ALBERT

Description: With decreasing water availability as a result of climate change and human activities, analysis of the influential factors and variation trends of chlorophyll a has become important to prevent reservoir eutrophication and ensure water supply safety. In this paper, a structurally simplified hybrid model of the genetic algorithm (GA) and the support vector machine (SVM) was developed for the prediction of monthly concentration of chlorophyll a in the Miyun Reservoir of northern China over the period from 2000 to 2010. Based on the influence factor analysis, the four most relevant influence factors of chlorophyll a (i.e., total phosphorus, total nitrogen, permanganate index, and reservoir storage) were extracted using the method of feature selection with the GA, which simplified the model structure, making it more practical and efficient for environmental management. The results showed that the developed simplified GA-SVM model could solve nonlinear problems of complex system, and was suitable for the simulation and prediction of chlorophyll a with better performance in accuracy and efficiency in the Miyun Reservoir.

Description: Inappropriate land use has caused a series of environmental disasters such as floods and sand storms, and some of them involved unused land changes with highly localized distributions in arid and semi-arid regions with fragile ecosystems. As the third-rank category of first-level land use/cover in China, unused land (UL) is now playing an increasingly important role in protection of the natural environment and sustainable utilization of land resources. In this article, we assessed the effects on regional eco-environments employing a quantitative EL (ecological effect index) model, which can be used to evaluate and represent the contribution of UL changes to the eco-environmental quality. Results show that UL changes generally contributed to the deterioration of eco-environmental quality during the study period. Some major contributors to improving eco-environmental quality were transformation of sandy land and saline-alkali lands to grasslands, expansion of water bodies in UL areas, and reclamation of farmland in UL areas (except for marsh lands). In contrast, the main contributors to worsening eco-environmental quality were grassland degradation to UL (except marshes), reclamation of marsh areas, and shrinkage of water bodies to leave desert or saline-alkali land. Some suggestions are provided about UL management, utilization, and protection issues.

Description: Recently; one-dimensional (1D) nanostructure field-effect transistors (FETs) have attracted much attention because of their potential application in gas sensing. Micro/nanoscaled field-effect sensors combine the advantages of 1D nanostructures and the characteristic of field modulation. 1D nanostructures provide a large surface area-volume ratio; which is an outstanding advantage for gas sensors with high sensitivity and fast response. In addition; the nature of the single crystals is favorable for the studies of the response mechanism. On the other hand; one main merit of the field-effect sensors is to provide an extra gate electrode to realize the current modulation; so that the sensitivity can be dramatically enhanced by changing the conductivity when operating the sensors in the subthreshold regime. This article reviews the recent developments in the field of 1D nanostructure FET for gas detection. The sensor configuration; the performance as well as their sensing mechanism are evaluated.

Description: Materials, Vol. 11, Pages 1551: Analysis of Geometrical Characteristics and Properties of Laser Cladding 85 wt.% Ti + 15 wt.% TiBCN Powder on 7075 Aluminum Alloy Substrate Materials doi: 10.3390/ma11091551 Authors: Yu-Xin Li Peng-Fei Zhang Pei-Kang Bai Zhan-Yong Zhao Bin Liu Ti/TiBCN composite coatings were prepared on a 7075 aluminum alloy surface by laser cladding. The relation between the main processing parameters (i.e., laser power, scanning speed, and powder feeding rate) and the geometrical characteristics (i.e., height, width, penetration depth, dilution and wetting angle) of single clad tracks is studied by linear regression analysis. The microstructure, micro-hardness and electrochemical corrosion were investigated by scanning electron microscopy, a Vickers micro-hardness machine, and a standard three-electrode cell, respectively. The results showed that all geometrical track characteristics are observed with high values of the correlation coefficient (R > 0.95). In addition, the average hardness value (750 HV0.2) was obtained of the Ti/TiBCN composite coating, and polarization curves indicated that the composite coatings were harder to corrode than the substrate.

Description: Remote Sensing, Vol. 9, Pages 971: Examining the Influence of Crop Residue Burning on Local PM2.5 Concentrations in Heilongjiang Province Using Ground Observation and Remote Sensing Data Remote Sensing doi: 10.3390/rs9100971 Authors: Ziyue Chen Danlu Chen Yan Zhuang Jun Cai Na Zhao Bin He Bingbo Gao Bing Xu Although a many studies concerning crop residue burning have been conducted, the influence of crop residue burning on local PM2.5 concentrations remains unclear. The number of crop residue burning spots was the highest in Heilongjiang province and we extracted crop residue burning spots for this region using MOD14A1 (Thermal Anomalies & Fire Daily L3 Global 1 km) data and national land cover data. By analyzing the temporal variation of crop residue burning and PM2.5 concentrations in Heilongjiang province, we found that the total number of crop residue burning spots was not correlated with the variations of PM2.5 concentrations at a provincial (regional) scale. However, crop residue burning exerted notable influence on the variations of PM2.5 concentrations at a local scale. We experimented with a set of buffer zone radiuses to examine the influencing area of crop residue burning. The results suggest that the valid influencing area of crop residue burning was between 50 and 80 km. The mean PM2.5 concentration measured at stations close to crop residue burning spots was more than 60 μg/m3 higher than that measured at stations not close to crop residue burning spots. However, no consistent, significant correlation existed between the existence of crop residue burning spots and local PM2.5 concentrations, indicating that local PM2.5 concentrations were influenced by a diversity of factors and not solely controlled by crop residue burning. This research also provides suggestions for better understanding the role of crop residue burning in local and regional air pollution.

Description: Symmetry, Vol. 9, Pages 180: Qinling: A Parametric Model in Speculative Multithreading Symmetry doi: 10.3390/sym9090180 Authors: Yuxiang Li Yinliang Zhao Bin Liu Speculative multithreading (SpMT) is a thread-level automatic parallelization technique that can accelerate sequential programs, especially for irregular applications that are hard to be parallelized by conventional approaches. Thread partition plays a critical role in SpMT. Conventional machine learning-based thread partition approaches applied machine learning to offline guide partition, but could not explicitly explore the law between partition and performance. In this paper, we build a parametric model (Qinling) with a multiple regression method to discover the inherent law between thread partition and performance. The paper firstly extracts unpredictable parameters that determine the performance of thread partition in SpMT; secondly, we build a parametric model Qinling with extracted parameters and speedups, and train Qinling offline, as well as apply it to predict the theoretical speedups of unseen applications. Finally, validation is done. Prophet, which consists of an automatic parallelization compiler and a multi-core simulator, is used to obtain real speedups of the input programs. Olden and SPEC2000 benchmarks are used to train and validate the parametric model. Experiments show that Qinling delivers a good performance to predict speedups of unseen programs, and provides feedback guidance for Prophet to obtain the optimal partition parameters.

Description: Sensors, Vol. 18, Pages 1134: Click Access to a Cyclodextrin-Based Spatially Confined AIE Material for Hydrogenase Recognition Sensors doi: 10.3390/s18041134 Authors: Rui Zhao Bin Li Yong Wang Wenping Hu The spatial confinement of conjugated phenyl rotators is a compulsory requirement for the fluorescence enhancement of aggregation induced emission (AIE) molecules. This work reports a novel spatially confined AIE material by restricting several tetraphenylethylene (TPE) molecules around the primary face of β-cyclodextrin (CD) via a Cu(I) catalytic 1,3-dipolar cycloaddition reaction (click chemistry). The spatial confinement effect was found to significantly enhance the fluorescence emission when compared with a single TPE modified CD. In addition, the emission maxima took place with the dimethyl sulfoxide volume ratio of 30% in a water mixture, which is remarkably different from traditional AIE molecules. Benefiting from the CD’s complexation effect, this material exhibits a selective fluorescence quenching property in certain hydrogenases and can be used as a fluorescence probe for hydrogenase sensing. This demonstrates the potential of the spatially confined AIECD for practical applications.

Description: Remote Sensing, Vol. 10, Pages 587: Least Squares Compactly Supported Radial Basis Function for Digital Terrain Model Interpolation from Airborne Lidar Point Clouds Remote Sensing doi: 10.3390/rs10040587 Authors: Chuanfa Chen Yanyan Li Na Zhao Bin Guo Naixia Mou To overcome the huge volume problem of light detection and ranging (LiDAR) data for the derivation of digital terrain models (DTMs), a least squares compactly supported radial basis function (CSRBF) interpolation method is proposed in this paper. The proposed method has a limited support radius and fewer RBF centers than the sample points, selected by a newly developed surface variation-based algorithm. Those make the linear system of the proposed method not only much sparser but also efficiently solvable. Tests on a synthetic dataset demonstrate that the proposed method is comparable to the smoothing RBF, and far superior to the exact RBF. Moreover, the first is much faster than the others. The proposed method with the RBF centers selected by the surface variation-based algorithm obviously outperforms that with the random selection of equal number. Real-world examples on one private and ten public datasets show that the surfaces of simple interpolation methods including inverse distance weighting, natural neighbor, linear and bicubic suffer from the problems of roughness, peak-cutting, discontinuity and subtle terrain feature loss, respectively. By contrast, the proposed method produces visually appealing results, keeping a good tradeoff between noise removal and terrain feature preservation. Additionally, the new method compares favorably with ordinary kriging (OK) for the generation of high-resolution DTMs in terms of interpolation accuracy, yet the former is much more robust to spatial resolution variation and terrain characteristics than the latter. More importantly, our method is about 4 times faster than OK. In conclusion, the proposed method has high potential for the interpolation of a large LiDAR dataset, especially when both interpolation accuracy and computational cost are taken into account.

Description: The precise and rapid estimation of grassland biomass is an important scientific issue in grassland ecosystem research. In this study, based on a field survey of 1205 sites together with biomass data of the Xilingol grassland for the years 2005–2012 and the “accumulated” MODIS productivity starting from the beginning of growing season, we built regression models to estimate the aboveground biomass of the Xilingol grassland during the growing season, then further analyzed the overall condition of the grassland and the spatial and temporal distribution of the aboveground biomass. The results are summarized as follows: (1) The unitary linear model based on the field survey data and “accumulated” MODIS productivity data is the optimum model for estimating the aboveground biomass of the Xilingol grassland during the growing period, with the model accuracy reaching 69%; (2) The average aboveground biomass in the Xilingol grassland for the years 2005–2012 was estimated to be 14.35 Tg, and the average aboveground biomass density was estimated to be 71.32 g∙m−2; (3) The overall variation in the aboveground biomass showed a decreasing trend from the eastern meadow grassland to the western desert grassland; (4) There were obvious fluctuations in the aboveground biomass of the Xilingol grassland for the years 2005–2012, ranging from 10.56–17.54 Tg. Additionally, several differences in the interannual changes in aboveground biomass were observed among the various types of grassland. Large variations occurred in the temperate meadow-steppe and the typical grassland; whereas there was little change in the temperate desert-steppe and temperate steppe-desert.

Description: In this paper, a novel direction of arrival (DOA) estimation algorithm called the Toeplitz fourth order cumulants multiple signal classification method (TFOC-MUSIC) algorithm is proposed through combining a fast MUSIC-like algorithm termed the modified fourth order cumulants MUSIC (MFOC-MUSIC) algorithm and Toeplitz approximation. In the proposed algorithm, the redundant information in the cumulants is removed. Besides, the computational complexity is reduced due to the decreased dimension of the fourth-order cumulants matrix, which is equal to the number of the virtual array elements. That is, the effective array aperture of a physical array remains unchanged. However, due to finite sampling snapshots, there exists an estimation error of the reduced-rank FOC matrix and thus the capacity of DOA estimation degrades. In order to improve the estimation performance, Toeplitz approximation is introduced to recover the Toeplitz structure of the reduced-dimension FOC matrix just like the ideal one which has the Toeplitz structure possessing optimal estimated results. The theoretical formulas of the proposed algorithm are derived, and the simulations results are presented. From the simulations, in comparison with the MFOC-MUSIC algorithm, it is concluded that the TFOC-MUSIC algorithm yields an excellent performance in both spatially-white noise and in spatially-color noise environments.