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Abstract Objective The efficacy of lymph node ratio (LNR) as a prognostic indicator in locally advanced gastric cancer (LAGC) patients underwent radical resection after neoadjuvant immunochemotherapy (NICT) remains to be demonstrated. The objective of the current retrospective study is to investigate the relationship between LNR and survival in patients with LAGC who underwent radical resection after NICT. Methods A retrospective analysis was performed on 121 cases of LAGC in patients underwent radical resection after NICT between July 2020 and October 2023. The LNR values of the patients were divided into two groups using X-tile software. The first group, designated the low LNR group, comprised patients with LNR values of ≤ 33%. The second group, designated the high LNR group, comprised patients with LNR values of > 33%. The correlation between patient survival rates and a range of clinical and pathological variables was examined. Results Overall, 121 patients were enrolled: 108 with low-LNR (LNR ≤ 33%) and 13 with high-LNR (LNR > 33%). A better 2-year overall survival (OS) (88.5% vs. 32.6%; p

Abstract Social isolation (SI) is a common phenomenon in the modern world, especially during the coronavirus disease 2019 pandemic, and causes lasting cognitive impairments and mental disorders. However, it is still unclear how SI alters molecules in the brain and induces behavioural dysfunctions. Here, we report that SI impairs cognitive function and induces depressive-like behaviours in C57BL/6 J mice, in addition to impairing synaptic plasticity and increasing the levels of APP cleavage-related enzymes, thereby promoting Aβ production. Moreover, we show that in APP/PS1 transgenic mice, SI accelerates pathological changes and behavioural deficits. Interestingly, downregulation of the expression of the BACE1 attenuates SI-induced Aβ toxicity and synaptic dysfunction. Furthermore, early intervention with BACE1 shRNA blocks SI-induced cognitive impairments. Together, our data strongly suggest that SI-induced upregulation of BACE1 expression mediates Aβ toxicity and induces behavioural deficits. Down-regulation of BACE1 may be a promising strategy for preventing SI-induced cognitive impairments.

Combining multiple crop protection Unmanned Aerial Vehicles (UAVs) as a team for a scheduled spraying mission over farmland now is a common way to significantly increase efficiency. However, given some issues such as different configurations, irregular borders, and especially varying pesticide requirements, it is more important and more complex than other multi-Agent Systems (MASs) in common use. In this work, we focus on the mission arrangement of UAVs, which is the foundation of other high-level cooperations, systematically propose Efficiency-first Spraying Mission Arrangement Problem (ESMAP), and try to construct a united problem framework for the mission arrangement of crop protection UAVs. Besides, to characterise the differences in sub-areas, the varying pesticide requirement per unit is well considered based on Normalized Difference Vegetation Index (NDVI). Firstly, the mathematical model of multiple crop-protection UAVs is established and ESMAP is defined. Furthermore, an acquisition method of a farmland’s NDVI map is proposed, and the calculation method of pesticide volume based on NDVI is discussed. Secondly, an improved Genetic Algorithm (GA) is proposed to solve ESMAP, and a comparable combination algorithm is introduced. Numerical simulations for algorithm analysis are carried out within MATLAB, and it is determined that the proposed GA is more efficient and accurate than the latter. Finally, a mission arrangement tested with three UAVs was carried out to validate the effectiveness of the proposed GA in spraying operation. Test results illustrated that it performed well, which took only 90.6 % of the operation time taken by the combination algorithm.

Heterotopic ossification of the elbow (HOE) is a complicated pathologic process characterized by extra bone formation in the elbow. Bone formation is a complex developmental process involving the differentiation of mesenchymal stem cells into osteoblasts. The aim of this study was to explore the cellular origin and progression of HOE by single-cell RNA sequencing. We identified 13 clusters of cells in HOE and further analyzed the subclusters for 4 of the main cell types. Six subclusters of osteoblasts, nine subclusters of chondrocytes, six subclusters of fibroblasts, and five subclusters of mononuclear phagocytes (MPs) were identified and analyzed. The new findings on osterix (OSX) and SOX9 expression in osteoblast subclusters and chondrocyte subclusters indicate that HOE is mediated through endochondral ossification. Further identification of the corresponding signature gene sets of distinct subclusters indicated that subclusters of osteoblasts_3, osteoblasts_4, osteoblasts_5, and osteoblasts_6 are relatively more mature during the osteoblastic progression of HOE. The trajectory analysis of the osteoblasts demonstrated that some genes were gradually downregulated, such as CRYAB, CCL3, SFRP4, WIF1, and IGFBP3, while other critical genes were upregulated, such as VCAN, IGFBP4, FSTL1, POSTN, MDK, THBS2, and ALPL, suggesting that these factors may participate in HOE progression. Cell–cell communication networks revealed extensive molecular interactions among the 13 HOE clusters. Ligand–receptor pairs for IL6, COL24A1, COL22A1, VWF, FZD6, FGF2, and NOTCH1 were identified, suggesting that multiple signaling pathways may be involved in HOE. In conclusion, this study provided the cellular atlas for HOE. We have established a greater extent of the heterogeneity of HOE cells than previously known through transcriptomic analysis at the single-cell level. We have observed gradual patterns of signature gene expression during the differentiation and maturation progression of osteoblasts from stem cells in HOE with higher resolution. The cell heterogeneity of HOE deserves further investigation to pave the way for identification of potential targets for HOE early diagnosis and therapeutic treatment.

Coherently combining distributed apertures adjusts the transmitted/received signals of multiple distributed small apertures, allowing coordinated distributed systems to obtain high power aperture products at much lower cost than large aperture. This is a promising and viable technology as an alternative to using large apertures. This study describes the concept and principles of coherently combining distributed apertures. Depending on whether external signal inputs at the combination destination are necessary, the implementation architecture of coherent combination is classified into two categories: closed- and open-loop. The development of coherently combining distributed apertures and their application in fields such as missile defense, deep space telemetry control, radar detection over ultralong range, and radio astronomy are then comprehensively presented. Furthermore, key techniques for aligning the time and phase of the transmitted/received signals for each aperture are elaborated, which are also necessary for coherently combining distributed apertures, including high-precision distributed time-frequency transfer and synchronization, and coherently combining parameters estimation, measurement and calibration, and prediction. Finally, summary is presented, and the scope of future works in this field is explored.

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.

Abstract Depression is a common chronic psychiatric illness, which is resistant to medical treatments. While melatonin may alleviate certain depression symptoms, evidence for its efficacy against core symptoms is lacking. Here, we tested a mechanism whereby melatonin rescues the behavioral outcomes of the chronic unpredictable mild stress (CUMS) mouse model of depression. CUMS mice showed depressive behaviors to tail suspension, open field behavior, and sucrose preference test, and cognitive dysfunction in the Morris water maze. Impairments in these measures were relieved by melatonin treatment. Moreover, CUMS mice had impaired glymphatic function across the sleep-wake cycle due to the astrocytic loss and disturbance of circadian regulation of the polarized expression of aquaporin-4 (AQP4) water channels in perivascular astrocytes. EEG results in CUMS mice showed a reduced total sleep time and non-rapid eye movement (NREM) sleep, due to sleep fragmentation in the light phase. CUMS mice lost the normal rhythmic expressions of circadian proteins Per2, Cry2, Bmal1, Clock, and Per1. However, the melatonin treatment restored glymphatic system function and the polarization of AQP4, while improving sleep structure, and rectifying the abnormal expression of Per2, Bmal1, Clock, and Per1 in CUMS mice. Interestingly, Per2 expression correlated negatively with the polarization of AQP4. Further studies demonstrated that Per2 directed the location of AQP4 expression via interactions with the α-dystrobrevin (Dtna) subunit of AQP4 in primary cultured astrocytes. In conclusion, we report a new mechanism whereby melatonin improves depression outcomes by regulating the expression of the circadian protein Per2, maintaining the circadian rhythm of astrocytic AQP4 polarization, and restoring glymphatic function.

The finite set state machine theory belongs to the field of computer science. It is used to model and create multitasking systems. The control and modulation system for power electronics converters is a multitasking system as well. To increase the control speed without being constrained by the triangular wave module, the finite set state machine theory can be applied to converter modulation. In this paper, a modulation technique for finite state machines was proposed. This strategy fully uses the relationship between the affective state and converter switch states. Then, the structure of the modulation modular is presented in detail. This paper has analyzed the mathematical model of the finite set state machine modular. Finally, finite set state machine theory is used to Boost converter modulation modular to control the output voltage stability. Additionally, the effectiveness of the modulation strategy is validated by the experimental results.

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.

Rice wine, known as yellow wine in China and Japan, possesses considerable nutritional value and holds significant global influence. This study addresses the challenge of preserving rice wine, which is prone to rancidity due to its low alcohol content. Conventional storage techniques employing pottery jars often result in substantial spoilage losses. Through rigorous investigation, this research identifies a polarization phenomenon exhibited by degraded rice wine when subjected to high-frequency microwaves(>60GHz), presenting a pioneering method for detecting spoilage, even within sealed containers. Employing a multi-channel microwave radar apparatus, the study delves into the susceptibility of rice wine to electromagnetic waves across various frequencies, uncovering pronounced polarization traits in deteriorated samples within the E-band microwave spectrum. Furthermore, lab-controlled simulations elucidate a direct correlation between physicochemical alterations and high-frequency Radar Cross Section (RCS) signals during the wine's deterioration process. A novel six-membered Hydrated Cluster hypothesis is proposed, offering insights into the molecular mechanisms underlying this phenomenon. Additionally, dielectric property assessments conducted using vector network analyzers (VNA) reveal noteworthy enhancements in the dielectric constant of deteriorated rice wine, particularly within the high-frequency domain, thereby augmenting detectability. These findings carry implications for refining rice wine preservation techniques and contribute to the advancement of non-destructive testing technologies, enabling the detection of rice wine deterioration or indications thereof, even within sealed vessels.

Abstract Extant research has demonstrated the positive intrapersonal effects of mindfulness training. However, the cognitive mechanisms underlying the effects of mindfulness training on interpersonal processes are less clear. Here, we combined a randomized control mindfulness training design with computational approach to moral decision-making and moral judgments. Participants were randomly assigned to a Training group (N = 32) who received an 8-week mindfulness training or a Control group (N = 26) who waited for the same period of time. Before and after the 8-week period, participants completed a moral decision-making task, where they made tradeoff between money for themselves and unpleasant electric shocks to another person, and a moral judgment task, where they evaluated the blameworthiness of someone else’s choices in the same moral decision-making task. Trait mindfulness, as measured by the Five-Facet Mindfulness Questionnaire, significantly increased from the pre- to post-training session for the Training group, but not the Control group, demonstrating the effectiveness of the mindfulness manipulation. For the Control group, participants’ moral preference in both the decision-making task and the judgment task declined over time, exhibiting a “slippery slope” effect. In contrast, for the Training group, mindfulness training prevented moral preferences from declining. Computational modeling revealed that mindfulness training specifically reduced the increase in the weights of money over time in both the decision-making and judgment tasks, thereby curbing the “slippery slope” effects. These findings provide a cognitive account of the prosocial effects of mindfulness training on moral decision-making and moral judgments.

In this paper, a model of electricity hydrogen integrated energy system considering virtual energy storage is proposed to enhance the penetration rate of renewable energy. Specifically, mathematical modeling is conducted for the integrated energy system of electricity hydrogen. Considering the thermal characteristics of buildings and demand response from users, the virtual energy storage system is integrated into the model to optimize the system’s overall performance. A day-ahead and intra-day optimization scheduling strategy is proposed to address the uncertainties of renewable energy generation, load forecasting, and power fluctuations. The day-ahead optimization model coordinates the output of various components to minimize the daily operating costs, resulting in a 24-hour operational plan. The intra-day scheduling model aims to smooth out power fluctuations and improve system stability by refining the day-ahead strategy through rolling optimization. Four comparative cases are presented to validate the proposed model. The quantitative results of this study show that introducing a virtual energy storage system has multiple significant advantages in an electric hydrogen integrated energy system. By optimizing operational costs, the equipment cost of the system is significantly reduced, and the integration of renewable energy is dramatically improved, effectively promoting sustainable development. In addition, the application of virtual energy storage maximizes the advantages of hydrogen energy while helping to reduce carbon emissions, providing strong support for the future sustainability of the energy system.

With the rapid development of modern industry, while improving people’s living standards, the over-exploitation of coal, oil and natural gas has led to a shortage of fossil energy, global warming and an increasingly serious deterioration of the ecological environment. To mitigate the greenhouse effect caused by excessive carbon emissions, the vigorous development of integrated electricity-gas system (IEGS) dominated by clean energy is the future trend of sustainable development of energy systems. In this paper, a bi-level optimal scheduling model is proposed for an IEGS considering carbon capture, utilization and storage (CCUS), and the ladder carbon trading mechanism is introduced to convert carbon emissions into economic benefits. The upper model is an optimal distribution model of natural gas network, and the lower model is a day-ahead economic dispatch model of power system. Based on the Karush-Kuhn-Tucher (KKT) condition and strong duality theory of the lower model, the bi-level model is transformed into a mixed integer linear programming (MILP), which is solved by calling CPLEX through the Yalmip toolbox of the Matlab platform. Finally, the reasonableness and validity of the model are verified by three arithmetic simulations. The results show that the proposed bi-level model for low-carbon economic dispatch of IEGS considering CCUS can effectively reduce the operating costs and carbon emissions of the system.

This paper proposes a method for retired batteries based on SOC consistency matching. This method takes the SOC consistency of the battery pack as the standard, which calculates the model parameter matching relationship to optimize the performance of the pack. Based on Thevenin equivalent circuit model, the mathematical model of lithium ion battery is established and the model is parameterized. Additionally, a battery pack reconfiguration criterion based on SOC consistency is proposed to reveal the mapping relationship between cell parameters and battery pack SOC consistency. Finally, the battery parameters are identified by the recursive least squares (RLS) algorithm to select two groups of batteries. Dynamic and constant current tests are performed on battery packs with different topologies, which are used to verify the feasibility of the proposed method. The experimental results show that the proposed reorganization scheme can better ensure the consistency of the SOC of the battery pack during charging and discharging.

Because of its high efficiency and flexible control, inverters are widely used in renewable energy generation. The inverter’s mathematical model is critical in system design and analysis. It is necessary to establish an accurate grid connected inverter model to carry out a reliable load flow analysis calculation. The amplitude and phase of the reference control strategy can be used to perform power transformation and control analysis. This paper proposed a steady-state power model controlled by amplitude and phase based on a two-level inverter. Then, the mathematical derivation of the proposed model is presented in detail. Furthermore, the simulation model’s details are then built. The simulation results validate the model’s effectiveness.

The development of power batteries has driven the popularity of electric vehicles (EVs). For EV, charging management directly affects battery pack performance and vehicle portability. In this paper, a multi-stage constant current charging mode considering the temperature rise, health loss, and charging time is proposed. Based on the equivalent circuit model, thermal model and aging empirical model of the battery, the objective function of charging optimization is constructed. The solution is solved by with different optimization algorithms. A comparative analysis of the different solutions on the Pareto front is performed, and the optimization results show that the non-dominated sorting genetic algorithm-II (NSGA-II) results are significantly better than the non-dominated sorting moth flame optimization (NSMFO). Finally, comparing the optimization results with the conventional constant current (CC) charging method shows that the charging optimization algorithm proposed in this work is able to reduce health loss and temperature rise. Although, there is a mutually binding relationship between charging time, temperature rise and health loss. However, the best charging optimization can be obtained on the Pareto front according to different charging requirements.

Effective state of health (SOH) estimation is of great significance for the maintenance and management of lithium-ion battery. A method for life prediction of lithium-ion batteries based on long short-term memory (LSTM) neural network is presented in this paper. To simulate the actual scene of the electric vehicle (EV), the dynamic aging experiment is carried out. In order to enhance the accuracy of parameter identification, the RLS algorithm is improved using fuzzy logic, the forgetting factor is adaptive according to the voltage error. Further, the internal parameters strongly related to SOH are extracted, and the SOH prediction model with LSTM neural network is established. The performance of the proposed algorithm is verified by comparing different algorithms with training sets of different scales.

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.

Abstract Background Many people suffered from emotional distress especially during the COVID-19 pandemic. In order to alleviate emotional distress, more accessible psychological intervention programs, such as online intervention programs, are needed. The study aimed to investigate the efficacy and the potential mechanism of a 4-week, online, self-help mindfulness-based intervention to manage emotional distress during the COVID-19 pandemic between February 3 and May 20, 2020. Methods A total of 302 individuals with high emotional distress completed a self-help mindfulness course, which lasted 30–60 min per day for 28 consecutive days. Participants who registered in the program later were included in the analyses as the control group (n = 315). Levels of mindfulness, perceived stress, emotional distress, anxiety and depression were assessed at baseline(T1), week 1(T2), week 2(T3), week 3(T4) and week 4(T5). Results Significant Group by Time interaction effects were found on mindfulness, perceived stress, emotional distress, anxiety and depression (p

Abstract White‐light‐emitting carbon dots (WCDs) show innate advantages as phosphors in white light‐emitting diodes (WLEDs). For WLEDs, the color rendering index (CRI) is the most important metric to evaluate its performance. Herein, WCDs are prepared by a facile one‐step solvothermal reaction of trimellitic acid and o‐phenylenediamine. It consists of four CDs identified by column chromatography as blue, green, yellow, red, and thus white light is a superposition of these four types of light. The mixture of the four CDs undergoes Förster resonance energy transfer to induce the generation of white light. The photoluminescence of WCDs originates from the synergistic effect of carbon core and surface states. Thereinto, the carbon core states dominate in RCDs, and the increase of amide contents and degree of conjugation promote the redshift of the emission spectra, which is further confirmed by theoretical calculations. In addition, a high CRI of 97 is achieved when the WCDs are used as phosphors to fabricate WLEDs, which is almost the highest value up to now. The multicolor LEDs can also be fabricated by using the four multicolor CDs as phosphors, respectively. This work provides a novel approach to explore the rapid preparation of low‐cost, high‐performance WCDs and CDs‐based WLEDs.

IntroductionAlzheimer’s disease (AD) is a progressive neurodegenerative disease that results in cognitive impairment and is often accompanied by anxiety. In this study, we investigated whether the activation of VTAVgat neurons could reduce anxiety in APP/PS1 mice. We hypothesized that acute social defeat stress (SDS) would lead to anxiety in APP/PS1 mice, and that the activation of VTAVgat neurons would alleviate this anxiety.MethodsWe exposed APP/PS1 mice to acute SDS and assessed anxiety using the open field test and elevated plus-arm test. Activated VTAVgat neurons was tested by cfos staining. Sleep quality was detected using electroencephalogram after SDS or non-SDS procedure. Sleep duration, sleep latency, and non-rapid eye movement (NREM) percentage were analyzed. VTAVgat neurons were chemogenetically activated by deschloroclozapine.ResultsOur results showed that acute SDS led to anxiety in APP/PS1 mice, as evidenced by increased anxiety-related behaviors in the open field and elevated plus-arm tests. Activation of VTAVgat neurons by SDS led to an increase in sleep duration, primarily due to a decrease in sleep latency and an increase in NREMs. However, the quality of sleep was poor. Chemogenetical activation of VTAVgat neurons improved sleep quality and relieved SDS-induced anxiety. Furthermore, the anxiety state correlated negatively with sleep duration and NREM percentage and correlated positively with theta power density in APP/PS1 mice.DiscussionOur study provides evidence that the activation of VTAVgat neurons alleviates SDS-induced anxiety in APP/PS1 mice, suggesting that poor sleep quality may exacerbate anxiety in AD. These findings may have important implications for the treatment of anxiety in AD, as targeting VTAVgat neurons could be a potential therapeutic approach.

Abstract This paper proposes a state estimation approach ‘robust strong tracking unscented Kalman filter with unknown inputs’ that can be applied to non‐linear systems with unknown inputs. Specifically, the non‐linear state and measurement equations are linearised by statistical linearisation. Then, the estimation equation of the unknown input is derived based on the weighted least squares method. The multiple suboptimal fading factor is introduced into a priori error covariance matrix to improve the tracking ability for the inaccuracy of the system model and the abrupt change of state variables caused by unknown inputs. Finally, based on the unbiased minimum variance estimation, the unbiased state estimation and the error covariance matrix are derived. Singular value decomposition is performed on the error covariance matrix to improve the stability of the algorithm. Simulated results validate the effectiveness of the proposed method.

With the crisis of energy and environment, the integrated energy systems (IES) have a bright prospect in future energy reform owing to the excellent economic and environmental performance. The IES combines a large amount of renewable energy (RE), which guarantees its economic and environmental benefits. Many uncertainties of RE threaten the performance of IES. How to promote RE consumption under strong uncertainties is a crucial problem in the scheduling of IES. In this work, a two-stage scheduling strategy for IES combining day-ahead scheduling and real-time scheduling is proposed to guide the system operation. The stage of day-ahead scheduling can obtain the optimal scheduling scheme one day in advance based on the forecast data of RE, and the stage of real-time scheduling is introduced to cope with the uncertainties of RE. The model of IES is established based on the energy hub (EH), and the entire strategy is implemented on this model. The model integrates various energy conversion equipment to give full play to the advantages of coordination and complementation of IES. The improved particle swarm optimization (IPSO) is proposed as the solution algorithm of the whole strategy, which improves the traditional PSO through random nonlinearity change inertia weight strategy and best solution perturbation operator (BSPO). Compared with the traditional PSO, IPSO has more excellent performance for solving IES scheduling model. Finally, different schemes under different situations are compared.

Research has shown mixed findings on the link between production diversity and household dietary diversity. The question is whether this link holds for children. In this study we examine the relationship between household's agricultural production diversity and child dietary diversity, and between production diversity and child nutritional status. Smallholder farm households (n = 1067) and children (n = 1067) aged 3-16 years from two then nationally designated poverty counties in Gansu Province of China were interviewed in 2019. Production diversity was assessed with the production richness score and production diversity score. Production diversity was calculated from agricultural production data covering a period of 12 months. Child dietary diversity was assessed with food variety score (FVS) and dietary diversity score (DDS). DDS was calculated based on 9 food groups using a 30-day recall method. Data were analysed using Poisson and Probit regression models. We find that both agricultural production richness score and revenue generated from selling agricultural products are positively associated with food variety score, with the relationship being stronger for the latter. Moreover, production diversity score is positively associated with children's dietary diversity score whereas negatively associated with their probability of being stunted, but not with their probabilities of being wasted or zinc deficient. Household social economics status were also positively associated with child dietary diversity.

Carbon dots (CDs) have been applied to solid lighting because of their excellent fluorescent properties, good chemical stability, easy functionalization, and other advantages. However, the low thermal stability of most CDs limits the wide application of CDs as phosphors in laser illumination field. Aiming at CDs with high thermal stability and low cost, Y-CDs with fluorescent quantum yield of 49% were prepared by one-step hydrothermal method using citric acid and 2，7-dihydroxynaphthalene as raw materials and adjusting the concentration of reactants. The YG-CDs after annealing only lost 10% of weight at 250 ℃, while their thermal stability was greatly improved. The fluorescent films with good transmittance, fluorescent property, and thermal stability were obtained by combining the annealed YG-CDs with three film-forming agents (polymethyl methacrylate, polyvinyl alcohol, and silane coupling agent). This research provides a theoretical and practical basis for the application of CDs-based fluorescent films for laser illumination.

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.

Abstract This paper shows a result for the load frequency control with event‐triggering mechanism under deception attacks. Specifically, the event‐triggering mechanism and deception attacks are considered in the dynamic model of power systems. The event‐triggering is used to reduce the frequency of controller update and communication between nodes. The H∞ controller is designed and the stability of the system is guaranteed by utilising the Lyapunov–Krasovskii functional method and truncated Bessel–Legendre inequality. Finally, a three‐area interconnected networked power system is given and the simulated results are presented to show the effectiveness of the developed theoretical results.

Dynamic acquisition of crop morphology is beneficial to real-time variable decision of precise spraying operations in fields. However, the existing spraying quantity regulation has high tolerance on the statistical characteristics of regional morphology, so expensive LiDAR and ultrasonic radar can't make full use of their high accuracy, and can reduce decision speed because of too much detail of branches and leaves. Therefore, designing a novel recognition system embedded machine learning with low-cost monocular vision is more feasible, especially in China, where the agricultural implements are medium sizes and cost-sensitive. In addition, we found that the growth period of crops is an important reference index for guiding spraying. So, taking cotton as a case study, a cotton morphology acquisition by a single camera is established, and a cotton growth period recognition algorithm based on Convolution Neural Network (CNN) is proposed in this paper. Through the optimization process based on confusion matrix and recognition efficiency, an optimized CNN model structure is determined from 9 different model structures, and its reliability was verified by changing training sets and test sets many times based on the idea of k-fold test. The accuracy, precision, recall, F1-score and recognition speed of this CNN model are 93.27%, 95.39%, 94.31%, 94.76% and 71.46 ms per image, respectively. In addition, compared with the performance of VGG16 and AlexNet, the convolution neural network model proposed in this paper has better performance. Finally, in order to verify the reliability of the designed recognition system and the feasibility of the spray decision-making algorithm based on CNN, spraying deposition experiments were carried out with 3 different growth-periods of cotton. The experiments’ results validate that after the optimal spray parameters were applied at different growth periods respectively, the average optimum index in 3 growth periods was 42.29%, which was increased up to 62.24% than the operations without distinguishing growth periods.

Abstract Background Since the outbreak of coronavirus disease 2019 in December 2019, more than 8 million cases have occurred worldwide as of June 16, 2020. However, it is important to distinguish COVID-19 from other respiratory infectious diseases, such as influenza. Here, we comparatively described the clinical characteristics of children with COVID-19 and paediatric patients with influenza. Methods In this retrospective, single-centre study, we reviewed the electronic medical records of 585 paediatric patients with COVID-19 or influenza in Wuhan Children’s Hospital, China. Clinical and epidemiological characteristics, laboratory findings, and clinical outcomes were comparatively analysed. Results The median ages were 6.96 years (IQR, 2–10.81) for children with confirmed COVID-19, 2.67 years (IQR, 1.03–15.25) for those with influenza A and 3.67 years (IQR, 1.62–5.54) for those with influenza B. Fever was a symptom in 84 (34.7%) COVID-19 cases, 132 (70.21%) influenza A cases and 111 (74.50%) influenza B cases. The median length of stay (LOS) was 11 (8–15) days for paediatric COVID-19 patients, 4 (3–6) days for influenza A patients and 5 (3–6) days for influenza B patients. Twenty-six (13.98%) influenza A patients and 18 (12.59%) influenza B patients presented with decreased white blood cell counts, while 13 (5.33%) COVID-19 patients presented with decreased white blood cell counts. Eight (3.28%) COVID-19 patients, 23 (12.71%) influenza A patients and 21 (14.79%) influenza B patients experienced lymphocytopenia. Acute cardiac injury occurred in 18 (7.29%) COVID-19 patients, while 37 (19.68%) influenza A and 27 (18.12%) influenza B patients had acute cardiac injury. Conclusion In this study, the illnesses of children with COVID-19 were demonstrated to be less severe than those of paediatric patients with influenza, and COVID-19 patients had milder illness and fewer complications.

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.

Accurate sag source location and precise sag type recognition are both essential to verifying the responsible party for the sag and taking countermeasures to improve power quality. In this paper, an attention-based independently recurrent neural network (IndRNN) for sag source location and sag type recognition in sparsely monitored power system is proposed. Specially, the given inputs are voltage waveforms collected by limited meters in sparsely monitored power system, and the desired outputs simultaneously contain the following information: the located lines where sag occurs; the corresponding sag types, including motor starting, transformer energizing and short circuit; and the fault phase for short circuit. In essence, the responsibility of the proposed method is to automatically establish a nonlinear function that relates the given inputs to the desired outputs with categorization labels as few as possible. A favorable feature of the proposed method is that it can be realized without system parameters or models. The proposed method is validated by IEEE 30-bus system and a real 134-bus system. Experimental results demonstrate that the accuracy of sag source location is higher than 99% for all lines, and the accuracy of sag type recognition is also higher than 99% for various sag sources including motor starting, transformer energizing and 7 different types of short circuits. Furthermore, a comparison among different monitor placements for the proposed method is conducted, which illustrates that the observability of power networks should be ensured to achieve satisfactory performance.

The test platforms of motor driver are relatively expensive, high energy consumption, complex mechanical structure, and not readily available. A three-phase power electronic load of simulated permanent magnet synchronous motor is used to eliminate the risk associated with the performance testing of motor driver, without complex mechanical system. This equipment not only can simulate a variety of load characteristics, but also has the ability to recover energy with little loss and high power factor. In this work, a novel current tracking control strategy based on dual-mode structure repetitive control (DMRC) is proposed to improve the tracking accuracy of alternating current. Compare to control strategy operated in the d-q coordinate system, the proposed control strategy can direct track sine wave commands on the stationary $\alpha $ - $\beta $ coordinate system, which is easier to implement with fewer intermediate coordinate transformation in practice. An inductor-capacitor-inductor (LCL) filter is used to interface the voltage source converter (VSC) and the tested inverter in the three-phase electronic load system. Adjusting the odd-harmonic gain and even-harmonic gain of DMRC controller can effectively suppress the current harmonics. Furthermore, the comparative evaluation of DMRC, repetitive control and conventional PI control is conducted to verify the superiority of DMRC at different AC voltage frequency of tested inverter. The simulation has been carried out on three-phase power electronic load. As a result, the proposed method can achieve low total harmonic distortion (THD) (< 1.8%) of AC current, It can provides nearly 20% reduction of THD in phase current when compared with PI controller.

Abstract Background Identifying the mutation status of KRAS is important for optimizing treatment in patients with colorectal cancer (CRC). The aim of this study was to investigate the predictive value of haematological parameters and serum tumour markers (STMs) for KRAS gene mutations. Methods The clinical data of patients with colorectal cancer from January 2014 to December 2018 were retrospectively collected, and the associations between KRAS mutations and other indicators were analysed. Receiver operating characteristic (ROC) curve analysis was performed to quantify the predictive value of these factors. Univariate and multivariate logistic regression models were applied to identify predictors of KRAS mutations by calculating the odds ratios (ORs) and their corresponding 95% confidence intervals (CIs). Results KRAS mutations were identified in 276 patients (35.2%). ROC analysis revealed that age, CA12–5, AFP, SCC, CA72–4, CA15–3, FERR, CYFRA21-1, MCHC, and tumor location could not predict KRAS mutations (P = 0.154, 0.177, 0.277, 0.350, 0.864, 0.941, 0.066, 0.279, 0.293, and 0.053 respectively), although CEA, CA19–9, NSE and haematological parameter values showed significant predictive value (P = 0.001,

Abstract Impregnated graphite has attracted considerable attention and has been widely used as an ideal friction material in many fields. However, the influence of the friction temperature on its tribological properties has not been clearly studied; furthermore, the evolution mechanism of transferred tribofilm is unknown. In this study, the tribological properties of impregnated graphite were investigated at different friction temperatures, and the evolution of the carbon-based tribofilm was also determined. The results revealed that the tribological properties significantly improved with an increase in friction temperature. The friction coefficient and wear depth of impregnated graphite reduced by 68% and 75%, respectively, at a high temperature of 160 °C compared with those of non-impregnated graphite. The significant properties of the impregnated graphite can be attributed to a transferred carbon-based tribofilm with an ordered structure induced by the friction temperature, which uniformly and stably adsorbs on friction interfaces. This study provides an important basis for designing graphite-based friction materials with improved properties suited for industrial applications.

Predicting stock movements is a valuable research field that can help investors earn more profits. As with time-series data, the stock market is time-dependent and the value of historical information may decrease over time. Accurate prediction can be achieved by mining valuable information with words on social platforms and further integrating it with actual stock market conditions. However, many methods still cannot effectively dig deep into hidden information, integrate text and stock prices, and ignore the temporal dependence. Therefore, to solve the above problems, we propose a transformer-based attention network framework that uses historical text and stock prices to capture the temporal dependence of financial data. Among them, the transformer model and attention mechanism are used for feature extraction of financial data, which has fewer applications in the financial field, and effective analysis of key information to achieve an accurate prediction. A large number of experiments have proved the effectiveness of our proposed method. The actual simulation experiment verifies that our model has practical application value.

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.

Previous studies have paid little attention to the spatial heterogeneity of residents' marginal willingness to pay (MWTP) for clean air at a city level. To fill this gap, this study adopts a geographically weighted regression (GWR) model to quantify the spatial heterogeneity of residents' MWTP for clean air in Shanghai. First, Shanghai was divided into 218 census tracts and each tract was the smallest research unit. Then, the impacts of air pollutants and other built environment variables on housing prices were chosen to reflect residents' MWTP and a GWR model was used to analyze the spatial heterogeneity of the MWTP. Finally, the total losses caused by air pollutants in Shanghai were estimated from the perspective of housing market value. Empirical results show that air pollutants have a negative impact on housing prices. Using the marginal rate of transformation between housing prices and air pollutants, the results show Shanghai residents, on average, are willing to pay 50 and 99 Yuan/m2 to reduce the mean concentration of PM2.5 and NO2 by 1 μg/m3, respectively. Moreover, residents' MWTP for clean air is higher in the suburbs and lower in the city center. This study can help city policymakers formulate regional air management policies and provide support for the green and sustainable development of the real estate market in China.

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.