Utilisation of noise level emitted by the electric arc furnace (EAF) for optimisation of the feeding process of slag foaming materials

The present paper reveals methodology of a research and the results of noise level measurements with respect to fluctuations of active power during EAF operation in one of the melt shops in Poland. In the current stage of experiment, the authors focused themselves on finding the optimum moment to start feeding of foaming material with the aim to obtain the most efficient slag foaming with the fastest coverage of electric arcs at the lowest losses of foaming material. Achieved results indicate that relation between the level of emitted noise and fluctuations of active power during melting can be utilised to define the moment to start feeding of slag foaming materials into the furnace

Utilisation of noise level emitted by the electric arc furnace (EAF) for optimisation of the feeding process of slag foaming materials

The present paper reveals methodology of a research and the results of noise level measurements with respect to fluctuations of active power during EAF operation in one of the melt shops in Poland. In the current stage of experiment, the authors focused themselves on finding the optimum moment to start feeding of foaming material with the aim to obtain the most efficient slag foaming with the fastest coverage of electric arcs at the lowest losses of foaming material. Achieved results indicate that relation between the level of emitted noise and fluctuations of active power during melting can be utilised to define the moment to start feeding of slag foaming materials into the furnace

Probing the radial temperature structure of protoplanetary disks with Herschel/HIFI

Herschel/HIFI spectroscopic observations of CO J=10-9, CO J=16-15 and [CII] towards HD 100546 are presented. The objective is to resolve the velocity profile of the lines to address the emitting region of the transitions and directly probe the distribution of warm gas in the disk. The spectra reveal double-peaked CO line profiles centered on the systemic velocity, consistent with a disk origin. The J=16-15 line profile is broader than that of the J=10-9 line, which in turn is broader than those of lower J transitions (6-5, 3-2, observed with APEX), thus showing a clear temperature gradient of the gas with radius. A power-law flat disk model is used to fit the CO line profiles and the CO rotational ladder simultaneously, yielding a temperature of T_0=1100 \pm 350 K (at r_0 = 13 AU) and an index of q=0.85 \pm 0.1 for the temperature radial gradient. This indicates that the gas has a steeper radial temperature gradient than the dust (mean q_{dust} ~ 0.5), providing further proof of the thermal decoupling of gas and dust at the disk heights where the CO lines form. The [CII] line profile shows a strong single-peaked profile red-shifted by 0.5 km s-1 compared to the systemic velocity. We conclude that the bulk of the [CII] emission has a non-disk origin (e.g., remnant envelope or diffuse cloud).Comment: Accepted for publication in ApJ

Determining the mid-plane conditions of circumstellar discs using gas and dust modelling: a study of HD 163296

The mass of gas in protoplanetary discs is a quantity of great interest for assessing their planet formation potential. Disc gas masses are, however, traditionally inferred from measured dust masses by applying an assumed standard gas-to-dust ratio of $g/d=100$. Furthermore, measuring gas masses based on CO observations has been hindered by the effects of CO freeze-out. Here we present a novel approach to study the mid-plane gas by combining C$^{18}$O line modelling, CO snowline observations and the spectral energy distribution (SED) and selectively study the inner tens of au where freeze-out is not relevant. We apply the modelling technique to the disc around the Herbig Ae star HD 163296 with particular focus on the regions within the CO snowline radius, measured to be at 90 au in this disc. Our models yield the mass of C$^{18}$O in this inner disc region of $M_{\text{C}^{18}\text{O}}(<90\,\text{au})\sim 2\times10^{-8}$ M$_\odot$. We find that most of our models yield a notably low $g/d<20$, especially in the disc mid-plane ($g/d<1$). Our only models with a more interstellar medium (ISM)-like $g/d$ require C$^{18}$O to be underabundant with respect to the ISM abundances and a significant depletion of sub-micron grains, which is not supported by scattered light observations. Our technique can be applied to a range of discs and opens up a possibility of measuring gas and dust masses in discs within the CO snowline location without making assumptions about the gas-to-dust ratio.This work has been supported by the DISCSIM project, grant agreement 341137 funded by the European Research Council under ERC-2013-ADG. DMB is funded by this ERC grant and an STFC studentship. OP is supported by the Royal Society Dorothy Hodgkin Fellowship. During a part of this project OP was supported by the European Union through ERC grant number 279973. TJH is funded by the STFC consolidated grant ST/K000985/1.This is the final version of the article. It first appeared from Oxford University Press via http://dx.doi.org/10.1093/mnras/stw132

Adapting TDMA arbitration for measurement-based probabilistic timing analysis

Critical Real-Time Embedded Systems require functional and timing validation to prove that they will perform their functionalities correctly and in time. For timing validation, a bound to the Worst-Case Execution Time (WCET) for each task is derived and passed as an input to the scheduling algorithm to ensure that tasks execute timely. Bounds to WCET can be derived with deterministic timing analysis (DTA) and probabilistic timing analysis (PTA), each of which relies upon certain predictability properties coming from the hardware/software platform beneath. In particular, specific hardware designs are needed for both DTA and PTA, which challenges their adoption by hardware vendors. This paper makes a step towards reconciling the hardware needs of DTA and PTA timing analyses to increase the likelihood of those hardware designs to be adopted by hardware vendors. In particular, we show how Time Division Multiple Access (TDMA), which has been regarded as one of the main DTA-compliant arbitration policies, can be used in the context of PTA and, in particular, of the industrially-friendly Measurement-Based PTA (MBPTA). We show how the execution time measurements taken as input for MBPTA need to be padded to obtain reliable and tight WCET estimates on top of TDMA-arbitrated hardware resources with no further hardware support. Our results show that TDMA delivers tighter WCET estimates than MBPTA-friendly arbitration policies, whereas MBPTA-friendly policies provide higher average performance. Thus, the best policy to choose depends on the particular needs of the end user.The research leading to these results has been funded by the EU FP7 under grant agreement no. 611085 (PROXIMA) and 287519 (parMERASA). This work has also been partially supported by the Spanish Ministry of Economy and Competitiveness (MINECO) under grant TIN2015-65316-P and the HiPEAC Network of Excellence. Miloˇs Pani´c is funded by the Spanish Ministry of Education under the FPU grant FPU12/05966. Jaume Abella has been partially supported by the MINECO under Ramon y Cajal postdoctoral fellowship number RYC-2013-14717.Peer ReviewedPostprint (author's final draft

Deep learning to segment liver metastases on CT images: Impact on a radiomics method to predict response to chemotherapy

Predicting response to neo-adjuvant chemotherapy of liver metastases (mts) using CT images is of key importance to provide personalized treatments. However, manual segmentation of mts should be avoid to develop methods that could be integrated into the clinical practice. The aim of this study is to evaluate if and how much automatic segmentation can affect a radiomics-based method to predict response to neoadjuvant chemotherapy of individual liver mts. To this scope, we developed an automatic deep learning method to segment liver mts, based on the U-net architecture, and we compared the classification results of a classifier fed with manual and automatic masks. In the validation set composed of 39 liver mts, the automatic deeplearning algorithm was able to detect 82% of mts, with a median precision of 67%. Using manual and automatic masks, we obtained the same classification in 19/32 mts. In case of mts with largest diameter > 20 mm, the precision of the segmentation does not impact the classification results and we obtained the same classification with both masks. Conversely, with smaller mts, we showed that a Dice coefficient of at least 0.5 should be obtained to extract the same information from the two segmentations. This are very important results in the perspective of using radiomics-based approach to predict response to therapy into clinical practice. Indeed, either precisely manually segment all lesions or refine them after automatic segmentation is a time-consuming task that cannot be performed on a daily basis

TW Hya: an old protoplanetary disc revived by its planet

Dark rings with bright rims are the indirect signposts of planets embedded in protoplanetary discs. In a recent first, an azimuthally elongated AU-scale blob, possibly a planet, was resolved with ALMA in TW Hya. The blob is at the edge of a cliff-like rollover in the dust disc rather than inside a dark ring. Here we build time-dependent models of TW Hya disc. We find that the classical paradigm cannot account for the morphology of the disc and the blob. We propose that ALMA-discovered blob hides a Neptune mass planet losing gas and dust. We show that radial drift of mm-sized dust particles naturally explains why the blob is located on the edge of the dust disc. Dust particles leaving the planet perform a characteristic U-turn relative to it, producing an azimuthally elongated blob-like emission feature. This scenario also explains why a 10 Myr old disc is so bright in dust continuum. Two scenarios for the dust-losing planet are presented. In the first, a dusty pre-runaway gas envelope of a ∼40M⊕ Core Accretion planet is disrupted, e.g. as a result of a catastrophic encounter. In the second, a massive dusty pre-collapse gas giant planet formed by Gravitational Instability is disrupted by the energy released in its massive core. Future modelling may discriminate between these scenarios and allow us to study planet formation in an entirely new way – by analysing the flows of dust and gas recently belonging to planets, informing us about the structure of pre-disruption planetary envelopes

Comparison of different classifiers to recognize active bone marrow from CT images

One of the main problems during in the treatment of anal cancer with chemotherapy and radiation is the occurrence of Hematologic Toxicity (HT). In particular, during radiotherapy it is crucial to spare Bone Marrow (BM), since the radiation dose received by BM in pelvic bones predicts the onset of HT. In this direction, the most popular strategies are based on the identification of the hematopoietically active BM (actBM), that is the part of BM in charge of blood cells generation, using MRI, SPECT or PET, but no approached have been proposed based on CT. In this study we compare four different classifiers in recognizing actBM from CT images using 36 radiomic features. We used Genetic Algorithms (GAs) to simultaneously optimize the feature subsets and the classifier parameters, separately for three pelvic subregions: iliac bone marrow (IBM), lower pelvis bone marrow (LPBM), and lumbosacral bone marrow (LSBM). The obtained classifiers were applied to CT sequences of a cohort of 25 patients affected by carcinoma of the anal canal. Classifiers results were compared with the actBM identified from 18FDG-PET (reference standard, RS). It emerged that the performances of the 4 classifiers are similar and they are satisfactory for IBM and LSBM subregions (Dice > 0.7) whereas they are poor for LPBM (Dice < 0.5)

Deep learning model for automatic prostate segmentation on bicentric T2w images with and without endorectal coil

Automatic segmentation of the prostate on Magnetic Resonance Imaging (MRI) is one of the topics on which research has focused in recent years as it is a fundamental first step in the building process of a Computer aided diagnosis (CAD) system for cancer detection. Unfortunately, MRI acquired in different centers with different scanners leads to images with different characteristics. In this work, we propose an automatic algorithm for prostate segmentation, based on a U-Net applying transfer learning method in a bi-center setting. First, T2w images with and without endorectal coil from 80 patients acquired at Center A were used as training set and internal validation set. Then, T2w images without endorectal coil from 20 patients acquired at Center B were used as external validation. The reference standard for this study was manual segmentation of the prostate gland performed by an expert operator. The results showed a Dice similarity coefficient >85% in both internal and external validation datasets.Clinical Relevance- This segmentation algorithm could be integrated into a CAD system to optimize computational effort in prostate cancer detection