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Drought has a significant impact on crop growth and productivity, highlighting the critical need for precise and timely soil moisture estimation to mitigate agricultural losses. This study focuses on soil moisture retrieval in northern Hebei Province during July 2012, utilizing eight widely employed remote sensing drought indices derived from MODIS satellite data. These indices were cross-referenced with measured soil moisture levels for analysis. Based on their correlation coefficients, a composite remote sensing drought index set comprising six indices was identified. Furthermore, a radial basis function neural network (RBFNN) was employed to estimate soil relative humidity. The accuracy evaluation of the soil moisture estimation model, which integrates multiple remote sensing drought indices and the RBFNN, demonstrated clear superiority over models relying on single drought indices. The model achieved an average estimation accuracy of 87.54 % for soil relative humidity at a depth of 10 cm (SM10) and 87.36 % for a 20 cm depth (SM20). The root mean square errors (RMSE) for the test sets were 0.093 and 0.092, respectively. Validation results for July 2013 indicated that the inversion accurately reflected the actual soil moisture conditions, effectively capturing dynamic moisture changes. These results fully verify the reliability and practicability of the model. These findings introduce a novel approach to local agricultural soil moisture estimation, with significant implications for enhancing agricultural water resource management and decision-making processes.

With the increasing penetration of natural gas in power systems, the resilience of electric-gas coupled distribution networks under extreme fault perturbations has received increasing attention. Since the entire phase resilience of an electric-gas coupled distribution network after an extreme event depends on its system function, it is necessary to consider a multi-phase recovery process in order to comprehensively improve the resilience and rapid recovery of electric-gas coupled distribution networks against extreme events. In this paper, a fast fault isolation and service restoration methodology that integrates active islanding effect and RCS is proposed to improve the resilience and fast recovery capability of electric-gas coupled distribution networks. Firstly, an electric-gas coupled distribution network model is constructed, after which the multi-stage recovery process is modeled in detail by combining the dynamic mechanism of the load supply curve of the electric-gas coupled distribution network under the whole process of extreme faults, including the topological constraints and power constraints of each stage and the coupling relationship between each stage. The effectiveness of the proposed multi-stage resilience optimization method is verified using the IEEE33 node power system and the natural gas 7-node distribution system as examples. It is demonstrated that the multi-stage restoration method proposed in this paper can greatly improve the resilience of coupled electric-gas distribution networks.

Purposes The construction of new low-carbon power grids for the whole society has an important role in promoting the development of low-carbon economy, and the establishment of a new power system whole life cycle low-carbon evaluation index system is of great significance to the development of low-carbon power grid. Methods On the basis of the analysis of the whole life cycle of the new power system low carbon grid, three primary indicators of economy, reliability, and low carbon were constructed at first, and then nine secondary indicators were screened on the basis of the primary indicators to evaluate the low carbon grid. A comprehensive evaluation index system with a core evaluation index system of the new power system low carbon grid for the whole life cycle was established, and a detailed explanation of the significance of each indicator in the system was provided. Results By using a large amount of statistical data from the power grid, typical scenarios with different new energy ratios were established and simulated. The obtained evaluation results of low carbon indicators for different scenarios prove the feasibility of the low carbon grid evaluation index system established in this paper.

Purposes In the context of China’s “double carbon” target, energy saving and emission reduction have become one of the primary strategic objectives of current power industry. In addition to system safety, reliability, and economy, the power system also needs to introduce the “carbon emission reduction constraint” into all aspects of the system, which will present a new low carbon operation and management mode. Methods Aiming at the new power system carbon measurement and low carbon regulation and operation problems, a low carbon demand response scheduling strategy based on carbon emission flow is proposed. For the new power system, on the basis of analyzing the carbon emission influencing factors of each link of source-network-load-storage, an accurate carbon metering model based on the carbon emission flow theory is established, and a low-carbon demand response mechanism is set up with the dynamic carbon emission factor as the guiding signal to carry out optimized scheduling. Findings It is verified that the new power system source-network-load-storage coordinated optimal scheduling method based on low-carbon demand response can effectively stimulate the emission reduction potential of each link of the system, reduce carbon emissions and improve the rate of new energy consumption.

Purposes Deep learning based on centralized data can effectively improve the prediction accuracy of wind power, but the serious consequences of data leakage make wind farms constantly pay attention to the confidentiality of their own data, which hinders data-driven wind power prediction methods. Methods To solve the above problems, a short-term power prediction method was proposed for distributed multi-wind farms oriented to data privacy protection, which uses the Horizontal Federated Learning framework to complete the power prediction task of wind farms. First, wind farms adopt the distributed training method, and use the Temporal Pattern Attention (TPA) mechanism and Long Short-Term Memory (LSTM) network to form a TPA-LSTM local model to complete local data training. Then, the parameters of these local models are aggregated, and the weight values of the models are introduced to improve the contribution rate of the local models with good fitting effect. Finally, the update of global model parameters is implemented. Findings The results of experiments show that the global model obtained by this method has good prediction performance and generalization ability in multiple scenarios under the premise of ensuring the privacy of wind farm data.

Purposes At present, the data driven method represented by deep learning has been widely used in power transient stability analysis. However, the existing transient stability models for researching data driving have some problems, such as limited generalization ability and insufficient model accuracy, when facing small samples, weak samples, and other actual scenarios. In order to improve the expression ability of the model, a refined transient stability assessment method is proposed in this paper according to operation data and fault data. Methods First, four fault information characteristics, namely fault time, fault location, disturbed line, and load level, are constructed according to the transient stability mechanism model of power system. Then, two feature fusion methods, parallel fusion and serial fusion, are proposed to realize the unified expression of operation features and fault features. The influence of multi-source feature fusion on transient stability analysis model is analyzed in depth. Findings The experimental results of the New England system example show that the transient stability analysis method based on multi-source data hybrid drive is conducive to improving the accuracy of the transient stability assessment model, and still has a high accuracy in practical scenarios such as small samples and weak samples.

The connection of single-phase AC loads to a DC microgrid not only large secondary currents flowing into energy storage units with droop control cause, but also lead to secondary voltage ripples appeared on the DC bus, which seriously affects the normal operation of DC microgrids. In this paper, the secondary currents and voltages can be suppressed simultaneously by means of reconstructing the output impedances of energy storage converter and active capacitor converter. The main characteristics, impedance model, control strategies, and system stability in terms of secondary ripples are comprehensively investigated. Particularly, it is shown that the output impedance of the energy storage converter with droop control is relatively small and the input impedance DC loads buck converter are relatively large, so the secondary currents mainly flow into energy storage units and DC bus capacitor. Based on this, the output impedance of the energy storage converter and active capacitor converter are reconstructed relatively larger and smaller respectively, so the secondary currents mainly flowing into the active capacitor. Therefore, the secondary ripple currents flowing into energy storage units and the secondary ripple voltages appeared on the DC bus can be suppressed. Finally, the proposed methods are varied through simulations results.

It is very important to clarify the optimization method of the rock-like material ratio for accurately characterizing mechanical properties similar to the original rock. In order to explore the optimal ratio of rock-like materials in gneissic granite, the water–paste ratio, iron powder content and coarse sand content were selected as the influencing factors of the ratio. An orthogonal test design and sensitivity analysis of variance were used to obtain the significant influencing factors of the ratio factors on seven macroscopic mechanical parameters, including compressive strength σc, tensile strength σt, shear strength τf, elastic modulus E, Poisson’s ratio ν, internal friction angle φ and cohesion c. A multivariate linear regression equation was constructed to obtain the quantitative relationship between the significant ratio factors and the macroscopic mechanical parameters. Finally, a rock-like material ratio optimization program based on genetic algorithm inversion was written. The results show that the water–paste ratio had extremely significant effects on σc, σt, τf, E, ν and c. The iron powder content had a highly significant effect on σc, σt, τf and c, and it had a significant effect on ν and φ. Coarse sand content had a significant effect on σc, E and c. The multiple linear regression model has good reliability after testing, which can provide theoretical support for predicting the macroscopic mechanical parameters of rock-like materials to a certain extent. After testing, the ratio optimization program works well. When the water–paste ratio is 0.5325, the iron powder content is 3.975% and the coarse sand content is 15.967%, it is the optimal ratio of rock-like materials.

Epigenomic imbalance drives abnormal transcriptional processes, promoting the onset and progression of cancer. Although defective gene regulation generally affects carcinogenesis and tumor suppression networks, tumor immunogenicity and immune cells involved in antitumor responses may also be affected by epigenomic changes, which may have significant implications for the development and application of epigenetic therapy, cancer immunotherapy, and their combinations. Herein, we focus on the impact of epigenetic regulation on tumor immune cell function and the role of key abnormal epigenetic processes, DNA methylation, histone post-translational modification, and chromatin structure in tumor immunogenicity, and introduce these epigenetic research methods. We emphasize the value of small-molecule inhibitors of epigenetic modulators in enhancing antitumor immune responses and discuss the challenges of developing treatment plans that combine epigenetic therapy and immunotherapy through the complex interaction between cancer epigenetics and cancer immunology.

Acute kidney injury (AKI) is a heterogeneous, high-mortality clinical syndrome with diverse pathogenesis and prognosis, but it lacks the effective therapy clinically. Its pathogenesis is associated with production of reactive oxygen/nitrogen species and infiltration of inflammatory cells. To overcome these pathogenic factors and improve the therapeutic efficiency, we synthesized triptolide-loaded mesoscale polydopamine melanin-mimetic nanoparticles (MeNP4TP) as the antioxidant plus anti-inflammatory therapeutic platform to synergistically scavenge reactive oxygen/nitrogen species (RONS), inhibit the activity of macrophages and dendritic cells, and generate Treg cells for AKI therapy. It was demonstrated that mesoscale size was beneficial for MeNP4TP to specifically accumulate at renal tubule cells, and MeNP4TP could significantly attenuate oxidative stress, reduce proinflammatory immune cells in renal, and repair renal function in cisplatin-induced AKI mouse model. MeNP4TP might be a potential candidate to inhibit oxidative damages and inflammatory events in AKI.

Pectobacterium brasiliense is a worldwide distributed bacterial pathogen causing soft rot and blackleg of a wide range of economically important crops. Here, we present the complete, high-quality genome of P. brasiliense strain TS20HJ1, which was isolated from a ginger (Zingiber officinale) rhizome showing soft rot symptoms. The genome assembly consists of a circular 4,766,353-bp chromosome with 52.1% GC content. The genome harbors 4,128 protein-coding genes, 22 rRNA genes, and 77 tRNA genes. We believe that the complete genome sequence of P. brasiliense TS20HJ1 will provide a valuable resource for understanding the molecular basis of host adaptation and evolution of pathogenesis. [Figure: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

The dynamic characteristics of the power system are becoming more and more complex, and the difficulty of operation control is increasing. Preventive control is the main means of power system transient stability control. This paper proposes a stacking ensemble learning-driven power system transient stability preventive control optimization method. Firstly, a transient stability assessment model based on Stacking Ensemble Deep Belief Nets (SEDBN) network is established in this research. The performance of weak classifiers is improved by SEDBN’s multi-layer ensemble structure, and the created transient stability estimator can extract diverse features and has better robustness and generalization abilities. Secondly, the trained transient stability estimator is integrated into the Aptenodytes Forsteri Optimization (AFO) algorithm as a “transient stability constraint discriminator”. Finally, with the goal of minimizing the cost of preventive control, an optimization algorithm for the preventive control of power system transient stability driven by SEDBN is established. Simulation results on IEEE 39-bus systems show that the proposed method can achieve highly efficient control solutions.

The transient stability assessment based on machine learning faces challenges such as sample data imbalance and poor generalization. To address these problems, this paper proposes an intelligent enhancement method for real-time adaptive assessment of transient stability. In the offline phase, a convolutional neural network (CNN) is used as the base classifier. A model training method based on contrastive learning is introduced, aiming to increase the spatial distance between positive and negative samples in the mapping space. This approach effectively improves the accuracy of the model in recognizing unbalanced samples. In the online phase, when real data with different distribution characteristics from the offline data are encountered, an active transfer strategy is employed to update the model. New system samples are obtained through instance transfer from the original system, and an active sampling strategy considering uncertainty is designed to continuously select high-value samples from the new system for labeling. The model parameters are then updated by fine-tuning. This approach drastically reduces the cost of updating while improving the model’s adaptability. Experiments on the IEEE39-node system verify the effectiveness of the proposed method.

Abstract The real‐time monitoring technology of power line is an important guarantee for the digitization and intelligence of the modern power system. Traditional current transformers cannot provide continuous and stable energy when the power line current fluctuates in a large range. This paper proposes a comprehensive design method of an active electronic current transformer (AECT) based on a single‐phase full‐bridge voltage‐type PWM rectifier (VSR), which can acquire continuous and stable energy from the power line with a minimum magnetic core. The control of the AECT is divided into four different operation modes to adapt the fluctuation of primary current. Maximum power point tracking (MPPT) mode and minimum loss (ML) mode is designed to harvest the maximum power with lower iron core loss when the primary current is relatively low. Excitation mode and demagnetization mode are designed to harvest stable power when the primary current is higher than a certain current. The specific mode operation principles and control strategies are analysed in detail. Besides, the calculation method of the primary current is also presented for the primary current measurement. Finally, simulations and experiments verify that the iron core can obtain power steadily (9.91 W/kg) when the current fluctuation between 50 and 600 A.

The incorporation of renewable energy on a broad scale into power grids increases the complexity to the issue of transient voltage stability in power systems. This research presents a rapid evaluation technique for assessing the stability of voltage fluctuations in power grids that receive electricity from photovoltaic sources. At first, a receiving-end system model was developed, which includes photovoltaics, and an alternative circuit of the virtual induction motor (IM) is obtained by utilizing the Thevenin equivalent. Second, the torque balance equation and the Kirchhoff voltage equation are interconnected to determine the unstable slip of the IM following a malfunction. Finally, by merging the unstable slip with the conventional transient stability discriminant index, the effects of different photovoltaic outputs, IM ratios, and system contact impedances on transient voltage stability are investigated. The proposed method avoids the computational burden of solving the differential-algebraic equations describing the complex transient processes of the IM. It also obviates the necessity of iteratively modifying the receiving load or fault clearance time in the simulation platform to achieve the constrained stability condition of the system’s transient voltage. The transient stabilization of voltage results is efficiently obtained by directly solving algebraic equations.

High-resistance ground faults are difficult to detect with existing ultrahigh voltage direct current (UHVDC) transmission fault detection systems because of their low sensitivity. To address this challenge, a straightforward mathematical method has been proposed for fault detection in UHVDC system based on the downsampling factor (DF) and approximation derivatives (AD). The signals at multiple sampling frequencies were analysed using the DF, and the AD approach was used to generate various levels of detail and approximation coefficients. Initially, the signals were processed with different DF values. The first, second, and third order derivatives of the generated signals were calculated by the AD method. Next, the entropy features of these signals were computed, and the Random Forest-Recursive feature elimination with cross-validation (RF-RFECV) algorithm was used to select a high-quality feature subset. Finally, an ensemble classifier consisting of Light Gradient Boosting Machine (LightGBM), K Nearest Neighbor (KNN), and Naive Bayes (NB) classifiers was utilized to identify UHVDC faults. The MATLAB/Simulink simulation software was used to develop a ±800 kV UHVDC transmission line model and perform simulation experiments with various fault locations and types. Based on the experiments, it has been established that the suggested approach is highly precise in detecting several faults on UHVDC transmission lines. The method is capable of accurately identifying low or high resistance faults, irrespective of their incidence, and is remarkably resistant to transitional resistance. Furthermore, it exhibits excellent performance in identifying faults using a small sample size and is highly reliable.

The implementation of public–private partnership (PPP) is perturbed by multiple factors, and it is difficult for the Chinese government to fully control the risks of PPP projects. Based on the DPSIR model, this study constructed the PPP projects risk evaluation index system by using the TOPSIS method. Then the spatial variation, standard deviation ellipse, and gray dynamic model were used to analyze the spatial–temporal dynamic evolution characteristics of the risk level of PPP projects from 2003 to 2019 and to make reasonable predictions of the future spatial distribution pattern. This study yielded the following five results: (1) the average risk level of China’s PPP projects is 0.722, with a decreasing trend of fluctuation and relatively stable risk; (2) high-risk provinces for PPP projects decrease, medium-risk provinces increase; (3) the spatial variability of risk is increasing, and the spatial differentiation is significant, showing a spatial evolution pattern of “west > central > east,” but areas with low-risk values are clearly migrating to the east; (4) the spatial distribution pattern of risk has a north-easterly orientation, with a “north-west to north-east” trend in the path of movement; (5) There are differences in the spatial distribution patterns in the east–west and north–south directions, and the spatial spillover effect is not obvious. Based on the above results, the risk center of China’s PPP projects will be shifted to the northeast in 2025–2035. Our study captures the evolution of PPP project risks in China in both temporal and spatial dimensions, which can provide lessons for optimizing global PPP project risk management.

Sodium‐ion batteries (SIBs) have attracted attention due to their potential applications for future energy storage devices. Despite significant attempts to improve the core electrode materials, only some work has been conducted on the chemistry of the interface between the electrolytes and essential electrode materials. Therefore, the different cathode–electrolyte interfaces (CEIs) that form between various cathode materials and liquid electrolytes for SIBs are briefly reviewed, and the requirements of CEI performance in low‐temperature SIBs. Modifying the cathode materials and electrolyte formulas offers an efficient strategy for CEI enhancement. The summary and analysis, given in this article, may serve as a reference for the development of better sodium‐ion batteries.

Transient stability preventive control (TSPC), a method to efficiently withstand the severe contingencies in a power system, is mathematically a transient stability constrained optimal power flow (TSC-OPF) issue, attempting to maintain the economical and secure dispatch of a power system via generation rescheduling. The traditional TSC-OPF issue incorporated with differential-algebraic equations (DAE) is time consumption and difficult to solve. Therefore, this paper proposes a new TSPC method driven by a naturally inspired optimization algorithm integrated with transient stability assessment. To avoid solving complex DAE, the stacking ensemble multilayer perceptron (SEMLP) is used in this research as a transient stability assessment (TSA) model and integrated into the optimization algorithm to replace transient stability constraints. Therefore, less time is spent on challenging calculations. Simultaneously, sensitivity analysis (SA) based on this TSA model determines the adjustment direction of the controllable generators set. The results of this SA can be utilized as prior knowledge for subsequent optimization algorithms, thus further reducing the time consumption process. In addition, a naturally inspired algorithm, Aptenodytes Forsteri Optimization (AFO), is introduced to find the best operating point with a near-optimal operational cost while ensuring power system stability. The accuracy and effectiveness of the method are verified on the IEEE 39-bus system and the IEEE 300-bus system. After the implementation of the proposed TSPC method, both systems can ensure transient stability under a given contingency. The test experiment using AFO driven by SEMLP and SA on the IEEE 39-bus system is completed in about 35 s, which is one-tenth of the time required by the time domain simulation method.

Abstract Tumors modify myeloid cell differentiation and induce an immunosuppressive microenvironment. Granulocytic myeloid-derived suppressor cells (G-MDSCs), the main subgroup of myeloid-derived suppressor cells (MDSCs), are immature myeloid cells (IMCs) with immunosuppressive activity and exist in tumor-bearing hosts. The reason why these cells diverge from a normal differentiation pathway and are shaped into immunosuppressive cells remains unclear. Here, we reported that the increase of granulocyte colony-stimulating factor (G-CSF) in mouse serum with tumor progression encouraged G-MDSCs to obtain immunosuppressive traits in peripheral blood through the PI3K-Akt/mTOR pathway. Importantly, we found that downregulation of type I interferon (IFN-I) signaling in G-MDSCs was a prerequisite for their immunosuppressive effects. Suppressor of cytokine signaling (SOCS1), the action of which is dependent on IFN-I signaling, inhibited the activation of the PI3K-Akt/mTOR pathway by directly interacting with Akt, indicating that the differentiation of immunosuppressive G-MDSCs involves a transition from immune activation to immune tolerance. Our study suggests that increasing IFN-I signaling in G-MDSCs may be a strategy for reprograming immunosuppressive myelopoiesis and slowing tumor progression.

Abstract With the adoption of gas‐fired units (GFU), the interaction between the electricity and gas systems has been intensified. The failure of the gas sources may lead to the insufficiency of the gas supply to the GFUs, and further result in the electricity supply shortage, threatening reliabilities of electricity and gas systems. However, compared with the electric power flow, the dynamics of the gas flow are much slower. Most of the existing studies evaluated the reliabilities of integrated electricity and gas systems (IEGS) without considering the slower dynamics of gas flow, which are not fully accurate in the short‐term. This paper proposes a short‐term reliability evaluation technique for IEGS considering the gas flow dynamics. Firstly, the short‐term reliability models of gas sources, GFUs, and gas compressors are developed. Then, the multi‐stage contingency management scheme is proposed, where gas flow dynamics are analysed for determining the time‐varying load curtailments of electricity and gas. Moreover, a time‐sequential Monte Carlo simulation technique is developed with the finite‐difference scheme to tackle the gas flow dynamics during the short‐term reliability evaluation. Finally, the proposed reliability evaluation technique is validated using an integrated IEEE reliability test system and the practical Belgium gas transmission system.

Sclerotinia stem rot, caused by Sclerotinia sclerotiorum, is a devastating disease of soybean. Biological control is a potential alternative to chemical fungicides for disease management, and provides broad benefits to the environment, farmers and consumers. Herein, we established a field application technique for biocontrol of Sclerotinia stem rot in soybean using wuyiencin, expanding on a previous study showing biocontrol potential. We used wuyiencin to reduce sclerotia in soybean seed, and disease incidence analysis by seed bioassay revealed an optimal wuyiencin seed soaking concentration of 12.5 μg/mL. We found that different application methods had different effects on soybean plant growth. Soybean pot experiments showed that 100 μg/mL wuyiencin was obtained a significant disease protection effect and promote soybean growth through root irrigation, and the optimal concentration for wuyiencin spraying was 100–200 μg/mL. We tested the efficacy of applying wuyiencin under field conditions, and the protection effect of 200 μg/mL wuyiencin sprayed three times was the best (64.0%), but this was slightly inferior to the protection effect of 200 μg/mL dimethachlon (77.6%).

Abstract An active power filter (APF), which operates under the power‐balance‐based scheme (PB scheme), suffers heavier DC‐link voltage fluctuation when the loads change suddenly, so that it is apt to be interfered and degraded or even out of stable operation. By analyzing the cause of DC‐link voltage fluctuation, this study proposes an improved PB scheme which feeds forward the increment of the fundamental‐frequency positive sequence component of the source current. The feed‐forward channel is designed by use of the cascaded delayed signal cancellation algorithm and integrated into the conventional PB scheme APF without adding any extra hardware circuit. The current increment fed forward is added into the output of the voltage controller to timely amend the reference current of the current control loop, and thus the active power flow, which caused by loads change, between the APF and its external circuitry can be suppressed. As a result, the DC‐link voltage fluctuation is mitigated to allow the APF to better accommodate the abrupt load changes. The configuration of the improved control system is introduced and the stability is analysed. The experiment results demonstrate the effectiveness of the proposed control scheme.

Multiple uncertainties from renewable energy sources, power loads and bidirectional AC/DC converter have brought great challenges to the energy management of AC/DC hybrid microgrid. In view of the issues, this paper proposes a temporally coordinated energy management strategy for AC/DC hybrid microgrid considering dynamic conversion efficiency of bidirectional AC/DC converter. According to the operation and loss characteristics of bidirectional AC/DC converter, a novel dynamic conversion efficiency model of bidirectional AC/DC converter is developed. To maintain high robustness at minimum operation cost, the proposed strategy is divided into two stages. The outputs of renewable energy sources, operation characteristics of microgrid components and time-of-use electricity price are comprehensively considered in the day-ahead economic energy management stage to minimize the daily operation cost. In the intraday rolling energy management stage, day-ahead schedules of controllable units are adjusted based on intraday ultra-short-term forecast data to suppress the intraday power fluctuations induced by day-ahead forecast errors. The simulation results demonstrate that the proposed strategy can effectively mitigate the impact of multiple uncertainties and realize the economic operation of AC/DC hybrid microgrid.

This study aimed at investigating the effects of phytosterols on the productive performance, egg quality, length of small intestine, and tibia quality in aged laying hens. A total of 960 Dawu Jinfeng commercial laying hens (75 weeks of age) were randomly assigned to three groups. Each group had 16 replicates and every replicate contained four cages (five birds/cage). The control group hens received the basal diet without phytosterols. The hens in the experimental groups received a diet containing phytosterols at concentrations of 20 mg/kg and 40 mg/kg for 7 weeks. The results showed that phytosterols had a linearly increasing effect on egg weight, eggshell surface area, albumen height, and haugh unit at week 5 of experiment (p < 0.05). Supplemental phytosterols linearly and quadratically increased eggshell thickness (p < 0.05). At week 7 of the experiment, dietary supplementation of phytosterols linearly increased egg weight and eggshell weight (p < 0.05). Supplementation of 20 mg/kg, but not 40 mg/kg, phytosterols increased the length of the small intestine. However, dietary phytosterols had no effect on the laying rate, mortality, or liver index (p > 0.1). The results of tibia quality detected by micro-CT also showed no difference in the treatment of phytosterols. Therefore, supplementation with 20 mg/kg phytosterols in the diet improves egg quality and increases the length of small intestine, but has no effects on the quality of the tibia.

The objective of this study was to compare two supplementary doses (6000 vs. 12,000 IU/kg) of vitamin A (VA) on the performance, development of intestine and immune organs, as well as gene expression of inflammatory factors in young Hy-Line Brown laying pullets. A total of 288 one-day-old Hy-Line Brown laying pullets (weighing 42.15 ± 0.23 g) were allotted into two treatments with 12 replicate cages and 12 birds per cage. During the 35-day period, the pullets were fed a basal diet supplemented with different doses of VA (6000 IU/kg VA in control group; 12,000 IU/kg VA in treatment group), respectively. The results showed that supplementary high doses of VA reduced the feed-to-gain ratio from day 21 to 35 (p < 0.05). Moreover, the pullets fed high doses of VA diets had increased length and relative weight of duodenum, jejunum, and ileum (p < 0.05). From observations on morphology, high doses of VA diets increased the villus height and the ratio of villus height to crypt depth in the jejunum and ileum (p < 0.05). High doses of VA diets also increased the relative weight of immune organs (p < 0.05). Furthermore, the gene expressions of inflammatory factors were decreased in the thymus of the pullets fed high doses of VA diets (p < 0.05). In summary, supplementary 12,000 IU/kg doses of VA improved performance and intestine and immune organ development, and alleviated gene expressions of inflammatory factors in young Hy-Line Brown laying pullets.

Smart grids are the developmental trend of power systems and they have attracted much attention all over the world. Due to their complexities, and the uncertainty of the smart grid and high volume of information being collected, artificial intelligence techniques represent some of the enabling technologies for its future development and success. Owing to the decreasing cost of computing power, the profusion of data, and better algorithms, AI has entered into its new developmental stage and AI 2.0 is developing rapidly. Deep learning (DL), reinforcement learning (RL) and their combination-deep reinforcement learning (DRL) are representative methods and relatively mature methods in the family of AI 2.0. This article introduces the concept and status quo of the above three methods, summarizes their potential for application in smart grids, and provides an overview of the research work on their application in smart grids.

Melanomas harboring BRAF mutation (V600E) are known to recur frequently following treatment with BRAF inhibitors (BRAFi) despite a high initial response rate. Our previous study has uncovered that BRAFi‐resistant melanoma (BR) cells are vulnerable to arginine deprivation. It has been reported that naïve melanoma cells undergo autophagy and re‐express argininosuccinate synthetase 1 (ASS1) to enable them to synthesize arginine for survival when encountering arginine deprivation. Abolishing these two factors in BR cells confers sensitivity to arginine deprivation. In this report, we further demonstrated that downregulation of AMPK‐α1 in BR cells is a major factor contributing to impairment of autophagy as evidenced by decreased autophagosome formation. These BR cells also showed a metabolic shift from glucose to arginine dependence, which was supported by decreased expressions of GLUT1 (glucose transporter) and hexokinase II (HKII) coupled with less glucose uptake but high levels of arginine transporter CAT‐2 expression. Furthermore, silencing CAT‐2 expression also distinctly attenuated BR cell proliferation. Notably, when naïve melanoma cells became BR cells by long‐term exposure to BRAFi, a stepwise degradation of AMPK‐α1 was initiated via ubiquitin‐proteasome system (UPS). We discovered that a novel E3 ligase, RING finger 44 (RNF44), is responsible for promoting AMPK‐α1 degradation in BR cells. RNF44 expression in BR cells was upregulated by transcription factor CREB triggered by hyperactivation of ERK/AKT. High levels of RNF44 corresponding to low levels of AMPK‐α1 appeared in BR xenografts and melanoma tumor samples from BR and BRAFi/MEK inhibitor (MEKi)‐resistant (BMR) melanoma patients. Similar to BR cells, BMR cells were also sensitive to arginine deprivation. Our study provides a novel insight into the mechanism whereby BRAFi or BRAFi/MEKi resistance drives proteasomal degradation of AMPK‐α1 and consequently regulates autophagy and metabolic reprogramming in melanoma cells.

The uncertainty associated with loads and renewable-energy sources affects active distribution networks in terms of the operation and voltage stability on different time scales. To address this problem, a multi-time-scale voltage stability constrained optimal scheduling framework is proposed, which includes a day-ahead model with a coarse-grained time resolution and an intra-day model with a fine-grained time resolution. The day-ahead economic-scheduling model maps out a scheme to operate different types of devices with the aim of minimizing the network losses. Following the scheme, the intra-day corrective-adjustment model based on model predictive control is proposed to regulate the flexible devices, such as the energy storage systems and the photovoltaic converters. In particular, the proposed optimal scheduling framework embeds a voltage stability constraint which is constructed by using a novel index, defined based on the Distflow model Jacobian. As the index at each bus is a linear function of the locally measurable power flow variables, the proposed constraint does not introduce additional computational burdens. Simulation results demonstrate the necessity and effectiveness of the proposed multi-time-scale voltage stability constrained optimal scheduling model. The results also show that the variation trend of the proposed index is consistent with that of the commonly used voltage stability index.

The operation state detection of numerous smart meters is a significant problem caused by manual on-site testing. This paper addresses the problem of improving the malfunction detection efficiency of smart meters using deep learning and proposes a novel evaluation model of operation state for smart meter. This evaluation model adopts recurrent neural networks (RNN) to predict power consumption. According to the prediction residual between predicted power consumption and the observed power consumption, the malfunctioning smart meter is detected. The training efficiency for the prediction model is improved by using transfer learning (TL). This evaluation uses an accumulator algorithm and threshold setting with flexibility for abnormal detection. In the simulation experiment, the detection principle is demonstrated to improve efficient replacement and extend the average using time of smart meters. The effectiveness of the evaluation model was verified on the actual station dataset. It has accurately detected the operation state of smart meters.

As the core component of the valve cooling system in a converter station, the main pump plays a major role in ensuring the stable operation of the valve. Thus, accurate and efficient fault diagnosis of the main pump according to vibration signals is of positive significance for the detection of failure equipment and reducing the maintenance cost. This paper proposed a new neural network based on the vibration signals of the main pump to classify four faults and one normal state of the main pump, which consisted of a convolutional neural network (CNN) and long short-term memory (LSTM). Multi-scale features were extracted by two CNNs with different kernel sizes, and temporal features were extracted by LSTM. Moreover, random sampling was used in data processing for imbalanced data, which is meaningful for data symmetry. Experimental results indicated that the accuracy of the network was 0.987 obtained from the test set, and the average values of F1-score, recall, and precision were 0.987, 0.987, and 0.988, respectively. It was found that the proposed network performed well in a multi-label fault diagnosis of the main pump and was superior to other methods.

Some experiments have proved that superhydrophobic behaviors can be achieved on inherently hydrophilic substrates without low surface energy modification at micro-scale. However, the thermodynamic mechanisms about these results have not been well-understood. In this work, a 2D analytical model was reported to analyze this unexpected experimental observations and wetting behaviors on trapezoidal, vertical and inverse-trapezoidal microstructure surfaces. Theoretical results showed that intrinsic contact angle, which was restricted by sidewall angle of micropillars, was not an independent parameter to affect superhydrophobicity. And re-entrant structures were critical in the realization of microstructures alone inducing transition from hydrophilicity to superhydrophobicity. The wetting transition criterion was that sidewall angle should be less than intrinsic contact angle. On this occasion, a positive energy barrier could support liquid/vapor interfaces and separate Wenzel and Cassie state on hydrophilic substrates. And the physical explanations can be found that the positive energy barrier mainly came from the growth of the high-energetic solid/vapor interfaces to be wetted by the drop with liquid/vapor interfaces moving down inverse-trapezoidal pillars. As for the optimal design of microstructures, considering the limitation of pillar width and the ‘sag’ transition caused by pillar height, T-shape microstructures could be a good choice.

The demand for electricity has been rising significantly over the past years and it is expected to rise further in the coming years due to economic and societal development. Smart grid technology is being developed in order to meet the rising electricity requirement. In order for the smart grid to perform its full functions, the Energy Management Systems (EMSs), especially Home Energy Management Systems (HEMS) are essential. It is necessary to understand the energy demand of the loads and the energy supply either from the national grid or from renewable energy technologies. To facilitate the Demand Side Management (DSM), Heat Pumps (HP) and air conditioning systems are often utilised for heating and cooling in residential houses due to their high-efficiency power output and low CO2 emissions. This paper presents a program for a HEMS using a Particle Swarm Optimisation (PSO) algorithm. A HP is used as the load and the aim of the optimisation program is to minimise the operational cost, i.e., the cost of electricity, while maintaining end-user comfort levels. This paper also details an indoor thermal model for temperature update in the heat pump control program. Real measured data from the UK Government’s Renewable Heat Premium Payment (RHPP) scheme was utilised to generate characteristic curves and equations that can represent the data. This paper compares different PSO variants with standard PSO and the unscheduled case calculated from the data for five winter days in 2019. Among all chosen algorithms, the Crossover Subswarm PSO (CSPSO) achieved an average saving of 25.61% compared with the cost calculated from the measured data with a short search time of 1576 ms for each subswarm. It is clear from this work that there is significant scope to reduce the cost of operating a HP while maintaining end user comfort levels.

Hybrid AC/DC microgrids (HMG) are emerging as an attracting method for integrating AC/DC distributed energy resources (DERs). In the isolated hybrid AC/DC microgrid (IHMG), the key problem is how to balance power variation and regulate voltage and frequency. Various energy storage systems (ESS) and interlinking converter (IC) technologies are viable for this application. The present study proposes a novel unified power flow (PF) model which can be applied to compare and analyze the practical operation modes of the IHMG and, further, to evaluate and compare the abilities of the ESS with different connection topologies and ICs with different control approaches to maintain the voltage and frequency stability of the IHMG. Five operation modes of the IHMG are defined and explained. Then, a set of generic PF equations are derived. Moreover, three binary matrices are applied as input parameters of the unified power equations. These matrices enable a single operation mode of the IHMG at a time to be constructed in the power equation. Finally, the accuracy and effectiveness of the proposed scheme are verified against the time domain simulation result. The quasi-steady-state behaviors of multi-DC subgrids IHMG in different modes after a range of load fluctuation are investigated. The results show that the use of multiple grid-forming units in the AC and DC subgrids, when IC adopted normalized.

Abstract Increasing evidence suggests that stem‐like cells mediate cancer therapy resistance and metastasis. Breast tumour‐initiating stem cells (T‐ISC) are known to be enriched in CD44+CD24neg/low cells. Here, we identify two T‐ISC subsets within this population in triple negative breast cancer (TNBC) lines and dissociated primary breast cancer cultures: CD44+CD24low+ subpopulation generates CD44+CD24neg progeny with reduced sphere formation and tumourigenicity. CD44+CD24low+ populations contain subsets of ALDH1+ and ESA+ cells, yield more frequent spheres and/or T‐ISC in limiting dilution assays, preferentially express metastatic gene signatures and show greater motility, invasion and, in the MDA‐MB‐231 model, metastatic potential. CD44+CD24low+ but not CD44+CD24neg express activated Notch1 intracellular domain (N1‐ICD) and Notch target genes. We show N1‐ICD transactivates SOX2 to increase sphere formation, ALDH1+ and CD44+CD24low+cells. Gamma secretase inhibitors (GSI) reduced sphere formation and xenograft growth from CD44+CD24low+ cells, but CD44+CD24neg were resistant. While GSI hold promise for targeting T‐ISC, stem cell heterogeneity as observed herein, could limit GSI efficacy. These data suggest a breast T‐ISC hierarchy in which distinct pathways drive developmentally related subpopulations with different anti‐cancer drug responsiveness.

With the high penetration of renewable energy sources, the reliability of power systems becomes more vulnerable than ever because of the greater uncertainty and intermittence in power generation. Reactive power plays an important role in the power system reliability, because it is closely related to the system voltage stability and voltage collapse. However, reactive power-related reliability issues are seldom emphasized in conventional power reliability evaluations. This article investigates power system reliability of real and reactive power. Real and reactive power shortages and the associated voltage violations due to system failures are considered on reliability evaluation of power systems. A three-stage load-shedding technique for post contingencies is implemented to determine the contributions of real and reactive power on the system reliability and to find an optimal way to release network violation. The results provide the detailed information on power system planning and operation for system planners and operators from real and reactive power aspects.