coefficients were comparable to those previously found for soils but did not correlate well with estimates derived from octanol–water partition coefficients. Roughing filtration exhibited low removal efficiencies, but antimicrobials with high sorption coefficients can accumulate in roughing filter waste and schmutzdecke. At the end of a 14‐day slow sand filtration period, tylosin was not detected in filter effluent and trimethoprim was >99% removed. Slow sand filtration, however, exhibited <25% removal of lincomycin and <4% removal of the sulfonamide class of antimicrobials. Multistage filtration is regarded as an ineffective treatment method for antimicrobials with low filter‐media sorption coefficients.
Lactobacillus salivarius UCC118™ Dampens Inflammation and Promotes Microbiota Recovery to Provide Therapeutic Benefit in a DSS-Induced Colitis Model

The use of probiotics such as Lactobacillus and Bifidobacterium spp. as a therapeutic against inflammatory bowel disease (IBD) is of significant interest. Lactobacillus salivarus strain UCC118TM is a commensal that has been shown to possess probiotic properties in vitro and anti-infective properties in vivo. However, the usefulness of UCC118 TM as a therapeutic against colitis remains unclear. This study investigates the probiotic potential of Lactobacillus salivarius, UCC118™ in a mouse model of colitis. DSS-induced colitis was coupled with pre-treatment or post-treatment with UCC118TM by daily oral gavage. In the pre-treatment model of colitis, UCC118TM reduced the severity of the disease in the early stages. Improvement in disease severity was coupled with an upregulation of tissue IL-10 levels and increased expression of macrophage M2 markers. This anti-inflammatory activity of UCC118TM was further confirmed in vitro, using a model of LPS-treated bone marrow-derived macrophages. Taken together, these results suggest that UCC118TM may promote the resolution of inflammation. This was supported in a mouse model of established DSS-induced colitis whereby UCC118TM treatment accelerated recovery, as evidenced by weight, stool, histological markers and the recovery of microbiome-associated dysbiosis with an increased abundance of beneficial commensal species. These results demonstrate the potential of Lactobacillus salivarius UCC118TM as a probiotic-based therapeutic strategy to promote health through the upregulation of anti-inflammatory IL-10 and protect against dysbiosis during IBD.