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Abstract The virtual synchronous generator (SG) (VSG) can not only enhance the inertia of the grid, but also introduce the oscillation characteristics of SG, which is easy to interact with the power angle of SG in the grid, and even produce low‐frequency oscillation (LFO). The authors first construct a two‐machine interconnected power system model containing VSG and traditional SG. The model is linearised to construct the state space equations to obtain the Phillips–Heffron model with VSG. The LFO path of action between VSG and SG is analysed. To reduce the negative damping torque provided by VSG to SG through this path, a virtual power system stabiliser (VPSS) is proposed and the controller parameters are adjusted according to the phase compensation method. Finally, the effectiveness of VPSS is verified by modal analysis and simulation comparison.

VSG is a pivotal control strategy for GFM, designed to enhance the damping and inertia of power systems, thereby bolstering their stability. Despite its benefits, VSG can induce a QEO due to dynamic interactions between the GFM and the power system. This QEO, is different from control issues, contrasts with the EO associated with conventional SG, which is rooted in electromechanical factors. To address this phenomenon, the influence mechanism of VSG on QEO of the system is studied. First, a comprehensive control model for the VSG is established and its efficacy in providing damping and inertia is assessed. Subsequently, a small-signal model for the VSG when connected to the grid is developed. Utilizing this model, the system’s characteristic roots and the trajectories of principal state variables, elucidating the impact of VSG control parameters on QEO are analyzed. The validity of the models and the underlying mechanisms are confirmed through extensive simulations. Furthermore, for the key problems that need to be solved urgently in the application of VSG in power system with renewable energy, further thinkings are prospected.

BackgroundPancreatic ductal adenocarcinoma (PDAC) is frequently diagnosed in advanced stages, necessitating pancreaticoduodenectomy (PD) as a primary therapeutic approach. However, PD surgery can engender intricate complications. Thus, understanding the factors influencing postoperative complications documented in electronic medical records and their impact on survival rates is crucial for improving overall patient outcomes.MethodsA total of 749 patients were divided into two groups: 598 (79.84%) chose the RPD (Robotic pancreaticoduodenectomy) procedure and 151 (20.16%) chose the LPD (Laparoscopic pancreaticoduodenectomy) procedure. We used correlation analysis, survival analysis, and decision tree models to find the similarities and differences about postoperative complications and prognostic survival.ResultsPancreatic cancer, known for its aggressiveness, often requires pancreaticoduodenectomy as an effective treatment. In predictive models, both BMI and surgery duration weigh heavily. Lower BMI correlates with longer survival, while patients with heart disease and diabetes have lower survival rates. Complications like delayed gastric emptying, pancreatic fistula, and infection are closely linked post-surgery, prompting conjectures about their causal mechanisms. Interestingly, we found no significant correlation between nasogastric tube removal timing and delayed gastric emptying, suggesting its prompt removal post-decompression.ConclusionThis study aimed to explore predictive factors for postoperative complications and survival in PD patients. Effective predictive models enable early identification of high-risk individuals, allowing timely interventions. Higher BMI, heart disease, or diabetes significantly reduce survival rates in pancreatic cancer patients post-PD. Additionally, there’s no significant correlation between DGE incidence and postoperative extubation time, necessitating further investigation into its interaction with pancreatic fistula and infection.

Rapid advances in modern industrial tribo-systems under high temperatures and heavy loads generate a growing demand for lubricating materials used in extreme environmental conditions. Metal-based lubricant coatings with excellent mechanical properties and thermal stability are widely used on core parts to reduce friction in harsh environments. This paper reviews the progress on modulating the frictional properties of coatings by designing the components and preparation techniques to prolong the lifetime of metal-based lubrication coatings. The impacts of the microstructural changes on the mechanical performances, including hardness, plasticity, interfacial adhesion, and environmental stability, were essential for the deformation and crack propagation of the coatings. Their performances and lubrication mechanisms were concerned under heavy loads, in a wide range of temperatures, and in corrosive marine environments. Finally, the study concluded the basic requirements of metal-based coatings for extreme environments at this stage. The research challenges and potential problems of metal-based coatings for long-life applications were emphasized. In the future, more research and breakthroughs of lubricant coatings are needed in the areas of super-lubrication and long-life service in order to expand their applications in extreme environments.

Abstract The long-term stability of perovskite solar cells remains one of the most important challenges for the commercialization of this emerging photovoltaic technology. Here, we adopt a non-noble metal/metal oxide/polymer multiple-barrier to suppress the halide consumption and gaseous perovskite decomposition products release with the chemically inert bismuth electrode and Al2O3/parylene thin-film encapsulation, as well as the tightly closed system created by the multiple-barrier to jointly suppress the degradation of perovskite solar cells, allowing the corresponding decomposition reactions to reach benign equilibria. The resulting encapsulated formamidinium cesium-based perovskite solar cells with multiple-barrier maintain 90% of their initial efficiencies after continuous operation at 45 °C for 5200 h and 93% of their initial efficiency after continuous operation at 75 °C for 1000 h under 1 sun equivalent white-light LED illumination.

Abstract As a superior modulation strategy, space vector pulse width modulation (SVPWM) provides redundant voltage vectors and adjustable action time, which can achieve multi‐objective control of modular multilevel converter (MMC). An SVPWM strategy suitable for MMC is proposed. The strategy is divided into three stages. In the first stage, the appropriate voltage vector, the action time and the basic sub‐module (SM) input number are quickly calculated to ensure the output quality by equating MMC as a 2‐level inverter. In the second stage, a finite set of the circulating current suppression is established on the basis of the basic SM input number. The optimal SM input number is selected through rolling optimisation. In the last stage, according to the SM voltage sorting and the optimal SM input number, the optimal switching state is determined to realise the SM voltage balance control. The proposed control strategy simplifies the design of the control system, reduces the computational burden and can be easily extended to MMC with any SM number. The simulation and experimental results show that the proposed SVPWM strategy can reduce the circulating current and balance the SM capacitor voltage while ensuring the output quality.

In the electricity spot market (ESM), thermal power plants (TPP) are quoted based on fuel costs and carbon costs, which impose carbon costs from the generation side to the load-side. The cost of load-side participation in the ESM will increase further. In addition, the traditional ESM centralized bidding model is based on the marginal clearing price, without considering the line transmission constraints, which may affect the security of the power system. To solve this challenge, firstly, an ESM centralized bidding clearing model including the generation side and the load-side is established based on the transaction mechanism of centralized bidding in the ESM. In this model, the marginal clearing prices of two possible bidding scenarios in the ESM are considered. Secondly, the marginal clearing price of the ESM is designed based on the security constraint of the power flow, which can avoid power transmission exceeding its capacity. Finally, a carbon emission cost settlement mechanism is proposed based on the carbon emission flow (CEF) method, which can reduce the carbon emission cost paid by the load-side. The effectiveness of the method is verified by simulations. Compared with a traditional load-side settlement model, the settlement cost on the load-side is reduced by 30.45 %.

In the microgrid, the battery energy storage system (BESS) is used in combination with renewable energy to solve the problem of renewable energy accommodation. Meanwhile, power sources with the BESS can participate in valley filling and reduce electric generation costs. However, the BESS cannot contribute sufficient capability to the microgrid and reduce costs due to its limited capabilities. To tackle this challenge, the output power model of the combined cooling, heating and power microgrid (CCHPM) with the BESS is proposed in this paper. The model takes the micro-gas turbine, the photovoltaic unit, the wind turbine, the electric refrigerator and the BESS as a whole CCHPM. Meanwhile, a capacity optimization method of the BESS is developed to select the appropriate capacity and promote renewable energy accommodation. The minimum cost of the objective function is established. The electric power balance of the CCHPM, charge state constraints of the BESS, capacity constraints and ramp-up/down constraints of the units are derived to optimize the output power of units and the capacity of the BESS. The effectiveness of the proposed method is verified by the system simulation of YALMIP and GUROBI. Compared with the traditional method, the total cost of the microgrid is decreased by 1.4%, and the microgrid’s renewable energy accommodation is enhanced by 12.2%. This capacity optimization method can enhance the applicability of BESS in CCHPM.

Abstract Background To assess the effectiveness of China's medicine and health care reform in promoting equity in health care utilization among rural residents, it is necessary to analyze temporal trends in equity in health care utilization among rural residents in China. This study is the first to assess horizontal inequity trends in health care utilization among rural Chinese residents from 2010 to 2018 and provides evidence for improving government health policies. Methods Longitudinal data obtained from China Family Panel Studies from 2010 to 2018 were used to determine trends in outpatient and inpatient utilization. Concentration index, concentration curve, and horizontal inequity index were calculated to measure inequalities. Decomposition analysis was applied to measure the contribution of need and non-need factors to the unfairness. Results From 2010 to 2018, outpatient utilization among rural residents increased by 35.10%, while inpatient utilization increased by 80.68%. Concentration indices for health care utilization were negative in all years. In 2012, there was an increase in the concentration index for outpatient utilization (CI = -0.0219). The concentration index for inpatient utilization decreased from -0.0478 in 2010 to -0.0888 in 2018. Except for outpatient utilization in 2012 (HI = 0.0214), horizontal inequity indices for outpatient utilization were negative in all years. The horizontal inequity index for inpatient utilization was highest in 2010 (HI = -0.0068) and lowest in 2018 (HI = -0.0303). The contribution of need factors to the inequity exceeded 50% in all years. Conclusions Between 2010 and 2018, low-income groups in rural China used more health services. This seemingly pro-poor income-related inequality was due in large part to the greater health care need among low-income groups. Government policies aimed at increasing access to health services, particularly primary health care had helped to make health care utilization in rural China more equitable. It is necessary to design better health policies for disadvantaged groups to reduce future inequities in the use of health services by rural populations.

The family of phosphatidylethanolamine-binding proteins (PEBPs) participates in various plant biological processes, mainly flowering regulation and seed germination. In cucurbit crops, several PEBP genes have been recognized to be responsible for flowering time. However, the investigation of PEBP family members across the genomes of cucurbit species has not been reported, and their conservation and divergence in structure and function remain largely unclear. Herein, PEBP genes were identified from seven cucurbit crops and were used to perform a comparative genomics analysis. The cucurbit PEBP proteins could be classified into MFT, FT, TFL, and PEBP clades, and further, the TFL clade was divided into BFT-like, CEN-like, and TFL1-like subclades. The MFT-like, FT-like, and TFL-like proteins were clearly distinguished by a critical amino acid residue at the 85th position of the Arabidopsis FT protein. In gene expression analysis, CsaPEBP1 was highly expressed in flowers, and its expression levels in females and males were 70.5 and 89.2 times higher, respectively, than those in leaves. CsaPEBP5, CsaPEBP6, and CsaPEBP7 were specifically expressed in male flowers, with expression levels 58.1, 17.3, and 15.7 times higher, respectively, than those of leaves. At least five CsaPEBP genes exhibited the highest expression during the later stages of corolla opening. Through clustering of time-series-based RNA-seq data, several potential transcription factors (TFs) interacting with four CsaPEBPs were identified during cucumber corolla opening. Because of the tandem repeats of binding sites in promoters, NF-YB (Csa4G037610) and GATA (Csa7G64580) TFs appeared to be better able to regulate the CsaPEBP2 and CsaPEBP5 genes, respectively. This study would provide helpful information for further investigating the roles of PEBP genes and their interacting TFs in growth and development processes, such as flowering time regulation in cucurbit crops.

The ubiquitously-expressed proteolytic enzyme furin is closely related to the pathogenesis of SARS-CoV-2 and therefore represents a key target for antiviral therapy. Based on bioinformatic analysis and pseudovirus tests, we discovered a second functional furin site located in the spike protein. Furin still increased the infectivity of mutated SARS-CoV-2 pseudovirus in 293T-ACE2 cells when the canonical polybasic cleavage site (682–686) was deleted. However, K814A mutation eliminated the enhancing effect of furin on virus infection. Furin inhibitor prevented infection by 682–686-deleted SARS-CoV-2 in 293T-ACE2-furin cells, but not the K814A mutant. K814A mutation did not affect the activity of TMPRSS2 and cathepsin L but did impact the cleavage of S2 into S2′ and cell–cell fusion. Additionally, we showed that this functional furin site exists in RaTG13 from bat and PCoV-GD/GX from pangolin. Therefore, we discovered a new functional furin site that is pivotal in promoting SARS-CoV-2 infection.

SARS-CoV-2 has caused the COVID-19 pandemic. B.1.617 variants (including Kappa and Delta) have been transmitted rapidly in India. The transmissibility, pathogenicity, and neutralization characteristics of these variants have received considerable interest. In this study, 22 pseudotyped viruses were constructed for B.1.617 variants and their corresponding single amino acid mutations. B.1.617 variants did not exhibit significant enhanced infectivity in human cells, but mutations T478K and E484Q in the receptor binding domain led to enhanced infectivity in mouse ACE2-overexpressing cells. Furin activities were slightly increased against B.1.617 variants and cell–cell fusion after infection of B.1.617 variants were enhanced. Furthermore, B.1.617 variants escaped neutralization by several mAbs, mainly because of mutations L452R, T478K, and E484Q in the receptor binding domain. The neutralization activities of sera from convalescent patients, inactivated vaccine-immunized volunteers, adenovirus vaccine-immunized volunteers, and SARS-CoV-2 immunized animals against pseudotyped B.1.617 variants were reduced by approximately twofold, compared with the D614G variant.

Abstract As a systematic investigation of the correlations between physical examination indicators (PEIs) is lacking, most PEIs are currently independently used for disease warning. This results in the general physical examination having limited diagnostic values. Here, we systematically analyzed the correlations in 221 PEIs between healthy and 34 unhealthy statuses in 803,614 individuals in China. Specifically, the study population included 711,928 healthy participants, 51,341 patients with hypertension, 12,878 patients with diabetes, and 34,997 patients with other unhealthy statuses. We found rich relevance between PEIs in the healthy physical status (7662 significant correlations, 31.5%). However, in the disease conditions, the PEI correlations changed. We focused on the difference in PEIs between healthy and 35 unhealthy physical statuses and found 1239 significant PEI differences, suggesting that they could be candidate disease markers. Finally, we established machine learning algorithms to predict health status using 15–16% of the PEIs through feature extraction, reaching a 66–99% accurate prediction, depending on the physical status. This new reference of the PEI correlation provides rich information for chronic disease diagnosis. The developed machine learning algorithms can fundamentally affect the practice of general physical examinations.

With the development of new energy, the ancillary service demand of power grid is increasing. The published literature focuses on building a blockchain ancillary service market to ensure the safety and efficiency of ancillary service transactions. However, due to the high threshold and poor scalability, the existing blockchain market is hard to meet the demand of the response rate of ancillary services. Therefore, this paper proposes an energy storage capacity competition-based demand response method in blockchain ancillary service market. Firstly, a new market structure is proposed, which uses the Proof of Capacity (POC) as the consensus mechanism. The structure uses POC to realize the competition among users’ energy storage capacity. Secondly, the demand response process of ancillary service market is designed. During the transaction processes, the probability of users participating in the transaction is changed according to the users’ storage capacity. Dealing with different demands, the market chooses the user with the largest capacity or the combination of users to respond. The simulation shows that, compared with the traditional ancillary service market, the capacity competition-based market demand response rate is increased by 20%, the time to fulling all users’ capacity in the market is reduced by 2 h, and the call rate of user’s capacity is increased by 10%.

Abstract Background In recent years, due to the increasing number of cross-regional medical patients, countries around the world have issued a series of policies or regulations to reduce their out-of-pocket burden. In this context, this study intended to explore the impact of the Spatio-temporal characteristics of cross-regional medical treatment on total medical expenses, medical insurance payments, and out-of-pocket expenses of patients with malignant tumors in low-income areas. Methods This study included 54,904 data of cross-provincial medical treatment of malignant tumor patients insured in Heilongjiang Province, China in 2020. Firstly, Microsoft Excel 2019 and ArcGIS 10.2 were applied to conduct a descriptive analysis of the Spatio-temporal characteristics of their cross-provincial medical treatment. Then, binary and multivariate logistic regression models were used to explore the specific impact of economic level and geographical distance of medical regions on total medical expenses, medical insurance payments, and out-of-pocket expenses. Results The number of cross-regional medical patients showed a gradual upward trend from February to December, and fell back in January. They were concentrated in regions with high economic level and short distance from the insured region, where were more likely to form the group with high out-of-pocket expenses (AOR = 3.620, P

With the increasing penetration of electric vehicles (EVs), the orderly charging–discharging (C–D) strategy of EVs can effectively alleviate the impact of large-scale grid-connected EVs. However, the schedule of the C–D system of EV clusters encounters the curse of dimensionality problem. In addition, the performance of the C–D control strategy faces great challenges of environmental uncertainty of user demand and electricity price. This paper proposes an EV cluster scheduling strategy considering real-time electricity prices based on deep reinforcement learning. Firstly, we establish a distributed real-time optimal scheduling structure according to the real-time price signals of distribution system operators (DSO). Furthermore, to alleviate the curse of dimensionality, we propose a C–D model of a single EV according to the C–D characteristics of EV, and we establish the C–D control model of EVs as a Markov decision process (MDP). Finally, to adapt to the uncertainty of the learning environment, we propose a model-based deep reinforcement learning to optimize the C–D behavior of EVs. After day-ahead training and parameter saving of the proposed model, the C–D scheduling strategy is generated for the real-time system state at each moment of the day. The simulation results of the C–D scheduling strategy for cost-oriented EV charging show that the proposed scheduling strategy effectively reduces the user charging cost by 133.7 dollars and the load peak–valley difference, stabilizes the load fluctuation, and achieves the win–win situation between the power grid and EV users.

The participation of incentive demand response (IDR) can improve power grid flexibility, and reduce peak shaving pressure. However, the further development of incentive demand response services will be limited by the uncertainty of user response behavior. To tackle this problem, this paper proposes a data-driven load forecasting method for IDR, which considers consumer behavior. Firstly, we describe the power market auxiliary service operation mechanism for load aggregators, through which the ability of demand-side resources to respond to auxiliary services is improved. Then, add an attention layer to improve the traditional long and short memory (LSTM) model, and propose an IDR load forecasting method based on this model, which considers the model’s learning of historical data of user behavior. Finally, establish a simulation model to verify the effectiveness of the improved LSTM model forecasting. The proposed method is simulated and compared with the traditional LSTM method and k-nearest neighbor prediction method. The results show that compared with the other two methods, the mean error, root means square error, and mean absolute percentage error in the improved LSTM method are reduced by 7.31 MW, 8.32 MW, 2.06% and 10.62 MW, 13.46 MW and 3.09%, respectively, which effectively improves the accuracy of forecasting and increase the participation of demand-side resources in the power auxiliary service market.

To handle the multiple-objective problem including carbon emission reduction and economic operation of integrated energy system (IES), this paper proposes an optimal scheduling model for IES considering combined weights for low-carbon and economic operation. In the dispatch model of IES, we mainly use the combined cold heat and power (CCHP) to optimize the energy distribution, and use the charge and discharge power of electric vehicles (EVs) to enhance the regulation capability of CCHP. Furthermore, aiming at the lowest carbon dioxide emission and economic cost, and the highest renewable energy accommodation, a multi-objective function considering combined weights is proposed, which combines subjective weights with objective weights. The balance constraints of electricity, heat and cold for the IES are established. Finally, the particle swarm optimization algorithm is used to optimize the multi-objective model based on the energy balance and unit constraints of IES. The proposed method is verified by the case studies. The results show that the proposed model can improve the accommodation of renewable energy and reduce carbon dioxide emissions.

In the collection and transmission of power big data, the problem of data missing exists. In response to this problem, this paper proposes a power data detection and repair method based on SOM-LSTM. Firstly, a large amount of collected power data is analyzed and the type of missing data is determined. Then, the SOM is used to classify the power data. The LSTM is trained according to the characteristic values of different users to complete the detection and repair of different types of missing power data. Finally, the analysis is based on actual data in some regional loads of China. Experimental results show that, compared with the extreme learning machine (ELM) and LSTM, the proposed SOM-LSTM model reduces the mean absolute error (MAE) by 0.2498 and 0.3425, and the root mean square error (RMSE) by 0.1048 and 0.1469, respectively.

The increase in the penetration rate of renewable energy (RE) and the number of electric vehicles (EVs) has created problems for the balance of energy supply and demand of the power system. To solve this problem, it is necessary to evaluate the flexibility of the power system and optimize scheduling flexible resources to enhance the flexibility of the system. This paper proposes a power system flexibility optimization scheduling strategy considering RE and EVs. First, the models of RE and EVs charging load are established. Second, it analyzes the flexibility of the power system and proposes system flexibility evaluation indexes taking into account RE and EVs charging load. Finally, an optimal scheduling model of the power system is established. The improved particle swarm optimization (PSO) algorithm is used to solve it. The optimization results show that the proposed strategy can effectively improve the flexibility of the power system.

The rapid development of electric vehicles (EVs) makes the load of electric vehicle charging stations (EVCSs) affect the power grid. Aiming at the low accuracy of charging station load forecasting caused by the number of EVs, temperature and electricity price, and other factors, this paper proposes a load forecasting method of EVCSs based on a combination of multivariable residual correction grey model (EMGM) and long short-term memory (LSTM) network. Firstly, load influencing factors are analysed, and the grey theory is introduced into the load forecast of EVCSs. The role of EMGM in taking into account the effects of multiple factors and eliminating cumulative errors is analysed. Then, the EMGM and LSTM networks are combined to establish a mapping from the influencing factor data to the forecast, reducing the load forecast error of EVCs. Simulation and experimental results show that the accuracy of EVCSs’ load forecasting can be improved by this method.

With the large-scale deployment of smart meters and wide application of various machine learning algorithms, non-intrusive load monitoring (NILM) has attracted the attention of academia and industry. However, machine learning algorithms often suffer from high variability in load identification performance due to different features. In view of the poor generalization ability and low accuracy of load identification using an individual feature or classifier model, this paper proposes a novel NILM method that accomplishes deep feature fusion and classifier model fusion by an improved Dempster–Shafer (D–S) evidence theory. Firstly, we extract the power features, current harmonic features, and voltage–current (V–I) trajectory features from the input signals. Then, K-nearest neighbor (KNN), random forest (RF), and convolutional neural networks (CNN) are employed to identify load appliances using three individual features. Finally, the probability estimates of each classifier are transmitted to the aggregator for aggregation to obtain the final identification results by the improved D–S evidence theory. The experimental results on the PLAID dataset show that the proposed method can significantly improve identification performances.

Li Zhang, Zhimin Cui, and Qianqian Li et al. compare the infectivity, host tropism, and antigenicity of 10 SARS-CoV-2 variants using a VSV-based pseudovirus system. Their results suggest that variants carrying E484K display the most significant reduction in sensitivity to neutralization, and may provide further insight into the development of relevant therapeutics for SARS-CoV-2 infection.

ObjectivesLack of assessment of 90-d perioperative morbidity in elderly patients after radical cystectomy and pelvic lymph node dissection (PLND) using a standard reporting methodology, and the Clavien–Dindo classification (CDC) does not accurately reflect the burden of complications. We aim to report the 90-d complications of elderly patients after radical cystectomy, and to compare the validity of the Comprehensive Complication Index (CCI) and CDC.MethodsRetrospective review of 280 patients aged ≥75 years who received radical cystectomy between 2006 and 2021. The 90-d complications of elderly patients after radical cystectomy were reported by implementing the EAU criteria. The CDC and CCI were both used for grading complications. The Spearman rank correlation coefficient was used to estimate the correlation between postoperative stay and CDC/CCI. Logistic regression was used to identify the risk factors for major complications. The sample size for a fictive superiority trial was calculated for different endpoints.ResultsA total of 225 (80.36%) patients suffered from 528 complications. The cumulative CCI had a more accurate prediction of postoperative stay than the CDC (r = 0.378, p < 0.001 vs. r = 0.349, p < 0.001). The need for sample size could decrease when CCI was used for the primary endpoint. More risk factors for major complications were identified when CCI ≥33.7 was defined as the endpoint of major complications.ConclusionCCI is better than CDC for grading the severity of complications in elderly patients after radical cystectomy and PLND.

BackgroundObesity among the elderly imposes a significant health and economic burden. The purpose of this study was to measure the obesity prevalence and income-related inequality among older adults in China and to explore the determinants of the inequity.MethodsData were obtained from 4,541 older adults (60 years and older) participating in the China Family Panel Study, 2018. Obesity was defined as body mass index (BMI) ≥28 kg/m2. Normalized concentration index and concentration curve were calculated to measure the income-related inequality. Decomposition analysis was used to measure the contribution of each factor to the overall unfairness.ResultsThe prevalence of obesity among the respondents was 7.99%. The 95% confidence interval for the overall prevalence was 7.20–8.78%. The normalized concentration index of obesity in the elderly was 0.075 (95% confidence interval: 0.047–0.103), indicating that obesity was more concentrated among the rich (p < 0.05). Socioeconomic factors contributed the most to the overall inequality (68.73%). Health behavior factors explained 16.38% of the observed income-related inequality in obesity among the elderly in China.ConclusionsIn 2018, obesity was more concentrated among the elderly with higher incomes in China. The pro-poor income-related inequality was mainly due to the higher socioeconomic status of higher-income older adults. Health behaviors and psychosocial factors could also exacerbate the inequality. To prevent the heavy burden of obesity on the health and finances of older adults, more attention should be paid to those who are financially better off, especially those who smoke and are physically inactive, while extroverted older adults also need to be focused on. For developing countries, concern needs to be given to the obesity of the wealthy elderly as a result of economic development.

The internal parameters and topology of the power converter are unknown in some practical cases. Existing modeling methods based on impedance frequency scanning method can only guarantee that the dynamic modeling is effective at a single working point. To make the established dynamic model effective in a wide range, an equivalent modeling method for power converter based LSTM Neural Network is presented. At first, the equivalence of black-box modeling problem and deep loop neural network is studied. Then, dynamic modeling method black-box power converter on wide operating range by using LSTM neural network is proposed. Finally, the simulation results under large disturbance and multi-operating points show that the proposed method is effective under wide operation range.

The optimal scheduling of integrated energy system (IES) can improve energy efficiency and economic operation. However, the existing scheduling methods cannot accurately handle the dynamic changes of supply and demand sides in the electricity-gas-heat IES thanks to their power uncertainties. To tackle this problem, an optimal dispatch framework based on the asynchronous advantage actor-critic (A3C) method of IES is proposed. Firstly, we describe the dispatch problem of IES with multiple uncertainties as Markov decision process (MDP) according to the corresponding mathematical models and constraints. Then, the dispatch framework based on A3C is developed to optimize the control decision for supply and demand sides by asynchronous learning of agents and to reduce the relevance of parameters update of neural networks by the multi-agent utilization of the central processing unit (CPU) multi-threading function. Finally, our proposed methods are verified by the simulations. Compared with the previous optimization algorithms, the training time of the proposed methods is shortened by 37% and 30%, and the daily average operating cost is reduced by 3%, 5.2% and 8.7%.

The dynamic behaviors of microgrid become more complicated due to the increasing implementation of distributed energy generation, energy storage. It is significant to study the dynamic response for power planning, analysis, and control of microgrid. Hence, an accurate dynamic equivalent model is essential, since it can help to evaluate the performance by simulation to avoid the loss and danger in practical test. However, the dynamic model based on differential equation cannot be established because of the lack of information in most of time. To build dynamic model when microgrid is a black-box system, a gated recurrent unit based neural network is proposed in this paper. The proposed neural network can be treated as a black-box differential–algebraic equations. The structure design and model training procedure are presented in detail. Study cases are implemented to evaluate the performance of modeling method. The comparison results show that the proposed dynamic modeling method can precisely estimate the dynamic response of microgrid.

This paper concerns a novel data-based policy iteration control method for discrete-time systems with disturbances. To avoid the need of system dynamic and state, a new system equation is constructed by using system operation data. Based on this, we derive a new data-based Bellman equation that can be applied to the design of model-free policy iteration algorithm. For discrete-time system with disturbances, we transform H∞ control into a zero-sum game problem, and then use the proposed new data-based PI method to solve it. Wind turbine control is a typical control problem with time delay and disturbance. Mathematical simulation results are given to demonstrate the effectiveness of the proposed data-based PI control method.

In the power sector, microgrids play a supportive role in bridging the adequacy gap in the conventional electricity supply. Trading of the generated energy has recently been improved by blockchain technology which offers a new cheap, secure, and decentralized transaction approach. Its operation is however associated with an undesired inherent delay during energy transactions initiated by the prosumers, thus, failure to timely attend to incidences of urgent demand could end up in catastrophe at the consumer's side. This article thus proposes a cyber-enhanced transactive microgrid model using blockchain technology with optimized participants' permission protocol to ameliorate this challenge. It is demonstrated that the optimized blockchain participants' permission model leads to improved transaction speed and greater convenience. The transaction speed simulation is thereafter performed and it was also demonstrated that the node population has a greater effect than the transaction block size on the transaction speed improvement.

High oleic acid peanut (HOAP) is extensively embraced in China because of its high nutritional value and enhanced oxidative stability. However, its dissemination has been severely constrained in high altitude and high latitude areas due to chilling stress during sowing, resulting in significant yield loss in these regions. Despite the lack of understanding of the molecular mechanisms underlying low temperature germination (LTG) in HOAP, discovering the quantitative trait loci (QTL) that confer this trait will undoubtedly benefit breeding efforts. In the present study, we identified putative genomic regions and single nucleotide polymorphisms (SNPs) that govern LTG tolerance of HOAP in an F2 population derived from the cross of chilling-tolerant YH65 and chilling-sensitive FL14 using bulk segregant analysis (BSA). Analysis of ΔSNP-index and Euclidean distance (ED) value association pinpointed the overlapped region to a 2.29 Mb interval on chromosome A05. The candidate interval showed that 122 genes were significantly related to response to abiotic stress and plant–pathogen interaction. Furthermore, an SNP site associated with LTG tolerance was discovered. The SNP site was employed as a Kompetitive Allele Specific PCR (KASP) marker and validated in a universal peanut panel. These findings may provide valuable insight into the molecular mechanism underpinning LTG tolerance and facilitate marker-assisted selective breeding in HOAP.

Renewable energy is usually connected to the DC micro-grid by a large number of power electronic devices, which have the advantages of a fast system response, but the disadvantage to reduce the inertia of the system, which makes the stability of the system worse. It is necessary to increase the inertia of DC micro-grid so that it can recover and stabilize well when it receives a disturbance. In this paper, a small-signal model of DC micro-grid with constant power load (CPL) is established, and a flexible virtual inertial (FVI) control method based on DC bus voltage real-time variation is proposed, by controlling the DC/DC converter of the energy storage system, the problem of system oscillation caused by introducing voltage differential link to the system is solved. Compared with the droop control method, the FVI control method can increase the inertia of DC micro-grid system, reduce the influence of small disturbances, and improve the stability of the system. Finally, the validity of the FVI control method based on small signal model is verified in dSPACE.

To determine whether the neutralization activity of monoclonal antibodies, convalescent sera and vaccine-elicited sera was affected by the top five epidemic SARS-CoV-2 variants in the UK, including D614G+L18F+A222V, D614G+A222V, D614G+S477N, VOC-202012/01(B.1.1.7) and D614G+69-70del+N439K, a pseudovirus-neutralization assay was performed to evaluate the relative neutralization titers against the five SARS-CoV-2 variants and 12 single deconvolution mutants based on the variants. In this study, 18 monoclonal antibodies, 10 sera from convalescent COVID-19 patients, 10 inactivated-virus vaccine-elicited sera, 14 mRNA vaccine-elicited sera, nine RBD-immunized mouse sera, four RBD-immunized horse sera, and four spike-encoding DNA-immunized guinea pig sera were tested and analyzed. The N501Y, N439K, and S477N mutations caused immune escape from nine of 18 mAbs. However, the convalescent sera, inactivated virus vaccine-elicited sera, mRNA vaccine-elicited sera, spike DNA-elicited sera, and recombinant RBD protein-elicited sera could still neutralize these variants (within three-fold changes compared to the reference D614G variant). The neutralizing antibody responses to different types of vaccines were different, whereby the response to inactivated-virus vaccine was similar to the convalescent sera.

Aiming at the large error of the traditional constant control method in predicting the maximum power of solar UAV, this paper proposed an improved mind evolutionary algorithm combined with BP neural network (BPNN), in which the improved mind evolutionary algorithm optimizes the BPNN. The optimized model is used to predict the voltage at the maximum power of the panel in the UAV. The constant voltage parameter based on the conventional constant pressure control algorithm is replaced by this value. At the same time, a new control simulation model of constant voltage solar panel maximum power tracking based on the improved mind evolution algorithm optimize BPNN was built. At last, the algorithm was simulated and validated in the MATLAB/Simulink environment. The simulation results show that this algorithm is better than simply using BPNN and genetic algorithm optimize BPNN stability and higher accuracy.

The H-bridge cascaded active power filter (APF) has broad application prospect in the harmonic procession of high voltage field. Due to differences between H bridges, the imbalance of DC side voltages affects not only compensation effect, but also the safe and stable operation of APF. By derivation of the power exchange model between APF and the power grid, the voltage balance among phases is achieved by the five-order zero-sequence voltage, and the global voltage balance is achieved by the fundamental positive-sequence active current. By appending an active voltage vector in the AC side of each H-bridge, the voltage balance is achieved within the single phase. This three-level equilibrium strategy controls each DC side voltage at the given value eventually. At the same time, the cascaded APF compensates harmonic and reactive current and reduces THD of the grid current. The feasibility of the method is verified by simulation and experimental results under low voltage conditions and provides the basis for application of the APF to a higher voltage level.

In a wind-diesel system with weak network architecture, many techniques are utilized to enhance its stability. However, the frequency oscillation between the grid-side converter and the diesel generator may be caused by their interactions, and there are only a few studies considering this issue, which is a major challenge for the stability. To tackle this challenge, the cause of frequency oscillation is studied, and an adaptive fuzzy PI controller based on the variable universe is proposed to suppress oscillation in this paper. The control parameter of the wind turbine generator (WTG) converter’s dc-link voltage loop and wind speed has an important influence on the oscillation. Our main idea is to suppress frequency oscillation by the optimizing control parameter. In order to analyze the interaction between WTG and diesel generator, we develop the small-signal models of the wind-diesel generator. Subsequently, the adaptive fuzzy PI controller based on the variable universe is proposed in the dc-link voltage loop to optimize control parameter instead of gain-scheduled PI controller, and its key feature is the innovative use of an extension factor with d-axis current in the output universe. The function of the extension factor is to adapt WTG to different wind speeds. The experimental results demonstrate that the proposed controller can significantly suppress frequency oscillation. The oscillation amplitude of isolated grid frequency is also significantly reduced by up to 0.175 Hz.

In order to study the solar cell power system of solar unmanned aerial vehicle (UAV), this paper builds a simulation model of solar cell according to the solar cell mathematical model. The characteristic curves of solar cells are obtained by MATLAB/Simulink. The characteristic curves of the solar cells are compared by changing the solar intensity and the external temperature. At the same time, the ground test of the solar cell module is carried out. The corresponding conclusions are obtained by the ground test and simulation analysis. The solar cells have a certain maximum power point at any condition, which is great significant to study the maximum power tracking of solar cells in solar UAVs.

Gold as a precious metal resource has high recycling significance. However, the current extraction methods cannot achieve the both efficiency and environmental friendliness. In this paper, we propose a new gold leaching agent, which can leach gold under light condition by mixing iodoform (CHI3) with 1-butyl-3-methylimidazolium dicyanamide (BmimN(CN)2) ionic liquid. Under 25 °C and 13 W incandescent lamp irradiation, the leaching yield of gold can achieve 100 wt%, and the average leaching rate is 945 mg Au/(h·mol·CHI3) (18.9 times of that of the cyanidation method). Through the analysis of the results of radical inhibition experiment, UV-Vis and XPS, a possible leaching mechanism is proposed: the iodine radical generated by light oxidizes Au0 to Au+, and then forms AuN(CN)2 by coordinating with N(CN)2−. Subsequently, the ionic liquid and Au N(CN)2 form a stable [Bmim]·[Au(N(CN)2)2] ion pair structure, further promoting the dissolution reaction. The leaching yield of gold can reach 81.9 wt% and 100 wt%, respectively, when applied to ore and waste electrical and electronic equipment (WEEE); the leaching yield of gold can also reach 100 wt% when applied to a waste catalyst by adding a Soxhlet extraction. The results show that this method is not only efficient, mild, and environmentally friendly, but also has strong adaptability and wide application prospects.

Nonsyndromic orofacial cleft (NSOFC) is a severe birth defect that occurs early in embryonic development and includes the subtypes cleft palate only (CPO), cleft lip only (CLO) and cleft lip with cleft palate (CLP). Given a lack of specific genetic factor analysis for CPO and CLO, the present study aimed to dissect the landscape of genetic factors underlying the pathogenesis of these two subtypes using 6,986 cases and 10,165 controls. By combining a genome-wide association study (GWAS) for specific subtypes of CPO and CLO, as well as functional gene network and ontology pathway analysis, we identified 18 genes/loci that surpassed genome-wide significance (P < 5 × 10-8) responsible for NSOFC, including nine for CPO, seven for CLO, two for both conditions and four that contribute to the CLP subtype. Among these 18 genes/loci, 14 are novel and identified in this study and 12 contain developmental transcription factors (TFs), suggesting that TFs are the key factors for the pathogenesis of NSOFC subtypes. Interestingly, we observed an opposite effect of the genetic variants in the IRF6 gene for CPO and CLO. Moreover, the gene expression dosage effect of IRF6 with two different alleles at the same single-nucleotide polymorphism (SNP) plays important roles in driving CPO or CLO. In addition, PAX9 is a key TF for CPO. Our findings define subtypes of NSOFC using genetic factors and their functional ontologies and provide a clue to improve their diagnosis and treatment in the future.

Due to increasing load and characteristic stagnation and fluctuations of existing generation systems capacity, the reliability assessment of generation systems is crucial to system adequacy. Furthermore, a rapid load increase could amount to a consequent sudden deficit in the generation supply before the next scheduled assessment. Hence, a reliability assessment is conducted at regular and close intervals to ensure adequacy. This study simulates and establishes the relationship between the load growth and generation capacity using the generation and load data of the IEEE reliability test system (IEEE RTS ‘96 standard). The generation capacity states and the risk model were obtained using the sequential Monte Carlo simulation (MCS) method. The load was gradually increased stepwise and is simulated against the constant generation capacity. In each case, the reliability index was recorded in terms of loss-of-load evaluation (LOLE). The recorded reliability index was thereafter fitted with the load-growth trend by the linear regression approach. A predictive assessment approach is thereafter proffered through the obtained fitting equation. In addition, a reliability threshold is effectively determined at a yield point for a reliability benchmark.

The volatility and uncertainty of high-penetration renewable energy (RE) challenge the stability of the power system. To tackle this challenge, an optimal dispatch of high-penetration RE based on flexible resources (FRs) is proposed to enhance the ability of the power system to cope with uncertain disturbances. Firstly, the flexibility of a high-penetration RE integrated power system is analyzed. The flexibility margin of power supply and flexible adaptability of RE are then introduced as the evaluation indices for optimal operation. Finally, a multi-objective optimal dispatch model for power system flexibility enhancement based on FRs under the constraint of flexibility indices is proposed. The simulation results show that the proposed optimal dispatch can effectively enhance the flexibility of the power system and the penetration of RE and reduce pollutant emissions. Compared with the conventional method, the daily average emissions of CO2, SO2, and NOx with the proposed method are reduced by about 83,600 kg, 870 kg, and 370 kg, respectively, the maximum allowable volatility of net load is increased by 7.63%, and the average volatility of net load is reduced by 2.67%.

In view of the low sensitivity, high operating temperature and poor selectivity of acetone measurements, in this paper much effort has been paid to improve the performance of acetone sensors from three aspects: increasing the surface area of the material, improving the surface activity and enhancing gas diffusion. A hierarchical flower-like Pt-doped (1 wt %) 3D porous SnO2 (3DPS) material was synthesized by a one-step hydrothermal method. The micropores of the material were constructed by subsequent annealing. The results of the experiments show that the 3DPS-based sensor's response is strongly dependent on temperature, exhibiting a mountain-like response curve. The maximum sensor sensitivity (Ra/Rg) was found to be as high as 505.7 at a heating temperature of 153 °C and with an exposure to 100 ppm acetone. Additionally, at 153 °C, the sensor still had a response of 2.1 when exposed to 50 ppb acetone gas. The 3DPS-based sensor also has an excellent selectivity for acetone detection. The high sensitivity can be explained by the increase in the specific surface area brought about by the hierarchical flower-like structure, the enhanced surface activity of the noble metal nanoparticles, and the rapid diffusion of free-gas and adsorbed gas molecules caused by the multiple channels of the microporous structure.

In order to reduce the pollution caused by coal-fired generating units during the heating season, and promote the wind power accommodation, an electrical and thermal system dispatch model based on combined heat and power (CHP) with thermal energy storage (TES) and demand response (DR) is proposed. In this model, the emission cost of CO2, SO2, NOx, and the operation cost of desulfurization and denitrification units is considered as environmental cost, which will increase the proportion of the fuel cost in an economic dispatch model. Meanwhile, the fuel cost of generating units, the operation cost and investment cost of thermal energy storage and electrical energy storage, the incentive cost of DR, and the cost of wind curtailment are comprehensively considered in this dispatch model. Then, on the promise of satisfying the load demand, taking the minimum total cost as an objective function, the power of each unit is optimized by a genetic algorithm. Compared with the traditional dispatch model, in which the environmental cost is not considered, the numerical results show that the daily average emissions CO2, SO2, NOx, are decreased by 14,354.35 kg, 55.5 kg, and 47.15 kg, respectively, and the wind power accommodation is increased by an average of 6.56% in a week.

In renewable energy generation applications, phase locked loop (PLL) is one of the most popular grid synchronization technique. The main objective of PLL is to rapidly and precisely extract phase and frequency especially when the grid voltage is under non-ideal conditions. This motivates the recent development of moving average filters (MAFs) based PLL in a quasi-type-1 system (i.e., QT1-PLL). Despite its success in certain applications, the transient response is still unsatisfactory, mainly due to the fact that the time delay caused by MAFs is still large. This has significantly limited the utilization of QT1-PLL, according to common grid codes such as German and Spanish grid codes. This challenge has been tackled in this paper. The basic idea is to develop a new hybrid filtering stage, consisting of adaptive notch filters (ANFs) and MAFs, arranged at the inner loop of QT1-PLL. Such an idea can greatly improve the transient response of QT1-PLL, owing to the fact that ANFs are utilized to remove the fundamental frequency negative voltage sequence (FFNS) component while other dominant harmonics can be removed by MAFs with a small time delay. By applying the proposed technique, the settling time is reduced to less than one cycle of grid frequency without any degradation in filtering capability. Moreover, the proposed PLL can be easily expanded to handle dc offset rejection. The effectiveness is validated by comprehensive experiments.

Frequency stability in an isolated grid can be easily impacted by sudden load or wind speed changes. Many frequency regulation techniques are utilized to solve this problem. However, there are only few studies designing torque compensation controllers based on power performances in different Speed Parts. It is a major challenge for a wind turbine generator (WTG) to achieve the satisfactory compensation performance in different Speed Parts. To tackle this challenge, this paper proposes a gain scheduled torque compensation strategy for permanent magnet synchronous generator (PMSG) based wind turbines. Our main idea is to improve the anti-disturbance ability for frequency regulation by compensating torque based on WTG speed Parts. To achieve higher power reserve in each Speed Part, an enhanced deloading method of WTG is proposed. We develop a new small-signal dynamic model through analyzing the steady-state performances of deloaded WTG in the whole range of wind speed. Subsequently, H∞ theory is leveraged in designing the gain scheduled torque compensation controller to effectively suppress frequency fluctuation. Moreover, since torque compensation brings about untimely power adjustment in over-rated wind speed condition, the conventional speed reference of pitch control system is improved. Our simulation and experimental results demonstrate that the proposed strategy can significantly improve frequency stability and smoothen power fluctuation resulting from wind speed variations. The minimum of frequency deviation with the proposed strategy is improved by up to 0.16 Hz at over-rated wind speed. Our technique can also improve anti-disturbance ability in frequency domain and achieve power balance.