Miracle-dz/UniFAR-FTUs huggingface dataset:
A Damping Control for DC Distribution System Under Isolated Operation Mode

There are risks of resonant instability exiting in the DC distribution system configured with battery energy storage devices because of capacitors and inductors in the network with fast speed-controlled DC-DC converters. The DC distribution system needs battery energy storage devices to provide DC voltage support when it operates in an isolated island mode because of no AC or DC sources at that moment. To explore the key factors affecting the resonant instability of DC network, a three-terminal DC network consisting of DC voltage control, constant current control, and constant power load is used for state space modeling. Research results show that the inductors for faults current limitation interacting with the capacitors under high constant power load is the main reason causing instability. The other reason is the large parameter of the current controller. To suppress the resonant instability of the DC distribution system, a damping control strategy by filtering DC voltage in advance to the current control loop is proposed, and the parameters are selected using the roots locus method. Simulations verify the correctness and effectiveness of the proposed method.
Single-Location and Multi-Locations Scenarios Generation for Wind Power Based On WGAN-GP

To address the randomness of renewable energy, scenario generation can simulate the random process of renewable energy. Still, most of the previous scenario generation algorithms are based on assumed probability distribution models, which are difficult to grasp the dynamic characteristics of renewable energy accurately. Based on an improved generative adversarial network algorithm, this paper generates single-site and multi-site scenarios for wind power generation data. The temporal and spatial correlations of the generated data were judged based on the maximum mean difference and the Pearson coefficient. Finally, multiple sets of wind power generation data are used to verify that the proposed method can reflect not only the volatility of renewable energy but also ensure the temporal and spatial correlation between the data.
Prediction of strength of multi-support overground segment of main pipeline

A problem of strength of a main pipeline in its zone of rectilinear overground passage through an obstacle is considered in the given case. In the given case. It is not the overground multi-support segment alone that is included in the calculation, the adjacent underground parts which are situated at a certain distance from the ground are also taken into account here. The pipeline is described by a beam model with piece-wise constant rigidity, i. e. each segment of the pipeline i of constant rigidity. This allows us to more accurately characterize different thicknesses of pipes of the overground segment of the main pipeline in its construction. There is also assumed that the overground segment of the pipeline is freely positioned and rests on the corresponding supporting elements. The latest can be mutually adjusted for the given altitude. This allows us to evaluate the strength in repairing lifting the pipe over supports. The supporting elements can also experience elastic deformations. Their case is the mechanical compliance of component parts of the supporting assembly and, especially that of the ground under the support. The entrance and the exit of the overground segment into the ground are also considered as supporting elements. Here, they can have vertical displacements and turns. They also take into account the weight of the ground over the pipeline. The calculation of a main oil pipeline with its overground segment resting on two supports has been carried out. There are taken real conditions of operational loading caused to be the following factors: internal pressure, temperature difference, and the weight of the product transported. It has been established that the maximal values of stresses are observed in support sections and between the supports. It is shown that the stresses caused by bending moment can amount to up to half the total value. The influence of altitudes upon the value caused by bending moment stresses in the pipeline is investigated. The change of stresses caused by mechanical compliance of elements of the supports and by adjusting vertical displacements of the supports during repair was studied.
Lepton Asymmetries for the $B_s -> \gamma l^+ l^- $ Decay in a Family Nonuniversal Z' Model

The exclusive $B_s -> \gamma l^+ l^- $ decay is analyzed in the framework of a family nonuniversal Z' model by calculating the differential branching ratio, double lepton polarizations and forward-backward asymmetries. Our results are compared against those of the Standard Model. The predictions of this work are hoped to can be tested in the near future at LHCb.
First Ejection from the PSR B1259-63/LS 2883 High Mass Gamma-Ray Binary Detected During the 2021–2024 Binary Cycle

LS 2883/PSR B1259-63 is a high mass, eccentric gamma-ray binary that has previously been observed to eject X-ray emitting material. We report the results of recent Chandra observations near binary apastron in which a new X-ray emitting clump of matter was detected. The clump has a high projected velocity of v ⊥ ≈ 0.07c and hard X-ray spectrum, which fits an absorbed power-law model with Γ = 1.1 ± 0.3. Although clumps with similar velocities and spectra were detected in some of the previous binary cycles, no resolved clumps were seen near apastron in the preceding cycle of 2017–2021.
Open-circuit fault diagnosis method for inverters using deep learning and the evidence reasoning rule

Inverters having high voltage levels, high power density, and high integration are widely used. However, many high-frequency switch units also increase the probability of failure. Therefore, developing an accurate and stable fault diagnosis method is necessary. This paper proposes a fault diagnosis algorithm based on deep learning and the evidence reasoning (ER) rule. It not only ensures high diagnostic accuracy, but also enhances the stability of the diagnostic results. The algorithm takes the three-phase voltage source inverter as the research object and extracts the three-phase current signals with different types of faults as features. First, Convolutional and Deep Neural Network methods were utilized independently to determine a preliminary diagnosis. Second, the softmax functions of the Convolutional and Deep Neural Network outputs provided the probability distribution of the fault category, which was used as the evidence body for the ER rule to construct the fusion diagnosis. In addition, a new method of determining the reliability and the importance factors of the evidence was proposed in which the evaluation index of the deep-learning diagnosis result was applied. Finally, the final classification result was obtained using the ER rule. The proposed method can effectively enhance the accuracy and robustness compared with a single classifier.
Association of radiological severity with inflammatory biomarkers for prognostic prediction in patients with COVID-19

Background: Covid 19 caused by SARSCoV2 has become a pandemic. It has a rapid disease progression and causes severe and fatal complications. Associating radiological severity with effective biomarkers like CRP, leucocytes, lymphocytes, DDimer, would be helpful in screening, categorizing patient, preventing serious complications. Aims and Objectives: The aim of the study was to investigate association between levels of inflammatory biomarkers and correlate it with HRCT chest finding to identify patients at risk of fatal complications. Materials and Methods: It was a retrospective monocentric observational study undertaken at Ibn Tofail hospital COVID19 dedicated center. 177 Patients>18 year of age who were admitted from september 1, 2020 up to november 30,2020 with laboratory confirmed diagnosis of Covid19 were included in the study. Data was collected on demography, disease severity, laboratory measurements, radiology imaging retrospectively from records of patients. The disease severity was classified into light, mild to severe and critic based on CT Severity scoring. HRCT Chest and inflammatory biomarkers were sent in every patient at the time of admission and the outcome was recorded. Results: There were 116 male patients, 61 female patients in our study. Average age of patients having severe lung involvement is 61.9years, whereas Average age of patients having non-severe lung involvement is 56.8 years and showed significant association with severity of lung involvement (p value : 0.017). Severity of lung involvement according to HRCT chest findings was greater in patients with both raised values of CRP <0.001), DDimer (P value 0.032) and low values of lymphocytes (P value : 0.001) . Capillary oxygen SATURATION was also found to be significantly associated with radiological severity among covid19 patients. Compared with CRP, leukocytes, lymphocytes, and DDimeres levels, the CT severity score had higher sensitivity, specificity, and overall accuracy in predicting severe, critical cases, and short term mortality. Conclusion: the severity of Covid19 disease is correlated with radiological severity andinflammatory markers thereby it will help in immediate categorization of patients into different risk groups following diagnosis, to ensure optimal resource allocation.
HMGA1 induces FGF19 to drive pancreatic carcinogenesis and stroma formation

High mobility group A1 (HMGA1) chromatin regulators are upregulated in diverse tumors where they portend adverse outcomes, although how they function in cancer remains unclear. Pancreatic ductal adenocarcinomas (PDACs) are highly lethal tumors characterized by dense desmoplastic stroma composed predominantly of cancer-associated fibroblasts and fibrotic tissue. Here, we uncover an epigenetic program whereby HMGA1 upregulates FGF19 during tumor progression and stroma formation. HMGA1 deficiency disrupts oncogenic properties in vitro while impairing tumor inception and progression in KPC mice and subcutaneous or orthotopic models of PDAC. RNA sequencing revealed HMGA1 transcriptional networks governing proliferation and tumor-stroma interactions, including the FGF19 gene. HMGA1 directly induces FGF19 expression and increases its protein secretion by recruiting active histone marks (H3K4me3, H3K27Ac). Surprisingly, disrupting FGF19 via gene silencing or the FGFR4 inhibitor BLU9931 recapitulates most phenotypes observed with HMGA1 deficiency, decreasing tumor growth and formation of a desmoplastic stroma in mouse models of PDAC. In human PDAC, overexpression of HMGA1 and FGF19 defines a subset of tumors with extremely poor outcomes. Our results reveal what we believe is a new paradigm whereby HMGA1 and FGF19 drive tumor progression and stroma formation, thus illuminating FGF19 as a rational therapeutic target for a molecularly defined PDAC subtype.
Time-variant coherence between heart rate variability and EEG activity in epileptic patients: an advanced coupling analysis between physiological networks

Time-variant coherence analysis between the heart rate variability (HRV) and the channel-related envelopes of adaptively selected EEG components was used as an indicator for the occurrence of (correlative) couplings between the central autonomic network (CAN) and the epileptic network before, during and after epileptic seizures. Two groups of patients were investigated, a group with left and a group with right hemispheric temporal lobe epilepsy. The individual EEG components were extracted by a signal-adaptive approach, the multivariate empirical mode decomposition, and the envelopes of each resulting intrinsic mode function (IMF) were computed by using Hilbert transform. Two IMFs, whose envelopes were strongly correlated with the HRV’s low-frequency oscillation (HRV-LF; ≈0.1 Hz) before and after the seizure were identified. The frequency ranges of these IMFs correspond to the EEG delta-band. The time-variant coherence was statistically quantified and tensor decomposition of the time-frequency coherence maps was applied to explore the topography-time-frequency characteristics of the coherence analysis. Results allow the hypothesis that couplings between the CAN, which controls the cardiovascular-cardiorespiratory system, and the ‘epileptic neural network’ exist. Additionally, our results confirm the hypothesis of a right hemispheric lateralization of sympathetic cardiac control of the HRV-LF.
Clinical Ratings of White Matter Hyperintensities, Hippocampal Ratings, and Neuropsychological Functioning from The Cache County Memory Study

Objective White matter burden and medial temporal atrophy are associated with cognitive health. A large epidemiological database, such as the Cache County Memory Study (CCMS), can provide additional insight into how visual clinical ratings of brain structural integrity predict cognition in older adults. Method We used the Scheltens Ratings Scale to quantify white matter lesion burden and medial temporal atrophy in the CCMS sample to determine if these qualitative markers are predictive of memory function. We performed clinical ratings of MRI scans across two ascertainment periods among 187 community-dwelling older adults and correlated these ratings with MMSE, CERAD memory performance, and general cognitive ability. Results Higher Scheltens ratings measuring white matter and basal ganglia hyperintensities were associated with lower memory performance (r = 0.21). The strongest correlations were observed between medial temporal atrophy and general cognition performance (r = 0.32). Conclusions The current findings support previous research that the integrity of different regions of the brain correlate to function in a meaningful way.
Modified (G′∕G) ‐expansion method for finding exact wave solutions of the coupled Klein–Gordon–Schrödinger equation

The coupled Klein–Gordon–Schrödinger equation is reduced to a nonlinear ordinary differential equation (ODE) by using Lie classical symmetries, and various solutions of the nonlinear ODE are obtained by the modified (G′∕G) ‐expansion method proposed recently. With the aid of solutions of the nonlinear ODE, more explicit traveling wave solutions of the coupled Klein–Gordon–Schrödinger equation are found out. The traveling wave solutions are expressed by the hyperbolic functions, trigonometric functions, and rational functions. Copyright © 2012 John Wiley & Sons, Ltd.
Quantitative Microroughness Analysis down to the Nanometer Scale

Cross characterizations of surface roughness of glassy materials have been performed using Atomic-Force Microscopy (AFM) and optical scattering techniques. The AFM measurements provide images of the surface height contours from the micrometer down to the nanometer scale. From a two-dimensional (2D) Fourier analysis of the images, the roughness power spectrum is measured for a range of spatial frequencies from 0.04 μm−1 up to 400 μm−1. An excellent agreement is obtained with parallel light scattering measurements of the surface roughness over the spatial frequencies ranging from 0.05 μm−1 to 1.54 μm−1, corresponding to the overlap bandwidth reached by the two techniques. From the power law dependence of the roughness spectrum vs. spatial frequency found on the whole range of AFM analysis, fractal properties of this self-affine surface are discussed.
Moral Narratives Around the Vaccination Debate on Facebook

Vaccine hesitancy is a complex issue with psychological, cultural, and even societal factors entangled in the decision-making process. The narrative around this process is captured in our everyday interactions; social media data offer a direct and spontaneous view of peoples’ argumentation. Here, we analysed more than 500,000 public posts and comments from Facebook Pages dedicated to the topic of vaccination to study the role of moral values and, in particular, the understudied role of the Liberty moral foundation from the actual user-generated text. We operationalise morality by employing the Moral Foundations Theory, while our proposed framework is based on recurrent neural network classifiers with a short memory and entity linking information. Our findings show that the principal moral narratives around the vaccination debate focus on the values of Liberty, Care, and Authority. Vaccine advocates urge compliance with the authorities as prosocial behaviour to protect society. On the other hand, vaccine sceptics mainly build their narrative around the value of Liberty, advocating for the right to choose freely whether to adhere or not to the vaccination. We contribute to the automatic understanding of vaccine hesitancy drivers emerging from user-generated text, providing concrete insights into the moral framing around vaccination decision-making. Especially in emergencies such as the Covid-19 pandemic, contrary to traditional surveys, these insights can be provided contemporary to the event, helping policymakers craft communication campaigns that adequately address the concerns of the hesitant population.
Rotation-Invariant Transformer for Point Cloud Matching

The intrinsic rotation invariance lies at the core of matching point clouds with handcrafted descriptors. However, it is widely despised by recent deep matchers that obtain the rotation invariance extrinsically via data augmentation. As the finite number of augmented rotations can never span the continuous $SO(3)$ space, these methods usually show instability when facing rotations that are rarely seen. To this end, we introduce RoITr, a Rotation-Invariant Transformer to cope with the pose variations in the point cloud matching task. We contribute both on the local and global levels. Starting from the local level, we introduce an attention mechanism embedded with Point Pair Feature (PPF)-based coordinates to describe the pose-invariant geometry, upon which a novel attention-based encoder-decoder architecture is constructed. We further propose a global transformer with rotation-invariant cross-frame spatial awareness learned by the self-attention mechanism, which significantly improves the feature distinctiveness and makes the model robust with respect to the low overlap. Experiments are conducted on both the rigid and non-rigid public benchmarks, where RoITr outperforms all the state-of-the-art models by a considerable margin in the low-overlapping scenarios. Especially when the rotations are enlarged on the challenging 3DLoMatch benchmark, RoITr surpasses the existing methods by at least 13 and 5 percentage points in terms of Inlier Ratio and Registration Recall, respectively. Code is publicly available 11https://github.com/haoyu94/RoITr.
Tuning energy dissipation via topologically electro-convoluted lipid-membrane boundary layers

It was recently discovered that friction between surfaces bearing phosphatidylcholine (PC) lipid bilayers can be increased by two orders of magnitude or more via an externally-applied electric field, and that this increase is fully reversible when the field is switched off. While this striking effect holds promising application potential, its molecular origin remains unknown due to difficulty in experimentally probing confined membrane structure at a molecular level. Our earlier molecular dynamics simulations revealed the equilibrium electroporated structure of such confined lipid membranes under an electric field; here we extend this approach to study the associated sliding friction between two solid surfaces across such PC bilayers. We identify the enhanced friction in the field as arising from membrane undulations due to the electroporation; this leads to some dehydration at the lipid-water interfaces, leading to closer contact and thus increased attraction between the zwitterionic headgroups, which results in increased frictional dissipation between the bilayers as they slide past each other. Additionally, the electric field facilitates formation of lipid bridges spanning the intersurface gap; at the sliding velocities of the experiments, these bridges increase the friction by topologically-forcing the slip-plane to pass through the acyl tail-tail interface, associated with higher dissipation during sliding. Our results account quantitatively for the experimentally-observed electro-modulated friction with boundary lipid bilayers, and indicate more generally how they may affect interactions between contacting surfaces, where high local transverse fields may be ubiquitous.
Distinguishing Cause from Effect on Categorical Data: The Uniform Channel Model

Distinguishing cause from effect using observations of a pair of random variables is a core problem in causal discovery. Most approaches proposed for this task, namely additive noise models (ANM), are only adequate for quantitative data. We propose a criterion to address the cause-effect problem with categorical variables (living in sets with no meaningful order), inspired by seeing a conditional probability mass function (pmf) as a discrete memoryless channel. We select as the most likely causal direction the one in which the conditional pmf is closer to a uniform channel (UC). The rationale is that, in a UC, as in an ANM, the conditional entropy (of the effect given the cause) is independent of the cause distribution, in agreement with the principle of independence of cause and mechanism. Our approach, which we call the uniform channel model (UCM), thus extends the ANM rationale to categorical variables. To assess how close a conditional pmf (estimated from data) is to a UC, we use statistical testing, supported by a closed-form estimate of a UC channel. On the theoretical front, we prove identifiability of the UCM and show its equivalence with a structural causal model with a low-cardinality exogenous variable. Finally, the proposed method compares favorably with recent state-of-the-art alternatives in experiments on synthetic, benchmark, and real data.
Mapping Urban Population Growth from Sentinel-2 MSI and Census Data Using Deep Learning: A Case Study in Kigali, Rwanda

To better understand current trends of urban population growth in Sub-Saharan Africa, high-quality spatiotemporal population estimates are necessary. While the joint use of remote sensing and deep learning has achieved promising results for population distribution estimation, most of the current work focuses on fine-scale spatial predictions derived from single date census, thereby neglecting temporal analyses. In this work, we focus on evaluating how deep learning change detection techniques can unravel temporal population dynamics at short intervals. Since Post-Classification Comparison (PCC) methods for change detection are known to propagate the error of the individual maps, we propose an end-to-end population growth mapping method. Specifically, a ResNet encoder, pretrained on a population mapping task with Sentinel-2 MSI data, was incorporated into a Siamese network. The Siamese network was trained at the census level to accurately predict population change. The effectiveness of the proposed method is demonstrated in Kigali, Rwanda, for the time period 2016–2020, using bi-temporal Sentinel-2 data. Compared to PCC, the Siamese network greatly reduced errors in population change predictions at the census level. These results show promise for future remote sensing-based population growth mapping endeavors. Code is available on GitHub1.