Removal of tungsten oxyanions from industrial wastewater by precipitation, coagulation and flocculation processes

Tungsten removal from industrial wastewater by precipitation, coagulation and flocculation processes using ferric chloride is reported. Suitable process conditions (pH, ferric chloride concentration) were established in jar tests performed with wastewater samples. Alkaline wastewater was treated with ferric chloride and pH was adjusted to various points using sulphuric acid. Tungsten removal was found to be most efficient (98-99%) in acidic conditions (pH<6). The process conditions were also found to be suitable for operation of an industrial scale wastewater treatment facility. More than 97% of tungsten were removed and the residual concentration was smaller than 10 ppm.     

An automated waveband selection technique for optimized hyperspectral mixture analysis

Linear spectral mixture analysis (SMA) has been used extensively in remote sensing studies to estimate the sub-pixel composition of spectral mixtures. The lack of ability to account for sufficient temporal and spatial variability between and among ground component or endmember spectra has been acknowledged as a major shortcoming of conventional SMA approaches. In an attempt to overcome this problem, a novel and automated linear spectral mixture protocol, referred to as stable zone unmixing (SZU), is presented and evaluated. Stable spectral features (i.e. least sensitive to spectral variability) are automatically selected for use in the mixture analysis based on a minimum InStability Index (ISI) criterion. ISI is defined as the ratio of the spectral variability within and the spectral variability among the endmember classes that are present within the mixture. The algorithm was tested on a set of scenarios, generated from in situ measured hyperspectral data. The scenarios covered both urban and natural environments under differing conditions. SZU provided reliable endmember cover distribution maps in all scenarios. On average, an absolute gain in R²—the coefficient of determination of the modelled versus the observed sub-pixel cover fractions—of 0.14 over the traditional SMA approaches was observed while the absolute gain in fraction abundance error was 0.06. It was concluded that the SZU protocol has potential to be an effective and efficient SMA algorithm for generating optimal cover fraction estimates regardless of the scenario considered. Moreover, the subset selection protocol, as implemented in SZU, can be regarded as complementary to conventional SMA approaches resulting in a further reduction of spectral variability.     

Thermoelastic Characterization of Changing Phase Distribution in Hardened Steel by Laser Ultrasonics

The increase of hardness of steel during a heat treatment intended to give components more performance is a result of a drastic change in grain size and microstructure, which in turn can be analyzed via changes in acoustic wave scattering. The degree of scattering is related to the grain size, alloy phases, elastic anisotropy, and phonon spectra, which are connected with the structural heterogeneity. In this study, an axially oriented hardening profile in a steel rod was induced by a Jominy test. All-optical photoacoustic excitation and detection schemes were used to establish the relation between the hardness, the elastic modulus, the elastic scattering, and the thermal diffusivity on a series of eight samples cut out from the gradient part of the rod. For each sample, the scattering of the photoacoustically excited traveling surface acoustic waves detected in a heterodyne diffraction and beam deflection setup was extracted from their damping behavior at different wavelengths and frequencies. Also, the thermal diffusivity was determined by fitting the slow time evolution of the laser-induced photo-thermoelastic displacement signal, and was found to be decreasing with decreasing grain size and increasing hardness.     

Soil moisture content retrieval based on apparent thermal inertia for Xinjiang province in China

In the arid to semi-arid regions of north-western China, soil moisture is the main hydrological driver for vegetation growth. With the launch of the Moderate Resolution Imaging Spectroradiometer (MODIS), the local MODIS reception capacity and the strong pressure on water resources in the province, the detection and mapping of soil moisture content (SMC) has become a major issue for the regional water management authorities of the province.

In this article, we apply the apparent thermal inertia approach to quantify SMC in the soils of the province of Xinjiang using locally received MODIS data. We report on SMC mapping for the entire province for the year 2005.

For the estimation, diurnal land surface temperature (LST (T_σ)), LST difference (ΔT_σ) and broadband albedo (α₀) were applied to determine the space–time variability of SMC. The retrieval of SMC was based on the rationale that high apparent thermal inertia (I_τ) values correspond to high SMCs and low I_τ values correspond to the minimal ones. To enable the application of the technique, a new classification of soil texture was established based on an existing Chinese soil type classification.

Typically only topsoil surface moisture content is retrieved with thermal remote sensing (RS). However, SMC retrieval for a 1 m soil profile was performed by applying a semi-empirical modelling approach. The model uses a two-layer water balance equation, and its SMC (θ_g) input is based on its linear relationship with the soil moisture saturation index (θ_si) at time t.

For validation purposes, the automatic weather station and time domain reflectometry (TDR) monitoring network included eight sites in the province, including the Mosuowan and Tianshan snow sites and the Turpan, Bayangburk, Kuerle, Yinsu, Alagan and Yiganbujima TDR sites for which data for the year of 2005 were acquired by the Xinjiang Meteorological Bureau (XMB), the Tarim Management Bureau (TMB) and the Xinjiang Institute for Ecology and Geography (XIEG).

When time series of SMC determined by using I_τ are compared with the measurements at the different validation sites, regression curve slopes of the validation relationships vary between 0.499 and 0.922. The R² values vary between 0.25 and 0.83. The minimum and maximum root mean square errors (RMSEs) are 0.001 and 0.028, respectively. Results suggest that apparent thermal inertia application is quite suitable for θ_g retrieval of a 1 m soil moisture profile in an arid to semi-arid region. The Aqua MODIS 10-day mean soil moisture product is proven to deliver quantitatively correct SMC imagery representing seasonal changes quite realistically.     

Trends in silicon radio large scale integration: zero IF receiver!Zero I&Q transmitter! Zero discrete passives!

After 10 years of advances in silicon RF integration, what used to be an art is becoming a “normal practice”. Historically, RF design was the art of s-parameters, shielding, impedance matching, and standing wave ratios. Modern silicon RFICs are designed using the same SPICE-like tools as in low-frequency analog ICs with the addition of important software for system-level simulation and mixer circuit noise analysis. New RF design practices away from the 50 ohm culture, novel chip architectures, and powerful technological advances will drive radio integration toward the ultimate single-chip phone. The obstacles in this quest are high system requirements on noise figure, substrate crosstalk, and parasitic coupling, not the silicon IC technology     

A dorsiventral leaf radiative transfer model: Development, validation and improved model inversion techniques

A Dorsiventral Leaf Model (DLM) is presented to simulate leaf radiative transfer. DLM was conceived as a plate model with a stochastic distribution of different groups of layers. Leaf asymmetry was modeled by assigning non-uniform distributions of pigments, water and dry matter to palisade and mesophyll layers and by simulating different amounts of light diffusion for adaxially and abaxially incident light. Surface reflections are based on micro-facets theory enabling the simulation of directional-hemispherical reflectance and a range of bidirectional reflectance factors. Adaxial and abaxial optical properties could be accurately simulated for a variety of leaf types with an overall error in reflectance and transmittance below 1.3%.Sensitivity analysis focused on optimizing model inversion schemes improves parameter estimation accuracy. Different inversion schemes were compared for two independent datasets. Results underpin most of the propositions of the sensitivity analysis: (i) masking the near-infrared wavelengths (band weighting) to account for variability in the dry matter composition consistently increased predicted accuracies for dry matter content, (ii) white reflectance measurements (reflectance with a 100% diffusely reflecting background) provided results superior to other optical measurements, making it a valuable and fast alternative and (iii) combining reflectance and transmittance into absorptance however did not result in improvements. Comparisons of DLM with the PROSPECT 5 model indicate an almost equal performance in content estimations. Improvements were thus not related to differences in model structure but to techniques that reduce the impact of leaf structure and compensate for sampling errors and variations in specific absorption spectra. DLM has important potential in the study of leaf radiative transfer and in the integration with canopy radiative transfer models.     

Magnitude- and Shape-Related Feature Integration in Hyperspectral Mixture Analysis to Monitor Weeds in Citrus Orchards

Traditionally, spectral mixture analysis (SMA) fails to fully account for highly similar ground components or endmembers. The high similarity between weed and crop spectra hampers the implementation of SMA for steering weed control management practices. To address this problem, this paper presents an alternative SMA technique, referred to as Integrated Spectral Unmixing (InSU). InSU combines both magnitude (i.e., reflectance) and shape (i.e., derivative reflectance) related features in an automated waveband selection protocol. Analysis was performed on different simulated mixed pixel spectra sets compiled from in situ-measured weed canopy, Citrus canopy, and soil spectra. Compared to traditional linear SMA, InSU significantly improved weed cover fraction estimations. An average decrease in fraction abundance error (Deltaf) of 0.09 was demonstrated for a signal-to-noise ratio (SNR) of 500 : 1, while for a SNR of 50 : 1, the decrease was 0.06.     

Nonlinear Hyperspectral Mixture Analysis for tree cover estimates in orchards

Accurate monitoring of spatial and temporal variation in tree cover provides essential information for steering management practices in orchards. In this light, the present study investigates the potential of Hyperspectral Mixture Analysis. Specific focus lies on a thorough study of non-linear mixing effects caused by multiple photon scattering. In a series of experiments the importance of multiple scattering is demonstrated while a novel conceptual Nonlinear Spectral Mixture Analysis approach is presented and successfully tested on in situ measured mixed pixels in Citrus sinensis L. orchards. The rationale behind the approach is the redistribution of nonlinear fractions (i.e., virtual fractions) among the actual physical ground cover entities (e.g., tree, soil). These ‘virtual’ fractions, which account for the extent and nature of multiple photon scattering only have a physical meaning at the spectral level but cannot be interpreted as an actual physical part of the ground cover. Results illustrate that the effect of multiple scattering on Spectral Mixture Analysis is significant as the linear approach provides a mean relative root mean square error (RMSE) for tree cover fraction estimates of 27%. While traditional nonlinear approaches only slightly reduce this error (RMSE = 23%), important improvements are obtained for the novel Nonlinear Spectral Mixture Analysis approach (RMSE = 12%).     

Neuropsychological functioning and determinants of morning alertness in patients with obstructive sleep apnea syndrome

Neuropsychological functioning is reported to be impaired in patients suffering from obstructive sleep apnea syndrome (OSAS). This syndrome is characterized by nocturnal respiratory disturbances, blood oxygen desaturations, sleep fragmentation, and excessive daytime sleepiness. Opinions are divided concerning the exact relationship between the observed cognitive deficits, nocturnal hypoxia, sleep disruption, and impaired daytime alertness. In the present study, morning neuropsychological function of 26 moderate to severe middle-aged sleep apneics is compared to that of 22 primary insomniacs. There were no performance differences on a range of neuropsychological tests among the two patient groups. In addition, the data suggest that morning alertness impairment, which is closely associated with a lack of slow wave sleep (SWS) and rapid eye movement (REM) sleep, is of major importance in inducing poorer cognitive performance in patients with moderate to severe sleep apnea.