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Accurate regional distributed PV power forecasting provides data support for power grid management and optimal operation. Distributed PV has the characteristics of large quantity, small capacity and difficulty in obtaining meteorological data. Statistical upscaling method is commonly used to forecast regional power. However, the current research ignores how to reasonably divide the sub-regions with similar output characteristics and mine the spatial and temporal correlation between different sub-regions. Therefore, this paper proposes a short-term regional distributed PV power forecasting method based on sub-region division considering spatio-temporal correlation. Firstly, the representative power plant is selected after dividing the sub-region by the AP clustering algorithm. Then, the GCN is used to extract spatial correlation features, and the LSTM is used to extract the evolution features of dynamic spatial correlation features, and the power forecasting models of representative plants in different weather types are established. Finally, the data integrity and similarity of the sub-region are scored, and the upscaling weight is determined to realize the power forecasting of the whole region. The distributed PV power generation data of Pingshan County, Hebei Province, China is used for simulation test. The results show that the forecasting method proposed has higher forecasting accuracy than the traditional model. • The spatio-temporal correlation between distributed PV power plants is studied. • Introduce how to divide appropriate distributed PV sub-regions. • A power forecasting method considering spatio-temporal correlation is proposed. • Sub-regional data evaluation improves the forecasting accuracy of regional PV. [ABSTRACT FROM AUTHOR]

Irradiance received on the earth’s surface is the main factor that affects the output power of solar PV plants, and is chiefly determined by the cloud distribution seen in a ground-based sky image at the corresponding moment in time. It is the foundation for those linear extrapolation-based ultra-short-term solar PV power forecasting approaches to obtain the cloud distribution in future sky images from the accurate calculation of cloud motion displacement vectors (CMDVs) by using historical sky images. Theoretically, the CMDV can be obtained from the coordinate of the peak pulse calculated from a Fourier phase correlation theory (FPCT) method through the frequency domain information of sky images. The peak pulse is significant and unique only when the cloud deformation between two consecutive sky images is slight enough, which is likely possible for a very short time interval (such as 1 min or shorter) with common changes in the speed of cloud. Sometimes, there will be more than one pulse with similar values when the deformation of the clouds between two consecutive sky images is comparatively obvious under fast changing cloud speeds. This would probably lead to significant errors if the CMDVs were still only obtained from the single coordinate of the peak value pulse. However, the deformation estimation of clouds between two images and its influence on FPCT-based CMDV calculations are terrifically complex and difficult because the motion of clouds is complicated to describe and model. Therefore, to improve the accuracy and reliability under these circumstances in a simple manner, an image-phase-shift-invariance (IPSI) based CMDV calculation method using FPCT is proposed for minute time scale solar power forecasting. First, multiple different CMDVs are calculated from the corresponding consecutive images pairs obtained through different synchronous rotation angles compared to the original images by using the FPCT method. Second, the final CMDV is generated from all of the calculated CMDVs through a centroid iteration strategy based on its density and distance distribution. Third, the influence of different rotation angle resolution on the final CMDV is analyzed as a means of parameter estimation. Simulations under various scenarios including both thick and thin clouds conditions indicated that the proposed IPSI-based CMDV calculation method using FPCT is more accurate and reliable than the original FPCT method, optimal flow (OF) method, and particle image velocimetry (PIV) method. [ABSTRACT FROM AUTHOR]

-Probabilistic wind power forecasting includes more detailed information than deterministic forecasting, which can provide reliable guidance for the optimal decisions of power system scheduling operation. However, there are certain laws in the magnitude and direction of the forecasting errors corresponding to different power series fluctuations, which leads to different predictability and forecasting accuracy of different power fluctuation patterns. As most studies still focused on the model algorithm improvement and pay less attention to the law of power data itself, this paper proposes a novel probabilistic forecasting method based on the swinging door algorithm (SDA), fuzzy c means (FCM) clustering method, long short-term memory (LSTM) neural network, and nonparametric kernel density estimation (KDE), considering the correlation between wind power fluctuation patterns and forecasting errors. SDA and FCM are used to assign appropriate pattern labels to the power fluctuations, and then LSTM and KDE are used to introduce pattern recognition results in probabilistic forecasting models, excavating the inherent law of the data for classification modeling. Simulation shows that the proposed model can adapt to different error distribution patterns, and the models introduced fluctuation pattern recognition can improve the skill score of probabilistic forecasting by 36.50% on average than those without pattern recognition. • Wind power fluctuation pattern recognition relies on fluctuation characteristics. • Model optimization is achieved through pattern forecasting and pattern search. • Forecasting error distribution affects the establishment of prediction intervals. • Pattern recognition can improve deterministic and probabilistic prediction accuracy. [ABSTRACT FROM AUTHOR]

Photovoltaic power forecasting (PVPF) can help energy management system and power grid to improve the proportion of solar energy in total energy consumption. Classification modeling according to different weather types is an effective means to improve the accuracy of PVPF under various weather statuses. However, the weather type of historical data (WTHD) is missing in some cases, which will cause great difficulties to classification modeling because the data without WTHD cannot be used for the model training. To identify the missing WTHD, a solar irradiance feature extraction and support vector machines (SVM) based weather statuses pattern recognition (WSPR) model for short-term PVPF (ST-PVPF) is presented. To ensure the feasibility and reduce the workload of classification modeling, four generalized weather classes (GWC) covering all weather types are constituted, and GWC based classification modeling approach for ST-PVPF is proposed subsequently. The SVM model for WSPR is built with input features extracted from solar irradiance data. Through a case study, the effectiveness and performance of the WSPR model are verified and evaluated. The influences of different input dimensions and feature combinations are also analyzed and discussed. The results indicate that the missing WTHD can be effectively recovered as GWC by the proposed model. [ABSTRACT FROM AUTHOR]

As the immune cells of the brain, microglia are crucial for the maintenance of brain function. The aims of the present study were to determine whether and how annexin-1 is able to affect microglial phenotype and migration in the lesion microenvironment. In the current experiment, we enhanced the interaction between annexin-1 and formyl peptide receptors in microglia and analyzed the function. We found that annexin-1 could polarize microglia to a beneficial phenotype and promote microglial migration to protect neurons from ischemia-like injury, and the annexin-1-mediated neuroprotective effect was dependent on the release of glutamate and ATP from the injured neurons. [ABSTRACT FROM AUTHOR]

• Explore the influence of wind process time scale on wind speed fluctuation law. • Propose wind speed forecasting model utilize time scale information of wind process. • Adopt complex network to mining the morphological characteristic of wind curve. The forecast of wind speed is prerequisite for wind power prediction, which is one of the most effective means of promoting wind power absorption. However, when modeling for wind speed sequences with different fluctuations, most existing researches ignore the influence of time scale of wind speed fluctuation period, let alone the low compatibility between training and testing samples that severely limit the training performance of forecasting model. To improve the accuracy of wind speed and wind power forecasting, an ultra-short-term wind speed forecasting model based on time scale recognition and dynamic adaptive modeling is proposed in this paper. First, a series of wind processes are divided from the historical wind speed sequence according to the natural variation characteristics of wind speed. Second, we divide all the wind processes into two patterns based on their time scale, and an SVC model with input features extracted from meteorological data is built to identify the time scale of the current wind process. Third, for a specifically identified wind process, the complex network algorithm is applied in data screening to select high compatible training samples to train the forecast model dynamically for current input. Simulation indicates that the proposed approach presents higher accuracy than benchmark models using the same forecasting algorithms but without considering the time scale and data screening. [ABSTRACT FROM AUTHOR]

Abstract: Thin films of Eu-doped lanthanum orthophosphate (LaPO4) were fabricated on glass substrates using the successive-ionic-layer-adsorption-and-reaction method and subsequent hydrothermal and furnace annealing. A monoclinic structure was observed to be present in LaPO4:Eu films after hydrothermal annealing at 200°C. Scanning electron microscopy and transmission electron microscopy images of LaPO4:Eu films show the homogenously close-packed morphology of micro-sized nanofibers at around 10 to 20nm in diameter. The red emission activated at 254nm was obtained from LaPO4:Eu films. [Copyright &y& Elsevier]

Abstract: The temperature effect on corrosion behaviors of PbCaSnCe alloy in 4.5M H2SO4 solution was investigated by using potentiodynamic curve, electrochemical impedance spectra (EIS), Mott–Schottky plot and photocurrent response methods. It was found that PbCaSnCe alloy was in passive state in sulfuric acid solution, a passive film can be formed on alloy surface. The compositions of passive films formed at 0.9V for 2h under different temperatures were detected by X-ray photoelectron spectroscopy (XPS). The results showed that the film resistance and the transfer resistance decreased with the increment of the solution temperature. Mott–Schottky analysis and the photocurrent response revealed that the passive film exhibited n-type semi-conductive character, the donor density of the passive film decreased with increasing the solution temperature. Photocurrent response revealed that the photocurrent increased with increasing temperature. XPS results indicated that the PbO2 content in passive films may increase with increasing the solution temperature. [Copyright &y& Elsevier]

Thioredoxin, DsbA, the N-terminal active-site domain a and the non-active-site domain b of protein-disulfide isomerase are all monomeric with a thioredoxin fold, and each exhibits Iow or no isomerase and chaperone activity. We have linked the N terminus of the above four monomers, individually, to the C terminus of the N-terminal domain of DsbC via the flexible linker helix of the latter to produce four domain hybrids, DsbCnTrx, DsbCn-DsbA, DsbCn-PDIa, and DsbCn-PDIb. These four hybrid proteins form homodimers, and except for DsbCn-PDIb they exhibit new or greatly elevated isomerase as well as chaperone activity. Three-dimensional structure prediction indicates that all the four domain hybrids adopt DsbC-like V-shaped structure with a broad uncharged cleft between the two arms for binding of non-native protein folding intermediates. The results provide strong evidence that dimerization creates chaperone and isomerase activity for monomeric thiol-protein oxidases or reductases, and suggesting a pathway for proteins to acquire new functions and/or higher biological efficiency during evolution. [ABSTRACT FROM AUTHOR]

For minute time scale solar photovoltaic (PV) power forecasting, the motion of clouds over PV power plant mainly contribute to the fluctuant and intermittent nature of solar PV power output. Therefore, research on cloud motion displacement (CMD) calculation to realize cloud motion prediction is a key sub-process for minute time scale solar PV power forecasting approaches. Fourier phase correlation theory (FPCT) is widely applied in CMD calculation for its superiority of simplicity and less computation, then an improved algorithm based on image-phase-shift-invariance (IPSI) is proposed to reduce the outlier probability of CMD results. However, at present, the current IPSI algorithm still has limitations and cannot avoid the occurrence of outliers altogether. In this paper, we presented a novel method, termed IPSI based multi-transform-fusion (MTF) method, to further improve the effectiveness compared with traditional FPCT and affine transform based IPSI method. First, three image transform methods satisfying IPSI condition, respectively wavelet transform (WT), affine transform (AT), and convolution transform (CT), are explored. Then the information increment of the transformed sky images using the above three methods is analyzed, respectively. Second, we determine the suitable image transform method for IPSI algorithm under specific cloud condition according to the corresponding information increment. Third, an IPSI based MTF method for CMD calculation in sky images is proposed. The original sky images are transformed through WT, AT, and CT to generate multiple images that maintain the same object motion information, then calculate the CMDs in each generated image. Finally, we apply Gaussian distribution to fit the multiple CMD values and taking its mathematical expectation as final CMD result. Various experimental results in 4 different scenarios show that the performance of the proposed approach is better than FPCT, AT based IPSI, and OF method, by reducing plenty of CMD outliers, thus delivering greater accuracy and robustness. [ABSTRACT FROM AUTHOR]

Conjugate heat transfer of air and steam in a rectangular channel with 90° ribs along two opposite walls was investigated experimentally and numerically. The stainless steel test section was 80 mm × 40 mm × 2.5 mm and the ribs were 80 mm × 2.5 mm × 2.5 mm with 25 mm between ribs. The tests investigated the effects of coolant mass flow rate (the corresponding Reynolds numbers in the range of 10,000-50,000) on the conjugate heat transfer enhancement with the ribs. Two conjugate heat transfer calculation methods with different models were developed. For the first model (CHT-Q model) solid domain was viewed as a uniform internal heat source with the adiabatic exterior surfaces, while for the second model (CHT-T model) the outwall temperature was specified by the fitting polynomials of measured data with the zero internal heat. Comparisons between the experimental and numerical results showed that the SST k–ω turbulence model was more suitable for the conjugate heat transfer in such channels. Regardless of numerical error, an approximation of heat loss was specified by the successive trial calculations of the CHT-Q model, while a relatively accurate heat loss was evaluated by the post-processing of the CHT-T calculation. Local heat transfer coefficient can be determined accurately by the quantified heat loss of test system. The critical impact of conjugate heat transfer was demonstrated. Furthermore, the steam coolant compared to air exhibited a higher heat transfer performance by 12–25% for both the ribbed and smooth walls at the same Reynolds number. [ABSTRACT FROM AUTHOR]

• The spatio-temporal correlation of adjacent wind farms is dynamic and time-varying. • Dynamic spatio-temporal correlation can alleviate lagging problem of forecast values. • Hierarchical directed graph can clarify the causal relationship among input variables. • Reduce error sources by hierarchical directed graph can improve forecast accuracy. Accurate wind farm cluster power forecasting is of great significance for the safe operation of the power system with high wind power penetration. However, most of the current neural network methods used for wind farm cluster power forecasting have the following three problems: (1) lack of consideration of dynamic spatio-temporal correlation among adjacent wind farms; (2) simultaneously forecasting all wind farms' power to obtain the total power will produce numerous error sources; (3) ignoring the causal relationship among input variables. Therefore, to solve the above problems, this paper proposes an ultra-short-term wind farm cluster power forecasting method based on dynamic spatio-temporal correlation and hierarchical directed graph structure. Firstly, three different types of nodes (wind speed nodes, wind power nodes, and target node) and input samples are defined, and then the spatio-temporal correlation matrices that can describe the correlation of adjacent wind farms are also calculated. Secondly, directed edges are defined to connect different nodes in order to obtain the hierarchical directed graph structure. Finally, this graph structure with dynamic spatio-temporal correlation information is used to train the forecasting model. In case study, compared with other benchmark methods, the proposed method shows excellent performance in improving accuracy of power forecasting. [ABSTRACT FROM AUTHOR]

Selective catalytic reduction technology using NH3 as a reducing agent (NH3-SCR) is an effective control method to remove nitrogen oxides. TiO2-supported vanadium oxide catalysts with different levels of Ce and Sb modification were prepared by an impregnation method and were characterized by X-ray diffractometer (XRD), Brunauer-Emmett-Teller (BET), Transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FT-IR), UV-Vis diffuse reflectance spectroscopy (UV-Vis DRS), Raman and Hydrogen temperature-programmed reduction (H2-TPR). The catalytic activities of V5CexSby/TiO2 catalysts for denitration were investigated in a fixed bed flow microreactor. The results showed that cerium, vanadium and antimony oxide as the active components were well dispersed on TiO2, and the catalysts exhibited a large number of d-d electronic transitions, which were helpful to strengthen SCR reactivity. The V5CexSby/TiO2 catalysts exhibited a good low temperature NH3-SCR catalytic activity. In the temperature range of 210 to 400°C, the V5CexSby/TiO2 catalysts gave NO conversion rates above 90%. For the best V5Ce35Sb2/TiO2 catalyst, at a reaction temperature of 210°C, the NO conversion rate had already reached 90%. The catalysts had different catalytic activity with different Ce loadings. With the increase of Ce loading, the NO conversion rate also increased. [ABSTRACT FROM AUTHOR]

Spondyloepiphyseal dysplasia tarda with progressive arthropathy (SEDT-PA) is an autosomal recessive skeletal disorder resulting from pathogenic mutations in the Wnt1-inducible signaling pathway protein 3 (WISP3) gene. This disorder predominantly involves the skeletal system, with the leading features of platyspondyly, metaphyseal dysplasia of limbs and extremities, and progressive degeneration of joints. To date, 53 distinct forms of WISP3 mutations have been detected globally, eleven of which originated from Chinese patients. In the current study, we reported the clinical manifestations and radiographic features of two unrelated Chinese SEDT-PA patients. Through genetic analysis, two novel mutations (c.624delA, c.105dupT) as well as one recurrent mutation (c.342T > G) were identified in the WISP3 gene. Our study contributed to the further expansion of the WISP3 mutation spectrum, and demonstrated the genotype-phenotype relationship between mutations in the WISP3 gene and clinical findings of SEDT-PA. [ABSTRACT FROM AUTHOR]

This research reported a micelle/microemulsion bimodal templating method to synthesize hierarchical porous silicas (HPS) by using a P123/cosurfactant/1,3,5-trimethylbenzene/water four-component microemulsion system. The influence of different diols (i.e., ethanediol, 1,3-propanediol, 1,4-butanediol, 1,5-pentadiol, and 1,6-hexanediol) as cosurfactants on the synthesis of HPS was systematically investigated. Among the obtained mesoporous silica materials, the HPS materials with pore structures of disordered-SBA-15-type (Dis-SBA-15) together with mesocellular foams (MCFs) could be clearly identified from the TEM images when using ethanediol, 1,3-propanediol, or 1,4-butanediol as the swelling agent of P123 in the initial synthesis environment, while MCFs were the main phases as using 1,5-pentadiol and 1,6-hexanediol. The results showed that those shorter chain diols with carbon number from 2 to 4 (involving ethanediol, 1,3-propanediol, and 1,4-butanediol) could more easily swell the P123 micelles compared with the longer chain diols (1,5-pentadiol and 1,6-hexanediol). Consequently, HPS materials with large pore volumes (up to 1.48 cm³ g-1) were successfully prepared by the oriented role from the templates of the swollen P123 micelles and TMB/P123 microemulsions. This approach provides a novel pathway for the facile synthesis of HPS materials with large pore volumes which may be a type of promising candidate for various applications. [ABSTRACT FROM AUTHOR]

• The temporal distribution of irradiance is a mapping of cloud spatial distribution. • Irradiance data can be achieved using cloud position and RGB values in sky images. • Sky image-irradiance mapping model is updated according to current cloud conditions. • Minutely irradiance forecasting is achieved by a sky image-irradiance mapping model. Accurate minutely solar irradiance forecasting is the basis of minute-level photovoltaic (PV) power forecasting. In this paper, a minutely solar irradiance forecasting method based on real-time surface irradiance mapping model is proposed, which is beneficial to achieve higher accuracy in solar power forecasting. First, we extract the red–green–blue (RGB) values and position information of pixels in sky images after background elimination and distortion rectification, to explore the mapping relationship between sky image and solar irradiance. Then a real-time sky image-irradiance mapping model is built, trained, and updated according to real-time sky images and solar irradiance. Finally, the future solar irradiance within the time horizons varying from 1 min to 10 min ahead are capable to be forecasted by using the latest updated surface irradiance mapping model with extracted input from the current sky image. The average measures of proposed method by using MAPE, RMSE, MBE are 22.66%, 92.72, −1.26% for blocky clouds; 20.44%, 132.15, −1.06% for thin clouds and 18.82%, 120.78, −0.98% for thick clouds, thus deliver much higher forecasting accuracy than other benchmarks. [ABSTRACT FROM AUTHOR]

• Photovoltaic power presents volatility, annual periodicity, and adjacent similarity. • Deep learning and time correlation principles are combined in a forecasting problem. • Time correlation modification improves the accuracy of LSTM-RNN forecasting model. • Partial daily pattern prediction can be used to optimize the modification process. Photovoltaic (PV) power generation is an effective means to realize solar energy utilization. Due to the natural characteristics of random fluctuations in solar energy, the applications of PV power such as grid-connected PV power plant, distributed PVs, and building integrated PVs will introduce new characteristics to the generation and load side of the power grid. Therefore, accurate day-ahead PV power forecasting is of great significance for enabling grid manager to achieve PV power output data in advance and mitigate the influence of random fluctuations. To tackle the deficiencies of conventional artificial intelligence (AI) modeling methods such as overfitting problem and insufficient generalization ability to complex nonlinear modeling, a day-ahead PV power forecasting model assembled by fusing deep learning modeling and time correlation principles under a partial daily pattern prediction (PDPP) framework is proposed. First, an independent day-ahead PV power forecasting model based on long-short-term memory recurrent neural network (LSTM-RNN) is established. Second, a modification method is proposed to update the forecasting results of LSTM-RNN model based on time correlation principles regarding different patterns of PV power in the forecasting day. Third, a partial daily pattern prediction (PDPP) framework is proposed to provide accurate daily pattern prediction information of particular days, which is used to guide the modification parameters. Simulation results show that the proposed forecasting method with time correlation modification (TCM) is more accurate than the individual LSTM-RNN model, and the performance of the forecasting model can be further improved for those days with accurate daily pattern predictions under the proposed PDPP framework. [ABSTRACT FROM AUTHOR]

A novel guided tissue regeneration (GTR) membrane with orthogonal aligned fibers in surface layers and random fibers within interlayer was developed by multilayer electrospinning. The mechanical strength of tri-layer membrane was between random and aligned membranes. The promotion of the proliferations of MC3T3-E1 and NIH-3 T3 cells on the tri-layer membrane were observed during monoculture. When cells were cocultured, the tri-layer membrane was effective in impeding MC3T3-E1 and NIH-3 T3 cell infiltration by guiding cells to grow along the direction of the fiber alignment. [ABSTRACT FROM AUTHOR]

Wind power has received extensive attention due to its superiorities of clean and pollution-free. However, because of the randomness and volatility of wind power, accurate power prediction is needed to help its consumption. Wind speed is the key to wind power prediction, but traditional prediction method cannot accurately grasp the wind speed variation trend and the traditional wind process partition method has some defects, an ultra-short-term wind speed hybrid prediction method based on wind process pattern forecasting is proposed in this paper. Firstly, a wind process (WP) division method considering the influence of wind speed on the operation state, mode switch and output power of wind turbine is proposed. Secondly, according to the operating characteristics of the wind turbine, all the WPs is classified into different wind process patterns (WPP), and the effectiveness of the classification is verified. Then, the Adaboost algorithm is used to forecast the WPP of the next 4 h. Finally, the wind speed hybrid prediction model of each pattern is established, the corresponding model is automatically selected based on WPP to predict the wind speed. Simulation results show that the proposed model can reliably forecast future WPP and the prediction accuracy is better than conventional models. • Wind fluctuation process is divided considering the key values of wind speed. • Wind process show varies fluctuation patterns according to operating characteristic. • Adaboost classification algorithm is applied to predict future wind process pattern. • Targeted modeling for specific wind process pattern can improve prediction accuracy. [ABSTRACT FROM AUTHOR]

Numerical Weather Prediction (NWP) is the key to precise wind power forecasting (WPF), which can be enhanced by the NWP correction and scenario partition techniques. However, on the one hand, existing NWP correction techniques may enlarge the volatility of ensemble NWP which disturbs the subsequent WPF. On the other hand, existing scenario partition techniques cannot precisely predict wind power in fluctuating scenarios by assuming NWP is totally reliable. Therefore, this paper proposes a novel NWP enhanced WPF method based on rank ensemble and probabilistic fluctuation awareness. Firstly, Rank Bayesian Ensemble (RBE) method is intended based on the stationary NWP rank, which generates a stable and accurate ensemble NWP. Secondly, a fluctuation scenarios partition framework is devised to establish a fluctuation awareness model with NWP's credibility quantified. The framework works in a three-step manner, including characterization, matching, and inference of wind fluctuation events: respectively as Fluctuation identification and feature embedding (FIGE), Fluctuating mapping algorithm (FMA), and Probabilistic fluctuation warning (PFW). Finally, we incorporate the two enhancement techniques in a forecasting method in the ultra-short-term. A real-world wind farm with four NWP sources data validates the superiority and robustness of the proposed WPF method. The result shows that our method can reduce the four hour-ahead rooted mean square error (RMSE) by 2.16%–4.36% compared to baseline models. Meanwhile, the stability of ensemble NWP and the effectiveness of fluctuation scenario partition are also discussed. • Proposed enhancement techniques improve NWP's contribution to forecasting accuracy. • NWP rank describes the stable performance of multi-source NWP in typical weather. • Scenario partition effectively models and predicts the wind fluctuation events. • Fluctuation probability is inferred in each moment with NWP credibility quantified. • Superiority of the two NWP enhancement techniques is proved in the real-world case. [ABSTRACT FROM AUTHOR]

The rhizome of Belamcanda chinensis (L.) DC. is a traditionally used medicinal material in China. Due to increasing demand, B. chinensis has been cultivated widely, and thus the study on its rational utilization of medicinal part and guidelines for the optimal cultivation and harvest is an important issue. Considering flavonoids were the main bioactive secondary metabolites of B. chinensis, fluorescence microscopy, laser microdissection (LMD), ultra-high performance liquid chromatography-quadrupole/time-of-flight-mass spectrometry (UHPLC-Q/TOF-MS), and UHPLC coupled with triple quadrupole mass spectrometer (UHPLC-QqQ-MS) were applied to profile and determine flavonoids in various tissues in this study. Consequently, 43 peaks were detected by UHPLC-Q/TOF-MS, and 26 flavonoid compounds combined with seven triterpene compounds were identified or tentatively identified in the tissue extractions. The results indicated that the hydrophobic compounds, especially flavonoid or isoflavonoid aglycones and xanthone mainly accumulated in the cork, whereas the hydrophilic compounds, namely the flavonoid and isoflavonoid glycosides were usually found in the cortex or center (the part inside of endodermis). Samples of rhizomes from different growth ages and origins were simultaneously analyzed. It was shown that the bulb or lateral part of the rhizome generally possessed more total flavonoids than the vertical part or the primordium. The present study established a new practical method to evaluate the quality of the rhizome of B. chinensis and to explore the relationship between distribution patterns of secondary metabolites and growth years of plants, thus important information for cultivation and processing was provided. [ABSTRACT FROM AUTHOR]

CeO2-TiO2 composite supports with different Ce/Ti molar ratios were prepared by a homogeneous precipitation method, and V2O5-WO3/CeO2-TiO2 catalysts for the selective catalytic reduction (SCR) of NOx with NH3 were prepared by an incipient-wetness impregnation method. These catalysts were characterized by means of BET, XRD, UV-Vis, Raman and XPS techniques. The results showed that the catalytic activity of V2O5-WO3/TiO2 was greatly enhanced by Ce doping (molar ratio of Ce/Ti = 1/10) in the TiO2 support. The catalysts that were predominantly anatase TiO2 showed better catalytic performance than the catalysts that were predominantly fluorite CeO2. The Ce additive could enhance the surface adsorbed oxygen and accelerate the SCR reaction. The effects of O2 concentration, ratio of NH3/NO, space velocity and SO2 on the catalytic activity were also investigated. The presence of oxygen played an important role in NO reduction. The optimal ratio of NH3/NO was 1/1 and the catalyst had good resistance to SO2 poisoning. [ABSTRACT FROM AUTHOR]

Highlights: [•] The corrosion mechanism of 10# steel in shell-side corrosion medium is displayed. [•] The results of this paper play a guidance role for the practical application in the field of refinery heat exchanger tube. [ABSTRACT FROM AUTHOR]

Accurate ultra-short-term PV power forecasting is essential for the power system with a high proportion of renewable energy integration, which can provide power fluctuation information hours ahead and help to mitigate the interference of the random PV power output. Most of the PV power forecasting methods mainly focus on employing local ground-based observation data, ignoring the spatial and temporal distribution and correlation characteristics of solar energy and meteorological impact factors. Therefore, a novel ultra-short-term PV power forecasting method based on the satellite image data is proposed in this paper, which combines the spatio-temporal correlation between multiple plants with power and cloud information. The associated neighboring plant is first selected by spatial-temporal cross-correlation analysis. Then the global distribution information of the cloud is extracted from satellite images as additional inputs with other general meteorological and power inputs to train the forecasting model. The proposed method is compared with several benchmark methods without considering the information of neighboring plants. Results show that the proposed method outperforms the benchmark methods and achieves a higher accuracy at 4.73%, 10.54%, and 4.88%, 11.04% for two target PV plants on a four-month validation dataset, in terms of root mean squared error and mean absolute error value, respectively. • The spatio-temporal correlation between PV power plants is investigated. • Introduce how to select an appropriate neighboring plant. • Verify the mapping relationship between cloud characteristics of neighboring plants and the power of target plants. • An ultra-short-term PV power forecasting method based on cloud information from neighboring plant is proposed. [ABSTRACT FROM AUTHOR]

[Display omitted] • An ultrafast method is developed to synthesize Cu 2 O octahedrons. • Highly dispersed Cu 2 O octahedrons inlaid in Ni Foam are obtained. • The sample displays superior catalytic activity in both alkaline water/seawater electrolytes. • The sample exhibits good catalytic durability and stability. Development of bifunctional cost-effective and self-supporting electrocatalysts for high-performance water/seawater electrolysis are vital for emerging energy storage and conversion technologies. Herein, an ultrafast strategy is reported to synthesis homogeneous Cu 2 O octahedrons inlaid in Ni foam (oct_Cu 2 O-NF) via spontaneous replacement of Ni with Cu, followed by rapid oxidization of Cu in air. Benefiting from the high dispersion of uniform octahedral structure and strong interaction between Cu 2 O and Ni foam, oct_Cu 2 O-NF displayed a superior activity for both oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) as well as outstanding stability. After assembling oct_Cu 2 O-NF as bifunctional electrodes for alkaline seawater splitting, the electrolyzer exhibited a very small cell voltage of 1.71 V to reach 10 mA cm−2. This brand-new way of ultrafast synthesis for oct_Cu 2 O-NF experimentally confirms the feasibility of Cu-based nanomaterials for efficient water/seawater electrolysis. [ABSTRACT FROM AUTHOR]

Folate plays a key role in DNA synthesis and methylation. Limited evidence suggests high intake may reduce risks of esophageal cancer overall; however, associations with esophageal cancer subtypes and Barrett's esophagus (BE), a precancerous lesion, remain unexplored. We evaluated the relation between intake of folate, B vitamins, and methyl-group donors (methionine, choline, betaine) from foods and supplements, polymorphisms in key folate-metabolizing genes, and risk of BE, esophageal adenocarcinoma (EAC), and esophageal squamous cell carcinoma (ESCC) in 2 population-based case-control studies in Australia. BE patients without (n = 266) or with (n = 101) dysplasia were compared with population controls (n = 577); similarly, EAC (n = 636) or ESCC (n = 245) patients were compared with population controls (n = 1507) using multivariable adjusted logistic regression. Increasing intake of folate from foods was associated with reduced EAC risk (P-trend = 0.01) and mitigated the increased risks of ESCC associated with smoking and alcohol consumption. In contrast, high intake of folic acid from supplements was associated with a significantly elevated risk of BE with dysplasia. High intakes of riboflavin and methionine from food were associated with increased EAC risk, whereas increasing betaine intake was associated with reduced risks of BE without (P-trend = 0.004) or with dysplasia (P-trend = 0.02). Supplemental thiamin, riboflavin, niacin, and vitamin B-12 were associated with increased EAC risk. There were no consistent associations between genetic polymorphisms studied and BE or EAC risk. High intake of folate-containing foods may reduce risk of EAC, but our data raise the possibility that folic acid supplementation may increase risks of BE with dysplasia and EAC. [ABSTRACT FROM AUTHOR]

Comparative studies of enzymatic acylation of troxerutin by the alkaline protease from Bacillus subtilis under ultrasound and shaking were carried out in nonaqueous media. Using divinyl dicarboxylates (CH2=CH–OOC–(CH2)n–COO–CH=CH2, n = 2, 3, 4, 7, 8, 11) featuring different chain length as acyl donors, troxerutin was regioselectively acylated at B ethoxyl group, whether under ultrasound or shaking. Ultrasonic treatment increased the reaction rate and led to high conversion. Several factors, such as pre-irradiation on the enzyme, the power and frequency of the ultrasound, operation manner, as well as the length of the acyl donors were investigated. Using enzyme pre-irradiated for 8 h, the conversion of troxerutin was increased to 87.3% compared with 56.3% obtained from the untreated enzyme. Experimental results also showed that continual ultrasound caused greater rate acceleration than interval ultrasound. Powers of 100, 150 and 200 W, frequencies of 40, 80 and 100 kHz all showed significant improvement on the transesterification, with the greatest effect observed at 150 W, 80 kHz. The acceleration effect increased as the chain length of the acyl donors decreased from C13 to C4. [Copyright &y& Elsevier]

Protein disulfide isomerase (PDI, EC 5.3.4.1) is a chaperone and catalyzes the formation and rearrangement of disulfide bonds in proteins. Domain c-(463–491), containing 18 acidic residues, is an interesting and important C-terminal extension of PDI. In this study, the PDI mutant abb′a′, in which domain c is truncated, was used to investigate the relationship between the C-terminal structure and chaperone function. Reactivation and light-scattering experiments show that both wild-type PDI and abb′a′ interact with lactate dehydrogenase (LDH, EC 1.1.1.27), which tends to self-aggregate during reactivation. The interaction enhances reactivation of LDH and reduces aggregation. According to these results, it seems as if domain c might be dispensable to the chaperone function of PDI. However, abb′a′ is prone to self-aggregation and causes increased aggregation of LDH during thermal denaturation. In contrast, wild-type PDI remains active as a chaperone under these conditions and prevents self-aggregation of LDH. Furthermore, measurements of intrinsic fluorescence and difference absorbance during denaturation show that abb′a′ is much more labile to heat or guanidine hydrochloride denaturation than wild-type PDI. This suggests that domain c is required for the stabilization and maintenance of the chaperone function of PDI under extreme conditions. [ABSTRACT FROM AUTHOR]