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Ground improvement is an effective way to improve the bearing capacity of a shallow foundation. However, the benefit of reducing uncertainties in soil parameters for shallow foundation design is rarely recognized. This study investigated the full effect of rapid impact compaction (RIC) on a strip foundation design. The finite difference method coupled Monte Carlo simulation were used to calculate the failure probability and the required width of the strip foundation, where the friction angle of soil was treated as a random variable. The results show that the foundation width reduces by 48.5% when considering the full effect of RIC, and a significant part of the reduction came from the decrease in the uncertainty of friction angle. Although the adopted relationship between the friction angle and tip resistance of cone penetration test affects the designed width of the foundation, the full effect of ground improvement contributed by the uncertainty reduction of soil parameters is still significant. The implication of the present study provides a basis for the performance-based bearing capacity design of shallow foundations. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) image ship target detection has attracted considerable attention. As a state-of-the-art method, the Constant False Alarm Rate (CFAR) detection algorithm is often used in SAR image ship target detection. However, the detection performance of the classical CFAR is easily affected by speckle noise. Moreover, the detection results based on the sliding window are sensitive to the size of the sliding window. Thus, ensuring that there are no target pixels in the cluttered background is difficult, which easily leads to a high computational load. This study proposes a new ship target detection method for SAR images based on fast superpixel-based non-window CFAR to solve these problems. The superpixel generation method of Density Based Spatial Clustering of Applications with Noise is used to generate superpixels for SAR images. Under the assumption that SAR data obey the Rayleigh mixture distribution, we define a superpixel dissimilarity measure. Then, the clutter parameters of each pixel are accurately estimated using superpixels, which can avoid the shortcomings of the traditional CFAR sliding window even in the case of multiple targets. A local contrast based on the Coefficient of Variation (CoV) of the SAR image is proposed to optimize the CFAR detection result, which can eliminate a large number of false alarms from man-made targets in urban areas. The experimental results of five real SAR images show that the proposed method for ship target detection in SAR images with different scenes is robust compared with other state-of-the-art methods.

[Objective] Understandings of the ecological evolution in the Hunshandake Sandy Land are divergent duo to the differences in study area, data used, and period of study. How the vegetation in the hinterland and marginal ecotones of the Hunshandake Sandy Land interacts with regional climate and responds to human activities remain to be addressed. [Methods] In this study, the Gobi desert area was set as the background area (CK), and the Hunshandake Sandy Land and its surrounding areas were divided into a core area (CA) and four ecotones. Based on the long-term GIMMS NDVI3g v1 (1982-2015) dataset, combined with climate data (annual temperature and precipitation) from 1982 to 2015, nighttime light dataset (1984-2015), and population and herbivore data of each banner/county (1986-2015), the correlations between NDVI and climate change and human activities in the CK, CA and the ecotones were analyzed. [Results] The results show that: (1) Vegetation phenology was regionally heterogeneous. The length of growing season (LOS) was between 158 and 196 days, and the phenological difference within the region reached 38 days. (2) The change rate of NDVI was spatially different. Total NDVI increased at a rate of 0.00021/10a, while the rate of change in each region varied, with a maximum difference of 17 folds. (3) The response of vegetation to climate variability was zonal. In these ecotones, NDVI change was more sensitive to monthly temperature than to monthly precipitation. (4) The coefficient of variation (Cov) of vegetation went through several stages. The Cov fluctuated little in the 1980s, was relatively stable in the 1990s, and had higher positive variation in the first 15 years of this century. (5) The response of vegetation cover to human activities was not significant. While NDVI was not correlated with population density and nighttime light intensity per square kilometer, it was locally positively correlated with sheep and total herbivorous livestock density per square kilometer. [Conclusion] These results suggest that the NDVI of the Hunshandake Sandy Land and its ecotones fluctuated in response to climate change, and although it has improved in this century, it has only reached the level of the mid- to late 1980s.

Characterizing spatial information as reinforcement of spectral signatures can largely assist the performance in hyperspectral image (HSI) classification. Markov random fields (MRFs) are probabilistic image texture models and are capable of encoding contextual dependencies through characterizing local conditional probabilities. As a representative standardized measure of dispersion of image probability distributions, the coefficient of variation (CoV) can be a useful tool for characterizing spatial heterogeneity. Their parameter derivation processes also share strong compatibility with convolutional neural networks (CNNs) that specify spatial correlations in local neighborhoods. In this work, we propose an MRF and CoV-based spectral–spatial convolutional network (MRF-CoV-CNN) for HSI classification. MRF models and CoVs are characterized as measures of spatial distributions and further combined with spectral information. Then the proposed MRF-CoV-CNN takes the fused features as input and produces reliable classification results. Comprehensive experiments have been conducted on the Pavia University dataset and the Salinas dataset to evaluate the proposed method both visually and quantitatively. [ABSTRACT FROM AUTHOR]

A large circular transmitter (power pad) is often utilized for inductively wireless charging so that the power receiver can position itself freely within the power pad. However, the uneven magnetic field distribution leads to the impedance mismatch and the variable transferred power due to the misalignment between a receiving coil and a relatively large transmitter coil. In this paper, the effects of the turn numbers, trace spacing, and coil size on the uniformity of magnetic field distribution over a planar spiral coil (PSC) are investigated based on an analytical model. Then, the genetic algorithm is used to optimize the trace width and spacing of a ten-turn PSC with the fixed outer and inner diameters, whereas the magnetic field is numerically calculated by HFSS software. The geometrically optimal PSC is obtained with the trace width approximately equal to trace spacing for each turn by minimizing the coefficient of variation (COV) of magnetic field within an effective charging area over the coil. The results showed that the simulated and measured COVs of magnetic field were 0.130 and 0.121, respectively, which are obviously less than that of the uniformly spaced regular coil and show a little less than that of the optimally spaced coil with fixed trace width. Therefore, the trace spacing is a major consideration to achieve the uniform magnetic field, whereas the trace width variation is applied to refine the evenness of field. It is also expected that the proposed GA-based optimization is well applied to design other Txs with conformable shape in inductively WPT systems for stable power delivery regardless of the receiver positions. [ABSTRACT FROM AUTHOR]

This paper presents a rational approach to answer the questions of how many field tests need to be performed at a test site to determine its subgrade strength properties, and what inferences can be drawn from limited number of field tests. MATLAB simulations were performed to generate various data sets that corresponded to site variability, with the coefficient of variation (CoV) ranging from 10 to 80%. Studies were performed based on coefficient of variation (CoV) and regression analysis. This study provides two important charts for evaluating subgrade properties at test sites: (1) to determine the number of field tests to be performed, after which there would not be any significant change in the inferences drawn from the test results; (2) inferences pertaining to subgrade soil property of a field test site, based on a limited number of tests. In order to demonstrate the applicability of the charts developed through this research, light weight deflectometer (LWD) spot tests that were performed at a test site, were considered. The results were used to draw inferences about the modulus of the subgrade material and the possible variability of the test site, in addition to the maximum number of tests that might be performed. [ABSTRACT FROM AUTHOR]

The prediction of carbonation depth for recycled aggregate concrete (RAC) is investigated in this paper. The existing prediction models were evaluated, and it showed that the coefficient of variation (COV) of model error for the existing models is high. By introducing the weighed water absorption of aggregates, the COV of model error can be effectively decreased. Compared with the existing models, the proposed model can predict more accurate carbonation depths. For RAC specimens, compared with the fib model and Xiao and Lei's model-a, the COV of model error of the proposed model is 0.36 which is decreased by 33.3%, and when compared with Xiao and Lei's model-b and Silva et al.’s model, the corresponding decreases are 55.2% and 16.2%. Finally, the proposed model is validated by a 10-year-old carbonation experiment, which indicates that the proposed model is reasonable and can be applied to predict the carbonation depth of RAC. [ABSTRACT FROM AUTHOR]

Highlights: [•] Gasoline–ethanol–methanol mixtures can be used as ‘drop-in’ fuels for E85. [•] These fuels can help to remove the biomass limit of ethanol. [•] Iso-stoichiometric GEM fuels are compared on an engine test bench. [•] Similar BTE, volumetric efficiency and knock behavior are reported. [Copyright &y& Elsevier]

Abstract: In this paper, a novel decentralized modal identification method is proposed utilizing the concepts of sparse blind source separation (BSS) and parallel factor decomposition. Unlike popular ambient modal identification methods which require large arrays of simultaneous vibration measurements, the decentralized algorithm presented here operates on partial measurements, utilizing a sub-set of sensors at-a-time. Mathematically, this leads to an underdetermined source separation problem, which is addressed using sparsifying wavelet transforms. The proposed method builds on a previously presented concept by the authors, which utilizes the stationary wavelet packet transform (SWPT) to generate an over-complete dictionary of sparse bases. However, the redundant SWPT can be computationally intensive depending on the bandwidth of the signals and the sampling frequency of the vibration measurements. This issue of computational burden is alleviated through a new method proposed here, which is based on a multi-linear algebra tool called PARAllel FACtor (PARAFAC) decomposition. At the core of this method, the wavelet packet decomposition coefficients are used to form a covariance tensor, followed by PARAFAC tensor decomposition to separate the modal responses. The underdetermined source identifiability of PARAFAC enables source separation in wavelet packet coefficients with considerable mode mixing, thereby relaxing the conditions to generate over-complete bases, thus reducing the computational burden. The proposed method is validated using a series of numerical simulations followed by an implementation on recorded ambient vibration measurements obtained from the UCLA factor building. [Copyright &y& Elsevier]

Highlights: [•] Evaluation of practicality of the Durability Index (DI) performance approach. [•] Practicality evaluated based on a large scale project which was the main case study. [•] Variability in test results, application of test, engineers’ perception considered. [•] High variability in results, high proportion that fails to comply with limit value. [•] Practical approach but further work required to address variability issues. [Copyright &y& Elsevier]

Abstract: Combustion in HCCI engines is a controlled auto ignition of well-mixed fuel, air and residual gas. Since onset of HCCI combustion depends on the auto ignition of fuel/air mixture, there is no direct control on the start of combustion process. Therefore, HCCI combustion becomes unstable rather easily, especially at lower and higher engine loads. In this study, cycle-to-cycle variations of a HCCI combustion engine fuelled with ethanol were investigated on a modified two-cylinder engine. Port injection technique is used for preparing homogeneous charge for HCCI combustion. The experiments were conducted at varying intake air temperatures and air–fuel ratios at constant engine speed of 1500rpm and P-θ diagram of 100 consecutive combustion cycles for each test conditions at steady state operation were recorded. Consequently, cycle-to-cycle variations of the main combustion parameters and performance parameters were analyzed. To evaluate the cycle-to-cycle variations of HCCI combustion parameters, coefficient of variation (COV) of every parameter were calculated for every engine operating condition. The critical optimum parameters that can be used to define HCCI operating ranges are ‘maximum rate of pressure rise’ and ‘COV of indicated mean effective pressure (IMEP)’. [ABSTRACT FROM AUTHOR]

Abstract: The lower Tarim River had dried up nearly 30years before an ecological water diversion project (EWDP) for ecological restoration was implemented in 2000. Since then, eight intermittent water deliveries have been carried out for restoring this seriously degraded riparian ecosystem. To evaluate the efficacy and effectiveness of these operations, the Normalized Difference Vegetation Index (NDVI) data set derived from bands 1 and 2 of the MODerate-resolution Imaging Spectroradiometer (MODIS) on board NASA''s Terra satellite was applied to identify the spatial and temporal variations of vegetation cover along the river corridor with about 320km in length. The goal of this study is thus to generate seasonally integrated NDVI (SINDVI) in growing seasons between April and October so as to investigate a general vegetation patterns as well as examine the inter-annual SINDVI for discerning the status of ecosystem restoration. The spatiotemporal variations of vegetation cover were further characterized based on those inter-annual SINDVI data with the aid of Coefficient of Variation (CoV). Research findings indicate that ecosystem integrity was strengthened after a series of water diversion efforts and groundwater table control in the past few years. As the degree of ecosystem restoration is in progress, continuous operation of water diversion is still necessary in response to the needs for restoration of dense vegetation in the riparian buffer within this arid or semi-arid region. [Copyright &y& Elsevier]

In this paper, the combustion stabilities and cycle-by-cycle variations of homogeneous charge compression ignition (HCCI) combustion using neat n-heptane, PRF20, PRF40, PRF50, and PRF60 were investigated. In-cylinder pressures of 100 combustion cycles for each test fuel on a single-cylinder engine were recorded. Consequently, cycle-to-cycle variations of the main combustion parameters and performance parameters were analysed, and the interdependency between the combustion parameter and performance parameters were also evaluated. The results reveal that the cycle-by-cycle variations deteriorate with the increase of the research octane number (RON). Especially, the coefficient of variations (COVs) of all parameters increase substantially when the RON of test fuels exceeds 40. For a certain test fuel, the COVs of combustion parameters that were used to depict the combustion characteristics during the early stage of combustion are very small, the COVs of combustion parameters that were used to describe the combustion characteristics of the post stage of the combustion are the largest, and the COVs of the i.m.e.p. and Pmax maintain at a middle level. Furthermore, a better interdependency also exists between the ignition timing of the low temperature reaction (LTR) and high temperature reaction (HTR), between the maximum pressure rise rate and its corresponding crank angle, between the peak values of the heat release and its corresponding crank angle. [ABSTRACT FROM AUTHOR]

GABA neurones in the dorsal raphe nucleus (DRN) influence ascending 5-hydroxytryptamine (5-HT) neurones but are not physiologically or anatomically characterised. Here, in vivo juxtacellular labelling methods in urethane-anaesthetised rats were used to establish the neurochemical and morphological identity of a fast-firing population of DRN neurones, which recent data suggest may be GABAergic. Slow-firing, putative 5-HT DRN neurones were also identified for the first time using this approach. Fast-firing, DRN neurones were successfully labelled with neurobiotin (n=10) and the majority (n=8/10) were immunoreactive for the GABA synthetic enzyme glutamic acid decarboxylase. These neurones were located in the DRN (mainly lateral regions), and consistently fired spikes with short width (1.1±0.1 ms) and high frequency (12.1±2.0 Hz). In most cases spike trains were regular but displayed low frequency oscillations (1–2 Hz). These neurones were morphologically heterogeneous but commonly had branching axons with varicosities and dendrites that extended across DRN subregions and the midline. Slow-firing DRN neurones were also successfully labelled with neurobiotin (n=24). These neurones comprised a population of neurones immunopositive for 5-HT and/or tryptophan hydroxylase (n=12) that fired broad spikes (2.2±0.2 ms) with high regularity and low frequency (1.7±0.2 Hz). However, a slow-firing, less regular population of neurones immunonegative for 5-HT/tryptophan hydroxylase (n=12) was also apparent.In summary, this study chemically identifies fast- and slow-firing neurones in the DRN and establishes for the first time that fast-firing DRN neurones are GABAergic. The electrophysiological and morphological properties of these neurones suggest a novel function involving co-ordination between GABA and 5-HT neurones dispersed across DRN subregions. [Copyright &y& Elsevier]

Hydrogen is of great significance for replacing fossil fuels and reducing carbon dioxide emissions. The application of hydrogen mixing with natural gas in gas network transportation not only improves the utilization rate of hydrogen energy, but also reduces the cost of large-scale updating household or commercial appliance. This paper investigates the necessity of a gas mixing device for adding hydrogen to existing natural gas pipelines in the industrial gas network. A three-dimensional helical static mixer model is developed to simulate the mixing behavior of the gas mixture. In addition, the model is validated with experimental results. Parametric studies are performed to investigate the effect of mixer on the mixing performance including the coefficient of variation (COV) and pressure loss. The research results show that, based on the, the optimum number of mixing units is three. The arrangement of the torsion angle of the mixing unit has a greater impact on the COV. When the torsion angle θ = 120°, the COV has a minimum value of 0.66%, and when the torsion angle θ = 60°, the COV has a maximum value of 8.54%. The distance of the mixing unit has little effect on the pressure loss of the mixed gas but has a greater impact on the COV. Consecutive arrangement of the mixing units (Case A) is the best solution. Increasing the distance of the mixing unit is not effective for the gas mixing effect. Last but not least, the gas mixer is optimized to improve the mixing performance. [ABSTRACT FROM AUTHOR]

The study assesses the long-term spatial and temporal solar resource variability in America using the 20-year National Renewable Energy Laboratory's (NREL's) National Solar Radiation Database (NSRDB). The coefficient of variation (COV) is used to analyze the spatial and temporal (interannual and seasonal) variability. Further, both spatial and temporal long-term variability are analyzed using the Köppen-Geiger climate classification. The temporal variability is found that, on average, the continental United States (CONUS) COV reaches up to 5% for global horizontal irradiance (GHI) and 10% for direct normal irradiance (DNI), and that the NSRDB domain's COV is roughly twice that of CONUS. For the seasonal variability analysis, the winter months are found to exhibit higher COV than the other seasons. In particular, December exhibits the highest variability, reaching on average 30% for DNI and 20% for GHI over various areas. On the other hand, the summer months demonstrate significantly lower variability, reaching only less than 20% for DNI and 10% for GHI, on average. Similarly, the spatial variability is analyzed by comparing each pixel to its neighbors. The long-term spatial variability is found to increase with the number of neighboring pixels being considered, which is equivalent to an increase in distance (within a 100-km x 100-km square grid). As expected, the DNI spatial variability is higher than that of GHI. Moreover, the annual solar irradiance anomalies are found to reach ±25% for both GHI and DNI (and even exceed those value in some instances) during each year of the 20-year period. • The NSRDB's 20-year satellite-based solar resource database is used over America. • The spatiotemporal variability in both DNI and GHI is studied. • Spatial variability is analyzed over an area of 100 × 100 km around each 4 × 4 km pixel. • Interannual variability and annual anomalies are analyzed during the 20-year record. • Köppen-Geiger climate zoning is used for additional insight at regional scale. [ABSTRACT FROM AUTHOR]

• A new reliability design and optimization method of the planetary gear using the genetic algorithm based on Kriging model. • Simulation of real physical environment of planetary gear by DEM. • The reliability kriging model of the planetary gear is established based on DEM and COV. • The relationship between the volume and the failure rate of the planetary gear is found by the simulation method. Based on stress-strength interference theory, calculating the reliability by using the coefficient of variation is a common method for the optimal design of a planetary transmission system. This method calculates the reliability by assuming the tangential force is zero while this is quite different from the real working condition. In this paper, the authors propose a novel reliability design and optimization method of the planetary gear, using the genetic algorithm, based on Kriging model. The Kriging method is used to establish the gear reliability model to simplify the reliability calculation. Then the Kriging model is optimized by using the genetic algorithm to ensure the global optimal solution. To simulate the real working conditions of planetary gears, the discrete element method (DEM) is adopted to calculates the load variation coefficient of the planetary gear. By taking the double toothed roller crusher as case study, the optimization results show that proposed method can significantly improve the calculation efficiency, and compared with the traditional design, the volume increases by 36.96%, and the failure rate of the planetary gear decreases by 17.05%. [ABSTRACT FROM AUTHOR]