Miracle-dz/UniFAR-FTUs huggingface dataset:
Mathematical modeling of unsteady processes in electromechanical system of ring-ball mill

We examine the mathematical model of start processes of the electromechanical system of the ring-ball mill, which was built taking into account the interaction of electromagnetic phenomena in asynchronous motor and mechanical vibrational phenomena. The specifics of transfer of the torque from the motor to the reducer by means of the centrifugal clutch with shot are taken into account in the motion equations of the mechanical system, as well as instability of reduced moment of inertia of the actuating mechanism and parameters of the technological load during filling or emptying of crushing cell. Electromagnetic state of asynchronous motor is described based on magnetic saturation. The mathematical model can be used to predict the fatigue strength of heavy loaded elements of ring-ball mill’s constructions. Substantiated practical recommendations aimed at reducing the dynamic loads of the main shaft of the mill.
Reference current regulation for inverter with virtual resistor damping control

Inverter with output LCL filter is widely used in power system. It has better switching frequency harmonic suppression effect, meanwhile it has resonance problem. Virtual resistor damping can suppress the resonance, but it affects the current control character, particularly for harmonic current. This paper develops the detailed models of the inverter with virtual resistor damping for output harmonic current control. Based on the analysis of current control loop with virtual resistor, the transfer function of current loop is obtained. Moreover, with the optimal value of virtual resistor, the performance of inverter is similar to 2nd order low pass filter. To control the output current equals to reference current, reference regulation method is proposed. At last, simulation and experimental results verify the correctness of theatrical analysis and effectiveness of the method.
Ion track reconstruction in 3D using alumina-based fluorescent nuclear track detectors

Fluorescent nuclear track detectors (FNTDs) based on Al2O3: C, Mg single crystal combined with confocal microscopy provide 3D information on ion tracks with a resolution only limited by light diffraction. FNTDs are also ideal substrates to be coated with cells to engineer cell-fluorescent ion track hybrid detectors (Cell-Fit-HD). This radiobiological tool enables a novel platform linking cell responses to physical dose deposition on a sub-cellular level in proton and heavy ion therapies. To achieve spatial correlation between single ion hits in the cell coating and its biological response the ion traversals have to be reconstructed in 3D using the depth information gained by the FNTD read-out. FNTDs were coated with a confluent human lung adenocarcinoma epithelial (A549) cell layer. Carbon ion irradiation of the hybrid detector was performed perpendicular and angular to the detector surface. In situ imaging of the fluorescently labeled cell layer and the FNTD was performed in a sequential read-out. Making use of the trajectory information provided by the FNTD the accuracy of 3D track reconstruction of single particles traversing the hybrid detector was studied. The accuracy is strongly influenced by the irradiation angle and therefore by complexity of the FNTD signal. Perpendicular irradiation results in highest accuracy with error of smaller than 0.10°. The ability of FNTD technology to provide accurate 3D ion track reconstruction makes it a powerful tool for radiobiological investigations in clinical ion beams, either being used as a substrate to be coated with living tissue or being implanted in vivo.
An Analytical Placement Framework for 3-D ICs and Its Extension on Thermal Awareness

In this paper, we present a high-quality analytical 3-D placement framework. We propose using a Huber-based local smoothing technique to work with a Helmholtz-based global smoothing technique to handle the nonoverlapping constraints. The experimental results show that this analytical approach is effective for achieving tradeoffs between the wirelength and the through-silicon-via (TSV) number. Compared to the state-of-the-art 3-D placer ntuplace3d, our placer achieves more than 20% wirelength reduction, on average, with a similar number of TSVs. Furthermore, we extend this analytical 3-D placement framework with thermal awareness. While 2-D thermal-aware placement simply follows uniform power distribution to minimize temperature, we show that the same criterion does not work for 3-D ICs. Instead, we are able to prove that when the TSV area in each bin is proportional to the lumped power consumption of that bin and the bins in all tiers directly above it, the peak temperature is minimized. Based on this criterion, we implement thermal awareness in our analytical 3-D placement framework. Compared with a TSV oblivious method, which only results in an 8% peak temperature reduction, our method reduces the peak temperature by 34%, on average, with slightly less wirelength overhead. These results suggest that considering the thermal effects of TSVs is necessary and effective during the placement stage.
EHDP: An efficient hybrid data dissemination protocol for dense irregular urban vehicular ad-hoc networks

In the urban scenario where the road topology is not regular, efficiently disseminating the data in vehicular ad-hoc networks (VANETs) is a critical issue. Several solutions have been proposed in the literature for data dissemination in VANET at high and low traffic density with consideration of various data dissemination issues like broadcast storm problem, network partition and resynchronization effect but, none of them could efficiently handle the broadcast storm and resynchronization effect for irregular urban road topology at high traffic density. In this paper, an inter-vehicular communication (IVC) protocol for the irregular urban scenario is presented which is efficient to handle the resynchronization effect and broadcast storm problem both at higher traffic density. The proposed “Efficient Hybrid Data Dissemination Protocol (EHDP)” for dense irregular urban VANET reduces the no. of packets sent and no. of collisions with an increased packet delivery ratio (PDR) as compared to the previously developed benchmark protocols.
SIMO-Model-Based Independent Component Analysis for High-Fidelity Blind Separation of Acoustic Signals

We newly propose a novel blind separation framework for SingleInput Multiple-Output (SIMO)-model-based acoustic signals using the extended ICA algorithm, SIMO-ICA. The SIMO-ICA consists of multiple ICAs and a fidelity controller, and each ICA runs in parallel under the fidelity control of the entire separation system. The SIMO-ICA can separate the mixed signals, not into monaural source signals but into SIMO-model-based signals from independent sources as they are at the microphones. Thus, the separated signals of SIMO-ICA can maintain the spatial qualities of each sound source. In order to evaluate its effectiveness, separation experiments are carried out under both nonreverberant and reverberant conditions. The experimental results reveal that (1) the signal separation performance of the proposed SIMO-ICA is the same as that of the conventional ICA-based method, and that (2) the spatial quality of the separated sound in SIMO-ICA is remarkably superior to that of the conventional method, particularly for the fidelity of the sound reproduction.
Insights into the detection of mutations and epigenetic markers using diamondoid-functionalized sensors

Nanogaps functionalized with small diamond-like particles, diamondoids, have been shown to effectively distinguish between different DNA nucleotides. Here, we focus on the detection of mutations and epigenetic markers using such devices. Based on quantum mechanical simulations within the density functional theory approach coupled with the non-equilibrium Green’s function scheme, we provide deeper insight into the inherent differences in detecting modified nucleotides. Our results strongly underline the influence of the type of functionalization molecule of the nanogap, as well its conformational details within the nanogap, on the sensing efficiency of the device. The electronic features for the mutations and epigenetic markers are compared to those for the respective canonical nucleotides that are detected by different devices. The calculations directly correlate the structural and electronic properties of the different nucleotides with the electronic transmission across the diamondoid-based device. The latter was found to be controlled by the functionalizing molecule and its binding to the nucleotides. We report on the direct connection of these characteristics to the sensitivity of the diamondoid-functionalized nanogaps, which could eventually be embedded in a nanopore device, and discuss the implications for DNA sensing.
A transfer learning based approach for automated grading of Gliomas using deep Residual Networks

We introduce a fully automated tool based on transfer learning that classifies gliomas in Magnetic Resonance (MR) volumes as either high grade (HGG) or low grade glioma (LGG). A deep residual network (ResNet-50), pre-trained on natural images was fine-tuned to accomplish the task & yield performance comparable to trained radiologists. Briefly, the slice in the MR volume that comprises of the largest lesion load was found by segmenting the lesion from the volume using a densely connected neural network. The slice and its neighbours were fed to the grading network (ResNet-50) to predict the degree of malignancy in the volume. On HGG volumes, it was observed that the class activation maps produced by the network could provide visual cues to radiologists by indicating regions within the lesion with higher malignancy. The performance of the network was validated on a publicly available dataset (n=165) and on a dataset acquired from a local hospital (n = 79). For the task of grading of gliomas (n=244), the network achieved a sensitivity, specificity & precision of 90.9 %, 98.2 % and 99.4% respectively. The grade map generated by the network showed correlation with the findings made by a radiologist and thus could potentially be used as a clinical tool for better surgical and treatment planning.
X-ray phase-contrast imaging: from pre-clinical applications towards clinics

Phase-contrast x-ray imaging (PCI) is an innovative method that is sensitive to the refraction of the x-rays in matter. PCI is particularly adapted to visualize weakly absorbing details like those often encountered in biology and medicine. In past years, PCI has become one of the most used imaging methods in laboratory and preclinical studies: its unique characteristics allow high contrast 3D visualization of thick and complex samples even at high spatial resolution. Applications have covered a wide range of pathologies and organs, and are more and more often performed in vivo. Several techniques are now available to exploit and visualize the phase-contrast: propagation- and analyzer-based, crystal and grating interferometry and non-interferometric methods like the coded aperture. In this review, covering the last five years, we will give an overview of the main theoretical and experimental developments and of the important steps performed towards the clinical implementation of PCI.
Measuring acuity of the approximate number system reliably and validly: the evaluation of an adaptive test procedure

Two studies investigated the reliability and predictive validity of commonly used measures and models of Approximate Number System acuity (ANS). Study 1 investigated reliability by both an empirical approach and a simulation of maximum obtainable reliability under ideal conditions. Results showed that common measures of the Weber fraction (w) are reliable only when using a substantial number of trials, even under ideal conditions. Study 2 compared different purported measures of ANS acuity as for convergent and predictive validity in a within-subjects design and evaluated an adaptive test using the ZEST algorithm. Results showed that the adaptive measure can reduce the number of trials needed to reach acceptable reliability. Only direct tests with non-symbolic numerosity discriminations of stimuli presented simultaneously were related to arithmetic fluency. This correlation remained when controlling for general cognitive ability and perceptual speed. Further, the purported indirect measure of ANS acuity in terms of the Numeric Distance Effect (NDE) was not reliable and showed no sign of predictive validity. The non-symbolic NDE for reaction time was significantly related to direct w estimates in a direction contrary to the expected. Easier stimuli were found to be more reliable, but only harder (7:8 ratio) stimuli contributed to predictive validity.
Campylobacter Jejuni Increases Transcribed Il-1 B and Causes Morphometric Changes in the Ileal Enterocytes of Chickens

Campylobacteriosis is a worldwide foodborne zoonosis disease caused by Campylobacter jejuni. This microorganism is considered a commensal bacterium in chicken hosts. C. jejuni produces epithelial cell modifications and induces a cytokine gene transcription innate immunity repertoire. In the present study, we describe the invasiveness, morphological cellular modifications, and transcript level expressions of innate immune cytokines from C. jejuni-inoculated chicken ileum explants. C. jejuni was internalized by epithelial ileum cells at 15 minutes postinoculation (p.i.) and was detected intracellularly for 4hs (p.i.). Inoculated explants displayed significant increases in cell height. C. jejuni induced a significant elevation of Transforming Growth Factor Beta 3 (TGF-b3) and Interleukin-1b (IL-1b) transcripts. In conclusion, C. jejuni is internalized in explanted epithelial ileum cells, produces morphological cell modifications, and induces gene transcription of both anti-inflammatory and pro-inflammatory cytokines.
Using Grid Benchmarks for Dynamic Scheduling of Grid Applications

Navigation or dynamic scheduling of applications on computational grids can be improved through the use of an application-specific characterization of grid resources. Current grid information systems provide a description of the resources, but do not contain any application-specific information. We define a GridScape as dynamic state of the grid resources. We measure the dynamic performance of these resources using the grid benchmarks. Then we use the GridScape for automatic assignment of the tasks of a grid application to grid resources. The scalability of the system is achieved by limiting the navigation overhead to a few percent of the application resource requirements. Our task submission and assignment protocol guarantees that the navigation system does not cause grid congestion. On a synthetic data mining application we demonstrate that Gridscape-based task assignment reduces the application tunaround time.
Learning and Using the Arrow of Time

We seek to understand the arrow of time in videos - what makes videos look like they are playing forwards or backwards? Can we visualize the cues? Can the arrow of time be a supervisory signal useful for activity analysis? To this end, we build three large-scale video datasets and apply a learning-based approach to these tasks. To learn the arrow of time efficiently and reliably, we design a ConvNet suitable for extended temporal footprints and for class activation visualization, and study the effect of artificial cues, such as cinematographic conventions, on learning. Our trained model achieves state-of-the-art performance on large-scale real-world video datasets. Through cluster analysis and localization of important regions for the prediction, we examine learned visual cues that are consistent among many samples and show when and where they occur. Lastly, we use the trained ConvNet for two applications: self-supervision for action recognition, and video forensics - determining whether Hollywood film clips have been deliberately reversed in time, often used as special effects.
Long‐term runoff and sediment yields from small semiarid watersheds in southern Arizona

This study presents analysis of 34 years of precipitation, runoff, and sediment data collected from eight small (1.1–4.0 ha) semiarid rangeland watersheds in southern Arizona, USA. Average annual precipitation ranged between 354 and 458 mm with 53% of the total rainfall occurring from July through September. Runoff depth was 3.5%–13.9% of annual precipitation depth for individual watersheds and 9.2% on average. Runoff events with missing sediment data were estimated to account for 30% of the total sediment yield. Sediment yields were highly variable, ranging between 0.85 t ha−1 yr−1 and 6.69 t ha−1 yr−1 with an average of 2.4 t ha−1 yr−1. Ten percent of rainfall events with the largest sediment yields produced over 50% of the total sediment yield during the 34 year period. Linear regression models were developed to relate precipitation and runoff characteristics to watershed sediment yield. Maximum 30 min precipitation intensity was the primary factor affecting runoff, and runoff was the best predictor for sediment yield, explaining up to 90% of its variability. Fire and drought may have significantly altered the hydrologic and sediment response on some of the watersheds, but lack of continuous monitoring of vegetation on the watershed areas complicated interpretation of both fire and grazing management effects.
A symbol by symbol clustering based blind equalizer

A new blind symbol by symbol equalizer is proposed. The operation of the proposed equalizer is based on the geometric properties of the two dimensional data constellation. An unsupervised clustering technique is used to locate the clusters formed by the received data. The symmetric properties of the clusters labels are subsequently investigated in order to label the clusters. Following this step, the received data are compared to clusters and decisions are made on a symbol by symbol basis by assigning to each data the label of the nearest cluster. The performance of the proposed equalizer is better compared to the performance of a CMA-based blind equalizer.
Expression of lncRNAs in Low-Grade Gliomas and Glioblastoma Multiforme: An In Silico Analysis

Background Each year, over 16,000 patients die from malignant brain cancer in the US. Long noncoding RNAs (lncRNAs) have recently been shown to play critical roles in regulating neurogenesis and brain tumor progression. To better understand the role of lncRNAs in brain cancer, we performed a global analysis to identify and characterize all annotated and novel lncRNAs in both grade II and III gliomas as well as grade IV glioblastomas (glioblastoma multiforme [GBM]). Methods and Findings We determined the expression of all lncRNAs in over 650 brain cancer and 70 normal brain tissue RNA sequencing datasets from The Cancer Genome Atlas (TCGA) and other publicly available datasets. We identified 611 induced and 677 repressed lncRNAs in glial tumors relative to normal brains. Hundreds of lncRNAs were specifically expressed in each of the three lower grade glioma (LGG) subtypes (IDH1/2 wt, IDH1/2 mut, and IDH1/2 mut 1p19q codeletion) and the four subtypes of GBMs (classical, mesenchymal, neural, and proneural). Overlap between the subtype-specific lncRNAs in GBMs and LGGs demonstrated similarities between mesenchymal GBMs and IDH1/2 wt LGGs, with 2-fold higher overlap than would be expected by random chance. Using a multivariate Cox regression survival model, we identified 584 and 282 lncRNAs that were associated with a poor and good prognosis, respectively, in GBM patients. We developed a survival algorithm for LGGs based on the expression of 64 lncRNAs that was associated with patient prognosis in a test set (hazard ratio [HR] = 2.168, 95% CI = 1.765–2.807, p < 0.001) and validation set (HR = 1.921, 95% CI = 1.333–2.767, p < 0.001) of patients from TCGA. The main limitations of this study are that further work is needed to investigate the clinical relevance of our findings, and that validation in an independent dataset is needed to determine the robustness of our survival algorithm. Conclusions This work identifies a panel of lncRNAs that appear to be prognostic in gliomas and provides a critical resource for future studies examining the role of lncRNAs in brain cancers.
To Study the Effects of Basis and Sensing Matrix on Compressive Sensing

Compressive sensing (CS) is the appropriate way to recover the compressible signal with very few observations or precisely very little number of measurements rather than the conventional methods. As per the theory given by Shannon for proper recovery of a signal the sampling frequency must be greater than or equal to the largest frequency component in that signal. So the storage requirement to store the data according to the Nyquist theorem is too high. So compressive sensing is used to reduce this storage requirement. There are two important parameters one is sensing matrix and another is measurement matrix by changing these two parameters we can change the quality of the recovered signal. There are various reconstruction algorithms which are used for proper reconstruction of signal. The work which is done in this paper comprises of various music signals on which the compressive sensing applied. As per the result the single tone music signal have less value of MSE than the multi tone and vocal song signal. The SNR value is quiet good for single tone than the multi tone & vocal song. This is due to the single frequency component in the single tone music signal.
Multivariate immune defences and fitness in the wild: complex but ecologically important associations among plasma antibodies, health and survival

Despite our rapidly advancing mechanistic understanding of vertebrate immunity under controlled laboratory conditions, the links between immunity, infection and fitness under natural conditions remain poorly understood. Antibodies are central to acquired immune responses, and antibody levels circulating in vivo reflect a composite of constitutive and induced functional variants of diverse specificities (e.g. binding antigens from prevalent parasites, self tissues or novel non-self sources). Here, we measured plasma concentrations of 11 different antibody types in adult females from an unmanaged population of Soay sheep on St Kilda. Correlations among antibody measures were generally positive but weak, and eight of the measures independently predicted body mass, strongyle parasite egg count or survival over the subsequent winter. These independent and, in some cases, antagonistic relationships point to important multivariate immunological heterogeneities affecting organismal health and fitness in natural systems. Notably, we identified a strong positive association between anti-nematode immunoglobulin (Ig) G antibodies in summer and subsequent over-winter survival, providing rare evidence for a fitness benefit of helminth-specific immunity under natural conditions. Our results highlight both the evolutionary and ecological importance and the complex nature of the immune phenotype in the wild.
Image retrieval with random bubbles

In this work we propose an algorithm for content based image retrieval based on random selection of circular bubbles on the reference image. More specifically, an image finger-print vector is extracted from the image, the components of which are simple statistical parameters associated to the luminance values in some selected circular areas of the image. The positions and radius of these bubbles result from a random selection, with characteristics defined by the user. In this way, the extracted fingerprint is very robust with respect to linear and nonlinear distortion of the image. Experiments based on the detection of various linearly and nonlinearly distorted versions of a test image in a large database have shown very promising results.