综合资源展示 综合资源展示


1 Multi-view multi-scale CNNs for lung nodule type classification from CT images 2018-01-18             

HTML   |   详细信息   |  

Publication date: May 2018
Source:Pattern Recognition, Volume 77

Author(s): Xinglong Liu, Fei Hou, Hong Qin, Aimin Hao

In this paper, we propose a novel convolution neural networks (CNNs) based method for nodule type classification. Compared with classical approaches that are handling four solid nodule types, i.e., well-circumscribed, vascularized, juxta-pleural and pleural-tail, our method could also achieve competitive classification rates on ground glass optical (GGO) nodules and non-nodules in computed tomography (CT) scans. The proposed method is based on multi-view multi-scale CNNs and comprises four main stages. First, we approximate the spherical surface centered at nodules using icosahedra and capture normalized sampling for CT values on each circular plane at a given maximum radius. Second, intensity analysis is applied based on the sampled values to achieve estimated radius for each nodule. Third, the re-sampling (which is the same as the first step but with estimated radius) is conducted, followed by a high frequency content measure analysis to decide which planes (views) are more abundant in information. Finally, with approximated radius and sorted circular planes, we build nodule captures at sorted scales and views to first pre-train a view independent CNNs model and then train a multi-view CNNs model with maximum pooling. The experimental results on both Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) [1] and Early Lung Cancer Action Program(ELCAP) [2] have shown the promising classification performance even with complex GGO and non-nodule types.

2 Estimating potential dust emissions from biochar amended soils under simulated tillage 2018-01-14             

HTML   |   详细信息   |  

Publication date: 1 June 2018
Source:Science of The Total Environment, Volume 625

Author(s): Chongyang Li, Daniel A. Bair, Sanjai J. Parikh

Although biochars may provide agricultural benefits, the potential risks related to agricultural dust emissions have not been adequately investigated. This study examines the impact of biochar type (WS 900: walnut shell, 900°C; PW 500, PW 700 and PW 900: pine wood, 500, 700, 900°C), biochar application rate (0, 1, 2, 5% wt.) and soil water content (low, medium and high) on dust emissions in two different textured-soils (silt loam, sandy loam). Dust was produced via a dust generator simulating soil disturbance (e.g, tillage) and dust fractions with an aerodynamic diameter under 100μm and 10μm (PM100 and PM10) were collected. The data indicate that the higher application rate of WS 900 led to higher PM100 and PM10 emissions while PW biochar treatments emitted equivalent amounts of dust as controls (non-amended soils). Dust emissions were exponentially reduced as soil water content increased, irrespective of biochar's presence. Specific markers for biochar, benzene polycarboxylic acids (BPCAs), were used to estimate the biochar content within dust. Results indicate that the increased dust emissions from WS 900 treatments mainly derive from soil particles due to the greater dispersion potential of WS 900 biochar. The collected data also reveal that PM10 dust contains less biochar particles than PM100, attributed to biochars originally containing negligible amounts of particles <10μm.

Graphical abstract


3 Image compression-encryption algorithms by combining hyper-chaotic system with discrete fractional random transform 2018-01-14             

HTML   |   详细信息   |  

Publication date: July 2018
Source:Optics & Laser Technology, Volume 103

Author(s): Lihua Gong, Chengzhi Deng, Shumin Pan, Nanrun Zhou

Based on hyper-chaotic system and discrete fractional random transform, an image compression-encryption algorithm is designed. The original image is first transformed into a spectrum by the discrete cosine transform and the resulting spectrum is compressed according to the method of spectrum cutting. The random matrix of the discrete fractional random transform is controlled by a chaotic sequence originated from the high dimensional hyper-chaotic system. Then the compressed spectrum is encrypted by the discrete fractional random transform. The order of DFrRT and the parameters of the hyper-chaotic system are the main keys of this image compression and encryption algorithm. The proposed algorithm can compress and encrypt image signal, especially can encrypt multiple images once. To achieve the compression of multiple images, the images are transformed into spectra by the discrete cosine transform, and then the spectra are incised and spliced into a composite spectrum by Zigzag scanning. Simulation results demonstrate that the proposed image compression and encryption algorithm is of high security and good compression performance.

4 Modeling soil organic carbon with Quantile Regression: Dissecting predictors' effects on carbon stocks 2018-01-12             

HTML   |   详细信息   |  

Publication date: 15 May 2018
Source:Geoderma, Volume 318

Author(s): Luigi Lombardo, Sergio Saia, Calogero Schillaci, P. Martin Mai, Raphaël Huser

Soil organic carbon (SOC) estimation is crucial to manage natural and anthropic ecosystems. Many modeling procedures have been tested in the literature, however, most of them do not provide information on predictors' behavior at specific sub-domains of the SOC stock. Here, we implement Quantile Regression (QR) to spatially predict the SOC stock and gain insight on the role of predictors (topographic and remotely sensed) at varying SOC stock (0–30cm depth) in the agricultural areas of an extremely variable semi-arid region (Sicily, Italy, around 25,000km 2). QR produces robust performances (maximum quantile loss = 0.49) and allows to recognize dominant effects among the predictors at varying quantiles. In particular, clay mostly contributes to maintain SOC stock at lower quantiles whereas rainfall and temperature influences are constantly positive and negative, respectively. This information, currently lacking, confirms that QR can discern predictor influences on SOC stock at specific SOC sub-domains. The QR map generated at the median shows a Mean Absolute Error of 17 t SOC ha -1 with respect to the data collected at sampling locations. Such MAE is lower than those of the Joint Research Centre at Global (18 t SOC ha -1) and at European (24 t SOC ha -1) scales and of the International Soil Reference and Information Centre (23 t SOC ha -1) while higher than the MAE reached in Schillaci et al. (2017b) (Geoderma, 2017, issue 286, page 35–45) using the same dataset (15 t SOC ha -1). The results suggest the use of QR as a comprehensive method to map SOC stock using legacy data in agro-ecosystems and to investigate SOC and inherited uncertainty with respect to specific subdomains. The R code scripted in this study for QR is included.

5 A unified framework for sparse non-negative least squares using multiplicative updates and the non-negative matrix factorization problem 2018-01-12             

HTML   |   详细信息   |  

Publication date: May 2018
Source:Signal Processing, Volume 146

Author(s): Igor Fedorov, Alican Nalci, Ritwik Giri, Bhaskar D. Rao, Truong Q. Nguyen, Harinath Garudadri

We study the sparse non-negative least squares (S-NNLS) problem. S-NNLS occurs naturally in a wide variety of applications where an unknown, non-negative quantity must be recovered from linear measurements. We present a unified framework for S-NNLS based on a rectified power exponential scale mixture prior on the sparse codes. We show that the proposed framework encompasses a large class of S-NNLS algorithms and provide a computationally efficient inference procedure based on multiplicative update rules. Such update rules are convenient for solving large sets of S-NNLS problems simultaneously, which is required in contexts like sparse non-negative matrix factorization (S-NMF). We provide theoretical justification for the proposed approach by showing that the local minima of the objective function being optimized are sparse and the S-NNLS algorithms presented are guaranteed to converge to a set of stationary points of the objective function. We then extend our framework to S-NMF, showing that our framework leads to many well known S-NMF algorithms under specific choices of prior and providing a guarantee that a popular subclass of the proposed algorithms converges to a set of stationary points of the objective function. Finally, we study the performance of the proposed approaches on synthetic and real-world data.

6 Hierarchical spheres In2S3-based cataluminescence sensor for ammonium sulfide 2018-01-12             

HTML   |   详细信息   |  

Publication date: May 2018
Source:Microchemical Journal, Volume 138

Author(s): Pingyang Cai, Hongjie Song, Yi Lv

In the present work, three different kinds of In2S3: hierarchical microspheres A, B and C (HMa, HMb and HMc), which were synthesized by a hydrothermal method in the sodium dodecyl sulfate-thiourea (SDS-thiourea) system. XRD, SEM and BET were used to characterize the prepared In2S3 materials. Compared with the other two kinds of In2S3, the as-prepared In2S3 hierarchical microspheres B (In2S3 HMb) exhibit the best cataluminescence response to ammonium sulfide. The response and recovery times of the home-made ammonium sulfide gas sensor with In2S3 hierarchical microspheres B as sensing materials were about 8 s and 24 s, respectively. The linear dependence of the sensitivity on the ammonium sulfide concentration was observed in the range of 4–200 ppm with an excellent selectivity. These results indicated that In2S3 hierarchical microspheres B would be a good candidate for fabricating practical cataluminescence ammonium sulfide sensor.

7 A comparison of 4D flow MRI-derived wall shear stress with computational fluid dynamics methods for intracranial aneurysms and carotid bifurcations — A review 2018-01-11             

HTML   |   详细信息   |  

Publication date: May 2018
Source:Magnetic Resonance Imaging, Volume 48

Author(s): Jeremy Szajer, Kevin Ho-Shon

Background 4D flow MRI is a relatively quick method for obtaining wall shear stress (WSS) in vivo, a hemodynamic parameter which has shown promise in risk stratification for rupture of cerebrovascular diseases such as intracranial aneurysms and atherosclerotic plaques. The accuracy of such measurements is still largely unknown. Objective To quantify the accuracy of 4D flow MRI-derived wall shear stress values for intracranial aneurysms and carotid bifurcations. Method We performed a review of all original research articles which compared the magnitudes of WSS derived from 4D flow MRI with corresponding values derived from computational fluid dynamics (CFD) within both intracranial aneurysms and carotid bifurcations. Result For intracranial aneurysms and carotid bifurcations, 4D flow MRI-derived WSS estimations are generally lower in magnitude compared to WSS derived by CFD methods. These differences are more pronounced in regions of higher WSS. However, the relative distributions of WSS derived from both methods are reasonably similar. Conclusion Pooled analysis suggests that WSS magnitudes obtained by 4D flow MRI are underestimated, while the relative distribution is reasonably accurate, the latter being an important factor for determining the natural history of intracranial aneurysms and other cerebrovascular diseases. 4D flow MRI shows enormous potential in providing new risk stratification parameters which could have significant impact on individualized treatment decisions and improved patient outcomes.

Graphical abstract


8 False positive rates in surface-based anatomical analysis 2018-01-11             

HTML   |   详细信息   |  

Publication date: 1 May 2018
Source:NeuroImage, Volume 171

Author(s): Douglas N. Greve, Bruce Fischl

The false positive rates (FPR) for surface-based group analysis of cortical thickness, surface area, and volume were evaluated for parametric and non-parametric clusterwise correction for multiple comparisons for a range of smoothing levels and cluster-forming thresholds (CFT) using real data under group assignments that should not yield significant results. For whole cortical surface analysis, thickness showed modest inflation in parametric FPRs above the nominal level (10% versus 5%). Surface area and volume FPRs were much higher (20–30%). In the analysis of interhemispheric thickness asymmetries, FPRs were well controlled by parametric correction, but FPRs for surface area and volume asymmetries were still inflated. In all cases, non-parametric permutation adequately controlled the FPRs. It was found that inflated parametric FPRs were caused by violations in the parametric assumptions, namely a heavier-than-Gaussian spatial correlation. The non-Gaussian spatial correlation originates from anatomical features unique to individuals (e.g., a patch of cortex slightly thicker or thinner than average) and is not a by-product of scanning or processing. Thickness performed better than surface area and volume because thickness does not require a Jacobian correction.

9 A new Hysteretic Nonlinear Energy Sink (HNES) 2018-01-11             

HTML   |   详细信息   |  

Publication date: July 2018
Source:Communications in Nonlinear Science and Numerical Simulation, Volume 60

Author(s): George C. Tsiatas, Aristotelis E. Charalampakis

The behavior of a new Hysteretic Nonlinear Energy Sink (HNES) coupled to a linear primary oscillator is investigated in shock mitigation. Apart from a small mass and a nonlinear elastic spring of the Duffing oscillator, the HNES is also comprised of a purely hysteretic and a linear elastic spring of potentially negative stiffness, connected in parallel. The Bouc-Wen model is used to describe the force produced by both the purely hysteretic and linear elastic springs. Coupling the primary oscillator with the HNES, three nonlinear equations of motion are derived in terms of the two displacements and the dimensionless hysteretic variable, which are integrated numerically using the analog equation method. The performance of the HNES is examined by quantifying the percentage of the initially induced energy in the primary system that is passively transferred and dissipated by the HNES. Remarkable results are achieved for a wide range of initial input energies. The great performance of the HNES is mostly evidenced when the linear spring stiffness takes on negative values.

10 GHG avoided emissions and economic analysis by power generation potential in posture aviaries in Brazil 2018-01-11             

HTML   |   详细信息   |  

Publication date: May 2018
Source:Renewable Energy, Volume 120

Author(s): Eruin Martuscelli Ribeiro, Regina Mambeli Barros, Geraldo Lúcio Tiago Filho, Ivan Felipe Silva dos Santos, Luma Canobre Sampaio, Ticiane Vasco dos Santos, Fernando das Graças Braga da Silva, Ana Paula Moni Silva, João Victor Rocha de Freitas

Intensification and mechanization of agricultural activities have brought numerous benefits, among which, is the increase in food production. However, waste generation from animal has also increased tremendously. Creation of laying hens in cage system produces many wastes, which must be removed daily. This is important for the cage and farm environmental protection; which should be properly managed to ensure public health protection. In the pursuit for effective management, by using experimental data of a chicken farm in Itanhandu-MG, Brazil, the possible production at national level, of electric energy by anaerobic digesters from dejections collected in laying hens cages. In the present study, the GHG emissions impacts assessment, by tCO2eq, in the COP21 from Paris 2015 context was evaluated using the Intergovernmental Panel on Climate Change (IPCC) methodology. For the economic viability of these wastes in energy generation, six scenarios of ten years were evaluated, all resulting from the combination between scenarios with and without funding, and with or without internal use of electrical power generated, but with real and presumed profit (in the framework of ANEEL no. 482/2012 and no.687/2015 resolutions) on micro-generation distribution. In all scenarios, energy was considered available to the Brazilian National Interconnected System (SIN), according to the 5th auction average price in 2015. The annual emissions avoided by methane burning and subsequent electricity generated was of 8.02 million of tCO2eq and 38.4 thousands of tCO2eq, respectively. Farms with more than 100,000 birds presented good probability in simulations of certainty, with regards to expected financial return. The most promising scenario was C6, tax modality of presumed profit, internal energy use, and sale of surplus energy (it would be also considered as credit on the next account), with financing of invested capital.

共计:2031条记录 页次:1/204    下一页 末页   跳转到第 页