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To obtain carbon dots (CDs) with narrow full width at half maximum (FWHM) and long-wavelength emission, carbon sources with high conjugate sizes and abundant functional groups can be employed to synthesize CDs. In this study, orange-emissive carbon dots (OCDs) were synthesized with phloroglucinol and rhodamine B as precursors. When the molar ratio of them was 30:1, and ethanol was served as the solvent, OCDs with optimized emission wavelength at approximately 580 nm, an FWHM of 30 nm, and a quantum yield (QY) of 27.31% were obtained. Subsequently, the OCDs were incorporated into polyvinyl alcohol (PVA) to fabricate solid-state OCD/PVA fluorescent films, which exhibited an FWHM of 47 nm. The PVA matrix facilitated the dispersion of OCDs, thereby suppressing non-radiative energy transfer among the OCDs and enhancing luminescence efficiency. Consequently, compared with OCDs, the OCD/PVA film exhibited significant luminescent enhancement, and the QY of the composite film was increased to 84.74%. Moreover, OCD/PVA film showed good transmittance and thermal stability. This research offers a solid theoretical and experimental foundation for the potential applications of CDs in the field of solid-state lighting.

Restricted by a metal-enclosed structure, the internal defects of large transformers are difficult to visually detect. In this paper, a micro-robot is used to visually inspect the interior of a transformer. For the micro-robot to successfully detect the discharge level and insulation degradation trend in the transformer, it is essential to segment the carbon trace accurately and rapidly from the complex background. However, the complex edge features and significant size differences of carbon traces pose a serious challenge for accurate segmentation. To this end, we propose the Hadamard production-Spatial coordinate attention-PixelShuffle UNet (HSP-UNet), an innovative architecture specifically designed for carbon trace segmentation. To address the pixel over-concentration and weak contrast of carbon trace image, the Adaptive Histogram Equalization (AHE) algorithm is used for image enhancement. To realize the effective fusion of carbon trace features with different scales and reduce model complexity, the novel grouped Hadamard Product Attention (HPA) module is designed to replace the original convolution module of the UNet. Meanwhile, to improve the activation intensity and segmentation completeness of carbon traces, the Spatial Coordinate Attention (SCA) mechanism is designed to replace the original jump connection. Furthermore, the PixelShuffle up-sampling module is used to improve the parsing ability of complex boundaries. Compared with UNet, UNet++, UNeXt, MALUNet, and EGE-UNet, HSP-UNet outperformed all the state-of-the-art methods on both carbon trace datasets. For dendritic carbon traces, HSP-UNet improved the Mean Intersection over Union (MIoU), Pixel Accuracy (PA), and Class Pixel Accuracy (CPA) of the benchmark UNet by 2.13, 1.24, and 4.68 percentage points, respectively. For clustered carbon traces, HSP-UNet improved MIoU, PA, and CPA by 0.98, 0.65, and 0.83 percentage points, respectively. At the same time, the validation results showed that the HSP-UNet has a good model lightweighting advantage, with the number of parameters and GFLOPs of 0.061 M and 0.066, respectively. This study could contribute to the accurate segmentation of discharge carbon traces and the assessment of the insulation condition of the oil-immersed transformer.

In the detection process of the internal defects of large oil-immersed transformers, due to the huge size of large transformers and metal-enclosed structures, the positional localization of miniature inspection robots inside the transformer faces great difficulties. To address this problem, this paper proposes a three-dimensional positional localization method based on adaptive denoising and the SCOT weighting function with the addition of the exponent β (SCOT-β) generalized cross-correlation for L-type ultrasonic arrays of transformer internal inspection robots. Aiming at the strong noise interference in the field, the original signal is decomposed by an improved Empirical Mode Decomposition (EMD) method, and the optimal center frequency and bandwidth of each mode are adaptively searched. By extracting the modes in the frequency band of the positional localization signal, suppressing the modes in the noise frequency band, and reconstructing the Intrinsic Mode Function (IMF) of the independently selected superior modal components, a signal with a high signal-to-noise ratio is obtained. In addition, for the traditional mutual correlation algorithm with a large delay estimation error at a low signal-to-noise ratio, this paper adopts an improved generalized joint weighting function, SCOT-β, which improves the anti-jamming ability of the generalized mutual correlation method at a low signal-to-noise ratio by adding an exponential function to the denominator term of the SCOT weighting function’s generalized cross-correlation. Finally, the accurate positional localization of the transformer internal inspection robot is realized based on the quadratic L-array and search-based maximum likelihood estimation method. Simulation and experimental results show the following: the improved EMD denoising method better improves the signal-to-noise ratio of the positional localization signal with a lower distortion rate; in the transformer test tank, which is 120 cm in length, 100 cm in width, and 100 cm in height, based on the positional localization method in this paper, the average relative positional localization error of the transformer internal inspection robot in three-dimensional space is 2.27%, and the maximum positional localization error is less than 2 cm, which meets the requirements of engineering positional localization.

It is difficult to visually detect internal defects in a large transformer with a metal closure. For convenient internal inspection, a micro-robot was adopted, and an inspection method based on an image-enhancement algorithm and an improved deep-learning network was proposed in this paper. Considering the dim environment inside the transformer and the problems of irregular imaging distance and fluctuating supplementary light conditions during image acquisition with the internal-inspection robot, an improved MSRCR algorithm for image enhancement was proposed. It could analyze the local contrast of the image and enhance the details on multiple scales. At the same time, a white-balance algorithm was introduced to enhance the contrast and brightness and solve the problems of overexposure and color distortion. To improve the target recognition performance of complex carbon-trace defects, the SimAM mechanism was incorporated into the Backbone network of the YOLOv8 model to enhance the extraction of carbon-trace features. Meanwhile, the DyHead dynamic detection Head framework was constructed at the output of the YOLOv8 model to improve the perception of local carbon traces with different sizes. To improve the defect target recognition speed of the transformer-inspection robot, a pruning operation was carried out on the YOLOv8 model to remove redundant parameters, realize model lightness, and improve detection efficiency. To verify the effectiveness of the improved algorithm, the detection model was trained and validated with the carbon-trace dataset. The results showed that the MSH-YOLOv8 algorithm achieved an accuracy of 91.80%, which was 3.4 percentage points higher compared to the original YOLOv8 algorithm, and had a significant advantage over other mainstream target-detection algorithms. Meanwhile, the FPS of the proposed algorithm was up to 99.2, indicating that the model computation and model complexity were successfully reduced, which meets the requirements for engineering applications of the transformer internal-inspection robot.

Gas film thickness significantly influences the performance prediction of Gas Foil Thrust Bearings (GFTB). However, the Classical Model (CM) for GFTBs exhibits inaccuracies in describing gas film thickness. In this paper, we explore the differences in the details of gas film thickness modeling and propose a Parallel Segmentation Model (PSM), which fixes the errors of the CM in describing the gas film thickness in the ramp section, and a Full-Ramp Model (FRM), to which a more realistic description of the gas film in the flat section is also added. Comparative analysis, utilizing a publicly available test dataset based on the open-source GFTB structure, establishes that the FRM surpasses the CM and PSM in accurately predicting load capacity. In-depth analysis shows that the location of the minimum gas film thickness for determining the load capacity is located at the innermost circle of the free end of the top foil, whereas the FRM is subjected to the same load with a larger film thickness at this location, which may be due to the unique geometry of the top foil of the FRM. Subsequently, employing the FRM, a parametric study explores load capacity in GFTB, considering variables such as ramp height, top foil thickness, bump foil stiffness, ramp section extent, and top foil area. The results demonstrate that GFTB load capacity exhibits a linear increase with the expansion of the top foil area. Moreover, the load capacity increases with augmented top foil thickness and bump foil stiffness, albeit at a decreasing rate. Additionally, an increase in ramp section extent initially enhances load capacity, reaching a maximum value before declining. Similarly, an increase in ramp height initially augments load capacity, attaining a maximum before subsequent diminution.

Our previous study has reported that metastasis-associated protein 2 (MTA2) plays essential roles in tumorigenesis and aggressiveness of gastric cancer (GC). However, the underlying molecular mechanisms of MTA2-mediated GC and its upstream regulation mechanism remain elusive. In this study, we identified a novel circular RNA (circRNA) generated from the MTA2 gene (circMTA2) as a crucial regulator in GC progression. CircMTA2 was highly expressed in GC tissues and cell lines, and circMTA2 promoted the proliferation, invasion, and metastasis of GC cells both in vitro and in vivo. Mechanistically, circMTA2 interacted with ubiquitin carboxyl-terminal hydrolase L3 (UCHL3) to restrain MTA2 ubiquitination and stabilize MTA2 protein expression, thereby facilitating tumor progression. Moreover, circMTA2 was mainly encapsulated and transported by exosomes to promote GC cell progression. Taken together, these findings uncover that circMTA2 suppresses MTA2 degradation by interacting with UCHL3, thereby promoting GC progression. In conclusion, we identified a cancer-promoting axis (circMTA2/UCHL3/MTA2) in GC progression, which paves the way for us to design and synthesize targeted inhibitors as well as combination therapies.

Because large oil-immersed transformers are enclosed by a metal shell, the on-site localization means it is difficult to achieve the accurate location of the patrol micro-robot inside a given transformer. To address this issue, a spatial ultrasonic localization method based on wavelet decomposition and PHAT-β-γ generalized cross correlation is proposed in this paper. The method is carried out with a five-element stereo ultrasonic array for the location of a transformer patrol robot. Firstly, the localization signal is decomposed into wavelet coefficients of different scales, which would realize the adaptive decomposition of the frequency of the localization signal from low frequencies to high frequencies. Then, the wavelet coefficients are denoised and reconstructed by using the semi-soft threshold function. Second, a modified phase transform-beta-gamma (PHAT-β-γ) method is used to calculate the exact time delay between different sensors by increasing the weights of the PHAT weighting function and introducing a correlation function. Finally, by using the proposed method, the accurate localization of the transformer patrol micro-robot is achieved with a five-element stereo ultrasonic array. The simulation and test results show that inside a transformer experimental oil tank (120 cm × 100 cm × 100 cm, L × W × H), the relative error of transformer patrol micro-robot spatial localization is within 4.1%, and the maximum localization error is less than 3 cm, which meets the requirement of engineering localization.

For cost-sensitive Chinese solar greenhouses (CSGs) with an uneven spatial distribution in temperature and humidity, there is a lack of effective strategies for sensor configuration that can reduce sensor usage while monitoring the microclimate precisely. A configuration strategy for integrated temperature–humidity sensors (THSs) based on the improved weighted Hilbert–Schmidt independence criterion (HSIC) is proposed in this paper. The data independence of the THSs in different sites was analyzed based on the improved HSIC, and the selection priority of the THSs was ranked based on the weighted independence of temperature and humidity. Then, according to different cost constraints and monitoring requirements, suitable THSs could be selected sequentially and constitute the monitoring solution. Compared with the original monitoring solution containing twenty-two THSs, the optimized solution used only four THSs (S6, S9 and H6, H5) under strict cost constraints, with a maximum RMSE of the temperature and relative humidity of 0.6 °C and 2.30%, as well as a maximum information gain rate (IGR) of 9.47% and 10.0%. If higher monitoring precision is required, we can increase the THS usage with a greater budget. The optimized solution with six THSs (S6, S9, S8 and H6, H5, H2) could further reduce the maximum RMSE of the temperature and relative humidity to 0.33 °C and 1.10% and the IGR to 6.9% and 8.7%. This indicated that the proposed strategy could use much fewer THSs to achieve accurate and comprehensive monitoring, which would provide efficient and low-cost solutions for CSG microclimate monitoring.

The critical speed is a crucial factor that impacts the stability of high-speed compressors. However, limited research has simultaneously considered the influence of gas foil bearings (GFBs), labyrinth seals, and impellers on critical speed. In this study, we develop a rotordynamic model that incorporates the aerodynamic forces of GFBs, labyrinth seals, and impellers to explore the effects of each component on the critical speed. To validate the developed model, experimental tests are conducted on a centrifugal compressor test bed, and the results exhibit a high level of agreement with the model calculations. By comparing the model calculations that include different components, we comprehensively analyze the influence of each component on the critical speed. The findings reveal that, for centrifugal compressors used in fuel cell vehicles, the rotordynamic coefficients resulting from GFBs are significantly larger than those resulting from impellers and labyrinth seals. Thus, it is reasonable to disregard the aerodynamic forces caused by impellers and labyrinth seals when calculating the critical speed. Furthermore, substituting rigid gas bearings for GFBs as a means to simplify the calculations has only a very slight impact on the results. This study aims to optimize the design process of centrifugal compressors for fuel cell vehicles and offers valuable insights for designing compressors of similar sizes.

A representative general polarimetric model-based decomposition framework was proposed by Chen et al., which implements a simultaneous full-parameter inversion by using complete polarimetric information and solves several limitations in previous decomposition methods. However, there are still shortcomings in Chen’s work. Firstly, only the real part of the parameter β in the generalized surface scattering model is considered. Secondly, inappropriate initial input values may lead to local optima in the nonlinear least squares optimization algorithm. Thirdly, the volume scattering component is underestimated in the volume scattering-dominated scene, but overestimated in buildings with large orientation (LOB) areas. Finally, nonlinear optimization is time-consuming computationally. To overcome those issues, an improved generalized polarimetric model-based decomposition method is proposed in this paper. The imaginary part of the parameter β is incorporated into the decomposition framework of the proposed method. Ingeniously utilizing the internal relationship in the generic equations composed of coherent matrix elements, the model parameters can be inversed by simplifying the nonlinear equations to linear equations. Therefore, compared with Chen’s method, the proposed method does not rely on the initial input values, and improves the computational efficiency. In addition, a hierarchical decomposition scheme is presented to solve the problem of underestimation or overestimation of volume scattering component mentioned above. The performance and advantages of this method are evaluated with L-band and C-band polarimetric synthetic aperture radar (PolSAR) data sets. Comparison studies are carried out with other model-based decomposition methods, demonstrating that the proposed method can further improve decomposition performance, especially in LOB areas.

Antibiotics are administered to livestock at subtherapeutic levels to promote growth, and their degradation in manure is slow. High antibiotic concentrations can inhibit bacterial activity. Livestock excretes antibiotics via feces and urine, leading to their accumulation in manure. This can result in the propagation of antibiotic-resistant bacteria and antibiotic resistance genes (ARGs). Anaerobic digestion (AD) manure treatment technologies are gaining popularity due to their ability to mitigate organic matter pollution and pathogens, and produce methane-rich biogas as renewable energy. AD is influenced by multiple factors, including temperature, pH, total solids (TS), substrate type, organic loading rate (OLR), hydraulic retention time (HRT), intermediate substrates, and pre-treatments. Temperature plays a critical role, and thermophilic AD has been found to be more effective in reducing ARGs in manure compared to mesophilic AD, as evidenced by numerous studies. This review paper investigates the fundamental principles of process parameters affecting the degradation of ARGs in anaerobic digestion. The management of waste to mitigate antibiotic resistance in microorganisms presents a significant challenge, highlighting the need for effective waste management technologies. As the prevalence of antibiotic resistance continues to rise, urgent implementation of effective treatment strategies is necessary.

Sepsis-associated encephalopathy (SAE) is a common complication of sepsis, which is a life-threatening condition resulting from a dysregulated host response to infection. Pyroptosis, a pro-inflammatory mode of lytic cell death mediated by GSDMD (Gasdermin D), is involved in the pathogenesis of SAE. While autophagy has been extensively studied in SAE, the role of nuclear autophagy is not yet well understood. In this study, we aimed to investigate the involvement of pyroptosis and neural nuclear autophagy in the pathogenesis of SAE. We analyzed a CLP (cecal ligation and puncture)-induced SAE model in wild-type and GSDMD−/− mice to gain insights into the underlying mechanisms. Here, we show that in sepsis, neural nuclear autophagy is extremely activated, and nuclear LaminB decreases and is accompanied by an increase in the ratio of LC3BII/I. These effects can be reversed in GSDMD−/− mice. The behavioral outcomes of septic wild-type mice are impaired by the evidence from the novel object recognition test (NORT) and open field test (OFT), but are improved in septic GSDMD−/− mice. In conclusion, our study demonstrates the activation of neural nuclear autophagy in SAE. The absence of GSDMD inhibits nuclear autophagy and improves the behavioral outcomes of SAE.

Load frequency control (LFC) is well known for balancing the load demand and frequency for a multi-area power system. Studies have proven that LFC can improve the global performance of multi-area power systems. In recent years, the increasing proportion of renewable energy, integration of EVs, and cyber-attacks have become the main challenges in LFC power systems. Different strategies have been applied in the literature for LFC power systems and the possible impacts of renewable energy, EVs, and cyber-attacks. This survey paper is devoted to the research on directions in LFC multi-area power systems. The mathematical model of recent challenges in LFC multi-area power systems is summarized and the similarities and differences of these challenges are analyzed. The uncertainty of renewable energy is a frequently noted issue in LFC power systems; however, the uncertainty that exists in controller design is often ignored. In this survey, we analyze methods for treating the uncertainty of renewable energy and controller. This survey paper introduces the most recent research on LFC and acquaints anyone interested in its development, such that the most effective strategies can be developed by the researchers.

Sodium–ion batteries (SIBs) are essential for large–scale energy storage attributed to the high abundance of sodium. Polyanion Na3V2(PO4)3 (NVP) is a dominant cathode candidate for SIBs because of its high-voltage and sodium superionic conductor (NASICON) framework. However, the electrochemical performance of NVP is hindered by the inherently poor electronic conductivity, especially for extreme fast charging and long-duration cycling. Herein, we develop a facile one-step in-situ polycondensation method to synthesize the three-dimensional (3D) Na3V2(PO4)3/holey-carbon frameworks (NVP@C) by using melamine as carbon source. In this architecture, NVP crystals intergrown with the 3D holey-carbon frameworks provide rapid transport pathways for ion/electron transmission to increase the ultrahigh rate ability and cycle capability. Consequently, the NVP@C cathode possesses a high reversible capacity of 113.9 mAh g−1 at 100 mA g−1 and delivers an outstanding high–rate capability of 75.3 mAh g−1 at 6000 mA g−1. Moreover, it shows that the NVP@C cathode is able to display a volumetric energy density of 54 Wh L−1 at 6000 mA g−1 (31 Wh L−1 for NVP bulk), as well as excellent cycling performance of 65.4 mAh g−1 after 1000 cycles at 2000 mA g−1. Furthermore, the NVP@C exhibits remarkable reversible capabilities of 81.9 mAh g−1 at a current density of 100 mA g−1 and 60.2 mAh g−1 at 1000 mA g−1 even at a low temperature of −15 °C. The structure of porous carbon frameworks combined with single crystal materials by in-situ polycondensation offers general guidelines for the design of sodium, lithium and potassium energy storage materials.

Intraday stock time series are noisier and more complex than other financial time series with longer time horizons, which makes it challenging to predict. We propose a hybrid CEGH model for intraday stock market forecasting. The CEGH model contains four stages. First, we use complete ensemble empirical mode decomposition (CEEMD) to decompose the original intraday stock market data into different intrinsic mode functions (IMFs). Then, we calculate the approximate entropy (ApEn) values and sample entropy (SampEn) values of each IMF to eliminate noise. After that, we group the retained IMFs into four groups and predict the comprehensive signals of those groups using a feedforward neural network (FNN) or gate recurrent unit with history attention (GRU-HA). Finally, we obtain the final prediction results by integrating the prediction results of each group. The experiments were conducted on the U.S. and China stock markets to evaluate the proposed model. The results demonstrate that the CEGH model improved forecasting performance considerably. The creation of a collaboration between CEEMD, entropy-based denoising, and GRU-HA is our major contribution. This hybrid model could improve the signal-to-noise ratio of stock data and extract global dependence more comprehensively in intraday stock market forecasting.

The rapid expansion of the high-speed railway (HSR) network in China has significantly shortened the space–time distance between cities. China is striving to enter an anti-poverty era, which is increasing the importance of research on the poverty reduction effect created by upgrading transportation infrastructure, in particular, HSR development. Describing the characteristics of accessibility and the mechanisms by which that accessibility reduces poverty could provide the insights needed for determining suitable anti-poverty paths. By using data for 2341 counties and equivalents in China during 2007–2018, this study analyses the railway accessibility improvements and the poverty reduction effect created by HSR development. On average, HSR in China contributed to a significant increase in potential economic accessibility (317.8%) and a decrease in weighted average travel time (39.9%) for counties. Based on accessibility calculations, the Theil index was used to measure the disparity level of regional accessibility and regional poverty measured based on the income of rural residents. The results indicate that HSR leads to an increase in inequality in terms of travel time and potential economic accessibility at a national level. Pearson coefficients reveal a strong correlation between disparities in accessibility and in rural income among provinces. Furthermore, using the full sample, and sub-samples of poor and non-poor counties in China, the association between regional accessibility and poverty was examined by using two-way fixed effect models and spatial econometric models. The estimated results show that a 1% improvement in potential economic accessibility leads to an aggregate rural income improvement of 0.03–0.17%; the ratio of rural income to urban income increases by 0.04–0.12% and a larger effect is observed in poor counties. The weighted average travel time reduction also leads to improvement in rural income and reduction in the urban–rural income gap. The empirical results obtained by different robust test methods, including different sample groups, different estimated methods and accessibility indicators, are shown to be robust. These findings can help transportation departments formulate poverty-alleviation-oriented transportation planning and investment policies and inform future policies for countries planning to construct HSRs.

Motion segmentation is one of the fundamental steps for detection, tracking, and recognition, and it can separate moving objects from the background. In this paper, we propose a spatial-motion-segmentation algorithm by fusing the events-dimensionality-preprocessing algorithm (EDPA) and the volume of warped events (VWE). The EDPA consists of depth estimation, linear interpolation, and coordinate normalization to obtain an extra dimension (Z) of events. The VWE is conducted by accumulating the warped events (i.e., motion compensation), and the iterative-clustering algorithm is introduced to maximize the contrast (i.e., variance) in the VWE. We established our datasets by utilizing the event-camera simulator (ESIM), which can simulate high-frame-rate videos that are decomposed into frames to generate a large amount of reliable events data. Exterior and interior scenes were segmented in the first part of the experiments. We present the sparrow search algorithm-based gradient ascent (SSA-Gradient Ascent). The SSA-Gradient Ascent, gradient ascent, and particle swarm optimization (PSO) were evaluated in the second part. In Motion Flow 1, the SSA-Gradient Ascent was 0.402% higher than the basic variance value, and 52.941% faster than the basic convergence rate. In Motion Flow 2, the SSA-Gradient Ascent still performed better than the others. The experimental results validate the feasibility of the proposed algorithm.

Background: Automated segmentation of COVID-19 infection lesions and the assessment of the severity of the infections are critical in COVID-19 diagnosis and treatment. Based on a large amount of annotated data, deep learning approaches have been widely used in COVID-19 medical image analysis. However, the number of medical image samples is generally huge, and it is challenging to obtain enough annotated medical images for training a deep CNN model. Methods: To address these challenges, we propose a novel self-supervised deep learning method for automated segmentation of COVID-19 infection lesions and assessing the severity of infection, which can reduce the dependence on the annotation of the training samples. In the proposed method, first, many unlabeled data are used to pre-train an encoder-decoder model to learn rotation-dependent and rotation-invariant features. Then, a small amount of labeled data is used to fine-tune the pre-trained encoder-decoder for COVID-19 severity classification and lesion segmentation. Results: The proposed methods were tested on two public COVID-19 CT datasets and one self-built dataset. Accuracy, precision, recall, and F1-score were used to measure classification performance and Dice coefficient was used to measure segmentation performance. For COVID-19 severity classification, the proposed method outperformed other unsupervised feature learning methods by about 7.16% in accuracy. For segmentation, when the amount of labeled data was 100%, the Dice value of the proposed method was 5.58% higher than that of U-Net.; in 70% of the cases, our method was 8.02% higher than U-Net; in 30% of the cases, our method was 11.88% higher than U-Net; and in 10% of the cases, our method was 16.88% higher than U-Net. Conclusions: The proposed method provides better classification and segmentation performance under limited labeled data than other methods.

Wind and solar energy are examples of clean energy that are widely developed and utilized in order to achieve the goal of carbon neutrality. Higher requirements for the safety and reliability of the power grid are put forward after they are connected to it. In the case of disconnectors, as the power system’s protection equipment, their arc interruption characteristics are closely tied to the safety and reliability of the power system. In addition, a disconnector is required to be able to break the DC arc in the photovoltaic power generation system. Therefore, this paper focuses on the arc evolution characteristics in disconnectors. A magnetohydrodynamics (MHD) model of disconnectors was built. In this model, not only are the coupling of the electromagnetic field and the airflow field considered, but also the characteristics of the external circuit. Therefore, not only can arc evolution characteristics be obtained through this simulation model, but the breaking performance will also be directly obtained. The temperature, pressure and velocity distribution are obtained to analyze the evolution process. The curve of current versus time is calculated to analyze the breaking performance. The evolution characteristics of AC and DC arcs in the disconnector are analyzed by calculation and comparison. This provides theoretical guidance for the optimal design of DC disconnectors through simulation analysis.

For the future development of integrated energy systems with high penetration of renewable energy, an integrated community energy systems (ICES) dispatch model is proposed including various renewable energy sources and energy conversion units. Energy coupling matrices of ICES based on traditional energy hub (EH) models are constructed. Uncertainties of long-term forecast data of renewable energy sources and internal loads are depicted by multi-interval uncertainty sets (MIUS). To cope with the impacts caused by uncertainties of renewable energy sources and internal loads, the whole dispatch process is divided into two stages. Considering various constraints of ICES, we solved the dispatch model through the improved particle swarm optimization (IPSO) algorithm in the first stage. The optimal evolutionary dispatch is then proposed in the second stage to overcome the evolution and errors of short-term forecast data and obtain the optimal dispatch plan. The effectiveness of the proposed dispatch method is demonstrated using an example considering dramatic uncertainties. Compared with the traditional methods, the proposed dispatch method effectively reduces system operating costs and improves the environmental benefits, which helps to achieve a win-win situation for both energy companies and users.

Existing empirical evidence suggests that the prevalence of undernutrition in remote and poor, rural areas is still high among Chinese children. While evidence reveals that undernutrition may detrimentally affect child development, studies focusing on rural Chinese preschoolers are sparse. Using the baseline survey of a preschool’s free nutritious lunch pilot program, this study examined the relationship between child undernutrition and developmental outcomes among a preschool-aged sample in poor, rural areas of China. We conducted the baseline survey in Hunan province in south central China in September 2018. A total of 1293 preschoolers living in two (then) nationally designated poverty counties in rural Hunan served as our study sample. Children’s nutritional statuses were measured using height-for-age z-score, weight-for-age z-score, and anemia, while their cognitive and socio-emotional skills were assessed using the Wechsler Preschool and Primary Scale of Intelligence (WPPSI) and Strengths and Difficulties Questionnaire (SDQ), respectively. We find that 33% of sample preschoolers were anemic, whereas the incidences of stunting and wasting were 11% and 2%, respectively. About 54% of the sample children had delay in at least one of the developmental domains measured in this study. Our findings provide suggestive evidence supporting that children from certain backgrounds tend to experience worse nutritional and developmental outcomes than their counterparts. After controlling for socioeconomic status, we observed that both anemia and stunting were negatively associated with children’s cognitive performance; however, they were not associated with socio-emotional performance. As such, this study suggests that free lunch programs have the potential to change children’s developmental trajectory in preschool. We believe that our results will contribute to the debate surrounding whether the nutritious lunch program in China should be expanded to the preschool education level.

In this paper, an extensible positioning system for mobile robots is proposed. The system includes a stereo camera module, inertial measurement unit (IMU) and an ultra-wideband (UWB) network which includes five anchors, one of which is with the unknown position. The anchors in the positioning system are without requirements of communication between UWB anchors and without requirements of clock synchronization of the anchors. By locating the mobile robot using the original system, and then estimating the position of a new anchor using the ranging between the mobile robot and the new anchor, the system can be extended after adding the new anchor into the original system. In an unfamiliar environment (such as fire and other rescue sites), it is able to locate the mobile robot after extending itself. To add the new anchor into the positioning system, a recursive least squares (RLS) approach is used to estimate the position of the new anchor. A maximum correntropy Kalman filter (MCKF) which is based on the maximum correntropy criterion (MCC) is used to fuse data from the UWB network and IMU. The initial attitude of the mobile robot relative to the navigation frame is calculated though comparing position vectors given by a visual simultaneous localization and mapping (SLAM) system and the UWB system respectively. As shown in the experiment section, the root mean square error (RMSE) of the positioning result given by the proposed positioning system with all anchors is 0.130 m. In the unfamiliar environment, the RMSE is 0.131 m which is close to the RMSE (0.137 m) given by the original system with a difference of 0.006 m. Besides, the RMSE based on Euler distance of the new anchor is 0.061 m.