Miracle-dz/UniFAR-FTUs huggingface dataset:
Manually-operated compressional optical coherence elastography with effective aperiodic averaging: demonstrations for corneal and cartilaginous tissues

Compressional elastography in optical coherence tomography (OCT) is based on relating the sought spatial distribution of stiffness with initially reconstructed strain distribution by comparing OCT scans of the tissue in reference and deformed states. To ensure acceptable signal-to-noise ratio, tissue straining with auxiliary periodic sources (e.g. piezo-actuators) allowing for periodic averaging is often used. However, for many practical biomedical applications and translation of optical coherence elastography (OCE) to clinics, realization of strain mapping in manually-operated regime is of key importance. Here, we demonstrate possibilities to realize manually-operated OCE, in which effective averaging does not require periodic actuation. Advantages of the proposed approach are discussed including possibilities of obtaining quantitative nonlinear stress–strain curves and estimating high-contrast stiffness differences. Experimental demonstrations are given using cornea and cartilage samples representing fairly ‘soft’ and ‘stiff’ tissues, respectively.
Real-time Monitoring and Early Detection of Diabetes with Bioactive and Biological Impedance Sensors using Hybrid Machine Learning Algorithm

Diabetes is an ongoing infection that influences a large number of individuals overall and can prompt serious unexpected issues whenever left untreated. Early identification of diabetes can altogether diminish the risk of intricacies and work on significant results. Lately, the utilization of wearable technology has arisen as a promising device for illness identification and checking. Smartwatches furnished with bioactive sensors can give ceaseless, painless observing of body vitals, making them ideal for diabetes screening. This study proposes a framework that uses patient information for preparing a hybrid AI model to distinguish the presence of diabetes. The framework consolidates body vitals estimated utilizing a smartwatch with a bioactive sensor to get exact and nonstop information on the wearer's wellbeing status. The mixture model coordinates both profound learning and conventional AI calculations to accomplish predominant precision in identifying diabetes. The framework gathers information on different body vitals, for example, pulse, circulatory strain, and skin conductance, which are known to be firmly connected with diabetes. The gathered information is pre-handled and afterward used to prepare the hybrid model. The profound learning calculation is utilized to remove significant level highlights from the crude information, while the conventional AI calculation is utilized to arrange the information into diabetic or non-diabetic classifications. The cross breed model is intended to work on the accuracy of diabetes location by integrating the qualities of both profound learning and conventional AI.
NAD+ Precursors and Intestinal Inflammation: Therapeutic Insights Involving Gut Microbiota

The oxidized form of nicotinamide adenine dinucleotide (NAD+) is a critical metabolite for living cells. NAD+ may act either as a cofactor for many cellular reactions as well as a coenzyme for different NAD+-consuming enzymes involved in the physiological homeostasis of different organs and systems. In mammals, NAD+ is synthesized from either tryptophan or other vitamin B3 intermediates that act as NAD+ precursors. Recent research suggests that NAD+ precursors play a crucial role in maintaining the integrity of the gut barrier. Indeed, its deficiency has been associated with enhanced gut inflammation and leakage, and dysbiosis. Conversely, NAD+-increasing therapies may confer protection against intestinal inflammation in experimental conditions and human patients, with accumulating evidence indicating that such favorable effects could be, at least in part, mediated by concomitant changes in the composition of intestinal microbiota. However, the mechanisms by which NAD+-based treatments affect the microbiota are still poorly understood. In this context, we have focused specifically on the impact of NAD+ deficiency on intestinal inflammation and dysbiosis in animal and human models. We have further explored the relationship between NAD+ and improved host intestinal metabolism and immunity and the composition of microbiota in vivo. Overall, this comprehensive review aims to provide a new perspective on the effect of NAD+-increasing strategies on host intestinal physiology.
Modified U Slot Patch Antenna with Large Frequency Ratio for Vehicle-to-Vehicle Communication

This paper presents a single-fed, single-layer, dual-band antenna with a large frequency ratio of 4.74:1 for vehicle-to-vehicle communication. The antenna consists of a 28 GHz inset-fed rectangular patch embedded into a 5.9 GHz patch antenna for dual-band operation. The designed dual-band antenna operates from 5.81 to 5.99 GHz (Dedicated Short Range Communications, DSRC) and 27.9 to 30.1 GHz (5G millimeter-wave (mm-wave) band). Furthermore, the upper band patch was modified by inserting slots near the inset feed line to achieve an instantaneous bandwidth of 4.5 GHz. The antenna was fabricated and measured. The manufactured prototype operates simultaneously from 5.8 to 6.05 GHz and from 26.8 to 31.3 GHz. Notably, the designed dual-band antenna offers a high peak gain of 7.7 dBi in the DSRC band and 6.38 dBi in the 5G mm-wave band.
Research Progress on the Mechanism of Nanoparticles Crossing the Intestinal Epithelial Cell Membrane

Improving the stability of drugs in the gastrointestinal tract and their penetration ability in the mucosal layer by implementing a nanoparticle delivery strategy is currently a research focus in the pharmaceutical field. However, for most drugs, nanoparticles failed in enhancing their oral absorption on a large scale (4 folds or above), which hinders their clinical application. Recently, several researchers have proved that the intestinal epithelial cell membrane crossing behaviors of nanoparticles deeply influenced their oral absorption, and relevant reviews were rare. In this paper, we systematically review the behaviors of nanoparticles in the intestinal epithelial cell membrane and mainly focus on their intracellular mechanism. The three key complex intracellular processes of nanoparticles are described: uptake by intestinal epithelial cells on the apical side, intracellular transport and basal side exocytosis. We believe that this review will help scientists understand the in vivo performance of nanoparticles in the intestinal epithelial cell membrane and assist in the design of novel strategies for further improving the bioavailability of nanoparticles.
Positivity, robust stability and comparison of dynamic systems

We study generalized classes of positive and monotone dynamic systems in a partially ordered Banach space. Using results from the nonlinear operators theory, we establish new algebraic conditions for stability of equilibrium states of a class of monotone-type di fferential and diff erence systems. Conditions for the positivity and absolute stability of diff erential systems with delay are proposed. Using new technique for constructing the invariant sets of diff erential systems, we generalize known positivity conditions for linear and nonlinear diff erential systems with respect to typical classes of cones. In addition, we generalize the comparison principle for a finite set of diff erential systems and formulate robust stability conditions for some families of diff erential systems in terms of cone inequalities.
Multi-task Bolasso based aircraft dynamics identification

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Multi-task Bolasso based aircraft dynamics identification Cédric Rommel, Joseph Frédéric Bonnans, Baptiste Gregorutti, Pierre Martinon
Espaces vectoriels bornologiques

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Approximations of option price elasticities for importance sampling

Importance sampling is a powerful instrument to reduce the standard error of Monte-Carlo estimators. Different importance sampling approaches have in common that the optimal importance sampling probability density is unknown. To approximate this unknown density, in this article we will analyze approximations of option price elasticities. The considered importance sampling approach involves adding an additional drift term. For models with stochastic volatility and for pathdependent options, we show that several approaches exist to achieve considerable variance reduction.
Fine-Grained Sports, Yoga, and Dance Postures Recognition: A Benchmark Analysis

Human body-pose estimation is a complex problem in computer vision. Recent research interests have been widened specifically on the sports, yoga, and dance (SYD) postures for maintaining health conditions. The SYD pose categories are regarded as a fine-grained image classification (FGIC) task due to the complex movement of body parts. Deep convolutional neural networks (CNNs) have attained significantly improved performance in solving various human body-pose estimation problems. Though decent progress has been achieved in yoga postures recognition using deep-learning techniques, fine-grained sports and dance recognition necessitates ample research attention. However, no benchmark public image dataset with sufficient interclass and intraclass variations is available yet to address sports and dance postures classification. To solve this limitation, we have proposed two image datasets, one for 102 sport categories and another for 12 dance styles. Two public datasets, Yoga-82 that contains 82 classes and Yoga-107 that represents 107 classes, are collected for yoga postures. These four SYD datasets are experimented with the proposed deep model, SYD-Net, which integrates a patch-based attention (PbA) mechanism on top of standard backbone CNNs. The PbA module leverages the self-attention mechanism that learns contextual information from a set of uniform and multiscale patches and emphasizes discriminative features to understand the semantic correlation among patches. Moreover, random erasing data augmentation is applied to improve performance. The proposed SYD-Net has achieved state-of-the-art accuracy on Yoga-82 using five base CNNs. SYD-Net’s accuracy on other datasets is remarkable, implying its efficiency. Our Sports-102 and Dance-12 datasets are publicly available at https://sites.google. com/view/syd-net/home.
Role of the Match Between Individual Levels of Anxious-Withdrawal and Affiliation Group Characteristics in Predicting Distress

To inform teaching practices, we examined elementary learners who were anxious-withdrawn within their naturally occurring affiliation groups at school. Hypotheses addressed the degree to which affiliation group characteristics moderate the relation between individual levels of anxious-withdrawal and internalizing distress. Data from 447 fourth and fifth graders who belonged in peer-reported affiliation groups at school were used. Peer reports assessed the degree to which participants’ affiliation groups were characterized as anxious-withdrawn, fun, and agreeable along with individual levels of anxious-withdrawal. Students also self-reported social dissatisfaction, and teachers reported students’ internalizing problems. Results indicate the impact of poor person-group fit on distress. For children with higher levels of anxious-withdrawal, affiliating with peers who have perceived socially desirable characteristics is associated with more internalizing distress and social dissatisfaction. In contrast, affiliating with anxious-withdrawn peers predicts increased distress only for those with low to average levels of anxious-withdrawal. Implications for peer-partnering in classrooms are discussed.
Enhancing the photocatalytic performance of g-C3N4 (GCN) via La–ZnO nanocomposite (Z-scheme mechanism) against toxic pharmaceutical pollutant

Highly effective Z-scheme La–ZnO/GCN nanocomposite (LZG) were synthesized via hydrothermal and ultrasonication methods. The prepared samples were further analyzed through varies techniques like X-ray diffraction (XRD), high-resolution scanning electron microscopy (HRSEM), X-ray photoelectron spectroscopy (XPS) and UV–visible spectroscopy. XRD confirms the non-detection of secondary phase formation and decrementing pattern of crystallite size confirm La ions presence in host lattice. Presence of La–ZnO nanorods on nanosheets of GCN are well observed from the HRSEM analysis. Enhancement in pollutant degradation was accredited due to higher charge transfer property observed from EIS (Electrochemical impedance spectroscopy). First-order Langmuir–Hinshelwood relation reveals about the higher rate of reaction (0.01796 × 10–2 min−1), around 84% of TC pollutant degradation by 10-10LZG nanocomposite within the time span of 80 min. The current research supports a novel design of nanocomposite with an electron trapper for hindering charge recombination process and enhancing the degradation of pharmaceutical pollutants.
Rayleigh-Taylor instability in oscillating liquid pistons

In this study, an experimental apparatus is used to excite four U-tube-shaped liquid pistons connected in series, and to study their behaviour. Some of the gas spaces are heated to induce piston oscillations; in others, gas expansion is utilised to produce a refrigeration effect. It was discovered that the liquid piston surface would become unstable and turbulent at relatively low gas charge pressures (2 bar–3 bar). Cylindrical polyethylene floats were employed at each piston surface in order to reduce the area of the free surface of each piston and allow experiments to be conducted over a wide range of operating conditions. Experiments were carried out using gas charge pressures in the range of 1 bar–6 bar. The resulting liquid piston oscillations were measured and analysed to assess the impact of any developing piston instability. Evidence of a liquid piston acceleration limit, likely resulting from the Rayleigh-Taylor instability phenomenon, is consistently observed during the experiments. The use of submerged polyethylene piston floats is found to increase the surface stability and enable maximum accelerations of 25 ms−2 to 30 ms−2.
On the Switch Markov Chain for Strongly Stable Degree Sequences

We show that the switch Markov chain for sampling undirected and bipartite graphs with a given degree sequence is rapidly mixing when the degree sequence is strongly stable. Strong stability is more restricted than the notion of stability introduced by Jerrum and Sinclair (TCS, 1990). Interestingly enough, strong stability is satisfied by all degree sequences for which the switch chain is known to be rapidly mixing based on Sinclair's multicommodity flow method. Our approach relies on a comparison argument with a Markov chain defined by Jerrum and Sinclair for sampling graphs that almost have a given degree sequence. This comparison provides a much shorter proof for the known rapid mixing results of the switch chain and extends them up to sharp characterizations of stability in terms of certain graph parameters. In particular, our work resolves an open problem posed by Greenhill and Sfragara (TCS, 2017) regarding the switch chain for undirected graphs.
A Comparative Analysis of Chest X-Rays and CT Scans Towards COVID-19 Detection

The diagnosis of COVID-19 is greatly aided by radiological chest examinations. In this study, we conduct a comprehensive comparison of various classification models to classify COVID-19 from Chest X-ray images and devise an automated, precise model to segment the COVID-19 infected regions present in Chest CT Scans and make the results interpretable with the use of GradCAM. A total of 4000 images from the COVID-19 Radiography dataset were used for training and evaluating the models. In order to predict COVID-19 in Chest X-rays, the transfuse segmentation model [1] is used to segment the lungs. Radiomic features are then extracted and the most essential features were selected using Principle Component Analysis which were then used to train six classification models, namely support vector machines (SVM), Logistic Regression (LR), Decision Trees, AdaBoost (AB), K-Nearest Neighbors (KNN) and Random Forest. Support vector machines was the best-performing classifier with an accuracy of 0.9115 and an AUC of 0.9117. In the case of CT scans, lungs were segmented using image processing techniques, and a total of 2482 CT Scans from the SARS-COV-2 CT-Scan Dataset were used to train a ResNet18 CNN architecture, whose results were made interpretable by employing gradCAM. Furthermore, infections were segmented from the CT scans using two image processing based approaches, the first making use of the Discrete Wavelet Transform and the second approach performing morphological operations. The ResNet18 CNN used for assisting GradCAM achieved a testing accuracy of 96.34%. In order to evaluate the results, the infections were compared with 25 manually segmented CT scans and achieved a precision of 0.975, a recall of 0.960, and an IOU score of 0.937.
Étude d'une nouvelle forme d'onde multiporteuses à PAPR réduit

L’OFDM est une technique de modulation multiporteuses largement utilisee dans des applications de communications filaires et sans-fils comme le DVB-T/T2, le Wifi, et la 4G, grâce a sa robustesse contre les canaux selectifs en frequence en comparaison avec la modulation monoporteuse. Cependant, le signal OFDM souffre de grandes variations d’amplitude. Les fluctuations de l’enveloppe du signal OFDM generent des distorsions non-lineaires quand on introduit le signal dans un equipement non-lineaire comme l’amplificateur de puissance. Reduire les variations du signal ameliore le rendement de l’amplificateur, reduit la consommation energetique et diminue les emissions de CO2 des transmissions numeriques.Le PAPR (rapport de la puissance crete sur la puissance moyenne) est une variable aleatoire qui a ete introduite pour mesurer les variations du signal. Il existe plusieurs systemes multiporteuses bases sur differentes bases de modulation et filtres de mise en forme. Nous prouvons d’abord dans ces travaux que le PAPR depend de cette structure de modulation. Ensuite, nous etudions le comportement du PAPR vis-a-vis des formes d’ondes utilisees dans la modulation. Le probleme de reduction du PAPR est ainsi formule en un probleme d’optimisation. Par ailleurs, une condition necessaire pour construire des formes d’ondes avec un meilleur PAPR que l’OFDM est developpee. Cette condition est notamment satisfaite par des bases en ondelettes. Enfin, une nouvelle forme d’onde en paquets d’ondelettes adaptative est proposee, permettant des gains significatifs en PAPR, tout en maintenant les avantages des modulations multiporteuses.
HUMAN POSE ESTIMATION SYSTEM USING DEEP LEARNING ALGORITHMS

The purpose of this work is the software implementation of neural network that can solve problem of Human Pose Estimation. With rapid improvements of neural network models and computing resources over last 10 years it’s become possible to automate a lot of processes, carry out research and improve quality of life. One of the directions is Computer Vision: it allows to recognize objects, track motions, image segmentation, facial recognition etc. Human pose estimation is the part of Computer Vision area of research. It allows to capture human pose from a video or an image and have many uses in medicine, sport, augmented reality, video games etc. Therefore, the goal of this work is to find and optimize algorithm, that is relatively accurate, for identifying and classifying the joints in the human body. To achieve the goal, the following tasks were solved: current methods and technologies that is commonly used to solve problem of human pose estimation were reviewed and analyzed, artificial neural networks were used as a mathematical apparatus for the model, software implementation for human pose estimation was developed and tested, outputs from model were analyzed and evaluated, results and conclusion were formulated.
Calabi--Yau geometry meets electrons in 2d

The B-model approach of topological string theory leads to difference equations by quantizing algebraic mirror curves. It is known that these quantum mechanical systems are solved by the refined topological strings. Recently, it was pointed out that the quantum eigenvalue problem for a particular Calabi--Yau manifold, known as local $\mathbb{F}_0$, is closely related to the Hofstadter problem for electrons on a two-dimensional square lattice. In this paper, we generalize this idea to a more complicated Calabi--Yau manifold. We find that the local $\mathcal{B}_3$ geometry, which is a three-point blow-up of local $\mathbb{P}^2$, is associated with electrons on a triangular lattice. This correspondence allows us to use known results in condensed matter physics to investigate the quantum geometry of the toric Calabi--Yau manifold.
Duplicate Genes Contribute to Variability in Abiotic Stress Resistance in Allopolyploid Wheat

Gene duplication is a universal biological phenomenon that drives genomic variation and diversity, plays a crucial role in plant evolution, and contributes to innovations in genetic engineering and crop development. Duplicated genes participate in the emergence of novel functionality, such as adaptability to new or more severe abiotic stress resistance. Future crop research will benefit from advanced, mechanistic understanding of the effects of gene duplication, especially in the development and deployment of high-performance, stress-resistant, elite wheat lines. In this review, we summarize the current knowledge of gene duplication in wheat, including the principle of gene duplication and its effects on gene function, the diversity of duplicated genes, and how they have functionally diverged. Then, we discuss how duplicated genes contribute to abiotic stress response and the mechanisms of duplication. Finally, we have a future prospects section that discusses the direction of future efforts in the short term regarding the elucidation of replication and retention mechanisms of repetitive genes related to abiotic stress response in wheat, excellent gene function research, and practical applications.