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Accurate Noctiluca scintillans bloom (NSB) recognition from space is of great significance for marine ecological monitoring and underwater target detection. However, most existing NSB recognition models require expert visual interpretation or manual adjustment of model thresholds, which limits model application in operational NSB monitoring. To address these problems, we developed a Noctiluca scintillans Bloom Recognition Network (NSBRNet) incorporating an Inception Conv Block (ICB) and a Swin Attention Block (SAB) based on the latest deep learning technology, where ICB uses convolution to extract channel and local detail features, and SAB uses self-attention to extract global spatial features. The model was applied to Coastal Zone Imager (CZI) data onboard Chinese ocean color satellites (HY1C/D). The results show that NSBRNet can automatically identify NSB using CZI data. Compared with other common semantic segmentation models, NSBRNet showed better performance with a precision of 92.22%, recall of 88.20%, F1-score of 90.10%, and IOU of 82.18%.

Atmospheric correction (AC) is a key step in ocean color remote sensing for obtaining oceanic and coastal water ecosystem products. However, traditional NIR-based AC scheme ( AC NIR ) often fails in turbid coastal waters dominated by high suspended particulate matter (SPM). SWIR-based AC is valid for turbid waters, but it cannot be applied to some satellite sensors (e.g., Sea-viewing Wide Field-of-view Sensor (SeaWiFS), MEdium Resolution Imaging Spectrometer (MERIS), and future small satellite sensors) that have no SWIR bands. To obtain reliable data of turbid coastal waters by these sensors without SWIR bands, an improved AC NIR ( AC NIR-PRO ) scheme is proposed herein based on the frame of traditional AC NIR ( AC NIR-STA ). The synthesized and in situ datasets are analyzed to extend bio-optical models of absorption and backscattering coefficients in AC NIR-STA to highly turbid waters. AC NIR-PRO utilizes concentrations of both chlorophyll-a ([Chla]) and SPM ([SPM]) to estimate water-leaving contributions at the NIR bands. Similar to NIR-SWIR AC, AC NIR-PRO can obtain the information of global coastal waters with remote sensing reflectance at 862 nm ( R r s ( 862 ) ) up to 0 . 025 ∼ 0 . 03 sr − 1 . R r s of AC NIR-PRO is also validated by AERONET-OC datasets (mean absolute percent difference (MAPD) of R r s ( 551 ) is approximately 16%) for various coastal waters including high colored dissolved organic matter. Further, the magnitude and spatial patterns of [SPM] and [Chla] derived from the R r s products after performing AC NIR-PRO are significantly better than those from AC NIR-STA , especially for turbid coastal waters.

Underwater ultraviolet radiation (UVR), which plays a significant role in photobiological and photochemical processes, is one of the key factors in marine ecosystems. A new algorithm KpcaUV, based on kernel principal component analysis (KPCA) and multiple linear regression (MLR), was proposed in this study for the retrieval of the UVR diffuse attenuation coefficient $ {K_{\mathrm {d}}(\lambda)}$ from remote sensing reflectance $ {R_{\mathrm {rs}}(\lambda)}$ in the global ocean. KPCA can be applied in all areas that principal components analysis (PCA) can be used. More importantly, KPCA can help mapping data into high dimensions and reducing the nonlinearity between inputs and outputs, which will improve the performance and robustness of algorithms when deriving large dynamic ranges parameters. Compared with SeaUVc, which is one of the most successful $ {K_{\mathrm {d}}(\lambda)}$ retrieval algorithms in UVR, the results showed that KpcaUV (with $ {R^{2}}$ : 0.970 and RMSE: 14.0%) performed similar to SeaUVc (with $\boldsymbol {R^{2}}$ : 0.963 and RMSE: 15.6%) when implemented with high-quality data. Nevertheless, KpcaUV was more robust and consistent than SeaUVc when implemented on the satellite images with different levels of quality control. The RMSD of SeaUVc had a significant reduction from 26.8% (QA ≥ 0.6) to 12.7% (QA = 1.0), and the RMSD of KpcaUV varied less than SeaUVc from 14.6% (QA ≥ 0.6) to 10.1% (QA = 1). Hence, considering its good nonlinear-problem-solving ability and robustness when applied to multiple satellites, KpcaUV proposed by this study can be used to obtain ${K_{\mathrm {d}}(380)}$ for the continuous observation of the large area.

Following the Aerosol Robotic Network-Ocean Color (AERONET-OC) network scheme and instrument deployment protocols, two fixed platforms (Muping and Dong’ou) in the Yellow Sea and East China Sea were implemented with the support of the China National Satellite Ocean Application Service. Optical radiometry instruments were established at the two sites to autonomously determine remote sensing reflectance (Rrs) and aerosol optical depth (AOD). Details about location selection, platform design, instrument deployment, and the associated data processing procedure are reported in this study. Rrs and AOD measured by independent instruments at the Muping site were compared and results showed that they were consistent, with a median relative percentage difference (MRPD) < 0.6% for AOD and

Red Noctiluca scintillans (RNS) is one of the major red tide species and dominant bioluminescent plankton in the global offshore. Bioluminescence offers a number of applications for ocean environment assessments such as interval waves study, fish stocks evaluation and underwater target detection making it of significant interest in forecasting bioluminescence occurrence and intensity. RNS is susceptible to changes in marine environmental factors. However, the effects of marine environmental factors on the bioluminescent intensity (BLI, photon s−1) of individual RNS cells (IRNSC) is poorly known. In this study, the effects of temperature, salinity and nutrients on the BLI were studied by field and laboratory culture experiments. In the field experiments, bulk BLI was measured by an underwater bioluminescence assessment tool at various temperature, salinity and nutrient concentrations. To exclude the contribution by other bioluminescent planktons, an identification method of IRNSC was first developed using the features of the bioluminescence flash kinetics (BFK) curve of RNS to identify and extract BLI emitted by an individual RNS cell. To decouple the effects of each environmental factor, laboratory culture experiments were conducted to examine the effects of a single factor on the BLI of IRNSC. The field experiments showed that BLI of IRNSC negatively correlated with temperature (3-27°C) and salinity (30-35‰). The logarithmic BLI can be well fitted using a linear equation with temperature or salinity with Pearson correlation coefficients of -0.95 and -0.80, respectively. The fitting function with salinity was verified by the laboratory culture experiment. On the other hand, no significant correlation was observed between BLI of IRNSC and nutrients. These relationships could be used in the RNS bioluminescence prediction model to improve the prediction accuracy of bioluminescent intensity and spatial distribution.

The power trading system has the characteristics of nonlinearity, dynamics, and complexity. Part of the business data in the trading system needs to be exposed to numerous external business systems. The traditional centralized power trading model has some problems, such as low data security and trust crisis of regulators. Blockchain technology provides prominent ideas for solving these problems. Firstly, the improved AdaBoost algorithm is used to predict the supply and demand gap of power trading nodes. Secondly, based on the fact that the information on the blockchain is only open to the power supply side, a two-stage game model of asymmetric information between the power supply side and the power user is constructed to capture the price competition behavior between them in order to find the Nash equilibrium price in two stages. Finally, the US PJM market electricity market data are used to carry out an example analysis to verify the effectiveness of the algorithm and model.

Red Noctiluca scintillans (RNS) red tides frequently occur in coastal waters in China, leading to great ecological and economic losses. The prewarning of red tides via the monitoring of RNS abundance in the field is of great importance. Bioluminescence sensors are convenient to deploy on multiple underwater platforms, and bioluminescence is related to the abundance and species of dinoflagellates. As an optical proxy, the maximum bioluminescence potential (MBP) could respond in a timely manner to changes in RNS abundance and be utilized to estimate it. A novel method with high correlation (R2=0.82) is proposed to estimate the RNS abundance from the MBP in this study. The maximum RNS abundance range of the method is 380 cell L−1. Furthermore, the bioluminescence flash kinetics of dinoflagellate individuals are analyzed to demonstrate the applicability of the method in the Yellow Sea and Bohai Sea.

The Chinese Ocean Colour and Temperature Scanner (COCTS), Coastal Zone Imager (CZI) and the novel Ultra-Violet Imager (UVI) which on-board the Chinese Ocean Satellite HY-1C was launched in September 2018. The atmospheric correction of ocean color sensors was a critical step for accurate retrieval of the remote sensing reflectance, and the look-up-tables (LUTs), for the Rayleigh scattering, the aerosol scattering, and the diffuse transmittance, which were built bases on a Successive Order Scattering Vector Radiative Transfer Solver, played an important role in the processing step. Preliminary evaluation has been performed using the SeaWiFS LUTs and the MODIS data, it showed that COCTS can provide accurate ocean color products.

This study explores second-order variability of inherent optical properties (IOPs) of particles caused by particle intrinsic attributes, including the mean particle size (DA) calculated by a LISST measurement restricted to a size range of 2.5–500 μm, mean apparent density (ρa), bulk refractive index (np), and composition (using a ratio of chlorophyll a to total suspended matter concentrations, Chl a/TSM, as the delegate), obtained from five field cruise observations collected from 2013 to 2015 in Bohai Sea (BS) and Yellow Sea (YS). Large variations in magnitude were observed in terms of mass-specific beam attenuation ( cp∗), scattering ( bp∗), and backscattering ( bbp∗), indicative of optically complex water conditions. cp∗ and bp∗ showed similarities in their change and driving mechanisms, in which DA explained the majority (35%), both np and ρa separately contributed variability levels of approximately 25%, and Chl a/TSM accounted for the lowest proportion (15%). Most (approximately 38%) of the variability in bbp∗ was found to be attributed to np when the other factors were responsible for the rest of the variation to different degrees of 9–29%. We also model cp∗, bp∗, and bbp∗ with direct linkages to DA, ρa, np, and Chl a/TSM using a multivariate linear regression method. The established cp∗, bp∗, and bbp∗ models generated very high goodness of fit results, with determination coefficients (R2) of above 0.970 (p

Remote sensing reflectance obtained from space-borne ocean color sensors is of great importance to carbon cycle and ocean-atmospheric interactions by providing biogeochemical parameters on the global scale using specific algorithms. Vicarious calibration is necessary for obtaining accurate remote sensing reflectance that meets the application demands of atmospheric correction algorithms. For ocean color sensors, vicarious calibration must be done prior to atmospheric correction. The third Chinese Ocean Color and Temperature Scanner (COCTS) aboard the HY1C satellite was launched on September 7, 2018, and it will provide essential ocean color data that will complement those of existing missions. We used field measurements from the Marine Optical Buoy (MOBY) and aerosol information provided by the MODerate Imaging Spectroradiometer (MODIS) aboard the Terra satellite to calculate vicarious calibration coefficients, and we further evaluated the applicability of the established vicarious calibration approach by cross-calibration using MODIS data on the global scale. Finally, the established vicarious calibration coefficients were used to retrieve the aerosol optical depth and remote sensing reflectance, which were compared to Aerosol Robotic Network-Ocean Color (AERONET-OC) data and MODIS-Terra and Ocean and Land Color Instrument (OLCI)-Sentinel-3A operational products. The results show that the vicarious calibration coefficients are relatively stable and reliable for all bands ranging from visible to near-infrared and can be used to obtain accurate high-quality data.

Daily cash flow forecasting plays a very important role in enterprise development planning and strategic deployment. This paper makes use of a deep recurrent neural network model and applies it to the forecast of daily sales cash flow. This model adopts GRU unit structure. Through analyzing and mining historical payment flow data, the neural network model is used to automatically learn and extract the internal characteristics of information, and finally the daily cash flow prediction results are obtained. This method is the first successful application of artificial intelligence algorithm in the daily cash flow prediction of power grid. Experimental results show that the model is more accurate than ARIMA method.

Accurate retrieval of biogeochemical components of the ocean at a global scale from space requires accurately calibrated top-of-atmosphere (TOA) radiance, which is usually achieved by deriving a vicarious gain coefficient (g-factor) through a process called system vicarious calibration (SVC). Currently, only two SVC sites, Marine Optical Buoy (MOBY) and BOUée pour l'acquiSition d'une Série Optique à Long termE (BOUSSOLE), are routinely operated to support the SVC process for all on-orbit ocean color satellite payloads. However, high-quality matchups between satellite observations and in situ measurements are rare because of the strict requirements of the SVC process. Meanwhile, a stable g-factor is usually computed by averaging sufficient gain measurements. Therefore, more SVC sites are required to derive a stable g-factor in a short duration, particularly for the initial calibration of newly launched satellite sensors. In this study, nearly twenty years of well-calibrated ocean color satellite data were used to calculate the mean and standard deviation of physical and optical properties of waters and the atmosphere in the South China Sea (SCS) to evaluate the feasibility of establishing a SVC site. A region was identified that meets all requirements that were used to evaluate the MOBY and BOUSSOLE sites. Two in situ measurements within this region were used to derive a g-factor for MODIS-Terra and MODIS-Aqua and were compared with the g-factor derived using MOBY data. The consistence of the two g-factors indicates that the identified region in the SCS could be a potential area for establishing a long-term moored SVC site.

Ten years (from 2005 to 2014) of satellite sea surface temperature (SST) data from the Advanced Very High Resolution Radiometer (AVHRR) are analyzed to reveal the monthly changes in surface cold patches (SCPs) in the main areas of the Northern Yellow Sea (NYS). The Canny edge detection algorithm is used to identify the edges of the patches. The monthly changes are described in terms of location, temperature and area. The inter-annual variations, including changes in the location and area of the SCPs from 2010 to 2014, are briefly discussed. The formation mechanisms of the SCPs in different periods are systematically analyzed using both in situ data and numerical simulation. The results show that from May to October, the location and area of the SCPs remain stable, with a north-south orientation. The SCPs altogether cover about 1° of longitude (124°E–125°E) in width and 2° of latitude (37.5°N–39.5°N) in length. In November, the SCP separates from the Jangsan Cape and forms a closed, isolated, and approximately circular cold patch in the central NYS. From May to October, the upwelling that leads to the formation of the SCP is mainly triggered by the headland residual current, wind field, climbing movement of the current and secondary circulation at the tide front. In November, cyclonic circulation in the NYS is primarily responsible for generating the upwelling that leads to the formation of the closed and isolated SCP.

The suspended sediment concentration (SSC) in the East China Seas (ECSs) is modulated by a host of physical processes such as wind waves, tides, and circulations on different timescales, which in turn lead to SSC variations on the corresponding timescales. However, understanding of SSC variations and their modulation mechanisms remain limited. This study aims to quantify SSC variations on multiple timescales (intratidal, spring-neap, seasonal and long-term timescales) and their modulation mechanisms using high spatial-temporal resolution satellite remote sensing data from MODIS and GOCI. The results indicate that in the ECSs (total area 13.0×105 km2), the seasonal variations of wind-waves and ocean stratification play the most important roles in modulating SSC, causing high SSC variation (> 30 mg/L) in 7.33% of the total area, and medium SSC variation (5–30 mg/L) in 31.57% of the total area. Tidal current variations on intratidal timescale play the next most important role, causing high SSC variation in 0.60% of the study area and medium SSC variation in 26.10% of the study area. Spring-neap tidal current variations also have a significant influence on modulating SSC, causing high SSC variation in 0.06% of the study area, and medium SSC variation in 13.10% of the study area. SSC variations on long-term timescale (10 years) are not significant, as strong long-term SSC variations are only observed in an area of 0.0012×105 km2 in the Bohai Sea. These new SSC datasets provide an important new data source to better understand SSC spatial-temporal variability and modulation mechanisms in the ECSs.

Cloud cover is one major obstacle to obtaining an accurate ocean colour signal by satellite remote sensing methods. Identifying the cloud pixels correctly is of great importance for satellite ocean colour data processing. Several cloud masking algorithms currently exist: near infrared threshold, shortwave infrared threshold, spatial variability threshold, and spectral variability threshold. Any of these algorithms can identify nearly all of the real cloud pixels. However, the influence of turbid waters, turbidity fronts, aerosols, and sunglint causes some amount of non-cloud pixels to be mistaken as clouds. This situation inevitably leads to the loss of valuable data. After reviewing these current approaches, this study proposes an improved cloud masking algorithm for the Moderate Resolution Imaging Spectroradiometer, which is based on the high spatial variability characteristic of clouds, as well as the small contribution from water reflection at shortwave bands. The new cloud masking algorithm reduces t...

This research used the profile data measured extensively in the Yellow Sea and Bohai Sea (YSBS) to explain the temporal and spatial distribution characteristics of optical properties and systematically analyzed the influencing mechanisms of the seasonal variations of optical properties in the YSBS in conjunction with synchronously measured hydrological and biogeochemical data in vertical profiles. The main conclusions obtained are as follows: the vertical variations in the optical properties in the YSBS are mainly influenced by the stratification effect, vertical mixing function, and phytoplankton growth process; and the variations of optical properties are dominated by the change of particle characteristics. The backscattering ratio can be used to discriminate particle types as a proxy of particulate bulk refractive index. In the YSBS, for waters with a backscattering ratio of less than 0.012, the variations of optical properties are dominated by the phytoplankton particles. For waters with a backscattering ratio greater than 0.012, the variations of optical properties are dominated by inorganic sediment particles. In addition, for the YSBS, the variations in optical properties of upper surface layer waters can be elucidated well by the vertical variations.

Distribution of absorption and backscattering coefficients (a(560) and bb(550)) is important for characterizing the marine optical environment. Satellite remote sensing is a useful tool for investigating the absorption and backscattering coefficients in coastal waters. A simple semi-analytical algorithm (SAABS) was developed for estimating a(560) and bb(550) in the Bohai Sea from Medium Resolution Imaging Spectrometer (MERIS) images. Using field measurements, the SAABS model attained root-mean-square (RMS) values of 13.25% and 12.75% for a(560) and bb(550), respectively. The SAABS model was also used to retrieve a(560) and bb(550) from the MERIS image. The match-up analysis results indicate that the RMS values of a(560) and bb(550) retrievals are 18.75% and 17%, respectively. These findings suggested that if the atmospheric correction scheme is available, the SAABS model may be used for the quantitative monitoring of the absorption and backscattering coefficients in the Bohai Sea from the MERIS images.

The performance of the near-infrared (NIR) and short-wave infrared (SWIR) combined atmospheric correction algorithm (NIR-SWIR) for Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua over the Eastern China Seas (ECS) was evaluated. The in situ data set for evaluation in this study was collected during 2005 and 2012 through eight cruises in the ECS, wherein 49 in situ observation points were successfully matched with MODIS-Aqua data. The remote-sensing reflectance derived from MODIS-Aqua data using the NIR-SWIR algorithm and the NIR algorithm were compared to the in situ measurements in the matched-up data set, which included ocean bands (412, 443, 488, 531, 547, 667, and 678 nm) and land bands (469, 555, and 645 nm). The results show that the performance of the NIR-SWIR algorithm has been improved in turbid waters, and the effect at the short-wave bands (blue and green bands) is more significant than that at the long-wave bands (red bands). In addition, MODIS-Aqua data at the land bands (469, 555, ...

Suspended particulate matter concentration (SPM) in coastal waters is of great importance for studying sediment resuspension and sediment transportation, especially for the profile distribution in bodies of water. The traditional SPM profile-obtaining method that uses a turbidity meter introduces large uncertainty for pigment particle-dominated waters. This research used a large amount of profile data on optical properties and hydrological measurements parameters (conductivity-temperature-depth (CTD), chlorophyll and turbidity), which were measured synchronously in the Bohai Sea and Yellow Sea; the data were used to analyze the relationships between the vertical distribution of the particulate backscattering ratio and the vertical distribution of pycnocline, chlorophyll and turbidity and to distinguish pigment particles and sediment particles in the vertical distribution. After considering the particle types, we also developed a novel method that exclusively depended on conventional hydrological measurements. The results show that the new method is simplified and effective and could significantly improve the accuracy of SPM estimations in coastal waters.

Understanding the optimal wavelength for detecting the water column profile from a light detection and ranging (lidar) system is important in the design of oceanographic lidar systems. In this research, the optimal wavelength for detecting the water column profile using a lidar system at the global scale was analyzed based on the inherent optical properties of water. In addition, assuming that the lidar system had a premium detection characteristic in its hardware design, the maximum detectable depth using the established optimal wavelength was analyzed and compared with the mixed layer depth measured by Argo data at the global scale. The conclusions drawn are as follows: first, the optimal wavelengths for the lidar system are between the blue and green bands. For the open ocean, the optimal wavelengths are between 420 and 510 nm, and for coastal waters, the optimal wavelengths are between 520 and 580 nm. To obtain the best detection ability using a lidar system, the best configuration is to use a lidar system with multiple bands. In addition, a 490 nm wavelength is recommended when an oceanographic lidar system is used at the global scale with a single wavelength. Second, for the recommended 490 nm band, a lidar system with the 4 attenuating length detection ability can penetrate the mixed layer for 80% of global waters.

The backscattering efficiency of particles is a crucial factor that relates light backscattering with biogeochemical properties. In this study, based on in situ measurements of the backscattering coefficient (bbp(λ)), particle biogeochemical variables and remote sensing reflectance (Rrs(λ)) in two typical shallow and semi-enclosed seas, namely the Bohai Sea (BS) and Yellow Sea (YS) during the late spring, late summer and late autumn, we examined particulate pseudo-backscattering efficiency variability at 640 nm (P_Qbbe(640)) and related optical effects. The results show that the P_Qbbe(640) levels varied by nearly two orders for all of the samples examined. This high degree of P_Qbbe(640) variability significantly affected bbp(640) and the mass-specific backscattering coefficient (bbp*(640)), showing that approximately 63.7% and 20.8% of the variability in the bbp*(640) and bbp(640) was attributed to the P_Qbbe(640), respectively. More importantly, consistent with the observations of Wang et al. [J. Geophys. Res.: Oceans 121, 3955 (2016)], the P_Qbbe(640) results clearly showed two clusters and this clustering changed the relationships between bbp*(640), bbp(640) and Rrs(640) with the biogeochemical variables. However, we confirm that P_Qbbe(640) clustering generally remained intact across seasons. Therefore, a simple scheme based on a threshold of the P_Qbbe(640) data is proposed for the classification of particle types. With this classification, impacts of P_Qbbe(640) on bbp*(640) and bbp(640) were clearly reduced, and co-variation trends of bbp*(640), bbp(640) and Rrs(640) with biogeochemical variables can be in turn more accurately described. Overall, this study provides general information on P_Qbbe(640) variability in the BS and the YS and consequent effects on optical properties. The scheme for particle type classification may also provide a useful basis for better modeling marine biogeochemical processes related to particulate backscattering and for the development of ocean color algorithms.

Asian dust storms originating from arid or semi-arid regions of China or her adjacent regions have important impact on the atmosphere and water composition, and ecological environment of the Eastern China Seas. This research used data collected in the middle of the South Yellow Sea, China, during a dust storm event from 23 April to 24 April 2006 to analyze the instantaneous influence of dust storms on optical scattering properties, which are closely related to particle characteristics. The analysis results showed that the dust storm had a remarkable influence on the optical scattering property in the upper mixed layer of water, and dust particles drily deposited from the dust storm with an aerosol optical depth of nearly 2.5 into the water could induce a 0.14 m-1 change in the water optical scattering coefficient at 532 nm at the depth of 4 m. The duration of the instantaneous influence of the dust storm on the water optical scattering properties was short, and this influence disappeared rapidly within approximately 3 hours after the end of the dust storm.

Ethylene–vinyl acetate rubber (EVM) vulcanizates with excellent mechanical properties were obtained with magnesium hydroxides (MH) and methacrylic acid (MAA). MAA significantly improved the mechanical properties of the MH-filled EVM vulcanizates. The tensile strength, Shore A hardness, and tensile set modulus of the EVM vulcanizates significantly increased with increasing MAA content. The tensile strength of the MH-filled EVM vulcanizates increased from 12.3 to 18.5 MPa after the addition of 2.5 phr MAA when the MH content was fixed at 60 phr. The gross crosslink density and ionic crosslink density increased with increasing MAA content. The glass-transition temperature and tan δ slightly decreased with increasing MAA content. Morphological study showed that MAA obviously improved the dispersion of the MH particles in the EVM matrix. The thermal stability of the EVM vulcanizates was obviously enhanced after the addition of MAA. Fourier transform infrared analysis indicated that MAA reacted with MH to form magnesium methacrylates, and there was a polymerization of MAA during the curing process in the existence of peroxide. An ionic crosslink bond was thought to be formed on the surface of the MH aggregates; this resulted in a possible structure where MH aggregates were considered as crosslink points. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2011

Ethylene-vinyl acetate rubber (EVM) vulcanizates cured by dicumyl peroxide (DCP) with excellent mechanical properties were obtained by adding superfluous magnesium hydroxides (MH)/methacrylic acid (MAA). Different factors such as the DCP content and MH content were investigated to reveal their effects on the properties of the MH/MAA-filled EVM vulcanizates. The formulation of DCP of 2 phr, MH of 60 phr, and MAA of 5 phr is recommended for the EVM vulcanizates with excellent mechanical properties. The stress relaxation and stress softening behavior of MH/MAA-filled EVM vulcanizates were studied. The stress relaxation and stress softening became faster and more obvious with increasing MH content. The hot air aging resistance of EVM vulcanizates filled with different fillers such as silica and high abrasion furnace were compared, and the MH/MAA-filled EVM vulcanizates had the best aging resistance at 40-phr filler content. The MH/MAA-filled EVM vulcanizates had excellent flame retardancy due to the high MH content. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2011

The mechanical properties, flame retardancy, hot-air ageing, and hot-oil ageing resistance of ethylene-vinyl acetate rubber (EVM)/hydrogenated nitrile-butadiene rubber (HNBR)/magnesium hydroxide (MH) composites were studied. With increasing HNBR fraction, elongation at break and tear strength of the EVM/HNBR/MH composites increased, whereas the limited oxygen index and Shore A hardness decreased slightly. Hot-air ageing resistance and hot-oil ageing resistance of the composites became better with increasing HNBR fraction. Thermal gravimetric analysis results demonstrated that the presence of MH and low HNBR fraction could improve the thermal stability of the composites. Differential scanning calorimeter revealed that the glass transition temperature (Tg) of the composites shifted toward low temperatures with increasing HNBR fraction, which was also confirmed by dynamic mechanical thermal analysis. Atomic force microscope images showed MH has a small particle size and good dispersion in the composites with high HNBR fraction. The flame retardancy, extremely good hot-oil ageing, and hot-air ageing resistance combined with good mechanical properties performance in a wide temperature range (−30°C to 150°C) make the EVM/HNBR/MH composites ideal for cables application. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2009

The thermal degradation of hydrogenated nitrile-butadiene rubber (HNBR)/clay and HNBR/clay/carbon nanotubes (CNTs) nanocomposites was investigated with thermogravimetric analysis (TGA) by using Kissinger method, Flynn–Wall–Ozawa method and Friedman method. The activation energy sequence of HNBR and its nanocomposites is HNBR/clay/CNTs > HNBR/clay > HNBR. HNBR/clay/CNTs nanocomposites had higher char yield at 600 °C than HNBR/clay, which was attributed to the interaction of network between clay and CNTs. The activation energies of HNBR and HNBR nanocomposites had a sharply increase in the low conversion degree area and a slow increase in the high conversion degree area. The gases involved during thermal degradation in nitrogen atmosphere were studied by Fourier transform infrared spectroscopy coupled with TGA. The HNBR/clay/CNTs nanocomposites had lower thermal degradation rate than HNBR/clay, which could be attributed to that the clay-CNTs filler network reduced the diffusion speed of degradation products. The coexistence of clay and CNTs could form compact char layers with better barrier property than clay and thus improved the thermal stability of HNBR.

The Geostationary Ocean Color Imager (GOCI) is the first geostationary ocean color satellite sensor launched in June 2010 on board the South Korean Communication, Ocean and Meteorological Satellite (COMS). The GOCI has a local coverage area of the western Pacific Ocean including Bohai, Yellow Sea and East Sea of China. Jiaozhou Bay is a semienclosed basin in the western part of the Yellow Sea, which is an important representative of gulf ecosystem in the North Temperate Zone. The GOCI data can provide useful information with an-hour temporal and 500-m spatial resolutions for monitoring oceanic and atmospheric process in Jiaozhou Bay. The performance of the atmospheric and optical algorithms of GOCI was evaluated by comparing with the simultaneous data from MODIS/Aqua. The match-up products include remote sensing reflectance (Rrs) and chlorophyll-a concentration ([chl-a]) and they were derived by GDPS software for GOCI data and SeaDAS software for MODIS and GOCI data, respectively, using default atmospheric correction and bio-optical algorithms. Our analyses show that GOCI products (i.e., Rrs(490), Rrs(555) and [chl-a]) are compared well with MODIS products. GOCI shows a potential capability of oceanography investigations in Jiaozhou Bay and Qingdao coastal area. The consistency of Rrs between GOCI and MODIS is relatively good but significant difference was observed in [chl-a] especially in Jiaozhou Bay. Moreover, it is suggested to improve GOCI standard atmospheric correction algorithm for high turbid water and cloud detection to increase data utilization.

PWM (Pulse Width Modulation) is the term used to describe using a digital signal to generate an analogue output signal. This paper is concerned with principles of PWM, PWM Signal Generators and its use in induction motor. At first the principles of PWM and PWM signal generators are discussed. Secondly, a designer's guide to Power MOS Devices for variable speed control of induction motors is analyzed. Lastly, the paper places the emphasis on the simulating of PWM frequency inverter for induction motor. Via the simulation of dynamic process for induction motor, it analyzes the instantaneous characteristics and change law of PWM control technology for induction motor, such as the rotor current, stator current, torque and speed in different PWM input frequency. And the validity of the simulation model is tested and verified via the simulations. The simulation results show that the way through the PWM can be achieved on the induction motor speed modulation.

A quasi-analytical algorithm (QAA)-based algorithm which is applied to moderate imaging spectroradiometer (MODIS) land band data (469, 555, and 645 nm) is proposed and named QAA-RGR (Red-Green-bands-Ratio). The performance has been evaluated using in situ measurements data and MODIS data from the Eastern China Seas (ECS). The QAA-RGR algorithm uses the ratio of the remote sensing reflectance at 645nm (Rsubrs/sub645) to the Rsubrs/sub555 to estimate the absorption coefficient at 555nm. In addition, the spectral slope of the backscattering coefficient (bsubb/sub) is estimated using a statistical relationship based on bsubb/sub555. The other steps of the retrieval algorithm are the same as those of the extensively used QAA version 5 (QAAv5). First, the QAA-RGR algorithm was applied to an in situ measurement data set for the ECS to retrieve inherent optical properties (IOPs), and the results were compared with the QAAv5. The results demonstrate that, the two algorithms exhibit similar performance for in situ measurements. Second, the algorithm was applied to the land bands data and ocean bands data of MODIS over the ECS to obtain the distribution of IOPs at 500m and 1000m resolutions. The results of the retrieval algorithm were evaluated against the corresponding in situ measurements and compared to those from QAAv5. The results demonstrate that, the QAA-RGR algorithm is characterized by a better performance than QAAv5 for MODIS data. For QAAv5, the averaged mean absolute percentage error (MAPE) values of retrieval results of absorption coefficients and backscattering coefficients compared with in situ match-up measurements are 25.2% and 22.2%, respectively. For QAA-RGR, the averaged MAPE values are 15.9% and 18.3%, respectively. The QAAv5 retrieval results are often significantly underestimated especially for turbid coastal waters because of the easy saturation at 667nm band in addition to a large uncertainty in the estimation of Rsubrs/subof the blue bands. The QAA-RGR algorithm may be used to retrieve IOPs from MODIS measurements over the ECS for the measurement periods used in the study.