ABSTRACT
A new upper limit on the 21 cm signal power spectrum at a redshift of z ≈ 9.1 is presented, based on 141 h of data obtained with the Low-Frequency Array (LOFAR). The analysis includes ...significant improvements in spectrally smooth gain-calibration, Gaussian Process Regression (GPR) foreground mitigation and optimally weighted power spectrum inference. Previously seen ‘excess power’ due to spectral structure in the gain solutions has markedly reduced but some excess power still remains with a spectral correlation distinct from thermal noise. This excess has a spectral coherence scale of 0.25–0.45 MHz and is partially correlated between nights, especially in the foreground wedge region. The correlation is stronger between nights covering similar local sidereal times. A best 2-σ upper limit of $\Delta ^2_{21} \lt (73)^2\, \mathrm{mK^2}$ at $k = 0.075\, \mathrm{h\, cMpc^{-1}}$ is found, an improvement by a factor ≈8 in power compared to the previously reported upper limit. The remaining excess power could be due to residual foreground emission from sources or diffuse emission far away from the phase centre, polarization leakage, chromatic calibration errors, ionosphere, or low-level radiofrequency interference. We discuss future improvements to the signal processing chain that can further reduce or even eliminate these causes of excess power.
An inexpensive and effective adsorbent was developed from waste tea leaves for the dynamic uptake of Pb(II). Characterization of the adsorbents showed a clear change between physico-chemical ...properties of activated tea waste and simply tea waste. The purpose of this work was to evaluate the potential of activated tea waste in continuous flow removal of Pb(II) ions from synthetic aqueous effluents. The performance of the system was evaluated to assess the effect of various process variables, viz., of bed height, hydraulic loading rate and initial feed concentration on breakthrough time and adsorption capacity. The shape of the breakthrough curves was determined for the adsorption of Pb(II) by varying different operating parameters like hydraulic loading rate (2.3–9.17
m
3/h
m
2), bed height (0.3–0.5
m) and feed concentration (2–10
mg/l). An attempt has also been made to model the data generated from column studies using the empirical relationship based on the Bohart–Adams model. There was an acceptable degree of agreement between the data for breakthrough time calculated from the Bohart–Adams model and the present experimental study with average absolute deviation of less than 5.0%. The activated tea waste in this study showed very good promise as compared with the other adsorbents available in the literature. The adsorbent could be suitable for repeated use (for more than four cycles) without noticeable loss of capacity.
All ten LIGO/Virgo binary black hole (BH-BH) coalescences reported following the O1/O2 runs have near-zero effective spins. There are only three potential explanations for this. If the BH spin ...magnitudes are large, then: (i) either both BH spin vectors must be nearly in the orbital plane or (ii) the spin angular momenta of the BHs must be oppositely directed and similar in magnitude. Then there is also the possibility that (iii) the BH spin magnitudes are small. We consider the third hypothesis within the framework of the classical isolated binary evolution scenario of the BH-BH merger formation. We test three models of angular momentum transport in massive stars: a mildly efficient transport by meridional currents (as employed in the Geneva code), an efficient transport by the Tayler-Spruit magnetic dynamo (as implemented in the MESA code), and a very-efficient transport (as proposed by Fuller et al.) to calculate natal BH spins. We allow for binary evolution to increase the BH spins through accretion and account for the potential spin-up of stars through tidal interactions. Additionally, we update the calculations of the stellar-origin BH masses, including revisions to the history of star formation and to the chemical evolution across cosmic time. We find that we can simultaneously match the observed BH-BH merger rate density and BH masses and BH-BH effective spins. Models with efficient angular momentum transport are favored. The updated stellar-mass weighted gas-phase metallicity evolution now used in our models appears to be key for obtaining an improved reproduction of the LIGO/Virgo merger rate estimate. Mass losses during the pair-instability pulsation supernova phase are likely to be overestimated if the merger GW170729 hosts a BH more massive than 50
M
⊙
. We also estimate rates of black hole-neutron star (BH-NS) mergers from recent LIGO/Virgo observations. If, in fact. angular momentum transport in massive stars is efficient, then any (electromagnetic or gravitational wave) observation of a rapidly spinning BH would indicate either a very effective tidal spin up of the progenitor star (homogeneous evolution, high-mass X-ray binary formation through case A mass transfer, or a spin- up of a Wolf-Rayet star in a close binary by a close companion), significant mass accretion by the hole, or a BH formation through the merger of two or more BHs (in a dense stellar cluster).
The phase III MONALEESA-2 study demonstrated significantly prolonged progression-free survival (PFS) and a manageable toxicity profile for first-line ribociclib plus letrozole versus placebo plus ...letrozole in patients with hormone receptor-positive (HR+), human epidermal growth factor receptor 2-negative (HER2–) advanced breast cancer. Here, we report updated efficacy and safety data, together with exploratory biomarker analyses, from the MONALEESA-2 study.
A total of 668 postmenopausal women with HR+, HER2– recurrent/metastatic breast cancer were randomized (1 : 1; stratified by presence/absence of liver and/or lung metastases) to ribociclib (600mg/day; 3-weeks-on/1-week-off; 28-day treatment cycles) plus letrozole (2.5mg/day; continuous) or placebo plus letrozole. The primary end point was locally assessed PFS. The key secondary end point was overall survival (OS). Other secondary end points included overall response rate (ORR) and safety. Biomarker analysis was an exploratory end point.
At the time of the second interim analysis, the median duration of follow-up was 26.4months. Median PFS was 25.3months 95% confidence interval (CI) 23.0–30.3 for ribociclib plus letrozole and 16.0months (95% CI 13.4–18.2) for placebo plus letrozole (hazard ratio 0.568; 95% CI 0.457–0.704; log-rank P=9.63×10−8). Ribociclib treatment benefit was maintained irrespective of PIK3CA or TP53 mutation status, total Rb, Ki67, or p16 protein expression, and CDKN2A, CCND1, or ESR1 mRNA levels. Ribociclib benefit was more pronounced in patients with wild-type versus altered receptor tyrosine kinase genes. OS data remain immature, with 116 deaths observed; 50 in the ribociclib arm and 66 in the placebo arm (hazard ratio 0.746; 95% CI 0.517–1.078). The ORR was 42.5% versus 28.7% for all patients treated with ribociclib plus letrozole versus placebo plus letrozole, respectively, and 54.5% versus 38.8%, respectively, for patients with measurable disease. Safety results, after a further 11.1months of follow-up, were comparable with those reported at the first analysis, with no new or unexpected toxicities observed, and no evidence of cumulative toxicity.
The improved efficacy outcomes and manageable tolerability observed with first-line ribociclib plus letrozole are maintained with longer follow-up, relative to letrozole monotherapy.
NCT01958021
ABSTRACT
We derive constraints on the thermal and ionization states of the intergalactic medium (IGM) at redshift ≈ 9.1 using new upper limits on the 21-cm power spectrum measured by the LOFAR radio ...telescope and a prior on the ionized fraction at that redshift estimated from recent cosmic microwave background (CMB) observations. We have used results from the reionization simulation code grizzly and a Bayesian inference framework to constrain the parameters which describe the physical state of the IGM. We find that, if the gas heating remains negligible, an IGM with ionized fraction ≳0.13 and a distribution of the ionized regions with a characteristic size ≳ 8 h−1 comoving megaparsec (Mpc) and a full width at half-maximum (FWHM) ≳16 h−1 Mpc is ruled out. For an IGM with a uniform spin temperature TS ≳ 3 K, no constraints on the ionized component can be computed. If the large-scale fluctuations of the signal are driven by spin temperature fluctuations, an IGM with a volume fraction ≲0.34 of heated regions with a temperature larger than CMB, average gas temperature 7–160 K, and a distribution of the heated regions with characteristic size 3.5–70 h−1 Mpc and FWHM of ≲110 h−1 Mpc is ruled out. These constraints are within the 95 per cent credible intervals. With more stringent future upper limits from LOFAR at multiple redshifts, the constraints will become tighter and will exclude an increasingly large region of the parameter space.
Manipulation of magnetisation with ultrashort laser pulses is promising for information storage device applications. The dynamics of the magnetisation response depends on the energy transfer from the ...photons to the spins during the initial laser excitation. A material of special interest for magnetic storage are FePt nanoparticles, for which switching of the magnetisation with optical angular momentum was demonstrated recently. The mechanism remained unclear. Here we investigate experimentally and theoretically the all-optical switching of FePt nanoparticles. We show that the magnetisation switching is a stochastic process. We develop a complete multiscale model which allows us to optimize the number of laser shots needed to switch the magnetisation of high anisotropy FePt nanoparticles in our experiments. We conclude that only angular momentum induced optically by the inverse Faraday effect will provide switching with one single femtosecond laser pulse.
Wearable Sensors for Remote Health Monitoring Majumder, Sumit; Mondal, Tapas; Deen, M Jamal
Sensors (Basel, Switzerland),
01/2017, Letnik:
17, Številka:
1
Journal Article, Book Review
Recenzirano
Odprti dostop
Life expectancy in most countries has been increasing continually over the several few decades thanks to significant improvements in medicine, public health, as well as personal and environmental ...hygiene. However, increased life expectancy combined with falling birth rates are expected to engender a large aging demographic in the near future that would impose significant burdens on the socio-economic structure of these countries. Therefore, it is essential to develop cost-effective, easy-to-use systems for the sake of elderly healthcare and well-being. Remote health monitoring, based on non-invasive and wearable sensors, actuators and modern communication and information technologies offers an efficient and cost-effective solution that allows the elderly to continue to live in their comfortable home environment instead of expensive healthcare facilities. These systems will also allow healthcare personnel to monitor important physiological signs of their patients in real time, assess health conditions and provide feedback from distant facilities. In this paper, we have presented and compared several low-cost and non-invasive health and activity monitoring systems that were reported in recent years. A survey on textile-based sensors that can potentially be used in wearable systems is also presented. Finally, compatibility of several communication technologies as well as future perspectives and research challenges in remote monitoring systems will be discussed.
This paper focuses on the application of machine learning algorithms for predicting spinal abnormalities. As a data preprocessing step, univariate feature selection as a filter based feature ...selection, and principal component analysis (PCA) as a feature extraction algorithm are considered. A number of machine learning approaches namely support vector machine (SVM), logistic regression (LR), bagging ensemble methods are considered for the diagnosis of spinal abnormality. The SVM, LR, bagging SVM and bagging LR models are applied on a dataset of 310 samples publicly available in Kaggle repository. The performance of classification of abnormal and normal spinal patients is evaluated in terms of a number of factors including training and testing accuracy, recall, and miss rate. The classifier models are also evaluated by optimizing the kernel parameters, and by using the results of receiver operating characteristic (ROC) and precision-recall curves. Results indicate that when 78% data are used for training, the observed training accuracies for SVM, LR, bagging SVM and bagging LR are 86.30%, 85.47%, 86.72% and 85.06%, respectively. On the other hand, the accuracies for the test dataset for SVM, LR, bagging SVM and bagging LR are the same being 86.96%. However, bagging SVM is the most attractive as it has a higher recall value and a lower miss rate compared to others. Hence, bagging SVM is suitable for the classification of spinal patients when applied on the most five important features of spinal samples.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Microplastics (MPs) pollution has become one of the most severe environmental concerns today. MPs persist in the environment and cause adverse effects in organisms. This review aims to present a ...state-of-the-art overview of MPs in the aquatic environment. Personal care products, synthetic clothing, air-blasting facilities and drilling fluids from gas-oil industries, raw plastic powders from plastic manufacturing industries, waste plastic products and wastewater treatment plants act as the major sources of MPs. For MPs analysis, pyrolysis-gas chromatography–mass spectrometry (Py-GC–MS), Py-MS methods, Raman spectroscopy, and FT-IR spectroscopy are regarded as the most promising methods for MPs identification and quantification. Due to the large surface area to volume ratio, crystallinity, hydrophobicity and functional groups, MPs can interact with various contaminants such as heavy metals, antibiotics and persistent organic contaminants. Among different physical and biological treatment technologies, the MPs removal performance decreases as membrane bioreactor (> 99%) > activated sludge process (~98%) > rapid sand filtration (~97.1%) > dissolved air floatation (~95%) > electrocoagulation (> 90%) > constructed wetlands (88%). Chemical treatment methods such as coagulation, magnetic separations, Fenton, photo-Fenton and photocatalytic degradation also show moderate to high efficiency of MP removal. Hybrid treatment technologies show the highest removal efficacies of MPs. Finally, future research directions for MPs are elaborated.
Display omitted
•MPs act as a significant vector of various organic and inorganic pollutants.•Best physical removal methods are UF, dynamic membrane filtration and rapid sand filtration.•Best chemical treatment methods are EC, sol-gel-agglomeration and electro-Fenton.•Constructed wetlands and MBRs are efficient biological treatment technologies.•Hybrid treatment technologies provide the highest MPs removal.
Lung disease is common throughout the world. These include chronic obstructive pulmonary disease, pneumonia, asthma, tuberculosis, fibrosis, etc. Timely diagnosis of lung disease is essential. Many ...image processing and machine learning models have been developed for this purpose. Different forms of existing deep learning techniques including convolutional neural network (CNN), vanilla neural network, visual geometry group based neural network (VGG), and capsule network are applied for lung disease prediction. The basic CNN has poor performance for rotated, tilted, or other abnormal image orientation. Therefore, we propose a new hybrid deep learning framework by combining VGG, data augmentation and spatial transformer network (STN) with CNN. This new hybrid method is termed here as VGG Data STN with CNN (VDSNet). As implementation tools, Jupyter Notebook, Tensorflow, and Keras are used. The new model is applied to NIH chest X-ray image dataset collected from Kaggle repository. Full and sample versions of the dataset are considered. For both full and sample datasets, VDSNet outperforms existing methods in terms of a number of metrics including precision, recall, F0.5 score and validation accuracy. For the case of full dataset, VDSNet exhibits a validation accuracy of 73%, while vanilla gray, vanilla RGB, hybrid CNN and VGG, and modified capsule network have accuracy values of 67.8%, 69%, 69.5% and 63.8%, respectively. When sample dataset rather than full dataset is used, VDSNet requires much lower training time at the expense of a slightly lower validation accuracy. Hence, the proposed VDSNet framework will simplify the detection of lung disease for experts as well as for doctors.
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