[Remediation Potential of Three Dwarf Bamboos on Farmland Soils Contaminated with Mixed Heavy Metals].

A two-year field experiment was carried out in a Cu-Zn-Cd-Pb-contaminated field with Sasa auricoma, Indocalamus tessellatus, and Shibataea chinensis Nakai to investigate their growth and accumulation characteristics. Based on changes in heavy metal content in the soil, we compared their phytoremediation potentials for the combined pollution of heavy metals. After two years of planting, S. auricoma showed the best tolerance to soil contaminated by heavy metals, with the number of plants being 63.8 times that at the time of planting. For each bamboo type, heavy metals were most concentrated in the root, with the best enrichment ability of Cd and worst ability of Pb. The bioconcentration factor of Cd in the roots was far greater than 1 for all three species, with 17.68 for S. auricoma and 14.63 for I. tessellatus. The accumulation of heavy metals was mainly concentrated in the roots and rhizomes, and the accumulations of Cu, Zn, and Cd in S. auricoma were 157.14, 363.3, and 7.18 g·hm-2, respectively, which were higher than those of I. tessellatus and S. chinensis Nakai. Compared with that before planting, the content of heavy metals in the non-rhizosphere soil decreased after two years of planting, among which Cd content decreased the most, from 39.6%-40.4%. In addition, the contents of Cu, Zn, and Cd in rhizosphere soil of S. auricoma and S. chinensis Nakai were significantly lower than those in non-rhizosphere soil (P<0.05). The results showed that the three species had strong accumulation ability of Cd and could be further studied as Cd-repaired plants. The growth condition and content and accumulation of heavy metals in S. auricoma were excellent among the dwarf bamboos, which showed the highest phytoremediation potential.

[Seasonal Characteristics and Source Analysis of Water-soluble Inorganic Ions in PM2.5 in Suqian City].

To study the seasonal pollution characteristics and sources of water-soluble inorganic ions in atmospheric PM2.5 in Suqian City, 171 samples were collected at three monitoring points, which were in the water vapor channel, from May 2017 to January 2018. The mass concentrations of PM2.5 and nine water-soluble inorganic ions were analyzed. The results showed that the annual average concentration of water-soluble inorganic ions in PM2.5 in Suqian City was (44.08±34.61) µg ·m-3, accounting for 41.8% of PM2.5. The concentrations of these species were in the order of ρ(NO3-) > ρ(SO42-) > ρ(NH4+) > ρ(ρl-) > ρ(Na+) > ρ(Ca2+) > ρ(K+) > ρ(F-) > ρ(Mg2+); NO3-, SO42-, and NH4+ accounted for 75.6% of the total water-soluble ions. The annual average ratio of ρ(NO3-) to ρ(SO42-) was 1.53±0.88, indicating that mobile sources contributed more to PM2.5 pollution. Based on the correlation analysis of NH4+ and SO42-, NO3- may exist in the form of (NH4)2 SO4, NH4HSO4, or NH4NO3. According to the principal component analysis, secondary transformation, industrial pollution, biomass burning, and dust were the major sources of water-soluble inorganic ions. PM2.5concentrations were positively related to relative humidity in winter. Water vapor transmission is more likely to promote PM2.5 accumulation in winter.

Three-dimensional display with directional beam splitter array.

Multi-view three-dimensional (3-D) displays using directional beam splitter array were proposed to achieve a perfect 3-D perception with low cross-talk. The multi-direction collimated light may project different images to different viewing zones to form the multi-view autostereoscopic display. Furthermore, a high resolution 3-D display can be realized with a sequential beam splitter array and a sequential liquid crystal display. By optimization, the cross-talk of the directional beam splitter backlight system was lowered to 5% to improve the perception of the 3-D displays.

[Local Regression Algorithm Based on Net Analyte Signal and Its Application in Near Infrared Spectral Analysis].

Abstract To overcome the problems of significant difference among samples and nonlinearity between the property and spectra of samples in spectral quantitative analysis, a local regression algorithm is proposed in this paper. In this algorithm, net signal analysis method(NAS) was firstly used to obtain the net analyte signal of the calibration samples and unknown samples, then the Euclidean distance between net analyte signal of the sample and net analyte signal of calibration samples was calculated and utilized as similarity index. According to the defined similarity index, the local calibration sets were individually selected for each unknown sample. Finally, a local PLS regression model was built on each local calibration sets for each unknown sample. The proposed method was applied to a set of near infrared spectra of meat samples. The results demonstrate that the prediction precision and model complexity of the proposed method are superior to global PLS regression method and conventional local regression algorithm based on spectral Euclidean distance.

[Variation of Size Distribution and the Influencing Factors of Aerosol in Northern Suburbs of Nanjing].

The size distribution of particulate was analyzed by the FA-3 9 stage sampler in Northern-suburb of Nanjing from January to November in 2014. First, the monitoring result from FA-3 was compared with the results of the same period obtained from a medium flow size grading sampler (KC-120H) and online monitoring instrument of the Environmental Protection Agency. The data correlation coefficients were all greater than 0.95. The fine particle concentration from FA-3 was lower by 13.9% and 16.6%, while PM10 concentration was higher by 15.2% and 13.3% respectively. However, the deviations were in the acceptable range of atmospheric sampling which could indicate the accurate classification and sampling of particulate for FA-3. Particulate pollution in Northern-suburb Nanjing was serous in which the annual average concentrations of PM1.1, PM2.1 and PM10 were(65.6±37.6), (91.0±54.7) and (168.0±87.0) µg·m-3 respectively; fine particles dominated and most of them had a diameter of less than 1.1 µm. Particle size distribution was bimodal with peaks at 0.43-0.65 and 9-10 µm; the median diameter was 1.83 µm which was in the accumulation mode. In winter, the concentration of fine particle size was higher and in spring the coarse particle size was higher; in summer, the fine particle size concentration was not significantly reduced but coarse particle size was obviously lower than those in other seasons. The differences of particle size distribution in day and at night were very small in coarse segment and in fine segment, the nocturnal concentrations were mostly higher than diurnal concentrations. The precipitation had cleaning effect for each size range of particulate except in summer and the effect was more distinct in fine particle size. In haze days, with the aggravation of haze level, the particle concentration in the diameter range of 0.43-2.1 µm increased gradually while in this segment the particle concentration was significantly negatively correlated with visibility. Using relative humidity of 70% as the demarcation, the particle size distribution changed significantly:when humidity was greater than 70%, mass concentration of particle with a diameter of less than 0.43 µm reduced significantly but that with diameter range of 0.43-2.1 µm increased obviously which should be related to the particle hygroscopic growth. The air mass sources could be divided into four categories in northern-suburb of Nanjing. Air mass from the northwest with rapid transport velocity was the cleanest in which the fine particle size concentration was significantly lower than those in other directions; the air mass from local and surrounding was the most severely polluted with high concentrations in both fine and coarse segment, its transmission distance was short and wind speed was small which contributed greatly to air pollution of Nanjing with probability of occurrence of pollution reaching 73.9%.

[An Improved ELM Algorithm for Near Infrared Spectral Quantitative Analysis].

Extreme learning machine (ELM) has been applied in near infrared spectral analysis as a novel chemometric method which attracted the attentions of various researchers. However, the dimension of spectral data is usually very high while more hidden nodes should be incorporated in original ELM model for spectral data. Thus the problems of high dimension and high colinearity in the output matrix of hidden layer of ELM model are inevitable. The solutions obtained with the existing Moore-Penrose generalized inverse can be ill-conditional due to the high dimension and high colinearity in the hidden layer output matrix. This study aims to propose an improved ELM to build spectral regression model. The proposed method firstly uses extreme learning machine (ELM) to relate spectral variables to response variable; then the output of each hidden node are treated as new variables; VIP-SPLS ( improved stacked PLS based on variable importance in the projection) proposed by our group recently is used to build the regression model between those new variables and the response variable. In this paper, this method is called as improved ELM (iELM). VIP-SPLS model can fully utilize the output information of each hidden node and can effectively solve the problems of high dimension and high colineariy. At the same time, VIP-SPLS also has the advantage of model ensemble. Therefore, the performance of ELM model used for spectral data can be improved if the VIP-SPLS is incorporated to relate the hidden layer output matrix and response variable. The proposed method is applied to a commonly used benchmark NIR spectral data for evaluation. The results demonstrate that the precision improvement of iELM model is 29.06% to PLS model and 27.47% to original ELM model, respectively.

Light extraction from electroluminescent devices using micro-rod array embedded within glass substrate.

The total internal reflection (TIR) effect in conventional electroluminescent devices causes a large amount of light energy trapped in the devices and result in heat energy that adversely affects the performance of the device. In order to enhance the light out-coupling efficiency without sacrificing the electrical properties, a micro-rod array (MRA) structure fabricated by a femtosecond laser was demonstrated. Green, blue, and red organic light-emitting diodes were employed to verify the effect of the proposed method, which increases out-coupling efficiencies by a factor of 1.9, 1.7, and 1.82, respectively, compared with conventional devices. This highly effective method is compatible with current device fabrication processes and is applicable to full-color electroluminescent devices.

Switchable viewing angle display with a compact directional backlight and striped diffuser.

A compact high-directionality backlight module combined with a striped diffuser is proposed to achieve an adjustable viewing angle for eco-display. The micro-prisms on the compact light guide plate guide the emitting rays to the normal viewing angle, whereas a set of striped diffusers scatter the rays to a wide viewing angle. View cones of ± 10° / ± 55° were obtained for narrow/wide viewing modes with 88% / 85% uniformity of spatial luminance, respectively. Compared with the conventional backlight, the optical efficiencies were increased by factors of 1.47 and 1.38 in narrow and wide viewing modes, respectively. In addition, only 5% of power consumption was needed when the backlight worked in private narrow viewing mode to maintain the same luminance as that of a conventional backlight.

High directional backlight using an integrated light guide plate.

A high directional backlight system that combined a composite microstructure light guide plate (LGP) with a collimated light source was proposed for eco-displays. The collimated planar light was expanded from a point light source and guided towards the normal direction by utilizing the micro-prism array on LGP. High uniformity of spatial luminous, 91%, with a narrow viewing cone of ± 4° can be achieved without additional optical films. Moreover, compared to the conventional backlight, only 5% of power consumption was needed to keep the same luminance, hence, the optical efficiency increased by a factor of 1.47.

[Discrimination of types of polyacrylamide based on near infrared spectroscopy coupled with least square support vector machine].

In this paper, a novel discriminant methodology based on near infrared spectroscopic analysis technique and least square support vector machine was proposed for rapid and nondestructive discrimination of different types of Polyacrylamide. The diffuse reflectance spectra of samples of Non-ionic Polyacrylamide, Anionic Polyacrylamide and Cationic Polyacrylamide were measured. Then principal component analysis method was applied to reduce the dimension of the spectral data and extract of the principal compnents. The first three principal components were used for cluster analysis of the three different types of Polyacrylamide. Then those principal components were also used as inputs of least square support vector machine model. The optimization of the parameters and the number of principal components used as inputs of least square support vector machine model was performed through cross validation based on grid search. 60 samples of each type of Polyacrylamide were collected. Thus a total of 180 samples were obtained. 135 samples, 45 samples for each type of Polyacrylamide, were randomly split into a training set to build calibration model and the rest 45 samples were used as test set to evaluate the performance of the developed model. In addition, 5 Cationic Polyacrylamide samples and 5 Anionic Polyacrylamide samples adulterated with different proportion of Non-ionic Polyacrylamide were also prepared to show the feasibilty of the proposed method to discriminate the adulterated Polyacrylamide samples. The prediction error threshold for each type of Polyacrylamide was determined by F statistical significance test method based on the prediction error of the training set of corresponding type of Polyacrylamide in cross validation. The discrimination accuracy of the built model was 100% for prediction of the test set. The prediction of the model for the 10 mixing samples was also presented, and all mixing samples were accurately discriminated as adulterated samples. The overall results demonstrate that the discrimination method proposed in the present paper can rapidly and nondestructively discriminate the different types of Polyacrylamide and the adulterated Polyacrylamide samples, and offered a new approach to discriminate the types of Polyacrylamide.