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The fundamental difference between quantum and traditional plasmas is the electron and ion composition, the former has a much higher density and extremely lower temperature, and it can be modelled by Zakharov-Kuznetsov (ZK) equation. In this paper, the Hirota bilinear method is used to study its solution properties. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is an illness that involves a gradual and irreversible degeneration of the brain. It is crucial to establish a precise diagnosis of AD early on in order to enable prompt therapies and prevent further deterioration. Researchers are currently focusing increasing attention on investigating the potential of machine learning techniques to simplify the automated diagnosis of AD using neuroimaging. The present study involved a comparison of models for the detection of AD through the utilization of 2D image slices obtained from magnetic resonance imaging brain scans. Five models, namely ResNet, ConvNeXt, CaiT, Swin Transformer, and CVT, were implemented to learn features and classify AD based on various perspectives of 2D image slices. A series of experiments were conducted using the dataset from the Alzheimer's Disease Neuroimaging Initiative. The results showed that ConvNeXt outperformed ResNet, CaiT, Swin Transformer, and CVT. ConvNeXt exhibited an average accuracy, precision, recall, and F1 score of 95.74%, 96.71%, 95.74%, and 96.14%, respectively, when applied to a 3-way classification task involving AD, mild cognitive impairment, and normal control subjects. The results suggest that the utilization of ConvNeXt may have potential in the identification of AD using 2D slice images. [ABSTRACT FROM AUTHOR]

In this paper, by combining sampling methods for food statistics with years of sample sampling experience, various sampling points and corresponding sampling methods are summarized. It hopes to discover food safety risks and improve the level of food safety. [ABSTRACT FROM AUTHOR]

As the global economy enters a new stage, Chinese dairy enterprises are gradually moving towards the road of internationalization. In this paper, the internationally common quality management system, food safety system and quality traceability system were analyzed and studied to promote the comprehensive transformation and upgrading of dairy industry and broaden the development road with the help of systematic construction. [ABSTRACT FROM AUTHOR]

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]

Yolk-shell SiO2 particles (YP) with center-radial meso-channels were fabricated through a simple and effective method. Al-containing YP-supported NiMo catalysts with different Al amounts (NiMo/AYP-x, x = Si/Al molar proportion) were prepared and dibenzothiophene (DBT) and 4,6-dimethyl-dibenzothiophene (4,6-DMDBT) were employed as the probes to evaluate the hydrodesulfurization (HDS) catalytic performance. The as-prepared AYP-x carriers and corresponding catalysts were characterized by some advanced characterizations to obtain deeper correlations between physicochemical properties and the HDS performance. The average pore sizes of series AYP-x supports are above 6.0 nm, which favors the mass transfer of organic sulfides. The cavity between the yolk and the shell is beneficial for the enrichment of S-containing compounds and the accessibility between reactants and active metals. Aluminum embedded into the silica framework could facilitate the formation of Lewis (L) and Brønsted (B) acid sites and adjust the metal-support interaction (MSI). Among all the as-synthesized catalysts, NiMo/AYP-20 catalyst shows the highest HDS activities. The improved HDS activity of NiMo/AYP-20 catalyst is attributed to the perfect combination of excellent structural properties of the yolk-shell mesoporous silica, enhanced acidity, moderate MSI, and good accessibility/dispersion of active components. [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]

Objective • The purpose of this retrospective cohort study was to evaluate clinical outcomes in high-risk patients with symptomatic intracranial atherosclerotic stenosis (sICAS) resulting from plaque enhancement who underwent balloon dilation or stent implantation. Plaque features were identified based on high-resolution magnetic resonance vessel wall imaging (HRMR-VWI). Methods • A total of 37 patients with sICAS (degree of stenosis ≥70%) were enrolled between January 2018 and March 2022 at a single center. All patients underwent HRMR-VWI and received standard drug treatment after hospital admission. The patients were divided into 2 groups based on whether they underwent interventional treatment (n = 18) or non-interventional treatment (n = 19). The grade of enhancement and enhancement rate (ER) of culprit plaque were evaluated using 3D-HRMR-VWI. The risk of symptom recurrence was compared between the 2 groups during follow-up. Results • There was no statistical difference between the intervention and non-intervention groups in the rate and type of enhancement. Median clinical follow-up time was 17.8 (10.0 to 26.0) months and median follow-up time was 3.6 (3.1 to 6.2) months. In the intervention group, 2 patients had stent restenosis, but no stroke or transient ischemia attacks (TIAs) occurred. In contrast, 1 patient in the non-intervention group had an ischemic stroke and 4 patients had TIAs. The incidence of the primary outcome was lower in the intervention group than in the nonintervention group (0 vs 26.3%; P = .046). Conclusions • High-resolution magnetic resonance intracranial vessel wall imaging (HR MR-IVWI) can be used to identify vulnerable plaque features. It is safe and effective in high-risk patients with sICAS with responsible plaque enhancement to undergo intravascular intervention combined with standard drug therapy. Further studies are needed to analyze the link between plaque enhancement and symptom recurrence in the medication group at baseline. [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]

The pathogenesis of high altitude-related gastric mucosal injury remains poorly understood, this study aimed to investigate the role of autophagy in hypoxia-induced apoptosis of rat gastric mucosal cells. Rats were randomized into four groups which were maintained at an altitude of 400 m (P) or received no treatment (H), autophagy inducer rapamycin (H+AI) or autophagy inhibitor 3-MA (H+AB) at an altitude of 4,300 m for 1, 7, 14 and 21 days, respectively, and the morphology, ultrastructure, autophagy, and apoptosis of gastric mucosal tissues were examined. Gastric mucosal epithelial cells CC-R039 were cultured under conditions of normoxia, 2% O2 (hypoxia), or 2% O2+anti-mTORC1 for 0, 24, 48, and 72 h, respectively, and the autophagy and apoptosis were analyzed. CC-R039 cells were transfected with siHIF-1α, siTERT, or siRNA and the autophagy was examined. The results showed that the exposure to hypoxia increased the autophagy and apoptosis of gastric mucosal cells in rats, and apoptosis was aggravated by rapamycin treatment but alleviated by 3-MA treatment. Increased duration of hypoxia from 0 to 72 h could increase the autophagy and apoptosis but decrease the proliferation of gastric mucosal cells. Inhibition of mTORC1 with rapamycin led to further increase in apoptosis and even substantial cell death, and inhibition of HIF- 1a and TERT increased mTORC1 expression and reduced autophagy. Moreover, the inhibition of HIF-1a reduced TERT expression. In conclusion, hypoxia could induce apoptosis of rat gastric mucosal cells by activating autophagy through HIF-1a/TERT/mTORC1 pathway. [ABSTRACT FROM AUTHOR]

Regional photovoltaic(PV) power forecasting provides a foundation for grid management and trading in the power markets. To tackle the deficiency of conventional regional PV power modeling methods, such as the problem of challenging to select useful meteorological information and the problem that a single model cannot fully learn the complex and diverse fluctuation characteristics of power curves, an ultrashort-term regional PV power forecasting framework assembled by fusing fluctuation pattern recognition (FPR) and deep learning modeling under a fluctuation pattern prediction (FPP) model was proposed. First of all, the fluctuation characteristics of the regional PV power curves were extracted, including sampling loss area, mean value, standard deviation, and third derivative. Then, a K-means algorithm based FPR model was established to obtained three kinds of patterns. FPP model based on convolutional neural network is used to predict future PV power fluctuation patterns with historical satellite images as input. Finally, the convolutional autoencoder is used to extract cloud distribution features, and a long- and short-term memory network based on the combination of cloud distribution features and historical output is proposed to construct prediction models of the three models. Through the analysis of the simulation data, the prediction method proposed in this article has higher prediction accuracy than the traditional prediction model. [ABSTRACT FROM AUTHOR]

The micro-electro-mechanical system is widely used for energy harvesting and thermal wind sensor, its efficiency and reliability depend upon the pull-in instability. This paper studies a micro-electro-mechanical system using He-Liu [34] formulation for finding its frequency-amplitude relationship. The system periodic motion, pull-in instability and pseudo-periodic motion are discussed. This paper offers a new window for security monitoring of the system reliable operation. [ABSTRACT FROM AUTHOR]

It is important to improve the comprehensive performance of the ductile iron bars (DIBs) for the cylinder block of the extra high pressure hydraulic plunger pump and accelerate the industrial application. In this work, the LZQT600-3 DIBs with the diameter of 145 mm were prepared by the horizontal continuous casting (HCC) process, that is, LZQT600-3 HCCDIBs. The microstructure and room temperature tensile properties of different sections [left-edge (surface layer), left-1/2R (left half of the radius), and the center of the HCCDIBs] were studied. The results show that the spheroidization of LZQT600-3 HCCDIBs matrix from the left-edge, left1/2R to the center is at nodulizing grade II and above. As the cooling rate gradually decreases from surface to the center of the HCCIBs, the number of spheroidized graphite is gradually reduced, the size is gradually increased, the shape factor is decreased, and the pearlite content and lamellate spacing are increased. Along the horizontal direction of the section, the hardness of the material is distributed symmetrically around the center of the HCCDIBs. In the vertical direction, the hardness distribution in the center of the HCCDIBs is asymmetrical due to the gravity during the solidification process. Therefore, the microstructure in the lower part of the section solidifies relatively quickly. The left-edge has the best tensile mechanical properties, and the ultimate tensile strength, yield tensile strength and elongation are 597.3 MPa, 418.5 MPa and 9.6%, respectively. The tensile fracture belongs to the ductile-brittle hybrid fracture. The comprehensive performances of LZQT600-3 HCCDIBs meet the actual application requirements of ultra-high pressure hydraulic plunger pump cylinder. [ABSTRACT FROM AUTHOR]

Ultra-short-term photovoltaic (PV) power forecasting can support the real-time dispatching of the power grid. However, PV power has great fluctuations due to various meteorological factors, which increase energy prices and cause difficulties in managing the grid. This article proposes an ultra-short-term PV power forecasting model based on the optimal frequency-domain decomposition and deep learning. First, the optimal frequency demarcation points for decomposition components are obtained through frequency-domain analysis. Then, the PV power is decomposed into the low-frequency and high-frequency components, which supports the rationality of decomposition results and solves the problem that the current decomposition model only uses the direct decomposition method and the decomposition components are not physical. Then, a convolutional neural network (CNN) is used to forecast the low-frequency and high-frequency components, and the final forecasting result is obtained by addition reconstruction. Based on the actual PV data in heavy rain days, the mean absolute percentage error (MAPE) of the proposed forecasting model is decreased by 52.97%, 64.07%, and 31.21%, compared with discrete wavelet transform, variational mode decomposition, and direct prediction models. In addition, compared with recurrent neural network and long–short-term memory model, the MAPE of the CNN forecasting model is decreased by 23.64% and 46.22%, and the training efficiency of the CNN forecasting model is improved by 85.63% and 87.68%. The results fully show that the proposed model in this article can improve both forecasting accuracy and time efficiency significantly. [ABSTRACT FROM AUTHOR]

The precise minute time scale forecasting of an individual PV power station output relies on accurate prediction of cloud distribution, which can lead to dramatic fluctuation of PV power generation. Precise cloud distribution information is mainly achieved by ground-based total sky imager, then the future cloud distribution can also be achieved by sky image prediction. In previous studies, traditional digital image processing technology (DIPT) has been widely used in predicting sky images. However, DIPT has two deficiencies: relatively limited input spatiotemporal information and linear extrapolation of images. The first deficiency makes the input spatiotemporal information not rich enough, while the second creates the prediction error from the beginning. To avoid these two deficiencies, convolutional autoencoder (CAE) based sky image prediction models are proposed due to the spatiotemporal feature extraction ability of two-dimensional (2-D) CAEs and 3-D CAEs. For 2-D CAEs and 3-D CAEs, four architectures are given respectively. To verify the effectiveness of the proposed models, two typical DIPT methods, including particle image velocimetry and Fourier phase correlation theory are introduced to build the benchmark models. Besides, five different scenarios are also set and the results show that the proposed models outperform the benchmark models in all scenarios. [ABSTRACT FROM AUTHOR]

Accurately forecasting regional distributed photovoltaic (DPV) power is crucial in mitigating the negative impact of high DPV penetration on the reliability and resilience of the distribution network. However, most of the current photovoltaic power forecasting methods suffer from two key problems: (1) ignoring the asymmetric influence relationship among DPV sites; (2) lack of consideration of dynamic spatiotemporal correlation among DPV sites. As a result, these methods are unable to fully adapt to the characteristics of DPV, making it challenging to directly apply the existing forecasting methods to improve the accuracy of DPV power forecasting. To conquer this limitation, a dynamic directed Graph Convolution Neural Network (DDGCN) is applied to regional DPV ultra‐short‐term power forecasting. Unlike the conventional Graph Convolution Neural Network (GCN) based forecasting methods, the proposed method improves GCN to process the directed graph. On this basis, to capture the dynamic and directed adjacency relationship among graph nodes, a temporal attention mechanism is proposed and combined with the directed GCN model. In this way, the dynamic and asymmetric/directed relationships among DPV sites can be taken into account. It is worth noting that the DPVs' adjacency relationship can be constructed without any prior knowledge by end‐to‐end model training. The simulation experiment proves that the prediction accuracy can be further improved by taking into account the dynamic directed relationship among the sites via a real DPV power dataset. [ABSTRACT FROM AUTHOR]

Selective oxidation of methane to organic oxygenates over metal–organic frameworks (MOFs) catalysts at low temperature is a challenging topic in the field of C1 chemistry because of the inferior stability of MOFs. Modifying the surface of Cu-BTC via hydrophobic polydimethylsiloxane (PDMS) at 235 °C under vacuum not only can dramatically improve its catalytic cycle stability in a liquid phase but also generate coordinatively unsaturated Cu(I) sites, which significantly enhances the catalytic activity of Cu-BTC catalyst. The results of spectroscopy characterizations and theoretical calculation proved that the coordinatively unsaturated Cu(I) sites made H2O2 dissociative into •OH, which formed Cu(II)-O active species by combining with coordinatively unsaturated Cu(I) sites for activating the C−H bond of methane. The high productivity of C1 oxygenates (CH3OH and CH3OOH) of 10.67 mmol gcat. −1h−1 with super high selectivity of 99.6% to C1 oxygenates was achieved over Cu-BTC-P-235 catalyst, and the catalyst possessed excellent reusability. [ABSTRACT FROM AUTHOR]

Introduction: The aim of this study is to investigate the role of genetic variation and DNA methylation of PEAR1 rs12041331 in high on-treatment platelet reactivity (HPR) and recurrent ischemic stroke (RIS). Methods: Genotype, methylation, and mRNA of PEAR1 rs12041331 were detected in patients with cerebral ischemia, for the analysis of the effect of PEAR1 rs12041331 on HPR and RIS. Results: The major G allele of PEAR1 rs12041331 was associated with hypermethylation, which was associated with HPR. This link was not observed for RIS. Conclusions: The PEAR1 rs12041331 genetic polymorphism and DNA methylation may be among the genetic factors affecting HPR. The correlation between PEAR1 and RIS needs to be studied further. [ABSTRACT FROM AUTHOR]

Propylene is a significant basic material for petrochemicals such as polypropylene, propylene oxide, etc. With abundant propane supply from shale gas, propane dehydrogenation (PDH) becomes extensively attractive as an on-purpose propylene production route in recent years. Nitrogen-doped carbon (NC) nanopolyhedra supported cobalt catalysts were synthesized in one-step of ZIF-67 pyrolysis and investigated further in PDH. XPS, TEM and N2 adsorption-desorption were used to study the influence of carbonization temperature on as-prepared NC supported cobalt catalysts. The temperature is found to affect the cobalt phase and nitrogen species of the catalysts. And the positive correlation was established between Co0 proportion and space time yield of propylene, indicating that the modulation of carbonization temperature could be important for catalytic performance. [ABSTRACT FROM AUTHOR]

In order to enhance the cooling performance of turbine blades, novel cooling channels filled with X-shaped truss array were investigated in this study. The flow mechanism and heat transfer characteristic of the cooling channel filled with X-shaped truss array were analyzed numerically. The empirical correlations of friction coefficient and Nusselt number related to the inlet Reynolds number (10000-60000) and truss rod inclination angle (30-45°) were fitted. The results show that the secondary flow vortex in the channel and the Nusselt number on the channel wall both show periodic distributions along the streamwise direction. The row-averaged Nusselt number and friction coefficient of the channel first decrease quickly and then decrease slowly along the streamwise direction. When truss rod inclination angle increases from 30-60°, the whole-averaged Nusselt number and the whole friction coefficient of the channel increase by 25.4-52.3% and 1.19-1.33 times, respectively under different Reynolds number. The channel with truss rod inclination angle of 45° has the best comprehensive thermal performance. In all cases, the ratio of heat transfer quantity of the truss rod surface to the total heat transfer quantity of the channel ranges from 22.9-42.3%. The increase of Reynolds number improves the heat transfer quantity of the channel wall and the increase of truss rod inclination angle reduces the heat transfer quantity of the channel wall. [ABSTRACT FROM AUTHOR]

The regeneration of the articular cartilage defects is characterized by the improvement in the quality of the repaired tissue and the reduction in the potential development of perifocal osteoarthritis (OA). Usually, the injection of dexamethasone (Dex) in the OA joints slows down the progression of inflammation and relieves pain. However, the anti-inflammatory Dex injected in the joint cavity is rapidly cleared, leading to a poor therapeutic effect. Multifunctional hydrogels with simultaneous chondrogenic differentiation, antioxidative, and anti-inflammatory capacities may represent a promising solution. Therefore, in this work, a novel injectable hydrogel based on double cross-linking of Schiff base bonds and coordination of catechol-Fe was developed. The obtained hydrogel (Gel-DA/DOHA/DMON@Dex@Fe) possessed molding performance in situ, excellent mechanical strength, controllable biodegradability, the on-demand release of the drug, and biocompatibility. The hydrogel system stimulated the HIF-1α signaling pathway and suppressed inflammation thanks to the introduction of DMON@Fe, consequently facilitating chondrogenic differentiation. The synergistic antiinflammatory effect together with the induction of chondrogenesis by Dex-loaded Gel-DA /DOHA/DMON@Fe hydrogel allowed the promotion of cartilage repair, as demonstrated by in vivo experiments. Hence, the proposed multifunctional scaffold provides a promising advancement in articular cartilage tissue engineering and may have great prospects in the prevention of OA. [ABSTRACT FROM AUTHOR]

Accurate monthly electricity consumption forecasting (MECF) is important for electricity retailers to mitigate trading risks in the electricity market. Clustering is commonly used to improve the accuracy of MECF. However, in the existing clustering-based forecasting methods, clustering and forecasting are independently performed and lack coordination, which limits the further improvement of forecasting accuracy. To address this issue, an adaptive optimal greedy clustering-based MECF method is proposed in this article. First, a metric of predictability is defined based on the goodness of fit and the cluster's average electricity consumption. Under a predefined number of clusters, the greedy clustering algorithm achieves the optimal division of individuals with the goal of maximizing predictability. Then, an adaptive method is designed to select the optimal number of clusters from a variety of clustering scenarios according to the prediction accuracy on the validation dataset. The effectiveness and superiority of the proposed method have been verified on a real-world dataset. [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]

Residential customers account for an indispensable part in the demand response (DR) program for their capability to provide flexibility when the system required. However, their available DR capacity has not been fully comprehended by the aggregator, who needs the information to bid accurately on behalf of the residential customers in the market transaction. To this end, this article devised an optimal bidding strategy for the aggregator considering the bottom-up responsiveness of residential customers. First, we attempt to establish the customers’ responsiveness function in relation to different incentives, during which a home energy management system is introduced to implement load adjustment for electrical appliances. Second, the functional relation is applied to the aggregator's decision-making process to formulate the optimal bidding strategy in the day-ahead market and the optimal scheduling scheme for the energy storage system with the aim to maximize its own revenue. Finally, the validity of the proposed method is verified using the dataset from the Pecan Street experiment in Austin. The obtained outcome demonstrates the practical rationality of the proposed method. [ABSTRACT FROM AUTHOR]

Furthermore, a subset of early Kras-mutant cell states exhibit marked similarity to either benign or malignant fates that emerge weeks to months later; for instance, Kras-mutant Nestin-positive progenitor-like cells display accessible chromatin near genes active in malignant tumors. These plastic cell states are enriched for open chromatin near cell-cell communication genes encoding ligands and cell-surface receptors, suggesting an increased propensity to communicate with the microenvironment. [Extracted from the article]

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]

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]