Identification of blurred terahertz images by improved cross-layer convolutional neural network.

Terahertz imaging technology has been gradually used in space communication, radar detection, aerospace and biomedical fields. Nevertheless, there are still some limits in terahertz image, such as single tone, fuzzy texture features, poor image resolution and less data, which seriously affect the application and popularization of Terahertz image technology in many fields. Traditional convolutional neural network (CNN) is an effective method for image recognition, but it is limited in highly blurred terahertz image recognition due to the great difference between terahertz image and traditional optical image. This paper presents a proven method for higher recognition rate of blurred terahertz images by using an improved Cross-Layer CNN model with different definition terahertz image dataset. Compared to employing clear image dataset, the accuracy of blurred image recognition can be improved from about 32% to 90% with different definition dataset. Meanwhile, the recognition accuracy of high blurred image can be improved by approximately 5% in contrast to the traditional CNN, which makes the higher recognition ability of neural network. It can be demonstrated that various types of blurred terahertz imaging data can be effectively identified by constructing different definition dataset combined with Cross-Layer CNN. A new method is proved to improve the recognition accuracy of terahertz imaging and application robustness in real scenarios.

Progress in semiconductor materials for photocathodic protection: Design strategies and applications in marine corrosion protection.

Metal protection of offshore equipment is very complicated owing to the complex marine environment. Photocathodic protection (PCP) is one of the popular research topics in marine metal protection. The protection efficiency of photoanode depends largely on the photoelectric properties of semiconductor materials, viz. the process of charge separation, charge migration, and light absorption. In this article, the enhancement strategies, photoelectrochemical properties, and electron transfer mechanisms of different composites for PCP were reviewed and highlighted. Some photoanodes with unusual and striking properties were emphasized. In addition, the outlooks and challenges of the application of PCP and the design of photoanodes materials are proposed.

Synthesis of environmental-friendly ion-imprinted magnetic nanocomposite bentonite for selective recovery of aqueous Sc(III).

A novel reusable ion imprinted nanocomposite magnetic bentonite(IIPNMB) was prepared for selective recovery of aqueous scandium. Based on the fact that oxyphosphorus functional groups in sodium tripolyphosphate have good affinity to Sc(III) and chitosan is rich in hydroxy and amino active sites, they were chosen to build ion imprinted layers. Mesoporous IIPNMB showed good adsorption performance. The pseudo second-order kinetic model and Langmuir model fit the experimental data. According to XPS features, the amino, hydroxyl, PO and PO bonds of the adsorbents had electrostatic interaction and complexation with Sc(III), leading to the good selectivity of IIPNMB for Sc(III). In addition, the material atomic structure was proposed based on the chemical structure of IIPNMB for DFT calculation of ion imprinting adsorption, which clearly proved that the adsorption process of Sc(III) was stable, and it gave another proof for the mechanism of the selective extraction.

Spatial and temporal distribution of rural settlements and influencing mechanisms in Inner Mongolia, China.

Revealing the patterns and influencing mechanisms of spatial and temporal distribution of rural settlements is crucial for rural revitalization and sustainable development. However, our limited understanding of the rural settlements in China's ethnic minority border areas has hindered the process of their agricultural and rural modernization. Based on data on rural settlements in Inner Mongolia, China in four periods between 1990 and 2020, this study reveals its spatial and temporal distribution characteristics and describes the dynamic transformation process of settlement. Using a geographical detector approach, 17 factors are explored to identify the influencing mechanisms of each factor on the distribution of rural settlements in different regions. The results show obvious regional differences in the spatial distribution of rural settlements in Inner Mongolia, with the largest kernel density values in the west (Hetao irrigation area) and higher kernel densities in the central (Hohhot) and the east (Chifeng and Tongliao). While rural population decreases, rural settlements expand into cultivated land, grassland, and unused land resources. Its spatial distribution is significantly influenced by the factors of distance to cultivated land, distance to towns, and population density. The east of the study area is mainly controlled by temperature, while vegetation type and vegetation coverage have a greater impact in the west. The interactions between two influencing factors possess bilinear or nonlinear enhancement relationships. This study enriches the understanding of the rural settlements in ethnic minority border areas, which provide reference for the improvement of rural human settlement environment in Inner Mongolia.

Spatiotemporal assessment of potential drivers of salt marsh dieback in the North Inlet-Winyah Bay estuary, South Carolina (1990-2019).

Previous studies in the Gulf of Mexico and Atlantic states have suggested that a suite of possibly abiotic and biotic attributes is responsible for salt marsh dieback, e.g., drought, soil waterlogging, soil chemistry, top-down consumers control, etc. However, there are no conclusive answers in current literature explaining what led to marsh dieback in past decades, especially from the spatiotemporal perspective. Exploring all Landsat-retrieved marsh dieback events in 1990-2019, this research investigates the spatiotemporal relationships between the dieback series and the associated environmental variables in an intertidal marsh in South Carolina (SC). Based on our previous study, a series of marsh dieback events in the past 30 years were identified and dieback pixels in the estuary were extracted. Among these were the most severe marsh dieback events (1991, 1999, 2000, 2002, 2004, and 2013). Daily Evaporative Demand Drought Index (EDDI), daily precipitation data from Parameter Elevation Regressions on Independent Slopes Model (PRISM), and station-based water quality observations (dissolved oxygen, specific conductivity, salinity, turbidity, pH, and temperature) in the estuary were retrieved. Integrated with the proof-by-exhaustion method, statistical analysis showed marsh dieback were highly related to moisture imbalance in a period of 90 days before the dieback events. Respectively, pH for Clambank and Debidue Creek, salinity and turbidity for Thousand Acre were found to be the key water quality variables influencing marsh dieback besides drought. This study cogitates the environmental influence on coastal marsh dieback from a spatiotemporal perspective using a long-term satellite time series analysis. The findings could provide insights into marsh ecological resilience and facilitate coastal ecosystem management.

At-Sensor Radiometric Correction of a Multispectral Camera (RedEdge) for sUAS Vegetation Mapping.

Rapid advancement of drone technology enables small unmanned aircraft systems (sUAS) for quantitative applications in public and private sectors. The drone-mounted 5-band MicaSense RedEdge cameras, for example, have been popularly adopted in the agroindustry for assessment of crop healthiness. The camera extracts surface reflectance by referring to a pre-calibrated reflectance panel (CRP). This study tests the performance of a Matrace100/RedEdge-M camera in extracting surface reflectance orthoimages. Exploring multiple flights and field experiments, an at-sensor radiometric correction model was developed that integrated the default CRP and a Downwelling Light Sensor (DLS). Results at three vegetated sites reveal that the current CRP-only RedEdge-M correction procedure works fine except the NIR band, and the performance is less stable on cloudy days affected by sun diurnal, weather, and ground variations. The proposed radiometric correction model effectively reduces these local impacts to the extracted surface reflectance. Results also reveal that the Normalized Difference Vegetation Index (NDVI) from the RedEdge orthoimage is prone to overestimation and saturation in vegetated fields. Taking advantage of the camera's red edge band centered at 717 nm, this study proposes a red edge NDVI (ReNDVI). The non-vegetation can be easily excluded with ReNDVI < 0.1. For vegetation, the ReNDVI provides reasonable values in a wider histogram than NDVI. It could be better applied to assess vegetation healthiness across the site.

Green synthesis of reusable super-paramagnetic diatomite for aqueous nickel (II) removal.

Adsorption is an effective method for treating wastewater containing nickel due to its minimal equipment requirements and flexible operation. Therefore, an environmental friendly, inexpensive, efficient and recyclable adsorbent is needed. In this work, a reusable dual-functional super-paramagnetic adsorbent was prepared by combining APTES (3-Aminopropyltriethoxysilane) and EDTA (ethylenediaminetetraacetic acid disodium) with magnetic diatomite for the removal of Ni2+. It is named diatomite/CoFe2O4@APTES-EDTA (DECFASEs). The synthetic material was characterized and studied by XRD (X-ray Powder Diffractometer), FTIR (Fourier Transform Infrared Spectrometer), SEM (Scanning Electron Microscope), TEM (Transmission Electron Microscope), EDS (Energy Dispersive Spectrometer), VSM (Vibrating-Sample Magnetometer), BET (Brunauer-Emmett-Teller) method, Zeta potential analyzer and XPS (X-ray Photoelectron Spectroscopy), respectively. The performance of adsorption Ni2+ by DECFASEs was studied on effect of pH, reaction time and initial concentrations. The adsorption and desorption capacity and recyclability of the adsorbent material were estimated. A adsorption kinetic data had a significant correlation with the pseudo second-order kinetic and also adsorption isotherm data corresponded well with Freundlich adsorption isotherm. The maximum adsorption capacity of the adsorbent material was 19.22 mg/g. The Ni2+ adsorption capacity of DECFASEs decreased slightly from 9.11 to 8.25 mg/g after 4 recycles. The XPS results of DECFASEs before and after Ni2+ uptake showed N and O participated in the complexation of Ni2+ in the adsorption process, which verified the chemical interaction between Ni2+ and DECFASEs. Modified-diatomite is a promising adsorbent for aqueous Ni2+ removal.

Green synthesis of reusable multifunctional γ-Fe2O3/bentonite modified by doped TiO2 hollow spherical nanocomposite for removal of BPA.

Eco-friendly treatment of refractory pollutants in wastewater is still full of challenge in catalytic oxidation and adsorption. In this study, based on the concept of green chemistry, sulfur-doped titanium dioxide hollow spheres modified by surfactant loaded on magnetic bentonite (CST/γ-Fe2O3-BT) is synthesized in two steps, and bisphenol A (BPA) was chosen as the representative organic pollutant. These materials were characterized by means of XRD, FTIR, SEM, EDS, TEM, XPS, BET, and VSM techniques. The adsorption and photodegradation behavior of CST/γ-Fe2O3-BT were examined. The Langmuir isotherm exhibited a better fit with a maximum adsorption capacity of 77.36 mg/g. At pH 7, the reaction rate constant (k) of the BPA photocatalytic degradation by product was 0.00104 min-1, and the adsorption equilibrium constant (K) was 0.04034L/mg. In addition, the composite can be recovered from the reaction mixture by applying an external magnetic field due to the existence of the superparamagnetic iron oxide nanoparticles in the construct. The recovered particles retained their catalytic activity which the catalytic activity of the material still reached 91% of the first catalytic experiment after 5 repetitive experiments. Results infer that the material has excellent reusability. Thus, CST/γ-Fe2O3-BT is a significant candidate for the treatment of recalcitrant organic pollutants in wastewater.

Synthesis of amino-functionalized bentonite/CoFe2O4@MnO2 magnetic recoverable nanoparticles for aqueous Cd2+ removal.

Magnetic nano-composite materials have been attracting considerable attention due to their unique properties and versatile applications. In this study, a novel magnetic amino-functionalized conjugate adsorbent, named as bentonite/CoFe2O4@MnO2-NH2 (BCFMNs), was synthesized by combining APTES and MnO2 with magnetic bentonite. XRD, FT-IR, SEM, EDS, TEM, and VSM techniques were used to characterize its structure and magnetic properties. Results were in indicative of productive synthesis, well-defined architecture and satisfactory magnetism. BET examinations illustrated 84.97m2/g of specific surface area, 0.15cm3/g of pore volume and 7.02nm average pore size. The effect parameters such as adsorbent dosage, contact time, initial concentration and ion selectivity and recycling were evaluated and optimized systematically. Also, the metal concentrations were measured by ICP-MS spectrometer. The feasibility of the BCFMNs for removal of Cd2+ from aqueous solution was also evaluated by adsorption experiments with the maximal adsorption efficiency for Cd2+ up to 98.88%. Cd2+ adsorption could be interpreted by the Langmuir adsorption isotherm and the maximum adsorption capacity was 115.79mg/g. The results revealed that the adsorbent still had higher selectivity of Cd2+ removal even in the presence of high concentration coexisting cations. The as-prepared magnetic conjugate adsorbent could be recycled by taking advantage of its magnetic properties. The distinctive structure of BCFMNs and its excellent adsorption performance of cadmium reflects its prospective application in water treatment.

Mapping Soil Alkalinity and Salinity in Northern Songnen Plain, China with the HJ-1 Hyperspectral Imager Data and Partial Least Squares Regression.

In arid and semi-arid regions, identifying and monitoring of soil alkalinity and salinity are in urgently need for preventing land degradation and maintaining ecological balances. In this study, physicochemical, statistical, and spectral analysis revealed that potential of hydrogen (pH) and electrical conductivity (EC) characterized the saline-alkali soils and were sensitive to the visible and near infrared (VIS-NIR) wavelengths. On the basis of soil pH, EC, and spectral data, the partial least squares regression (PLSR) models for estimating soil alkalinity and salinity were constructed. The R² values for soil pH and EC models were 0.77 and 0.48, and the root mean square errors (RMSEs) were 0.95 and 17.92 dS/m, respectively. The ratios of performance to inter-quartile distance (RPIQ) for the soil pH and EC models were 3.84 and 0.14, respectively, indicating that the soil pH model performed well but the soil EC model was not considerably reliable. With the validation dataset, the RMSEs of the two models were 1.06 and 18.92 dS/m. With the PLSR models applied to hyperspectral data acquired from the hyperspectral imager (HSI) onboard the HJ-1A satellite (launched in 2008 by China), the soil alkalinity and salinity distributions were mapped in the study area, and were validated with RMSEs of 1.09 and 17.30 dS/m, respectively. These findings revealed that the hyperspectral images in the VIS-NIR wavelengths had the potential to map soil alkalinity and salinity in the Songnen Plain, China.

Assessment of heavy metal in coal gangue: distribution, leaching characteristic and potential ecological risk.

In the process of excavation and utilization of the coal gangue hill, gangue at different weathering degree was exposed to the environment, which can be harmful to the surroundings. In order to find the law of heavy metal release and to evaluate the potential ecological risk, five kinds of coal gangue at different weathering degrees were collected from a coal mine named Suncun, an over 100-year-old mine of Xinwen coal mining field located in Tai'an city, Shandong Province of China. Samples were processed with microwave digestion for total content determination of heavy metals, and another part of samples was processed by Tessier sequential extraction for chemical forms analysis. Leaching tests at various pH were carried out to investigate the release of heavy metal. The laws of transformation and release of heavy metals were discussed and potential ecological risk was evaluated. The results indicated that the weathering degree had a significant impact on the content of heavy metal. Exchangeable and carbonate fractions of Cr and Pb were a large proportion of the total and should attract great attention. Potential ecological risk was at strong level (light black) and was up to very strong level (deep black) because of Cd. But Cr had contributed the most for gray gangue, which was 71% of the total. The species of heavy metal in gangue changed due to weathering and lead to the difference of the leaching characteristic and risk.

Geospatial assessment of bioenergy land use and its impacts on soil erosion in the U.S. Midwest.

Agricultural land use change, especially corn expansion since 2000s, has been accelerating to meet the growing bioenergy demand of the United States. This study identifies the environmentally sensitive lands (ESLs) in the U.S. Midwest using the distance-weighted Revised Universal Soil Loss Equation (RUSLE) associated with bioenergy land uses extracted from USDA Cropland Data Layers. The impacts of soil erosion to downstream wetlands and waterbodies in the river basin are counted in the RUSLE with an inverse distance weighting approach. In a GIS-ranking model, the ESLs in 2008 and 2011 (two representative years of corn expansion) are ranked based on their soil erosion severity in crop fields. Under scenarios of bioenergy land use change (corn to grass and grass to corn) on two land types (ESLs and non-ESLs) at three magnitudes (5%, 10% and 15% change), this study assesses the potential environmental impacts of bioenergy land use at a basin level. The ESL distributions and projected trends vary geographically responding to different agricultural conversions. Results support the idea of re-planting native prairie grasses in the identified High and Severe rank ESLs for sustainable bioenergy management in this important agricultural region.

Assessing phenological change and climatic control of alpine grasslands in the Tibetan Plateau with MODIS time series.

The Tibetan Plateau, a unique cold and dry region recognized as the Earth's third pole, is primarily composed of alpine grasslands (>60 %). While a warming climate in the plateau is being recorded, phenology of alpine grasslands and its climatic dependencies are less investigated. This study tests the feasibility of the frequently observed Moderate Resolution Imaging Spectroradiometer (MODIS) time series (500 m, 8 days) in examining alpine phenology in the plateau. A set of phenological metrics are extracted from the MODIS Normalized Difference Vegetation Index (NDVI) series in each year, 2000-2010. A nonparametric Mann-Kendall trend analysis is performed to find the trends of these phenological metrics, which are then linked to monthly climatic records in the growing season. Opposite trends of phenological change are observed between the east and west of the plateau, with delayed start of season, peak date, and end of season in the west and advanced phenophases in the east. The correlation analysis indicates that precipitation, with a decreasing trend in the west and increasing trend in the east, may serve as the primary driver of the onset and peak dates of greenness. Temperature increases all over the plateau. While the delay of the end of season in the west could be related to higher late-season temperature, its advance in the east needs further investigation in this unique cold region. This study demonstrates that frequent satellite observations are able to extract phenological features of alpine grasslands and to provide spatiotemporally detailed base information for long-term monitoring on the plateau under rapid climate change.

Detecting invasive sericea lespedeza (Lespedeza cuneata) in Mid-Missouri pastureland using hyperspectral imagery.

Missouri ranks second in cow-calf numbers in the United States and its pastureland has experienced invasion of various plant species. As one of the invasive weeds, sericea lespedeza is becoming a serious threat to pasturelands in this state. The in-situ field survey in these vast pastures is time consuming and often impossible because of accessibility. Typical aerial survey is also difficult to detect sericea because the plant is of similar size and color as natural grass and, thus, cannot be effectively discriminated in broadband aerial color photos. This study used an airborne hyperspectral image to map sericea and its invasiveness in a public grass field in Mid-Missouri. The maximal 1st-order derivative in red-near infrared region (650-800nm) was derived to separate sericea from fescue, the dominant grass in pastures in Missouri. With a simple threshold approach, sericea of various sizes were identified in the study area. It was also found that the maximal 1st-order derivatives of sericea patches were log-linearly related to sericea "volume," a quasi 3-dimensional biophysical variable as an approximate measure of sericea invasiveness. The squared correlation coefficient (r2) of the regression was 0.65 and the estimation error of sericea "volume" estimation was 11% based on ground measurements at 27 sample sites. With this empirical regression model, the quantitative distribution of sericea volume was mapped, which could serve as a first step in alerting landowners and the general public about the seriousness of sericea invasion in Missouri pasturelands.