Miracle-dz/UniFAR-FTUs huggingface dataset:
Numerical simulation of the evolution of Tollmien–Schlichting waves over finite compliant panels

The evolution of two-dimensional Tollmien–Schlichting waves propagating along a wall shear layer as it passes over a compliant panel of finite length is investigated by means of numerical simulation. It is shown that the interaction of such waves with the edges of the panel can lead to complex patterns of behaviour. The behaviour of the Tollmien–Schlichting waves in this situation, particularly the effect on their growth rate, is pertinent to the practical application of compliant walls for the delay of laminar–turbulent transition. If compliant panels could be made sufficiently short whilst retaining the capability to stabilize Tollmien–Schlichting waves, there is a good prospect that multiple-panel compliant walls could be used to maintain laminar flow at indefinitely high Reynolds numbers. We consider a model problem whereby a section of a plane channel is replaced with a compliant panel. A growing Tollmien–Schlichting wave is then introduced into the plane, rigid-walled, channel flow upstream of the compliant panel. The results obtained are very encouraging from the viewpoint of laminar-flow control. They indicate that compliant panels as short as a single Tollmien–Schlichting wavelength can have a strong stabilizing effect. In some cases the passage of the Tollmien–Schlichting wave over the panel edges leads to the excitation of stable flow-induced surface waves. The presence of these additional waves does not appear to be associated with any adverse effect on the stability of the Tollmien–Schlichting waves. Except very near the panel edges the panel response and flow perturbation can be represented by a superposition of the Tollmien–Schlichting wave and two other eigenmodes of the coupled Orr–Sommerfeld/compliant-wall eigensystem. The numerical scheme employed for the simulations is derived from a novel vorticity–velocity formulation of the linearized Navier–Stokes equations and uses a mixed finite-difference/spectral spatial discretization. This approach facilitated the development of a highly efficient solution procedure. Problems with numerical stability were overcome by combining the inertias of the compliant wall and fluid when imposing the boundary conditions. This allowed the interactively coupled fluid and wall motions to be computed without any prior restriction on the form taken by the disturbances.
A CRLH-TL Inspired Dual-Band Antenna With Polarization Diversity for Wrist-Worn Wireless Body Area Network Devices

A wrist-worn dual-band smartwatch antenna with polarization diversity is proposed for smartwatches. The antenna has a cylindrical structure with a diameter of 33.6 mm and a height of 6.8 mm. The radiated structure is inspired by composite right-/left-handed transmission lines (CRLH-TLs) to realize a dual-band operation (2.4 and 5.8 GHz WLAN band). The unit cell of the CRLH-TL is analyzed, and its equivalent circuit model and the dispersion curves are studied for mode analyzation. The +1 and −1 modes of CRLH TL are employed for dual-band operating, and two orthogonal-polarized modes are excited with different types of feeding structure to realize the polarization diversity with low coupling and low envelope correlation coefficients (ECCs). The wrist model is built in both simulation and experiment to evaluate the performance of the antenna, which shows a good radiation and diversity performances. The measured degree of freedom (DoF) is 1.92 and the capacity is 17.39 bit/s/Hz with 30 dB signal-noise rate (SNR). The results of specific absorption rate (SAR) are also studied and they are pretty low. These characteristics of the proposed antenna make it a competitive candidate for wrist-worn wireless body area network (WBAN) devices.
Multi-energy CT material decomposition using Bayesian deep convolutional neural network with explicit penalty of uncertainty and bias

Convolutional neural network (CNN)-based material decomposition has the potential to improve image quality (visual appearance) and quantitative accuracy of material maps. Most methods use deterministic CNNs with mean-square-error loss to provide point-estimates of mass densities. Point estimates can be over-confident as the reliability of CNNs is frequently compromised by bias and two major uncertainties – data and model uncertainties originating from noise in inputs and train-test data dissimilarity, respectively. Also, mean-square-error lacks explicit control of uncertainty and bias. To tackle these problems, a Bayesian dual-task CNN (BDT-CNN) with explicit penalization of uncertainty and bias was developed. It is a probabilistic CNN that concurrently conducts material classification and quantification and allows for pixel-wise modeling of bias, data uncertainty, and model uncertainty. CNN was trained with images of physical and simulated tissue-mimicking inserts at varying mass densities. Hydroxyapatite (nominal density 400mg/cc) and blood (nominal density 1095mg/cc) inserts were placed in different-sized body phantoms (30 – 45cm) and used to evaluate mean-absolute-bias (MAB) in predicted mass densities across different images at routine- and half-routine-dose. Patient CT exams were collected to assess generalizability of BDT-CNN in the presence of anatomical background. Noise insertion was used to simulate patient exams at half- and quarter-routine-dose. The deterministic dual-task CNN was used as baseline. In phantoms, BDT-CNN improved consistency of insert delineation, especially edges, and reduced overall bias (average MAB for hydroxyapatite: BDT-CNN 5.4mgHA/cc, baseline 11.0mgHA/cc and blood: BDT-CNN 8.9mgBlood/cc, baseline 14.0mgBlood/cc). In patient images, BDT-CNN improved detail preservation, lesion conspicuity, and structural consistency across different dose levels.
Group Synchrony for Emotion Recognition Using Physiological Signals

During group interactions, we react and modulate our emotions and behaviour to the group through phenomena including emotion contagion and physiological synchrony. Previous work on emotion recognition through video/image has shown that group context information improves the classification performance. However, when using physiological data, literature mostly focuses on intrapersonal models that leave-out group information, while interpersonal models are unexplored. This paper introduces a new interpersonal Weighted Group Synchrony approach, which relies on Electrodermal Activity (EDA) and Heart-Rate Variability (HRV). We perform an analysis of synchrony metrics applied across diverse data representations (EDA and HRV morphology and features, recurrence plot, spectrogram), to identify which metrics and modalities better characterise physiological synchrony for emotion recognition. We explored two datasets (AMIGOS and K-EmoCon), covering different group sizes (4 vs dyad) and group-based activities (video-watching vs conversation). The experimental results show that integrating group information improves arousal and valence classification, across all datasets, with the exception of K-EmoCon on valence. The proposed method was able to attain mean M-F1 of <inline-formula><tex-math notation="LaTeX">$\approx$</tex-math><alternatives><mml:math><mml:mo>≈</mml:mo></mml:math><inline-graphic xlink:href="bota-ieq1-3265433.gif"/></alternatives></inline-formula> 72.15% arousal and 81.16% valence for AMIGOS, and M-F1 of <inline-formula><tex-math notation="LaTeX">$\approx$</tex-math><alternatives><mml:math><mml:mo>≈</mml:mo></mml:math><inline-graphic xlink:href="bota-ieq2-3265433.gif"/></alternatives></inline-formula> 52.63% arousal, 65.09% valence for K-EmoCon, surpassing previous work results for K-EmoCon on arousal, and providing a new baseline on AMIGOS for long-videos.
Statistical model of seasonal forecasting the completed suicides number in the regions of Ukraine

Suicide prevention efforts require conscious coordination and close collaboration between health agencies. They should be based on an understanding of the true picture of the prevalence of this phenomenon in a particular area, the characteristics of the dynamics of changes in the frequency of suicides, and high-risk factors, namely age, gender, climatic and social components. The purpose of this study was an attempt to create for each region of Ukraine a statistical model of the dynamics of the frequency of completed suicides depending on the time component (months of the year) and to build on its basis a forecast of the dynamics of the indicator of the number of deaths due to intentional self-harm. For this, the autocorrelation of absolute indicators was carried out and correlograms of time series of indicators of the deaths' number due to intentional self-harm were constructed. The obtained correlograms had sufficiently pronounced features, which made it possible to structure them into 4 separate groups. For further analysis, we used the time series of the areas that made up the first two groups, characterized by a trend and seasonality. For further analysis, only models of exponential smoothing of the time series of areas were used, whose indicators of Ljung-Box Q-statistics, coefficient of determination, mean modulus of error, and smoothing of the mean were in an acceptable range. Based on the created time series model, it is possible to assume that the period from August 2021 to September 2022, will increase in the absolute indicator of the number of deaths due to intentional self-harm in the spring months and, for most regions, in January. For the autumn period, on the contrary, a decrease in the number of completed suicides is characteristic. the characteristics of the time series models for a whole group of regions did not allow us to use them to build a forecast. These regions are represented by two different geographical groups – a group of regions of Western Ukraine and three Black Sea regions. Created for each region of Ukraine, a statistical model of the frequency dynamics of the completed suicides depending on the time component (months of the year) made it possible to build an annual forecast for the number of deaths dynamics due to intentional self-harm. Longer-term forecasts are possible by analyzing more data.
Identification of rogue waves from scattering transform analysis of periodized waveforms

The nonlinear Schr\"odinger equation (NLSE) stands out as the dispersive nonlinear partial differential equation that plays prominent role in the modeling and understanding of features relevant to many fields of nonlinear physics. The question of random input problems in the one-dimensional and integrable NLSE enters within the framework of integrable turbulence, and the specific question of formation of rogue waves has been recently extensively studied in this context. The determination of exact analytic solutions of the focusing 1D-NLSE prototyping rogue waves of statistical relevance is now considered as the problem of central importance. Here we address this question from the perspective of the inverse scattering transform (IST) method that relies on the integrable nature of the wave equation. We develop a conceptually new approach to the rogue wave identification in which appropriate, locally coherent structures are specifically isolated from a globally incoherent wave train to be subsequently analyzed by implementing a numerical IST procedure relying on a spatial periodization of the object under consideration. Using this approach we extend the existing classifications of the prototypes of rogue waves from standard breathers and their collisions to more general nonlinear modes characterized by their nonlinear spectra.
Theoretical Modelling of the Energy Surface (001) and Topology of CaZrO3 Perovskite

I present the results of ab initio calculations of surface relaxations, rumplings, energetics, optical band gaps, and charge distribution for the CaZrO3 (001) surfaces using computer code CRYSTAL and a hybrid description of exchange and correlation. I consider both CaO and ZrO2-terminations of the CaZrO3 (001) surface. On the both CaO and ZrO2-terminated CaZrO3 (001) surfaces, I find that all upper and third layer atoms relax inwards, whereas all second layer atoms relax upwards. I predict a considerable increase of the Zr-O chemical bond covalency (0.102e) near the ZrO2-terminated CaZrO3 (001) surface relative to the CaZrO3 bulk (0.086e). My calculated CaO (0.87 eV) and ZrO2-terminated (1.33 eV) CaZrO3 (001) surface energies are considerably smaller than another ABO3 perovskite polar (011) and (111) surface energies. The calculated optical band gaps near the CaO (5.00 eV) and ZrO2-terminated (5.22 eV) CaZrO3 (001) surfaces are considerably reduced with respect to the bulk band gap (5.40 eV).
Evolutionary history of New World crakes (Aves: Rallidae) with emphasis on the tribe Laterallini

New World crakes are intriguing, poorly known birds with cursorial cryptic habits; they belong to two unrelated tribes: Laterallini and Pardirallini. Recent genetic studies contributed to the phylogenetic knowledge of these tribes, but a complete phylogenetic reconstruction is still missing. Here, we present the most taxonomically complete dated gene‐based phylogeny of New World crakes, with an emphasis on the Laterallini, including for the first time sequences of Coturnicops notatus, Laterallus levraudi, L. jamaicensis tuerosi and L. xenopterus. We used multilocus (mtDNA + nucDNA) phylogenetic analyses and interpreted our results in light of the natural history of the group. Our novel results show that: (1) L. xenopterus is sister to L. leucopyrrhus; (2) L. j. tuerosi is sister to L. spilonota; (3) C. notatus is sister to the clade that groups the remaining New World black crakes (L. jamaicensis, L. j. tuerosi, L. rogersi, L. spilonota and L. spilopterus); and (4) L. levraudi and L. melanophaius conform a recently diverged clade. Thus, our results indicate polyphyly in this group, particularly for Laterallus and, for the first time, for Coturnicops. We show that some discrepancies with previous studies stem from misidentified samples or sequences of L. leucopyrrhus and Anurolimnas fasciatus. Finally, we provide new preliminary insights into species limits of some taxa, specifically for L. levraudi, L. melanophaius and L. j. tuerosi. Our results set the stage for integrative taxonomic assessments at the generic level in the Laterallini by incorporating phylogenetic (gene‐based) and natural history data.
Biopsy and surgical specimen specific deep learning models for prostate cancer detection on digitized pathology images

The detection of prostate cancer within H&E-stained tissue slides helps identify the presence, location, and extent of the disease. Machine learning approaches have been developed to accomplish this task for biopsies and radical prostatectomies. Deep learning approaches, primarily using convolutional neural networks (CNN), have demonstrated substantial ability to identify cancer in each of these sample types. Some models trained on biopsies have even been approved for clinical application. Biopsies are small tissue samples acquired through a syringe to establish cancer diagnosis while radical prostatectomies are large planar sections of prostate tissue acquired post-surgery. This leads to differing morphology and heterogeneity between sample types, making it unclear whether algorithms trained on biopsies are robust enough to perform well on radical prostatectomies and vice versa. Our goal was to investigate whether morphological differences between sample types affected the performance of radical prostatectomy trained CNN models when applied to biopsies and vice versa. Radical prostatectomies (N=100) and biopsies (N=50) were acquired from The University of Pennsylvania to train (80%) and validate (20%) a CNN using the Densenet architecture for biopsies (MB), radical prostatectomies (MR), and a combined dataset (MB+R). Model performance was compared using sensitivity, specificity, and F1 score. To isolate the effect of morphological differences on performance, we acquired all data from the same institution, ensured no batch effects were present via UMAP, applied data augmentation during training, and applied stain normalization. Additionally, the radical prostatecomy training set was optimized for transfer to biopsies by reducing heterogeneity (MR*). This reduction was based on distance from the centroid of a texture feature distribution. Models performed well when applied to their own sample type (F1 > 0.88) but performed poorly when applied across sample types (F1 < 0.65). Reducing heterogeneity to optimize the radical prostatectomy training set resulted in MR* (F1=0.72) noticeably outperforming MR (F1=0.53) on biopsies. This indicates that differences in morphology and heterogeneity drive performance differences between cancer detection models trained on different sample types. Our results suggest that these morphological differences can be overcome with sample type specific models or training set optimization methods.
Analog Integrated Circuit Routing Techniques: An Extensive Review

Routing techniques for analog and radio-frequency (A/RF) integrated circuit (IC) design automation have been proposed in the literature for over three decades. On those, an extensive set of geometric constraints have already been covered as surrogates for routing quality, but also, performance-related criteria were progressively included. However, as A/RF design moved into advanced integration technology nodes, the increasing number of design rules/constraints, wire resistance, congestion, and interwire parasitic growth is constantly challenging existing automatic routing techniques and keeping pressure on their improvement. Fortunately, recent developments in modern workstations’ capabilities allowed the growth of sophisticated routing processes, including some assisted by the latest machine and deep learning methods, offering unprecedented solutions for the automation of this task. Still, as the correlation between routing-induced parasitic structures and the circuit’s functional behavior is far from simple, computational-intensive parasitic-inclusive and layout-aware synthesis techniques have also been proposed, where automatic routing techniques play a decisive role. This paper conducts an extensive review of A/RF IC routing techniques, from the digitally-inspired earliest approaches to state-of-the-art developments, providing a complete and comprehensive guide for circuit designers and design automation developers while defining research lines to facilitate more activities within this field.
Glucagon Promotes Gluconeogenesis through the GCGR/PKA/CREB/PGC-1α Pathway in Hepatocytes of the Japanese Flounder Paralichthys olivaceus

In order to investigate the mechanism of glucagon regulation of gluconeogenesis, primary hepatocytes of the Japanese flounder (Paralichthys olivaceus) were incubated with synthesized glucagon, and methods based on inhibitors and gene overexpression were employed. The results indicated that glucagon promoted glucose production and increased the mRNA levels of glucagon receptor (gcgr), guanine nucleotide-binding protein Gs α subunit (gnas), adenylate cyclase 2 (adcy2), protein kinase A (pka), cAMP response element-binding protein 1 (creb1), peroxisome proliferator-activated receptor-γ coactivator 1α (pgc-1α), phosphoenolpyruvate carboxykinase 1 (pck1), and glucose-6-phosphatase (g6pc) in the hepatocytes. An inhibitor of GCGR decreased the mRNA expression of gcgr, gnas, adcy2, pka, creb1, pgc-1α, pck1, g6pc, the protein expression of phosphorylated CREB and PGC-1α, and glucose production. The overexpression of gcgr caused the opposite results. An inhibitor of PKA decreased the mRNA expression of pgc-1α, pck1, g6pc, the protein expression of phosphorylated-CREB, and glucose production in hepatocytes. A CREB-targeted inhibitor significantly decreased the stimulation by glucagon of the mRNA expression of creb1, pgc-1α, and gluconeogenic genes, and glucose production decreased accordingly. After incubating the hepatocytes with an inhibitor of PGC-1α, the glucagon-activated mRNA expression of pck1 and g6pc was significantly down-regulated. Together, these results demonstrate that glucagon promotes gluconeogenesis through the GCGR/PKA/CREB/PGC-1α pathway in the Japanese flounder.
Estimating Periodic Length and Phase Shifts of Business Cycles: An Approach from Model-based Uni- and Multi-variate Bandpass Filter(in Japanese)

This paper verified possible application of model-based univariate and multivariate Bandpass filter for Japanese time series data. Firstly, we extracted cycle components and estimated periodic length of monthly univariate time series such as coincident index (CCI) and the index of industrial production (IIP) using Generalized Butterworth Filter, a kind of unobservable component models, following Harvey and Trimbur (2003). The feature of the method is to estimate the parameters of models from actual data, unlike HP filter and Baxter-King Filter. Secondly, we turned to estimate phase shifts of multivariate time series such as inventoryproduction diagram and leading, coincident and lagged series using Multivariate Bandpass filter following Valla e Azevedo, Koopman and Rua (2006). The advantage of the method is to extract common cycle component from multivariate series even with phase shifts among them, whereas earlier methods can do it for only univariate series.
Comprison of Model Selection Critaria for Multivariate Regression Model with Mixed Model

This a rticle considers the analysis of m ultivariate regression experiment t hat i s u sed frequently in v ariety o f ap plications re search. W e used simulation study to compare five model selection criteria in terms of their ability to i dentify the right multivariate regression model that has the right covariance structure a nd i n t he same time t he right multivariate mod el structure. The comparison of the five model selection criteria was in terms of t heir p ercentage of nu mber o f t imes tha t t hey id entify the right m odel. The simulation results indicate that overall, the percentages of identifying the right multivariate regression model f rom both sta ndard and non-standard multivariate model stru ctures were low except f or sp ecific models that involve the indicator variable. In the same time the five criterions showed similar performance where CAIC and BIC have the best p erformance in the case of suc ceed in selecting the right m ultivariate regression mo del that has the right covariance s tructure a nd i n t he same time t he right multivariate mod el structure.
Effect of Rotation on the Onset of Compressible Rivlin-Ericksen Fluid Heated from Below Saturating a Darcy-Brinkman Porous Medium

The effect of rotation on the onset of convection in compressible Rivlin-Ericksen elastico-viscous fluid heated from below saturating a porous medium is considered. For the porous medium, the Brinkman model is employed. By applying normal mode analysis method, the dispersion relation has been derived and solved analytically. It is observed that the medium permeability, compressibility, gravity field and viscoelasticity introduce oscillatory modes. For stationary convection, the rotation has stabilizing effect whereas Darcy number and medium permeability have destabilizing/stabilizing effect on the system under certain conditions. The effects of rotation, Darcy number and medium permeability have also been shown graphically.