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With the rapid development of urban power grids and the large-scale integration of renewable energy, traditional power grid fault diagnosis techniques struggle to address the complexities of diagnosing faults in intricate power grid systems. Although artificial intelligence technologies offer new solutions for power grid fault diagnosis, the difficulty in acquiring labeled grid data limits the development of AI technologies in this area. In response to these challenges, this study proposes a semi-supervised learning framework with self-supervised and adaptive threshold (SAT-SSL) for fault detection and classification in power grids. Compared to other methods, our method reduces the dependence on labeling data while maintaining high recognition accuracy. First, we utilize frequency domain analysis on power grid data to filter abnormal events, then classify and label these events based on visual features, to creating a power grid dataset. Subsequently, we employ the Yule–Walker algorithm extract features from the power grid data. Then we construct a semi-supervised learning framework, incorporating self-supervised loss and dynamic threshold to enhance information extraction capabilities and adaptability across different scenarios of the model. Finally, the power grid dataset along with two benchmark datasets are used to validate the model’s functionality. The results indicate that our model achieves a low error rate across various scenarios and different amounts of labels. In power grid dataset, When retaining just 5% of the labels, the error rate is only 6.15%, which proves that this method can achieve accurate grid fault detection and classification with a limited amount of labeled data.

Multi-energy synergy systems integrating high-penetration large-scale plug-in electric vehicles, distributed renewable energy generations, and battery energy storage systems have great potential to reduce the reliance of the grid on traditional fossil fuels. However, the random charging characteristics of plug-in electric vehicles and the uncertainty of photovoltaics may impose an additional burden on the grid and affect the supply–demand equilibrium. To address this issue, judicious scheduling optimization offers an effective solution. In this study, considering charge and discharge management of plug-in electric vehicles and intermittent photovoltaics, a novel Multi-energy synergy systems scheduling framework is developed for solving grid instability and unreliability issues. This formulates a large-scale mixed-integer problem, which calls for a powerful and effective optimizer. The new binary level-based learning optimization algorithm is proposed to address nonlinear large-scale high-coupling unit commitment problems. To investigate the feasibility of the proposed scheme, numerical experiments have been carried out considering multiple scales of unit numbers and various scenarios. Finally, the results confirm that the proposed scheduling framework is reasonable and effective in solving unit commitment problems, can achieve 3.3% cost reduction and demonstrates superior performance in handling large-scale energy optimization problems. The integration of plug-in electric vehicles, distributed renewable energy generations, and battery energy storage systems is verified to reduce the output power of 192.72 MW units during peak periods to improve grid stability. Therefore, optimizing energy utilization and distribution will become an indispensable part of future power systems.

Cu-WS2 composites are being used as self-lubricating materials for various engineering applications. The poor wettability between Cu and WS2 is a main obstacle for obtaining high-performance Cu-WS2. In order to improve the wettability, a thin Cu layer was deposited on the surface of the WS2 powder through an electroless plating technology. Cu-WS2 composites were prepared via spark plasma sintering (SPS) using the mix powders of Cu and Cu-WS2. Microstructure, mechanical properties and tribological properties of the Cu-WS2 composites were investigated, and that of the Cu were also comparatively studied. Results show that the WS2 powder coated with Cu (abbreviated as WS2@Cu powder) improves the poor wettability between Cu and WS2 and meanwhile the distribution of WS2. Cu-WS2 composites sintered below 750 °C could reduce the reaction between Cu and WS2 and ensure more WS2 in the composites. Both the coefficient of friction (COF) and the wear rate of the Cu-WS2 are lower than that of the Cu. A lubricant film leads to the excellent tribological properties as the Cu-WS2 composites were tested at 25−200 °C. However, the disappearance of WS2 and the formation of Cu2O cause to the decreasing self-lubricating performances at 300−500 °C. With an increase in the test temperature, the dominant wear mechanisms of the Cu-WS2 composites change from adhesive wear to a mixture of abrasive and oxidation wear, and that of the Cu change from adhesive wear to a mixture of fatigue and oxidation wear.

The increasing integration of distributed generations brings great challenges to the power grid. In this paper, a distributed photovoltaic (PV) integration methodology in distribution network is established for large-scale PV penetration. Firstly, a PV integration model was formulated with the aim of maximizing PV integration capacity and enhancing the voltage profile. Specially, the PV large-scale integration model for county-wide promotion is proposed by considering various typical integration scenarios. Additionally, a novel improved multi-objective Teaching-Learning based optimization (TLBO) algorithm, namely, IM-TLBO, was proposed to seek an optimal Pareto front of the PV integration model. The IM-TLBO algorithm innovatively incorporates the elite reverse learning search strategy to enhance exploration in the solution space. Moreover, the differentiated teaching guided by optimal individual and central location is employed to improve the efficiency of the “teaching” process. Meanwhile, a cyclic crowded sort deletion based on crowding distance is developed to enhance the diversity of elite individuals and the distribution characteristics of the Pareto Frontier. Finally, the performance of IM-TLBO is tested in benchmark functions. Also, a simulation case in IEEE 33 bus system is performed to verify the proposed PV integration method. It is observed that the proposed method in this paper can not only realize the overall optimal integration of roof distributed PV, but also improve voltage profile. The results of IM-TLBO are compared to other classical algorithms, and it is shown that IM-TLBO outperformed them in terms of convergence, distribution and diversity.

Abstract The effective use of wind energy is an essential part of the sustainable development of human society, in particular, at the recent unprecedented pressure in shaping a low carbon energy environment. Accurate wind resource and power forecasting play a key role in improving the wind penetration. However, it has not been well adopted in the real‐world applications due to the strong stochastic characteristics of wind energy. In recent years, the application boost of deep learning methods provides new effective tools in wind forecasting. This paper provides a comprehensive overview of the forecasting models based on deep learning in the field of wind energy. Featured approaches include time‐series‐based recurrent neural networks, restricted Boltzmann machines, convolutional neural networks as well as auto‐encoder‐based approaches. In addition, future development directions of deep‐learning‐based wind energy forecasting have also been discussed.

As the urgency to adopt renewable energy sources escalates, so does the need for accurate forecasting of power output, particularly for wind and solar power. Existing models often struggle with noise and temporal intricacies, necessitating more robust solutions. In response, our study presents the SL-Transformer, a novel method rooted in the deep learning paradigm tailored for green energy power forecasting. To ensure a reliable basis for further analysis and modeling, free from noise and outliers, we employed the SG filter and LOF algorithm for data cleansing. Moreover, we incorporated a self-attention mechanism, enhancing the model’s ability to discern and dynamically fine-tune input data weights. When benchmarked against other premier deep learning models, the SL-Transformer distinctly outperforms them. Notably, it achieves a near-perfect R2 value of 0.9989 and a significantly low SMAPE of 5.8507% in wind power predictions. For solar energy forecasting, the SL-Transformer has achieved a SMAPE of 4.2156%, signifying a commendable improvement of 15% over competing models. The experimental results demonstrate the efficacy of the SL-Transformer in wind and solar energy forecasting.

Mature cardiac muscle requires high mitochondrial ATP production and specialized contractile proteins. Here the authors demonstrate that cardiomyocyte-specific contractile maturation involves cooperation between the nuclear receptor ERRγ and cardiogenic transcription factor GATA4, but ERRγ controls metabolic genes independently.

Abstract This paper explores the wake of a wind turbine yaw and its influence on the output power of the downstream wind turbine. A wind turbine model (hub height and diameter of 1.2 m and 1.0 m, respectively) was used in the experiments, and the yaw angle of the upstream wind turbine was successively varied through values of 0°, 15°, and 30°. The wind speed and the intensity of turbulence in the wake region were measured using a hot‐wire anemometer. The measuring positions were changed to 3, 5, and 7 rotor diameters behind the upstream wind turbine. In addition, the output power of the downstream wind turbine model was measured using the rotational speed torque meter. The results show that, in the case of large‐angle yaw of the upstream wind turbine, the recovery of the wind speed in the wake area was greatly improved, and up to 30% recovery was achieved. The power output of the downstream wind turbine was also significantly increased. Under the yaw conditions of 15° and 30° for the upstream wind turbine, the average output power of the downstream wind turbine increased by 17.6% and 21.6%, respectively.

Dynamic economic dispatch (DED) considering valve-point effects and transmission losses is a complex constrained optimization problem with non-smooth, non-linear and non-convex characteristics which has always been a significant challenge. To tackle the DED problem, an enhanced exploratory whale optimization algorithm (EEWOA) and a constraint handling technique considering the unit efficiency are proposed. Firstly, an adaptive enhanced exploratory prey mechanism is created to enhance population diversity of whale swarm, and the EEWOA method is developed. Secondly, a constraint handling technology is designed to improve the variable repairing ability, including coarse-tuning by heuristics and fine-tuning by forced repair technique. The proposed EEWOA is compared with several state-of-the-art optimization algorithms in terms of efficiency and effectiveness on 13 classical benchmarks, and three DED with different scales. According to the simulation results, EEWOA shows outstanding advantages on the benchmarks and the DED cases.

The health status of equipment is of paramount importance during the operation of nuclear power plants. The occurrence of faults not only leads to significant economic losses but also poses risks of casualties and even major accidents, with unimaginable consequences. This paper proposed a deep learning framework called PT-Informer for fault prediction, detection, and localization in order to address the challenges of online monitoring of the operating health of nuclear steam turbines. Unlike traditional approaches that involve separate design and execution of feature extraction for fault diagnosis, classification, and prediction, PT-Informer aims to extract fault features from the raw vibration signal and perform ultra-real-time fault prediction prior to their occurrence. Specifically, the encoding and decoding structure in PT-Informer ensures the capture of temporal dependencies between input features, enabling accurate time series prediction. Subsequently, the predicted data are utilized for fault detection using PCA in the PT-Informer framework, aiming to assess the likelihood of equipment failure in the near future. In the event of potential future failures, t-SNE is utilized to project high-dimensional data into a lower-dimensional space, facilitating the identification of clusters or groups associated with different fault types or operational conditions, thereby achieving precise fault localization. Experimental results on a nuclear steam turbine rotor demonstrate that PT-Informer outperformed the traditional GRU with a 4.94% improvement in R2 performance for prediction. Furthermore, compared to the conventional model, the proposed PT-Informer enhanced the fault classification accuracy of the nuclear steam turbine rotor from 97.4% to 99.6%. Various comparative experiments provide strong evidence for the effectiveness of PT-Informer framework in the diagnosis and prediction of nuclear steam turbine.

Due to the considerable number of electric vehicles and the characteristics of energy storage, it is possible for these new energy factors to participate in the operation and regulation of the power system and provide reserve service. In view of this, a multi-objective optimal scheduling model is established, aiming at the economic benefits of electricity collectors, the microgrid power fluctuations, and user satisfaction. Among them, the expression paradigm of user satisfaction is proposed. At the same time, an improved adaptive non-dominated sorting genetic algorithm (NSGA-III-W) was proposed to solve the problem of large-scale and high-dimensional multi-objective in the model. First, an adaptive T-crossover operator is proposed to increase the search and optimization capability of NSGA-III. Second, an adaptive crossover mutation mechanism is proposed to improve the convergence performance of the algorithm. In addition, a compromise solution is selected from the obtained Pareto-dominated solutions through the distance ranking method of superior and inferior solutions (TOPSIS). The improved NSGA-III algorithm, namely the NSGA-III-W algorithm, is compared with the mainstream intelligent optimization algorithms non-dominated sorting genetic algorithm II (NSGA-II) and decomposition-based multi-objective evolutionary algorithm (MOEA\D), and the simulation results demonstrate the feasibility of the proposed model and the effectiveness of the proposed algorithm.

In order to deal with the fluctuation and intermittency of photovoltaic (PV) cells, the battery energy storage system (BESS) as a supplementary power source has been widely concerned. In BESS, the unknown parameters of the battery can affect its output, and its structure determines these parameters. Therefore, it is essential to establish the battery model and extract the parameters accurately, and the existing methods cannot effectively solve this problem. This study proposes an adaptive differential evolution algorithm with the dynamic opposite learning strategy (DOLADE) to deal with the issue. In DOLADE, the number of elite particles and particles with poor performance is expanded, the population’s search area is increased, and the population’s exploration capability is improved. The particles’ search area is dynamically changed to ensure the population has a good exploitation capability. The dynamic opposite learning (DOL) strategy increases the population’s diversity and improves the probability of obtaining the global optimum with a considerable convergence rate. The various discharging experiments are performed, the battery model parameters are identified, and the results are compared with the existing well-established algorithms. The comprehensive results indicate that DOLADE has excellent performance and could deal with similar problems.

The degradation modes and characteristics of different pesticides were introduced. In addition, this paper also describes the degradation mechanism of different pesticides, classifies, and summarizes the methods of degradation products identification. For the sake of human life health and better biological environment, we should have a familiar knowledge of the natural degradation of pesticides and understand the photo-hydrolysis and its influencing factors (temperature, pH, light, etc.). Through the degradation mechanism and influencing factors, the degradation time could be accelerated and it also provides a theoretical basis and basic support for the treatment of pesticide residues in the future.

With the development of society, the demand for energy keeps increasing. Solar energy has received widespread concern for its renewable and environmentally friendly advantages. As one of the most efficient solar energy devices, the output power of photovoltaic (PV) cells is easily affected by the external environment. In order to solve the problem of the maximum power output of PV cells, this paper proposed a maximum power point tracking (MPPT) method. Based on the online particle swarm optimization (PSO) variable step length algorithm, the pulse width modulation (PWM) control module parameters are set according to the parameters of the PV cells’ output voltage. By dynamically adjusting the output voltage step of the PV cells online, the output of the PV cells is stabilized near the maximum power point (MPP). The simulation results concluded that the method and model could accurately adjust the output voltage according to the external environment changes in real time and reduce the voltage fluctuation at the MPP, providing a new idea to solve the problem of MPPT of PV cells.

Unit commitment is an intractable issue aiming to reduce the overall economic cost of power system operation while maintaining the system constraints. Due to the emerging scenario of global warming, many countries are vigorously developing renewable energy to replace the traditional fossil power plant, in order to reduce the environmental and carbon emission. The increasing penetration of renewable generation significantly challenge the economic and security of power system operation. In this paper, a low carbon multi-objective objective unit commitment model considering economic cost, environmental cost and, more importantly, the carbon emission is established, integrating wind and solar power and therefore generating a multi-objective, high-dimensional, strong non-linear, multi-constraint and mixed integer optimization problem. The non-dominated sorting genetic algorithm-III is tailored and adopted for solving the proposed challenging task, where the decision-making scheme is designed according to the normalization method and weighted sum function. Numerical results show that the proposed complex many-objective low carbon unit commitment model can be successfully solved by the proposed algorithm and the carbon emission is effectively reduced by the integration of renewable generations.

The state of charge (SOC) estimation of Li-ion batteries has attracted substantial interests in recent years. Kalman Filter has been widely used in real-time battery SOC estimation, however, to build a suitable dynamic battery state-space model is a key challenge, and most existing methods still use the off-line modelling approach. This paper tackles the challenge by proposing a novel sparse learning machine for real-time SOC estimation. This is achieved first by developing a new learning machine based on the traditional least squares support vector machine (LS-SVM) to capture the process dynamics of Li-ion batteries in real-time. The least squares support vector machine is the least squares version of the conventional support vector machines (SVMs) which suffers from low model sparseness. The proposed learning machine reduces the dimension of the projected high dimensional feature space with no loss of input information, leading to improved model sparsity and accuracy. To accelerate computation, mapping functions in the high feature space are selected using a fast recursive method. To further improve the model accuracy, a weighted regularization scheme and the differential evolution (DE) method are used to optimize the parameters. Then, an unscented Kalman filter (UKF) is used for real-time SOC estimation based on the proposed sparse learning machine model. Experimental results on the Federal Urban Drive Schedule (FUDS) test data reveal that the performance of the proposed algorithm is significantly enhanced, where the maximum absolute error is only one sixth of that obtained by the conventional LS-SVMs and the mean square error of the SOC estimations reaches to 10-7, while the proposed method is executed nearly 10 times faster than the conventional LS-SVMs.

Summary: The role of the transforming growth factor (TGF)-β pathway in myocardial fibrosis is well recognized. However, the precise role of this signaling axis in cardiomyocyte (CM) biology is not defined. In TGF-β signaling, SMAD4 acts as the central intracellular mediator. To investigate the role of TGF-β signaling in CM biology, the authors deleted SMAD4 in adult mouse CMs. We demonstrate that CM-SMAD4–dependent TGF-β signaling is critical for maintaining cardiac function, sarcomere kinetics, ion-channel gene expression, and cardiomyocyte survival. Thus, our findings raise a significant concern regarding the therapeutic approaches that rely on systemic inhibition of the TGF-β pathway for the management of myocardial fibrosis. Key Words: cardiomyocyte, cardiomyopathy, fibrosis, heart failure, SMAD4, TGF-β

In this study, polyacrylic acid (PAA) films were deposited onto a quartz surface acoustic wave (SAW) resonator using a spin-coating technique for ammonia sensing operated at room temperature, and the sensing mechanisms and performance were systematically studied. The oxygen-containing functional groups on the surfaces of the PAA film make it sensitive and selective to ammonia molecules, even when tested at room temperature. The ammonia molecules adsorbed by the oxygen-containing functional groups of PAA (e.g., hydroxyl and epoxy groups) increase the membrane’s stiffness, which was identified as the primary mechanism leading to the positive frequency shifts. However, mass loading due to adsorption of ammonia molecules is not a major reason as it will result in a negative frequency shifts. When the PAA coated SAW sensor was exposed to ammonia with a low concentration of 500 ppb, it showed a positive frequency shift of 225 Hz, with both good repeatability and stability, as well as a good selectivity to ammonia compared with those to C2H5OH, H2, HCl, H2S, CO, NO2, NO, and CH3COCH3.

Battery energy storage is the pivotal project of renewable energy systems reform and an effective regulator of energy flow. Parallel battery packs can effectively increase the capacity of battery modules. However, the power loss caused by the uncertainty of parallel battery branch current poses severe challenge to the economy and safety of electric vehicles. Accuracy of battery branch current prediction is needed to improve the parallel connection. This paper proposes a radial basis function neural network model based on the pigeon-inspired optimization method and successfully applies the algorithm to predict the parallel branch current of the battery pack. Numerical results demonstrate the high accuracy of the proposed pigeon-inspired optimized RBF model for parallel battery branch forecasting and provide a useful tool for the prediction of parallel branch currents of battery packs.

In this paper, a deep learning fault detection and prediction framework combining principal component analysis (PCA) and Informer is proposed to solve the problem of online monitoring of nuclear power valves which is hard to implement. More specifically, PCA plays the role of dimensionality reduction and fault feature extraction. It maps data with multi-dimensional space to low-dimensional space and extracts the main features. At the same time, the T-square and Q statistic thresholds are also provided to realize abnormal status monitoring. Meanwhile, Informer is a long-term series prediction method. It encrypts and decrypts data through the encoder and decoder to train a prediction model. Through the training of fault data, fault prediction can be realized. Experiments based on the sound waves collected from real valves can be continued, which also illustrates the effectiveness of the PCA–Informer model for fault diagnosis and fault prediction of nuclear power valves. Therefore, the online monitoring and maintenance of nuclear valves and other important equipment, without shutting down the nuclear station, can be achieved.

Economic dispatch (ED) aims to allocate the generation of units to minimize the total production cost. This dispatch is generally formulated with nonsmooth and nonconvex cost function due to valve-point effects and various constraints, where the conventional methods are inapplicable. An improved social spider optimization algorithm, namely, ISSO, is proposed in this paper to solve the ED problem with valve-point effects. That is, dynamic updating mechanism of the subpopulations, Gaussian mating radius, and multimating strategy are introduced into the ISSO. These mechanisms facilitate a compromise between the global exploration and local exploitation of the search process. Numerical experiments are conducted on benchmark functions and different scale generation units commonly considered in the literature to validate the feasibility of the proposed ISSO. Computational results are analyzed in terms of solution quality by the statistical method, which shows the superiority of the ISSO algorithm in comparison with the state-of-the-art algorithms.

Lithium-ion batteries have been widely used as energy storage systems and in electric vehicles due to their desirable balance of both energy and power densities as well as continual falling price. Accurate estimation of the state-of-charge (SOC) of a battery pack is important in managing the health and safety of battery packs. This paper proposes a compact radial basis function (RBF) neural model to estimate the state-of-charge (SOC) of lithium battery packs. Firstly, a suitable input set strongly correlated with the package SOC is identified from directly measured voltage, current, and temperature signals by a fast recursive algorithm (FRA). Secondly, a RBF neural model for battery pack SOC estimation is constructed using the FRA strategy to prune redundant hidden layer neurons. Then, the particle swarm optimization (PSO) algorithm is used to optimize the kernel parameters. Finally, a conventional RBF neural network model, an improved RBF neural model using the two stage method, and a least squares support vector machine (LSSVM) model are also used to estimate the battery SOC as a comparative study. Simulation results show that generalization error of SOC estimation using the novel RBF neural network model is less than half of that using other methods. Furthermore, the model training time is much less than the LSSVM method and the improved RBF neural model using the two-stage method.

Whale optimization algorithm (WOA), known as a novel nature-inspired swarm optimization algorithm, demonstrates superiority in handling global continuous optimization problems. However, its performance deteriorates when applied to large-scale complex problems due to rapidly increasing execution time required for huge computational tasks. Based on interactions within the population, WOA is naturally amenable to parallelism, prompting an effective approach to mitigate the drawbacks of sequential WOA. In this paper, field programmable gate array (FPGA) is used as an accelerator, of which the high-level synthesis utilizes open computing language (OpenCL) as a general programming paradigm for heterogeneous System-on-Chip. With above platform, a novel parallel framework of WOA named PWOA is presented. The proposed framework comprises two feasible parallel models called partial parallel and all-FPGA parallel, respectively. Experiments are conducted by performing WOA on CPU and PWOA on OpenCL-based FPGA heterogeneous platform, to solve ten well-known benchmark functions. Meanwhile, other two classic algorithms including particle swarm optimization (PSO) and competitive swarm optimizer (CSO) are adopted for comparison. Numerical results show that the proposed approach achieves a promising computational performance coupled with efficient optimization on relatively large-scale complex problems.

In the practical scenario of construction sites with extremely complicated working environment and numerous personnel, it is challenging to detect safety helmet wearing (SHW) in real time on the premise of ensuring high precision performance. In this paper, a novel SHW detection model on the basis of improved YOLOv3 (named CSYOLOv3) is presented to heighten the capability of target detection on the construction site. Firstly, the backbone network of darknet53 is improved by applying the cross stage partial network (CSPNet), which reduces the calculation cost and improves the training speed. Secondly, the spatial pyramid pooling (SPP) structure is employed in the YOLOv3 model, and the multi-scale prediction network is improved by combining the top-down and bottom-up feature fusion strategies to realize the feature enhancement. Finally, the safety helmet wearing detection dataset containing 10,000 images is established using the construction site cameras, and the manual annotation is required for the model training. Experimental data and contrastive curves demonstrate that, compared with YOLOv3, the novel method can largely ameliorate mAP by 28% and speed is improved by 6 fps.

Efficient and valuable strategies provided by large amount of available data are urgently needed for a sustainable electricity system that includes smart grid technologies and very complex power system situations. Big Data technologies including Big Data management and utilization based on increasingly collected data from every component of the power grid are crucial for the successful deployment and monitoring of the system. This paper reviews the key technologies of Big Data management and intelligent machine learning methods for complex power systems. Based on a comprehensive study of power system and Big Data, several challenges are summarized to unlock the potential of Big Data technology in the application of smart grid. This paper proposed a modified and optimized structure of the Big Data processing platform according to the power data sources and different structures. Numerous open-sourced Big Data analytical tools and software are integrated as modules of the analytic engine, and self-developed advanced algorithms are also designed. The proposed framework comprises a data interface, a Big Data management, analytic engine as well as the applications, and display module. To fully investigate the proposed structure, three major applications are introduced: development of power grid topology and parallel computing using CIM files, high-efficiency load-shedding calculation, and power system transmission line tripping analysis using 3D visualization. The real-system cases demonstrate the effectiveness and great potential of the Big Data platform; therefore, data resources can achieve their full potential value for strategies and decision-making for smart grid. The proposed platform can provide a technical solution to the multidisciplinary cooperation of Big Data technology and smart grid monitoring.

Temperature is a crucial state to guarantee the reliability and safety of a battery during operation. The ability to estimate battery temperature, especially the internal temperature, is of paramount importance to the battery management system for monitoring and thermal control purposes. In this paper, a data-driven approach combining the RBF neural network (NN) and the extended Kalman filter (EKF) is proposed to estimate the internal temperature for lithium-ion battery thermal management. To be specific, the suitable input terms and the number of hidden nodes for the RBF NN are first optimized by a two-stage stepwise identification algorithm (TSIA). Then, the teaching-learning-based optimization (TLBO) algorithm is developed to optimize the centres and widths in every neuron of basis function. After optimizing the RBF NN model, a battery lumped thermal model is adopted as the state function with the EKF to filter out the outliers of the RBF model and reduce the estimation error. This data-driven approach is validated under four different conditions in comparison with the linear NN models. The experimental results demonstrate that the proposed RBF data-driven approach outperforms the other approaches and can be extended to other types of batteries for thermal monitoring and management.

BackgroundThe amino acid response (AAR) is an evolutionarily conserved protective mechanism activated by amino acid deficiency through a key kinase, general control nonderepressible 2. In addition to mobilizing amino acids, the AAR broadly affects gene and protein expression in a variety of pathways and elicits antifibrotic, autophagic, and anti‐inflammatory activities. However, little is known regarding its role in cardiac stress. Our aim was to investigate the effects of halofuginone, a prolyl‐tRNA synthetase inhibitor, on the AAR pathway in cardiac fibroblasts, cardiomyocytes, and in mouse models of cardiac stress and failure. Methods and ResultsConsistent with its ability to inhibit prolyl‐tRNA synthetase, halofuginone elicited a general control nonderepressible 2–dependent activation of the AAR pathway in cardiac fibroblasts as evidenced by activation of known AAR target genes, broad regulation of the transcriptome and proteome, and reversal by l‐proline supplementation. Halofuginone was examined in 3 mouse models of cardiac stress: angiotensin II/phenylephrine, transverse aortic constriction, and acute ischemia reperfusion injury. It activated the AAR pathway in the heart, improved survival, pulmonary congestion, left ventricle remodeling/fibrosis, and left ventricular function, and rescued ischemic myocardium. In human cardiac fibroblasts, halofuginone profoundly reduced collagen deposition in a general control nonderepressible 2–dependent manner and suppressed the extracellular matrix proteome. In human induced pluripotent stem cell–derived cardiomyocytes, halofuginone blocked gene expression associated with endothelin‐1‐mediated activation of pathologic hypertrophy and restored autophagy in a general control nonderepressible 2/eIF2α‐dependent manner. ConclusionsHalofuginone activated the AAR pathway in the heart and attenuated the structural and functional effects of cardiac stress.

Load forecasting is one of the major challenges of power system operation and is crucial to the effective scheduling for economic dispatch at multiple time scales. Numerous load forecasting methods have been proposed for household and commercial demand, as well as for loads at various nodes in a power grid. However, compared with conventional loads, the uncoordinated charging of the large penetration of plug-in electric vehicles is different in terms of periodicity and fluctuation, which renders current load forecasting techniques ineffective. Deep learning methods, empowered by unprecedented learning ability from extensive data, provide novel approaches for solving challenging forecasting tasks. This research proposes a comparative study of deep learning approaches to forecast the super-short-term stochastic charging load of plug-in electric vehicles. Several popular and novel deep-learning based methods have been utilized in establishing the forecasting models using minute-level real-world data of a plug-in electric vehicle charging station to compare the forecasting performance. Numerical results of twelve cases on various time steps show that deep learning methods obtain high accuracy in super-short-term plug-in electric load forecasting. Among the various deep learning approaches, the long-short-term memory method performs the best by reducing over 30% forecasting error compared with the conventional artificial neural network model.

The unit commitment (UC) problem is a critical task in power system operation process. The units realize reasonable start-up and shut-down scheduling and would bring considerable economic savings to the grid operators. However, unit commitment is a high-dimensional mixed-integer optimisation problem, which has long been intractable for current solvers. Competitive swarm optimizer is a recent proposed meta-heuristic algorithm specialized in solving the high-dimensional problem. In this paper, a novel binary competitive swarm optimizer (BCSO) is proposed for solving the UC problem associated with lambda iteration method. To verify the effectiveness of the proposed algorithm, comprehensive numerical studies on different sizes units ranging from 10 to 100 are proposed, and the algorithm is compared with other counterparts. Results clearly show that BCSO outperforms all the other counterparts and is therefore completely capable of solving the UC problem.

Short-term load forecasting is a key task to maintain the stable and effective operation of power systems, providing reasonable future load curve feeding to the unit commitment and economic load dispatch. In recent years, the boost of internal combustion engine (ICE) based vehicles leads to the fossil fuel shortage and environmental pollution, bringing significant contributions to the greenhouse gas emissions. One of the effective ways to solve problems is to use electric vehicles (EVs) to replace the ICE based vehicles. However, the mass rollout of EVs may cause severe problems to the power system due to the huge charging power and stochastic charging behaviors of the EVs drivers. The accurate model of EV charging load forecasting is, therefore, an emerging topic. In this paper, four featured deep learning approaches are employed and compared in forecasting the EVs charging load from the charging station perspective. Numerical results show that the gated recurrent units (GRU) model obtains the best performance on the hourly based historical data charging scenarios, and it, therefore, provides a useful tool of higher accuracy in terms of the hourly based short-term EVs load forecasting.

Abstract Background To introduce a model of community health service organization (as implemented in urban areas) to less developed rural areas in China and evaluate the impact of this model on health care utilization. Methods The intervention involved developing leadership at county level, training rural health practitioners, providing clinical management guidelines and standards, encouraging clinic improvements and providing access to subsidies for public health work. We chose 7 townships and 49 administrative villages in Chongyi County as the intervention sites; 3 townships and 9 administrative villages in Luxi County as the comparison sites. Officers from county health bureaus and postgraduates from School of Public Health, Nanchang University visited each township hospital and village clinic in field together and made observations and interviewed clinic staff. Results There was little change in health facilities or workforce in the two areas. However, there was an increase in the use of public health services at township and village level in the intervention sites in Chongyi. In these, the proportion of clinics which had developed a child health (under the age of 3) management system, maternal postpartum visit and chronic disease management increased from 53%, 51% and 47% to 78%, 73%, and 71% respectively. There was no significant change in the comparison sites. Conclusions The trial demonstrated that it was feasible to implement a model of community health service delivery that was adapted to depressed rural areas because it required little organizational change, additional funding or personnel. The model had a positive impact on the provision of public health programs, a finding which has implications for efforts to improve access to primary health care in rural China.